Accurate observational estimation of the ocean surface heat, momentum, and freshwater fluxes is crucial for studies of the global climate system. Estimating surface flux using satellite remote sensing techniques is one possible answer to this challenge. In this paper, we introduce J-OFURO3, a third-generation data set developed by the Japanese Ocean Flux Data Sets with Use of Remote-Sensing Observations (J-OFURO) research project, which represents a significant improvement from older data sets as the result of research and development conducted from several perspectives. J-OFURO3 offers data sets for surface heat, momentum, freshwater fluxes, and related parameters over the global oceans (except regions of sea ice) from 1988 to 2013. The surface flux data, based on a 0.25° grid system, have a higher spatial resolution and are more accurate than the previous efforts. This has been achieved through the adopting of the state-of-the-art algorithms that estimate the near-surface air specific humidity and the improvement of techniques using observations from multi-satellite sensors. Comparisons with in situ observations using a systematic system developed along with the J-OFURO3 data set confirmed these improvements in accuracy, as did comparisons with other data sets. J-OFURO3 data are of good quality, facilitating a clearer understanding of more fine-scale ocean-atmosphere features (such as ocean fronts, mesoscale eddies, and geographic features) and their effects on surface fluxes. The information contained in this long-term (26 year) data set is demonstrably beneficial to understanding climate change and its relationship to oceans and the atmosphere.

A multi-scale three-dimensional variational (MS-3DVAR) scheme is developed to assimilate high-resolution Himawari-8 sea surface temperature (SST) data for the first time into an operational ocean nowcast/forecast system targeting the North Western Pacific, JCOPE2. MS-3DVAR improves representation of the Kuroshio path south of Japan, its associated sea level variations, and temperature/ salinity profiles south of Japan, the Kuroshio/Oyashio mixed water region, and the Japan Sea as compared to those of the products by the traditional single-scale 3DVAR. Validation results demonstrate that MS-3DVAR well assimilates the sparsely distributed in situ temperature and salinity profiles data by spreading the information over the large scale and by representing the detailed information near the measurement points. MS-3DVAR succeeds to assimilate the Himawari-8 SST product without noisy features caused by the cloud effects. We also find that MS-3DVAR is more effective for estimating oceanic conditions in regions with smaller mesoscale variability including the mixed water region and Japan Sea than in south of Japan.Keywords Himawari-8 sea surface temperature data . Multi-scale three-dimensional variational scheme . Operational ocean nowcast/forecast system

Near‐surface air‐specific humidity is a key variable in the estimation of air‐sea latent heat flux and evaporation from the ocean surface. An accurate estimation over the global ocean is required for studies on global climate, air‐sea interactions, and water cycles. Current remote sensing techniques are problematic and a major source of errors for flux and evaporation. Here we propose a new method to estimate surface humidity using satellite microwave radiometer instruments, based on a new finding about the relationship between multichannel brightness temperatures measured by satellite sensors, surface humidity, and vertical moisture structure. Satellite estimations using the new method were compared with in situ observations to evaluate this method, confirming that it could significantly improve satellite estimations with high impact on satellite estimation of latent heat flux. We recommend the adoption of this method for any satellite microwave radiometer observations.

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We have developed a new algorithm to estimate the surface air specific humidity over the ocean from AMSR-E data. It should be noted that remarkably reduced random errors of the estimated surface air specific humidity result from using the surface air specific humidity provided by reanalysis data. We validated our new algorithm using independent ship and buoy data. The bias, RMS error, and correlation coefficient of the products obtained using our algorithm for global buoys are 0.38 g/kg, 0.61 g/kg and 0.99, respectively. It should be noted that surface specific humidity having similar accuracy to the reanalysis data near in situ data could be derived from AMSR-E data by the present algorithm.