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

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

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

Estimation of Time-Averaged Surface Divergence and Vorticity from Satellite Ocean Vector Winds

Two methods of computing the time-mean divergence and vorticity from satellite vector winds in rain-free (RF) and all-weather (AW) conditions are investigated. Consequences of removing rain-contaminated winds on the mean divergence and vorticity depend strongly on the order in which the time-average and spatial derivative operations are applied. Taking derivatives first and averages second (DFAS_RF) incorporates only those RF winds measured at the same time into the spatial derivatives. While preferable mathematically, this produces mean fields biased relative to their AW counterparts because of the exclusion of convergence and cyclonic vorticity often associated with rain. Conversely, taking averages first and derivatives second (AFDS_RF) incorporates all RF winds into the time-mean spatial derivatives, even those not measured coincidentally. While questionable, the AFDS_RF divergence and vorticity surprisingly appears qualitatively consistent with the AW means, despite using only RF winds. The analysis addresses whether the AFDS_RF method accurately estimates the AW mean divergence and vorticity.Model simulations indicate that the critical distinction between these two methods is the inclusion of typically convergent and cyclonic winds bordering rain patches in the AFDS_RF method. While this additional information removes some of the sampling bias in the DFAS_RF method, it is shown that the AFDS_RF method nonetheless provides only marginal estimates of the mean AW divergence and vorticity given sufficient time averaging and spatial smoothing. Use of the AFDS_RF method is thus not recommended.C-2013 ASCAT data are produced by Remote Sensing Systems and sponsored by the NASA Ocean Vector Winds Science Team. The JPL version 3 QuikSCAT dataset (https://podaac.jpl.nasa.gov/dataset/QSCAT_LEVEL_2B_OWV_COMP_12) was obtained via the PO.DAAC web portal and was processed by the SeaWinds Processing and Analysis Center (SeaPAC).This is the publisher’s final pdf. The published article is copyrighted by American Meteorological Society and can be found at: https://www2.ametsoc.org/ams/index.cfm/publications/journals/journal-of-climate/Keywords: North Atlantic Ocean, Satellite observations, Error analysis, Mesoscale models, Marine boundary laye

On the Use of QuikSCAT Scatterometer Measurements of Surface Winds for Marine Weather Prediction

The value of Quick Scatterometer (QuikSCAT) measurements of 10-m ocean vector winds for marine weather prediction is investigated from two Northern Hemisphere case studies. The first of these focuses on an intense cyclone with hurricane-force winds that occurred over the extratropical western North Pacific on 10 January 2005. The second is a 17 February 2005 example that is typical of sea surface temperature influence on low-level winds in moderate wind conditions in the vicinity of the Gulf Stream in the western North Atlantic. In both cases, the analyses of 10-m winds from the NCEP and ECMWF global numerical weather prediction models considerably underestimated the spatial variability of the wind field on scales smaller than 1000 km compared with the structure determined from QuikSCAT observations. The NCEP and ECMWF models both assimilate QuikSCAT observations. While the accuracies of the 10-m wind analyses from these models measurably improved after implementation of the QuikSCAT data assimilation, the information content in the QuikSCAT data is underutilized by the numerical models. QuikSCAT data are available in near–real time in the NOAA/NCEP Advanced Weather Interactive Processing System (N-AWIPS) and are used extensively in manual analyses of surface winds. The high resolution of the QuikSCAT data is routinely utilized by forecasters at the NOAA/NCEP Ocean Prediction Center, Tropical Prediction Center, and other NOAA weather forecast offices to improve the accuracies of wind warnings in marine forecasts

Assessment and Analysis of QuikSCAT and COAMPS Model Vector Wind Products for the Gulf of Mexico: A Long-Term and Hurricane Perspective

Global weather changes have become a matter of grave concern in hurricane prone areas as intensities of hurricanes are observed to be increasing every year, necessitating improved monitoring capabilities. NASA’s QuikSCAT satellite sensor has provided significant support in analyzing and forecasting winds for the past 8 years. In this study, the performance of QuikSCAT products, including JPL’s latest L2B 12.5km swath winds, was evaluated against buoy-measured winds in the Gulf of Mexico. The long-term study period was 1/2005 – 2/2007. The Coupled Ocean/Atmospheric Mesoscale Prediction System (COAMPS) was also assessed. The regression analyses showed very good results for QuikSCAT products, with the best results obtained from L2B winds. R2 values for moderate wind speeds were 0.75 and 0.89, 0.88 and 0.93, 0.66 and 0.77 for speed and direction and for L3, L2B and COAMPS respectively. The National Weather Product (NWP) model winds provided in the L2B dataset were also studied. Hurricanes that took place from 2002 to 2006 were studied individually to obtain regressions of QuikSCAT and COAMPS versus buoys for those events. The correlations were very high indicating that QuikSCAT is at par with buoys during hurricanes. These measurements were compared with the NHC best track analyses to determine the accuracy and found to be almost half those obtained by NHC, possibly due to rain contamination. Sea Surface Height Anomaly (SSHA) measurements by Jason-1 and sea surface temperature (SST) measurements by the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua and GOES-12 (Geostationary) were compared with wind fields during hurricanes to study the effects of the Loop Current and Warm Core Rings on the intensification of the hurricanes. A preliminary study was conducted in which the regions of enhanced wind speeds were observed by studying the longitudinal and latitudinal transects across the hurricane for two hurricanes, namely Hurricanes Ivan and Katrina. This study would act as a precursor to further analysis of the radius of maximum wind and critical wind radii

Development Of An Improved Microwave Ocean Surface Emissivity Radiative Transfer Model

An electromagnetic model is developed for predicting the microwave blackbody emission from the ocean surface over a wide range of frequencies, incidence angles, and wind vector (speed and direction) for both horizontal and vertical polarizations. This ocean surface emissivity model is intended to be incorporated into an oceanic radiative transfer model to be used for microwave radiometric applications including geophysical retrievals over oceans. The model development is based on a collection of published ocean emissivity measurements obtained from satellites, aircraft, field experiments, and laboratory measurements. This dissertation presents the details of methods used in the ocean surface emissivity model development and comparisons with current emissivity models and aircraft radiometric measurements in hurricanes. Especially, this empirically derived ocean emissivity model relates changes in vertical and horizontal polarized ocean microwave brightness temperature measurements over a wide range of observation frequencies and incidence angles to physical roughness changes in the ocean surface, which are the result of the air/sea interaction with surface winds. Of primary importance are the Stepped Frequency Microwave Radiometer (SFMR) brightness temperature measurements from hurricane flights and independent measurements of surface wind speed that are used to define empirical relationships between C-band (4 - 7 GHz) microwave brightness temperature and surface wind speed. By employing statistical regression techniques, we develop a physical-based ocean emissivity model with empirical coefficients that depends on geophysical parameters, such as wind speed, wind direction, sea surface temperature, and observational parameters, such as electromagnetic frequency, electromagnetic polarization, and incidence angle

Ocean Vector Wind Measurement Potential from the Global Precipitation Measurement Mission using a Combined Active and Passive Algorithm

Ocean surface vector wind (OVW) is an essential parameter for understanding the physics and dynamics of the ocean-atmosphere system, thereby improving weather forecasting and climate studies. Satellite scatterometers, synthetic aperture radars, and polarimetric microwave radiometers have provided almost global coverage of ocean surface vector wind for the last four decades. Nonetheless, a consistent and uninterrupted long-time data record with the capability of resolving sub-diurnal variability has remained a critical challenge over the years. The Global Precipitation Measurement Mission (GPM) is a satellite mission designed to provide space-based precipitation information on a global scale with complete diurnal sampling. This dissertation presents a combined active and passive retrieval algorithm to investigate the feasibility of ocean surface vector wind measurements from the GPM core satellite by utilizing its Ku- and Ka-band Dual-frequency Precipitation Radar (DPR) and the multi-frequency GPM Microwave Imager (GMI) observations. The unique GPM active and passive geophysical model functions were empirically developed by characterizing the anisotropic nature of ocean backscatter of normalized radar cross-section (δ°) and brightness temperature (TB) at multiple bands. For passive GMF, the modified 2nd Stoke\u27s parameter (linear combination of V and H-pol TBs) was used to mitigate the atmospheric contamination and to enhance the anisotropic wind direction signal superimposed on GMI TBs. The GMFs were combined in a maximum likelihood estimation (MLE) algorithm to infer the OVW. Finally, the retrieval algorithm was validated by comparing OVW retrievals with collocated NASA Advanced Scatterometer (ASCAT) wind vectors. The wind speed and direction retrieval performance statistics are promising and comparable with those of conventional scatterometer and polarimetric radiometer data products. The algorithm demonstrates the capability of the GPM to provide a long-term OVW data record for the entire GPM-TRMM era, which may include unique monthly diurnal OVW statistics

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