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":" ","pages":""},"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":" ","pages":""},"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":" ","pages":""},"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":" ","pages":""},"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":" ","pages":""},"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}
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":" ","pages":""},"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}
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":" ","pages":""},"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}
Pub Date : 2023-03-29DOI: 10.54302/mausam.v74i2.6118
M. Bushair, D. Pattanaik, M. Mohapatra
The southwest (SW) monsoon season from June to September (JJAS) is the major rainfall period over most parts of Indian regions. Accurate rainfall forecast is one of the most crucial and least predictable parameters of the numerical weather prediction (NWP) models because of its uneven distribution and patterns over the globe. During the last decade many studies have been carried out using different NWP models to predict rainfall incidents, and it is found that the forecast skill has been improved considerably. In the present study, a multi-model ensemble (MME) based tool has been developed for the prediction of SW monsoon rainfall at the district level over India for five days. The precipitation forecasts from five operational NWP modelling systems, viz., (i) Global Forecast System (GFS) and (ii) Global Ensemble Forecasting System (GEFS) running at India Meteorological Department, (iii) Global Forecast System model running at National Centre for Environment Prediction (NCEP), (iv) Unified Model running at National Centre for Medium-Range Weather Forecasting (NCMRWF) and (v) Global Spectral Model (GSM) running at Japan Meteorological Agency (JMA) have been used for developing the MME forecasts for SW monsoon 2021. The prediction skill of the MME and the individual model forecast is evaluated against observed district rainfall. The district-level heavy rainfall forecast from individual models and MME is also evaluated against the observed rainfall events useful for warning services. Different verification scores like Correlation Coefficient (CC), Root Mean Square Error (RMSE), Mean Bias, Probability of Detection (POD), False Alarm Ratio (FAR), Equitable Treat Score (ETS), Critical Success Index (CSI), etc. are calculated for the verification purpose. The different verification score shows that MME rainfall forecast has performed well than the individual models in different spatial domains and temporal scales. The CC between observed rainfall and day 1 MME forecast is 0.58, whereas GFS, GEFS, NCEP, NCUM and JMA are showing 0.43, 0.47, 0.49, 0.49 and 0.46 respectively. The RMSE observed for MME, GFS, GEFS, NCEP, NCUM and JMA are 12.7, 15.2, 14.1, 14.3, 16.6 and 14.1 mm/day respectively when compared with IMD observed rainfall. The inter-comparison of the model forecasts reveal that the MME method can generate skillful district rainfall forecast over India for operational use during the monsoon season.
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Pub Date : 2023-03-29DOI: 10.54302/mausam.v74i2.5980
B. Golding
A priority of weather services is to protect lives and property from hazardous weather. Research on how to achieve that most effectively is the mission of the World Weather Research Programme’s High Impact Weather (HIWeather) project. HIWeather brings together physical and social scientists from a wide variety of disciplines and from across the world to study each step of the process from monitoring the weather to making effective protective responses. HIWeather uses a simple model of the warning production and communication chain that highlights the roles of key actors and organisations involved in forecasting the weather, the resulting hazard and its socio-economic impacts, in formulating the warning and communicating it to the end-user. In this paper I draw on the results of that research which has now been published in our book, “Towards the ‘perfect’ weather warning: bridging disciplinary gaps through partnership and communication” (Golding, 2022). In the context of severe weather associated with monsoons, I shall identify key principles for the design of weather-related warning systems, connecting this work with ideas from the design of community-based warning systems, developments in social media communication, research on impact-based forecasting and with progress in convection-permitting and higher resolution NWP models. A key result is that the communication of knowledge is at least as important as its content and that the creation and nurturing of partnerships between organisations is critical to that.
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Pub Date : 2023-03-29DOI: 10.54302/mausam.v74i2.6000
Mong-Ming Lu, Y. Cho, C. Sui
The decadal-scale variations of the Asian summer monsoon and the tropical cyclone (TC) activity over the western North Pacific (WNP) and the South China Sea (SCS) are of great scientific and societal importance. The period of 2010-2019 was identified as the most inactive decade since 1961 in terms of TC genesis over the SCS and the Philippine Sea during May (Cho et al. 2022). In this paper we extended the analysis by using 40-yr (1981-2020) data to illustrate the relationship between the SCS TC frequency in May and the spring-to-summer transition of Asian monsoon systems. The results show clear decadal-scale variations of TC frequency with two active decades during the 1980s and 2000s, and two inactive decades during the 1990s and 2010s. The circulation and surface air temperature contrast during the earlier two decades is drastically different from the contrast during later two decades. The difference can be understood as decadal-scale variations of two leading modes of the 40-yr March-June precipitation in Asian-Australian-Pacific monsoon region. For the earlier two decades, the contrast of active and inactive SCS TC frequency in May can be explained by the difference in EOF2. The positive EOF2 corresponds to a wet and dry dipole pattern of the concurrent anomalies with enhanced convection over the eastern Indian Ocean and suppressed convection over the western Pacific warm pool. For the later two decades, the contrast can be explained by the difference in EOF1, which shows a meridional dipole pattern over eastern Indian Ocean reflecting the northward movement of the ITCZ. Among four decades, the decade of 2001-2010 shows the earliest northward transition of the ITCZ and the most active SCS TC frequency in May. Although the decades of 1981-1990 and 1991-2000 show strong difference in TC frequency, no discernable difference in monsoon seasonal transition is detected.
亚洲夏季风的年代际变化以及北太平洋西部和南海的热带气旋活动具有重要的科学意义和社会意义。就5月份南海和菲律宾海的TC形成而言，2010-2019年被确定为1961年以来最不活跃的十年(Cho et al. 2022)。本文利用40年(1981-2020年)的数据进一步分析了南海5月TC频率与亚洲季风系统春夏转换的关系。结果表明，20世纪80年代和2000年代有两个活跃年，90年代和2010年代有两个不活跃年，TC频率具有明显的年代际变化。前20年的环流和地面气温对比与后20年的对比有很大的不同。这种差异可以理解为亚洲-澳大利亚-太平洋季风区40年3 - 6月降水的两个主要模态的年代际变化。在前20年，5月活跃和不活跃SCS TC频率的对比可以用EOF2的差异来解释。正EOF2对应于东印度洋对流增强和西太平洋暖池对流抑制的同步异常的干湿偶极子型。在之后的20年里，这种对比可以用EOF1的差异来解释，EOF1显示了东印度洋上空的经向偶极子模式，反映了ITCZ的北移。其中，2001-2010年是过渡带北移最早的10年，5月是南海TC频率最活跃的10年。尽管1981-1990年和1991-2000年的年代际变化显示出较强的TC频率差异，但季风季节转换没有明显的差异。
{"title":"Decadal-scale changes in the seasonal transition patterns of the Asian summer monsoon and the South China Sea tropical cyclone frequency during May","authors":"Mong-Ming Lu, Y. Cho, C. Sui","doi":"10.54302/mausam.v74i2.6000","DOIUrl":"https://doi.org/10.54302/mausam.v74i2.6000","url":null,"abstract":"The decadal-scale variations of the Asian summer monsoon and the tropical cyclone (TC) activity over the western North Pacific (WNP) and the South China Sea (SCS) are of great scientific and societal importance. The period of 2010-2019 was identified as the most inactive decade since 1961 in terms of TC genesis over the SCS and the Philippine Sea during May (Cho et al. 2022). In this paper we extended the analysis by using 40-yr (1981-2020) data to illustrate the relationship between the SCS TC frequency in May and the spring-to-summer transition of Asian monsoon systems. The results show clear decadal-scale variations of TC frequency with two active decades during the 1980s and 2000s, and two inactive decades during the 1990s and 2010s. The circulation and surface air temperature contrast during the earlier two decades is drastically different from the contrast during later two decades. The difference can be understood as decadal-scale variations of two leading modes of the 40-yr March-June precipitation in Asian-Australian-Pacific monsoon region. For the earlier two decades, the contrast of active and inactive SCS TC frequency in May can be explained by the difference in EOF2. The positive EOF2 corresponds to a wet and dry dipole pattern of the concurrent anomalies with enhanced convection over the eastern Indian Ocean and suppressed convection over the western Pacific warm pool. For the later two decades, the contrast can be explained by the difference in EOF1, which shows a meridional dipole pattern over eastern Indian Ocean reflecting the northward movement of the ITCZ. Among four decades, the decade of 2001-2010 shows the earliest northward transition of the ITCZ and the most active SCS TC frequency in May. Although the decades of 1981-1990 and 1991-2000 show strong difference in TC frequency, no discernable difference in monsoon seasonal transition is detected.","PeriodicalId":18363,"journal":{"name":"MAUSAM","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45528310","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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