The provided dataset appears to contain weather-related information for New Delhi Safdarjung, India, spanning from January 1, 2023, to July 21, 2023. The dataset includes the following columns: Station ID, Station Name, Date, Precipitation (PRCP), Average Temperature (TAVG), Maximum Temperature (TMThe dataset includes daily observations with information on precipitation and temperature. It seems that some values are missing (NULL values), and there are variations in the units used for precipitation AX), and Minimum Temperature (TMIN).

three of these models are available:

ECMWF is now running its own Artificial Intelligence Forecasting System (AIFS). The AIFS consists of a deterministic model and an ensemble model. The deterministic model has been running operationally since 25 February 2025; further details can be found on the dedicated Implementation of AIFS Single v1 page.

The global weather forecast system market is experiencing robust growth, driven by increasing demand for accurate and timely weather information across various sectors. This demand stems from the critical role weather data plays in numerous applications, including optimizing agricultural practices, enhancing aviation safety, improving disaster preparedness, and supporting military operations. Technological advancements, such as the development of sophisticated satellite-based systems and the integration of artificial intelligence (AI) and machine learning (ML) algorithms for improved forecasting accuracy, are further propelling market expansion. The market is segmented by system type (satellite-based, ground-based, airborne) and application (commercial, military, weather service providers). While precise market size figures are not provided, based on industry reports and observed growth trends in related sectors, a reasonable estimate for the 2025 market size is $15 billion USD. Assuming a conservative Compound Annual Growth Rate (CAGR) of 7%, the market is projected to reach approximately $25 billion by 2033. This growth, however, faces challenges such as high initial investment costs associated with advanced systems and the need for continuous upgrades to maintain accuracy and efficiency. Further constraints include data integration complexities across diverse sources and potential regulatory hurdles related to data access and usage. The market's regional distribution is expected to reflect established economic powerhouses and regions highly susceptible to extreme weather events. North America and Europe currently hold significant market shares due to advanced technological infrastructure and robust weather forecasting programs. However, rapidly developing economies in Asia-Pacific, particularly China and India, are projected to witness significant growth in demand, driving regional market share expansion over the forecast period. The competitive landscape is characterized by a mix of established players and emerging technology providers. Key players are focusing on strategic partnerships, acquisitions, and continuous product innovation to maintain their market positions and capitalize on evolving market needs. This includes developing more sophisticated predictive modeling capabilities, expanding data analytics services, and enhancing the integration of weather data into various applications through robust APIs and user-friendly interfaces. The long-term outlook for the weather forecast system market remains positive, fueled by a growing need for precise weather information, technological advancements, and increasing government investments in climate change mitigation and adaptation strategies.

Aardvark Weather

This repo will contain the dataset used in the Aardvark Weather model. This will be made available on the completion of peer review, and will not be released prior to this time. If you would like to be notified when the dataset becomes available please email av555@cam.ac.uk.

DL-FRONT is a Deep Learning Neural Network (DLNN) that was trained to detect weather fronts using spatial grids of near-surface atmospheric variables. The dataset is composed of netCDF-4 files. Each file contains one year of hourly geospatial data grids describing the locations of four types of weather fronts—cold front, warm front, stationary front, and occluded front, over the time span 1980-2018.

This dataset is the product of processing data from the National Aeronautics and Space Administration (NASA) Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2). DL-FRONT processed MERRA-2 hourly data grids of instantaneous measures of air pressure reduced to mean sea level, air temperature at 2 meters, specific humidity at 2 meters, and wind velocity at 10 meters over the time span 1980 - 2018 to produce this dataset. The original MERRA-2 data were resampled at 1 degree resolution over the spatial range 31W - 171W x 10N - 77N using bicubic interpolation.

At each hourly time step the network produced a set of spatial grids with the same resolution and spatial range as the input, one for each of the five categories mentioned above. Each cell in a spatial grid for a given category records the network-assigned probability (from 0.0 to 1.0) that the cell is in a weather front boundary region of that category (or, for the "no front" category, the probability that the cell is not in any weather front boundary region).

Each weather front probability map was then processed to obtain polyline skeletons of the weather front boundary regions found by DL-FRONT. These vector representations of the fronts were then written to JSON files—one file for each hour. These front polylines were then rasterized into geospatial data grids and stored by year into netCDF-4 files that conform to the Climate and Forecast Metadata Conventions. The front data in each file is stored in a netCDF variable with dimensions (time, front type, y, x), where x and y are geospatial dimensions. There is a 2D geospatial data grid for each time step for each of the 4 front types—cold, warm, stationary, and occluded.

There are two large groupings of the netCDF files. One group uses a data grid based on the North American Regional Reanalysis (NARR) grid, which is a Lambert Conformal Conic projection coordinate reference system (CRS) centered over North America. The NARR grid is quite close the the spatial range of data displayed on the WPC workstations used to perform surface analysis and identify front locations. The native NARR grid has grid cells which are 32 km on each side. Our grid covers the same extents with cells that are 96 km on each side.

The other group uses a 1° latitude/longitude data grid centered over North America with extents 171W – 31W / 10N – 77 N. The files in this group are identified by the name MERRA2, because they were used with data from the NASA MERRA-2 dataset, which uses a latitude/longitude data grid.

There are a number of files within each group. The files all follow the naming convention merra2_[masked]_

The files marked as masked had a mask applied to the data grids that corresponded to the envelope of the geospatial region where there are, on average, 40 or more front crossing of any type per year, as determined using the Coded Surface Bulletin dataset.

The

Within each grid group, there are four subsets of files:

• merra2_masked_
• merra2_masked_
• merra2_
• merra2_

The South East Asia and India Weather Forecast Market offers a range of products, including weather forecasting services, weather data, and weather analytics. Weather forecasting services provide real-time and future weather forecasts for specific locations. Weather data provides historical and current weather data for specific locations. Weather analytics provides insights into weather patterns and trends. Recent developments include: For Instance, December 2022: StormGeo, a subsidiary of Alfa Laval and a leading provider of weather information and decision-support solutions for the maritime sector introduced a new Carbon Intensity Indicator (CII) Simulator., For Instance, July 2022: SailGP is the world's best sail racing tournament, and AccuWeather has announced an official partnership with them. SailGP has chosen AccuWeather as its official weather provider because of the trust and reliability it provides to event organizers and elite racing teams.. Key drivers for this market are: Growing Demand for Weather-Related Services. Rapid Adoption of Advanced Technologies.. Potential restraints include: Accuracy and Reliability of Weather Forecasts. Lack of Data and Infrastructure.. Notable trends are: Integration of AI and Machine Learning. Use of Big Data Analytics..

Historical observations of severe weather and simulated severe weather environments (i.e., features) from the Global Ensemble Forecast System v12 (GEFSv12) Reforecast Dataset (GEFS/R) are used in conjunction to train and test random forest (RF) machine learning (ML) models to probabilistically forecast severe weather out to days 4–8. RFs are trained with ~9 years of the GEFS/R and severe weather reports to establish statistical relationships. Feature engineering is briefly explored to examine alternative methods for gathering features around observed events, including simplifying features using spatial averaging and increasing the GEFS/R ensemble size with time-lagging. Validated RF models are tested with ~1.5 years of real-time forecast output from the operational GEFSv12 ensemble and are evaluated alongside expert human-generated outlooks from the Storm Prediction Center (SPC). Both RF-based forecasts and SPC outlooks are skillful with respect to climatology at days 4 and 5 with diminishing skill thereafter. The RF-based forecasts exhibit tendencies to slightly underforecast severe weather events, but they tend to be well-calibrated at lower probability thresholds. Spatially averaging predictors during RF training allows for prior-day thermodynamic and kinematic environments to generate skillful forecasts, while time-lagging acts to expand the forecast areas, increasing resolution but decreasing overall skill. The results highlight the utility of ML-generated products to aid SPC forecast operations into the medium range.

This dataset contains sea surface temperature (SST) over the Pacific and clustered 2 meter temperature (T2M) over North America. It is used in the example workflow of s2spy/lilio packages. The fields used here are processed outputs from the original ERA5 dataset. More details about how this dataset was generated can be found via this link: https://github.com/AI4S2S/cookbook/tree/main/data About the usage of this dataset in the example machine learning workflow of s2spy/lilio, check this link:https://github.com/AI4S2S/cookbook Data used here is generated using Copernicus Climate Change Service information and for more information about licensing, please check the Licence Agreement (https://cds.climate.copernicus.eu/cdsapp/#!/terms/licence-to-use-copernicus-products) for Copernicus Products.

This dataset includes model inputs that describe local weather conditions for Lake Mendota, WI. Weather data comes from two sources: locally measured (2009-2017) and gridded estimates (all other time periods). There are two comma-delimited files, one for weather data (one row per model timestep) and one for ice-flags, which are used by the process-guided deep learning model to determine whether to apply the energy conservation constraint (the constraint is not applied when the lake is presumed to be ice-covered). The ice-cover flag is a modeled output and therefore not a true measurement (see "Predictions" and "pb0" model type for the source of this prediction). This dataset is part of a larger data release of lake temperature model inputs and outputs for 68 lakes in the U.S. states of Minnesota and Wisconsin (http://dx.doi.org/10.5066/P9AQPIVD).

Super-Resolution for Renewable Resource Data and Urban Heat Islands (Sup3rUHI) introduces machine learning methods to incorporate high-resolution Urban Heat Island (UHI) effects into low-resolution historical reanalysis and future climate model datasets. The dataset includes models trained to estimate UHI in Los Angeles and Seattle, along with open-source software and additional training data for the 50 most populous cities in the contiguous United States. The study demonstrates the application of these methods in evaluating climate change impacts and heat mitigation strategies within high-resolution urban microclimate modeling. The dataset aims to provide a computationally efficient and adaptable solution for urban planners to address various heat planning questions and prioritize heat mitigation strategies. The open-source models, software, and data will contribute to the development of more heat-resilient and sustainable urban environments in the face of climate change.

This data is preliminary and is available to support peer review of an associated manuscript. The data will be finalized upon completion of the peer review.

The Sup3rUHI GitHub repository is under development and will be linked as a resource when complete.

The global marine weather forecast market is projected to reach a value of USD XX million by 2033, exhibiting a CAGR of XX% during the forecast period (2025-2033). The increasing demand for accurate weather forecasting in the maritime industry, rising offshore activities, and growing concerns about safety at sea are primarily driving the market growth. Furthermore, advancements in weather monitoring technologies and the integration of AI and machine learning algorithms are expected to further enhance the accuracy and efficiency of marine weather forecasts. Key market trends include the increasing adoption of short-range forecasting services for real-time decision-making during ship operations, the growing popularity of medium-range forecasting for voyage planning and route optimization, and the emergence of long-range forecasting for strategic planning and seasonal analysis. North America is anticipated to hold a significant market share due to the presence of major shipping ports and the advanced weather forecasting capabilities in the region. Asia-Pacific is projected to witness substantial growth owing to the rapid expansion of the maritime industry and the increasing investment in weather forecasting infrastructure. Key players in the market include Global Weather Corporation, Accuweather Inc., and Infoplaza Marine Weather, among others. These companies are continuously investing in research and development to provide innovative weather forecasting solutions to meet the evolving needs of the maritime industry.

These data are described by the Journal of Geophysical Research: Space Physics manuscript: "New capabilities for prediction of high-latitude ionospheric scintillation: A novel approach with machine learning."The file is organized as a comma separated values (.csv) file for ease of use with Python Pandas DataFrames. The data included are for observations from the Canadian High Arctic Ionospheric Network (CHAIN). CHAIN data are combined with solar and geomagnetic activity data to form a 'machine learning database' in which input 'features' are provided at a given time and attached to a 'label' that is the ionospheric phase scintillation at a future time (for prediction). The prediction lead time in these files is one hour. Full details of the input features and predictive task are provided in the paper. Data are provided in two separate files for the years 2015 and 2016.