(MENAFN- Trend News Agency)
Scientists have proposed a new method to forecast precipitation
over the western Himalayas by using real time satellite images,
which they claim is more accurate and takes less time to prepare,
Trend reports
citing The Tribune .
Termed as the Artificial Neural Network (ANN) model, it extracts
the pixel values corresponding to infra-red and water vapour images
from the Kalpana-I satellite to generate precipitation
forecast.
A geostationary satellite, Kalpana-1 satellite provides images
in visible, infra-red and water vapour bands at an interval of
every half an hour, which provide information about the cloud
movement and water content present in the atmosphere over South
Asia.
ANN has been developed by four scientists from the Defence
Geoinformatics Research Establishment, Chandigarh, Defence
Institute of Bio Energy Research, Haldwani and National Institute
of Technology, Kurukshetra.
“The model is capable of interpreting satellite images in a
generalised manner without biasing. Results show good
correspondence between pixel values and observed precipitation,”
the scientists have claimed in a research paper published recently
by the India Meteorological Department (IMD).
Six locations, Haddantaj, Gulmarg, Dhundi, Drass Stage-II and
Patsio, in various mountain ranges of the western Himalayas were
considered for the study and computer software was used to develop
an algorithm for extracting the pixel values. Relationship between
the extracted pixel values and associated precipitation was modeled
using a three-layer Artificial Neural Net.
A multiple regression model with the same input and output
parameters for training and validating data was also developed for
all the six locations and the results were compared with the ANN
model. There were a total 1,012 data points, out of which 785
points were taken for training and 227 points were taken as
independent data set for validation of the developed ANN model.
“Results show that ANN model has better skill to predict the
precipitation at any specific location. Hence the model can be used
as a good supporting tool for operational weather forecasts,” the
paper states. The computational time is also comparatively
less.
According to the researchers, accurate and timely prediction of
weather has a large impact on our day to day activities, water
resources, agriculture as well as the economy of the country.
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