Assimilation of SMOS Retrievals in the Land Information System

The Soil Moisture and Ocean Salinity (SMOS) satellite provides retrievals of soil moisture in the upper 5 cm with a 30-50 km resolution and a mission accuracy requirement of 0.04 cm(sub 3 cm(sub -3). These observations can be used to improve land surface model soil moisture states through data assimilation. In this paper, SMOS soil moisture retrievals are assimilated into the Noah land surface model via an Ensemble Kalman Filter within the NASA Land Information System. Bias correction is implemented using Cumulative Distribution Function (CDF) matching, with points aggregated by either land cover or soil type to reduce sampling error in generating the CDFs. An experiment was run for the warm season of 2011 to test SMOS data assimilation and to compare assimilation methods. Verification of soil moisture analyses in the 0-10 cm upper layer and root zone (0-1 m) was conducted using in situ measurements from several observing networks in the central and southeastern United States. This experiment showed that SMOS data assimilation significantly increased the anomaly correlation of Noah soil moisture with station measurements from 0.45 to 0.57 in the 0-10 cm layer. Time series at specific stations demonstrate the ability of SMOS DA to increase the dynamic range of soil moisture in a manner consistent with station measurements. Among the bias correction methods, the correction based on soil type performed best at bias reduction but also reduced correlations. The vegetation-based correction did not produce any significant differences compared to using a simple uniform correction curve

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

Hydrologic Remote Sensing and Land Surface Data Assimilation

Accurate, reliable and skillful forecasting of key environmental variables such as soil moisture and snow are of paramount importance due to their strong influence on many water resources applications including flood control, agricultural production and effective water resources management which collectively control the behavior of the climate system. Soil moisture is a key state variable in land surface?atmosphere interactions affecting surface energy fluxes, runoff and the radiation balance. Snow processes also have a large influence on land-atmosphere energy exchanges due to snow high albedo, low thermal conductivity and considerable spatial and temporal variability resulting in the dramatic change on surface and ground temperature. Measurement of these two variables is possible through variety of methods using ground-based and remote sensing procedures. Remote sensing, however, holds great promise for soil moisture and snow measurements which have considerable spatial and temporal variability. Merging these measurements with hydrologic model outputs in a systematic and effective way results in an improvement of land surface model prediction. Data Assimilation provides a mechanism to combine these two sources of estimation. Much success has been attained in recent years in using data from passive microwave sensors and assimilating them into the models. This paper provides an overview of the remote sensing measurement techniques for soil moisture and snow data and describes the advances in data assimilation techniques through the ensemble filtering, mainly Ensemble Kalman filter (EnKF) and Particle filter (PF), for improving the model prediction and reducing the uncertainties involved in prediction process. It is believed that PF provides a complete representation of the probability distribution of state variables of interests (according to sequential Bayes law) and could be a strong alternative to EnKF which is subject to some limitations including the linear updating rule and assumption of jointly normal distribution of errors in state variables and observation

Soil Moisture Data Assimilation

Accurate knowledge of soil moisture at the continental scale is important for improving predictions of weather, agricultural productivity and natural hazards, but observations of soil moisture at such scales are limited to indirect measurements, either obtained through satellite remote sensing or from meteorological networks. Land surface models simulate soil moisture processes, using observation-based meteorological forcing data, and auxiliary information about soil, terrain and vegetation characteristics. Enhanced estimates of soil moisture and other land surface variables, along with their uncertainty, can be obtained by assimilating observations of soil moisture into land surface models. These assimilation results are of direct relevance for the initialization of hydro-meteorological ensemble forecasting systems. The success of the assimilation depends on the choice of the assimilation technique, the nature of the model and the assimilated observations, and, most importantly, the characterization of model and observation error. Systematic differences between satellite-based microwave observations or satellite-retrieved soil moisture and their simulated counterparts require special attention. Other challenges include inferring root-zone soil moisture information from observations that pertain to a shallow surface soil layer, propagating information to unobserved areas and downscaling of coarse information to finer-scale soil moisture estimates. This chapter summarizes state-of-the-art solutions to these issues with conceptual data assimilation examples, using techniques ranging from simplified optimal interpolation to spatial ensemble Kalman filtering. In addition, operational soil moisture assimilation systems are discussed that support numerical weather prediction at ECMWF and provide value-added soil moisture products for the NASA Soil Moisture Active Passive mission

Soil Moisture Analysis Based On Microwave Brightness Temperatures : A Study on Systematic and Random Errors

In the context of the EU research project ELDAS, the European Centre for Medium-Range Weather Forecasts developed an experimental soil moisture analysis system which is able to assimilate both screen-level variables (2-metre air temperature and relative humidity) and satellite-observed land surface brightness temperatures at low microwave frequencies. Based on measurements from the Southern Great Plains Hydrology Experiments (SGP97 and SGP99), this study investigates the impact of potential systematic and random errors on the performance of the ELDAS soil moisture analysis and discusses how to cope with these errors in operational applications. Three topics are addressed in detail: (a) An error propagation experiment simulates the effects of erroneous precipitation forcing on the soil moisture of different model layers. The resulting depth-dependent uncertainties are integrated into the model error covariance matrix. Analysed soil moisture and modelled surface heat fluxes from assimilation runs using this covariance matrix are compared to results from reference runs using a vertically uniform model error. The different model error covariance matrices significantly affect model soil moisture and fluxes; a preferable setting, however, can not be identified. (b) An easy-to-apply method of accounting for systematic errors of observations, forward operators and the background soil moisture in an operational large-scale forecast environment is to correct the observations used for the assimilation procedure by the innovation bias (the systematic deviation of the observations from the model equivalents). Such a correction is carried out based on data from an SGP97 site and is shown to improve the performance of the soil moisture analysis. The simulation of the surface latent and sensible heat fluxes, however, does not benefit from the improved analysis. Significant contributions to the innovation biases are shown to result from the microwave forward operator and a dry bias of the modelled near-surface soil moisture. (c) Land surface schemes of current weather forecast models do not sufficiently resolve the top few centimetres of the soil from where the main brightness temperature signal originates. In case of non-uniform near-surface soil moisture and temperature profiles in reality, the assimilation of the corresponding brightness temperature observations can lead to misinterpretations by the soil moisture analysis. The relevance of this model shortcoming is investigated with artificial profiles created on the basis of SGP99 soil moisture and temperature measurements. Mean brightness temperature errors of up to 5 K are found depending on the days elapsed after a rainfall event. A simple bias correction method is presented and applied for the SGP97 period.Bodenfeuchteanalyse mittels Helligkeitstemperaturen - Eine Studie über systematische und zufällige Fehler Im Rahmen des EU-Forschungsprojekts ELDAS hat das Europäische Zentrum für mittelfristige Wettervorhersage ein experimentelles Bodenfeuchteanalysesystem entwickelt, mit dem sowohl Messungen der 2m-Lufttemperatur und -feuchte als auch Satellitenbeobachtungen der Landoberflächenhelligkeitstemperatur im niederfrequenten Mikrowellenbereich assimiliert werden können. Basierend auf Feldmessungen der Southern Great Plains Hydrology Experiments (SGP97 und SGP99) untersucht diese Studie, welche Auswirkungen potentielle systematische und zufällige Fehler auf die Güte der ELDAS-Bodenfeuchteanalyse haben und wie diese Fehler im operationellen Modellbetrieb berücksichtigt werden können. Drei Themenschwerpunkte werden diskutiert: (a) In einem Fehlerausbreitungsexperiment werden die Auswirkungen von fehlerhaften Niederschlagsdaten auf die Bodenfeuchte in verschiedenen Modellschichten simuliert. Die resultierenden, von der Bodentiefe abhängigen Unsicherheiten werden in die Kovarianzmatrix des Modellfehlers integriert. Die auf Basis dieser Kovarianzmatrix berechneten Bodenfeuchten und turbulenten Wärmeflüsse werden mit Ergebnissen von Referenzläufen, bei denen ein vertikal konstanter Modellfehler angenommen wurde, verglichen. Die unterschiedlichen Kovarianzmatrizen des Modellfehlers beeinflussen signifikant die Modellbodenfeuchte und -flüsse. Welche Kovarianzmatrix insgesamt besser ist, kann aber anhand der vorhandenen Ergebnisse nicht festgestellt werden. (b) Eine einfach anzuwendende Methode, um systematische Fehler der Beobachtungen, der Vorwärtsoperatoren und der Hintergrundbodenfeuchte im operationellen Betrieb eines großskaligen Vorhersagemodells zu berücksichtigen, ist die Korrektur der zu assimilierenden Beobachtungen um den Betrag des Innovationsbias' (die systematische Abweichung der Beobachtungen von den entsprechenden Modellwerten). Eine solche Korrektur wurde anhand von Daten eines SGP97-Messfeldes durchgeführt. Die Güte der Bodenfeuchteanalyse konnte dadurch verbessert werden. Die Simulation der turbulenten Wärmeflüsse profitiert jedoch nicht von der verbesserten Bodenfeuchteanalyse. Es wird gezeigt, dass der Mikrowellen-Vorwärtsoperator und ein trockener Bias der oberflächennahen Modellbodenfeuchte die wesentlichen Beiträge zum Innovationsbias liefern. (c) Die Bodenmodelle in gegenwärtigen Wettervorhersagemodellen erfassen die obersten Zentimeter des Bodens, von wo der wesentliche Beitrag zum Helligkeitstemperatursignal kommt, nur ungenügend. Die Assimilation von Helligkeitstemperaturen kann deshalb zu falschen Interpretationen durch die Bodenfeuchteanalyse führen. Die Relevanz dieser Modellunzulänglichkeit wird anhand von künstlichen Bodenfeuchte- und Bodentemperaturprofilen, die auf Basis von SGP99-Messungen erzeugt wurden, untersucht. Bei den modellierten Helligkeitstemperaturen ergeben sich durchschnittliche Fehler von bis zu 5 Kelvin, abhängig von der Anzahl der Tage, die seit dem Auftreten eines Regenereignisses vergangen sind. Es wird eine einfache Biaskorrektur vorgeschlagen und für den SGP97-Zeitraum angewendet

Land Surface Data Assimilation of Satellite Derived Surface Soil Moisture : Towards an Integrated Representation of the Arctic Hydrological Cycle

The ability to accurately determine soil water content (soil moisture) over large areas of the Earth’s surface has potential implications in meteorology, hydrology, water and natural hazards management. The advent of space-based microwave sensors, found to be sensitive to surface soil moisture, has allowed for long-term studies of soil moisture dynamics at the global scale. There are, however, areas where remote sensing of soil moisture is prone to errors because, e.g., complex topography, surface water, dense vegetation, frozen soil or snow cover affect the retrieval. This is particularly the case for the northern high latitudes, which is a region subject to more rapid warming than the global mean and also is identified as an important region for studying 21st century climate change. Land surface models can help to close these observation gaps and provide high spatiotemporal coverage of the variables of interest. Models are only approximations of the real world and they can experience errors in, for example, their initialization and/or parameterization. In the past 20 years the research field of land surface data assimilation has undergone rapid developments, and it has provided a potential solution to the aforementioned problems. Land surface data assimilation offers a compromise between model and observations, and by minimization of their total errors it creates an analysis state which is superior to the model and observation alone. This thesis focuses on the implementation of a land surface data assimilation system, its applications and how to improve the separate elements that goes into such a framework. My ultimate goal is to improve the representation of soil moisture over northern high latitudes using land surface data assimilation. In my three papers, I first show how soil moisture data assimilation can correct random errors in the precipitation fields used to drive the land surface model. A result which indicates that a land surface model, driven by uncorrected precipitation, can have the same skill as a land surface model driven by bias-corrected precipitation. I show that passive microwave remote sensing can be utilized to monitor drought over regions of the world where this was thought to be impractical. I do this by creating a novel drought index based on passive microwave observations, and I validate the new index by comparing it with output from a land surface data assimilation system. Finally, I address knowledge gaps in the modelling of microwave emissions over northern high latitudes. In particular, I study the impact of neglecting multiplescattering terms from vegetation in the radiative transfer models of microwave emission. My three papers show that: (i) land surface data assimilation can improve surface soil moisture estimates at regional scales, (ii) passive microwave observations carries more information about the land surface over northern high latitudes than explored in the retrieval processing chain and (iii) including multiple-scattering terms in microwave radiative transfer models has the potential to increase the sensitivity for surface soil moisture below dense vegetation, and decrease biases between modelled and observed brightness temperature. In sum, my three papers lay the foundation for a land data assimilation system applicable to monitor the hydrological cycle over northern high latitudes

Leveraging Soil Moisture Assimilation in Permafrost Affected Regions

The transfer of water and energy fluxes between the ground and the atmosphere is influenced by soil moisture (SM), which is an important factor in land surface dynamics. Accurate representation of SM over permafrost-affected regions remains challenging. Leveraging blended SM from microwave satellites, this study examines the potential for satellite SM assimilation to enhance LSM (Land Surface Model) seasonal dynamics. The Ensemble Kalman Filter (EnKF) is used to integrate SM data across the Iya River Basin, Russia. Considering the permafrost, only the summer months (June to August) are utilized for assimilation. Field data from two sites are used to validate the study’s findings. Results show that assimilation lowers the dry bias in Noah LSM by up to 6%, which is especially noticeable in the northern regions of the Iya Basin. Comparison with in situ station data demonstrates a considerable improvement in correlation between SM after assimilation (0.94) and before assimilation (0.84). The findings also reveal a significant relationship between SM and surface energy balance.publishedVersio

Implementation of surface soil moisture data assimilation with watershed scale distributed hydrological model

This paper aims to investigate how surface soil moisture data assimilation affects each hydrologic process and how spatially varying inputs affect the potential capability of surface soil moisture assimilation at the watershed scale. The Ensemble Kalman Filter (EnKF) is coupled with a watershed scale, semi-distributed hydrologic model, the Soil and Water Assessment Tool (SWAT), to assimilate surface (5 cm) soil moisture. By intentionally setting inaccurate precipitation with open loop and EnKF scenarios in a synthetic experiment, the capability of surface soil moisture assimilation to compensate for the precipitation errors were examined. Results show that daily assimilation of surface soil moisture for each HRU improves model predictions especially reducing errors in surface and profile soil moisture estimation. Almost all hydrological processes associated with soil moisture are also improved with decreased root mean square error (RMSE) values through the EnKF scenario. The EnKF does not produce as much a significant improvement in streamflow predictions as compared to soil moisture estimates in the presence of large precipitation errors and the limitations of the infiltration–runoff model mechanism. Distributed errors of the soil water content also show the benefit of surface soil moisture assimilation and the influences of spatially varying inputs such as soil and landuse types. Thus, soil moisture update through data assimilation can be a supplementary way to overcome the errors created by inaccurate rainfall. Even though this synthetic study shows the potential of remotely sensed surface soil moisture measurements for applications of watershed scale water resources management, future studies are necessary that focus on the use of real-time observational data

Defining a Trade-Off Between Spatial and Temporal Resolution of a Geosynchronous SAR mission for Soil Moisture Monitoring

The next generation of synthetic aperture radar (SAR) systems could foresee satellite missions based on a geosynchronous orbit (GEO SAR). These systems are able to provide radar images with an unprecedented combination of spatial ( 641 km) and temporal ( 6412 h) resolutions. This paper investigates the GEO SAR potentialities for soil moisture (SM) mapping finalized to hydrological applications, and defines the best compromise, in terms of image spatio-temporal resolution, for SM monitoring. A synthetic soil moisture\u2013data assimilation (SM-DA) experiment was thus set up to evaluate the impact of the hydrological assimilation of different GEO SAR-like SM products, characterized by diverse spatio-temporal resolutions. The experiment was also designed to understand if GEO SAR-like SM maps could provide an added value with respect to SM products retrieved from SAR images acquired from satellites flying on a quasi-polar orbit, like Sentinel-1 (POLAR SAR). Findings showed that GEO SAR systems provide a valuable contribution for hydrological applications, especially if the possibility to generate many sub-daily observations is sacrificed in favor of higher spatial resolution. In the experiment, it was found that the assimilation of two GEO SAR-like observations a day, with a spatial resolution of 100 m, maximized the performances of the hydrological predictions, for both streamflow and SM state forecasts. Such improvements of the model performances were found to be 45% higher than the ones obtained by assimilating POLAR SAR-like SM maps