Evaluation of GPM IMERG Products and Estimation of Warm-Season Precipitation in Crimea

A. E. Anisimov1, ✉, V. V. Efimov1, M. V. Lvova2

1 Marine Hydrophysical Institute, Russian Academy of Sciences, Sevastopol, Russian Federation

2 Voeikov Main Geophysical Observatory, Saint Petersburg, Russian Federation

✉ e-mail: anatolii.anisimov@mhi-ras.ru

Abstract

Purpose. The study was aimed at the evaluation of the Integrated MultisatellitE Retrievals from GPM (IMERG) remote sensing dataset using ground observations and estimation of the 2006–2018 warm-season precipitation in the Crimean Peninsula.

Methods and Results. Evaluation of the IMERG dataset was performed using the meteorological station observations treated as the ground truth. We provided the multiyear statistical characteristics of precipitation amounts, frequency and intensity for different climate zones of the Crimean Peninsula. We considered the spatial variability of summer precipitation, bias and correlation between IMERG and the ground observations.

Conclusions. IMERG has a weaker spatial variability compared to the ground observations. The warm-season IMERG bias is small in the central and mountainous parts of Crimea, whereas the precipitation estimates in the coastal zones are substantially overestimated. The IMERG wet bias is mostly caused by the excessive rainfall frequency. The temporal variability of IMERG is in good agreement with the observations with an average correlation coefficient of 0.73. For most of the metrics considered, warm-season IMERG precipitation significantly outperforms the other datasets in the central and mountainous parts of Crimea and could be used for practical tasks with certain precautions. At the same time, due to the lack of calibration over the marine areas, the quality of IMERG precipitation estimates in the coastal zones is reduced.

Keywords

GPM, IMERG, TRMM, E-OBS, evaluation, atmospheric precipitation, Crimea

Acknowledgements

The study of precipitation variability based on station data was funded by RFBR and Sevastopol municipality, research project No. 20‑45‑920017 “Quantitative estimates of precipitation in Southwestern Crimea and Sevastopol based on numerical modeling and radar observations”. The processing of remotely sensed data was done within the framework of the state research project No. 0827-2018-0001 “Fundamental studies of the interaction processes in the ocean-atmosphere system conditioning the regional spatial-temporal variability of natural environment and climate”.

Original russian text

Original Russian Text © A. E. Anisimov, V. V. Efimov, M. V. Lvova, 2021, published in MORSKOY GIDROFIZICHESKIY ZHURNAL, Vol. 37, Iss. 4, pp. 490-504 (2021)

For citation

Anisimov, A.E., Efimov, V.V. and Lvova, M.V., 2021. Evaluation of GPM IMERG products and estimation of warm-season precipitation in Crimea. Physical Oceanography, 28(4), pp. 454-467. doi:10.22449/1573-160X-2021-4-454-467

DOI

10.22449/1573-160X-2021-4-454-467

References

  1. Spinoni, J., Barbosa, P., Bucchignani, E., Cassano, J., Cavazos, T., Christensen, J.H., Christensen, O.B., Coppola, E. and Evans, J. [et al.], 2020. Future Global Meteorological Drought Hot Spots: A Study Based on CORDEX Data. Journal of Climate, 33(9), pp. 3635- 3661. https://doi.org/10.1175/JCLI-D-19-0084.1
  2. Entekhabi, D., Njoku, E.G., O’Neill, P.E., Kellogg, K.H., Crow, W.T., Edelstein, W.N., Entin, J.K., Goodman, S.D. and Jackson, T.J. [et al.], 2010. The Soil Moisture Active Passive (SMAP) Mission. Proceedings of the IEEE, 98(5), pp. 704-716. https://doi.org/10.1109/JPROC.2010.2043918
  3. Maggioni, V. and Massari, C., 2018. On the Performance of Satellite Precipitation Products in Riverine Flood Modeling: A Review. Journal of Hydrology, 558, pp. 214-224. https://doi.org/10.1016/j.jhydrol.2018.01.039
  4. Hou, A.Y., Kakar, R.K., Neeck, S., Azarbarzin, A.A., Kummerow, C.D., Kojima, M., Oki, R., Nakamura, K. and Iguchi, T., 2014. The Global Precipitation Measurement Mission. Bulletin of the American Meteorological Society, 95(5), pp. 701-722. https://doi.org/10.1175/BAMS- D-13-00164.1
  5. Navarro, A., García-Ortega, E., Merino, A., Sánchez, J.L., Kummerow, C. and Tapiador, F.J., 2019. Assessment of IMERG Precipitation Estimates over Europe. Remote Sensing, 11(21), 2470. https://doi.org/10.3390/rs11212470
  6. Tapiador, F.J., Navarro, A., García-Ortega, E., Merino, A., Sánchez, J.L., Marcos, C. and Kummerow, C., 2020. The Contribution of Rain Gauges in the Calibration of the IMERG Product: Results from the First Validation over Spain. Journal of Hydrometeorology, 21(2), pp. 161-182. https://doi.org/10.1175/JHM-D-19-0116.1
  7. Rios Gaona, M.F., Overeem, A., Leijnse, H. and Uijlenhoet, R., 2016. First-Year Evaluation of GPM Rainfall over the Netherlands: IMERG Day 1 Final Run (V03D). Journal of Hydrometeorology, 17(11), pp. 2799-2814. https://doi.org/10.1175/JHM-D-16-0087.1
  8. Guo, H., Chen, S., Bao, A., Behrangi, A., Hong, Y., Ndayisaba, F., Hu, J. and Stepanian, P.M., 2016. Early Assessment of Integrated Multi-Satellite Retrievals for Global Precipitation Measurement over China. Atmospheric Research, 176–177, pp. 121-133. https://doi.org/10.1016/j.atmosres.2016.02.020
  9. Wang, H. and Yong, B., 2020. Quasi-Global Evaluation of IMERG and GSMaP Precipitation Products over Land Using Gauge Observations. Water, 12(1), 243. https://doi.org/10.3390/w12010243
  10. Gavrikov, A.V., 2017. Estimating the Reproduction Quality of Precipitation over the North Atlantic and Influence of the Hydrostatic Approximation in the WRF–ARW Atmospheric Model. Oceanology, 57(2), pp. 232-238. https://doi.org/10.1134/S0001437017020047
  11. Lockhoff, M., Zolina, O., Simmer, C. and Schulz, J., 2019. Representation of Precipitation Characteristics and Extremes in Regional Reanalyses and Satellite- and Gauge-Based Estimates over Western and Central Europe. Journal of Hydrometeorology, 20(6), pp. 1123- 1145. https://doi.org/10.1175/JHM-D-18-0200.1
  12. Koistinen, J. and Saltikoff, E., 1998. Experience of Customer Products of Accumulated Snow, Sleet and Rain. In: C. G. Collier, ed., 1998. COST 75 Advanced Weather Radar Systems: International Seminar, Locarno, Switzerland, 23–27 March 1998: Proceedings. Luxembourg: European Commission, pp. 397-406. Available at: https://op.europa.eu/fr/publication-detail/-/publication/404fb559-9d41-4002-a7e0-9a08c81fd264 [Accessed: 1 September 2021].
  13. Huffman, G., Bolvin, D.T., Braithwaite, D., Hsu, K., Joyce, R., Kidd, C., Nelkin, E.J., Sorooshian, S., Tan, J. and Xie, P., 2020. Algorithm Theoretical Basis Document (ATBD) Version 06. NASA Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG). [online] Available at: https://gpm.nasa.gov/sites/default/files/2020-05/IMERG_ATBD_V06.3.pdf [Accessed: 1 September 2021].
  14. Tan, J., Huffman, G.J., Bolvin, D.T. and Nelkin, E.J., 2019. IMERG V06: Changes to the Morphing Algorithm. Journal of Atmospheric and Oceanic Technology, 36(12), pp. 2471- 2482. https://doi.org/10.1175/JTECH-D-19-0114.1
  15. Schneider, U., Becker, A., Finger, P., Meyer-Christoffer, A., Ziese, M. and Rudolf, B., 2014. GPCC’s New Land Surface Precipitation Climatology Based on Quality-Controlled In Situ Data and Its Role in Quantifying the Global Water Cycle. Theoretical and Applied Climatology, 115(1–2), pp. 15-40. https://doi.org/10.1007/s00704-013-0860-x
  16. Cornes, R.C., van der Schrier, G., van den Besselaar, E.J.M. and Jones, P.D., 2018. An Ensemble Version of the E-OBS Temperature and Precipitation Data Sets. Journal of Geophysical Research: Atmospheres, 123(17), pp. 9391-9409. https://doi.org/10.1029/2017JD028200
  17. Kotlarski, S., Keuler, K., Christensen, O.B., Colette, A., Déqué, M., Gobiet, A., Goergen, K., Jacob, D. and Lüthi, D. [et al.], 2014. Regional Climate Modeling on European Scales: A Joint Standard Evaluation of the EURO-CORDEX RCM Ensemble. Geoscientific Model Development, 7(4), pp. 1297-1333. https://doi.org/10.5194/gmd-7-1297-2014
  18. Aleshina, M.A., Semenov, V.A. and Chernokulsky, A.V., 2019. Study of the Role of Global and Regional Factors in Changing the Extremes of Summer Precipitation on the Black Sea Coast of the Caucasus with the Use of Results of Experiments with the Climate Model. Fundamental and Аpplied Сlimatology, 3, pp. 59-75. doi:10.21513/2410-8758-2019-3-59-75 (in Russian).
  19. Raimonet, M., Oudin, L., Thieu, V., Silvestre, M., Vautard, R., Rabouille, C. and Le Moigne, P., 2017. Evaluation of Gridded Meteorological Datasets for Hydrological Modeling. Journal of Hydrometeorology, 18(11), pp. 3027-3041.doi:10.1175/JHM-D-17-0018.1
  20. Laiti, L., Mallucci, S., Piccolroaz, S., Bellin, A., Zardi, D., Fiori, A., Nikulin, G. and Majone, B., 2018. Testing the hydrological coherence of High-Resolution Gridded Precipitation and Temperature Data Sets. Water Resources Research, 54(3), pp. 1999-2016. https://doi.org/10.1002/2017WR021633
  21. Camici, S., Ciabatta, L., Massari, C. and Brocca, L., 2018. How Reliable are Satellite Precipitation Estimates for Driving Hydrological Models: A Verification Study over the Mediterranean Area. Journal of Hydrology, 563, pp. 950-961. https://doi.org/10.1016/j.jhydrol.2018.06.067
  22. Ved, I.P., 2000. [Climatological Atlas of Crimea]. Simferopol: Tavriya-Plus, 120 p. (in Russian).
  23. Lipinskyi, V.M., Diachuk, V.A. and Babichenko, V.M., eds., 2003. [Climate of Ukraine]. Kyiv: Vydavnytstvo Raievskoho, 343 p. (in Ukrainian).
  24. Efimov, V.V. and Komarovskaya, O.I., 2015. Breeze Circulation in the Crimean Region Atmosphere. Physical Oceanography, (6), pp. 69-78. https://doi.org/10.22449/1573-160X- 2015-6-69-78
  25. Efimov, V.V., 2017. Numerical Simulation of Breeze Circulation over the Crimean Peninsula. Izvestiya, Atmospheric and Ocean Physics, 53(1), pp. 84-94. https://doi.org/10.1134/S0001433817010042
  26. Prakash, S., Ramesh Kumar, M.R., Mathew, S. and Venkatesan, R., 2018. How Accurate are Satellite Estimates of Precipitation over the North Indian Ocean? Theoretical and Applied Climatology, 134(1–2), pp. 467-475. https://doi.org/10.1007/s00704-017-2287-2
  27. Dunaieva, Ye.A. and Kovalenko, P.I., 2013. River Basins Typification of Crimea by Agrolandscapes and Ecological Load. Scientific Journal of Russian Scientific Research Institute of Land Improvement Problems, (4), pp. 157-167 (in Russian).
  28. Tian, F., Hou, S., Yang, L., Hu, H. and Hou, A., 2018. How Does the Evaluation of the GPM IMERG Rainfall Product Depend on Gauge Density and Rainfall Intensity? Journal of Hydrometeorology, 19(2), pp. 339-349. https://doi.org/10.1175/JHM-D-17-0161.1

Download the article (PDF)