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At least 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Site B - NREL ASSIST (SN11) Thermodynamic Retrievals TROPoe / Derived Data

This dataset contains daily files with thermodynamic profiles retrieved with the optimal estimation physical retrieval TROPoe v0.12 (Turner and Löhnert 2014; Turner and Blumberg 2019; Turner and Löhnert 2021). The profiles are retrieved every 10 minutes from instantaneous observations from the NREL ASSIST-II (SN 11) infrared spectrometer. Observations are noise-filtered but not averaged in time to minimize errors due to non-uniform clouds. Additional input data in TROPoe are cloud base height (CBH), which is a combined data product that uses data from ceilometers at sites A1 and H and scanning lidars from ARM sites C1 and E37. The CBH is weighted inversely proportionally to the distance to the respective site to take into account the spatial variability of clouds (see https://github.com/StefanoWind/ASSIST_analysis/blob/main/awaken_processing/combine_cbh.py). The full pipeline for running the retrieval is available at https://github.com/StefanoWind/TROPoe_processor. Met data was not ingested. In addition to these temporally resolved input data, TROPoe requires an a priori dataset (prior) that provides mean climatological estimates of thermodynamic profiles and specifies how temperature and humidity covary with height as an input (for details see, e.g., Djalalova et al. 2022). The prior is a key component of the retrieval and provides a constraint on the ill-posed inversion problem. A monthly prior was computed from operational radiosonde launches at ARM SGP, OK.

17 WIND ENERGY↗
DOE OSTI2025

Site G - NREL ASSIST (SN10) Thermodynamic Retrievals TROPoe / Derived Data

This dataset contains daily files with thermodynamic profiles retrieved with the optimal estimation physical retrieval TROPoe v0.12 (Turner and Löhnert 2014; Turner and Blumberg 2019; Turner and Löhnert 2021). The profiles are retrieved every 10 minutes from instantaneous observations from the NREL ASSIST-II (SN 10) infrared spectrometer. Observations are noise-filtered but not averaged in time to minimize errors due to non-uniform clouds. Additional input data in TROPoe are cloud base height (CBH), which is a combined data product that uses data from ceilometers at sites A1 and H and scanning lidars from ARM sites C1 and E37. The CBH is weighted inversely proportionally to the distance to the respective site to take into account the spatial variability of clouds (see https://github.com/StefanoWind/ASSIST_analysis/blob/main/awaken_processing/combine_cbh.py). The full pipeline for running the retrieval is available at https://github.com/StefanoWind/TROPoe_processor. Met data was not ingested. In addition to these temporally resolved input data, TROPoe requires an a priori dataset (prior) that provides mean climatological estimates of thermodynamic profiles and specifies how temperature and humidity covary with height as an input (for details see, e.g., Djalalova et al. 2022). The prior is a key component of the retrieval and provides a constraint on the ill-posed inversion problem. A monthly prior was computed from operational radiosonde launches at ARM SGP, OK.

17 WIND ENERGY↗
DOE OSTI2025

Site C1a - NREL ASSIST (SN12) Thermodynamic Retrievals TROPoe / Derived Data

This dataset contains daily files with thermodynamic profiles retrieved with the optimal estimation physical retrieval TROPoe v0.12 (Turner and Löhnert 2014; Turner and Blumberg 2019; Turner and Löhnert 2021). The profiles are retrieved every 10 minutes from instantaneous observations from the NREL ASSIST-II (SN 12) infrared spectrometer. Observations are noise-filtered but not averaged in time to minimize errors due to non-uniform clouds. Additional input data in TROPoe are cloud base height (CBH), which is a combined data product that uses data from ceilometers at sites A1 and H and scanning lidars from ARM sites C1 and E37. The CBH is weighted inversely proportionally to the distance to the respective site to take into account the spatial variability of clouds (see https://github.com/StefanoWind/ASSIST_analysis/blob/main/awaken_processing/combine_cbh.py). The full pipeline for running the retrieval is available at https://github.com/StefanoWind/TROPoe_processor. Met data was not ingested. In addition to these temporally resolved input data, TROPoe requires an a priori dataset (prior) that provides mean climatological estimates of thermodynamic profiles and specifies how temperature and humidity covary with height as an input (for details see, e.g., Djalalova et al. 2022). The prior is a key component of the retrieval and provides a constraint on the ill-posed inversion problem. A monthly prior was computed from operational radiosonde launches at ARM SGP, OK.

17 WIND ENERGY↗
DOE OSTI2025

Rhode Island Site - NREL ASSIST Thermodynamic Retrievals TROPoe v0.19 / Derived Data

This dataset contains daily files with thermodynamic profiles retrieved with the optimal estimation physical retrieval TROPoe v0.19 (Turner and Löhnert, 2014; Turner and Blumberg, 2019; Turner and Löhnert, 2021). The profiles are retrieved every 10 minutes from instantaneous observations from the NREL ASSIST-II (SN 12) infrared spectrometer. Observations are noise-filtered but not averaged in time to minimize errors due to non-uniform clouds. Additional input data in TROPoe are cloud base height from the collocated NREL upgraded Galion lidar (Newsom et al., 2019), and surface temperature, relative humidity, and pressure from the collocated PNNL met tower. The full pipeline to run the retrieval is available at https://github.com/StefanoWind/TROPoe_processor. In addition to these temporally resolved input data, TROPoe requires an a priori dataset (prior) that provides mean climatological estimates of thermodynamic profiles and specifies how temperature and humidity covary with height as an input (for details see, e.g., Djalalova et al., 2022). The prior is a key component of the retrieval and provides a constraint on the ill-posed inversion problem. A monthly prior was computed from operational radiosonde launches at Upton, NY.

17 WIND ENERGY↗
DOE OSTI2026

BLOC Site - ASSIST Thermodynamic Retrievals TROPoe v0.18 / Derived Data

This dataset contains daily files with thermodynamic profiles retrieved with the optimal estimation physical retrieval TROPoe (Turner and Löhnert 2014; Turner and Blumberg 2019; Turner and Löhnert 2021). This is a post-processed dataset and recommended for use. The profiles are retrieved every 10 minutes from instantaneous radiances observed with an Atmospheric Sounder Spectrometer by Infrared Spectral Technology (ASSIST, Michaud-Belleau et al. 2025) operated by NOAA Physical Sciences Laboratory (PSL) on Block Island for WFIP3. The spectral bands used in the retrieval are in the wavenumber range from 612 - 905.4 cm-1 and are specified in Turner and Löhnert (2021). Additional input data in TROPoe are cloud base height from a collocated ceilometer operated by NOAA GML and temperature, water vapor mixing ratio, and pressure from a collocated surface tower operated by NOAA PSL. In addition to these temporally resolved input data, TROPoe requires an a priori dataset (prior) that provides mean climatological estimates of thermodynamic profiles and specifies how temperature and humidity covary with height as an input (for details see, e.g., Djalalova et al. 2022). The prior is a key component of the retrieval and provides a constraint on the ill-posed inversion problem. A monthly prior was computed from operational radiosonde launches at Upton, NY. The TROPoe docker container (version 0.18) is available from Docker Hub at https://hub.docker.com/r/davidturner53/tropoe/tags, and the source code code is available in the GitHub repository https://github.com/OAR-atmospheric-observations/TROPoe.

17 WIND ENERGY↗
DOE OSTI2026

NANT Site - ASSIST Thermodynamic Retrievals TROPoe v0.18 / Derived Data

This dataset contains daily files with thermodynamic profiles retrieved with the optimal estimation physical retrieval TROPoe (Turner and Löhnert 2014; Turner and Blumberg 2019; Turner and Löhnert 2021). This is a post-processed dataset and recommended for use. The profiles are retrieved every 10 minutes from instantaneous radiances observed with an Atmospheric Sounder Spectrometer by Infrared Spectral Technology (ASSIST, Michaud-Belleau et al. 2025) operated by NOAA Physical Sciences Laboratory (PSL) on Nantucket Island for WFIP3. The spectral bands used in the retrieval are in the wavenumber range from 612 - 905.4 cm-1 and are specified in Turner and Löhnert (2021). Additional input data in TROPoe are cloud base height from a collocated ceilometer operated by NOAA GML and temperature, water vapor mixing ratio, and pressure from a collocated surface tower operated by NOAA PSL. In addition to these temporally resolved input data, TROPoe requires an a priori dataset (prior) that provides mean climatological estimates of thermodynamic profiles and specifies how temperature and humidity covary with height as an input (for details see, e.g., Djalalova et al. 2022). The prior is a key component of the retrieval and provides a constraint on the ill-posed inversion problem. A monthly prior was computed from operational radiosonde launches at Upton, NY. The TROPoe docker container (version 0.18) is available from Docker Hub at https://hub.docker.com/r/davidturner53/tropoe/tags, and the source code code is available in the GitHub repository https://github.com/OAR-atmospheric-observations/TROPoe.

17 WIND ENERGY↗
DOE OSTI2026

FC Site 4.0 - NLR Thermodynamic profiler (ASSIST II-11) Thermodynamic Retrievals TROPoe

This dataset contains daily files with thermodynamic profiles retrieved with the optimal estimation physical retrieval TROPoe v0.19 (Turner and Löhnert 2014; Turner and Blumberg 2019; Turner and Löhnert 2021). The profiles are retrieved every 10 minutes from instantaneous observations from the NLR ASSIST II infrared spectrometer. Observations are noise-filtered but not averaged in time to minimize errors due to non-uniform clouds. Additional input data in TROPoe are cloud base height (CBH) from co-located scanning lidar. The full pipeline for running the retrieval is available at https://github.com/StefanoWind/TROPoe_processor. Met data was not ingested. In addition to these temporally resolved input data, TROPoe requires an a priori dataset (prior) that provides mean climatological estimates of thermodynamic profiles and specifies how temperature and humidity covary with height as an input (for details see, e.g., Djalalova et al. 2022). The prior is a key component of the retrieval and provides a constraint on the ill-posed inversion problem. A monthly prior was computed from operational radiosonde launches in Denver, CO.

17 WIND ENERGY↗
DOE OSTI2026

NREL ASSIST Rhode Island / Thermodynamic retrievals TROPoe

This dataset contains daily files with thermodynamic profiles retrieved with the optimal estimation physical retrieval TROPoe (Turner and Löhnert 2014; Turner and Blumberg 2019; Turner and Löhnert 2021). The profiles are retrieved every 10 minutes from instantaneous observations from the NREL ASSIST-II (SN 11) infrared spectrometer. Observations are noise-filtered but not averaged in time to minimize errors due to non-uniform clouds. Additional input data in TROPoe are cloud base height from a collocated NREL Upgraded Galion lidar #1212-60 and temperature, RH, and pressure from collocated PNNL met station. The lidar beta is used to estimate the cloud base height through a modified algorithm by Newsom et al., 2019 used at ARM SGP. The full pipeline for running the retrieval is available at https://github.com/StefanoWind/TROPoe_processor. In addition to these temporally resolved input data, TROPoe requires an a priori dataset (prior) which provides mean climatological estimates of thermodynamic profiles and specifies how temperature and humidity covary with height as an input (for details see e.g. Djalalova et al. 2022). The prior is a key component of the retrieval and provides a constraint on the ill-posed inversion problem. A monthly prior was computed from operational radiosonde launches at Upton, NY.

17 WIND ENERGY↗
DOE OSTI2020

ExoReL{sup R}: A Bayesian Inverse Retrieval Framework for Exoplanetary Reflected Light Spectra

The high-contrast imaging technique is meant to provide insight into those planets orbiting several astronomical units from their host star. Space missions such as Wide-Field InfraRed Survey Telescope, Habitable Exoplanet Imaging Mission, and Large Ultra-Violet/Optical/InfraRed Surveyor will measure reflected light spectra of cold gaseous and rocky planets. To interpret these observations, we introduce ExoReL{sup R} (Exoplanetary Reflected Light Retrieval), a novel Bayesian retrieval framework to retrieve cloud properties and atmospheric structures from exoplanetary reflected light spectra. As a unique feature, it assumes a vertically nonuniform volume mixing ratio (VMR) profile of water and ammonia, and uses it to construct cloud densities. In this way, clouds and molecular mixture ratios are consistent. We apply ExoReL{sup R} on three test cases: two exoplanets (υ And e and 47 Uma b) and Jupiter. We show that we are able to retrieve the concentration of methane in the atmosphere, and estimate the position of clouds when the signal-to-noise ratio of the spectrum is higher than 15, in line with previous works. Moreover, we described the ability of our model to give a chemical identity to clouds, and we discussed whether or not we can observe this difference in the planetary reflection spectrum. Finally, we demonstrate how it could be possible to retrieve molecular concentrations (water and ammonia in this work) below the clouds by linking the nonuniform VMR profile to the cloud presence. This will help to constrain the concentration of water and ammonia unseen in direct measurements.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Sensitivity of thermodynamic profiles retrieved from ground-based microwave and infrared observations to additional input data from active remote sensing instruments and numerical weather prediction models

Accurate and continuous estimates of the thermodynamic structure of the lower atmosphere are highly beneficial to meteorological process understanding and its applications, such as weather forecasting. In this study, the Tropospheric Remotely Observed Profiling via Optimal Estimation (TROPoe) physical retrieval is used to retrieve temperature and humidity profiles from various combinations of input data collected by passive and active remote sensing instruments, in situ surface platforms, and numerical weather prediction models. Among the employed instruments are microwave radiometers (MWRs), infrared spectrometers (IRSs), radio acoustic sounding systems (RASSs), ceilometers, and surface sensors. TROPoe uses brightness temperatures and/or radiances from MWRs and IRSs, as well as other observational inputs (virtual temperature from the RASS, cloud-base height from the ceilometer, pressure, temperature, and humidity from the surface sensors) in a physical iterative retrieval approach. This starts from a climatologically reasonable profile of temperature and water vapor, with the radiative transfer model iteratively adjusting the assumed temperature and humidity profiles until the derived brightness temperatures and radiances match those observed by the MWR and/or IRS instruments within a specified uncertainty, as well as within the uncertainties of the other observations, if used as input. In this study, due to the uniqueness of the dataset that includes all the above-mentioned sensors, TROPoe is tested with different observational input combinations, some of which also include information higher than 4 km above ground level (a.g.l.) from the operational Rapid Refresh numerical weather prediction model. These temperature and humidity retrievals are assessed against independent collocated radiosonde profiles under non-cloudy conditions to assess the sensitivity of the TROPoe retrievals to different input combinations.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1978

An extended Kalman-Bucy filter for atmospheric temperature profile retrieval with a passive microwave sounder

An extended Kalman-Bucy filter has been implemented for atmospheric temperature profile retrievals from observations made using the Scanned Microwave Spectrometer (SCAMS) instrument carried on the Nimbus 6 satellite. This filter has the advantage that it requires neither stationary statistics in the underlying processes nor linear production of the observed variables from the variables to be estimated. This extended Kalman-Bucy filter has yielded significant performance improvement relative to multiple regression retrieval methods. A multi-spot extended Kalman-Bucy filter has also been developed in which the temperature profiles at a number of scan angles in a scanning instrument are retrieved simultaneously. These multi-spot retrievals are shown to outperform the single-spot Kalman retrievals.

Ledsham, W. H.↗
NASA NTRS1979

Development of concepts for satellite retrieval devices

The teleoperator being developed to augment the Space Transportation System (STS) for satellite placement, retrieval, or servicing at altitudes or orbital planes where it would be impractical to use the shuttle is primarily a general purpose propulsion stage that can be fitted with manipulator arms, automated servicers and satellite retrieval devices for particular missions. Design concepts for a general purpose retrieval device for docking with a satellite to which a grappling fixture has been attached, and for a retrieval device for docking with the Solar Maximum Mission (SMM) spacecraft were defined. The mechanical aspects of these two devices are discussed as well as the crew operations involved and problems created by the requirement for remote control. Drawings for the two retrieval device concepts are included.

Pruett, E. C.↗
NASA NTRS1991

Retrievable Payload Carrier (RPC): Next generation Long Duration Exposure Facility

The Retrievable Payload Carrier (RPC) is described which is a multi-experiment, free-flyer spacecraft being privately developed to make in-space experimentation more accessible and economically feasible for research and development organizations. The carrier concept was derived from NASA's highly successful and flight proven Long Duration Exposure Facility (LDEF). The LDEF capabilities were enhanced to meet new customer requirements. This reusable payload carrier is planned for launch and retrieval by the space shuttle on a regular basis. The RPC's compact design facilitates flexible manifesting of the shuttle cargo bay space, thereby, permitting timely launches and retrievals. The vehicle is designed so that either the entire carrier can be retrieved from orbit, or individual experiment pallets can be removed and replaced in orbit. This gives customers even greater control over their experiment recoveries. A fully operational RPC System consists of: (1) a carrier spacecraft; (2) a ground control station for communication with customer payloads and the carrier; (3) a ground processing facility to provide refurbishment, final checkout, and integration of customer payloads and the carrier; and (4) the personnel required to support both the development and operation phases of the program. Customer payloads will fly 6 to 18 month missions, or longer, as required. Spreading launch, operations, and retrieval costs over multiple experimental payloads results in minimized customer costs. The RPC Program's initial objective is to operate a simple carrier system which meets basic customer service needs.

Perry, Arthur T.↗
NASA NTRS1990

Retrieval of water vapor profiles from microwave radiometric measurements near 90 and 183 GHz

Upwelling radiometric measurements at 90 GHz and three side bands near 183 GHz are used to retrieve water vapor profiles over the ocean surface. An algorithm incorporating a new technique of handling moderate cloud cover is illustrated for the profiling of both relative humidity and water vapor burden. It is shown that the retrieved relative humidity profiles reflect gross features of the corresponding profiles recorded by the radiosondes. However, the retrieval generally cannot produce fine details of the observed profiles at altitudes where a rapid change in relative humidity occurs. For this reason, comparison of retrieved and observed values at a given altitude often yields an appreciable rms error. Profiling of water vapor burden, a parameter equivalent to total integrated water vapor above a certain altitude, results in much better agreement, as expected. The rms error obtained from the results of the retrieval at the surface is comparable to that derived from the combination of measurements at 18 GHz and 21 GHz channels of the Scanning Multichannel Microwave Radiometer aboard the Nimbus 7 satellite.

Wang, J. R.↗
NASA NTRS1990

Retrieval methods for ground-based millimeter-wave measurements for the network for the detection of stratospheric change

The fundamental objective is to determine the information available in ground-based millimeter-wave measurements of stratospheric constituent profiles, to identify the optimum method of retrieving this profile information, and to characterize the errors in the final result. A secondary objective is to produce retrieval software for operational use with Network for the Detection of Stratospheric Change (NDSC) measurements of O3, H2O, ClO, and perhaps N2O. Tests were performed on existing ozone retrieval programs in support of ongoing NDSC field measurements. The results show that if random spectral errors and retrieval bias errors are considered, accuracy of the retrieved profile is about 5 percent from 20-50 km, and about 10 percent from 50-60 km.

Connor, Brian J.↗
NASA NTRS1993

Algorithms and sensitivity analyses for Stratospheric Aerosol and Gas Experiment II water vapor retrieval

The operational inversion algorithm used for the retrieval of the water-vapor vertical profiles from the Stratospheric Aerosol and Gas Experiment II (SAGE II) occultation data is presented. Unlike the algorithm used for the retrieval of aerosol, O3, and NO2, the water-vapor retrieval algorithm accounts for the nonlinear relationship between the concentration versus the broad-band absorption characteristics of water vapor. Problems related to the accuracy of the computational scheme, the accuracy of the removal of other interfering species, and the expected uncertainty of the retrieved profile are examined. Results are presented on the error analysis of the SAGE II water vapor retrieval, indicating that the SAGE II instrument produced good quality water vapor data.

Chu, W. P.↗
NASA NTRS1994

Improvements to TOVS retrievals over sea ice and applications to estimating Arctic energy fluxes

Modeling studies suggest that polar regions play a major role in modulating the Earth's climate and that they may be more sensitive than lower latitudes to climate change. Until recently, however, data from meteorological stations poleward of 70 degs have been sparse, and consequently, our understanding of air-sea-ice interaction processes is relatively poor. Satellite-borne sensors now offer a promising opportunity to observe polar regions and ultimately to improve parameterizations of energy transfer processes in climate models. This study focuses on the application of the TIROS-N operational vertical sounder (TOVS) to sea-ice-covered regions in the nonmelt season. TOVS radiances are processed with the improved initialization inversion ('3I') algorithm, providng estimates of layer-average temperature and moisture, cloud conditions, and surface characteristics at a horizontal resolution of approximately 100 km x 100 km. Although TOVS has flown continuously on polar-orbiting satellites since 1978, its potential has not been realized in high latitudes because the quality of retrievals is often significantly lower over sea ice and snow than over the surfaces. The recent availability of three Arctic data sets has provided an opportunity to validate TOVS retrievals: the first from the Coordinated Eastern Arctic Experiment (CEAREX) in winter 1988/1989, the second from the LeadEx field program in spring 1992, and the third from Russian drifting ice stations. Comparisons with these data reveal deficiencies in TOVS retrievals over sea ice during the cold season; e.g., ice surface temperature is often 5 to 15 K too warm, microwave emissivity is approximately 15% too low at large view angles, clear/cloudy scenes are sometimes misidentified, and low-level inversions are often not captured. In this study, methods to reduce these errors are investigated. Improvements to the ice surface temperature retrieval have reduced rms errors from approximately 7 K to 3 K; correction of microwaves (50 GHz) brightness temperatures for view angle dependence now allows the surface type (open water versus sea ice) to be determined; modifications to the clear/cloud tests have improved cloud detection over sea ice, especially where it is inhomogeneous; and improved surface temperature estimates have resulted in more successful diagnoses of low-level stratification. Applications of improved retrievals to studies of the Arctic energy budget are encouraging. Preliminary calculations of air-ice stress vectors, 10-m wind speed, and horizontal advection of heat and moisture are presented.

Francis, Jennifer A.↗
NASA NTRS1995

Millimeter-wave Imaging Radiometer (MIR) data processing and development of water vapor retrieval algorithms

This document describes the progress of the task of the Millimeter-wave Imaging Radiometer (MIR) data processing and the development of water vapor retrieval algorithms, for the second six-month performing period. Aircraft MIR data from two 1995 field experiments were collected and processed with a revised data processing software. Two revised versions of water vapor retrieval algorithm were developed, one for the execution of retrieval on a supercomputer platform, and one for using pressure as the vertical coordinate. Two implementations of incorporating products from other sensors into the water vapor retrieval system, one from the Special Sensor Microwave Imager (SSM/I), the other from the High-resolution Interferometer Sounder (HIS). Water vapor retrievals were performed for both airborne MIR data and spaceborne SSM/T-2 data, during field experiments of TOGA/COARE, CAMEX-1, and CAMEX-2. The climatology of water vapor during TOGA/COARE was examined by SSM/T-2 soundings and conventional rawinsonde.

Chang, L. Aron↗