Relative effects of statistical preprocessing and postprocessing on a regional hydrological ensemble prediction system
The relative roles of statistical weather preprocessing and streamflow postprocessing in hydrological ensemble forecasting at short- to medium-range forecast lead times (day 1–7) are investigated. For this purpose, a regional hydrologic ensemble prediction system (RHEPS) is developed and impleme...
Main Authors: | , , , , , |
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Format: | Article |
Language: | English |
Published: |
Copernicus Publications
2018-03-01
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Series: | Hydrology and Earth System Sciences |
Online Access: | https://www.hydrol-earth-syst-sci.net/22/1831/2018/hess-22-1831-2018.pdf |
Summary: | The relative roles of statistical weather preprocessing and
streamflow postprocessing in hydrological ensemble forecasting at short- to
medium-range forecast lead times (day 1–7) are investigated. For this
purpose, a regional hydrologic ensemble prediction system (RHEPS) is
developed and implemented. The RHEPS is comprised of the following
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 (HCLR) as the statistical
preprocessor; (v) two statistical postprocessors, an autoregressive model
with a single exogenous variable (ARX(1,1)) and quantile regression (QR);
and (vi) a comprehensive verification strategy. To implement the RHEPS, 1 to
7 days weather forecasts from the GEFSRv2 are used to force HL-RDHM and
generate raw ensemble streamflow forecasts. Forecasting experiments are
conducted in four nested basins in the US Middle Atlantic region, ranging
in size from 381 to 12 362 km<sup>2</sup>.
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Results show that the HCLR preprocessed ensemble precipitation forecasts
have greater skill than the raw forecasts. These improvements are more
noticeable in the warm season at the longer lead times (> 3 days). Both postprocessors, ARX(1,1) and QR, show gains in skill relative to
the raw ensemble streamflow forecasts, particularly in the cool season, but
QR outperforms ARX(1,1). The scenarios that implement preprocessing and
postprocessing separately tend to perform similarly, although the
postprocessing-alone scenario is often more effective. The scenario
involving both preprocessing and postprocessing consistently outperforms the
other scenarios. In some cases, however, the differences between this
scenario and the scenario with postprocessing alone are not as significant.
We conclude that implementing both preprocessing and postprocessing ensures
the most skill improvements, but postprocessing alone can often be a
competitive alternative. |
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ISSN: | 1027-5606 1607-7938 |