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Published March 2018 | Published
Journal Article Open

Application and Evaluation of an Explicit Prognostic Cloud Cover Scheme in GRAPES Global Forecast System

Abstract

An explicit prognostic cloud‐cover scheme (PROGCS) is implemented into the Global/Regional Assimilation and Prediction System (GRAPES) for global middle‐range numerical weather predication system (GRAPES_GFS) to improve the model performance in simulating cloud cover and radiation. Unlike the previous diagnostic cloud‐cover scheme (DIAGCS), PROGCS considers the formation and dissipation of cloud cover by physically connecting it to the cumulus convection and large‐scale stratiform condensation processes. Our simulation results show that clouds in mid‐high latitudes arise mainly from large‐scale stratiform condensation processes, while cumulus convection and large‐scale condensation processes jointly determine cloud cover in low latitudes. Compared with DIAGCS, PROGCS captures more consistent vertical distributions of cloud cover with the observations from Atmospheric Radiation Measurements (ARM) program at the Southern Great Plains (SGP) site and simulates more realistic diurnal cycle of marine stratocumulus with the ERA‐Interim reanalysis data. The low, high, and total cloud covers that are determined via PROGCS appear to be more realistic than those simulated via DIAGCS when both are compared with satellite retrievals though the former maintains slight negative biases. In addition, the simulations of outgoing longwave radiation (OLR) at the top of the atmosphere (TOA) from PROGCS runs have been considerably improved as well, resulting in less biases in radiative heating rates at heights below 850 hPa and above 400 hPa of GRAPES_GFS. Our results indicate that a prognostic method of cloud‐cover calculation has significant advantage over the conventional diagnostic one, and it should be adopted in both weather and climate simulation and forecast.

Additional Information

© 2017 American Geophysical Union. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. Received 17 NOV 2017; Accepted 17 FEB 2018; Accepted article online 21 FEB 2018; Published online 8 MAR 2018. This paper is jointly supported by the Ministry of Science and Technology of China (2017YFC1501403 and 2017YFC1501406), the National Natural Science Foundation of China (NSFC) (grants 41575143, 41105067, and 41375107), Special Fund for Meteorology Scientific Research in the Public Interest (grants GYHY201506018 and GYHY201406005), the State Key Laboratory of Earth Surface Processes and Resource Ecology (2017‐ZY‐02), and the Fundamental Research Funds for the Central Universities. Coauthor Y.W. appreciates the funding support provided by US National Science Foundation (NSF, award 1700727). Coauthor J.H.J. acknowledges the support by the Jet Propulsion Laboratory, California Institute of Technology, sponsored by NASA. The data used in this study can be downloaded via ftp: nwpc.nmc.cn (user: pub, pswd: verygood) and are also available by request to Zhanshan Ma through mazs@cma.gov.cn.

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Published - Ma_et_al-2018-Journal_of_Advances_in_Modeling_Earth_Systems.pdf

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Created:
August 21, 2023
Modified:
October 18, 2023