How Well Does the DOE Global Storm Resolving Model Simulate Clouds and Precipitation Over the Amazon?
Abstract This study assesses a 40‐day 3.25‐km global simulation of the Simple Cloud‐Resolving E3SM Model (SCREAMv0) using high‐resolution ground‐based observations from the Atmospheric Radiation Measurement (ARM) Green Ocean Amazon (GoAmazon) field campaign. SCREAMv0 reasonably captures the diurnal...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-07-01
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| Series: | Geophysical Research Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2023GL108113 |
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| _version_ | 1849321353643032576 |
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| author | Jingjing Tian Yunyan Zhang Stephen A. Klein Christopher R. Terai Peter M. Caldwell Hassan Beydoun Peter Bogenschutz Hsi‐Yen Ma Aaron S. Donahue |
| author_facet | Jingjing Tian Yunyan Zhang Stephen A. Klein Christopher R. Terai Peter M. Caldwell Hassan Beydoun Peter Bogenschutz Hsi‐Yen Ma Aaron S. Donahue |
| author_sort | Jingjing Tian |
| collection | DOAJ |
| description | Abstract This study assesses a 40‐day 3.25‐km global simulation of the Simple Cloud‐Resolving E3SM Model (SCREAMv0) using high‐resolution ground‐based observations from the Atmospheric Radiation Measurement (ARM) Green Ocean Amazon (GoAmazon) field campaign. SCREAMv0 reasonably captures the diurnal timing of boundary layer clouds yet underestimates the boundary layer cloud fraction and mid‐level congestus. SCREAMv0 well replicates the precipitation diurnal cycle, however it exhibits biases in the precipitation cluster size distribution compared to scanning radar observations. Specifically, SCREAMv0 overproduces clusters smaller than 128 km, and does not form enough large clusters. Such biases suggest an inhibition of convective upscale growth, preventing isolated deep convective clusters from evolving into larger mesoscale systems. This model bias is partially attributed to the misrepresentation of land‐atmosphere coupling. This study highlights the potential use of high‐resolution ground‐based observations to diagnose convective processes in global storm resolving model simulations, identify key model deficiencies, and guide future process‐oriented model sensitivity tests and detailed analyses. |
| format | Article |
| id | doaj-art-a254aec4d3bc4ef788a67f21d9bc2f55 |
| institution | Kabale University |
| issn | 0094-8276 1944-8007 |
| language | English |
| publishDate | 2024-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geophysical Research Letters |
| spelling | doaj-art-a254aec4d3bc4ef788a67f21d9bc2f552025-08-20T03:49:46ZengWileyGeophysical Research Letters0094-82761944-80072024-07-015114n/an/a10.1029/2023GL108113How Well Does the DOE Global Storm Resolving Model Simulate Clouds and Precipitation Over the Amazon?Jingjing Tian0Yunyan Zhang1Stephen A. Klein2Christopher R. Terai3Peter M. Caldwell4Hassan Beydoun5Peter Bogenschutz6Hsi‐Yen Ma7Aaron S. Donahue8Lawrence Livermore National Laboratory Livermore CA USALawrence Livermore National Laboratory Livermore CA USALawrence Livermore National Laboratory Livermore CA USALawrence Livermore National Laboratory Livermore CA USALawrence Livermore National Laboratory Livermore CA USALawrence Livermore National Laboratory Livermore CA USALawrence Livermore National Laboratory Livermore CA USALawrence Livermore National Laboratory Livermore CA USALawrence Livermore National Laboratory Livermore CA USAAbstract This study assesses a 40‐day 3.25‐km global simulation of the Simple Cloud‐Resolving E3SM Model (SCREAMv0) using high‐resolution ground‐based observations from the Atmospheric Radiation Measurement (ARM) Green Ocean Amazon (GoAmazon) field campaign. SCREAMv0 reasonably captures the diurnal timing of boundary layer clouds yet underestimates the boundary layer cloud fraction and mid‐level congestus. SCREAMv0 well replicates the precipitation diurnal cycle, however it exhibits biases in the precipitation cluster size distribution compared to scanning radar observations. Specifically, SCREAMv0 overproduces clusters smaller than 128 km, and does not form enough large clusters. Such biases suggest an inhibition of convective upscale growth, preventing isolated deep convective clusters from evolving into larger mesoscale systems. This model bias is partially attributed to the misrepresentation of land‐atmosphere coupling. This study highlights the potential use of high‐resolution ground‐based observations to diagnose convective processes in global storm resolving model simulations, identify key model deficiencies, and guide future process‐oriented model sensitivity tests and detailed analyses.https://doi.org/10.1029/2023GL108113cloud and precipitationconvective processglobal storm resolving modelatmospheric radiation measurement observationsremote sensingmodel evaluation |
| spellingShingle | Jingjing Tian Yunyan Zhang Stephen A. Klein Christopher R. Terai Peter M. Caldwell Hassan Beydoun Peter Bogenschutz Hsi‐Yen Ma Aaron S. Donahue How Well Does the DOE Global Storm Resolving Model Simulate Clouds and Precipitation Over the Amazon? Geophysical Research Letters cloud and precipitation convective process global storm resolving model atmospheric radiation measurement observations remote sensing model evaluation |
| title | How Well Does the DOE Global Storm Resolving Model Simulate Clouds and Precipitation Over the Amazon? |
| title_full | How Well Does the DOE Global Storm Resolving Model Simulate Clouds and Precipitation Over the Amazon? |
| title_fullStr | How Well Does the DOE Global Storm Resolving Model Simulate Clouds and Precipitation Over the Amazon? |
| title_full_unstemmed | How Well Does the DOE Global Storm Resolving Model Simulate Clouds and Precipitation Over the Amazon? |
| title_short | How Well Does the DOE Global Storm Resolving Model Simulate Clouds and Precipitation Over the Amazon? |
| title_sort | how well does the doe global storm resolving model simulate clouds and precipitation over the amazon |
| topic | cloud and precipitation convective process global storm resolving model atmospheric radiation measurement observations remote sensing model evaluation |
| url | https://doi.org/10.1029/2023GL108113 |
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