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Transmission model effects on water vapor mixing ratios and radiance calculations for infrared horizon scanners
The Soil Moisture and Ocean Salinity (SMOS) satellite mission provides global measurements of L-band brightness temperatures at horizontal and vertical polarization and a variety of incidence angles that are sensitive to moisture and temperature conditions in the top few centimeters of the soil. These L-band observations can therefore be assimilated into a land surface model to obtain surface and root zone soil moisture estimates. As part of the observation operator, such an assimilation system requires a radiative transfer model (RTM) that converts geophysical fields (including soil moisture and soil temperature) into modeled L-band brightness temperatures. At the global scale, the RTM parameters and the climatological soil moisture conditions are still poorly known. Using look-up tables from the literature to estimate the RTM parameters usually results in modeled L-band brightness temperatures that are strongly biased against the SMOS observations, with biases varying regionally and seasonally. Such biases must be addressed within the land data assimilation system. In this presentation, the estimation of the RTM parameters is discussed for the NASA GEOS-5 land data assimilation system, which is based on the ensemble Kalman filter (EnKF) and the Catchment land surface model. In the GEOS-5 land data assimilation system, soil moisture and brightness temperature biases are addressed in three stages. First, the global soil properties and soil hydraulic parameters that are used in the Catchment model were revised to minimize the bias in the modeled soil moisture, as verified against available in situ soil moisture measurements. Second, key parameters of the "tau-omega" RTM were calibrated prior to data assimilation using an objective function that minimizes the climatological differences between the modeled L-band brightness temperatures and the corresponding SMOS observations. Calibrated parameters include soil roughness parameters, vegetation structure parameters, and the single scattering albedo. After this climatological calibration, the modeling system can provide L-band brightness temperatures with a global mean absolute bias of less than 10K against SMOS observations, across multiple incidence angles and for horizontal and vertical polarization. Third, seasonal and regional variations in the residual biases are addressed by estimating the vegetation optical depth through state augmentation during the assimilation of the L-band brightness temperatures. This strategy, tested here with SMOS data, is part of the baseline approach for the Level 4 Surface and Root Zone Soil Moisture data product from the planned Soil Moisture Active Passive (SMAP) satellite mission.
A comparison of model- and satellite-inferred clear sky top-of-atmosphere (TOA) albedos is presented. The clear sky albedos were inferred from GOES-2 observations for November 1978 over South America and most of North America and adjacent ocean regions. The model albedos were computed on a 1 deg X 1 deg latitude-longitude grid, allowing for variations in surface vegetation type, solar zenith angle, orography, and spectral absorption/scattering. Over the ocean areas, the observed and calculated TOA albedos agree within + or - 1 percent, while the albedos over land mostly agree within + or - 2 percent for the entire range of significant geographical variation of albedo from 13 percent over the Amazon Basin to 24 percent over mountains of western North America. Both the ocean and land agreements lie within the theoretical and observational uncertainties.
The potential for polarimetric Doppler radar measurements to improve predictions of ice microphysical processes within an idealized model–observational framework is examined. In an effort to more rigorously constrain ice growth processes (e.g., vapor deposition) with observations of natural clouds, a novel framework is developed to compare simulated and observed radar measurements, coupling a bulk adaptive-habit model of vapor growth to a polarimetric radar forward model. Bayesian inference on key microphysical model parameters is then used, via a Markov chain Monte Carlo sampler, to estimate the probability distribution of the model parameters. The statistical formalism of this method allows for robust estimates of the optimal parameter values, along with (non-Gaussian) estimates of their uncertainty. To demonstrate this framework, observations from Department of Energy radars in the Arctic during a case of pristine ice precipitation are used to constrain vapor deposition parameters in the adaptive habit model. The resulting parameter probability distributions provide physically plausible changes in ice particle density and aspect ratio during growth. A lack of direct constraint on the number concentration produces a range of possible mean particle sizes, with the mean size inversely correlated to number concentration. Consistency is found between the estimated inherent growth ratio and independent laboratory measurements, increasing confidence in the parameter PDFs and demonstrating the effectiveness of the radar measurements in constraining the parameters. The combined Doppler and polarimetric observations produce the highest-confidence estimates of the parameter PDFs, with the Doppler measurements providing a stronger constraint for this case.
We have investigated the influences of lateral variations of viscosity on the moment of inertia tensor from viscous flows due to the density anomalies in the mantle inferred from seismic tomographic models. The scaling relations between the density and the seismic anomalies is taken as either a constant or a function increasing with depth in accord with the recent high-pressure experimental studies. The viscosity is taken as an exponential function of the 3D density anomaly. In models with an isoviscous background, the effects on the perturbed moment of inertia tensor from the lateral viscosity variations are smaller than those due to variations in the radial viscosity profiles. In mantle models with a background viscosity increasing with depth, the influences of the lateral viscosity variations are significant. The most striking feature in the latter case is that the two off-diagonal elements delta I(sub xz) and delta I(sub yz) in the inertia tensor exhibit greatest sensitivity to lateral variations of the viscosity. While the other elements of the inertia change by only about a few tens of percent in the range of lateral viscosity contrast considered (less than 300), delta I(sub xz) and delta I(sub yz) can vary up to 40 times even with a change in sign, depending on the radial viscosity stratification and the location of the strongest lateral variations. The increase in the velocity-density scaling relation with depth can reduce the influences of the lateral viscosity variations, but it does not change the overall sensitive nature of delta I(sub xz) and delta I(sub yz). This study demonstrates clearly that the lateral viscosity variations, especially in the upper mantle, must be considered in the determination of long-term polar wander, since the variations in the delta I(sub xz) and delta I(sub yz) terms are directly responsible for exciting rotational movements.
Uncertainties in L-band (1.4 GHz) radiative transfer modeling (RTM) affect the simulation of brightness temperatures (Tb) over land and the inversion of satellite-observed Tb into soil moisture retrievals. In particular, accurate estimates of the microwave soil roughness, vegetation opacity and scattering albedo for large-scale applications are difficult to obtain from field studies and often lack an uncertainty estimate. Here, a Markov Chain Monte Carlo (MCMC) simulation method is used to determine satellite-scale estimates of RTM parameters and their posterior uncertainty by minimizing the misfit between long-term averages and standard deviations of simulated and observed Tb at a range of incidence angles, at horizontal and vertical polarization, and for morning and evening overpasses. Tb simulations are generated with the Goddard Earth Observing System (GEOS-5) and confronted with Tb observations from the Soil Moisture Ocean Salinity (SMOS) mission. The MCMC algorithm suggests that the relative uncertainty of the RTM parameter estimates is typically less than 25 of the maximum a posteriori density (MAP) parameter value. Furthermore, the actual root-mean-square-differences in long-term Tb averages and standard deviations are found consistent with the respective estimated total simulation and observation error standard deviations of m3.1K and s2.4K. It is also shown that the MAP parameter values estimated through MCMC simulation are in close agreement with those obtained with Particle Swarm Optimization (PSO).
Models of the radial variation of effective viscosity inferred from the earth's response to surface loads associated with Pleistocene deglaciation are compared to structures inferred from models of geodynamic phenomena associated with convection: the geoid, plate-driving forces, and advected heat flux. While observations of the earth's response to surface loads do not have sufficient resolution to justify more than two viscous layers, adequately matching the observed long-wavelength geoid anomalies associated with density contrasts in the lower mantle (inferred from seismic tomography) and in the upper mantle (inferred from a model of subducted slabs) requires more structure. It is possible to explain the geoid, observed plate velocities, the advected heat flux in the lower mantle, and relative sea-level variations in oceanic regions, all with a mantle with a high-viscosity/elastic lid, an asthenospheric channel of 2 x 10 exp 19 Pa s from 100 to 400-km depth, a 6 x 10 exp 20 Pa s transition zone, and a lower mantle of 6 x 10 exp 21 Pa s. The uplift history of Australia, Fennoscandia, and Laurentia can be explained with an asthenospheric viscosity less than a factor of 10 higher. Lateral variations in lower mantle viscosity are not required. Transient creep appears to be unimportant for the recent response-to-surface loads from Pleistocene deglaciation.
Summer hot oxygen densities of 10,000-30,000 per cubic cm at 1100 km (or 100,000-300,000 at 550 km) for low to moderate solar activities are inferred from differences between satellite drag and mass-spectrometer-based models. At high solar activities, densities at 925 km from 100,000-400,000 (or 5 x 10 to the 5th to 2 x 10 to the 6th at 550 km) are inferred from a limited number of Dynamics Explorer mass spectrometer measurements. The densities are of the same order as those reported previously from ground-based optical measurements. It is pointed out that a detailed theoretical calculation of the hot oxygen distribution consistent with the cool populations in mass-spectrometer-based models would provide the basis for a more complete interpretation of mass spectrometer data and of high-altitude differences between drag-based and mass-spectrometer-based models. It is concluded that hot oxygen could be providing the major contribution to satellite drag in the summer lower exosphere during low solar activity.
The 4.8 hour X-ray light curve of Cygnus X-3 is quantitatively described by several proposed models under the assumption that the binary orbit of Cyg X-3 is elliptic. The cocoon model implies an apparent size for the X-ray star's companion, which is larger than that of its critical lobe, and it is suggested that the emission of a dense stellar wind by the companion could lead to such a situation. It is shown that a helium star, with or without a hydrogen-rich envelope, seems to be the most likely Cyg X-3 companion, and that some of the observed variations in the 4.8 hour period can be accounted for by the apparent period variations due to apsidal motion.
Report consists of: 1. List of accomplishments 2. List of publications 3. Abstracts of published or submitted papers and 4. Subject invention disclosure. The accomplishments of the grant listed are: 1. Improved the third-order turbulence closure in cloud resolving models to remove the liquid water oscillation. 2. Used the University of California-Los Angeles (UCLA) large-eddy simulation (LES) model to provide data for radiation transfer testing. 3. Revised shortwave k-distribution models based on HITRAN 2000. 4. Developed a gamma-weighted two-stream radiative transfer model for radiation budget estimate applications. 5. Estimated the effect of spherical geometry to the earth radiation budget. 6. Estimated top-of-atmosphere irradiance over snow and sea ice surfaces. 7. Estimated the aerosol direct radiative effect at the top of the atmosphere. 8. Estimated the top-of-atmosphere reflectance of the clear-sky molecular atmosphere over ocean. 9. Developed and validated new set of Angular Distribution Models for the CERES TRMM satellite instrument (tropical) 10. Developed and validated new set of Angular Distribution Models for the CERES Terra satellite instrument (global) 11. Quantified the top-of-atmosphere direct radiative effect of aerosols over global oceans from merged CERES and MODIS observations 12 Clarified the definition of TOA flux reference level for radiation budget studies 13. Developed new algorithm for unfaltering CERES measured radiances 14. Used multiangle POLDER measurements to produce narrowband angular distribution models and examine the effect of scene identification errors on TOA albedo estimates 15. Developed and validated a novel algorithm called the Multidirectional Reflectance Matching (MRM) model for inferring TOA albedos from ice clouds using multi-angle satellite measurements. 16. Developed and validated a novel algorithm called the Multidirectional Polarized Reflectance Matching (MPRM) model for inferring particle shapes from ice clouds using multi-angle polarized satellite measurements. 17. Developed 4 advanced light scattering models including the three-dimensional (3D) uniaxial perfectly matched layer (UPML) finite-difference time-domain (FDTD) model. 18. Develop sunglint in situ measurement and study reflectance distribution in the sunglint area. 19. Lead a balloon-borne radiometer TOA albedo validation effort. 20. Developed a CERES surface UVB, UVA, and UV index product.
Multi-planet systems provide crucial insights into the architectures and correlations within planetary systems, which in turn offer clues into their formation and evolution histories. Through the thousands of exoplanet candidates that it discovered, NASA's Kepler mission revealed that systems with multiple transiting planets are common. We describe a new model for inferring the exoplanet population observed by Kepler, which combines a semi-parametric description of planetary systems with a prescription for envelope mass-loss due to photoevaporation. This new model is a"hybrid" of the maximum AMD (angular momentum deficit) model in which the orbital excitations of the planets are drawn by distributing the system's critical AMD (He et al. 2020), and a photoevaporation model for sculpting the observed radius valley (Neil & Rogers 2020). This is an update to the SysSim suite of exoplanet population models derived from forward modeling the Kepler detection pipeline. The hybrid model is constrained by the observed statistics of multi-transiting systems, including additional constraints from the distribution of planet radii and their intra-system size patterns. This model is capable of producing a planet radius valley as well as a preference for planet size similarity. However, we find that the Kepler catalog exhibits features that are even more significant (i.e., it has a deeper observed radius valley and stronger size similarity) than the best-fit model. We use this model to infer the intrinsic planet radius valley as well as the distribution of primordial planet masses and core masses.
Heat source information is derived here from the Voyager 2 Ultraviolet Spectrometer occultation data of Uranus. Analytic functions for the local heat dependence on altitude are used to obtain a temperature profile by solving the heat equation. The stellar entrance and exit occultation and a solar occultation are used to infer the thermal and density structure of the atmosphere. The least squares fit solution to the solar occultation data gives one source located at 1.8 x 10 exp -5 microbar with a strength of 0.056 +/- 0.01 erg/sq cm/s. Latitudinal temperature gradients are obtained.
An intimate relation exists between the vertical shear and the horizontal eddy heat flux within the atmosphere. In the present investigation empirical means are employed to provide clues concerning the relationship between the shear and eddy heat flux. In particular, linear regression models are applied to individual and joint time series of the shear and eddy heat flux. These discrete models are used as a basis to infer continuous models. A description is provided of the observed relationship between the flux and the shear, taking into account means, standard deviations, and lag correction functions.
Part I presented a Monte Carlo Bayesian method for constraining a complex statistical model of GCM sub-gridcolumn moisture variability using high-resolution MODIS cloud data, thereby permitting large-scale model parameter estimation and cloud data assimilation. This part performs some basic testing of this new approach, verifying that it does indeed significantly reduce mean and standard deviation biases with respect to the assimilated MODIS cloud optical depth, brightness temperature and cloud top pressure, and that it also improves the simulated rotational-Ramman scattering cloud optical centroid pressure (OCP) against independent (non-assimilated) retrievals from the OMI instrument. Of particular interest, the Monte Carlo method does show skill in the especially difficult case where the background state is clear but cloudy observations exist. In traditional linearized data assimilation methods, a subsaturated background cannot produce clouds via any infinitesimal equilibrium perturbation, but the Monte Carlo approach allows finite jumps into regions of non-zero cloud probability. In the example provided, the method is able to restore marine stratocumulus near the Californian coast where the background state has a clear swath. This paper also examines a number of algorithmic and physical sensitivities of the new method and provides guidance for its cost-effective implementation. One obvious difficulty for the method, and other cloud data assimilation methods as well, is the lack of information content in the cloud observables on cloud vertical structure, beyond cloud top pressure and optical thickness, thus necessitating strong dependence on the background vertical moisture structure. It is found that a simple flow-dependent correlation modification due to Riishojgaard (1998) provides some help in this respect, by better honoring inversion structures in the background state.
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It is widely believed that the Martian polar layered deposits record climate variations over at least the last 10 to 100 m.y., but the details of the processes involved and their relative roles in layer formation and evolution remain obscure. Weathering of the Martian layered deposits by sublimation of water ice can account for the thermal inertias, water vapor abundances, and geologic relationships observed in the Martian polar regions. The nonvolatile components of the layered deposits appears to consist mainly of bright red dust, with small amounts of dark dust. Dark dust, perhaps similar to the magnetic material found at the Viking Lander sites, may preferentially form filamentary residue particles upon weathering of the deposits. Once eroded, these particles may saltate to form the dark dunes found in both polar regions. This scenario for the origin and evolution of the dark material within the polar layered deposits is consistent with the available imaging and thermal data. Further experimental measurements of the thermophysical properties of magnetite and maghemite under Martian conditions are needed to better test this hypothesis.
A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge and support human space missions. Through artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in space biosciences and engineered astronaut health systems, to enable Earth-independence and mission operations autonomy. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated mission biomonitoring, and 8) a Precision Space Health system. AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the space biology field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics to phenotypic data using an ensemble model to infer causality of rodent liver health disruption, 2) usage of explainable ML to interrogate muscular underpinnings of muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interactions, and 5) a suite of benchmarked open science datasets enabling programmers to identify best algorithms to answer space biology questions.