Pub Date : 2023-03-29DOI: 10.54302/mausam.v74i2.5997
N. Acharya, K. Hall
Due to the uncertainty associated with Indian summer monsoon rainfall (ISMR), probabilistic seasonal forecasts which can convey the inherent uncertainty of ISMR are more useful to the user community than a single deterministic forecast. While such probabilistic seasonal forecasts can be produced from general circulation model (GCM) output, one single model generally does not represent all sources of error. The probabilistic multi model ensemble (PMME) is a well-accepted way to improve on the skill of probabilistic forecasts by individual GCMs. PMME can be constructed with one of two approaches: non-parametric, or parametric with respect to the occurrence of three categories of seasonal total rainfall—below, near, and above normal as defined by the climatological base period. Both the methods have their limitations. Non-parametric PMME use a smaller ensemble size which results in overconfident forecasts, and parametric PMME make the inaccurate assumption that total rainfall follows a Gaussian distribution. To avoid these problems, we propose the use of the Extreme Learning Machine (ELM), a novel machine learning (ML) approach, to construct PMME for ISMR forecasting. ELM is a state-of-the-art generalized form of single-hidden-layer feed-forward neural network. However, since the traditional ELM network only produces a deterministic outcome, we use a modified version of ELM called Probabilistic Output Extreme Learning Machine (PO-ELM). PO-ELM uses sigmoid additive neurons and slightly different linear programming to make probabilistic predictions. The performance of such PO-ELM based PMME is assessed rigorously in terms of Generalized Receiver Operating Characteristic scores and reliability diagrams over a 37 years period spanning from 1982 to 2018 following a leave-three-year-out cross-validation scheme. It is demonstrated that our new strategy for PMME based on ML is capable of producing skillful MME forecasts over large regions of India.
{"title":"A Machine Learning Approach for Probabilistic Multi-Model Ensemble Predictions of Indian Summer Monsoon Rainfall","authors":"N. Acharya, K. Hall","doi":"10.54302/mausam.v74i2.5997","DOIUrl":"https://doi.org/10.54302/mausam.v74i2.5997","url":null,"abstract":"Due to the uncertainty associated with Indian summer monsoon rainfall (ISMR), probabilistic seasonal forecasts which can convey the inherent uncertainty of ISMR are more useful to the user community than a single deterministic forecast. While such probabilistic seasonal forecasts can be produced from general circulation model (GCM) output, one single model generally does not represent all sources of error. The probabilistic multi model ensemble (PMME) is a well-accepted way to improve on the skill of probabilistic forecasts by individual GCMs. PMME can be constructed with one of two approaches: non-parametric, or parametric with respect to the occurrence of three categories of seasonal total rainfall—below, near, and above normal as defined by the climatological base period. Both the methods have their limitations. Non-parametric PMME use a smaller ensemble size which results in overconfident forecasts, and parametric PMME make the inaccurate assumption that total rainfall follows a Gaussian distribution. To avoid these problems, we propose the use of the Extreme Learning Machine (ELM), a novel machine learning (ML) approach, to construct PMME for ISMR forecasting. ELM is a state-of-the-art generalized form of single-hidden-layer feed-forward neural network. However, since the traditional ELM network only produces a deterministic outcome, we use a modified version of ELM called Probabilistic Output Extreme Learning Machine (PO-ELM). PO-ELM uses sigmoid additive neurons and slightly different linear programming to make probabilistic predictions. The performance of such PO-ELM based PMME is assessed rigorously in terms of Generalized Receiver Operating Characteristic scores and reliability diagrams over a 37 years period spanning from 1982 to 2018 following a leave-three-year-out cross-validation scheme. It is demonstrated that our new strategy for PMME based on ML is capable of producing skillful MME forecasts over large regions of India.","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":"46277443","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.v74i2.6006
Donaldi S Permana, Supari Supari, R. Hutauruk, D. Nuryanto, N. Riama
Gridded precipitation datasets are widely available from satellite observations and reanalysis model outputs. However, its performance in specific regions in the world may vary and depends on several factors, such as grid data spatial resolution, rainfall estimation algorithms, geographical location, elevation and regional climate conditions. This study aims to report on 13 gridded precipitation datasets' performance over Indonesia through direct comparisons with rain gauge measurements at various time scales over a 12-year period (2001-2012). The results show that, at daily timescales, the MERRA2 and CPC outperformed other datasets but tended to underestimate the rain gauge data in Indonesia, followed by GPCC. However, MERRA2 has smaller variation and bias than CPC. On monthly and annually timescales, CPC was found to be the best-performing dataset, followed by MERRA2, GPM-IMERG, GPCC and TRMM (TMPA), while JRA55 registered the worst performance at all timescales, followed by ERA-Interim. The performance of all datasets was better during JJA and SON than during DJF and MAM. The best performances were found in the southern (S) region of Indonesia, while the worst were in the northeast (NE) region for all months and datasets. The best performances during DJF (Asian Winter Monsoon) and JJA/SON (Australian Winter Monsoon) were found in the northwest (NW) and southern (S) regions, respectively. Most datasets overestimate the rain gauge data over Indonesia, except for GSMaP, MERRA2, CPC and CMORPH.
{"title":"Evaluation of multiple gridded precipitation datasets using gauge observations over Indonesia during the Asian-Australian monsoon period","authors":"Donaldi S Permana, Supari Supari, R. Hutauruk, D. Nuryanto, N. Riama","doi":"10.54302/mausam.v74i2.6006","DOIUrl":"https://doi.org/10.54302/mausam.v74i2.6006","url":null,"abstract":"Gridded precipitation datasets are widely available from satellite observations and reanalysis model outputs. However, its performance in specific regions in the world may vary and depends on several factors, such as grid data spatial resolution, rainfall estimation algorithms, geographical location, elevation and regional climate conditions. This study aims to report on 13 gridded precipitation datasets' performance over Indonesia through direct comparisons with rain gauge measurements at various time scales over a 12-year period (2001-2012). The results show that, at daily timescales, the MERRA2 and CPC outperformed other datasets but tended to underestimate the rain gauge data in Indonesia, followed by GPCC. However, MERRA2 has smaller variation and bias than CPC. On monthly and annually timescales, CPC was found to be the best-performing dataset, followed by MERRA2, GPM-IMERG, GPCC and TRMM (TMPA), while JRA55 registered the worst performance at all timescales, followed by ERA-Interim. The performance of all datasets was better during JJA and SON than during DJF and MAM. The best performances were found in the southern (S) region of Indonesia, while the worst were in the northeast (NE) region for all months and datasets. The best performances during DJF (Asian Winter Monsoon) and JJA/SON (Australian Winter Monsoon) were found in the northwest (NW) and southern (S) regions, respectively. Most datasets overestimate the rain gauge data over Indonesia, except for GSMaP, MERRA2, CPC and CMORPH.","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":"44661687","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.v74i2.6029
K. Tsuboki
In East Asia, heavy-rain-producing mesoscale convective systems (MCSs) often develop in monsoon systems and cause severe floods and landslides. To understand and forecast these high-resolution simulations using cloud-resolving models (CRMs) are necessary. Tsuboki and Luo (2020) reviewed recent studies of MCSs using CRMs and remarked that data assimilation (DA) of radar observations to CRMs is promising for the improvement of simulation and numerical weather prediction (NWP) of MCSs. The DA of radar observations to CRMs is very effective for short-range NWP of MCSs using CRMs. Various convective-scale DAs have been developed to improve the NWP of heavy-rain-producing MCSs. Following Tsuboki and Luo (2020), this paper introduces recent studies on MCS using CRMs, phased array weather radars and DAs of radar observations.
{"title":"High-Resolution simulations of Heavy-Rain-Producing Mesoscale Convective Systems using Cloud-Resolving models","authors":"K. Tsuboki","doi":"10.54302/mausam.v74i2.6029","DOIUrl":"https://doi.org/10.54302/mausam.v74i2.6029","url":null,"abstract":"In East Asia, heavy-rain-producing mesoscale convective systems (MCSs) often develop in monsoon systems and cause severe floods and landslides. To understand and forecast these high-resolution simulations using cloud-resolving models (CRMs) are necessary. Tsuboki and Luo (2020) reviewed recent studies of MCSs using CRMs and remarked that data assimilation (DA) of radar observations to CRMs is promising for the improvement of simulation and numerical weather prediction (NWP) of MCSs. The DA of radar observations to CRMs is very effective for short-range NWP of MCSs using CRMs. Various convective-scale DAs have been developed to improve the NWP of heavy-rain-producing MCSs. Following Tsuboki and Luo (2020), this paper introduces recent studies on MCS using CRMs, phased array weather radars and DAs of radar observations.","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":"43801761","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.v74i2.5992
P. Roundy
Convectively coupled equatorial Rossby waves are the dominant mode of westward-moving subseasonal convection in the tropics. A portion of the variance in these waves has been shown to associate with the tropical intraseasonal oscillation, along with a process often mediated by the extratropical Rossby wave response to tropical convection that yields Rossby waves breaking back into the tropical atmosphere. The potential vorticity anomalies driven by Rossby wave breaking become the equatorial Rossby waves. This work creates an index of planetary scale equatorial Rossby waves and applies the method of seasonally varying regression slope coefficients to diagnose their preferred associations with tropical and extratropical circulation features. Results confirm the already known association between these waves and the extratropical atmosphere and they reveal a pattern of westward-and northward-moving anomalies of tropical convection over the Indian Ocean and Southern Asia during the Northern Hemisphere summer. These patterns are associated with a cycle of suppression and enhancement of convection in which negative anomalies of outgoing longwave radiation are found to be 3-times as likely during the wet than the dry phases of the waves.
{"title":"Equatorial Rossby waves and their impacts on monsoon region deep convection","authors":"P. Roundy","doi":"10.54302/mausam.v74i2.5992","DOIUrl":"https://doi.org/10.54302/mausam.v74i2.5992","url":null,"abstract":"Convectively coupled equatorial Rossby waves are the dominant mode of westward-moving subseasonal convection in the tropics. A portion of the variance in these waves has been shown to associate with the tropical intraseasonal oscillation, along with a process often mediated by the extratropical Rossby wave response to tropical convection that yields Rossby waves breaking back into the tropical atmosphere. The potential vorticity anomalies driven by Rossby wave breaking become the equatorial Rossby waves. This work creates an index of planetary scale equatorial Rossby waves and applies the method of seasonally varying regression slope coefficients to diagnose their preferred associations with tropical and extratropical circulation features. Results confirm the already known association between these waves and the extratropical atmosphere and they reveal a pattern of westward-and northward-moving anomalies of tropical convection over the Indian Ocean and Southern Asia during the Northern Hemisphere summer. These patterns are associated with a cycle of suppression and enhancement of convection in which negative anomalies of outgoing longwave radiation are found to be 3-times as likely during the wet than the dry phases of the waves.","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":"43708577","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.v74i2.6068
Bin Wang, Chunhan Jin, Jian Liu
Monsoon has been studied for centuries, yet only recently have regional monsoons been recognized as a global system. This paper begins with a review of the concept of Global Monsoon and related debating issues. We argue that GM drives annual cycles of Hadley circulation, Intertropical Convergence Zone, and subtropical high and dry climate regions. Land monsoon rainfall (LMR) provides water resources for about 70% of the world’s population. Here we review the climate sensitivity of global and regional LMR to anthropogenic warming projected by models participating in phase six of the Coupled Model Intercomparison Project (CMIP6), focusing on critical physical processes responsible for projected changes. In theory, regional mean LMR changes can be approximated by the changes in the product of the mid-tropospheric ascent and 850-hPa specific humidity, plus moderate contribution from evaporation. The greenhouse gases (GHGs) forcing increases moisture content but stabilizes the atmosphere; the two thermodynamic effects offset each other, resulting in a moderate thermodynamic impact on LMR. The GHGs-induced horizontally differential warming results in robust ‘‘northern hemisphere (NH)-warmer than- southern hemisphere (SH)’’, ‘‘land-warmer-than-ocean’’, and an El Nino–like warming pattern. The enhanced NH–SH thermal contrast will increase NH monsoon rainfall and reduce SH monsoon rainfall. The enhanced land–ocean thermal contrast will increase monsoon rainfall over the Asian–northern African monsoon regions. The projected eastern Pacific warming will reduce the North American monsoon. The Inter-model spread analysis suggests that the GHGs-induced circulation changes (dynamic effects) are primarily responsible for the regional differences. The last section discusses conceivable ways forward.
{"title":"Global monsoon: Concept and dynamic response to anthropogenic warming","authors":"Bin Wang, Chunhan Jin, Jian Liu","doi":"10.54302/mausam.v74i2.6068","DOIUrl":"https://doi.org/10.54302/mausam.v74i2.6068","url":null,"abstract":"Monsoon has been studied for centuries, yet only recently have regional monsoons been recognized as a global system. This paper begins with a review of the concept of Global Monsoon and related debating issues. We argue that GM drives annual cycles of Hadley circulation, Intertropical Convergence Zone, and subtropical high and dry climate regions. Land monsoon rainfall (LMR) provides water resources for about 70% of the world’s population. Here we review the climate sensitivity of global and regional LMR to anthropogenic warming projected by models participating in phase six of the Coupled Model Intercomparison Project (CMIP6), focusing on critical physical processes responsible for projected changes. In theory, regional mean LMR changes can be approximated by the changes in the product of the mid-tropospheric ascent and 850-hPa specific humidity, plus moderate contribution from evaporation. The greenhouse gases (GHGs) forcing increases moisture content but stabilizes the atmosphere; the two thermodynamic effects offset each other, resulting in a moderate thermodynamic impact on LMR. The GHGs-induced horizontally differential warming results in robust ‘‘northern hemisphere (NH)-warmer than- southern hemisphere (SH)’’, ‘‘land-warmer-than-ocean’’, and an El Nino–like warming pattern. The enhanced NH–SH thermal contrast will increase NH monsoon rainfall and reduce SH monsoon rainfall. The enhanced land–ocean thermal contrast will increase monsoon rainfall over the Asian–northern African monsoon regions. The projected eastern Pacific warming will reduce the North American monsoon. The Inter-model spread analysis suggests that the GHGs-induced circulation changes (dynamic effects) are primarily responsible for the regional differences. The last section discusses conceivable ways forward.","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":"45040512","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.v74i2.5981
Yali Luo, Yanyu Gao, Yangruixue Chen
{"title":"New insights on the convective and microphysical characteristics of heavyrainfallin monsoon coastalareas (South China)","authors":"Yali Luo, Yanyu Gao, Yangruixue Chen","doi":"10.54302/mausam.v74i2.5981","DOIUrl":"https://doi.org/10.54302/mausam.v74i2.5981","url":null,"abstract":"","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":"44371822","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.v74i2.5998
A. Datye, S. Chakraborty, R. Chattopadhyay, MohammadArzoo Ansari, A. Deodhar, P. Mohan
The isotopic composition of precipitation was studied over a terrestrial environment in western India and an island region in the Bay of Bengal. We have examined the precipitation isotopes’ response to the surface temperature and the tropospheric warming during the monsoon season. We observed that tropospheric temperature and surface temperature are positively correlated over the ocean while they are negatively correlated over the land. As a result, the precipitation isotopes in these environments show the opposite behavior to surface temperature variability. Despite this difference, precipitation isotopes in both environments respond positively to the tropospheric temperature variability, though the relationship is weaker in the terrestrial environment. The precipitation isotopic response to tropospheric temperature may provide an alternative to the precipitation and precipitation isotope relation widely used in past monsoon reconstruction.
{"title":"Precipitation isotopes’ response to the atmospheric processes over the mainland and the island region in the northern Indian Ocean: Implications to the paleo-monsoon study","authors":"A. Datye, S. Chakraborty, R. Chattopadhyay, MohammadArzoo Ansari, A. Deodhar, P. Mohan","doi":"10.54302/mausam.v74i2.5998","DOIUrl":"https://doi.org/10.54302/mausam.v74i2.5998","url":null,"abstract":"The isotopic composition of precipitation was studied over a terrestrial environment in western India and an island region in the Bay of Bengal. We have examined the precipitation isotopes’ response to the surface temperature and the tropospheric warming during the monsoon season. We observed that tropospheric temperature and surface temperature are positively correlated over the ocean while they are negatively correlated over the land. As a result, the precipitation isotopes in these environments show the opposite behavior to surface temperature variability. Despite this difference, precipitation isotopes in both environments respond positively to the tropospheric temperature variability, though the relationship is weaker in the terrestrial environment. The precipitation isotopic response to tropospheric temperature may provide an alternative to the precipitation and precipitation isotope relation widely used in past monsoon reconstruction.","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":"46677894","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.v74i2.6031
Mvs Ramarao, D. Ayantika, R. Krishnan, J. Sanjay, T. Sabin, M. Mujumdar, KK Singh
Evapotranspiration (ET) is the primary process of water transfer in the hydrological cycle over land and is linked to water, energy and carbon cycles. While the global hydrological cycle is expected to intensify in a warming climate with enhanced ET and precipitation, the magnitude and spatial distribution of regional scale response of ET to climate change remains uncertain. Here we present an analysis of in-situ observations of ET from 23 stations in India during 1979-2008, which shows that the annual ET has declined by about 9% over the humid sub-regions of the Indo-Gangetic Plain (IGP). Additional analysis from high-resolution climate model simulations and observed climate datasets lend support to the role of aerosol-induced solar-dimming in intensifying ET reductions, in a background of decreasing monsoon precipitation and soil-moisture levels, over the IGP
{"title":"Signatures of aerosol-induced decline in evapotranspiration over the Indo-Gangetic Plain during the recent decades","authors":"Mvs Ramarao, D. Ayantika, R. Krishnan, J. Sanjay, T. Sabin, M. Mujumdar, KK Singh","doi":"10.54302/mausam.v74i2.6031","DOIUrl":"https://doi.org/10.54302/mausam.v74i2.6031","url":null,"abstract":" Evapotranspiration (ET) is the primary process of water transfer in the hydrological cycle over land and is linked to water, energy and carbon cycles. While the global hydrological cycle is expected to intensify in a warming climate with enhanced ET and precipitation, the magnitude and spatial distribution of regional scale response of ET to climate change remains uncertain. Here we present an analysis of in-situ observations of ET from 23 stations in India during 1979-2008, which shows that the annual ET has declined by about 9% over the humid sub-regions of the Indo-Gangetic Plain (IGP). Additional analysis from high-resolution climate model simulations and observed climate datasets lend support to the role of aerosol-induced solar-dimming in intensifying ET reductions, in a background of decreasing monsoon precipitation and soil-moisture levels, over the IGP","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":"47411364","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.v74i2.5983
M. Kaur, S. Joseph, R. Phani, A. Sahai, A. Dey, R. Mandal
Approximated and simplified real-atmospheric process impact in physical parameterization is a primary correspondent of biases in the model, particularly for extreme events. The present study discusses how event genesis in the small and large-scale quintessential environment is incongruously simulated within a set of multiple convection parameterizations. Despite a few inherent errors, most of the selected convective parameterization schemes could indicate 10-15 days in advance the Uttarakhand heavy rains resulted from large-scale background interaction. The runs without any convection scheme, followed by new-Tiedtke and BMJ schemes, outperform in this case. Further, almost all schemes except new-Tiedtke flunked for the case of Mount-Abu flood originated from relatively local-scale interaction even from 5-day advance initialization. Results are further extended for a few other cases using best performers of both extreme events and new-Tiedtke found to be more efficient. The better representation of convection (especially the shallow) and low clouds in this scheme makes it superior to other schemes for simulating extreme precipitation events.
{"title":"Impact of genesis conditions on regional simulations of extreme rainfall : A convection parameterization sensitivity study","authors":"M. Kaur, S. Joseph, R. Phani, A. Sahai, A. Dey, R. Mandal","doi":"10.54302/mausam.v74i2.5983","DOIUrl":"https://doi.org/10.54302/mausam.v74i2.5983","url":null,"abstract":"Approximated and simplified real-atmospheric process impact in physical parameterization is a primary correspondent of biases in the model, particularly for extreme events. The present study discusses how event genesis in the small and large-scale quintessential environment is incongruously simulated within a set of multiple convection parameterizations. Despite a few inherent errors, most of the selected convective parameterization schemes could indicate 10-15 days in advance the Uttarakhand heavy rains resulted from large-scale background interaction. The runs without any convection scheme, followed by new-Tiedtke and BMJ schemes, outperform in this case. Further, almost all schemes except new-Tiedtke flunked for the case of Mount-Abu flood originated from relatively local-scale interaction even from 5-day advance initialization. Results are further extended for a few other cases using best performers of both extreme events and new-Tiedtke found to be more efficient. The better representation of convection (especially the shallow) and low clouds in this scheme makes it superior to other schemes for simulating extreme precipitation events.","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":"42065033","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.v74i2.5925
Y. Takaya, Hongli Ren, F. Vitart, A. Robertson
The Asian summer monsoon (ASM) has a considerable impact on human lives in the most populated region in the world. Thus, its seasonal prediction is a high-profile application in Earth Science. However, the prediction skill of the regional ASM variability has long been limited due to a formidable difficulty in accurately simulating the complex interactions of the atmosphere-ocean variability and its remote influence on regional climate in numerical models. This study updates the current status and assesses progress in the ASM seasonal prediction performance. This study evaluated the seasonal prediction skill of two generations of models in hindcast data archived by the WCRP Climate-system Historical Forecast Project (CHFP) and Copernicus Climate Change Service (C3S). A special focus was put on the representation of the predominant teleconnections associated with the ENSO and Indian Ocean variability. It was found that the latest seasonal prediction systems (C3S) generally outperform previous-generation systems (CHFP) in terms of the reproducibility of the observed precipitation climatology and the prediction skill of the interannual variability of seasonal precipitation over the ASM region. Furthermore, the results suggested that the improvement of the prediction skill of the ASM likely stems from the improved representation of the monsoon climatology and teleconnections in the models. These analyses highlight the steady progress of the atmosphere-ocean coupled modelling and promise future improvements in the seasonal ASM prediction.
{"title":"Current status and progress in the seasonal prediction of the Asian summer monsoon","authors":"Y. Takaya, Hongli Ren, F. Vitart, A. Robertson","doi":"10.54302/mausam.v74i2.5925","DOIUrl":"https://doi.org/10.54302/mausam.v74i2.5925","url":null,"abstract":"The Asian summer monsoon (ASM) has a considerable impact on human lives in the most populated region in the world. Thus, its seasonal prediction is a high-profile application in Earth Science. However, the prediction skill of the regional ASM variability has long been limited due to a formidable difficulty in accurately simulating the complex interactions of the atmosphere-ocean variability and its remote influence on regional climate in numerical models. This study updates the current status and assesses progress in the ASM seasonal prediction performance. This study evaluated the seasonal prediction skill of two generations of models in hindcast data archived by the WCRP Climate-system Historical Forecast Project (CHFP) and Copernicus Climate Change Service (C3S). A special focus was put on the representation of the predominant teleconnections associated with the ENSO and Indian Ocean variability. It was found that the latest seasonal prediction systems (C3S) generally outperform previous-generation systems (CHFP) in terms of the reproducibility of the observed precipitation climatology and the prediction skill of the interannual variability of seasonal precipitation over the ASM region. Furthermore, the results suggested that the improvement of the prediction skill of the ASM likely stems from the improved representation of the monsoon climatology and teleconnections in the models. These analyses highlight the steady progress of the atmosphere-ocean coupled modelling and promise future improvements in the seasonal ASM prediction.","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":"48334946","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：