Evaluating Operational and Experimental HRRR model forecasts of Atmospheric River events in California

Autor: William R. Moninger, David D. Turner, Robert Cifelli, Jason M. English, Melinda Marquis, Trevor I. Alcott, Janice L. Bytheway
Rok vydání: 2021
Předmět:
Zdroj: Weather and Forecasting.
ISSN: 1520-0434
0882-8156
DOI: 10.1175/waf-d-21-0081.1
Popis: Improved forecasts of Atmospheric River (AR) events, which provide up to half the annual precipitation in California, may reduce impacts to water supply, lives, and property. We evaluate Quantitative Precipitation Forecasts (QPF) from the High-Resolution Rapid Refresh model version 3 (HRRRv3) and version 4 (HRRRv4) for five AR events that occurred in Feb-Mar 2019 and compare them to Quantitative Precipitation Estimates (QPE) from Stage IV and Mesonet products. Both HRRR versions forecast spatial patterns of precipitation reasonably well, but are drier than QPE products in the Bay Area and wetter in the Sierra Nevada range. The HRRR dry bias in the Bay Area may be related to biases in the model temperature profile, while IWV, wind speed, and wind direction compare reasonably well. In the Sierra Nevada range, QPE and QPF agree well at temperatures above freezing. Below freezing, the discrepancies are due in part to errors in the QPE products, which are known to underestimate frozen precipitation in mountainous terrain. HRRR frozen QPF accuracy is difficult to quantify, but the model does have wind speed and wind direction biases near the Sierra Nevada range. HRRRv4 is overall more accurate than HRRRv3, likely due to data assimilation improvements, and possibly physics improvements. Applying a Neighborhood Maximum method impacted performance metrics, but did not alter general conclusions, suggesting closest grid box evaluations may be adequate for these types of events. Improvements to QPF in the Bay Area and QPE/QPF in the Sierra Nevada range would be particularly useful to provide better understanding of AR events.
Databáze: OpenAIRE
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