The performance of the operational Extended-range forecast (ERF) issued by IMD for the recent two monsoon seasons (2020 and 2021) at 676 districts over India has been evaluated. The ERF prepared in real-time up to 4 weeks is evaluated at the district level in terms of predicting above-normal (AN), normal (NN) and below-normal (BN) rainfall. Statistical scores like Forecast Accuracy, Bias Score (BS), Probability of Detection (POD), False Alarm Ratio (FAR), Critical Success Index (CSI), Equitable Threat Score (ETS) etc. have been calculated for ‘AN’ ‘NN’ and ‘BN categories separately at the district level. During the 2020 monsoon season, the forecast accuracy in terms of predicting ‘above-normal’ normal and below-normal categories show 65–68

This research focuses on developing a rainfall prediction model using a polynomial regression approach with nine weather variables as weather indices. The dataset comprises 2806 values for each weather variable obtained from the Indian summer monsoon of June to September months over 23 years (2000–2022). Performance evaluation of the polynomial regression model is conducted using metrics such as RMSE, MAE, MSE, and R2, along with cross-validation techniques to determine the optimal polynomial order and prevent overfitting. The results indicate that the second-degree polynomial regression model demonstrates the best accuracy and precision in forecasting rainfall. Scatter plots and residual distribution analysis further confirm the model's effectiveness in predicting rainfall for different categories. Additionally, forecast skill analysis shows satisfactory correlations between observed and predicted rainfall, especially for light rain and medium rain categories. This study contributes valuable insights into enhancing weather forecasting models for rainfall prediction.

The forecasted rainfall in the coupled Climate Forecast System Version 2 (CFSv2) (operational in the India Meteorological Department) during the Indian summer monsoon 2020 is assessed herein for river basins in an extended-range forecast (ERF). The CFSv2 was implemented in IMD during 2017 for operational forecasting over an extended time period. In this work, the week 1 (W1), W2, W3 and W4 cumulative rainfall is assessed for the initial condition every Wednesday for the summer monsoon months (June, July, August and September) in India. Statistical measures including correlation coefficients (CC), root mean square error (RMSE), mean bias error (MBE), mean absolute error (MAE) and normalised RMSE (NRMSE) are calculated for nine different river basins, and the model forecasting skill is evaluated against the gridded data of the IMD. The ERF shows reasonable skill up to W2 to W3 over most river basins. However, higher CC is found for the larger river basins of Hirakud and Tapi compared to the other river basins that have a smaller spatial extent. The raw forecast is bias-corrected by applying a normal ratio method, and an improvement in RMSE and NRMSE is observed for the bias-corrected compared to the raw CFSv2 forecast. The ERF may provide a valuable means of forecasting cumulative rainfall for an extended period to closely manage hydrological activity. There is further potential to apply the bias correction for other river basins of India for skilful forecasting in ERF.

The performance of the operational extended-range forecast (ERF) issued by IMD is evaluated for the southwest monsoon 2020. The normal onset of monsoon over Kerala (the southern tip of India) with subsequent rapid progress northward in covering the entire country is very well captured in the ERF with 2 to 3 weeks lead time. The ERF also captured very well the transitions from normal to weaker phase of monsoon in July and the active phase of monsoon during entire August with a lead time of about 3 weeks. The active monsoon condition in the second half of September associated with delayed withdrawal from northwest India was also reasonably well captured in the ERF. Quantitatively, the ERF shows significant skill up to 3 weeks on all India levels. On smaller spatial domains for 36 meteorological subdivisions (met-subdivisions) over India, the performance of category forecasts is evaluated in terms of above normal, normal and below normal. The spatial distribution of the met-subdivision level forecast skill of predicting above normal, normal and below normal categories for the 36 subdivisions during the entire monsoon season of 2020 in terms of correct (forecast and observed category matching) to partially correct (forecast and observed category out by one category) combined categories is found to be 89%, 83%, 80% and 78% for week 1 to week 4 forecasts respectively. The wrong forecasts (forecast and observed category out by two categories) are found to be between 11% in week 1 and 22% in week 4 forecast. Thus, the met-subdivision level forecast shows useful skill and is being used operationally for agrometeorological advisory services of IMD.

Dynamic recrystallization (DRX) in a Ni alloy has been studied using a cellular automata model. The dislocation density evolves according to the Kocks-Mecking equation and nucleation is modeled according to an Arrhenius type equation. Nucleation occurs at grain boundaries when the dislocation densities in neighboring grains exceed a threshold value. A new methodology is developed to predict the nucleation probability for the selection of recrystallizing grains based on the deformation temperature and strain rate. Grain growth is modeled based on a driving pressure and grain boundary mobility, which are functions of the grain boundary energy and misorientation between neighboring grains, respectively. The flow stress response and grain size evolution of the material have been found to closely agree with experimental and simulated results from literature. We have systematically analyzed the effect of various combinations of preferred and random orientations of both pre-existing and new grains on the microstructure evolution as a result of DRX and growth. The growth kinetics have been determined for all orientation combinations. It is identified that the DRX kinetics are the fastest when preferred crystallographic orientations are assigned to both pre-existing and new grains. Through inverse pole figures and pole figures, the weakening of texture intensity has been observed in cases when random orientation is assigned to new grains and vice versa for the reverse case. The shifting of the direction of peak crystallographic texture intensity has been observed in several cases when random orientation is assigned to new grains, hereby implying grain re-orientation. Additionally, the effect of randomness has been evaluated by conducting three simulations of each set of orientations. The present study can help design thermo-mechanical treatments and metal forming process schedules to achieve tailored sets of mechanical properties for specific applications.