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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.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/improving-thermodynamic-profile-retrievals-from-microwave-radiometers-by-including-radio-acoustic-sounding-system-rass-observations/137704/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/improving-thermodynamic-profile-retrievals-from-microwave-radiometers-by-including-radio-acoustic-sounding-system-rass-observations/137704.png","ImageObject",300,407,{"name":92,"@type":93},"Kyle","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-19","2026-08-22",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What does the physical-iterative retrieval method do in this study?","Question",{"text":112,"@type":113},"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.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How was RASS used to improve retrievals?",{"text":117,"@type":113},"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.",{"name":119,"@type":110,"acceptedAnswer":120},"Against what measurements were the retrieved profiles evaluated?",{"text":121,"@type":113},"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.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},137704,1787438211,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":39,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":129,"read_time":144},3985741905716,"https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d","1 Improving thermodynamic profile retrievals from microwave  \n2 radiometers by including Radio Acoustic Sounding System (RASS)  \n3 observations  \n4  \n5  \n6 Irina V. Djalalova1,2, David D. Turner3, Laura Bianco1,2,  \n7 James M. Wilczak2, James Duncan1,2*, Bianca Adler1,2 and Daniel Gottas2  \n8  \n9 1 Cooperative Institute for Research in Environmental Sciences (CIRES), Boulder, CO, USA  \n10 2 National Oceanic and Atmospheric Administration, Physical Sciences Laboratory, Boulder, CO, USA  \n11 3 National Oceanic and Atmospheric Administration, Global Systems Laboratory, Boulder, CO USA  \n12 * Now at WindESCo, Burlington, MA  \n13  \n14  \n15  \n16  \n17  \n18 Corresponding author address: Irina V. Djalalova ([Irina.V.Djalalova@noaa.gov), NOAA/Physical](Irina.V.Djalalova@noaa.gov), NOAA/Physical)  \n[19](19 Science Laboratory)[ Science Laboratory](19 Science Laboratory), 325 Broadway, mail stop: PSD3, Boulder, CO 80305. Tel.: 303‐497‐6238.  \n20 Fax: 303‐497‐6181.  \n21  \n22 Outline  \n23 Abstract  \n24 1. Introduction  \n25 2. XPIA dataset  \n26 2.1 MWR measurements  \n27 2.2 WPR‐RASS measurements  \n28 2.3 BAO data  \n29 2.4 Radiosonde measurements  \n30 3. Physical retrievals  \n31 3.1 Iterative retrieval technique  \n32 3.2 Bias‐correction of MWR observations using radiosondes or climatology  \n33 3.3 Analysis of physical retrieval characteristics  \n34 4. Results  \n35 4.1 Statistical analysis of the physical retrievals up to 3 km AGL  \n36 4.2 Statistics for the profiles least close to the climatology  \n37 4.3 Virtual temperature statistics  \n38 5. Conclusions  \n39 Appendix A  \n40 Data availability  \n41 Author contribution  \n42 Acknowledgments  \n43 References  \n44 Abstract  \n45 Thermodynamic profiles are often retrieved from the multi‐wavelength brightness  \n46 temperature observations made by microwave radiometers (MWRs) using regression methods  \n47 (linear, quadratic approaches), artificial intelligence (neural networks), or physical‐iterative  \n48 methods. Regression and neural network methods are tuned to mean conditions derived from  \n49 a climatological dataset of thermodynamic profiles collected nearby. In contrast, physical‐  \n50 iterative retrievals use a radiative transfer model starting from a climatologically reasonable  \n51 profile of temperature and water vapor, with the model running iteratively until the derived  \n52 brightness temperatures match those observed by the MWR within a specified uncertainty.  \n53 In this study, a physical‐iterative approach is used to retrieve temperature and humidity  \n54 profiles from data collected during XPIA (eXperimental Planetary boundary layer Instrument  \n55 Assessment), a field campaign held from March to May 2015 at NOAA’s Boulder Atmospheric  \n56 Observatory (BAO) facility. During the campaign, several passive and active remote sensing  \n57 instruments as well as in‐situ platforms were deployed and evaluated to determine their  \n58 suitability for the verification and validation of meteorological processes. Among the deployed  \n59 remote sensing instruments were a multi‐channel MWR, as well as two radio acoustic sounding  \n60 systems (RASS), associated with 915‐MHz and 449‐MHz wind profiling radars.  \n61 In this study the physical‐iterative approach is tested with different observational  \n62 inputs: first using data from surface sensors and the MWR in different configurations, and then  \n63 including data from the RASS into the retrieval with the MWR data. These temperature  \n64 retrievals are assessed against co‐located radiosonde profiles. Results show that the  \n65 combination of the MWR and RASS observations in the retrieval allows for a more accurate  \n66 characterization of low‐level temperature inversions, and that these retrieved temperature  \n67 profiles match the radiosonde observations better than the temperature profiles retrieved from  \n68 only the MWR in the layer between the surface and 3 km above ground level (AGL) . Specifically,  \n69 in this layer of the atmosphere, both r","cbCaioIJVrPS4gYP","https://ap.wps.com/l/cbCaioIJVrPS4gYP","pdf",1567036,53,"English","# Outline\n## Abstract\n# 1. Introduction\n# 2. XPIA dataset\n## 2.1 MWR measurements\n## 2.2 WPR-RASS measurements\n## 2.3 BAO data\n## 2.4 Radiosonde measurements\n# 3. Physical retrievals\n## 3.1 Iterative retrieval technique\n## 3.2 Bias-correction of MWR observations using radiosondes or climatology\n## 3.3 Analysis of physical retrieval characteristics\n# 4. Results\n## 4.1 Statistical analysis of the physical retrievals up to 3 km AGL\n## 4.2 Statistics for the profiles least close to the climatology\n## 4.3 Virtual temperature statistics\n# 5. Conclusions\n# Appendix A\n## Data availability\n## Author contribution\n## Acknowledgments\n## References","[{\"question\":\"What does the physical-iterative retrieval method do in this study?\",\"answer\":\"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.\"},{\"question\":\"How was RASS used to improve retrievals?\",\"answer\":\"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.\"},{\"question\":\"Against what measurements were the retrieved profiles evaluated?\",\"answer\":\"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.\"}]","Improving Thermodynamic Profile Retrievals from Microwave Radiometers by Including Radio Acoustic Sounding System (RASS) Observations | PDF",134]