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Dataset results
514 results for “meteorological data”
Simulation data for WRF-GC (v2.0): online two-way coupling of WRF (v3.9.1.1) and GEOS-Chem (v12.7.2) for modeling regional atmospheric chemistry–meteorology interactions
<p>This repository provides the test simulation data for "WRF-GC (v2.0): online two-way coupling of WRF (v3.9.1.1) and GEOS-Chem (v12.7.2) for modeling regional atmospheric chemistry–meteorology interactions" published in Geoscientific Model Development. The configurations for sensitivity experiments are described in this paper. Please contact the corresponding author Tzung-May Fu (fuzm@sustech.edu.cn) for more details.</p>
EpiGraph 1.3 meteorological-based experimental data
<p>This file contains the complete data set of EpiGraph version 1.3.</p> <p>EpiGraph is a scalable, fully distributed simulator that is able to perform large scale stochastic and realistic simulations of the propagation of the flu virus. The current implementation of EpiGraph allows modeling the population via a realistic local interconnection network based on actual individual interactions extracted from social networks and demographic data. At inter-urban scale, we use a transportation model which allows the study of the spatial dynamics of the virus propagation over large geographical areas. EpiGraph also includes a model of the interaction between influenza spreading and climatic and meteorological factors, such as temperature, atmospheric pressure and humidity levels. From the computational perspective, EpiGraph is a network I/O bound application written in C language and implemented on MPI. Internally, EpiGraph performs irregular memory access patterns related to sparse matrices used to model the individual interactions.</p> <p>This distribution contains the meteorological-based experimental data used to run the experiments for the paper entitled <strong><em>Evaluating the impact of the weather conditions on the influenza propagation</em></strong></p> <p><br> In order to install this data set, you first need to install EpiGraph. You can download it from:</p> <p>https://gitlab.arcos.inf.uc3m.es:8380/desingh/EpiGraph</p> <p>Then, to install this package take the following steps:</p> <p>1.- Download the file and place it in EpiGraphHome directory.</p> <p>2.- Extract the tarbal in this directory. </p> <p> tar -zxvf EpiGraph.1.3.DataMeteo.tar.gz</p>
An Excel spreadsheet including eddy covariance and meteorological data measured at northernmost restored mangrove ecosystem from 2017 to 2023.
<p>The data was measured using an open-path eddy covariance system in the northernmost restored mangrove ecosystem afforested in Zhejiang Province, China. The results will be published in a paper entitled "Net Carbon Uptake During the Wet Seasons Dominates the Northernmost Restored Mangrove Ecosystem in Southern China". Contact Xianglan Li at Beijing Normal University (xlli@bnu.edu.cn) if you have any question.</p>
Meteorological data_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>Meteorological data_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
URA dataset - 40 Basque Country catchments hourly hydro-meteorological data + Catchments Attributes
<p>Basque Country is located in north of Spain on the Atlantic coast. This region, characterized by its humid climatology, has abundant water resources. The catchments, noted for their flashy and humid characteristics, span an area of 4494 Km<sup>2</sup> and include a diverse range of basin sizes from 4 to 1000 Km<sup>2</sup>.</p> <p>The Basque Water Agency (URA), a regional governmental entity in the Basque Country, is responsible for managing water policies and resources in this territory. To support water resources planning and management, URA has compiled a high-quality dataset suitable for data mining using deep learning models. This dataset includes hourly hydro-meteorological timeseries, reflecting the region’s steep, flashy, and humid hydrological dynamics.</p> <p>URA catchments are situated between the Cantabrian Mountains (reaching up to 1300 meters in elevation) in the northwest and the Atlantic Ocean to the north. The region is predominantly covered by grasslands and evergreen forests and benefits from the warming effects of the Gulf Stream. The climate is humid and temperate, with mean annual temperatures ranging from 9°<sup>C</sup> in the mountains to 15°<sup>C</sup> in lower regions. Annual rainfall varies between 1200 and 1600 millimeters, primarily due to the advection of North Atlantic fronts.</p> <p>URA has collected hourly hydro-meteorological timeseries from approximately 100 stations distributed across the region, including rain gauges and water level measurement sites. Accurate rainfall-runoff modeling is critical in this region due to its susceptibility to flash floods. This dataset presents 21 years of hourly timeseries from 40 catchments within the region. This extensive temporal coverage and high-resolution data offer valuable insights into the region’s hydrological dynamics.</p> <p>This Dataset is published with the papers:</p> <p>1- <span>Hosseini, F.; Prieto, C.; Álvarez, C. (2025). </span><span><strong>Ensemble learning of catchment-wise optimized LSTMs enhances regional rainfall-runoff modelling</strong>—Case Study: Basque Country, Spain. </span><span>J. Hydrol. 132269. doi: </span><span><a href="https://doi.org/10.1016/j.jhydrol.2024.132269"><span>10.1016/j.jhydrol.2024.132269</span></a></span></p> <p>2- <span>Hosseini, F., Prieto, C., & Álvarez, C. (2024a). <strong>Hyperparameter optimization of regional hydrological LSTMs by random search</strong>: A case study from Basque Country, Spain. Journal of Hydrology, 132003. doi: </span><span><a href="https://doi.org/10.1016/j.jhydrol.2024.132003"><span>10.1016/j.jhydrol.2024.132003</span></a></span></p> <p> </p>
Meteorological Data for the Bonneville Salt Flats, Utah, USA
<p>Modern and historical meteorological measurements from the Bonneville Salt Flats, with a compilation of evaporation measurements at comparable saline pans. </p>
2019-2020 AR station alpine meadow ecosystem tower-based observation spectra, GPP and meteorological data
<p> This is the dataset used in the <em>Investigating the Performance of Red and Far-Red SIF for Monitoring GPP of Alpine Meadow Ecosystems</em> paper. The dataset contains canopy red and far-red SIF data, GPP data, NDVI data, photosynthetically active radiation(PAR) data, temperature(Ta) data, and vapor pressure deficit(VPD) data during the 2019 and 2020 growing seasons in the alpine meadow ecosystem at the AR site(100.4643 E, 38.0473 N, altitude 3033 m).</p>
Meteorological and Flux Data in Two Rubber Plantations and a Natural Forest in Mainland Southeast Asia
<p>Rubber plantations have rapidly replaced natural forests in Mainland Southeast Asia, yet the relevant impacts on the terrestrial carbon cycle remain uncertain, especially with an increase in drought frequency. Based on the meteorological and flux data from two rubber sites and one natural forest site, our study investigated the rubber ecosystem fluxes in a marginal (drier) plantation and their responses to drought 2015/2016 compared to a local natural forest, and the differences in ecosystem fluxes and their climatic drivers between the marginal plantation and a traditional humid plantation. </p>
Animation for "Capturing synoptic-scale variations in surface aerosol pollution using deep learning with meteorological data"
<p>Animation for "Capturing synoptic-scale variations in surface aerosol pollution using deep learning with meteorological data"</p>
Meteorological, hydrological and water quality data of the Bahe River
<p>This dataset includes climate data from 1951 to 2020, and monthly streamflow, sediment, total nitrogen (TN), and phosphorus (TP) concentrations data observed from 2016 to 2020 for Bahe River Basin. The data is intended for research on SWAT setup, calibration, and validation.</p>
An Excel spreadsheet including eddy covariance and meteorological data measured at Nanji Island flux tower, Southern China.
<p>The data was measured using an open-path eddy covariance system in a subtropical island forest located in the Nanji Islands National Marine Protected Area in southern China. The results will support for a paper entitled "Carbon sink potential and its driving mechanisms in an island forest nature reserve in southern China". Contact Xianglan Li at Beijing Normal University (xlli@bnu.edu.cn) if you have any question.</p>
Meteorological data of Ali_Tazhong_Minfeng and Potential Source Analysis Code_FEAST
<p>Meteorological data of Ali_Tazhong_Minfeng and Potential Source Analysis Code_FEAST</p>
Supporting Data and Code for "Managing to Climatology: Improving semi-arid agricultural risk management using crop models and a dense meteorological network"
<p>Without reliable seasonal climate forecasts, farmers and managers in other weather-sensitive sectors might adopt practices that are optimal for recent climate conditions. To demonstrate this principle, crop simulation models driven by a dense meteorological network were used to identify climate-optimal planting dates for U.S. Southern High Plains (SHP) un-irrigated agriculture. This method converted large samples of SHP growing season weather outcomes into climate-representative cotton and sorghum yield distributions over a range of planting dates. Best planting dates were defined as those that maximized median cotton lint (April 24) and sorghum grain (July 1) yields. Those optimal yield distributions were then converted into corresponding profit distributions reflecting 2005-2019 commodity prices and fixed production costs. Both crop's profitability under variable price conditions and current SHP climate conditions were then compared based on median profits and loss probability, and through stochastic dominance analyses that assumed a slightly risk-averse producer.</p>
Meteorological, catchment wetness, natural tracers and stable isotopes of water data in Schwingbach Environmental Observatory, Germany
<p>Dataset includes the following high-resolution (3 h) variables in 2018 and 2019 :</p> <ul> <li>Meteorological data</li> <li>Discharge</li> <li>Soil moisture</li> <li>Water temperature</li> <li>pH</li> <li>Electrical conductivity</li> <li>stable isotopes of water</li> </ul>
1-hour meteorological data set
<p>1-hour meteorological data set</p>
Aerosol and meteorological data measured at LACMS from December 2019 to November 2020
<p>The data in this meteorological data set include concentration data of major pollutants, ion concentration data, meteorological elements data, etc. Detailed information can be seen in the 'Data Ddescription'.</p>
Data of the paper "The ionospheric exploration based on TJU#01 meteorological microsatellite mission: initial results"
<p>Ionospheric occultation data of the paper "The ionospheric exploration based on TJU#01 meteorological microsatellite mission: initial results"</p>
FNL data used for producing meteorological ICs/BCs of WRF v4.1.1-CMAQ v5.3.1, WRF-Chem v4.1.1 and WRF v3.7.1-CHIMERE v2020r1
<p>The National Center for Environmental Prediction Final Analysis (NCEP-FNL) datasets with a horizontal resolution of 1° × 1° at 6-hour intervals are used for producing meteorological initial and boundary conditions of WRF v4.1.1-CMAQ v5.3.1, WRF-Chem v4.1.1 and WRF v3.7.1-CHIMERE v2020r1.</p>
Meteorological data-MK
<p>Meteorological data for the region of Ohrid and Prespa Lakes in the region of North Macedonia Mean monthly air temperature from the meteorological station in Ohrid and Pretor (North Macedopnia)</p>
Data from "Connecting large-scale meteorological patterns to extratropical cyclones in CMIP6 climate models using self-organizing maps"
<p>The following files were used as data and analysis in the article "Connecting large-scale meteorological patterns to extratropical cyclones in CMIP6 climate models using self-organizing maps" (<a href="https://doi.org/10.1029/2022EF003211">https://doi.org/10.1029/2022EF003211</a>). In the study, we applied self-organizing maps (SOMs) as an automated machine-learning approach to characterize the large-scale meteorological patterns (LSMP) and associated frequency and intensity of discrete extratropical cyclone (ETC) events over the northeastern U.S. The dominant patterns of geopotential height variability are identified through SOM analysis of five reanalysis products during 1980 - 2019. ETC events are tracked using TempestExtremes and are integrated with SOMs to classify the accumulated cyclone activity associated with each pattern. We then evaluate the skill of CMIP6 historical experiments in simulating the LSMP and ETC events identified in the SOM. Please see the published paper for more details. Here we have archived: </p> <p>- data pre-processing scripts</p> <p>- code to run the self-organizing map analysis</p> <p>- code to calculate the SOM and ETC statistics</p> <p>- composites of 500-hPa geopotential height for each dataset as organized by the SOM</p> <p>- ETC tracking script and tracking output for each dataset</p> <p>- SOM output for each dataset </p>
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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.