Comment on gmd-2022-246

Abstract. Traditionally, tephra transport and dispersal models have evolved decoupled (offline) from numerical weather prediction models. There is a concern that inconsistencies and shortcomings associated with this coupling strategy might lead to errors in the ash cloud forecast. Despite this concern and the significant progress in improving the accuracy of tephra dispersal models in the aftermath of the 2010 Eyjafjallajökull and 2011 Cordón Caulle eruptions, to date, no operational online dispersal model is available to forecast volcanic ash. Here, we describe and evaluate NMMB-MONARCH-ASH, a new online multi-scale meteorological and transport model that attempts to pioneer the forecast of volcanic aerosols at operational level. The model forecasts volcanic ash cloud trajectories, concentration of ash at relevant flight levels, and the expected deposit thickness for both regional and global configurations. Its online coupling approach improves the current state-of-the-art tephra dispersal models, especially in situations where meteorological conditions are changing rapidly in time, two-way feedbacks are significant, or distal ash cloud dispersal simulations are required. This work presents the model application for the first phases of the 2011 Cordón Caulle and 2001 Mount Etna eruptions. The computational efficiency of NMMB-MONARCH-ASH and its application results compare favorably with other long-range tephra dispersal models, supporting its operational implementation.

The tephra dispersal model used by the Yucca Mountain Project (YMP) to evaluate the potential consequences of a volcanic eruption through the waste repository must incorporate simplifications in order to function within a large Monte-Carlo style performance assessment framework. That is, the explicit physics of the conduit, vent, and eruption column processes are abstracted to a 2-D, steady-state advection-dispersion model (ASHPLUME) that can be run quickly over thousands of realizations of the overall system model. Given the continuous development of tephra dispersal modeling techniques in the last few years, we evaluated the adequacy of this simplified model for its intended purpose within the YMP total system performance assessment (TSPA) model. We evaluated uncertainties inherent in model simplifications including (1) instantaneous, steady-state vs. unsteady eruption, which affects column height, (2) constant wind conditions, and (3) power-law distribution of the tephra blanket; comparisons were made to other models and published ash distributions. Spatial statistics are useful for evaluating differences in these model output vs. results using more complex wind, column height, and tephra deposition patterns. However, in order to assess the adequacy of the model for its intended use in TSPA, we evaluated the propagation of these uncertainties through FAR, the YMP ash redistribution model, which utilizes ASHPLUME tephra deposition results to calculate the concentration of nuclear waste-contaminated tephra at a dose-receptor population as a result of sedimentary transport and mixing processes on the landscape. Questions we sought to answer include: (1) what conditions of unsteadiness, wind variability, or departure from simplified tephra distribution result in significant effects on waste concentration (related to dose calculated for the receptor population)? (2) What criteria can be established for the adequacy of a tephra dispersal model within the TSPA framework?

Challenges for data assimilation – from convective‐scale to climate

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 first<br/>phase 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. Furthermore<br/>it 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.<br/><br/>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.<br/><br/>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 matrix<br/>for 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.<br/><br/>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.<br/><br/>Infrared satellite measurements of volcanic ash mass loadings are often used as<br/>input observations for the assimilation scheme. However, these satellite-retrieved<br/>data 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.

A 20-member WRF-based regional Ensemble Prediction System (WEPS) is operationally run at the Central Weather Administration (CWA) to provide up to 5-day ensemble forecasts over the East Asian region with a 15-km grid spacing and a 3-km nest centered over Taiwan. Since becoming operational in 2011, WEPS has been under continuous development that aims to improve the construction of its perturbations in initial conditions (IC), boundary conditions (BC), as well as model uncertainties due to numerical approximations and physical parameterizations. Among these uncertainties, construction of IC perturbations for WEPS is the focus of this study. An initialization method named ensemble partial cycling (EnPC) is proposed for the WEPS. The EnPC method combines partial cycling data assimilation (DA) and the ensemble of DA approach with an additional blending procedure that merges large-scale global features with small-scale regional information, leveraging the DA efforts from the deterministic system of CWA. EnPC is compared with three other initialization methods that are popularly used for regional ensemble forecasting, including dynamic downscaling from a global EPS, Ensemble Adjustment Kalman Filter (EAKF) based regional ensemble DA, and a blended version of the two, the last of which is equivalent to the current operational configuration of WEPS. Among all 4 methods, EnPC is the only method that allows separate initializations for the parent and the nested domains while the initialization for the nested domain in the other three methods is simply a downscale-interpolation from the corresponding parent grid. Several sets of WEPS experiments are conducted over a 5-week period, including five typhoons. EnPC-initialized WEPS forecasts are found to be comparable to the dynamically downscaled forecasts in many evaluation metrics and have more accurate near-surface forecasts over the first 12 h and better precipitation forecast discrimination ability for typhoon events. Compared to the EAKF and the blended methods, forecasts initialized from EnPC have overall smaller errors in most of the evaluation metrics by both deterministic and probabilistic measures and better spread-to-error ratios. As an alternative initialization method, EnPC not only adds some regional benefits on top of downscaling, but also shows some advantages over the operational method. With the planned retirement of EAKF and the anticipation of a more unified production suite at CWA, EnPC will replace the current operational method.

A data assimilation problem for unsteady models is considered as a sequence of coupled inverse problems of reconstruction of the space-time structure of the state functions with various sets of measurement data. The data assimilation is carried out jointly with the identification of an additional unknown function, which is interpreted as a function of model uncertainty. A variational principle serves as the basis for constructing the algorithms. Various versions of the algorithms are presented and analyzed. Based on the principle of the residual, a computationally efficient algorithm for data assimilation in a locally one-dimensional case is constructed. A theoretical estimate of its performance is obtained. This algorithm is one of the main components of a data assimilation system based on a splitting scheme for unsteady three-dimensional transport and transformation models of atmospheric chemistry.

The quality of the retrieved temperature and moisture profiles acquired from the Atmospheric Infrared Sounder (AIRS) aboard the NASA Aqua spacecraft was evaluated by comparing the data with dropsonde observations obtained from NASA African Monsoon Multidisciplinary Analyses (NAMMA 2006) and international THORPEX Pacific Asian Regional Campaign (T‐PARC 2008) field programs. Results indicate that the AIRS retrieved temperature profiles are in good agreement with dropsonde observations. However, the AIRS retrieved moisture profiles show a larger bias compared with the dropsondes over the tropical oceans where tropical cyclones developed. A series of data assimilation experiments is then performed with an advanced research version of the Weather Research and Forecasting model and its three‐dimensional variational data assimilation system. Results show that the assimilation of the AIRS retrieved temperature and moisture profiles has a significant impact on the numerical simulations of tropical cyclones. However, the overall impacts of the data assimilation on numerical simulations of tropical cyclones are very sensitive to the bias corrections of the data. Specifically, the dry biases of moisture profiles cause the decay of Tropical Storm Debby (2006) in the numerical simulations. Only with bias correction can data assimilation result in a reasonable portrayal of storm development. Compared with the moisture profiles, temperature profiles show a larger impact on the track forecasting. Assimilation of the temperature profiles resulted in significant improvements in the track forecasts for both Debby (2006) and Typhoon Jangmi (2008).

Geochemical characterization of mid-distal Nisyros tephra on Datça peninsula (southwestern Anatolia)

Chapter 1 - Field Observations of Tephra Fallout Deposits

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.

Data assimilation is a critical component for short-range weather forecasting; a number of algorithms have been developed and applied for assimilation of different kind of observations. However, an important but less explored question is the (optimal) amount of observation for maximum improvement in forecasts through data assimilation. Because the meteorological fields at different spatial and temporal resolutions are not necessarily mutually independent, indefinite increase in resolution of observations may be ineffective; thus data optimality in this sense can be defined as the maximum resolution of observation beyond which no appreciable improvement occurs due to assimilation of data. Based on forecasts of seven events over a complex terrain (urban location, Delhi) with different combinations of observations, we show that improvement in forecast skill does not saturate even with assimilation of observations a few kilometers (<10 km) apart. The improvement due to assimilation of data from each of the profilers is appreciable; however, the impact was generally the highest for assimilation of data from all the four profilers. Applicable strategies for observation system design over high-impact areas are discussed.

Ensemble simulations have been performed with a climate model constrained to follow temperature histories obtained from a recent compilation of 56 well‐calibrated surface temperature proxy records, using a new data assimilation technique. First, we demonstrate that the data assimilation technique provides a faithful representation in the Northern Hemisphere of the signal recorded by the climate proxies at both the regional and gridbox scales. Second, by varying the external forcing, the parameters of the data assimilation method, and the parameters controlling the equilibrium climate sensitivity of the climate model, we demonstrate that the uncertainty in model results is much lower in simulations using data assimilation than in those without it. This observation implies that the data assimilation, using a set of 56 proxies, is providing an efficient and robust constraint on the simulated climate variability over the past centuries. At the hemispheric and continental scales, the model reconstructions using data assimilation are in good agreement with both the instrumental record of the past 150 years and reconstructions of climate in past centuries derived from the application of traditional statistical approaches to networks of proxy data. This increases the confidence in both the data assimilation and traditional statistical approaches. Our data assimilation method, however, is unable to provide a reliable reconstruction over the North Atlantic Ocean, which we attribute to the paucity of proxy data in that region.

The High-Resolution Rapid Refresh (HRRR) is a convection-allowing implementation of the Advanced Research version of the Weather Research and Forecasting (WRF-ARW) Model with hourly data assimilation that covers the conterminous United States and Alaska and runs in real time at the NOAA/National Centers for Environmental Prediction (NCEP). Implemented operationally at NOAA/NCEP in 2014, the HRRR features 3-km horizontal grid spacing and frequent forecasts (hourly for CONUS and 3-hourly for Alaska). HRRR initialization is designed for optimal short-range forecast skill with a particular focus on the evolution of precipitating systems. Key components of the initialization are radar-reflectivity data assimilation, hybrid ensemble-variational assimilation of conventional weather observations, and a cloud analysis to initialize stratiform cloud layers. From this initial state, HRRR forecasts are produced out to 18 h every hour, and out to 48 h every 6 h, with boundary conditions provided by the Rapid Refresh system. Between 2014 and 2020, HRRR development was focused on reducing model bias errors and improving forecast realism and accuracy. Improved representation of the planetary boundary layer, subgrid-scale clouds, and land surface contributed extensively to overall HRRR improvements. The final version of the HRRR (HRRRv4), implemented in late 2020, also features hybrid data assimilation using flow-dependent covariances from a 3-km, 36-member ensemble (“HRRRDAS”) with explicit convective storms. HRRRv4 also includes prediction of wildfire smoke plumes. The HRRR provides a baseline capability for evaluating NOAA’s next-generation Rapid Refresh Forecast System, now under development. Significance Statement NOAA’s operational hourly updating, convection-allowing model, the High-Resolution Rapid Refresh (HRRR), is a key tool for short-range weather forecasting and situational awareness. Improvements in assimilation of weather observations, as well as in physics parameterizations, have led to improvements in simulated radar reflectivity and quantitative precipitation forecasts since the initial implementation of HRRR in September 2014. Other targeted development has focused on improved representation of the diurnal cycle of the planetary boundary layer, resulting in improved near-surface temperature and humidity forecasts. Additional physics and data assimilation changes have led to improved treatment of the development and erosion of low-level clouds, including subgrid-scale clouds. The final version of HRRR features storm-scale ensemble data assimilation and explicit prediction of wildfire smoke plumes.

This article reviews the characteristics of forecast error statistics in meteorological data assimilation from the substantial literature on this subject. It is shown how forecast error statistics appear in the data assimilation problem through the background error covariance matrix, B. The mathematical and physical properties of the covariances are surveyed in relation to a number of leading systems that are in use for operational weather forecasting. Different studies emphasize different aspects of B, and the known ways that B can impact the assimilation are brought together. Treating B practically in data assimilation is problematic. One such problem is in the numerical measurement of B, and five calibration methods are reviewed, including analysis of innovations, analysis of forecast differences and ensemble methods. Another problem is the prohibitive size of B. This needs special treatment in data assimilation, and is covered in a companion article (Part II). Examples are drawn from the literature that show the univariate and multivariate structure of the B‐matrix, in terms of variances and correlations, which are interpreted in terms of the properties of the atmosphere. The need for an accurate quantification of forecast error statistics is emphasized. Copyright © 2008 Royal Meteorological Society

Stratigraphic analysis of fallout tephra deposits in and around a volcano provides a framework for understanding the long-term temporal change in the volcano’s activities. Here, we reconstruct the evolution of the volcanic activities of Sakurajima volcano based on reconstructed tephra stratigraphy by original field surveys, compilations of geological and archeological data, and new 14C dating. We define three eruption stages of Stage 1 (30–24 ka), Stage 2 (12.8–4.8 ka), and Stage 3 (4.5 ka–present), based on a major hiatus and shifting of the volcanic centers. Stages 2 and 3 are further subdivided according to the predominant mode of volcanic activity. Revised distribution of tephra deposits indicates that a total of 14.5 km3 (5.8 km3 dense rock equivalent) of fallout tephra has erupted from Sakurajima. Among them, Stage 2a (13–8 ka) produced the largest volume of tephra fallout deposits, suggesting that Sakurajima peaked in magma discharge during Stage 2a (2.9 km3/kyr) and then decreased rapidly toward Stage 2b (8–4.8 ka; 0.07 km3/kyr). The eruption of large-volume tephra deposits in Stage 2a, followed by the development of a thick volcanic ash layer in Stage 2b, indicates the eruption style shifted from explosive pumice eruptions in Stage 2a to ash-producing Vulcanian activity in Stage 2b, with decreasing magma discharge rate. Thick volcanic ash deposits covered by several fallout tephra deposits during Stage 3 also reflect a shift in activity from repeated Vulcanian explosions with lava effusions in Stage 3a (4.5–1.6 ka) to predominantly pumice eruptions during the historical period (Stage 3b) with an increasing magma discharge rate. The case study of Sakurajima presented here demonstrates that the combined analysis of the distribution, stratigraphy, and age of pumice fall layers with the lava and pyroclastic cone deposits on the volcanic edifice is a powerful tool for deciphering the growth history of complex stratovolcanoes.