Global Weather VERIFIED

The world weather is an ever-changing and complex phenomenon that plays a significant role in shaping the lives of people and ecosystems across the globe. Weather conditions such as temperature, precipitation, wind, and humidity can have significant impacts on agriculture, transportation, infrastructure, and public health.

Looking for Global CFSR weather data mapping application? This is no longer available online. We recommend using the new global weather data site, or you may download the CFSR data zipped by continent here.In addition, we also have CHIRPS/CHIRTS data zipped by continent. We hope to have another mapping appilication available in the future.

Global Weather

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NCEI, the National Centers for Environmental Prediction (NCEP), and the GeophysicalFluid Dynamics Laboratory provide remote access to high-volume numerical weatherprediction and global climate models and data.

The Service Records Retention System (SRRS) stores weather observations, summaries,forecasts, warnings, and advisories provided by the U.S. National Weather Service(NWS) for public use. Congress mandates that NCEI store and retain SRRS productsfor a five-year period. SRRS records can be used for accident investigations andlitigations.

Weather forecasting is an important application of scientific computing that aims to predict future weather changes, especially in regards to extreme weather events. In the past decade, high-performance computing systems have greatly accelerated research in the field of numerical weather prediction (NWP) methods1. Conventional NWP methods are primarily concerned with describing the transitions between discretized grids of atmospheric states using partial differential equations (PDEs) and then solving them with numerical simulations4,5,6. These methods are often slow; a single simulation for a ten-day forecast can take hours of computation in a supercomputer that has hundreds of nodes7. In addition, conventional NWP algorithms largely rely on parameterization, which uses approximate functions to capture unresolved processes, where errors can be introduced by approximation8,9.

Figure 3 shows a visualization of the 3-day forecast results of Pangu-Weather. We studied two upper-air variables, Z500 and T850 (850 hPa temperature), and two surface variables, 2-m temperature and 10-m wind speed, and compared the results with the operational IFS and the ERA5 ground truth. The results of both Pangu-Weather and operational IFS are sufficiently close to the ground truth, yet there are visible differences between them. Pangu-Weather produced smoother contour lines, implying that the model tends to forecast similar values for neighbouring regions. It is a general property of any regression algorithm (including deep neural networks) to converge on average values. In contrast, the operational IFS forecast is less smooth, because it calculates a single estimated value at each grid cell by solving a system of PDEs with initial conditions, while the chaotic nature of weather and the inevitably imprecise knowledge of the initial conditions and sub-grid scale processes can cause statistical uncertainties in each forecast.

We follow a recent work35 to compute two metrics for ensemble weather forecast, namely, the continuous ranked probability score (CRPS) and the spread-skill ratio (SSR). Mathematically, CRPS is defined as

Each perturbation generated for ensemble weather forecast contains 3 octaves of Perlin noise, with the scales being 0.2, 0.1 and 0.05, and the number of periods to generate along each axis (the longitude or the latitude) being 12, 24 and 48, respectively. We used the code provided in a GitHub repository ( -numpy) and modified the code for acceleration. We added a section to the pseudocode.

NWP methods often partition the atmospheric states into discretized grids, use PDEs to describe the transition between them1,39,40 and solve the PDEs using numerical simulations. The spacing of grids is key to forecast accuracy, but it is constrained by the computational budget and thus the spatial resolution of weather forecasts is often limited. Parameterization41 is an effective method for capturing unresolved processes. NWP methods have been widely applied, but they are troubled by the super-linearly increasing computational overhead1,42 and it is often difficult to perform efficient parallelization for them43. The heavy computational overhead of NWP also restricts the number of ensemble members, hence weakening the diversity and accuracy of probabilistic weather forecasts.

AI-based methods offer a complementary path for weather forecasting. The cutting-edge technology of AI lies in deep learning10, which assumes that the complex relationship between input and output data can be learned from abundant training data without knowing the actual physical procedure and/or formulae. In the scope of weather forecasting, AI-based methods were first applied to the problems of precipitation forecasting based on radar data44,45,46,47 or satellite data48,49, where the traditional methods that are much influenced by the initial conditions were replaced by deep-learning-based methods. The powerful expressive ability of deep neural networks led to success in these problems, which further encouraged researchers to delve into medium-range weather forecasting2,11,12,13,14,15,16 as a faster complement or surrogate of NWP methods. State-of-the-art deep-learning methods mostly rely on large models (that is, with large numbers of learnable parameters) to learn complex patterns from the training data.

Pangu is a primordial being and creation figure in Chinese mythology who separated heaven and earth and became geographic features such as mountains and rivers (see ). Pangu is also a series of pre-trained AI models developed by Huawei Cloud that covers computer vision, natural language processing, multimodal understanding, scientific computing (including weather forecasting) and so on.

Make an API call to receive access to the various data: Current weather and forecasts: minute forecast for 1 hour hourly forecast for 48 hours daily forecast for 8 days and government weather alerts Weather data for any timestamp for 40+ years historical archive and 4 days ahead forecast Daily aggregation of weather data for 40+ years archive and 1.5 years ahead forecast

For professionals and specialists with middle sized project, we recommend our Professional collections, which included Current & Forecasts collection, Historical weather data collection, Weather Maps collection and other APIs. For Enterprise level projects we provide Enterprise license, which is included all forecast products and current state, along with alerts, maps, and other products. Learn more

Access current weather data for any location We collect and process weather data from different sources such as global and local weather models, satellites, radars and a vast network of weather stations JSON, XML, and HTML formats Included in both free and paid subscriptions

Hourly forecast is available for 4 days Forecast weather data for 96 timestamps JSON and XML formats Included in the Developer, Professional and Enterprise subscription plans

Current weather, a variety of weather forecasts and their 7-day archive via regularly updated files Weather bulks are grouped by types of weather data and location lists (global city lists or ZIP code lists of EU, UK, and US) Bulk files are available via CSV and JSON formats Included in the Professional and Enterprise subscription plans

Get all the warnings from national weather agencies Weather alerts are pushed to your endpoint as soon as they occur Data feed provides all active weather alerts from the entire world Each alert contents date, time, location, and detailed description Monthly subscription. Please contact us to get access.

We have combined Weather services and Satellite imagery in a simple and fast Agro API. We have also launched a Dashboard for it - it is a visual service where you can easily work with satellite, weather and historical data, soil temperature and moisture, accumulated temperature and precipitation, etc. Learn more

Specify your route and get weather data and national alerts for the point of destination and along the route Current, forecast for 5 days and historical weather data for 1 year for your route Weather data are available for any point on the globe To receive information on price and access the data, please contact us

Through our API we provide historical weather data for any location on the globe Weather data have 1-hour step The depth of historical data depends on your subscription plan JSON format

Historical archive of 16-days forecast weather data Historical forecast data is available from October 7, 2017 CSV and JSON formats You can purchase the product from our Marketplace

Statistical data on main weather parameters for any day and monthof the year The statistics is calculated based on our Historical weather data JSON format The weather data updated every hour Included in Medium and Advanced subscription plans

Forecast, Historical, Current weather maps with 3-hour step 15 weather map layers The maps can be used as layers in Direct Tiles, OpenLayers, Leaflet, and Google Maps Included in Developer, Professional and Enterprise subscription plans

Forecast, Historical, Current weather maps with 1-hour step 14 weather map layers The maps can be used as layers in Direct Tiles, OpenLayers, Leaflet, and Google Maps Please contact us to get access 17dc91bb1f