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

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

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

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

Confidence and sensitivity study of the OAFlux multisensor synthesis of the global ocean surface vector wind from 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): 6842–6862, doi:10.1002/2014JC010194.This study presented an uncertainty assessment of the high-resolution global analysis of daily-mean ocean-surface vector winds (1987 onward) by the Objectively Analyzed air-sea Fluxes (OAFlux) project. The time series was synthesized from multiple satellite sensors using a variational approach to find a best fit to input data in a weighted least-squares cost function. The variational framework requires the a priori specification of the weights, or equivalently, the error covariances of input data, which are seldom known. Two key issues were investigated. The first issue examined the specification of the weights for the OAFlux synthesis. This was achieved by designing a set of weight-varying experiments and applying the criteria requiring that the chosen weights should make the best-fit of the cost function be optimal with regard to both input satellite observations and the independent wind time series measurements at 126 buoy locations. The weights thus determined represent an approximation to the error covariances, which inevitably contain a degree of uncertainty. Hence, the second issue addressed the sensitivity of the OAFlux synthesis to the uncertainty in the weight assignments. Weight perturbation experiments were conducted and ensemble statistics were used to estimate the sensitivity. The study showed that the leading sources of uncertainty for the weight selection are high winds (>15 ms−1) and heavy rain, which are the conditions that cause divergence in wind retrievals from different sensors. Future technical advancement made in wind retrieval algorithms would be key to further improvement of the multisensory synthesis in events of severe storms.The project is sponsored by the NASA Ocean Vector Wind Science Team (OVWST) activities under grant NNA10AO86G. The database of 126 buoys was established during the development of the OAFlux surface turbulent latent and sensible heat fluxes under the auspices of the NOAA grant NA09OAR4320129.2015-04-1

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

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