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Aerosol optical depths (AODs) from the new Himawari-8 satellite instrument have been assimilated in a dust simulation model over East Asia. This advanced geostationary instrument is capable of monitoring the East Asian dust storms which usually have great spatial and temporal variability. The quality of the data has been verified through a comparison with AErosol RObotic NETwork AODs. This study focuses on extreme dust events only when dust aerosols are dominant; promising results are obtained in AOD assimilation experiments during a case in May 2017. The dust emission fields that drive the simulation model are strongly improved by the inverse modeling, and consequently, the simulated dust concentrations are in better agreements with the observed AOD as well as ground-based observations of PM 10 . However, some satellite AODs show significant inconsistence with the simulations and the PM 10 and AErosol RObotic NETwork observations, which might arise from retrieval errors over a partially clouded scene. The data assimilation procedure therefore includes a screening method to exclude these observations in order to avoid unrealistic results. A dust mask screening method is designed, which is based on selecting only those observations where the deterministic model produces a substantial amount of dust. This screen algorithm is tested to give more accurate result compared to the traditional method based on background covariance in the case study. Note that our screen method would exclude valuable information in case the model is not able to simulate the dust plume shape correctly; hence, applications in related studies require inspections of simulations and observations by user. ©2019. The Authors.

Numerical models of chemical transport have been used to simulate the complex processes involved in the formation and transport of air pollutants. Although these models can predict the spatiotemporal variability of a variety of chemical species, the accuracy of these models is often limited. Therefore, in the past two decades, data assimilation methods have been applied to use the available measurements for improving the forecast. Nowadays, machine learning techniques provide new opportunities for improving the air quality forecast. A case study on PM10 concentrations during a dust storm is performed. It is known that the PM10 concentrations are caused by multiple emission sources, e.g., dust from the desert and anthropogenic emissions. Accurate modeling of the PM10 concentration levels owing to the local anthropogenic emissions is essential for an adequate evaluation of the dust level. However, real-time measurement of local emissions is not possible, so no direct data is available. Actually, the lack of in-time emission inventories is one of the main reasons that current numerical chemical transport models cannot produce accurate anthropogenic PM10 simulations. Using machine learning techniques to generate local emissions based on past observations is a promising approach. We report how it can be combined with data assimilation to improve the accuracy of air quality forecast considerably.

The transition to electric vehicles (EVs) reduces vehicle emissions to combat climate change. EVs raise concerns regarding the production of lithium-ion batteries and related emissions; while batteries can also provide energy storage services for the electricity system. Here we use the material flow analysis method to quantify the future material demand for lithium-ion batteries and the prospective life cycle assessment method to quantify future emissions of battery production. Further combined with battery technology modelling, future energy storage potential of EV batteries is evaluated. Results show the demand for battery raw materials will increase by a factor of over 15 in the next three decades, which requires a drastic expansion of battery supply chains. The increasing utilization of renewable energy and improved mining technology of raw materials for battery production will result in a 50% decrease in emissions per lithium-ion battery production between 2020-2050. Renewable energy transition contributes largely to this emission reduction, but EV battery storage can provide short-term grid services for complementing variable renewable generation. EV batteries alone could satisfy short-term grid storage demand by as early as 2030. This research reveals environmental challenges and opportunities for EV batteries as well as options to improve EV battery sustainability.

Coastal flooding is threatening the personal safety, property, and social development of the low-lying land around the coast worldwide. Stormsurge is one of the main sources of coastal flooding. Tide and surge models can provide timely water level forecasts for coastal management with the early warning of flooding. Although a regional model can be used to study effects of climate change in a specific area, global water level modeling provides some advantages, such as the long-term response of the extreme sea level and coastal flooding due to global warming and comparison of global surge differences between regions. Global hydrodynamic modeling is becoming an increasingly important research topic. Nowadays, with ever increasing resolution, neglected physical processes and parameter uncertainties due to the inaccurate input or empirical values is becoming more and more dominating the model accuracy. At the same time, measurements like the satellite altimeter and the in-situ tide gauges are able to monitor the water level changes, which offers the possibility to estimate uncertain parameters. In this thesis, we develop a parameter estimation scheme and implement it to a global tide and surge model, and subsequently, apply to improve the water level forecast skill. Themain challenges for large-scale parameter assimilation for tide models are in assessing parameter uncertainties, large computational demand, large memory requirement and insufficient observations. In this thesis, we explore these challenges using an application to the Global Tide and Surge Model (GTSM). A computationally efficient and low memory usage iterative estimation scheme is designed and applied to GTSM for bathymetry and bottomfriction coefficient calibration. In addition, we study how to make the best use of spatial sparse distributed observations...

Volcanic eruptions release a large amount of volcanic ash, which can pose hazard to human and animal health, land transportation, and aviation safety. Volcanic Ash Transport and Dispersion (VATD) models are critical tools to provide advisory information and timely volcanic ash forecasts. Due to the complexity and the uncertainty of many dynamic processes involved in the volcanic ash distribution, even the most advanced VATDs today are not capable to reproduce the reality accurately. It is necessary to integrate available observations in the models for more accurate predictions by employing data assimilation techniques.In addition to a valid VATD, ash emissions, usually used as input so the model, are crucial for the forecasts of the locations and shapes of the ash cloud. In general, the eruption source parameters for the construction of the emission are poorly known, which include Plume Height (PH), Mass Eruption Rate (MER) and vertical distribution of the emission rate. Even when PH can be obtained from ground-based observations in some cases, the emission source computed from this PH and a MER empirically related to this PH remains highly uncertain. Not to mention the volcanoes which are unmonitored or hardly accessible, the PH can merely be retrieved from satellite data with a large uncertainty and temporal insufficiency. Fortunately, satellite instruments are able to observe the movement of an ash cloud with a global coverage. Therefore, this thesis focuses on the estimation of the volcanic ash emissions by assimilating Ash Mass Loadings (AMLs) retrieved from satellite data to improve the accuracy of forecasts. Among all available data assimilation approaches, Four Dimensional Variational assimilation (4D-Var) approach was chosen as a suitable one. 4D-Var seeks an optimal set of parameters, including model states, initial conditions and systematic parameters, by minimizing a cost function which combines the model simulations and observations over a period according to their statistic properties. 4D-Var with a standard form of the cost function is tested in a twin experiment framework, where synthetic observations of ash columns computed from model simulated 3D ash concentrations are used. The results show that Standard 4DVar (Std4DVar) is unable to reconstruct the vertical profile of the emission. The injection layer containing the maximal amount of emission rate cannot be accurately determined. This failure is attributed to the fact that AML data lacks vertical resolution. Using the AMLs, it is difficult to reconstruct the volcanic ash emission presented in forms of an eruption column.To deal with this problem, a Trajectory-based 4D-Var (Trj4DVar) approach is proposed. Trj4DVar reformulates the cost function in a regression type which computes the total difference between observed ash columns and a linear combination of simulated trajectories coupled with a priori emission knowledge. The results of twin experiments show that, for most cases, Trj4DVar is capable of estimating the input emission column when a large assimilation window (> 6 hours) is used. The twin experiments is repeated where different values of noise are given in the synthetic observations or perturbations are used in the meteorologic data. The outcomes show that there is still a small possibility that Trj4DVar fails to determine the injection height accurately. Being disturbed by the weather condition (light and cloud, etc) at that moment, satellite instrument can be hampered to observe the ash cloud, which may increase the possibility of failure for the use of Trj4DVar. To remedy this, Trj4DVar is modified to incorporate observations of PH and MER in addition to satellite AMLs. The modified Trj4DVar is shown to be able accurately estimate the injection height based on the results of twin experiments.When it comes to using real-life field data, the situation is more complicated. The detection of volcanic ash can be disturbed by the weather condition such as water vapor. This will result in observations of undetected or wrongly-detected ash. Besides, many sensors ,such as UV and visible sensors, have limited temporal coverage which can only observe during daylight. In order to find effective method in dealing with the temporal and sometimes spatial insufficiency of the data, investigations are carried out on how to use the data properly to benefit more and produce a reasonable estimate. A prepossessing procedure and guidance on the proper use of satellite data are presented.Finally, a deeper analysis is given on the failure of using Std4DVar in this application. It is found that using Std4DVar to assimilate remote sensing data can be tricky. Remote sensing measures quantities that combine several state variables. This creates Sensor-Induced Correlations between the state variables which share the same observation variable and may be physically unrelated. This may cause numerical problems resulting in a low convergence rate or inaccurate estimates of parameters using gradient-based variational assimilation if an erroneous or improper specification of error statistics is adopted. These problems are usually ignored when a reasonable result is obtained, or are avoided by reducing the 3D model to a 2D model. However, it results in significantly unreliable and misguiding estimates for the application in this thesis. Two criteria are proposed to quantify the negative effects of the SICs, which give indications of the effectiveness of the assimilation process and the forecast quality. They are simple to implement and very practical for the use of remote sensing data. They are tested in the twin experiments. The results show that they are able to give evaluation on the design and configuration of the assimilation system with remote sensing data.

The 2010 Eyjafjallajökull volcano eruption had serious consequences to civil aviation. This has initiated a lot of research on volcanic ash forecasting in recent years. For forecasting the volcanic ash transport after eruption onset, a volcanic ash transport and diffusion model (VATDM) needs to be run with Eruption Source Parameters (ESPs) such as plume height and mass eruption rate as input, and with data assimilation techniques to continuously improve the forecast. Reliable and accurate ash measurements are crucial for providing successful ash clouds advices. In the firstphase of this research work, simulated aircraft-based volcanic ash measurements, will be assimilated into a transport model to identify the potential benefit of this kind of observations in an assimilation system. The results show that assimilating aircraft-based measurements can improve the state of ash clouds, and can provide an improved forecast. We also show that for an advice on the aeroplane flying level, aircraft-based measurements should preferably be taken at this level. Furthermoreit is shown that in order to make an acceptable advice for aviation decision makers, accurate knowledge about uncertainties of ESPs and measurements is of great importance.The forecast accuracy of distal volcanic ash clouds is important for providing valid aviation advice during volcanic ash eruptions. However, because the distal part of a volcanic ash plume is far from the volcano, the influence of eruption information on this part becomes rather indirect and uncertain, resulting in inaccurate volcanic ash forecasts in these distal areas. In this thesis, we use real-life aircraft in situ observations, measured in the North-West part of Germany during the 2010 Eyjafjallajökull eruption, in an ensemble-based data assimilation system to investigate the potential improvement on the forecast accuracy with regard to the distal volcanic ash plume. We show that the error of the analyzed volcanic ash state can be significantly reduced by assimilating real-life in situ measurements. After assimilation, it is shown that the model-based aviation advice for Germany, the Netherlands and Luxembourg can be improved. We suggest that with suitable aircrafts measuring once per day across the distal volcanic ash plume, the description and prediction of volcanic ash clouds in these areas can be improved significantly.Among the data assimilation approaches, the ensemble Kalman filter (EnKF) is a well-known and popular method. A proper covariance localization strategy in the analysis step of EnKF is essential for reducing spurious covariances caused by the finite ensemble size, as shown for this application for assimilation of aircraft in situ measurements. After analyzing the characteristics of the physical forecast error covariances, we present a two-way tracking approach to define the localization matrixfor covariance localization. The result shows that the Two-way-tracking Localized EnKF (TL-EnKF) effectively maintains the correctly specified physical covariances and largely reduces the spurious ones. The computational cost of TL-EnKF is also evaluated and is shown to be advantageous for both serial and parallel implementations. Compared to the commonly used distance-based covariance localization, the two-way tracking approach is shown to be more suitable. In addition, the covariance inflation approach is verified as an additional improvement to TL-EnKF to achieve more accurate results.A timely prediction requires that the computations of the data assimilation system can be performed quickly (at least than the Wall-clock). We therefore investigate strategies for accelerating the data assimilation algorithm. Based on evaluations of the computational time, the analysis step of the assimilation turns out to be the most expensive part. After a study on the characteristics of the ensemble ash state, we propose a mask-state algorithm which records the sparsity information of the full ensemble state matrix and transforms the full matrix into a relatively small one. This will reduce the computational cost in the analysis step. Experimental results show the mask-state algorithm significantly speeds up the analysis step. Subsequently, the total amount of computing time for volcanic ash data assimilation is reduced to an acceptable level. The mask-state algorithm is generic and thus can be embedded in any ensemble-based data assimilation framework. Moreover, ensemble-based data assimilation with the mask-state algorithm is promising and flexible, because it implements exactly the standard data assimilation without any approximation and it realizes the satisfying performance without any change of the full model.Infrared satellite measurements of volcanic ash mass loadings are often used asinput observations for the assimilation scheme. However, these satellite-retrieveddata are often two-dimensional (2D), and cannot easily be combined with a three-dimensional (3D) volcanic ash model to improve the volcanic ash state. By integrating available data including ash mass loadings, cloud top heights and thickness information, we propose a satellite observational operator (SOO) that translates satellite-retrieved 2D volcanic ash mass loadings to 3D concentrations at the top layer of the ash cloud. Ensemble-based data assimilation is used to assimilate the extracted measurements of ash concentrations. The results show that satellite data assimilation can force the volcanic ash state to match the satellite observations, and that it improves the forecast of the ash state. Comparison with highly accurate aircraft in situ measurements shows that the effective duration of the improved volcanic ash forecasts is about a half day.