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A new tool for predicting drought: an application over India.

¹Indian Institute of Tropical Meteorology, Dr. Homi Bhabha Road, Pashan, Pune 411008, State Maharashtra, India.

Scientific Reports

|January 9, 2015

Summary

This summary is machine-generated.

Atmospheric electrical columnar resistance, derived from satellite data, offers a novel, non-probabilistic method for rainfall prediction. Data from the Bay of Bengal alone can forecast drought conditions across India.

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Area of Science:

Atmospheric electricity
Meteorology
Climate science

Background:

Traditional rainfall prediction methods often rely on probabilistic models.
Existing drought prediction techniques have limitations in accuracy and early identification.
Atmospheric electrical properties have not been extensively explored for rainfall forecasting.

Purpose of the Study:

To introduce and validate atmospheric electrical columnar resistance as a new predictor for rainfall and drought.
To develop novel methods for drought prediction and forecasting using this new parameter.
To assess the predictor's efficacy over India and its potential for global application.

Main Methods:

Utilizing satellite data to calculate atmospheric electrical columnar resistance.
Developing model calculations based on atmospheric electricity.
Applying the new predictor to historical data for drought identification and forecasting over India.
Analyzing sub-regional scale associations between rainfall and the new predictor.

Main Results:

Atmospheric electrical columnar resistance proves to be a physically sound and simple predictor.
Data solely from the Bay of Bengal were sufficient to predict country-wide drought conditions in India.
New drought prediction methods achieved 100% success in identifying drought conditions in pre-drought years (1981-1990, 2001-2013).
A preliminary drought forecast for India in 2014 was provided.

Conclusions:

Atmospheric electrical columnar resistance is a promising new tool for rainfall and drought prediction.
The predictor demonstrates high accuracy and potential for early drought detection.
The findings suggest global applicability and integration into climate models for improved weather forecasting.