Assimilation of Freeze - Thaw Observations into the NASA Catchment Land Surface Model

The land surface freeze-thaw (F-T) state plays a key role in the hydrological and carbon cycles and thus affects water and energy exchanges and vegetation productivity at the land surface. In this study, we developed an F-T assimilation algorithm for the NASA Goddard Earth Observing System, version 5 (GEOS-5) modeling and assimilation framework. The algorithm includes a newly developed observation operator that diagnoses the landscape F-T state in the GEOS-5 Catchment land surface model. The F-T analysis is a rule-based approach that adjusts Catchment model state variables in response to binary F-T observations, while also considering forecast and observation errors. A regional observing system simulation experiment was conducted using synthetically generated F-T observations. The assimilation of perfect (error-free) F-T observations reduced the root-mean-square errors (RMSE) of surface temperature and soil temperature by 0.206 C and 0.061 C, respectively, when compared to model estimates (equivalent to a relative RMSE reduction of 6.7 percent and 3.1 percent, respectively). For a maximum classification error (CEmax) of 10 percent in the synthetic F-T observations, the F-T assimilation reduced the RMSE of surface temperature and soil temperature by 0.178 C and 0.036 C, respectively. For CEmax=20 percent, the F-T assimilation still reduces the RMSE of model surface temperature estimates by 0.149 C but yields no improvement over the model soil temperature estimates. The F-T assimilation scheme is being developed to exploit planned operational F-T products from the NASA Soil Moisture Active Passive (SMAP) mission

Assimilation of Freeze–Thaw Observations into the NASA Catchment Land Surface Model

The land surface freeze–thaw (F/T) state plays a key role in the hydrological and carbon cycles and thus affects water and energy exchanges and vegetation productivity at the land surface. In this study, an F/T assimilation algorithm was developed for the NASA Goddard Earth Observing System, version 5 (GEOS-5), modeling and assimilation framework. The algorithm includes a newly developed observation operator that diagnoses the landscape F/T state in the GEOS-5 Catchment land surface model. The F/T analysis is a rule-based approach that adjusts Catchment model state variables in response to binary F/T observations, while also considering forecast and observation errors. A regional observing system simulation experiment was conducted using synthetically generated F/T observations. The assimilation of perfect (error free) F/T observations reduced the root-mean-square errors (RMSEs) of surface temperature and soil temperature by 0.206° and 0.061°C, respectively, when compared to model estimates (equivalent to a relative RMSE reduction of 6.7% and 3.1%, respectively). For a maximum classification error CEmax of 10% in the synthetic F/T observations, the F/T assimilation reduced the RMSE of surface temperature and soil temperature by 0.178° and 0.036°C, respectively. For CEmax = 20%, the F/T assimilation still reduces the RMSE of model surface temperature estimates by 0.149°C but yields no improvement over the model soil temperature estimates. The F/T assimilation scheme is being developed to exploit planned F/T products from the NASA Soil Moisture Active Passive (SMAP) mission

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

SAR (Synthetic Aperture Radar). Earth observing system. Volume 2F: Instrument panel report

The scientific and engineering requirements for the Earth Observing System (EOS) imaging radar are provided. The radar is based on Shuttle Imaging Radar-C (SIR-C), and would include three frequencies: 1.25 GHz, 5.3 GHz, and 9.6 GHz; selectable polarizations for both transmit and receive channels; and selectable incidence angles from 15 to 55 deg. There would be three main viewing modes: a local high-resolution mode with typically 25 m resolution and 50 km swath width; a regional mapping mode with 100 m resolution and up to 200 km swath width; and a global mapping mode with typically 500 m resolution and up to 700 km swath width. The last mode allows global coverage in three days. The EOS SAR will be the first orbital imaging radar to provide multifrequency, multipolarization, multiple incidence angle observations of the entire Earth. Combined with Canadian and Japanese satellites, continuous radar observation capability will be possible. Major applications in the areas of glaciology, hydrology, vegetation science, oceanography, geology, and data and information systems are described

Characterization of the spatial and temporal variability in pan-Arctic, terrestrial hydrology

Arctic hydrology represents an important component of the larger global climate system, and there are signs that significant water-cycle changes, involving complex feedbacks, have occurred. This dissertation explores the methods to estimate components of the arctic hydrological cycle, the numerous biases and uncertainties associated with the techniques, and suggestions for future research needs. The studies described here focus on quantitative models and methods for predicting the spatial and temporal variability in pan-Arctic hydrology. This dissertation discusses pan-Arctic water budgets drawn from a hydrological model which is appropriate for applications across the terrestrial Arctic. Including effects from soil-water phase changes results in increases in simulated annual runoff of 7% to 27%. A sensitivity analysis reveals that simulated runoff is far more sensitive to the time-varying climate drivers than to parameterization of the landscape. When appropriate climate data are used, the Pan-Arctic Water Balance Model (PWBM) is able to capture well the variability in seasonal river discharge at the scale of arctic sea basins. This dissertation also demonstrated a method to estimate snowpack thaw timing from radar data. Discrepancies between thaw timing inferred from the microwave backscatter data and the hydrological model are less than one week. The backscatter signal-to-noise values are highest in areas of higher seasonal snow accumulation, low to moderate tree cover and low topographic complexity. An evaluation of snow water equivalent (SWE) estimates drawn from land surface models and microwave remote sensing data suggests that simulated SWE from a hydrological model like PWBM, when forced with appropriate climate data, is far superior to current snow mass estimate derived from passive microwave data. Biases arising from interpolations from sparse, uneven networks can be significant. A bias of well over +10 mm yr-1 was found in the early network representations of spatial precipitation across Eurasia. When examining linkages between precipitation and river discharge, these biases limit our confidence in the accuracy of historical precipitation reconstructions. This dissertation assess our current capabilities in estimating components of arctic water cycle and reducing the uncertainties in predictions of arctic climate change

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

Selection of the key earth observation sensors and platforms focusing on applications for Polar Regions in the scope of Copernicus system 2020-2030

An optimal payload selection conducted in the frame of the H2020 ONION project (id 687490) is presented based on the ability to cover the observation needs of the Copernicus system in the time period 2020–2030. Payload selection is constrained by the variables that can be measured, the power consumption, and weight of the instrument, and the required accuracy and spatial resolution (horizontal or vertical). It involved 20 measurements with observation gaps according to the user requirements that were detected in the top 10 use cases in the scope of Copernicus space infrastructure, 9 potential applied technologies, and 39 available commercial platforms. Additional Earth Observation (EO) infrastructures are proposed to reduce measurements gaps, based on a weighting system that assigned high relevance for measurements associated to Marine for Weather Forecast over Polar Regions. This study concludes with a rank and mapping of the potential technologies and the suitable commercial platforms to cover most of the requirements of the top ten use cases, analyzing the Marine for Weather Forecast, Sea Ice Monitoring, Fishing Pressure, and Agriculture and Forestry: Hydric stress as the priority use cases.Peer ReviewedPostprint (published version

Assimilation of backscatter observations into a hydrological model: a case study in Belgium using ASCAT data

We investigated the possibilities of improving hydrological simulations by assimilating radar backscatter observations from the advanced scatterometer (ASCAT) in the hydrological model SCHEME using a calibrated water cloud model (WCM) as an observation operator. The WCM simulates backscatter based on soil moisture and vegetation data and can therefore be used to generate observation predictions for data assimilation. The study was conducted over two Belgian catchments with different hydrological regimes: the Demer and the Ourthe catchment. The main differences between the two catchments can be summarized in precipitation and streamflow levels, which are higher in the Ourthe. The data assimilation method adopted here was the ensemble Kalman filter (EnKF), whereby the uncertainty of the state estimate was described via the ensemble statistics. The focus was on the optimization of the EnKF, and possible solutions to address biases introduced by ensemble perturbations were investigated. The latter issue contributes to the fact that backscatter data assimilation only marginally improves the overall scores of the discharge simulations over the deterministic reference run, and only for the Ourthe catchment. These performances, however, considerably depend on the period considered within the 5 years of analysis. Future lines of research on bias correction, the data assimilation of soil moisture and backscatter data are also outlined

Microwave Indices from Active and Passive Sensors for Remote Sensing Applications

Past research has comprehensively assessed the capabilities of satellite sensors operating at microwave frequencies, both active (SAR, scatterometers) and passive (radiometers), for the remote sensing of Earth’s surface. Besides brightness temperature and backscattering coefficient, microwave indices, defined as a combination of data collected at different frequencies and polarizations, revealed a good sensitivity to hydrological cycle parameters such as surface soil moisture, vegetation water content, and snow depth and its water equivalent. The differences between microwave backscattering and emission at more frequencies and polarizations have been well established in relation to these parameters, enabling operational retrieval algorithms based on microwave indices to be developed. This Special Issue aims at providing an overview of microwave signal capabilities in estimating the main land parameters of the hydrological cycle, e.g., soil moisture, vegetation water content, and snow water equivalent, on both local and global scales, with a particular focus on the applications of microwave indices