This paper describes first results obtained from the SWIM (Surface Waves Investigation and Monitoring) instrument carried by CFOSAT (China France Oceanography Satellite), which was launched on October 29 th , 2018. SWIM is a Ku-Band radar with a near-nadir scanning beam geometry. It was designed to measure the spectral properties of surface ocean waves. First, the good behavior of the instrument is illustrated. It is then shown that the nadir products (significant wave height, normalized radar cross-section and wind speed) exhibit an accuracy similar to standard altimeter missions, thanks to a new retracking algorithm, which compensates a lower sampling rate compared to standard altimetry missions. The off-nadir beam observations are analyzed in details. The normalized radar cross-section varies with incidence and wind speed as expected from previous studies presented in the literature. We illustrate that, in order to retrieve the wave spectra from the radar backscattering fluctuations, it is crucial to apply a speckle correction derived from the observations. Directional spectra of ocean waves and their mean parameters are then compared to wave model data at the global scale and to in situ data from a selection of case studies. The good efficiency of SWIM to provide the spectral properties of ocean waves in the wavelength range [70m-500m] is illustrated. The main limitations are discussed, and the perspectives to improve data quality are presented. 1

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This letter uses a large ocean satellite data set to 5 document relationships between Ku-band radar backscatter (σ 0 ) 6 of the sea surface, near-surface wind speed (U ), and ocean wave 7 height (SWH). The observations come from satellite crossovers 8 of the Tropical Rainfall Mapping Mission (TRMM) precipita-9 tion radar (PR) and two satellite altimeters, namely: 1) Ja-10 son-1 and 2) Environmental Satellite. At these nodes, we obtain 11 TRMM clear-air normalized radar cross-section data along with 12 coincident altimeter-derived significant wave height. Wind speed 13 estimates come from the European Centre for Medium-Range 14 Weather Forecast. TRMM PR is the first satellite to measure low 15 incidence Ku-band ocean backscatter at a continuum of incidence 16 angles from 0 • to 18 • . This letter utilizes these global ocean 17 data to assess hypotheses developed in past theoretical and field 18 studies-namely that variations in ocean sea state are measur-19 ably and systematically related to Ku-band σ 0 , that the impact 20 changes with incidence angle, and that it will affect the retrieval of 21 wind speed from σ 0 . Results have bearing on near-nadir ocean 22 radar missions such as Surface Waves Investigation and Moni-23 toring from Satellite, Advanced Scatterometer, TRMM, and the 24 wide-swath altimeter. 25 Index Terms-Author, please supply your own keywords or send Q1 26 a blank e-mail to keywords@ieee.org to receive a list of suggested 27 keywords. 28 I. INTRODUCTION 29 S ATELLITE radars are used to infer the wind speed 30 just above the sea surface through their measurement of 31 backscattered signal power, which is a signal that changes with 32 the amount and steepness of ocean waves. This normalized 33 radar backscatter cross-section (σ 0 ) term also depends on the 34 frequency, polarization, and incidence angle (θ) of the incident 35 radiation. Two now-standard satellite systems for ocean wind 36 estimation are the altimeter and the scatterometer. The former 37 views the sea from a downlooking (θ = 0 • ) incidence angle, 38 whereas the latter uses side-looking angles from 20 • to 60 • . It is 39 widely held that centimeter-scale ocean gravity-capillary waves 40 and their growth or decay with wind forcing are the dominant 41 controls of σ 0 variation for both sensors, but the ocean reflec-42 tion is distinctly different for these two systems that is con-43 sistent with the optical expectation; increased wave roughness 44 Manuscript

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The Envisat microwave radiometer is designed to correct the satellite altimeter data for the excess path delay resulting from tropospheric humidity. Neural networks have been used to formulate the inversion algorithm to retrieve this quantity from the measured brightness temperatures. The learning database has been built with European Centre for Medium-Range Weather Forecasts (ECMWF) analyses and simulated brightness temperatures by a radiative transfer model. The in-flight calibration has been performed in a consistent way by adjusting measurements on simulated brightness temperatures. Finally, coincident radiosonde measurements are used to validate the Envisat wet-tropospheric correction, and this comparison shows the good performances of the method.

[1] This study documents a method for increasing the precision of satellite-derived sea level measurements. Results are achieved using an enhanced three-dimensional (3-D) sea state bias (SSB) correction model derived from both Jason-1 altimeter ocean observations (i.e., sea state and wind) and estimates of mean wave period from a numerical ocean wave model, NOAA's WAVEWATCH III. A multiyear evaluation of Jason-1 data indicates sea surface height variance reduction of 1.26 (±0.2) cm 2 in comparison to the commonly applied two-parameter SSB model. The improvement is similar for two separate variance reduction metrics and for separate annual data sets spanning [2002][2003][2004]. Spatial evaluation of improvement shows skill increase at all latitudes. Results indicate the new model can reduce the total Jason-1 and Jason-2 altimeter range error budgets by $7.5%. In addition to the 2-D (two-dimensional) and 3-D model differences in correcting the range for wavefield variability, mean model regional differences also occur across the globe and indicate a possible 1-2 cm gradient across ocean basins linked to the zonal variation in wave period (short fetch and period in the west, swells and long period in the east). Overall success of this model provides first evidence that operational wave modeling can support improved ocean altimetry. Future efforts will attempt to work within the limits of wave modeling capabilities to maximize their benefit to Jason-1 and Jason-2 SSB correction methods.

[1] A new sea state bias modeling approach is presented that makes use of altimeter-derived marine geoid estimates. This method contrasts with previous models that require differencing between repeat altimeter passes for SSB isolation, along with complex bivariate inversion, to derive a relation between wind speed, wave height and SSB. Here one directly bin-averages sea height residuals over the wind and wave correlatives. Comparison with the most current nonparametric repeat-pass model shows close agreement and provides a first validation of this simpler and more direct technique. Success is attributed mainly to extensive space and time averaging. Ease in implementation and benefits in working with absolute levels provide much appeal. Further advantages and potential limitations, centered on the need to effectively randomize large sea level anomaly components to expose the bias, are also discussed.