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Completed TRL 2 (started at 1, targeting 2)
This project will provide instrument concept design and software development focused on a window channel passive microwave radiometer at 18, 37, and 89 GHz. The 2017 Decadal Strategy for Earth Observation from Space identified the need and desire to continue to (a) advance the quality of space borne instantaneous precipitation measurements beyond GPM, and (b) improve the quality as well as space/time resolution of global measurements of precipitation. With the GPM core satellite likely to de-orbit in the mid-late 2020’s and the aging of “radiometers of opportunity”, the field must look ahead to new possibilities for continued monitoring. Due to cost constraints related to antenna size, most planned radiometers are expected to carry only frequencies greater than 85 GHz, at which the relationship to hydrometeors becomes more indirect and retrieval algorithms more uncertain, particularly over ocean surfaces. For instruments including the desired lower frequencies, these same constraints lead to coarse resolution, which is less desirable for applications. In this project we will demonstrate that lower frequency window channel observations can be made with appropriate use of oversampling techniques in a way that could make long-term monitoring cheaper and more attractive for implementation. Specifically, passive microwave observations with the footprint and sampling characteristics of the proposed instrument will be simulated using state-of-the-art radiative transfer and atmospheric electromagnetic property models. A deep machine learning model (DMLM) will be trained to inversely relate simulated microwave radiometer observations (brightness temperatures) to high-resolution brightness temperatures. Resolution enhancement of this type has not been fully explored using machine learning techniques, and has potential to yield greatly improved solutions.
This project is envisioned as the first step in demonstration of a new concept for long-term global precipitation mapping using small, low-cost radiometers at the frequencies and enhanced resolution desired for applications including decision makers, hydrologists, hurricane forecasters, and researchers. While the planned AOS mission will greatly advance understanding of convective processes, mapping and other key questions posed in the 2017 Decadal Survey such as changes in the water cycle are not currently addressed beyond the lifetime of GPM. The problems inherent in the continuation of precipitation monitoring include antenna cost, and coarse resolution at the lower frequencies, and we are addressing the second issue here. The advancement of lower cost deployable antennas show great potential to alleviate the first. Lower frequency window channel measurements are not being pursued in current missions under development due to cost/antenna size requirements but are highly desired for applications and for more direct precipitation measurements. Innovating the machine learning component of optimally assembling the oversampled signal is a key component in demonstrating this concept and enabling it to move forward.
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