The concept of a sea state bias (SSB) correction, i.e. using a function of wave height (and other terms) to improve sea surface height, has been accepted and applied for many decades. Recent papers have demonstrated afresh that the correction operates on at least two different scales — one, associated with physical processes, is pertinent at wavelengths of 10 km and greater, whilst the other attributed to retracker noise, is effectively independent at 20 Hz i.e every one third of a kilometre along track. Passaro et al. (2018) showed how regional high-frequency corrections could enable a better removal of SSB than the standard 1 Hz implementation.
This work utilizes the observed connection between high-frequency anomalies in range and wave height in the reverse manner, namely using range or epoch to improve wave height. The scaling factors, obtained through a separate linear regression process, are not simply the inverse of those used for the SSB implementation. Initial results suggest that this technique will not be efficacious if applied to each waveform independently, as there are numerous processes affecting the position of the waveform within the tracker window. However, by focussing on the highest frequency variations in leading edge position (using anomalies relative to a broader running mean for that value) we were able to effect a significant reduction in the short-term variability in wave height. This was quantified by comparisons of sigma_Hs (the standard deviation of wave height estimates in each 1 Hz record), and also by calculation of along-track spectra.
The Sea State CCI (Climate Change Initiative) is seeking to improve the consistency within individual altimeter datasets and between missions. This technique will be considered as a post-processing option and evaluated against strict and transparent criteria. This more detailed investigation may uncover further challenges in implementing this correlated error mitigation strategy.

Standard methods to retrieve sea level from satellite altimetry usually also provide significant wave height (SWH) and backscatter coefficient (sigma0), which are fundamental to describe the sea state. Since the traditional waveform fitting (retracking) methods have been tuned to sea level retrieval, the sea state record is currently affected by high noise (over half a meter in terms of SWH even for calm seas), problems in estimating extreme values and scarce usability close to the coast. The Sea State Climate Change Initiative of the European Space Agency aims at improving this picture and compiling a new sea state record of climate quality standard.

We present a new retracking algorithm meant to achieve this objective. The algorithm, called Wave Height Adaptive Leading Edge Subwaveform retracker (WHALES), can be applied to any Low Resolution Mode (LRM) mission and its rationale is preparatory for the improvement of the estimates from SAR-mode altimetry as well.

The LRM waveforms are characterised by a rising leading edge that becomes less steep as the SWH increases, and a slowly decreasing trailing edge. The advent of the ALES retracker has already showed the way to keep the quality of the retrievals in the open ocean while improving the data quality and quantity in the last ~25 km from the coast, where waveforms are particularly corrupted by heterogeneous backscattering from land and sheltered water. Nevertheless, besides still using the full echo to retrieve parameters that are located on the leading edge, the standard retracking method is still affected by a suboptimal distribution of the residuals in the fitting process, which usually are far from being of similar magnitude across the different samples of the waveform.

WHALES is designed as a unified way to solve these and other problems currently affecting the standard product, and is based on three principles:

1) The application of a weighted fitting solution, whose weights are adapted to the SWH in order to guarantee a more uniform distribution of the residuals during the iterative fitting. This guarantees significantly more precise estimations.

2) A subwaveform strategy to focus the retracking on the portion of the signal of interest, avoiding heterogeneous backscattering in the trailing edge (partially inherited from the ALES retracker). This guarantees efficiency in the coastal zone and a better representation of the oceanic scales of variability.

3) The decorrelation between SWH and sea level estimation, which corrects for possible covariant errors and increase furthermore the precision.

Moreover, a revisiting of the look-up tables used to correct for the Gaussian approximation of the Point Target Response in the Brown model ensures that the accuracy in the estimation is tailored to the new retracking solution.

First results show that the noise in the SWH can be abated by over 50% compared with the current standards, while reliable performances in the coastal ocean match with improvements in all the statistics of comparison with the MeteoFrance WAM wave model in the open Southern Ocean.

Besides presenting the WHALES algorithm and its performance, this presentation will also discuss a way to compare it against different retrackers in the framework of the SS_CCI. The altimetry community has in fact debated for a long time about the need of establishing a way to provide a quantitative evaluation of different retracking techniques, while keeping an independent, more extensive validation process. Here we propose a series of criteria (a Round Robin exercise) based on internal statistics and comparisons with buoys and models: these statistics will be produced by routines that will be made publicly available, therefore entirely transparent and replicable. They are meant to reflect the different interests that scientists have when using sea state data, by differentiating into coastal and open ocean environments, standard and extreme sea states, precision and accuracy statistics.

The improvement of sea state estimation from satellite altimetry would have a very strong impact on the understanding of the climatic changes of these variables, the forcing of reanalysis and forecasting models and the improvements in the knowledge in areas with strong societal impact, such as coast and storms. The success of the Round Robin exercise could constitute a milestone in the attempt to harmonise and evaluate different approaches to treat the radar signal for this constantly evolving and expanding remote sensing technique.

Figure caption: Left panel: Noise performances w.r.t. different SWH levels for the standard Jason 3 data (blue), an unweighted subwaveform estimator (such as ALES, in green), the weighted subwaveform solution proposed by WHALES (purple) and the same WHALES solution once decorrelated from the sea level estimation (red). Right panel: Performances of the raw WHALES-retracked SWH data on Jason-3 against estimations from MFWAM in a subsection of the Southern Ocean. Different colours correspond to different tracks.

The Safety of Life at Sea (SOLAS) convention grew out of the sinking of the Titanic in 1912. The United States is one of the original signatories to SOLAS, in 1914, and continues to support maritime safety conventions of the International Maritime Organization and World Meteorological Organization. To prevent loss of life and property at sea and as a contributor to the Global Maritime Distress and Safety System, the U. S. National Oceanic and Atmospheric Administration (NOAA)'s Ocean Prediction Center provides hazardous marine weather warnings and forecasts for much of the north Atlantic and Pacific Oceans.

OPC forecasters use significant wave height (SWH) and near-surface wind speed (WS) estimates derived from short-time-critical satellite altimeter data to verify the performance of forecast models and to make decisions about issuing warnings. Satellite altimeters are one of the few ways to measure the extreme SWH and WS values that may occur in tropical and extra-tropical cyclonic storms. At this writing, SWH estimates from the CryoSat-2, Jason-2, Jason-3, Sentinel-3A, Sentinel-3B, and SARAL/AltiKa altimeters are routinely displayed on the workstations of OPC forecasters.

When satellite altimeter measurements of sea surface height (SSH) are assimilated into operational models of ocean circulation, the SSH data associated with unusual SWH or WS values are edited (not used), because large values occur in storms under conditions such that SSH anomalies likely would not be indicative of dynamic geostrophic heights. However, OPC does not want an editing scheme that would reject large but accurate SWH or WS values, as these are needed to make a decision about issuing a warning to ships at sea. We find that altimeters do seem to be measuring extreme SWH reliably in a variety of storms we have observed.

During the 2018 North Atlantic hurricane season Hurricanes Florence and Michael occasionally caused the Jason-2 altimeter to estimate truly spurious wave heights exceeding 20 m SWH that we have been able to correlate with bands of extreme rain intensity measured by ground-based weather radar observations of dBZ.

During its primary mission Jason-2 flew an exact-repeat orbit along a known ground track, and its radar measurement was controlled by the DIODE/DEM ("open loop tracking") system. Since the end of the exact-repeat mission Jason-2 has been on a geodetic mission orbit, over which it does not use DIODE/DEM but strives to follow the sea surface automatically ("closed loop tracking"). We think that extreme rain intensity causes the Jason-2 altimeter to lose track of the sea surface, producing the spurious SWH values. We are developing a new editing scheme specific to the Jason-2 GM data that will allow large but likely correct SWH values to pass through to OPC forecasters, while editing SWH values that are likely due to the altimeter's loss of track.

Swell systems are fingerprints of the extreme ocean storms, and can propagate all the way across ocean basins from the area of high winds that generated them. Very long-period swells have been observed to propagate up to halfway around the globe (e.g. Munk et al., 1963). More recent investigations (e.g. Collard et al., 2009, Hannafin et al., 2011) clearly confirmed swell space-time persistence. Space-time analysis of ocean waves spectra measured from space at global scale can thus be used to monitor the same swell field over 3 to 10 days along their initial propagation direction. To date, this has only been applied to synthetic aperture radar (SAR) Wave Mode data in the case of extra-tropical storm. The proper combination of regularly sampled acquisitions over the ocean surface at different places and times can yield the position of the generating storm [Heimbach and Hasselmann, 2000, Husson et al., 2012], and predictions for the arrival time of swells with different wavelengths and directions. An ensemble of data acquired at different times and locations can thus be gathered to document a given extreme event. Currently available, global Wave Mode data from two identical C-Band SAR instruments, on board Sentinel-1 A and Sentinel-1 B, can already be used to identify and track swell systems. Wave mode data, consisting of 20 km by 20 km imagettes, are acquired alternatively at near range (incidence angle: 23.5 deg) and far range (incidence angle: 36.5 deg), every 100 km along the orbit. Moreover, existing altimeter can be used to complement Sentinel-1 by providing significant wave height measurements.
In addition, recent methods have been developed to measure ocean surface wind speed over hurricanes. In particular, radiometers such as L-band radiometers SMOS [Reul et al., 2012], SMAP [Meissner et al., 2017] and or the multi-frequencies radiometer AMSR2 [Zabolotskikh et al., 2013] can provide medium resolution (about 50 km) wind speeds with a large coverage (swath is about 1000km wide). Their combination allows a description of the hurricane intensity and extent during its whole lifetime [Reul et al., 2018]. Thanks to the new capabilities (co- and cross- polarization) Synthetic Aperture Radar such as Sentinel-1 [Mouche et al., 2017], Radarsat-2 [Zhang & Perrie, 2012] or Gaofeng-3 can also provide ocean surface wind field at very high resolution, including measurements in the inner core.
In order to consistently analyse hurricane properties (intensity, extent, speed) and Tropical Cyclones (TC) generated waves, a refocusing technique has then been developed to precisely study the distribution of the swell wavelengths and directions as measured by Sentinel-1 with respect to the hurricane track far from their generation area. All swell detected systems are back-propagated in space and time using the estimated group velocity. Swell-current interactions are neglected, and back-propagated tracks are then cross-assigned with the hurricane Best Track, i.e. the space-time location of the hurricane center. For a given location, we select all swell systems with trajectories crossing the storm center, e.g. found +/- 3 hours around the track time and within a 2° by 2° box around the storm-center.
Based on these remote sensing data enables we analyse the distribution of hurricane generated waves wavelengths with respect to their direction of propagation outside of the TC together with the hurricane characteristics given by SAR and radiometers. Impact of the extended fetch effect is discussed and comparison with wind and waves models are presented.

Global information on the motion near the ocean surface is generally lacking, limiting the physical modelling capabilities of the forcing of the world’s water surfaces by the atmosphere. This also limits our knowledge of the exchange of momentum across the ocean-atmosphere interface, affecting meteorological and ocean applications. A particularly pressing requirement in the Ocean Surface Vector Wind (OSVW) community is to obtain reliable extreme winds in hurricanes (> 30 m/s) from wind scatterometers, since extreme weather classification, surge and wave forecasts for societal warning are a high priority in nowcasting and in Numerical Weather Prediction (NWP). The main goal of this study is therefore to consolidate a wind reference for assessing scatterometer high and extreme-force wind capabilities.
Scatterometers have proved to have very good performances when retrieving low to moderate winds. Over the years, improvements have been done on the development of the empirical Geophysical Model Functions (GMF) in order to obtain more accurate wind estimates with respect to in situ measurements. However, measuring high and extreme winds is still challenging as vicarious calibration is needed and calibrated in situ reference winds are scarce. Theoretical statistical descriptions of the high-wind ocean surface, where patchy foam, droplets, spume and wave breaking occurs are much simplified, while the microwave interaction on cm scales is rather complex too.
Moored buoy data are usually used as absolute reference to calibrate the GMFs, however, for very high and extreme winds above 25 m/s, moored buoys may not be reliable. Moreover, controversy exists in the OSVW satellite community on the quality of moored buoys above 15 m/s rather than 25 m/s. Therefore, collaboration has been sought with the National Oceanic and Atmospheric Administration (NOAA) Ocean Science Team hurricane hunters to have additional high and extreme winds reference data sets.
The NOAA hurricane hunters fly into hurricanes to drop sondes, and thus obtain wind profiles in the lowest few kilometers of hurricanes, and operate dedicated microwave instrumentation on aircraft to obtain detailed wind patterns in hurricanes, such as the Stepped-Frequency Microwave Radiometer (SFMR). Ideally, local dropsonde winds may be statistically used to calibrate SFMR as they have similar spatial representation (“footprint”), which in turn, after spatial aggregation to scatterometer footprints, may be used to calibrate satellite scatterometers and radiometers in overflights.
Although this approach is credible in principle and physically more consistent than any other global method to obtain maximum winds in hurricanes, research is ongoing to understand the exact physical interpretation of dropsonde winds and SFMR in the inherently extremely variable conditions in tropical hurricanes for which more hurricane flight data is required. Since processing artifacts are known to exist for both dropsondes and SFMR, NOAA has recently reprocessed both data sets to provide a more accurate reference.
In the framework of the EUMETSAT-funded “C-band High and Extreme-Force Speeds (CHEFS)” project, the main goal is to assess the extreme wind capabilities of the next generation of C-band wind scatterometers onboard Metop Second Generation. One important goal within CHEFS is to provide an appropriate high and extreme-wind reference at scatterometer scales. To this end, five wind data sets have been collected so far: (i) different types of moored buoy data; (ii) NOAA-reprocessed SFMR 10m surface winds from 2008 to 2017; (iii) NOAA dropsonde wind profiles from 1996 to 2017; (iv) reprocessed ASCAT-A winds at 12.5 km resolution; and (v) the latest European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis dataset ERA5 from 2007 to 2017.
The quality of buoy winds between 15 m/s and 25 m/s is thoroughly evaluated. Analysis of the wind characteristics of different types of moored buoys in terms of height and mooring is performed. The buoy wind performance, estimated with triple collocation analyses of buoy, ASCAT and ERA5 winds, shows that although buoy wind uncertainty increases with wind speed, the quality of buoy wind vectors up to 25 m/s is within 2 m/s, indicating that buoy winds can indeed be used for wind scatterometer GMF calibration in the mentioned wind range.
An accurate calibration procedure for surface high winds retrieved from SFMR will be also developed using dropsonde data as reference. To this end, a SFMR/dropsonde collocation criterion is defined. In addition, the so-called WL150 algorithm used to estimate 10m surface winds from dropsonde wind profiles is also revised and its impact on the SFMR calibration process is tested. SFMR wind data are also analyzed at different temporal/spatial scales in order to assess the spatial representativeness error when compared against 12.5 km resolution ASCAT winds for different wind conditions. Moreover, the SFMR rain data are also used to evaluate the impact of rain on both SMFR and ASCAT winds, notably for winds higher than 25 m/s. The findings of this analysis will be then used to investigate extreme winds cases such as those close to the hurricane eyewall. In such high wind gradient conditions, new SMFR-dropsonde collocation approaches will also be explored in order to reduce the collocation error and improve the wind consistency in the analysis, which should lead to a more suitable SFMR calibration at extreme wind conditions. Finally, SFMR calibrated winds can be aggregated along the flight track and used as wind reference for re-tuning the C-band vertically-polarized (e.g., ASCAT) and cross-polarized (e.g., Sentinel-1) GMFs in preparation for the Metop Second Generation scatterometer (SCA). The latest results of this ongoing work will be presented at the conference.

In a period of climate change, understanding the global wind and wave climate, and how it may be changing is important for a number of reasons. Ocean waves play an important role in defining the air-water boundary roughness and hence the magnitude of fluxes of energy and CO2 between the atmosphere and ocean. Sea level rise is one of the most immediate consequences of a warming planet and breaking wave setup is an important component of total water level during storms. Projections of future ocean wind and wave states, and whether extreme conditions are changing become important elements of projections of total sea level and will be included in IPCC.

A key baseline for future projection is understanding whether ocean wind and wave climate has changed in recent years (i.e. the past 30 years) and the mechanisms which may be driving such changes. Accurate determination of relatively small trends in measured records has however, to date, proved problematic. The most obvious data source for long term oceanic wind and wave observations are ocean buoys. However, a number of studies have indicated that changes in buoy hulls, instrumentation packages and processing methods means that the buoy dataset is not homogeneous and generally unsuitable for long term trend analysis. The satellite record, which provides global coverage, is now 33 years long (1985-2018) and includes altimeters (wave height and wind speed) and radiometers (wind speed). Scatterometers, which measure wind speed and direction are also useful, but have a shorter time span. Initial applications of the satellite record (Young et al., 2011) has proved promising. However, the potential for bias in this record has raised doubts about the veracity of resulting trends. In particular, concerns have been raised about: the consistency of calibration across the multiple satellite platforms, the apparent differences in observed magnitudes of wind speed trends between altimeter and radiometer data, potential for under-sampling and “fair-weather” bias and the validity of observations of extreme conditions.

In this presentation, we report on the analysis of an extensive satellite database consisting of 13 altimeters and 12 radiometer missions, spanning a total of 33 years (1985-2018). The data set is analyzed to determine long-term global trends in wind speed and wave height. The analysis is able to address many of the previous concerns about such satellite records. Specifically, we show consistent trends between altimeter and radiometer wind speed, we resolve apparent inconsistencies in the magnitudes of wind speed and wave height trends and we add confidence as to the ability to extract not only trends in mean values but also higher percentile extremes (i.e. 90th percentiles).

The data shows a global increase in mean wind speed over the measurement period with a stronger increase in extreme (90th percentile) winds. There are strong regional variations in the trend, with the Southern Ocean showing the largest increase. The trend in mean wave height is weak, with only some regions of the Southern Ocean showing a statistically significant positive trend. As with wind speed, the 90th percentile wave height shows a stronger positive trend. The presentation will show that there has been a distinct change in the wind speed pdf, which is reflected in a nonlinear manner by the waves. As a result, the increase in mean wind speed is not matched by a similar response in the waves. The analysis will show that the results, including the higher percentiles are robust.