Investigation of WRF’s ability to simulate the monsoon-related seasonal variability in the thermodynamics and precipitation over southern peninsular India
The goal of this study is to evaluate the ability of the state-of-the-art, higher-resolution, convection-permitting, weather research forecasting (WRF) model in predicting the changes in precipitation regimes which come in response to the seasonal changes in the large-scale environmental forcing. Th...
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description | The goal of this study is to evaluate the ability of the state-of-the-art, higher-resolution, convection-permitting, weather research forecasting (WRF) model in predicting the changes in precipitation regimes which come in response to the seasonal changes in the large-scale environmental forcing. The simulation days are selected in the year 2009 and according to four environmental regimes defined by the daily flow direction (Ragi et al. (IEEE Trans Geosci Remote Sens 55:3466–3474,
2017
)) using QuikSCAT scatterometer and the comparison of the same with National Center for Environmental Prediction (NCEP) final analysis (FNL) data. The observations used for analysis are from Indian Meteorological Department, Wyoming, TRMM satellite data, and NCEP-NCAR reanalysis data. This study finds that WRF is capable of reproducing the season-specific differences in the precipitating patterns that reflect the different phases of the monsoon. Extensive comparisons to observations point out that the model simulates reasonably well the temperature and the humidity fields, including their diurnal variability and vertical structure. However, the model-produced precipitation and winds do not compare so well, especially the winds. The simulated large-scale monsoon circulation and rainfall patterns indicate a wet bias in the model rainfall simulations than the TRMM rainfall observations over the selected region. In particular, WRF overestimates the rain. The base variables such as outgoing longwave radiation (OLR), latent and sensible heat fluxes, and convective available potential energy (CAPE) and convective inhibition energy (CIN) are nearly in agreement with the observations. In effect, WRF is skilled to represent the variability in different seasons and its spatial distribution, an important characteristic of the precipitation, especially concerning prediction of the monsoon onset. The disagreements between the observed and the model precipitation and winds can be due to the WRF model physics which generates different dynamics and different precipitating systems and initial conditions. |
doi_str_mv | 10.1007/s00704-020-03240-1 |
format | Article |
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2017
)) using QuikSCAT scatterometer and the comparison of the same with National Center for Environmental Prediction (NCEP) final analysis (FNL) data. The observations used for analysis are from Indian Meteorological Department, Wyoming, TRMM satellite data, and NCEP-NCAR reanalysis data. This study finds that WRF is capable of reproducing the season-specific differences in the precipitating patterns that reflect the different phases of the monsoon. Extensive comparisons to observations point out that the model simulates reasonably well the temperature and the humidity fields, including their diurnal variability and vertical structure. However, the model-produced precipitation and winds do not compare so well, especially the winds. The simulated large-scale monsoon circulation and rainfall patterns indicate a wet bias in the model rainfall simulations than the TRMM rainfall observations over the selected region. In particular, WRF overestimates the rain. The base variables such as outgoing longwave radiation (OLR), latent and sensible heat fluxes, and convective available potential energy (CAPE) and convective inhibition energy (CIN) are nearly in agreement with the observations. In effect, WRF is skilled to represent the variability in different seasons and its spatial distribution, an important characteristic of the precipitation, especially concerning prediction of the monsoon onset. The disagreements between the observed and the model precipitation and winds can be due to the WRF model physics which generates different dynamics and different precipitating systems and initial conditions.</description><identifier>ISSN: 0177-798X</identifier><identifier>EISSN: 1434-4483</identifier><identifier>DOI: 10.1007/s00704-020-03240-1</identifier><language>eng</language><publisher>Vienna: Springer Vienna</publisher><subject>Analysis ; Aquatic Pollution ; Atmospheric precipitations ; Atmospheric Protection/Air Quality Control/Air Pollution ; Atmospheric Sciences ; Climate science ; Climatology ; Computer simulation ; Convection ; Convective available potential energy ; Diurnal ; Earth and Environmental Science ; Earth Sciences ; Enthalpy ; Environmental law ; Heat flux ; Heat transfer ; Initial conditions ; Investigations ; Long wave radiation ; Meteorological research ; Monsoon circulation ; Monsoon forecasting ; Monsoon onset ; Monsoon rainfall ; Monsoon winds ; Monsoons ; Numerical weather forecasting ; Original Paper ; Physics ; Potential energy ; Precipitation ; Radiation ; Rain ; Rain and rainfall ; Rainfall ; Rainfall patterns ; Rainfall simulators ; Satellite data ; Scatterometers ; Seasonal variability ; Seasonal variation ; Seasonal variations ; Seasons ; Sensible heat ; Spatial distribution ; Thermodynamics ; TRMM satellite ; Tropical Rainfall Measuring Mission (TRMM) ; Vertical profiles ; Waste Water Technology ; Water Management ; Water Pollution Control ; Weather ; Weather forecasting ; Wind ; Winds</subject><ispartof>Theoretical and applied climatology, 2020-08, Vol.141 (3-4), p.1025-1043</ispartof><rights>Springer-Verlag GmbH Austria, part of Springer Nature 2020</rights><rights>COPYRIGHT 2020 Springer</rights><rights>Springer-Verlag GmbH Austria, part of Springer Nature 2020.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c392t-22f12927051b2df7596134a1c94ea712c12a857a019a9250fe4409339113daf03</citedby><cites>FETCH-LOGICAL-c392t-22f12927051b2df7596134a1c94ea712c12a857a019a9250fe4409339113daf03</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00704-020-03240-1$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00704-020-03240-1$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27923,27924,41487,42556,51318</link.rule.ids></links><search><creatorcontrib>Ragi, A.R.</creatorcontrib><creatorcontrib>Sharan, Maithili</creatorcontrib><creatorcontrib>Haddad, Z.S.</creatorcontrib><title>Investigation of WRF’s ability to simulate the monsoon-related seasonal variability in the thermodynamics and precipitation over southern peninsular India</title><title>Theoretical and applied climatology</title><addtitle>Theor Appl Climatol</addtitle><description>The goal of this study is to evaluate the ability of the state-of-the-art, higher-resolution, convection-permitting, weather research forecasting (WRF) model in predicting the changes in precipitation regimes which come in response to the seasonal changes in the large-scale environmental forcing. The simulation days are selected in the year 2009 and according to four environmental regimes defined by the daily flow direction (Ragi et al. (IEEE Trans Geosci Remote Sens 55:3466–3474,
2017
)) using QuikSCAT scatterometer and the comparison of the same with National Center for Environmental Prediction (NCEP) final analysis (FNL) data. The observations used for analysis are from Indian Meteorological Department, Wyoming, TRMM satellite data, and NCEP-NCAR reanalysis data. This study finds that WRF is capable of reproducing the season-specific differences in the precipitating patterns that reflect the different phases of the monsoon. Extensive comparisons to observations point out that the model simulates reasonably well the temperature and the humidity fields, including their diurnal variability and vertical structure. However, the model-produced precipitation and winds do not compare so well, especially the winds. The simulated large-scale monsoon circulation and rainfall patterns indicate a wet bias in the model rainfall simulations than the TRMM rainfall observations over the selected region. In particular, WRF overestimates the rain. The base variables such as outgoing longwave radiation (OLR), latent and sensible heat fluxes, and convective available potential energy (CAPE) and convective inhibition energy (CIN) are nearly in agreement with the observations. In effect, WRF is skilled to represent the variability in different seasons and its spatial distribution, an important characteristic of the precipitation, especially concerning prediction of the monsoon onset. The disagreements between the observed and the model precipitation and winds can be due to the WRF model physics which generates different dynamics and different precipitating systems and initial conditions.</description><subject>Analysis</subject><subject>Aquatic Pollution</subject><subject>Atmospheric precipitations</subject><subject>Atmospheric Protection/Air Quality Control/Air Pollution</subject><subject>Atmospheric Sciences</subject><subject>Climate science</subject><subject>Climatology</subject><subject>Computer simulation</subject><subject>Convection</subject><subject>Convective available potential energy</subject><subject>Diurnal</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Enthalpy</subject><subject>Environmental law</subject><subject>Heat flux</subject><subject>Heat transfer</subject><subject>Initial conditions</subject><subject>Investigations</subject><subject>Long wave radiation</subject><subject>Meteorological research</subject><subject>Monsoon circulation</subject><subject>Monsoon forecasting</subject><subject>Monsoon onset</subject><subject>Monsoon rainfall</subject><subject>Monsoon winds</subject><subject>Monsoons</subject><subject>Numerical weather forecasting</subject><subject>Original Paper</subject><subject>Physics</subject><subject>Potential energy</subject><subject>Precipitation</subject><subject>Radiation</subject><subject>Rain</subject><subject>Rain and rainfall</subject><subject>Rainfall</subject><subject>Rainfall patterns</subject><subject>Rainfall simulators</subject><subject>Satellite data</subject><subject>Scatterometers</subject><subject>Seasonal variability</subject><subject>Seasonal variation</subject><subject>Seasonal variations</subject><subject>Seasons</subject><subject>Sensible heat</subject><subject>Spatial distribution</subject><subject>Thermodynamics</subject><subject>TRMM satellite</subject><subject>Tropical Rainfall Measuring Mission (TRMM)</subject><subject>Vertical profiles</subject><subject>Waste Water Technology</subject><subject>Water Management</subject><subject>Water Pollution Control</subject><subject>Weather</subject><subject>Weather forecasting</subject><subject>Wind</subject><subject>Winds</subject><issn>0177-798X</issn><issn>1434-4483</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kd9qFTEQxhex4LH1BbwKeOXF1smf3ZxclmL1QEGolnoXprvJMWU3WZPsoefO1xB8Op_EnG5FeiNhEhh-30xmvqp6TeGUAsh3qVwgamBQA2cCavqsWlHBRS3Emj-vVkClrKVaf31RvUzpDgBY28pV9WvjdyZlt8XsgifBkpuri98_fiaCt25weU9yIMmN84DZkPzNkDH4FIKvozmkepIMpuBxIDuM7q_I-Qe2RBxDv_c4uq6U9D2Zounc5PJjv52JJIX5AHoyGe98Kq0i2fje4Ul1ZHFI5tXje1xdX7z_cv6xvvz0YXN-dll3XLFcM2YpU0xCQ29Zb2WjWsoF0k4Jg5KyjjJcNxKBKlSsAWuEAMW5opT3aIEfV2-WulMM3-eyDn0X5lhmSpoJxlULDVeFOl2oLQ5GO29DjtiV05syXfDGupI_a5lq-ZpzVgRvnwgKk8193uKckt58vnrKsoXtYkgpGqun6EaMe01BHyzWi8W6WKwfLNa0iPgiSgX2WxP__fs_qj_edKux</recordid><startdate>20200801</startdate><enddate>20200801</enddate><creator>Ragi, 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B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>ISR</scope><scope>3V.</scope><scope>7QH</scope><scope>7TG</scope><scope>7TN</scope><scope>7UA</scope><scope>7XB</scope><scope>88I</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F1W</scope><scope>GNUQQ</scope><scope>H96</scope><scope>HCIFZ</scope><scope>KL.</scope><scope>L.G</scope><scope>L6V</scope><scope>M2P</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PCBAR</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>Q9U</scope></search><sort><creationdate>20200801</creationdate><title>Investigation of WRF’s ability to simulate the monsoon-related seasonal variability in the thermodynamics and precipitation over southern peninsular India</title><author>Ragi, A.R. ; Sharan, Maithili ; Haddad, Z.S.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c392t-22f12927051b2df7596134a1c94ea712c12a857a019a9250fe4409339113daf03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Analysis</topic><topic>Aquatic Pollution</topic><topic>Atmospheric precipitations</topic><topic>Atmospheric Protection/Air Quality Control/Air Pollution</topic><topic>Atmospheric Sciences</topic><topic>Climate science</topic><topic>Climatology</topic><topic>Computer simulation</topic><topic>Convection</topic><topic>Convective available potential energy</topic><topic>Diurnal</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>Enthalpy</topic><topic>Environmental law</topic><topic>Heat flux</topic><topic>Heat transfer</topic><topic>Initial conditions</topic><topic>Investigations</topic><topic>Long wave radiation</topic><topic>Meteorological research</topic><topic>Monsoon circulation</topic><topic>Monsoon forecasting</topic><topic>Monsoon onset</topic><topic>Monsoon rainfall</topic><topic>Monsoon winds</topic><topic>Monsoons</topic><topic>Numerical weather forecasting</topic><topic>Original Paper</topic><topic>Physics</topic><topic>Potential energy</topic><topic>Precipitation</topic><topic>Radiation</topic><topic>Rain</topic><topic>Rain and rainfall</topic><topic>Rainfall</topic><topic>Rainfall patterns</topic><topic>Rainfall simulators</topic><topic>Satellite data</topic><topic>Scatterometers</topic><topic>Seasonal variability</topic><topic>Seasonal variation</topic><topic>Seasonal variations</topic><topic>Seasons</topic><topic>Sensible heat</topic><topic>Spatial distribution</topic><topic>Thermodynamics</topic><topic>TRMM satellite</topic><topic>Tropical Rainfall Measuring Mission (TRMM)</topic><topic>Vertical profiles</topic><topic>Waste Water Technology</topic><topic>Water Management</topic><topic>Water Pollution Control</topic><topic>Weather</topic><topic>Weather forecasting</topic><topic>Wind</topic><topic>Winds</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ragi, A.R.</creatorcontrib><creatorcontrib>Sharan, Maithili</creatorcontrib><creatorcontrib>Haddad, Z.S.</creatorcontrib><collection>CrossRef</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Aqualine</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Oceanic Abstracts</collection><collection>Water Resources Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology 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climatology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ragi, A.R.</au><au>Sharan, Maithili</au><au>Haddad, Z.S.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Investigation of WRF’s ability to simulate the monsoon-related seasonal variability in the thermodynamics and precipitation over southern peninsular India</atitle><jtitle>Theoretical and applied climatology</jtitle><stitle>Theor Appl Climatol</stitle><date>2020-08-01</date><risdate>2020</risdate><volume>141</volume><issue>3-4</issue><spage>1025</spage><epage>1043</epage><pages>1025-1043</pages><issn>0177-798X</issn><eissn>1434-4483</eissn><abstract>The goal of this study is to evaluate the ability of the state-of-the-art, higher-resolution, convection-permitting, weather research forecasting (WRF) model in predicting the changes in precipitation regimes which come in response to the seasonal changes in the large-scale environmental forcing. The simulation days are selected in the year 2009 and according to four environmental regimes defined by the daily flow direction (Ragi et al. (IEEE Trans Geosci Remote Sens 55:3466–3474,
2017
)) using QuikSCAT scatterometer and the comparison of the same with National Center for Environmental Prediction (NCEP) final analysis (FNL) data. The observations used for analysis are from Indian Meteorological Department, Wyoming, TRMM satellite data, and NCEP-NCAR reanalysis data. This study finds that WRF is capable of reproducing the season-specific differences in the precipitating patterns that reflect the different phases of the monsoon. Extensive comparisons to observations point out that the model simulates reasonably well the temperature and the humidity fields, including their diurnal variability and vertical structure. However, the model-produced precipitation and winds do not compare so well, especially the winds. The simulated large-scale monsoon circulation and rainfall patterns indicate a wet bias in the model rainfall simulations than the TRMM rainfall observations over the selected region. In particular, WRF overestimates the rain. The base variables such as outgoing longwave radiation (OLR), latent and sensible heat fluxes, and convective available potential energy (CAPE) and convective inhibition energy (CIN) are nearly in agreement with the observations. In effect, WRF is skilled to represent the variability in different seasons and its spatial distribution, an important characteristic of the precipitation, especially concerning prediction of the monsoon onset. The disagreements between the observed and the model precipitation and winds can be due to the WRF model physics which generates different dynamics and different precipitating systems and initial conditions.</abstract><cop>Vienna</cop><pub>Springer Vienna</pub><doi>10.1007/s00704-020-03240-1</doi><tpages>19</tpages></addata></record> |
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subjects | Analysis Aquatic Pollution Atmospheric precipitations Atmospheric Protection/Air Quality Control/Air Pollution Atmospheric Sciences Climate science Climatology Computer simulation Convection Convective available potential energy Diurnal Earth and Environmental Science Earth Sciences Enthalpy Environmental law Heat flux Heat transfer Initial conditions Investigations Long wave radiation Meteorological research Monsoon circulation Monsoon forecasting Monsoon onset Monsoon rainfall Monsoon winds Monsoons Numerical weather forecasting Original Paper Physics Potential energy Precipitation Radiation Rain Rain and rainfall Rainfall Rainfall patterns Rainfall simulators Satellite data Scatterometers Seasonal variability Seasonal variation Seasonal variations Seasons Sensible heat Spatial distribution Thermodynamics TRMM satellite Tropical Rainfall Measuring Mission (TRMM) Vertical profiles Waste Water Technology Water Management Water Pollution Control Weather Weather forecasting Wind Winds |
title | Investigation of WRF’s ability to simulate the monsoon-related seasonal variability in the thermodynamics and precipitation over southern peninsular India |
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