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...
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doaj-59ad220ceb0747be8f2835e936b216522020-11-24T23:16:38ZengCopernicus PublicationsHydrology and Earth System Sciences1027-56061607-79382018-03-01221831184910.5194/hess-22-1831-2018Relative effects of statistical preprocessing and postprocessing on a regional hydrological ensemble prediction systemS. Sharma0R. Siddique1S. Reed2P. Ahnert3P. Mendoza4A. Mejia5Department of Civil and Environmental Engineering, The Pennsylvania State University, University Park, PA, USANortheast Climate Science Center, University of Massachusetts, Amherst, MA, USANational Weather Service, Middle Atlantic River Forecast Center, State College, PA, USANational Weather Service, Middle Atlantic River Forecast Center, State College, PA, USAAdvanced Mining Technology Center (AMTC), Universidad de Chile, Santiago, ChileDepartment of Civil and Environmental Engineering, The Pennsylvania State University, University Park, PA, USAThe 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>. <br><br> 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.https://www.hydrol-earth-syst-sci.net/22/1831/2018/hess-22-1831-2018.pdf |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
S. Sharma R. Siddique S. Reed P. Ahnert P. Mendoza A. Mejia |
spellingShingle |
S. Sharma R. Siddique S. Reed P. Ahnert P. Mendoza A. Mejia Relative effects of statistical preprocessing and postprocessing on a regional hydrological ensemble prediction system Hydrology and Earth System Sciences |
author_facet |
S. Sharma R. Siddique S. Reed P. Ahnert P. Mendoza A. Mejia |
author_sort |
S. Sharma |
title |
Relative effects of statistical preprocessing and postprocessing on a regional hydrological ensemble prediction system |
title_short |
Relative effects of statistical preprocessing and postprocessing on a regional hydrological ensemble prediction system |
title_full |
Relative effects of statistical preprocessing and postprocessing on a regional hydrological ensemble prediction system |
title_fullStr |
Relative effects of statistical preprocessing and postprocessing on a regional hydrological ensemble prediction system |
title_full_unstemmed |
Relative effects of statistical preprocessing and postprocessing on a regional hydrological ensemble prediction system |
title_sort |
relative effects of statistical preprocessing and postprocessing on a regional hydrological ensemble prediction system |
publisher |
Copernicus Publications |
series |
Hydrology and Earth System Sciences |
issn |
1027-5606 1607-7938 |
publishDate |
2018-03-01 |
description |
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>.
<br><br>
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. |
url |
https://www.hydrol-earth-syst-sci.net/22/1831/2018/hess-22-1831-2018.pdf |
work_keys_str_mv |
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