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

Satellite data for high resolution offshore wind resource mapping: A data fusion approach

International audienceWind resource mapping needs accurate high spatial and temporal resolutions wind measurements. Offshore, satellite data are an accurate and economic way to access wind measurements. Previous studies showed the capabilities of some remote sensing instruments to measure the wind. Synthetic aperture radars (SAR) have a high spatial resolution but are associated to a low temporal repetitiveness, preventing resource assessment reliability due to the small number of samples. Scatterometers have a sufficient temporal repetitiveness for assessment reliability but have a low spatial resolution (25 km). In this paper we apply a data fusion method to these different datasets. The fusion of scatterometer and SAR data sets results on a synthetic data set having the high spatial resolution of SAR measurements and the temporal repetitiveness of scatterometer measurements. This synthetic data set, having high spatial resolution, can be used to assess the resource at a high spatial resolution. It meets the requirements of the computation of wind statistics

Review and assessment of latent and sensible heat flux accuracy over the global oceans

For over a decade, several research groups have been developing air-sea heat flux information over the global ocean, including latent (LHF) and sensible (SHF) heat fluxes over the global ocean. This paper aims to provide new insight into the quality and error characteristics of turbulent heat flux estimates at various spatial and temporal scales (from daily upwards). The study is performed within the European Space Agency (ESA) Ocean Heat Flux (OHF) project. One of the main objectives of the OHF project is to meet the recommendations and requirements expressed by various international programs such as the World Research Climate Program (WCRP) and Climate and Ocean Variability, Predictability, and Change (CLIVAR), recognizing the need for better characterization of existing flux errors with respect to the input bulk variables (e.g. surface wind, air and sea surface temperatures, air and surface specific humidities), and to the atmospheric and oceanic conditions (e.g. wind conditions and sea state). The analysis is based on the use of daily averaged LHF and SHF and the asso- ciated bulk variables derived from major satellite-based and atmospheric reanalysis products. Inter-comparisons of heat flux products indicate that all of them exhibit similar space and time patterns. However, they also reveal significant differences in magnitude in some specific regions such as the western ocean boundaries during the Northern Hemisphere winter season, and the high southern latitudes. The differences tend to be closely related to large differences in surface wind speed and/or specific air humidity (for LHF) and to air and sea temperature differences (for SHF). Further quality investigations are performed through comprehensive comparisons with daily-averaged LHF and SHF estimated from moorings. The resulting statistics are used to assess the error of each OHF product. Consideration of error correlation between products and observations (e.g., by their assimilation) is also given. This reveals generally high noise variance in all products and a weak signal in common with in situ observations, with some products only slightly better than others. The OHF LHF and SHF products, and their associated error characteristics, are used to compute daily OHF multiproduct-ensemble (OHF/MPE) estimates of LHF and SHF over the ice-free global ocean on a 0.25° × 0.25° grid. The accuracy of this heat multiproduct, determined from comparisons with mooring data, is greater than for any individual product. It is used as a reference for the anomaly characterization of each individual OHF product

Improved near real time surface wind resolution over the Mediterranean Sea

International audienceSeveral scientific programs, including the Mediterranean Forecasting System Toward Environmental Predictions (MFSTEP project), request high space and time resolutions of surface wind speed and direction. The purpose of this paper is to focus on surface wind improvements over the global Mediterranean Sea, based on the blending near real time remotely sensed wind observations and ECMWF wind analysis. Ocean surface wind observations are retrieved from QuikSCAT scatterometer and from SSM/I radiometers available at near real time at Météo-France. Using synchronous satellite data, the number of remotely sensed data available for each analysis epoch (00:00 h; 06:00 h; 12:00 h; 18:00 h) is not uniformly distributed as a function of space and time. On average two satellite wind observations are available for each analysis time period. The analysis is performed by optimum interpolation (OI) based on the kriging approach. The needed covariance matrixes are estimated from the satellite wind speed, zonal and meridional component observations. The quality of the 6-hourly resulting blended wind fields on 0.25° grid are investigated trough comparisons with the remotely sensed observations as well as with moored buoy wind averaged wind estimates. The blended wind data and remotely wind observations, occurring within 3 h and 0.25° from the analysis estimates, compare well over the global basin as well as over the sub-basins. The correlation coefficients exceed 0.95 while the rms difference values are less than 0.30 m/s. Using measurements from moored buoys, the high-resolution wind fields are found to have similar accuracy as satellite wind retrievals. Blended wind estimates exhibit better comparisons with buoy moored in open sea than near shore

A comparison of SSM/I-derived global marine surface specific humidity datasets

Satellite-based microwave sensors have, since the 1980s, provided a means to retrieve near-surface marine specific humidity (qa), accurate estimation of which is necessary for climate and air–sea interaction applications. Seven satellite measurement-derived monthly mean humidity datasets are compared with one another and with a dataset constructed from in situ measurements. The means, spatial and temporal structures of the datasets are shown to be markedly different, with a range of yearly, global mean qa of ?1?g?kg–1. Comparison of the datasets derived using the same satellite measurements of brightness temperature reveals differences in qa that depend on the source of satellite data; the processing and quality control applied to the data; and the algorithm used to derive qa from the satellite measurements of brightness temperature. Regional differences between satellite-derived qa due to the choice of input data, quality control and retrieval algorithm can all exceed the accuracy requirements for surface flux calculation of ?0.3?g?kg–1 and in some cases can be several g kg–1 in monthly means for some periods and regions

Use of satellite observations for operational oceanography: recent achievements and future prospects

The paper gives an overview of the development of satellite oceanography over the past five years focusing on the most relevant issues for operational oceanography. Satellites provide key essential variables to constrain ocean models and/or serve downstream applications. New and improved satellite data sets have been developed and have directly improved the quality of operational products. The status of the satellite constellation for the last five years was, however, not optimal. Review of future missions shows clear progress and new research and development missions with a potentially large impact for operational oceanography should be demonstrated. Improvement of data assimilation techniques and developing synergetic use of high resolution satellite observations are important future priorities

ICOADS Release 3.0: a major update to the historical marine climate record

We highlight improvements to the International Comprehensive Ocean-Atmosphere Data Set (ICOADS) in the latest Release 3.0 (R3.0; covering 1662–2014). ICOADS is the most widely used freely available collection of surface marine observations, providing data for the construction of gridded analyses of sea surface temperature, estimates of air–sea interaction and other meteorological variables. ICOADS observations are assimilated into all major atmospheric, oceanic and coupled reanalyses, further widening its impact. R3.0 therefore includes changes designed to enable effective exchange of information describing data quality between ICOADS, reanalysis centres, data set developers, scientists and the public. These user-driven innovations include the assignment of a unique identifier (UID) to each marine report – to enable tracing of observations, linking with reports and improved data sharing. Other revisions and extensions of the ICOADS' International Maritime Meteorological Archive common data format incorporate new near-surface oceanographic data elements and cloud parameters. Many new input data sources have been assembled, and updates and improvements to existing data sources, or removal of erroneous data, made. Coupled with enhanced ‘preliminary’ monthly data and product extensions past 2014, R3.0 provides improved support of climate assessment and monitoring, reanalyses and near-real-time applications

Probability Distribution Characteristics for Surface Air–Sea Turbulent Heat Fluxes over the Global Ocean

To analyze the probability density distributions of surface turbulent heat fluxes, the authors apply the twoparametric modified Fisher–Tippett (MFT) distribution to the sensible and latent turbulent heat fluxes recomputed from 6-hourly NCEP–NCAR reanalysis state variables for the period from 1948 to 2008. They derived the mean climatology and seasonal cycle of the location and scale parameters of the MFT distribution. Analysis of the parameters of probability distributions identified the areas where similar surface turbulent fluxes are determined by the very different shape of probability density functions. Estimated extreme turbulent heat fluxes amount to 1500–2000 W m22 (for the 99th percentile) and can exceed 2000 W m22 for higher percentiles in the subpolar latitudes and western boundary current regions. Analysis of linear trends and interannual variability in the mean and extreme fluxes shows that the strongest trends in extreme fluxes (more than 15 W m22 decade21) in the western boundary current regions are associated with the changes in the shape of distribution. In many regions changes in extreme fluxes may be different from those for the mean fluxes at interannual and decadal time scales. The correlation between interannual variability of themean and extreme fluxes is relatively low in the tropics, the SouthernOcean, and the Kuroshio Extension region.Analysis of probability distributions in turbulent fluxes has also been used in assessing the impact of sampling errors in theVoluntaryObserving Ship (VOS)-based surface flux climatologies, allowed for the estimation of the impact of sampling in extreme fluxes. Although sampling does not have a visible systematic effect onmean fluxes, sampling uncertainties result in the underestimation of extreme flux values exceeding 100 W m22 in poorly sampled regions