On the Collective Importance of Model Physics and Data Assimilation on Mesoscale Convective System and Precipitation Forecasts over Complex Terrain

Christoforus Bayu Risanto aDepartment of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, Arizona

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https://orcid.org/0000-0003-3225-4948
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James M. Moker Jr., b56th Operations Support Squadron Weather Flight, Luke Air Force Base, Glendale, Arizona

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Avelino F. Arellano Jr., aDepartment of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, Arizona

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Christopher L. Castro aDepartment of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, Arizona

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Yolande L. Serra cCooperative Institute for Climate, Ocean, and Ecosystem Studies, University of Washington, Seattle, Washington

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Thang M. Luong dPhysical Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia

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David K. Adams eInstituto de Ciencias de la Atmósfera y Cambio Climático, Universidad Nacional Autónoma de México, Mexico City, Mexico

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Abstract

Forecasting mesoscale convective systems (MCSs) and precipitation over complex terrain is an ongoing challenge even for convective-permitting numerical models. Here, we show the value of combining mesoscale constraints to improve short-term MCS forecasts for two events during the North American monsoon season in 2013, including the following: 1) the initial specification of moisture, via GPS-precipitable water vapor (PWV) data assimilation (DA); 2) kinematics via modification of cumulus parameterization; and 3) microphysics via modification of cloud microphysics parameterization. A total of five convective-permitting Weather Research and Forecasting (WRF) Model experiments is conducted for each event to elucidate the impact of these constraints. Results show that combining GPS-PWV DA with a modified Kain–Fritsch scheme and double-moment microphysics provides relatively the best forecast of both North American monsoon MCSs and convective precipitation in terms of timing, location, and intensity relative to available precipitation and cloud-top temperature observations. Additional examination on the associated reflectivity, vertical wind field, equivalent potential temperature, and hydrometeor distribution of MCS events show the added value of each individual constraint to forecast performance.

Significance Statement

Forecasting thunderstorm clouds and rain over mountainous regions is challenging because of limitations in having radar and rain gauges and in resolving physical drivers in forecast models. We examine the value of considering all possible constraints by incorporating moisture into these models, and correcting physics in the model treatment of cumulus and cloud microphysics parameterizations. This study demonstrates that assimilating moisture and using modified Kain–Fritsch and double-moment microphysics schemes provides the best thunderstorm cloud and rain forecasts in terms of timing, location, and intensity. Each correction improves key properties of these storms such as vertical wind, along with distribution of water in various phases. We highlight the need to improve our efforts on effectively integrating these constraints into current and future forecasts.

© 2023 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: Christoforus Bayu Risanto, cbrisanto@arizona.edu

Abstract

Forecasting mesoscale convective systems (MCSs) and precipitation over complex terrain is an ongoing challenge even for convective-permitting numerical models. Here, we show the value of combining mesoscale constraints to improve short-term MCS forecasts for two events during the North American monsoon season in 2013, including the following: 1) the initial specification of moisture, via GPS-precipitable water vapor (PWV) data assimilation (DA); 2) kinematics via modification of cumulus parameterization; and 3) microphysics via modification of cloud microphysics parameterization. A total of five convective-permitting Weather Research and Forecasting (WRF) Model experiments is conducted for each event to elucidate the impact of these constraints. Results show that combining GPS-PWV DA with a modified Kain–Fritsch scheme and double-moment microphysics provides relatively the best forecast of both North American monsoon MCSs and convective precipitation in terms of timing, location, and intensity relative to available precipitation and cloud-top temperature observations. Additional examination on the associated reflectivity, vertical wind field, equivalent potential temperature, and hydrometeor distribution of MCS events show the added value of each individual constraint to forecast performance.

Significance Statement

Forecasting thunderstorm clouds and rain over mountainous regions is challenging because of limitations in having radar and rain gauges and in resolving physical drivers in forecast models. We examine the value of considering all possible constraints by incorporating moisture into these models, and correcting physics in the model treatment of cumulus and cloud microphysics parameterizations. This study demonstrates that assimilating moisture and using modified Kain–Fritsch and double-moment microphysics schemes provides the best thunderstorm cloud and rain forecasts in terms of timing, location, and intensity. Each correction improves key properties of these storms such as vertical wind, along with distribution of water in various phases. We highlight the need to improve our efforts on effectively integrating these constraints into current and future forecasts.

© 2023 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: Christoforus Bayu Risanto, cbrisanto@arizona.edu
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  • Zhang, Y., J. Fan, Z. Li, and D. Rosenfeld, 2021: Impacts of cloud microphysics parameterizations on simulated aerosol–cloud interactions for deep convective clouds over Houston. Atmos. Chem. Phys., 21, 23632381, https://doi.org/10.5194/acp-21-2363-2021.

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