Advancements in mesoscale ensemble prediction strategies: Application to Mediterranean high-impact weather

[cat] La predictibilitat d'esdeveniments d'alt impacte a la regi o Mediterr ania ha millorat substancialment al llarg de les darreres d ecades. No obstant aix o, una representaci o precisa d'aspectes dels sistemes convectius rellevants per la societat, tals com el moment en qu e es produeixen, i la seva localitzaci o i intensitat encara suposen un repte. Aquestes febleses de la predicci o a escala convectiva provenen d'imprecisions a l'estimaci o de l'estat atmosf eric inicial, la formulaci o de processos f sics rellevants i la natura ca otica dels sistema associada a la seva no linealitat. En el marc probabil stic imposat per les incerteses intr nseques implicades en la predicci o num erica del temps, l'entitat matem atica que quanti ca la incertesa en l'estat atmosf eric es la funci o densitat de probabilitat. Malgrat aix o, el c alcul de la seva evoluci o temporal es inviable per situacions realistes amb els recursos computacionals disponibles actualment. La modesta aproximaci o habitual per estimar aquesta evoluci o es l' us d'un discret i petit nombre de mostres de l'estat del sistema, que es coneix com a predicci o per conjunts (ensemble forecasting). L'objectiu general d'aquesta Tesi es entendre millor els l mits de la predictibilitat i contribuir a una millora de la predicci o de temps sever a la regi o Mediterr ania. En primer lloc, s'avalua l'evoluci o temporal de les funcions densitat de probabilitat per sistemes de baixa complexitat amb un cert grau de realisme adoptant el formalisme de Liouville. En segon lloc, es dissenya una estrat egia de mostreig per crear pertorbacions a les condicions inicials per abastos de predicci o curts (24-36 h). La t ecnica es basa en el m etode de breeding, que utilitza la din amica completa no lineal per identi car modes de creixement r apid. La modi caci o proposada est a dirigida a ajustar l'escala de les pertorbacions per tal de cobrir l'ample rang d'escales rellevants per la predicci o de curt abast. En tercer lloc, s'investiga el potencial de varis m etodes per tenir en compte la incertesa en el model per a un episodi recent de precipitacions intenses i inundacions que va oc orrer al llarg de la costa Mediterr ania espanyola (12-13 setembre de 2019). S'avaluen m ultiples estrat egies estoc astiques en front l'aproximaci o ordin aria de multif sica en termes de diversitat i habilitat de l'ensemble. Les t ecniques considerades inclouen pertorbacions estoc astiques a les tend encies f siques i pertorbacions a par ametres in uents de l'esquema de microf sica. Finalment, aquestes estrat egies de generaci o d'ensembles s'utilitzen com a for cament meteorol ogic per a un model hidrol ogic per tal d'investigar la predictibilitat 21 22 CONTENTS hidrometeorol ogica de l'episodi del 12-13 setembre de 2019. Les t ecniques desenvolupades, juntament amb l'assimilaci o de dades mitjan cant Ensemble Kalman Filter es comparen amb altres estrat egies populars, tals com el downscaling d'un model global i l'aproximaci o de multif sica. Els resultats d'aquesta Tesi s on rellevants des d'una perspectiva te orica, ja que la soluci o de l'equaci o de Liouville revela estructures complexes per la funci o densitat de probabilitat que podrien comprometre les hip otesis de compacitat i suavitat assumides per la majoria d'eines d'interpretaci o i post proc es d'ensembles. Per altra banda, les estrat egies de generaci o d'ensembles desenvolupades mostren potencial per millorar la predicci o d'esdeveniments d'alt impacte, que es demostra per una major diversitat i habilitat dels ensembles comparades amb les estrat egies de refer encia. Aquests resultats prometedors posen les bases per un sistema avan cat d'alertes a la regi o Mediterr ania per encarar els esdeveniments de temps sever.[spa] La predictibilidad de eventos de alto impacto en la regi on Mediterr anea ha mejorado sustancialmente a lo largo de las ultimas d ecadas. No obstante, una representaci on precisa de aspectos relevantes de los sistemas convectivos relevantes para la sociedad, como el momento en el que se producen, su localizaci on e intensidad a un suponen un reto. Estas debilidades de la predicci on a escala convectiva provienen de imprecisiones en la estimaci on del estado atmosf erico inicial, la formulaci on de los procesos f sicos relevantes y la naturaleza ca otica del sistema asociada a su no linealidad. En el marco probabilista impuesto por las incertidumbres intr nsecas implicadas en la predicci on num erica del tiempo, la entidad matem atica que cuanti ca la incertidumbre en el estado atmosf erico inicial es la funci on densidad de probabilidad. Sin embargo, el c alculo de su evoluci on temporal es inviable para situaciones realistas con los recursos computacionales disponibles actualmente. La modesta aproximaci on habitual para estimar esta evoluci on en el uso de un discreto y peque~no n umero de muestras del estado del sistema, lo que se conoce como predicci on por conjuntos (ensemble forecasting). El objetivo general de esta Tesis es entender mejor los l mites de la predictibilidad y contribuir a una mejora de la predicci on del tiempo severo en la regi on Mediterr anea. En primer lugar, se eval ua la evoluci on temporal de las funciones densidad de probabilidad para sistemas de baja complejidad con un cierto grado de realismo adoptando el formalismo te orico de Liouville. En segundo lugar, se dise~na una estrategia de muestreo para crear perturbaciones en les condiciones iniciales para alcances de predicci on cortos (24-36 h). La t ecnica se basa en el m etodo de breeding, que utiliza la din amica completa no lineal para identi car modos de crecimiento r apido. La modi caci on propuesta est a dirigida a ajustar la escala de las perturbaciones para cubrir el amplio rango de escalas relevantes para la predicci on de corto alcance. En tercer lugar, se investiga el potencial de varios m etodos para tener en cuenta la incertidumbre en el modelo para un episodio reciente de precipitaciones intensas e inundaciones que ocurri o a lo largo de la costa Mediterr anea espa~nola (12-13 de septiembre de 2019). Se eval uan m ultiples estrategias estoc asticas frente a la aproximaci on ordinaria de multif sica en t erminos de diversidad y habilidad del ensemble. Las t ecnicas consideradas incluyen perturbaciones estoc asticas en las tendencias f sicas y perturbaciones en par ametros in uyentes del esquema de microf sica. Finalmente, estas estrategias de generaci on de ensembles se usan como forzamiento meteorol ogico para un modelo hidrol ogico con el n de investigar la predictibilidad hidrometeorol ogica del episodio del 12-13 de septiembre de 2019. Las t ecnicas desarrolladas, junto a la asimilaci on de datos mediante Ensemble Kalman Filter se comparan con otras estrategias populares, como el dowscaling de un modelo global y la aproximaci on de multif sica. Los resultados de esta Tesis son relevantes desde una perspectiva te orica, ya que la soluci on de la ecuaci on de Liouville revela estructuras complejas para la funci on densidad de probabilidad que podr an comprometer las hip otesis de compacidad y suavidad asumidas por la mayor a de herramientas de interpretaci on y pos proceso de ensembles. Por otro lado, las estrategias de generaci on de ensembles desarrolladas muestran potencial para mejorar la predicci on de eventos de alto impacto, que se demuestra por una mayor diversidad y habilidad de los ensembles comparadas con las estrategias de referencia. Estos resultados prometedores sientan las bases para un sistema avanzado de alertas en la regi on Mediterr anea para afrontar los eventos de tiempo severo.[eng] The predictability of meteorological high-impact events in the Mediterranean region has substantially improved over the last decades. Nevertheless, a precise representation of socially relevant aspects of convective systems, such as their timing, location, and intensity is still challenging. These weaknesses of convective-scale forecasting stem from inaccuracies in the estimation of the atmospheric initial state, formulation of relevant physical processes, and the chaotic nature of the system associated with its nonlinearity. In the probabilistic framework imposed by the intrinsic uncertainties involved in numerical weather prediction, the mathematical entity that quanti es the uncertainty in the atmospheric state is the probability density function. However, the computation of its time evolution is unfeasible for realistic situations with the current available computational resources. The usual modest approach to estimate this evolution is the use of a discrete and small number of samples of the state of the system, which is known as ensemble forecasting. The general aim of this Thesis is to better understand the predictability limits and contribute towards the improvement of severe weather forecasting in the Mediterranean region. Firstly, the time evolution of probability density functions for low complexity systems with a certain degree of realism is evaluated by adopting the Liouville formalism. Secondly, a sampling strategy to create initial condition perturbations for the short-range (24-36 h) is designed. The technique is based on the breeding method, which uses the full nonlinear dynamics to identify fast-growing modes. The proposed modi cation is aimed at tailoring the scale of the perturbations in order to cover the wide range of scales relevant for short-range forecasting. Thirdly, the potential of several methods to account for model uncertainty is investigated for a recent heavy precipitation and ash ood episode occurred along the Spanish Mediterranean coast (12-13 September 2019). Multiple stochastic strategies are evaluated against the ordinary multiphysics approach in terms of ensemble diversity and skill. The considered techniques include stochastically perturbed physics tendencies and perturbations to in uential parameters within the microphysics scheme. Finally, these ensemble generation strategies are used as the meteorological forcing for a hydrological model in order to investigate the hydrometeorological predictability of the 12-13 September 2019 episode. The developed techniques, along with data assimilation by means of Ensemble Kalman Filter are compared to other popular strategies, such as the downscaling from a global model and the multiphysics approach. The results of this Thesis are relevant from a theoretical perspective, as the solution of the Liouville equation reveals complex structures for the probability density function that could compromise the hypothesis of compactness and smoothness assumed by most current ensemble interpretation and postprocessing tools. Conversely, the ensemble generation strategies developed show potential to improve the forecasting of high-impact events, proven by higher ensemble diversity and skill compared to the benchmark strategies. These encouraging results lay the foundations for an advanced warning system in the Mediterranean region to deal with severe weather events

Development of integrated approaches for hydrological data assimilation through combination of ensemble Kalman filter and particle filter methods

Natural Science Foundation of China; National Key Research and Development Plan; Natural Sciences and Engineering Research Council of Canada

Methods for assimilating remotelysensed water storage changes into hydrological models

Understanding physical processes within the water cycle is a challenging issue that requires merging information from various disciplines. The Gravity Recovery And Climate Experiment (GRACE) mission provides a unique opportunity to measure time-variable gravity fields, which can be converted to global total water storage anomalies (TWSA). These observations represent a vertical integral of all individual water compartments, which is difficult to observe by in-situ or other remote-sensing techniques. Knowledge about interactions between hydrological fluxes and terrestrial water storage compartments is reflected in large-scale hydrological models that nowadays increase in complexity to simulate all relevant physical processes within the global water cycle. Hydrological models are driven by climate forcing fields and their parameters are usually calibrated against river discharge to ensure a realistic water balance on river basin scale. However, errors in climate forcing fields, model parameters, and model structure limit the reliability of hydrological models. Therefore, it is necessary to improve model simulations by introducing measurements, which is known as data assimilation or data-model fusion. In this thesis, a novel calibration and data assimilation (C/DA) framework is developed to merge remotely-sensed large scale TWSA with hydrological models. To implement this framework, the WaterGAP Global Hydrology Model (WGHM) is chosen, which is a sophisticated 0.5°x0.5° conceptual model that simulates daily water changes in surface and sub-surface water compartments (including groundwater), and considers water consumption. In particular, a flexible approach is introduced to assimilate GRACE TWSA as (sub-)basin or gridded averages into WGHM, while for the first time, implementing the observation error correlations in the C/DA system. A sensitivity analysis is performed to identify significant parameters in the largest river basins world-wide. It is also investigated whether GRACE TWSA can be used to calibrate model parameters. To reduce sampling errors and to improve the computational efficiency, the classical ensemble Kalman filter (EnKF) technique is extended to a square root analysis (SQRA) scheme, and the singular evolutive interpolated Kalman (SEIK) filter. The relationships between these algorithms are addressed. A simple model and WGHM are used to describe the mathematical details of the data assimilation techniques. The observation error model, spatial resolution of observations, choice of filtering algorithm, and model ensemble size are assessed within a realistic synthetic experiment designed for the Mississippi River Basin, USA. Real GRACE products are also integrated into WGHM over this region. Investigations indicate that introducing GRACE TWSA constrains the water balance equation and corrects for insufficiently known climate forcing, in particular precipitation. Individual water states and fluxes are also adjusted but more improvements are expected by assimilating further in-situ and remotely-sensed observations. The processing choices represent important impacts on the final results. The C/DA framework is transferred to the Murray-Darling River Basin, Australia, to improve the simulation of hydrological changes under a long-term drought condition. GRACE C/DA introduces a negative trend to WGHM simulated TWSA. A validation with in-situ groundwater measurements indicates that the trend is correctly associated with the groundwater compartment. Thus, the C/DA helps to identify deficits in model simulations and improves the understanding of hydrological processes. The promising results provide a first step towards more complex C/DA applications on global scale and in conjunction with further terrestrial water storage observations.Methoden zur Assimilierung von satelliten-basierten Wasserspeicheränderungen in hydrologische Modelle Zum Verständnis der physikalischen Prozesse des Wasserkreislaufes ist das Zusammenführen von Kenntnissen verschiedener Disziplinen erforderlich. Die Messungen zeitabhängiger Gravitationsfelder der Gravity Recovery And Climate Experiment (GRACE) Satellitenmission liefern einzigartige Erkenntnisse über globale Gesamtwasserspeicher-änderungen (GWSA). Diese Größe repräsentiert die Summe aller einzelnen Wasserspeicherkomponenten, welche nur unzulänglich durch lokale oder andere satellitengestützte Verfahren beobachtet werden kann. Großskalige hydrologische Modelle simulieren Interaktionen zwischen terrestrischen Wasserspeicherkomponenten. Ihre Komplexität steigt heutzutage immer weiter, um alle relevanten physikalischen Prozesse im globalen Wasserkreislauf abzubilden. Sie werden durch Klimadaten angetrieben und durch Modellparameter gesteuert. Zur Gewährleistung einer realistischen Wasserbilanz in Flusseinzugsgebieten werden letztere üblicherweise gegen Durchflussmessungen kalibriert. Dennoch limitieren Unsicherheiten in den Klimadaten, in den Modellparametern und in der Modellstruktur die Zuverlässigkeit hydrologischer Prädiktionen. Um Simulationen zu verbessern ist daher die Integration von Beobachtungsdaten notwendig, welches unter dem Begriff der Datenassimilierung bekannt ist. In dieser Arbeit wird ein neuer Kalibrierungs- und Datenassimilierungsansatz (K/DA) zur Kombination von großskalig beobachteten GWSA und hydrologischen Modellen am Beispiel des WaterGAP Global Hydrology Model (WGHM) entwickelt. WGHM ist ein konzeptionelles Wasserbilanzmodell, das tägliche Wasseränderungen auf und im Boden (inklusive Grundwasser) auf einer räumlichen Skala von 0,5°x0,5° berechnet und anthropogene Wasserentnahmen berücksichtigt. Insbesondere wird ein flexibler Ansatz zur Integration gegitterter und räumlich gemittelter GWSA eingeführt, während die Korrelationen der Beobachtungsfehler zum ersten Mal in der Assimilierung berücksichtigt werden. Eine Sensitivitätsanalyse identifiziert maßgebliche Parameter für die weltweit größten Flusseinzugsgebiete. Es wird außerdem untersucht, ob GRACE-GWSA zur Parameter-kalibrierung herangezogen werden können. Um Stichprobenfehler zu reduzieren und um die rechnerische Effizienz zu steigern, wird die klassische Ensemble Kalman Filter (EnKF) Methode um das Square Root Analysis (SQRA) Schema und den Singular Evolutive Interpolated Kalman (SEIK) Filter erweitert. Die Zusammenhänge dieser Algorithmen werden dargestellt. Die mathematischen Details der Methoden werden anhand eines einfachen Modells und des WGHM beschrieben. Das Modell der Beobachtungsfehler, die Auflösung der Beobachtungen, die Auswahl der Filteralgorithmen und die Größe des Modellensembles werden in einem realistischen synthetischen Experiment für das Flusseinzugsgebiet des Mississippis (USA) analysiert. GRACE-GWSA werden ebenfalls für dieser Region in das WGHM integriert. Untersuchungen zeigen, dass die Wasserbilanz an die Daten angepasst wird und ungenaue Klimadaten, insbesondere Niederschlag, ausgeglichen werden. Wasserspeicher-komponenten werden ebenfalls angepasst, würden aber durch die Assimilierung weiterer lokaler und satellitengestützter Daten profitieren. Der K/DA Ansatz hat einen entscheidenden Einfluss auf die Ergebnisse. Der entwickelte Ansatz wird auf das Einzugsgebiet des Murray und Darling Flusses (Australien) übertragen, um die Simulation hydrologischer Änderungen während einer Trockenperiode zu verbessern. GRACE-K/DA führt einen negativen Trend in das Modell ein. Die Validierung mit lokalen Grundwasserdaten bestätigt, dass der Trend korrekt mit dem Grundwasserspeicher assoziiert wird. Die K/DA ermöglicht somit Defizite in Modellsimulationen zu identifizieren und verbessert das Verständnis hydrologischer Prozesse. Die vielversprechenden Ergebnisse bereiten einen ersten Schritt in Richtung globaler K/DA in Verbindung mit weiteren hydrologischen Beobachtungen

Ecohydrological Footprints:Quantitative Response of Ecosystems to Changes in their Hydrological Drivers

Ecohydrological footprints are defined as the response of ecosystem functions or services to changes in their hydrologic drivers. In this thesis, several diverse footprints are addressed: noise-driven effects on storage-discharge relations and catchment streamflow distributions, that are important drivers of biodiversity; soil salinization and its ecohydrological implications; topological effects of the ecological interaction networks on living communities (e.g. on their species persistence); and form and function of the global virtual water trade network. The coherence of the conceptual framework is provided by the study of drivers and controls of ecohydrological variability using methodological approaches based on statistical mechanics. In fact, this thesis work outlines a significant portion of environmental statistical mechanics, an overarching discipline that is emerging in recent years, which applies mathematical tools from statistical mechanics to model several ecohydrological processes. The proposed relevance of this thesis lies in the major effects of hydrologic drivers on ecological process. The view that emerges from current research in ecohydrology, that this thesis supports, is that there exists a definite need for an integrated understanding of ecological and hydrological processes. Because stochasticity is intrinsic to environmental and ecohydrological variability, noise plays an important and constructive role in ecohydrological processes. In this thesis, a stochastic approach is applied to analyze different ecohydrological processes, ranging from green and blue water flows in river basins (part I), ecosystem dynamics affected by the directional dispersal provided by river networks (part II) to water footprints of human society (part III).Methods range from novel exact solutions to stochastic differential equations to random graph theory applications, and imply the analysis of suitable field data. An analytical framework for quantitative analysis is laid out to tackle complex problems and to estimate the effects of environmental change on the interaction of the hydrologic processes with the biota. The main results of this thesis are: i) the achievement of exact solutions for the probability distribution of catchment streamflow, that takes in account stochastic fluctuations in the storage-discharge relation and for the condition of a noise induced phenomena to the streamflows regimes; ii) the stationary solutions of soil salinity under stochastic hydrologic forcing; iii) a novel solution of the Ito-Stratonovich problem in multiplicative Poisson processes; iv) the proper framework for species' persistence time distributions, as a function of topological constraints on the ecosystem, and its connection with other important macroecological laws. A related length-bias sampling problem is also solved. v) A statistical analysis of the global virtual trade network and a semi-analytical model that is able to describe most of the observed properties

Nonlinear dynamics of water and energy balance in land-atmosphere interaction

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 1995.Includes bibliographical references (leaves 141-147).by Kaye Lorraine.Ph.D

Development of a copula-based particle filter (CopPF) approach for hydrologic data assimilation under consideration of parameter interdependence

National Natural Science Foundation of China, the National Key Research and Development Plan, and the Natural Sciences and Engineering Research Council of Canad

Fecal Coliform Release Studies and Development of a Preliminary Nonpoint Source Transport Model for Indicator Bacteria

The effect of grazing on water quality has been documented by bacteriological studies of streams adjacent to grazed areas. Bacterial release from fecal deposits is a parameter of the pollution transport mechanism that is poorly understood. The objective of this study was to determine a fecal coliform release function for cattle fecal deposits. Standard cowpies were rained on with a rainfall simulator, and the fecal coliform counts were determined using the most probably number (MPN) method of enumeration. The fecal deposits were rained on at ages 2 through 100 days. The effects of rainfall intensity and recurrent rainfall were tested. Naturally occurring fecal deposits were also tested to compare their results with the results from the standard cowpies. A log-log regression was found to describe the decline in peak fecal coliform release with fecal deposit age. The 100-day-old fecal deposits produced peak counts of 4,200 fecal coliforms per 100 milliliters of water. This quantity of release is insignificant compared to the release from fresher fecal material. Rainfall intensity had little effect on peak fecal coliform release from fecal deposits that ere 2 or 10 days old. At age 20 days the effect of rainfall intensity was significant; the highest intensity gave the lowest peak counts, and the lowest intensity gave the highest peak counts. The effect of rainfall intensity appears to be related to the dryness of the fecal deposits. Peak fecal coliform counts were significantly lowered by raining on the fecal deposits more than once. This decline was thought to be produced by the loss of bacteria from the fecal deposits during the previous wettings. Standard cowpies produced a peak release regression that was not significantly different from the regression for the natural fecal deposits. Apparently, grossly manipulating the fecal deposits did not significantly change the release patterns

Assimilation of Remotely Sensed Soil Moisture in the MESH Model

Soil moisture information is critically important to weather, climate, and hydrology forecasts since the wetness of the land strongly affects the partitioning of energy and water at the land surface. Spatially distributed soil moisture information, especially at regional, continental, and global scales, is difficult to obtain from ground-based (in situ) measurements, which are typically based upon sparse point sources in practice. Satellite microwave remote sensing can provide large-scale monitoring of surface soil moisture because microwave measurements respond to changes in the surface soil’s dielectric properties, which are strongly controlled by soil water content. With recent advances in satellite microwave soil moisture estimation, in particular the launch of the Soil Moisture and Ocean Salinity (SMOS) satellite and the Soil Moisture Active Passive (SMAP) mission, there is an increased demand for exploiting the potential of satellite microwave soil moisture observations to improve the predictive capability of hydrologic and land surface models. In this work, an Ensemble Kalman Filter (EnKF) scheme is designed for assimilating satellite soil moisture into a land surface-hydrological model, Environment Canada’s standalone MESH to improve simulations of soil moisture. After validating the established assimilation scheme through an observing system simulation experiment (synthetic experiment), this study explores for the first time the assimilation of soil moisture retrievals, derived from SMOS, the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) and the Advanced Microwave Scanning Radiometer 2 (AMSR2), in the MESH model over the Great Lakes basin. A priori rescaling on satellite retrievals (separately for each sensor) is performed by matching their cumulative distribution function (CDF) to the model surface soil moisture’s CDF, in order to reduce the satellite-model bias (systematic error) in the assimilation system that is based upon the hypothesis of unbiased errors in model and observation. The satellite retrievals, the open-loop model soil moisture (no assimilation) and the assimilation estimates are, respectively, validated against point-scale in situ soil moisture measurements in terms of the daily-spaced time series correlation coefficient (skill R). Results show that assimilating either L-band retrievals (SMOS) or X-band retrievals (AMSR-E/AMSR2) can favorably influence the model soil moisture skill for both surface and root zone soil layers except for the cases with a small observation (retrieval) skill and a large open-loop skill. The skill improvement ΔRA-M, defined as the skill for the assimilation soil moisture product minus the skill for the open-loop estimates, typically increases with the retrieval skill and decreases with increased open-loop skill, showing a strong dependence upon ΔRS-M, defined as the retrieval skill minus the model (open-loop) surface soil moisture skill. The SMOS assimilation reveals that the cropped areas typically experience large ΔRA-M, consistent with a high satellite observation skill and a low open-loop skill, while ΔRA-M is usually weak or even negative for the forest-dominated grids due to the presence of a low retrieval skill and a high open-loop skill. The assimilation of L-band retrievals (SMOS) typically results in greater ΔRA-M than the assimilation of X-band products (AMSR-E/AMSR2), although the sensitivity of the assimilation to the satellite retrieval capability may become progressively weaker as the open-loop skill increases. The joint assimilation of L-band and X-band retrievals does not necessarily yield the best skill improvement. As compared to previous studies, the primary contributions of this thesis are as follows. (i) This work examined the potential of latest satellite soil moisture products (SMOS and AMSR2), through data assimilation, to improve soil moisture model estimates. (ii) This work, by taking advantage of the ability of SMOS to estimate surface soil moisture underneath different vegetation types, revealed the vegetation cover modulation of satellite soil moisture assimilation. (iii) The assimilation of L-band retrievals (SMOS) was compared with the assimilation of X-band retrievals (AMSR-E/AMSR2), providing new insight into the dependence of the assimilation upon satellite retrieval capability. (iv) The influence of satellite-model skill difference ΔRS-M on skill improvement ΔRA-M was consistently demonstrated through assimilating soil moisture retrievals derived from radiometers operating at different microwave frequencies, different vegetation cover types, and different retrieval algorithms

Multi-sensor large scale land surface data assimilation using ensemble approaches

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2006.Includes bibliographical references (p. 223-234).One of the ensemble Kalman filter's (EnKF) attractive features in land surface applications is its ability to provide distributional information. The EnKF relies on normality approximations that improve its efficiency but can also compromise the accuracy of its distributional estimates. The effects of these approximations are evaluated by comparing the conditional marginal distributions and moments estimated by the EnKF to those obtained from an SIR particle filter, which gives exact solutions for large ensemble sizes. The results show that overall the EnKF appears to provide a good approximation for nonlinear, non-normal land surface problems. A difficulty in land data assimilation problems results from the high dimensionality of states created by spatial discretization over large computational grids. The high dimensionality can be reduced by exploiting the fact that soil moisture field may have significant spatial correlation structure especially after extensive rainfall while it may have local structure determined by soil and vegetation variability after prolonged drydown. This is confirmed by SVD of the replicate matrix produced in an ensemble forecasting experiment. Local EnKF's are suitable for problems during dry periods but give less accurate results after rainfall.(cont.) The most promising option is to develop a generalized method that reflects structural changes in the ensemble. A highly efficient ensemble multiscale filter (EnMSF) is then proposed to solve large scale nonlinear estimation problems with arbitrary uncertainties. At each prediction step realizations of the state variables are propagated. At update times, joint Gaussian distribution of states and measurements are assumed and the Predictive Efficiency method is used to identify a multiscale tree to approximate statistics of the propagated ensemble. Then a two-sweep update is performed to estimate the state variables using all the data. By controlling the tree parameters, the EnMSF can reduce sampling error while keep long range correlation in the ensemble. Applications of the EnMSF to Navier-Stokes equation and a nonlinear diffusion problem are demonstrated. Finally, the EnMSF is successfully applied to soil moisture and surface fluxes estimation over the Great Plains using synthetic multiresolution L-band passive and active microwave soil moisture measurements following HYDROS specifications.by Yuhua Zhou.Ph.D