Pub Date : 2023-07-03DOI: 10.54302/mausam.v74i3.5993
N. Nissanka, E. Lokupitiya, Shiromani Jayawardena
Climate change-related changes in temperature and precipitation trends must be investigated at local, regional and global levels. Temperature and precipitation trends in two selected regions having tropical wet and tropical montane climates (i.e., Colombo and Nuwara Eliya respectively) in Sri Lanka were studied for a 30 year period from 1989 to 2019, to evaluate the temporal dynamics of climate change. Precipitation trends were analyzed on annual, monthly, and seasonal scales, while the trends in mean, minimum, and maximum temperatures were examined on annual and monthly scales. Decadal time series plots were used to study decadal variations in average temperature and precipitation. The trends in extreme temperature and precipitation events were also evaluated. In addition, the trends in diurnal temperature range (DTR), cool and warm nights, and heat index (HI) were studied. The significance of trends was evaluated using the Mann-Kendall test, while the magnitude of the slope was assessed by Sen’s slope estimator. Clear statistically significant increasing trends were observed for the mean annual temperatures under the tropical wet and tropical montane climates, and no clear trends were observed in annual precipitation in both districts. There were decreasing trends in south-west monsoon rainfall, with a significant decrease in Nuwara Eliya under the tropical montane climate. Increasing trends were observed for the average monthly precipitation in November (i.e., during the inter-monsoonal rains) and average monthly temperature in April (i.e., the hottest month) over the last decade (i.e., 2010-2019) in Colombo. The DTR has significantly decreased over the last three decades in Colombo. A significant upward trend was observed for HI values during the last decade in Colombo. Colombo also showed a statistically significant decreasing trend in the number of cool nights and a statistically significant decreasing trend in the number of warm nights over the last decade.
{"title":"Trends in climate change observed under tropical wet and tropical montane climates; A case study from Sri Lanka","authors":"N. Nissanka, E. Lokupitiya, Shiromani Jayawardena","doi":"10.54302/mausam.v74i3.5993","DOIUrl":"https://doi.org/10.54302/mausam.v74i3.5993","url":null,"abstract":"Climate change-related changes in temperature and precipitation trends must be investigated at local, regional and global levels. Temperature and precipitation trends in two selected regions having tropical wet and tropical montane climates (i.e., Colombo and Nuwara Eliya respectively) in Sri Lanka were studied for a 30 year period from 1989 to 2019, to evaluate the temporal dynamics of climate change. Precipitation trends were analyzed on annual, monthly, and seasonal scales, while the trends in mean, minimum, and maximum temperatures were examined on annual and monthly scales. Decadal time series plots were used to study decadal variations in average temperature and precipitation. The trends in extreme temperature and precipitation events were also evaluated. In addition, the trends in diurnal temperature range (DTR), cool and warm nights, and heat index (HI) were studied. The significance of trends was evaluated using the Mann-Kendall test, while the magnitude of the slope was assessed by Sen’s slope estimator. Clear statistically significant increasing trends were observed for the mean annual temperatures under the tropical wet and tropical montane climates, and no clear trends were observed in annual precipitation in both districts. There were decreasing trends in south-west monsoon rainfall, with a significant decrease in Nuwara Eliya under the tropical montane climate. Increasing trends were observed for the average monthly precipitation in November (i.e., during the inter-monsoonal rains) and average monthly temperature in April (i.e., the hottest month) over the last decade (i.e., 2010-2019) in Colombo. The DTR has significantly decreased over the last three decades in Colombo. A significant upward trend was observed for HI values during the last decade in Colombo. Colombo also showed a statistically significant decreasing trend in the number of cool nights and a statistically significant decreasing trend in the number of warm nights over the last decade.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41262251","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 : 2023-07-03DOI: 10.54302/mausam.v74i3.5898
M. Gundalia
Rain is a meager and crucial hydrological variable in arid and semi-arid region. Junagadh (Gujarat-India) reels under monsoon rainfall uncertainties and thereby the agriculture and other water resources management activities suffer. Therefore, urgent attention is needed to address water resources conservation and crop damage issues due to deficits or excess rainfall. Water resources development of any locality depends on amount of runoff generated and rainfall received. Appropriate probability distributions need to be selected and fitted to the historical time series of rainfall for better frequency analysis and forecasting of the rainfall. The daily rainfall data was collected for a period of 38 years i.e., from 1984 to 2021. This research attempts to fit eightdifferent theoretical probability distributions to the monthly and annual maximum rainfall for one to five consecutive days to select the best one for the better prediction of maximum rainfall. For determination of goodness of fit Chi-Square and Nash-Sutcliffe Efficiency were carried out by comparing the expected values with the observed values. The results indicated that the Gumbel distribution emerged to be the best fit for the prediction of monthly and annual maximum rainfall of Junagadh Region.
{"title":"Best Fitting of Probability Distribution for Monthly and Annual Maximum Rainfall Prediction in Junagadh Region (Gujarat-India)","authors":"M. Gundalia","doi":"10.54302/mausam.v74i3.5898","DOIUrl":"https://doi.org/10.54302/mausam.v74i3.5898","url":null,"abstract":"Rain is a meager and crucial hydrological variable in arid and semi-arid region. Junagadh (Gujarat-India) reels under monsoon rainfall uncertainties and thereby the agriculture and other water resources management activities suffer. Therefore, urgent attention is needed to address water resources conservation and crop damage issues due to deficits or excess rainfall. Water resources development of any locality depends on amount of runoff generated and rainfall received. Appropriate probability distributions need to be selected and fitted to the historical time series of rainfall for better frequency analysis and forecasting of the rainfall. The daily rainfall data was collected for a period of 38 years i.e., from 1984 to 2021. This research attempts to fit eightdifferent theoretical probability distributions to the monthly and annual maximum rainfall for one to five consecutive days to select the best one for the better prediction of maximum rainfall. For determination of goodness of fit Chi-Square and Nash-Sutcliffe Efficiency were carried out by comparing the expected values with the observed values. The results indicated that the Gumbel distribution emerged to be the best fit for the prediction of monthly and annual maximum rainfall of Junagadh Region.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43021892","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 : 2023-07-01DOI: 10.54302/mausam.v74i3.5331
S. Sandhu, P. Kaur
Rainfall is an important part of hydrological cycle and any alteration in its pattern influence water resources. In Punjab, the monsoon season of 77 days extending during three months July, August and September, receives rainfall at an average rate of 6 mm/day. In the present study, monsoon rainfall data for three parts of the state, viz., the north eastern region (1984-2020), Central plain region (1970-2020) and the south western region (1977-2020) of the state have been analyzed using non-parametric tests, i.e., descriptive statistics, trend analysis, Mann Kendall test and Sen’s slope. Though, the duration of the monsoon season has increased over the last two decades at 0.8 day/year, the rate of rainfall has decreased as rainfall has been less than normal during 17 of the past 20 years. The monsoon rainfall analysis for the five decades indicates a significant decrease in rainfall at 0.7 mm/year which has mainly been due to a decline in rainfall in the north eastern region. The Sen’s slope value of -4.77 (Ballowal) and -0.60 (Bathinda) indicate a decreasing trend of rainfall in the region. The decreasing trend in rainfall received during the July-August months with Sen’s slope values ranging between -0.04 to -2.50 and -0.24 to -3.14, indicates that the months which contribute 70 percent to total rainfall are not a good signal for the agriculture sector in the state.
{"title":"A case study on the changing pattern of monsoon rainfall duration and its amount during recent five decades in different agroclimatic zones of Punjab state of India","authors":"S. Sandhu, P. Kaur","doi":"10.54302/mausam.v74i3.5331","DOIUrl":"https://doi.org/10.54302/mausam.v74i3.5331","url":null,"abstract":"Rainfall is an important part of hydrological cycle and any alteration in its pattern influence water resources. In Punjab, the monsoon season of 77 days extending during three months July, August and September, receives rainfall at an average rate of 6 mm/day. In the present study, monsoon rainfall data for three parts of the state, viz., the north eastern region (1984-2020), Central plain region (1970-2020) and the south western region (1977-2020) of the state have been analyzed using non-parametric tests, i.e., descriptive statistics, trend analysis, Mann Kendall test and Sen’s slope. Though, the duration of the monsoon season has increased over the last two decades at 0.8 day/year, the rate of rainfall has decreased as rainfall has been less than normal during 17 of the past 20 years. The monsoon rainfall analysis for the five decades indicates a significant decrease in rainfall at 0.7 mm/year which has mainly been due to a decline in rainfall in the north eastern region. The Sen’s slope value of -4.77 (Ballowal) and -0.60 (Bathinda) indicate a decreasing trend of rainfall in the region. The decreasing trend in rainfall received during the July-August months with Sen’s slope values ranging between -0.04 to -2.50 and -0.24 to -3.14, indicates that the months which contribute 70 percent to total rainfall are not a good signal for the agriculture sector in the state.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44833167","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 : 2023-07-01DOI: 10.54302/mausam.v74i3.931
R. J, R. Lalitha, SVallal Kannan, K. Sivasubramanian
The performance of sixteen Valiantzas’ reference evapotranspiration models was investigated using the meteorological data obtained from Agricultural Engineering College and Research Institute, Kumulur, Lalgudi Taluk of Tiruchirapalli district, which is a semi-arid region located in Tamil Nadu, India. The Valiantzas’ reference evapotranspiration was compared with the globally used FAO56 Penman-Monteith method. The indexes used for comparison are coefficient of determination (R2), Standard Error Estimate (SEE) and long-term average ratio (RT). The Valiantzas’ models requiring complete dataset performed excellently in this station. The models not requiring wind speed data also performed equally well in this station and exhibited a fairly good correlation with FAO56-PM method. The other formulae accounting for local average wind conditions, reduced set formulae with temperature and relative humidity data alone and reduced set formulae with temperature and radiation data alone also performed well in this station. The investigation showed fair accuracy of Valiantzas’ Models and hence researchers can use these models in the absence of availability of the complete dataset.
{"title":"Investigation of Valiantzas’ Simplified forms of FAO56 Penman-Monteith Reference Evapotranspiration Models in a semi-arid region","authors":"R. J, R. Lalitha, SVallal Kannan, K. Sivasubramanian","doi":"10.54302/mausam.v74i3.931","DOIUrl":"https://doi.org/10.54302/mausam.v74i3.931","url":null,"abstract":"The performance of sixteen Valiantzas’ reference evapotranspiration models was investigated using the meteorological data obtained from Agricultural Engineering College and Research Institute, Kumulur, Lalgudi Taluk of Tiruchirapalli district, which is a semi-arid region located in Tamil Nadu, India. The Valiantzas’ reference evapotranspiration was compared with the globally used FAO56 Penman-Monteith method. The indexes used for comparison are coefficient of determination (R2), Standard Error Estimate (SEE) and long-term average ratio (RT). The Valiantzas’ models requiring complete dataset performed excellently in this station. The models not requiring wind speed data also performed equally well in this station and exhibited a fairly good correlation with FAO56-PM method. The other formulae accounting for local average wind conditions, reduced set formulae with temperature and relative humidity data alone and reduced set formulae with temperature and radiation data alone also performed well in this station. The investigation showed fair accuracy of Valiantzas’ Models and hence researchers can use these models in the absence of availability of the complete dataset.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42433452","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 : 2023-07-01DOI: 10.54302/mausam.v74i3.1486
Man Jeet, A. Rag, Ram Niwas, Anil Kumar, ML Khichar, Chander Shekhar, Naresh Kumar
This paper presents the evaluation of the air quality in different districts of Haryana. Geo-spatial techniques were used to estimate the spatial and temporal variation (2019-2020) of gaseous and particulate pollutants. Data of six fixed pollutants were collected from Central Pollution Control Board (CPCB). In this context, data of the air pollutant (PM10, PM2.5, O3, NOx, SO2 and CO) were analyzed seasonally for 2019 and 2020. The spatio-temporal distribution of the air quality index (AQI) clearly depicted changes indifferent meteorological and crop seasons in 2019 and 2020. The result showed that the air quality was very poor in winter and the post-monsoon seasons in 2019 and slightly improved in 2020 due to COVID 19 lockdown and satisfactory air quality was observed in the monsoon and the pre-monsoon seasons for both years. It was also observed that the air quality was poor in the rabi seasons (October to March) as compared to the kharif seasons (April to September) in 2019 and 2020. The study suggested that the air quality can be improved by the best management of straw waste instead of burning, along with reducing major pollutant sources like automobiles.
{"title":"Spatial and temporal variation in the seasonal air quality index of Haryana, India","authors":"Man Jeet, A. Rag, Ram Niwas, Anil Kumar, ML Khichar, Chander Shekhar, Naresh Kumar","doi":"10.54302/mausam.v74i3.1486","DOIUrl":"https://doi.org/10.54302/mausam.v74i3.1486","url":null,"abstract":"This paper presents the evaluation of the air quality in different districts of Haryana. Geo-spatial techniques were used to estimate the spatial and temporal variation (2019-2020) of gaseous and particulate pollutants. Data of six fixed pollutants were collected from Central Pollution Control Board (CPCB). In this context, data of the air pollutant (PM10, PM2.5, O3, NOx, SO2 and CO) were analyzed seasonally for 2019 and 2020. The spatio-temporal distribution of the air quality index (AQI) clearly depicted changes indifferent meteorological and crop seasons in 2019 and 2020. The result showed that the air quality was very poor in winter and the post-monsoon seasons in 2019 and slightly improved in 2020 due to COVID 19 lockdown and satisfactory air quality was observed in the monsoon and the pre-monsoon seasons for both years. It was also observed that the air quality was poor in the rabi seasons (October to March) as compared to the kharif seasons (April to September) in 2019 and 2020. The study suggested that the air quality can be improved by the best management of straw waste instead of burning, along with reducing major pollutant sources like automobiles.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44868023","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 : 2023-07-01DOI: 10.54302/mausam.v74i3.4933
ParthaPratim Sarkar
The proposed study employs a long short-term memory (LSTM) neural network (NN) to forecast monthly rainfall in the Barak river basin in the northeastern region of India for a prediction horizon up to 4 months in advance. Out of nine significant climate variables, sea surface temperature (SST), sea level pressure (SLP), Nino 3.4 index, the Indian summer monsoon rainfall (ISMR) anomalies and dipole mode index (DMI) were identified to be the best-suited predictors and were introduced as the inputs in the NN. The LSTM is a special kind of recurrent neural network (RNN) which specializes in feature extraction and storing memory in its cell state cumulatively. The model results display strong correlations between the potential predictor sets and the rainfall distribution across the basin. The obtained forecast results were scrutinized in terms of various statistical measures and the predictions were found to be at par with the real time observations (correlations greater than 0.90 and hit score greater than 85%). The testing phase of model produced root mean square errors in the range of 12.45% to 15.65% highlighting satisfactory model performance. The proposed method of incorporating different climate indices form a novel approach to forecast rainfall in the region which may lead to timely and effective management of water resources.
{"title":"Rainfall forecasting in the Barak river basin, India using a LSTM network based on various climate indices","authors":"ParthaPratim Sarkar","doi":"10.54302/mausam.v74i3.4933","DOIUrl":"https://doi.org/10.54302/mausam.v74i3.4933","url":null,"abstract":"The proposed study employs a long short-term memory (LSTM) neural network (NN) to forecast monthly rainfall in the Barak river basin in the northeastern region of India for a prediction horizon up to 4 months in advance. Out of nine significant climate variables, sea surface temperature (SST), sea level pressure (SLP), Nino 3.4 index, the Indian summer monsoon rainfall (ISMR) anomalies and dipole mode index (DMI) were identified to be the best-suited predictors and were introduced as the inputs in the NN. The LSTM is a special kind of recurrent neural network (RNN) which specializes in feature extraction and storing memory in its cell state cumulatively. The model results display strong correlations between the potential predictor sets and the rainfall distribution across the basin. The obtained forecast results were scrutinized in terms of various statistical measures and the predictions were found to be at par with the real time observations (correlations greater than 0.90 and hit score greater than 85%). The testing phase of model produced root mean square errors in the range of 12.45% to 15.65% highlighting satisfactory model performance. The proposed method of incorporating different climate indices form a novel approach to forecast rainfall in the region which may lead to timely and effective management of water resources.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42824208","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 : 2023-07-01DOI: 10.54302/mausam.v74i3.1383
R. Jaiswal, Vinotha M, T. K., S. M.
In this paper, the authors have made an effort to investigate the impact of the solar cycle on Earth’s climate in the context of rainfall and temperature over a location, on El Nino/ La Nina, and world famines. The study shows that the peak sunspot number (SSN) often occurs in pairs. Multiple peaks are also seen frequently. The La Ninas follow multiple peaks, or sometimes associated with it. The El Ninos usually follow the solar minima, though not always. This study shows that the SSN trough will occur in 2020, thereby causing El Nino during 2019-2021. The multiple SSN peak is likely to occur during 2023-2028, predicting a La-Nina during this period. Multiple SSN peaks and very high SSN values bring about famines. The study shows that the total solar irradiance (TSI) bears a strong correlation with the SSN. Besides, the cosmic ray flux decreases as the SSN and the TSI increases. The monthly and yearly variations of SSN, TSI, and temperature show increasing trends over the years, indicating increased warming as the years advance. However, none of these parameters bears significant correlations with the temperature, either independently or together, implying that some other factors are also responsible for determining the temperature. The study shows no direct relationship between rainfall and the SSN. However, several years show a similar trend between the two. The investigation indicates a strong influence of the solar cycle on world climate.
{"title":"Linking the solar cycle and Earth’s climate","authors":"R. Jaiswal, Vinotha M, T. K., S. M.","doi":"10.54302/mausam.v74i3.1383","DOIUrl":"https://doi.org/10.54302/mausam.v74i3.1383","url":null,"abstract":" In this paper, the authors have made an effort to investigate the impact of the solar cycle on Earth’s climate in the context of rainfall and temperature over a location, on El Nino/ La Nina, and world famines. The study shows that the peak sunspot number (SSN) often occurs in pairs. Multiple peaks are also seen frequently. The La Ninas follow multiple peaks, or sometimes associated with it. The El Ninos usually follow the solar minima, though not always. This study shows that the SSN trough will occur in 2020, thereby causing El Nino during 2019-2021. The multiple SSN peak is likely to occur during 2023-2028, predicting a La-Nina during this period. Multiple SSN peaks and very high SSN values bring about famines. The study shows that the total solar irradiance (TSI) bears a strong correlation with the SSN. Besides, the cosmic ray flux decreases as the SSN and the TSI increases. The monthly and yearly variations of SSN, TSI, and temperature show increasing trends over the years, indicating increased warming as the years advance. However, none of these parameters bears significant correlations with the temperature, either independently or together, implying that some other factors are also responsible for determining the temperature. The study shows no direct relationship between rainfall and the SSN. However, several years show a similar trend between the two. The investigation indicates a strong influence of the solar cycle on world climate.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44498720","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 : 2023-03-29DOI: 10.54302/mausam.v74i2.5948
S. Yoden, Vinay Kumar, S. Dhaka, M. Hitchman
Monthly-mean data of ERA-Interim reanalysis, precipitation, outgoing longwave radiation (OLR) and sea surface temperature(SST) are investigated for 40 years (1979-2018) to reveal the modulation of the global monsoon systems by the equatorial quasi-biennial oscillation (QBO), focusing only on the neutral El Niño-Southern Oscillation (ENSO) periods (in total 374 months). First, the climatology of the global monsoon systems is viewed with longitude-latitude plots of the precipitation, its proxies and lower tropospheric circulations for the annual mean and two solstice seasons, together with the composite differences between the two seasons. In addition to seasonal variations of Intertropical Convergence Zones (ITCZs), several regional monsoon systems are well identified with an anti-phase of the annual cycle between the two hemispheres. Precipitation-related quantities (OLR and specific humidity), surface conditions [i.e., mean sea level pressure (MSLP) and SST] and circulation fields related to moist convection systems show fundamental features of the global monsoon systems. After introducing eight QBO phases based on the leading two principal components of the zonal-mean zonal wind variations in the equatorial lower-stratosphere, the statistical significance of the composite difference in the precipitation and tropospheric circulation is evaluated for the opposite QBO phases. The composite differences show significant modulations in some regional monsoon systems, dominated by zonally asymmetric components, with the largest magnitudes for specific QBO-phases corresponding to traditional indices of the equatorial zonal-mean zonal wind at 20 and 50 hPa. Along the equator, significant QBO influence is characterized by the modulation of the Walker circulation over the western Pacific. In middle latitudes during boreal summer, for a specific QBO-phase, statistically significant modulation of low-pressure cyclonic perturbation is obtained over the Northern-Hemisphere western Pacific Ocean associated with statistically significant features of heavier precipitation over the eastern side of the cyclonic perturbation and the opposite lighter precipitation over the western side. During boreal winter, similar significant low-pressure cyclonic perturbations were found over the Northern-Hemisphere eastern Pacific and Atlantic Oceans for specific phases.
{"title":"Global monsoon systems and their modulation by the equatorial Quasi-Biennial Oscillation","authors":"S. Yoden, Vinay Kumar, S. Dhaka, M. Hitchman","doi":"10.54302/mausam.v74i2.5948","DOIUrl":"https://doi.org/10.54302/mausam.v74i2.5948","url":null,"abstract":"Monthly-mean data of ERA-Interim reanalysis, precipitation, outgoing longwave radiation (OLR) and sea surface temperature(SST) are investigated for 40 years (1979-2018) to reveal the modulation of the global monsoon systems by the equatorial quasi-biennial oscillation (QBO), focusing only on the neutral El Niño-Southern Oscillation (ENSO) periods (in total 374 months). First, the climatology of the global monsoon systems is viewed with longitude-latitude plots of the precipitation, its proxies and lower tropospheric circulations for the annual mean and two solstice seasons, together with the composite differences between the two seasons. In addition to seasonal variations of Intertropical Convergence Zones (ITCZs), several regional monsoon systems are well identified with an anti-phase of the annual cycle between the two hemispheres. Precipitation-related quantities (OLR and specific humidity), surface conditions [i.e., mean sea level pressure (MSLP) and SST] and circulation fields related to moist convection systems show fundamental features of the global monsoon systems. After introducing eight QBO phases based on the leading two principal components of the zonal-mean zonal wind variations in the equatorial lower-stratosphere, the statistical significance of the composite difference in the precipitation and tropospheric circulation is evaluated for the opposite QBO phases. The composite differences show significant modulations in some regional monsoon systems, dominated by zonally asymmetric components, with the largest magnitudes for specific QBO-phases corresponding to traditional indices of the equatorial zonal-mean zonal wind at 20 and 50 hPa. Along the equator, significant QBO influence is characterized by the modulation of the Walker circulation over the western Pacific. In middle latitudes during boreal summer, for a specific QBO-phase, statistically significant modulation of low-pressure cyclonic perturbation is obtained over the Northern-Hemisphere western Pacific Ocean associated with statistically significant features of heavier precipitation over the eastern side of the cyclonic perturbation and the opposite lighter precipitation over the western side. During boreal winter, similar significant low-pressure cyclonic perturbations were found over the Northern-Hemisphere eastern Pacific and Atlantic Oceans for specific phases.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45070192","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 : 2023-03-29DOI: 10.54302/mausam.v74i2.6180
M. Mohapatra, Anshul Chauhan, Avnish Varshney, Suman Gurjar, M. Bushair, M. Sharma, RK Jenamani, K. Srivastava, P. Guhathakurta, R. Chattopadhyay, Mamta Yadav, Radheshyam Sharma, AK Mitra, Ananda KumarDas, S. Nath, Naresh Kumar, S. Senroy, T. Arulalan, Amit Bharadwaj, D. Pattanaik, BP Yadav, R. Saxena, Ashok KumarDas, Asok Raja, B. Hemlata, Kvh Arun, S. Nitha, Atul KSingh, Shobhit Katiyar, K. Mishra, Surendra PratapSingh, Shashikant Mishra, A. Srivastava, B. Geetha, M. Rahul, K. Nagaratna, H. Biswas, M. Mohanty, R. Thapliyal, Shivinder Singh, S. Lotus, Sandeep KumarSharma, V. Mini, S. Das, Gk Das, A. Anand, Gayatri KVani
There have been major advances in the last few decades in our understanding of heavy rainfall during monsoon season due to substantial progress in both observation and numerical modelling of monsoon. All these resulted in more accurate forecast of heavy rainfall in short to medium range, (upto five days) with 40% improvement in accuracy of heavy rainfall forecast in recent five years (2018-2022) as compared to previous five years. However, improvement of forecast and warning skill is not sufficient to minimize damage to lives and property. It is essential to extend to hazard forecast systems (hazard models) and then to impact and risk assessment with stakeholder interaction for risk based warning (RBW) and response action to protect lives and livelihoods Considering all these, India Meteorological Department (IMD) has introduced impact based forecast (IBF) for heavy rainfall at meteorological sub-division level since July 2013 and at district and city scale in August, 2019 in its short to medium range forecasts and nowcasts indicating the likely impact of the heavy rainfall in different sectors and required response actions. Thereafter the IBF of heavy rainfall has undergone several changes over the years. Currently, the IBF being implemented by IMD includes all the four components, viz., (i) meteorological hazards, (ii) geophysical hazards, (iii) geospatial applications and (iv) socio-economic conditions and it utilises a web-GIS based decision support system (DSS). In this study we have reviewed various approaches and stages of development of IBF of heavy rainfall in India. The success of IBF of heavy rainfall will enhance the management of critical resources like agriculture, water & power and support urban and disaster management sectors among others while reducing loss of life and property.
{"title":"Short to medium range impact based forecasting of heavy rainfall in India","authors":"M. Mohapatra, Anshul Chauhan, Avnish Varshney, Suman Gurjar, M. Bushair, M. Sharma, RK Jenamani, K. Srivastava, P. Guhathakurta, R. Chattopadhyay, Mamta Yadav, Radheshyam Sharma, AK Mitra, Ananda KumarDas, S. Nath, Naresh Kumar, S. Senroy, T. Arulalan, Amit Bharadwaj, D. Pattanaik, BP Yadav, R. Saxena, Ashok KumarDas, Asok Raja, B. Hemlata, Kvh Arun, S. Nitha, Atul KSingh, Shobhit Katiyar, K. Mishra, Surendra PratapSingh, Shashikant Mishra, A. Srivastava, B. Geetha, M. Rahul, K. Nagaratna, H. Biswas, M. Mohanty, R. Thapliyal, Shivinder Singh, S. Lotus, Sandeep KumarSharma, V. Mini, S. Das, Gk Das, A. Anand, Gayatri KVani","doi":"10.54302/mausam.v74i2.6180","DOIUrl":"https://doi.org/10.54302/mausam.v74i2.6180","url":null,"abstract":"There have been major advances in the last few decades in our understanding of heavy rainfall during monsoon season due to substantial progress in both observation and numerical modelling of monsoon. All these resulted in more accurate forecast of heavy rainfall in short to medium range, (upto five days) with 40% improvement in accuracy of heavy rainfall forecast in recent five years (2018-2022) as compared to previous five years. However, improvement of forecast and warning skill is not sufficient to minimize damage to lives and property. It is essential to extend to hazard forecast systems (hazard models) and then to impact and risk assessment with stakeholder interaction for risk based warning (RBW) and response action to protect lives and livelihoods\u0000 \u0000Considering all these, India Meteorological Department (IMD) has introduced impact based forecast (IBF) for heavy rainfall at meteorological sub-division level since July 2013 and at district and city scale in August, 2019 in its short to medium range forecasts and nowcasts indicating the likely impact of the heavy rainfall in different sectors and required response actions. Thereafter the IBF of heavy rainfall has undergone several changes over the years. Currently, the IBF being implemented by IMD includes all the four components, viz., (i) meteorological hazards, (ii) geophysical hazards, (iii) geospatial applications and (iv) socio-economic conditions and it utilises a web-GIS based decision support system (DSS). In this study we have reviewed various approaches and stages of development of IBF of heavy rainfall in India. The success of IBF of heavy rainfall will enhance the management of critical resources like agriculture, water & power and support urban and disaster management sectors among others while reducing loss of life and property.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43593908","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}
The Bay of Bengal (BoB) receives a large amount of freshwater from rains and rivers, resulting in large upper-ocean stratification due to the freshening effect. This salinity stratification has been theorized to impact sea-surface temperature (SST) and convection on intra-seasonal time scales by affecting the ocean mixed layer and the barrier layer. This article aims to quantify the impact of salinity stratification on the sub-seasonal variability in SST and convection by using in-situ ocean observations and coupled model experiments. It is shown that monsoon intra-seasonal oscillations (MISOs) exhibit varied levels of intra-seasonal variability in SST and rainfall based on the underlying ocean conditions. The largest intra-seasonal variability in SST does not cause the largest convection variability in the north-western BoB. Instead, moderate variability in SST and rainfall associated with MISOs co-occur with deep mixed layer and thick barrier layer conditions. Realistic representation of river freshwater fluxes in a coupled ocean-atmosphere model leads to improved intra-seasonal SST and rainfall variability. Thick barrier layers in the north-western Bay attenuates the entrainment cooling of the mixed layer, and the high mixed layer heat content provides conducive oceanic conditions for the genesis of monsoon low-pressure systems (LPS), thereby affecting rainfall over India. This study has important implications for operation forecasting using coupled models.
{"title":"Bay of Bengal upper-ocean stratification and the sub-seasonal variability in convection: Role of rivers in a coupled ocean-atmosphere model","authors":"Ankur Srivastava, Suryachandra ARao, Subimal Ghosh","doi":"10.54302/mausam.v74i2.6011","DOIUrl":"https://doi.org/10.54302/mausam.v74i2.6011","url":null,"abstract":"The Bay of Bengal (BoB) receives a large amount of freshwater from rains and rivers, resulting in large upper-ocean stratification due to the freshening effect. This salinity stratification has been theorized to impact sea-surface temperature (SST) and convection on intra-seasonal time scales by affecting the ocean mixed layer and the barrier layer. This article aims to quantify the impact of salinity stratification on the sub-seasonal variability in SST and convection by using in-situ ocean observations and coupled model experiments. It is shown that monsoon intra-seasonal oscillations (MISOs) exhibit varied levels of intra-seasonal variability in SST and rainfall based on the underlying ocean conditions. The largest intra-seasonal variability in SST does not cause the largest convection variability in the north-western BoB. Instead, moderate variability in SST and rainfall associated with MISOs co-occur with deep mixed layer and thick barrier layer conditions. Realistic representation of river freshwater fluxes in a coupled ocean-atmosphere model leads to improved intra-seasonal SST and rainfall variability. Thick barrier layers in the north-western Bay attenuates the entrainment cooling of the mixed layer, and the high mixed layer heat content provides conducive oceanic conditions for the genesis of monsoon low-pressure systems (LPS), thereby affecting rainfall over India. This study has important implications for operation forecasting using coupled models.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48371588","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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