Sea Surface Reflectivity Variation With Ocean Temperature at Ka-Band Observed Using Near-Nadir Satellite Radar Data

Satellite ocean radar data are used to assess the flat surface reflectivity for seawater at 36 GHz by comparison to an existing model for dielectric constant variation. Sea surface temperature (SST) is the dominant control, and results indicate a 14% variation in the normalized radar cross section (NRCS) at Ka-band (35.75 GHz) that is in close agreement with model prediction. Consistent results are obtained globally using near-nadir incidence data from both the SARAL AltiKa radar altimeter and Global Precipitation Measurement mission rain radar. The observations affirm that small but systematic SST-dependent corrections at Ka-band may require consideration prior to NRCS use in ocean surface wave investigations and applications. As an example, we demonstrate a systematic improvement in AltiKa ocean wind speed inversions after such an SST adjustment. Lower frequency C-and Ku-band results are also assessed to confirm the general agreement with prediction and a much smaller variation due to SST

Ocean Vector Wind Measurement Potential from the Global Precipitation Measurement Mission using a Combined Active and Passive Algorithm

Ocean surface vector wind (OVW) is an essential parameter for understanding the physics and dynamics of the ocean-atmosphere system, thereby improving weather forecasting and climate studies. Satellite scatterometers, synthetic aperture radars, and polarimetric microwave radiometers have provided almost global coverage of ocean surface vector wind for the last four decades. Nonetheless, a consistent and uninterrupted long-time data record with the capability of resolving sub-diurnal variability has remained a critical challenge over the years. The Global Precipitation Measurement Mission (GPM) is a satellite mission designed to provide space-based precipitation information on a global scale with complete diurnal sampling. This dissertation presents a combined active and passive retrieval algorithm to investigate the feasibility of ocean surface vector wind measurements from the GPM core satellite by utilizing its Ku- and Ka-band Dual-frequency Precipitation Radar (DPR) and the multi-frequency GPM Microwave Imager (GMI) observations. The unique GPM active and passive geophysical model functions were empirically developed by characterizing the anisotropic nature of ocean backscatter of normalized radar cross-section (δ°) and brightness temperature (TB) at multiple bands. For passive GMF, the modified 2nd Stoke\u27s parameter (linear combination of V and H-pol TBs) was used to mitigate the atmospheric contamination and to enhance the anisotropic wind direction signal superimposed on GMI TBs. The GMFs were combined in a maximum likelihood estimation (MLE) algorithm to infer the OVW. Finally, the retrieval algorithm was validated by comparing OVW retrievals with collocated NASA Advanced Scatterometer (ASCAT) wind vectors. The wind speed and direction retrieval performance statistics are promising and comparable with those of conventional scatterometer and polarimetric radiometer data products. The algorithm demonstrates the capability of the GPM to provide a long-term OVW data record for the entire GPM-TRMM era, which may include unique monthly diurnal OVW statistics