Original scientific paper

Improving the quality of INSAT derived quantitative precipitation estimates using an neural network method

Sankar Nath ; Indian Meteorological Department, New Delhi, India
A. K. Mitra ; Indian Meteorological Department, New Delhi, India
S. K. Roy Bhowmik ; Indian Meteorological Department, New Delhi, India

Abstract

In this paper an Artificial Neural Network (NN) approach has been applied
to improve the quality of the INSAT derived sub-division quantitative
precipitation estimates (IMD-QPE) over the Indian region for the summer
monsoon season. Data for the years 2001, 2003 and 2004 have been used as
the training sample. The method is tested with independent sample data for
the year 2005. For the subdivisions over the domains of high orographic and
monsoon low pressure system, where very rainfall occasionally occurs, different
network architectures are applied to minimize the IMD-QPE errors. An
inter-comparison between NNQPE (NN model output IMD-QPE), IMD-QPE
and actual rainfall indicates that the pattern of NNQPE is closer to the observed
rainfall distribution. The weekly mean absolute error of IMD-QPE
with respect to observed rainfall, which ranges between 10–99 mm, becomes
4–70 mm in case of NNQPE. The performance statistics shows that the proposed
NN model is able to produce better IMD-QPE with higher skill score
and correlation co-efficient with respect to observation in most of the sub-divisions.
The method is found to be promising for operational application.

Keywords

artificial neural network; INSAT derived QPE; weekly subdivision rainfall; orographic; skill score

Hrčak ID:

25449

URI

https://hrcak.srce.hr/25449

Publication date:

1.7.2008.

Article data in other languages: croatian

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