PRASAD V S
Articles written in Journal of Earth System Science
Volume 128 Issue 6 August 2019 Article ID 0155 Research Article
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.
Volume 129 All articles Published: 23 July 2020 Article ID 0163 Research Article
India Meteorological Department (IMD) is operationally producing forecasts at T1534 resolution using NCMRWF GFS (NGFS) model and biases are reported in some regions. In order to identify the model biases and applying necessary correction measures to improve forecast, retrospective forecast is carried out for the 20 yrs period from 1999–2018 using operational version of NGFS model. In this study, model’s ability to predict extreme temperature and rainfall events in Indian region irrespective of model biases is investigated. It is found that model is able to predict extreme temperature events accurately with sufficiently long lead time (7 days). In case of extreme rainfall at shorter lead time (3 days), model is able to predict accurately and accuracy decreases with increase in lead time. Employing bias correction methods reduced large biases in some regions.
Volume 129, 2020
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