Homogenization of scatterometer wind retrievals

Abstract

Surface winds (10 m equivalent neutral wind velocity) from scatterometer missions since 1992 to present require homogenization to meet the requirements for oceanic and atmospheric climate data records. Sources of differences between winds retrieved from different scatterometer measurements mainly arise from calibration/validation procedures used for each scatterometer and differences in measurement physics. In this study, we focus on the calibration/validation component of the European Remote Sensing Satellite (ERS)-1 and ERS-2 wind speed biases. ERS-1 and ERS-2 data, named as WNF products, are from the Institut Français de Recherche pour l'Exploitation de la MER (IFREMER). In addition to WNF data, the newly calibrated ERS-2 products provided by the European Space Agency (ESA), indicated as ASPS2.0 products, are also used. Our approach utilizes collocated satellite-buoy data. Expected values of the normalized radar cross section (NRCS) are calculated from buoy winds for each antenna beam using the Cmod5.n geophysical model function. The comparisons between expected and measured NRCS examine differences along with variables such as backscatter coefficient and incidence angle ranges. The difference between the expected and measured NRCS is then used to set up empirical models aiming at the correction for biases in ERS-1 and ERS-2 WNF NRCS calibrations. Finally, ERS-1 and ERS-2 wind retrievals are reprocessed using the corrected NRCS and Cmod5.n. These earlier corrected ERS-1/2 winds are analysed along with later scatterometer data (QuikSCAT and ASCAT-A) for their deviations from in situ buoy winds during 1992-2011 period. The scatterometer data homogeneity is also investigated at global scales based on the use of collocated scatterometer retrievals and atmospheric re-analyses winds derived from ERA Interim and CFSR models.

FluxSat: Measuring the Ocean–Atmosphere Turbulent Exchange of Heat and Moisture from Space

“…Wind speeds in the global flux products can be derived from either passive or active microwave measurements, or both, but are inferred from wind effects on the surface roughness, with differing retrieval algorithms and satellites contributing to differences between the derived flux fields. Some of the algorithms for deriving global fluxes infer near-surface specific humidity from passive microwave measurements (Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite (HOAPS4; [33,34]) and SeaFlux CDR [35]), while others use reanalysis products or a blend of reanalysis and passive microwave measurements (Institut Français pour la Recherche et l'Exploitation de la Mer, IFREMER 4, [36,37]), and Japanese ocean flux data set using remote-sensing observations (J-OFURO3, [38]). Near-surface air temperature is most commonly estimated using reanalysis products (IFREMER4, J-OFURO3), by using an air-sea temperature difference derived from SST (HOAPS4) or a neural net retrieval from passive microwave (SeaFlux CDR).…”

Multivariate Sub-Regional Ocean Indicators in the Mediterranean Sea: From Event Detection to Climate Change Estimations

“…The wind speed and direction (WIND) are monitored using the CMEMS OSI-TAC product for the global ocean, from blended ASCAT-SSM/I analysis processed by CERSAT (Bentamy et al, 2017;Desbiolles et al, 2017). Both the REP and NRT products used are interpolated data into a 1/4 • × 1/4 • grid with a 6-h resolution over the 1992-2018 and 2019-present periods, respectively.…”

Intercomparison of freshwater fluxes over ocean and investigations into water budget closure

“…The BTs used for retrievals are intercalibrated by Colorado State University (CSU; Sapiano et al, 2013), except for data beyond June 2017, where CSU data end and a switch to BTs from RSS (Wentz et al, 2013) is made. Intercalibrated scatterometer wind data (Bentamy et al, 2017a) are supplemented by wind speeds determined by RSS from the SSM/I, SSMIS, and WindSat instruments. SST are from OISST (Reynolds et al, 2007).…”