How well do stratospheric reanalyses reproduce high-resolution satellite temperature measurements?

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Abstract

Atmospheric reanalyses are data-assimilating weather models which are widely used as proxies for the true state of the atmosphere in the recent past. This is particularly the case for the stratosphere, where historical observations are sparse. But how realistic are these stratospheric reanalyses? Here, we resample stratospheric temperature data from six modern reanalyses (CFSR, ERA-5, ERA-Interim, JRA-55, JRA-55C and MERRA-2) to produce synthetic satellite observations, which we directly compare to retrieved satellite temperatures from COSMIC, HIRDLS and SABER and to brightness temperatures from AIRS for the 10-year period of 2003–2012. We explicitly sample standard public-release products in order to best assess their suitability for typical usage. We find that all-time all-latitude correlations between limb sounder observations and synthetic observations from full-input reanalyses are 0.97–0.99 at 30km in altitude, falling to 0.84–0.94 at 50km. The highest correlations are seen at high latitudes and the lowest in the sub-tropics, but root-mean-square (RMS) differences are highest (10K or greater) in high-latitude winter. At all latitudes, differences increase with increasing height. High-altitude differences become especially large during disrupted periods such as the post-sudden stratospheric warming recovery phase, in which zonal-mean differences can be as high as 18K among different datasets. We further show that, for the current generation of reanalysis products, a full-3-D sampling approach (i.e. one which takes full account of the instrument measuring volume) is always required to produce realistic synthetic AIRS observations, but is almost never required to produce realistic synthetic HIRDLS observations. For synthetic SABER and COSMIC observations full-3-D sampling is required in equatorial regions and regions of high gravity-wave activity but not otherwise. Finally, we use cluster analyses to show that full-input reanalyses (those which assimilate the full suite of observations, i.e. excluding JRA-55C) are more tightly correlated with each other than with observations, even observations which they assimilate. This may suggest that these reanalyses are over-tuned to match their comparators. If so, this could have significant implications for future reanalysis development.
LanguageEnglish
Number of pages29
JournalAtmospheric Chemistry and Physics
Volume18
Issue number18
DOIs
StatusPublished - 27 Sep 2018

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COSMIC
AIRS
temperature
sampling
brightness temperature
gravity wave
stratosphere
limb
weather
atmosphere
winter
product
observation satellite
public
sudden stratospheric warming
measuring instrument
tropics

Cite this

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title = "How well do stratospheric reanalyses reproduce high-resolution satellite temperature measurements?",
abstract = "Atmospheric reanalyses are data-assimilating weather models which are widely used as proxies for the true state of the atmosphere in the recent past. This is particularly the case for the stratosphere, where historical observations are sparse. But how realistic are these stratospheric reanalyses? Here, we resample stratospheric temperature data from six modern reanalyses (CFSR, ERA-5, ERA-Interim, JRA-55, JRA-55C and MERRA-2) to produce synthetic satellite observations, which we directly compare to retrieved satellite temperatures from COSMIC, HIRDLS and SABER and to brightness temperatures from AIRS for the 10-year period of 2003–2012. We explicitly sample standard public-release products in order to best assess their suitability for typical usage. We find that all-time all-latitude correlations between limb sounder observations and synthetic observations from full-input reanalyses are 0.97–0.99 at 30km in altitude, falling to 0.84–0.94 at 50km. The highest correlations are seen at high latitudes and the lowest in the sub-tropics, but root-mean-square (RMS) differences are highest (10K or greater) in high-latitude winter. At all latitudes, differences increase with increasing height. High-altitude differences become especially large during disrupted periods such as the post-sudden stratospheric warming recovery phase, in which zonal-mean differences can be as high as 18K among different datasets. We further show that, for the current generation of reanalysis products, a full-3-D sampling approach (i.e. one which takes full account of the instrument measuring volume) is always required to produce realistic synthetic AIRS observations, but is almost never required to produce realistic synthetic HIRDLS observations. For synthetic SABER and COSMIC observations full-3-D sampling is required in equatorial regions and regions of high gravity-wave activity but not otherwise. Finally, we use cluster analyses to show that full-input reanalyses (those which assimilate the full suite of observations, i.e. excluding JRA-55C) are more tightly correlated with each other than with observations, even observations which they assimilate. This may suggest that these reanalyses are over-tuned to match their comparators. If so, this could have significant implications for future reanalysis development.",
author = "Corwin Wright and Neil Hindley",
year = "2018",
month = "9",
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doi = "10.5194/acp-18-13703-2018",
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journal = "Atmospheric Chemistry and Physics",
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TY - JOUR

T1 - How well do stratospheric reanalyses reproduce high-resolution satellite temperature measurements?

AU - Wright, Corwin

AU - Hindley, Neil

PY - 2018/9/27

Y1 - 2018/9/27

N2 - Atmospheric reanalyses are data-assimilating weather models which are widely used as proxies for the true state of the atmosphere in the recent past. This is particularly the case for the stratosphere, where historical observations are sparse. But how realistic are these stratospheric reanalyses? Here, we resample stratospheric temperature data from six modern reanalyses (CFSR, ERA-5, ERA-Interim, JRA-55, JRA-55C and MERRA-2) to produce synthetic satellite observations, which we directly compare to retrieved satellite temperatures from COSMIC, HIRDLS and SABER and to brightness temperatures from AIRS for the 10-year period of 2003–2012. We explicitly sample standard public-release products in order to best assess their suitability for typical usage. We find that all-time all-latitude correlations between limb sounder observations and synthetic observations from full-input reanalyses are 0.97–0.99 at 30km in altitude, falling to 0.84–0.94 at 50km. The highest correlations are seen at high latitudes and the lowest in the sub-tropics, but root-mean-square (RMS) differences are highest (10K or greater) in high-latitude winter. At all latitudes, differences increase with increasing height. High-altitude differences become especially large during disrupted periods such as the post-sudden stratospheric warming recovery phase, in which zonal-mean differences can be as high as 18K among different datasets. We further show that, for the current generation of reanalysis products, a full-3-D sampling approach (i.e. one which takes full account of the instrument measuring volume) is always required to produce realistic synthetic AIRS observations, but is almost never required to produce realistic synthetic HIRDLS observations. For synthetic SABER and COSMIC observations full-3-D sampling is required in equatorial regions and regions of high gravity-wave activity but not otherwise. Finally, we use cluster analyses to show that full-input reanalyses (those which assimilate the full suite of observations, i.e. excluding JRA-55C) are more tightly correlated with each other than with observations, even observations which they assimilate. This may suggest that these reanalyses are over-tuned to match their comparators. If so, this could have significant implications for future reanalysis development.

AB - Atmospheric reanalyses are data-assimilating weather models which are widely used as proxies for the true state of the atmosphere in the recent past. This is particularly the case for the stratosphere, where historical observations are sparse. But how realistic are these stratospheric reanalyses? Here, we resample stratospheric temperature data from six modern reanalyses (CFSR, ERA-5, ERA-Interim, JRA-55, JRA-55C and MERRA-2) to produce synthetic satellite observations, which we directly compare to retrieved satellite temperatures from COSMIC, HIRDLS and SABER and to brightness temperatures from AIRS for the 10-year period of 2003–2012. We explicitly sample standard public-release products in order to best assess their suitability for typical usage. We find that all-time all-latitude correlations between limb sounder observations and synthetic observations from full-input reanalyses are 0.97–0.99 at 30km in altitude, falling to 0.84–0.94 at 50km. The highest correlations are seen at high latitudes and the lowest in the sub-tropics, but root-mean-square (RMS) differences are highest (10K or greater) in high-latitude winter. At all latitudes, differences increase with increasing height. High-altitude differences become especially large during disrupted periods such as the post-sudden stratospheric warming recovery phase, in which zonal-mean differences can be as high as 18K among different datasets. We further show that, for the current generation of reanalysis products, a full-3-D sampling approach (i.e. one which takes full account of the instrument measuring volume) is always required to produce realistic synthetic AIRS observations, but is almost never required to produce realistic synthetic HIRDLS observations. For synthetic SABER and COSMIC observations full-3-D sampling is required in equatorial regions and regions of high gravity-wave activity but not otherwise. Finally, we use cluster analyses to show that full-input reanalyses (those which assimilate the full suite of observations, i.e. excluding JRA-55C) are more tightly correlated with each other than with observations, even observations which they assimilate. This may suggest that these reanalyses are over-tuned to match their comparators. If so, this could have significant implications for future reanalysis development.

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DO - 10.5194/acp-18-13703-2018

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JO - Atmospheric Chemistry and Physics

T2 - Atmospheric Chemistry and Physics

JF - Atmospheric Chemistry and Physics

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