Multivariate data assimilation in snow modelling at Alpine sites

The knowledge of snowpack dynamics is of critical importance to several real-time applications such as agricultural production, water resource management, flood prevention, hydropower generation, especially in mountain basins. Snowpack state can be estimated by models or from observations, even though both these sources of information are affected by several errors

ESTIMATING LAND SURFACE ALBEDO FROM SATELLITE DATA

Land surface albedo, defined as the ratio of the surface reflected incoming and outgoing solar radiation, is one of the key geophysical variables controlling the surface radiation budget. Surface shortwave albedo is widely used to drive climate and hydrological models. During the last several decades, remotely sensed surface albedo products have been generated through satellite-acquired data. However, some problems exist in those products due to instrument measurement inaccuracies and the failure of current retrieving procedures, which have limited their applications. More significantly, it has been reported that some albedo products from different satellite sensors do not agree with each other and some even show the opposite long term trend regionally and globally. The emergence of some advanced sensors newly launched or planned in the near future will provide better capabilities for estimating land surface albedo with fine resolution spatially and/or temporally. Traditional methods for estimating the surface shortwave albedo from satellite data include three steps: first, the satellite observations are converted to surface directional reflectance using the atmospheric correction algorithms; second, the surface bidirectional reflectance distribution function (BRDF) models are inverted through the fitting of the surface reflectance composites; finally, the shortwave albedo is calculated from the BRDF through the angular and spectral integration. However, some problems exist in these algorithms, including: 1) "dark-object" based atmospheric correction methods which make it difficult to estimate albedo accurately over non-vegetated or sparsely vegetated area; 2) the long-time composite albedo products cannot satisfy the needs of weather forecasting or land surface modeling when rapid changes such as snow fall/melt, forest fire/clear-cut and crop harvesting occur; 3) the diurnal albedo signature cannot be estimated in the current algorithms due to the Lambertian approximation in some of the atmospheric correction algorithms; 4) prior knowledge has not been effectively incorporated in the current algorithms; and 5) current observation accumulation methods make it difficult to obtain sufficient observations when persistent clouds exist within the accumulation window. To address those issues and to improve the satellite surface albedo estimations, a method using an atmospheric radiative transfer procedure with surface bidirectional reflectance modeling will be applied to simultaneously retrieve land surface albedo and instantaneous aerosol optical depth (AOD). This study consists of three major components. The first focuses on the atmospheric radiative transfer procedure with surface reflectance modeling. Instead of executing atmospheric correction first and then fitting surface reflectance in the previous satellite albedo retrieving procedure, the atmospheric properties (e.g., AOD) and surface properties (e.g., BRDF) are estimated simultaneously to reduce the uncertainties produced in separating the entire radiative transfer process. Data from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra and Aqua are used to evaluate the performance of this albedo estimation algorithm. Good agreement is reached between the albedo estimates from the proposed algorithm and other validation datasets. The second part is to assess the effectiveness of the proposed algorithm, analyze the error sources, and further apply the algorithm on geostationary satellite - the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard Meteosat Second Generation (MSG). Extensive validations on surface albedo estimations from MSG/SEVIRI observations are conducted based on the comparison with ground measurements and other satellite products. Diurnal changes and day-to-day changes in surface albedo are accurately captured by the proposed algorithm. The third part of this study is to develop a spatially and temporally complete, continuous, and consistent albedo maps through a data fusion method. Since the prior information (or climatology) of albedo/BRDF plays a vital role in controlling the retrieving accuracy in the optimization method, currently available multiple land surface albedo products will be integrated using the Multi-resolution Tree (MRT) models to mitigate problems such as data gaps, systematic bias or low information-noise ratio due to instrument failure, persistent clouds from the viewing direction and algorithm limitations. The major original contributions of this study are as follows: 1) this is the first algorithm for the simultaneous estimations of surface albedo/reflectance and instantaneous AOD by using the atmospheric radiative transfer with surface BRDF modeling for both polar-orbiting and geostationary satellite data; 2) a radiative transfer with surface BRDF models is used to derive surface albedo and directional reflectance from MODIS and SEVIRI observations respectively; 3) extensive validations are made on the comparison between the albedo and AOD retrievals, and the satellite products from other sensors; 4) the slightly modified algorithm has been adopted to be the operational algorithm of Advanced Baseline Imager (ABI) in the future Geostationary Operational Environmental Satellite-R Series (GOES-R) program for estimating land surface albedo; 5) a framework of using MRT is designed to integrate multiple satellite albedo products at different spatial scales to build the spatially and temporally complete, continuous, and consistent albedo maps as the prior knowledge in the retrieving procedure

Désagrégation de l'humidité du sol issue des produits satellitaires micro-ondes passives et exploration de son utilisation pour l'amélioration de la modélisation et la prévision hydrologique

De plus en plus de produits satellitaires en micro-ondes passives sont disponibles. Cependant, leur large résolution spatiale (25-50 km) n’en font pas un outil adéquat pour des applications hydrologiques à une échelle locale telles que la modélisation et la prévision hydrologiques. Dans de nombreuses études, une désagrégation d’échelle de l’humidité du sol des produits satellites micro-ondes est faite puis validée avec des mesures in-situ. Toutefois, l’utilisation de ces données issues d’une désagrégation d’échelle n’a pas encore été pleinement étudiée pour des applications en hydrologie. Ainsi, l’objectif de cette thèse est de proposer une méthode de désagrégation d’échelle de l’humidité du sol issue de données satellitaires en micro-ondes passives (Satellite Passive Microwave Active and Passive - SMAP) à différentes résolutions spatiales afin d’évaluer leur apport sur l’amélioration potentielle des modélisations et prévisions hydrologiques. À partir d’un modèle de forêt aléatoire, une désagrégation d’échelle de l’humidité du sol de SMAP l’amène de 36-km de résolution initialement à des produits finaux à 9-, 3- et 1-km de résolution. Les prédicteurs utilisés sont à haute résolution spatiale et de sources différentes telles que Sentinel-1A, MODIS et SRTM. L'humidité du sol issue de cette désagrégation d’échelle est ensuite assimilée dans un modèle hydrologique distribué à base physique pour tenter d’améliorer les sorties de débit. Ces expériences sont menées sur les bassins versants des rivières Susquehanna (de grande taille) et Upper-Susquehanna (en comparaison de petite taille), tous deux situés aux États-Unis. De plus, le modèle assimile aussi des données d’humidité du sol en profondeur issue d’une extrapolation verticale des données SMAP. Par ailleurs, les données d’humidité du sol SMAP et les mesures in-situ sont combinées par la technique de fusion conditionnelle. Ce produit de fusion SMAP/in-situ est assimilé dans le modèle hydrologique pour tenter d’améliorer la prévision hydrologique sur le bassin versant Au Saumon situé au Québec. Les résultats montrent que l'utilisation de l’humidité du sol à fine résolution spatiale issue de la désagrégation d’échelle améliore la représentation de la variabilité spatiale de l’humidité du sol. En effet, le produit à 1- km de résolution fournit plus de détails que les produits à 3- et 9-km ou que le produit SMAP de base à 36-km de résolution. De même, l’utilisation du produit de fusion SMAP/ in-situ améliore la qualité et la représentation spatiale de l’humidité du sol. Sur le bassin versant Susquehanna, la modélisation hydrologique s’améliore avec l’assimilation du produit de désagrégation d’échelle à 9-km, sans avoir recours à des résolutions plus fines. En revanche, sur le bassin versant Upper-Susquehanna, c’est le produit avec la résolution spatiale la plus fine à 1- km qui offre les meilleurs résultats de modélisation hydrologique. L’assimilation de l’humidité du sol en profondeur issue de l’extrapolation verticale des données SMAP n’améliore que peu la qualité du modèle hydrologique. Par contre, l’assimilation du produit de fusion SMAP/in-situ sur le bassin versant Au Saumon améliore la qualité de la prévision du débit, même si celle-ci n’est pas très significative.Abstract: The availability of satellite passive microwave soil moisture is increasing, yet its spatial resolution (i.e., 25-50 km) is too coarse to use for local scale hydrological applications such as streamflow simulation and forecasting. Many studies have attempted to downscale satellite passive microwave soil moisture products for their validation with in-situ soil moisture measurements. However, their use for hydrological applications has not yet been fully explored. Thus, the objective of this thesis is to downscale the satellite passive microwave soil moisture (i.e., Satellite Microwave Active and Passive - SMAP) to a range of spatial resolutions and explore its value in improving streamflow simulation and forecasting. The random forest machine learning technique was used to downscale the SMAP soil moisture from 36-km to 9-, 3- and 1-km spatial resolutions. A combination of host of high-resolution predictors derived from different sources including Sentinel-1A, MODIS and SRTM were used for downscaling. The downscaled SMAP soil moisture was then assimilated into a physically-based distributed hydrological model for improving streamflow simulation for Susquehanna (larger in size) and Upper Susquehanna (relatively smaller in size) watersheds, located in the United States. In addition, the vertically extrapolated SMAP soil moisture was assimilated into the model. On the other hand, the SMAP and in-situ soil moisture were merged using the conditional merging technique and the merged SMAP/in-situ soil moisture was then assimilated into the model to improve streamflow forecast over the au Saumon watershed. The results show that the downscaling improved the spatial variability of soil moisture. Indeed, the 1-km downscaled SMAP soil moisture presented a higher spatial detail of soil moisture than the 3-, 9- or original resolution (36-km) SMAP product. Similarly, the merging of SMAP and in-situ soil moisture improved the accuracy as well as spatial representation soil moisture. Interestingly, the assimilation of the 9-km downscaled SMAP soil moisture significantly improved the accuracy of streamflow simulation for the Susquehanna watershed without the need of going to higher spatial resolution, whereas for the Upper Susquehanna watershed the 1-km downscaled SMAP showed better results than the coarser resolutions. The assimilation of vertically extrapolated SMAP soil moisture only slightly further improved the accuracy of the streamflow simulation. On the other hand, the assimilation of merged SMAP/in-situ soil moisture for the au Saumon watershed improved the accuracy of streamflow forecast, yet the improvement was not that significant. Overall, this study demonstrated the potential of satellite passive microwave soil moisture for streamflow simulation and forecasting

Fast 3D Inhomogeneous Radiative Transfer Model Incorporating Aspherical Frozen Hydrometeors with Application to Precipitation Locking

A horizontally inhomogeneous unified microwave radiative transfer (HI-UMRT) model incorporating aspherical frozen hydrometeors based on the NASA/GSFC OpenSSP database is presented to study 3-dimensional (3D) effects of horizontal inhomogeneous clouds on computed microwave radiances and facilitate satellite radiance assimilation over horizontally inhomogeneous weather conditions. HI-UMRT provides a coupled two-Stokes parameter numerical radiance solution of the 3D radiative transfer equation by embedding the existing 1D UMRT algorithm into an iterative perturbation scheme. The horizontal derivatives in radiances of lower perturbation order are treated as the source functions of the azimuthal harmonic perturbation radiative transfer equations that are readily solved by the planar-stratified 1D UMRT algorithm.The 1D UMRT algorithm requires symmetry of the transition matrix for the discretized planar-stratified radiative transfer equation to realize numerically stable and accurate matrix operations as required by the discrete-ordinate eigenanalysis method. In this thesis, the necessary block-diagonal structure of the full Stokes matrix for randomly oriented OpenSSP aspherical hydrometeors is shown to be maintained, albeit with small asymmetric deviations which introduce small asymmetric components into the transition matrix that are negligible for most passive microwave remote sensing applications. An upper bound of the brightness temperature error calculated by neglecting the asymmetric components of the transition matrix under even extreme atmospheric conditions is shown to be small. Hence the OpenSSP hydrometeor database can be reliably used within the UMRT model.Block-diagonal Stokes matrix elements along with other single-scattering parameters of OpenSSP hydrometeors were subsequently used in radiative simulations of multi-stream dual-polarization radiances for a simulated hurricane event to demonstrate the inherent numerical stability and utility of the extended 1D UMRT algorithm. An intercomparison of computed upwelling radiances for a multiphase distribution of aspherical OpenSSP hydrometeors versus a mass-equivalent Mie hydrometeor polydispersion for key sensing frequencies from 10 to 874 GHz shows the considerable impact of complex (versus simple spherical) hydrometeors on predicted microwave radiances.Further, a numerical performance assessment shows that the increase in computing time for the 3D HI-UMRT model relative to the 1D UMRT model is moderate since (i) the computationally efficient UMRT engine is applied only to the perturbation equations with non-trivial solutions, and (ii) the layer parameters for the 1D solution are reused for all higher perturbation orders. Numerical simulations using HI-UMRT based on 3D cloud profiles simulated by the WRF numerical weather model illustrate the convergence of the iterative perturbation series. An intercomparison of top-of-atmosphere brightness temperature images for HI-UMRT versus the planar-stratified UMRT model illustrates the considerable impact of cloud horizontal inhomogeneities on computed upwelling microwave radiances.The microwave radiances simulated using UMRT at 118 and 183 GHz based on the Orbital Micro Systems Inc. Global Earth Monitoring System (GEMS) CubeSat constellation concept have been used in an all-weather microwave data assimilation scheme to facilitate precipitation locking of hydrometeor state variables in severe weather. The capability of first frame precipitation locking can be achieved based on constrained extended Kalman filtering (XKF), statistical estimation of a flow-dependent background error covariance matrix, and appropriate update of state variables using nonlinear iterative method. Preliminary simulation results demonstrate the potential for assimilating both thermodynamic and hydrometeor variables in first-frame locking iterations

Atmospheric transport on Mars

The workshop covered topics such as: Atmospheric dynamics and circulation; Dust; Volatiles; Mars Observer and future spacecraft missions.sponsored by Lunar and Planetary Institute, ... [and others].edited by J.R. Barnes and R.M. HaberleHydrological consequences of ponded water on Mars / Baker, V.R. -- Morphologic and morphometric studies of impact craters in the northern plains of Mars / Barlow, N.G. -- Calderas produced by hydromagmatic eruptions through permafrost in northwest Alaska / Beget, J.E. -- The fate of water deposited in the low-lying northern plains / Carr, M.H. -- Evidence for an ice sheet/frozen lake in Utopia Planitia, Mars / Chapman, M.G

MODELLING WATER AND ENERGY BALANCE OF THE LAND-ATMOSPHERE SYSTEM USING HIGH RESOLUTION REMOTE SENSING DATA

La rilevanza assunta dal risparmio della risorsa idrica negli ultimi anni ha spinto verso una corretta quanti cazione delle perdite legate al processo evapotraspirativo, al ne di una gestione parsimoniosa della risorsa stessa. In particolare nei sistemi agricoli soggetti a stress severo, sia la misura che la stima dell'evapotraspirazione (ET) ad un'adeguata risoluzione spaziale e temporale sono uno dei principali problemi da a rontare per la comunit a scienti ca. Recentemente, le tecniche di telerilevamento sono divenute un ulteriore strumento a supporto della modellistica idrologica distribuita; in particolare, le immagini acquisite nelle onde corte e nell'infrarosso termico risultano essere di notevole interesse. In questo contesto, i due scopi principali di questa ricerca sono stati: la quanti cazione dell'accuratezza delle misure micro-meteorologiche in sistemi agricoli vegetati con colture alte e sparse; e l'analisi dei modelli basati su dati telerilevati per la stima di ET ad alta risoluzione spaziale e temporale. L'area di studio e caratterizzata da un tipico clima Mediterraneo e da colture olivicole, e si trova localizzata nei pressi di Castelvetrano (Italia). Quest'area e stata oggetto nella primavera-estate 2008 di una campagna di misura mediante istallazioni eddy covariance e scintillometrica, e, contestualmente, dall'acquisizione di 7 immagini multi-spettrali ad alta risoluzione. L'analisi delle misure micro-meteorologiche ha permesso di quanti care l'accordo tra le due tecniche e ha portato allo sviluppo di un nuovo approccio di calibrazione dei dati scintillometrici. Inoltre, alcune ipotesi alla base della stima dei ussi giornalieri sono state discusse in dettaglio. L'analisi degli algoritmi per la simulazione dei processi di scambio nel continuo suolo-pianta-atmosfera e stata focalizzata: i) sulle stime hot-spot di ET mediante un approccio di bilancio energetico residuale, ii) sulla stima in continuo di ET alla scala di campo mediante diversi approcci. Quest'ultima analisi ha evidenziato i buoni risultati del modello accoppiato energetico/idrologico per la stima dei ussi di acqua ed energia sia a scala oraria che giornaliera. In ne, l'applicabilit a di due approcci di data assimilation e stata testata utilizzando sia osservazioni arti ciali che reali.In view of the increased relevance of water saving issues in the last decades, the correct quanti cation of water loss due to evapotranspirative process became fundamental for a parsimonious management of this resource. Especially in agricultural systems subjected to severe water stress, both the measurement and the modelling of evapotranspiration (ET) at adequate temporal and spatial resolution, are important topics for the hydrologist scienti c community. Recently, the remote sensing techniques provide an additional tool to support the hydrologic spatially distributed models; in particular, images acquired in the short-wave and the thermal spectral regions have quite interesting applications. Within this framework, the two principal aims of this work were: to quantify the accuracy of surface energy uxes measured by micro-meteorological techniques in sparse tall vegetated system; and to analyze the capability of remote sensing-based approach to retrieve ET at high temporal and spatial resolution. The selected test site was an area characterized by Mediterranean climate and olive crops, located near Castelvetrano (Italy). This area, during the spring-summer period in 2008, was interested by in-situ measurements campaigns with eddy covariance and scintillometer instruments, and, contextually, by the acquisition of 7 high resolution multi-spectral images. The analysis of micro-meteorological measurements allows to evaluate the agreement between these techniques in the study site, also by means of a novel algorithm for the elaboration of scintillometer data. Moreover, some fundamental hypothesis of daily uxes estimation was critically discussed. The analysis of the algorithms for the simulation of the exchange processes in the continuum soil-plant-atmosphere was focused on: i) the retrieval of hot-spot ET maps by means of residual energy balance approach and ii) the continuous ET estimation at eld scale using di erent approaches. This latter analysis highlights the good performance of a coupled energy/hydrological model for the assessment of energy and water uxes at both hourly and daily scale. Finally, the applicability of two data assimilation schemes was tested using both arti cial and real observations

Statistically Optimized Inversion Algorithm for Enhanced Retrieval of Aerosol Properties from Spectral Multi-Angle Polarimetric Satellite Observations

The proposed development is an attempt to enhance aerosol retrieval by emphasizing statistical optimization in inversion of advanced satellite observations. This optimization concept improves retrieval accuracy relying on the knowledge of measurement error distribution. Efficient application of such optimization requires pronounced data redundancy (excess of the measurements number over number of unknowns) that is not common in satellite observations. The POLDER imager on board the PARASOL microsatellite registers spectral polarimetric characteristics of the reflected atmospheric radiation at up to 16 viewing directions over each observed pixel. The completeness of such observations is notably higher than for most currently operating passive satellite aerosol sensors. This provides an opportunity for profound utilization of statistical optimization principles in satellite data inversion. The proposed retrieval scheme is designed as statistically optimized multi-variable fitting of all available angular observations obtained by the POLDER sensor in the window spectral channels where absorption by gas is minimal. The total number of such observations by PARASOL always exceeds a hundred over each pixel and the statistical optimization concept promises to be efficient even if the algorithm retrieves several tens of aerosol parameters. Based on this idea, the proposed algorithm uses a large number of unknowns and is aimed at retrieval of extended set of parameters affecting measured radiation

Advanced Processing of Multispectral Satellite Data for Detecting and Learning Knowledge-based Features of Planetary Surface Anomalies

abstract: The marked increase in the inflow of remotely sensed data from satellites have trans- formed the Earth and Space Sciences to a data rich domain creating a rich repository for domain experts to analyze. These observations shed light on a diverse array of disciplines ranging from monitoring Earth system components to planetary explo- ration by highlighting the expected trend and patterns in the data. However, the complexity of these patterns from local to global scales, coupled with the volume of this ever-growing repository necessitates advanced techniques to sequentially process the datasets to determine the underlying trends. Such techniques essentially model the observations to learn characteristic parameters of data-generating processes and highlight anomalous planetary surface observations to help domain scientists for making informed decisions. The primary challenge in defining such models arises due to the spatio-temporal variability of these processes. This dissertation introduces models of multispectral satellite observations that sequentially learn the expected trend from the data by extracting salient features of planetary surface observations. The main objectives are to learn the temporal variability for modeling dynamic processes and to build representations of features of interest that is learned over the lifespan of an instrument. The estimated model parameters are then exploited in detecting anomalies due to changes in land surface reflectance as well as novelties in planetary surface landforms. A model switching approach is proposed that allows the selection of the best matched representation given the observations that is designed to account for rate of time-variability in land surface. The estimated parameters are exploited to design a change detector, analyze the separability of change events, and form an expert-guided representation of planetary landforms for prioritizing the retrieval of scientifically relevant observations with both onboard and post-downlink applications.Dissertation/ThesisDoctoral Dissertation Computer Engineering 201

A physics-constrained machine learning method for mapping gapless land surface temperature

More accurate, spatio-temporally, and physically consistent LST estimation has been a main interest in Earth system research. Developing physics-driven mechanism models and data-driven machine learning (ML) models are two major paradigms for gapless LST estimation, which have their respective advantages and disadvantages. In this paper, a physics-constrained ML model, which combines the strengths in the mechanism model and ML model, is proposed to generate gapless LST with physical meanings and high accuracy. The hybrid model employs ML as the primary architecture, under which the input variable physical constraints are incorporated to enhance the interpretability and extrapolation ability of the model. Specifically, the light gradient-boosting machine (LGBM) model, which uses only remote sensing data as input, serves as the pure ML model. Physical constraints (PCs) are coupled by further incorporating key Community Land Model (CLM) forcing data (cause) and CLM simulation data (effect) as inputs into the LGBM model. This integration forms the PC-LGBM model, which incorporates surface energy balance (SEB) constraints underlying the data in CLM-LST modeling within a biophysical framework. Compared with a pure physical method and pure ML methods, the PC-LGBM model improves the prediction accuracy and physical interpretability of LST. It also demonstrates a good extrapolation ability for the responses to extreme weather cases, suggesting that the PC-LGBM model enables not only empirical learning from data but also rationally derived from theory. The proposed method represents an innovative way to map accurate and physically interpretable gapless LST, and could provide insights to accelerate knowledge discovery in land surface processes and data mining in geographical parameter estimation