Le Roux Frederique

Frédérique Le Roux
email: fleroux@sb-roscoff.fr

Diagnosis of vibriosis in the era of genomics: lessons from invertebrates

Keywords: Ecology, Emergence, Evolution, Marine invertebrate, Vibriosis, Virulence

Global changes linked to increases in temperature and ocean acidification, but also to more direct anthropogenic influences such as aquaculture, have caused a worldwide increase in the reports of Vibrio-associated illnesses affecting humans and also animals such as shrimp and molluscs. Investigation of the emergence of Vibrio pathogenesis events requires the analysis of microbial evolution at the gene, genome and population levels, in order to identify genomic modifications linked to increased virulence, resistance and/or prevalence, or to recent host shift. From a more applied point of view, the elucidation of virulence mechanisms is a prerequisite to devising prophylactic methods to fight infectious agents. In comparison with human pathogens, fairly little is known about the requirements for virulence in vibrios pathogenic to animals. However, the advent of genome sequencing, especially nextgeneration technologies,the possibility of genetically manipulating most of the Vibrio strains, and the recent availability of standardised animals for experimental infections have now compensated for the considerable delay in advancement of the knowledge of non-model pathogens such as Vibrio and have led to new scientific questions.

Increase in sea surface temperature and ocean acidification linked to global change and human activities such as aquaculture, have caused a worldwide increase in the reports of Vibrio-associated diseases, with ecosystem-wide direct and indirect effects on humans and marine animals (For a review see 1). In Europe, several studies have reported the presence of human pathogens (e.g., V. parahaemolyticus, V. cholerae non-O1/non-O139) in shellfish as well as in coastal and estuarine waters [START_REF] Igbinosa | Emerging Vibrio species: an unending threat to public health in developing countries[END_REF][START_REF] Baker-Austin | Emerging Vibrio risk at high latitudes in response to ocean warming[END_REF][START_REF] Huehn | Pathogenic vibrios in environmental, seafood and clinical sources in Germany[END_REF][START_REF] Andersson | Wound infections due to Vibrio cholerae in Sweden after swimming in the Baltic Sea, summer 2006[END_REF][START_REF] Lukinmaa | Territorial waters of the Baltic Sea as a source of infections caused by Vibrio cholerae non-O1, non-O139: report of 3 hospitalized cases[END_REF][START_REF] Dalsgaard | Clinical manifestations and molecular epidemiology of Vibrio vulnificus infections in Denmark[END_REF][START_REF] Stypulkowska-Misiurewicz | Two unrelated cases of septicaemia due to Vibrio cholerae non-O1, non-O139 in Poland, July and August 2006[END_REF][START_REF] Reilly | Vibrio alginolyticus-associated wound infection acquired in British waters, Guernsey[END_REF][START_REF] Cantet | Quantification of Vibrio parahaemolyticus, Vibrio vulnificus and Vibrio cholerae in French Mediterranean coastal lagoons[END_REF][START_REF] Schets | Pathogenic Vibrio spp. in Northern European Waters[END_REF][START_REF] Bisharat | Clinical, epidemiological, and microbiological features of Vibrio vulnificus biogroup 3 causing outbreaks of wound infection and bacteraemia in Israel. Israel Vibrio Study Group[END_REF][START_REF] Torres | Wound Infection due to Vibrio vulnificus in Spain[END_REF][START_REF] Partridge | Vibrio vulnificus: an unusual mode of acquisition and novel use of rapid susceptibility testing[END_REF][START_REF] Huhulescu | Occurrence of Vibrio cholerae serogroups other than O1 and O139 in Austria[END_REF][START_REF] Kirschner | Rapid growth of planktonic Vibrio cholerae non-O1/non-O139 strains in a large alkaline lake in Austria: dependence on temperature and dissolved organic carbon quality[END_REF]. Several lines of evidences suggest that these infections are increasing and tend to follow regional climatic trends with outbreaks typically following episodes of unusually warm weather. In northern Europe, the salmonid farming industry is constantly threatened by pathogens, such as Vibrio salmonicida and V. anguillarum [START_REF] Austin | Taxonomy of bacterial fish pathogens[END_REF]. In France, several Vibrio species have been associated with recent and massive losses in the oyster industry [START_REF] Goudenege | A single regulatory gene is sufficient to alter Vibrio aestuarianus pathogenicity in oysters[END_REF][START_REF] Fouz | Characterization of Vibrio damsela strains from turbot Scophthalmus maximus in Spain[END_REF]. In Spain Photobacterium damselae is associated with diseases of cultured fish species [START_REF] Fouz | Characterization of Vibrio damsela strains from turbot Scophthalmus maximus in Spain[END_REF] and V. vulnificus with hemorrhagic septicaemia in eels [START_REF] Haenen | Bacterial infections from aquatic species: potential for and prevention of contact zoonoses[END_REF]. Both fish pathogens deserve special attention since they are able to cause septicemia in humans. Finally, evidence has also been gathered linking Vibrio infections (e.g. Vibrio coralliilyticus) to increasing mass mortality of benthic corals (e.g. Paramuricea clavata) in the NW Mediterranean Sea [START_REF] Vezzulli | Vibrio infections triggering mass mortality events in a warming Mediterranean Sea[END_REF]. In this context, the development of operational tools to identify and detect emergent pathogens is essential to zoosanitary monitoring of cultivated species as well as to inform and enhance studies on wild animal populations.

Experimental challenge still remains the only way to determine the virulence of an isolate

Vibrio infections of invertebrates are widely documented (for review see [START_REF] Thompson | Biodiversity of vibrios[END_REF]. However, compared to human pathogens, little is known regarding the mode of pathogenesis. More often, the virulence of a given strain must be assessed by experimental challenge. However, analysis of the data generated by this approach is hampered by the variability of the physiological state and a heterogeneous genetic background of the animals, combined with a continuously changing environment (food, temperature, salinity, pollutants). Even more disturbing is the fact that invertebrate tissues naturally housing bacteria that can lead to misinterpretations of obtained results [START_REF] Pruzzo | Persistence of vibrios in marine bivalves: the role of interactions with haemolymph components[END_REF].

The use of gnotobiotic systems (i.e. animals cultured in axenic conditions or with a known microflora) is a promising tool to diagnose pathogenic strains and extend our understanding of the mechanisms involved in host-microbe interactions (for review see [START_REF] Marques | Gnotobiotically grown aquatic animals: opportunities to investigate host-microbe interactions[END_REF]. Most studies performed so far have used axenic Artemia fransciscana as a test organism. This system has allowed for the investigation of several aspects of crustacean-Vibrio interactions, such as colonization (of pathogenic or probiotic isolates), biofilm formation, toxicity of exoenzymes, and infection route [START_REF] Defoirdt | Antivirulence therapy for animal production: filling an arsenal with novel weapons for sustainable disease control[END_REF][START_REF] Defoirdt | The impact of mutations in the quorum sensing systems of Aeromonas hydrophila, Vibrio anguillarum and Vibrio harveyi on their virulence towards gnotobiotically cultured Artemia franciscana[END_REF][START_REF] Defoirdt | Quorum sensing negatively regulates chitinase in Vibrio harveyi[END_REF][START_REF] Defoirdt | Monitoring of Vibrio harveyi quorum sensing activity in real time during infection of brine shrimp larvae[END_REF]. However as a heterologous system, this model may exclude species-specific pathogens and specific virulence mechanisms.

Experimental challenge in larvae seems to give reproducible results, and as a consequence, the pathogenic status of tested strains is less controversial than in adult animals [START_REF] Wendling | Persistence, seasonal dynamics and pathogenic potential of Vibrio communities from Pacific oyster hemolymph[END_REF]. Generally, bacterial isolates are incubated directly with larval cultures and mortalities appear rapidly, i.e. from 1-10 days after infection. These tests can be miniaturized and therefore permit the screening of large numbers of isolates. However, because several strains are pathogenic only from certain developmental stages (for example Vibrio penaeicida in the shimp Litopenaeus stylirostris, see 31), this procedure cannot be used as a broad test for virulence.

Putative pathogens are frequently selected by injection of bacterial suspensions in the animals The results are obtained quickly since mortality often appears in a few days. The recent development of specific-pathogen-free (SPF) and standardized spats of Crassostrea gigas [START_REF] Petton | Temperature influence on pathogen transmission and subsequent mortalities in juvenile Pacific oysters Crassostrea gigas[END_REF][START_REF] Petton | Factors influencing disease-induced mortality of Pacific oysters Crassostrea gigas[END_REF] has made high-throughput screening of oyster pathogenic isolates by injection possible [START_REF] Goudenege | A single regulatory gene is sufficient to alter Vibrio aestuarianus pathogenicity in oysters[END_REF][START_REF] Lemire | Populations, not clones, are the unit of vibrio pathogenesis in naturally infected oysters[END_REF]. However, injection techniques do not reflect the natural route of infection, thus precluding other factors (e.g. chemotaxis, colonisation etc.) that may be involved in the infection process (Figure 1). Furthermore, these laboratory analyses do not capture the complexity of infection occurring in the natural environment. For instance, oysters are typically injected with a single bacterial strain, whereas in the environment, animals are typically colonized by a diverse assemblage of Vibrio species [START_REF] Lemire | Populations, not clones, are the unit of vibrio pathogenesis in naturally infected oysters[END_REF][START_REF] Wendling | Persistence, seasonal dynamics and pathogenic potential of Vibrio communities from Pacific oyster hemolymph[END_REF].

Experimental infection by immersion has gained success in demonstrating the virulence of some Vibrio strains towards shrimp [START_REF] Le Roux | Virulence of an emerging pathogenic lineage of Vibrio nigripulchritudo is dependent on two plasmids[END_REF], oyster [START_REF] Waechter | Characterisation of Crassostrea gigas spat pathogenic bacteria[END_REF] clam [START_REF] Gomez-Leon | Isolation of Vibrio alginolyticus and Vibrio splendidus from aquacultured carpet shell clam (Ruditapes decussatus) larvae associated with mass mortalities[END_REF] and octopus [START_REF] Farto | Vibrio lentus associated with diseased wild octopus (Octopus vulgaris)[END_REF]. However, this method frequently does not result in any mortality at all. This lack of reproducibility may be due to the fact that the ability of vibrios to induce disease in the wild may depend on associations with other bacteria or attachment to other organisms and particles. For instance, a recent study has reported that marine aggregates facilitate retention of nanoparticles (including bacteria) by suspension-feeding bivalves [START_REF] Froelich | Vibrio vulnificus integration into marine aggregates and subsequent uptake by the oyster, Crassostrea virginica[END_REF]. Thus, monitoring of animals in an environment in which bacteria are not simply in a planktonic form may yield a more accurate understanding of the factors that contribute to virulence. Identifying the microhabitats of pathogens (39) may facilitate further development of an experimental infection model representative of the natural route of infection (e.g. through the use of adapted polymeric substrates and or cellular vectors). Such an approach would also allow for a better understanding of the mode of transmission and primary target tissues or organs for these pathogens.

The lack of knowledge concerning the vibrio pathogenesis in marine animals is in part a consequence of the absence of standardized models for in vivo studies. Indeed, with no inbred animal lines, the genetic background of the experimental animals is heterogeneous. Furthermore as bacteria naturally infect invertebrates, the diversity of the natural microflora may also contribute to a lack of reproducibility. Of the recent work aiming to improve the in vivo model, the standardization of animal hatching and attempts to mimic the natural route of infection determination seem to have the most promising perspectives.

Cellular and/or molecular tests to evaluate potential virulence

In light of the difficulties faced by current techniques of experimental infection in nondomesticated animals, the development of cellular and/or molecular tests to evaluate the potential virulence of strains is necessary. The development of such bioassays could be informed by descriptions of host alterations to define the virulence mechanisms involved in pathogenesis. However, in these systems, it is difficult to establish a specific clinical table of disease characteristics for four main reasons: 1) it is necessary to analyze a high number of samples to investigate any one specific observation; 2) the absence of external symptoms frequently leads to the observation/description of post-mortem lesions; 3) the size of the pathogen is inhibitory to detection; and 4) invertebrates are already colonized by commensal community of microbes. Thus, specific molecular tools, such as in situ hybridization and fluorescent-labeled bacteria, should be developed to describe the infection route and eventual specific localization and migrations of Vibrio pathogens. GFP-labeled Vibrio have already been used to investigate Vibrio-invertebrate interactions at both organismal and cellular levels (Figure 2) [START_REF] Travers | Construction of a stable GFP-tagged Vibrio harveyi strain for bacterial dynamics analysis of abalone infection[END_REF][START_REF] Duperthuy | Use of OmpU porins for attachment and invasion of Crassostrea gigas immune cells by the oyster pathogen Vibrio splendidus[END_REF][START_REF] Goulden | Pathogenicity and infection cycle of Vibrio owensii in larviculture of the ornate spiny lobster (Panulirus ornatus)[END_REF].

Standardized in vitro assays are also necessary to screen for distinct activities covered by the generic term virulence, such as adherence and cytolytic effects. As marine invertebrate cell lines are not yet available, experiments aiming to understand interactions between host cells and pathogenic Vibrios have thus far relied mainly on primary lines of hemocytes [START_REF] Duperthuy | Use of OmpU porins for attachment and invasion of Crassostrea gigas immune cells by the oyster pathogen Vibrio splendidus[END_REF][START_REF] Labreuche | Vibrio aestuarianus zinc metalloprotease causes lethality in the Pacific oyster Crassostrea gigas and impairs the host cellular immune defenses[END_REF]. However, the use of such a cell population has drawbacks. The hemocyte population is made of distinct cell types that have yet to be fully characterized. Moreover, each animal provides a limited number of cells (about 10 6 ), and experiments using this hemocyte procedure would require the pooling of hemocytes from different individuals with heterogeneous genetic backgrounds in order to achieve the required sample size. Additionally, during an experiment of sufficient duration, bacteria that naturally occur in the hemolymph will proliferate. Interestingly, a recent report has described the use of primary cell cultures from abalone gills to investigate the V. harveyi-abalone interaction [START_REF] Pichon | Characterization of abalone Haliotis tuberculata-Vibrio harveyi interactions in gill primary cultures[END_REF]. Also, heterologous cell lines, such as Bge (mollusk from fresh water) or NIH3T3 (mouse fibroblastic cell lines), have been used to describe the cytopathic effects of a metalloprotease expressed by an oyster pathogen [START_REF] Binesse | Metalloprotease vsm is the major determinant of toxicity for extracellular products of Vibrio splendidus[END_REF]. However, due to osmolarity restrictions, such an approach is still limited to extracellular product analyses.

Bacterial pathogenicity is known to be associated with the structural components of the cells (e.g. capsules, fimbriae, LPS, endotoxins, and other cell wall components), or with the active secretion of substances that either damage host tissues or protect the bacteria against host defenses (invasin enzymes, hemolysin, coagulase, toxins). Several virulence factors have already been identified in Vibrio pathogenic to invertebrates. A metalloprotease has been demonstrated to be a key factor in the extracellular product (ECP) toxicity in V. splendidus [START_REF] Le Roux | Construction of a Vibrio splendidus mutant lacking the metalloprotease gene vsm by use of a novel counterselectable suicide vector[END_REF] V. aestuarianus (43)V. tubiashi (47) and V. corallyliticus [START_REF] Santos Ede | Genomic and proteomic analyses of the coral pathogen Vibrio coralliilyticus reveal a diverse virulence repertoire[END_REF]. The porin OmpU was reported to be an essential virulence factor in the V. splendidus-Crassostrea gigas interaction [START_REF] Duperthuy | The major outer membrane protein OmpU of Vibrio splendidus contributes to host antimicrobial peptide resistance and is required for virulence in the oyster Crassostrea gigas[END_REF]. More recently, a serine protease was found to be specifically secreted through outer membrane vesicles (OMVs) and shown to participate in the virulence of a V. splendidusrelated strain [START_REF] Vanhove | Outer membrane vesicles are vehicles for the delivery of Vibrio tasmaniensis virulence factors to oyster immune cells[END_REF]. DjlA, an inner membrane co-chaperone belonging to the DnaJ family, was shown to be necessary for cytotoxicity of the clam pathogen V. tapetis to hemocytes [START_REF] Lakhal | DjlA, a membrane-anchored DnaJ-like protein, is required for cytotoxicity of clam pathogen Vibrio tapetis to hemocytes[END_REF]. Additionally, from the genomic identification of homologues of genes involved in virulence in other bacterial pathogens, several putative virulence factors have been identified [START_REF] Le Roux | Genome sequence of Vibrio splendidus: an abundant planctonic marine species with a large genotypic diversity[END_REF]. These include potential toxins such as hemolysins, MARTX, proteases, a type VI secretion system, adhesins, as well as genes for siderophore production, transport and utilization. A caveat of this analysis is that the majority of these homologues has been identified on the basis of vertebrate pathogen studies, possibly precluding the discovery of new mechanisms specific to invertebrate pathogens. Furthermore the knowledge of the absence/presence/diversity of these genes does not appear sufficient for the determination of a strain's pathogenicity.

These last few years, few likely virulence mechanisms of vibrios pathogenic for invertebrates have been described at the cellular and molecular level, leading to a better understanding of some pathogenic effects. However to date, detection of these genes is not sufficient to conclude about the pathogenicity of the strain. Microbial pathogenesis is often multifactorial, and pathogens use several biochemical mechanisms operating in concert to produce infections and diseases. In addition, intra and inter-species diversity in virulence mechanisms complicates the development of diagnostic tools to determine the pathogenicity of the strain.

Genome sequence and typing virulent strains

Due to the lack of knowledge of marine bacterial virulence mechanisms and diversity, a "blind" approach has been necessarily employed in order to correlate a genotype (or a group of related genotypes) with virulence potential. In the last few years, significant progress has been made in understanding the population structure and diversity of the Vibrio [START_REF] Thompson | Genotypic diversity within a natural coastal bacterioplankton population[END_REF]. Despite their enormous microdiversity, these organisms fall into well-defined genetic clusters that have similar resource preferences. These clusters have been hypothesized to correspond to populations that act as cohesive ecological units, i.e. ecological populations [START_REF] Hunt | Resource partitioning and sympatric differentiation among closely related bacterioplankton[END_REF]. However, a link between ecological populations and pathogenicity has not been demonstrated, and it is unclear whether pathogenicity is a trait primarily linked to clones, or to populations comprising a large number of distinct genotypes.

Combining experimental ecology, a high throughput infection assay, and genome sequencing, we were recently able to show that Vibrio pathogenicity for oyster spat in France can be ascribed to a cluster of genetically related strains that coincides with a previously defined ecologically cohesive population and to the V. crassostreae species delineation [START_REF] Lemire | Populations, not clones, are the unit of vibrio pathogenesis in naturally infected oysters[END_REF]. Despite a strong clonal frame in the core genome (genes shared by all the strains of the population), the flexible genome was highly diverse with 100-1200 strain specific genes. Genes specific to this population likely reflect the selective pressure associated with population specialization, and we have demonstrated that one of these genes is required for pathogenicity. Thus, in the case of the V. crassostreae infection, the functional unit of virulence is the population (or species) and diagnostic tools can be based on taxonomic markers polymorphisms or population specific gene detection (Figure 3).

The genome based phylogeny of V. aestuarianus related strains shows that the isolates infecting diseased oysters are clustered into two specific lineages [START_REF] Goudenege | A single regulatory gene is sufficient to alter Vibrio aestuarianus pathogenicity in oysters[END_REF]. These lineages contain a large majority of the virulent strains. The strong clonal frame in the core genome and absence of strain specific genes within the lineages led us to hypothesize that the common ancestor was pathogenic and that a few modern strains have lost some key virulence factor(s). This has been recently illustrated by the identification of a frameshift in a non-virulent strain, in a gene coding for a histidine kinase (VarS). A knock out of this gene in a virulent strain confirmed its role as a regulator for V. aestuarianus virulence. Thus, in the case of V. aestuarianus, the functional unit of virulence is the lineage, probably as a consequence of the clonal expansion of a virulent strain (Figure 3). The diagnosis should map taxonomic markers as soon as it leads to the discrimination of the lineage within the species.

The population structure of Vibrio nigripulchritudo related strains highlights a high genetic diversity [START_REF] Goudenège | Comparative genomics of pathogenic lineages of Vibrio nigripulchritudo identifies virulence-associated traits[END_REF]. However virulent strains associated to shrimp disease are found exclusively in specific lineages. Within each lineage, the strains are nearly clonal but only some of them are virulent. Complete sequencing and comparative genome analysis demonstrated that the virulent strains contain specific mobile genetic elements further reported to be necessary for virulence. These elements are different between virulent strains from each lineage but a single toxin is shared by all of them. Thus, in the case of V. nigripulchritudo, the functional unit is the strain within lineage and the diagnosis should map the toxin (Figure 3).

To summarize, we have illustrated here the difficulty in developing diagnostic tools based on genotyping methods resulting from the diversity of pathogen evolutionary scenarios within the Vibrios. In some cases, pathogens arise from the clonal expansion of a strain via lateral gene transfer. In other cases, virulence seems to be linked to the differentiation of ecological populations or species. It should be noted that none of these data would have been obtained from single gene (more often 16S analyses) which is unfortunately still being used to identify "putative" pathogens in aquaculture.

With the revolution of high throughput technologies, it is now possible to sequence a large collection of genomes, perform population structure analyses at the genome scale, and investigate the functional unit of virulence for each putative pathogen. In addition, comparing the genome of virulent strains or populations with their closest non-virulent phylogenetic neighbors will lead to the identification of virulence markers. Then, genetic analyses will highlight the genes that are necessary for infection -a prerequisite to developing cellular and/or molecular tests to diagnose pathogenicity.

Metagenomic and exploring a polymicrobial disease

Marine invertebrates house abundant communities of microbes, which are increasingly recognized to influence the health of these animals. This association can be beneficial as some microbes provide additional energy sources to the host or prevent the establishment of pathogens. For instance, a recent study showed that about 3% of oyster haemolymphassociated cultivable bacteria displayed antibacterial activity toward vibrio [START_REF] Desriac | Exploring the hologenome concept in marine bivalvia: haemolymph microbiota as a pertinent source of probiotics for aquaculture[END_REF]. Using a metabarcoding approach, Wegner and coll showed in oyster that microbial communities are stable over time and assemble in a animal's genotype specific manner. However a change of the environment, such as temperature stress, can disrupt such associations and promote dominance of opportunists [START_REF] Lokmer | Hemolymph microbiome of Pacific oysters in response to temperature, temperature stress and infection[END_REF]. In addition two studies support the hypothesis of increased virulence due to microbe interaction in oysters. First, experimental infections have demonstrated that some Vibrio strains are moderately virulent when injected into animals individually, and display heightened virulence in mixed experimental infections [START_REF] Gay | Two vibrio splendidus related strains collaborate to kill Crassostrea gigas: taxonomy and host alterations[END_REF]. Second, the analyses of oyster mortality following experimental infection suggest that disease onset can be facilitated by the presence of non-virulent strains [START_REF] Lemire | Populations, not clones, are the unit of vibrio pathogenesis in naturally infected oysters[END_REF]. Hence although non-virulent strains are not sufficient for pathogenesis, they clearly have some features (as yet undetermined) that contribute either directly or indirectly to pathogenicity mechanisms. One possibility is that non-virulent strains provide resources required by the virulent strains, acting as "cheaters". This phenomenon has been seen in some analyses of siderophore synthesis and utilization [START_REF] Cordero | Ecological populations of bacteria act as socially cohesive units of antibiotic production and resistance[END_REF]. An alternate role for the non-virulent strains may be to generate a sufficient bacterial load, either to overcome host defenses or to induce expression of virulence factors that are regulated by quorum sensing [START_REF] Bassler | Small talk. Cell-to-cell communication in bacteria[END_REF].

Therefore in the future, the investigation of vibrio pathogenicity for oyster, as for other marine invertebrates, should incorporate metagenomic analysis on the whole microbial community. This approach should lead to correlate spatio-temporal dynamic of populations with cooperative behavior (e.g. quorum sensing, public good) and weapon sharing (e.g. synergic/additional virulence traits; lateral gene transfer).

Figure 1:

Vibrio nigripulchritudo is an emerging pathogen of farmed shrimp in New Caledonia and other regions in the Indo-Pacific. Molecular epidemiological studies have suggested that pathogenicity is linked to particular lineages (A, B and M). The pathogenicity has been assessed using 3 experimental models: shrimp transiently immersed into bacteriacontaminated waters, shrimp intramuscularly injected with bacteria, and shrimp intramuscularly injected with bacterial culture supernatants, and yielded the distinction between Highly, Moderately and Non pathogenic strain (HP in red, MP in orange and NP in green). Each contemporary lineage is comprised of nearly identical strains but comparative genomics allowed differentiation of genetic elements specific to shrimp pathogenesis of varying severity [START_REF] Vanhove | Outer membrane vesicles are vehicles for the delivery of Vibrio tasmaniensis virulence factors to oyster immune cells[END_REF]. In clade A, the highly pathogenic phenotype coincides with the presence of two plasmids pB1067 (11 kbp) and pA1066 (240kbp). The role of each replicon was investigating genetically using plasmid cured HP derivatives. In the immersion model, only strains containing both pA1066 and pB1067 were virulent, while in bacterial injection model, the wild type strain which contains both plasmids, was more virulent than a strain containing pA1066 alone. When supernatants were injected, toxicity was independent of the presence of pB1067. Collectively, these findings suggest that there may be interactions between factors encoded on the two plasmids. It is likely that differences between results obtained with the models reflect the different host-imposed barriers that are encountered by the bacteria.

  Figure 1:Vibrio nigripulchritudo is an emerging pathogen of farmed shrimp in New Caledonia and other regions in the Indo-Pacific. Molecular epidemiological studies have suggested that pathogenicity is linked to particular lineages (A, B and M). The pathogenicity has been assessed using 3 experimental models: shrimp transiently immersed into bacteriacontaminated waters, shrimp intramuscularly injected with bacteria, and shrimp intramuscularly injected with bacterial culture supernatants, and yielded the distinction between Highly, Moderately and Non pathogenic strain (HP in red, MP in orange and NP in green). Each contemporary lineage is comprised of nearly identical strains but comparative genomics allowed differentiation of genetic elements specific to shrimp pathogenesis of varying severity[START_REF] Vanhove | Outer membrane vesicles are vehicles for the delivery of Vibrio tasmaniensis virulence factors to oyster immune cells[END_REF]. In clade A, the highly pathogenic phenotype coincides with the presence of two plasmids pB1067 (11 kbp) and pA1066 (240kbp). The role of each replicon was investigating genetically using plasmid cured HP derivatives. In the immersion model, only strains containing both pA1066 and pB1067 were virulent, while in bacterial injection model, the wild type strain which contains both plasmids, was more virulent than a strain containing pA1066 alone. When supernatants were injected, toxicity was independent of the presence of pB1067. Collectively, these findings suggest that there may be interactions between factors encoded on the two plasmids. It is likely that differences between results obtained with the models reflect the different host-imposed barriers that are encountered by the bacteria. They may also indicate that there are multiple pathways by which the bacterium impairs shrimp viability (LeRoux et al., 2011;[START_REF] Goudenège | Comparative genomics of pathogenic lineages of Vibrio nigripulchritudo identifies virulence-associated traits[END_REF].

Figure 2 :Figure 3 :

 23 Figure 2: GFP labeled Vibrio to investigate interactions with the host at both organismal (A) and cellular levels (B). A-Artemia fransciscana was immersed into GFP labeled Vibrio sp. contaminated waters, fluorescence is visible by transparency in the animal alive. B-Hemocyte invasion by V. crassostreae strain J2-9. GFP-expressing J2-9 in hemocytes stained with Dapi, and rhodamin-coupled phalloidin (Courtezy from Guillaume Charrière).

Acknowledgements

I warmly thank Teagan Mayer (Rebecca Case lab, University of Alberta, Canada) and Dr Yannick Labreuche for critically reading the manuscript and for their help in editing the English language. This work was supported in part by grants from the ANR (11-BSV7-023-01 « VIBRIOGEN » and 13-ADAP-0007-01« OPOPOP»).


Bentamy Abderrahim
email: abderrahim.bentamy@ifremer.fr

Semyon A Grodsky

Elyouncha Anis

Chapron Bertrand

Desbiolles Fabien

Homogenization of scatterometer wind retrievals

Keywords: scatterometer, surface wind, homogenization

Surface winds (10 m equivalent neutral wind velocity) from scatterometer missions since 1992 to present require homogenization to meet the requirements for oceanic and atmospheric climate data records. Sources of differences between winds retrieved from different scatterometer measurements mainly arise from calibration/validation procedures used for each scatterometer and differences in measurement physics. In this study, we focus on the calibration/validation component of the European Remote Sensing Satellite (ERS)-1 and ERS-2 wind speed biases. ERS-1 and ERS-2 data, named as WNF products, are from the Institut Français de Recherche pour l'Exploitation de la MER (IFREMER). In addition to WNF data, the newly calibrated ERS-2 products provided by the European Space Agency (ESA), indicated as ASPS2.0 products, are also used. Our approach utilizes collocated satellite-buoy data. Expected values of the normalized radar cross section (NRCS) are calculated from buoy winds for each antenna beam using the Cmod5.n geophysical model function. The comparisons between expected and measured NRCS examine differences along with variables such as backscatter coefficient and incidence angle ranges. The difference between the expected and measured NRCS is then used to set up empirical models aiming at the correction for biases in ERS-1 and ERS-2 WNF NRCS calibrations. Finally, ERS-1 and ERS-2 wind retrievals are reprocessed using the corrected NRCS and Cmod5.n. These earlier corrected ERS-1/2 winds are analysed along with later scatterometer data (QuikSCAT and ASCAT-A) for their deviations from in situ buoy winds during 1992-2011 period. The scatterometer data homogeneity is also investigated at global scales based on the use of collocated scatterometer retrievals and atmospheric re-analyses winds derived from ERA Interim and CFSR models.

Introduction

Surface wind vectors are vital for operational and scientific issues. For instance, they are routinely used as primary forcing function component for ocean circulation and wave models at global and/or local scales. They are considered as the most important variable for investigating storm surges and wave forecasts at various space and time scales. They have great impact on coastal upwelling, primary productivity, cross shelf transport, deep water formation, ice transport and variability. They are essential for reliable estimation of momentum (wind stress vector), heat fluxes (latent and sensible), and gas fluxes (e.g. CO 2 and H 2 O). Long term change in global winds is an important forcing and indicator of the climate change (e.g. [START_REF] Bourassa | Remotely Sensed Winds and Wind Stresses for Marine Forecasting and Ocean Modeling in Proceedings of OceanObs'09[END_REF]. . One of the main sources of surface wind speed and direction over the global ocean are scatterometers on board polar satellites. Since the launch of the European Remote Sensing Satellite (ERS-1) in August 1991, a total of 10 independent scatterometer missions have been operational. The European Space Agency (ESA) operated two scatterometers onboard the European Remote Sensing Satellites ERS-1 (1991ERS-1 ( -1996) ) and ERS-2 (1995ERS-2 ( -2011)). Three scatterometers have been operated by the National Aeronautic Space Administration (NASA):

NASA scatterometer (NSCAT, 1996(NSCAT, -1997) ) onboard the Japanese Advanced Earth Observing Satellite (ADEOS-1), SeaWinds onboard QuikSCAT satellite (1999 -2009), and SeaWinds onboard (ADEOS-2/Midori) (2002 -2003). The latest scatterometers are the Advanced SCATterometer ASCAT-A (2006 -present) and ASCAT-B (2013 -present) onboard METOP satellites, Ocean SCATterometer (OSCAT) onboard OCEANSAT2 satellite (2009 -present), andHY-2A scatterometer (2011 -present). ASCAT-A/B, OSCAT, and HY-2A, and RapidScat are operated by European Meteorological Satellite organization (EUMETSAT), Indian Space Research Organization (ISRO), the Chinese National Space Center (CAS),and by NASA, respectively. Thanks to the overlapping periods between missions, scatterometer winds are continuously available over global ocean and span more than two decades (1992 -present).

The quality of each scatterometer retrievals have been investigated by a number of authors (e.g. [START_REF] Quilfen | ERS-1 off-line wind scatterometer products[END_REF][START_REF] Graber | Evaluation of ERS-1 scatterometer winds with wind and wave ocean buoy observations[END_REF][START_REF] Freilich | The accuracy of the NSCAT 1 vector winds: Comparisons with National Data Buoy Center buoys[END_REF]Dunbar, 1999, Bentamy et al, 2002;[START_REF] Ebuchi | Evaluation of wind vectors observed by QuikSCAT/SeaWinds using ocean buoy data[END_REF][START_REF] Bentamy | Characterization of ASCAT measurements based on buoy and QuikSCAT wind vector observations[END_REF][START_REF] Verspeek | Validation and Calibration of ASCAT Using CMOD5[END_REF][START_REF] Sudha | Comparison of Oceansat-2 scatterometer winds with buoy observations over the Indian Ocean and the Pacific Ocean[END_REF]. They found that remotely sensed winds are comparable to in situ measurements (mainly from moored buoys). Furthermore, scatterometer products relied on each mission have been successfully used for numerous purposes such as ocean model forcing experiments (e.g. [START_REF] Grima | Sensitivity of an oceanic general circulation model forced by satellite wind stress fields[END_REF][START_REF] Ayina | The impact of satellite winds and latent heat fluxes in a numerical simulation of the tropical Pacific Ocean[END_REF], extreme event studies (e.g. Le Marchal et al, 2000;[START_REF] Katsaros | QuikSCAT facilitates early identification of tropical depressions in 1999 hurricane season[END_REF], and upwelling space and time patterns [START_REF] Blanke | Modeling the structure and variability of the southern Benguela upwelling using QuikSCAT wind forcing[END_REF][START_REF] Penven | Average circulation, seasonal cycle, and mesoscale dynamics of the Peru Current System: A modeling approach[END_REF].

The main weakness of using scatterometer data for a long term wind analysis is the lack of consistency between retrievals from different scatterometers. [START_REF] Bentamy | Matching ASCAT and QuikSCAT winds[END_REF][START_REF] Bentamy | Compatibility of C-and Kuband scatterometer winds: ERS-2 and QuikSCAT[END_REF] have highlighted the differences between surface winds retrieved from ASCAT-A and QuikSCAT, and between ERS-2 and QuikSCAT. Differences in wind retrievals originate from differences in radar physics and procedures used to retrieve near surface winds from measured backscatter coefficients. In the two above papers, a chain of corrections has been applied to individual mission winds to improve the consistency between ERS-2, QuikSCAT, and ASCAT-A. Application of those corrections reduces the global mean of intermission wind difference as well as reduces the magnitude of its geographical patterns.

This study considers four scatterometer missions (ERS-1, ERS-2, QuikSCAT, and ASCAT) spanning two decades from 1992 through 2011. Comparisons of each mission winds with collocated in situ winds from ocean buoys reveal stronger deviations for both ERS missions. Calibration of normalized radar backscatter ( 0 σ , NRCS) is revisited for ERS-1 and ERS-2 by comparing expected NRCS (calculated from collocated buoy winds using the Cmod5.n Geophysical Model Function, GMF) with measured NRCS. The difference between the expected and measured 0 σ is then used to correct biases in measured NRCS. Finally, ERS-1 and ERS-2 winds are reprocessed using the corrected 0 σ and Cmod5.n. Resulting winds from the four scatterometers are analyzed for their inter mission deviations and consistency with buoy winds during 1992 -2011

Data

Scatterometer data

For more than two decades, a sequence of scatterometers onboard polar satellites has been providing unique observations of winds over the global ocean. Continuous surface wind records are available from ten missions spanning late 1991 through present. These missions include C-band (5.3 GHz) scatterometers onboard ERS-1, ERS-2 and METOP-A(ASCAT), and higher frequency Ku-band (13.4 GHz) SeaWinds onboard QuikSCAT (indicated as QSCAT hereafter)

Scatterometers are microwave radars that infer near-surface wind velocity from NRCS, ( 0 σ ) measured at a variety of azimuth (χ) and incidence angles (θ). The ocean surface radar backscatter occurs primarily from centimeter-scale capillary/gravity waves (ripples), whose amplitude is in equilibrium with the local near-surface wind. Measurements of NRCS are used to estimate equivalent neutral wind (ENW) speed (W) and direction (ϕ) at 10m height Equivalent neutral wind is the wind speed that would be associated with the actual wind stress if the atmospheric boundary layer was neutrally stratified. W and ϕ are related to 0 σ through the Geophysical Model Function (GMF). The latter is a nonlinear function involving θ and χ dependency (e.g. [START_REF] Bentamy | Ocean surface wind fields estimated from satellite active and passive microwave instruments[END_REF][START_REF] Wentz | A model function for the ocean-normalized radar cross section at 14 GHz derived from NSCAT observations[END_REF][START_REF] Hersbach | Comparison of C-Band Scatterometer CMOD5.N Equivalent Neutral Winds with ECMWF[END_REF]. Wind retrievals are available at Wind Vector Cell (WVC) along scatterometer swath. Spatial resolution varies between 12.5km² and 50km² for different scatterometer products.

In this study we consider the swath data (level 2, L2b) from ERS-1, ERS-2, QSCAT, and ASCAT-A (hereafter ASCAT). Original data listed in Table 1 have been corrected (except ERS-2 ASPS2.0, assumed well calibrated) to decrease inter-mission biases. In particular, the original QSCAT winds distributed by NASA/JPL have been corrected for a SST-related bias. This correction depends on wind speed and the sea surface temperature (SST) and accounts for stronger viscous dissipation (especially noticeable at cold SST<5°C) of Bragg waves in Ku-band in comparison with C-band [START_REF] Bentamy | Satellite Air -Sea fluxes[END_REF][START_REF] Grodsky | Does direct impact of SST on short wind waves matter for scatterometry?[END_REF][START_REF] Bentamy | Compatibility of C-and Kuband scatterometer winds: ERS-2 and QuikSCAT[END_REF].

This new QSCAT wind is referred to as QSCAT/N. It also employs an enhanced rain filtering based on the regular rain flag along with the multidimensional rain probability, which must be <0.05 for rain free data [START_REF] Bentamy | Satellite Air -Sea fluxes[END_REF].

ASCAT winds have been also modified from their original version distributed by the EUMETSAT. These modified winds are referred to as ASCAT/N. They are corrected for GMF-related bias, which has been parameterized in [START_REF] Bentamy | Satellite Air -Sea fluxes[END_REF] as a function of ASCAT wind speed and direction relative to the mid-beam azimuth.

Scatterometers onboard ERS-1 and ERS-2 are identical in design. Both are C-band radars that have three antennae looking 45° forward (fore-beam), perpendicular (mid-beam), and 45° backward (aft-beam) relative to the satellite track and illuminating a 500km wide swath to the right of the satellite track. Fore-beam and aft-beam incidence angles vary from 24° (inner swath) to 57° (outer swath), whereas mid-beam incidence angles vary from 18° to 46°.

ERS-1 and ERS-2 winds are inferred at 50km² spatial resolution using IFREMER version 2 GMF (CMODIFR2 of [START_REF] Bentamy | Ocean surface wind fields estimated from satellite active and passive microwave instruments[END_REF]. CMODIFR2 was derived by fitting ERS-1 winds to collocated National Data Buoy Center (NDBC) buoy winds. CMODIFR2 has been applied to ERS-2 without any further adjustments. These ERS-1 and ERS-2 datasets including their corresponding NRCS values are distributed by IFREMER and indicated as WNF products. Land, ice, and rain contaminations are excluded using quality flags included into WNF products.

Previous studies have found that ERS-2 WNF wind speed is underestimated versus buoy and QSCAT/N winds (e.g. [START_REF] Bentamy | Daily surface wind fields produced by merged satellite data[END_REF][START_REF] Bentamy | Compatibility of C-and Kuband scatterometer winds: ERS-2 and QuikSCAT[END_REF]. This ERS-2 wind bias is related to biases in radar calibration and GMF. In fact, the bias versus QSCAT/N is apparently reduced if Cmod5.n GMF is applied instead of CMODIFR2 [START_REF] Bentamy | Compatibility of C-and Kuband scatterometer winds: ERS-2 and QuikSCAT[END_REF].

Buoy data

The ground truth surface wind speed and direction and accompanying oceanic and atmospheric parameters are obtained from moored buoys. [START_REF] Fairall | Bulk parameterization of airsea fluxes: updates and verification for the COARE algorithm[END_REF]. This algorithm requires the knowledge of sea surface temperature (SST), air temperature (Ta), and relative humidity (Rh) (or related variables). All these parameters are available from the tropical buoys. However, only a few NDBC buoys provide air humidity measurements. Therefore, Rh is set to 80% when it is unavailable.

Numerical Weather Prediction (NWP) data

Era-Interim [START_REF] Simmons | ERA-Interim: New ECMWF reanalysis products from 1989 onwards[END_REF] Statistical Interpolation (GSI) data assimilation system for the atmosphere. Flow dependence for the background error variances is included as well as first order time interpolation to the observation. Variational quality control of observations (Andersson and J ärvinen 1999) is also included. An ocean analysis for SST is also performed using Optimal Interpolation (OI).

A full range of observations is used as in the other re-analyses which are quality controlled and bias corrected, including satellite radiances. Observations of ocean temperature and salinity are also used.

Data collocation

In this study we use satellite-buoy and satellite-satellite collocations to assess the accuracy of wind retrievals and to separate and correct for biases in radar calibration.

Sattelite-buoy.

The accuracy of wind retrievals is evaluated by comparing in-situ buoy measurements with quasi-simultaneous scatterometer data. Here we use satellite-buoy data pairs collocated in space and time. The spatial limit for collocation is set based on the spatial resolution of particular satellite wind product, which is 25km for ERS-2 UWI, ASCAT, and QuikSCAT and increases to 50km for ERS-

Satellite-model

ERA Interim and CFSR are available four times a day (00h:00; 06h:00; 12h:00; 18h:00 UTC) on their corresponding regular grids. Atmospheric re-analyses winds are interpolated in space and time over scatterometer swaths using a bilinear method.

Validation method

Buoy comparison results

Comparisons are performed for all valid collocated data available during the periods , which accounts for errors in both, buoy and satellite data Two types of correlation coefficients are included. Scalar correlation ( ρ ) for wind speed is the conventional Pearson's correlation coefficient. Wind direction correlation is evaluated as a vector correlation [START_REF] Crosby | A proposed definition for vector correlation in geophysics: theory and application[END_REF]. It provides a scalar measure of consistency between two vector fields and varies between -2 and +2.

The agreement between buoy and scatterometer wind speeds is excellent. The wind speed correlation coefficients of all scatterometer products are higher than 0.87. The associated b s values are close to unity. The RMS differences (estimated from Bias and STD values) <1.60m/s are lower than scatterometer accuracy specifications. The wind speed RMS at Tropical buoys is slightly weaker than that at NDBC. This is mainly related to difference in wind speed distributions over NDBC and Tropical network regions (e.g. [START_REF] Bentamy | Characterization of ASCAT measurements based on buoy and QuikSCAT wind vector observations[END_REF]. One should notice that ERS-2 UWI and ASCAT have similar statistics. They rely on the use of same C-band geophysical model GMF to retrieve surface winds from the two scatterometers. Cmod5.n was developed on the basis of ERS-2 scatterometer backscatter coefficient measurements [START_REF] Hersbach | An improved C-band scatterometer ocean geophysical model function: CMOD5[END_REF]. Quite similar results are found for wind direction comparisons. Indeed, for ERS-1 WNF, ERS-2 WNF, QSCAT, and ASCAT the correlation coefficients characterizing NDBC and Tropical comparisons are higher than 1.20 and 1.60, respectively. The lowest wind direction correlations are found for ERS-2 UWI.

Furthermore, except for ERS-2 UWI, STD values are lower than 20°.

Reprocessing of ERS-1 and ERS-2 WNF wind retrievals

Both ERS-1 WNF and especially ERS-2 WNF winds tend to be underestimated in comparison with buoy winds (Tables 1,2). This is associated with the calibration of ERS-1 and ERS-2 radars and GMF issues (e.g. [START_REF] Bentamy | Compatibility of C-and Kuband scatterometer winds: ERS-2 and QuikSCAT[END_REF], see also references therein).

Indeed, the reprocessing of ERS-2 wind retrievals based on the use of Cmod5.n GMF has improved correspondence with buoy data [START_REF] Bentamy | Compatibility of C-and Kuband scatterometer winds: ERS-2 and QuikSCAT[END_REF]. Therefore, we achieve better consistency between ERS-1 and ERS-2 winds as a result of their reprocessing with Cmod5.n GMF.

Prior to applying Cmod5.n for the wind reprocessing we compare measured ( 0 m σ ) and expected ( 0 e σ ) backscatter coefficients to estimate radar calibration issues that may be still present in the WNF products. The expected 0 e σ is calculated from Cmod5.n forced by collocated buoy wind velocity. The measured backscatter coefficient is evaluated from scatterometer data following the [START_REF] Bentamy | Characterization of ASCAT measurements based on buoy and QuikSCAT wind vector observations[END_REF] approach. The azimuth dependence of 0 σ is represented by truncated Fourier series:

σ 0 = A 0 + A 1 cosχ + A 2 cos2χ (1)
χ is the wind direction relative to antenna azimuth .

The coefficient A 0 in ( 1), referred to as the power coefficient, mainly carries the information on wind speed. The harmonic coefficient A 1 describes the upwind and downwind asymmetry. The coefficient A 2 describes the difference in backscatter coefficient extrema. A 0 , A 1 , and A 2 are functions of wind speed, incidence angle, and polarization. According to the study objective dealing with wind speed consistency, we next focus on A 0 , which is calculated as:

A 0 = (σ 0 u + σ 0 d + 2σ 0 c )/4 (2)
where σ 0 u , σ 0 d , σ 0 c are the upwind (χ= 0°), downwind (χ = 180°), and crosswind (χ = 90° or 270°) values of the backscatter coefficient. Using buoy wind direction, we selected only those data that correspond within ± 10° either to up-, down-, or cross-wind direction.

The selected data are binned 1m/s in buoy wind speed and 0.2° in incidence angle (θ ), from which measured A 0 is calculated based on (2). Only bins for which data count exceeds 10 are retained for investigations. The calculations are performed for A 0 derived from ERS-1 WNF, ERS-2 WNF, and ERS-2 UWI backscatter measurements, 0 m σ .

For each wind speed and incidence angle the expected backscatter coefficients ( 0 e σ ), calculated from Cmod5.n forced by collocated buoy winds, are used to estimate the expected A 0 based on (2). The best correspondence between expected and measured A 0 is found for ERS-2 UWI (Figures 1 g, 1h, 1i) that reflects better post flight backscatter calibration in the UWI product. Mean differences (resp. STD) between expected and measured power coefficients A 0 for ERS-2 UWI are -0.36dB (resp. 1.08dB), -0.60dB (1.54dB), and -0.57dB

(1.10dB) for fore-, mid-, and aft-beam, respectively. The three associated correlation coefficients exceed 0.98. The discrepancy between expected and measured power coefficients is stronger for ERS-1 WNF and ERS-2 WNF than that for ERS-2 UWI. It is more noticeable for low A 0 corresponding to low winds. But, even for buoy winds >3m/s, the mean difference and STD reach -1dB and 2dB for ERS-1 WNF and ERS-2 WNF, respectively. Correlation between expected and measured power coefficients remain relatively high (~0.95) for the two WNF products, but it is lower than that for the UWI. Various regression analyses would be used to determine the linear relationship between measured and predicted σ 0 (e.g. [START_REF] Bentamy | Satellite Air -Sea fluxes[END_REF]. However, one should notice that both 0 m σ and 0 e σ have their own uncertainties, neither measured nor expected σ 0 can be selected as a reference ('ground truth') for the analysis (e.g. [START_REF] Bentamy | Satellite Air -Sea fluxes[END_REF]. Indeed, both sources have errors related to buoy wind and scatterometer backscatter coefficient measurements.

Additional errors are related to the procedure of estimation of 0 e σ and to the spatial and temporal separations between buoy and scatterometer data. In this study, the symmetrical regression analysis, also called the reduction of major axis is used (e.g. [START_REF] Trauth | Matlab Recipes for Earth Sciences[END_REF][START_REF] Bentamy | Satellite Air -Sea fluxes[END_REF]. It aims to minimize the distances separating regression fit and both, 0 m σ and 0 e σ . Outliers, detected through the application of robust regression algorithm (Street et al, 1988), are excluded.

The regression is performed for each incidence angle bin because Figure 1 (low incidence angles). For instance, the associated RMS difference related to ERS-1 midbeam vary between 1 and 1.5dB for θ < 25°, while RMS values reach 1.90dB for θ > 40°.

To further assess the quality of the correction procedure reliability, corrected ERS-2 WNF σ 0 are compared to ERS-2 UWI over global ocean. Indeed, the latter exhibit the best comparison results versus predicated backscatter coefficient (Figure 1 g), h), and i)). The comparison is performed based on the use of collocated ERS-2 WNF and UWI (section 2.4).

Figure 3 shows mean differences between ERS-2 UWI and ERS-2 WNF backscatter coefficients as a function of incidence angles. The statistics are estimated from remotely sensed data collected from January through March 2000. Dashed and full lines illustrate the result associated with uncorrected and corrected ERS-2 WNF σ°, while the colors aim to distinguish the results associated with each beam like in buoy comparisons (Figure 2) the uncorrected ERS-2 WNF σ° tend to be overestimated versus ERS-2 UWI. On average, biases related to the uncorrected σ° differences fall within -0.20dB and -0.10 dB range and are reduced as a result of the correction. Indeed the biases are lower than 0.07dB.

Based on the method and the related results described in [START_REF] Bentamy | Compatibility of C-and Kuband scatterometer winds: ERS-2 and QuikSCAT[END_REF], ERS-1 and ERS-2 scatterometer wind speeds are reprocessed using the corrected backscatter coefficients and Cmod5.n GMF. As for ASCAT and QSCAT, these newly reprocessed ERS-1 and ERS-2 retrievals are referred as ERS-1/N and ERS-2/N. The gross statistics of deviations from buoy data for these new retrievals are listed in brackets in Table 2 andTable 3. Because neither of buoys locates in cold SST<5°C, the results for QSCAT/N are very close to those for QSCAT and therefore not shown. Although, the statistics obtained for ASCAT L2b and ASCAT/N are slightly different, the results are statically comparable. Indeed, the main impact of ASCAT bias correction relies on high wind conditions (wind speed exceeding 18 m/s). The latter are undersampled in buoy and ASCAT collocated data set. The main changes are found for the reprocessed ERS winds in comparison with WNF winds. Indeed, the reprocessed ERS-1 and ERS-2 winds show significant reduction in the time mean as well as in RMS wind speed differences. The new bias values are quite small, and the associated RMS differences are even lower that for ERS-2 UWI, QSCAT, and ASCAT.

The results in Tables 2 and3 and3). No significant time changes are depicted. Based on mean differences and the associated STD, results shown in Figures 4 and5 state that no systematic biases are found.

Changes of a few tenths of m/s are mainly due to changes in sampling lengths of collocated data and/or to wind seasonal variability. For instance there is a factor of 3 between the number of collocated NDBC and ERS-1 and NDBC and QSCAT. The change in sampling lengths arises from changes between scatterometer sampling schemes and especially on changes in the number of buoys available for the collocation. For instance, the numbers of Tropical buoy available for ERS-1, ERS-2, QSCAT, and ASCAT collocations are on average about of 19, 24, 32, and 28, respectively. As expected the inter-annual wind distributions may also have significant impact on buoy and scatterometer comparisons. For instance, the percentage of low winds (lower than 3m/s) reported from NDBC buoys reaches a maximum of 37% in August 1992, whereas it is only about 20% during summer months (June-July-August). Low winds that are not adequately detected by ERS 1/2 lead to a negative bias between NDBC and ERS-1 wind speeds. Monthly difference time series calculated from NDBC and scatterometer winds (Figure 4) indicate different trends for ERS-1 and ERS-2 biases. The latter tends to be negative. Such changes between ERS-1 and ERS-2 bias trends, is mainly relied on changes in low wind speed distribution derived from NDBC buoy measurements. Indeed, the sampling lengths of NDBC low wind speeds (<4m/s), reported from collocated buoy and scatterometer data, are of 1175 and 1935 for ERS-1 and ERS-2 periods, respectively. We may notice that during overlapping periods of any pair of scatterometers, their associated biases are very close. However, some discrepancies are depicted. For instance, Figure 4 indicates QSCAT/N and ASCAT/N bias difference during the period April -December 2007 when original ASCAT retrievals are estimated as "real", not ENW at 10m, winds [START_REF] Bentamy | Characterization of ASCAT measurements based on buoy and QuikSCAT wind vector observations[END_REF].

Correlation coefficient time series (not shown) vary mostly between 0.88 and 0.97. The highest values are found for QSCAT/N and ASCAT/N whereas the lowest are related to ERS-1 and ERS-2 and especially for summer months. This is clearly due to the sampling issues mentioned above.

Global consistency analysis

In the previous section, we established that ERS-1/N, ERS-2/N, QSCAT/N, and ASCAT/N exhibit quite similar statistics with respect to buoy data. Although such comparisons remain the most reliable method to estimate the quality of remotely sensed data, it is spatially limited. This section aims to investigate, based on statistical analysis, the consistency of the four scatterometer wind sources at global as well as at regional and local Therefore, hereafter, ERA Interim and CFSR wind speeds are assumed time consistent.

To reduce the impact of difference in spatial resolutions for NWP and scatterometer winds, both are averaged over 1°×1° grid cells over scatterometer swaths. The resulting collocated data are monthly averaged. The monthly averages are arithmetically formed on the 1°×1° grid using all available and valid data falling within the grid cell. As expected, the sampling lengths of 1°×1° collocated NWP and remotely sensed data rely on scatterometer characteristics. On average, in inter-tropical oceanic basins the monthly number of collocated data at grid of 1° is about 15 for ERS-1 and ERS-2 and of 40 for QSCAT and ASCAT. At high latitudes, this number reaches or exceeds 30 for ERS-1 and ERS-2, and 70 for QSCAT and ASCAT.

Global spatial distributions

The currents may lead to differences between NWP and remotely sensed wind data (e.g. [START_REF] Quilfen | The ERS Scatterometer Wind Measurement Accuracy: Evidence of Seasonal and Regional Biases[END_REF]. Rain impact on scatterometer measurements may also induce a departure from model estimates. Indeed, scatterometer, and especially, radars operating at Ku band such as QSCAT, are rain affected. In the presence of rain, Ku-band backscatter coefficients are affected by the roughening of the sea surface by rain drops and also by scattering and absorption by rain drops in the atmosphere. [START_REF] Sobieski | Scatterometric signatures of multivariate drop impacts on fresh and salt water surfaces[END_REF] showed that rain impact on QSCAT retrievals may lead to an overestimation reaching 2m/s in the tropical rainy regions.

Another source of the difference between model and scatterometer winds is associated to the atmospheric stability effect. Indeed, NWP wind estimation includes air-sea stratification, whereas scatterometer winds are equivalent neutral wind. Neutral wind speeds are stronger, on average, than stability-dependent wind speeds (e.g. [START_REF] Mears | Comparison of Special Sensor Microwave Imager and buoy-measured wind speeds from 1987-1997[END_REF]. To assess the stability impact, 10m neutral winds are calculated from ERA Interim 10m stability-dependent wind speed, air and sea surface temperatures, and specific air humidity based on the use of COARE3.0 bulk parameterization [START_REF] Fairall | Bulk parameterization of airsea fluxes: updates and verification for the COARE algorithm[END_REF]. Comparisons between ERA Interim 10m neutral and 10m stability-dependent wind speeds are performed for ERS-1, ERS-2, QSCAT, and ASCAT periods (not shown). The mean differences between the two ERA Interim wind speeds are of about 0.20m/s for the four scatterometer periods. It is also found that differences vary as a function of oceanic zone and season. For instance, along north hemisphere western boundary currents, the difference reaches 0.40m/s during winter season.

The comparisons of mean wind speed patterns (Figures 6 and7) are not straightforward due to differences between scatterometer sampling schemes. To assess the impact of sampling 6) reveal that the associated patterns are quite similar. Same results are found for bias determined with respect to ERA Interim (Figure 7). For the four scatterometers, about 95% of bias absolute values are lower than 1m/s. Global distributions of both mean differences and STD of ERS-1/N and ERS-2/N are remarkably similar. Similar results are found for QSCAT/N and ASCAT/N and especially for CFSR comparisons. These results are relied on the scatterometer sampling schemes characteristics. On average, the four scatterometer winds tend to be slightly overestimated compared to CFSR and ERA Interim re-analyses. As expected, the four STD spatial distributions exhibit small (lower than 1m/s) and high (great than 2m/s) values in the tropical and high latitude regions, respectively. Both regions are characterized by low and high wind variablities. Surface winds occurring over extra-tropical regions are known dominated by more dynamic synoptic variability as compared to less variable tropical winds.

Although, NWP and scatterometers are in good agreements, significant departures are found at some specific regions. For instance, ERS-1/N winds are underestimated with respect to CFSR estimates (Figure 6b) along south trade wind zone in the Atlantic Ocean. Indeed, mean differences between CFSR and ERS-1/N annual winds are about 0.40m/s, and reach 0.80m/s at some locations. Similar difference pattern, even though slightly lower, is found for CFSR and ERS-2/N comparison (Figure 6e). For CFSR and QSCAT/N (Figure 6h) as well as for CFSR and ASCAT/N (Figure 6k) comparisons do not lead to any significant patterns of wind differences along south Atlantic trade wind area. These specific difference patterns are also depicted from ERA Interim and scatterometer comparisons (Figure 7b, e, h, and k).

However their magnitudes are lower. These results suggest that CFSR and somehow ERA-Interim south Atlantic trade winds decrease from 1992 through 2011.

Noticeable differences are depicted along the three ocean equatorial areas.

Scatterometer monthly winds are overestimated with respect to NWP estimates. The highest departures are found for ASCAT and especially for QSCAT/N. These equatorial difference patterns are more pronounced for ERA-Interim comparisons. For instance, mean difference between ERA-Interim and QSCAT/N may reach 2m/s. Such difference behaviors along the equator may partly rely on impact of rain (non detected through quality control procedure), especially for QSCAT/N, current on scatterometer retrievals, and of stability (see above).

However, such impacts cannot account for the total differences between ERA Interim and scatterometer winds along equatorial zones. Indeed, these equatorial differences are not found for CFSR and scatterometer comparisons (Figure 6). The former exhibit lower difference values. Somewhat high discrepancies between ERA Interim and scatterometer winds are also depicted along western boundary current regions. ERS-1/N, ERS-2/N, and QSCAT/N winds are slightly overestimated compared to ERA Interim. These patterns are not found for ERA Interim and ASCAT/N and for CFSR and scatterometer comparisons.

Long time series

In section above, the consistency of scatterometer retrievals has been assessed through the spatial differences versus CFSR and ERA Interim analyses. Such consistency would be further assessed through the investigation of long time series of surface wind speeds derived from scatterometers and NWP over ocean basins or at some locations of interest. [START_REF] Bentamy | Satellite Air -Sea fluxes[END_REF], and in [START_REF] Grodsky | Does direct impact of SST on short wind waves matter for scatterometry?[END_REF]. However, one should notice that departure between QSCAT/N and ASCAT/N mean values do not exceed 0.20m/s.

Further investigation of remotely sensed data consistency as well as of comparison between satellite and NWP are performed at some specific locations for monthly time scales. At the north Atlantic location (Figure 10a and 11a) , both scatterometer and NWP wind speeds exhibit very similar wind patterns. The two types of wind sources lead to the expected robust seasonal variation of surface wind speed. Most of maximum and minimum winds occur during north hemisphere winter (December, January, February (DJF)) and summer (June, July, August (JJA)), respectively. However, one should notice that the inter-annual variability is significant. Despite of sampling scheme impact, the four scatterometer retrievals are consistently higher than CFSR as well as than ERA Interim wind estimates.

The scatterometer as well as NWP wind speed variablities at the Atlantic northwest location (Figures 10b and11b) illustrate the typical annual features of surface wind occurring in the midlatitude of the North Atlantic and the Pacific Oceans. They are mainly characterized by a robust seasonal variability where the maximum and minimum occur in winter and summer seasons, respectively. The month to month variability derived from scatterometer and NWP agree well. However, one should notice that departure found between ERA Interim and ERS-1/N during the period 1992 through 1994 is higher than that depicted from ERS-1/N and CFSR comparison.

At the location of Gulf of Lion in the Mediterranean Sea (Figure 10c and11c), scatterometer and NWP winds reveal similar month to month variablities characterized by a seasonal features. The highest and lowest winds occur during winter and summer seasons.

Due to sampling issues, monthly estimated from collocated NWP, ERS-1/N, and ERS-2/N data are more variable. Monthly scatterometer winds are consistently higher than ERA Interim estimates (Figure 11c), while no systematic bias is found out from CFSR comparisons (Figure 10c). The comparisons of monthly winds derived from buoys moored in Gulf of Lion (buoys WMO61001 (43.3°N7.8E) and WMO61002 (42.1°N4.7°E)) and scatterometers (QSCAT/N and ASCAT/N) do not reveal any overestimation of scatterometer estimates (not shown).

Time series of wind speeds at equatorial site (Figure 10d and Figure 11d) reveal less robust seasonal variability. The wind variability is more characterized by large interannual patterns. Even though NWP and scatterometer exhibit quite similar variablities, the month to month agreement is quite poor compared to previous sites. This is mostly pronounced for ERS-1/N and ERS-2/N. Furthermore, due to wind speed distribution along the equatorial region, the departure between ERA Interim and the four scatterometer winds is further highlighted. Better results are drawn from CFSR and scatterometer wind time series comparisons. The latter are similar to those obtained from the comparisons of monthly winds estimated from PIRATA buoy (0°N, 10°W) and from scatterometers (ERS-2/N, QSCAT/N, and ASCAT/N).

The wind speed variations obtained at location in the bay of Bengal of the Indian Ocean (Figures 10e and11e) exhibit a major peak occurring in summer season and related to the Indian monsoon season, while a second weaker peak occurs mostly in January when the region experiences winter weather conditions. The difference between the two peaks indicates that the climate in this region is overwhelmingly controlled by the monsoon. Although, ERA Interim winds are slightly underestimated (Figure 11e), their comparisons to scatterometer are of same order that found for CFSR.

Summary and discussion

Twenty years of scatterometer measurements and wind retrievals are investigated to assess the consistency between the various satellite missions. This study is a continuation of those previously set up to assess the comparisons of scatterometer winds derived from ASCAT and QSCAT [START_REF] Bentamy | Matching ASCAT and QuikSCAT winds[END_REF][START_REF] Grodsky | Does direct impact of SST on short wind waves matter for scatterometry?[END_REF], and from ERS-2 and QSCAT [START_REF] Bentamy | Compatibility of C-and Kuband scatterometer winds: ERS-2 and QuikSCAT[END_REF]. It aims at the characterization of the source of errors yielding to significant differences between scatterometer retrievals and reference data, and at the correction of systematic biases between scatterometer missions.

In this study focuses on the four scatterometers missions: ERS-1 (March 1992-May 1996), ERS-2 (March 1996-January 2001), QSCAT (July 1999-November 2009), ASCAT (April 2007-March 2011), which represent more than two decades of the global ocean winds. The study investigates the long-term consistency of the scatterometer missions. For such purpose, bias corrections found for QSCAT and ASCAT and detailed in [START_REF] Bentamy | Matching ASCAT and QuikSCAT winds[END_REF], Grodsky et al, 2012) are applied without any further investigations. These datasets are referred as QSCAT/N and ASCAT/N. The results drawn from [START_REF] Bentamy | Compatibility of C-and Kuband scatterometer winds: ERS-2 and QuikSCAT[END_REF] dealing with the assessment of ERS-2 and QSCAT are used as guideline for further investigation of ERS-2 winds and then for ERS-1.

The wind speed retrieval consistency is mainly based on the use of collocated moorings and scatterometer data. Similar space and time collocation procedure is applied for the four These new datasets derived from "calibrated" scatterometer measurements for a period exceeding 20 years would be useful for several studies aiming at the characterization of longterm of surface parameters such as wind stress, wind stress curl, latent and sensible heat fluxes at various spatial and temporal scales. We do believe they provide a better resource for such studies, which are ongoing research.

Tables Table 1: Summary of scatterometer characteristics.

March 1992 -

 1992 June 1996 for ERS-1 WNF, March 1996 -January 2001 for ERS-2 WNF, April 1997 -January 2001 for ERS-2 UWI, July 1999 -November 2009 for QSCAT, and April 2007 -March 2011 for ASCAT. Statistical parameters of scatterometer and buoy winds are summarized in Tables2 and 3(see[START_REF] Bentamy | Satellite Air -Sea fluxes[END_REF] for parameter's description). To avoid an impact of outliers a robust regression method of[START_REF] Street | A Note on Computing Robust Regression Estimates via Iteratively Reweighted Least Squares[END_REF], which iteratively assigns weights to data, is applied. Outliers are filtered by excluding data with the assigned weight <0.05. The regression symmetrical coefficient ( s b ) in Tables 2, 3 is produced by linear regression of satellite and buoy winds,

Figure 1

 1 Figure 1 illustrates a persistent overestimation of measured A 0 in comparison with expected values, which is indicative of a low bias in ERS wind retrievals and suggests the need for correction of the WNF backscatter coefficients to reduce the bias in the scatterometer winds.NRCS correction for ERS-1 WNF and ERS-2 WNF is based on a linear regression analysis of expected ( 0 e σ ) and measured ( 0 m σ ) NRCS using a linear regression analysis.

σ

  clearlyindicates that NRCS correction should depend on θ. Such kind of analysis requires a large sampling length of collocated buoy and scatterometer data. All available and valid NDBC and tropical (TAO and PIRATA) hourly data are used together and collocated with ERS-1 and with ERS-2 measurements based on space and time criteria described above. The resulting sampling lengths of ERS-1/buoy and ERS-2/buoy collocations are 17156 and 23405, respectively. These collocated data are split into two subsamples, which are randomly selected. The first subsample (67% of collocated data) is used to determine the regression slope and intercept coefficients, whilst the second subsample is utilized for the validation.of the corrected σ°. Figure 2 shows examples of the validation results. It indicates the mean differences between predicted 0 e σ and measured 0 m σ as a function of incidence angles.Results related to ERS-1 and ERS-2 are shown in Figure2aand Figure2b, respectively.Dashed and Full and lines illustrate the results of ( tend to be overestimated with respect to the expected backscatter coefficients, 0 e σ , (Figure2) Not surprisingly, the results are in agreement with the power coefficient A 0 in Figure1. The difference, 0 e σ -0 m σ , is reduced for the corrected measured backscatter coefficients calculated from the application of the symmetrical regression results. The correction reduces the mean difference 0 e σ -0 m σ by about 30%. The lowest mean and standard deviation (not shown) are found for inner swath locations

  reflect statistics averaged over the period of operation of each satellite. It is important to ensure that validation results are not time dependent. This could be determined by examining differences, in terms of bias and STD, between buoy and scatterometer data as a function of time. The results are illustrated in Figure4and 5 for NDBC and Tropical buoy comparisons, respectively. For each month of comparison periods, difference between available collocated buoy and scatterometer wind speeds are arithmetically averaged and the associated STD are calculated. Full lines indicate time series of monthly mean differences, while dashed lines indicate bias±STD. Along the duration of each scatterometer period, the related monthly biases are quite close to the total bias (Tables2

  scales. For such purpose, comparisons are performed versus ERA Interim and CFSR 10m wind speed estimates. This analysis aims first to assess the long term consistency of the remotely sensed data, and secondly to assess the comparisons of scatterometer and NWP (wind speeds at various spatial and temporal scales. CFSR as well as ERA Interim are considered as references. However, they are assumed ensuring long time series consistency of surface winds.The purpose aiming to assess the consistency of long time series of surface wind speed retrievals based on the use of NWP requires ensuring that their long time variations are not scatterometer dependant. Indeed, both numerical models assimilate ERS-1, ERS-2, QuikSCAT and ASCAT observations. To meet this requirement, monthly ERA Interim and CFSR wind speed anomalies are calculated over North, Tropical, and South Atlantic Oceans during the period March 1992 -March 2011 (not shown). The departures from annual mean concurrent with start and/or end periods of a given scatterometer observations would be used as dependency indicators. The analysis of anomaly time series do not lead to any significant change associated with change in scatterometer availability for assimilation processing.

  monthly averages are utilized to estimate annual wind speed means during operating period of each scatterometer. Spatial variability of annual scatterometer wind speeds (first column) are shown in Figures 6 and 7, along with mean (second column) and STD (Third column) differences between NWP and scatterometer data, for CFSR and ERA Interim comparisons, respectively. Spatial statistics associated to ERS-1/N, ERS-2/N, QSCAT/N, and to ASCAT/N are shown in first, second, third, and fourth Figure rows, respectively. Some difference patterns revealed in Figures 6 and 7 are inherent to the characteristics of scatterometer and NWP wind determinations. For instance, model surface wind speeds are earth-relative, whereas scatterometer retrievals are surface-relative. Therefore, surface

Figures 8

 8 Figures 8 and 9 show time series of yearly-averaged wind speeds estimated from available monthly-averaged collocated CFSR and scatterometer. and from ERA Interim and scatterometer data, respectively. They also show annual mean wind speeds calculated from 6hourly CFSR and ERA Interim analyses (dashed lines). They are calculated for five oceanic zones : Northern high-latitudes (50°N -70°N), Northern mid-latitudes (20°N-40°N), tropical (10°S -10°N), Southern mid-latitudes (40°S-20°S), and Southern high-latitudes (70°S -50°S). Changes of mean values associated with changes of remotely sensed data sources are related in part to the sampling scheme issues. Indeed, annual means derived from CFSR (Figure 8) as well from Era Interim (Figure 9) show small change over time, except for the period beyond 2009, and along southern high latitudes (Figure 8e and 9e). But, stronger changes are present in the time series derived from the collocated data, including discontinuities associated with changes of satellites, thus associated with sampling schemes.

Figures

  Figures 10 and 11 show time series of monthly-averaged wind speeds estimated from collocated scatterometers and CFSR, and from scatterometers and ERA Interim, respectively.They are shown at locations supposed representing different surface wind characteristics associated with North Atlantic (59°N11°W), Gulf stream (41°N66°W), the Mediterranean Sea (41°N6°E), equatorial (0°N10°W), and Bay of Bengal (15°N90°E ) temporal wind patterns. Furthermore, moored buoy measuring winds are also available at these specific locations or nearby. They would be valuable for triplet buoy / scatterometer / NWP collocation analysis.

σ

  scatterometers. The analysis of collocated data clearly shows that the source of WNF ERS-1 and ERS-2 wind underestimation with respect to buoys is related in part with the backscatter coefficient bias. This bias characterizes the mean differences between measured ( from buoy wind and Cmod5.n GMF) backscatter coefficients. ERS-1 and ERS-2 backscatter coefficient biases do not exhibit similar behavior as a function of σ° and incidence angle ranges. For each ERS beam and each incidence angle bin, is determined and used for backscatter coefficient correction. This approach has a serious shortcoming issues. Indeed, moorings have a limited geographical coverage and the associated reported and collocated atmospheric and oceanic conditions are not representative well of for global conditions, especially at high latitudes where buoys are almost missing. However over the global ocean, it is found that corrected ERS-2 NRCS compare better with ERS-2 UWI NRCS (assumed well calibrated) than uncorrected NRCS.The corrected ERS-1 and ERS-2 NRCS are used for reprocessing wind velocity based on CMOD5.n GMF. As expected, the new ERS-1 and ERS-2 winds (referred as ERS-1/N, and ERS-2/N) exhibit better comparisons with independent mooring data not used for the correction procedure. Indeed, both mean and STD characterizing buoy and ERS-1/N and buoy and ERS-2/N are reduced significantly. They of same order that found for QSCAT/N and ASCAT/N. The determination of ERS-1/N , ERS-2/N, QSCAT/N, and ASCAT/N retrievals over global ocean and from March 1992 through March 2011, leads to further investigations of consistency at global and regional scales and as a function of time. The four remotely sensed data sources show very similar spatial and temporal patterns in agreement with the main known wind distributions. The main discrepancies are found along the equatorial areas where ERS-1/N and ERS-2/N winds are slightly higher than QSCAT/N and ASCAT/N. The study of the long-term wind speed time series, do not show any step change associated with the change of scatterometer. The differences between two scatterometer winds, operating at same dates, rely on their space and time sampling schemes

Figure 8 :

 8 Figure 1: C-band model power coefficient A 0 from observed scatterometer backscatter coefficients (eq. 2) versus its predicted values from Cmod5.n GMF and collocated buoy winds. Red, blue, and green colors dots indicate A 0 values associated to inner-(mid beam θ of 18°), middle-(θ of 27°), and outer-(θ of 45°) swaths, respectively.Figure 2: Differences between expected and measured backscatter (uncorrected -dashed, corrected -solid) coefficients as a function of the incidence angle.Figure 3: Mean Differences between ERS-2 UWI and ERS-2 WNF backscatter coefficients (uncorrected -dashed, corrected -solid) as a function of the incidence angle.Figure 4: Time series of monthly differences between NDBC buoy and scatterometer wind speeds. Full and dashed lines indicate running bias and bias±STD, respectively.Figure 5: Time series of monthly differences between Tropical (TAO, PIRATA, RAMA) buoy and scatterometer wind speeds. Full and dashed lines indicate bias and bias±STD, respectively.Figure6: Annual mean scatterometer wind speeds (1st column), mean (2 nd column) and STD (3 rd column) differences (CFSR minus scatterometer). First, second, third, and fourth rows show statistics for ERS-1/N, ERS-2/N, QuikSCAT, and ASCAT/N, respectively. Figure7: Annual mean scatterometer wind speeds (1st column), bias (2 nd column), and STD (3 rd column) differences ERA Interim minus scatterometer wind speeds. First, second, third, and fourth rows are related to statistics estimated for ERS-1/N, ERS-2/N, QuikSCAT, and ASCAT/N, respectively. Figure 8: Annual mean wind speed estimated from available monthly averaged collocated scatterometer (ERS-1/N, ERS-2/N, QSCAT, ASCAT/N) and CFSR data. They are calculated for the period March 1992 -March 2011, and for latitudinal oceanic zones: a) 50°N -70°N; b) 20°N -40°N; c) 10°S -10°N; d) 40°S -20°S; and e) 70°S -50°S . CFSR_Scatt (thin black line) indicate CFSR data collocated with scatterometer retrievals, whereas CFSR (dashed black line) indicates annual winds calculated from all CFSR data.Figure 9: Annual mean speed estimated from available monthly averaged collocated scatterometer (ERS-1/N, ERS-2/N, QSCAT, ASCAT/N) and ERA Interim data. They are calculated for the period March 1992 -March 2011, and for latitudinal oceanic zones: a) 50°N -70°N; b) 20°N -40°N; c) 10°S -10°N; d) 40°S -20°S; and e) 70°S -50°S . ERAI_Scatt (thin black line) indicate ERA Interim data collocated with scatterometer retrievals, whereas ERAI (dashed black line) indicates annual winds calculated from 6-hourly analyses. Figure 10: Time series of monthly-averaged wind speeds estimated from collocated scatterometer (ERS-1/N, ERS-2/N, QSCAT, ASCAT/N) and CFSR data. They are shown for five locations which coordinates are indicated in the top/leftcorner of each panel.Figure 11: Time series of monthly-averaged wind speeds estimated from collocated scatterometer (ERS-1/N, ERS-2/N, QSCAT, ASCAT/N) and ERA Interim data. They are shown at five locations which latitudes and longitudes are indicated at top/left of each panel.

Figure 9 :Figure 10 :

 910 Figure 1: C-band model power coefficient A 0 from observed scatterometer backscatter coefficients (eq. 2) versus its predicted values from Cmod5.n GMF and collocated buoy winds. Red, blue, and green colors dots indicate A 0 values associated to inner-(mid beam θ of 18°), middle-(θ of 27°), and outer-(θ of 45°) swaths, respectively.Figure 2: Differences between expected and measured backscatter (uncorrected -dashed, corrected -solid) coefficients as a function of the incidence angle.Figure 3: Mean Differences between ERS-2 UWI and ERS-2 WNF backscatter coefficients (uncorrected -dashed, corrected -solid) as a function of the incidence angle.Figure 4: Time series of monthly differences between NDBC buoy and scatterometer wind speeds. Full and dashed lines indicate running bias and bias±STD, respectively.Figure 5: Time series of monthly differences between Tropical (TAO, PIRATA, RAMA) buoy and scatterometer wind speeds. Full and dashed lines indicate bias and bias±STD, respectively.Figure6: Annual mean scatterometer wind speeds (1st column), mean (2 nd column) and STD (3 rd column) differences (CFSR minus scatterometer). First, second, third, and fourth rows show statistics for ERS-1/N, ERS-2/N, QuikSCAT, and ASCAT/N, respectively. Figure7: Annual mean scatterometer wind speeds (1st column), bias (2 nd column), and STD (3 rd column) differences ERA Interim minus scatterometer wind speeds. First, second, third, and fourth rows are related to statistics estimated for ERS-1/N, ERS-2/N, QuikSCAT, and ASCAT/N, respectively. Figure 8: Annual mean wind speed estimated from available monthly averaged collocated scatterometer (ERS-1/N, ERS-2/N, QSCAT, ASCAT/N) and CFSR data. They are calculated for the period March 1992 -March 2011, and for latitudinal oceanic zones: a) 50°N -70°N; b) 20°N -40°N; c) 10°S -10°N; d) 40°S -20°S; and e) 70°S -50°S . CFSR_Scatt (thin black line) indicate CFSR data collocated with scatterometer retrievals, whereas CFSR (dashed black line) indicates annual winds calculated from all CFSR data.Figure 9: Annual mean speed estimated from available monthly averaged collocated scatterometer (ERS-1/N, ERS-2/N, QSCAT, ASCAT/N) and ERA Interim data. They are calculated for the period March 1992 -March 2011, and for latitudinal oceanic zones: a) 50°N -70°N; b) 20°N -40°N; c) 10°S -10°N; d) 40°S -20°S; and e) 70°S -50°S . ERAI_Scatt (thin black line) indicate ERA Interim data collocated with scatterometer retrievals, whereas ERAI (dashed black line) indicates annual winds calculated from 6-hourly analyses. Figure 10: Time series of monthly-averaged wind speeds estimated from collocated scatterometer (ERS-1/N, ERS-2/N, QSCAT, ASCAT/N) and CFSR data. They are shown for five locations which coordinates are indicated in the top/leftcorner of each panel.Figure 11: Time series of monthly-averaged wind speeds estimated from collocated scatterometer (ERS-1/N, ERS-2/N, QSCAT, ASCAT/N) and ERA Interim data. They are shown at five locations which latitudes and longitudes are indicated at top/left of each panel.

Figure 11 :

 11 Figure 1: C-band model power coefficient A 0 from observed scatterometer backscatter coefficients (eq. 2) versus its predicted values from Cmod5.n GMF and collocated buoy winds. Red, blue, and green colors dots indicate A 0 values associated to inner-(mid beam θ of 18°), middle-(θ of 27°), and outer-(θ of 45°) swaths, respectively.Figure 2: Differences between expected and measured backscatter (uncorrected -dashed, corrected -solid) coefficients as a function of the incidence angle.Figure 3: Mean Differences between ERS-2 UWI and ERS-2 WNF backscatter coefficients (uncorrected -dashed, corrected -solid) as a function of the incidence angle.Figure 4: Time series of monthly differences between NDBC buoy and scatterometer wind speeds. Full and dashed lines indicate running bias and bias±STD, respectively.Figure 5: Time series of monthly differences between Tropical (TAO, PIRATA, RAMA) buoy and scatterometer wind speeds. Full and dashed lines indicate bias and bias±STD, respectively.Figure6: Annual mean scatterometer wind speeds (1st column), mean (2 nd column) and STD (3 rd column) differences (CFSR minus scatterometer). First, second, third, and fourth rows show statistics for ERS-1/N, ERS-2/N, QuikSCAT, and ASCAT/N, respectively. Figure7: Annual mean scatterometer wind speeds (1st column), bias (2 nd column), and STD (3 rd column) differences ERA Interim minus scatterometer wind speeds. First, second, third, and fourth rows are related to statistics estimated for ERS-1/N, ERS-2/N, QuikSCAT, and ASCAT/N, respectively. Figure 8: Annual mean wind speed estimated from available monthly averaged collocated scatterometer (ERS-1/N, ERS-2/N, QSCAT, ASCAT/N) and CFSR data. They are calculated for the period March 1992 -March 2011, and for latitudinal oceanic zones: a) 50°N -70°N; b) 20°N -40°N; c) 10°S -10°N; d) 40°S -20°S; and e) 70°S -50°S . CFSR_Scatt (thin black line) indicate CFSR data collocated with scatterometer retrievals, whereas CFSR (dashed black line) indicates annual winds calculated from all CFSR data.Figure 9: Annual mean speed estimated from available monthly averaged collocated scatterometer (ERS-1/N, ERS-2/N, QSCAT, ASCAT/N) and ERA Interim data. They are calculated for the period March 1992 -March 2011, and for latitudinal oceanic zones: a) 50°N -70°N; b) 20°N -40°N; c) 10°S -10°N; d) 40°S -20°S; and e) 70°S -50°S . ERAI_Scatt (thin black line) indicate ERA Interim data collocated with scatterometer retrievals, whereas ERAI (dashed black line) indicates annual winds calculated from 6-hourly analyses. Figure 10: Time series of monthly-averaged wind speeds estimated from collocated scatterometer (ERS-1/N, ERS-2/N, QSCAT, ASCAT/N) and CFSR data. They are shown for five locations which coordinates are indicated in the top/leftcorner of each panel.Figure 11: Time series of monthly-averaged wind speeds estimated from collocated scatterometer (ERS-1/N, ERS-2/N, QSCAT, ASCAT/N) and ERA Interim data. They are shown at five locations which latitudes and longitudes are indicated at top/left of each panel.

Figure 1 :

 1 Figure 1: C-band model power coefficient A 0 from observed scatterometer backscatter coefficients (eq. 2) versus its predicted values from Cmod5.n GMF and collocated buoy winds. Red, blue, and green colors dots indicate A 0 values associated to inner-(mid beam θ of 18°), middle-(θ of 27°), and outer-(θ of 45°) swaths, respectively.

Figure 2 :

 2 Figure 2: Differences between expected and measured backscatter (uncorrected corrected -solid) coefficients as a function of the incidence angle.

Figure 2 :Figure 2 :

 22 Figure 2: Differences between expected and measured backscatter (uncorrected solid) coefficients as a function of the incidence angle.

Figure 4 :

 4 Figure 4: Time series of monthly differences between NDBC buoy and scatterometer wind speeds. Full and dashed lines indicate running bias and bias±STD, respectively.

Figure 5 :

 5 Figure 5: Time series of monthly differences between Tropical (TAO, PIRATA, RAMA) buoy and scatterometer wind speeds. Full and dashed lines indicate bias and bias±STD, respectively.

Figure 6 :

 6 Figure 6: Annual mean scatterometer wind speeds (1st column), mean (2 nd column) and STD (3 rd column) differences (CFSR minus scatterometer). First, second, third, and fourth rows show statistics for ERS-1/N, ERS-2/N, QuikSCAT, and ASCAT/N, respectively.

Figure 7 :Figure 8 : 1 Figure 9 :

 7819 Figure 7: Annual mean scatterometer wind speeds (1st column), bias (2 nd column), and STD (3 rd column) differences ERA Interim minus scatterometer wind speeds. First, second, third, and fourth rows are related to statistics estimated for ERS-1/N, ERS-2/N, QuikSCAT, and ASCAT/N, respectively.

Figure 10 :

 10 Figure 10: Time series of monthly-averaged wind speeds estimated from collocated scatterometer (ERS-1/N, ERS-2/N, QSCAT, ASCAT/N) and CFSR data. They are shown for five locations which coordinates are indicated in the top/leftcorner of each panel.

Figure 11 :

 11 Figure 11: Time series of monthly-averaged wind speeds estimated from collocated scatterometer (ERS-1/N, ERS-2/N, QSCAT, ASCAT/N) and ERA Interim data. They are shown at five locations which latitudes and longitudes are indicated at top/left of each panel.

Table 1

 1

provides particular characteristics of each scatterometer including its operating period, repeat cycle, radar frequency/wavelength, GMF used for wind retrieval processing, the version of L2b product used in this study, , and WVC spatial resolution. The agencies processing and distributing these data (shown in the last column of Table

1

) are Institut Français pour la Recherche et l'Exploitation de la MER (IFREMER), European Space Agency (ESA), NASA Jet Propulsion Laboratory (JPL), and European Meteorological Satellite (EUMETSAT).

Table 2 :

 2 Statistical comparison results of collocated 10m wind speeds and direction from NDBC buoys and scatterometer ERS-1, ERS-2, QSCAT, and ASCAT products. Bias is defined as mean difference between buoy and scatterometer winds (in this order). Std, bs, ρ, and ρ² indicate standard deviation, regression symmetrical coefficient, scalar correlation coefficient, and vector correlation coefficient. The latter varies between -2 and +2.

			Wind Speed		Wind Direction
		Bias	STD	bs	ρ	Bias	STD	ρ²
		(m/s)	(m/s)			(deg)	(deg)
	ERS-1 WNF	0.42 (-0.06)	1. 36 (1.10)	1.02 (1.00)	0.92 (0.94)	-4	19	1.84
	ERS-2 WNF	0.70 (-0.15)	1.41 (1.09)	1.01 (1.02)	0.93 (0.95)	-5	18	1.86
	ERS-2 UWI	0.13	1.31	0.99	0.93	-2	38	1.33
	QSCAT	-0.01	1.21	0.99	0.94	-4	21	1.85
	ASCAT	0.15 (0.10)	1.21 (1.26)	1.00 (1.01)	0.94 (0.94)	0	18	1.90

Table 3 :

 3 Statistical comparison results of collocated 10m wind speeds and direction from Tropical (TAO, PIRATA, RAMA) buoys and scatterometer ERS-1, ERS-2, QSCAT, and ASCAT products. Bias is defined as mean difference between buoy and scatterometer winds (in this order). Std, bs, ρ, and ρ² indicate standard deviation, regression symmetrical coefficient, scalar correlation coefficient, and vector correlation coefficient. The latter varies between -2 and +2. Annual mean scatterometer wind speeds (1st column), bias (2 nd column), and STD (3 rd column) differences ERA Interim minus scatterometer wind speeds. First, second, third, and fourth rows are related to statistics estimated for ERS-1/N, ERS-2/N, QuikSCAT, and ASCAT/N, respectively.

			Wind Speed		Wind Direction
		Bias	STD	bs	ρ	Bias	STD	ρ²
		(m/s)	(m/s)			(deg)	(deg)
	ERS-1 WNF	0.77 (0.26)	1.23 (0.99)	0.99 (0.95)	0.90 (0.91)	-9	19	1.64
	ERS-2 WNF	0.88 (0.12)	1.32 (0.91)	1.01 (0.99)	0.90 (0.92)	-10	20	1.68
	ERS-2 UWI	0.46	1.12	0.95	0.90	0	31	1.24
	QSCAT	0.19	0.95	0.96	0.91	1	16	1.74
	ASCAT	0.45 (0.32)	1.02 (1.01)	0.95 (0.94)	0.91 (0.90)	-3	15	1.78

Acknowledgements. This research was supported by the NASA International Ocean Vector Wind Science Team (NASA NNXIOAD99G), and by TOSCA (Terre, Océan, Surfaces continentales, Atmosphère) project. We thank D. Croizé-Fillon, J. F. Piollé, F. Paul, and IFREMER/CERSAT for data processing support. The authors are grateful to ESA, EUMETSAT, CERSAT, JPL, Météo-France, NDBC, PMEL, and UK MetOffice for providing numerical, satellite, and in-situ data used in this study.