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LEO Sensor to GEO Sensor Algorithm Transfer Models for Land Surface Temperature
Land surface temperature (LST) is a key climate observable used to detect changes in the Earth’s surface energy budget that influence carbon and water cycles. Land surface temperature exhibits strong diurnal variability, which geostationary satellites can observe at scale thanks to their temporal resolution. Due to anthropogenic climate and land use changes, the surface energy balance has been considerably modified and may be described by changes in diurnal temperature range and extremes. Using high performance computing and datasets from the NASA Earth Exchange, we exploit co-located, co-temporal observations from low-earth orbit (LEO) and geostationary (GEO) sensors to develop a deep learning-based method for LEO-to-GEO algorithm emulation. Our model is trained to predict MODIS Terra LST from GOES-16 thermal bands and achieves validation error <2K. Application of the model to unseen times of day (observed by MODIS Aqua) and a new GEO sensor (Himawari-8) observing an unseen spatial domain, demonstrate the generalization of the deep learning model across space, time and spectra. Communicating diurnal LST variability observed by geostationary satellites can have impacts in multiple disciplines, from understanding of snow, vegetation and soil dynamics, to recognizing trends in heat events relevant to human health.
Implementation of a New Scattering Database and a Radar Simulator into Community Radiative Transfer Model
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Pushing the Limits of Aquatic Remote Sensing: Synthetic Data and Deep Learning for Fast Inverse Emulation of A Coupled Ocean-Atmosphere Radiative Transfer Model
The inversion of electromagnetic information to physical and biological properties of the water column is a notoriously difficult problem, yet fundamental to our ability of understanding aquatic processes on large time and space scales. There is now a growing necessity to develop pragmatic approaches that allow timely and effective extrapolation of local processes, to spatially resolved global products, and to promote operational and sustainable resource policy management. This presentation will discuss research integrating advanced biological and radiative modeling, high-end computation, and machine learning to develop a portable global processor for simultaneous retrieval of atmosphere and water optics for diverse aquatic systems from the open and coastal ocean to optically extreme inland waters and harmful algal blooms. We will discuss some of the basic concepts behind the forward modeling approach including DEAP, the novel Distributed Equivalent Algal Populations model, for developing large spectral libraries of aquatic particle optics to aid in our ability to distinguish phytoplankton functional types (PFTs) and inorganic material, as well as other factors which enable comprehensive modeling from the benthos to top-of-atmosphere (TOA). This information is being used to understand how we can leverage next-generation deep learning methods for maximum information retrieval and rapid image processing, while also providing capabilities to identify minimum sensor spectral requirements necessary for certain aquatic applications. Further, I will touch on how we envision this research to enable the aquatic community for science discovery and how we are moving closer towards the capability for high-fidelity global analysis of aquatic ecosystems.
Significance of Radiative Transfer Models in Satellite Instrumentation Development and OSSEs
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Global Clear-Sky Downwelling Shortwave Flux Using CCCma and MODTRAN6 Radiative Transfer Models
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Impact of Ice Cloud Microphysics on Satellite Cloud Retrievals and Broadband Flux Radiative Transfer Model Calculations
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Optimizing Assimilation of Microwave and Radar Observations in the NWP Models
Radiative transfer models are extensively used for the assimilation of satellite observations into NWP models as well as retrieving geophysical products from satellite measurements. The Community Radiative Transfer Model (CRTM) is a community model developed by NOAA JCSDA and widely used for different purposes requiring RT calculations. CRTM requires bulk optical properties of hydrometeors in the form of lookup tables in order to perform all-sky RT calculations. However, the current cloud scattering lookup tables in CRTM assume spherical shapes for all frozen hydrometeors, whereas actual clouds contain frozen particles with diverse shapes. The first part of this talk presents the implementation and validation of a comprehensive Discrete Dipole Approximation (DDA) cloud scattering database into CRTM, specifically targeting microwave frequencies. The DDA technique proves effective in simulating the optical properties of non-spherical hydrometeors in the microwave region. The original DDA database assumes total random orientation in calculating single scattering properties. To generate the required mass scattering parameters for CRTM, the single scattering properties and water content dependent particle size distributions are used. The evaluation of results involved a collocated dataset comprising short-term forecasts from the Integrated Forecast System of the European Center for Medium-Range Weather Forecasts and satellite microwave data. The findings demonstrate that the DDA lookup tables significantly reduce discrepancies between simulated and observed values when compared to the Mie tables. Passive instruments lack the ability to provide vertically resolved measurements of clouds and precipitation, which can be obtained by active radar instruments. However, incorporating these active measurements into data assimilation systems presents challenges due to the absence of fast forward radiative transfer models and difficulties in error modeling. The second part of the talk focuses on the development, evaluation, and sensitivity analysis of a forward radar model integrated into CRTM. The forward radar model utilizes scattering properties obtained from hydrometeor lookup tables generated using the discrete dipole approximation. By utilizing CRTM instrument-specific coefficients, the model can calculate both reflectivity and attenuated reflectivity for any given radar instrument and zenith angles. Evaluation using CloudSat measurements demonstrates a strong agreement between simulations and observations when the input profiles of hydrometeors align with the measured reflectivity profiles.
Mathematical models for radiation transfer
A radiation transfer model was modified to include semitransparent and opaque layers as well as molecular constituents. An example of the use of the program and an analysis of the mathematical model are included.
Modeling the radiant transfers of sparse vegetation canopies
The scattering dynamics of sparse vegetation canopies were studied within the framework of the three-dimensional radiative transfer model of Kimes (1984). The model was upgraded by including an algorithm for the anisotropic scattering of a soil boundary. Validation of the model was carried out using measured directional reflectance data for two canopies exhibiting typical scattering behavior with low and intermediate vegetation density. The canopies were: an orchard grass canopy; and a hard wheat canopy. A number of factors were found contributing to the final reflectance distribution of the canopies, including: (1) the strong anisotropic scattering properties of the soil; (2) the geometric effect of the vegetation probability gap function on the soil anisotropy and solar irradiance; and (3) the anisotropic scattering of vegetation which is controlled by the phase function and the layering of leaves. The application of the theoretical results to the development of earth-observing sensor systems is discussed.
A cloud model-radiative model combination for determining microwave TB-rain rate relations
The development of a cloud model-radiative transfer model combination for computing average brightness temperature, T(B), is discussed. The cloud model and radiative transfer model used in this study are described. The relations between rain rate, cloud and rain water, cloud and precipitation ice, and upwelling radiance are investigated. The effects of the rain rate relations on T(B) under different climatological conditions are examined. The model-derived T(B) results are compared to the 92 and 183 GHz aircraft observations of Hakkarinen and Adler (1984, 1986) and the radar-estimated rain rate of Hakkarinen and Adler (1986); good correlation between the data is detected.
The Climatology of Carbon Monoxide and Water Vapor on Mars as Observed by CRISM and Modeled by the GEM-Mars General Circulation Model
Radiative transfer modeling of near-infrared spectra taken by the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) instrument onboard Mars Reconnaissance Orbiter (MRO) enables the column-integrated abundance of carbon monoxide (CO) and water vapor (H 2 O) to be retrieved. These results provide a detailed global description of the seasonal and spatial distribution of CO in the Mars atmosphere and new information about the interannual variability of H2O. The CRISM retrievals show the seasonally and globally averaged carbon monoxide mixing ratio to be near 800 parts per million, but with strong seasonal variations, especially at high latitudes. At low latitudes, the carbon monoxide mixing ratio varies in response to the mean seasonal cycle of surface pressure and shows little variation with topography. At high latitudes, carbon monoxide is depleted in the summer hemisphere by a factor of two or more, while in the winter hemisphere there is relatively higher mixing ratio in regions with low-lying topography. Water vapor shows only modest interannual variations, with the largest observed difference being unusually dry conditions in the wake of the Mars Year 28 global dust storm. Modeling results from the GEM (Global Environmental Multiscale) - Mars general circulation model generally reproduce the observed seasonal and spatial trends and provide insight into the underlying physical processes.
Transfer Function Models and Sensitivity Analysis
In many situations, real or induced flaws such as tight cracks with known morphology cannot be manufactured in part configuration specimens or in real parts. Typically, fatigue cracks are manufactured in simple geometry specimens such as flat plates, dog-bone shaped flat or round specimens. If a nondestructive evaluation (NDE) technique is required to provide a reliably detectable target flaw size denoted as α_(90/95) for induced flaws in real part, then the direct method for qualifying the NDE procedure is to use the appropriate induced flaw specimens and perform probability of detection analysis using these flaws. This can be described as direct POD demonstration testing, which may follow guidelines of MIL-HDBK-1823. This paper considers a case, where induced flaws are not available in part configuration specimens. Therefore, a direct POD demonstration study cannot be undertaken. In such situation, general practice for NDE procedure qualification is to use artificial flaws in simple geometry and part configuration specimens, and induced flaws in the chosen simple geometry specimens. Signal response data is taken on all sets of artificial and induced flaws. NDE procedure testing on induced flaw in simple geometry specimen is called NDE demonstration testing here. A transfer function NDE procedure qualification method for forward case calculates predicted induced flaw size for demonstration using a chosen target flaw size. Another transfer function method for inverse case, calculates the target flaw size using a given demonstration flaw size. The transfer function analysis assumes relationship of artificial flaw signal responses in real parts and simple geometry specimens; and induced flaw responses in simple geometry specimens to induced flaws in real parts. The signal response transfer relationships model should be defined before transfer function models can be devised. Assuming that the signal response transfer relationships model is valid, forward and inverse case transfer function methods have been devised. Because of lack of signal response data from induced flaws in real part, 90/95% POD/confidence (P/C) cannot be demonstrated directly. However, the transfer function method may be assessed using simulation to evaluate whether the resulting target flaw size or demonstration flaw size provides adequate confidence to the assumed signal response transfer relationships model. Therefore, the transfer function approach is a risk assessment approach. Both the signal response transfer relationships model and the transfer function model are important in managing risk in results provided by the transfer function NDE technique qualification or assessment. The signal response transfer relationships model needs to be validated with empirical data and then transfer function model needs to be validated for desired P/C on case-by-case basis.
Enhancements to the SSME transfer function modeling code
This report details the results of a one year effort by Ohio University to apply the transfer function modeling and analysis tools developed under NASA Grant NAG8-167 (Irwin, 1992), (Bartholomew, 1992) to attempt the generation of Space Shuttle Main Engine High Pressure Turbopump transfer functions from time domain data. In addition, new enhancements to the transfer function modeling codes which enhance the code functionality are presented, along with some ideas for improved modeling methods and future work. Section 2 contains a review of the analytical background used to generate transfer functions with the SSME transfer function modeling software. Section 2.1 presents the 'ratio method' developed for obtaining models of systems that are subject to single unmeasured excitation sources and have two or more measured output signals. Since most of the models developed during the investigation use the Eigensystem Realization Algorithm (ERA) for model generation, Section 2.2 presents an introduction of ERA, and Section 2.3 describes how it can be used to model spectral quantities. Section 2.4 details the Residue Identification Algorithm (RID) including the use of Constrained Least Squares (CLS) and Total Least Squares (TLS). Most of this information can be found in the report (and is repeated for convenience). Section 3 chronicles the effort of applying the SSME transfer function modeling codes to the a51p394.dat and a51p1294.dat time data files to generate transfer functions from the unmeasured input to the 129.4 degree sensor output. Included are transfer function modeling attempts using five methods. The first method is a direct application of the SSME codes to the data files and the second method uses the underlying trends in the spectral density estimates to form transfer function models with less clustering of poles and zeros than the models obtained by the direct method. In the third approach, the time data is low pass filtered prior to the modeling process in an effort to filter out high frequency characteristics. The fourth method removes the presumed system excitation and its harmonics in order to investigate the effects of the excitation on the modeling process. The fifth method is an attempt to apply constrained RID to obtain better transfer functions through more accurate modeling over certain frequency ranges. Section 4 presents some new C main files which were created to round out the functionality of the existing SSME transfer function modeling code. It is now possible to go from time data to transfer function models using only the C codes; it is not necessary to rely on external software. The new C main files and instructions for their use are included. Section 5 presents current and future enhancements to the XPLOT graphics program which was delivered with the initial software. Several new features which have been added to the program are detailed in the first part of this section. The remainder of Section 5 then lists some possible features which may be added in the future. Section 6 contains the conclusion section of this report. Section 6.1 is an overview of the work including a summary and observations relating to finding transfer functions with the SSME code. Section 6.2 contains information relating to future work on the project.
A Principal Component-Based Radiative Transfer Forward Model (PCRTM) for Vertically in Homogeneous Cloud
A principal-component based radiative transfer model (PCRTM) is developed for simulating the infrared spectral radiance at the top of the atmosphere (TOA). The PCRTM approach removes the redundancy in radiative transfer calculation in high resolution infrared spectra, and saves significant amount of computational time with great accuracy. In PCRTM, both ice and water clouds are treated as effective transmissivity and reflectivity stored in a pre-calculated lookup tables. These quantities are calculated using cloud single scattering properties such as cloud optical depth, cloud particle size, cloud phase, etc. The cloud can be inserted into any pressure layer in the PCRTM model (up to 100 layers). The effective temperature of each cloud layer is treated as a function of its optical depth. To test the accuracy of this approximation, the results are compared with the more rigorous DISORT model, which treats cloud as a plane parallel layer. The root-mean-square error of PCRTM, with respect to DISORT results, is generally less than 0.5 K in brightness temperature. However, the CPU time required by PCRTM was approximately two orders of magnitude less than that required by DISORT.
Global Propagation of Gravity Waves Generated with the Whole Atmosphere Transfer Function Model
A brief review is presented of the Transfer Function Model (TFM) [e.g., Mayr et al., Space Science Reviews, 1990], which describes acoustic gravity waves (AGW) that propagate across the globe in a dissipative and static (no winds) background atmosphere with globally uniform temperature and density variations extending from the ground to 700 km. Unique among existing models, the TFM can be placed between the analytical approach on one end, and the rigorous numerical approach of general circulation models (GCM). The time consuming numerical integration of the conservation equations is restricted to compute the transfer function (TF) for a broad range of frequencies and spherical harmonics. Given TF, the atmospheric response for a chosen source configuration is then obtained in short order. Computationally efficient, the model is well suited to serve as experimental and educational tool for simulating propagating wave patterns across the globe. By design, the TFM is also semi-analytical and therefore well suited to explore the different wave modes that can be generated under different dynamical conditions.