In collaboration with VisualCrossing as a Weather Ambassador, I am creating a python wrapper that can fetch the historical, latest, and forecast weather data. The Visual Crossing Weather API provides developers with weather data for any programming language or script, using simple .get() requests. The Weather API provides instant and scalable access to historical weather reports and forecast data in an easy-to-use JSON or text format.
VisualCrossing provides five different types of subscriptions, with either monthly or annual payable plans. The annual payable plans offer two months of free data usage, i.e., about 16% cheaper than the monthly plan. The table below highlights the plan details (which may change over time), for more details visit VisualCrossing/pricing.
| Plan Name | Costing Details | No. API Calls |
|---|---|---|
| Free Tier | $ 0.00 / month | 1,000 (1k) Records/Month |
| Professional Tier | $ 35.00 / month | 10,000,000 (10M) Records/Month |
| Metered Connection (pay as you use) | $ 0.0001 / record | unlimited |
| Corporate Tier | $ 150.00 / month | unlimited |
In addition, you can contact the support for enterprise solutions. The free and professional tier has a single concurrency usage, while the metered connection has unlimited concurrency while the corporate account has a team concurrency and is billed together. The API service provided historical data of the last 50 years (currently, the data is available from 1975, as of 21.01.2024), while a 15-Days forecast data is available under each tier.
Weather data is a critical parameter in various forecasting projects (1, 2) due to its widespread impact. Popular tools like VisualCrossing fetch data from various weather stations and provide an API-based service to query onto. A weather data record is typically divided into two tables/files - (I) daily: where the day-level information of a particular "location" is stored, and (II) an hourly value for the location.
To fetch the data easily, a WorldDB.db file is provided with basic location information like - countries, states, and city names. The data is sourced and parsed from OSS World-DB Site and the data can be read and used in python using SQLite engine.
import sqlite3
con = sqlite3.connect("WorldDB.db")
records = pd.read_sql("SELECT * FROM vwWorldDB", con) # using pandas
records.sample()The table vwWorldDB is the combined view of the mwCountries, mwStates, and mwCities tables, and the location of states/cities are in the format latitude,longitude and thus can be easily parsed using the eval statement in python. This is specifically done, because VisualCrossing accepts location information in lat,lon format, and thus the table can be easily used to fetch the data.
Thus said, it is recommended to integrate the data into your own database using an already existing key, or creating a new key for the use. Also, it is recommended to not use SQLite3 as database to save weather data as for large tables the query can be slow.
Contributions are welcomed. Do check our contributing guide for more information.