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

Development of high-resolution L4 ocean wind products

[eng] Heat, moisture, gas, and momentum exchanges at the oceanic and atmospheric interface modulate, inter alia, the Earth’s heat and carbon budgets, global circulation, and dynamical modes. Sea surface winds are fundamental to these exchanges and, as such, play a major role in the evolution and dynamics of the Earth’s climate. For ocean and atmospheric modeling purposes, and for their coupling, accurate sea-surface winds are therefore crucial to properly estimate these turbulent fluxes. Over the last decades, as numerical models became more sophisticated, the requirements for higher temporal and spatial resolution ocean forcing products grew. Sea surface winds from numerical weather prediction (NWP) models provide a convenient temporal and spatial coverage to force ocean models, and for that they are extensively used, e.g., the European Centre for Medium-range Weather Forecasts (ECMWF) latest reanalysis, ERA5, with ubiquitous hourly estimates of sea-surface wind available globally on a 30-km spatial grid. However, local systematic errors have been reported in global NWP fields using collocated scatterometer observations as reference. These rather persistent errors are associated with physical processes that are absent or misrepresented by the NWP models, e.g., strong current effects like the Western Boundary Current Systems (highly stationary), wind effects as- sociated with the oceanic mesoscale (sea surface temperature gradients), coastal effects (land see breezes, katabatic winds), Planetary Boundary Layer parameterization errors, and large-scale circulation effects, such as those associated with moist convection areas. In contrast, the ocean surface vector wind or wind stress derived from scatterometers, although intrinsically limited by temporal and spatial sampling, exhibits considerable spatial detail and accuracy. The latter has an effective resolution of 25 km while that of NWP models is of 150 km. Consequently, the biases between the two mostly represent the physical processes unresolved by NWP models. In this thesis, a high-resolution ocean surface wind forcing, the so-called ERAú, that combines the strengths of both the scatterometer observations and of the atmospheric model wind fields is created using a scatterometer-based local NWP wind vector model bias correction. ERAú stress equivalent wind (U10S) is generated by means of a geolocated scatterometer-based correction applied separately to two different ECMWF reanalyses, the nowadays obsolete ERA-interim (ERAi) and the most recent ERA5. Several ERAú configurations using complementary scatterometer data accumulated over different temporal windows (TW) are generated and verified against independent wind sources (scatterometer and moored buoys), through statistical and spectral analysis of spatial structures. The newly developed method successfully corrects for local wind vector biases in the reanalysis output, particularly in open ocean regions, by introducing the oceanic mesoscales captured by the scatterometers into the ERAi/ERA5 NWP reanalyses. However, the effectiveness of the method is intrinsically dependent on regional scatterometer sampling, wind variability and local bias persistence. The optimal ERAú uses multiple complementary scatterometers and a 3-day TW. Bias patterns are the same for ERAi and ERA5 SC to the reanalyses, though the latter shows smaller bias amplitudes and hence smaller error variance reduction differences in verification (up to 8% globally). However, because of ERA5 being more accurate than ERAi, ERAú derived from ERA5 turns out to be the highest quality product. ERAú ocean forcing does not enhance the sensitivity in global circulation models to highly localized transient events, however it improves large-scale ocean simulations, where large- scale corrections are relevant. Besides ocean forcing studies, the developed methodology can be further applied to improve scatterometer wind data assimilation by accounting for the persistent model biases. In addition, since the biases can be associated with misrepresented processes and parmeterizations, empirical predictors of these biases can be developed for use in forecasting and to improve the dynamical closure and parameterizations in coupled ocean-atmosphere models.[spa] Los vientos de la superficie del mar son fundamentales para estimar los flujos de calor y momento en la interfaz oceánica-atmosfera, ocupando un papel importante en la evolución y la dinámica del clima del planeta. Por tanto, en modelación (oceánica y atmosférica), vientos de calidad son cruciales para estimar adecuadamente estos flujos turbulentos. Vientos de la superficie del mar de salidas de modelos de predicción numérica del tiempo (NWP) proporcionan una cobertura temporal y espacial conveniente para forzar los modelos oceánicos, y todavía se utilizan ampliamente. Sin embargo, se han documentado errores sistemáticos locales en campos de NWP globales utilizando observaciones de dispersómetros co-ubicados como referencia (asociados con procesos físicos que ausentes o mal representados por los modelos). Al contrario, el viento de la superficie del mar derivado de los dispersómetros, aunque intrínsecamente limitado por el muestreo temporal y espacial, presenta una precisión y un detalle espacial considerables. Consecuentemente, los sesgos entre los dos representan principalmente los procesos físicos no resueltos por los modelos NWP. En esta tesis, se crea un producto de forzamiento del viento en la superficie del océano de alta resolución, el ERAú. ERAú se genera con una corrección media basada en diferencias geolocalizadas entre dispersometro y modelo, aplicadas por separado a dos reanálisis diferentes, el ERA-interim (ERAi) y el ERA5. Varias configuraciones de ERAú utilizando datos de dispersómetros complementarios acumulados en diferentes ventanas tempo- rales (TW) se generan y validan frente a datos de viento independientes, a través de análisis estadísticos y espectrales de estructuras espaciales. El método corrige con éxito los sesgos del vector de viento local de la reanálisis. Sin embargo, su eficacia depende del muestreo del dispersómetro regional, la variabilidad del viento y la persistencia del sesgo local. El ERAú óptimo utiliza múltiples dispersómetros complementarios y un TW de 3 días. Las dos reanálisis muestran los mismos patrones de sesgo en la SC, debido a que ERA5 es más preciso que ERAi, ERAú derivado de ERA5 es el producto de mayor calidad. El forzamiento oceánico ERAú mejora las simulaciones oceánicas a gran escala, donde las correcciones a gran escala son relevantes

EPS/Metop-SG Scatterometer Mission Science Plan

89 pages, figures, tablesThis Science Plan describes the heritage, background, processing and control of C-band scatterometer data and its remaining exploitation challenges in view of SCA on EPS/MetOp-SGPeer reviewe

ERA*

Presentación para la ponencia Scatterometer new products en el 2nd Globcurrent User Consultation Meeting, 4-6 November 2015, Brest, France.-- 35 pagesSurface winds derived from earth Observation satellites are increasingly required for use in monitoring and forecasting of the ocean. A drawback of space-borne wind observing systems, such as scatterometers, is that they provide time and space coverage unsuitable for, among others, high-resolution ocean model forcing. As such, blended ocean forcing products combining scatterometer data and numerical weather prediction (NWP) output, are being developed over the past few years. These products, which provide full global coverage at increased temporal resolution (e.g., daily), however, generally only resolve spatial scales closer to NWP-resolved (200km) rather than scatterometer-resolved scales (25 km). Therefore, information on wind-current interaction, on the diurnal wind cycle and on wind variability in moist convection areas is lost in these blended products. Moreover, known systematic NWP model (parameterization) errors are propagated in the blended products at times and locations where no scatterometer winds are available. Direct forcing from ERA-interim or an operational global meteorological model results in even more extensive physical drawbacks, but has the advantage of increased temporal coverage. We propose to maintain this increased temporal coverage in a gridded wind and stress product, but also to maintain most beneficial physical qualities of the scatterometer winds, i.e., 25-km spatial resolution, wind-current interaction, variability due to moist convection, etc., and, at the same time avoid the large-scale NWP parameterization and dynamical errors. In fact, collocations of scatterometer and global NWP winds show these physical differences, where the local mean and variability of these differences are rather constant in time and thus could be added to the ERA-interim time record in order to better represent physical interaction processes and avoid NWP model errors. Correction of either the wind vector biases and wind vector variability is expected to affect ocean forcing. Moreover, the collocation process provides NWP winds, but sampled like a scatterometer and, therefore, provides information on the scatterometer wind sampling error. Prior to merging different scatterometer data sources, a comprehensive characterization of the scatterometer corrections is required. We provide an assessment of the corrections and sampling errors for the tandem scatterometer data set composed by ASCAT-A/B, RapidScat, Oceansat-2 and HY-2A, which, so far offer the most complementary orbits in terms of the diurnal cycle. All comparisons involve the stress-equivalent 10m wind, U10S, which avoids effects of atmospheric stratification and mass density to affect the computed wind differences. U10S may be easily computed from global NWP or moored buoy measurements for comparison to the scatterometer equivalents. U10S, in turn, can be easily related to ocean surface stressPeer 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

Satellite remote sensing of surface winds, waves, and currents: Where are we now?

This review paper reports on the state-of-the-art concerning observations of surface winds, waves, and currents from space and their use for scientific research and subsequent applications. The development of observations of sea state parameters from space dates back to the 1970s, with a significant increase in the number and diversity of space missions since the 1990s. Sensors used to monitor the sea-state parameters from space are mainly based on microwave techniques. They are either specifically designed to monitor surface parameters or are used for their abilities to provide opportunistic measurements complementary to their primary purpose. The principles on which is based on the estimation of the sea surface parameters are first described, including the performance and limitations of each method. Numerous examples and references on the use of these observations for scientific and operational applications are then given. The richness and diversity of these applications are linked to the importance of knowledge of the sea state in many fields. Firstly, surface wind, waves, and currents are significant factors influencing exchanges at the air/sea interface, impacting oceanic and atmospheric boundary layers, contributing to sea level rise at the coasts, and interacting with the sea-ice formation or destruction in the polar zones. Secondly, ocean surface currents combined with wind- and wave- induced drift contribute to the transport of heat, salt, and pollutants. Waves and surface currents also impact sediment transport and erosion in coastal areas. For operational applications, observations of surface parameters are necessary on the one hand to constrain the numerical solutions of predictive models (numerical wave, oceanic, or atmospheric models), and on the other hand to validate their results. In turn, these predictive models are used to guarantee safe, efficient, and successful offshore operations, including the commercial shipping and energy sector, as well as tourism and coastal activities. Long-time series of global sea-state observations are also becoming increasingly important to analyze the impact of climate change on our environment. All these aspects are recalled in the article, relating to both historical and contemporary activities in these fields

Buoy perspective of a high-resolution global ocean vector wind analysis constructed from passive radiometers and active scatterometers (1987–present)

Author Posting. © American Geophysical Union, 2012. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research 117 (2012): C11013, doi:10.1029/2012JC008069.The study used 126 buoy time series as a benchmark to evaluate a satellite-based daily, 0.25-degree gridded global ocean surface vector wind analysis developed by the Objectively Analyzed airs-sea Fluxes (OAFlux) project. The OAFlux winds were produced from synthesizing wind speed and direction retrievals from 12 sensors acquired during the satellite era from July 1987 onward. The 12 sensors included scatterometers (QuikSCAT and ASCAT), passive microwave radiometers (AMSRE, SSMI and SSMIS series), and the passive polarimetric microwave radiometer from WindSat. Accuracy and consistency of the OAFlux time series are the key issues examined here. A total of 168,836 daily buoy measurements were assembled from 126 buoys, including both active and archive sites deployed during 1988–2010. With 106 buoys from the tropical array network, the buoy winds are a good reference for wind speeds in low and mid-range. The buoy comparison shows that OAFlux wind speed has a mean difference of −0.13 ms−1 and an RMS difference of 0.71 ms−1, and wind direction has a mean difference of −0.55 degree and an RMS difference of 17 degrees. Vector correlation of OAFlux and buoy winds is of 0.9 and higher over almost all the sites. Influence of surface currents on the OAFlux/buoy mean difference pattern is displayed in the tropical Pacific, with higher (lower) OAFlux wind speed in regions where wind and current have the opposite (same) sign. Improved representation of daily wind variability by the OAFlux synthesis is suggested, and a decadal signal in global wind speed is evident.The authors are grateful for the support of the NASA Ocean Vector Wind Science Team (OVWST) under grant NNA10AO86G during the five-year development of the OAFlux wind synthesis products. Support from the NOAA Office of Climate Observation (OCO) under grant NA09OAR4320129 in establishing and maintaining the buoy validation database for surface fluxes is gratefully acknowledged.2013-05-1

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