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

ASCAT pintatuulihavaintojen käyttökelpoisuus rajatun alueen sääennustemallissa

Sea-surface wind observations of previous generation scatterometers have been successfully assimilated into Numerical Weather Prediction (NWP) models. Impact studies conducted with these assimilation implementations have shown a distinct improvement to model analysis and forecast accuracies. The Advanced Scatterometer (ASCAT), flown on Metop-A, offers an improved sea-surface wind accuracy and better data coverage when compared to the previous generation scatterometers. Five individual case studies are carried out. The effect of including ASCAT data into High Resolution Limited Area Model (HIRLAM) assimilation system (4D-Var) is tested to be neutral-positive for situations with general flow direction from the Atlantic Ocean. For northerly flow regimes the effect is negative. This is later discussed to be caused by problems involving modeling northern flows, and also due to the lack of a suitable verification method. Suggestions and an example of an improved verification method is presented later on. A closer examination of a polar low evolution is also shown. It is found that the ASCAT assimilation scheme improves forecast of the initial evolution of the polar low, but the model advects the strong low pressure centre too fast eastward. Finally, the flaws of the implementation are found small and implementing the ASCAT assimilation scheme into the operational HIRLAM suite is feasible, but longer time period validation is still required.Edellisen sukupolven skatterometrien merenpinnan tuulihavaintoja on tuloksellisesti assimiloitu numeerisiin sääennustemalleihin. Näistä assimilaatiototeutuksista tehdyt vaikutustutkimukset ovat osoittaneet selviä parannuksia mallien analyysi- ja ennustetarkkuuksiin. Metop-A:n hyötykuormana oleva kehittynyt skatterometri (ASCAT) tarjoaa tarkempia havaintoja merenpintatuulista sekä kattaa mittauksillaan suuremman alueen edellisiin skatterometreihin verrattuna. ASCAT:in havaintojen assimilaatiota korkean resoluution rajatun alueen malliin (HIRLAM 4D-Var) tutkitaan viidessä erillisessä tapaustutkimuksessa. Vaikutuksen huomataan olevan neutraali-positiivinen tapauksille, joissa yleinen virtaussuunta on Atlantilta, mutta pohjoisille polaarivirtauksille vaikutuksen havaitaan olevan negatiivinen. Tämän päätellään johtuvan ongelmista pohjoisten virtausten mallintamisessa, mutta myös sopivien verifikaatiometodien puutteellisuudesta. Ehdotuksia sekä esimerkki verifikaatiometodien kehittämisestä esitellään myöhemmässä vaiheessa. Työssä tarkastellaan myös lähemmin polaarimatalatapausta. Tarkastelusta selviää, että ASCAT:in assimilaatio analyysijärjestelmään parantaa polaarimatalan alkukehityksen ennustetta, mutta mallin dynamiikka/fysiikka advektoi tämän voimakkaan matalapainekeskuksen liian nopeasti itään. Assimilaation toteutuksen viat havaitaan kuitenkin pieniksi ja ASCAT-havaintojen assimilaation operatiiviseen HIRLAM sääennustemalliin nähdään olevan toteuttamiskelpoinen, tosin pidemmän aikavälin validoinnin tarve nousee vielä esiin

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

An Improved Ocean Vector Winds Retrieval Approach Using C- And Ku-band Scatterometer And Multi-frequency Microwave Radiometer Measurements

This dissertation will specifically address the issue of improving the quality of satellite scatterometer retrieved ocean surface vector winds (OVW), especially in the presence of strong rain associated with tropical cyclones. A novel active/passive OVW retrieval algorithm is developed that corrects Ku-band scatterometer measurements for rain effects and then uses them to retrieve accurate OVW. The rain correction procedure makes use of independent information available from collocated multi-frequency passive microwave observations provided by a companion sensor and also from simultaneous C-band scatterometer measurements. The synergy of these active and passive measurements enables improved correction for rain effects, which enhances the utility of Ku-band scatterometer measurements in extreme wind events. The OVW retrieval algorithm is based on the next generation instrument conceptual design for future US scatterometers, i.e. the Dual Frequency Scatterometer (DFS) developed by NASA’s Jet Propulsion Laboratory. Under this dissertation research, an end-to-end computer simulation was developed to evaluate the performance of this active/passive technique for retrieving hurricane force winds in the presence of intense rain. High-resolution hurricane wind and precipitation fields were simulated for several scenes of Hurricane Isabel in 2003 using the Weather Research and Forecasting (WRF) Model. Using these numerical weather model environmental fields, active/passive measurements were simulated for instruments proposed for the Global Change Observation Mission- Water Cycle (GCOM-W2) satellite series planned by the Japanese Aerospace Exploration Agency. Further, the quality of the simulation was evaluated using actual hurricane measurements from the Advanced Microwave Scanning Radiometer and iv SeaWinds scatterometer onboard the Advanced Earth Observing Satellite-II (ADEOS-II). The analysis of these satellite data provided confidence in the capability of the simulation to generate realistic active/passive measurements at the top of the atmosphere. Results are very encouraging, and they show that the new algorithm can retrieve accurate ocean surface wind speeds in realistic hurricane conditions using the rain corrected Ku-band scatterometer measurements. They demonstrate the potential to improve wind measurements in extreme wind events for future wind scatterometry missions such as the proposed GCOM-W2

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

Challenges to Satellite Sensors of Ocean Winds: Addressing Precipitation Effects

Measurements of global ocean surface winds made by orbiting satellite radars have provided valuable information to the oceanographic and meteorological communities since the launch of the Seasat in 1978, by the National Aeronautics and Space Administration (NASA). When Quick Scatterometer (QuikSCAT) was launched in 1999, it ushered in a new era of dual-polarized, pencil-beam, higher-resolution scatterometers for measuring the global ocean surface winds from space. A constant limitation on the full utilization of scatterometer-derived winds is the presence of isolated rain events, which affect about 7% of the observations. The vector wind sensors, the Ku-band scatterometers [NASA\u27s SeaWinds on the QuikSCAT and Midori-II platforms and Indian Space Research Organisation\u27s (ISRO\u27s) Ocean Satellite (Oceansat)-2], and the current C-band scatterometer [Advanced Wind Scatterometer (ASCAT), on the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)\u27s Meteorological Operation (MetOp) platform] all experience rain interference, but with different characteristics. Over this past decade, broad-based research studies have sought to better understand the physics of the rain interference problem, to search for methods to bypass the problem (using rain detection, flagging, and avoidance of affected areas), and to develop techniques to improve the quality of the derived wind vectors that are adversely affected by rain. This paper reviews the state of the art in rain flagging and rain correction and describes many of these approaches, methodologies, and summarizes the results

Insights on the OAFlux ocean surface vector wind analysis merged from scatterometers and passive microwave radiometers (1987 onward)

Author Posting. © American Geophysical Union, 2014. 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: Oceans 119 (2014): 5244–5269, doi:10.1002/2013JC009648.A high-resolution global daily analysis of ocean surface vector winds (1987 onward) was developed by the Objectively Analyzed air-sea Fluxes (OAFlux) project. This study addressed the issues related to the development of the time series through objective synthesis of 12 satellite sensors (two scatterometers and 10 passive microwave radiometers) using a least-variance linear statistical estimation. The issues include the rationale that supports the multisensor synthesis, the methodology and strategy that were developed, the challenges that were encountered, and the comparison of the synthesized daily mean fields with reference to scatterometers and atmospheric reanalyses. The synthesis was established on the bases that the low and moderate winds (<15 m s−1) constitute 98% of global daily wind fields, and they are the range of winds that are retrieved with best quality and consistency by both scatterometers and radiometers. Yet, challenges are presented in situations of synoptic weather systems due mainly to three factors: (i) the lack of radiometer retrievals in rain conditions, (ii) the inability to fill in the data voids caused by eliminating rain-flagged QuikSCAT wind vector cells, and (iii) the persistent differences between QuikSCAT and ASCAT high winds. The study showed that the daily mean surface winds can be confidently constructed from merging scatterometers with radiometers over the global oceans, except for the regions influenced by synoptic weather storms. The uncertainties in present scatterometer and radiometer observations under high winds and rain conditions lead to uncertainties in the synthesized synoptic structures.The project is sponsored by the NASA Ocean Vector Wind Science Team (OVWST) activities under grant NNA10AO86G.2015-02-1

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

Characterization of ASCAT measurements based on buoy and QuikSCAT wind vector observations

International audienceThe new scatterometer Advanced SCATterometer (ASCAT) onboard MetOp-A satellite provides surface wind speed and direction over global ocean with a spatial resolution of 25 km square over two swaths of 550 km widths. The accuracy of ASCAT wind retrievals is determined through various comparisons with moored buoys. The comparisons indicate that the remotely sensed wind speeds and directions agree well with buoy data. The root-mean-squared differences of the wind speed and direction are less than 1.72 m/s and 18°, respectively. At global scale, ASCAT winds are compared with surface winds derived from QuikSCAT scatterometer. The results confirm the buoy analyses, especially for wind speed ranging between 3 m/s and 20 m/s. For higher wind conditions, ASCAT is biased low. The ASCAT underestimation with respect to QuikSCAT winds is wind speed dependent. The comparisons based on the collocated scatterometer data collected after 17 October 2007 indicate that there are significant improvements compared to previous periods