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

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

Second-order structure function analysis of scatterometer winds over the Tropical Pacific

22 pages, 16 figures, 1 tableKolmogorov second-order structure functions are used to quantify and compare the small-scale information contained in near-surface ocean wind products derived from measurements by ASCAT on MetOp-A and SeaWinds on QuikSCAT. Two ASCAT and three SeaWinds products are compared in nine regions (classified as rainy or dry) in the tropical Pacific between 10°S and 10°N and 140° and 260°E for the period November 2008 to October 2009. Monthly and regionally averaged longitudinal and transverse structure functions are calculated using along-track samples. To ease the analysis, the following quantities were estimated for the scale range 50 to 300 km and used to intercompare the wind products: (i) structure function slopes, (ii) turbulent kinetic energies (TKE), and (iii) vorticity-to-divergence ratios. All wind products are in good qualitative agreement, but also have important differences. Structure function slopes and TKE differ per wind product, but also show a common variation over time and space. Independent of wind product, longitudinal slopes decrease when sea surface temperature exceeds the threshold for onset of deep convection (about 28°C). In rainy areas and in dry regions during rainy periods, ASCAT has larger divergent TKE than SeaWinds, while SeaWinds has larger vortical TKE than ASCAT. Differences between SeaWinds and ASCAT vortical TKE and vorticity-to-divergence ratios for the convectively active months of each region are large. © 2014. American Geophysical Union. All Rights ReservedThe ASCAT-12.5 and ASCAT-25 data used in this work can be ordered online from the EUMETSAT Data Centre (www.eumetsat.int) as SAF type data in BUFR or NetCDF format. They can also be ordered from PO.DAAC (podaac.jpl.nasa.gov) in NetCDF format only. The SeaWinds-NOAA and QuikSCAT-12.5 data are also available from PO.DAAC. The SeaWinds-KNMI data are available from the KNMI archive upon an email request to [email protected]. Rain-rates and sea surface temperatures were obtained from the Tropical Rainfall Measuring Mission's (TRMM) Microwave Imager (TMI) archive at the Remote Sensing Systems web site (www.ssmi.com). SeaWinds Radiometer (SRAD) rain-rates were obtained from the QuikSCAT 25 km L2B science data product that is available from PO.DAAC. This work has been funded by EUMETSAT in the context of the Numerical Weather Prediction Satellite Applications Facility (NWP SAF). The contribution of GPK has been supported by EUMETSAT as part of the SAF Visiting Scientists programmePeer Reviewe

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

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

Measurements of Air-Sea Interaction from the HY-2A Scatterometer

International Ocean Vector Wind Science Team Meeting (IOVWST), 2-4 June 2014, Brest, France.-- 21 pagesPeer Reviewe

On the utilization of meso-scale models for offshore wind atlases

Two different offshore wind atlases based on the meso-scale model WRF are presented and discussed in this paper. The Work is part of the EU-funded project NORSEWIND (Northern Seas Wind Index Database). Validations show that annual average wind speeds and windroses at hub-height (100m) are well represented by the model, while the model accuracy is poorer for vertical wind profile, wind shear parameters and static stability

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