Improving volcanic ash forecasts with ensemble-based data assimilation

<p>Explosive volcanic eruptions release high amounts of ash into the atmosphere. Accurate tracking and forecasting of ash dispersal into the atmosphere and quantification of its uncertainty is of fundamental importance for volcanic hazard mitigation. Numerical models represent a powerful tool to monitor ash clouds in real-time, but limits and uncertainties affect numerical results. A way to improve numerical forecasts is by assimilating satellite observations of ash clouds through Data Assimilation algorithms, such as Ensemble-based Kalman Filters. In this study, we present the implementation of the so-called Local Ensemble Transform Kalman Filters inside a numerical procedure which simulates the release and transport of volcanic ash during explosive eruptions. The numerical procedure consists of the eruptive column model PLUME-MoM coupled with the tephra transport and dispersal model HYSPLIT. When satellite observations are available, ash maps supplied by PLUME-MoM/HYSPLIT are sequentially corrected/modified using ash column loading as retrieved from space. The new volcanic ash state represents the optimal solution with minimized uncertainties with respect to numerical estimates and observations. To test the Data Assimilation procedure, we used satellite observations of the volcanic cloud released during the explosive eruption that occurred at Mt. Etna (Italy) on 24 December 2018. Satellite observations have been carried out by the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) instrument, on board the Meteosat Second Generation (MSG) geostationary satellite. Results show that the assimilation procedure significantly improves the current ash state and the forecast. In addition, numerical tests show that the use of sequential Kalman Filters does not require a precise initialization of the numerical model, being able to improve the forecasts as the assimilation cycles are performed.</p>

Bayesian state estimation of a dynamical system utilising a stream of noisy measurements is important in many geophysical and engineering applications. In these cases, nonlinearities, high (or infinite) dimensionality of the state space, and sparse observations pose key challenges for deriving efficient and accurate data assimilation (DA) techniques. A number of DA algorithms used commonly in practice, such as the Ensemble Kalman Filter (EnKF) or Ensemble Square Root Kalman Filter (EnSRKF), suffer from serious drawbacks such as catastrophic filter divergence and filter instability. The analysis of stability and accuracy of these DA schemes has thus far focused either on finite-dimensional dynamics, or on complete observations in case of infinite-dimensional dissipative systems. We develop a unified framework for the analysis of several well-known and empirically efficient DA techniques derived from various Gaussian approximations of the Bayesian filtering schemes for geophysical-type dissipative dynamics with quadratic nonlinearities. We establish rigorous results on (time-asymptotic) accuracy and stability of these algorithms with general covariance and observation operators. The accuracy and stability results for EnKF and EnSRKF for dissipative PDEs are, to the best of our knowledge, completely new in this general setting. It turns out that a hitherto unexploited cancellation property involving the ensemble covariance and observation operators and the concept of covariance localization in conjunction with covariance inflation play a pivotal role in the accuracy and stability for EnKF and EnSRKF. Our approach also elucidates the links, via determining functionals, between the approximate-Bayesian and control-theoretic approaches to DA. We consider the ‘model’ dynamics governed by the two-dimensional incompressible Navier–Stokes equations (NSEs) and observations given by noisy measurements of averaged volume elements or spectral/modal observations of the velocity field. In this setup, several continuous-time DA techniques, namely the so-called 3DVar, EnKF and EnSRKF reduce to a stochastically forced NSEs. For the first time, we derive conditions for accuracy and stability of EnKF and EnSRKF. The derived bounds are given for the limit supremum of the expected value of the L 2 norm and of the H 1 Sobolev norm of the difference between the approximating solution and the actual solution as the time tends to infinity. Moreover, our analysis reveals an interplay between the resolution of the observations (roughly, the ‘richness’ of the observation space) associated with the observation operator underlying the DA algorithms and covariance inflation and localization which are employed in practice for improved filter performance.

A localized ensemble Kalman filter (EnKF) method is developed to assimilate transient flow data to calibrate a heterogeneous conductivity field. To update conductivity value at a point in a study domain, instead of assimilating all the measurements in the study domain, only limited measurement data in an area around the point are used for the conductivity updating in the localized EnKF method. The localized EnKF is proposed to solve the problems of the filter divergence usually existing in a data assimilation method without localization. The developed method is applied, in a synthetical two dimensional case, to calibrate a heterogeneous conductivity field by assimilating transient hydraulic head data. The simulations by the data assimilation with and without localized EnKF are compared. The study results indicate that the hydraulic conductivity field can be updated efficiently by the localized EnKF, while it cannot be by the EnKF. The covariance inflation and localization are found to solve the problem of the filter divergence efficiently. In comparison with the EnKF method without localization, the localized EnKF method needs smaller ensemble size to achieve stabilized results. The simulation results by the localized EnKF method are much more sensitive to conductivity correlation length than to the localization radius. The developed localized EnKF method provides an approach to improve EnKF method in conductivity calibration.

Reduced-order flow modeling and geological parameterization for ensemble-based data assimilation

Because of its ease of implementation, computational efficiency and the fact that it generates multiple history-matched models, which conceptually allows one to characterize the uncertainty in reservoir description and future performance predictions, the ensemble Kalman filter (EnKF) provides a highly attractive technique for history matching production data. In this work, we apply EnKF with a recently proposed method of covariance localization to history-match production data from a real field to generate multiple realizations of the permeability field. A single manually history-matched model is available for comparisons. Only 7.6 years of the 10 years of history were matched with the remaining 2.4 years of history used to assess the predictive capability of the history-matched models. For this field case, covariance localization was necessary to avoid the propagation of spurious correlations and loss of variance and also resulted in better data matches and predictions than were obtained with EnKF without localization. EnKF with covariance localization also gave better data matches, more accurate "future" predictions and far more geologically realistic models than were obtained by manually matching production data. We also present results obtained using half-iteration EnKF (HI-EnKF) with covariance localization. For this field case, HI-EnKF gave a significant further improvement in the data match and predictions. However, as HI-EnKF requires rerunning the ensemble from time zero at every data assimilation step, it leads to a considerable increase in the computational time. The results for this field case indicate that we can reduce the computational cost of HI-EnKF, without compromising the quality of the results, by rerunning the ensemble from time zero only when "large" changes in the state vector occur.

INTRODUCTION Past volcanic eruptions in Iceland have sometimes led to long-distance distribution of fine volcanic tephra (volcanic ash). Evidence for volcanic ash from, for example, the ca. 4250 BP eruption in Hekla (H4) and the 17831784 AD Laki eruption in southern Iceland has been found in wetlands at Svalbard (Kekonen et al. 2005), Scotland (Charman et al. 1995) and Scandinavia (Thorarinsson 1981). Basaltic volcanic ash contains several compounds, e.g. mixed sulphates, ammonium (NH4 +) and nitrate (NO3 -) (Ayris and Delmelle 2012), all of which have the potential to affect various soil processes, such as N cycling and greenhouse gas (GHG) production. Such direct effects of long distance volcanic ash transport have, however, been little studied so far. The Eyjafjallajokull volcano eruption started in Iceland in March, 2010. A major outbreak of the central crater under the covering ice cap started on 14 April and continued until 24 May, ejecting a very large amount of gases and fine ash into the atmosphere. This ash contained mainly silicon dioxide, but also other compounds including sulphates (O’Dowd et al. 2012). This eruption gave us an opportunity to study the fresh volcanic ash and its effects on soil properties. Because this volcanic ash was alkaline, it could increase soil pH and affect the microbiological processes behind GHG emissions in a similar way to that suggested for wood ash (Maljanen et al. 2014, Klemedtsson et al. 2010). We used fresh volcanic ash from the Eyjafjallajokull volcano in a laboratory experiment conducted on samples of drained peat soil from western Finland that were also used for studying the effects of wood ash recycling from bioenergy power plants (Maljanen et al. 2014). The aim of this study was to test whether this volcanic ash affected GHG production rates in drained peat soil in our laboratory incubation experiments and to compare the results with wood ash.

Volcanic eruptions are common in Iceland and have caused health problems ever since the settlement of Iceland. Here we describe volcanic activity and the effects of volcanic gases and ash on human health in Iceland. Volcanic gases expelled during eruptions can be highly toxic for humans if their concentrations are high, irritating the mucus membranes of the eyes and upper respiratory tract at lower concentrations. They can also be very irritating to the skin. Volcanic ash is also irritating for the mucus membranes of the eyes and upper respiratory tract. The smalles particles of volcanic ash can reach the alveoli of the lungs. Described are four examples of volcanic eruptions that have affected the health of Icelanders. The eruption of Laki volcanic fissure in 1783-1784 is the volcanic eruption that has caused the highest mortality and had the greatest effects on the well-being of Icelanders. Despite multiple volcanic eruptions during the last decades in Iceland mortality has been low and effects on human health have been limited, although studies on longterm effects are lacking. Studies on the effects of the Eyjafjallajökul eruption in 2010 on human health showed increased physical and mental symptoms, especially in those having respiratory disorders. The Directorate of Health in Iceland and other services have responded promptly to recurrent volcanic eruptions over the last few years and given detailed instructions on how to minimize the effects on the public health. Key words: volcanic eruptions, Iceland, volcanic ash, volcanic gases, health effects, mortality. Correspondence: Gunnar Guðmundsson, ggudmund@landspitali.is.

Ash plumes erupting from volcanoes can cause a range of health effects following exposure either to the initial emissions or to deposited material that becomes resuspended days, months, or years later.Researchers from the United States, Iceland, and Australia now report in EHP evidence that exposure to volcanic ash may both foster bacterial growth in the respiratory system and weaken the ability of the immune system to limit that growth, possibly contributing to respiratory infections that have been documented in relation to past eruptions. 1ach year eruptions occur at about 50-70 above-sea locations; eruptions have been documented for about 550 volcanoes in all, and roughly 1,300 have been active at some point in the past 10,000 years. 2 Some of this activity generates ash plumes that affect potentially millions of people.Volcanic ash can contain significant quantities of respirable particles of rock, minerals, and volcanic glass, and eruptions also emit hazardous gases such as sulfur dioxide, hydrogen sulfide, hydrochloric acid, and hydrofluoric acid. 3he 20 March 2010 eruption of the Icelandic volcano Eyjafjallajökull (pronounced, roughly, AY-yah-FYET-lah-YOH-kuhl), another larger eruption on 14 April 2010, and ongoing volcanic activity through late May 2011 ejected numerous major ash plumes that spread far and wide.For the current study, the researchers primarily used one sample of this ash that was collected on the ground 15 April 2010 at a location 58 km from the volcano.The ash was composed of a complex mixture of aluminosilicate volcanic glass and minerals including iron, titanium, manganese, zinc, strontium, and barium.The researchers used a ubiquitous and potent

A Neurocomputing Approach for Monitoring Plinian Volcanic Eruptions Using Infrasound

The ensemble Kalman filter (EnKF) has been shown repeatedly to be an effective method for data assimilation in large-scale problems, including those in petroleum engineering. Data assimilation for multiphase flow in porous media is particularly difficult, however, because the relationships between model variables (e.g., permeability and porosity) and observations (e.g., water cut and gas–oil ratio) are highly nonlinear. Because of the linear approximation in the update step and the use of a limited number of realizations in an ensemble, the EnKF has a tendency to systematically underestimate the variance of the model variables. Various approaches have been suggested to reduce the magnitude of this problem, including the application of ensemble filter methods that do not require perturbations to the observed data. On the other hand, iterative least-squares data assimilation methods with perturbations of the observations have been shown to be fairly robust to nonlinearity in the data relationship. In this paper, we present EnKF with perturbed observations as a square root filter in an enlarged state space. By imposing second-order-exact sampling of the observation errors and independence constraints to eliminate the cross-covariance with predicted observation perturbations, we show that it is possible in linear problems to obtain results from EnKF with observation perturbations that are equivalent to ensemble square-root filter results. Results from a standard EnKF, EnKF with second-order-exact sampling of measurement errors that satisfy independence constraints (EnKF (SIC)), and an ensemble square-root filter (ETKF) are compared on various test problems with varying degrees of nonlinearity and dimensions. The first test problem is a simple one-variable quadratic model in which the nonlinearity of the observation operator is varied over a wide range by adjusting the magnitude of the coefficient of the quadratic term. The second problem has increased observation and model dimensions to test the EnKF (SIC) algorithm. The third test problem is a two-dimensional, two-phase reservoir flow problem in which permeability and porosity of every grid cell (5,000 model parameters) are unknown. The EnKF (SIC) and the mean-preserving ETKF (SRF) give similar results when applied to linear problems, and both are better than the standard EnKF. Although the ensemble methods are expected to handle the forecast step well in nonlinear problems, the estimates of the mean and the variance from the analysis step for all variants of ensemble filters are also surprisingly good, with little difference between ensemble methods when applied to nonlinear problems.

Mineralogical, Chemical, and Isotopic (Sr, Pb) Composition of Atmospheric Mineral Dusts in an Ombrotrophic Peat Bog, Southern South America

In this study, we developed a data assimilation (DA) system for chemical transport model (CTM) simulations using an ensemble Kalman filter (EnKF) technique. This DA technique is easy to implement to an existing system without seriously modifying the original CTM, and can provide flow-dependent corrections based on error covariance by short-term ensemble propagations. First, the PM2.5 observations at ground stations were assimilated in this DA system every 6 hours over South Korea for the period of the KORUS–AQ campaign, from 1 May to 12 June, 2016. The DA performances with the EnKF were then compared to a control run (CTR) without DA, as well as a run with three-dimensional variational (3DVAR) DA. Consistent improvements due to the ICs assimilated with the EnKF were found in the DA experiments with 6 h interval, compared to the CTR run, and to the run with 3DVAR. In addition, we attempted to assimilate the ground observations from China to examine the impacts of improved boundary concentrations (BCs) on the PM2.5 predictability over South Korea. The contributions of the ICs and BCs to improvements in the PM2.5 predictability were also quantified. For example, the relative reductions in terms of the normalized mean bias (NMB) were found to be about 27.2 % for the 6 h reanalysis run. A series of 24 hour PM2.5 predictions were additionally conducted each day at 00 UTC with the optimized initial concentrations (ICs). The relative reduction of the NMB was 17.3 % for the 24 h prediction run, when the updated ICs were applied at 00 UTC. This means that after the application of the updated BCs, an additional 9.0 % reduction in the NMB was achieved for 24 h PM2.5 predictions in South Korea.

This sensor detects volcanic ash particles and distinguishes them from cloud droplets. Operated on an airplane, this detector can quantify the exposure to hazardous refractory ash and the in-situ measurement is not biased by the presence of cloud particles. A volcanic eruption emits a significant amount of hazardous ash particles into the air. If the event is strong enough, the volcanic ash plume can reach high altitudes and can be a serious security risk for airplanes. We have developed a new prototype aerosol sensor for the reliable detection of volcanic ash. The envisaged application is the employment of this new technique on board of passenger aircraft. It allows in-situ monitoring of the airplane's exposure to volcanic ash. The challenge of this development is the requirement that the sensor can distinguish cloud droplets (or ice crystals) from the hazardous refractory ash particles. At aviation altitudes, water droplets and ice crystals are often present in the particle size region of the ash (1–20 micrometer) and their concentrations can reach the levels that are considered as the limits of the different volcanic ash contamination zones. Therefore, it is crucial that the sensor can differentiate between volcanic ash and water or ice particles. The sensor measures the scattered light intensities from individual particles outside of the airplane cabin through a glass window. The desired discrimination is achieved with two lasers operating at different wavelengths. Ash concentrations (in terms of number and mass) are derived, and the exposure of the airplane is recorded and transmitted in real time to the pilot. The volcanic ash detector was tested in the laboratory with various test aerosols and micrometer-sized water droplets. Then, ground-based outdoor measurements were conducted and the instrument response to mineral dust (a surrogate for volcanic ash) and natural cloud droplets (and ice crystals) was investigated. In a next step, this new technique will be tested in summer 2016 on-board of a research aircraft.

Previous research suggests that an ensemble Kalman filter (EnKF) data assimilation and modeling system can produce accurate atmospheric analyses and forecasts at 30–50-km grid spacing. This study examines the ability of a mesoscale EnKF system using multiscale (36/12 km) Weather Research and Forecasting (WRF) model simulations to produce high-resolution, accurate, regional surface analyses, and 6-h forecasts. This study takes place over the complex terrain of the Pacific Northwest, where the small-scale features of the near-surface flow field make the region particularly attractive for testing an EnKF and its flow-dependent background error covariances. A variety of EnKF experiments are performed over a 5-week period to test the impact of decreasing the grid spacing from 36 to 12 km and to evaluate new approaches for dealing with representativeness error, lack of surface background variance, and low-level bias. All verification in this study is performed with independent, unassimilated observations. Significant surface analysis and 6-h forecast improvements are found when EnKF grid spacing is reduced from 36 to 12 km. Forecast improvements appear to be a consequence of increased resolution during model integration, whereas analysis improvements also benefit from high-resolution ensemble covariances during data assimilation. On the 12-km domain, additional analysis improvements are found by reducing observation error variance in order to address representativeness error. Removing model surface biases prior to assimilation significantly enhances the analysis. Inflating surface wind and temperature background error variance has large impacts on analyses, but only produces small improvements in analysis RMS errors. Both surface and upper-air 6-h forecasts are nearly unchanged in the 12-km experiments. Last, 12-km WRF EnKF surface analyses and 6-h forecasts are shown to generally outperform those of the Global Forecast System (GFS), North American Model (NAM), and the Rapid Update Cycle (RUC) by about 10%–30%, although these improvements do not extend above the surface. Based on these results, future improvements in multiscale EnKF are suggested.

Improving flood forecasting using conditional bias-aware assimilation of streamflow observations and dynamic assessment of flow-dependent information content