Pub Date : 2023-12-31DOI: 10.54302/mausam.v75i1.6037
Priyanka Singh, R. Mall, K. K. Singh, A. K. Das
Weather forecasting with high spatial resolution become increasingly relevant for decision support in agriculture and water management. Present work is carried out for verification of IMD-WRF Model rainfall forecast with 3 days lead time over Nalanda, Supaul and East Champaran districts in Bihar, India. The model’s skill up to a lead time of 3 days is evaluated with panchayat level daily in situ observations for Monsoon 2020 and 2021. Results show good agreement of forecast and observation throughout the domain and particularly over Supaul district, where about 70% of rain and no-rain days are correctly predicted for all panchayat. Also, FAR is <.3 in 90 percent of the panchayat and HK is also found >.25 in almost all places. This evaluation supports the use of WRF model forecast in agriculture up to 3 days in advance. However the quantitative verification suggests that model output is more reliable for moderate rainfall
{"title":"Verification of WRF model forecasts and their use for agriculture decision support in Bihar, India","authors":"Priyanka Singh, R. Mall, K. K. Singh, A. K. Das","doi":"10.54302/mausam.v75i1.6037","DOIUrl":"https://doi.org/10.54302/mausam.v75i1.6037","url":null,"abstract":"Weather forecasting with high spatial resolution become increasingly relevant for decision support in agriculture and water management. Present work is carried out for verification of IMD-WRF Model rainfall forecast with 3 days lead time over Nalanda, Supaul and East Champaran districts in Bihar, India. The model’s skill up to a lead time of 3 days is evaluated with panchayat level daily in situ observations for Monsoon 2020 and 2021. Results show good agreement of forecast and observation throughout the domain and particularly over Supaul district, where about 70% of rain and no-rain days are correctly predicted for all panchayat. Also, FAR is <.3 in 90 percent of the panchayat and HK is also found >.25 in almost all places. This evaluation supports the use of WRF model forecast in agriculture up to 3 days in advance. However the quantitative verification suggests that model output is more reliable for moderate rainfall","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":" 10","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139135612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-31DOI: 10.54302/mausam.v75i1.3892
Raktim Jyoti Saikia, P. Neog, R. L. Deka, K. Medhi
A field experiment was conducted at Assam Agricultural University, Jorhat, Assam during rabi 2018-19 for assessing the PAR interception and radiation use efficiency in potato variety Kufri Jyoti under different microclimates, which was planted in split plot design with 4 dates of plantings and three mulching treatments with water hyacinth, black polythene and without mulching. The incident, reflected and transmitted PAR were measured periodically over the crop with line quantum sensor and daily incident radiation were calculated from incident PAR and bright sunshine hours. The interception of PAR (iPAR) varied considerably among different treatments, while highest iPAR was recorded under first date of planting and mulching treatment with water hyacinth. The leaf area index (LAI) and biomass production was highest in crop planted in first date planting and grown under water hyacinth mulch. The RUE for tuber yield was highest under water hyacinth (2.35 g MJ-1) followed by black polythene (2.03 g MJ-1) and non-mulched (1.67 g MJ-1) condition, while among planting dates it was highest in case of first date of planting. The LAI, biomass production and yield of potato were found to be significantly correlated with iPAR and RUE. The predictive models were developed by using stepwise regression method to predict tuber yield from iPAR and REU, which have R2 value of 0.96 and 0.99, respectively.
阿萨姆邦乔哈特的阿萨姆农业大学于2018-19年秋季进行了一项田间试验，以评估马铃薯品种Kufri Jyoti在不同小气候条件下的PAR截获和辐射利用效率，该试验采用分小区设计，有4个种植日期，并有水葫芦、黑色聚乙烯和无覆盖物三种覆盖物处理。用线量子传感器定期测量作物的入射、反射和透射 PAR，并根据入射 PAR 和日照时数计算日入射辐射。不同处理的截获 PAR（iPAR）差异很大，而在第一种植日和布袋莲覆盖处理下的 iPAR 最高。叶面积指数（LAI）和生物量产量在首播日种植和布袋莲覆盖下的作物中最高。在布袋莲（2.35 克 MJ-1）条件下，块茎产量的 RUE 值最高，其次是黑色聚乙烯（2.03 克 MJ-1）和无覆盖物（1.67 克 MJ-1）条件下，而在不同的种植日期中，第一种植日期的 RUE 值最高。发现马铃薯的 LAI、生物量产量和产量与 iPAR 和 RUE 显著相关。利用逐步回归法建立了预测模型，通过 iPAR 和 REU 预测块茎产量，其 R2 值分别为 0.96 和 0.99。
{"title":"Importance of PAR interception and radiation use efficiency on growth and yield of Potatoes under different microclimates in the upper Brahmaputra valley zone of Assam","authors":"Raktim Jyoti Saikia, P. Neog, R. L. Deka, K. Medhi","doi":"10.54302/mausam.v75i1.3892","DOIUrl":"https://doi.org/10.54302/mausam.v75i1.3892","url":null,"abstract":"A field experiment was conducted at Assam Agricultural University, Jorhat, Assam during rabi 2018-19 for assessing the PAR interception and radiation use efficiency in potato variety Kufri Jyoti under different microclimates, which was planted in split plot design with 4 dates of plantings and three mulching treatments with water hyacinth, black polythene and without mulching. The incident, reflected and transmitted PAR were measured periodically over the crop with line quantum sensor and daily incident radiation were calculated from incident PAR and bright sunshine hours. The interception of PAR (iPAR) varied considerably among different treatments, while highest iPAR was recorded under first date of planting and mulching treatment with water hyacinth. The leaf area index (LAI) and biomass production was highest in crop planted in first date planting and grown under water hyacinth mulch. The RUE for tuber yield was highest under water hyacinth (2.35 g MJ-1) followed by black polythene (2.03 g MJ-1) and non-mulched (1.67 g MJ-1) condition, while among planting dates it was highest in case of first date of planting. The LAI, biomass production and yield of potato were found to be significantly correlated with iPAR and RUE. The predictive models were developed by using stepwise regression method to predict tuber yield from iPAR and REU, which have R2 value of 0.96 and 0.99, respectively.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":"78 24","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139130530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-31DOI: 10.54302/mausam.v75i1.6016
T. S. Bajirao, D. Madane
{"title":"STUDY ON STATISTICAL DISTRIBUTION OF MONTHLY RAINFALL IN PUNJAB, INDIA","authors":"T. S. Bajirao, D. Madane","doi":"10.54302/mausam.v75i1.6016","DOIUrl":"https://doi.org/10.54302/mausam.v75i1.6016","url":null,"abstract":"","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":"43 15","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139131440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-31DOI: 10.54302/mausam.v75i1.5015
Shubhika Goel, Jaya Dhami, S. R K
The study is conducted for the Tarai region of Uttarakhand regarding the trend analysis of the weather parameters, namely maximum temperature, minimum temperature, rainfall, sunshine hours and evaporation on an annual basis over the periods from 1981-2020. The moving average for 5-year, 10-year intervals and the pentadal, decadal variations has been studied for the above stated parameters. The results revealed that there is an increasing trend in the maximum and minimum temperature of about 0.0004°C/year and 0.0180°C/year respectively. The decreasing trend in the rainfall, sunshine hours and evaporation is observed of about 1.461 mm/year, 0.042 hr/year and 0.028 mm/year respectively.
{"title":"Climate and its variability over Tarai region of Uttarakhand","authors":"Shubhika Goel, Jaya Dhami, S. R K","doi":"10.54302/mausam.v75i1.5015","DOIUrl":"https://doi.org/10.54302/mausam.v75i1.5015","url":null,"abstract":"The study is conducted for the Tarai region of Uttarakhand regarding the trend analysis of the weather parameters, namely maximum temperature, minimum temperature, rainfall, sunshine hours and evaporation on an annual basis over the periods from 1981-2020. The moving average for 5-year, 10-year intervals and the pentadal, decadal variations has been studied for the above stated parameters. The results revealed that there is an increasing trend in the maximum and minimum temperature of about 0.0004°C/year and 0.0180°C/year respectively. The decreasing trend in the rainfall, sunshine hours and evaporation is observed of about 1.461 mm/year, 0.042 hr/year and 0.028 mm/year respectively.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":"1 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139131564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-31DOI: 10.54302/mausam.v75i1.5886
Nilesh Wagh, P. Guhathakurta
Annual rainfall and temperature data series of all climate stations in Maharashtra & Goa are statistically tested for data homogeneity. To inspect homogeneity of a station, a two-step approach is followed. First, four homogeneity tests Standard normal homogeneity test, Pettit’s test, Buishand’s range test and Von Neumann ration test at 5% level of significance are used to determine test hypothesis for homogeneity on testing parameters of annual rainfall and temperature. Second, results from all these four tests aggregated together into three different classes as ‘useful’, ‘doubtful’ and ‘suspect’. Here 30 rainfall, 29 maximum and minimum temperature climate stations were tested. The results showed 80% stations as ‘useful’, 7% as ‘suspect’ and 13% as ‘doubtful’ for rainfall, for maximum temperature series these results are 17% as ‘useful’, 7% as ‘suspect’ and 76% as ‘doubtful’, while for minimum temperature series these results are 21% as ‘useful’, 10% as ‘suspect’ and 69% as ‘doubtful’. Further, in this study an attempt is also made to correct the monthly rainfall and temperature data series for homogeneity. Stations categorised as ‘useful’ are used as reference series to remove inhomogeneities from ‘suspect’ and ‘doubtful’ stations. To correct rainfall series ratio’s method is used while for temperature series addition method is used. Correction results showed significant improvement in ‘suspect’ category stations. After correction of inhomogeneous series, the results shows all 100% of rainfall stations and more than 65% of temperature stations are now in ‘useful’ category. The corrected stations may be included in further climate research studies.
{"title":"Homogenizing Monthly Rainfall and Temperature Data Series in Maharashtra & Goa","authors":"Nilesh Wagh, P. Guhathakurta","doi":"10.54302/mausam.v75i1.5886","DOIUrl":"https://doi.org/10.54302/mausam.v75i1.5886","url":null,"abstract":"Annual rainfall and temperature data series of all climate stations in Maharashtra & Goa are statistically tested for data homogeneity. To inspect homogeneity of a station, a two-step approach is followed. First, four homogeneity tests Standard normal homogeneity test, Pettit’s test, Buishand’s range test and Von Neumann ration test at 5% level of significance are used to determine test hypothesis for homogeneity on testing parameters of annual rainfall and temperature. Second, results from all these four tests aggregated together into three different classes as ‘useful’, ‘doubtful’ and ‘suspect’. Here 30 rainfall, 29 maximum and minimum temperature climate stations were tested. The results showed 80% stations as ‘useful’, 7% as ‘suspect’ and 13% as ‘doubtful’ for rainfall, for maximum temperature series these results are 17% as ‘useful’, 7% as ‘suspect’ and 76% as ‘doubtful’, while for minimum temperature series these results are 21% as ‘useful’, 10% as ‘suspect’ and 69% as ‘doubtful’. Further, in this study an attempt is also made to correct the monthly rainfall and temperature data series for homogeneity. Stations categorised as ‘useful’ are used as reference series to remove inhomogeneities from ‘suspect’ and ‘doubtful’ stations. To correct rainfall series ratio’s method is used while for temperature series addition method is used. Correction results showed significant improvement in ‘suspect’ category stations. After correction of inhomogeneous series, the results shows all 100% of rainfall stations and more than 65% of temperature stations are now in ‘useful’ category. The corrected stations may be included in further climate research studies.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":"120 38","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139134064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-01DOI: 10.54302/mausam.v74i4.5603
GOKUL KRISHNAN B., VISHAL MEHTA, V. N. RAI
The variations in climatic conditions depend on seasonal changes throughout the year. Modelling and prediction of climatic conditions can help to determine the impacts of seasonal changes in climate over a specific period of time. Climate change can directly and indirectly affect agricultural, industrial, geographical and technological sectors in our society. Agriculture and the allied sector are seriously affected by changes in climate since it leads to complete destruction of cultivated crops. In this study, in order to model and forecast relative humidity and wind speed for northern, central and southern zones of Kerala, stochastic approach using SARIMA (Seasonal Autoregressive Integrated Moving Average) model was employed. The monthly weather data for the northern zone and the central zone of Kerala was taken from the location of RARS Pilicode and RARS Pattambi for a period of 39 years (1982-2020) whereas for southern zone, data was collected from the location of RARS, Vellayani for a period of 36 years (1985-2020) with the help of data access viewer. The model validation was done using MSE (mean square error), RMSE (root mean square error), MAE (mean absolute error) and RMAPE (relative mean absolute percentage error). The RMAPE values of relative humidity and wind speed in different zones of Kerala was less than 10 per cent which indicated that fitted model is showing accurate performance. The best selected SARIMA model is used in attaining anticipated values of relative humidity and wind speed for the next 5 years.
{"title":"Stochastic modelling and forecasting of relative humidity and wind speed for different zones of Kerala","authors":"GOKUL KRISHNAN B., VISHAL MEHTA, V. N. RAI","doi":"10.54302/mausam.v74i4.5603","DOIUrl":"https://doi.org/10.54302/mausam.v74i4.5603","url":null,"abstract":"The variations in climatic conditions depend on seasonal changes throughout the year. Modelling and prediction of climatic conditions can help to determine the impacts of seasonal changes in climate over a specific period of time. Climate change can directly and indirectly affect agricultural, industrial, geographical and technological sectors in our society. Agriculture and the allied sector are seriously affected by changes in climate since it leads to complete destruction of cultivated crops. In this study, in order to model and forecast relative humidity and wind speed for northern, central and southern zones of Kerala, stochastic approach using SARIMA (Seasonal Autoregressive Integrated Moving Average) model was employed. The monthly weather data for the northern zone and the central zone of Kerala was taken from the location of RARS Pilicode and RARS Pattambi for a period of 39 years (1982-2020) whereas for southern zone, data was collected from the location of RARS, Vellayani for a period of 36 years (1985-2020) with the help of data access viewer. The model validation was done using MSE (mean square error), RMSE (root mean square error), MAE (mean absolute error) and RMAPE (relative mean absolute percentage error). The RMAPE values of relative humidity and wind speed in different zones of Kerala was less than 10 per cent which indicated that fitted model is showing accurate performance. The best selected SARIMA model is used in attaining anticipated values of relative humidity and wind speed for the next 5 years.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134934172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-01DOI: 10.54302/mausam.v74i4.4359
KUMARASWAMY KANDUKURI, BHATRACHARYULU N. CH.
There is a lot of time series data in many realistic sectors with different forecast techniques over the years. However there is no unanimous conclusion on forecast techniques such as individual forecasts Autoregressive, Moving averages, Autoregressive Moving average, Autoregressive Integrated Moving average, Artificial Neural Network, Long Short Term Memory network and Auto-Regressive Conditional Heteroscedasticity / Generalized Autoregressive Conditional Heteroskedasticity and combination of forecast (simple Average of forecasts, Minimum variance method, and Regression method of the combine). The most empirical hydrological time series models do not accurately forecast the weather. This paper focuses on a comparative study of different existing individual and combination forecasts with the proposed Hybrid Stochastic Model (HSM) forecast procedure. For this we consider a hydrological time series data of the Indian subcontinent to test the proposed forecast model. As a whole in comparison to all other traditional model's contributions accuracy, the proposed model performed well, and also we examined the model's dimension reduction approach to choose an optimum number of forecast techniques to be included in the model to yield the best forecasts.
{"title":"New method of precipitation forecast model and validation","authors":"KUMARASWAMY KANDUKURI, BHATRACHARYULU N. CH.","doi":"10.54302/mausam.v74i4.4359","DOIUrl":"https://doi.org/10.54302/mausam.v74i4.4359","url":null,"abstract":"There is a lot of time series data in many realistic sectors with different forecast techniques over the years. However there is no unanimous conclusion on forecast techniques such as individual forecasts Autoregressive, Moving averages, Autoregressive Moving average, Autoregressive Integrated Moving average, Artificial Neural Network, Long Short Term Memory network and Auto-Regressive Conditional Heteroscedasticity / Generalized Autoregressive Conditional Heteroskedasticity and combination of forecast (simple Average of forecasts, Minimum variance method, and Regression method of the combine). The most empirical hydrological time series models do not accurately forecast the weather. This paper focuses on a comparative study of different existing individual and combination forecasts with the proposed Hybrid Stochastic Model (HSM) forecast procedure. For this we consider a hydrological time series data of the Indian subcontinent to test the proposed forecast model. As a whole in comparison to all other traditional model's contributions accuracy, the proposed model performed well, and also we examined the model's dimension reduction approach to choose an optimum number of forecast techniques to be included in the model to yield the best forecasts.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134934396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-01DOI: 10.54302/mausam.v74i4.6176
C. S. TOMAR, RAJIV BHATLA, V. K. SONI, R. K. GIRI
Pre-monsoon season (March to May) is very challenging as convective activities prevails almost throughout the country. Most of the Rabi crops harvesting affected and sometimes suffer great losses due to sudden rain or high winds. INSAT-3D/3DR satellite images and derived products provides continuous support to the forecasters and end users in monitoring such events and thereafter significant value addition improves the prediction. This information was found to be very useful where actual ground based or upper air observations are limited or especially over data sparse or difficult terrain regions. In this work, we have examined three weather events at different Geographical locations (i) Rainfall over Bihar-24-26 June, 2020 (ii) Delhi & NCR region on 17 June, 2022 (iii) NE region activity in 16-18 June, 2022. The Real Time Analysis of Products and Information Dissemination (RAPID) web based tool was utilized in monitoring and diagnosing the convective weather events based on the brightness temperature & derived products like Outgoing longwave radiation, upper tropospheric humidity, insolation etc & RGB imagery composite in terms of day & night time microphysics daily operational products. The time series of the wind derived products for Delhi NCR rainfall and NE rainfall products also generated through RAPID. The synoptic model analysis provides valuable inputs for these mesoscale convective weather events. The southerly wind flow (at 925 hPa) and velocity convergence (at 500 hPa) analysis of European Centre for Medium Range Weather Forecasting (ECMWF) supports the severity of NE event occurred on 16-18 June, 2022. Therefore, utilization of near real time INSAT-3D/3DR products along with appropriate synoptic model analysis can help the forecasters to understand better about such mesoscale convective events & accurate forecast with sufficient lead time can save the life and property.
{"title":"Convective weather event monitoring with multispectral image analysis of INSAT-3D/3DR over Indian domain","authors":"C. S. TOMAR, RAJIV BHATLA, V. K. SONI, R. K. GIRI","doi":"10.54302/mausam.v74i4.6176","DOIUrl":"https://doi.org/10.54302/mausam.v74i4.6176","url":null,"abstract":"Pre-monsoon season (March to May) is very challenging as convective activities prevails almost throughout the country. Most of the Rabi crops harvesting affected and sometimes suffer great losses due to sudden rain or high winds. INSAT-3D/3DR satellite images and derived products provides continuous support to the forecasters and end users in monitoring such events and thereafter significant value addition improves the prediction. This information was found to be very useful where actual ground based or upper air observations are limited or especially over data sparse or difficult terrain regions. In this work, we have examined three weather events at different Geographical locations (i) Rainfall over Bihar-24-26 June, 2020 (ii) Delhi & NCR region on 17 June, 2022 (iii) NE region activity in 16-18 June, 2022. The Real Time Analysis of Products and Information Dissemination (RAPID) web based tool was utilized in monitoring and diagnosing the convective weather events based on the brightness temperature & derived products like Outgoing longwave radiation, upper tropospheric humidity, insolation etc & RGB imagery composite in terms of day & night time microphysics daily operational products. The time series of the wind derived products for Delhi NCR rainfall and NE rainfall products also generated through RAPID. The synoptic model analysis provides valuable inputs for these mesoscale convective weather events. The southerly wind flow (at 925 hPa) and velocity convergence (at 500 hPa) analysis of European Centre for Medium Range Weather Forecasting (ECMWF) supports the severity of NE event occurred on 16-18 June, 2022. Therefore, utilization of near real time INSAT-3D/3DR products along with appropriate synoptic model analysis can help the forecasters to understand better about such mesoscale convective events & accurate forecast with sufficient lead time can save the life and property.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134934951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-01DOI: 10.54302/mausam.v74i4.6040
B. LALMUANZUALA, NK. SATHYAMOORTHY, S. KOKILAVANI, R. JAGADEESWARAN, BALAJI KANNAN
Drought is a natural phenomenon caused due to inadequate rainfall over a region as compared to the expected amount, which when sustained over an extended period of time, eventually leads to shortage of water to sustain various human activities. One-month SPI showed that the southern zone is highly prone to moderate drought conditions. The seasonal analysis of SPI showed that the region faced more drought instances during the South West Monsoon compared with North East Monsoon season. Thoothukudi, Dindigul, Pudukkottai and Virudhunagar showed the high occurrences of drought at seasonal and annual scale. The weekly MAI calculated indicated a risk in the rainfed cropping season. Tirunelveli and Tenkasi showed highly vulnerable to moderate drought. NDVI during the NEM 2016, 2017 and 2018 showed that more than 80 per cent of the total area in the southern districts was under drought stress. NDVI analysis showed that Thoothukudi, Ramanathapuram, Pudukkottai, Sivagangai and Virudhunagar districts are highly vulnerable to drought. NDWI analysis during the NEM 2016, 2017 and 2018 showed high drought stresses with more than 90 per cent of the area showing drought stress during these three years. NDVI and NDWI analysis showed that the Southern Zone of Tamil Nadu was most vulnerable to Moderate and Severe droughts. The comparison of NDVI and NDWI and 3-, 6-, 9- and 12-month SPI showed that the three indices are fairly accurate with each other and hence are useful in the analysis of drought. However, just a single drought index cannot clearly define accurately the spatial and temporal extent of drought. Thus, a combination of meteorological and remote sensing indices gave a detailed idea about the spatio-temporal extent of drought.
{"title":"Drought analysis in southern region of Tamil Nadu using meteorological and remote sensing indices","authors":"B. LALMUANZUALA, NK. SATHYAMOORTHY, S. KOKILAVANI, R. JAGADEESWARAN, BALAJI KANNAN","doi":"10.54302/mausam.v74i4.6040","DOIUrl":"https://doi.org/10.54302/mausam.v74i4.6040","url":null,"abstract":"Drought is a natural phenomenon caused due to inadequate rainfall over a region as compared to the expected amount, which when sustained over an extended period of time, eventually leads to shortage of water to sustain various human activities. One-month SPI showed that the southern zone is highly prone to moderate drought conditions. The seasonal analysis of SPI showed that the region faced more drought instances during the South West Monsoon compared with North East Monsoon season. Thoothukudi, Dindigul, Pudukkottai and Virudhunagar showed the high occurrences of drought at seasonal and annual scale. The weekly MAI calculated indicated a risk in the rainfed cropping season. Tirunelveli and Tenkasi showed highly vulnerable to moderate drought. NDVI during the NEM 2016, 2017 and 2018 showed that more than 80 per cent of the total area in the southern districts was under drought stress. NDVI analysis showed that Thoothukudi, Ramanathapuram, Pudukkottai, Sivagangai and Virudhunagar districts are highly vulnerable to drought. NDWI analysis during the NEM 2016, 2017 and 2018 showed high drought stresses with more than 90 per cent of the area showing drought stress during these three years. NDVI and NDWI analysis showed that the Southern Zone of Tamil Nadu was most vulnerable to Moderate and Severe droughts. The comparison of NDVI and NDWI and 3-, 6-, 9- and 12-month SPI showed that the three indices are fairly accurate with each other and hence are useful in the analysis of drought. However, just a single drought index cannot clearly define accurately the spatial and temporal extent of drought. Thus, a combination of meteorological and remote sensing indices gave a detailed idea about the spatio-temporal extent of drought.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134934175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-01DOI: 10.54302/mausam.v74i4.5267
DR. A. SRAVANI, DR. K. NAGA RATNA, R. SUDHEER KUMAR, N. REKHA
In the present study, we have constructed a frequency of occurrence of rainfall over each sub-catchment of the Godavari river catchment using the synoptic analogue method for the years 2012-2019. Using the Frequency of the Areal average precipitations the model is verified for the AAP of the synoptic situations for the years 2020. The model has observed the 62% percentage of correct for the monsoon season 2020 and it gives the 90% correct to 50-100 and >100 AAP events. Using the frequency of the AAP events w have constructed the percentage of probability of the AAP of the synoptic events which occur over the Sub-basin. This model is generally accurate for the generation of QPF before the 24hr provided the synoptic conditions over the Region which will be very helpful to facilitate the 48hrs forecast to the flood forecasters and end-users like the central Water commission and Disaster management authorities.
{"title":"Quantitative precipitation forecast for the Godavari basin using the Synoptic analogue method","authors":"DR. A. SRAVANI, DR. K. NAGA RATNA, R. SUDHEER KUMAR, N. REKHA","doi":"10.54302/mausam.v74i4.5267","DOIUrl":"https://doi.org/10.54302/mausam.v74i4.5267","url":null,"abstract":"In the present study, we have constructed a frequency of occurrence of rainfall over each sub-catchment of the Godavari river catchment using the synoptic analogue method for the years 2012-2019. Using the Frequency of the Areal average precipitations the model is verified for the AAP of the synoptic situations for the years 2020. The model has observed the 62% percentage of correct for the monsoon season 2020 and it gives the 90% correct to 50-100 and >100 AAP events. Using the frequency of the AAP events w have constructed the percentage of probability of the AAP of the synoptic events which occur over the Sub-basin. This model is generally accurate for the generation of QPF before the 24hr provided the synoptic conditions over the Region which will be very helpful to facilitate the 48hrs forecast to the flood forecasters and end-users like the central Water commission and Disaster management authorities.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134934388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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