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Title: Statistical Study of SCM Simulations Using Continuous Forcing Data Derived from NWP Products with the ARM Data Constraints

Abstract

Statistical study of Single Column Model (SCM) results has been recently advocated by the ARM cloud parameterization and modeling working group. This is partly due to the sensitivity nature of Single Column Models (SCMs) to uncertainties in the initial conditions and the specified large-scale forcing. In addition, given the limitation of SCM framework (e.g. the lack of effective internal feedback between the SCM and the specified forcing) and the inevitable error in the initial conditions and the large-scale forcing, it might not be realistic to expect that SCMs can correctly capture every individual synoptic event. Statistical studies can help smooth out those random errors related to uncertainties in the initial conditions and the specified large-scale forcing so that one can focus on those physically important systematic errors from SCM simulations. Noted that, for climate simulations, it is more important for a given physical parameterization to successfully simulate statistics right for the process that is being parameterized. This study conducts a statistical study of SCM simulations by using the ARM recently developed continuous forcing data for the year 2000. The NCAR CCM3 SCM is used in this study. The long-term continuous forcing data were developed from the NOAA mesoscale model RUCmore » (Rapid Update Cycle) analysis using the ARM objective variational analysis approach, in which the ARM surface and the top of the atmosphere (TOA) measurements at Southern Great Plains (SGP) site are used as the constraining data. Seasonal averaged simulation biases in temperature, moisture, and surface precipitation rates are analyzed. Performance of the SCM to simulate the ARM observed seasonal averaged diurnal variations of surface precipitation and outgoing longwave radiative flux (OLR) is also discussed.« less

Authors:
; ; ;
Publication Date:
Research Org.:
Lawrence Livermore National Lab., CA (US)
Sponsoring Org.:
US Department of Energy (US)
OSTI Identifier:
15005401
Report Number(s):
UCRL-JC-151604
TRN: US200322%%417
DOE Contract Number:  
W-7405-ENG-48
Resource Type:
Conference
Resource Relation:
Conference: ARM Science Team Meeting, Broomfield, CO (US), 03/31/2003--04/04/2003; Other Information: PBD: 30 Jun 2003
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; CLIMATES; CLOUDS; DAILY VARIATIONS; FEEDBACK; MOISTURE; PERFORMANCE; PRECIPITATION; SENSITIVITY; SIMULATION; STATISTICS; USA

Citation Formats

Xie, S, Cederwall, R, Zhang, M, and Yio, J J. Statistical Study of SCM Simulations Using Continuous Forcing Data Derived from NWP Products with the ARM Data Constraints. United States: N. p., 2003. Web.
Xie, S, Cederwall, R, Zhang, M, & Yio, J J. Statistical Study of SCM Simulations Using Continuous Forcing Data Derived from NWP Products with the ARM Data Constraints. United States.
Xie, S, Cederwall, R, Zhang, M, and Yio, J J. Mon . "Statistical Study of SCM Simulations Using Continuous Forcing Data Derived from NWP Products with the ARM Data Constraints". United States. https://www.osti.gov/servlets/purl/15005401.
@article{osti_15005401,
title = {Statistical Study of SCM Simulations Using Continuous Forcing Data Derived from NWP Products with the ARM Data Constraints},
author = {Xie, S and Cederwall, R and Zhang, M and Yio, J J},
abstractNote = {Statistical study of Single Column Model (SCM) results has been recently advocated by the ARM cloud parameterization and modeling working group. This is partly due to the sensitivity nature of Single Column Models (SCMs) to uncertainties in the initial conditions and the specified large-scale forcing. In addition, given the limitation of SCM framework (e.g. the lack of effective internal feedback between the SCM and the specified forcing) and the inevitable error in the initial conditions and the large-scale forcing, it might not be realistic to expect that SCMs can correctly capture every individual synoptic event. Statistical studies can help smooth out those random errors related to uncertainties in the initial conditions and the specified large-scale forcing so that one can focus on those physically important systematic errors from SCM simulations. Noted that, for climate simulations, it is more important for a given physical parameterization to successfully simulate statistics right for the process that is being parameterized. This study conducts a statistical study of SCM simulations by using the ARM recently developed continuous forcing data for the year 2000. The NCAR CCM3 SCM is used in this study. The long-term continuous forcing data were developed from the NOAA mesoscale model RUC (Rapid Update Cycle) analysis using the ARM objective variational analysis approach, in which the ARM surface and the top of the atmosphere (TOA) measurements at Southern Great Plains (SGP) site are used as the constraining data. Seasonal averaged simulation biases in temperature, moisture, and surface precipitation rates are analyzed. Performance of the SCM to simulate the ARM observed seasonal averaged diurnal variations of surface precipitation and outgoing longwave radiative flux (OLR) is also discussed.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Mon Jun 30 00:00:00 EDT 2003},
month = {Mon Jun 30 00:00:00 EDT 2003}
}

Conference:
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