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All sourcesNASA NTRSDOE OSTI
NASA NTRS2024

Assessment of Dust Size Retrievals Based on AERONET: A Case Study of Radiative Closure From Visible-Near-Infrared to Thermal Infrared

Super-coarse dust particles (diameters >10 μm) are evidenced to be more abundant in the atmosphere than model estimates and contribute significantly to the dust climate impacts. Since super-coarse dust accounts for less dust extinction in the visible-to-near-infrared (VIS-NIR) than in the thermal infrared (TIR) spectral regime, they are suspected to be underestimated by remote sensing instruments operates only in VIS-NIR, including Aerosol Robotic Networks (AERONET), a widely used data set for dust model validation. In this study, we perform a radiative closure assessment using the AERONET-retrieved size distribution in comparison with the collocated Atmospheric Infrared Sounder (AIRS) TIR observations with comprehensive uncertainty analysis. The consistently warm bias in the comparisons suggests a potential underestimation of super-coarse dust in the AERONET retrievals due to the limited VIS-NIR sensitivity. An extra super-coarse mode included in the AERONET-retrieved size distribution helps improve the TIR closure without deteriorating the retrieval accuracy in the VIS-NIR.

thermal infrared (TIR)↗
NASA NTRS2023

Retrieval of Aerosol Optical Properties From GOCI-II Observations: Continuation of Long-Term Geostationary Aerosol Monitoring Over East Asia

Since the Geostationary Ocean Color Imager (GOCI) was successfully launched in 2010, the GOCI Yonsei aerosol retrieval (YAER) algorithm has been continuously updated to retrieve hourly aerosol optical properties. GOCI-II has 4 more channels including UV, finer spatial resolution (250 m), and daily full disk coverage as compared to GOCI, and was launched in February 2020, onboard the GEO-KOMPSAT-2B (GK-2B) satellite. In this study, we extended the YAER algorithm to GOCI-II data based on its improved performance in many aspects and present the first results of aerosol optical properties retrieved from GOCI-II data. Utilizing the overlapping period between the GOCI-II and GOCI in geostationary Earth orbit, we present GOCI-II aerosol retrievals for high aerosol-loading cases over East Asia and show that these have a consistent spatial distribution with those from GOCI. Furthermore, GOCI-II provides AOD at an even higher spatial resolution, revealing finer changes in aerosol concentrations. Validation results for one year data show that the GOCI-II AOD has a correlation coefficient of 0.83 and a ratio within the expected error (EE) of 59.4 % when compared with the aerosol robotic network (AERONET) data. We compared statistical metrics for the GOCI and GOCI-II AODs to assess the consistency between the two datasets. In addition, it was found that there is a strong correlation between the two datasets from the comparison of gridded GOCI and GOCI-II AOD products. It is expected that data from GOCI-II will continue long-term aerosol records with high accuracy that can be used to address air-quality issues over East Asia.

AOD↗
DOE OSTI2020

Phase retrieval for short wavelength orbital angular momentum beams using knife-edge diffraction

Laser beams operating in high order transverse modes, especially orbital angular momentum (OAM) modes, have been used increasingly in many frontier research areas. The characterization of these laser modes and the related component analysis can be carried out using the retrieved optical phase. However, conventional iterative phase retrieval methods, when applied to pure OAM modes, may fail because their convergence depends on the choice of the initial phase. Here, we present an improved phase retrieval method which can incorporate additional phase information obtained using knife-edge diffraction. Using no additional optics or few optics available in a wide spectral range, this method is particularly suitable for short wavelength lasers such as free-electron lasers (FELs) in the VUV, EUV, and X-ray regimes. Finally, this method has been tested using simulated beams, and successfully applied to experimentally generated beams from a HeNe laser and an FEL.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

One-dimensional phase retrieval: Regularization, box relaxation and uniqueness

Recovering a signal from its Fourier magnitude is referred to as phase retrieval, which occurs in different fields of engineering and applied physics. This paper gives a new characterization of the phase retrieval problem. Particularly useful is the analysis revealing that the common gradient-based regularization does not restrict the set of solutions to a smaller set. Specifically focusing on binary signals, we show that a box relaxation is equivalent to the binary constraint for Fourier-types of phase retrieval. Here, we further prove that binary signals can be recovered uniquely up to trivial ambiguities under certain conditions. Finally, we use the characterization theorem to develop an efficient denoising algorithm.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Using ARM Data to Retrieve Entrainment Rates in Stratocumulus Cloud Systems (Final Report)

A stratocumulus-topped boundary layer (STBL) is a shallow convecting layer in which both the updraft and the downdraft branches of the convective circulation are saturated (cloudy) in the upper part of the boundary layer. The convective circulation is vigorous, yet the cloud-top entrainment velocity, $w_e$, is small and difficult to measure. Differences between currently used parameterizations of $w_e$ in GCMs can lead to STBLs that differ by as much as a factor of two in climatologically important properties such as liquid water path and boundary layer depth. We have developed a new method to retrieve entrainment rates in STBLs which uses vertical velocity retrievals from surface-based cloud radar, in conjunction with other surface-based measurements. These new estimates will allow a more comprehensive evaluation of parameterizations of entrainment rate for STBLs. The new method will also provide ground-based estimates of entrainment in STBLs over long time periods. We use retrievals of vertical velocity and liquid water content profiles to estimate the turbulent liquid water flux, which is then used in combination with observed cloud-top jumps of temperature, water vapor, and calculated radiative flux profiles to estimate the entrainment rate. We present results for three days for which observational data was not significantly contaminated by drizzle. The resulting average entrainment rates are 0.58 cm/s, 1.51 cm/s, and 0.31 cm/s.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Retrieved Number concentration of Cloud Condensation Nuclei (RNCCN) Value-Added Product (VAP)

The objective of Retrieved Number concentration of Cloud Condensation Nuclei (RNCCN) Value-Added Product (VAP) is to provide the vertical distribution of cloud condensation nuclei (CCN) number concentrations to better represent aerosol indirect effects in climate models. The RNCCN VAP allows to collect a large number of independent samples of vertical CCN measurements at various supersaturation values from the ground. This helps to avoid the high cost of aircraft and increases the temporal distribution of retrieved CCN in the planetary boundary layer. The VAP method is based on Ghan and Collins (2004) and Ghan et al. (2006) proposed parameterizations that retrieve the vertical profiles of CCN directly from the ground level CCN, humidification factor, and in-situ lidar measurements. Further, the VAP accuracy is improved by using the quality controlled data that ensures that the boundary layer is well mixed.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2013

Enhanced Deep Blue Aerosol Retrieval Algorithm: The Second Generation

The aerosol products retrieved using the MODIS collection 5.1 Deep Blue algorithm have provided useful information about aerosol properties over bright-reflecting land surfaces, such as desert, semi-arid, and urban regions. However, many components of the C5.1 retrieval algorithm needed to be improved; for example, the use of a static surface database to estimate surface reflectances. This is particularly important over regions of mixed vegetated and non- vegetated surfaces, which may undergo strong seasonal changes in land cover. In order to address this issue, we develop a hybrid approach, which takes advantage of the combination of pre-calculated surface reflectance database and normalized difference vegetation index in determining the surface reflectance for aerosol retrievals. As a result, the spatial coverage of aerosol data generated by the enhanced Deep Blue algorithm has been extended from the arid and semi-arid regions to the entire land areas.

Deep Blue↗
NASA NTRS2013

Application of OMI, SCIAMACHY and GOME-2 Satellite SO2 Retrievals for Detection of Large Emission Sources

Retrievals of sulfur dioxide (SO2) from space-based spectrometers are in a relatively early stage of development. Factors such as interference between ozone and SO2 in the retrieval algorithms often lead to errors in the retrieved values. Measurements from the Ozone Monitoring Instrument (OMI), Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY), and Global Ozone Monitoring Experiment-2 (GOME-2) satellite sensors, averaged over a period of several years, were used to identify locations with elevated SO2 values and estimate their emission levels. About 30 such locations, detectable by all three sensors and linked to volcanic and anthropogenic sources, were found after applying low and high spatial frequency filtration designed to reduce noise and bias and to enhance weak signals to SO2 data from each instrument. Quantitatively, the mean amount of SO2 in the vicinity of the sources, estimated from the three instruments, is in general agreement. However, its better spatial resolution makes it possible for OMI to detect smaller sources and with additional detail as compared to the other two instruments. Over some regions of China, SCIAMACHY and GOME-2 data show mean SO2 values that are almost 1.5 times higher than those from OMI, but the suggested spatial filtration technique largely reconciles these differences.

SO2 retrievals↗
NASA NTRS2015

Validation of Rain Rate Retrievals for the Airborne Hurricane Imaging Radiometer (HIRAD)

The NASA Hurricane and Severe Storm Sentinel (HS3) mission is an aircraft field measurements program using NASA's unmanned Global Hawk aircraft system for remote sensing and in situ observations of Atlantic and Caribbean Sea hurricanes. One of the principal microwave instruments is the Hurricane Imaging Radiometer (HIRAD), which measures surface wind speeds and rain rates. For validation of the HIRAD wind speed measurement in hurricanes, there exists a comprehensive set of comparisons with the Stepped Frequency Microwave Radiometer (SFMR) with in situ GPS dropwindsondes [1]. However, for rain rate measurements, there are only indirect correlations with rain imagery from other HS3 remote sensors (e.g., the dual-frequency Ka- & Ku-band doppler radar, HIWRAP), which is only qualitative in nature. However, this paper presents results from an unplanned rain rate measurement validation opportunity that occurred in 2013, when HIRAD flew over an intense tropical squall line that was simultaneously observed by the Tampa NEXRAD meteorological radar (Fig. 1). During this experiment, Global Hawk flying at an altitude of 18 km made 3 passes over the rapidly propagating thunderstorm, while the TAMPA NEXRAD perform volume scans on a 5-minute interval. Using the well-documented NEXRAD Z-R relationship, 2D images of rain rate (mm/hr) were obtained at two altitudes (3 km & 6 km), which serve as surface truth for the HIRAD rain rate retrievals. A preliminary comparison of HIRAD rain rate retrievals (image) for the first pass and the corresponding closest NEXRAD rain image is presented in Fig. 2 & 3. This paper describes the HIRAD instrument, which 1D synthetic-aperture thinned array radiometer (STAR) developed by NASA Marshall Space Flight Center [2]. The rain rate retrieval algorithm, developed by Amarin et al. [3], is based on the maximum likelihood estimation (MLE) technique, which compares the observed Tb's at the HIRAD operating frequencies of 4, 5, 6 and 6.6 GHz with corresponding theoretical Tb values from a forward radiative transfer model (RTM). The optimum solution is the integrated rain rate that minimizes the difference between RTM and observed values. Because the excess Tb from rain comes from the direct upwelling and the indirect reflected downwelling paths through the atmosphere, there are several assumptions made for the 2D rain distribution in the antenna incident plane (crosstrack to flight direction). The opportunity to knowing 2D rain surface truth from NEXRAD at two different altitudes will enable a comprehensive evaluation to be preformed and reported in this paper.

hurricane↗
NASA NTRS2016

Evaluating a Priori Ozone Profile Information Used in TEMPO Tropospheric Ozone Retrievals

Ozone (O3) is a greenhouse gas and toxic pollutant which plays a major role in air quality. Typically, monitoring of surface air quality and O3 mixing ratios is primarily conducted using in situ measurement networks. This is partially due to high-quality information related to air quality being limited from space-borne platforms due to coarse spatial resolution, limited temporal frequency, and minimal sensitivity to lower tropospheric and surface-level O3. The Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite is designed to address these limitations of current space-based platforms and to improve our ability to monitor North American air quality. TEMPO will provide hourly data of total column and vertical profiles of O3 with high spatial resolution to be used as a near-real-time air quality product.TEMPO O3 retrievals will apply the Smithsonian Astrophysical Observatory profile algorithm developed based on work from GOME, GOME-2, and OMI. This algorithm uses a priori O3 profile information from a climatological data-base developed from long-term ozone-sonde measurements (tropopause-based (TB) O3 climatology). It has been shown that satellite O3 retrievals are sensitive to a priori O3 profiles and covariance matrices. During this work we investigate the climatological data to be used in TEMPO algorithms (TB O3) and simulated data from the NASA GMAO Goddard Earth Observing System (GEOS-5) Forward Processing (FP) near-real-time (NRT) model products. These two data products will be evaluated with ground-based lidar data from the Tropospheric Ozone Lidar Network (TOLNet) at various locations of the US. This study evaluates the TB climatology, GEOS-5 climatology, and 3-hourly GEOS-5 data compared to lower tropospheric observations to demonstrate the accuracy of a priori information to potentially be used in TEMPO O3 algorithms. Here we present our initial analysis and the theoretical impact on TEMPO retrievals in the lower troposphere.

TEMPO↗
NASA NTRS2021

Changes in Satellite Retrievals of Atmospheric Composition over Eastern China During the 2020 COVID-19 Lockdowns

We examined daily level-3 satellite retrievals of Atmospheric Infrared Sounder (AIRS) CO, Ozone Monitoring Instrument (OMI) SO2 and NO2, and Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) over eastern China to understand how COVID-19 lockdowns affected atmospheric composition. Changes in 2020 were strongly dependent on the choice of background period since 2005 and whether trends in atmospheric composition were accounted for. Over central east China during the 23 January–8 April lockdown window, CO in 2020 was between 3 % and 12 % lower than average depending on the background period. The 2020 CO was not consistently less than expected from trends beginning between 2005 and 2016 and ending in 2019 but was 3 %–4 % lower than the background mean during the 2017–2019 period when CO changes had flattened. Similarly for AOD, 2020 was between 14 % and 30 % lower than averages beginning in 2005 and 14 %–17 % lower compared to different background means beginning in 2016. NO2 in 2020 was between 30 % and 43 % lower than the mean over different background periods and between 17 % and 33 % lower than what would be expected for trends beginning later than 2011. Relative to the 2016–2019 period when NO2 had flattened, 2020 was 30 %–33 % lower. Over southern China, 2020 NO2 was between 23 % and 27 % lower than different background means beginning in 2013, the beginning of a period of persistently lower NO2. CO over southern China was significantly higher in 2020 than what would be expected, which we suggest was partly because of an active fire season in neighboring countries. Over central east and southern China, 2020 SO2 was higher than expected, but this depended strongly on how daily regional values were calculated from individual retrievals and reflects background values approaching the retrieval detection limit. Future work over China, or other regions, needs to take into account the sensitivity of differences in 2020 to different background periods and trends in order to separate the effects of COVID-19 on air quality from previously occurring changes or from variability in other sources.

atmospheric composition↗
NASA NTRS2022

Understanding Model-Observation Discrepancies in Satellite Retrievals of Atmospheric Temperature Using GISS ModelE

We examine multiple factors in the representation of satellite-retrieved atmospheric temperature diagnostics in historical simulations of climate change during the satellite era (specifically 1979–2021) using GISS ModelE contributions to the Coupled Model Intercomparison Project (Phase 6) (CMIP6). The tropospheric and stratospheric trends in these diagnostics are affected by greenhouse gases (notably carbon dioxide and ozone), coupling with the ocean, volcanic aerosols, solar activity and compositional and dynamic feedbacks. We explore the impacts of internal variability, changing forcing specifications, composition interactivity, the quality of the stratospheric circulation, vertical resolution, and possible impacts of the mis-specification of volcanic aerosol optical depths. Overall temperature trends throughout the satellite period are well captured, but discrepancies at all levels exist and have multiple distinct causes. We find that stratospheric comparisons (using Stratospheric Sounding Unit (SSU) retrievals and successor instruments) are most affected by variations in the representation of ozone depletion and feedbacks, followed by the volcanic signals. Tropospheric skill (using the Microwave Sounding Unit (MSU) retrievals) is affected by the trends in ocean heat uptake and tropospheric aerosols, but also by the representation of stratospheric processes through the impact of the Brewer-Dobson circulation on the height of the tropical tropopause. We demonstrate that no single factor is the dominant cause of the discrepancies and that almost all observations lie within the broad envelope of structural uncertainty.

satellite retrievals of atmospheric temperature↗
NASA NTRSDate not supplied

Skillful Forecasts of Basic Hydrological Quantities Through the Application of SMAP-Based Soil Moisture Retrievals

The top five centimeters of soil lie at the interface between the atmosphere and land; hydrological variations in the atmosphere communicate themselves to the land largely through this layer, and vice-versa. The estimates of near-surface soil moisture provided by the SMAP mission are thus central to studies of hydrological variability. In fact, recent analyses show that the hydrological variability captured in the SMAP soil moisture retrievals can be parlayed into useful hydrological predictions at various leads (weekly out to seasonal). Specifically, at a given location, using the antecedent time series of SMAP Level 2 soil moisture retrievals up to the start of a forecast, skillful predictions can be made of surface soil moisture anomalies at a 1-week lead, of evapotranspiration stress anomalies at a ~1 month lead, and of streamflow anomalies at a multi-month lead. The skill is derived in part from soil moisture memory (along with the interpretation of deeper soil moisture from the surface measurements) and from joint analyses, outside the forecast period, of the SMAP retrievals with existing observational hydrological datasets.

soil moisture retrievals↗
DOE OSTI2024

Formation and retrieval of cell assemblies in a biologically realistic spiking neural network model of area CA3 in the mouse hippocampus

The hippocampal formation is critical for episodic memory, with area Cornu Ammonis 3 (CA3) a necessary substrate for auto-associative pattern completion. Recent theoretical and experimental evidence suggests that the formation and retrieval of cell assemblies enable these functions. Yet, how cell assemblies are formed and retrieved in a full-scale spiking neural network (SNN) of CA3 that incorporates the observed diversity of neurons and connections within this circuit is not well understood. Here, we demonstrate that a data-driven SNN model quantitatively reflecting the neuron type-specific population sizes, intrinsic electrophysiology, connectivity statistics, synaptic signaling, and long-term plasticity of the mouse CA3 is capable of robust auto-association and pattern completion via cell assemblies. Our results show that a broad range of assembly sizes could successfully and systematically retrieve patterns from heavily incomplete or corrupted cues after a limited number of presentations. Furthermore, performance was robust with respect to partial overlap of assemblies through shared cells, substantially enhancing memory capacity. These novel findings provide computational evidence that the specific biological properties of the CA3 circuit produce an effective neural substrate for associative learning in the mammalian brain.

hetero-association↗
DOE OSTI2022

The effects of advanced spectral line shapes on atmospheric carbon dioxide retrievals

Line shape effects such as speed-dependence and collisional (Dicke) narrowing are manifest in high signal-to-noise ratio laboratory spectra for a wide range of molecular species and sample conditions. However, these effects are usually neglected in spectroscopic models used in remote sensing of atmospheric greenhouse gases. Here, in this study, we examine the effects of line profile choice on laboratory spectra and XCO 2 values retrieved from Total Carbon Column Observing Network (TCCON) spectra. To this end, we utilize limiting cases of the Hartmann-Tran profile (HTP) with shape parameters recently measured by cavity ring-down spectroscopy. Significant differences highlighting the importance of including speed-dependence effects in forward models were observed both in scaling retrievals as well as profile retrievals. However, because of uncertainties associated with the atmospheric temperature gradient, it was not possible to extract accurate carbon column profiles even with inclusion of these new line shape parameters and advanced line profiles.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Can We Rely on Satellite Visible/Infrared Microphysical Retrievals of Boundary Layer Clouds in Partially Cloudy Scenes? Implications for Climate Research

This study addresses the longstanding question of the reliability of gridded visible/infrared satellite cloud properties in partially cloudy scenes. By using in-situ cloud probes and airborne Research Scanning Polarimeter (RSP) observations, we analyze bias changes in satellite retrievals from the Spinning Enhanced Visible Infra-Red Imager (SEVIRI) geostationary sensor during the ORACLES campaign. Biases in cloud optical depth (τ) and droplet effective radius (r e ) modestly change for cloud area fraction greater than 35%. The agreement between SEVIRI and RSP r e substantially improves when the retrievals are averaged after removing pixels with τ < 3.0, yielding biases indistinguishable from overcast scenes. In addition, satellite and RSP show an excellent agreement for closed- and open-cell stratocumulus clouds, showing that the satellite retrievals capture spatial changes of r e , and confirming that satellites can faithfully reproduce real physical features for optically thick and partially cloudy scenes. We demonstrate that a simple methodology can minimize uncertainties in satellite-based climate studies.

Painemal, David [NASA Langley Research Center, Ham↗
DOE OSTI2022

Dynamics retrieval from stochastically weighted incomplete data by low-pass spectral analysis

Time-resolved serial femtosecond crystallography (TR-SFX) provides access to protein dynamics on sub-picosecond timescales, and with atomic resolution. Due to the nature of the experiment, these datasets are often highly incomplete and the measured diffracted intensities are affected by partiality. To tackle these issues, one established procedure is that of splitting the data into time bins, and averaging the multiple measurements of equivalent reflections within each bin. This binning and averaging often involve a loss of information. Here, we propose an alternative approach, which we call low-pass spectral analysis (LPSA). In this method, the data are projected onto the subspace defined by a set of trigonometric functions, with frequencies up to a certain cutoff. This approach attenuates undesirable high-frequency features and facilitates retrieving the underlying dynamics. A time-lagged embedding step can be included prior to subspace projection to improve the stability of the results with respect to the parameters involved. Subsequent modal decomposition allows to produce a low-rank description of the system's evolution. Using a synthetic time-evolving model with incomplete and partial observations, we analyze the LPSA results in terms of quality of the retrieved signal, as a function of the parameters involved. We compare the performance of LPSA to that of a range of other sophisticated data analysis techniques. We show that LPSA allows to achieve excellent dynamics reconstruction at modest computational cost. Finally, we demonstrate the superiority of dynamics retrieval by LPSA compared to time binning and merging, which is, to date, the most commonly used method to extract dynamical information from TR-SFX data.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗