OUSD (R&E) CRITICAL TECHNOLOGY AREA(S): Trusted AI and Autonomy OBJECTIVE: Develop and demonstrate a software system that unifies high resolution radiative transfer modeling from the UV, optical, infrared, microwave, and radio wavelengths with a database of physical earth system radiative spectra properties (including emission, absorption, transmission, reflection, and scattering) to inform a software package that supports purpose-driven remote sensing sensor selection and algorithmic development. DESCRIPTION: With expanded proliferation of remote sensing tools, especially from satellite orbit, there is a greater variation of wavelengths observed with differing physical signals that result in non-uniform interpretation of phenomena. Specifically for sensing of the earth system environment, properties of all the constituents of the land, water, atmosphere, and space environment have unique properties in the EM spectrum. There are currently no consolidated capabilities to interrogate multiple/mixed physical environments and their characteristics toward developing and optimizing remote sensing observation. This SBIR topic aims to provide that holistic capability to understand comparative observing characteristics of environmental signals, focusing on two specific use cases: (1) developing new hardware capability to optimally and generically observe desired environmental features (for example, determining the top three frequencies to maximally differentiate cloud water, snow over land, and glaciated ice); and (2) given legacy algorithms that leverage specific observing frequencies and bandwidths, optimally re-deriving those algorithms using the spectral characteristics of a new set of observing frequencies (e.g., porting products developed from one satellite constellation to another). While radiative transfer technology is relatively mature, much of the focus of this effort will be the identification, compilation, and characterization of the physical spectra database and the software implementation for straightforward model and simulation for a purpose-driven target enhancement and background minimization. PHASE I: Demonstrate the technical capability to leverage a radiative transfer model (such as RRTMG, CRTM, or other related tool suite) and a set of selected physical radiative spectra characteristics in a software suite to model multiple use case scenarios. Clearly scope the full range of possible environmental characteristics in a possible physical database (including, but not limited to, oceanic states, atmospheric water/particles/chemistry/thermodynamics from troposphere through thermosphere and ionosphere, land surface characteristics, and sea ice). Identify methodological details needed to run radiative transfer modeling and calculate different use scenarios (such as algorithmic porting, new sensor development, model and simulation for extrapolated new frequencies, etc.), highlighting automated steps from user-defined entries in a man-in-the-loop system. Develop a final summary report, including literature review and overall conclusions and recommendations, to be presented at the end of this Phase. Develop a Phase II plan. PHASE II: Conduct expanded technical development and validation of a robust prototype system for end-to-end modeling and simulation of radiative transfer characteristics of the earth system. Largely focus on the development and validation of the database on physical radiative characteristics and the software maturation, focusing on two specific use cases: (1) developing new hardware capability to optimally observe desired environmental features (for example, determining the top three frequencies to maximally differentiate cloud water, snow over land, and glaciated ice. This is only a display example, not a requested solution.); and (2) given legacy algorithms that leverage specific observing frequencies and bandwidths, optimally re-deriving those algorithms using the spectral characteristics of a new set of observing frequencies (for example, porting the dynamic enhancement with background reduction algorithm (DEBRA) from MeteoSat to Himawari. This is only a display example, not a requested solution.). The demonstration software package will include a fully connected radiative transfer model, complete physical radiative spectra database as outlined in Phase I, and will be compatible with running from open source python data analysis libraries. Delivery of the prototype software package and final verification report is expected at the end of this Phase. PHASE III DUAL USE APPLICATIONS: A prototype software suite that provides generic capability to interrogate radiation spectra characteristics for different phenomena has potentially wide use cases, both within the earth science community and beyond. In addition to a more robust validation and verification of the software capabilities, Phase III efforts include expansion of frequency spectra for the radiative transfer, developing an expanded emissivity database for broader use case and material scenarios (potentially for higher resolution surface characteristics), and refinement/optimization of software usage. Developers of remote sensing tools and software engineers refining algorithmic uses, especially for the earth system environment in this instantiation, will have immediate ability to leverage this work for their efforts. More broadly, satellite, aircraft, ship-based, and land-based remote sensing all need information on observed physical phenomena to properly calibrate their sensor and develop downstream applications. Use cases span DoD, civil, and private sectors. Should this demonstration provide comprehensive capability for the meteorological use case, this methodology could be ported to use cases beyond the environment where radiative spectra database development would be useful. REFERENCES: 1. Clough, S. A., et al. "Atmospheric radiative transfer modeling: A summary of the AER codes." Journal of Quantitative Spectroscopy and Radiative Transfer 91.2 (2005): 233-244. 2. Saunders, Roger, et al. "An update on the RTTOV fast radiative transfer model (currently at version 12)." Geoscientific Model Development 11.7 (2018): 2717-2737. 3. Lyapustin, Alexei, et al. "MODIS collection 6 MAIAC algorithm." Atmospheric Measurement Techniques 11.10 (2018): 5741-5765. 4. Vicent, Jorge, et al. "Comparative analysis of atmospheric radiative transfer models using the Atmospheric Look-up table Generator (ALG) toolbox (version 2.0)." Geoscientific Model Development 13.4 (2020): 1945-1957. 5. Hall, Forrest G., et al. "ISLSCP Initiative II global data sets: Surface boundary conditions and atmospheric forcings for land-atmosphere studies." Journal of Geophysical Research: Atmospheres 111.D22 (2006). 6. Miller, Steven D., et al. "A dynamic enhancement with background reduction algorithm: Overview and application to satellite-based dust storm detection." Journal of Geophysical Research: Atmospheres 122.23 (2017): 12-938. KEYWORDS: Radiative Transfer; Background; Emission; Atmospheric Science; satellite; satellite based environmental monitoring; remote sensing; spectral analysis
