Results 51 to 60 of about 109,140 (374)
A comparative study of convolutional neural network models for wind field downscaling [PDF]
Meteorological Applications, 2020 We analyze the applicability of convolutional neural network (CNN) architectures for downscaling of short‐range forecasts of near‐surface winds on extended spatial domains.
Kevin Höhlein +3 more
semanticscholar +1 more source
Comparison of data-driven methods for downscaling ensemble weather forecasts [PDF]
Hydrology and Earth System Sciences, 2008 This study investigates dynamically different data-driven methods, specifically a statistical downscaling model (SDSM), a time lagged feedforward neural network (TLFN), and an evolutionary polynomial regression (EPR) technique for downscaling numerical ...
Xiaoli Liu, P. Coulibaly, N. Evora
doaj
Hydrology and Earth System Sciences, 2021 . General circulation models (GCMs) are the primary tools for evaluating the possible impacts of climate change; however, their results are coarse in temporal and spatial dimensions. In addition, they often show systematic biases compared to observations.
H. Tabari +3 more
semanticscholar +1 more source
Progress in Earth and Planetary Science, 2018 The hybrid dynamical-statistical downscaling approach is an effort to combine the ability of dynamical downscaling to resolve fine-scale climate changes with the low computational cost of statistical downscaling.
Quan Tran Anh, Kenji Taniguchi
doaj +1 more source
Enhancing Regional Climate Downscaling Through Advances in Machine Learning
Artificial Intelligence for the Earth SystemsDespite the sophistication of Global Climate Models (GCMs), their coarse spatial resolution limits their ability to resolve important aspects of climate variability and change at the local scale.
Neelesh Rampal +9 more
semanticscholar +1 more source
Climate Dynamics, 2020 General circulation models (GCMs) allow the analysis of potential changes in the climate system under different emissions scenarios. However, their spatial resolution is too coarse to produce useful climate information for impact/adaptation assessments ...
Daniela Araya-Osses +4 more
semanticscholar +1 more source
Impact of day/night time land surface temperature in soil moisture disaggregation algorithms [PDF]
, 2016 Since its launch in 2009, the ESA’s SMOS mission is providing global soil moisture (SM) maps at ~40 km, using the first L-band microwave radiometer on space.
Camps Carmona, Adriano José +5 more
core +2 more sources
Precipitation Dynamical Downscaling Over the Great Plains
Journal of Advances in Modeling Earth Systems, 2018 Detailed, regional climate projections, particularly for precipitation, are critical for many applications. Accurate precipitation downscaling in the United States Great Plains remains a great challenge for most Regional Climate Models, particularly for ...
Xiao‐Ming Hu +5 more
doaj +1 more source
Remote SensingSoil moisture (SM) is a key variable driving hydrologic, climatic, and ecological processes. Although it is highly variable, both spatially and temporally, there is limited data availability to inform about SM conditions at adequate spatial and temporal ...
I. P. Senanayake +5 more
semanticscholar +1 more source
Geoscientific Model Development, 2022 . Deep learning (DL) has recently emerged as an innovative tool to downscale climate variables from large-scale atmospheric fields under the perfect-prognosis (PP) approach.
J. Baño-Medina +6 more
semanticscholar +1 more source