Characterizing sampling and quality screening biases in infrared and microwave limb sounding
This study investigates orbital sampling biases and evaluates the additional impact caused by data quality screening for the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) and the Aura Microwave Limb Sounder (MLS). MIPAS acts as a proxy for typical infrared limb emission sounde...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2018-03-01
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| Series: | Atmospheric Chemistry and Physics |
| Online Access: | https://www.atmos-chem-phys.net/18/4187/2018/acp-18-4187-2018.pdf |
| _version_ | 1818173608245067776 |
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| author | L. F. Millán N. J. Livesey M. L. Santee T. von Clarmann |
| author_facet | L. F. Millán N. J. Livesey M. L. Santee T. von Clarmann |
| author_sort | L. F. Millán |
| collection | DOAJ |
| description | This study investigates orbital sampling biases and evaluates the additional
impact caused by data quality screening for the Michelson Interferometer for
Passive Atmospheric Sounding (MIPAS) and the Aura Microwave Limb Sounder
(MLS). MIPAS acts as a proxy for typical infrared limb emission sounders,
while MLS acts as a proxy for microwave limb sounders. These biases were
calculated for temperature and several trace gases by interpolating model
fields to real sampling patterns and, additionally, screening those locations
as directed by their corresponding quality criteria. Both instruments have
dense uniform sampling patterns typical of limb emission sounders, producing
almost identical sampling biases. However, there is a substantial difference
between the number of locations discarded. MIPAS, as a mid-infrared
instrument, is very sensitive to clouds, and measurements affected by them
are thus rejected from the analysis. For example, in the tropics, the MIPAS
yield is strongly affected by clouds, while MLS is mostly unaffected.
<br><br>
The results show that upper-tropospheric sampling biases in zonally averaged
data, for both instruments, can be up to 10 to 30 %, depending on the
species, and up to 3 K for temperature. For MIPAS, the sampling reduction
due to quality screening worsens the biases, leading to values as large as
30 to 100 % for the trace gases and expanding the 3 K bias region for
temperature. This type of sampling bias is largely induced by the geophysical
origins of the screening (e.g. clouds). Further, analysis of long-term time
series reveals that these additional quality screening biases may affect the
ability to accurately detect upper-tropospheric long-term changes using such
data. In contrast, MLS data quality screening removes
sufficiently few points that no additional bias is introduced, although its
penetration is limited to the upper troposphere, while MIPAS may cover well
into the mid-troposphere in cloud-free scenarios. We emphasize that the
results of this study refer only to the representativeness of the respective
data, not to their intrinsic quality. |
| first_indexed | 2024-12-11T19:31:12Z |
| format | Article |
| id | doaj.art-c13e00029eb44a93adc48ef94491fd5d |
| institution | Directory Open Access Journal |
| issn | 1680-7316 1680-7324 |
| language | English |
| last_indexed | 2024-12-11T19:31:12Z |
| publishDate | 2018-03-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Atmospheric Chemistry and Physics |
| spelling | doaj.art-c13e00029eb44a93adc48ef94491fd5d2022-12-22T00:53:17ZengCopernicus PublicationsAtmospheric Chemistry and Physics1680-73161680-73242018-03-01184187419910.5194/acp-18-4187-2018Characterizing sampling and quality screening biases in infrared and microwave limb soundingL. F. Millán0N. J. Livesey1M. L. Santee2T. von Clarmann3Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USAJet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USAJet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USAInstitut für Meteorologie und Klimaforschung, Karlsruhe Institute of Technology, Karlsruhe, GermanyThis study investigates orbital sampling biases and evaluates the additional impact caused by data quality screening for the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) and the Aura Microwave Limb Sounder (MLS). MIPAS acts as a proxy for typical infrared limb emission sounders, while MLS acts as a proxy for microwave limb sounders. These biases were calculated for temperature and several trace gases by interpolating model fields to real sampling patterns and, additionally, screening those locations as directed by their corresponding quality criteria. Both instruments have dense uniform sampling patterns typical of limb emission sounders, producing almost identical sampling biases. However, there is a substantial difference between the number of locations discarded. MIPAS, as a mid-infrared instrument, is very sensitive to clouds, and measurements affected by them are thus rejected from the analysis. For example, in the tropics, the MIPAS yield is strongly affected by clouds, while MLS is mostly unaffected. <br><br> The results show that upper-tropospheric sampling biases in zonally averaged data, for both instruments, can be up to 10 to 30 %, depending on the species, and up to 3 K for temperature. For MIPAS, the sampling reduction due to quality screening worsens the biases, leading to values as large as 30 to 100 % for the trace gases and expanding the 3 K bias region for temperature. This type of sampling bias is largely induced by the geophysical origins of the screening (e.g. clouds). Further, analysis of long-term time series reveals that these additional quality screening biases may affect the ability to accurately detect upper-tropospheric long-term changes using such data. In contrast, MLS data quality screening removes sufficiently few points that no additional bias is introduced, although its penetration is limited to the upper troposphere, while MIPAS may cover well into the mid-troposphere in cloud-free scenarios. We emphasize that the results of this study refer only to the representativeness of the respective data, not to their intrinsic quality.https://www.atmos-chem-phys.net/18/4187/2018/acp-18-4187-2018.pdf |
| spellingShingle | L. F. Millán N. J. Livesey M. L. Santee T. von Clarmann Characterizing sampling and quality screening biases in infrared and microwave limb sounding Atmospheric Chemistry and Physics |
| title | Characterizing sampling and quality screening biases in infrared and microwave limb sounding |
| title_full | Characterizing sampling and quality screening biases in infrared and microwave limb sounding |
| title_fullStr | Characterizing sampling and quality screening biases in infrared and microwave limb sounding |
| title_full_unstemmed | Characterizing sampling and quality screening biases in infrared and microwave limb sounding |
| title_short | Characterizing sampling and quality screening biases in infrared and microwave limb sounding |
| title_sort | characterizing sampling and quality screening biases in infrared and microwave limb sounding |
| url | https://www.atmos-chem-phys.net/18/4187/2018/acp-18-4187-2018.pdf |
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