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An Evaluation of Radiation Parameterizations in a Meso-Scale Weather Prediction Model Using Satellite Flux Observations.

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Název: An Evaluation of Radiation Parameterizations in a Meso-Scale Weather Prediction Model Using Satellite Flux Observations.
Autoři: Choi, Jihee1 (AUTHOR), Roh, Soonyoung2 (AUTHOR), Song, Hwan-Jin1,3 (AUTHOR), Baek, Sunghye4 (AUTHOR), Choi, Minjin1,2 (AUTHOR), Choi, Won-Jun2,3 (AUTHOR)
Zdroj: Remote Sensing. Oct2025, Vol. 17 Issue 19, p3312. 17p.
Témata: *RADIATIVE transfer, *NUMERICAL weather forecasting, *SOLAR radiation, *ATMOSPHERIC radiation, *WEATHER forecasting, *INFRARED radiation
Abstrakt: Highlights: What are the main findings? RRTMG-K reduced OLR RMSE by 4.8%, OSR RMSE by 17.5%, and bias by over 40% compared to RRTMG, mainly due to improved shortwave processes. RRTMG-KNN achieved similar or better accuracy than RRTMG-K, while offering 60-fold higher computational efficiency. What is the implication of the main finding? This study is the first to validate the performance of RRTMG-K using CERES satellite fluxes in a high-resolution NWP framework. CERES fluxes provide a reliable benchmark for radiation scheme evaluation, and RRTMG-KNN offers a practical, fast alternative for radiative transfer. This study evaluates the forecast performance of four radiation parameterization schemes—the Rapid Radiative Transfer Model for General Circulation Models (RRTMG), its improved version RRTMG-K, the infrequently applied variant, RRTMG-K60x, and the neural network emulator, RRTMG-KNN, within a high-resolution numerical weather prediction (NWP) model. The evaluation uses satellite-derived observations of Outgoing Longwave Radiation (OLR) and Outgoing Shortwave Radiation (OSR) from the Clouds and the Earth's Radiant Energy System (CERES) over the Korean Peninsula during 2020, including an extreme case study of Typhoon Haishen. Results show that RRTMG-K reduces RMSEs by 4.8% for OLR and 17.5% for OSR relative to RRTMG, primarily due to substantial bias reduction (42.3% for OLR, 60.4% for OSR). The RRTMG-KNN scheme achieves approximately 60-fold computational speedup while maintaining similar or slightly better accuracy than RRTMG-K; specifically, it reduces OLR errors by 1.2% and OSR errors by 1.6% compared to the infrequently applied RRTMG-K60x. In contrast, the infrequent application of RRTMG-K (RRTMG-K60x) slightly increases errors, underscoring the trade-off between computational efficiency and accuracy. These findings demonstrate the value of integrating advanced satellite flux observations and machine learning techniques into the evaluation and optimization of radiation schemes, providing a robust framework for improving cloud–radiation interaction representation in NWP models. [ABSTRACT FROM AUTHOR]
Databáze: Academic Search Index
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Abstrakt:Highlights: What are the main findings? RRTMG-K reduced OLR RMSE by 4.8%, OSR RMSE by 17.5%, and bias by over 40% compared to RRTMG, mainly due to improved shortwave processes. RRTMG-KNN achieved similar or better accuracy than RRTMG-K, while offering 60-fold higher computational efficiency. What is the implication of the main finding? This study is the first to validate the performance of RRTMG-K using CERES satellite fluxes in a high-resolution NWP framework. CERES fluxes provide a reliable benchmark for radiation scheme evaluation, and RRTMG-KNN offers a practical, fast alternative for radiative transfer. This study evaluates the forecast performance of four radiation parameterization schemes—the Rapid Radiative Transfer Model for General Circulation Models (RRTMG), its improved version RRTMG-K, the infrequently applied variant, RRTMG-K60x, and the neural network emulator, RRTMG-KNN, within a high-resolution numerical weather prediction (NWP) model. The evaluation uses satellite-derived observations of Outgoing Longwave Radiation (OLR) and Outgoing Shortwave Radiation (OSR) from the Clouds and the Earth's Radiant Energy System (CERES) over the Korean Peninsula during 2020, including an extreme case study of Typhoon Haishen. Results show that RRTMG-K reduces RMSEs by 4.8% for OLR and 17.5% for OSR relative to RRTMG, primarily due to substantial bias reduction (42.3% for OLR, 60.4% for OSR). The RRTMG-KNN scheme achieves approximately 60-fold computational speedup while maintaining similar or slightly better accuracy than RRTMG-K; specifically, it reduces OLR errors by 1.2% and OSR errors by 1.6% compared to the infrequently applied RRTMG-K60x. In contrast, the infrequent application of RRTMG-K (RRTMG-K60x) slightly increases errors, underscoring the trade-off between computational efficiency and accuracy. These findings demonstrate the value of integrating advanced satellite flux observations and machine learning techniques into the evaluation and optimization of radiation schemes, providing a robust framework for improving cloud–radiation interaction representation in NWP models. [ABSTRACT FROM AUTHOR]
ISSN:20724292
DOI:10.3390/rs17193312