Improving Thermodynamic Profile Retrievals from Microwave Radiometers by Including Radio Acoustic Sounding System (RASS) Observations

Aug 22, 2026

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Thermodynamic temperature and humidity profiles are retrieved from multi-wavelength microwave radiometer brightness temperature observations using regression, neural networks, and physical-iterative methods. Regression and neural networks are tuned to climatological mean conditions, whereas physical-iterative retrievals run a radiative transfer model iteratively from a climatologically reasonable profile until modeled brightness temperatures match observations within an uncertainty. This work tests the physical-iterative approach on XPIA field campaign data (2015, Boulder Atmospheric Observatory), including optional RASS inputs, and evaluates results against co-located radiosondes, improving low-level inversion characterization and reducing profile errors versus MWR-only retrievals.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages53

Reading Time2 h 14 min

AccessFree to download

Outline

Abstract

1. Introduction

2. XPIA dataset

2.1 MWR measurements

2.2 WPR-RASS measurements

2.3 BAO data

2.4 Radiosonde measurements

3. Physical retrievals

3.1 Iterative retrieval technique

3.2 Bias-correction of MWR observations using radiosondes or climatology

3.3 Analysis of physical retrieval characteristics

4. Results

4.1 Statistical analysis of the physical retrievals up to 3 km AGL

4.2 Statistics for the profiles least close to the climatology

4.3 Virtual temperature statistics

5. Conclusions

Appendix A

Data availability

Author contribution

Acknowledgments

References

1 Improving thermodynamic profile retrievals from microwave 2 radiometers by including Radio Acoustic Sounding System (RASS) 3 observations 4 5 6 Irina V. Djalalova1,2, David D. Turner3, Laura Bianco1,2, 7 James M. Wilczak2, James Duncan1,2*, Bianca Adler1,2 and Daniel Gottas2 8 9 1 Cooperative Institute for Research in Environmental Sciences (CIRES), Boulder, CO, USA 10 2 National Oceanic and Atmospheric Administration, Physical Sciences Laboratory, Boulder, CO, USA 11 3 National Oceanic and Atmospheric Administration, Global Systems Laboratory, Boulder, CO USA 12 * Now at WindESCo, Burlington, MA 13 14 15 16 17 18 Corresponding author address: Irina V. Djalalova ([Irina.V.Djalalova@noaa.gov), NOAA/Physical](Irina.V.Djalalova@noaa.gov), NOAA/Physical) [19](19 Science Laboratory)[ Science Laboratory](19 Science Laboratory), 325 Broadway, mail stop: PSD3, Boulder, CO 80305. Tel.: 303‐497‐6238. 20 Fax: 303‐497‐6181. 21 22 Outline 23 Abstract 24 1. Introduction 25 2. XPIA dataset 26 2.1 MWR measurements 27 2.2 WPR‐RASS measurements 28 2.3 BAO data 29 2.4 Radiosonde measurements 30 3. Physical retrievals 31 3.1 Iterative retrieval technique 32 3.2 Bias‐correction of MWR observations using radiosondes or climatology 33 3.3 Analysis of physical retrieval characteristics 34 4. Results 35 4.1 Statistical analysis of the physical retrievals up to 3 km AGL 36 4.2 Statistics for the profiles least close to the climatology 37 4.3 Virtual temperature statistics 38 5. Conclusions 39 Appendix A 40 Data availability 41 Author contribution 42 Acknowledgments 43 References 44 Abstract 45 Thermodynamic profiles are often retrieved from the multi‐wavelength brightness 46 temperature observations made by microwave radiometers (MWRs) using regression methods 47 (linear, quadratic approaches), artificial intelligence (neural networks), or physical‐iterative 48 methods. Regression and neural network methods are tuned to mean conditions derived from 49 a climatological dataset of thermodynamic profiles collected nearby. In contrast, physical‐ 50 iterative retrievals use a radiative transfer model starting from a climatologically reasonable 51 profile of temperature and water vapor, with the model running iteratively until the derived 52 brightness temperatures match those observed by the MWR within a specified uncertainty. 53 In this study, a physical‐iterative approach is used to retrieve temperature and humidity 54 profiles from data collected during XPIA (eXperimental Planetary boundary layer Instrument 55 Assessment), a field campaign held from March to May 2015 at NOAA’s Boulder Atmospheric 56 Observatory (BAO) facility. During the campaign, several passive and active remote sensing 57 instruments as well as in‐situ platforms were deployed and evaluated to determine their 58 suitability for the verification and validation of meteorological processes. Among the deployed 59 remote sensing instruments were a multi‐channel MWR, as well as two radio acoustic sounding 60 systems (RASS), associated with 915‐MHz and 449‐MHz wind profiling radars. 61 In this study the physical‐iterative approach is tested with different observational 62 inputs: first using data from surface sensors and the MWR in different configurations, and then 63 including data from the RASS into the retrieval with the MWR data. These temperature 64 retrievals are assessed against co‐located radiosonde profiles. Results show that the 65 combination of the MWR and RASS observations in the retrieval allows for a more accurate 66 characterization of low‐level temperature inversions, and that these retrieved temperature 67 profiles match the radiosonde observations better than the temperature profiles retrieved from 68 only the MWR in the layer between the surface and 3 km above ground level (AGL) . Specifically, 69 in this layer of the atmosphere, both r

FAQs

What does the physical-iterative retrieval method do in this study?

It uses a radiative transfer model starting from a climatologically reasonable temperature and water vapor profile, then iterates until modeled brightness temperatures match the microwave radiometer observations within a specified uncertainty.

How was RASS used to improve retrievals?

The retrievals were tested with different observational inputs, first using surface sensors and MWR in different configurations, and then including RASS into the retrieval together with MWR data.

Against what measurements were the retrieved profiles evaluated?

Retrieved temperature profiles were assessed against co-located radiosonde profiles, showing improved agreement—especially for low-level inversions between the surface and 3 km AGL—when MWR and RASS observations were combined.

Aug 22, 2026

Upload by Kyle