Abstract
In the present work, daily rainfall is simulated over the core monsoon region of India by using a feedforward multilayer perceptron (MLP) model. Daily rainfall is found to be optimally dependent on four concurrent meteorological parameters, namely, geopotential height, specific humidity, zonal, and meridional wind at 1000 mb, 925 mb, 850 mb, and 700 mb pressure levels during 00, 06, 12, and 18 Greenwich Mean Time (GMT). The architecture of the optimized feedforward MLP model consists of 64 nodes in the input layer, 10 nodes in the hidden layer, and 1 node in the output layer. The results from the model are compared with the 3B42 (version 7) rainfall product. In terms of root mean square error (rmse) and correlation coefficient (cc), the model is performing better compared to the satellite-derived 3B42 rainfall product, whereas in terms of bias, the performance of the 3B42 product is better compared to the model. The weight matrices of the feedforward MLP model are estimated at a particular location (22.5°N, 82.5°E). These weight matrices are able to simulate daily rainfall at neighbourhood locations also with reasonably good accuracy with cc in the range of 0.41 to 0.55. The performance of the model improves in case of an aerial average of daily rainfall with significantly enhanced cc (0.72). The model is able to capture monthly and intraseasonal variation of rainfall with reasonably good accuracy, with cc of 0.88 and 0.68, respectively. The simulation model has a limitation that it is not able to simulate extreme high rainfall events (>60 mm/day). Overall, the developed model is performing reasonably well. This approach has a potential to be used as a rain parameterization scheme in the dynamical atmospheric and coupled models to simulate daily rainfall. Nevertheless, the present approach can also be used for multistep prediction of rainfall.
Highlights
Rainfall is a core component of the hydrological cycle. e knowledge of rainfall at different time scales is of paramount importance for many scientific and operational applications
To simulate daily rainfall by using a feedforward multilayer perceptron (MLP) model, working methodology is planned into four steps, namely, (i) data preparation, (ii) identi cation of optimum depth of pressure level to be considered for the model, (iii) selection of a suitable number of input concurrent meteorological parameters for the model, and (iv) development of optimum weight matrices of an MLP model at 22.5°N, 82.5°E location. ereafter, by using the developed weight matrices, daily rainfall is simulated at neighboring locations. e detail is provided in the following subsections
E idea of the present work is inspired by the concept of hybrid environmental model, where a deterministic numerical model can be combined with an artificial neural networks (ANNs)-based parameterization scheme which takes into account the underlying physical processes [61]
Summary
Simulation of Daily Rainfall from Concurrent Meteorological Parameters over Core Monsoon Region of India: A Novel Approach. Daily rainfall is simulated over the core monsoon region of India by using a feedforward multilayer perceptron (MLP) model. E weight matrices of the feedforward MLP model are estimated at a particular location (22.5°N, 82.5°E). Ese weight matrices are able to simulate daily rainfall at neighbourhood locations with reasonably good accuracy with cc in the range of 0.41 to 0.55. E model is able to capture monthly and intraseasonal variation of rainfall with reasonably good accuracy, with cc of 0.88 and 0.68, respectively. Is approach has a potential to be used as a rain parameterization scheme in the dynamical atmospheric and coupled models to simulate daily rainfall. The present approach can be used for multistep prediction of rainfall
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