Daily evaluation of 26 precipitation datasets using Stage-IV gauge-radar data for the CONUS
<p>New precipitation (<span class="inline-formula"><i>P</i></span>) datasets are released regularly, following innovations in weather forecasting models, satellite retrieval methods, and multi-source merging techniques. Using the conterminous US as a case stud...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2019-01-01
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| Series: | Hydrology and Earth System Sciences |
| Online Access: | https://www.hydrol-earth-syst-sci.net/23/207/2019/hess-23-207-2019.pdf |
| _version_ | 1831548645314396160 |
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| author | H. E. Beck M. Pan T. Roy G. P. Weedon F. Pappenberger A. I. J. M. van Dijk G. J. Huffman R. F. Adler E. F. Wood |
| author_facet | H. E. Beck M. Pan T. Roy G. P. Weedon F. Pappenberger A. I. J. M. van Dijk G. J. Huffman R. F. Adler E. F. Wood |
| author_sort | H. E. Beck |
| collection | DOAJ |
| description | <p>New precipitation (<span class="inline-formula"><i>P</i></span>) datasets are released regularly, following
innovations in weather forecasting models, satellite retrieval methods, and
multi-source merging techniques. Using the conterminous US as a case study,
we evaluated the performance of 26 gridded (sub-)daily <span class="inline-formula"><i>P</i></span> datasets to obtain
insight into the merit of these innovations. The evaluation was performed at
a daily timescale for the period 2008–2017 using the Kling–Gupta efficiency
(KGE), a performance metric combining correlation, bias, and variability. As
a reference, we used the high-resolution (4 km) Stage-IV gauge-radar <span class="inline-formula"><i>P</i></span>
dataset. Among the three KGE components, the <span class="inline-formula"><i>P</i></span> datasets performed worst
overall in terms of correlation (related to event identification). In terms
of improving KGE scores for these datasets, improved <span class="inline-formula"><i>P</i></span> totals (affecting
the bias score) and improved distribution of <span class="inline-formula"><i>P</i></span> intensity (affecting the
variability score) are of secondary importance. Among the 11 gauge-corrected
<span class="inline-formula"><i>P</i></span> datasets, the best overall performance was obtained by MSWEP V2.2,
underscoring the importance of applying daily gauge corrections and
accounting for gauge reporting times. Several uncorrected <span class="inline-formula"><i>P</i></span> datasets
outperformed gauge-corrected ones. Among the 15 uncorrected <span class="inline-formula"><i>P</i></span> datasets, the
best performance was obtained by the ERA5-HRES fourth-generation reanalysis,
reflecting the significant advances in earth system modeling during the last
decade. The (re)analyses generally performed better in winter than in summer,
while the opposite was the case for the satellite-based datasets. IMERGHH V05
performed substantially better than TMPA-3B42RT V7, attributable to the many
improvements implemented in the IMERG satellite <span class="inline-formula"><i>P</i></span> retrieval algorithm.
IMERGHH V05 outperformed ERA5-HRES in regions dominated by convective storms,
while the opposite was observed in regions of complex terrain. The ERA5-EDA
ensemble average exhibited higher correlations than the ERA5-HRES
deterministic run, highlighting the value of ensemble modeling. The WRF
regional convection-permitting climate model showed considerably more
accurate <span class="inline-formula"><i>P</i></span> totals over the mountainous west and performed best among the
uncorrected datasets in terms of variability, suggesting there is merit in
using high-resolution models to obtain climatological <span class="inline-formula"><i>P</i></span> statistics. Our
findings provide some guidance to choose the most suitable <span class="inline-formula"><i>P</i></span> dataset for a
particular application.</p> |
| first_indexed | 2024-12-17T02:23:11Z |
| format | Article |
| id | doaj.art-168503cebfb947729f9f15f9ab166f09 |
| institution | Directory Open Access Journal |
| issn | 1027-5606 1607-7938 |
| language | English |
| last_indexed | 2024-12-17T02:23:11Z |
| publishDate | 2019-01-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Hydrology and Earth System Sciences |
| spelling | doaj.art-168503cebfb947729f9f15f9ab166f092022-12-21T22:07:11ZengCopernicus PublicationsHydrology and Earth System Sciences1027-56061607-79382019-01-012320722410.5194/hess-23-207-2019Daily evaluation of 26 precipitation datasets using Stage-IV gauge-radar data for the CONUSH. E. Beck0M. Pan1T. Roy2G. P. Weedon3F. Pappenberger4A. I. J. M. van Dijk5G. J. Huffman6R. F. Adler7E. F. Wood8Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey, USADepartment of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey, USADepartment of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey, USAMet Office, JCHMR, Maclean Building, Benson Lane, Crowmarsh Gifford, Oxfordshire, UKEuropean Centre for Medium-Range Weather Forecasts (ECMWF), Reading, UKFenner School for Environment and Society, Australian National University, Canberra, AustraliaNASA Goddard Space Flight Center (GSFC), Greenbelt, Maryland, USAUniversity of Maryland, Earth System Science Interdisciplinary Center, College Park, Maryland, USADepartment of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey, USA<p>New precipitation (<span class="inline-formula"><i>P</i></span>) datasets are released regularly, following innovations in weather forecasting models, satellite retrieval methods, and multi-source merging techniques. Using the conterminous US as a case study, we evaluated the performance of 26 gridded (sub-)daily <span class="inline-formula"><i>P</i></span> datasets to obtain insight into the merit of these innovations. The evaluation was performed at a daily timescale for the period 2008–2017 using the Kling–Gupta efficiency (KGE), a performance metric combining correlation, bias, and variability. As a reference, we used the high-resolution (4 km) Stage-IV gauge-radar <span class="inline-formula"><i>P</i></span> dataset. Among the three KGE components, the <span class="inline-formula"><i>P</i></span> datasets performed worst overall in terms of correlation (related to event identification). In terms of improving KGE scores for these datasets, improved <span class="inline-formula"><i>P</i></span> totals (affecting the bias score) and improved distribution of <span class="inline-formula"><i>P</i></span> intensity (affecting the variability score) are of secondary importance. Among the 11 gauge-corrected <span class="inline-formula"><i>P</i></span> datasets, the best overall performance was obtained by MSWEP V2.2, underscoring the importance of applying daily gauge corrections and accounting for gauge reporting times. Several uncorrected <span class="inline-formula"><i>P</i></span> datasets outperformed gauge-corrected ones. Among the 15 uncorrected <span class="inline-formula"><i>P</i></span> datasets, the best performance was obtained by the ERA5-HRES fourth-generation reanalysis, reflecting the significant advances in earth system modeling during the last decade. The (re)analyses generally performed better in winter than in summer, while the opposite was the case for the satellite-based datasets. IMERGHH V05 performed substantially better than TMPA-3B42RT V7, attributable to the many improvements implemented in the IMERG satellite <span class="inline-formula"><i>P</i></span> retrieval algorithm. IMERGHH V05 outperformed ERA5-HRES in regions dominated by convective storms, while the opposite was observed in regions of complex terrain. The ERA5-EDA ensemble average exhibited higher correlations than the ERA5-HRES deterministic run, highlighting the value of ensemble modeling. The WRF regional convection-permitting climate model showed considerably more accurate <span class="inline-formula"><i>P</i></span> totals over the mountainous west and performed best among the uncorrected datasets in terms of variability, suggesting there is merit in using high-resolution models to obtain climatological <span class="inline-formula"><i>P</i></span> statistics. Our findings provide some guidance to choose the most suitable <span class="inline-formula"><i>P</i></span> dataset for a particular application.</p>https://www.hydrol-earth-syst-sci.net/23/207/2019/hess-23-207-2019.pdf |
| spellingShingle | H. E. Beck M. Pan T. Roy G. P. Weedon F. Pappenberger A. I. J. M. van Dijk G. J. Huffman R. F. Adler E. F. Wood Daily evaluation of 26 precipitation datasets using Stage-IV gauge-radar data for the CONUS Hydrology and Earth System Sciences |
| title | Daily evaluation of 26 precipitation datasets using Stage-IV gauge-radar data for the CONUS |
| title_full | Daily evaluation of 26 precipitation datasets using Stage-IV gauge-radar data for the CONUS |
| title_fullStr | Daily evaluation of 26 precipitation datasets using Stage-IV gauge-radar data for the CONUS |
| title_full_unstemmed | Daily evaluation of 26 precipitation datasets using Stage-IV gauge-radar data for the CONUS |
| title_short | Daily evaluation of 26 precipitation datasets using Stage-IV gauge-radar data for the CONUS |
| title_sort | daily evaluation of 26 precipitation datasets using stage iv gauge radar data for the conus |
| url | https://www.hydrol-earth-syst-sci.net/23/207/2019/hess-23-207-2019.pdf |
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