Skip to main content

Advertisement

Log in

Evaluation of short-period rainfall estimates from Kalpana-1 satellite using MET software

  • Published:
Journal of Earth System Science Aims and scope Submit manuscript

The INSAT Multispectral Rainfall Algorithm (IMSRA) technique for rainfall estimation, has recently been developed to meet the shortcomings of the Global Precipitation Index (GPI) technique of rainfall estimation from the data of geostationary satellites; especially for accurate short period rainfall estimates. This study evaluates the 3-hourly precipitation estimates by this technique as well as the rainfall estimates by the GPI technique using data of the Kalpana-1 satellite, over the Indian region for the south-west monsoon season of 2010 to understand their relative strengths and weaknesses in estimating short period rainfall. The gridded 3 hourly accumulated TRMM satellite (3B42 V6 product or TMPA product) and surface raingauge data for stations over the Indian region for the same period is used as the standard measure of rainfall estimates. The Method for Object-based Diagnostic Evaluation (MODE) utility of the METv3.0 software, has been used for the evaluation purpose. The results show that the new IMSRA technique is closer to the TMPA rainfall estimate, in terms of areal spread, geometric shape and location of rainfall areas, as compared to the GPI technique. The overlap of matching rainfall areas with respect to TMPA rainfall patches is also higher for the IMSRA estimates as compared to the GPI values. However, both satellite rainfall estimates are observed to be generally higher compared to the TMPA measurements. However, the values for the highest 10% of the rainfall rates in any rainfall patch, is generally higher for rainfall measured by the IMSRA technique, as compared to the estimates by the GPI technique. This may partly be due to the capping maximum limit of 3 mm/hr for rainfall measured by the GPI technique limits the total 3-hour accumulation to 9 mm even during heavy rainfall episodes. This is not so with IMSRA technique, which has no such limiting value. However, this general overestimation of the rainfall amount, measured by both techniques, and the greater error in case of IMSRA estimates, is also validated independently with respect to surface raingauge observations. Hence the observed overestimation by the IMSRA technique for the highest 10th percentile rainfall rates in rainfall episodes, is real. This overestimation by the latter technique may become a significant source of error, if the IMSRA estimate is used for monitoring very heavy rainfall episodes. In all other respects, since the IMSRA method shows significant improvement over the GPI, the rainfall estimates by the IMSRA method may be used for operational short period rainfall estimation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Figure 1
Figure 2
Figure 2
Figure 3
Figure 4

Similar content being viewed by others

References

  • Adler R F, Huffman G J, Chang A, Ferraro R, Xie P P, Janowiak J, Rudolf B, Schneider U, Curtis S, Bolvin D, Gruber A, Susskind J, Arkin P and Nelkin E 2003 The version-2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979–Present); J. Hydrometeorol. 4 1147–1167.

    Article  Google Scholar 

  • Arkin P A 1979 The relationship between fractional coverage of high cloud and rainfall accumulations during GATE over the B-scale array; Mon. Weather Rev. 107 1382–1387.

    Article  Google Scholar 

  • Arkin P A and Ardanuy P E 1989 Estimating climatic-scale precipitation from space: A review; J. Climate 2 1229–1238.

    Article  Google Scholar 

  • Arkin P A, Krishna Rao A V R and Kelkar R R 1989 Large scale precipitation and outgoing longwave radiation from INSAT-1B during the 1986 south west monsoon season; J. Climate 2 619–628.

    Article  Google Scholar 

  • Brown B G, Bullock R, Halley Gotway J, Ahijevych D, Davis C, Gilleland E and Holland L 2007 Application of the MODE object-based verification tool for the evaluation of model precipitation fields; AMS 22nd Conference on Weather Analysis and Forecasting and 18th Conference on Numerical Weather Prediction, 25–29 June, Park City, Utah, American Meteorological Society (Boston), available at http://ams.confex.com/ams/pdfpapers/124856.pdf.

  • Chokngamwong R and Chiu L 2007 Thailand daily rainfall and comparison with TRMM products; J. Hydrometeorol. 9(2) 256–266.

    Article  Google Scholar 

  • Davis C A, Brown B G and Bullock R G 2006a Object-based verification of precipitation forecasts, Part I: Methodology and application to mesoscale rain areas; Mon. Weather Rev. 134 1772–1784.

    Article  Google Scholar 

  • Davis C A, Brown B G and Bullock R G 2006b Object-based verification of precipitation forecasts, Part II: Application to convective rain systems; Mon. Weather Rev. 134 1785–1795.

    Article  Google Scholar 

  • Dinku T, Ceccato P, Grover-Kopec E, Lemma M, Connor S J and Ropelewski C F 2007 Validation of satellite rainfall products over East Africa’s complex topography; Int. J. Remote Sens. 28(7) 1503–1526.

    Article  Google Scholar 

  • Durai V R, Roy Bhowmik S K and Mukhopadhyay B 2010 Evaluation of Indian summer monsoon rainfall features using TRMM and Kalpana-1 satellite derived precipitation and rain gauge observations; Mausam 61(3) 317–336.

    Google Scholar 

  • Feidas H 2010 Validation of satellite rainfall products over Greece; Theor. Appl. Climatol. 99 193–216.

    Article  Google Scholar 

  • Gairola R M and Krishnamurti T N 1992 Rain rates based on SSM/I, OLR and rainguage data sets; Meteorol. Atmos. Phys. 50(4) 165–174.

    Article  Google Scholar 

  • Gairola R M, Mishra A, Prakash S and Mahesh C 2010 Development of INSAT multi-spectral rainfall algorithm (IMSRA) for monitoring rainfall events over India using Kalpana-IR and TRMM-precipitation radar observations, Scientific Report, SAC/EPSA/AOSG/INSAT/SR-39/2010.

  • Han W S, Burian S J and Shepherd J M 2011 Assessment of satellite-based rainfall estimates in urban areas in different geographic and climatic regions; Nat. Hazards 56(3) 733–747.

    Article  Google Scholar 

  • Huffman G J, Bolvin D T, Nelkin E J, Wolff D B, Adler R F, Gu G, Hong Y, Bowman K P and Stocker E F 2007 The TRMM multisatellite precipitation analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales; J. Hydrometeorol. 8 38–55.

    Article  Google Scholar 

  • Islam N and Uyeda H 2005 Comparison of TRMM 3B42 products with surface rainfall over Bangladesh; In: Proceedings of Geoscience and Remote Sensing Symposium, IGARSS ’05, 2005, IEEE International 6 4112–4115.

  • Kummerow C, Barnes W, Kozu T, Shiue J and Simpson J 1998 The status of the Tropical Rainfall Measuring Mission (TRMM) sensor package; J. Atmos. Oceanic Technol. 15 809–817.

    Article  Google Scholar 

  • Krajewski W F 1993 Global estimation of rainfall: Certain methodological issues; In: World at Risk: Global Climate Change and Natural Hazards (ed.) Bras R, Proc. Conf. American Institute of Physics 277 180–192.

  • Li Li, Yang Hong, Jiahu Wang, Adler R F, Policelli F S, Habib S, Irwn D, Korme T and Okello L 2008 Evaluation of the real-time TRMM-based multi-satellite precipitation analysis for an operational flood prediction system in Nzoia Basin, Lake Victoria, Africa; Nat. Hazards 50(1) 109–123.

    Article  Google Scholar 

  • Mishra A, Gairola R M, Varma A K and Agarwal V K 2010 Remote sensing of precipitation over Indian land and oceanic regions by synergistic use of multi-satellite sensors; J. Geophys. Res. 115 D08106, doi: 10.1029/2009JD012157.

    Article  Google Scholar 

  • Nair S, Srinivasan G and Nemani R 2009 Evaluation of multi- satellite TRMM derived rainfall estimates over a western state of India; J. Meteorol. Soc. Japan 87(6) 927–939.

    Article  Google Scholar 

  • Prakash S, Mahesh C, Gairola R M and Pal P K 2010 Estimation of Indian summer monsoon rainfall using Kalpana-1 VHRR data and its validation using rain gauge and GPCP data; Meteorol. Atmos. Phys. 110(1–2) 45–57.

    Article  Google Scholar 

  • Prakash S, Mahesh C and Gairola R M 2011 Large-scale precipitation estimation using Kalpana-1 IR measurements and its validation using GPCP and GPCC data; Theor. Appl. Climatol. 106(3–4) 283–293, doi: 10.1007/s00704-011-0435-7.

    Article  Google Scholar 

  • Rahman H and Sengupta D 2007 Preliminary comparison of daily rainfall from satellites and Indian gauge data, CAOS Technical Report No. 2007AS1, Centre for Atmospheric and Oceanic Sciences, Indian Institute of Science, Bangalore.

  • Rahman S H, Sengupta D and Ravichandran M 2009 Variability of Indian summer monsoon rainfall in daily data from gauge and satellite; J. Geophys. Res. 114 D17113, doi: 10.1029/2008JD011694.

    Article  Google Scholar 

  • Richards F and Arkin P A 1981 On the relationship between satellite-observed cloud cover and precipitation; Mon. Weather Rev. 109 1081–1093.

    Article  Google Scholar 

  • Roca R, Viollier M, Picon L and Desbois M 2002 A multisatellite analysis of deep convection and its moist environment over the Indian Ocean during the winter monsoon; J. Geophys. Res. 107(INX2) 1–25.

    Google Scholar 

  • Su F, Hong Y and Lettenmaier D P 2008 Evaluation of TRMM multisatellite precipitation analysis (TMPA) and its utility in hydrologic prediction in the La Plata Basin; J. Hydrometeorol. 9 622–664.

    Article  Google Scholar 

  • Todd M C, Kidd C, Kniveton D and Bellerby T J 2001 A combined satellite infrared and passive microwave technique for estimation of small-scale rainfall; J. Atmos. Oceanic. Technol. 18 742–755.

    Article  Google Scholar 

  • Villarini G 2010 Evaluation of the research-version TMPA rainfall estimate at its finest spatial and temporal scales over the Rome metropolitan area; J. Appl. Meteorol. Climatol. 49(12) 2591–2602.

    Google Scholar 

  • Xie P and Arkin P A 1995 An intercomparison of gauge observations and satellite estimates of monthly precipitation; J. Appl. Meteorol. 34 1143–1160.

    Article  Google Scholar 

  • Yatagai A, Arakawa O, Kamiguchi K, Kawamoto H, Nodzu M I and Hamada A 2009 A 44-year daily gridded precipitation dataset for Asia based on a dense network of rain gauges; SOLA 5 137–140, doi: 10.2151/sola.2009-035.

    Article  Google Scholar 

Download references

Acknowledgements

The authors are grateful to the Director General of Meteorology, India Meteorological Department, for constant encouragement during the course of this study. We are grateful to a large number of officers, namely Dr V Rajeswara Rao and other staff members at IMD, New Delhi for providing the IMSRA and GPI estimates of rainfall.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to SOMA SEN ROY.

Rights and permissions

Reprints and permissions

About this article

Cite this article

ROY, S.S., SAHA, S.B., FATIMA, H. et al. Evaluation of short-period rainfall estimates from Kalpana-1 satellite using MET software. J Earth Syst Sci 121, 1113–1123 (2012). https://doi.org/10.1007/s12040-012-0218-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12040-012-0218-2

Keywords

Navigation