Evaluating Convective Initiation in High-Resolution Numerical Weather Prediction Models Using GOES-16 Infrared Brightness Temperatures

David S. Henderson Space Science and Engineering Center, University of Wisconsin–Madison, Madison, Wisconsin

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Jason A. Otkin Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin–Madison, Madison, Wisconsin

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John R. Mecikalski Atmospheric Sciences Department, University of Alabama in Huntsville, Huntsville, Alabama

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Abstract

The evolution of model-based cloud-top brightness temperatures (BT) associated with convective initiation (CI) is assessed for three bulk cloud microphysics schemes in the Weather Research and Forecasting Model. Using a composite-based analysis, cloud objects derived from high-resolution (500 m) model simulations are compared to 5-min GOES-16 imagery for a case study day located near the Alabama–Mississippi border. Observed and simulated cloud characteristics for clouds reaching CI are examined by utilizing infrared BTs commonly used in satellite-based CI nowcasting methods. The results demonstrate the ability of object-based verification methods with satellite observations to evaluate the evolution of model cloud characteristics, and the BT comparison provides insight into a known issue of model simulations producing too many convective cells reaching CI. The timing of CI from the different microphysical schemes is dependent on the production of ice in the upper levels of the cloud, which typically occurs near the time of maximum cloud growth. In particular, large differences in precipitation formation drive differences in the amount of cloud water able to reach upper layers of the cloud, which impacts cloud-top glaciation. Larger cloud mixing ratios are found in clouds with sustained growth leading to more cloud water lofted to the upper levels of the cloud and the formation of ice. Clouds unable to sustain growth lack the necessary cloud water needed to form ice and grow into cumulonimbus. Clouds with slower growth rates display similar BT trends as clouds exhibiting growth, which suggests that forecasting CI using geostationary satellites might require additional information beyond those derived at cloud top.

SIGNIFICANCE STATEMENT

Several studies have used weather satellites to examine storm properties; however, they do not provide information about processes occurring within clouds. To address this limitation, we used numerical weather prediction model simulations and an object-based analysis method to learn more about in-cloud processes that influence the evolution of thunderstorms in the southeastern United States. The model and satellite comparison helped demonstrate that differences in the timing of rainfall formation can impact the amount of ice reaching the upper portion of the cloud. When ice forms, the cloud begins to grow rapidly and is more likely to become a long-lived thunderstorm. The results highlight the importance of using satellite data sensitive to clouds to evaluate the conditions under which cumulus clouds transition into severe storms.

© 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: David Henderson, dshenderson@wisc.edu

Abstract

The evolution of model-based cloud-top brightness temperatures (BT) associated with convective initiation (CI) is assessed for three bulk cloud microphysics schemes in the Weather Research and Forecasting Model. Using a composite-based analysis, cloud objects derived from high-resolution (500 m) model simulations are compared to 5-min GOES-16 imagery for a case study day located near the Alabama–Mississippi border. Observed and simulated cloud characteristics for clouds reaching CI are examined by utilizing infrared BTs commonly used in satellite-based CI nowcasting methods. The results demonstrate the ability of object-based verification methods with satellite observations to evaluate the evolution of model cloud characteristics, and the BT comparison provides insight into a known issue of model simulations producing too many convective cells reaching CI. The timing of CI from the different microphysical schemes is dependent on the production of ice in the upper levels of the cloud, which typically occurs near the time of maximum cloud growth. In particular, large differences in precipitation formation drive differences in the amount of cloud water able to reach upper layers of the cloud, which impacts cloud-top glaciation. Larger cloud mixing ratios are found in clouds with sustained growth leading to more cloud water lofted to the upper levels of the cloud and the formation of ice. Clouds unable to sustain growth lack the necessary cloud water needed to form ice and grow into cumulonimbus. Clouds with slower growth rates display similar BT trends as clouds exhibiting growth, which suggests that forecasting CI using geostationary satellites might require additional information beyond those derived at cloud top.

SIGNIFICANCE STATEMENT

Several studies have used weather satellites to examine storm properties; however, they do not provide information about processes occurring within clouds. To address this limitation, we used numerical weather prediction model simulations and an object-based analysis method to learn more about in-cloud processes that influence the evolution of thunderstorms in the southeastern United States. The model and satellite comparison helped demonstrate that differences in the timing of rainfall formation can impact the amount of ice reaching the upper portion of the cloud. When ice forms, the cloud begins to grow rapidly and is more likely to become a long-lived thunderstorm. The results highlight the importance of using satellite data sensitive to clouds to evaluate the conditions under which cumulus clouds transition into severe storms.

© 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: David Henderson, dshenderson@wisc.edu
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