3/26/18: Thomas M. Hamill

Please join us for a Great Lakes Seminar Series presentation:
Time: 1:30-2:30pm
Location: NOAA Great Lakes Environmental Research Laboratory, Lake Superior Hall
Presenter: Thomas M. Hamill, NOAA Earth System Research Lab
Title: Improved Statistical Postprocessing of Precipitation for the NWS Forecasts and Hydrologic Applications

Event Flyer

Webinar Recording: https://www.youtube.com/watch?v=dgKfsajlwNQ&t=492s

Abstract: Accurate numerical forecasts of precipitation have many purposes, including providing forcings to hydrologic models used to predict river flow and Great Lakes water levels. Commonly, the raw model guidance may exhibit both random and systematic errors, such as an overabundance of light precipitation and not enough heavy precipitation. If the raw guidance is used to provide precipitation forcings to hydrologic predictions, the accuracy of those predictions will suffer. A potential solution is the statistical postprocessing of the numerical guidance, using past forecasts and observations (or analyses) to statistically adjust the current model guidance. Statistical postprocessing of precipitation amount is quite challenging; to have an adequate training sample of unusual events such as heavy precipitation, either a long time series of past forecasts and analyses are needed (i.e., reforecasts) or advanced statistical methods to leverage shorter training data sets.

In this seminar I will describe an advanced precipitation postprocessing algorithm in development that is intended for operational use in the NWS roughly a year hence. Each of the ensemble predictions are statistically postprocessed to adjust for biases in the mean amount and for excessive confidence. Short training data sets (the last 60 days of forecasts and analyses) are used. Biases in the mean amount are addressed through “quantile mapping” using cumulative distributions functions (CDFs) of analyzed and forecast precipitation. To address the small training sample size, the training sample used to populate CDFs at a particular location are supplemented by data from other locations with similar precipitation climatologies and terrain characteristics. The final probabilities are determined from a weighted linear combination of “dressed” ensemble members.

The resultant probabilistic forecasts are greatly improved, as will be demonstrated with verification statistics and some case studies. After presentation of results, I welcome discussion about the potential relevance of this algorithm for forcing hydrologic predictions of the Great Lakes.

Bio: Thomas performs, coordinates, and leads R&D to improve NOAA’s probabilistic weather forecasts on time scales of days to several weeks. The probabilistic forecasts are based on ensembles of weather simulations using different initial conditions and methods for simulating imperfections in the forecast model. Thomas is also involved in the “post-processing” of ensembles of forecasts, making corrections to the real-time forecasts based on discrepancies noted between past forecasts and observations or analyses. Thomas and his team develop these research methods, demonstrate their suitability for operational use, and then work with colleagues in the National Weather Service to adapt them for daily use by forecasters and the public. They document their work in the form of peer-reviewed journal articles, presentations, white papers, book chapters, and so forth.

Thomas also has several additional roles. He is co-chairperson of the World Meteorological Organization’s (WMO’s) Data Assimilation and Observing Systems committee. He is on the management board of the NOAA-NCAR Developmental Test Center, and on Cornell University’s Department of Earth and Atmospheric Sciences review board. Thomas has previously been an editor of the American Meteorological Society’s journal “Monthly Weather Review” and served for many years on the WMO’s Working Group for Numerical Experimentation. Recently, Thomas contributed to the NWS Service Assessment in the wake of the severe flooding in Boulder (Sep 2013).

Important Visitor Information
All in-person seminar attendees are required to receive a visitor badge from the front desk at the NOAA Great Lakes Environmental Research Laboratory facility. Seminar attendees need to present a valid U.S. photo ID or green card. If you are a Foreign National, advance notification of at least 48 hours is needed so that security guidelines are followed. You will need to present your passport (a copy will NOT work). For questions regarding building access, or assistance in obtaining Foreign National clearance, please call 734-741-2393. Email contact: [email protected]
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Questions? Contact Mary Ogdahl: [email protected]