Works matching DE "NUMERICAL weather forecasting"
Results: 3836
基于多特征提取−卷积神经网络−长短期记忆网络的 短期风电功率预测方法.
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- Power Generation Technology, 2025, v. 46, n. 1, p. 93, doi. 10.12096/j.2096-4528.pgt.23101
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Optimizing Numerical Weather Prediction Utility of the Maryland Mesonet with Observing System Simulation Experiments.
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- Weather & Forecasting, 2024, v. 39, n. 12, p. 1849, doi. 10.1175/WAF-D-24-0089.1
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Short- and Medium-Range Predictability of Warm-Season Derechos. Part II: Convection-Allowing Ensemble Forecasts.
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- Weather & Forecasting, 2024, v. 39, n. 12, p. 1889, doi. 10.1175/WAF-D-24-0052.1
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Development of a Wave Model Component in the First Coupled Global Ensemble Forecast System at NOAA.
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- Weather & Forecasting, 2024, v. 39, n. 12, p. 1761, doi. 10.1175/WAF-D-24-0048.1
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Development and Validation of NOAA's 20-Year Global Wave Ensemble Reforecast.
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- Weather & Forecasting, 2024, v. 39, n. 11, p. 1651, doi. 10.1175/WAF-D-24-0043.1
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Observation Definitions and Their Implications in Machine Learning–Based Predictions of Excessive Rainfall.
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- Weather & Forecasting, 2024, v. 39, n. 11, p. 1733, doi. 10.1175/WAF-D-24-0033.1
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Sources of Tropical Cyclone Track Bifurcation in the COAMPS-TC and GFS Ensembles.
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- Weather & Forecasting, 2024, v. 39, n. 11, p. 1513, doi. 10.1175/WAF-D-24-0012.1
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Predictability of Severe Convective Storm Environments in Global Ensemble Forecast System, Version 12, Reforecasts.
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- Weather & Forecasting, 2024, v. 39, n. 11, p. 1571, doi. 10.1175/WAF-D-24-0004.1
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Updates to NOAA's Unified Forecast System's Cumulus Convection Parameterization Scheme between GFSv16 and GFSv17.
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- Weather & Forecasting, 2024, v. 39, n. 11, p. 1559, doi. 10.1175/WAF-D-23-0232.1
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Spatial Aligned Mean: A Method to Improve Consensus Forecasts of Precipitation from Convection-Allowing Model Ensembles.
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- Weather & Forecasting, 2024, v. 39, n. 11, p. 1545, doi. 10.1175/WAF-D-23-0229.1
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Application of a Two-Step Space–Time EOF Statistical Postprocessing Algorithm to Mitigate Subseasonal 200-hPa Geopotential Height Forecast Error.
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- Weather & Forecasting, 2024, v. 39, n. 11, p. 1621, doi. 10.1175/WAF-D-23-0168.1
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Development of a Two-Step EOF Statistical Postprocessing Algorithm to Identify Patterns of Systematic Error and Variance within GEFSv12 Reforecasts.
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- Weather & Forecasting, 2024, v. 39, n. 11, p. 1605, doi. 10.1175/WAF-D-23-0167.1
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Improvement of Wind Power Prediction by Assimilating Principal Components of Cabin Radar Observations.
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- Weather & Forecasting, 2024, v. 39, n. 9, p. 1335, doi. 10.1175/WAF-D-24-0058.1
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A Combined Scheme based on the Multiscale Stochastic Perturbed Parameterization Tendencies and Perturbed Boundary Layer Parameterization for a Global Ensemble Prediction System.
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- Weather & Forecasting, 2024, v. 39, n. 9, p. 1279, doi. 10.1175/WAF-D-24-0022.1
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Representation of Blowing Snow and Associated Visibility Reduction in an Operational High-Resolution Weather Model.
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- Weather & Forecasting, 2024, v. 39, n. 9, p. 1319, doi. 10.1175/WAF-D-23-0195.1
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Verification of Tropical Cyclogenesis Forecasts of the Korean Integrated Model for 2020–21.
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- Weather & Forecasting, 2024, v. 39, n. 9, p. 1247, doi. 10.1175/WAF-D-23-0175.1
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The Meteorology of the August 2023 Maui Wildfire.
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- Weather & Forecasting, 2024, v. 39, n. 8, p. 1097, doi. 10.1175/WAF-D-23-0210.1
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Adjusting Soil Temperatures with a Physics-Informed Deep Learning Model for a High-Resolution Numerical Weather Prediction System.
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- Atmosphere, 2025, v. 16, n. 2, p. 207, doi. 10.3390/atmos16020207
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Correcting Forecast Time Biases in CMA-MESO Using Himawari-9 and Time-Shift Method.
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- Remote Sensing, 2025, v. 17, n. 4, p. 617, doi. 10.3390/rs17040617
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Monte Carlo Guidance for Better Imaging of Boreal Lakes in the Wavelength Region of 400–800 nm.
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- Sensors (14248220), 2025, v. 25, n. 4, p. 1020, doi. 10.3390/s25041020
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The Meteorological Device: Literary Modernism, the Daily Weather Forecast and the Productions of Anxiety.
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- Modernism/Modernity, 2024, v. 31, n. 1, p. 23, doi. 10.1353/mod.2024.a935443
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Numerical ragweed pollen forecasts using different source maps: a comparison for France.
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- International Journal of Biometeorology, 2017, v. 61, n. 1, p. 23, doi. 10.1007/s00484-016-1188-x
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Dynamic and thermodynamic processes related to precipitation diurnal cycle simulated by GRIST.
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- Climate Dynamics, 2023, v. 61, n. 7/8, p. 3935, doi. 10.1007/s00382-023-06779-7
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Investigation of future climate change over the British Isles using weather patterns.
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- Climate Dynamics, 2022, v. 58, n. 9/10, p. 2405, doi. 10.1007/s00382-021-06031-0
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Deterministic prediction of stratospheric sudden warming events in the Global/Regional Integrated Model system (GRIMs).
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- Climate Dynamics, 2020, v. 55, n. 5/6, p. 1209, doi. 10.1007/s00382-020-05320-4
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Calibration of WRF model parameters using multiobjective adaptive surrogate model-based optimization to improve the prediction of the Indian summer monsoon.
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- Climate Dynamics, 2020, v. 55, n. 3/4, p. 631, doi. 10.1007/s00382-020-05288-1
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Assessment of MPAS variable resolution simulations in the grey-zone of convection against WRF model results and observations: An MPAS feasibility study of three extreme weather events in Europe.
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- Climate Dynamics, 2020, v. 55, n. 1/2, p. 253, doi. 10.1007/s00382-018-4562-z
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Coupled data assimilation and parameter estimation in coupled ocean–atmosphere models: a review.
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- Climate Dynamics, 2020, v. 54, n. 11/12, p. 5127, doi. 10.1007/s00382-020-05275-6
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Phase inconsistency as a major source of error in NGFS forecast.
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- Climate Dynamics, 2020, v. 54, n. 5/6, p. 2797, doi. 10.1007/s00382-020-05148-y
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GEOS-5 seasonal forecast system.
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- Climate Dynamics, 2019, v. 53, n. 12, p. 7335, doi. 10.1007/s00382-017-3835-2
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Predictability of the European heat and cold waves.
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- Climate Dynamics, 2019, v. 52, n. 3/4, p. 2481, doi. 10.1007/s00382-018-4273-5
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Attractor radius and global attractor radius and their application to the quantification of predictability limits.
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- Climate Dynamics, 2018, v. 51, n. 5/6, p. 2359, doi. 10.1007/s00382-017-4017-y
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Impact of moisture divergence on systematic errors in precipitation around the Tibetan Plateau in a general circulation model.
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- Climate Dynamics, 2016, v. 47, n. 9/10, p. 2923, doi. 10.1007/s00382-016-3005-y
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Can we trust climate models to realistically represent severe European windstorms?
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- Climate Dynamics, 2016, v. 46, n. 11/12, p. 3431, doi. 10.1007/s00382-015-2777-9
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Grappling with uncertainties in physical climate impact projections of water resources.
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- Climatic Change, 2020, v. 163, n. 3, p. 1379, doi. 10.1007/s10584-020-02858-4
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Potential impacts of climate change on wind and solar electricity generation in Texas.
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- Climatic Change, 2020, v. 163, n. 2, p. 745, doi. 10.1007/s10584-020-02891-3
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DADA: data assimilation for the detection and attribution of weather and climate-related events.
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- Climatic Change, 2016, v. 136, n. 2, p. 155, doi. 10.1007/s10584-016-1595-3
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Adding to Fire Fighter Safety by Including Real-Time Radar Data in Short-Range Forecasts of Thunderstorm-Induced Wind Shifts.
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- Fire (2571-6255), 2021, v. 4, n. 3, p. 1, doi. 10.3390/fire4030055
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The New Zealand Reanalysis (NZRA): development and preliminary evaluation.
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- Weather & Climate (01115499), 2023, v. 42, n. 1, p. 58
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Evaluation of global and regional reanalyses performance over New Zealand.
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- Weather & Climate (01115499), 2021, v. 41, n. 1, p. 52, doi. 10.2307/27127989
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Solar and photovoltaic forecasting through post-processing of the Global Environmental Multiscale numerical weather prediction model.
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- Progress in Photovoltaics, 2013, v. 21, n. 3, p. 284, doi. 10.1002/pip.1180
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Time series forecasting on multivariate solar radiation data using deep learning (LSTM).
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- Turkish Journal of Electrical Engineering & Computer Sciences, 2020, v. 28, n. 1, p. 211, doi. 10.3906/elk-1907-218
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分布函数差异化导向的风电功率预测误差 气象条件概率建模方法.
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- Electric Power Automation Equipment / Dianli Zidonghua Shebei, 2022, v. 42, n. 12, p. 58, doi. 10.16081/j.epae.202205056
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基于多源气象预报总辐照度修正的光伏功率短期预测.
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- Electric Power Automation Equipment / Dianli Zidonghua Shebei, 2022, v. 42, n. 3, p. 104, doi. 10.16081/j.epae.202201019
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A database of in situ water temperatures for large inland lakes across the coterminous United States.
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- Scientific Data, 2024, v. 11, n. 1, p. 1, doi. 10.1038/s41597-024-03103-8
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风云四号A星可见光反射率同化神经网络观测算子.
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- Journal of Remote Sensing, 2024, v. 28, n. 12, p. 3261, doi. 10.11834/jrs.20243348
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FY-3D星微波湿温探测仪通道响应函数的影响分析.
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- Journal of Remote Sensing, 2023, v. 27, n. 2, p. 394, doi. 10.11834/jrs.20220450
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Improving Numerical Weather Prediction of Rainfall Events Using Radar Data Assimilation.
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- Indonesian Journal of Geography, 2019, v. 51, n. 3, p. 273, doi. 10.22146/ijg.44924
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Revealing short-term dynamics of tropical cyclone wind speeds from satellite synthetic aperture radar.
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- Scientific Reports, 2024, v. 14, n. 1, p. 1, doi. 10.1038/s41598-024-61384-w
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Isotonic distributional regression.
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- Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2021, v. 83, n. 5, p. 963, doi. 10.1111/rssb.12450
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