Rainfall Estimation using Image Processing and Regression Model on DWR Rainfall Product for Delhi-NCR Region

Source:
By:Author(s)

DOI: https://doi.org/10.30564/jasr.v3i1.1859

Abstract:

Observed rainfall is a very essential parameter for the analysis of rainfall, day to day weather forecast and its validation. The observed rainfall data is only available from five observatories of IMD; while no rainfall data is available at various important locations in and around Delhi-NCR. However, the 24-hour rainfall data observed by Doppler Weather Radar (DWR) for entire Delhi and surrounding region (up to 150 km) is readily available in a pictorial form. 

In this paper, efforts have been made to derive/estimate the rainfall at desired locations using DWR hydrological products. Firstly, the rainfall at desired locations has been estimated from the precipitation accumulation product (PAC) of the DWR using image processing in Python language. After this, a linear regression model using the least square method has been developed in R language. Estimated and observed rainfall data of year 2018 (July, August and September) was used to train the model. After this, the model was tested on rainfall data of year 2019 (July, August and September) and validated.

With the use of linear regression model, the error in mean rainfall estimation reduced by 46.58% and the error in max rainfall estimation reduced by 84.53% for the year 2019. The error in mean rainfall estimation reduced by 81.36% and the error in max rainfall estimation reduced by 33.81% for the year 2018. Thus, the rainfall can be estimated with a fair degree of accuracy at desired locations within the range of the Doppler Weather Radar using the radar rainfall products and the developed linear regression model.

References:

Anagnostou E.N. and Krajewski W.F. Real-Time Radar Rainfall Estimation. Part I: Algorithm Formulation. Journal of Atmospheric and Oceanic Technology. 16 (1999a) 189-197 Anagnostou E.N. and Krajewski W.F. Real-Time Radar Rainfall Estimation. Part II: Case Study. Journal of Atmospheric and Oceanic Technology. 16 (1999b) 198-205 Anagnostou E.N., Anagnostou M.N., Krajewski W.F., Kruger A. and Miriovsky B.J. High-Resolution Rainfall Estimation from X-Band Polarimetric Radar Measurements. Journal of Hydrometeorology. 5 (2004) 110-128 Brandes E.A. Optimizing Rainfall Estimates with the Aid of Radar. Journal of Applied Meteorology. 14 (1975) 1339-1345 Brandes E.A., Zhang G. and Vivekanandan J. Experiments in Rainfall Estimation with a Polarimetric Radar in a Subtropical Environment. Journal of Applied Meteorology. 41 (2012) 674-685 Dutta D., Sharma S., Sen G.K., Kannan B.A.M, Venketswarlu S., Gairola R.M., Das J., Viswanathan G. An Artificial Neural Network based approach for estimation of rain intensity from spectral moments of a Doppler Weather Radar. Advances in Space Research. 47 (2011) 1949-1957 Krajewski W.F., Smith J.A. Radar hydrology: rainfall estimation. Advances in Water Resources. 25 (2002) 1387-1394 Marshall J.S., Langille R.C., Palmer W. McK. Measurement of Rainfall by Radar. Journal of Meteorology. 4 (1947) 186-192 Smith J.A., Baeck M.L., Meierdiercks K.L., Miller A.J., Krajewski W.F. Radar rainfall estimation for flash flood forecasting in small urban watersheds. Advances in Water Resources. 30 (2007) 2087-2097 Sun X., Mein R.G., Keenan T.D., Elliott J.F. Flood estimation using radar and rain gauge data. Journal of Hydrology. 239 (2000) 4-18 Wilson J.W., Brandes E.A. Radar Measurement of Rainfall – A Summary. Bulletin American Meteorological Society. 60 (1979) 1048-1058 Xiao R., Chandrasekar V. Development of a neural network-based algorithm for rainfall estimation from radar observations. IEEE Transactions on Geoscience and Remote Sensing. 35 (1997) 160-171