2021
DOI: 10.1007/s00382-021-05833-6
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Modeling high-resolution precipitation by coupling a regional climate model with a machine learning model: an application to Sai Gon–Dong Nai Rivers Basin in Vietnam

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Cited by 8 publications
(3 citation statements)
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“…1, studies have indicated that including atmospheric covariates is helpful for estimating precipitation (e.g., Baño-Medina et al, 2020;Li et al, 2022;Rasp and Lerch, 2018). The other three scenarios also consider atmospheric covariates of P from MERRA2 as predictors, which include geopotential height, specific humidity, air temperature, eastward wind, and northward wind at three different vertical levels (250, 500, 850 hPa) (e.g., Baño-Medina et al, 2020;Rasp and Lerch, 2018) as well as vertical wind (e.g., Trinh et al, 2021) at 500 hPa (OMEGA500), sea level pressure and 2 m air temperature in a single level (e.g., Panda et al, 2022;Rasp and Lerch, 2018) (see Table 2). We chose these variables based on precipitation formation theory (cloud mass movements and thermodynamics) as well as findings from previous studies as already indicated.…”
Section: Experimental Designmentioning
confidence: 99%
“…1, studies have indicated that including atmospheric covariates is helpful for estimating precipitation (e.g., Baño-Medina et al, 2020;Li et al, 2022;Rasp and Lerch, 2018). The other three scenarios also consider atmospheric covariates of P from MERRA2 as predictors, which include geopotential height, specific humidity, air temperature, eastward wind, and northward wind at three different vertical levels (250, 500, 850 hPa) (e.g., Baño-Medina et al, 2020;Rasp and Lerch, 2018) as well as vertical wind (e.g., Trinh et al, 2021) at 500 hPa (OMEGA500), sea level pressure and 2 m air temperature in a single level (e.g., Panda et al, 2022;Rasp and Lerch, 2018) (see Table 2). We chose these variables based on precipitation formation theory (cloud mass movements and thermodynamics) as well as findings from previous studies as already indicated.…”
Section: Experimental Designmentioning
confidence: 99%
“…This method primarily involves nesting regional climate models within global models to predict regional future climate change scenarios. Currently, widely used RCMs include models such as Weather Research and Forecasting (WRF), Regional Climate Mode (RegCM), and PRECIS [8,9]. These RCMs, to some extent, can effectively simulate the geographical distribution and seasonal variation characteristics of temperature and precipitation at the regional scale.…”
Section: Introductionmentioning
confidence: 99%
“…Climate models are developed using well-established earth physical principles ( 17) and applying those principles allows the modelling of past, current and future climates ( 17,18 ). The IPCC global model has been widely recognised and used for a variety of climate change modelling ( [19][20][21][22] ).…”
Section: Introductionmentioning
confidence: 99%