Skip to main content
Log in

A quantitative precipitation forecast model using convective-cloud tracking in satellite thermal infrared images and adaptive regression: a case study along East Coast of India

  • Original Article
  • Published:
Modeling Earth Systems and Environment Aims and scope Submit manuscript

Abstract

The current work addresses issues related to quantitative estimation of precipitation caused by convective clouds, using thermal infrared images, and adaptive regression modeling. The developed methodology has been implemented on the Indian sector during the period 22–25 October 2013. The importance of the developed methodology lies in the fact that the information obtained from it can facilitate further studies intended for the prediction of flood events. This study is the continuation of existing work of identification of convective clouds and the analysis of the Mesoscale Convective Systems (MCS). In the current work, forecast of rainfall in terms of millimeter has been proposed. The entire work has been carried out on thermal infrared (TIR) images obtained from geostationary satellites and the results have been validated by actual rainfall data measured by rain gauges. The results obtained from the developed methodology were found to be fairly close to actual values.

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Amorati R, Alberoni P, Levizzani V, Nanni S (2000) IR-based satellite and radar rainfall estimates of convective storms over northern Italy. Meteorol Appl 7(1):1–18

    Google Scholar 

  • Arkin PA, Meisner BN (1987) The relationship between large-scale convective rainfall and cold cloud over the western hemisphere during 1982–84. Mon Weather Rev Am Meteorol Soc 115(1):51–74

    Google Scholar 

  • Ba MB, Gruber A (2001) GOES multispectral rainfall algorithm (GMSRA). J Appl Meteorol Am Meteorol Soc 40:1500–1514

    Google Scholar 

  • Behrangi A, Hsu K, Imam B, Sorooshian S (2010) Daytime precipitation estimation using bispectral cloud classification system. J Appl Meteorol Climatol Am Meteorol Soc 49(5):1015–1031

    Google Scholar 

  • Berges JC, Jobard I, Chopin F, Roca R (2009) EPSAT-SG: a satellite method for precipitation estimation; its concepts and implementation for the AMMA experiment. Ann Geophys 27:1–20

    Google Scholar 

  • Chadwick RS, Grimes DIF, Saunders RW, Francis PN, Blackmore TA (2010) The TAMORA algorithm: satellite rainfall estimates over West Africa using multi-spectral SEVIRI data. Adv Geosci 25(25):3–9

    Google Scholar 

  • Ducrocq V, Ricard D, Lafore J, Orain F (2002) Storm-scale numerical rainfall prediction for five precipitating events over france: on the importance of the initial humidity field. Weather Forecast Am Meteorol Soc 17:1236–1256

    Google Scholar 

  • Ebert EE, Manton MJ (1998) Performance of satellite rainfall estimation algorithms during TOGA COARE. J Atmos Sci Am Meteorol Soc 55(9):1537–1557

    Google Scholar 

  • Ebert EE, Janowiak JE, Kidd C (2007) Comparison of near-real-time precipitation estimates from satellite observations and numerical models. Bull Am Meteorol Soc 88(1):47–64

    Google Scholar 

  • Ferraro R, Pellegrino P, Turk M, Chen W, Qiu S, Kuligowski R, Kusselson S, Irving A, Kidder S, Knaff J (2005) The Tropical Rainfall Potential (TRaP) technique. part ii: validation. Weather Forecast Am Meteorol Soc 20:465–475

    Google Scholar 

  • Goswami B and Bhandari G (2011) Cloud motion prediction using mean path adjustment method from satellite infrared images. In: Proceedings of IEEE CALCON 2011, pp 313–316

  • Goswami B and Bhandari G (2011) Automatically Adjusting Cloud Movement Prediction Model from Satellite Infrared Images. In: Proceedings of IEEE India Conference on engineering sustainable Solutions (INDICON 2011), pp 1–4

  • Goswami B and Bhandari G (2012) Development of Irregular Cloud Cluster Encapsulating Structure from Satellite Infrared Images. In: Proceedings of 33rd Asian Conference on remote sensing (ACRS 2012).

  • Goswami B, Bhandari G (2013a) Convective cloud detection and tracking from series of infrared images. J Indian Soc Remote Sens 41(2):291–299

    Google Scholar 

  • Goswami B and Bhandari G (2013b) Application of general fuzzy min-max neural network for the clustering of satellite thermal infrared images. In: Proceedings of 4th International Conference on technical and managerial innovation in computing and communications in industry and academia (IEMCON 2013), 240–244.

  • Goswami B, Bhandari G and Goswami S (2012) Fuzzy Min-Max Neural Network for Satellite Infrared Image Clustering. In: Proceedings of 3rd IEEE International Conference on Emerging Applications of Information Technology (EAIT 2012), I: 239–242

  • Goswami B, Bhandari G and Goswami S (2014a) Temperature induced mean based cloud motion prediction model for multiple cloud clusters in satellite infrared images. In: Proceedings of the 4th IEEE International Conference on Emerging Applications of Information Technology (EAIT 2014), 279–282.

  • Goswami B, Bhandari G and Goswami S (2014) Mesoscale Convective System Tracking in Satellite Thermal Infrared Images. In: Proceedings of the IEEE India Conference on Emerging Trends and Innovation in Technology (INDICON 2014), pp 1–4.

  • Gourley JJ, Vieux BE (2005) A method for evaluating the accuracy of quantitative precipitation estimates from a hydrologic modeling perspective. J Hydrometeorol Am Meteorol Soc 6(2):115–133

    Google Scholar 

  • Gourley JJ, Maddox RA, Howard KW, Burgess DW (2002) An exploratory multisensor technique for quantitative estimation of stratiform rainfall. J Hydrometeorol Am Meteorol Soc 3:166–180

    Google Scholar 

  • Griffith CG, Woodley WL, Browner S, Teijeiro J, Maier M, Martin DW, Stout J, Sikdar DN (1976) Rainfall estimation from geosynchronous satellite imagery during daylight hours. NOAA Tech. Rep., ERL, 106

  • Hong Y, Hsu KL, Sorooshian S, Gao XG (2004) Precipitation Estimation from Remotely Sensed Imagery Using an Artificial Neural Network Cloud Classification System. J Appl Meteorol Am Meteorol Soc 43(12):1834–1853

    Google Scholar 

  • Hong Y, Hsu KL, Sorooshian S, Gao X (2005) Self-Organizing Nonlinear Output (SONO): A neural network suitable for cloud patch–based rainfall estimation at small scales. Water Resour Res 41:3

    Google Scholar 

  • Hossain F, Anagnostou EN (2006) Assessment of a multidimensional satellite rainfall error model for ensemble generation of satellite rainfall data. IEEE Geosci Remote Sens Lett 3(3):419–423

    Google Scholar 

  • Huffman GJ, Adler RF, Morrissey MM, Bolvin DT, Curtis S, Joyce R, Mcgavock B, Susskind J (2001) Global precipitation at one-degree daily resolution from multi-satellite observations. J Hydrometeorol Amer Meteor Soc 2(1):36–50

    Google Scholar 

  • Inoue T, Vila D, Rajendran K, Hamada A, Wu X, Machado LAT (2009) Life cycle of deep convective systems over the eastern tropical pacific observed by TRMM and GOES-W. J Meteorol Soc Jpn 87A:381–391

    Google Scholar 

  • Islam MN, Islam AS, Hayashi T, Terao T, Uyeda H (2002) Application of a method to estimate rainfall in Bangladesh using GMS-5 data. J Nat Disaster Sci 24(2):83–89

    Google Scholar 

  • Kidder SQ, Kusselson SJ, Knaff JA, Ferraro RR, Kuligowski RJ, Turk M (2005) The Tropical Rainfall Potential (TRaP) technique. Part I: description and examples. Weather Forecast Am Meteorol Soc 20:456–464

    Google Scholar 

  • Kondo Y, Higuchi A, Nakamura K (2006) Small-scale cloud activity over the Maritime Continent and the Western Pacific as revealed by satellite data. Mon Weather Rev 134(6):1581–1599

    Google Scholar 

  • Liu Y, Geerts B, Miller M, Daum P, McGraw R (2008) Threshold radar reflectivity for drizzling clouds. Geophys Res Lett 35:3

    Google Scholar 

  • Machado LAT, Rossow WB, Guedes RL, Walker AW (1998) Life cycle variations of mesoscale convective systems over the Americas. Mon Weather Rev Am Meteorol Soc 126(6):1630–1654

    Google Scholar 

  • Mathon V, Laurent H (2001) Life cycle of Sahelian mesoscale convective cloud systems. Q J R Meteorol Soc 127(5):377–406

    Google Scholar 

  • Murao H, Nishikawa I, Kitamura S, Yamada M, Xie P (1993) A hybrid neural network system for the rainfall estimation using satellite imagery. In: Proceedings of 1993 IEEE International Joint Conference on Neural Networks 1993, IJCNN'93-Nagoya, 2:1211–1214.

  • Nasrollahi N, Hsu K, Sorooshian S (2013) An artificial neural network model to reduce false alarms in satellite precipitation products using MODIS and CloudSat observations. J Hydrometeorol Am Meteorol Soc 14(6):1872–1883

    Google Scholar 

  • Ohara N, Kavvas ML, Kure S, Chen ZQ, Jang S, Tan E (2011) Physically based estimation of maximum precipitation over American River watershed, California. J Hydrol Eng 16(4):351–361

    Google Scholar 

  • Ouallouche F, Lazri M, Ameur S, Brucker JM, Sehad M (2014) Infrared and Microwave Image Fusion for Rainfall Detection over Northern Algeria. Int J Image Graph Signal Process 6:11–18

    Google Scholar 

  • Palmeira FLB, Morales CA, Franςa GB, Landau L (2004) Rainfall estimation using satellite data for Paraίba Do Sul Basin (Brazil). In” The XXth ISPRS International Society for Photogrammetry and Remote Sensing Congress.

  • Petty GW (1999) Prevalence of precipitation from warm-topped clouds over eastern Asia and the western Pacific. J Clim Am Meteorol Soc 12(1):220–229

    Google Scholar 

  • Petty GW, Krajewski WF (1996) Satellite estimation of precipitation over land. Hydrol Sci J 41(4):433–451

    Google Scholar 

  • Qin Y, Chen Z, Shen Y, Zhang S, Shi R (2014) Evaluation of Satellite Rainfall Estimates over the Chinese Mainland. Remote Sens 6(11):11649–11672

    Google Scholar 

  • Rivolta G, Marzano FS, Coppola E, Verdecchia M (2006) Artificial neural-network technique for precipitation nowcasting from satellite imagery. Adv Geosci 7(7):97–103

    Google Scholar 

  • Saxen TR, Rutledge SA (2000) Surface rainfall-cold cloud fractional coverage relationship in TOGA COARE: a function of vertical wind shear. Mon Weather Rev Am Meteorol Soc 128(2):407–415

    Google Scholar 

  • Scherer WD, Hudlow MD (1971) A technique for assessing probable distributions of tropical precipitation echo lengths for X-band radar from Nimbus 3HRIR data. BOMEX Bull 10:63–68

    Google Scholar 

  • Scofield RA (1987) The NESDIS operational convective precipitation-estimation technique. Mon Weather Rev 115(8):1773–1793

    Google Scholar 

  • Scofield RA, Kuligowski RJ (2003) Status and outlook of operational satellite precipitation algorithms for extreme-precipitation events. Weather Forecast Am Meteorol Soc 18(6):1037–1051

    Google Scholar 

  • Scofield RA, Oliver VJ (1977) A scheme for estimating convective rainfall from satellite imagery. NOAA Tech. Memo. NESS 86, US Dept. of Commerce, Wasington, DC, 47.

  • Sorooshian S, Hsu KL, Gao X, Gupta HV, Imam B, Braithwaite D (2000) Evaluation of PERSIANN system satellite-based estimates of tropical rainfall. Bull Am Meteor Soc 81(9):2035–2046

    Google Scholar 

  • Sorooshian S, Hsu KL, Imam B, Hong Y (2005) Global precipitation estimation from satellite image using artificial neural networks. J Appl Meteorol Climatol Am Meteorol Soc 36:1176–1190

    Google Scholar 

  • Staelin DH, Chen FW (2000) The remote sensing of clouds and precipitation from space: a review. IEEE Trans Geosci Remote Sens 38(5):2322–2332

    Google Scholar 

  • Stephens GL, Kummerow CD (2007) The remote sensing of clouds and precipitation from space: a review. J Atmos Sci Sp Sect 64:3742–3765

    Google Scholar 

  • Stout JS, Martin DW, Sikdar DN (1979) Estimating GATE Rainfall with Geosynchronous Satellite Images. Mon Weather Rev Am Meteorol Soc 107(5):585–598

    Google Scholar 

  • Tapiador FJ, Kidd C, Levizzani V, Marzano FS (2004) A maximum entropy approach to satellite quantitative precipitation estimation (QPE). Int J Remote Sens 25(21):4629–4639

    Google Scholar 

  • Tarruella R, Jorge J (2003) Comparison of three infrared satellite techniques to estimate accumulated rainfall over the Iberian Peninsula. Int J Climatol 23(14):1757–1769

    Google Scholar 

  • Todd MC, Kidd C, Kniveton D, Bellerby TJ (2001) A combined satellite infrared and passive microwave technique for estimation of small-scale rainfall. J Atmos Oceanic Technol Am Meteorol Soc 18(5):742–755

    Google Scholar 

  • Turk FJ, Ebert EE, Oh HJ, Sohn BJ (2002) Validation and applications of a real-time global precipitation analysis. Proc IEEE Int Geosci Remote Sens Symp IGARSS 2:705–707

    Google Scholar 

  • Vicente GA, Costa MH (2001) (2001) Real time satellite rainfall estimation over the Amazon region for hydrological applications. Proc IEEE Geosci Remote Sens Symp IGARSS 5:2121–2123

    Google Scholar 

  • Vicente GA, Scofield RA, Menzel WP (1998) The operational GOES infrared rainfall estimation technique. Bull Am Meteorol Soc 79(9):1883–1898

    Google Scholar 

  • Wardah T, Abu Bakar SH, Bardossy A, Maznorizan M (2008) Use of geostationary meteorological satellite images in convective rain estimation for flash-flood forecasting. J Hydrol 365(3):283–298

    Google Scholar 

  • Wardah T, Ibrahim Z, Ramli S (2009) Geostationary meteorological satellite-based quantitative rainfall estimation (GMS-Rain) for flood forecasting. Malays J Civ Eng 21(1):1–16

    Google Scholar 

  • Wardah T, Kamil AA, Sahol Hamid AB, Maisarah WWI (2011) Quantitative precipitation forecast using MM5 and WRF models for Kelantan River Basin. J World Acad Sci Eng Technol 59:2469–2473

    Google Scholar 

  • Wei C, Hung WC, Cheng KS (2006) A multi-spectral spatial convolution approach of rainfall forecasting using weather satellite imagery. Adv Sp Res 37:747–753

    Google Scholar 

  • Woodley WL, Sancho B (1971) A first step towards rainfall estimation from satellite cloud photographs. Weather 26(7):279–289

    Google Scholar 

  • Xu L, Gao X, Sorooshian S, Arkin PA, Imam B (1999) A microwave infrared threshold technique to improve the GOES precipitation index. J Appl Meteorol Am Meteorol Soc 38(5):569–579

    Google Scholar 

  • Yang Y, Lin H, Guo Z, Jiang J (2007) A data mining approach for heavy rainfall forecasting based on satellite image sequence analysis. Comput Geosci 33(1):20–30

    Google Scholar 

  • Yilmaz KK, Hogue TS, Hsu KL, Sorooshian S, Gupta HV, Wagener T (2005) Intercomparison of rain gauge, radar and satellite-based precipitation estimates with emphasis on hydrologic forecasting. J Hydrometeorol Am Meteorol Soc 6(4):497–517

    Google Scholar 

Download references

Acknowledgements

The authors are especially indebted to Meteorological and Oceanographic Satellite Data Archival Centre (MOSDAC), Indian Space Research Organization, Government of India, for availing the images of Indian subcontinent from the Indian Meteorological Satellite, Kalpana-1, for performing the study. Special thanks to National Climate Centre (NCC), Indian Meteorological Department, Pune, India for providing rainfall data to validate the developed model.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sanjay Goswami.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Goswami, B., Bhandari, G. & Goswami, S. A quantitative precipitation forecast model using convective-cloud tracking in satellite thermal infrared images and adaptive regression: a case study along East Coast of India. Model. Earth Syst. Environ. 7, 1097–1105 (2021). https://doi.org/10.1007/s40808-020-00968-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40808-020-00968-7

Keywords

Navigation