Results 41 to 50 of about 4,321,601 (325)
Handling Missing Values and Unusual Observations in Statistical Downscaling Using Kalman Filter
, 2021 Rainfall forecasting model using data Global Circular Model (GCM) with Statistical Downscaling technique has a fairly high accuracy. However, missing local climate information poses a constraint in data analysis and forecasting.
M. D. Saputra +3 more
semanticscholar +1 more source
Review of Downscaling Methodologies for Africa Climate Applications [PDF]
, 2008 Downscaling is the term used to describe the various methods used to translate the climate projections from coarse resolution GCMs to finer resolutions deemed more useful for assessing impacts.
Block, Paul J. +3 more
core +2 more sources
International Journal of Climatology, 2020 AbstractFuture climate projections illuminate our understanding of the climate system and generate data products often used in climate impact assessments. Statistical downscaling (SD) is commonly used to address biases in global climate models (GCM) and to translate large‐scale projected changes to the higher spatial resolutions desired for regional ...
Adrienne M. Wootten +3 more
openaire +2 more sources
Statistical downscaling of seasonal wave forecasts [PDF]
Ocean Modelling, 2019 P.C. acknowledges the support of the Spanish Ministerio de Economía y Competitividad (MINECO) and European Regional Development Fund (FEDER) under Grant BIA2015-70644-R (MINECO/FEDER, UE). The authors acknowledge funding from the ERANET ERA4CS (ECLISEA project) and the government of Cantabria and FEDER under the project CLISMO.
Camus Braña, Paula +3 more
openaire +5 more sources
Statistical Learning-Based Spatial Downscaling Models for Precipitation Distribution
Advances in Meteorology, 2022 The downscaling technique produces high spatial resolution precipitation distribution in order to analyze impacts of climate change in data-scarce regions or local scales.
Yichen Wu +3 more
doaj +1 more source
pyESDv1.0.1: an open-source Python framework for empirical-statistical downscaling of climate information [PDF]
Geoscientific Model Development, 2023 The nature and severity of climate change impacts vary significantly from region to region. Consequently, high-resolution climate information is needed for meaningful impact assessments and the design of mitigation strategies.
D. Boateng, S. G. Mutz, S. G. Mutz
doaj +1 more source
Atmosphere, 2011 When assessing the magnitude of climate signals in a regional scale, a host of optional approaches is feasible. This encompasses the use of regional climate models (RCM), nested into global climate models (GCM) for an area of interest as well as ...
Arne Spekat +3 more
doaj +1 more source
Blue Nile Runoff Sensitivity to Climate Change [PDF]
, 2010 This study describes implementation of hydrological climate change impact assessment tool utilising a combination of statistical spatiotemporal downscaling and an operational hydrological model known as the Nile Forecasting System.
Bellerby, T +3 more
core +2 more sources
DL4DS—Deep learning for empirical downscaling
Environmental Data Science, 2023 A common task in Earth Sciences is to infer climate information at local and regional scales from global climate models. Dynamical downscaling requires running expensive numerical models at high resolution, which can be prohibitive due to long model ...
Carlos Alberto Gomez Gonzalez
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Statistical downscaling for precipitation projections in West Africa
Theoretical and Applied Climatology, 2023 Abstract The West Africa region (5to 20N and 10E to 20W) is particularly vulnerable to climate change due to a combination of unique geographic features, meteorological conditions, and socio-economic factors. Drastic changes in precipitation (e.g., droughts or floods) in the region can have dramatic impacts on rain-fed agriculture, water ...
Andrew Polasky +2 more
openaire +1 more source