Pub Date : 2023-12-31DOI: 10.54302/mausam.v75i1.299
Balaji Thirugnanasambandam, Kalamegam Kaliyaperumal, Sagaya Alfred Raymond
{"title":"HEAT WAVE ANALYSIS FOR THE REGION OF PUDUCHERRY AND KARAIKAL IN THE U.T. OF PUDUCHERRY","authors":"Balaji Thirugnanasambandam, Kalamegam Kaliyaperumal, Sagaya Alfred Raymond","doi":"10.54302/mausam.v75i1.299","DOIUrl":"https://doi.org/10.54302/mausam.v75i1.299","url":null,"abstract":"","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139132274","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.v75i1.5960
R. Jayabalakrishnan, G. Sivasankaran, M. Maheswari, R. Kumaraperumal, C. Poornachandra
Climate change has been worsened by aerosols which got a significant place in the scientific research to understand climate change dynamics. Hence, the optical properties of the aerosols play an important role in the earth’s energy radiation budget. The Aerosol Optical Depth was measured at high altitude region in Ooty from December 2020 to May 2021. The spectral, monthly and diurnal variation of AOD were assessed and showed their seasonal variability. The mean AOD value at 500 nm was higher during the Summer season (0.625±0.323) than in the Winter season (0.213±0.006). The Black Carbon (BC) was measured using an Aethalo meter from December 2020 to September 2021. The average season wise concentrations of BC were 0.680±0.206µg m-3, 1.128±0.393 µg m-3 and 0.189±0.06 µg m-3 for the Winter, Summer and Monsoon seasons, respectively. The sources of BC mass concentration were apportioned based on fossil fuel (BCff) and biomass burning (BCbb). The fossil fuel based contribution was higher than the biomass based contribution to the total BC concentration. The comparative study of BC concentration with the AOD, it was projected that the AOD had increased in line with surging BC concentration up to April, 2021. The ground-based daily AOD measurements were compared with the MODIS retrieved AOD. The MODIS retrieved AOD was positively correlated with the ground measured AOD during the Winter and Summer seasons. The HYSPLIT trajectory presented the pathways of the source from the long range regions. The Winter season trajectory was attributed to the North-easterly and easterly winds and the Summer season was attributed to the North-westerly and westerly winds that exhibited the long-range transport of aerosols from the neighbouring cities. The meteorological parameters significantly affected the loading of aerosols during all the seasons, denoting that they were supposed to the local prevailing meteorological conditions.
气溶胶加剧了气候变化，在了解气候变化动态的科学研究中占有重要地位。因此，气溶胶的光学特性在地球能量辐射预算中发挥着重要作用。2020 年 12 月至 2021 年 5 月期间，在奥蒂的高海拔地区测量了气溶胶光学深度。对 AOD 的光谱、月变化和日变化进行了评估，并显示出其季节性变化。夏季 500 nm 处的平均 AOD 值（0.625±0.323）高于冬季（0.213±0.006）。2020 年 12 月至 2021 年 9 月期间，使用 Aethalo 测量仪对黑碳（BC）进行了测量。冬季、夏季和季风季节的 BC 平均浓度分别为 0.680±0.206µg m-3、1.128±0.393 µg m-3 和 0.189±0.06 µg m-3。化石燃料（BCff）和生物质燃烧（BCbb）是 BC 质量浓度的来源。在 BC 总浓度中，化石燃料的贡献率高于生物质的贡献率。通过对 BC 浓度与 AOD 的比较研究，预计到 2021 年 4 月，AOD 与激增的 BC 浓度保持一致。对地基每日日照时数的测量值与 MODIS 的日照时数进行了比较。在冬季和夏季，中分辨率成像分 辨系统获取的 AOD 与地面测量的 AOD 呈正相关。HYSPLIT 轨迹显示了来自远距离区域的源路径。冬季的轨迹归因于东北风和东风，而夏季则归因于西北风和西风，这显示了气溶胶从邻近城市的长程飘移。气象参数对所有季节的气溶胶负荷都有重大影响，这表明气溶胶负荷与当地盛行的气象条件有关。
{"title":"Seasonal characterization of aerosols over high altitude location of southern India, Ooty, Tamilnadu","authors":"R. Jayabalakrishnan, G. Sivasankaran, M. Maheswari, R. Kumaraperumal, C. Poornachandra","doi":"10.54302/mausam.v75i1.5960","DOIUrl":"https://doi.org/10.54302/mausam.v75i1.5960","url":null,"abstract":"Climate change has been worsened by aerosols which got a significant place in the scientific research to understand climate change dynamics. Hence, the optical properties of the aerosols play an important role in the earth’s energy radiation budget. The Aerosol Optical Depth was measured at high altitude region in Ooty from December 2020 to May 2021. The spectral, monthly and diurnal variation of AOD were assessed and showed their seasonal variability. The mean AOD value at 500 nm was higher during the Summer season (0.625±0.323) than in the Winter season (0.213±0.006). The Black Carbon (BC) was measured using an Aethalo meter from December 2020 to September 2021. The average season wise concentrations of BC were 0.680±0.206µg m-3, 1.128±0.393 µg m-3 and 0.189±0.06 µg m-3 for the Winter, Summer and Monsoon seasons, respectively. The sources of BC mass concentration were apportioned based on fossil fuel (BCff) and biomass burning (BCbb). The fossil fuel based contribution was higher than the biomass based contribution to the total BC concentration. The comparative study of BC concentration with the AOD, it was projected that the AOD had increased in line with surging BC concentration up to April, 2021. The ground-based daily AOD measurements were compared with the MODIS retrieved AOD. The MODIS retrieved AOD was positively correlated with the ground measured AOD during the Winter and Summer seasons. The HYSPLIT trajectory presented the pathways of the source from the long range regions. The Winter season trajectory was attributed to the North-easterly and easterly winds and the Summer season was attributed to the North-westerly and westerly winds that exhibited the long-range transport of aerosols from the neighbouring cities. The meteorological parameters significantly affected the loading of aerosols during all the seasons, denoting that they were supposed to the local prevailing meteorological conditions.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139134747","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.v75i1.834
Giar No, Muna War, Ervan Ferdiansyah, Fendy Arifianto, A. Pratiwi, Silvia Yulianti
The metropolis Jakarta is a place where floods often occur which are detrimental to both property and life. Weather forecast information released by Meteorology, Climatology, and Geophysical Agency (BMKG) has very important in anticipating this disaster. Hence, it is important to pay attention to the weather forecast accuracy. The purpose of this study was to examine the effect of variations accuracy in rain events of the Jakarta area includes Central Jakarta, East Jakarta, West Jakarta, North Jakarta, South Jakarta, Bekasi, Tangerang, Depok, and Bogor as known Jabotabek. School of Meteorology Climatology and Geophysics or STMKG Weather Care developed voluntary observations of weather conditions especially rain events. Respondents filled out the form whether there was rain in the location where they lived and would be evaluated using the dichotomous method. This study shows the accuracy of rain prediction in the Jabotabek area of 66.8%, with prediction failures generally is an overestimation. The highest number of correct predictions occurred when the location was not raining. Moreover, the best accuracy is in Bekasi City and South Jakarta and West Jakarta is the worst. The evaluation confirms that it is not easy to predict rain events in a detailed location and the prediction terms used.
{"title":"The influence variability of weather condition on predicting rain events in surrounding Jakarta","authors":"Giar No, Muna War, Ervan Ferdiansyah, Fendy Arifianto, A. Pratiwi, Silvia Yulianti","doi":"10.54302/mausam.v75i1.834","DOIUrl":"https://doi.org/10.54302/mausam.v75i1.834","url":null,"abstract":"The metropolis Jakarta is a place where floods often occur which are detrimental to both property and life. Weather forecast information released by Meteorology, Climatology, and Geophysical Agency (BMKG) has very important in anticipating this disaster. Hence, it is important to pay attention to the weather forecast accuracy. The purpose of this study was to examine the effect of variations accuracy in rain events of the Jakarta area includes Central Jakarta, East Jakarta, West Jakarta, North Jakarta, South Jakarta, Bekasi, Tangerang, Depok, and Bogor as known Jabotabek. School of Meteorology Climatology and Geophysics or STMKG Weather Care developed voluntary observations of weather conditions especially rain events. Respondents filled out the form whether there was rain in the location where they lived and would be evaluated using the dichotomous method. This study shows the accuracy of rain prediction in the Jabotabek area of 66.8%, with prediction failures generally is an overestimation. The highest number of correct predictions occurred when the location was not raining. Moreover, the best accuracy is in Bekasi City and South Jakarta and West Jakarta is the worst. The evaluation confirms that it is not easy to predict rain events in a detailed location and the prediction terms used.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139135536","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.v75i1.6037
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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：