From c317655742e0263d2c2ee3dab6a1ac1aedb7dd0e Mon Sep 17 00:00:00 2001 From: Ben Tupper Date: Tue, 28 Nov 2023 19:09:26 -0500 Subject: [PATCH] rebuild after nbs update --- ...omp_SCIMonthlyGlobal.u_wind.2000-01-01.tif | Bin 13990 -> 0 bytes ...omp_SCIMonthlyGlobal.v_wind.2000-01-01.tif | Bin 13990 -> 0 bytes ..._SCIMonthlyGlobal.windspeed.2000-01-01.tif | Bin 13990 -> 0 bytes ...omp_SCIMonthlyGlobal.u_wind.2000-02-01.tif | Bin 13990 -> 0 bytes ...omp_SCIMonthlyGlobal.v_wind.2000-02-01.tif | Bin 13990 -> 0 bytes ..._SCIMonthlyGlobal.windspeed.2000-02-01.tif | Bin 13990 -> 0 bytes ...omp_SCIMonthlyGlobal.u_wind.2000-03-01.tif | Bin 13990 -> 0 bytes ...omp_SCIMonthlyGlobal.v_wind.2000-03-01.tif | Bin 13990 -> 0 bytes ..._SCIMonthlyGlobal.windspeed.2000-03-01.tif | Bin 13990 -> 0 bytes ...omp_SCIMonthlyGlobal.u_wind.2000-04-01.tif | Bin 13990 -> 0 bytes ...omp_SCIMonthlyGlobal.v_wind.2000-04-01.tif | Bin 13990 -> 0 bytes ..._SCIMonthlyGlobal.windspeed.2000-04-01.tif | Bin 13990 -> 0 bytes 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zox_l>h#>G~1lM1N^F+iteVxMjF7hm?6~UbOF%TWO2dr{>fTpMiMXVEJ$3l216Uv7r zp{U*qCG

        3.4 Data storage

        -

        We have set up a data directory, data, for storing data collected for the project. To start out there isn’t much more than the downloaded data set, but we’ll added to it as we go. We try to keep the data storage area tidy by using subdirectories.

        +

        We have set up a data directory, data, for storing data collected for the project. To start out there isn’t much more than the downloaded data set, but we’ll added to it as we go. We try to keep the data storage area tidy by using subdirectories. Below we print the directory tree, your’s might look slightly different until you have run this code.

        -
        fs::dir_tree("data", recurse = TRUE)
        +
        fs::dir_tree("data", recurse = 1)
        data
         ├── bkg
        @@ -351,1187 +351,34 @@ 

        diff --git a/docs/modeling-02.html b/docs/modeling-02.html index 6072200..54eb2ef 100644 --- a/docs/modeling-02.html +++ b/docs/modeling-02.html @@ -252,7 +252,7 @@

        Modeling each month

        1 Load data

        Here we load the observation and background data points. We add a column identifying the month of the year.

        -
        +
        source("setup.R")
         obs = sf::read_sf(file.path("data", "obs", "obs-covariates.gpkg")) |>
           sf::st_set_geometry("geometry") |>
        @@ -267,7 +267,7 @@ 

        2 Do we model every month?

        Let’s do a quick check by counting each by month. Note that we drop the spatial info so that we can make simply tallies.

        -
        +
        counts = sf::st_drop_geometry(obs) |> 
           dplyr::count(month, name = "n_obs") |>
           dplyr::left_join(sf::st_drop_geometry(bkg) |> dplyr::count(month, name = "n_bkg"), 
        @@ -277,25 +277,25 @@ 

        # A tibble: 12 × 3 month n_obs n_bkg <fct> <int> <int> - 1 Jan 33 51 - 2 Feb 40 57 - 3 Mar 50 79 - 4 Apr 341 528 - 5 May 541 943 - 6 Jun 2137 3471 - 7 Jul 2108 3233 - 8 Aug 1698 2597 - 9 Sep 725 1205 -10 Oct 328 485 -11 Nov 494 739 -12 Dec 66 90

        + 1 Jan 33 2288 + 2 Feb 40 2140 + 3 Mar 50 2312 + 4 Apr 341 2232 + 5 May 541 2305 + 6 Jun 2137 2291 + 7 Jul 2108 2371 + 8 Aug 1698 2502 + 9 Sep 725 2275 +10 Oct 328 2390 +11 Nov 494 2267 +12 Dec 66 2201

        So the colder months have fewer observations than the warmer months. We already knew that, but it will be interesting to see how that manifests itself in the models.

        2.1 Build the monthly models

        Since we are building 12 models (rather than one) it is useful to create a function that computes a model for any month, and then iterate through the months of the year.

        -
        +
        # A function for making one month's model
         #
         # @param tbl a data frame of one month's observations
        @@ -338,7 +338,7 @@ 

        rlang::set_names(levels(obs$month))

        We can look at the response plots for every month, but for demonstration purposes, we’ll just show one month. It is interesting to compare this respinse to that for the basic model.

        -
        +
        plot(models[['Jun']], type = 'cloglog')

        @@ -352,15 +352,15 @@

        3.1 Load the raster databases (sst and u_wind and v_wind)

        We also make sure they are in date order and add a “month” variable to each.

        -
        +
        sst_path = "data/oisst"
         sst_db = oisster::read_database(sst_path) |>
           dplyr::arrange(date) |>
           dplyr::mutate(month = format(date, "%b"))
           
         
        -wind_path = "data/nbs"
        -wind_db = nbs::read_database(wind_path) |>
        +nbs_path = "data/nbs2"
        +wind_db = nbs::read_database(nbs_path) |>
           dplyr::arrange(date)|>
           dplyr::mutate(month = format(date, "%b"))
         
        @@ -374,16 +374,15 @@ 

        3.2 Iterate through the months making predictions

        Now we can build an iterator function that will make a prediction for each month. Let’s narrow our predictions to just those for a particular year, 2019, and read the rasters in all at once.

        -
        +
        dates = as.Date(c("2019-01-01", "2019-12-31"))
         x = read_predictors(
           sst_db = dplyr::filter(sst_db, dplyr::between(date, dates[1], dates[2])),
           u_wind_db = dplyr::filter(u_wind_db, dplyr::between(date, dates[1], dates[2])),
        -  v_wind_db = dplyr::filter(v_wind_db, dplyr::between(date, dates[1], dates[2]))
        -)
        + v_wind_db = dplyr::filter(v_wind_db, dplyr::between(date, dates[1], dates[2])) )

        Now we can iterate through the months.

        -
        +
        date_sequence = seq(from = dates[1], to = dates[2], by = "month")
         pred_rasters = lapply(names(models),
           function(mon){
        @@ -393,7 +392,7 @@ 

        pred_rasters = do.call(c, append(pred_rasters, list(along = list(time = date_sequence))))

        Let’s plot them.

        -
        +
        coast = rnaturalearth::ne_coastline(scale = 'large', returnclass = 'sf') |>
           sf::st_geometry() |>
           sf::st_crop(pred_rasters)
        @@ -407,7 +406,7 @@ 

        +
        pred_obs = stars::st_extract(pred_rasters, 
                                      dplyr::filter(obs, dplyr::between(date, dates[1], dates[2])),
                                      time_column = "month_id") |>
        @@ -429,22 +428,22 @@ 

        +
        pp = paucs |>
           dplyr::group_by(month) |>
           dplyr::group_map(
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zpq!_3I`DVy=kuqkPhP(De*piKsVM*e diff --git a/docs/predictors.html b/docs/predictors.html index 2617c92..bac9011 100644 --- a/docs/predictors.html +++ b/docs/predictors.html @@ -249,7 +249,7 @@

        Predictors

        1 Why predictor variables?

        Predictor variables (aka “covariate variables” and “environmental variables”) are provided in the downloaded observations. We can certainly use that data, but to use MaxEnt presence only modeling, we need a way to characterize the background. For that we select random points from the same region and times that we have observations. Well, those data do not come with the OBIS download, we have to get them ourselves.

        -

        Another nice thing about using external predictor variables, is that we can use them, once we have a model in hand, to make predictive maps.

        +

        Another nice thing about using external predictor variables is that we can use them, once we have a model in hand, to make predictive maps.

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        2 What predictor variables?

        @@ -261,8 +261,10 @@

        source("setup.R")
        -ok = dir.create("data/oisst", recursive = TRUE, showWarnings = FALSE)
        -ok = dir.create("data/nbs2", recursive = TRUE, showWarnings = FALSE)
        +oisst_path = "data/oisst" +nbs_path = 'data/nbs2' +ok = dir.create(oisst_path, recursive = TRUE, showWarnings = FALSE) +ok = dir.create(nbs_path, recursive = TRUE, showWarnings = FALSE)

        It is not unreasonable to keep your data in your project directory, but gridded data can be quite large. Sometimes it gets so large that the size limit on github repositories can be exceeded. The simplest way to prevent that is to prevent git from tracking your large data directories by adding filters to your local .gitignore file. Here’s what we put in our project’s .gitignore file. These lines tell git to not catalog items found in those two directories.

        # Biggish data directories
        @@ -282,20 +284,19 @@ 

        -
        oisst_path = "data/oisst"
        -oisst_db_file = file.path(oisst_path, "database.csv.gz")
        -
        -if (file.exists(oisst_db_file)){
        -  sst_db = oisster::read_database(oisst_path)
        -} else {
        -  sst = oisster::fetch_month(dates = seq(from = as.Date("2000-01-01"),
        -                                       to = current_month(offset = -1),
        -                                       by = "month"),
        -                           bb = bb,
        -                           path = oisst_path)
        -  sst_db = oisster::build_database(oisst_path, save_db = TRUE)
        -}
        -sst_db
        +
        oisst_db_file = file.path(oisst_path, "database.csv.gz")
        +
        +if (file.exists(oisst_db_file)){
        +  sst_db = oisster::read_database(oisst_path)
        +} else {
        +  sst = oisster::fetch_month(dates = seq(from = as.Date("2000-01-01"),
        +                                       to = current_month(offset = -1),
        +                                       by = "month"),
        +                           bb = bb,
        +                           path = oisst_path)
        +  sst_db = oisster::build_database(oisst_path, save_db = TRUE)
        +}
        +sst_db
        # A tibble: 285 × 5
            date       param per   trt   ltm  
        @@ -346,30 +347,30 @@ 

        This can take a long time, so we test for the existence of the local NBS database first. If the database doesn’t yet exist, then we download the data.

        -
        wind_path = 'data/nbs'
        -wind_db_file = file.path(wind_path, "database.csv.gz")
        -
        -if (file.exists(wind_db_file)){
        -  wind_db = nbs::read_database(wind_path)
        -} else {
        -  wind = nbs::fetch_month(dates = seq(from = as.Date("2000-01-01"),
        -                                       to = Sys.Date() - 75,
        -                                       by = "month"),
        -                           bb = bb,
        -                           path = wind_path)
        -  wind_db = nbs::build_database(wind_path, save_db = TRUE)
        +
        wind_db_file = file.path(nbs_path, "database.csv.gz")
        +if (file.exists(wind_db_file)){
        +  wind_db = nbs::read_database(nbs_path)
        +} else {
        +  wind_db = query_nbs(period = "monthly",
        +                      dates = c(as.Date("2000-01-01"), Sys.Date()-75),
        +                      product = "uvcomp") |>
        +  fetch_nbs(param = 'all', 
        +            bb = bb, 
        +            path = nbs_path, 
        +            verbose = FALSE) |>
        +    write_database(nbs_path)
         }

        Just as with sst, we can select from the database a small number of file to load and view.

        # first we filter
         sub_wind_db = wind_db |>
        -  dplyr::filter(dplyr::between(date, as.Date("2000-01-01"), as.Date("2000-12-01")),
        -                param == "windspeed")
        +  dplyr::filter(param == "windspeed",
        +  dplyr::between(date, as.Date("2000-01-01"), as.Date("2000-12-01")))
         
         
         # then read into a multi=layer stars object
        -wind = stars::read_stars(nbs::compose_filename(sub_wind_db, wind_path),
        +wind = stars::read_stars(nbs::compose_filename(sub_wind_db, nbs_path),
                                 along = list(date = sub_wind_db$date)) |>
           rlang::set_names("windspeed")
         plot(wind)
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zjO*Xq>ccr87Jd^LtZaGI_(7QZd7O%0{ziWJawC)D&9YLm%`n*jEQs@-XnAr`El?kr z+23b{7f243p7l#4hid1A$l7nXvJes3-BVbR1N#EM?Iqe!)r}pfqM`)ph7%~2uMQ3n zTD2x|wC`?>)R=p_(!J0Y2SK1qXT(r5Iyfi%@BbP^?&{&I)QGAGOoHiv9{HLs-el_ok`Gfxkk>#v$ diff --git a/docs/search.json b/docs/search.json index e9c24eb..4eb325b 100644 --- a/docs/search.json +++ b/docs/search.json @@ -130,7 +130,7 @@ "href": "index.html#getting-started", "title": "Home", "section": "3 Getting started", - "text": "3 Getting started\n\n“According to the ancient Chinese proverb, A journey of a thousand miles must begin with a single step.” ~ John F. Kennedy\n\n\n3.1 Handling spatial data\nIn this tutorial we use the sf and stars packages to handle spatial data. These tutorials for sf and for stars will help you off to a great start.\n\n\n3.2 Project-specific functions\nWe have developed a suite of functions that facilitate accessing and working with data. These can be loaded into your R session by source()-ing the setup.R file. Here’s an example where we show the study area using the ancillary function get_bb() to retrieve the project’s bounding box.\n\nsource(\"setup.R\", echo = FALSE)\nbb = get_bb(form = 'polygon')\ncoast = rnaturalearth::ne_coastline(scale = 'large', returnclass = 'sf')\nplot(sf::st_geometry(coast), extent = bb, axes = TRUE, reset = FALSE)\nplot(bb, lwd = 2, border = 'orange', add = TRUE)\n\n\n\n\nThe setup file also checks for the required packages, and will attempt to install them into the user’s R library directory if not already installed.\n\n\n3.3 Fetching data\nThe robis package facilitates easy access to OBIS which is a huge public database for oceanographic species information. We have written a wrapper function to download the Mola mola species records in our study region. To simplify our task we drop may columns of data from that delivered by OBIS, but there is much in the original worth exploring. Note you can use this function to access other species in other parts of the world.\n\n\n\n\n\n\nNote\n\n\n\nNote that we have already fetched the data, so we don’t run this next step (but you can if you like to get updated data.)\n\n\n\nif (!file.exists('data/obis/Mola_mola.gpkg')){\n x = fetch_obis(scientificname = 'Mola mola')\n}\n\nSince we already have the data, we need only read it which we do below. We also “glimpse” at the data so we can get a sense of the variables within the data frame and their respective data types.\n\nx = read_obis(scientificname = 'Mola mola') |>\n dplyr::glimpse()\n\nRows: 10,646\nColumns: 8\n$ occurrenceID <chr> \"1817_15415\", \"283_15788\", \"513_6751\", \"513_49055\", \"513…\n$ date <date> 2016-08-07, 1980-05-23, 2002-06-17, 2009-07-10, 2009-08…\n$ basisOfRecord <chr> \"HumanObservation\", \"HumanObservation\", \"HumanObservatio…\n$ bathymetry <dbl> 121, 102, 65, 166, 133, 19, 218, 90, 1283, 160, 219, 97,…\n$ shoredistance <dbl> 134559, 121292, 77684, 127643, 58028, 524, 94816, 141127…\n$ sst <dbl> 16.36, 15.44, 13.38, 11.70, 9.20, 12.35, 11.79, 13.90, 1…\n$ sss <dbl> 34.10, 33.41, 32.64, 32.22, 30.99, 31.38, 32.12, 33.38, …\n$ geom <POINT [°]> POINT (-72.8074 39.056), POINT (-72.283 39.733), P…\n\n\nLet’s see what we found on a map. We first plot the coastline, but provide the bounding box to establish the limits of the plot. Then we add the box itself and then the points.\n\nplot(sf::st_geometry(coast), extent = bb, axes = TRUE, reset = FALSE)\nplot(bb, lwd = 2, border = 'orange', add = TRUE)\nplot(sf::st_geometry(x), pch = \"+\", col = 'blue', add = TRUE)\n\n\n\n\n\n\n3.4 Data storage\nWe have set up a data directory, data, for storing data collected for the project. To start out there isn’t much more than the downloaded data set, but we’ll added to it as we go. We try to keep the data storage area tidy by using subdirectories.\n\nfs::dir_tree(\"data\", recurse = TRUE)\n\ndata\n├── bkg\n│ ├── bkg-covariates.gpkg\n│ └── buffered-polygon.gpkg\n├── model\n│ ├── v1\n│ │ └── v1.0\n│ │ └── model_v1.0.rds\n│ ├── v2\n│ │ ├── v2.Apr.rds\n│ │ ├── v2.Aug.rds\n│ │ ├── v2.Dec.rds\n│ │ ├── v2.Feb.rds\n│ │ ├── v2.Jan.rds\n│ │ ├── v2.Jul.rds\n│ │ ├── v2.Jun.rds\n│ │ ├── v2.Mar.rds\n│ │ ├── v2.May.rds\n│ │ ├── v2.Nov.rds\n│ │ ├── v2.Oct.rds\n│ │ └── v2.Sep.rds\n│ └── v3\n│ └── v3.0\n│ └── model_v3.0.rds\n├── nbs\n│ ├── 2000\n│ │ ├── 0101\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2000-01-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2000-01-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2000-01-01.tif\n│ │ ├── 0201\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2000-02-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2000-02-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2000-02-01.tif\n│ │ ├── 0301\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2000-03-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2000-03-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2000-03-01.tif\n│ │ ├── 0401\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2000-04-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2000-04-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2000-04-01.tif\n│ │ ├── 0501\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2000-05-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2000-05-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2000-05-01.tif\n│ │ ├── 0601\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2000-06-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2000-06-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2000-06-01.tif\n│ │ ├── 0701\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2000-07-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2000-07-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2000-07-01.tif\n│ │ ├── 0801\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2000-08-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2000-08-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2000-08-01.tif\n│ │ ├── 0901\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2000-09-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2000-09-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2000-09-01.tif\n│ │ ├── 1001\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2000-10-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2000-10-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2000-10-01.tif\n│ │ ├── 1101\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2000-11-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2000-11-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2000-11-01.tif\n│ │ └── 1201\n│ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2000-12-01.tif\n│ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2000-12-01.tif\n│ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2000-12-01.tif\n│ ├── 2001\n│ │ ├── 0101\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2001-01-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2001-01-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2001-01-01.tif\n│ │ ├── 0201\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2001-02-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2001-02-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2001-02-01.tif\n│ │ ├── 0301\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2001-03-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2001-03-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2001-03-01.tif\n│ │ ├── 0401\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2001-04-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2001-04-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2001-04-01.tif\n│ │ ├── 0501\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2001-05-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2001-05-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2001-05-01.tif\n│ │ ├── 0601\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2001-06-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2001-06-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2001-06-01.tif\n│ │ ├── 0701\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2001-07-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2001-07-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2001-07-01.tif\n│ │ ├── 0801\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2001-08-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2001-08-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2001-08-01.tif\n│ │ ├── 0901\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2001-09-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2001-09-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2001-09-01.tif\n│ │ ├── 1001\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2001-10-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2001-10-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2001-10-01.tif\n│ │ ├── 1101\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2001-11-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2001-11-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2001-11-01.tif\n│ │ └── 1201\n│ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2001-12-01.tif\n│ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2001-12-01.tif\n│ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2001-12-01.tif\n│ ├── 2002\n│ │ ├── 0101\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2002-01-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2002-01-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2002-01-01.tif\n│ │ ├── 0201\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2002-02-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2002-02-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2002-02-01.tif\n│ │ ├── 0301\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2002-03-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2002-03-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2002-03-01.tif\n│ │ ├── 0401\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2002-04-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2002-04-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2002-04-01.tif\n│ │ ├── 0501\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2002-05-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2002-05-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2002-05-01.tif\n│ │ ├── 0601\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2002-06-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2002-06-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2002-06-01.tif\n│ │ ├── 0701\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2002-07-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2002-07-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2002-07-01.tif\n│ │ ├── 0801\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2002-08-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2002-08-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2002-08-01.tif\n│ │ ├── 0901\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2002-09-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2002-09-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2002-09-01.tif\n│ │ ├── 1001\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2002-10-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2002-10-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2002-10-01.tif\n│ │ ├── 1101\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2002-11-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2002-11-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2002-11-01.tif\n│ │ └── 1201\n│ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2002-12-01.tif\n│ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2002-12-01.tif\n│ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2002-12-01.tif\n│ ├── 2003\n│ │ ├── 0101\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2003-01-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2003-01-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2003-01-01.tif\n│ │ ├── 0201\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2003-02-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2003-02-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2003-02-01.tif\n│ │ ├── 0301\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2003-03-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2003-03-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2003-03-01.tif\n│ │ ├── 0401\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2003-04-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2003-04-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2003-04-01.tif\n│ │ ├── 0501\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2003-05-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2003-05-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2003-05-01.tif\n│ │ ├── 0601\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2003-06-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2003-06-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2003-06-01.tif\n│ │ ├── 0701\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2003-07-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2003-07-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2003-07-01.tif\n│ │ ├── 0801\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2003-08-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2003-08-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2003-08-01.tif\n│ │ ├── 0901\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2003-09-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2003-09-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2003-09-01.tif\n│ │ ├── 1001\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2003-10-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2003-10-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2003-10-01.tif\n│ │ ├── 1101\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2003-11-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2003-11-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2003-11-01.tif\n│ │ └── 1201\n│ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2003-12-01.tif\n│ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2003-12-01.tif\n│ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2003-12-01.tif\n│ ├── 2004\n│ │ ├── 0101\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2004-01-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2004-01-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2004-01-01.tif\n│ │ ├── 0201\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2004-02-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2004-02-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2004-02-01.tif\n│ │ ├── 0301\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2004-03-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2004-03-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2004-03-01.tif\n│ │ ├── 0401\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2004-04-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2004-04-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2004-04-01.tif\n│ │ ├── 0501\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2004-05-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2004-05-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2004-05-01.tif\n│ │ ├── 0601\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2004-06-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2004-06-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2004-06-01.tif\n│ │ ├── 0701\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2004-07-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2004-07-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2004-07-01.tif\n│ │ ├── 0801\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2004-08-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2004-08-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2004-08-01.tif\n│ │ ├── 0901\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2004-09-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2004-09-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2004-09-01.tif\n│ │ ├── 1001\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2004-10-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2004-10-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2004-10-01.tif\n│ │ ├── 1101\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2004-11-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2004-11-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2004-11-01.tif\n│ │ └── 1201\n│ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2004-12-01.tif\n│ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2004-12-01.tif\n│ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2004-12-01.tif\n│ ├── 2005\n│ │ ├── 0101\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2005-01-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2005-01-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2005-01-01.tif\n│ │ ├── 0201\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2005-02-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2005-02-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2005-02-01.tif\n│ │ ├── 0301\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2005-03-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2005-03-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2005-03-01.tif\n│ │ ├── 0401\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2005-04-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2005-04-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2005-04-01.tif\n│ │ ├── 0501\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2005-05-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2005-05-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2005-05-01.tif\n│ │ ├── 0601\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2005-06-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2005-06-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2005-06-01.tif\n│ │ ├── 0701\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2005-07-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2005-07-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2005-07-01.tif\n│ │ ├── 0801\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2005-08-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2005-08-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2005-08-01.tif\n│ │ ├── 0901\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2005-09-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2005-09-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2005-09-01.tif\n│ │ ├── 1001\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2005-10-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2005-10-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2005-10-01.tif\n│ │ ├── 1101\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2005-11-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2005-11-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2005-11-01.tif\n│ │ └── 1201\n│ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2005-12-01.tif\n│ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2005-12-01.tif\n│ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2005-12-01.tif\n│ ├── 2006\n│ │ ├── 0101\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2006-01-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2006-01-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2006-01-01.tif\n│ │ ├── 0201\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2006-02-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2006-02-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2006-02-01.tif\n│ │ ├── 0301\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2006-03-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2006-03-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2006-03-01.tif\n│ │ ├── 0401\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2006-04-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2006-04-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2006-04-01.tif\n│ │ ├── 0501\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2006-05-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2006-05-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2006-05-01.tif\n│ │ ├── 0601\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2006-06-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2006-06-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2006-06-01.tif\n│ │ ├── 0701\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2006-07-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2006-07-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2006-07-01.tif\n│ │ ├── 0801\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2006-08-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2006-08-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2006-08-01.tif\n│ │ ├── 0901\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2006-09-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2006-09-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2006-09-01.tif\n│ │ ├── 1001\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2006-10-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2006-10-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2006-10-01.tif\n│ │ ├── 1101\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2006-11-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2006-11-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2006-11-01.tif\n│ │ └── 1201\n│ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2006-12-01.tif\n│ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2006-12-01.tif\n│ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2006-12-01.tif\n│ ├── 2007\n│ │ ├── 0101\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2007-01-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2007-01-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2007-01-01.tif\n│ │ ├── 0201\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2007-02-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2007-02-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2007-02-01.tif\n│ │ ├── 0301\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2007-03-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2007-03-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2007-03-01.tif\n│ │ ├── 0401\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2007-04-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2007-04-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2007-04-01.tif\n│ │ ├── 0501\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2007-05-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2007-05-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2007-05-01.tif\n│ │ ├── 0601\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2007-06-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2007-06-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2007-06-01.tif\n│ │ ├── 0701\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2007-07-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2007-07-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2007-07-01.tif\n│ │ ├── 0801\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2007-08-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2007-08-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2007-08-01.tif\n│ │ ├── 0901\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2007-09-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2007-09-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2007-09-01.tif\n│ │ ├── 1001\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2007-10-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2007-10-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2007-10-01.tif\n│ │ ├── 1101\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2007-11-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2007-11-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2007-11-01.tif\n│ │ └── 1201\n│ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2007-12-01.tif\n│ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2007-12-01.tif\n│ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2007-12-01.tif\n│ ├── 2008\n│ │ ├── 0101\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2008-01-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2008-01-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2008-01-01.tif\n│ │ ├── 0201\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2008-02-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2008-02-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2008-02-01.tif\n│ │ ├── 0301\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2008-03-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2008-03-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2008-03-01.tif\n│ │ ├── 0401\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2008-04-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2008-04-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2008-04-01.tif\n│ │ ├── 0501\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2008-05-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2008-05-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2008-05-01.tif\n│ │ ├── 0601\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2008-06-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2008-06-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2008-06-01.tif\n│ │ ├── 0701\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2008-07-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2008-07-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2008-07-01.tif\n│ │ ├── 0801\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2008-08-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2008-08-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2008-08-01.tif\n│ │ ├── 0901\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2008-09-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2008-09-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2008-09-01.tif\n│ │ ├── 1001\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2008-10-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2008-10-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2008-10-01.tif\n│ │ ├── 1101\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2008-11-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2008-11-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2008-11-01.tif\n│ │ └── 1201\n│ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2008-12-01.tif\n│ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2008-12-01.tif\n│ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2008-12-01.tif\n│ ├── 2009\n│ │ ├── 0101\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2009-01-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2009-01-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2009-01-01.tif\n│ │ ├── 0201\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2009-02-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2009-02-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2009-02-01.tif\n│ │ ├── 0301\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2009-03-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2009-03-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2009-03-01.tif\n│ │ ├── 0401\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2009-04-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2009-04-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2009-04-01.tif\n│ │ ├── 0501\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2009-05-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2009-05-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2009-05-01.tif\n│ │ ├── 0601\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2009-06-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2009-06-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2009-06-01.tif\n│ │ ├── 0701\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2009-07-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2009-07-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2009-07-01.tif\n│ │ ├── 0801\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2009-08-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2009-08-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2009-08-01.tif\n│ │ ├── 0901\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2009-09-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2009-09-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2009-09-01.tif\n│ │ ├── 1001\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2009-10-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2009-10-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2009-10-01.tif\n│ │ ├── 1101\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2009-11-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2009-11-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2009-11-01.tif\n│ │ └── 1201\n│ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2009-12-01.tif\n│ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2009-12-01.tif\n│ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2009-12-01.tif\n│ ├── 2010\n│ │ ├── 0101\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2010-01-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2010-01-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2010-01-01.tif\n│ │ ├── 0201\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2010-02-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2010-02-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2010-02-01.tif\n│ │ ├── 0301\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2010-03-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2010-03-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2010-03-01.tif\n│ │ ├── 0401\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2010-04-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2010-04-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2010-04-01.tif\n│ │ ├── 0501\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2010-05-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2010-05-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2010-05-01.tif\n│ │ ├── 0601\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2010-06-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2010-06-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2010-06-01.tif\n│ │ ├── 0701\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2010-07-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2010-07-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2010-07-01.tif\n│ │ ├── 0801\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2010-08-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2010-08-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2010-08-01.tif\n│ │ ├── 0901\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2010-09-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2010-09-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2010-09-01.tif\n│ │ ├── 1001\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2010-10-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2010-10-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2010-10-01.tif\n│ │ ├── 1101\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2010-11-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2010-11-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2010-11-01.tif\n│ │ └── 1201\n│ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2010-12-01.tif\n│ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2010-12-01.tif\n│ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2010-12-01.tif\n│ ├── 2011\n│ │ ├── 0101\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2011-01-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2011-01-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2011-01-01.tif\n│ │ ├── 0201\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2011-02-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2011-02-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2011-02-01.tif\n│ │ ├── 0301\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2011-03-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2011-03-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2011-03-01.tif\n│ │ ├── 0401\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2011-04-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2011-04-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2011-04-01.tif\n│ │ ├── 0501\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2011-05-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2011-05-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2011-05-01.tif\n│ │ ├── 0601\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2011-06-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2011-06-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2011-06-01.tif\n│ │ ├── 0701\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2011-07-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2011-07-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2011-07-01.tif\n│ │ ├── 0801\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2011-08-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2011-08-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2011-08-01.tif\n│ │ ├── 0901\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2011-09-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2011-09-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2011-09-01.tif\n│ │ ├── 1001\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2011-10-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2011-10-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2011-10-01.tif\n│ │ ├── 1101\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2011-11-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2011-11-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2011-11-01.tif\n│ │ └── 1201\n│ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2011-12-01.tif\n│ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2011-12-01.tif\n│ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2011-12-01.tif\n│ ├── 2012\n│ │ ├── 0101\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2012-01-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2012-01-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2012-01-01.tif\n│ │ ├── 0201\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2012-02-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2012-02-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2012-02-01.tif\n│ │ ├── 0301\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2012-03-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2012-03-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2012-03-01.tif\n│ │ ├── 0401\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2012-04-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2012-04-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2012-04-01.tif\n│ │ ├── 0501\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2012-05-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2012-05-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2012-05-01.tif\n│ │ ├── 0601\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2012-06-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2012-06-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2012-06-01.tif\n│ │ ├── 0701\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2012-07-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2012-07-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2012-07-01.tif\n│ │ ├── 0801\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2012-08-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2012-08-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2012-08-01.tif\n│ │ ├── 0901\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2012-09-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2012-09-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2012-09-01.tif\n│ │ ├── 1001\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2012-10-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2012-10-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2012-10-01.tif\n│ │ ├── 1101\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2012-11-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2012-11-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2012-11-01.tif\n│ │ └── 1201\n│ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2012-12-01.tif\n│ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2012-12-01.tif\n│ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2012-12-01.tif\n│ ├── 2013\n│ │ ├── 0101\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2013-01-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2013-01-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2013-01-01.tif\n│ │ ├── 0201\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2013-02-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2013-02-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2013-02-01.tif\n│ │ ├── 0301\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2013-03-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2013-03-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2013-03-01.tif\n│ │ ├── 0401\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2013-04-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2013-04-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2013-04-01.tif\n│ │ ├── 0501\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2013-05-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2013-05-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2013-05-01.tif\n│ │ ├── 0601\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2013-06-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2013-06-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2013-06-01.tif\n│ │ ├── 0701\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2013-07-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2013-07-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2013-07-01.tif\n│ │ ├── 0801\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2013-08-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2013-08-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2013-08-01.tif\n│ │ ├── 0901\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2013-09-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2013-09-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2013-09-01.tif\n│ │ ├── 1001\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2013-10-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2013-10-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2013-10-01.tif\n│ │ ├── 1101\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2013-11-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2013-11-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2013-11-01.tif\n│ │ └── 1201\n│ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2013-12-01.tif\n│ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2013-12-01.tif\n│ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2013-12-01.tif\n│ ├── 2014\n│ │ ├── 0101\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2014-01-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2014-01-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2014-01-01.tif\n│ │ ├── 0201\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2014-02-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2014-02-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2014-02-01.tif\n│ │ ├── 0301\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2014-03-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2014-03-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2014-03-01.tif\n│ │ ├── 0401\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2014-04-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2014-04-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2014-04-01.tif\n│ │ ├── 0501\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2014-05-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2014-05-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2014-05-01.tif\n│ │ ├── 0601\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2014-06-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2014-06-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2014-06-01.tif\n│ │ ├── 0701\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2014-07-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2014-07-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2014-07-01.tif\n│ │ ├── 0801\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2014-08-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2014-08-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2014-08-01.tif\n│ │ ├── 0901\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2014-09-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2014-09-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2014-09-01.tif\n│ │ ├── 1001\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2014-10-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2014-10-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2014-10-01.tif\n│ │ ├── 1101\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2014-11-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2014-11-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2014-11-01.tif\n│ │ └── 1201\n│ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2014-12-01.tif\n│ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2014-12-01.tif\n│ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2014-12-01.tif\n│ ├── 2015\n│ │ ├── 0101\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2015-01-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2015-01-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2015-01-01.tif\n│ │ ├── 0201\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2015-02-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2015-02-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2015-02-01.tif\n│ │ ├── 0301\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2015-03-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2015-03-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2015-03-01.tif\n│ │ ├── 0401\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2015-04-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2015-04-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2015-04-01.tif\n│ │ ├── 0501\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2015-05-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2015-05-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2015-05-01.tif\n│ │ ├── 0601\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2015-06-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2015-06-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2015-06-01.tif\n│ │ ├── 0701\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2015-07-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2015-07-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2015-07-01.tif\n│ │ ├── 0801\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2015-08-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2015-08-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2015-08-01.tif\n│ │ ├── 0901\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2015-09-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2015-09-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2015-09-01.tif\n│ │ ├── 1001\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2015-10-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2015-10-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2015-10-01.tif\n│ │ ├── 1101\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2015-11-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2015-11-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2015-11-01.tif\n│ │ └── 1201\n│ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2015-12-01.tif\n│ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2015-12-01.tif\n│ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2015-12-01.tif\n│ ├── 2016\n│ │ ├── 0101\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2016-01-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2016-01-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2016-01-01.tif\n│ │ ├── 0201\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2016-02-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2016-02-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2016-02-01.tif\n│ │ ├── 0301\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2016-03-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2016-03-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2016-03-01.tif\n│ │ ├── 0401\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2016-04-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2016-04-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2016-04-01.tif\n│ │ ├── 0501\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2016-05-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2016-05-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2016-05-01.tif\n│ │ ├── 0601\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2016-06-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2016-06-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2016-06-01.tif\n│ │ ├── 0701\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2016-07-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2016-07-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2016-07-01.tif\n│ │ ├── 0801\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2016-08-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2016-08-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2016-08-01.tif\n│ │ ├── 0901\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2016-09-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2016-09-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2016-09-01.tif\n│ │ ├── 1001\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2016-10-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2016-10-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2016-10-01.tif\n│ │ ├── 1101\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2016-11-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2016-11-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2016-11-01.tif\n│ │ └── 1201\n│ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2016-12-01.tif\n│ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2016-12-01.tif\n│ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2016-12-01.tif\n│ ├── 2017\n│ │ ├── 0101\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2017-01-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2017-01-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2017-01-01.tif\n│ │ ├── 0201\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2017-02-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2017-02-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2017-02-01.tif\n│ │ ├── 0301\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2017-03-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2017-03-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2017-03-01.tif\n│ │ ├── 0401\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2017-04-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2017-04-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2017-04-01.tif\n│ │ ├── 0501\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2017-05-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2017-05-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2017-05-01.tif\n│ │ ├── 0601\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2017-06-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2017-06-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2017-06-01.tif\n│ │ ├── 0701\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2017-07-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2017-07-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2017-07-01.tif\n│ │ ├── 0801\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2017-08-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2017-08-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2017-08-01.tif\n│ │ ├── 0901\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2017-09-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2017-09-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2017-09-01.tif\n│ │ ├── 1001\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2017-10-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2017-10-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2017-10-01.tif\n│ │ ├── 1101\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2017-11-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2017-11-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2017-11-01.tif\n│ │ └── 1201\n│ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2017-12-01.tif\n│ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2017-12-01.tif\n│ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2017-12-01.tif\n│ ├── 2018\n│ │ ├── 0101\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2018-01-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2018-01-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2018-01-01.tif\n│ │ ├── 0201\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2018-02-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2018-02-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2018-02-01.tif\n│ │ ├── 0301\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2018-03-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2018-03-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2018-03-01.tif\n│ │ ├── 0401\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2018-04-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2018-04-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2018-04-01.tif\n│ │ ├── 0501\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2018-05-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2018-05-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2018-05-01.tif\n│ │ ├── 0601\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2018-06-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2018-06-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2018-06-01.tif\n│ │ ├── 0701\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2018-07-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2018-07-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2018-07-01.tif\n│ │ ├── 0801\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2018-08-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2018-08-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2018-08-01.tif\n│ │ ├── 0901\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2018-09-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2018-09-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2018-09-01.tif\n│ │ ├── 1001\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2018-10-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2018-10-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2018-10-01.tif\n│ │ ├── 1101\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2018-11-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2018-11-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2018-11-01.tif\n│ │ └── 1201\n│ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2018-12-01.tif\n│ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2018-12-01.tif\n│ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2018-12-01.tif\n│ ├── 2019\n│ │ ├── 0101\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2019-01-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2019-01-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2019-01-01.tif\n│ │ ├── 0201\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2019-02-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2019-02-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2019-02-01.tif\n│ │ ├── 0301\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2019-03-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2019-03-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2019-03-01.tif\n│ │ ├── 0401\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2019-04-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2019-04-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2019-04-01.tif\n│ │ ├── 0501\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2019-05-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2019-05-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2019-05-01.tif\n│ │ ├── 0601\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2019-06-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2019-06-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2019-06-01.tif\n│ │ ├── 0701\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2019-07-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2019-07-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2019-07-01.tif\n│ │ ├── 0801\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2019-08-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2019-08-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2019-08-01.tif\n│ │ ├── 0901\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2019-09-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2019-09-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2019-09-01.tif\n│ │ ├── 1001\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2019-10-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2019-10-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2019-10-01.tif\n│ │ ├── 1101\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2019-11-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2019-11-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2019-11-01.tif\n│ │ └── 1201\n│ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2019-12-01.tif\n│ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2019-12-01.tif\n│ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2019-12-01.tif\n│ ├── 2020\n│ │ ├── 0101\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2020-01-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2020-01-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2020-01-01.tif\n│ │ ├── 0201\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2020-02-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2020-02-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2020-02-01.tif\n│ │ ├── 0301\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2020-03-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2020-03-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2020-03-01.tif\n│ │ ├── 0401\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2020-04-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2020-04-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2020-04-01.tif\n│ │ ├── 0501\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2020-05-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2020-05-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2020-05-01.tif\n│ │ ├── 0601\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2020-06-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2020-06-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2020-06-01.tif\n│ │ ├── 0701\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2020-07-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2020-07-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2020-07-01.tif\n│ │ ├── 0801\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2020-08-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2020-08-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2020-08-01.tif\n│ │ ├── 0901\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2020-09-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2020-09-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2020-09-01.tif\n│ │ ├── 1001\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2020-10-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2020-10-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2020-10-01.tif\n│ │ ├── 1101\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2020-11-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2020-11-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2020-11-01.tif\n│ │ └── 1201\n│ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2020-12-01.tif\n│ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2020-12-01.tif\n│ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2020-12-01.tif\n│ ├── 2021\n│ │ ├── 0101\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2021-01-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2021-01-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2021-01-01.tif\n│ │ ├── 0201\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2021-02-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2021-02-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2021-02-01.tif\n│ │ ├── 0301\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2021-03-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2021-03-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2021-03-01.tif\n│ │ ├── 0401\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2021-04-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2021-04-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2021-04-01.tif\n│ │ ├── 0501\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2021-05-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2021-05-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2021-05-01.tif\n│ │ ├── 0601\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2021-06-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2021-06-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2021-06-01.tif\n│ │ ├── 0701\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2021-07-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2021-07-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2021-07-01.tif\n│ │ ├── 0801\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2021-08-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2021-08-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2021-08-01.tif\n│ │ ├── 0901\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2021-09-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2021-09-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2021-09-01.tif\n│ │ ├── 1001\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2021-10-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2021-10-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2021-10-01.tif\n│ │ ├── 1101\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2021-11-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2021-11-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2021-11-01.tif\n│ │ └── 1201\n│ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2021-12-01.tif\n│ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2021-12-01.tif\n│ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2021-12-01.tif\n│ ├── 2022\n│ │ ├── 0101\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2022-01-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2022-01-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2022-01-01.tif\n│ │ ├── 0201\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2022-02-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2022-02-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2022-02-01.tif\n│ │ ├── 0301\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2022-03-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2022-03-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2022-03-01.tif\n│ │ ├── 0401\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2022-04-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2022-04-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2022-04-01.tif\n│ │ ├── 0501\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2022-05-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2022-05-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2022-05-01.tif\n│ │ ├── 0601\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2022-06-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2022-06-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2022-06-01.tif\n│ │ ├── 0701\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2022-07-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2022-07-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2022-07-01.tif\n│ │ ├── 0801\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2022-08-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2022-08-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2022-08-01.tif\n│ │ ├── 0901\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2022-09-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2022-09-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2022-09-01.tif\n│ │ ├── 1001\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2022-10-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2022-10-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2022-10-01.tif\n│ │ ├── 1101\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2022-11-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2022-11-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2022-11-01.tif\n│ │ └── 1201\n│ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2022-12-01.tif\n│ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2022-12-01.tif\n│ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2022-12-01.tif\n│ ├── 2023\n│ │ ├── 0101\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2023-01-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2023-01-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2023-01-01.tif\n│ │ ├── 0201\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2023-02-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2023-02-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2023-02-01.tif\n│ │ ├── 0301\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2023-03-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2023-03-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2023-03-01.tif\n│ │ ├── 0401\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2023-04-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2023-04-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2023-04-01.tif\n│ │ ├── 0501\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2023-05-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2023-05-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2023-05-01.tif\n│ │ ├── 0601\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2023-06-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2023-06-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2023-06-01.tif\n│ │ ├── 0701\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2023-07-01.tif\n│ │ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2023-07-01.tif\n│ │ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2023-07-01.tif\n│ │ └── 0801\n│ │ ├── uvcomp_SCIMonthlyGlobal.u_wind.2023-08-01.tif\n│ │ ├── uvcomp_SCIMonthlyGlobal.v_wind.2023-08-01.tif\n│ │ └── uvcomp_SCIMonthlyGlobal.windspeed.2023-08-01.tif\n│ └── database.csv.gz\n├── nbs2\n├── obis\n│ └── Mola_mola.gpkg\n├── obs\n│ ├── obs-covariates.gpkg\n│ └── obs.gpkg\n└── oisst\n ├── 2000\n │ ├── 0101\n │ │ └── sst.mon.mean.2000-01-01.tif\n │ ├── 0201\n │ │ └── sst.mon.mean.2000-02-01.tif\n │ ├── 0301\n │ │ └── sst.mon.mean.2000-03-01.tif\n │ ├── 0401\n │ │ └── sst.mon.mean.2000-04-01.tif\n │ ├── 0501\n │ │ └── sst.mon.mean.2000-05-01.tif\n │ ├── 0601\n │ │ └── sst.mon.mean.2000-06-01.tif\n │ ├── 0701\n │ │ └── sst.mon.mean.2000-07-01.tif\n │ ├── 0801\n │ │ └── sst.mon.mean.2000-08-01.tif\n │ ├── 0901\n │ │ └── sst.mon.mean.2000-09-01.tif\n │ ├── 1001\n │ │ └── sst.mon.mean.2000-10-01.tif\n │ ├── 1101\n │ │ └── sst.mon.mean.2000-11-01.tif\n │ └── 1201\n │ └── sst.mon.mean.2000-12-01.tif\n ├── 2001\n │ ├── 0101\n │ │ └── sst.mon.mean.2001-01-01.tif\n │ ├── 0201\n │ │ └── sst.mon.mean.2001-02-01.tif\n │ ├── 0301\n │ │ └── sst.mon.mean.2001-03-01.tif\n │ ├── 0401\n │ │ └── sst.mon.mean.2001-04-01.tif\n │ ├── 0501\n │ │ └── sst.mon.mean.2001-05-01.tif\n │ ├── 0601\n │ │ └── sst.mon.mean.2001-06-01.tif\n │ ├── 0701\n │ │ └── sst.mon.mean.2001-07-01.tif\n │ ├── 0801\n │ │ └── sst.mon.mean.2001-08-01.tif\n │ ├── 0901\n │ │ └── sst.mon.mean.2001-09-01.tif\n │ ├── 1001\n │ │ └── sst.mon.mean.2001-10-01.tif\n │ ├── 1101\n │ │ └── sst.mon.mean.2001-11-01.tif\n │ └── 1201\n │ └── sst.mon.mean.2001-12-01.tif\n ├── 2002\n │ ├── 0101\n │ │ └── sst.mon.mean.2002-01-01.tif\n │ ├── 0201\n │ │ └── sst.mon.mean.2002-02-01.tif\n │ ├── 0301\n │ │ └── sst.mon.mean.2002-03-01.tif\n │ ├── 0401\n │ │ └── sst.mon.mean.2002-04-01.tif\n │ ├── 0501\n │ │ └── sst.mon.mean.2002-05-01.tif\n │ ├── 0601\n │ │ └── sst.mon.mean.2002-06-01.tif\n │ ├── 0701\n │ │ └── sst.mon.mean.2002-07-01.tif\n │ ├── 0801\n │ │ └── sst.mon.mean.2002-08-01.tif\n │ ├── 0901\n │ │ └── sst.mon.mean.2002-09-01.tif\n │ ├── 1001\n │ │ └── sst.mon.mean.2002-10-01.tif\n │ ├── 1101\n │ │ └── sst.mon.mean.2002-11-01.tif\n │ └── 1201\n │ └── sst.mon.mean.2002-12-01.tif\n ├── 2003\n │ ├── 0101\n │ │ └── sst.mon.mean.2003-01-01.tif\n │ ├── 0201\n │ │ └── sst.mon.mean.2003-02-01.tif\n │ ├── 0301\n │ │ └── sst.mon.mean.2003-03-01.tif\n │ ├── 0401\n │ │ └── sst.mon.mean.2003-04-01.tif\n │ ├── 0501\n │ │ └── sst.mon.mean.2003-05-01.tif\n │ ├── 0601\n │ │ └── sst.mon.mean.2003-06-01.tif\n │ ├── 0701\n │ │ └── sst.mon.mean.2003-07-01.tif\n │ ├── 0801\n │ │ └── sst.mon.mean.2003-08-01.tif\n │ ├── 0901\n │ │ └── sst.mon.mean.2003-09-01.tif\n │ ├── 1001\n │ │ └── sst.mon.mean.2003-10-01.tif\n │ ├── 1101\n │ │ └── sst.mon.mean.2003-11-01.tif\n │ └── 1201\n │ └── sst.mon.mean.2003-12-01.tif\n ├── 2004\n │ ├── 0101\n │ │ └── sst.mon.mean.2004-01-01.tif\n │ ├── 0201\n │ │ └── sst.mon.mean.2004-02-01.tif\n │ ├── 0301\n │ │ └── sst.mon.mean.2004-03-01.tif\n │ ├── 0401\n │ │ └── sst.mon.mean.2004-04-01.tif\n │ ├── 0501\n │ │ └── sst.mon.mean.2004-05-01.tif\n │ ├── 0601\n │ │ └── sst.mon.mean.2004-06-01.tif\n │ ├── 0701\n │ │ └── sst.mon.mean.2004-07-01.tif\n │ ├── 0801\n │ │ └── sst.mon.mean.2004-08-01.tif\n │ ├── 0901\n │ │ └── sst.mon.mean.2004-09-01.tif\n │ ├── 1001\n │ │ └── sst.mon.mean.2004-10-01.tif\n │ ├── 1101\n │ │ └── sst.mon.mean.2004-11-01.tif\n │ └── 1201\n │ └── sst.mon.mean.2004-12-01.tif\n ├── 2005\n │ ├── 0101\n │ │ └── sst.mon.mean.2005-01-01.tif\n │ ├── 0201\n │ │ └── sst.mon.mean.2005-02-01.tif\n │ ├── 0301\n │ │ └── sst.mon.mean.2005-03-01.tif\n │ ├── 0401\n │ │ └── sst.mon.mean.2005-04-01.tif\n │ ├── 0501\n │ │ └── sst.mon.mean.2005-05-01.tif\n │ ├── 0601\n │ │ └── sst.mon.mean.2005-06-01.tif\n │ ├── 0701\n │ │ └── sst.mon.mean.2005-07-01.tif\n │ ├── 0801\n │ │ └── sst.mon.mean.2005-08-01.tif\n │ ├── 0901\n │ │ └── sst.mon.mean.2005-09-01.tif\n │ ├── 1001\n │ │ └── sst.mon.mean.2005-10-01.tif\n │ ├── 1101\n │ │ └── sst.mon.mean.2005-11-01.tif\n │ └── 1201\n │ └── sst.mon.mean.2005-12-01.tif\n ├── 2006\n │ ├── 0101\n │ │ └── sst.mon.mean.2006-01-01.tif\n │ ├── 0201\n │ │ └── sst.mon.mean.2006-02-01.tif\n │ ├── 0301\n │ │ └── sst.mon.mean.2006-03-01.tif\n │ ├── 0401\n │ │ └── sst.mon.mean.2006-04-01.tif\n │ ├── 0501\n │ │ └── sst.mon.mean.2006-05-01.tif\n │ ├── 0601\n │ │ └── sst.mon.mean.2006-06-01.tif\n │ ├── 0701\n │ │ └── sst.mon.mean.2006-07-01.tif\n │ ├── 0801\n │ │ └── sst.mon.mean.2006-08-01.tif\n │ ├── 0901\n │ │ └── sst.mon.mean.2006-09-01.tif\n │ ├── 1001\n │ │ └── sst.mon.mean.2006-10-01.tif\n │ ├── 1101\n │ │ └── sst.mon.mean.2006-11-01.tif\n │ └── 1201\n │ └── sst.mon.mean.2006-12-01.tif\n ├── 2007\n │ ├── 0101\n │ │ └── sst.mon.mean.2007-01-01.tif\n │ ├── 0201\n │ │ └── sst.mon.mean.2007-02-01.tif\n │ ├── 0301\n │ │ └── sst.mon.mean.2007-03-01.tif\n │ ├── 0401\n │ │ └── sst.mon.mean.2007-04-01.tif\n │ ├── 0501\n │ │ └── sst.mon.mean.2007-05-01.tif\n │ ├── 0601\n │ │ └── sst.mon.mean.2007-06-01.tif\n │ ├── 0701\n │ │ └── sst.mon.mean.2007-07-01.tif\n │ ├── 0801\n │ │ └── sst.mon.mean.2007-08-01.tif\n │ ├── 0901\n │ │ └── sst.mon.mean.2007-09-01.tif\n │ ├── 1001\n │ │ └── sst.mon.mean.2007-10-01.tif\n │ ├── 1101\n │ │ └── sst.mon.mean.2007-11-01.tif\n │ └── 1201\n │ └── sst.mon.mean.2007-12-01.tif\n ├── 2008\n │ ├── 0101\n │ │ └── sst.mon.mean.2008-01-01.tif\n │ ├── 0201\n │ │ └── sst.mon.mean.2008-02-01.tif\n │ ├── 0301\n │ │ └── sst.mon.mean.2008-03-01.tif\n │ ├── 0401\n │ │ └── sst.mon.mean.2008-04-01.tif\n │ ├── 0501\n │ │ └── sst.mon.mean.2008-05-01.tif\n │ ├── 0601\n │ │ └── sst.mon.mean.2008-06-01.tif\n │ ├── 0701\n │ │ └── sst.mon.mean.2008-07-01.tif\n │ ├── 0801\n │ │ └── sst.mon.mean.2008-08-01.tif\n │ ├── 0901\n │ │ └── sst.mon.mean.2008-09-01.tif\n │ ├── 1001\n │ │ └── sst.mon.mean.2008-10-01.tif\n │ ├── 1101\n │ │ └── sst.mon.mean.2008-11-01.tif\n │ └── 1201\n │ └── sst.mon.mean.2008-12-01.tif\n ├── 2009\n │ ├── 0101\n │ │ └── sst.mon.mean.2009-01-01.tif\n │ ├── 0201\n │ │ └── sst.mon.mean.2009-02-01.tif\n │ ├── 0301\n │ │ └── sst.mon.mean.2009-03-01.tif\n │ ├── 0401\n │ │ └── sst.mon.mean.2009-04-01.tif\n │ ├── 0501\n │ │ └── sst.mon.mean.2009-05-01.tif\n │ ├── 0601\n │ │ └── sst.mon.mean.2009-06-01.tif\n │ ├── 0701\n │ │ └── sst.mon.mean.2009-07-01.tif\n │ ├── 0801\n │ │ └── sst.mon.mean.2009-08-01.tif\n │ ├── 0901\n │ │ └── sst.mon.mean.2009-09-01.tif\n │ ├── 1001\n │ │ └── sst.mon.mean.2009-10-01.tif\n │ ├── 1101\n │ │ └── sst.mon.mean.2009-11-01.tif\n │ └── 1201\n │ └── sst.mon.mean.2009-12-01.tif\n ├── 2010\n │ ├── 0101\n │ │ └── sst.mon.mean.2010-01-01.tif\n │ ├── 0201\n │ │ └── sst.mon.mean.2010-02-01.tif\n │ ├── 0301\n │ │ └── sst.mon.mean.2010-03-01.tif\n │ ├── 0401\n │ │ └── sst.mon.mean.2010-04-01.tif\n │ ├── 0501\n │ │ └── sst.mon.mean.2010-05-01.tif\n │ ├── 0601\n │ │ └── sst.mon.mean.2010-06-01.tif\n │ ├── 0701\n │ │ └── sst.mon.mean.2010-07-01.tif\n │ ├── 0801\n │ │ └── sst.mon.mean.2010-08-01.tif\n │ ├── 0901\n │ │ └── sst.mon.mean.2010-09-01.tif\n │ ├── 1001\n │ │ └── sst.mon.mean.2010-10-01.tif\n │ ├── 1101\n │ │ └── sst.mon.mean.2010-11-01.tif\n │ └── 1201\n │ └── sst.mon.mean.2010-12-01.tif\n ├── 2011\n │ ├── 0101\n │ │ └── sst.mon.mean.2011-01-01.tif\n │ ├── 0201\n │ │ └── sst.mon.mean.2011-02-01.tif\n │ ├── 0301\n │ │ └── sst.mon.mean.2011-03-01.tif\n │ ├── 0401\n │ │ └── sst.mon.mean.2011-04-01.tif\n │ ├── 0501\n │ │ └── sst.mon.mean.2011-05-01.tif\n │ ├── 0601\n │ │ └── sst.mon.mean.2011-06-01.tif\n │ ├── 0701\n │ │ └── sst.mon.mean.2011-07-01.tif\n │ ├── 0801\n │ │ └── sst.mon.mean.2011-08-01.tif\n │ ├── 0901\n │ │ └── sst.mon.mean.2011-09-01.tif\n │ ├── 1001\n │ │ └── sst.mon.mean.2011-10-01.tif\n │ ├── 1101\n │ │ └── sst.mon.mean.2011-11-01.tif\n │ └── 1201\n │ └── sst.mon.mean.2011-12-01.tif\n ├── 2012\n │ ├── 0101\n │ │ └── sst.mon.mean.2012-01-01.tif\n │ ├── 0201\n │ │ └── sst.mon.mean.2012-02-01.tif\n │ ├── 0301\n │ │ └── sst.mon.mean.2012-03-01.tif\n │ ├── 0401\n │ │ └── sst.mon.mean.2012-04-01.tif\n │ ├── 0501\n │ │ └── sst.mon.mean.2012-05-01.tif\n │ ├── 0601\n │ │ └── sst.mon.mean.2012-06-01.tif\n │ ├── 0701\n │ │ └── sst.mon.mean.2012-07-01.tif\n │ ├── 0801\n │ │ └── sst.mon.mean.2012-08-01.tif\n │ ├── 0901\n │ │ └── sst.mon.mean.2012-09-01.tif\n │ ├── 1001\n │ │ └── sst.mon.mean.2012-10-01.tif\n │ ├── 1101\n │ │ └── sst.mon.mean.2012-11-01.tif\n │ └── 1201\n │ └── sst.mon.mean.2012-12-01.tif\n ├── 2013\n │ ├── 0101\n │ │ └── sst.mon.mean.2013-01-01.tif\n │ ├── 0201\n │ │ └── sst.mon.mean.2013-02-01.tif\n │ ├── 0301\n │ │ └── sst.mon.mean.2013-03-01.tif\n │ ├── 0401\n │ │ └── sst.mon.mean.2013-04-01.tif\n │ ├── 0501\n │ │ └── sst.mon.mean.2013-05-01.tif\n │ ├── 0601\n │ │ └── sst.mon.mean.2013-06-01.tif\n │ ├── 0701\n │ │ └── sst.mon.mean.2013-07-01.tif\n │ ├── 0801\n │ │ └── sst.mon.mean.2013-08-01.tif\n │ ├── 0901\n │ │ └── sst.mon.mean.2013-09-01.tif\n │ ├── 1001\n │ │ └── sst.mon.mean.2013-10-01.tif\n │ ├── 1101\n │ │ └── sst.mon.mean.2013-11-01.tif\n │ └── 1201\n │ └── sst.mon.mean.2013-12-01.tif\n ├── 2014\n │ ├── 0101\n │ │ └── sst.mon.mean.2014-01-01.tif\n │ ├── 0201\n │ │ └── sst.mon.mean.2014-02-01.tif\n │ ├── 0301\n │ │ └── sst.mon.mean.2014-03-01.tif\n │ ├── 0401\n │ │ └── sst.mon.mean.2014-04-01.tif\n │ ├── 0501\n │ │ └── sst.mon.mean.2014-05-01.tif\n │ ├── 0601\n │ │ └── sst.mon.mean.2014-06-01.tif\n │ ├── 0701\n │ │ └── sst.mon.mean.2014-07-01.tif\n │ ├── 0801\n │ │ └── sst.mon.mean.2014-08-01.tif\n │ ├── 0901\n │ │ └── sst.mon.mean.2014-09-01.tif\n │ ├── 1001\n │ │ └── sst.mon.mean.2014-10-01.tif\n │ ├── 1101\n │ │ └── sst.mon.mean.2014-11-01.tif\n │ └── 1201\n │ └── sst.mon.mean.2014-12-01.tif\n ├── 2015\n │ ├── 0101\n │ │ └── sst.mon.mean.2015-01-01.tif\n │ ├── 0201\n │ │ └── sst.mon.mean.2015-02-01.tif\n │ ├── 0301\n │ │ └── sst.mon.mean.2015-03-01.tif\n │ ├── 0401\n │ │ └── sst.mon.mean.2015-04-01.tif\n │ ├── 0501\n │ │ └── sst.mon.mean.2015-05-01.tif\n │ ├── 0601\n │ │ └── sst.mon.mean.2015-06-01.tif\n │ ├── 0701\n │ │ └── sst.mon.mean.2015-07-01.tif\n │ ├── 0801\n │ │ └── sst.mon.mean.2015-08-01.tif\n │ ├── 0901\n │ │ └── sst.mon.mean.2015-09-01.tif\n │ ├── 1001\n │ │ └── sst.mon.mean.2015-10-01.tif\n │ ├── 1101\n │ │ └── sst.mon.mean.2015-11-01.tif\n │ └── 1201\n │ └── sst.mon.mean.2015-12-01.tif\n ├── 2016\n │ ├── 0101\n │ │ └── sst.mon.mean.2016-01-01.tif\n │ ├── 0201\n │ │ └── sst.mon.mean.2016-02-01.tif\n │ ├── 0301\n │ │ └── sst.mon.mean.2016-03-01.tif\n │ ├── 0401\n │ │ └── sst.mon.mean.2016-04-01.tif\n │ ├── 0501\n │ │ └── sst.mon.mean.2016-05-01.tif\n │ ├── 0601\n │ │ └── sst.mon.mean.2016-06-01.tif\n │ ├── 0701\n │ │ └── sst.mon.mean.2016-07-01.tif\n │ ├── 0801\n │ │ └── sst.mon.mean.2016-08-01.tif\n │ ├── 0901\n │ │ └── sst.mon.mean.2016-09-01.tif\n │ ├── 1001\n │ │ └── sst.mon.mean.2016-10-01.tif\n │ ├── 1101\n │ │ └── sst.mon.mean.2016-11-01.tif\n │ └── 1201\n │ └── sst.mon.mean.2016-12-01.tif\n ├── 2017\n │ ├── 0101\n │ │ └── sst.mon.mean.2017-01-01.tif\n │ ├── 0201\n │ │ └── sst.mon.mean.2017-02-01.tif\n │ ├── 0301\n │ │ └── sst.mon.mean.2017-03-01.tif\n │ ├── 0401\n │ │ └── sst.mon.mean.2017-04-01.tif\n │ ├── 0501\n │ │ └── sst.mon.mean.2017-05-01.tif\n │ ├── 0601\n │ │ └── sst.mon.mean.2017-06-01.tif\n │ ├── 0701\n │ │ └── sst.mon.mean.2017-07-01.tif\n │ ├── 0801\n │ │ └── sst.mon.mean.2017-08-01.tif\n │ ├── 0901\n │ │ └── sst.mon.mean.2017-09-01.tif\n │ ├── 1001\n │ │ └── sst.mon.mean.2017-10-01.tif\n │ ├── 1101\n │ │ └── sst.mon.mean.2017-11-01.tif\n │ └── 1201\n │ └── sst.mon.mean.2017-12-01.tif\n ├── 2018\n │ ├── 0101\n │ │ └── sst.mon.mean.2018-01-01.tif\n │ ├── 0201\n │ │ └── sst.mon.mean.2018-02-01.tif\n │ ├── 0301\n │ │ └── sst.mon.mean.2018-03-01.tif\n │ ├── 0401\n │ │ └── sst.mon.mean.2018-04-01.tif\n │ ├── 0501\n │ │ └── sst.mon.mean.2018-05-01.tif\n │ ├── 0601\n │ │ └── sst.mon.mean.2018-06-01.tif\n │ ├── 0701\n │ │ └── sst.mon.mean.2018-07-01.tif\n │ ├── 0801\n │ │ └── sst.mon.mean.2018-08-01.tif\n │ ├── 0901\n │ │ └── sst.mon.mean.2018-09-01.tif\n │ ├── 1001\n │ │ └── sst.mon.mean.2018-10-01.tif\n │ ├── 1101\n │ │ └── sst.mon.mean.2018-11-01.tif\n │ └── 1201\n │ └── sst.mon.mean.2018-12-01.tif\n ├── 2019\n │ ├── 0101\n │ │ └── sst.mon.mean.2019-01-01.tif\n │ ├── 0201\n │ │ └── sst.mon.mean.2019-02-01.tif\n │ ├── 0301\n │ │ └── sst.mon.mean.2019-03-01.tif\n │ ├── 0401\n │ │ └── sst.mon.mean.2019-04-01.tif\n │ ├── 0501\n │ │ └── sst.mon.mean.2019-05-01.tif\n │ ├── 0601\n │ │ └── sst.mon.mean.2019-06-01.tif\n │ ├── 0701\n │ │ └── sst.mon.mean.2019-07-01.tif\n │ ├── 0801\n │ │ └── sst.mon.mean.2019-08-01.tif\n │ ├── 0901\n │ │ └── sst.mon.mean.2019-09-01.tif\n │ ├── 1001\n │ │ └── sst.mon.mean.2019-10-01.tif\n │ ├── 1101\n │ │ └── sst.mon.mean.2019-11-01.tif\n │ └── 1201\n │ └── sst.mon.mean.2019-12-01.tif\n ├── 2020\n │ ├── 0101\n │ │ └── sst.mon.mean.2020-01-01.tif\n │ ├── 0201\n │ │ └── sst.mon.mean.2020-02-01.tif\n │ ├── 0301\n │ │ └── sst.mon.mean.2020-03-01.tif\n │ ├── 0401\n │ │ └── sst.mon.mean.2020-04-01.tif\n │ ├── 0501\n │ │ └── sst.mon.mean.2020-05-01.tif\n │ ├── 0601\n │ │ └── sst.mon.mean.2020-06-01.tif\n │ ├── 0701\n │ │ └── sst.mon.mean.2020-07-01.tif\n │ ├── 0801\n │ │ └── sst.mon.mean.2020-08-01.tif\n │ ├── 0901\n │ │ └── sst.mon.mean.2020-09-01.tif\n │ ├── 1001\n │ │ └── sst.mon.mean.2020-10-01.tif\n │ ├── 1101\n │ │ └── sst.mon.mean.2020-11-01.tif\n │ └── 1201\n │ └── sst.mon.mean.2020-12-01.tif\n ├── 2021\n │ ├── 0101\n │ │ └── sst.mon.mean.2021-01-01.tif\n │ ├── 0201\n │ │ └── sst.mon.mean.2021-02-01.tif\n │ ├── 0301\n │ │ └── sst.mon.mean.2021-03-01.tif\n │ ├── 0401\n │ │ └── sst.mon.mean.2021-04-01.tif\n │ ├── 0501\n │ │ └── sst.mon.mean.2021-05-01.tif\n │ ├── 0601\n │ │ └── sst.mon.mean.2021-06-01.tif\n │ ├── 0701\n │ │ └── sst.mon.mean.2021-07-01.tif\n │ ├── 0801\n │ │ └── sst.mon.mean.2021-08-01.tif\n │ ├── 0901\n │ │ └── sst.mon.mean.2021-09-01.tif\n │ ├── 1001\n │ │ └── sst.mon.mean.2021-10-01.tif\n │ ├── 1101\n │ │ └── sst.mon.mean.2021-11-01.tif\n │ └── 1201\n │ └── sst.mon.mean.2021-12-01.tif\n ├── 2022\n │ ├── 0101\n │ │ └── sst.mon.mean.2022-01-01.tif\n │ ├── 0201\n │ │ └── sst.mon.mean.2022-02-01.tif\n │ ├── 0301\n │ │ └── sst.mon.mean.2022-03-01.tif\n │ ├── 0401\n │ │ └── sst.mon.mean.2022-04-01.tif\n │ ├── 0501\n │ │ └── sst.mon.mean.2022-05-01.tif\n │ ├── 0601\n │ │ └── sst.mon.mean.2022-06-01.tif\n │ ├── 0701\n │ │ └── sst.mon.mean.2022-07-01.tif\n │ ├── 0801\n │ │ └── sst.mon.mean.2022-08-01.tif\n │ ├── 0901\n │ │ └── sst.mon.mean.2022-09-01.tif\n │ ├── 1001\n │ │ └── sst.mon.mean.2022-10-01.tif\n │ ├── 1101\n │ │ └── sst.mon.mean.2022-11-01.tif\n │ └── 1201\n │ └── sst.mon.mean.2022-12-01.tif\n ├── 2023\n │ ├── 0101\n │ │ └── sst.mon.mean.2023-01-01.tif\n │ ├── 0201\n │ │ └── sst.mon.mean.2023-02-01.tif\n │ ├── 0301\n │ │ └── sst.mon.mean.2023-03-01.tif\n │ ├── 0401\n │ │ └── sst.mon.mean.2023-04-01.tif\n │ ├── 0501\n │ │ └── sst.mon.mean.2023-05-01.tif\n │ ├── 0601\n │ │ └── sst.mon.mean.2023-06-01.tif\n │ ├── 0701\n │ │ └── sst.mon.mean.2023-07-01.tif\n │ ├── 0801\n │ │ └── sst.mon.mean.2023-08-01.tif\n │ └── 0901\n │ └── sst.mon.mean.2023-09-01.tif\n └── database.csv.gz" + "text": "3 Getting started\n\n“According to the ancient Chinese proverb, A journey of a thousand miles must begin with a single step.” ~ John F. Kennedy\n\n\n3.1 Handling spatial data\nIn this tutorial we use the sf and stars packages to handle spatial data. These tutorials for sf and for stars will help you off to a great start.\n\n\n3.2 Project-specific functions\nWe have developed a suite of functions that facilitate accessing and working with data. These can be loaded into your R session by source()-ing the setup.R file. Here’s an example where we show the study area using the ancillary function get_bb() to retrieve the project’s bounding box.\n\nsource(\"setup.R\", echo = FALSE)\nbb = get_bb(form = 'polygon')\ncoast = rnaturalearth::ne_coastline(scale = 'large', returnclass = 'sf')\nplot(sf::st_geometry(coast), extent = bb, axes = TRUE, reset = FALSE)\nplot(bb, lwd = 2, border = 'orange', add = TRUE)\n\n\n\n\nThe setup file also checks for the required packages, and will attempt to install them into the user’s R library directory if not already installed.\n\n\n3.3 Fetching data\nThe robis package facilitates easy access to OBIS which is a huge public database for oceanographic species information. We have written a wrapper function to download the Mola mola species records in our study region. To simplify our task we drop may columns of data from that delivered by OBIS, but there is much in the original worth exploring. Note you can use this function to access other species in other parts of the world.\n\n\n\n\n\n\nNote\n\n\n\nNote that we have already fetched the data, so we don’t run this next step (but you can if you like to get updated data.)\n\n\n\nif (!file.exists('data/obis/Mola_mola.gpkg')){\n x = fetch_obis(scientificname = 'Mola mola')\n}\n\nSince we already have the data, we need only read it which we do below. We also “glimpse” at the data so we can get a sense of the variables within the data frame and their respective data types.\n\nx = read_obis(scientificname = 'Mola mola') |>\n dplyr::glimpse()\n\nRows: 10,646\nColumns: 8\n$ occurrenceID <chr> \"1817_15415\", \"283_15788\", \"513_6751\", \"513_49055\", \"513…\n$ date <date> 2016-08-07, 1980-05-23, 2002-06-17, 2009-07-10, 2009-08…\n$ basisOfRecord <chr> \"HumanObservation\", \"HumanObservation\", \"HumanObservatio…\n$ bathymetry <dbl> 121, 102, 65, 166, 133, 19, 218, 90, 1283, 160, 219, 97,…\n$ shoredistance <dbl> 134559, 121292, 77684, 127643, 58028, 524, 94816, 141127…\n$ sst <dbl> 16.36, 15.44, 13.38, 11.70, 9.20, 12.35, 11.79, 13.90, 1…\n$ sss <dbl> 34.10, 33.41, 32.64, 32.22, 30.99, 31.38, 32.12, 33.38, …\n$ geom <POINT [°]> POINT (-72.8074 39.056), POINT (-72.283 39.733), P…\n\n\nLet’s see what we found on a map. We first plot the coastline, but provide the bounding box to establish the limits of the plot. Then we add the box itself and then the points.\n\nplot(sf::st_geometry(coast), extent = bb, axes = TRUE, reset = FALSE)\nplot(bb, lwd = 2, border = 'orange', add = TRUE)\nplot(sf::st_geometry(x), pch = \"+\", col = 'blue', add = TRUE)\n\n\n\n\n\n\n3.4 Data storage\nWe have set up a data directory, data, for storing data collected for the project. To start out there isn’t much more than the downloaded data set, but we’ll added to it as we go. We try to keep the data storage area tidy by using subdirectories. Below we print the directory tree, your’s might look slightly different until you have run this code.\n\nfs::dir_tree(\"data\", recurse = 1)\n\ndata\n├── bkg\n│ ├── bkg-covariates.gpkg\n│ └── buffered-polygon.gpkg\n├── model\n│ ├── v1\n│ ├── v2\n│ └── v3\n├── nbs2\n│ ├── 2000\n│ ├── 2001\n│ ├── 2002\n│ ├── 2003\n│ ├── 2004\n│ ├── 2005\n│ ├── 2006\n│ ├── 2007\n│ ├── 2008\n│ ├── 2009\n│ ├── 2010\n│ ├── 2011\n│ ├── 2012\n│ ├── 2013\n│ ├── 2014\n│ ├── 2015\n│ ├── 2016\n│ ├── 2017\n│ ├── 2018\n│ ├── 2019\n│ ├── 2020\n│ ├── 2021\n│ ├── 2022\n│ ├── 2023\n│ └── database.csv.gz\n├── obis\n│ └── Mola_mola.gpkg\n├── obs\n│ ├── obs-covariates.gpkg\n│ └── obs.gpkg\n└── oisst\n ├── 2000\n ├── 2001\n ├── 2002\n ├── 2003\n ├── 2004\n ├── 2005\n ├── 2006\n ├── 2007\n ├── 2008\n ├── 2009\n ├── 2010\n ├── 2011\n ├── 2012\n ├── 2013\n ├── 2014\n ├── 2015\n ├── 2016\n ├── 2017\n ├── 2018\n ├── 2019\n ├── 2020\n ├── 2021\n ├── 2022\n ├── 2023\n └── database.csv.gz" }, { "objectID": "observations.html", @@ -207,14 +207,14 @@ "href": "modeling-02.html#do-we-model-every-month", "title": "Modeling each month", "section": "2 Do we model every month?", - "text": "2 Do we model every month?\nLet’s do a quick check by counting each by month. Note that we drop the spatial info so that we can make simply tallies.\n\ncounts = sf::st_drop_geometry(obs) |> \n dplyr::count(month, name = \"n_obs\") |>\n dplyr::left_join(sf::st_drop_geometry(bkg) |> dplyr::count(month, name = \"n_bkg\"), \n by = 'month') |>\n print(n = 12)\n\n# A tibble: 12 × 3\n month n_obs n_bkg\n <fct> <int> <int>\n 1 Jan 33 51\n 2 Feb 40 57\n 3 Mar 50 79\n 4 Apr 341 528\n 5 May 541 943\n 6 Jun 2137 3471\n 7 Jul 2108 3233\n 8 Aug 1698 2597\n 9 Sep 725 1205\n10 Oct 328 485\n11 Nov 494 739\n12 Dec 66 90\n\n\nSo the colder months have fewer observations than the warmer months. We already knew that, but it will be interesting to see how that manifests itself in the models.\n\n2.1 Build the monthly models\nSince we are building 12 models (rather than one) it is useful to create a function that computes a model for any month, and then iterate through the months of the year.\n\n# A function for making one month's model\n#\n# @param tbl a data frame of one month's observations\n# @param key a data frame that holds the current iteration's month name\n# @param bkg a complete data frame of background data (which we filter for the given month)\n# @param path the path where the model is saved\n# @return a model, which is also saved in \"data/model/v2/v2.<monthname>\"\nmodel_month = function(tbl, key, bkg = NULL, path = \".\"){\n \n bkg = bkg |>\n dplyr::filter(month == key$month) |>\n sf::st_drop_geometry() |>\n dplyr::select(dplyr::all_of(c(\"sst\", \"u_wind\", \"v_wind\"))) |>\n na.omit()\n \n obs = tbl |>\n sf::st_drop_geometry() |>\n dplyr::select(dplyr::all_of(c(\"sst\", \"u_wind\", \"v_wind\"))) |>\n na.omit()\n \n # these are the predictor variables row bound\n x = dplyr::bind_rows(obs, bkg)\n \n # and the flag indicating presence/background\n flag = c(rep(1, nrow(obs)), rep(0, nrow(bkg)))\n \n model_path = file.path(path, paste0(\"v2.\", key$month, \".rds\"))\n\n model = maxnet::maxnet(flag, x) |>\n maxnetic::write_maxnet(model_path)\n \n model\n}\n\npath = file.path(\"data\", \"model\", \"v2\")\nok = dir.create(path, recursive = TRUE, showWarnings = FALSE)\nmodels = obs |>\n dplyr::group_by(month) |>\n dplyr::group_map(model_month, bkg = bkg, path = path) |>\n rlang::set_names(levels(obs$month))\n\nWe can look at the response plots for every month, but for demonstration purposes, we’ll just show one month. It is interesting to compare this respinse to that for the basic model.\n\nplot(models[['Jun']], type = 'cloglog')" + "text": "2 Do we model every month?\nLet’s do a quick check by counting each by month. Note that we drop the spatial info so that we can make simply tallies.\n\ncounts = sf::st_drop_geometry(obs) |> \n dplyr::count(month, name = \"n_obs\") |>\n dplyr::left_join(sf::st_drop_geometry(bkg) |> dplyr::count(month, name = \"n_bkg\"), \n by = 'month') |>\n print(n = 12)\n\n# A tibble: 12 × 3\n month n_obs n_bkg\n <fct> <int> <int>\n 1 Jan 33 2288\n 2 Feb 40 2140\n 3 Mar 50 2312\n 4 Apr 341 2232\n 5 May 541 2305\n 6 Jun 2137 2291\n 7 Jul 2108 2371\n 8 Aug 1698 2502\n 9 Sep 725 2275\n10 Oct 328 2390\n11 Nov 494 2267\n12 Dec 66 2201\n\n\nSo the colder months have fewer observations than the warmer months. We already knew that, but it will be interesting to see how that manifests itself in the models.\n\n2.1 Build the monthly models\nSince we are building 12 models (rather than one) it is useful to create a function that computes a model for any month, and then iterate through the months of the year.\n\n# A function for making one month's model\n#\n# @param tbl a data frame of one month's observations\n# @param key a data frame that holds the current iteration's month name\n# @param bkg a complete data frame of background data (which we filter for the given month)\n# @param path the path where the model is saved\n# @return a model, which is also saved in \"data/model/v2/v2.<monthname>\"\nmodel_month = function(tbl, key, bkg = NULL, path = \".\"){\n \n bkg = bkg |>\n dplyr::filter(month == key$month) |>\n sf::st_drop_geometry() |>\n dplyr::select(dplyr::all_of(c(\"sst\", \"u_wind\", \"v_wind\"))) |>\n na.omit()\n \n obs = tbl |>\n sf::st_drop_geometry() |>\n dplyr::select(dplyr::all_of(c(\"sst\", \"u_wind\", \"v_wind\"))) |>\n na.omit()\n \n # these are the predictor variables row bound\n x = dplyr::bind_rows(obs, bkg)\n \n # and the flag indicating presence/background\n flag = c(rep(1, nrow(obs)), rep(0, nrow(bkg)))\n \n model_path = file.path(path, paste0(\"v2.\", key$month, \".rds\"))\n\n model = maxnet::maxnet(flag, x) |>\n maxnetic::write_maxnet(model_path)\n \n model\n}\n\npath = file.path(\"data\", \"model\", \"v2\")\nok = dir.create(path, recursive = TRUE, showWarnings = FALSE)\nmodels = obs |>\n dplyr::group_by(month) |>\n dplyr::group_map(model_month, bkg = bkg, path = path) |>\n rlang::set_names(levels(obs$month))\n\nWe can look at the response plots for every month, but for demonstration purposes, we’ll just show one month. It is interesting to compare this respinse to that for the basic model.\n\nplot(models[['Jun']], type = 'cloglog')" }, { "objectID": "modeling-02.html#predict-with-rasters", "href": "modeling-02.html#predict-with-rasters", "title": "Modeling each month", "section": "3 Predict with rasters", - "text": "3 Predict with rasters\nFirst we load the raster databases as these are lightweight to pass into a function that iterates through the months.\n\n3.1 Load the raster databases (sst and u_wind and v_wind)\nWe also make sure they are in date order and add a “month” variable to each.\n\nsst_path = \"data/oisst\"\nsst_db = oisster::read_database(sst_path) |>\n dplyr::arrange(date) |>\n dplyr::mutate(month = format(date, \"%b\"))\n \n\nwind_path = \"data/nbs\"\nwind_db = nbs::read_database(wind_path) |>\n dplyr::arrange(date)|>\n dplyr::mutate(month = format(date, \"%b\"))\n\nu_wind_db = wind_db |>\n dplyr::filter(param == \"u_wind\")\n\nv_wind_db = wind_db |>\n dplyr::filter(param == \"v_wind\")\n\n\n\n3.2 Iterate through the months making predictions\nNow we can build an iterator function that will make a prediction for each month. Let’s narrow our predictions to just those for a particular year, 2019, and read the rasters in all at once.\n\ndates = as.Date(c(\"2019-01-01\", \"2019-12-31\"))\nx = read_predictors(\n sst_db = dplyr::filter(sst_db, dplyr::between(date, dates[1], dates[2])),\n u_wind_db = dplyr::filter(u_wind_db, dplyr::between(date, dates[1], dates[2])),\n v_wind_db = dplyr::filter(v_wind_db, dplyr::between(date, dates[1], dates[2]))\n)\n\nNow we can iterate through the months.\n\ndate_sequence = seq(from = dates[1], to = dates[2], by = \"month\")\npred_rasters = lapply(names(models),\n function(mon){\n ix = which(month.abb %in% mon)\n predict(models[[mon]], dplyr::slice(x, time, ix, drop), type = \"cloglog\")\n }) \npred_rasters = do.call(c, append(pred_rasters, list(along = list(time = date_sequence))))\n\nLet’s plot them.\n\ncoast = rnaturalearth::ne_coastline(scale = 'large', returnclass = 'sf') |>\n sf::st_geometry() |>\n sf::st_crop(pred_rasters)\n\nplot_coast = function() {\n plot(coast, col = 'green', add = TRUE)\n}\nplot(pred_rasters, hook = plot_coast)\n\n\n\n\nLet’s see what we can discern from the predict abilities. We can extract the predicted values at the observed locations. Having those in hand allows us to compute pAUC for each month.\n\npred_obs = stars::st_extract(pred_rasters, \n dplyr::filter(obs, dplyr::between(date, dates[1], dates[2])),\n time_column = \"month_id\") |>\n dplyr::mutate(month = factor(format(month_id, \"%b\"), levels = month.abb)) |>\n dplyr::group_by(month)\n\npaucs = dplyr::group_map(pred_obs,\n function(x, y) {\n ix = month.abb %in% y$month\n s = dplyr::slice(pred_rasters, \"time\", ix)\n pauc = maxnetic::pAUC(s,x)\n dplyr::tibble(month = y$month, \n auc = pauc$area,\n pauc = list(pauc))\n })|>\n dplyr::bind_rows() |>\n print(n = 12)\n\n# A tibble: 12 × 3\n month auc pauc \n <fct> <dbl> <list> \n 1 Jan 0.703 <pAUC [3]>\n 2 Feb 0.689 <pAUC [3]>\n 3 Mar 0.698 <pAUC [3]>\n 4 Apr 0.677 <pAUC [3]>\n 5 May 0.654 <pAUC [3]>\n 6 Jun 0.662 <pAUC [3]>\n 7 Jul 0.665 <pAUC [3]>\n 8 Aug 0.696 <pAUC [3]>\n 9 Sep 0.663 <pAUC [3]>\n10 Oct 0.633 <pAUC [3]>\n11 Nov 0.627 <pAUC [3]>\n12 Dec 0.665 <pAUC [3]>\n\n\nNote that last element, pauc, is the result returned by the maxnetic::pAUC() function which we can plot.\n\npp = paucs |>\n dplyr::group_by(month) |>\n dplyr::group_map(\n function(tbl, key){\n plot(tbl$pauc[[1]], title = key$month, xlab = \"\", ylab = \"\")\n }\n )\npatchwork::wrap_plots(pp, ncol = 4)\n\n\n\n\nWell, it would be easy to become dispirited by this result. It would be reasonable to expect AUC values to improve if we built monthly models rather than a single model applied to any month. But it seems to not be the dramatic improvement hoped for. Darn!" + "text": "3 Predict with rasters\nFirst we load the raster databases as these are lightweight to pass into a function that iterates through the months.\n\n3.1 Load the raster databases (sst and u_wind and v_wind)\nWe also make sure they are in date order and add a “month” variable to each.\n\nsst_path = \"data/oisst\"\nsst_db = oisster::read_database(sst_path) |>\n dplyr::arrange(date) |>\n dplyr::mutate(month = format(date, \"%b\"))\n \n\nnbs_path = \"data/nbs2\"\nwind_db = nbs::read_database(nbs_path) |>\n dplyr::arrange(date)|>\n dplyr::mutate(month = format(date, \"%b\"))\n\nu_wind_db = wind_db |>\n dplyr::filter(param == \"u_wind\")\n\nv_wind_db = wind_db |>\n dplyr::filter(param == \"v_wind\")\n\n\n\n3.2 Iterate through the months making predictions\nNow we can build an iterator function that will make a prediction for each month. Let’s narrow our predictions to just those for a particular year, 2019, and read the rasters in all at once.\n\ndates = as.Date(c(\"2019-01-01\", \"2019-12-31\"))\nx = read_predictors(\n sst_db = dplyr::filter(sst_db, dplyr::between(date, dates[1], dates[2])),\n u_wind_db = dplyr::filter(u_wind_db, dplyr::between(date, dates[1], dates[2])),\n v_wind_db = dplyr::filter(v_wind_db, dplyr::between(date, dates[1], dates[2])) )\n\nNow we can iterate through the months.\n\ndate_sequence = seq(from = dates[1], to = dates[2], by = \"month\")\npred_rasters = lapply(names(models),\n function(mon){\n ix = which(month.abb %in% mon)\n predict(models[[mon]], dplyr::slice(x, time, ix, drop), type = \"cloglog\")\n }) \npred_rasters = do.call(c, append(pred_rasters, list(along = list(time = date_sequence))))\n\nLet’s plot them.\n\ncoast = rnaturalearth::ne_coastline(scale = 'large', returnclass = 'sf') |>\n sf::st_geometry() |>\n sf::st_crop(pred_rasters)\n\nplot_coast = function() {\n plot(coast, col = 'green', add = TRUE)\n}\nplot(pred_rasters, hook = plot_coast)\n\n\n\n\nLet’s see what we can discern from the predict abilities. We can extract the predicted values at the observed locations. Having those in hand allows us to compute pAUC for each month.\n\npred_obs = stars::st_extract(pred_rasters, \n dplyr::filter(obs, dplyr::between(date, dates[1], dates[2])),\n time_column = \"month_id\") |>\n dplyr::mutate(month = factor(format(month_id, \"%b\"), levels = month.abb)) |>\n dplyr::group_by(month)\n\npaucs = dplyr::group_map(pred_obs,\n function(x, y) {\n ix = month.abb %in% y$month\n s = dplyr::slice(pred_rasters, \"time\", ix)\n pauc = maxnetic::pAUC(s,x)\n dplyr::tibble(month = y$month, \n auc = pauc$area,\n pauc = list(pauc))\n })|>\n dplyr::bind_rows() |>\n print(n = 12)\n\n# A tibble: 12 × 3\n month auc pauc \n <fct> <dbl> <list> \n 1 Jan 0.684 <pAUC [3]>\n 2 Feb 0.735 <pAUC [3]>\n 3 Mar 0.693 <pAUC [3]>\n 4 Apr 0.719 <pAUC [3]>\n 5 May 0.704 <pAUC [3]>\n 6 Jun 0.706 <pAUC [3]>\n 7 Jul 0.697 <pAUC [3]>\n 8 Aug 0.730 <pAUC [3]>\n 9 Sep 0.696 <pAUC [3]>\n10 Oct 0.676 <pAUC [3]>\n11 Nov 0.661 <pAUC [3]>\n12 Dec 0.659 <pAUC [3]>\n\n\nNote that last element, pauc, is the result returned by the maxnetic::pAUC() function which we can plot.\n\npp = paucs |>\n dplyr::group_by(month) |>\n dplyr::group_map(\n function(tbl, key){\n plot(tbl$pauc[[1]], title = key$month, xlab = \"\", ylab = \"\")\n }\n )\npatchwork::wrap_plots(pp, ncol = 4)\n\n\n\n\nWell, it would be easy to become dispirited by this result. It would be reasonable to expect AUC values to improve if we built monthly models rather than a single model applied to any month. But it seems to not be the dramatic improvement hoped for. Darn!" }, { "objectID": "modeling-04.html", diff --git a/functions/stars.R b/functions/stars.R index b8f13fa..d6c62f6 100644 --- a/functions/stars.R +++ b/functions/stars.R @@ -5,11 +5,19 @@ #' @param u_wind_db NULL or a database table #' @param v_wind_db NULL or a database table #' @param sst_path char, the path to sst data -#' @param wind_path char, the path to wind data +#' @param nbs_path char, the path to wind data +#' @param nbs_shift numeric, days to shift nbs days by because nbs, natively, +#' is time-stamped to the middle of each month. Ignored if per in db +#' is not "month" #' @return stars object with one or more attributes (variables) read_predictors = function( - sst_db = NULL, windspeed_db = NULL, u_wind_db = NULL, v_wind_db = NULL, - sst_path = "data/oisst", wind_path = "data/nbs"){ + sst_db = NULL, + windspeed_db = NULL, + u_wind_db = NULL, + v_wind_db = NULL, + sst_path = "data/oisst", + nbs_path = "data/nbs2", + nbs_shift = -14){ if (all(c(is.null(sst_db), is.null(windspeed_db), is.null(u_wind_db), is.null(v_wind_db)))){ @@ -22,11 +30,20 @@ read_predictors = function( if (is.null(db[[name]])) return(NULL) files = if(name == "sst"){ filenames = oisster::compose_filename(db[[name]], sst_path) + x = stars::read_stars(filenames, along = list(time = db[[name]]$date)) |> + rlang::set_names(name) } else { - filenames = nbs::compose_filename(db[[name]], wind_path) + filenames = nbs::compose_filename(db[[name]], nbs_path) + x = stars::read_stars(filenames, along = list(time = db[[name]]$date)) |> + rlang::set_names(name) + if (db[[name]]$per[1] == "month"){ + d = stars::st_dimensions(x) + d$time$values = d$time$values + nbs_shift + stars::st_dimensions(x) <- d + } } - stars::read_stars(filenames, along = list(time = db[[name]]$date)) |> - rlang::set_names(name) + + x }) # purge any NULLS diff --git a/index.qmd b/index.qmd index 7efe7e1..d594688 100644 --- a/index.qmd +++ b/index.qmd @@ -73,8 +73,8 @@ plot(sf::st_geometry(x), pch = "+", col = 'blue', add = TRUE) ### Data storage -We have set up a data directory, `data`, for storing data collected for the project. To start out there isn't much more than the downloaded data set, but we'll added to it as we go. We try to keep the data storage area tidy by using subdirectories. +We have set up a data directory, `data`, for storing data collected for the project. To start out there isn't much more than the downloaded data set, but we'll added to it as we go. We try to keep the data storage area tidy by using subdirectories. Below we print the directory tree, your's might look slightly different until you have run this code. ```{r} -fs::dir_tree("data", recurse = TRUE) +fs::dir_tree("data", recurse = 1) ``` diff --git a/modeling-02.qmd b/modeling-02.qmd index 47ef788..6ea9b87 100644 --- a/modeling-02.qmd +++ b/modeling-02.qmd @@ -102,8 +102,8 @@ sst_db = oisster::read_database(sst_path) |> dplyr::mutate(month = format(date, "%b")) -wind_path = "data/nbs" -wind_db = nbs::read_database(wind_path) |> +nbs_path = "data/nbs2" +wind_db = nbs::read_database(nbs_path) |> dplyr::arrange(date)|> dplyr::mutate(month = format(date, "%b")) @@ -122,8 +122,7 @@ dates = as.Date(c("2019-01-01", "2019-12-31")) x = read_predictors( sst_db = dplyr::filter(sst_db, dplyr::between(date, dates[1], dates[2])), u_wind_db = dplyr::filter(u_wind_db, dplyr::between(date, dates[1], dates[2])), - v_wind_db = dplyr::filter(v_wind_db, dplyr::between(date, dates[1], dates[2])) -) + v_wind_db = dplyr::filter(v_wind_db, dplyr::between(date, dates[1], dates[2])) ) ``` Now we can iterate through the months. diff --git a/modeling-02_files/execute-results/html.json b/modeling-02_files/execute-results/html.json deleted file mode 100644 index b3b8f68..0000000 --- a/modeling-02_files/execute-results/html.json +++ /dev/null @@ -1,14 +0,0 @@ -{ - "hash": "0c7a92cf7e0db38fdefb25315df58884", - "result": { - "markdown": "---\ntitle: \"Modeling each month\"\ncache: true\n---\n\n\nHere we modify our first modeling workflow to produce a model for each month. In the [previous workflow](modeling-01.qmd) we produced one model covering observations covering all times; we then applied the model to the various months. \n\n## Load data\n\nHere we load the observation and background data points. We add a column identifying the month of the year. \n\n\n::: {.cell hash='modeling-02_cache/html/unnamed-chunk-1_e35d59038fe3799ba4e644ab0d67b2d0'}\n\n```{.r .cell-code}\nsource(\"setup.R\")\nobs = sf::read_sf(file.path(\"data\", \"obs\", \"obs-covariates.gpkg\")) |>\n sf::st_set_geometry(\"geometry\") |>\n dplyr::mutate(month = factor(format(month_id, \"%b\"), levels = month.abb), \n .before = geometry)\nbkg = sf::read_sf(file.path(\"data\", \"bkg\", \"bkg-covariates.gpkg\")) |>\n sf::st_set_geometry(\"geometry\") |>\n dplyr::mutate(month = factor(format(month_id, \"%b\"), levels = month.abb), \n .before = geometry)\n```\n:::\n\n\n## Do we model every month?\n\nLet's do a quick check by counting each by month. Note that we drop the spatial info so that we can make simply tallies.\n\n\n::: {.cell hash='modeling-02_cache/html/unnamed-chunk-2_80a187bb1c38cbffb49e1feee10e7252'}\n\n```{.r .cell-code}\ncounts = sf::st_drop_geometry(obs) |> \n dplyr::count(month, name = \"n_obs\") |>\n dplyr::left_join(sf::st_drop_geometry(bkg) |> dplyr::count(month, name = \"n_bkg\"), \n by = 'month') |>\n print(n = 12)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 12 × 3\n month n_obs n_bkg\n \n 1 Jan 33 51\n 2 Feb 40 57\n 3 Mar 50 79\n 4 Apr 341 528\n 5 May 541 943\n 6 Jun 2137 3471\n 7 Jul 2108 3233\n 8 Aug 1698 2597\n 9 Sep 725 1205\n10 Oct 328 485\n11 Nov 494 739\n12 Dec 66 90\n```\n:::\n:::\n\n\nSo the colder months have fewer observations than the warmer months. We already knew that, but it will be interesting to see how that manifests itself in the models.\n\n### Build the monthly models\n\nSince we are building 12 models (rather than one) it is useful to create a function that computes a model for any month, and then iterate through the months of the year.\n\n\n::: {.cell hash='modeling-02_cache/html/unnamed-chunk-3_14408e426c7c9e84885d104d1381d64b'}\n\n```{.r .cell-code}\n# A function for making one month's model\n#\n# @param tbl a data frame of one month's observations\n# @param key a data frame that holds the current iteration's month name\n# @param bkg a complete data frame of background data (which we filter for the given month)\n# @param path the path where the model is saved\n# @return a model, which is also saved in \"data/model/v2/v2.\"\nmodel_month = function(tbl, key, bkg = NULL, path = \".\"){\n \n bkg = bkg |>\n dplyr::filter(month == key$month) |>\n sf::st_drop_geometry() |>\n dplyr::select(dplyr::all_of(c(\"sst\", \"u_wind\", \"v_wind\"))) |>\n na.omit()\n \n obs = tbl |>\n sf::st_drop_geometry() |>\n dplyr::select(dplyr::all_of(c(\"sst\", \"u_wind\", \"v_wind\"))) |>\n na.omit()\n \n # these are the predictor variables row bound\n x = dplyr::bind_rows(obs, bkg)\n \n # and the flag indicating presence/background\n flag = c(rep(1, nrow(obs)), rep(0, nrow(bkg)))\n \n model_path = file.path(path, paste0(\"v2.\", key$month, \".rds\"))\n\n model = maxnet::maxnet(flag, x) |>\n maxnetic::write_maxnet(model_path)\n \n model\n}\n\npath = file.path(\"data\", \"model\", \"v2\")\nok = dir.create(path, recursive = TRUE, showWarnings = FALSE)\nmodels = obs |>\n dplyr::group_by(month) |>\n dplyr::group_map(model_month, bkg = bkg, path = path) |>\n rlang::set_names(levels(obs$month))\n```\n:::\n\n\nWe can look at the response plots for every month, but for demonstration purposes, we'll just show one month. It is interesting to compare this respinse to that for the [basic model](modeling-01.qmd).\n\n\n::: {.cell hash='modeling-02_cache/html/unnamed-chunk-4_56127f4832cd034c70d9efb8b0100319'}\n\n```{.r .cell-code}\nplot(models[['Jun']], type = 'cloglog')\n```\n\n::: {.cell-output-display}\n![](modeling-02_files/figure-html/unnamed-chunk-4-1.png){width=672}\n:::\n:::\n\n\n## Predict with rasters\nFirst we load the raster databases as these are lightweight to pass into a function that iterates through the months.\n\n### Load the raster databases (`sst` and `u_wind` and `v_wind`)\n\nWe also make sure they are in date order and add a \"month\" variable to each.\n\n\n::: {.cell hash='modeling-02_cache/html/unnamed-chunk-5_aec22c8734e04228e9c12ddd7dbebd94'}\n\n```{.r .cell-code}\nsst_path = \"data/oisst\"\nsst_db = oisster::read_database(sst_path) |>\n dplyr::arrange(date) |>\n dplyr::mutate(month = format(date, \"%b\"))\n \n\nwind_path = \"data/nbs\"\nwind_db = nbs::read_database(wind_path) |>\n dplyr::arrange(date)|>\n dplyr::mutate(month = format(date, \"%b\"))\n\nu_wind_db = wind_db |>\n dplyr::filter(param == \"u_wind\")\n\nv_wind_db = wind_db |>\n dplyr::filter(param == \"v_wind\")\n```\n:::\n\n\n### Iterate through the months making predictions\nNow we can build an iterator function that will make a prediction for each month. Let's narrow our predictions to just those for a particular year, 2019, and read the rasters in all at once.\n\n\n::: {.cell hash='modeling-02_cache/html/unnamed-chunk-6_c05a1ac68684d54c6ff8b072d57654ad'}\n\n```{.r .cell-code}\ndates = as.Date(c(\"2019-01-01\", \"2019-12-31\"))\nx = read_predictors(\n sst_db = dplyr::filter(sst_db, dplyr::between(date, dates[1], dates[2])),\n u_wind_db = dplyr::filter(u_wind_db, dplyr::between(date, dates[1], dates[2])),\n v_wind_db = dplyr::filter(v_wind_db, dplyr::between(date, dates[1], dates[2]))\n)\n```\n:::\n\n\nNow we can iterate through the months.\n\n\n::: {.cell hash='modeling-02_cache/html/unnamed-chunk-7_88b6e121185be4feda4a2f5f5d3eb399'}\n\n```{.r .cell-code}\ndate_sequence = seq(from = dates[1], to = dates[2], by = \"month\")\npred_rasters = lapply(names(models),\n function(mon){\n ix = which(month.abb %in% mon)\n predict(models[[mon]], dplyr::slice(x, time, ix, drop), type = \"cloglog\")\n }) \npred_rasters = do.call(c, append(pred_rasters, list(along = list(time = date_sequence))))\n```\n:::\n\n\nLet's plot them.\n\n\n::: {.cell hash='modeling-02_cache/html/unnamed-chunk-8_004777e1101dedbce37699ae5d54f4c2'}\n\n```{.r .cell-code}\ncoast = rnaturalearth::ne_coastline(scale = 'large', returnclass = 'sf') |>\n sf::st_geometry() |>\n sf::st_crop(pred_rasters)\n\nplot_coast = function() {\n plot(coast, col = 'green', add = TRUE)\n}\nplot(pred_rasters, hook = plot_coast)\n```\n\n::: {.cell-output-display}\n![](modeling-02_files/figure-html/unnamed-chunk-8-1.png){width=672}\n:::\n:::\n\n\nLet's see what we can discern from the predict abilities. We can extract the predicted values at the observed locations. Having those in hand allows us to compute pAUC for each month.\n\n\n\n\n::: {.cell hash='modeling-02_cache/html/unnamed-chunk-9_a7e317262834717357a0fd9d3b7b9f20'}\n\n```{.r .cell-code}\npred_obs = stars::st_extract(pred_rasters, \n dplyr::filter(obs, dplyr::between(date, dates[1], dates[2])),\n time_column = \"month_id\") |>\n dplyr::mutate(month = factor(format(month_id, \"%b\"), levels = month.abb)) |>\n dplyr::group_by(month)\n\npaucs = dplyr::group_map(pred_obs,\n function(x, y) {\n ix = month.abb %in% y$month\n s = dplyr::slice(pred_rasters, \"time\", ix)\n pauc = maxnetic::pAUC(s,x)\n dplyr::tibble(month = y$month, \n auc = pauc$area,\n pauc = list(pauc))\n })|>\n dplyr::bind_rows() |>\n print(n = 12)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n# A tibble: 12 × 3\n month auc pauc \n \n 1 Jan 0.703 \n 2 Feb 0.689 \n 3 Mar 0.698 \n 4 Apr 0.677 \n 5 May 0.654 \n 6 Jun 0.662 \n 7 Jul 0.665 \n 8 Aug 0.696 \n 9 Sep 0.663 \n10 Oct 0.633 \n11 Nov 0.627 \n12 Dec 0.665 \n```\n:::\n:::\n\n\nNote that last element, `pauc`, is the result returned by the `maxnetic::pAUC()` function which we can plot.\n\n\n::: {.cell hash='modeling-02_cache/html/unnamed-chunk-10_d4439b1d52a25443e93dea2dfb9413f1'}\n\n```{.r .cell-code}\npp = paucs |>\n dplyr::group_by(month) |>\n dplyr::group_map(\n function(tbl, key){\n plot(tbl$pauc[[1]], title = key$month, xlab = \"\", ylab = \"\")\n }\n )\npatchwork::wrap_plots(pp, ncol = 4)\n```\n\n::: {.cell-output-display}\n![](modeling-02_files/figure-html/unnamed-chunk-10-1.png){width=672}\n:::\n:::\n\n\nWell, it would be easy to become dispirited by this result. It would be reasonable to expect AUC values to improve if we built monthly models rather than a single model applied to any month. But it seems to not be the dramatic improvement hoped for. 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zpq!_3I`DVy=kuqkPhP(De*piKsVM*e diff --git a/predictors.qmd b/predictors.qmd index 8bacea4..f70a774 100644 --- a/predictors.qmd +++ b/predictors.qmd @@ -7,7 +7,7 @@ execute: ## Why predictor variables? Predictor variables (aka "covariate variables" and "environmental variables") are provided in the downloaded [observations](observations.html#examining-emnbedded-covariates). We can certainly use that data, but to use [MaxEnt](https://biodiversityinformatics.amnh.org/open_source/maxent/) presence only modeling, we need a way to characterize the background. For that we select random points from the same region and times that we have observations. Well, those data do not come with the OBIS download, we have to get them ourselves. -Another nice thing about using external predictor variables, is that we can use them, once we have a model in hand, to make predictive maps. +Another nice thing about using external predictor variables is that we can use them, once we have a model in hand, to make predictive maps. ## What predictor variables? Here we chose to use monthly means of sea surface temperature [OISST](https://www.ncei.noaa.gov/products/optimum-interpolation-sst) and sea winds [NBS v2](https://coastwatch.noaa.gov/cwn/products/noaa-ncei-blended-seawinds-nbs-v2.html). @@ -20,8 +20,10 @@ Let's make two sub-directories in our `data` directory; one each for OISST and N ```{r} source("setup.R") -ok = dir.create("data/oisst", recursive = TRUE, showWarnings = FALSE) -ok = dir.create("data/nbs2", recursive = TRUE, showWarnings = FALSE) +oisst_path = "data/oisst" +nbs_path = 'data/nbs2' +ok = dir.create(oisst_path, recursive = TRUE, showWarnings = FALSE) +ok = dir.create(nbs_path, recursive = TRUE, showWarnings = FALSE) ``` It is not unreasonable to keep your data in your project directory, but gridded data can be quite large. Sometimes it gets so large that the size limit on github repositories can be exceeded. The simplest way to prevent that is to prevent git from tracking your large data directories by adding filters to your local `.gitignore` file. Here's what we put in our project's `.gitignore` file. These lines tell `git` to not catalog items found in those two directories. @@ -50,7 +52,6 @@ And now get the rasters. This can take a long time, so we test for the existenc ```{r oisst, warning = FALSE, message = FALSE} -oisst_path = "data/oisst" oisst_db_file = file.path(oisst_path, "database.csv.gz") if (file.exists(oisst_db_file)){ @@ -91,18 +92,18 @@ The NBS dataset for monthly UV has some issues - certain months are not present This can take a long time, so we test for the existence of the local NBS database first. If the database doesn't yet exist, then we download the data. ```{r nbs, warning = FALSE, message = FALSE} -wind_path = 'data/nbs' -wind_db_file = file.path(wind_path, "database.csv.gz") - +wind_db_file = file.path(nbs_path, "database.csv.gz") if (file.exists(wind_db_file)){ - wind_db = nbs::read_database(wind_path) + wind_db = nbs::read_database(nbs_path) } else { - wind = nbs::fetch_month(dates = seq(from = as.Date("2000-01-01"), - to = Sys.Date() - 75, - by = "month"), - bb = bb, - path = wind_path) - wind_db = nbs::build_database(wind_path, save_db = TRUE) + wind_db = query_nbs(period = "monthly", + dates = c(as.Date("2000-01-01"), Sys.Date()-75), + product = "uvcomp") |> + fetch_nbs(param = 'all', + bb = bb, + path = nbs_path, + verbose = FALSE) |> + write_database(nbs_path) } ``` @@ -111,12 +112,12 @@ Just as with sst, we can select from the database a small number of file to load ```{r nbs-load} # first we filter sub_wind_db = wind_db |> - dplyr::filter(dplyr::between(date, as.Date("2000-01-01"), as.Date("2000-12-01")), - param == "windspeed") + dplyr::filter(param == "windspeed", + dplyr::between(date, as.Date("2000-01-01"), as.Date("2000-12-01"))) # then read into a multi=layer stars object -wind = stars::read_stars(nbs::compose_filename(sub_wind_db, wind_path), +wind = stars::read_stars(nbs::compose_filename(sub_wind_db, nbs_path), along = list(date = sub_wind_db$date)) |> rlang::set_names("windspeed") plot(wind) diff --git a/predictors_files/figure-html/nbs-load-1.png b/predictors_files/figure-html/nbs-load-1.png index 0889070a5327a6e2b066c4f823d7f72399bf7db8..a83b90e8b8aa48e4ce836d5ded881b3dad87f450 100644 GIT binary patch literal 68874 zcmeFZXHZn#)-DPN3aEgR6eK8;qvRY!K$IwwlZfObl2bRl3Mf&MNNfbjp_MGDktjLm zBstS$XrSTFe)rzr+2`2b`LXMss&lLE53MfN>M+L|OW)@w6!3ndFji#Imr&y*hG z;E0BYYa3hN(Wa12`ccApzx(ZAR#r60xlnAW{j)pksx zmXeW>8l?rxePx|Gvjg56mc&O%$2k*H8qL5{`Bj;?$^uB84<@3$AU{c-+&n3 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