Wind-Wave LETKF Data Assimilation of Remote Sensing and In Situ Observations-Updates on the 3D-RTMA Marine Component
Panagiotis Mitsopoulos, Malaquias Peña, and Manuel Pondeca
University of Connecticut
20 July, 2026, 12-1pm
Abstract:
A prototype Data Assimilation system using the Local Ensemble Transform Kalman Filter (LETKF) in a wind-wave Weakly Coupled DA (WCDA) configuration has been developed to improve the marine 3D-RTMA component and forecast verification offshore the Contiguous United States (CONUS). This talk will describe the workflow and main components of the system. The DA system uses Background fields from the 30-member GEFSv12-Wave ensemble. LETKF WCDA experiments were conducted offshore CONUS, assimilating significant wave height observations from ten satellite altimeters and surface wind speed observations from six scatterometers. The experiments covered six months, from January 1 to July 1, 2024, with supplementary in situ data used to maintain temporal continuity when remote sensing data were unavailable. The mesoscale features in the flow-dependent Analysis improve the long-term statistics (Bias, RMSE, Scatter Index) relative to the Background fields. This improvement is confirmed against assimilated remote sensing observations and independent, non-assimilated observations. We demonstrate the diurnal cycle in the spatial coverage and temporal sampling of the state-of-the-art altimeter and scatterometer constellations over the 6-month period offshore the maritime CONUS and adjacent ocean waters. A Pacific storm case study further highlights the spatial impact of assimilating observations at Analysis time. Overall, the results demonstrate the significant influence of assimilating satellite altimeter and scatterometer data into the LETKF on improving surface wave and wind speed Analyses and on better representing the ocean surface state offshore CONUS. In this talk, we will also discuss plans for and preliminary results of the strongly coupled wave-wind data assimilation scheme.