Theoretical modeling of dual-frequency scatterometer response: improving ocean wind and rainfall effects

Ocean surface wind is a key parameter of the Earth’s climate system. Occurring at the interface between the ocean and the atmosphere, ocean winds modulate fluxes of heat, moisture and gas exchanges. They reflect the lower branch of the atmospheric circulation and represent a major driver of the ocean circulation. Studying the long-term trends and variability of the ocean surface winds is of key importance in our effort to understand the Earth’s climate system and the causes of its changes. More than three decades of surface wind data are available from spaceborne scatterometer/radiometer missions and there is an ongoing effort to inter-calibrate all these measurements with the aim of building a complete and continuous picture of the ocean wind variability. Currently, spaceborne scatterometer wind retrievals are obtained by inversion algorithms of empirical Geophysical Model Functions (GMFs), which represent the relationship between ocean surface backscattering coefficient and the wind parameters. However, by being measurement-dependent, the GMFs are sensor-specific and, in addition, they may be not properly defined in all weather conditions. This may reduce the accuracy of the wind retrievals in presence of rain and it may also lead to inconsistencies amongst winds retrieved by different sensors. Theoretical models of ocean backscatter have the big potential of providing a more general and understandable relation between the measured microwave backscatter and the surface wind field than empirical models. Therefore, the goal of our research is to understand and address the limitations of the theoretical modeling, in order to propose a new strategy towards the definition of a unified theoretical model able to account for the effects of both wind and rain. In this work, it is described our approach to improve the theoretical modeling of the ocean response, starting from the Ku-band (13.4 GHz) frequency and then broadening the analysis at C-band (5.3 GHz) frequency. This research has revealed the need for new understanding of the frequency-dependent modeling of the surface backscatter in response to the wind-forced surface wave spectrum. Moreover, our ocean wave spectrum modification introduced to include the influences of the surface rain, allows the interpretation/investigation of the scatterometer observations in terms not only of the surface winds but also of the surface rain, defining an additional step needed to improve the wind retrievals algorithms as well as the possibility to jointly estimate wind and rain from scatterometer observations

EPS/Metop-SG Scatterometer Mission Science Plan

89 pages, figures, tablesThis Science Plan describes the heritage, background, processing and control of C-band scatterometer data and its remaining exploitation challenges in view of SCA on EPS/MetOp-SGPeer reviewe

Development of high-resolution L4 ocean wind products

[eng] Heat, moisture, gas, and momentum exchanges at the oceanic and atmospheric interface modulate, inter alia, the Earth’s heat and carbon budgets, global circulation, and dynamical modes. Sea surface winds are fundamental to these exchanges and, as such, play a major role in the evolution and dynamics of the Earth’s climate. For ocean and atmospheric modeling purposes, and for their coupling, accurate sea-surface winds are therefore crucial to properly estimate these turbulent fluxes. Over the last decades, as numerical models became more sophisticated, the requirements for higher temporal and spatial resolution ocean forcing products grew. Sea surface winds from numerical weather prediction (NWP) models provide a convenient temporal and spatial coverage to force ocean models, and for that they are extensively used, e.g., the European Centre for Medium-range Weather Forecasts (ECMWF) latest reanalysis, ERA5, with ubiquitous hourly estimates of sea-surface wind available globally on a 30-km spatial grid. However, local systematic errors have been reported in global NWP fields using collocated scatterometer observations as reference. These rather persistent errors are associated with physical processes that are absent or misrepresented by the NWP models, e.g., strong current effects like the Western Boundary Current Systems (highly stationary), wind effects as- sociated with the oceanic mesoscale (sea surface temperature gradients), coastal effects (land see breezes, katabatic winds), Planetary Boundary Layer parameterization errors, and large-scale circulation effects, such as those associated with moist convection areas. In contrast, the ocean surface vector wind or wind stress derived from scatterometers, although intrinsically limited by temporal and spatial sampling, exhibits considerable spatial detail and accuracy. The latter has an effective resolution of 25 km while that of NWP models is of 150 km. Consequently, the biases between the two mostly represent the physical processes unresolved by NWP models. In this thesis, a high-resolution ocean surface wind forcing, the so-called ERAú, that combines the strengths of both the scatterometer observations and of the atmospheric model wind fields is created using a scatterometer-based local NWP wind vector model bias correction. ERAú stress equivalent wind (U10S) is generated by means of a geolocated scatterometer-based correction applied separately to two different ECMWF reanalyses, the nowadays obsolete ERA-interim (ERAi) and the most recent ERA5. Several ERAú configurations using complementary scatterometer data accumulated over different temporal windows (TW) are generated and verified against independent wind sources (scatterometer and moored buoys), through statistical and spectral analysis of spatial structures. The newly developed method successfully corrects for local wind vector biases in the reanalysis output, particularly in open ocean regions, by introducing the oceanic mesoscales captured by the scatterometers into the ERAi/ERA5 NWP reanalyses. However, the effectiveness of the method is intrinsically dependent on regional scatterometer sampling, wind variability and local bias persistence. The optimal ERAú uses multiple complementary scatterometers and a 3-day TW. Bias patterns are the same for ERAi and ERA5 SC to the reanalyses, though the latter shows smaller bias amplitudes and hence smaller error variance reduction differences in verification (up to 8% globally). However, because of ERA5 being more accurate than ERAi, ERAú derived from ERA5 turns out to be the highest quality product. ERAú ocean forcing does not enhance the sensitivity in global circulation models to highly localized transient events, however it improves large-scale ocean simulations, where large- scale corrections are relevant. Besides ocean forcing studies, the developed methodology can be further applied to improve scatterometer wind data assimilation by accounting for the persistent model biases. In addition, since the biases can be associated with misrepresented processes and parmeterizations, empirical predictors of these biases can be developed for use in forecasting and to improve the dynamical closure and parameterizations in coupled ocean-atmosphere models.[spa] Los vientos de la superficie del mar son fundamentales para estimar los flujos de calor y momento en la interfaz oceánica-atmosfera, ocupando un papel importante en la evolución y la dinámica del clima del planeta. Por tanto, en modelación (oceánica y atmosférica), vientos de calidad son cruciales para estimar adecuadamente estos flujos turbulentos. Vientos de la superficie del mar de salidas de modelos de predicción numérica del tiempo (NWP) proporcionan una cobertura temporal y espacial conveniente para forzar los modelos oceánicos, y todavía se utilizan ampliamente. Sin embargo, se han documentado errores sistemáticos locales en campos de NWP globales utilizando observaciones de dispersómetros co-ubicados como referencia (asociados con procesos físicos que ausentes o mal representados por los modelos). Al contrario, el viento de la superficie del mar derivado de los dispersómetros, aunque intrínsecamente limitado por el muestreo temporal y espacial, presenta una precisión y un detalle espacial considerables. Consecuentemente, los sesgos entre los dos representan principalmente los procesos físicos no resueltos por los modelos NWP. En esta tesis, se crea un producto de forzamiento del viento en la superficie del océano de alta resolución, el ERAú. ERAú se genera con una corrección media basada en diferencias geolocalizadas entre dispersometro y modelo, aplicadas por separado a dos reanálisis diferentes, el ERA-interim (ERAi) y el ERA5. Varias configuraciones de ERAú utilizando datos de dispersómetros complementarios acumulados en diferentes ventanas tempo- rales (TW) se generan y validan frente a datos de viento independientes, a través de análisis estadísticos y espectrales de estructuras espaciales. El método corrige con éxito los sesgos del vector de viento local de la reanálisis. Sin embargo, su eficacia depende del muestreo del dispersómetro regional, la variabilidad del viento y la persistencia del sesgo local. El ERAú óptimo utiliza múltiples dispersómetros complementarios y un TW de 3 días. Las dos reanálisis muestran los mismos patrones de sesgo en la SC, debido a que ERA5 es más preciso que ERAi, ERAú derivado de ERA5 es el producto de mayor calidad. El forzamiento oceánico ERAú mejora las simulaciones oceánicas a gran escala, donde las correcciones a gran escala son relevantes

Satellite remote sensing of surface winds, waves, and currents: Where are we now?

This review paper reports on the state-of-the-art concerning observations of surface winds, waves, and currents from space and their use for scientific research and subsequent applications. The development of observations of sea state parameters from space dates back to the 1970s, with a significant increase in the number and diversity of space missions since the 1990s. Sensors used to monitor the sea-state parameters from space are mainly based on microwave techniques. They are either specifically designed to monitor surface parameters or are used for their abilities to provide opportunistic measurements complementary to their primary purpose. The principles on which is based on the estimation of the sea surface parameters are first described, including the performance and limitations of each method. Numerous examples and references on the use of these observations for scientific and operational applications are then given. The richness and diversity of these applications are linked to the importance of knowledge of the sea state in many fields. Firstly, surface wind, waves, and currents are significant factors influencing exchanges at the air/sea interface, impacting oceanic and atmospheric boundary layers, contributing to sea level rise at the coasts, and interacting with the sea-ice formation or destruction in the polar zones. Secondly, ocean surface currents combined with wind- and wave- induced drift contribute to the transport of heat, salt, and pollutants. Waves and surface currents also impact sediment transport and erosion in coastal areas. For operational applications, observations of surface parameters are necessary on the one hand to constrain the numerical solutions of predictive models (numerical wave, oceanic, or atmospheric models), and on the other hand to validate their results. In turn, these predictive models are used to guarantee safe, efficient, and successful offshore operations, including the commercial shipping and energy sector, as well as tourism and coastal activities. Long-time series of global sea-state observations are also becoming increasingly important to analyze the impact of climate change on our environment. All these aspects are recalled in the article, relating to both historical and contemporary activities in these fields

Assimilation de données satellitaires pour le suivi des ressources en eau dans la zone Euro-Méditerranée

Une estimation plus précise de l'état des variables des surfaces terrestres est requise afin d'améliorer notre capacité à comprendre, suivre et prévoir le cycle hydrologique terrestre dans diverses régions du monde. En particulier, les zones méditerranéennes sont souvent caractérisées par un déficit en eau du sol affectant la croissance de la végétation. Les dernières simulations du GIEC (Groupe d'Experts Intergouvernemental sur l'Evolution du Climat) indiquent qu'une augmentation de la fréquence des sécheresses et des vagues de chaleur dans la région Euro-Méditerranée est probable. Il est donc crucial d'améliorer les outils et l'utilisation des observations permettant de caractériser la dynamique des processus des surfaces terrestres de cette région. Les modèles des surfaces terrestres ou LSMs (Land Surface Models) ont été développés dans le but de représenter ces processus à diverses échelles spatiales. Ils sont habituellement forçés par des données horaires de variables atmosphériques en point de grille, telles que la température et l'humidité de l'air, le rayonnement solaire et les précipitations. Alors que les LSMs sont des outils efficaces pour suivre de façon continue les conditions de surface, ils présentent encore des défauts provoqués par les erreurs dans les données de forçages, dans les valeurs des paramètres du modèle, par l'absence de représentation de certains processus, et par la mauvaise représentation des processus dans certaines régions et certaines saisons. Il est aussi possible de suivre les conditions de surface depuis l'espace et la modélisation des variables des surfaces terrestres peut être améliorée grâce à l'intégration dynamique de ces observations dans les LSMs. La télédétection spatiale micro-ondes à basse fréquence est particulièrement utile dans le contexte du suivi de ces variables à l'échelle globale ou continentale. Elle a l'avantage de pouvoir fournir des observations par tout-temps, de jour comme de nuit. Plusieurs produits utiles pour le suivi de la végétation et du cycle hydrologique sont déjà disponibles. Ils sont issus de radars en bande C tels que ASCAT (Advanced Scatterometer) ou Sentinel-1. L'assimilation de ces données dans un LSM permet leur intégration de façon cohérente avec la représentation des processus. Les résultats obtenus à partir de l'intégration de données satellitaires fournissent une estimation de l'état des variables des surfaces terrestres qui sont généralement de meilleure qualité que les simulations sans assimilation de données et que les données satellitaires elles-mêmes. L'objectif principal de ce travail de thèse a été d'améliorer la représentation des variables des surfaces terrestres reliées aux cycles de l'eau et du carbone dans le modèle ISBA grâce à l'assimilation d'observations de rétrodiffusion radar (sigma°) provenant de l'instrument ASCAT. Un opérateur d'observation capable de représenter les sigma° ASCAT à partir de variables simulées par le modèle ISBA a été développé. Une version du WCM (water cloud model) a été mise en œuvre avec succès sur la zone Euro-Méditerranée. Les valeurs simulées ont été comparées avec les observations satellitaires. Une quantification plus détaillée de l'impact de divers facteurs sur le signal a été faite sur le sud-ouest de la France. L'étude de l'impact de la tempête Klaus sur la forêt des Landes a montré que le WCM est capable de représenter un changement brutal de biomasse de la végétation. Le WCM est peu efficace sur les zones karstiques et sur les surfaces agricoles produisant du blé. Dans ce dernier cas, le problème semble provenir d'un décalage temporel entre l'épaisseur optique micro-ondes de la végétation et l'indice de surface foliaire de la végétation. Enfin, l'assimilation directe des sigma° ASCAT a été évaluée sur le sud-ouest de la France.More accurate estimates of land surface conditions are important for enhancing our ability to understand, monitor, and predict key variables of the terrestrial water cycle in various parts of the globe. In particular, the Mediterranean area is frequently characterized by a marked impact of the soil water deficit on vegetation growth. The latest IPCC (Intergovernmental Panel on Climate Change) simulations indicate that occurrence of droughts and warm spells in the Euro-Mediterranean region are likely to increase. It is therefore crucial to improve the ways of understanding, observing and simulating the dynamics of the land surface processes in the Euro-Mediterranean region. Land surface models (LSMs) have been developed for the purpose of representing the land surface processes at various spatial scales. They are usually forced by hourly gridded atmospheric variables such as air temperature, air humidity, solar radiation, precipitation, and are used to simulate land surface states and fluxes. While LSMs can provide a continuous monitoring of land surface conditions, they still show discrepancies due to forcing and parameter errors, missing processes and inadequate model physics for particular areas or seasons. It is also possible to observe the land surface conditions from space. The modelling of land surface variables can be improved through the dynamical integration of these observations into LSMs. Remote sensing observations are particularly useful in this context because they are able to address global and continental scales. Low frequency microwave remote sensing has advantages because it can provide regular observations in all-weather conditions and at either daytime or night-time. A number of satellite-derived products relevant to the hydrological and vegetation cycles are already available from C-band radars such as the Advanced Scatterometer (ASCAT) or Sentinel-1. Assimilating these data into LSMs permits their integration in the process representation in a consistent way. The results obtained from assimilating satellites products provide land surface variables estimates that are generally superior to the model estimates or satellite observations alone. The main objective of this thesis was to improve the representation of land surface variables linked to the terrestrial water and carbon cycles in the ISBA LSM through the assimilation of ASCAT backscatter (sigma°) observations. An observation operator capable of representing the ASCAT sigma° from the ISBA simulated variables was developed. A version of the water cloud model (WCM) was successfully implemented over the Euro-Mediterranean area. The simulated values were compared with those observed from space. A more detailed quantification of the influence of various factors on the signal was made over southwestern France. Focusing on the Klaus storm event in the Landes forest, it was shown that the WCM was able to represent abrupt changes in vegetation biomass. It was also found that the WCM had shortcomings over karstic areas and over wheat croplands. It was shown that the latter was related to a discrepancy between the seasonal cycle of microwave vegetation optical depth (VOD) and leaf area index (LAI). Finally, the direct assimilation of ASCAT sigma° observations was assessed over southwestern France

Validation of the NSCAT-5 Geophysical Model Function for Scatsat-1 Wind Scatterometer

2018 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2018), Observing, Understanding And ForecastingThe Dynamics Of Our Planet, 22-27 July 2018, Valencia, SpainRecent developments on the wind geophysical model function (GMF) of Ku-band scatterometers include a sea surface temperature (SST) dependent term. It has been found that the SST effects on the radar backscatter are wind speed dependent and more pronounced in vertical polarization (VV) than in horizontal polarisation (HH) at higher incidence angles, and are mainly relevant at radar wavelengths smaller than C-band. The new Ku-band GMF, NSCAT-5, is developed based on a physical model and RapidScat radar backscatter measurements, which are only available at two incidence angles, i.e., 48.8° and 55.2°, for HH and VV beams, respectively. The objective of this paper is to verify the NSCAT-5 GMF at similar incidence angles, using data from the scatterometer onboard Indian SCATSat-1 satellite, which operates at 49.1° (HH) and 57.9° (VV) incidence angles. First, the SCATSat-l backscatter sensitivity to sea surface wind and SST is assessed using the C-band Advance Scatterometer (ASCAT) winds as reference. Second, the approach used to derive the NSCAT-5 GMF for RapidScat is adapted to derive a SST-dependent GMF for SCATSat-l. The new GMF will be used to consolidate the current NSCAT-5 model, and then evaluated for SCATSat-l wind retrievalPeer Reviewe