Watershed

Oral

394308 - Impact of statistical processing on short-to medium-range flood forecasts from a regional hydrological ensemble prediction system

Thursday, June 7
10:30 AM - 12:00 PM
Location: Greenway IJ
Co-Authors: Alfonso Mejia, State College – The Pennsylvania State University

In this study, we investigate the interactions between a statistical weather preprocessor and streamflow postprocessor in hydrologic ensemble forecasting at short-to medium-range forecast lead times (day 1-7). For this, we develop and implement a regional hydrologic ensemble prediction system (RHEPS). The RHEPS is a multicomponent, ensemble-based, research hydrological forecast emulator. In this case, the RHEPS is comprised by the following system components: i) hydrometeorological observations (multisensor precipitation estimates, gridded surface temperature, and gauged streamflow); ii) weather ensemble forecasts (precipitation and near-surface temperature) from the National Centers for Environmental Prediction 11-member Global Ensemble Forecast System Reforecast version 2 (GEFSRv2); iii) NOAA’s Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM); iv) heteroscedastic censored logistic regression as the statistical preprocessor; v) quantile regression as the statistical postprocessors; and vi) a comprehensive verification strategy. To implement the RHEPS, the forecasting outputs from the GEFSRv2 are used to force HL-RDHM and generate raw ensemble streamflow forecasts. Additionally, to study the role played by preprocessing and postprocessing in streamflow forecasts, the RHEPS is implemented at multiple gauged locations within the middle Atlantic region (MAR) under three different forecasting scenarios. The first and second scenarios implement the RHEPS using the preprocessor and postprocessor alone, respectively, while the third scenario explores the effect of both preprocessing and postprocessing on the RHEPS. Overall, analyses from the verification of different forecasting scenarios indicate that preprocessing alone has little effect on improving the skill of ensemble flood forecasts. Indeed, postprocessing alone results in improved forecast quality, comparable to that of the more involved scenario combining preprocessing and postprocessing.

Sanjib Sharma


The Pennsylvania State University

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394308 - Impact of statistical processing on short-to medium-range flood forecasts from a regional hydrological ensemble prediction system



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