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815 results for “Forecasting”
Spatial Damped Anomaly Persistence (SDAP) Forecasts of Sea Ice Presence in the Arctic between 1999 and 2020
<p>Spatial Damped Anomaly Persistence Forecasts of Sea Ice Presence in the Arctic between 1999 and 2020. Each netcdf file corresponds to a single initialisation, done at the start of the stated month, and the forecasts for the following 120 days, both probabilistic (SDAP) and deterministic (SAP) forecasts. The forecasts were derived using OSI SAF sea-ice concentration records (OSI SAF 450 and 430b) and follow the resolution of that dataset (25 km EASE-2 grid). Further details regarding the forecasting method and the results can be found in Niraula et Goessling, 2021 (in review).</p> <p> </p> <p>Please note that while the filenames say "DampedForecast", each file contains both Damped or Deterministic forecasts associated with the date.</p> <p> </p>
Data from: Pathways to global-change effects on biodiversity: New opportunities for dynamically forecasting demography and species interactions
<p>In structured populations, persistence under environmental change is threatened when abiotic factors simultaneously negatively affect survival and reproduction of several life-cycle stages. Such effects can then be exacerbated when species interactions generate reciprocal feedbacks between the demographic rates of the different species. Despite the importance of such demographic feedbacks, forecasts that account for them are severely limited as individual-based data on interacting species are perceived to be essential for such mechanistic forecasting - but are rarely available. This dataset is the input to showcase a state-of-the-art Bayesian method to infer and project stage-specific survival and reproduction from abundance data for several interacting species in a Mediterranean shrub community.</p>
WRF output for the Geophysical Research Letters publication "Potential Impacts of Radio Occultation Data Assimilation on Forecast Skill of Tropical Cyclone Formation in the Western North Pacific"
<p>This dataset is the Weather Research and Forecasting (WRF) model output for the <em>Geophysical Research Letters</em> publication entitled "Potential Impacts of Radio Occultation Data Assimilation on Forecast Skill of Tropical Cyclone Formation in the Western North Pacific". The dataset includes the azimuthal-averaged parameters with 0.2°resolution, three-day forecast, and two experiments for all cases analyzed in the publication. Due to the data size, only the variables used in the figures are uploaded (i.e., relative humidity, relative vorticity, temperature, and water vapor mixing ratio). Detailed information, composite calculation, and model settings can be found in the publication.</p>
The benefit of augmenting open data with clinical data-warehouse EHR for forecasting SARS-CoV-2 hospitalizations in Bordeaux area, France
<p><strong>Objective</strong></p> <p>The aim of this study was to develop an accurate regional forecast algorithm to predict the number of hospitalized patients and to assess the benefit of the Electronic Health Records (EHR) information to perform those predictions. Materials and Methods Aggregated data from SARS-CoV-2 and weather public database and data warehouse of the Bordeaux hospital were extracted from May 16, 2020, to January 17, 2022. The outcomes were the number of hospitalized patients in the Bordeaux Hospital at 7 and 14 days. We compared the performance of different data sources, feature engineering, and machine learning models.</p> <p><strong>Results </strong></p> <p>During the period of 88 weeks, 2561 hospitalizations due to COVID-19 were recorded at the Bordeaux Hospital. The model achieving the best performance was an elastic-net penalized linear regression using all available data with a median relative error at 7 and 14 days of 0.136 [0.063; 0.223] and 0.198 [0.105; 0.302] hospitalizations, respectively. Electronic health records (EHRs) from the hospital data warehouse improved median relative error at 7 and 14 days by 10.9% and 19.8%, respectively. Graphical evaluation showed remaining forecast error was mainly due to delay in slope shift detection.</p> <p><strong>Discussion </strong></p> <p>Forecast models showed overall good performance both at 7 and 14 days which was improved by the addition of the data from Bordeaux Hospital data warehouse.</p> <p><strong>Conclusions </strong></p> <p>The development of hospital data warehouses might help to get more specific and faster information than traditional surveillance systems, which in turn will help to improve epidemic forecasting at a larger and finer scale.</p>
MERICS China Forecast Conference 2023
<p>This is the YouTube Livestream of the 4th MERICS China Forecast Conference which was hosted on Wednesday, January 18, 2023.</p> <p>2023 will be a critical year for China. Following the sudden about-turn in Beijing’s Covid-19 policies, the country is currently struggling with an unprecedented health crisis. The economy is in deep trouble. At the same time, Xi Jinping looks set to hardwire the political agenda he pushed through at the historic 20th Party Congress in October and establish a new leadership team at the National People’s Congress in spring. Internationally, Beijing is also facing multiple challenges, including its relationship with the US, its "no-limits" partnership with Russia and a re-calibration of China policies across Europe. To anticipate what 2023 might have in store for us, we hosted the 4th MERICS China Forecast conference on Wednesday, January 18, 2023, in collaboration with our media partner Handelsblatt. MERICS presented findings from our annual survey among leading European experts and professionals and among a wider global audience on key developments in Europe-China relations. A prominent line-up of high-level speakers shared and discussed their expectations for the turbulent year ahead, including China’s political and economic trajectory and challenges for European governments and companies.</p> <p>The MERICS China Forecast 2023 has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement number 101061700.</p> <p>Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.</p>
An Urban Scheme for the ECMWF Integrated Forecasting System: Global Forecasts and Residential CO2 Emissions - Dataset
<p>These data support the journal article : An Urban Scheme for the ECMWF Integrated Forecasting System: Global Forecasts and Residential CO2 Emissions (Journal of Advances in Modeling Earth Systems).</p> <p>The files provided are as follows:</p> <p>SITE_RMSE* - These files provided the computed RMSE values for SYNOP site evaluation using the control IFS and the urban IFS. Results are given for different forecast lead times, different seasons and for both 2 m and 10 m wind speed. </p> <p>DIURNAL* - These files provide the diurnal 2 m temperature output from the model and the comparison of those with observations.</p> <p>For more information please contact or access to alternative data related to the publication please contact: joe.mcnorton@ecmwf.int</p> <p> </p>
Health Risks Forecast of Regional Air Pollution on Allergic Rhinitis: High-Resolution City-Scale Simulations in Changchun, China
<p>Here presented the forcasted results of Potential Morbidity Risk Index (PMRI) for the personal patients with allerigc rhinitis and the public health administrations, and these results are supplied to the published paper of "Health Risks Forecast of Regional Air Pollution on Allergic Rhinitis: High-Resolution City-Scale Simulations in Changchun, China".</p>
Datasets and Codesets for "Wavelet Decomposition and Neural Networks: A Potent Combination for Short Term Wind Speed and Power Forecasting"
<p>This is the datasets and codesets used in the paper:</p> <p>A. E. Kio, J. Xu, N. Gautam, and Y. Ding, 2024, “Wavelet decomposition and neural networks: A potent combination for short term wind speed and power forecasting,” Frontiers in Energy Research, section of Wind Energy, Vol. 12, pp. 1277464. </p> <p>The PDF file, "Reproducibility Report," explains how to reproduce the results in the tables and figures.</p>
Forecasting and Tracking Volcanic Explosions using Shannon Entropy at Volcán de Colima
<pre><strong>Forecasting and Tracking Volcanic Explosions using Shannon Entropy at Volcán de Colima</strong>. by: Pablo Rey-Devesa (1,2),*, Janire Prudencio (1,2), Carmen Benítez (3), Mauricio Bretón (4), Imelda Plasencia (4), Zoraida León (4), Félix Ortigosa (4), Ligdamis Gutiérrez (1,2), Raúl Arámbula (4) and Jesús M. Ibáñez (1,2). <strong>Institutions associated</strong>: (1) Department of Theoretical Physics and Cosmos. Science Faculty. Avd. Fuentenueva s/n. University of Granada. 18071. Granada. Spain. (2) Andalusian Institute of Geophysiscs. Campus de Cartuja. University of Granada. C/Profesor Clavera 12. 18071. Granada. Spain. (3) Department of Signal Theory, Telematics and Communication. University of Granada. Informatics and Telecommunication School. 18071. Granada. Spain. (4) Centro Universitario de Estudios Vulcanológicos (CUEV), Observatorio Vulcanológico, Universidad de Colima, Colima, México <strong>Acknowledgment</strong>: a) This study was partially supported by the Spanish FEMALE (PID2019-106260GB-I00) and PROOF-FOREVER (EUR2022.134044) projects. P. Rey-Devesa was funded by the Ministerio de Ciencia e Innovación del Gobierno de España (MCIN), Agencia Estatal de Investigación (AEI), Fondo Social Europeo (FSE) and Programa Estatal de Promoción del Talento y su Empleabilidad en I+D+I Ayudas para contratos predoctorales para la formación de doctores 2020 (PRE2020-092719). b) To the Visual Monitoring and Seismicity Monitoring projects, both from the Center for Volcanological Studies of the Colima University. <strong>Data availability statemen</strong>t: Seismic data from Volcán de Fuego de Colima. <strong>Contents</strong>: Seismic Data from Volcán de Fuego de Colima recorded at stations INCA and SOMA. The data represent the vertical component of the seismic signal, associated to the period analyzed in the study: "<em>Forecasting and Tracking Volcanic Explosions using Shannon Entropy at Volcán de Colima</em>" Data available between January 2015 and May 2017.</pre>
The SafeSpace magnetospheric models sample forecast for the 2015 St Patrick's geomagnetic storm
<p>This dataset presents the sample forecast of the magnetosphere models in the SafeSpace project, for the March 2015 St Patrick's storm. It is build from a synthetic solar wind forecast at L1 and corresponding Kp forecast. This forecast if fed in the SPM plasma density model, as well as in a VLF wave intensities model, yielding the dataset presented here.</p> <p>All files are in the CDF file format.</p> <ul> <li>The <a href="https://zenodo.org/api/files/eb6a5ea3-366a-4830-bb14-71799a6f00cf/GEOIDX_20150301.cdf">GEOIDX_20150301.cdf</a> file contains the synthetic solar wind and Kp ensemble forecast.</li> <li>The <a href="https://zenodo.org/api/files/eb6a5ea3-366a-4830-bb14-71799a6f00cf/Bw2_20150301.cdf">Bw2_20150301.cdf</a> file contains the corresponding VLF wave intensities.</li> <li>The <a href="https://zenodo.org/api/files/eb6a5ea3-366a-4830-bb14-71799a6f00cf/SPM_dens_20150301.cdf">SPM_dens_20150301.cdf</a> file contains the corresponding plasma densities.</li> <li>The <a href="https://zenodo.org/api/files/eb6a5ea3-366a-4830-bb14-71799a6f00cf/SafeSpace_RBSP_A_Nowcast.cdf">SafeSpace_RBSP_A_Nowcast.cdf</a> and <a href="https://zenodo.org/api/files/eb6a5ea3-366a-4830-bb14-71799a6f00cf/SafeSpace_RBSP_A_Nowcast.cdf">SafeSpace_RBSP_B_Nowcast.cdf</a> files contains the reconstructed electron fluxes along the RBSP spacecrafts for the whole March 2015 month, using data assimilation in the SafeSpace pipeline.</li> <li>The <a href="https://zenodo.org/api/files/eb6a5ea3-366a-4830-bb14-71799a6f00cf/SafeSpace_RBSP_A_Nowcast.cdf">SafeSpace_RBSP_A_Forecast.cdf</a> and <a href="https://zenodo.org/api/files/eb6a5ea3-366a-4830-bb14-71799a6f00cf/SafeSpace_RBSP_A_Nowcast.cdf">SafeSpace_RBSP_B_Nowcast.cdf</a> files contains a 4 days forecast of the electron fluxes along the RBSP spacecrafts for March 17th to March 20th, 2015.</li> </ul> <p>This dataset and the SafeSpace pipeline is described in details in the article by Brunet et al. "Improving the electron radiation belt nowcast and forecast using the SafeSpace data assimilation modelling pipeline", currently in review in AGU Space Weather.</p> <p> </p>
Animations of Tropospheric Signatures Preceding and Following Sudden Stratospheric Warmings in Extended-Range Ensemble Forecasts
<p>Animations of Tropospheric Signatures Preceding and Following Sudden Stratospheric Warmings in Extended-Range Ensemble Forecasts</p>
Data from: Comparing winter versus summer deepwater dissolved oxygen depletion with the potential for cross-seasonal forecasting of deepwater oxygen availability
<p>Depletion of deepwater dissolved oxygen (DO) in lakes has become increasingly prevalent and severe due to many external stressors, potentially threatening human-derived ecosystem services ranging from drinking water quality to fisheries. Using year-round, high-frequency DO data from 12 dimictic lakes, we compared three measures of deepwater DO depletion during winter and summer: DO depletion rate, DO minimum, and hypoxia duration. Hypoxia (DO < 3 mg L<sup>-1</sup>) occurred in over half of the lakes and persisted an average of 83% longer in summer than in winter. While we found no difference in DO depletion rates between winter versus summer, these rates were strongly related to lake morphology in winter but water transparency and temperature in summer. Winter hypoxia duration was negatively related to summer hypoxia duration, suggesting potential utility for forecasting DO depletion in the subsequent summer. Spring mixing efficacy was strongly related to winter minimum DO saturation and hypoxia duration, and was also a strong predictor of summer minimum DO saturation and hypoxia duration. Hence, these cross-seasonal patterns suggest deepwater DO metrics can be used to forecast DO availability in subsequent seasons, modified by the relative importance of morphology, water transparency, and temperature. These findings can allow for improved, early management when DO is predicted to be critically low based on previous seasons’ DO measurements, which can work to minimize the negative consequences for water quality and fisheries health associated with severe DO depletion.</p>
Supporting data and tool, for the paper "A standardized methodology for the validation of air quality forecast applications (F-MQO): Lessons learnt from its application across Europe"
<p>This 'Zenodo' contains supporting data and tools, for the paper 'A standardized methodology for the validation of air quality forecast applications (F-MQO): Lessons learnt from its application across Europe'.</p> <p>The repository includes the source code (<a href="https://zenodo.org/api/files/fdcaf2a5-5289-44f5-96db-6dda29c5cb6c/DELTA_7.2.zip">DELTA_7.2.zip</a>) and the dataset (<a href="https://zenodo.org/api/files/fdcaf2a5-5289-44f5-96db-6dda29c5cb6c/GMD_Vitali_et_all_data_20230516.zip">GMD_Vitali_et_all_data_20230516.zip</a>).</p>
Input and output data for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 4)
<p>The dataset contains:</p> <p>i. the meteorological forcing, hydrological boundary condition and chlorophyll-a files used as an input</p> <p>ii. the model output and skill produced</p> <p>for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 4)</p>
Seasonal forecasts of ocean heat content in ECMWF-SEAS5 and CMCC-SPS3
<p>Seasonal forecasts of ocean heat content in the upper 300m from two Copernicus Climate Change Service Systems: ECMWF SEAS5 and CMCC SPS3. Data was used for the following study:</p> <p>McAdam, R., Masina, S., Balmaseda, M. <em>et al.</em> Seasonal forecast skill of upper-ocean heat content in coupled high-resolution systems. <em>Clim Dyn</em> 58, 3335–3350 (2022). https://doi.org/10.1007/s00382-021-06101-3</p>
Data set for the ensemble postprocessing of 2m surface temperature forecasts in Germany for 24 hours lead time
<p>Full data set for the ensemble postprocessing of 2m surface temperature forecasts at 462 observation stations in Germany for 24 hours lead time in the years 2015-2020. The data set is provided in .Rdata format supported by the statistical software <a href="https://www.r-project.org">R</a>. The ensemble forecasts are retrieved from <a href="https://www.ecmwf.int">ECMWF</a> and the observation data from the <a href="https://opendata.dwd.de/climate_environment/CDC/observations_germany/climate/hourly/air_temperature/historical/BESCHREIBUNG_obsgermany_climate_hourly_tu_historical_de.pdf">German Weather Service</a> (<a href="https://www.dwd.de/">DWD</a>). <br> <br> For more information about the data set see: <a href="https://github.com/jobstdavid/paper_gamvinereg">https://github.com/jobstdavid/paper_gamvinereg</a></p>
Railbelt 2050 Load, Electrification, and Behind-the-Meter Solar Hourly Load Demand for Aggressive and Moderate Electrification Forecasts
<p>The data in this file is comprised of hourly load demand data for the year of 2050 for Alaska's Railbelt transmission system from the aggressive and moderate load, electrification adoption, and behind-the-meter solar forcasts generated by the ACEP Railbelt Decarbonization Study. </p>
Data set for the ensemble postprocessing of 2m surface temperature forecasts in Germany for five different lead times
<p>Full data set for the ensemble postprocessing of 2m surface temperature forecasts at 462 observation stations in Germany for the lead times 24, 48, 72, 96 and 120 hours in the years 2015-2020. The data set is provided in .RData format supported by the statistical software <a href="https://www.r-project.org">R</a>. The ensemble forecasts are retrieved from <a href="https://www.ecmwf.int">ECMWF</a> and the observation data from the <a href="https://opendata.dwd.de/climate_environment/CDC/observations_germany/climate/hourly/air_temperature/historical/BESCHREIBUNG_obsgermany_climate_hourly_tu_historical_de.pdf">German Weather Service</a> (<a href="https://www.dwd.de/">DWD</a>). <br> <br> For more information about the data set see: <a href="https://github.com/jobstdavid/paper_tsEMOS">https://github.com/jobstdavid/paper_tsEMOS</a></p>
Submitted forecasts and analysis code for "Predicting spring phenology in deciduous broadleaf forests: NEON Phenology Forecasting Community Challenge"
<p>Submitted forecasts for the 2021 Ecological Forecasting Initiative NEON Phenology Forecast Challenge and the analysis code for the accompanying manuscript. </p>
Dataset for "Assessing Storm Surge Multi-Scenarios based on Ensemble Tropical Cyclone Forecasting" paper
<p>1000 ensemble track forecast of tropical cyclone Hagibis (2019) is provided in NetCDF format and the computed storm surge forecast is provided in the Excel file.</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.