• Durai V R

Articles written in Journal of Earth System Science

• MOS guidance using a neural network for the rainfall forecast over India

In the present study, a model output statistics (MOS) guidance model was developed by using the neural network technique for a bias-corrected rainfall forecast. The model was developed over the Indian window (0–40$^{\circ}$N and 60–100$^{\circ}$E) by using the observed and global forecast system (GFS) T-1534 model output (up to 5 days) at a 0.125$^{\circ} \times$ 0.125$^{\circ}$ regular grid during the summer monsoon (June–September) 2016. The skill of the developed MOS model forecast against the observed 0.125$^{\circ} \times$ 0.125$^{\circ}$ grid rainfall data is obtained for the summer monsoon (June–September) 2017. The skill of the MOS model rainfall forecast is found to show good improvement over the T-1534 model’s direct forecast over the Indian window. In general, the T-1534 model’s direct forecast shows high skill but the forecast obtained by using the MOS model shows better skill than the direct model’s forecast, although a major improvement is seen for the Day 1 forecast at the national level. So the skill of the bias-corrected rainfall forecast by using the MOS guidance and the T-1534 model output is high and has the potential of being used as an operational forecast over the Indian region.

• Performance of a very high-resolution global forecast system model (GFS T1534) at 12.5 km over the Indian region during the 2016–2017 monsoon seasons

A global forecast system model at a horizontal resolution of T1534 ($\sim$12.5 km) has been evaluated for the monsoon seasons of 2016 and 2017 over the Indian region. It is for the first time that such a high-resolution global model is being run operationally for monsoon weather forecast. A detailed validation of the model therefore is essential. The validation of mean monsoon rainfall for the season and individual months indicates a tendency for wet bias over the land region in all the forecast lead time. The probability distribution of forecast rainfall shows an overestimation (underestimation) of rainfall for the lighter (heavy) categories. However, the probability distribution functions of moderate rainfall categories are found to be reasonable. The model shows fidelity in capturing the extremely heavy rainfall categories with shorter lead times. The model reasonably predicts the large-scale parameters associated with the Indian summer monsoon, particularly, the vertical profile of the moisture. The diurnal rainfall variability forecasts in all lead times show certain biases over different land and oceanic regions and, particularly, over the north–west Indian region. Although the model has a reasonable fidelity in capturing the spatio-temporal variability of the monsoon rain, further development is needed to enhance the skill of forecast of a higher rain rate with a longer lead time.

• # Journal of Earth System Science

Volume 129, 2020
All articles
Continuous Article Publishing mode

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Posted on July 25, 2019