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Department of Atmospheric Science

Tues., Feb. 26, 3:10 pm, EN6085

Advances and challenges in the understanding and prediction of extreme rainfall

Dr. Russ Schumacher

Colorado State University


Extreme precipitation can cause profound impacts, including deadly and destructive flooding, yet it continues to present a major forecasting challenge.  This is for many reasons: the often small spatial and temporal scales on which it occurs, the wide variety of synoptic-scale patterns that can support it, the intertwined set of processes on multiple scales that are involved, and the fundamental limits on the predictability of heavy-rain-producing storms.  But a great deal of recent progress has also been made, through efforts to better observe and understand the storms that produce extreme rainfall and advances in numerical models and how we process their output.

This presentation will outline my research group's recent work to integrate theory, observations, and models with the goal of increasing understanding and improving prediction of extreme precipitation.  In two recent field campaigns---Plains Elevated Convection At Night (PECAN) in 2015 and RELAMPAGO in 2018---a large number of high-resolution radiosonde observations were collected in the environments near convective systems that produced heavy precipitation.  These observations have revealed the rapid changes that take place near these convective systems, as well as the difficulties that numerical models have in accurately representing them.  In particular, the vertical profile of water vapor is not observed or modeled particularly well with operational systems, and in turn, the timing, location, and intensity of precipitation---which is very sensitive to moisture---can have substantial forecast errors.

Next, observations show that many cases of extremely large short-term rain rates occur in environments with strong low-level vertical wind shear, which is known to support storms with strong rotation.  Rotating storms can also dynamically induce strong low-level updrafts.  A series of numerical model experiments is used to test the hypothesis that rain rates are increased in situations with storm-scale to mesoscale rotation.  These simulations show that as low-level wind shear is increased, dynamical accelerations associated with rotation are capable of enhancing updrafts that, along with increased storm-relative inflow, result in more precipitation. 

Lastly, I will discuss a promising approach that combines historical forecasts, observations, and machine-learning algorithms to generate improved probabilistic guidance to forecasters responsible for predicting heavy rainfall.  I will show results from objective and subjective evaluations of these forecasts, and outline plans for future development.

Contact Us

University of Wyoming,

Atmospheric Science,

EN 6034

Dept. 3038

1000 E. University Ave.

Laramie, WY 82071

Phone: (307)766-3245


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