reanalysis intercomparison
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2017 ◽  
Vol 17 (23) ◽  
pp. 14593-14629 ◽  
Author(s):  
Craig S. Long ◽  
Masatomo Fujiwara ◽  
Sean Davis ◽  
Daniel M. Mitchell ◽  
Corwin J. Wright

Abstract. Two of the most basic parameters generated from a reanalysis are temperature and winds. Temperatures in the reanalyses are derived from conventional (surface and balloon), aircraft, and satellite observations. Winds are observed by conventional systems, cloud tracked, and derived from height fields, which are in turn derived from the vertical temperature structure. In this paper we evaluate as part of the SPARC Reanalysis Intercomparison Project (S-RIP) the temperature and wind structure of all the recent and past reanalyses. This evaluation is mainly among the reanalyses themselves, but comparisons against independent observations, such as HIRDLS and COSMIC temperatures, are also presented. This evaluation uses monthly mean and 2.5° zonal mean data sets and spans the satellite era from 1979–2014. There is very good agreement in temperature seasonally and latitudinally among the more recent reanalyses (CFSR, MERRA, ERA-Interim, JRA-55, and MERRA-2) between the surface and 10 hPa. At lower pressures there is increased variance among these reanalyses that changes with season and latitude. This variance also changes during the time span of these reanalyses with greater variance during the TOVS period (1979–1998) and less variance afterward in the ATOVS period (1999–2014). There is a distinct change in the temperature structure in the middle and upper stratosphere during this transition from TOVS to ATOVS systems. Zonal winds are in greater agreement than temperatures and this agreement extends to lower pressures than the temperatures. Older reanalyses (NCEP/NCAR, NCEP/DOE, ERA-40, JRA-25) have larger temperature and zonal wind disagreement from the more recent reanalyses. All reanalyses to date have issues analysing the quasi-biennial oscillation (QBO) winds. Comparisons with Singapore QBO winds show disagreement in the amplitude of the westerly and easterly anomalies. The disagreement with Singapore winds improves with the transition from TOVS to ATOVS observations. Temperature bias characteristics determined via comparisons with a reanalysis ensemble mean (MERRA, ERA-Interim, JRA-55) are similarly observed when compared with Aura HIRDLS and Aura MLS observations. There is good agreement among the NOAA TLS, SSU1, and SSU2 Climate Data Records and layer mean temperatures from the more recent reanalyses. Caution is advised for using reanalysis temperatures for trend detection and anomalies from a long climatology period as the quality and character of reanalyses may have changed over time.


2017 ◽  
Vol 12 (11) ◽  
pp. 114019 ◽  
Author(s):  
Verónica Torralba ◽  
Francisco J Doblas-Reyes ◽  
Nube Gonzalez-Reviriego

2017 ◽  
Author(s):  
Craig S. Long ◽  
Masatomo Fujiwara ◽  
Sean Davis ◽  
Daniel M. Mitchell ◽  
Corwin J. Wright

Abstract. Abstract. Two of the most basic parameters generated from a reanalysis are temperature and winds. Temperatures in the reanalyses are derived from conventional (surface and balloon), aircraft, and satellite observations. Winds are both observed by conventional systems, cloud tracked, and derived from height fields which in turn are derived from the vertical temperature structure. In this paper we evaluate as part of the SPARC-Reanalysis Intercomparison Project (S-RIP) the temperature and wind structure of all the recent and past reanalyses. This evaluation is mainly between the reanalyses themselves, but comparisons against independent observations such as HIRDLS temperatures are also presented. This evaluation uses monthly mean and 2.5 degree zonal mean data sets and spans the satellite era from 1979–2014. There is very good agreement in temperature seasonally and latitudinally between the more recent reanalyses (CFSR, MERRA, ERA-Interim, JRA-55, and MERRA-2) between the surface and 10 hPa. At lower pressures there is increased variance between these reanalyses that changes with season and latitude. This variance also changes during the time span of these reanalyses with greater variance during the TOVS period (1979–1998) and less variance afterward in the ATOVS period (1999–2014). There is a distinct change in the temperature structure in the middle and upper stratosphere during this transition from TOVS to ATOVS systems. Zonal winds are in greater agreement than temperatures and this agreement extends to lower pressures than the temperatures. Older reanalyses (NCEP/NCAR, NCEP/DOE, ERA-40, JRA-25) have larger temperature and zonal wind disagreement from the more recent reanalyses. All reanalyses to date have issues analysing the Quasi-Biennial Oscillation (QBO) winds. Comparisons with Singapore QBO winds show disagreement in the amplitude of the westerly and easterly anomalies. The disagreement with Singapore winds improves with the transition from TOVS to ATOVS observations. Temperature bias characteristics determined via comparisons with a Reanalysis Ensemble Mean (MERRA, ERA-Interim, JRA-55) are similarly observed when compared with Aura/HIRDLS and Aura/MLS observations. There is good agreement between NOAA's TLS, SSU1 and SSU2 Climate Data Records and layer mean temperatures from the more recent reanalyses. Caution is advised for using reanalysis temperatures for trend detection.


2017 ◽  
Vol 17 (2) ◽  
pp. 1417-1452 ◽  
Author(s):  
Masatomo Fujiwara ◽  
Jonathon S. Wright ◽  
Gloria L. Manney ◽  
Lesley J. Gray ◽  
James Anstey ◽  
...  

Abstract. The climate research community uses atmospheric reanalysis data sets to understand a wide range of processes and variability in the atmosphere, yet different reanalyses may give very different results for the same diagnostics. The Stratosphere–troposphere Processes And their Role in Climate (SPARC) Reanalysis Intercomparison Project (S-RIP) is a coordinated activity to compare reanalysis data sets using a variety of key diagnostics. The objectives of this project are to identify differences among reanalyses and understand their underlying causes, to provide guidance on appropriate usage of various reanalysis products in scientific studies, particularly those of relevance to SPARC, and to contribute to future improvements in the reanalysis products by establishing collaborative links between reanalysis centres and data users. The project focuses predominantly on differences among reanalyses, although studies that include operational analyses and studies comparing reanalyses with observations are also included when appropriate. The emphasis is on diagnostics of the upper troposphere, stratosphere, and lower mesosphere. This paper summarizes the motivation and goals of the S-RIP activity and extensively reviews key technical aspects of the reanalysis data sets that are the focus of this activity. The special issue The SPARC Reanalysis Intercomparison Project (S-RIP) in this journal serves to collect research with relevance to the S-RIP in preparation for the publication of the planned two (interim and full) S-RIP reports.


2016 ◽  
Author(s):  
Masatomo Fujiwara ◽  
Jonathon S. Wright ◽  
Gloria L. Manney ◽  
Lesley J. Gray ◽  
James Anstey ◽  
...  

Abstract. The climate research community uses atmospheric reanalysis data sets to understand a wide range of processes and variability in the atmosphere, yet different reanalyses may give very different results for the same diagnostics. The Stratosphere–troposphere Processes And their Role in Climate (SPARC) Reanalysis Intercomparison Project (S-RIP) is a coordinated activity to compare reanalysis data sets using a variety of key diagnostics. The objectives of this project are to identify differences among reanalyses and understand their underlying causes, to provide guidance on appropriate usage of various reanalysis products in scientific studies, particularly those of relevance to SPARC, and to contribute to future improvements in the reanalysis products by establishing collaborative links between reanalysis centres and data users. The project focuses predominantly on differences among reanalyses, although studies that include operational analyses and studies comparing reanalyses with observations are also included when appropriate. The emphasis is on diagnostics of the upper troposphere, stratosphere, and lower mesosphere. This overview paper for the S-RIP special issue summarizes the motivation and goals of the S-RIP activity, and reviews key technical aspects of the reanalysis data sets that are the focus of the S-RIP report.


2014 ◽  
Vol 95 (11) ◽  
pp. 1671-1678 ◽  
Author(s):  
Catherine A. Smith ◽  
Gilbert P. Compo ◽  
Don K. Hooper

While atmospheric reanalysis datasets are widely used in climate science, many technical issues hinder comparing them to each other and to observations. The reanalysis fields are stored in diverse file architectures, data formats, and resolutions. Their metadata, such as variable name and units, can also differ. Individual users have to download the fields, convert them to a common format, store them locally, change variable names, regrid if needed, and convert units. Even if a dataset can be read via the Open-Source Project for a Network Data Access Protocol (commonly known as OPeNDAP) or a similar protocol, most of this work is still needed. All of these tasks take time, effort, and money. Our group at the Cooperative Institute for Research in the Environmental Sciences at the University of Colorado and affiliated colleagues at the NOAA's Earth System Research Laboratory Physical Sciences Division have expertise both in making reanalysis datasets available and in creating web-based climate analysis tools that have been widely used throughout the meteorological community. To overcome some of the obstacles in reanalysis intercomparison, we have created a set of web-based Reanalysis Intercomparison Tools (WRIT) at www.esrl.noaa.gov/psd/data/writ/. WRIT allows users to easily plot and compare reanalysis datasets, and to test hypotheses. For standard pressure-level and surface variables there are tools to plot trajectories, monthly mean maps and vertical cross sections, and monthly mean time series. Some observational datasets are also included. Users can refine date, statistics, and plotting options. WRIT also facilitates the mission of the Reanalyses.org website as a convenient toolkit for studying the reanalysis datasets.


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