2000 days of SMOS at the Barcelona Expert Centre: a tribute to the work of Jordi Font

Soil Moisture and Ocean Salinity (SMOS) is the first satellite mission capable of measuring sea surface salinity and soil moisture from space. Its novel instrument (the L-band radiometer MIRAS) has required the development of new algorithms to process SMOS data, a challenging task due to many processing issues and the difficulties inherent in a new technology. In the wake of SMOS, a new community of users has grown, requesting new products and applications, and extending the interest in this novel brand of satellite services. This paper reviews the role played by the Barcelona Expert Centre under the direction of Jordi Font, SMOS co-principal investigator. The main scientific activities and achievements and the future directions are discussed, highlighting the importance of the oceanographic applications of the mission.Peer ReviewedPostprint (published version

Climate change initiative+ sea surface salinity: Product validation and intercomparison report (PVIR)

The purpose of this document (D.4 Product Validation and Intercomparison Report, PVIR, document version v1.0) is to describe the results of the validation of the Sea Surface Salinity (SSS) products obtained during the ESA CCI+ SSS project when compared with other data sources. The PVIR is a requirement of the Statement of Work (Task 3 SoW ref. ESA-CCI-PRGM-EOPS-SW-17- 0032). The PVIR contains a list of all reference datasets used for validation of each SSS product. Two products are assessed, the level 4 (1) monthly and (2) weekly products based on a temporal optimal interpolation of SSS data measured by SMOS, Aquarius-SAC and SMAP satellite missions. Both gridded products have a resolution of ~25 km on an EASE 2 grid

Comparison of measured brightness temperatures from SMOS with modelled ones from ORCHIDEE and H-TESSEL over the Iberian Peninsula

L-band radiometry is considered to be one of the most suitable techniques to estimate surface soil moisture (SSM) by means of remote sensing. Brightness temperatures are key in this process, as they are the main input in the retrieval algorithm which yields SSM estimates. The work exposed compares brightness temperatures measured by the SMOS mission to two different sets of modelled ones, over the Iberian Peninsula from 2010 to 2012. The two modelled sets were estimated using a radiative transfer model and state variables from two land-surface models: (i) ORCHIDEE and (ii) H-TESSEL. The radiative transfer model used is the CMEM. Measured and modelled brightness temperatures show a good agreement in their temporal evolution, but their spatial structures are not consistent. An empirical orthogonal function analysis of the brightness temperature's error identifies a dominant structure over the south-west of the Iberian Peninsula which evolves during the year and is maximum in autumn and winter. Hypotheses concerning forcing-induced biases and assumptions made in the radiative transfer model are analysed to explain this inconsistency, but no candidate is found to be responsible for the weak spatial correlations at the moment. Further hypotheses are proposed and will be explored in a forthcoming paper. The analysis of spatial inconsistencies between modelled and measured TBs is important, as these can affect the estimation of geophysical variables and TB assimilation in operational models, as well as result in misleading validation studies

Perspectives and Integration in SOLAS Science

Why a chapter on Perspectives and Integration in SOLAS Science in this book? SOLAS science by its nature deals with interactions that occur: across a wide spectrum of time and space scales, involve gases and particles, between the ocean and the atmosphere, across many disciplines including chemistry, biology, optics, physics, mathematics, computing, socio-economics and consequently interactions between many different scientists and across scientific generations. This chapter provides a guide through the remarkable diversity of cross-cutting approaches and tools in the gigantic puzzle of the SOLAS realm. Here we overview the existing prime components of atmospheric and oceanic observing systems, with the acquisition of ocean–atmosphere observables either from in situ or from satellites, the rich hierarchy of models to test our knowledge of Earth System functioning, and the tremendous efforts accomplished over the last decade within the COST Action 735 and SOLAS Integration project frameworks to understand, as best we can, the current physical and biogeochemical state of the atmosphere and ocean commons. A few SOLAS integrative studies illustrate the full meaning of interactions, paving the way for even tighter connections between thematic fields. Ultimately, SOLAS research will also develop with an enhanced consideration of societal demand while preserving fundamental research coherency. The exchange of energy, gases and particles across the air-sea interface is controlled by a variety of biological, chemical and physical processes that operate across broad spatial and temporal scales. These processes influence the composition, biogeochemical and chemical properties of both the oceanic and atmospheric boundary layers and ultimately shape the Earth system response to climate and environmental change, as detailed in the previous four chapters. In this cross-cutting chapter we present some of the SOLAS achievements over the last decade in terms of integration, upscaling observational information from process-oriented studies and expeditionary research with key tools such as remote sensing and modelling. Here we do not pretend to encompass the entire legacy of SOLAS efforts but rather offer a selective view of some of the major integrative SOLAS studies that combined available pieces of the immense jigsaw puzzle. These include, for instance, COST efforts to build up global climatologies of SOLAS relevant parameters such as dimethyl sulphide, interconnection between volcanic ash and ecosystem response in the eastern subarctic North Pacific, optimal strategy to derive basin-scale CO2 uptake with good precision, or significant reduction of the uncertainties in sea-salt aerosol source functions. Predicting the future trajectory of Earth’s climate and habitability is the main task ahead. Some possible routes for the SOLAS scientific community to reach this overarching goal conclude the chapter

Exploiting the multiscale synergy among ocean variables : application to the improvement of remote sensing salinity maps

Les imatges de teledetecció de la superfície oceànica proporcionen una vista sinòptica de la complexa geometria de la circulació oceànica, dominada per la variabilitat de mesoescala. Estructures com filaments i vòrtex són presents en els diferents escalars advectats pel flux oceànic. L’origen més probable d’aquestes estructures és el caràcter turbulent dels corrents, aquestes estructures són persistents amb el temps i compatibles amb la dinàmica mesoscalar oceànica. A escales espacials de quilòmetres o més, la turbulència és principalment 2D, i una complexa geometria, plena de filaments i remolins de mides diferents, emergeix en les imatges superficials de teledetecció de concentració de clorofil·la-a, salinitat superficial, així com en altres escalars més coneguts com són la temperatura superficial i la topografia dinàmica. L’objectiu d’aquesta tesi és explorar i aplicar metodologies de mapatge que permeten millorar la qualitat de mapes de teledetecció oceànica en general, i en particular de la salinitat superficial del mar (SSS). Les diferents metodologies emprades en aquesta tesi han estat aplicades amb l’objectiu específic de millorar els mapes de teledetecció de salinitat superficial del mar proveïts per la missió SMOS de l’Agència Espaial Europea. SMOS és el primer satèl·lit capaç de mesurar la humitat del sol i salinitat oceànica des de l’espai a escala global. La primera part d’aquesta tesi se centra a analitzar les característiques dels productes de nivell 2 (L2) de salinitat de SMOS i produir mapes de nivell 3 (L3) de salinitat utilitzant aproximacions clàssiques: millora del filtratge, mitjana ponderada i Interpolació Òptima. En el curs de la nostra recerca obtenim un conjunt de recomanacions de com processar les dades de SMOS començant des del nivell L2. Aquesta tesi també presenta una nova tècnica de fusió de dades que permet explotar les estructures turbulentes comunes entre diferents variables oceàniques, representant un pas endavant en la cadena de processat per generar mapes de nivell 4 (L4). Aquesta tècnica de fusió es basa teòricament en les propietats geomètriques dels traçadors advectats per la dinàmica oceànica (Turiel et al., 2005a). Degut a l’efecte de forta cissalla als fluits turbulents, l’estructura espacial d’un traçador oceànic hereta algunes propietats del flux subjacent, i en particular el seu arranjament geomètric. Com a conseqüència, les diferents variables oceàniques mostren propietats d’escala similars a la cascada d’energia turbulenta (Seuront and Schmitt, 2005; Nieves et al., 2007; Nieves and Turiel, 2009; Isern-Fontanet et al., 2007). El mètode de fusió agafa un senyal de menor qualitat (afectat per soroll, forats de dades i/o de resolució més baixa) i en millora la seva qualitat. A més d’això, el mètode de fusió és capaç d’extrapolar les dades de forma geofísicament coherent. Aquesta millora del senyal s’aconsegueix utilitzant una altra variable oceànica adquirida amb major qualitat, cobertura espacial més gran i/o millor resolució. Un punt clau d’aquesta aproximació és la suposició de l’existència d’una estructura multifractal de les imatges de teledetecció oceànica (Lovejoy et al., 2001b), i que les línies de singularitat de les diferents variables de l’oceà coincideixen. Sota aquestes premises, els gradients de les dues variables a fusionar estan relacionats per una matriu suau. Com a primera i simple aproximació, s’assumeix que aquesta matriu és proporcional a la identitat; això porta a un esquema de regressió lineal local. Aquesta tesi mostra que aquesta aproximació senzilla permet reduir l’error i millorar la cobertura del producte de nivell 4 resultant. D’altra banda, s’obté informació sobre la relació estadística entre les dues variables fusionades, ja que la dependència funcional entre elles es determina per cada punt de la imatge.Remote sensing imagery of the ocean surface provides a synoptic view of the complex geometry of ocean circulation, which is dominated by mesoscale variability. The signature of filaments and vortices is present in different ocean scalars advected by the oceanic flow. The most probable origin of the observed structures is the turbulent character of ocean currents, and those signatures are persistent over time scales compatible with ocean mesoscale dynamics. At spatial scales of kilometers or more, turbulence is mainly 2D, and a complex geometry, full of filaments and eddies of different sizes, emerges in remote sensing images of surface chlorophyll-a concentration and surface salinity, as well as in other scalars acquired with higher quality such as surface temperature and absolute dynamic topography. The aim of this thesis is to explore and apply mapping methodologies to improve the quality of remote sensing maps in general, but focusing in the case of remotely sensed sea surface salinity (SSS) data. The different methodologies studied in this thesis have been applied with the specific goal of improving surface salinity maps generated from data acquired by the European Space Agency's mission SMOS, the first satellite able to measure soil moisture and ocean salinity from space at a global scale. The first part of this thesis will introduce the characteristics of the operational SMOS Level 2 (L2) SSS products and the classical approaches to produce the best possible SSS maps at Level 3 (L3), namely data filtering, weighted average and Optimal Interpolation. In the course of our research we will obtain a set of recommendations about how to process SMOS data starting from L2 data. A fusion technique designed to exploit the common turbulent signatures between different ocean variables is also explored in this thesis, in what represents a step forward from L3 to Level 4 (L4). This fusion technique is theoretically based on the geometrical properties of advected tracers. Due to the effect of the strong shear in turbulent flows, the spatial structure of tracers inherit some properties of the underlying flow and, in particular, its geometrical arrangement. As a consequence, different ocean variables exhibit scaling properties, similar to the turbulent energy cascade. The fusion method takes a signal affected by noise, data gaps and/or low resolution, and improves it in a geophysically meaningful way. This signal improvement is achieved by using an appropriate data, which is another ocean variable acquired with higher quality, greater spatial coverage and/or finer resolution. A key point in this approach is the assumption of the existence of a multifractal structure in ocean images, and that singularity lines of the different ocean variables coincide. Under these assumptions, the horizontal gradients of both variables, signal and template, can be related by a smooth matrix. The first, simplest approach to exploit such an hypothesis assumes that the relating matrix is proportional to the identity, leading to a local regression scheme. As shown in the thesis, this simple approach allows reducing the error and improving the coverage of the resulting Level 4 product; Moreover, information about the statistical relationship between the two fields is obtained since the functional dependence between signal and template is determined at each point