Improving thermodynamic profile retrievals from microwave radiometers by including radio acoustic sounding system (RASS) observations
<p>Thermodynamic profiles are often retrieved from the multi-wavelength brightness temperature observations made by microwave radiometers (MWRs) using regression methods (linear, quadratic approaches), artificial intelligence (neural networks), or physical iterative methods. Regression and neu...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2022-01-01
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| Series: | Atmospheric Measurement Techniques |
| Online Access: | https://amt.copernicus.org/articles/15/521/2022/amt-15-521-2022.pdf |
| _version_ | 1831641828296753152 |
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| author | I. V. Djalalova I. V. Djalalova D. D. Turner L. Bianco L. Bianco J. M. Wilczak J. Duncan J. Duncan J. Duncan B. Adler B. Adler D. Gottas |
| author_facet | I. V. Djalalova I. V. Djalalova D. D. Turner L. Bianco L. Bianco J. M. Wilczak J. Duncan J. Duncan J. Duncan B. Adler B. Adler D. Gottas |
| author_sort | I. V. Djalalova |
| collection | DOAJ |
| description | <p>Thermodynamic profiles are often retrieved from the multi-wavelength
brightness temperature observations made by microwave radiometers (MWRs)
using regression methods (linear, quadratic approaches), artificial
intelligence (neural networks), or physical iterative methods. Regression
and neural network methods are tuned to mean conditions derived from a
climatological dataset of thermodynamic profiles collected nearby. In
contrast, physical iterative retrievals use a radiative transfer model
starting from a climatologically reasonable profile of temperature and water
vapor, with the model running iteratively until the derived brightness
temperatures match those observed by the MWR within a specified uncertainty.</p>
<p>In this study, a physical iterative approach is used to retrieve temperature
and humidity profiles from data collected during XPIA (eXperimental
Planetary boundary layer Instrument Assessment), a field campaign held from
March to May 2015 at NOAA's Boulder Atmospheric Observatory (BAO) facility.
During the campaign, several passive and active remote sensing instruments
as well as in situ platforms were deployed and evaluated to determine their
suitability for the verification and validation of meteorological processes.
Among the deployed remote sensing instruments were a multi-channel MWR as
well as two radio acoustic sounding systems (RASSs) associated with 915
and 449 MHz wind profiling radars.</p>
<p>In this study the physical iterative approach is tested with different
observational inputs: first using data from surface sensors and the MWR in
different configurations and then including data from the RASS in the
retrieval with the MWR data. These temperature retrievals are assessed
against co-located radiosonde profiles. Results show that the combination of the MWR and RASS observations in the retrieval allows for a more accurate characterization of low-level temperature inversions and that these retrieved temperature profiles match the radiosonde observations better than
the temperature profiles retrieved from only the MWR in the layer between
the surface and 3 km above ground level (a.g.l.). Specifically, in this layer
of the atmosphere, both root mean square errors and standard deviations of
the difference between radiosonde and retrievals that combine MWR and RASS
are improved by mostly 10 %–20 % compared to the configuration that does not
include RASS observations. Pearson correlation coefficients are also
improved.</p>
<p>A comparison of the temperature physical retrievals to the
manufacturer-provided neural network retrievals is provided in Appendix A.</p> |
| first_indexed | 2024-12-19T12:31:39Z |
| format | Article |
| id | doaj.art-2e23613c90e4411391c7fa90fb47d0dd |
| institution | Directory Open Access Journal |
| issn | 1867-1381 1867-8548 |
| language | English |
| last_indexed | 2024-12-19T12:31:39Z |
| publishDate | 2022-01-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Atmospheric Measurement Techniques |
| spelling | doaj.art-2e23613c90e4411391c7fa90fb47d0dd2022-12-21T20:21:22ZengCopernicus PublicationsAtmospheric Measurement Techniques1867-13811867-85482022-01-011552153710.5194/amt-15-521-2022Improving thermodynamic profile retrievals from microwave radiometers by including radio acoustic sounding system (RASS) observationsI. V. Djalalova0I. V. Djalalova1D. D. Turner2L. Bianco3L. Bianco4J. M. Wilczak5J. Duncan6J. Duncan7J. Duncan8B. Adler9B. Adler10D. Gottas11Weather and Climate Dynamics, Cooperative Institute for Research in Environmental Sciences (CIRES), Boulder, CO, USAPhysical Sciences Laboratory, National Oceanic and Atmospheric Administration, Boulder, CO, USAGlobal Systems Laboratory, National Oceanic and Atmospheric Administration, Boulder, CO, USAWeather and Climate Dynamics, Cooperative Institute for Research in Environmental Sciences (CIRES), Boulder, CO, USAPhysical Sciences Laboratory, National Oceanic and Atmospheric Administration, Boulder, CO, USAPhysical Sciences Laboratory, National Oceanic and Atmospheric Administration, Boulder, CO, USAWeather and Climate Dynamics, Cooperative Institute for Research in Environmental Sciences (CIRES), Boulder, CO, USAPhysical Sciences Laboratory, National Oceanic and Atmospheric Administration, Boulder, CO, USAnow at: WindESCo, Burlington, MA, USAWeather and Climate Dynamics, Cooperative Institute for Research in Environmental Sciences (CIRES), Boulder, CO, USAPhysical Sciences Laboratory, National Oceanic and Atmospheric Administration, Boulder, CO, USAPhysical Sciences Laboratory, National Oceanic and Atmospheric Administration, Boulder, CO, USA<p>Thermodynamic profiles are often retrieved from the multi-wavelength brightness temperature observations made by microwave radiometers (MWRs) using regression methods (linear, quadratic approaches), artificial intelligence (neural networks), or physical iterative methods. Regression and neural network methods are tuned to mean conditions derived from a climatological dataset of thermodynamic profiles collected nearby. In contrast, physical iterative retrievals use a radiative transfer model starting from a climatologically reasonable profile of temperature and water vapor, with the model running iteratively until the derived brightness temperatures match those observed by the MWR within a specified uncertainty.</p> <p>In this study, a physical iterative approach is used to retrieve temperature and humidity profiles from data collected during XPIA (eXperimental Planetary boundary layer Instrument Assessment), a field campaign held from March to May 2015 at NOAA's Boulder Atmospheric Observatory (BAO) facility. During the campaign, several passive and active remote sensing instruments as well as in situ platforms were deployed and evaluated to determine their suitability for the verification and validation of meteorological processes. Among the deployed remote sensing instruments were a multi-channel MWR as well as two radio acoustic sounding systems (RASSs) associated with 915 and 449 MHz wind profiling radars.</p> <p>In this study the physical iterative approach is tested with different observational inputs: first using data from surface sensors and the MWR in different configurations and then including data from the RASS in the retrieval with the MWR data. These temperature retrievals are assessed against co-located radiosonde profiles. Results show that the combination of the MWR and RASS observations in the retrieval allows for a more accurate characterization of low-level temperature inversions and that these retrieved temperature profiles match the radiosonde observations better than the temperature profiles retrieved from only the MWR in the layer between the surface and 3 km above ground level (a.g.l.). Specifically, in this layer of the atmosphere, both root mean square errors and standard deviations of the difference between radiosonde and retrievals that combine MWR and RASS are improved by mostly 10 %–20 % compared to the configuration that does not include RASS observations. Pearson correlation coefficients are also improved.</p> <p>A comparison of the temperature physical retrievals to the manufacturer-provided neural network retrievals is provided in Appendix A.</p>https://amt.copernicus.org/articles/15/521/2022/amt-15-521-2022.pdf |
| spellingShingle | I. V. Djalalova I. V. Djalalova D. D. Turner L. Bianco L. Bianco J. M. Wilczak J. Duncan J. Duncan J. Duncan B. Adler B. Adler D. Gottas Improving thermodynamic profile retrievals from microwave radiometers by including radio acoustic sounding system (RASS) observations Atmospheric Measurement Techniques |
| title | Improving thermodynamic profile retrievals from microwave radiometers by including radio acoustic sounding system (RASS) observations |
| title_full | Improving thermodynamic profile retrievals from microwave radiometers by including radio acoustic sounding system (RASS) observations |
| title_fullStr | Improving thermodynamic profile retrievals from microwave radiometers by including radio acoustic sounding system (RASS) observations |
| title_full_unstemmed | Improving thermodynamic profile retrievals from microwave radiometers by including radio acoustic sounding system (RASS) observations |
| title_short | Improving thermodynamic profile retrievals from microwave radiometers by including radio acoustic sounding system (RASS) observations |
| title_sort | improving thermodynamic profile retrievals from microwave radiometers by including radio acoustic sounding system rass observations |
| url | https://amt.copernicus.org/articles/15/521/2022/amt-15-521-2022.pdf |
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