29 Jan 2021

29 Jan 2021

Review status: this preprint is currently under review for the journal AMT.

Improving thermodynamic profile retrievals from microwave radiometers by including Radio Acoustic Sounding System (RASS) observations

Irina V. Djalalova1,2, David D. Turner3, Laura Bianco1,2, James M. Wilczak2, James Duncan1,2, Bianca Adler1,2, and Daniel Gottas2 Irina V. Djalalova et al.
  • 1Cooperative Institute for Research in Environmental Sciences (CIRES), Boulder, CO, USA
  • 2National Oceanic and Atmospheric Administration, Physical Sciences Laboratory, Boulder, CO, USA
  • 3National Oceanic and Atmospheric Administration, Global Systems Laboratory, Boulder, CO USA

Abstract. 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 value of temperature and water vapor, with the model run iteratively until the derived brightness temperatures match those observed by the MWR within a specified uncertainty.

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 was a multi-channel MWR, as well as two radio acoustic sounding systems (RASS), associated with 915-MHz and 449-MHz wind profiling radars.

Having the possibility to combine the information provided by the MWR and RASS systems, 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 RASSs. These temperature retrievals are also compared to those derived by a neural network method, assessing their relative accuracy against 58 co-located radiosonde profiles. Results show that the combination of the MWR and RASS observations in the physical-iterative approach allows for a more accurate characterization of low-level temperature inversions, and that these retrieved temperature profiles match the radiosonde observations better than all other approaches, including the neural network, in the atmospheric layer between the surface and 5 km AGL. 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 ~0.5 °C compared to the other methods. Pearson correlation coefficients are also improved.

Irina V. Djalalova et al.

Status: open (until 26 Mar 2021)

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Irina V. Djalalova et al.

Irina V. Djalalova et al.


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Short summary
In this paper we show the usefulness of observational data in the radiative transfer model to retrieve the reliable atmospheric temperature profiles. In particular, the additional data from radio acoustic sounding systems (RASS) could improve the temperature in terms of bias and RMSE in comparison to the radiosonde temperature profiles not only in the 200–2000 m atmospheric layer above the ground where RASS data available, but in the deep 0–5000 m atmosphere stratum.