Why Weather Data Should Be Open And Nearly Free
Posted on 08 Jul 2020
By: Bill Doerrfeld
Weather data should be open to everyone. Above all other types of data, the weather is the most critical to our daily lives. Since everyone can benefit from it, weather data shouldn't cost a lot of money at all.
This is especially true since so much raw weather data is already freely available. Open databases like NOAA and Environment Canada share loads of valuable data free of charge. Since so much is already open, companies shouldn't be charging much at all for weather data.
Furthermore, big data technology companies that apply machine learning to predict forecasts shouldn't have a high price tag, since weather algorithms are open and standardized. Since the models have been widely known throughout the industry for years, weather forecasting should be extremely affordable.
Open web APIs help democratize real-time weather data, allowing developers to program highly-pinpointed forecasts into any application for all to use. The OpenWeatherMap APIs, for example, give away a lot of weather data free of charge and provide advanced services at a very affordable rate.
No One Should "Own" Weather Data
Weather data should be open for all — no one "owns" the weather. Accurate weather information aids our day-to-day, informs decision-making, and improves overall living conditions. Some practical use cases for weather data include:
Planning of safe routes for transport companies;
Gauging consumer demand for retailers;
Timely watering of crops for farmers;
Accurate evaluation of customers;
Informing insurance company cases;
Sensitive planning of energy consumption for households.
Precise climate models can reroute a shipment around inclement weather, saving companies a lot of money. Accurate weather data could improve resiliency against natural disasters, saving lives. Since weather data has so many helpful use cases, everyone should have access to it nearly for free.
Raw Weather Datasets Are Already Free
Global meteorological services such as NOAA, MetOffice, Environment Canada, EMCWF, and others supply enormous data feeds from radars, weather satellites, and weather stations. They also provide specialized raw data sets for road alerts, traffic risks, and marine conditions.
For the valuation of complex hydrometeorological models, most global forecasters rely on NOAA or ECMWF. Some platforms take this data, add additional forecasting calculations, and expose it as a middleman to third parties. Such platforms can bring usability benefits for developers. However, since the information is already free, companies shouldn't be charging much at all for weather data.
Forecasting AI Is Well-Known And Open
Forecasting services that enhance weather data shouldn't be expensive since the technology that powers them is known and freely distributed. Short-term nowcast predictions, for example, utilize well-known algorithms. The minute forecast formulas are based on a mathematical approach established in the 1950s that still works like a charm today.
Open-source frameworks, like Python libraries for STEP (Short-Term Ensemble Prediction System), provide Artificial Intelligence to enhance resolution into nowcast models for specific geographic regions. Open frameworks for Machine Learning improve long-term forecasting models too, such as Numerical Weather Prediction (NWP) algorithms.
Given that academia has nearly standardized the mathematical forecasting models or keep them open, and that many machine learning packages are open-source, weather data companies shouldn't put a high price tag on open technology.
Weather APIs Should Be Nearly Free, Too
Nowadays, developers use Application Programming Interfaces (APIs) to insert all kinds of data into their applications. Web APIs are the modern building blocks. The benefit of using an API to access weather data is that it improves accessibility by using a format that programmers are familiar with.
For an example of a weather data API doing it right, take OpenWeatherMap. OpenWeatherMap is a leading resource for free current weather, historical data, and extended forecasts for any location. OpenWeatherMap aggregates and processes with its ML data from an immense network of 82,000 weather stations, radars, weather satellite data, and rain gauges, among other global weather sensors.
Developers access these vast amounts of data via light-speed, elegant APIs. Using OpenWeatherMap APIs, you can retrieve data for any location on the globe: minute forecast, hourly forecast, current weather, and history, with a vast range of meteorological parameters. Many of these products are available for free with up to 1,000,000 calls per month.
Using a smart third-party weather layer like OpenWeatherMap grants a few other advantages:
Freemium: A weather data layer can offer free services, democratizing access to essential weather data, while permitting larger-scale enterprise abilities on the side.
Aggregation: A weather data layer can combine data from numerous international collection services. This results in a thorough coverage of the global surface area.
AI/ML: A weather data layer can extend raw data with its own machine learning algorithms, enriching the accuracy of results, and extending forecast timelines.
Usability: A weather data layer can specialize in making weather data consumable for app developers. This is accomplished with a developer portal that adopts modern integration standards.
Programmatic: A weather API can respond with JSON-formatted web Application Programming interfaces (APIs). This enables developers to program automated capabilities.
Exploring OpenWeatherMap APIs
Let's look a little bit closer at OpenWeatherMap. The Current Weather Data API is updated every 10 minutes and returns temperature, humidity, pressure, wind, precipitation, among other variables. Other OpenWeather APIs offer more advanced functionality. The Hourly Forecast API provides a four-day hourly weather forecast with highly accurate geographic targeting.
For larger-scale deployments, OpenWeatherMap provides several paid subscriptions and a range of historical products. OpenWeather provides more long-term forecasts, such as the 16 Day Weather Forecast API, and the Historical Data service to download 40 years of weather data for any coordinate. Compared to other weather data APIs on the market, OpenWeather offers an impressive free plan, and a broad scope of parameters to satisfy nearly any weather data use case.
Getting started with OpenWeatherMap APIs is straightforward. Developers simply sign up to obtain an API key to make calls and peruse the easy-to-read public API documentation. All OpenWeather requires is a city, zip code, or coordinates to return up-to-date weather information in JSON, XML, or HTML formats.
Weather Data Should Be Open
The need for accurate, open weather data is universal. Applications for global logistics, agriculture, commerce, natural disaster mitigation, and many other areas require valid weather outlooks.
Thankfully, the weather industry is quite mature, with a global ecosystem of sensors storing data every second, and extensive databases opening up raw data. Academia has also produced powerful open-source machine learning technologies, advancing both the accuracy and computing speed of global forecasting models.
Since both global weather datasets and AI weather calculations are freely available, the APIs that enable access to them should be open as well. Thus, services like OpenWeatherMap are keeping costs down while providing a usable API platform to access enhanced weather data.