Validation of Satellite-Based Gridded Rainfall Products with Station Data Over Major Cities in Punjab

Syeda Nimra Raza Geelani; Sawaid Abbas; Muhammad Umar; Muhammad Usman; Irum Yousfani

Authors

Syeda Nimra Raza Geelani Smart Sensing for Climate and Development, Center for Geographical Information System, University of the Punjab, Lahore, Pakistan
Sawaid Abbas Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong SAR
Muhammad Umar Smart Sensing for Climate and Development, Center for Geographical Information System, University of the Punjab, Lahore, Pakistan
Muhammad Usman Interdisciplinary Research Center for Aviation and Space Exploration, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia
Irum Yousfani Department of Computer Science, Asian Institute of Technology, Thailand

Keywords:

Rainfall, CHIRPS, IMERG, ERA-5, APHRODITE, Punjab

Abstract

Introduction A critical evaluation of the newly developed gridded rainfall data set is important for its effective applications. Over the past two decades, the availability of gridded rainfall measurements has increased, however, it remains challenging to identify suitable proxies for conventional station-based measurements.

Novelty Statement

Therefore, this study performed a comparative assessment of rainfall estimates from IMERG, CHIRPS, ERA-5, and APHRODITE, with meteorological station data of five cities in Pakistan including Lahore, Faisalabad, Multan, Islamabad, and Murree.

Material and Method

The assessment was performed at multiple temporal scales (daily, monthly, and yearly) using the daily data recorded between 2001 to 2022. The analytical metrics applied in this study included Bias, Mean Error (ME), Root Mean Square Error (RMSE), Correlation Coefficient (CC), and Coefficient of determination (R²).

Result and Discussion

The results showed interesting spatial and temporal patterns of agreement among the datasets. Correlation for daily data was weak for all the gridded data sets, and among all of these, APHRODITE performed better. The monthly aggregates showed that the IMERG has the highest association with the ground data, followed by the CHRIPS. Likewise, yearly accumulated rainfall records indicated IMERG with the highest correlation, followed by CHIRPS. Overall, IMERG has shown higher consistency across the stations at monthly and yearly temporal scales. CHIRPS excelled with low errors (RMSE and bias) for most locations especially Lahore, but higher errors were found in Murree at the monthly time scale.

Concluding Remarks

This study concluded that a single satellite alone cannot show significant results over large areas, rather hybrid of products may be required for better estimation.

References

K. E. Trenberth, “Changes in precipitation with climate change,” Clim. Res., vol. 47, no. 1–2, pp. 123–138, 2011, doi: 10.3354/cr00953.

Q. You, J. Min, W. Zhang, N. Pepin, and S. Kang, “Comparison of multiple datasets with gridded precipitation observations over the Tibetan Plateau,” Clim. Dyn., vol. 45, no. 3–4, pp. 791–806, 2015, doi: 10.1007/s00382-014-2310-6.

M. Luo, J. Feng, Z. Xu, Y. Wang, and L. Dan, “Evaluating the performance of five twentieth-century reanalysis datasets in reproducing the severe drought in northern China during the 1920s-1930s,” Theor. Appl. Climatol., vol. 137, no. 1–2, pp. 187–199, 2019, doi: 10.1007/s00704-018-2591-5.

S. Feng, Q. Hu, and W. Qian, “Quality control of daily meteorological data in China, 1951-2000: A new dataset,” Int. J. Climatol., vol. 24, no. 7, pp. 853–870, 2004, doi: 10.1002/joc.1047.

A. Asfaw, B. Simane, A. Hassen, and A. Bantider, “Variability and time series trend analysis of rainfall and temperature in northcentral Ethiopia: A case study in Woleka sub-basin,” Weather Clim. Extrem., vol. 19, no. June 2017, pp. 29–41, 2018, doi: 10.1016/j.wace.2017.12.002.

E. Sharifi, R. Steinacker, and B. Saghafian, “Assessment of GPM-IMERG and other precipitation products against gauge data under different topographic and climatic conditions in Iran: Preliminary results,” Remote Sens., vol. 8, no. 2, 2016, doi: 10.3390/rs8020135.

M. Adnan et al., “Snowmelt runoff modelling under projected climate change patterns in the Gilgit river basin of northern Pakistan,” Polish J. Environ. Stud., vol. 26, no. 2, pp. 525–542, 2017, doi: 10.15244/pjoes/66719.

M. Latif, F. S. Syed, and A. Hannachi, “Rainfall trends in the South Asian summer monsoon and its related large-scale dynamics with focus over Pakistan,” Clim. Dyn., vol. 48, no. 11–12, pp. 3565–3581, 2017, doi: 10.1007/s00382-016-3284-3.

C. Funk et al., “The climate hazards infrared precipitation with stations - A new environmental record for monitoring extremes,” Sci. Data, vol. 2, pp. 1–21, 2015, doi: 10.1038/sdata.2015.66.

C. Toté, D. Patricio, H. Boogaard, R. van der Wijngaart, E. Tarnavsky, and C. Funk, “Evaluation of satellite rainfall estimates for drought and flood monitoring in Mozambique,” Remote Sens., vol. 7, no. 2, pp. 1758–1776, 2015, doi: 10.3390/rs70201758.

F. Zambrano, B. Wardlow, T. Tadesse, M. Lillo-Saavedra, and O. Lagos, “Evaluating satellite-derived long-term historical precipitation datasets for drought monitoring in Chile,” Atmos. Res., vol. 186, no. November, pp. 26–42, 2017, doi: 10.1016/j.atmosres.2016.11.006.

M. Arshad et al., “Evaluation of GPM-IMERG and TRMM-3B42 precipitation products over Pakistan,” Atmos. Res., vol. 249, p. 105341, 2021, doi: 10.1016/j.atmosres.2020.105341.

M. Nawaz, M. F. Iqbal, and I. Mahmood, “Validation of CHIRPS satellite-based precipitation dataset over Pakistan,” Atmos. Res., vol. 248, p. 105289, 2021, doi: 10.1016/j.atmosres.2020.105289.

Y. Yang, G. Wang, L. Wang, J. Yu, and Z. Xu, “Evaluation of Gridded Precipitation Data for Driving SWAT Model in Area Upstream of Three Gorges Reservoir,” PLoS One, vol. 9, no. 11, p. e112725, Nov. 2014, doi: 10.1371/journal.pone.0112725.

N. Khan, S. Shahid, T. Ismail, K. Ahmed, and N. Nawaz, “Trends in heat wave related indices in Pakistan,” Stoch. Environ. Res. Risk Assess., vol. 33, no. 1, pp. 287–302, 2019, doi: 10.1007/s00477-018-1605-2.

M. Dembélé and S. J. Zwart, “Evaluation and comparison of satellite-based rainfall products in Burkina Faso, West Africa,” Int. J. Remote Sens., vol. 37, no. 17, pp. 3995–4014, Sep. 2016, doi: 10.1080/01431161.2016.1207258.

G. T. Ayehu, T. Tadesse, B. Gessesse, and T. Dinku, “Validation of new satellite rainfall products over the Upper Blue Nile Basin, Ethiopia,” Atmos. Meas. Tech., vol. 11, no. 4, pp. 1921–1936, 2018, doi: 10.5194/amt-11-1921-2018.

L. Bai, C. Shi, L. Li, Y. Yang, and J. Wu, “Accuracy of CHIRPS Satellite-Rainfall Products over Mainland China,” Remote Sens., vol. 10, no. 3, p. 362, Feb. 2018, doi: 10.3390/rs10030362.

H. Feidas, “Validation of satellite rainfall products over Greece,” Theor. Appl. Climatol., vol. 99, no. 1–2, pp. 193–216, 2010, doi: 10.1007/s00704-009-0135-8.

C. Funk, A. Verdin, J. Michaelsen, P. Peterson, D. Pedreros, and G. Husak, “A global satellite-assisted precipitation climatology,” Earth Syst. Sci. Data, vol. 7, no. 2, pp. 275–287, 2015, doi: 10.5194/essd-7-275-2015.

M. F. Iqbal and H. Athar, “Validation of satellite based precipitation over diverse topography of Pakistan,” Atmos. Res., vol. 201, pp. 247–260, 2018, doi: 10.1016/j.atmosres.2017.10.026.

S. Ullah, Q. You, W. Ullah, and A. Ali, “Observed changes in precipitation in China-Pakistan economic corridor during 1980–2016,” Atmos. Res., vol. 210, pp. 1–14, Sep. 2018, doi: 10.1016/j.atmosres.2018.04.007.

A. Banerjee et al., “Tracking 21st century climate dynamics of the Third Pole: An analysis of topo-climate impacts on snow cover in the central Himalaya using Google Earth Engine,” Int. J. Appl. Earth Obs. Geoinf., vol. 103, no. July, p. 102490, 2021, doi: 10.1016/j.jag.2021.102490.

Z. Nawaz, X. Li, Y. Chen, N. Nawaz, R. Gull, and A. Elnashar, “Spatio-temporal assessment of global precipitation products over the largest agriculture region in pakistan,” Remote Sens., vol. 12, no. 21, pp. 1–24, 2020, doi: 10.3390/rs12213650.

A. P. Dimri et al., “Western Disturbances: A review,” Rev. Geophys., vol. 53, no. 2, pp. 225–246, 2015, doi: 10.1002/2014RG000460.

U. Asmat and H. Athar, “Run-based multi-model interannual variability assessment of precipitation and temperature over Pakistan using two IPCC AR4-based AOGCMs,” Theor. Appl. Climatol., vol. 127, no. 1–2, pp. 1–16, 2017, doi: 10.1007/s00704-015-1616-6.

S. Ullah, Q. You, W. Ullah, and A. Ali, “Observed changes in precipitation in China-Pakistan economic corridor during 1980–2016,” Atmos. Res., vol. 210, no. April, pp. 1–14, 2018, doi: 10.1016/j.atmosres.2018.04.007.

M. Arshad, X. Ma, J. Yin, W. Ullah, M. Liu, and I. Ullah, “Performance evaluation of ERA-5, JRA-55, MERRA-2, and CFS-2 reanalysis datasets, over diverse climate regions of Pakistan,” Weather Clim. Extrem., vol. 33, p. 100373, 2021, doi: 10.1016/j.wace.2021.100373.

M. W. Ashiq, C. Zhao, J. Ni, and M. Akhtar, “GIS-based high-resolution spatial interpolation of precipitation in mountain-plain areas of Upper Pakistan for regional climate change impact studies,” Theor. Appl. Climatol., vol. 99, no. 3–4, pp. 239–253, 2010, doi: 10.1007/s00704-009-0140-y.

F. Abbas, “Analysis of a historical (1981-2010) temperature record of the Punjab Province of Pakistan,” Earth Interact., vol. 17, no. 15, pp. 1–23, 2013, doi: 10.1175/2013EI000528.1.

S. Khan and M.-U.- Hasan, “Climate Classification of Pakistan,” Int. J. Econ. Environ. Geol., vol. 10, no. 2, pp. 60–71, 2019, doi: 10.46660/ojs.v10i2.264.

U. Asmat, H. Athar, A. Nabeel, and M. Latif, “An AOGCM based assessment of interseasonal variability in Pakistan,” Clim. Dyn., vol. 50, no. 1–2, pp. 349–373, Jan. 2018, doi: 10.1007/s00382-017-3614-0.

Z. Nawaz et al., “Spatiotemporal Assessment of Temperature Data Products for the Detection of Warming Trends and Abrupt Transitions over the Largest Irrigated Area of Pakistan,” Adv. Meteorol., vol. 2020, 2020, doi: 10.1155/2020/3584030.

G. Huffman et al., “NASA GPM Integrated Multi-satellitE Retrievals for GPM (IMERG) Algorithm Theoretical Basis Document (ATBD) Version 06,” Nasa/Gsfc, no. January, p. 29, 2020, [Online]. Available: https://pmm.nasa.gov/sites/default/files/imce/times_allsat.jpg%0Ahttps://pmm.nasa.gov/sites/default/files/document_files/IMERG_ATBD_V06.pdf

A. Yatagai, K. Kamiguchi, O. Arakawa, A. Hamada, N. Yasutomi, and A. Kitoh, “APHRODITE: Constructing a Long-Term Daily Gridded Precipitation Dataset for Asia Based on a Dense Network of Rain Gauges,” Bull. Am. Meteorol. Soc., vol. 93, no. 9, pp. 1401–1415, Sep. 2012, doi: 10.1175/BAMS-D-11-00122.1.

H. Hersbach et al., “The ERA5 global reanalysis,” Q. J. R. Meteorol. Soc., vol. 146, no. 730, pp. 1999–2049, Jul. 2020, doi: 10.1002/qj.3803.

C. C. Funk et al., “A Quasi-Global Precipitation Time Series for Drought Monitoring,” U.S. Geol. Surv. Data Ser., vol. 832, no. January, p. 4, 2014, doi: 10.3133/ds832.

C. Funk et al., “The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes,” Sci. Data, vol. 2, no. 1, p. 150066, Dec. 2015, doi: 10.1038/sdata.2015.66.

M. N. Anjum et al., “Assessment of PERSIANN-CCS, PERSIANN-CDR, SM2RAIN-ASCAT, and CHIRPS-2.0 Rainfall Products over a Semi-Arid Subtropical Climatic Region,” Water, vol. 14, no. 2, p. 147, Jan. 2022, doi: 10.3390/w14020147.

A. Banerjee, R. Chen, M. E. Meadows, R. B. Singh, S. Mal, and D. Sengupta, “An Analysis of Long-Term Rainfall Trends and Variability in the Uttarakhand Himalaya Using Google Earth Engine,” Remote Sens., vol. 12, no. 4, p. 709, Feb. 2020, doi: 10.3390/rs12040709.

G. Artan, H. Gadain, J. L. Smith, K. Asante, C. J. Bandaragoda, and J. P. Verdin, “Adequacy of satellite derived rainfall data for stream flow modeling,” Nat. Hazards, vol. 43, no. 2, pp. 167–185, Oct. 2007, doi: 10.1007/s11069-007-9121-6.

M. Dembélé and S. J. Zwart, “Evaluation and comparison of satellite-based rainfall products in Burkina Faso, West Africa,” Int. J. Remote Sens., vol. 37, no. 17, pp. 3995–4014, 2016, doi: 10.1080/01431161.2016.1207258.