Lidar / X-band Doppler velocity article code

This repository contains scripts for creating the figures for the article "Complementarity of Wind Measurements from Co-located X-band Weather Radar and Doppler Lidar" by Ritvanen et al., published in the Atmospheric Measurement Techniques journal (https://doi.org/10.5194/amt-15-6507-2022).

Python scripts

Script	Description
browse_netcdf_data.py	Browse lidar netcdf files and write information of the files to a csv file.
browse_raw_radar_data.py	Browse RAW (IRIS/Sigmet) radar files and write information of the files to a csv file.
get_xband_lidar_time_differences.py	Calculate time differences between lidar and X-band radar scans.
plot_lidar_xband_case.py	Plot 4-panel case figures of X-band radar and lidar data.
plot_lidar_ppi.py	Plot 2-panel figures of lidar PPIs with CNR and Doppler velocity.
plot_lidar_figure_list.py	Run plot_lidar_ppi.py for a list produced by browse_netcdf_data.py.
compute_measurement_availability.py	Computes the fraction of available measurements over the given month for lidar and X-band radar measurements. The files for the measurements are searched according to the information given in config.py in RADAR_INFO["fivxt"] and LIDAR_INFO["vaisala"]. Produces also the masks to filter out blocked rays for the analysis.
plot_ppi_blockage_map.py	Plots the PPI availability figure based on output from compute_measurement_availability.py.
compute_measurement_availability_weather.py	Computes measurement availability as function of range for binned by different surface station measurements. Currently, bins scans by cloud base height and horizontal visibility if --tol 1, by precipitation intensity if --tol 10, and by nothing if --tol 0. Note that after running the script once, the calculation can be sped up by reading stored data with --only-read, even if --tol changes. Note that for --tol 0, the results differ from the results produced by compute_measurement_availability.py, because here we only consider measurement times where both lidar and radar measurements exist, while that script calculates over all measurements separately.
plot_measurement_ranges.py	Plot measurement availability as function of range, calculated by compute_measurement_availability_weather.py with --tol 0.
plot_measurement_ranges_weather.py	Plot measurement availability as function of range binned by surface measurements, calculated by compute_measurement_availability_weather.py with --tol 1 or ``--tol 10`.
compute_gridded_lidar_xband.py	Grid lidar and X-band radar observations to a Cartesian grid and calculate statistics from paired scans. Configurations given in config.py. Third argument is the radar task name.
plot_gridded_lidar_xband_scatterplot.py	Plot results from compute_gridded_lidar_xband.py calculations. Produces a scatterplot, text file with linear fit statistics, figure with correlation values, and pair-wise scatterplots for lidar and X-band radar variables. (Small modifications allow also plotting gridded measurements with surface measurements.)
plot_surface_meas_distributions.py	Plot distributions of fraction of available measurements a surface measurement based on output from plot_gridded_lidar_xband_scatterplot.py

How to plot figures from the article

Figure 2 (measurement availability PPI)

Run script compute_measurement_availability.py. This produces files with the bin-wise information of available measurements. The PPI is then plotted with plot_ppi_blockage_map.py.

For example:

python compute_measurement_availability.py 20210501 20211130 --task-name WND-03 --outpath results

# After calculation is done
python plot_ppi_blockage_map.py 20210501 20211130 results --outpath results

Figure 3 (scatterplot of gridded measurements)

Run script compute_gridded_lidar_xband.py for the interval. Note that it might be better to run this for one month at a time, especially if you want to compare the results per month. Remember to set paths in config.py. Then plot the results with plot_gridded_lidar_xband_scatterplot.py. The script should be run with only one process (in config.py DASK_NWORKERS=1), as writing the data is not currently safe to do concurrently. The script can be run for longer periods with the helper script run_scatterplot.py.

# Run script, with each month splitted to 4 intervals. Output goes to "results" directory
python run_scatterplot.py WND-03 202105 202111 results --month_splits 4

# When done, plot with this
python plot_gridded_lidar_xband_scatterplot.py WND-03 results 202105 202111 --outpath results

Figure 4 (measurement availability as function of range)

Run script compute_measurement_availability_weather.py with --tol 0, and plot the results with plot_measurement_ranges.py.

For example:

python compute_measurement_availability_weather.py 20210501 20211130 --tol 0 --outpath results --var none

# After calculation is done
python plot_measurement_ranges.py results --outpath results

Figures 5, 9, and 11 (distributions of fraction of available measurements)

Run script compute_gridded_lidar_xband.py for the interval, and plot with plot_surface_meas_distributions.py.

# Run per month (same as for figure 4)
# Run script, with each month splitted to 4 intervals. Output goes to "results" directory
python run_scatterplot.py WND-03 202105 202111 results --month_splits 4

# When done, plot with these
# Horizontal visibility
python plot_surface_meas_distributions.py WND-03 results 202105 202109 --outpath results --tol 1--var vis
python plot_surface_meas_distributions.py WND-03 results 202110 202111 --outpath results --tol 1--var vis

# cloud base height
python plot_surface_meas_distributions.py WND-03 results 202105 202109 --outpath results --tol 1--var clhb
python plot_surface_meas_distributions.py WND-03 results 202110 202111 --outpath results --tol 1--var clhb


# Precipitation intensity
python plot_surface_meas_distributions.py WND-03 results 202105 202111 --outpath results --tol 10 --var prio

Figures 6 and 10 (measurement availability as function of range for cloud base height and visibility)

Run script compute_measurement_availability_weather.py with --tol 1

# If script was run before
python compute_measurement_availability_weather.py 202105 202111 --tol 1 --outpath results --only-read --var vis

python compute_measurement_availability_weather.py 202105 202111 --tol 1 --outpath results --only-read --var clhb

# If running first time
python compute_measurement_availability_weather.py 202105 202111 --tol 1 --outpath results --var vis

python compute_measurement_availability_weather.py 202105 202111 --tol 1 --outpath results --var clhb

# Plot as function of cloud base height
python plot_measurement_ranges_weather.py results CLHB_PT1M_INSTANT_3000 --log-scale  --outpath results --formatter m2km

# Plot as function of horizontal visibility
python plot_measurement_ranges_weather.py results VIS_PT1M_AVG_75000   --outpath results --formatter m2km

Figure 12 (measurement availability as function of range for precipitation)

Run script compute_measurement_availability_weather.py with --tol 10

# If script was run before
python compute_measurement_availability_weather.py 202105 202111 --tol 10 --outpath results --only-read --var prio

# If running first time
python compute_measurement_availability_weather.py 202105 202111 --tol 10 --outpath results --var prio

# Plot as function of precipitation intensity
python plot_measurement_ranges_weather.py results PRIO_PT10M_AVG_4  --outpath results --formatter none

Figures 7, 8, and 13 (case figures)

Run make_case_figures.sh. Set settings and file root path in the beginning of the script.