Climate change & artificial intelligence (AI) is both multidisciplinary topics with many connections. In the coming years, both will profoundly impact every aspect of the economy. As a result, there is no single “magic bullet” application of environmental intelligence to address climate change. Instead, a variety of machine learning applications can aid in the quest to decarbonize our planet.
Making stuff, moving things, generating things, eating foods, computing things: nearly every significant activity that humanity does today adds to the carbon footprint to a certain level. There is a subset of these decarbonization programs where artificial intelligence can be used effectively. There is a subset of these for which there are appealing startup business models.
It’s worth noting that, in addition to being a tool in the battle against climate change, computer vision contributes to the problem due to its ravenous appetite for computer resources and, consequently, power. As discussed in this column, this is a significant ongoing difficulty for the AI community.
One of the reasons businesses are making climate change a top focus is that extreme weather events can, and increasingly will, disrupt supply chains, force mass evacuees due to wildfires, flood facilities near coasts, halt outdoor sports due to extreme heat, and make certain regions less livable. As humans become more affected by climate change, developers may want to create or enhance apps using climate data, and the easiest method is through weather APIs.
What is a weather API?
An Application Program Interface (API) for climate data allows developers to interact with it and integrate it into their programs.
1. Ambee weather API
Weather forecasting is made possible through Ambee’s weather API, which provides real-time weather data. The data models are created with data validation as a principal focus to provide reliable data with relevant insights. Private data insights are simple to incorporate into weather-related industry applications.
Ambee combines weather data to ensure accuracy and availability by integrating on-ground sensors, satellite images, and statistical analysis. The data models rely on weather data from the past.
Ambee’s unique AI-powered algorithms perform data analysis to validate and offer reliable real-time weather data. Weather data allows for weather forecasting by supplying real-time weather data such as temperature, pressure, cloud cover, wind, or other variables. Weather data also gives actionable insight and guidance that may be readily integrated with any product, system, or service to improve user experience, sales, and performance.
2. World Air Quality Index API
The World Air Quality Data Index initiative raises awareness of air pollution and provides weather data in various locations worldwide. The World Air Quality Index REST API Track provides developers access to and integrates weather data for more than 70 nations, including 9000 stations in 600 large cities. API methods include geo-based inquiries, obtaining air quality data for cities and areas, and retrieving present weather conditions.
3. UrtheCast Platform API
UrtheCast serves the industry with satellite remote sensing technologies. Track the Geosys Bridge API, which gives you programmatic access to Geosys’ small detecting data processing facilities. The API allows a company to access satellite imagery for weather and agriculture, combining analytical data, improving data flow, or filtering alternatives for complex requests.
4. Airchecks API
The aircheck is a program that calculates and analyzes nitrogen dioxide, chlorine, and particulate matter in the outside air. This API, aircheck API Track, delivers hyper-local, near-real-time air quality data. It provides Air Quality Index (AQI) data from territories, cities, and regions at the street level. With this API, you may also access long-term AQI statistics for a given location.
5. Carbon Interface API
This API calculates CO2 emissions from various actions. Users give information about the activity, and the API calculates the quantity of CO2 emitted using the most precise estimation methodology. Emissions data can be returned for power, airplane flights, product manufacture, shipping, and other specified operations.
6. Solcast API
Solcast provides solar energy projections. Track the Solcast REST API, which provides weather forecasts, solar power estimations, radiation, and cloud data. This API allows developers to get photovoltaic (PV) output power forecasts, solar radiation estimates, real-time data, and analytics for tailored forecasts.
What is the scope of the Weather API’s coverage?
Consider the level of weather data relevance you want to provide to your target audience: Is your target audience but their most pressing needs addressed? Users in nations with recognized pollution problems, for example, will probably find air quality data beneficial, while users in areas prone to forest fires may benefit from wildfire tracking tools. Consider the weather data’s resolution as well. What level of granularity does the weather API provide? Will, a simple daily forecast of temperature and rain, suffice, or are you seeking ‘feels like’ recommendations or visualizations to add additional value and boost engagement?
Conclusion
Temperature, wind speed, and rainfall readings are only a tiny part of the data used in weather forecasting. In addition to complicated analytics, climate intelligence and historical data can indicate climatic trends that can be utilized to determine the best places to build renewable technology with the most significant production potential. Wind farms, for instance, must have a mean wind speed of 8m/s at a turbine nominal condition to be called ‘good,’ and historical wind speed data can reveal where this is likely to be attainable. When it comes to new solar installations, asset owners and potential investors must look at historical weather data, which includes not only the number of sunshine hours but also parameters like hail forecasts and wind speeds, to ensure that the site can deliver on its expected yield as well as withstand adverse conditions – both of which are critical for highest profitability and financial return on capital. As we’ve seen, the energy business uses weather and climatic data extensively. Although forecasting techniques and products are getting more technologically sophisticated, combining them with current energy IT, architecture remains a significant barrier. We’ve seen this hurdle with energy data across the value chain – typical procurement and account management processes are lengthy and done offline, data processing sourcing and integration a resource-intensive operation. As the renewable sector (and the energy business) evolves, we need to streamline the interchange of precision meteorological data, making communication between provider and customer far more efficient.
The most economical option is to employ weather data APIs, which can provide a basic data access solution of weather packages tailored for the energy sector. One of the most well-known meteorological data sources, Dark Sky, was acquired by Apple last year. Dark Sky provided hyper-local weather forecasts, and its API was used as a critical source of relatively inexpensive meteorological data by any third parties, notably those in the energy sector, in addition to its standalone app. The announcement that Dark Sky would be shutting down its API entirely by 2022 opened the door to a plethora of alternative weather API choices, each of which offers a diverse set of weather intelligence capabilities, with much-demonstrating functionalities specifically tailored to the needs of the energy industry.