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

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


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.


Keywords


Rainfall estimation; Rainfall analysis; Doppler Weather Radar; Precipitation Accumulation Product; Image processing; Linear regression model

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