Ash fallout scenarios at Vesuvius: Numerical simulations and implications for hazard assessment

Volcanic ash fallout subsequent to a possible renewal of the Vesuvius activity represents a serious threat to the highly urbanized area around the volcano. In order to assess the relative hazard we consider three different possible scenarios such as those following Plinian, Sub-Plinian, and violent Strombolian eruptions. Reference eruptions for each scenario are similar to the 79 AD (Pompeii), the 1631 AD (or 472 AD) and the 1944 AD Vesuvius events, respectively. Fallout deposits for the first two scenarios are modeled using HAZMAP, a model based on a semi-analytical solution of the 2D advection–diffusion–sedimentation equation. In contrast, fallout following a violent Strombolian event is modeled by means of FALL3D, a numerical model based on the solution of the full 3D advection–diffusion–sedimentation equation which is valid also within the atmospheric boundary layer. Inputs for models are total erupted mass, eruption column height, bulk grain-size, bulk component distribution, and a statistical set of wind profiles obtained by the NCEP/NCAR re-analysis. We computed ground load probability maps for different ash loadings. In the case of a Sub-Plinian scenario, the most representative tephra loading maps in 16 cardinal directions were also calculated. The probability maps obtained for the different scenarios are aimed to give support to the risk mitigation strategies.     

龙岗金龙顶子火山空降碎屑物数值模拟及概率性灾害评估

空降碎屑物为爆炸式火山喷发产生的一种重要的灾害类型,数值模拟已成为一个快速有效地确定火山灰扩散和沉积范围的方法。本文根据改进的Suzuki(1983)二维扩散模型,编写了基于Windows环境下的火山灰扩散程序。通过对前人资料的分析,模拟了龙岗火山群中最新火山喷发——金龙顶子火山喷发产生的空降碎屑物扩散范围,与实测结果具有很好的一致性,证实了模型的可靠性和参数的合理性。根据该区10年的风参数,模拟了7021次不同风参数时金龙顶子火山灰的扩散范围,以此制作了火山灰沉积厚度超过1cm和0.5cm时的概率性空降碎屑灾害区划图。本文的研究可为龙岗火山区火山危险性分析和灾害预警与对策提供重要的科学依据。     

A three-dimensional Eulerian model for transport and deposition of volcanic ashes

Volcanic ash fallout represents a serious threat to people living near active volcanoes because it can produce several undesirable effects such as collapse of roofs by ash loading, respiratory sickness, air traffic disruption, or damage to agriculture. The assessment of such volcanic risk is therefore an issue of vital importance for public safety and its mitigation often requires to evaluate the temporal evolution of the phenomenon through reliable computational models.We develop an Eulerian model, named FALL3D, for the transport and deposition of volcanic ashes. The model is based on the advection–diffusion–sedimentation equation with a turbulent diffusion given by the gradient transport theory, a wind field obtained from a meteorological limited area model (LAM) and the source term derived from by buoyant plume theory. It can be used to forecast either ash concentration in the atmosphere or ash loading on the ground. Model inputs are topography, meteorological data given by a LAM, mass eruption rate, and a particle settling velocity distribution. A test application to the July 2001 Etna eruption is presented.     

Very short-term spatio-temporal wind power prediction using a censored Gaussian field

Wind power is a renewable energy resource, that has relatively cheap installation costs and it is highly possible that will become the main energy resource in the near future. Wind power needs to be integrated efficiently into electricity grids, and to optimize the power dispatch, techniques to predict the level of wind power and the associated variability are critical. Ideally, one would like to obtain reliable probability density forecasts for the wind power distributions. We aim at contributing to the literature of wind power prediction by developing and analysing a spatio-temporal methodology for wind power production, that is tested on wind power data from Denmark. We use anisotropic spatio-temporal correlation models to account for the propagation of weather fronts, and a transformed latent Gaussian field model to accommodate the probability masses that occur in wind power distribution due to chains of zeros. We apply the model to generate multi-step ahead probability predictions for wind power generated at both locations where wind farms already exist but also to nearby locations.     

Tephra fallout hazard assessment at the Campi Flegrei caldera (Italy)

Tephra fallout associated with renewal of volcanism at the Campi Flegrei caldera is a serious threat to the Neapolitan area. In order to assess the hazards related with tephra loading, we have considered three different eruption scenarios representative of past activity: a high-magnitude event similar to the 4.1 ka Agnano-Monte Spina eruption, a medium-magnitude event, similar to the ∼3.8 ka Astroni 6 eruption, and a low-magnitude event similar to the Averno 2 eruption. The fallout deposits were reconstructed using the HAZMAP computational model, which is based on a semi-analytical solution of the two-dimensional advection–diffusion–sedimentation equation for volcanic tephra. The input parameters into the model, such as total erupted mass, eruption column height, and bulk grain-size and components distribution, were obtained by best-fitting field data. We carried out tens of thousands simulations using a statistical set of wind profiles, obtained from NOAA re-analysis. Probability maps, relative to the considered scenarios, were constructed for several tephra loads, such as 200, 300 and 400 kg/m². These provide a hazard assessment for roof collapses due to tephra loading that can be used for risk mitigation plans in the area.     

太阳等离子体和磁场输出源表面结构的一种MHD模型

以观测到的光球视向磁场、Ｋ－日冕亮度作为输入,以相应的统计结果为约束条件,利用磁流体力学方程组,给出了等离子体及磁场各参数在源表面上的二维分布．其结果与同期的卫星观测数据和已有的统计结果相比较,显示出相当程度的一致性．     

A One-dimensional Ensemble Forecast and Assimilation System for Fog Prediction

A probabilistic fog forecast system was designed based on two high resolution numerical 1-D models called COBEL and PAFOG. The 1-D models are coupled to several 3-D numerical weather prediction models and thus are able to consider the effects of advection. To deal with the large uncertainty inherent to fog forecasts, a whole ensemble of 1-D runs is computed using the two different numerical models and a set of different initial conditions in combination with distinct boundary conditions. Initial conditions are obtained from variational data assimilation, which optimally combines observations with a first guess taken from operational 3-D models. The design of the ensemble scheme computes members that should fairly well represent the uncertainty of the current meteorological regime. Verification for an entire fog season reveals the importance of advection in complex terrain. The skill of 1-D fog forecasts is significantly improved if advection is considered. Thus the probabilistic forecast system has the potential to support the forecaster and therefore to provide more accurate fog forecasts.     

Comparison of different mass transport calculation methods for wind erosion quantification purposes

Quantitative estimation of the material transported by the wind under field conditions is essential for the study and control of wind erosion. A critical step of this calculation is the integration of the curve that relates the variation of the amount of the material carried by the wind with height. Several mathematical procedures have been proposed for this calculation, but results are scarce and controversial. One objective of this study was to assess the efficiency of three mathematical models (a rational, an exponential, and a simplified Gaussian function) for the calculation of the mass transport, as compared to the linear spline interpolation. Another objective of this study was to compare the mass transport calculated from field measurements obtained from a minimum of three discrete sampling heights with measurements of nine sampling heights. With this purpose, wind erosion was measured under low surface roughness conditions on an Entic Haplustoll during 25 events. The rational function was found to be mathematically limited for the estimation of wind eroded sediment mass flux. The simplified Gaussian model did not fit to the vertical mass flux profile data. Linear spline interpolation generally produced higher mass transport estimates than the exponential equation, and it proved to be a very flexible and robust method. Using different sampling arrangements and different mass flux models can produce differences of more than 45% in mass transport estimates, even under similar field conditions. Under the conditions of this study, at least three points between the soil surface and 1·5 m high, including one point as closest as possible to the surface, should be sampled in order to obtain accurate mass transport estimates. Additionally, the linear spline interpolation and the non‐linear regression using an exponential model, proved to be mathematically reliable methods for calculating the mass transport. Copyright © 2010 John Wiley & Sons, Ltd.     

DEPTH-INTEGRATED MODELING FOR AGGRADING/DEGRADING MOBILE CHANNELS I： MODEL DEVELOPMENT

1 INTRODUCTION The study of sediment transport in alluvial river is one of the most important fields in hydraulic engineering. Sediment transport has direct influence on the evolution of riverbeds, estuaries and coastlines, and, in turn, affects decision-making of flood control, operating rule of reservoir, design of hydraulic structure and many other aspects. Models with different orders of dimensions were presented in the literature, and most of them had common basis that they were formu…     

Coseismic Three-Dimensional Displacements Determined Using SAR Data: Theory and an Application Test

We present a methodology to construct three-dimensional deformation maps using different parameters that can be extracted using SAR data. We apply the methodology using ENVISAT SAR data before and after the December 26^th, 2003 M_w 6.6 Bam earthquake in Iran to determine spatial quasi-continuous three-dimensional coseismic deformation maps. Two near vertical deformation offset components are computed using Envisat ASAR differential interferometry (DInSAR), while two horizontal deformation offset components are measured using sub-pixel correlation techniques applied to ASAR amplitude images. Using the presented methodology, we combine four unevenly precise independent projections of surface coseismic deformation to obtain the full three-dimensional coseismic deformation field caused by an earthquake. The full 3-D coseismic displacement vector is modeled using elastic deformation models constraining details of the fault geometry and slip distribution at depth. Results from the inversion are interpreted in the framework of the Iranian present-day tectonism. Full exploitation of dense 3-D coseismic surface deformation using SAR data, even for moderate earthquakes, should facilitate distinguishing between different interpretations of the mechanical properties of seismically active areas and within the inherent ambiguity of the geophysical inversion solutions.     

A New Integrated Weighted Model in SNOW-V10: Verification of Categorical Variables

This paper presents the verification results for nowcasts of seven categorical variables from an integrated weighted model (INTW) and the underlying numerical weather prediction (NWP) models. Nowcasting, or short range forecasting (0–6 h), over complex terrain with sufficient accuracy is highly desirable but a very challenging task. A weighting, evaluation, bias correction and integration system (WEBIS) for generating nowcasts by integrating NWP forecasts and high frequency observations was used during the Vancouver 2010 Olympic and Paralympic Winter Games as part of the Science of Nowcasting Olympic Weather for Vancouver 2010 (SNOW-V10) project. Forecast data from Canadian high-resolution deterministic NWP system with three nested grids (at 15-, 2.5- and 1-km horizontal grid-spacing) were selected as background gridded data for generating the integrated nowcasts. Seven forecast variables of temperature, relative humidity, wind speed, wind gust, visibility, ceiling and precipitation rate are treated as categorical variables for verifying the integrated weighted forecasts. By analyzing the verification of forecasts from INTW and the NWP models among 15 sites, the integrated weighted model was found to produce more accurate forecasts for the 7 selected forecast variables, regardless of location. This is based on the multi-categorical Heidke skill scores for the test period 12 February to 21 March 2010.     

Evaluating suitability of a tephra dispersal model as part of a risk assessment framework

In volcanic risk assessment it is necessary to determine the appropriate level of sophistication for a given predictive model within the contexts of multiple sources of uncertainty and coupling between models. A component of volcanic risk assessment for the proposed radioactive waste repository at Yucca Mountain (Nevada, USA) involves prediction of dispersal of contaminated tephra during violent Strombolian eruptions and the subsequent transport of that tephra toward a hypothetical individual via surface processes. We test the suitability of a simplified model for volcanic plume transport and fallout tephra deposition (ASHPLUME) coupled to a surface sediment-transport model (FAR) that calculates the redistribution of tephra, and in light of inherent uncertainties in the system. The study focuses on two simplifying assumptions in the ASHPLUME model: 1) constant eruptive column height and 2) constant wind speed and direction during an eruption. Variations in tephra dispersal resulting from unsteady column height and wind conditions produced variations up to a factor of two in the concentration of tephra in sediment transported to the control population. However, the effects of watershed geometry and terrain, which control local remobilization of tephra, overprint sensitivities to eruption parameters. Because the combination of models used here shows limited sensitivity to the actual details of ash fall, a simple fall model suffices to estimate tephra mass delivered to the hypothetical individual.     

A New Integrated Weighted Model in SNOW-V10: Verification of Continuous Variables

This paper presents the verification results of nowcasts of four continuous variables generated from an integrated weighted model and underlying Numerical Weather Prediction (NWP) models. Real-time monitoring of fast changing weather conditions and the provision of short term forecasts, or nowcasts, in complex terrain within coastal regions is challenging to do with sufficient accuracy. A recently developed weighting, evaluation, bias correction and integration system was used in the Science of Nowcasting Olympic Weather for Vancouver 2010 project to generate integrated weighted forecasts (INTW) out to 6 h. INTW forecasts were generated with in situ observation data and background gridded forecasting data from Canadian high-resolution deterministic NWP system with three nested grids at 15-, 2.5- and 1-km horizontal grid-spacing configurations. In this paper, the four variables of temperature, relative humidity, wind speed and wind gust are treated as continuous variables for verifying the INTW forecasts. Fifteen sites were selected for the comparison of the model performances. The results of the study show that integrating surface observation data with the NWP forecasts produce better statistical scores than using either the NWP forecasts or an objective analysis of observed data alone. Overall, integrated observation and NWP forecasts improved forecast accuracy for the four continuous variables. The mean absolute errors from the INTW forecasts for the entire test period (12 February to 21 March 2010) are smaller than those from NWP forecasts with three configurations. The INTW is the best and most consistent performer among all models regardless of location and variable analyzed.     

Conditional Probabilities for Large Events Estimated by Small Earthquake Rate

We examined forecasting quiescence and activation models to obtain the conditional probability that a large earthquake will occur in a specific time period on different scales in Taiwan. The basic idea of the quiescence and activation models is to use earthquakes that have magnitudes larger than the completeness magnitude to compute the expected properties of large earthquakes. We calculated the probability time series for the whole Taiwan region and for three subareas of Taiwan—the western, eastern, and northeastern Taiwan regions—using 40 years of data from the Central Weather Bureau catalog. In the probability time series for the eastern and northeastern Taiwan regions, a high probability value is usually yielded in cluster events such as events with foreshocks and events that all occur in a short time period. In addition to the time series, we produced probability maps by calculating the conditional probability for every grid point at the time just before a large earthquake. The probability maps show that high probability values are yielded around the epicenter before a large earthquake. The receiver operating characteristic (ROC) curves of the probability maps demonstrate that the probability maps are not random forecasts, but also suggest that lowering the magnitude of a forecasted large earthquake may not improve the forecast method itself. From both the probability time series and probability maps, it can be observed that the probability obtained from the quiescence model increases before a large earthquake and the probability obtained from the activation model increases as the large earthquakes occur. The results lead us to conclude that the quiescence model has better forecast potential than the activation model.     

Inversion of surface gravity data for 3-D density modeling of geologic structures using total variation regularization

We develop an inversion procedure using the total variation (TV) regularization method as a stabilizing function to invert surface gravity data to retrieve 3-D density models of geologic structures with sharp boundaries. The developed inversion procedure combines several effective algorithms to solve the TV regularized problem. First, a matrix form of the gradient vector is designed using the Kronecker product to numerically approximate the 3-D TV function. The piecewise polynomial truncated singular value decomposition (PP-TSVD) algorithm is then used to solve the TV regularized inverse problem. To obtain a density model with depth resolution, we use a sensitivity-based depth weighting function. Finally, we apply the Genetic Algorithm (GA) to select the best combination of the PP-TSVD algorithm and the depth weighting function parameters. 3-D simulations conducted with synthetic data show that this approach produces sub-surface images in which the structures are well separated in terms of sharp boundaries, without the need of a priori detailed density model. The method applied to a real dataset from a micro-gravimetry survey of Gotvand Dam, southwestern Iran, clearly delineates subsurface cavities starting from a depth of 40 m within the area of the dam reservoir.     

A Procedure for Calculating Extreme Characteristics of the Northern Caspian Sea Level

A procedure is proposed for calculating extreme characteristics of the level of a sea with allowance for positive and negative setups. Analysis is made of past storm events in the Northern Caspian Sea that have caused strong setups. Sixty-three storm weather patterns are chosen from a period of 45 years. Time ring synoptic maps are used to digitize the atmospheric pressure fields and calculate the field of its gradient and the wind near water surface. Based on these data, the sea level values and currents are calculated through two- and three-dimensional hydrodynamic models. A probabilistic model along with computer-aided data treatment procedures are used to calculate the fields of extreme characteristics of the sea level at the Lagan gage with the occurrence of once per N years at the average Caspian Sea level of 27 m below SL.     

An Application of the Multi-Physics Ensemble Kalman Filter to Typhoon Forecast

This study examines the roles of the multi-physics approach in accounting for model errors for typhoon forecasts with the local ensemble transform Kalman filter (LETKF). Experiments with forecasts of Typhoon Conson (2010) using the weather research and forecasting (WRF) model show that use of the WRF’s multiple physical parameterization schemes to represent the model uncertainties can help the LETKF provide better forecasts of Typhoon Conson in terms of the forecast errors, the ensemble spread, the root mean square errors, the cross-correlation between mass and wind field as well as the coherent structure of the ensemble spread along the storm center. Sensitivity experiments with the WRF model show that the optimum number of the multi-physics ensemble is roughly equal to the number of combinations of different physics schemes assigned in the multi-physics ensemble. Additional idealized experiments with the Lorenz 40-variable model to isolate the dual roles of the multi-physics ensemble in correcting model errors and expanding the local ensemble space show that the multi-physics approach appears to be more essential in augmenting the local rank representation of the LETKF algorithm rather than directly accounting for model errors during the early cycles. The results in this study suggest that the multi-physics approach is a good option for short-range forecast applications with full physics models in which the spinup of the ensemble Kalman filter may take too long for the ensemble spread to capture efficiently model errors and cross-correlations among model variables.     

Using wind run to predict sand drift

Conventional aeolian sand transport models relate mass transport rate to wind speed or shear velocity, usually expressed and empirically tested on a 1-s time scale. Projections of total sand delivery over long time scales based on these models are highly sensitive to any small bias arising from statistical fitting on empirical data. We analysed time series of wind speed and sand transport rate collected at 14 independent measurement stations on a beach during a prior field experiment. The results show that relating total sand drift to cumulative above-threshold wind run yields models which are more statistically robust when fitted on empirical data, generating smaller prediction errors when projected to longer time scales. Testing of different power exponents indicates that a linear relationship between sand drift and above-threshold wind run yields the best results. These findings inspire a speculative novel phenomenological model relating the mass flow of air in the boundary layer to the mass transport of sand over the surface. © 2020 John Wiley & Sons, Ltd.     

Assimilation of Doppler Weather Radar Data in WRF Model for Simulation of Tropical Cyclone Aila

For the accurate and effective forecasting of a cyclone, it is critical to have accurate initial structure of the cyclone in numerical models. In this study, Kolkata Doppler weather radar (DWR) data were assimilated for the numerical simulation of a land-falling Tropical Cyclone Aila (2009) in the Bay of Bengal. To study the impact of radar data on very short-range forecasting of a cyclone's path, intensity and precipitation, both reflectivity and radial velocity were assimilated into the weather research and forecasting (WRF) model through the ARPS data assimilation system (ADAS) and cloud analysis procedure. Numerical experiment results indicated that radar data assimilation significantly improved the simulated structure of Cyclone Aila. Strong influences on hydrometeor structures of the initial vortex and precipitation pattern were observed when radar reflectivity data was assimilated, but a relatively small impact was observed on the wind fields at all height levels. The assimilation of radar wind data significantly improved the prediction of divergence/convergence conditions over the cyclone's inner-core area, as well as its wind field in the low-to-middle troposphere (600–900 hPa), but relatively less impact was observed on analyzed moisture field. Maximum surface wind speed produced from DWR–Vr and DWR–ZVr data assimilation experiments were very close to real-time values. The impact of radar data, after final analysis, on minimum sea level pressure was relatively less because the ADAS system does not adjust for pressure due to the lack of pressure observations, and from not using a 3DVAR balance condition that includes pressure. The greatest impact of radar data on forecasting was realized when both reflectivity and wind data (DWR–ZVr and DWR–ZVr00 experiment) were assimilated. It is concluded that after final analysis, the center of the cyclone was relocated very close to the observed position, and simulated cyclone maintained its intensity for a longer duration. Using this analysis, different stages of the cyclone are better captured, and cyclone structure, intensification, direction of movement, speed and location are significantly improved when both radar reflectivity and wind data are assimilated. As compared to other experiments, the maximum reduction in track error was noticed in the DWR–ZVr and DWR–ZVr00 experiments, and the predicted track in these experiments was very close to the observed track. In the DWR–ZVr and DWR–ZVr00 experiments, rainfall pattern and amount of rainfall forecasts were remarkably improved and were similar to the observation over West Bengal, Orissa and Jharkhand; however, the rainfall over Meghalaya and Bangladesh was missed in all the experiments. The influence of radar data reduces beyond a 12-h forecast, due to the dominance of the flow from large-scale, global forecast system models. This study also demonstrates successful coupling of the data assimilation package ADAS with the WRF model for Indian DWR data.     

Wave measurement and modeling in Chesapeake Bay

Three recently measured wind and wave data sets in the northern part of Chesapeake Bay (CB) are presented. Two of the three data sets were collected in late 1995. The third one was collected in July of 1998. The analyzed wind and wave data show that waves were dominated by locally generated, fetch limited young wind seas. Significant wave heights were highly correlated to the local driving wind speeds and the response time of the waves to the winds was about 1 h. We also tested two very different numerical wave models, Simulation of WAves Nearshore (SWAN) and Great Lakes Environmental Research Laboratory (GLERL), to hind-cast the wave conditions against the data sets. Time series model–data comparisons made using SWAN and GLERL showed that both models behaved well in response to a suddenly changing wind. In general, both SWAN and GLERL over-predicted significant wave height; SWAN over-predicted more than GLERL did. SWAN had a larger scatter index and a smaller correlation coefficient for wave height than GLERL had. In addition, both models slightly under-predicted the peak period with a fairly large scatter and low correlation coefficient. SWAN predicted mean wave direction better than GLERL did. Directional wave spectral comparisons between SWAN predictions and the data support these statistical comparisons. The GLERL model was much more computationally efficient for wind wave forecasts in CB. SWAN and GLERL predicted different wave height field distributions for the same winds in deeper water areas of the Bay where data were not available, however. These differences are as yet unresolved.