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WRF Model Moisture Adjustment Method: A Case Study with Wintertime Cloudy Biases in Xinjiang, China

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  • 1 Envision Digital International Pte. Ltd., Singapore
  • 2 Shenzhen Institute of Artificial Intelligence and Robotics for Society, and Institute of Robotics and Intelligent Manufacturing, The Chinese University of Hong Kong, Shenzhen, China
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Abstract

For most locations on Earth the ability of a numerical weather prediction (NWP) model to accurately simulate surface irradiance relies heavily on the NWP model being able to resolve cloud coverage and thickness. At horizontal resolutions at or below a few kilometers NWP models begin to explicitly resolve convection and the clouds that arise from convective processes. However, even at high resolutions, biases may remain in the model and result in under- or overprediction of surface irradiance. In this study we explore the correction of such systematic biases using a moisture adjustment method in tandem with the Weather Research and Forecasting (WRF) Model for a location in Xinjiang, China. After extensive optimization of the configuration of the WRF Model we show that systematic biases still exist—in particular for wintertime in Xinjiang. We then demonstrate the moisture adjustment method with cloudy days for January 2019. Adjusting the relative humidity by 12% through the vertical led to a root-mean-square error (RMSE) improvement of 57.8% and a 90.5% reduction in bias for surface irradiance.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Robert Huva, robert.huva@envision-digital.com

Abstract

For most locations on Earth the ability of a numerical weather prediction (NWP) model to accurately simulate surface irradiance relies heavily on the NWP model being able to resolve cloud coverage and thickness. At horizontal resolutions at or below a few kilometers NWP models begin to explicitly resolve convection and the clouds that arise from convective processes. However, even at high resolutions, biases may remain in the model and result in under- or overprediction of surface irradiance. In this study we explore the correction of such systematic biases using a moisture adjustment method in tandem with the Weather Research and Forecasting (WRF) Model for a location in Xinjiang, China. After extensive optimization of the configuration of the WRF Model we show that systematic biases still exist—in particular for wintertime in Xinjiang. We then demonstrate the moisture adjustment method with cloudy days for January 2019. Adjusting the relative humidity by 12% through the vertical led to a root-mean-square error (RMSE) improvement of 57.8% and a 90.5% reduction in bias for surface irradiance.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Robert Huva, robert.huva@envision-digital.com
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