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Not AvailableIn this study, performance of seasonal temperature and rainfall forecast were evaluated for forecasting of wheat yield using CERES-wheat model for Himachal Pradesh region. Using the historical monthly total rainfall and mean maximum and minimum temperature values, 1000 daily weather realizations were generated using disaggregation technique. The moving average correlation between observed and generated weather sequences were calculated for different parameters viz., rainfall intensity, rainfall frequency and total monthly rainfall. The correlation values for all these parameters increased with the increase in number of weather realizations and attained its peak near 200 realizations. Thereafter, the correlation became almost constant which concluded that 200 weather realizations were optimum to know the behaviour of predicted weather sequences and its application in crop simulation models. The lead-1 temperature and rainfall forecast for November-April (6 months forecast), December-April (5 months forecast), January-April (4 months forecast), February-April (3 months forecast), March-April (2 months forecast) and monthly forecast of April month (1 month forecast) were generated for 1984-2008 in hindcast mode using multi-model ensemble technique. The monthly and seasonal forecasts were converted into daily weather sequences using the stochastic disaggregation technique. For each generated forecast, observed weather was merged with the respective forecast to make it entire season weather observations so that it can be used in crop models. CERES-wheat model was calibrated and validated for variety HPW-89 for Palampur region and the genetic coefficients thus generated were used to simulate the wheat yield in different years starting from 1984- 2008. It was found that six month forecast i.e. October start NovemberApril did not have significant skill and it failed to capture the variation in wheat yield for different years. However, with the advancement of season thereby reducing the period of forecast, the forecast skill improved progressively. It was also observed that 3 or 4 months seasonal forecast have almost similar results with a slight variation. However, forecast for three months or lesser period was able to simulate the wheat yield in an efficient manner.Department of Agriculture and Cooperation, Government of Indi

Subseasonal Forecasts of the 2018 Indian Summer Monsoon Over Bihar

Calibrated probabilistic forecasts of weekly rainfall were developed for the state of Bihar in northern India and issued in real time during the June–September 2018 monsoon period, up to 2 weeks in advance. The forecasts are based on subseasonal forecasts from the U.S. National Centers for Environmental Prediction CFSv2 model and were calibrated against observed gridded rainfall fields from the India Meteorological Department using canonical correlation analysis. Hindcasts over the 1999–2010 period exhibit appreciable skill at Week 1 lead (Days 3–9), with some skill at Week 2 (Days 10–16), over Bihar as well as over a larger region. Forecasts were issued in real time during the 2018 Indian summer monsoon season for four districts in Bihar on a 1° grid in tercile probability format every Thursday. Verification of the district‐level real‐time forecasts over the 2018 season is evaluated and moderate skill demonstrated in terms of the Brier and Heidke skill scores, especially for the northern districts and for the below‐normal category. Successful monsoon onset and break phase forecasts in 2018 over Bihar are related to episodes of the Madden‐Julian Oscillation, which the model is shown to capture quite well at 1–2 week lead.NOAA's International Research and Applications Program (IRAP)6 month embargo; published online: 26 December 2019This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Long-Term Spatiotemporal Investigation of Various Rainfall Intensities over Central India Using EO Datasets

This study involved an investigation of the long-term seasonal rainfall patterns in central India at the district level during the period from 1991 to 2020, including various aspects such as the spatiotemporal seasonal trend of rainfall patterns, rainfall variability, trends of rainy days with different intensities, decadal percentage deviation in long-term rainfall patterns, and decadal percentage deviation in rainfall events along with their respective intensities. The central region of India was meticulously divided into distinct subparts, namely, Gujarat, Daman and Diu, Maharashtra, Goa, Dadra and Nagar Haveli, Madhya Pradesh, Chhattisgarh, and Odisha. The experimental outcomes represented the disparities in rainfall distribution across different districts of central India with the spatial distribution of mean rainfall ranges during winter (2.08 mm over Dadra and Nagar Haveli with an average of 24.19 mm over Odisha), premonsoon (6.65 mm over Gujarat to 132.89 mm over Odisha), monsoon (845.46 mm over Gujarat to 3188.21 mm over Goa), and post-monsoon (30.35 mm over Gujarat to 213.87 mm over Goa), respectively. Almost all the districts of central India displayed an uneven pattern in the percentage deviation of seasonal rainfall in all three decades for all seasons, which indicates the seasonal rainfall variability over the last 30 years. A noticeable variation in the percentage deviation of seasonal rainfall patterns has been observed in the following districts: Rewa, Puri, Anuppur, Ahmadabad, Navsari, Chhindwara, Devbhumi Dwarka, Amreli, Panch Mahals, Kolhapur, Kandhamal, Ratnagiri, Porbandar, Bametara, and Sabar Kantha. In addition, a larger number of rainy days of various categories occurred in the monsoon season in comparison to other seasons. A higher contribution of trace rainfall events was found in the winter season. The highest contributions of very light, light rainfall, moderate, rather high, and high events were found in the monsoon season in central India. The percentage of various categories of rainfall events has decreased over the last two decades (2001–2020) in comparison to the third decade (1991–2000), according to the mean number of rainfall events in the last 30 years. This spatiotemporal analysis provides valuable insights into the rainfall trends in central India, which represent regional disparities and the potential challenges impacted by climate patterns. This study contributes to our understanding of the changing rainfall dynamics and offers crucial information for effective water resource management in the region