Passive microwave remote sensing of surface turbulent fluxes: the role of clouds and statistics

Detailed studies of the energy and water cycles require the use of data records of the turbulent moisture and heat fluxes across the atmosphere-ocean interface from the process to global scale. Estimates of these fluxes are made available from satellite-based observations primarily through retrievals of near-surface bulk variables — sea surface temperature, wind speed, air temperature and humidity — and the application of a suitable bulk flux parameterization. Historically, these retrievals have been implemented with empirical algorithms including both linear and nonlinear regression. Recent studies have revealed large differences between remotely sensed estimates of the turbulent fluxes that can be directly traced to discrepancies in the near-surface retrievals. Upon closer inspection, there is systematic co-organization of these inter-product differences with cloud-based weather states. This work examines the remote sensing of turbulent fluxes in several current data products. Particular emphasis is placed on investigating the underlying roots of the observed inconsistencies in near-surface parameter retrievals across ocean basins. The role of cloud-based weather states and their interconnection to the development of conditional biases in empirical retrievals is explored. Further, the development of alternative strategies to account for these issues in near-surface retrievals is presented.