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Comparison of Biases in Warm-Season WRF Forecasts in North and South America

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  • 1 a Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado
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Abstract

Ensemble forecasts using the WRF Model at 20-km grid spacing with varying parameterizations are used to investigate and compare precipitation and atmospheric profile forecast biases in North and South America. By verifying a 19-member ensemble against NCEP Stage-IV precipitation analyses, it is shown that the cumulus parameterization (CP), in addition to precipitation amount and season, had the largest influence on precipitation forecast skill in North America during 2016–17. Verification of an ensemble subset against operational radiosondes in North and South America finds that forecasts in both continents feature a substantial midlevel dry bias, particularly at 700 hPa, during the warm season. Case-by-case analysis suggests that large midlevel error is associated with mesoscale convective systems (MCSs) east of the high terrain and westerly subsident flow from the Rocky and Andes Mountains in North and South America. However, error in South America is consistently greater than North America. This is likely attributed to the complex terrain and higher average altitude of the Andes relative to the Rockies, which allow for a deeper low-level jet and long-lasting MCSs, both of which 20-km simulations struggle to resolve. In the wake of data availability from the RELAMPAGO field campaign, the authors hope that this work motivates further comparison of large precipitating systems in North and South America, given their high impact in both continents.

Piersante’s current affiliation: Department of Atmospheric and Environmental Sciences, University at Albany, State University of New York, Albany, New York.

Corresponding author: Jeremiah O. Piersante, jpiersante@albany.edu

This article is included in the RELAMPAGO-CACTI Special Collection.

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

Abstract

Ensemble forecasts using the WRF Model at 20-km grid spacing with varying parameterizations are used to investigate and compare precipitation and atmospheric profile forecast biases in North and South America. By verifying a 19-member ensemble against NCEP Stage-IV precipitation analyses, it is shown that the cumulus parameterization (CP), in addition to precipitation amount and season, had the largest influence on precipitation forecast skill in North America during 2016–17. Verification of an ensemble subset against operational radiosondes in North and South America finds that forecasts in both continents feature a substantial midlevel dry bias, particularly at 700 hPa, during the warm season. Case-by-case analysis suggests that large midlevel error is associated with mesoscale convective systems (MCSs) east of the high terrain and westerly subsident flow from the Rocky and Andes Mountains in North and South America. However, error in South America is consistently greater than North America. This is likely attributed to the complex terrain and higher average altitude of the Andes relative to the Rockies, which allow for a deeper low-level jet and long-lasting MCSs, both of which 20-km simulations struggle to resolve. In the wake of data availability from the RELAMPAGO field campaign, the authors hope that this work motivates further comparison of large precipitating systems in North and South America, given their high impact in both continents.

Piersante’s current affiliation: Department of Atmospheric and Environmental Sciences, University at Albany, State University of New York, Albany, New York.

Corresponding author: Jeremiah O. Piersante, jpiersante@albany.edu

This article is included in the RELAMPAGO-CACTI Special Collection.

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

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