Abstract
In this study, a machine-learning model was used to produce surface rainfall estimates from Temporal Experiment for Storms and Tropical Systems – Demonstration (TEMPEST-D) microwave radiance observations from a CubeSat. The machine-learning model is based on an artificial neural network (ANN). The space-borne TEMPEST-D sensor performed brightness temperature (TB) observations at five frequencies (i.e., 87, 164, 174, 178, and 181 GHz) during its nearly three-year mission. The TEMPEST-D TBs were used as inputs, and the multiradar/multisensor system (MRMS) radar-only quantitative precipitation estimation product at the surface was used as the ground truth to train the ANN model. A total of 19 storms were identified that were simultaneously observed by TEMPEST-D and ground weather radar over the contiguous United States. The training dataset used 14 of the 19 storm cases. The other five storm cases, consisting of three continental storms and two land-falling hurricanes, were used for independent testing. A spatial alignment algorithm was developed to align the TEMPEST-D observed storm with the ground radar measurement of the storm. This study showed that the TEMPEST-D TBs captured storm features as well as current-generation satellite sensors, such as the global precipitation mission microwave imager. The results of this study demonstrated that the rainfall estimated from TEMPEST-D matches well with the MRMS surface rainfall product in terms of rainfall intensity, area, and precipitation system pattern. The average structural similarity index measure score for the five independent test cases is 0.78.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.