Spatial and Temporal Variability of ERA5 Precipitation Accuracy over Russia
Sparse rain gauge grid over Russia and instrumental heterogeneity of the measurements make use of reanalysis data more suitable for some researches. We examined the accuracy of daily precipitation by ERA5 over Russia in 1950–2020 against the gauge observations over 526 locations, including 457 locat...
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| Language: | Russian |
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Izvestiya Rossiiskoi Akademii Nauk. Seriya Geograficheskaya
2022
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| Online Access: | https://izvestia.igras.ru/jour/article/view/1596 https://doi.org/10.31857/S2587556622030062 |
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ftjiransg:oai:oai.sergeogr.elpub.ru:article/1596 |
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Open Polar |
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Izvestiya Rossiiskoi Akademii Nauk. Seriya Geograficheskaya |
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ftjiransg |
| language |
Russian |
| topic |
пространственно-временная изменчивость precipitations systematic and random error Russia spatio-temporal variability осадки случайные и систематические ошибки Россия |
| spellingShingle |
пространственно-временная изменчивость precipitations systematic and random error Russia spatio-temporal variability осадки случайные и систематические ошибки Россия V. Yu. Grigorev N. L. Frolova M. B. Kireeva V. M. Stepanenko В. Ю. Григорьев Н. Л. Фролова М. Б. Киреева В. М. Степаненко Spatial and Temporal Variability of ERA5 Precipitation Accuracy over Russia |
| topic_facet |
пространственно-временная изменчивость precipitations systematic and random error Russia spatio-temporal variability осадки случайные и систематические ошибки Россия |
| description |
Sparse rain gauge grid over Russia and instrumental heterogeneity of the measurements make use of reanalysis data more suitable for some researches. We examined the accuracy of daily precipitation by ERA5 over Russia in 1950–2020 against the gauge observations over 526 locations, including 457 locations with bias-corrected observations. The main flaws of ERA5 precipitations are overestimation of their amount and too high number of days with false detected precipitations. On average, ERA5 overestimate precipitation amount from 14% in summer to 37% in spring. Comparison with bias-corrected observations for ERA5 shows the least systematic error in winter and more even spatial distribution of the error. ERA5 false detected from 30% (winter and fall) to 40% (spring and summer) days without precipitation. However, the random error in general is less than 2/3 of daily precipitation variability. The error is more in spring and summer and less in winter and fall. The share of days with precipitation identified by ERA5 is about 84–89%. The share in general less in summer than in other seasons. Overall, ERA5 shows less accuracy in dry area with few days with precipitation. The tendency is most pronounce for systematic error and for share of days with false identified precipitations. Редкая сеть наземных наблюдений за осадками на территории России и статистическая неоднородность рядов наблюдений на ней обуславливают в ряде исследований предпочтительность использования данных реанализа. Авторы статьи исследовали точность воспроизведения суточных сумм осадков на территории России за 1950–2020 гг. реанализом ERA5 при сравнении с данными наземных наблюдений на 526 метеостанциях, для 457 из которых привлекались также месячные суммы осадков с устраненной систематической ошибкой. Было выявлено, что наименее удовлетворительные результаты реанализ ERA5 показывает по величине систематической ошибки и доле дней с ложно обнаруженными осадками. В среднем по территории России ERA5 завышает количество осадков от 14% летом до 37% весной. ... |
| author2 |
The study was financially supported by the Russian Foundation for Basic Research (project no. 20-05-00773 reanalysis accuracy estimate) and by the Russian Science Foundation grants nos. 21-47-00008 (spring precipitation variability analysis) and 19-77-10032 (calculation methods and use of grid archives data) Работа выполнена в рамках гранта РФФИ № 20-0500773 в части оценки точности данных реанализа и грантов РНФ № 21-47-00008 в части анализа данных осадков в весенний период и № 19-77-10032 в части методов расчетов и использования данных сеточных архивов |
| format |
Article in Journal/Newspaper |
| author |
V. Yu. Grigorev N. L. Frolova M. B. Kireeva V. M. Stepanenko В. Ю. Григорьев Н. Л. Фролова М. Б. Киреева В. М. Степаненко |
| author_facet |
V. Yu. Grigorev N. L. Frolova M. B. Kireeva V. M. Stepanenko В. Ю. Григорьев Н. Л. Фролова М. Б. Киреева В. М. Степаненко |
| author_sort |
V. Yu. Grigorev |
| title |
Spatial and Temporal Variability of ERA5 Precipitation Accuracy over Russia |
| title_short |
Spatial and Temporal Variability of ERA5 Precipitation Accuracy over Russia |
| title_full |
Spatial and Temporal Variability of ERA5 Precipitation Accuracy over Russia |
| title_fullStr |
Spatial and Temporal Variability of ERA5 Precipitation Accuracy over Russia |
| title_full_unstemmed |
Spatial and Temporal Variability of ERA5 Precipitation Accuracy over Russia |
| title_sort |
spatial and temporal variability of era5 precipitation accuracy over russia |
| publisher |
Izvestiya Rossiiskoi Akademii Nauk. Seriya Geograficheskaya |
| publishDate |
2022 |
| url |
https://izvestia.igras.ru/jour/article/view/1596 https://doi.org/10.31857/S2587556622030062 |
| genre |
Arctic |
| genre_facet |
Arctic |
| op_source |
Izvestiya Rossiiskoi Akademii Nauk. Seriya Geograficheskaya; № 3 (2022); 435-446 Известия Российской академии наук. Серия географическая; № 3 (2022); 435-446 2658-6975 2587-5566 |
| op_relation |
https://izvestia.igras.ru/jour/article/view/1596/871 Жаков И.С. Общие закономерности режима тепла и увлажнения на территории СССР. Л.: Гидрометеоиздат, 1982. 231 с. Кислов А.В., Китаев Л.М., Константинов И.С. Статистическая структура крупномасштабных особенностей поля снежного покрова // Метеорология и гидрология. 2001. № 8. С. 98–104. Amjad M., Yilmaz M.T., Yucel I., Yilmaz K.K. Performance evaluation of satellite- and model-based precipitation products over varying climate and complex topography // J. Hydrol. 2020. V. 584. P. 124707. https://doi.org/10.1016/j.jhydrol.2020.124707 An Y., Zhao W., Li C., Liu Y. Evaluation of Six Satellite and Reanalysis Precipitation Products Using Gauge Observations over the Yellow River Basin, China // Atmosphere. 2020. V. 11. № 11. P. 1223. https://doi.org/10.3390/atmos11111223 Beck H.E., Pan M., Roy T., Weedon G.P., Pappenberger F., Dijk A.I.J.M. Van, Huffman G.J., Adler R.F., Wood E.F. Daily evaluation of 26 precipitation datasets using Stage-IV gauge-radar data for the CONUS // Hydrol. and Earth Sys. Sci. 2019. V. 23. № 1. P. 207–224. https://doi.org/10.5194/hess-23-207-2019 Behrangi A., Singh A., Song Y., Panahi M. Assessing Gauge Undercatch Correction in Arctic Basins in Light of GRACE Observations // Geophys. Res. Let. 2019. V.46. № 20. P. 11358–11366. https://doi.org/10.1029/2019GL084221 Benavidez R., Jackson B., Maxwell D., Norton K. A review of the (Revised) Universal Soil Loss Equation ((R)USLE): With a view to increasing its global applicability and improving soil loss estimates // Hydrol. and Earth Sys. Sci. 2018. V. 22. № 11. P. 6059–6086. https://doi.org/10.5194/hess-22-6059-2018 Betts A.K., Chan D.Z., Desjardins R.L. Near-Surface Biases in ERA5 Over the Canadian Prairies // Frontiers in Environ. Sci. 2019. V. 7. P. 129. https://doi.org/10.3389/fenvs.2019.00129 Bogdanova E.G., Gavrilova S.Y. Correction of the precipitation time series nonhomogeneity caused by replacement of the Nipher shielded rain gauge by a Tretyakov precipitation gauge // Russian Meteorol. and Hydrol. 2008. V. 33. № 8. P. 525–536. https://doi.org/10.3103/S1068373908080074 Chernokulsky A., Kozlov F., Zolina O., Bulygina O., Mokhov I.I., Semenov V.A. Observed changes in convective and stratiform precipitation in Northern Eurasia over the lastfive decades // Environ. Res. Let. 2019. V. 14. № 4. P.045001. https://doi.org/10.1088/1748-9326/aafb82 Emmanouil S., Langousis A., Nikolopoulos E.I., Anagnostou E.N. An ERA-5 Derived CONUS-Wide High-Resolution Precipitation Dataset Based on a Refined Parametric Statistical Downscaling Framework // Wat. Res. Res. 2021. V. 57. № 6. P. e2020WR029548. https://doi.org/10.1029/2020WR029548 Gleixner S., Demissie T., Diro G.T. Did ERA5 improve temperature and precipitation reanalysis over East Africa? // Atmosphere. 2020. V. 11. № 9. P. 996. https://doi.org/10.3390/atmos11090996 Groisman P.Y., Koknaeva V.V., Belokrylova T.A., Karl T.R. Overcoming biases of precipitation measurement: a history of the USSR experience // Bulletin of the American Meteorol. Soc. 1991. V. 72. № 11. P. 1725–1733. https://doi.org/10.1175/1520-0477(1991)072<1725:OBOPMA>2.0.CO;2 Hersbach H., Bell B., Berrisford P. et al. The ERA5 global reanalysis // Quart. J. Royal Meteorol. Soc. 2020. V. 146. № 730. P. 1999–2049. https://doi.org/10.1002/qj.3803 Jiang Q., Li W., Fan Z., He X., Sun W., Chen S., Wen J., GaoJ., Wang J. Evaluation of the ERA5 reanalysis precipitation dataset over Chinese Mainland // J. Hydrol. 2021. V. 595. P. 125660. https://doi.org/10.1016/j.jhydrol.2020.125660 Nogueira M. Inter-comparison of ERA-5, ERA-interim and GPCP rainfall over the last 40 years: Process-based analysis of systematic and random differences // J. Hydrol. 2020. V. 583. P. 124632. https://doi.org/10.1016/j.jhydrol.2020.124632 Rivoire P., Martius O., Naveau P.A. Comparison of Moderate and Extreme ERA-5 Daily Precipitation With Two Observational Data Sets // Earth and Space Sci. 2021. V. 8. № 4. P. e2020EA001633. https://doi.org/10.1029/2020EA001633 Singh T., Saha U., Prasad V.S., Gupta M.D. Assessment of newly-developed high resolution reanalyses (IMDAA, NGFS and ERA5) against rainfall observations for Indian region // Atmospher. Res. 2021. V. 259. P. 105679. https://doi.org/10.1016/j.atmosres.2021.105679 Sun S., Shi W., Zhou S., Chai R., Chen H., Wang G., Zhou Y., Shen H. Capacity of satellite-based and reanalysis precipitation products in detecting long-term trends across Mainland China // Remote Sens. 2020. V. 12. № 18. P.2902. https://doi.org/10.3390/RS12182902 Tang G., Behrangi A., Long D., Li C., Hong Y. Accounting for spatiotemporal errors of gauges: A critical step to evaluate gridded precipitation products // J. Hydrol. 2018. V. 559. P. 294–306. https://doi.org/10.1016/j.jhydrol.2018.02.057 Voropay N., Ryazanova A., Dyukarev E. High-resolution bias-corrected precipitation data over South Siberia, Russia // Atmospher. Res. 2021. V. 254. P. 105528. https://doi.org/10.1016/j.atmosres.2021.105528 https://izvestia.igras.ru/jour/article/view/1596 doi:10.31857/S2587556622030062 |
| op_doi |
https://doi.org/10.31857/S2587556622030062 https://doi.org/10.1016/j.jhydrol.2020.124707 https://doi.org/10.3390/atmos11111223 https://doi.org/10.5194/hess-23-207-2019 https://doi.org/10.1029/2019GL084221 https://doi.org/10.5194/hess-22-6059-201 |
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ftjiransg:oai:oai.sergeogr.elpub.ru:article/1596 2023-05-15T14:28:23+02:00 Spatial and Temporal Variability of ERA5 Precipitation Accuracy over Russia Пространственно-временная изменчивость ошибки воспроизведения осадков реанализом ERA5 на территории России V. Yu. Grigorev N. L. Frolova M. B. Kireeva V. M. Stepanenko В. Ю. Григорьев Н. Л. Фролова М. Б. Киреева В. М. Степаненко The study was financially supported by the Russian Foundation for Basic Research (project no. 20-05-00773 reanalysis accuracy estimate) and by the Russian Science Foundation grants nos. 21-47-00008 (spring precipitation variability analysis) and 19-77-10032 (calculation methods and use of grid archives data) Работа выполнена в рамках гранта РФФИ № 20-0500773 в части оценки точности данных реанализа и грантов РНФ № 21-47-00008 в части анализа данных осадков в весенний период и № 19-77-10032 в части методов расчетов и использования данных сеточных архивов 2022-09-17 application/pdf https://izvestia.igras.ru/jour/article/view/1596 https://doi.org/10.31857/S2587556622030062 rus rus Izvestiya Rossiiskoi Akademii Nauk. Seriya Geograficheskaya Известия Российской академии наук. Серия географическая https://izvestia.igras.ru/jour/article/view/1596/871 Жаков И.С. Общие закономерности режима тепла и увлажнения на территории СССР. Л.: Гидрометеоиздат, 1982. 231 с. Кислов А.В., Китаев Л.М., Константинов И.С. Статистическая структура крупномасштабных особенностей поля снежного покрова // Метеорология и гидрология. 2001. № 8. С. 98–104. Amjad M., Yilmaz M.T., Yucel I., Yilmaz K.K. Performance evaluation of satellite- and model-based precipitation products over varying climate and complex topography // J. Hydrol. 2020. V. 584. P. 124707. https://doi.org/10.1016/j.jhydrol.2020.124707 An Y., Zhao W., Li C., Liu Y. Evaluation of Six Satellite and Reanalysis Precipitation Products Using Gauge Observations over the Yellow River Basin, China // Atmosphere. 2020. V. 11. № 11. P. 1223. https://doi.org/10.3390/atmos11111223 Beck H.E., Pan M., Roy T., Weedon G.P., Pappenberger F., Dijk A.I.J.M. Van, Huffman G.J., Adler R.F., Wood E.F. Daily evaluation of 26 precipitation datasets using Stage-IV gauge-radar data for the CONUS // Hydrol. and Earth Sys. Sci. 2019. V. 23. № 1. P. 207–224. https://doi.org/10.5194/hess-23-207-2019 Behrangi A., Singh A., Song Y., Panahi M. Assessing Gauge Undercatch Correction in Arctic Basins in Light of GRACE Observations // Geophys. Res. Let. 2019. V.46. № 20. P. 11358–11366. https://doi.org/10.1029/2019GL084221 Benavidez R., Jackson B., Maxwell D., Norton K. A review of the (Revised) Universal Soil Loss Equation ((R)USLE): With a view to increasing its global applicability and improving soil loss estimates // Hydrol. and Earth Sys. Sci. 2018. V. 22. № 11. P. 6059–6086. https://doi.org/10.5194/hess-22-6059-2018 Betts A.K., Chan D.Z., Desjardins R.L. Near-Surface Biases in ERA5 Over the Canadian Prairies // Frontiers in Environ. Sci. 2019. V. 7. P. 129. https://doi.org/10.3389/fenvs.2019.00129 Bogdanova E.G., Gavrilova S.Y. Correction of the precipitation time series nonhomogeneity caused by replacement of the Nipher shielded rain gauge by a Tretyakov precipitation gauge // Russian Meteorol. and Hydrol. 2008. V. 33. № 8. P. 525–536. https://doi.org/10.3103/S1068373908080074 Chernokulsky A., Kozlov F., Zolina O., Bulygina O., Mokhov I.I., Semenov V.A. Observed changes in convective and stratiform precipitation in Northern Eurasia over the lastfive decades // Environ. Res. Let. 2019. V. 14. № 4. P.045001. https://doi.org/10.1088/1748-9326/aafb82 Emmanouil S., Langousis A., Nikolopoulos E.I., Anagnostou E.N. An ERA-5 Derived CONUS-Wide High-Resolution Precipitation Dataset Based on a Refined Parametric Statistical Downscaling Framework // Wat. Res. Res. 2021. V. 57. № 6. P. e2020WR029548. https://doi.org/10.1029/2020WR029548 Gleixner S., Demissie T., Diro G.T. Did ERA5 improve temperature and precipitation reanalysis over East Africa? // Atmosphere. 2020. V. 11. № 9. P. 996. https://doi.org/10.3390/atmos11090996 Groisman P.Y., Koknaeva V.V., Belokrylova T.A., Karl T.R. Overcoming biases of precipitation measurement: a history of the USSR experience // Bulletin of the American Meteorol. Soc. 1991. V. 72. № 11. P. 1725–1733. https://doi.org/10.1175/1520-0477(1991)072<1725:OBOPMA>2.0.CO;2 Hersbach H., Bell B., Berrisford P. et al. The ERA5 global reanalysis // Quart. J. Royal Meteorol. Soc. 2020. V. 146. № 730. P. 1999–2049. https://doi.org/10.1002/qj.3803 Jiang Q., Li W., Fan Z., He X., Sun W., Chen S., Wen J., GaoJ., Wang J. Evaluation of the ERA5 reanalysis precipitation dataset over Chinese Mainland // J. Hydrol. 2021. V. 595. P. 125660. https://doi.org/10.1016/j.jhydrol.2020.125660 Nogueira M. Inter-comparison of ERA-5, ERA-interim and GPCP rainfall over the last 40 years: Process-based analysis of systematic and random differences // J. Hydrol. 2020. V. 583. P. 124632. https://doi.org/10.1016/j.jhydrol.2020.124632 Rivoire P., Martius O., Naveau P.A. Comparison of Moderate and Extreme ERA-5 Daily Precipitation With Two Observational Data Sets // Earth and Space Sci. 2021. V. 8. № 4. P. e2020EA001633. https://doi.org/10.1029/2020EA001633 Singh T., Saha U., Prasad V.S., Gupta M.D. Assessment of newly-developed high resolution reanalyses (IMDAA, NGFS and ERA5) against rainfall observations for Indian region // Atmospher. Res. 2021. V. 259. P. 105679. https://doi.org/10.1016/j.atmosres.2021.105679 Sun S., Shi W., Zhou S., Chai R., Chen H., Wang G., Zhou Y., Shen H. Capacity of satellite-based and reanalysis precipitation products in detecting long-term trends across Mainland China // Remote Sens. 2020. V. 12. № 18. P.2902. https://doi.org/10.3390/RS12182902 Tang G., Behrangi A., Long D., Li C., Hong Y. Accounting for spatiotemporal errors of gauges: A critical step to evaluate gridded precipitation products // J. Hydrol. 2018. V. 559. P. 294–306. https://doi.org/10.1016/j.jhydrol.2018.02.057 Voropay N., Ryazanova A., Dyukarev E. High-resolution bias-corrected precipitation data over South Siberia, Russia // Atmospher. Res. 2021. V. 254. P. 105528. https://doi.org/10.1016/j.atmosres.2021.105528 https://izvestia.igras.ru/jour/article/view/1596 doi:10.31857/S2587556622030062 Izvestiya Rossiiskoi Akademii Nauk. Seriya Geograficheskaya; № 3 (2022); 435-446 Известия Российской академии наук. Серия географическая; № 3 (2022); 435-446 2658-6975 2587-5566 пространственно-временная изменчивость precipitations systematic and random error Russia spatio-temporal variability осадки случайные и систематические ошибки Россия info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 2022 ftjiransg https://doi.org/10.31857/S2587556622030062 https://doi.org/10.1016/j.jhydrol.2020.124707 https://doi.org/10.3390/atmos11111223 https://doi.org/10.5194/hess-23-207-2019 https://doi.org/10.1029/2019GL084221 https://doi.org/10.5194/hess-22-6059-201 2022-09-20T16:46:00Z Sparse rain gauge grid over Russia and instrumental heterogeneity of the measurements make use of reanalysis data more suitable for some researches. We examined the accuracy of daily precipitation by ERA5 over Russia in 1950–2020 against the gauge observations over 526 locations, including 457 locations with bias-corrected observations. The main flaws of ERA5 precipitations are overestimation of their amount and too high number of days with false detected precipitations. On average, ERA5 overestimate precipitation amount from 14% in summer to 37% in spring. Comparison with bias-corrected observations for ERA5 shows the least systematic error in winter and more even spatial distribution of the error. ERA5 false detected from 30% (winter and fall) to 40% (spring and summer) days without precipitation. However, the random error in general is less than 2/3 of daily precipitation variability. The error is more in spring and summer and less in winter and fall. The share of days with precipitation identified by ERA5 is about 84–89%. The share in general less in summer than in other seasons. Overall, ERA5 shows less accuracy in dry area with few days with precipitation. The tendency is most pronounce for systematic error and for share of days with false identified precipitations. Редкая сеть наземных наблюдений за осадками на территории России и статистическая неоднородность рядов наблюдений на ней обуславливают в ряде исследований предпочтительность использования данных реанализа. Авторы статьи исследовали точность воспроизведения суточных сумм осадков на территории России за 1950–2020 гг. реанализом ERA5 при сравнении с данными наземных наблюдений на 526 метеостанциях, для 457 из которых привлекались также месячные суммы осадков с устраненной систематической ошибкой. Было выявлено, что наименее удовлетворительные результаты реанализ ERA5 показывает по величине систематической ошибки и доле дней с ложно обнаруженными осадками. В среднем по территории России ERA5 завышает количество осадков от 14% летом до 37% весной. ... Article in Journal/Newspaper Arctic Izvestiya Rossiiskoi Akademii Nauk. Seriya Geograficheskaya |