Department of Physics Seminar Series: Andrew Martin
Monday, November 27, 2017 - 3:15pm

SB1 107, 1025 SW Mill Street
Free and open to the public
Light refreshments will be served


Dr. Andrew Martin
Climate, Atmospheric Science & Physical Oceanography
Scripps Institution of Oceanography
UC San Diego
"The Atmospheric River Low-Level Jet: Its Role in Driving Precipitation and Forecast Errors"


Accurate forecasts of precipitation during landfalling atmospheric rivers (AR) are critical because AR play a large role in water supply and flooding for many regions. Regional numerical weather prediction models may offer accurate predictions because they are able to resolve the fine scales critical to determining orographic uplift. A comprehensive analysis of predictability
limit and the dependence of precipitation errors on simulated physical processes by regional numerical weather prediction models during ARs has not been performed. In this study, we have used an unprecedented number of observations from within ARs to verify global and
regional model forecasts of ARs. We have characterized forecast error and the predictability limit in
AR water vapor transport, static stability, precipitation and standard atmospheric fields. A unique analysis is also presented that apportions the role of orographic forcing and precipitation response in driving errors in forecast precipitation. It is found that the global model and the higher-resolution regional model reach their predictability limit in forecasting atmospheric state during ARs at similar lead time, and both present similar and important errors in low-level water vapor flux, moist-static stability, and precipitation. However, the relative contribution of forcing and response to the incurred precipitation error is very different in the two models. It can be demonstrated using the analysis presented herein that improving water vapor transport accuracy can significantly reduce regional model precipitation errors during ARs, while the same cannot be demonstrated for the global model.


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