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182 results for “Weather forecast”
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result / 10 Days / Created By Jihyeon Yoon(somehowme@gmail.com, flyingtext@nate.com, flyingtext@hotmail.com) Latest prediction can be available on map at https://sonagi-weather.web.app Prediction is based on the observation data of - Korea Meteorological Administration API - United States National Weather Service API - Canada MSC GeoMet API
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result / 10 Days / Created By Jihyeon Yoon(somehowme@gmail.com, flyingtext@nate.com, flyingtext@hotmail.com) Latest prediction can be available on map at https://sonagi-weather.web.app Prediction is based on the observation data of - Korea Meteorological Administration API - United States National Weather Service API - Canada MSC GeoMet API
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result / 15 Days / Created By Jihyeon Yoon(somehowme@gmail.com, flyingtext@nate.com, flyingtext@hotmail.com) Latest prediction can be available on map at https://sonagi-weather.web.app Prediction is based on the observation data of - Korea Meteorological Administration API - United States National Weather Service API - Canada MSC GeoMet API
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result / 15 Days / Created By Jihyeon Yoon(somehowme@gmail.com, flyingtext@nate.com, flyingtext@hotmail.com) Latest prediction can be available on map at https://sonagi-weather.web.app Prediction is based on the observation data of - Korea Meteorological Administration API - United States National Weather Service API - Canada MSC GeoMet API
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result / 15 Days / Created By Jihyeon Yoon(somehowme@gmail.com, flyingtext@nate.com, flyingtext@hotmail.com) Latest prediction can be available on map at https://sonagi-weather.web.app Prediction is based on the observation data of - Korea Meteorological Administration API - United States National Weather Service API - Canada MSC GeoMet API
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result / 15 Days / Created By Jihyeon Yoon(somehowme@gmail.com, flyingtext@nate.com, flyingtext@hotmail.com) Latest prediction can be available on map at https://sonagi-weather.web.app Prediction is based on the observation data of - Korea Meteorological Administration API - United States National Weather Service API - Canada MSC GeoMet API
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result / 15 Days / Created By Jihyeon Yoon(somehowme@gmail.com, flyingtext@nate.com, flyingtext@hotmail.com) Latest prediction can be available on map at https://sonagi-weather.web.app Prediction is based on the observation data of - Korea Meteorological Administration API - United States National Weather Service API - Canada MSC GeoMet API
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result / 15 Days / Created By Jihyeon Yoon(somehowme@gmail.com, flyingtext@nate.com, flyingtext@hotmail.com) Latest prediction can be available on map at https://sonagi-weather.web.app Prediction is based on the observation data of - Korea Meteorological Administration API - United States National Weather Service API - Canada MSC GeoMet API - ECMWF IFS04 within Europe area
Ephemeris data for: "Planet activeness: a new concept to enhance the accuracy of Astromet weather forecast"
<p><strong>Background: </strong>Astrometeorology is an ancient science, that addresses the relationship between planet position and weather events. Several Indian studies proved that astrometeorology could be a complementary method to improve numerical weather forecast accuracy. Since 2011, Tamil Nadu Agricultural University is involved in astrometeorological research and devised a novel concept "Planet Activeness Chart". The principle is that "planets' influence on a location's weather varies throughout the day and may be negative, inactive, active, highly active and rule depending on their angle to that location". Most existing astrometeorological studies used planetary position to predict the occurrence of weather events (yes/no) but failed to capture the intensity of such events. The "Planet Activeness Concept" could address this limitation and enhance forecast usability.</p> <p><strong>Methods</strong>: A study was carried out from 2018 to 2021 with six years of data (2011-16) to verify the "planet activeness" on hourly rainfall and wind speed events in Tamil Nadu. The frequency of planet activeness for a weather event was calculated by dividing the number of times a planet was in the selected activeness during a specific event category by the total number of events.</p> <p><strong>Results:</strong> The results indicated that the negative state of the Sun, and the active status of Saturn, Uranus, Venus, and Moon were positively associated with rainfall intensity. The windy planets Mercury and Neptune being in active states, the Sun and Saturn in rule states, Venus and Uranus in negative states, and Jupiter at a highly active state all had a significant influence on the increased wind speed.</p> <p><strong>Conclusion</strong>: Applying the planet activeness concept with azimuth could enhance the accuracy and usability of Astrometeorological forecasts. This study establishes a mathematical relationship between planet activeness and weather as a first step to understanding the science behind this relationship. It is suggested to study different combinations of planets' activeness during a weather event for more insights.</p>
Advancing Mars space weather forecasting: multi-point validation during a major solar storm
Open the record for dataset details and reuse information.
Ephemeris data for: "Planet activeness: a new concept to enhance the accuracy of Astromet weather forecast"
Open the record for dataset details and reuse information.
GPM Ground Validation Weather Research and Forecasting (WRF) Images MC3E V1
The GPM Ground Validation Weather Research and Forecasting (WRF) Images MC3E dataset consists of browse only images showing radar reflectivity, radar echo top, convective available potential energy (CAPE), temperature, geopotential height, wind speed, relative humidity, rain water, snow, cloud water, cloud ice, and graupel. These data were simulated by the Weather Research and Forecasting (WRF) for the period of the GPM Ground Validation Mid-latitude Continental Convective Cloud Experiment (MC3E) field campaign. The overarching goal of the MC3E field campaign was to provide the most complete characterization of convective cloud systems, precipitation, and the environment ever obtained and to provide new constraints for model cumulus parameterizations and space-based rainfall retrieval algorithms over land. Browse imagery files in PNG and GIF formats are available for April 19, 2011 through June 6, 2011.
Weather Research and Forecasting (WRF) North American Mountain Snow Data, Version 1
This data set consists of modeled snow water equivalent (SWE) data for 10 mountain ranges in North America, simulated by the Weather Research and Forecasting (WRF) regional climate model.
Wakasa Bay Weather Forecast Maps, Version 1
The AMSR-E Wakasa Bay Field Campaign was conducted over Wakasa Bay, Japan. The Wakasa Bay Field Campaign includes joint research observations, such as precipitation amount, pressure thickness, seal level pressure, and surface winds by the Japan Aerospace Exploration Agency (JAXA), the AMSR precipitation validation team, and the NASA AMSR-E team.
Evaluation of a customized variable-resolution global model and its application for high-resolution weather forecasts in East Asia
<p>The data for shallow water test for the paper "Evaluation of a customized variable-resolution global model and its application for high-resolution weather forecasts in East Asia" are in the two sw[2,5]_GlobalIntegrals.tar.bz2 files.</p> <p>The data of MPAS model output is stored as Demo data of the <a href="https://cpas.earth/">CPAS cloud platform</a> with Jupyter visualization tools and user interface. Please see "How-to-visualize-MPAS-model-data.pdf" on how to use it.</p>
Verification datasets for publication "Seasonal and diurnal performance of daily forecasts with WRF-NOAHMP over the United Arab Emirates" - UAE weather station data
<p>These are excel files containing measurement data for 2015 in the UAE, used for the submission to GMD journal, "Seasonal and diurnal performance of daily forecasts with WRF-NOAHMP over the United Arab Emirates". All data is courtesy of NCM, UAE.</p> <p>Also included is a station_ids.csv file. </p>
Tourism Forecast with Weather, Event, and Cross-Industry Data
<p><strong>Introduction</strong></p> <p>Substantial short-term demand fluctuations are common in the tourism industry. Therefore, tourism companies such as accommodation, transportation, catering, and leisure facilities have a vital interest in precise forecasts of the number of customers.</p> <p>We present a novel forecasting dataset for tourism, consisting of four Swiss companies, one accommodation, two transportation, and one indoor leisure businesses, all located in the same touristic region. It covers a total of ten years starting in 2007 and ending in 2016. The dataset allows using cross-series information and includes explanatory variables, such as calendar effects, event data, and weather forecast information.</p> <p>Machine learning (ML) practitioners, statisticians, and experts of tourism sectors are invited to investigate our dataset for new insights on short-term forecasting for industries in tourism.</p> <p>The Algorithmic Business Research Lab (ABIZ) of the Lucerne University of Applied Sciences and Arts, Switzerland, researches ML algorithms for businesses to support industry partners in developing business models and services based on complex algorithms as well as in the induced digital transformation. In a joint effort with Institute of Tourism (ITW) of the School of Business of the Lucerne University of Applied Sciences and Arts, Switzerland, we provide the present tourism dataset as part of a publication.</p> <p>Contacts and further information can be found at <a href="http://www.abiz.ch/.">http://www.abiz.ch/.</a></p> <p><strong>The dataset</strong></p> <p>The dataset comprises 3653 days of customer numbers of four Swiss companies in the tourism sector. There are 556 feature columns, four target columns, and two mask columns, 562 columns in total.</p> <p><strong>Target variables</strong></p> <p>The customer volume data has daily resolution and features at worst minor interruptions of a few days over a common period of ten years, starting in 2007 and ending in 2016. The missing values for one transportation and the indoor leisure company are masked, and the masks are available as indicator variables.</p> <p><strong>Feature Variables</strong></p> <p>The dataset contains both numerical and categorical,</p> <ul> <li>calendar effects, such as day of the week, weekend, and month features,</li> <li>event data, e.g., public and school holidays, free-time regional events, promotions or revisions for the facilities under exam,</li> <li>weather forecast features provided by the Federal Office of Meteorology and Climatology (MeteoSwiss), which encodes information about conditions in the locations of the four businesses and neighboring regions.</li> </ul> <p>The weather forecast data consist of information about temperature, sunshine, precipitation, and wind, forecasted up to 3 days in advance. Note that the weather forecast model is updated regularly, and therefore many features do not cover the entire period. We want to point out that there are categorical weather summary annotations created by meteorologists, which are only provided for the last year.</p> <p><strong>Dataset Download</strong></p> <p>In the published version of the dataset, feature names are replaced with pseudonyms, but descriptions are given to identify feature groups with similar meaning. The content available for download consists of </p> <ul> <li>data.csv,<br> CSV format, 11.3 MB, 3654 rows, and 562 columns with the time series data;<br> </li> <li>data-description.csv,<br> CSV format, 36 KB, 563 rows and 12 columns with feature name and short description, minimal statistics for cross-checking, and indicator variables that specify which single-company datasets a feature belongs.</li> </ul> <p> </p>
GPM Ground Validation Weather Research and Forecasting (WRF) Model LPVEx V1
The GPM Ground Validation Weather Research and Forecasting (WRF) Images LPVEx includes model data simulated by the Weather Research and Forecasting (WRF) model for the GPM Ground Validation Light Precipitation Validation Experiment (LPVEx). This field campaign took place around the Gulf of Finland in September and October of 2010. The goal of the campaign was to provide additional high-latitude, light rainfall measurements for the improvement of GPM satellite precipitation algorithms. The WRF model provided simulations of the precipitation events that were observed during the campaign. The LPVEx WRF dataset files are available from September 20 through October 20, 2010 in netCDF-3 format.
Weather Research and Forecasting (WRF) Model IMPACTS
The Weather Research and Forecasting (WRF) Model IMPACTS dataset includes model data simulated by the Weather Research and Forecasting (WRF) model for the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The WRF model provided simulations of the precipitation events that were observed during the campaign using initial and boundary conditions from the Global Forecast System (GFS) model and the North American Mesoscale Forecast System (NAM). The WRF IMPACTS dataset files are available from January 12, 2020, through March 4, 2023, in netCDF-3 format.
TCSP European Centre for Medium-Range Weather Forecasts (ECMWF) V1
The TCSP European Centre for Medium-Range Weather Forecasts (ECMWF) dataset consists of three-hour forecast/analysis data for the Tropical Cloud Systems and Processes (TCSP) field campaign, supplied by ECMWF. The TCSP field campaign was conducted from July 1 through July 27, 2005 out of the Juan Santamaria Airfield in San Jose, Costa Rica. TCSP collected data for research and documentation of cyclogenesis, the interaction of temperature, humidity, precipitation, wind, and air pressure that creates ideal birthing conditions for tropical storms, hurricanes, and related phenomena. The goal of this mission was to help better understand how hurricanes and other tropical storms are formed and intensify. The ECMWF three-hour forecast/analysis data are in a gridded binary (GRIB) format and tarred into daily files.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.