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NASA NTRSDate not supplied

Towards an Optimal Estimation Retrieval of Cirrus Cloud Optical and Microphysical Properties Using Hyperspectral Shortwave Instruments and A Fast Radiative Transfer Algorithm

Cirrus cloud retrieval products (here, cloud optical depth, effective particle size, and cloud top height) are important inputs into numerical weather and climate models. Uncertainties in such retrieval products, as a matter of course, propagate downstream, impacting model calculations. Improvements in high-quality global cirrus cloud optical and microphysical data products from satellite observations are needed to understand and reduce retrieval uncertainties. Hyperspectral shortwave instruments produce high-resolution and information-dense spectra, thus offering the opportunity to reduce uncertainties in retrieval products. We are in the process of developing a retrieval that uses the very fast Principal Component Radiative Transfer Model in the solar spectral region (PCRTM-Solar) in the forward model calculations. This retrieval will use measured reflectances from the NASA Earth Surface Mineral Dust Source Investigation (EMIT) and the forthcoming Climate Absolute Radiance and Refractivity Observatory Pathfinder (CLARREO-Pathfinder) instruments. In this manuscript we present progress towards a reference retrieval employing a widely used, verified, accurate, yet computationally slower radiative transfer modeling technique. The reference retrieval, while too slow for using the complete hyperspectral measurement, will allow us to study the behavior of retrieval products and help us verify results from our in-development fast retrieval. Both retrievals will use the optimal estimation retrieval framework. In this manuscript we present results from an uncertainty analysis considering three uncertainty sources for a cirrus cloud retrieval in the form of error covariance matrices: reflectance uncertainty due to water vapor, the reflectance uncertainty due to ice crystal scattering assumptions, and the instrument measurement uncertainty. Results show that the uncertainty due to habit selection is the largest, while that due to water vapor is at most 0.6% relative to channel reflectance. As a first step, the retrieval is being designed for single layer ice clouds over open ocean water.

Cirrus cloud↗
NASA NTRSDate not supplied

Newly Available Single Field-of-view Sounder Atmospheric Product (SiFSAP) and Its Derivative Product

The Single Field-of-view Sounder Atmospheric product (SiFSAP) has been developed and delivered to NASA GES DISC. The SiFSAP Algorithm Theoretical Basis Documents (ATBD) and users manuals are ready to be released to public. This novel data product supplements other operational products such as AIRS version 7 and the Community Long-term Infrared Microwave Combined Atmospheric Product System (CLIMCAPS) from two main perspectives: 1) improving the spatial resolution of sounder Level-2 data to extend its usage in weather and dynamics focus area; 2) establishing radiance closure between Level-2 data and directly measured Level-1 radiances to facilitate the climate trend analysis. SiFSAP has 3-times higher spatial resolution and 9-times denser data products comparing to current NASA and NOAA operational IR sounder products. SiFSAP is derived using the optimal estimation method based physical retrieval algorithm. The Principal Component-based Radiative Transfer Model (PCRTM) which includes the cloud scattering simulation is used for the forward model so that the solution can fit the spectral radiances under all-sky conditions for individual single field-of-view (SFOV) measurements. A general introduction of the SiFSAP algorithm and corresponding validation work will be presented. Also introduced here is the Climate Fingerprinting Sounder product (ClimFiSP) that is the derivative product of SiFSAP and will be released in the near future. The ClimFiSP algorithm uses pre-constructed fingerprinting relationship to achieve a low-latency Level-3 data production and facilitate the fusion of data of different sounders.

Wan Wu↗
NASA NTRS2009

Radiative Transfer Modeling and Retrievals for Advanced Hyperspectral Sensors

A novel radiative transfer model and a physical inversion algorithm based on principal component analysis will be presented. Instead of dealing with channel radiances, the new approach fits principal component scores of these quantities. Compared to channel-based radiative transfer models, the new approach compresses radiances into a much smaller dimension making both forward modeling and inversion algorithm more efficient.

Liu, Xu↗
NASA NTRS2016

Thermophysical Properties Along Curiosity's Traverse in Gale Crater, Mars, Derived from the REMS Ground Temperature Sensor

The REMS instrument onboard the Mars Science Laboratory rover, Curiosity, has measured ground temperature nearly continuously at hourly intervals for two Mars years. Coverage of the entire diurnal cycle at 1 Hz is available every few martian days. We compare these measurements with predictions of surface atmosphere thermal models to derive the apparent thermal inertia and thermally derived albedo along the rovers traverse after accounting for the radiative effects of atmospheric water ice during fall and winter, as is necessary to match the measured seasonal trend. The REMS measurements can distinguish between active sand, other loose materials, mudstone, and sandstone based on their thermophysical properties. However, the apparent thermal inertias of bedrock dominated surfaces [approx. 350-550 J m(exp. -2) K(exp. -1 s(exp. -1/2 )] are lower than expected. We use rover imagery and the detailed shape of the diurnal ground temperature curve to explore whether lateral or vertical heterogeneity in the surface materials within the sensor footprint might explain the low inertias. We find that the bedrock component of the surface can have a thermal inertia as high as 650-1700 J m(exp. -2) K(exp. -1) s(exp. -1/2) for mudstone sites and approx. 700 J m(exp. -2) K(exp. -1) s(exp. - 1/2) for sandstone sites in models runs that include lateral and vertical mixing. Although the results of our forward modeling approach may be non-unique, they demonstrate the potential to extract information about lateral and vertical variations in thermophysical properties from temporally resolved measurements of ground temperature.

Vasavada, Ashwin R.↗
NASA NTRS2019

Understanding Heating in Active Region Cores through Machine Learning. I. Numerical Modeling and Predicted Observables

To adequately constrain the frequency of energy deposition in active region cores in the solar corona, systematic comparisons between detailed models and observational data are needed. In this paper, we describe a pipeline for forward modeling active region emission using magnetic field extrapolations and field-aligned hydrodynamic models. We use this pipeline to predict time-dependent emission from active region NOAA 1158 for low-, intermediate-, and high-frequency nanoflares. In each pixel of our predicted multi-wavelength, time-dependent images, we compute two commonly used diagnostics: the emission measure slope and the time lag. We find that signatures of the heating frequency persist in both of these diagnostics. In particular, our results show that the distribution of emission measure slopes narrows and the mean decreases with decreasing heating frequency and that the range of emission measure slopes is consistent with past observational and modeling work. Furthermore, we find that the time lag becomes increasingly spatially coherent with decreasing heating frequency while the distribution of time lags across the whole active region becomes more broad with increasing heating frequency. In a follow-up paper, we train a random forest classifier on these predicted diagnostics and use this model to classify real observations of NOAA 1158 in terms of the underlying heating frequency.

UV radiation↗
NASA NTRS2004

Local Scale Radiobrightness Modelling during Intensive Observing Period-4 of the Cold Land Processes Experiment-1

The NASA Cold Land Processes Field Experiment (CLPX-1) was designed to provide microwave remote sensing observations and ground truth for studies of snow and frozen ground remote sensing, particularly issues related to scaling. CLPX-1 was conducted in the spring of 2003 in Colorado, USA. Initial forward model validation work is concentrating on the Local-Scale Observation Site (LSOS), a 0.8 ha study site consisting of open meadows separated by trees where the most detailed measurements were made of snow depth and temperature, density, and grain size profiles. This paper will focus on the ability of forward Dense Medium Radiative Transfer (DMRT) modelling, combined with snowpack measurements to reproduce the radiobrightness signatures observed by the University of Michigan s Truck-Mounted Radiometer System at 19 and 37 GHz during the 4th Intensive Observing Period (IOP4) in March, 2003. Unlike the earlier IOP3, conditions during IOP4 include both wet and dry periods, providing a valuable test of DMRT model performance. Observations of upwelling and downwelling tree radiobrightness will be used to formulate a simple model for the effect of trees within the field of view. In addition, a comparison will be made for the one day of coincident observations by the University of Tokyo s Ground- Based Microwave Radiometer-7 (GBMR-7). These analyses will help guide the choice of future snow retrieval algorithms and the design of future Cold Lands observing systems.

Kim, Edward J.↗
NASA NTRS2018

A Singular Value Decomposition Framework for Retrievals with Vertical Distribution Information from Greenhouse Gas Column Absorption Spectroscopy Measurements

We describe a variation of the Optimal Estimation (OE) method for greenhouse gas remote sensing retrievals using a singular value decomposition (SVD) and an uninformative prior. The SVD method is capable of discerning vertical information in column integrated absorption measurements. While traditional Bayesian optimal estimation (OE) assumes a prior distribution in order to regularize the inversion problem, the SVD approach identifies principal components that can be retrieved from the measurement without explicitly specifying a prior mean and prior covariance matrix. We discuss the method, illustrate its use on an integrated path differential absorption CO2 lidar measurement model, and compare it to traditional optimal estimation using numerical simulations. In the absence of forward model error, the mathematics behind the SVD method guarantee it to be bias-free, which is confirmed by the numerical simulations. In contrast, traditional OE retrievals exhibit bias when the prior mean used in the retrieval differs from the true mean. While the SVD approach can be used for most trace gas retrievals, it is particularly useful for situations where prior knowledge of the trace gas profile is poor. The SVD analysis is also useful in illustrating how vertical information is treated by the traditional OE approach.

Ramanathan, Anand K.↗
NASA NTRSDate not supplied

Hover and Forward Flight Performance Modeling of the Ingenuity Mars Helicopter

In 2015, NASA’s Jet Propulsion Laboratory partnered with Ames Research Center, Langley Research Center, and AeroVironment to develop Ingenuity, a small coaxial helicopter capable of flying within Mars’ unique atmospheric conditions. Ingenuity was successfully deployed from its protective shroud on the underside of the Mars 2020 Perseverance Rover and has flown 17 flights on Mars as of December 2021. A number of rotorcraft analysis tools were utilized, and a series of experimental tests were performed to ready Ingenuity for its launch with the Perseverance Rover in July 2020. In this paper, RotCFD, a Reynolds-averaged Navier-Stokes flow solver, is used to model Ingenuity in hover and forward flight for the purposes of validating tools to aid in the development of a future generation of Mars rotorcraft. The results from the RotCFD modeling are benchmarked against results from hover performance tests of the Ingenuity prototype in the 25-Foot Space Simulator at the Jet Propulsion Laboratory and are also compared to hover and forward flight predictions made by CAMRAD II, a well-known comprehensive rotorcraft analysis code. Surrogate performance models are trained to obtain a set of trimmed rotor settings for Ingenuity at different forward flight speeds, which are then used as inputs for the RotCFD forward flight simulations. Additionally, a study of the airframe-rotor interaction and a study of the aerodynamics of the individual airframe components of Ingenuity in forward flight are performed. Finally, to better understand performance predictions by RotCFD and CAMRAD II, a study is conducted on how sectional angles of attack in each code vary with radial station and azimuth.

Hover↗
NASA NTRSDate not supplied

Deriving a Suite of Climate Data Records from 21-years of Sounder Observations

Two retrieval algorithms, a Single Field-of-view Sounder Atmospheric Product (SiFSAP) Level 2 algorithm and a Climate Fingerprinting Sounder Product (ClimFiSP) Level 3 algorithm, have been developed at NASA Langley Research Center. In this presentation, we will demonstrate the radiance closure build into these two algorithms. This is possible due to the use of a principal-component-based radiative transfer model (PCRTM). The PCRTM enables the inclusion of all available spectral information from hyperspectral IR sounders. It also has fast and accurate capability of modeling observed cloudy radiance spectra directly. The SiFSAP, which fits sounder radiance spectra using PCRTM as forward model and an optimal estimation as inverse model, has 3-times higher spatial resolution and 9-times denser data products comparing to current NASA and NOAA operational IR sounder products. With great support from NASA sounder SIPS, the SiFSAP algorithm has been delivered to NASA GES DISC for L2 and L3 data generation and releasing. The ClimFiSP uses a spectral fingerprinting method to directly generate L3 data products from spatiotemporally averaged L1 data. The goal of ClimFiSP is to generate high-quality climate data records (CDRs) from multiple IR sounders (e.g. Aqua AIRS, SNPP CrIS, NOAA-20 and NOAA-21 CrIS) using a consistent retrieval methodology. Using CHIRP L1 data, we have generated 20-years of CDRs for atmospheric temperature, water vapor, and trace gas profiles, as well as cloud and surface properties. The algorithm will be transitioned to Sounder SIPS soon and eventually to NASA GES DISC.

Xu Liu↗
NASA NTRS2001

CLAES Product Improvement by use of GSFC Data Assimilation System

Recent development in chemistry transport models (CTM) and in data assimilation systems (DAS) indicate impressive predictive capability for the movement of airparcels and the chemistry that goes on within these. This project was aimed at exploring the use of this capability to achieve improved retrieval of geophysical parameters from remote sensing data. The specific goal was to improve retrieval of the CLAES CH4 data obtained during the active north high latitude dynamics event of 18 to 25 February 1992. The model capabilities would be used: (1) rather than climatology to improve on the first guess and the a-priori fields, and (2) to provide horizontal gradients to include in the retrieval forward model. The retrieval would be implemented with the first forward DAS prediction. The results would feed back to the DAS and a second DAS prediction for first guess, a-priori and gradients would feed to the retrieval. The process would repeat to convergence and then proceed to the next day.

Kumer, J. B.↗
NASA NTRS2012

Conceptual Problems in Land Surface Data Assimilation

A land data assimilation system (LDAS) merges observations (or satellite retrievals) of land surface hydrological conditions, including soil moisture, snow, and terrestrial water storage (TWS), into a numerical model of land surface processes. In theory, the output from such a system is superior to estimates based on the observations or the model alone, thereby enhancing our ability to understand, monitor, and predict key elements of the terrestrial water cycle. In practice, however, several conceptual problems can interfere with realizing the potential improvements from data assimilation. Of particular concern is the frequent mismatch between the assimilated observations and the land surface model variables of interest. The seminar will discuss recent research with the ensemble-based NASA GEOS-S LDAS to address various aspects of this mismatch. These aspects include (i) the assimilation of coarse-scale observations into higher-resolution land surface models, (ii) the partitioning of satellite observations (such as TWS retrievals) into their constituent water cycle components, (iii) the forward modeling of microwave brightness temperatures over land for radiance-based land surface data aSSimilation, and (iv) the selection of the most relevant types of observations for the analysis of a specific water cycle variable (such as root zone soil moisture). At its core, the solution to the above challenges involves the careful construction of an observation operator that maps from the land surface model variables of interest to the space of the assimilated observations.

Reichle, Rolf↗
NASA NTRS2012

Spatial and Temporal Variations in Titan's Surface Temperatures from Cassini CIRS Observations

We report a wide-ranging study of Titan's surface temperatures by analysis of the Moon's outgoing radiance through a spectral window in the thermal infrared at 19 mm (530/cm) characterized by lower atmospheric opacity. We begin by modeling Cassini Composite Infrared Spectrometer (CIRS) far infrared spectra collected in the period 2004-2010, using a radiative transfer forward model combined with a non-linear optimal estimation inversion method. At low-latitudes, we agree with the HASI near-surface temperature of about 94 K at 101S (Fulchignoni et al., 2005). We find a systematic decrease from the equator toward the poles, hemispherically asymmetric, of approx. 1 K at 60 deg. south and approx. 3 K at 60 deg. north, in general agreement with a previous analysis of CIRS data and with Voyager results from the previous northern winter. Subdividing the available database, corresponding to about one Titan season, into 3 consecutive periods, small seasonal changes of up to 2 K at 60 deg N became noticeable in the results. In addition, clear evidence of diurnal variations of the surface temperatures near the equator are observed for the first time: we find a trend of slowly increasing temperature from the morning to the early afternoon and a faster decrease during the night. The diurnal change is approx. 1.5 K, in agreement with model predictions for a surface with a thermal inertia between 300 and 600 J/ sq. m s (exp -1/2) / K. These results provide important constraints on coupled surface-atmosphere models of Titan's meteorology and atmospheric dynamic.

titan↗
NASA NTRS2013

Connecting Satellite Observations with Water Cycle Variables Through Land Data Assimilation: Examples Using the NASA GEOS-5 LDAS

A land data assimilation system (LDAS) can merge satellite observations (or retrievals) of land surface hydrological conditions, including soil moisture, snow, and terrestrial water storage (TWS), into a numerical model of land surface processes. In theory, the output from such a system is superior to estimates based on the observations or the model alone, thereby enhancing our ability to understand, monitor, and predict key elements of the terrestrial water cycle. In practice, however, satellite observations do not correspond directly to the water cycle variables of interest. The present paper addresses various aspects of this seeming mismatch using examples drawn from recent research with the ensemble-based NASA GEOS-5 LDAS. These aspects include (1) the assimilation of coarse-scale observations into higher-resolution land surface models, (2) the partitioning of satellite observations (such as TWS retrievals) into their constituent water cycle components, (3) the forward modeling of microwave brightness temperatures over land for radiance-based soil moisture and snow assimilation, and (4) the selection of the most relevant types of observations for the analysis of a specific water cycle variable that is not observed (such as root zone soil moisture). The solution to these challenges involves the careful construction of an observation operator that maps from the land surface model variables of interest to the space of the assimilated observations.

Land data assimilation↗
NASA NTRS1983

Identification of helicopter rotor dynamic models

A recursive, extended Kalman-filter approach is applied to the identifiction of rotor damping levels of representative helicopter dynamic systems. The general formulation of the approach is presented in the context of a typically posed stochastic estimation problem, and the method is analytically applied to determining the damping levels of a coupled rotor-body system. The identified damping covergence characteristics are studied for sensitivity to both constant-coefficient and periodic-coefficient measurement models, process-noise covariance levels, and specified initial estimates of the rotor-system damping. A second application of the method to identifying the plant model for a highly damped, isolated flapping blade with a constant-coefficient state model (hover) and a periodic-coefficient state model (forward flight) is also investigated. The parameter-identification capability is evaluated for the effect of periodicity on the plant model coefficients and the influence of different measurement noise levels.

Molusis, J. A.↗
NASA NTRS2015

Evaluating the Utility of Satellite Soil Moisture Retrievals over Irrigated Areas and the Ability of Land Data Assimilation Methods to Correct for Unmodeled Processes

Earth's land surface is characterized by tremendous natural heterogeneity and human-engineered modifications, both of which are challenging to represent in land surface models. Satellite remote sensing is often the most practical and effective method to observe the land surface over large geographical areas. Agricultural irrigation is an important human-induced modification to natural land surface processes, as it is pervasive across the world and because of its significant influence on the regional and global water budgets. In this article, irrigation is used as an example of a human-engineered, often unmodeled land surface process, and the utility of satellite soil moisture retrievals over irrigated areas in the continental US is examined. Such retrievals are based on passive or active microwave observations from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), the Advanced Microwave Scanning Radiometer 2 (AMSR2), the Soil Moisture Ocean Salinity (SMOS) mission, WindSat and the Advanced Scatterometer (ASCAT). The analysis suggests that the skill of these retrievals for representing irrigation effects is mixed, with ASCAT-based products somewhat more skillful than SMOS and AMSR2 products. The article then examines the suitability of typical bias correction strategies in current land data assimilation systems when unmodeled processes dominate the bias between the model and the observations. Using a suite of synthetic experiments that includes bias correction strategies such as quantile mapping and trained forward modeling, it is demonstrated that the bias correction practices lead to the exclusion of the signals from unmodeled processes, if these processes are the major source of the biases. It is further shown that new methods are needed to preserve the observational information about unmodeled processes during data assimilation.

land data↗
NASA NTRS2012

SLR Station Recovery, Center of Frame Motion, and Time Varying Gravity

Weekly station position estimates, beginning with 1993, are derived from the ITRF2008-based SLR processing of up to four satellites: Lageos 1, Lageos2, Starlette, and Stella. Helmert parameters obtained from c omparison of weekly SLR station positions and the a-priori SLRF2008 station complement are evaluated for geocenter motion and scale. Two me thods for modeling time varying gravity are employed in the SLR satel lite POD processing, with GGM03S serving as the static gravity field. Both methods forward model atmosphere gravity derived from 6-hour ECM WF pressure data. The standard approach applies an annual 20x20 field estimated from 4 years of GRACE data, and the IERS2003 recommended linear rates for C20, C30, C40, C21, and S21. The alternate approach us es a new set of low-order/degree 4x4 coefficients estimated weekly fr om SLR & DORIS processing to 10 satellites from 1993-2012. This exper imental tvg4x4 model has been shown to improve the TOPEX, Jason-1, and Jason-2 altimeter satellite orbits,. In this paper we apply the more detailed time-variable gravity modeling to the SLR satellite POD pro cessing and subsequent reference frame analyses. For this study we will evaluate the orbit differences (periodic and secular) for the satel lites concerned, characterize the impact on the station coordinate solutions, and the impact on reference frame parameters (geocenter and s cale).

Zelensky, Nikita P.↗
NASA NTRS2017

The MERRA-2 Aerosol Reanalysis, 1980 Onward. Part II: Evaluation and Case Studies

The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) is NASA’s latest reanalysis for the satellite era (1980-present) using the Goddard Earth Observing System version 5 (GEOS-5) Earth system model. MERRA-2 provides several improvements over its predecessor (MERRA), including the inclusion of interactive aerosols for the entire period. In addition to assimilating bias-corrected Aerosol Optical Depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua satellites, it also includes the assimilation of bias-corrected AOD from Advanced Very High Resolution Spectroradiometer (AVHRR) instruments, Multi-angle Imaging SpectroRadiometer (MISR) AOD over bright surfaces, and ground-based Aerosol Robotic Network (AERONET) AOD. This paper is the second of a pair that summarizes our efforts to assess the quality of the MERRA-2 aerosol assimilation. In this study, we first follow previous work performed with version 1 of the MERRA Aerosol Reanalysis (MERRAero) using independent observations. The evaluation of MERRA-2 Absorption Aerosol Optical Depth (AAOD) and ultra-violet Aerosol Index (UV-AI) against the Ozone Monitoring Instrument (OMI) observations show good agreement, particularly over dusty regions where our previous efforts improved model aerosol optical properties. Next, we find that aerosol assimilation system improves the aerosol vertical structure when compared to estimates from the same version of the model without AOD assimilation. A similar conclusion is found for MERRA-2 aerosol surface fine particulate matter (PM (sub 2.5)). However, deficiencies in the forward model such as missing emissions noted during the MERRAero study still explain the MERRA-2 PM(sub 2.5) bias relative to observations over the United States. Finally, to illustrate successes and weaknesses of the AOD assimilation, we focus on the performance of the MERRA-2 aerosol system during several major aerosol events: the Mount Pinatubo eruption in 1991, a Saharan dust event and transportation over the Atlantic Ocean during April 2010, the Rim Fire of summer 2013 in California, and an extreme pollution event over China in January 2013. We conclude with a summary that points to best practices for utilizing the MERRA-2 aerosol reanalysis in future studies.

V Buchard↗
NASA NTRS1982

Investigation of the effects of external current systems on the MAGSAT data utilizing grid cell modeling techniques

The feasibility of modeling magnetic fields due to certain electrical currents flowing in the Earth's ionosphere and magnetosphere was investigated. A method was devised to carry out forward modeling of the magnetic perturbations that arise from space currents. The procedure utilizes a linear current element representation of the distributed electrical currents. The finite thickness elements are combined into loops which are in turn combined into cells having their base in the ionosphere. In addition to the extensive field modeling, additional software was developed for the reduction and analysis of the MAGSAT data in terms of the external current effects. Direct comparisons between the models and the MAGSAT data are possible.

Klumpar, D. M.↗