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Potential Impacts of Assimilating All-Sky Satellite Infrared Radiances on Convection-Permitting Analysis and Prediction of Tropical Convection
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Citation:
Citation_Information:
Originator:The Pennsylvania State University
Publication_Date:2020
Title:
Potential Impacts of Assimilating All-Sky Satellite Infrared Radiances on Convection-Permitting Analysis and Prediction of Tropical Convection
Online_Linkage: http://www.datacommons.psu.edu/
Description:
Abstract: 
Geostationary infrared satellite observations are spatially dense [ >1/(20 km)2] and temporally frequent ( > 1/hour). These suggest the possibility of using these observations to constrain sub-synoptic features over data sparse regions, such as tropical oceans. In this study, the potential impacts of assimilating water vapor channel brightness temperature (WV-BT) observations from the geostationary Meteorological Satellite 7 (Meteosat-7) on tropical convection analysis and prediction were systematically examined through a series of ensemble data assimilation experiments. WV-BT observations were assimilated hourly into convection-permitting ensembles using Penn State’s ensemble square root filter (EnSRF).  Comparisons against independently observed Meteosat-7 window channel brightness temperature (Window-BT) show that the assimilation of WV-BT generally improved the intensities and locations of large-scale cloud patterns at spatial scales larger than 100 km. However, comparisons against independent soundings indicate that the EnSRF analysis produced a much stronger dry bias than the no data assimilation experiment. This strong dry bias is associated with the use of the simulated WV-BT from the prior mean during the EnSRF analysis step. A stochastic variant of the ensemble Kalman filter (NoMeanSF) is proposed. The NoMeanSF algorithm was able to assimilate the WV-BT without causing such a strong dry bias and the quality of the analyses’ horizontal cloud pattern is similar to EnSRF’s analyses. Finally, deterministic forecasts initiated from the NoMeanSF analyses possess better horizontal cloud patterns above 500 km than those of the EnSRF. These results suggest that it might be better to assimilate all-sky WV-BT through the NoMeanSF algorithm than the EnSRF algorithm.
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Single_Date/Time:
Calendar_Date:2020
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publication date
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Theme_Keyword_Thesaurus:ISO 19115 Topic Categories
Theme_Keyword:climatologyMeteorologyAtmosphere
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Point_of_Contact:
Contact_Information:
Contact_Person_Primary:
Contact_Person:Man-Yau Chan
Contact_Organization:Penn State
Contact_Position:Department of Meteorology and Atmospheric Science
Contact_Address:
Address_Type:mailing address
Address: 
624 Walker Building
City:University Park
State_or_Province:Pennsylvania
Postal_Code:16802
Country:USA
Contact_Electronic_Mail_Address:chanmanyau@gmail.com
Contact_Electronic_Mail_Address:mxc98@psu.edu
Contact Instructions: 
Authors:
Man-Yau Chan*, Fuqing Zhang*, Xingchao Chen*, L. Ruby Leung**
* Department of Meteorology and Atmospheric Science, and Center for Advanced Data Assimilation and Predictability Techniques, The Pennsylvania State University, University Park, Pennsylvania, USA
**Atmospheric Sciences and Global Change, Pacific Northwest National Laboratory, Richland, Washington, USA
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Contact_Organization:Penn State Data Commons
Contact_Address:
Address_Type:mailing and physical address
Address: 
115 Land and Water Building
City:University Park
State_or_Province:Pennsylvania
Postal_Code:16802
Country:United States
Contact_Voice_Telephone:(814) 865 - 8792
Contact_Electronic_Mail_Address:datacommons@psu.edu
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The USER shall indemnify, save harmless, and, if requested, defend those parties involved with the development and distribution of this data, their officers, agents, and employees from and against any suits, claims, or actions for injury, death, or property damage arising out of the use of or any defect in the FILES or any accompanying documentation. Those parties involved with the development and distribution excluded any and all implied warranties, including warranties or merchantability and fitness for a particular purpose and makes no warranty or representation, either express or implied, with respect to the FILES or accompanying documentation, including its quality, performance, merchantability, or fitness for a particular purpose. The FILES and documentation are provided "as is" and the USER assumes the entire risk as to its quality and performance. Those parties involved with the development and distribution of this data will not be liable for any direct, indirect, special, incidental, or consequential damages arising out of the use or inability to use the FILES or any accompanying documentation.
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Contact_Information:
Contact_Organization_Primary:
Contact_Organization:Penn State Data Commons
Contact_Position:Metadata Coordinator
Contact_Address:
Address_Type:mailing address
Address: 
115 Land and Water Building
City:University Park
State_or_Province:Pennsylvania
Postal_Code:16802
Country:United States
Contact_Voice_Telephone:814-865-8792
Contact_Electronic_Mail_Address:datacommons@psu.edu
Metadata_Standard_Name:FGDC Content Standards for Digital Geospatial Metadata
Metadata_Standard_Version:FGDC-STD-001-1998
Metadata_Time_Convention:local time
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