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Improving Cloud–Radiation Interaction Simulations with an Observation-Constrained Symbolic-Regression Cloud-Fraction Diagnostic

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 Biases in boundary-layer clouds and cloud–radiation interactions remain a leading source of uncertainty in Earth’s energy budget and global-model performance. Using multiple satellite datasets (MODIS, PATMOS-x, CLARA-AVHRR, and CERES-EBAF), we diagnose biases in cloud fraction and cloud–radiation interaction in the Korea Meteorological Administration’s global Korean Integrated Model (KIM; 8-km horizontal resolution). We further propose an observation-constrained approach to improve a cloud fraction parameterization by combining observations with machine-learning-based diagnostics. Evaluation of forecasts for July 2022 and January 2023 shows that KIM overestimates the global-mean low-level cloud fraction by about 30%, while underestimating cloud fraction over major marine stratocumulus decks. In the tropics, KIM simulates excessive high-level cloud fraction, consistent with overly vigorous deep convection. Neural-network-based permutation importance and sensitivity analyses indicate that temperature, relative humidity, and lower-tropospheric stability (e.g., inversion strength) are key controls on cloud fraction. However, KIM fails to capture the dependence of cloud fraction on these controls in stratus and stratocumulus regimes. To address this limitation, we retune the parameters of the previously developed symbolic regression based cloud-fraction diagnostic parameterization for the KIM grid scale. We retune it using a CloudSat–ERA5 matched dataset. Specifically, we sample ERA5 along CloudSat tracks, upscale the matched dataset to 8 km (horizontal) and 20 hPa (vertical), and optimize the diagnostic parameters using differential evolution. The retuned diagnostic formulation reduces low- and high-cloud biases across the low to mid-latitudes and correspondingly reduces biases in surface shortwave radiation and outgoing longwave radiation (OLR). Notably, cloud fraction in the cumulus regime within the stratocumulus-to-cumulus transition region decreases substantially and becomes much closer to observations. These improvements are accompanied by a more realistic thermodynamic structure near the planetary boundary-layer top. 
Title: Improving Cloud–Radiation Interaction Simulations with an Observation-Constrained Symbolic-Regression Cloud-Fraction Diagnostic
Description:
 Biases in boundary-layer clouds and cloud–radiation interactions remain a leading source of uncertainty in Earth’s energy budget and global-model performance.
Using multiple satellite datasets (MODIS, PATMOS-x, CLARA-AVHRR, and CERES-EBAF), we diagnose biases in cloud fraction and cloud–radiation interaction in the Korea Meteorological Administration’s global Korean Integrated Model (KIM; 8-km horizontal resolution).
We further propose an observation-constrained approach to improve a cloud fraction parameterization by combining observations with machine-learning-based diagnostics.
Evaluation of forecasts for July 2022 and January 2023 shows that KIM overestimates the global-mean low-level cloud fraction by about 30%, while underestimating cloud fraction over major marine stratocumulus decks.
In the tropics, KIM simulates excessive high-level cloud fraction, consistent with overly vigorous deep convection.
Neural-network-based permutation importance and sensitivity analyses indicate that temperature, relative humidity, and lower-tropospheric stability (e.
g.
, inversion strength) are key controls on cloud fraction.
However, KIM fails to capture the dependence of cloud fraction on these controls in stratus and stratocumulus regimes.
To address this limitation, we retune the parameters of the previously developed symbolic regression based cloud-fraction diagnostic parameterization for the KIM grid scale.
We retune it using a CloudSat–ERA5 matched dataset.
Specifically, we sample ERA5 along CloudSat tracks, upscale the matched dataset to 8 km (horizontal) and 20 hPa (vertical), and optimize the diagnostic parameters using differential evolution.
The retuned diagnostic formulation reduces low- and high-cloud biases across the low to mid-latitudes and correspondingly reduces biases in surface shortwave radiation and outgoing longwave radiation (OLR).
Notably, cloud fraction in the cumulus regime within the stratocumulus-to-cumulus transition region decreases substantially and becomes much closer to observations.
These improvements are accompanied by a more realistic thermodynamic structure near the planetary boundary-layer top.
 .

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