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DOE OSTI2021

A Novel Machine Learning Algorithm for Planetary Boundary Layer Height Estimation Using AERI Measurement Data

Accurately determining the height of the planetary boundary layer (PBL) is important since it can affect the climate, weather, and air quality. Ground-based infrared hyperspectral remote sensing is an effective way to obtain this parameter. Compared with radiosonde measurements, its temporal resolution is much higher. In this study, a method to retrieve the PBL height (PBLH) from the ground-based infrared hyperspectral radiance data is proposed based on machine learning. In this method, the channels that are sensitive to temperature and humidity profiles are selected as the feature vectors, and the PBLHs derived from radiosonde are taken as the true values. The support vector machine (SVM) is applied to train and test the data set, and the parameters are optimized in the process. The data set collected at the Atmospheric Radiation Measurement (ARM) program Southern Great Plains (SGP) from 2012 to 2015 is analyzed. The instruments used in this letter include Atmospheric Emitted Radiance Interferometer (AERI), Vaisala CL31 ceilometer, and radiosonde. It shows that the root mean square error (RMSE) between the PBLHs calculated by the proposed method using AERI data and those from radiosonde data can be within 370 m, and the square correlation coefficient (SCC) is greater than 0.7. Compared with the PBLHs derived from the ceilometer, it can be found that the new method is more stable and less affected by clouds.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Using Downwelling Far- and Thermal-Infrared Hyperspectral Radiance for Cloud Phase Classification in the Antarctic

The cloud phase is one of the most important parameters of clouds. In this paper, we propose a method for cloud phase classification that synergistically utilizes the far- and thermal-infrared bands based on the Atmospheric Emitted Radiance Interferometer (AERI) at the Atmospheric Radiation Measurement West Antarctic Radiation Experiment (AWARE) observatory in 2016. The possible features in the far- and thermal-infrared bands are analyzed based on the differences in the simulated cloud brightness temperature (BT) spectra with different cloud phases. Using the support vector machine (SVM) algorithm, four features are determined to identify the cloud phase, which include the BT at 900 cm -1 , the slope of the fitted function of BT in the 900–1000 cm -1 interval, the BT difference (BTD) between 512 cm -1 and 726 cm -1 , and the BTD between 550 cm -1 and 726 cm -1 . Here, the performance of the proposed method is evaluated with Shupe’s and Turner’s method. The monthly average accuracy of the proposed method, the method without the two far-infrared features, and Turner’s method are about 76%, 36%, and 49%, respectively, which infer the good performance of the proposed method and also indicate that the far-infrared band features can effectively enhance cloud phase classification. It is notable that, compared to Shupe’s method, the accuracy for the proposed method is only 61% during the Antarctic summer, which results from the definitions of cloud phase and radiative effect. In addition, the accuracy is only 44% for Turner’s method in seasons with a low frequency of mixed clouds due to the significant effect of water vapor.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

An Observational Evaluation of RKW Theory over the U.S. Southern Great Plains

The theory of Rotunno et al. (“RKW” theory) addresses the behavior of squall-line cold pools in vertically sheared flows. It predicts that, within a given thermodynamic environment, a balance between baroclinic vorticity generation by the cold pool and low-level environmental vertical wind shear induces an upright updraft along the gust front that maximizes the initiation of new convective cells. Although this theory has been evaluated numerically, its applicability to observed systems remains unclear and is limited by a lack of critical measurements, including high-frequency thermodynamic and wind profiles across the gust front. Herein, observations from the Atmospheric Radiation Measurement Southern Great Plains (ARM-SGP) observatory near Lamont, Oklahoma, are used to evaluate RKW theory for 10 well-observed squall lines over a 11-yr period. For this evaluation, RKW parameters including cold-pool intensity (c), low-level ambient, line-normal vertical shear (ΔV n ), subcloud and cloud-layer updraft tilts, and multiple measures of system intensity are estimated. Furthermore, the c estimates rely on thermodynamic retrievals from the Atmosphere Emitted Radiance Interferometer (AERI), which are uncertain but verify reasonably well against independent observations. As predicted by the theory, for c/ΔV n ≥ 1, c/ΔV n correlates positively with updraft tilt and negatively with system intensity, but these results are not always statistically significant and are also sensitive to the method by which ΔV n is evaluated. Specifically, ΔV n evaluations that extend above the cold-pool top yield greater consistency with RKW predictions. Also, some measures of intensity correlate more strongly with standard moist instability metrics than with RKW parameters.

Cold pools↗

DOE OSTI2022

A Novel Machine Learning Algorithm for Cloud Detection Using AERI Measurement Data

Infrared hyperspectral remote sensing has been widely used in the field of meteorology. Many scientists have carried out research on inversion methods of meteorological elements such as thermodynamic profile, boundary layer height, cloud base height, etc. In this study, a method based on machine learning for cloud detection using ground-based infrared hyperspectral radiation data is proposed. The features of outliers, the cloudy and cloud-free data of Atmospheric Emitted Radiance Interferometer (AERI) radiation are extracted. The “reference values” of cloudy and cloud-free are determined based on the observation data of Vaisala CL31 ceilometer within the time range of 8 min before the corresponding time of AERI. A support vector machine (SVM) algorithm is used for training. The dataset comes from the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site and North Slope Alaska (NSA) site from 2015 to 2017, and the ARM West Antarctic Radiation Experiment (AWARE) site in 2016 is also analyzed. The instruments used in this paper include AERI, ceilometer, etc. The experimental results reveal that the agreement of cloud detection results between the proposed algorithm and ceilometer is about 93% at each site. However, for high clouds or optically thin clouds, the agreement will decrease.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

Trends in Downwelling Longwave Radiance Over the Southern Great Plains

Downwelling longwave radiation is an important part of the surface energy budget. Spectral trends in the downwelling longwave radiance (DLR) provide insight into the radiative drivers of climate change. In this research, we process and analyze a 23-year DLR record measured by the Atmospheric Emitted Radiance Interferometer (AERI) at the U.S. Department of Energy Atmospheric Radiation Program Southern Great Plains (SGP) site. Two AERIs were deployed at SGP with an overlapping observation period of about 10 years, which allows us to examine the consistency and accuracy of the measurements and to account for discrepancies between them due to errors associated with the instruments themselves. We then analyzed the all-sky radiance trends in DLR, which are associated with the surface warming trend at SGP during this same period and also the complex changes in meteorological conditions. For instance, the observed radiance in the CO2 absorption band follows closely the near-surface air temperature variations. The significant positive radiance trends in weak absorption channels, such as in the wings of the CO 2 band and in the weak absorption channels in the H 2 O vibration-rotational band, show earlier detectability of climate change. The magnitude of the radiance trend uncertainty in the DLR record mainly results from internal climate variability rather than from measurement error, which highlights the importance of continuing the DLR spectral measurements to unambiguously detect and attribute climate change.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

TRACER CLAMPS1 AERI TROPoe Retrievals

These files contain 24 hour periods of retrieved thermodynamic profiles derived from observations collected by the CLAMPS1 Atmospheric Emitted Radiance Interferometer (AERI). The TROPoe algorithm is a python equivalent to the AERIoe algorithm (see Turner and Loehnert 2014; Turner and Blumberg 2019). These data were collected during the TRACER-CUBIC project. Due to issues with the CLAMPS1 microwave radiometer, only retrievals that used AERI data are available.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

AWAKEN 2023 CLAMPS2 AERI TROPoe Retrievals

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

AWAKEN 2023 CLAMPS1 AERI TROPoe Retrievals

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

TRACER CLAMPS2 AERI_MWR TROPoe Retrievals

These files contain 24 hour periods of retrieved thermodynamic profiles derived from observations collected by the CLAMPS2 Atmospheric Emitted Radiance Interferometer (AERI) and microwave radiometer (MWR). The TROPoe algorithm is a python equivalent to the AERIoe algorithm (see Turner and Loehnert, 2014; Turner and Blumberg 2019). These data were collected during the TRACER-CUBIC project. MWR only and combined AERI and MWR retrievals are available. During this deployment the AERI had high noise so AERI only retrievals are unavailable and the combined AERI and MWR retrievals should be used with extreme caution.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Evaluation and Applications of Multi-Instrument Boundary-Layer Thermodynamic Retrievals

Recent reports have highlighted the need for improved observations of the atmosphere boundary layer. In this study, we explore the combination of ground-based active and passive remote sensors deployed for thermodynamic profiling to analyze various boundary-layer observation strategies. Optimal-estimation retrievals of thermodynamic profiles from Atmospheric Emitted Radiance Interferometer (AERI) observed spectral radiance are compared with and without the addition of active sensor observations from a May–June 2017 observation period at the Atmospheric Radiation Measurement Southern Great Plains site. In all, three separate thermodynamic retrievals are considered here: retrievals including AERI data only, retrievals including AERI data and Vaisala water vapour differential-absorption lidar data, and retrievals including AERI data and Raman lidar data. First, the three retrievals are compared to each other and to reference radiosonde data over the full observation period to obtain a bulk understanding of their differences and characterize the impact of clouds on these retrieved profiles. These analyses show that the most significant differences are in the water vapour field, where the active sensors are better able to represent the moisture gradient in the entrainment zone near the boundary-layer top. Furthermore, we also explore how differences in retrievals may affect results of applied analyses including land–atmosphere coupling, convection indices, and severe storm environmental characterization. Overall, adding active sensors to the optimal-estimation retrieval shows some added information, particularly in the moisture field. Given the costs of such platforms, the value of that added information must be weighed for the application at hand.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Information Content of a Synergy of Ground-Based and Space-Based Infrared Sounders. Part I: Clear-Sky Environments

Abstract Profiles of atmospheric temperature and water vapor from remotely sensed platforms provide critical observations within the temporal and spatial gaps of the radiosonde network. The 2017 National Academies of Science Decadal Survey highlighted that observations of the planetary boundary layer (PBL) from the current space-based observing system are not of the necessary accuracy or resolution for monitoring and predicting high-impact weather phenomena. One possible solution to improving observations of the PBL is supplementing the existing space-based observing system with a network of ground-based profilers. A synthetic information content study is developed utilizing profiles from the Atmospheric Radiation Measurement (ARM) program sites at the Southern Great Plains (SGP), east North Atlantic (ENA), and North Slope of Alaska (NSA) to assess the benefits, in terms of degrees of freedom (DOF), vertical resolution, and uncertainties, of a synergy between the ground-based Atmospheric Emitted Radiance Interferometer (AERI) with space-based hyperspectral infrared (IR) sounders. A combination of AERI with any of the three polar-orbiting IR sounders: the Atmospheric Infrared Sounder (AIRS), the Cross-track Infrared Sounder (CrIS), or the Infrared Atmospheric Sounding Interferometer (IASI), results in a DOF increase of 30%–40% in the surface-to-700-hPa layer compared to the space-based instrument alone. Introducing AERI measurements to the observing system also results in significant improvements to vertical resolution and uncertainties in the bottom 1000 m of the atmosphere compared to CrIS measurements alone. A synergy of CrIS and AERI exceeds the 1-km-vertical-resolution goal set by the Decadal Survey in the lowest 1000 m.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

TRACER SPARC AERI Data

These files contain 24 hour periods of retrieved thermodynamic profiles derived from observations collected by the University of Wisconsin–Madison Space Science and Engineering Center Portable Atmospheric Research Center (SPARC) Atmospheric Emitted Radiance Interferometer (AERI). The TROPoe algorithm is a python equivalent to the AERIoe algorithm (see Turner and Loehnert, 2014; Turner and Blumberg 2019). These data were collected during the TRACER project.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Evaluation of a land-atmosphere coupling metric computed from a ground-based infrared interferometer

Land-atmosphere feedbacks are a critical component of the hydrologic cycle. Vertical profiles of boundary layer temperature and moisture, together with information about the land surface, are used to compute land-atmosphere coupling metrics. Ground based remote sensing platforms, such as the Atmospheric Emitted Radiance Interferometer (AERI), can provide high temporal resolution vertical profiles of temperature and moisture. When co-located with soil moisture, surface flux, and surface meteorological observations, such as at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site, it is possible to observe both the terrestrial and atmospheric legs of land-atmosphere feedbacks. In this study, we compare a commonly used coupling metric computed from radiosonde-based data to that obtained from the AERI to characterize the accuracy and uncertainty in the metric derived from the two distinct platforms. This approach demonstrates the AERI’s utility where radiosonde observations are absent in time and/or space. Radiosonde and AERI based observations of the Convective Triggering Potential and Low-Level Humidity Index (CTP-HI low ) were computed during the 1200 UTC observation time and displayed good agreement during both 2017 and 2019 warm seasons. Furthermore, radiosonde and AERI derived metrics diagnosed the same atmospheric preconditioning based upon the CTP-HI low framework a majority of the time. When retrieval uncertainty was considered, even greater agreement was found between radiosonde and AERI derived classification. The AERI’s ability to represent this coupling metric well enabled novel exploration of temporal variability within the overnight period in CTP and HI low . Observations of CTP-HI low computed within a few hours of 1200 UTC were essentially equivalent, however with greater differences in time arose greater differences in CTP and HI low .

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Climate-Relevant Gas Absorption Properties from AWARE and Other ARM Spectral Measurements (Final Report)

The objective of this project has been to use measurements from the Atmospheric Emitted Radiance Interferometer (AERI) deployed at ARM sites to improve our knowledge of uncertain infrared spectroscopic parameters of importance to climate, remote sensing, and data assimilation. A primary focus has been the water vapor continuum in the infrared atmospheric window. Radiation codes used to predict climate and weather base their representation of the water vapor continuum in this window on the MT_CKD model, but in this spectral region the MT_CKD water vapor continuum absorption coefficients were derived almost two decades ago by an analysis of AERI measurements for conditions with a limited range of precipitable water vapor (PWV) and temperature values, leading to uncertainty in the derived coefficients. This project has included a new, comprehensive analysis of all aspects of the water vapor continuum in the atmospheric window -- the self continuum, the foreign continuum, and the self continuum temperature dependence, all resolved spectrally. Having a wide range of PWV amounts and temperatures from deployments of the ARM Mobile Facility for GoAmazon as well as the more than a decade of AERI measurements at SGP has provided an appropriate foundation for such a comprehensive analysis.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

Atmospheric Emitted Radiance Interferometer Optimal Estimation (AERIoe) Value-Added Product Report

The Atmospheric Emitted Radiance Interferometer (AERI) Optimal Estimation (AERIoe) algorithm (Turner and Löhnert 2014, Turner and Blumberg 2019) retrieves boundary-layer profiles of temperature and water vapor mixing ratio, together with cloud properties for a single-layer cloud (i.e., LWP, effective radius), from AERI-observed infrared radiance spectra and other measurements. The current AERIoe Value-Added Product (VAP) uses version 2.8 of David Turner's code, which uses additional inputs to constrain the retrieval including surface meteorology, microwave radiometer brightness temperatures at any number of frequencies (the U.S. Department of Energy Atmospheric Radiation Measurement [ARM] user facility typically used observations at 23.8 and 30.0 GHz), and Rapid Refresh numerical weather prediction model output. The method is a physical iterative retrieval that uses the optimal estimation framework (Rodgers 2000), so a full error covariance of the solution and the information content (in the form of degrees of freedom for signal) is provided for each retrieval.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Southern Hemisphere Polar Infrared Radiation Experiment (SHIRE) Field Campaign Report

An extended-range atmospheric emitted radiance interferometer (AERI) instrument to be deployed to Scott Base (78 S, 166 E) in McMurdo Sound, Antarctica for the purpose of synergizing with the National Aeronautics and Space Administration (NASA) Polar Radiant Energy in the Far-InfraRed Experiment (PREFIRE) satellite mission (https://prefire.ssec.wisc.edu/), examining the closure of atmospheric thermal emission, and providing validation for PREFIRE. The PREFIRE mission is the first satellite mission to map Earth’s spectrally resolved thermal emission, from 5 to 50 microns, over seasonal to annual timescales. These new observations represent the beginning of an intensive examination of the nature of thermal emission from Earth (emission from both the surface and atmosphere). A primary requirement of the PREFIRE mission is to provide validation of the retrieved variables. Numerous field sites in the Arctic (such as the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) North Slope of Alaska observatory and Summit Station, Greenland) have existing instrumentation to facilitate validation, leaving an observational gap in Antarctica. Through SHIRE, we propose to establish a temporary, intensive measurement site in Antarctica to ensure that both poles are suitably represented, and benefit from this state-of-the art science. This will also allow Arctic and Antarctic atmospheric radiative states to be compared and contrasted. For SHIRE specifically, we also propose further objectives to examine the spectrally resolved thermal radiative closure of the polar atmosphere and the historical changes in Antarctic radiative forcing, using previous Antarctic field campaigns, such as the ARM West Antarctic Radiation Experiment (AWARE).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Retrieval of temperature and humidity profiles from ground-based high-resolution infrared observations using an adaptive fast iterative algorithm

Various retrieval algorithms have been developed for retrieving temperature and water vapor profiles from Atmospheric Emitted Radiance Interferometer (AERI) observations. The physical retrieval algorithm, named AERI Optimal Estimation (AERIoe), outperforms other retrieval algorithms in many aspects except the retrieval time, which is significantly increased due to the complex radiative transfer process. The calculation of the Jacobian matrix is the most computationally intensive step of the physical retrieval algorithm. Interestingly, an analysis of the change in AERI observations' information content with respect to Jacobians revealed that the AERIoe algorithm's performance presents negligible dependence on these metrics. Thus, the Jacobian matrix could remain unchanged when the variation in the atmospheric state is small in the retrieval process to reduce the most time-consuming computation. On the basis of the above findings, a fast physical–iterative retrieval algorithm was proposed by adaptively recalculating Jacobians in keeping with the changes in the atmospheric state. Experiments with synthetic observations demonstrate that the proposed method experiences an average reduction in retrieval time by an impressive 59 % compared to the original AERIoe algorithm while achieving maximum root-mean-square errors of less than 0.95 K and 0.22 log(ppmv) for heights below 3 km for the temperature and water vapor profile, respectively. Further analyses revealed that the fast-retrieval algorithm reached an acceptable convergence rate of 98.7 %, marginally lower than AERIoe's 99.9 % convergence rate for the 826 cases used in this study.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

NWB Sensors Infrared Cloud Imager Data Products from SGP

NWB Sensors is a company which has developed a commercially available Infrared Cloud Imager (ICI). For more information, consult the company's webpage, https://www.nwbsensors.com/infrared-cloud-imager. To validate the radiometric accuracy of the ICI, NWB Sensors deployed it to the ARM SGP User Facility in 2023. The primary motivation of this deployment was to perform an intercomparison between the ICI and the Atmospheric Emitted Radiance Interferometer (AERI). The AERI spectral radiance data product can be integrated across the response function of the ICI and directly compared to the zenith radiance observed by the ICI. In addition, the ICI uses proprietary models of the downwelling clear-sky radiance in its cloud processing algorithms. They are based on surface meteorology and precipitable water vapor (PWV). These models were validated by comparing their predicted radiances to those derived from radiative transfer models of the ARM radiosondes. Finally, PWV observations derived from the ICI's onboard GNSS-based PWV retrieval system were compared against those from the microwave radiometer. This dataset contains the ICI radiance and cloud data products.

Atmosphere↗