Articles written in Journal of Earth System Science

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

      Ashok Kumar Sridevi Ch Durai V R Singh K K Mukhopadhyay P Chattopadhyay N

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      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.

    • Monitoring agricultural drought using combined drought index in India


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      Long period data and information indicate that India faced number of droughts-like situation from colonial period. A number of indices have been developed nationally and internationally to monitor agricultural drought based on remote sensing; however, to predict the onset of agricultural drought and its evolution and monitoring in time and space in a more efficient way qualitatively, Combined Drought Index (CDI) has been developed using meteorological, land based and remote sensing observations. In this study, an effort has been made to monitor agricultural drought based on exploitation of new data, methodologies and metrics that would aid the experts to make best judgments of regional-scale drought conditions through CDI using geospatial technology. The present study has been carried out for three consecutive years of 2014, 2015 and 2016 in five states (Andhra Pradesh, Chhattisgarh, Haryana, Maharashtra and Telangana) in India at district level for southwest monsoon season when rainfed kharif crops are grown extensively across the above-mentioned states in India. CDI gives a synthetic and synoptic overview of the drought situations using a classification scheme derived from various individual indices as it has been developed to combine the strength of various indices.

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