ERAstar: A high-resolution ocean forcing product

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksTo address the growing demand for accurate high-resolution ocean wind forcing from the ocean modeling community, we develop a new forcing product, ERA*, by means of a geolocated scatterometer-based correction applied to the European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis or ERA-interim (hereafter referred to as ERAi). This method successfully corrects for local wind vector biases present in the ERAi output globally. Several configurations of the ERA* are tested using complementary scatterometer data [advanced scatterometer (ASCAT)-A/B and oceansat-2 scatterometer (OSCAT)] accumulated over different temporal windows, verified against independent scatterometer data [HY-2A scatterometer (HSCAT)], and evaluated through spectral analysis to assess the geophysical consistency of the new stress equivalent wind fields (U10S). Due to the high quality of the scatterometer U10S, ERA* contains some of the physical processes missing or misrepresented in ERAi. Although the method is highly dependent on sampling, it shows potential, notably in the tropics. Short temporal windows are preferred, to avoid oversmoothing of the U10S fields. Thus, corrections based on increased scatterometer sampling (use of multiple scatterometers) are required to capture the detailed forcing errors. When verified against HSCAT, the ERA* configurations based on multiple scatterometers reduce the vector root-mean-square difference about 10% with respect to that of ERAi. ERA* also shows a significant increase in small-scale true wind variability, observed in the U10S spectral slopes. In particular, the ERA* spectral slopes consistently lay between those of HSCAT and ERAi, but closer to HSCAT, suggesting that ERA* effectively adds spatial scales of about 50 km, substantially smaller than those resolved by global numerical weather prediction (NWP) output over the open ocean (about 150 km).Peer ReviewedPostprint (author's final draft

On buoys, scatterometers and reanalyses for globally representative winds

15 pages, 3 figures, 2 tablesMoored buoy winds are of high quality and our only absolute reference for satellite wind calibration and monitoring. General Circulation Models (GCMs) and satellites lack absolute calibration otherwise. Maintaining a long-term data record of surface wind measurements is thus critical to the cross-calibration of satellite winds from different satellite missions and different satellite sensor types (e.g., the SSM/I series microwave radiometers, Ku- vs C- vs L-band scatterometers). The current non-uniform distribution of moored buoys makes them rather unsuitable for global change metrics. The geographical distribution of moored buoys points to a glaring hole in the southern hemisphere. With 60m of global water level stored in the southern hemisphere, scientific misjudgement may have rather drastic consequences. However, buoy monitoring in the SH extratropics is essentially missing and should be recommended in our view. It would be much appreciated if (particularly southern hemisphere governments) would take responsibility in this area. We perform triple collocation (TC) with moored buoys, scatterometers and GCMs to establish the accuracy and calibration of the scatterometer winds and the GCMs at the moored buoy positions. By physical inference, we assume that the spatial sample of buoys is sufficient to obtain a globally representative absolute calibration. This can obviously not be proven, as no globally representative in situ wind network is available. However, given such plausible inference, it appears possible to reach the 0.1 m/s per decade stability in a representative global metric. Moreover, randomly reducing the density of the current spatial distribution of moored buoys, does not appear too harmful. We note that different global metrics provide different trends though, as they cover different spatio-temporal domains, e.g., at all global buoy measurement positions (as in TC), at model grid positions (either regular or uniformly spaced), or at all satellite measurement points (after QC usually). The satellite or GCM representations of the global waters appear clearly the most faithful (see above). The IOVWST community currently converges in the understanding that stress-equivalent wind (U10S) is the most practical retrieval quantity for scatterometers and radiometers, as it may be well validated by GCM and buoy data. This implies that for an accurate computation of U10S from buoys, we ideally need continuous buoy series of: the 10-m wind, SST, air temperature, air humidity, air pressure and ocean current. These variables are used to respectively take out effects of atmospheric stratification, air mass density and ocean mean motion (as the sensed ocean roughness depends on the mean relative difference between water and air motion). As less of this information would become available at the buoys, it will be harder to stay within the climate requirement of 0.1 m/s per decade in the more representative global metrics. Recent publications suggest that observation of OSVW variability in the tropics is quite relevant, e.g., Sherwood et al. (2014), Lin et al. (2015), King et al. (2014) or Sandu et al. (2011), suggesting that spread in climate model sensitivity and model bias can be related to subtle dynamical model aspects, such as moist convection. Another question is thus how dynamical meteorological and oceanographic interaction processes, relevant for the realism of climate models should be addressed by measurement capability in the satellite era. This question is not further addressed in this report.This documentation was developed within the context of the EUMETSAT Satellite Application Facility on Numerical Weather Prediction (NWP SAF), under the Cooperation Agreement dated 16 December, 2003, between EUMETSAT and the Met Office, UK, by one or more partners within the NWP SAF. The partners in the NWP SAF are the Met Office, ECMWF, KNMI and Météo FrancePeer Reviewe

Accuracy of wind observations from open-ocean buoys: Correction for flow distortion

The comparison of equivalent neutral winds obtained from (a) four WHOI buoys in the subtropics and (b) scatterometer estimates at those locations reveals a root-mean-square (RMS) difference of 0.56-0.76 m/s. To investigate this RMS difference, different buoy wind error sources were examined. These buoys are particularly well suited to examine two important sources of buoy wind errors because: (1) redundant anemometers and a comparison with numerical flow simulations allow us to quantitatively assess flow distortion errors, and (2) one-minute sampling at the buoys allows us to examine the sensitivity of buoy temporal sampling/averaging in the buoy-scatterometer comparisons. The inter-anemometer difference varies as a function of wind direction relative to the buoy wind vane and is consistent with the effects of flow distortion expected based on numerical flow simulations. Comparison between the anemometers and scatterometer winds supports the interpretation that the inter-anemometer disagreement, which can be up to 5% of the wind speed, is due to flow distortion. These insights motivate an empirical correction to the individual anemometer records and subsequent comparison with scatterometer estimates show good agreement

Extended triple collocation: estimating errors and correlation coefficients with respect to an unknown target

Calibration and validation of geophysical measurement systems typically require knowledge of the true value of the target variable. However, the data considered to represent the true values often include their own measurement errors, biasing calibration, and validation results. Triple collocation (TC) can be used to estimate the root-mean-square-error (RMSE), using observations from three mutually independent, error-prone measurement systems. Here, we introduce Extended Triple Collocation (ETC): using exactly the same assumptions as TC, we derive an additional performance metric, the correlation coefficient of the measurement system with respect to the unknown target, rho(t,Xi). We demonstrate that rho(2)(t,Xi) is the scaled, unbiased signal-to-noise ratio and provides a complementary perspective compared to the RMSE. We apply it to three collocated wind data sets. Since ETC is as easy to implement as TC, requires no additional assumptions, and provides an extra performance metric, it may be of interest in a wide range of geophysical disciplines.Peer ReviewedPostprint (published version

On mesoscale analysis and ASCAT ambiguity removal

45 pages, 17 figures, 7 tablesIn the so-called two-dimensional variational ambiguity removal (2DVAR) scheme [Vogelzanget al., 2010], the scatterometer observations and the model background (fromthe European Centre for Medium-range Weather Forecasts, ECMWF) are combined using a two-dimensional variational approach, similar to that used in meteorological data assimilation, to provide an analyzed wind field. Since scatterometers provide unique mesoscale information on the wind field, mesoscale analysis is a common challenge for 2DVAR and for mesoscale data assimilation in 4D-var or 3D-var, such as applied using the Integrated Forecasting System (IFS) at ECMWF, Meteo France or in the HIRLAM project (www.hirlam.org). This study elaborates on the common problem of specifying the observation and background error covariances in data assimilationThis documentation was developed within the context of the EUMETSAT Satellite Application Facility on Numerical Weather Prediction (NWP SAF), under the Cooperation Agreement dated 29 June 2011, between EUMETSAT and the Met Office, UK, by one or more partners within the NWP SAF. The partners in the NWP SAF are the Met Office, ECMWF, KNMI and Météo FrancePeer Reviewe

Accuracy of wind observations from open-ocean buoys: Correction for flow distortion

The comparison of equivalent neutral winds obtained from (a) four WHOI buoys in the subtropics and (b) scatterometer estimates at those locations reveals a root-mean-square (RMS) difference of 0.56-0.76 m/s. To investigate this RMS difference, different buoy wind error sources were examined. These buoys are particularly well suited to examine two important sources of buoy wind errors because: (1) redundant anemometers and a comparison with numerical flow simulations allow us to quantitatively assess flow distortion errors, and (2) one-minute sampling at the buoys allows us to examine the sensitivity of buoy temporal sampling/averaging in the buoy-scatterometer comparisons. The inter-anemometer difference varies as a function of wind direction relative to the buoy wind vane and is consistent with the effects of flow distortion expected based on numerical flow simulations. Comparison between the anemometers and scatterometer winds supports the interpretation that the inter-anemometer disagreement, which can be up to 5% of the wind speed, is due to flow distortion. These insights motivate an empirical correction to the individual anemometer records and subsequent comparison with scatterometer estimates show good agreement

Temporal stability of soil moisture and radar backscatter observed by the advanced Synthetic Aperture Radar (ASAR)

The high spatio-temporal variability of soil moisture is the result of atmospheric forcing and redistribution processes related to terrain, soil, and vegetation characteristics. Despite this high variability, many field studies have shown that in the temporal domain soil moisture measured at specific locations is correlated to the mean soil moisture content over an area. Since the measurements taken by Synthetic Aperture Radar (SAR) instruments are very sensitive to soil moisture it is hypothesized that the temporally stable soil moisture patterns are reflected in the radar backscatter measurements. To verify this hypothesis 73 Wide Swath (WS) images have been acquired by the ENVISAT Advanced Synthetic Aperture Radar (ASAR) over the REMEDHUS soil moisture network located in the Duero basin, Spain. It is found that a time-invariant linear relationship is well suited for relating local scale (pixel) and regional scale (50 km) backscatter. The observed linear model coefficients can be estimated by considering the scattering properties of the terrain and vegetation and the soil moisture scaling properties. For both linear model coefficients, the relative error between observed and modelled values is less than 5 % and the coefficient of determination (R-2) is 86 %. The results are of relevance for interpreting and downscaling coarse resolution soil moisture data retrieved from active (METOP ASCAT) and passive (SMOS, AMSR-E) instruments

Wind speed retrieval from the Gaofen-3 synthetic aperture radar for VV- and HH-polarization using a re-tuned algorithm

In this study, a re-tuned algorithm based on the geophysical model function (GMF) C-SARMOD2 is proposed to retrieve wind speed from Synthetic Aperture Radar (SAR) imagery collected by the Chinese C-band Gaofen-3 (GF-3) SAR. More than 10,000 Vertical-Vertical (VV) and Horizontal-Horizontal (HH) polarization GF-3 images acquired in quad-polarization stripmap (QPS) and wave (WV) modes have been collected during the last three years, in which wind patterns are observed over open seas with incidence angles ranging from 18° to 52°. These images, collocated with wind vectors from the European Centre for Medium-Range Weather Forecast (ECMWF) reanalysis at 0.125° resolution, are used to re-tune the C-SARMOD2 algorithm to specialize it for the GF-3 SAR (CSARMOD-GF). In particular, the CSARMOD-GF performs differently from the C-SARMOD2 at low-to-moderate incidence angles smaller than about 34°. Comparisons with wind speed data from the Advanced Scatterometer (ASCAT), Chinese Haiyang-2B (HY-2B) and buoys from the National Data Buoy Center (NDBC) show that the root-mean-square error (RMSE) of the retrieved wind speed is approximately 1.8 m/s. Additionally, the CSARMOD-GF algorithm outperforms three state-of-the-art methods – C-SARMOD, C-SARMOD2, and CMOD7 – that, when applied to GF-3 SAR imagery, generating a RMSE of approximately 2.0–2.4 m/s

RapidScat winds from the OSI SAF

2015 EUMETSAT Meteorological Satellite Conference, 21-25 September 2015, Toulouse.-- 1 page, 2 figures, 3 tablesThe RapidScat scatterometer instrument is a speedy and cost-effective replacement for the National Aeronautics and Space Administration (NASA) QuikSCAT satellite, which provided a decade-long ocean vector wind observations. RapidScat was launched on 20 September 2014 and mounted on the International Space Station (ISS). The use of generic algorithms for Ku-band scatterometer wind processing allowed us to develop a good quality wind product in a very short time. The wind products with development status are available to users since early December 2014, only one month after the level 2a data became available. Operational status was achieved in March 2015. The good quality of the winds is confirmed by comparisons of RapidScat with NWP, buoy and ASCAT windsPeer Reviewe