Estimation and propagation of volcanic source parameter uncertainty in an ash transport and dispersal model: application to the Eyjafjallajokull plume of 14–16 April 2010     

Marcus Bursik  Matthew Jones  Simon Carn  Ken Dean  Abani Patra  Michael Pavolonis  E. Bruce Pitman  Tarunraj Singh  Puneet Singla  Peter Webley  Halldor Bjornsson  Maurizio Ripepe 《Bulletin of Volcanology》2012,74(10):2321-2338

Data on source conditions for the 14 April 2010 paroxysmal phase of the Eyjafjallaj?kull eruption, Iceland, have been used as inputs to a trajectory-based eruption column model, bent. This model has in turn been adapted to generate output suitable as input to the volcanic ash transport and dispersal model, puff, which was used to propagate the paroxysmal ash cloud toward and over Europe over the following days. Some of the source parameters, specifically vent radius, vent source velocity, mean grain size of ejecta, and standard deviation of ejecta grain size have been assigned probability distributions based on our lack of knowledge of exact conditions at the source. These probability distributions for the input variables have been sampled in a Monte Carlo fashion using a technique that yields what we herein call the polynomial chaos quadrature weighted estimate (PCQWE) of output parameters from the ash transport and dispersal model. The advantage of PCQWE over Monte Carlo is that since it intelligently samples the input parameter space, fewer model runs are needed to yield estimates of moments and probabilities for the output variables. At each of these sample points for the input variables, a model run is performed. Output moments and probabilities are then computed by properly summing the weighted values of the output parameters of interest. Use of a computational eruption column model coupled with known weather conditions as given by radiosonde data gathered near the vent allows us to estimate that initial mass eruption rate on 14 April 2010 may have been as high as 10⁸?kg/s and was almost certainly above 10⁷?kg/s. This estimate is consistent with the probabilistic envelope computed by PCQWE for the downwind plume. The results furthermore show that statistical moments and probabilities can be computed in a reasonable time by using 9⁴?=?6,561 PCQWE model runs as opposed to millions of model runs that might be required by standard Monte Carlo techniques. The output mean ash cloud height plus three standard deviations??encompassing c. 99.7?% of the probability mass??compares well with four-dimensional ash cloud position as retrieved from Meteosat-9 SEVIRI data for 16 April 2010 as the ash cloud drifted over north-central Europe. Finally, the ability to compute statistical moments and probabilities may allow for the better separation of science and decision-making, by making it possible for scientists to better focus on error reduction and decision makers to focus on ??drawing the line?? for risk assessment.  相似文献   

A Genetic Algorithm Variational Approach to Data Assimilation and Application to Volcanic Emissions     

Kerrie?J.?Schmehl  Sue?Ellen?HauptEmail author  Michael?J.?Pavolonis 《Pure and Applied Geophysics》2012,169(3):519-537

Variational data assimilation methods optimize the match between an observed and a predicted field. These methods normally require information on error variances of both the analysis and the observations, which are sometimes difficult to obtain for transport and dispersion problems. Here, the variational problem is set up as a minimization problem that directly minimizes the root mean squared error of the difference between the observations and the prediction. In the context of atmospheric transport and dispersion, the solution of this optimization problem requires a robust technique. A genetic algorithm (GA) is used here for that solution, forming the GA-Variational (GA-Var) technique. The philosophy and formulation of the technique is described here. An advantage of the technique includes that it does not require observation or analysis error covariances nor information about any variables that are not directly assimilated. It can be employed in the context of either a forward assimilation problem or used to retrieve unknown source or meteorological information by solving the inverse problem. The details of the method are reviewed. As an example application, GA-Var is demonstrated for predicting the plume from a volcanic eruption. First the technique is employed to retrieve the unknown emission rate and the steering winds of the volcanic plume. Then that information is assimilated into a forward prediction of its transport and dispersion. Concentration data are derived from satellite data to determine the observed ash concentrations. A case study is made of the March 2009 eruption of Mount Redoubt in Alaska. The GA-Var technique is able to determine a wind speed and direction that matches the observations well and a reasonable emission rate.  相似文献   

Coastal-Fog Microphysics Using In-Situ Observations and GOES-R Retrievals     

Gultepe  I.  Pardyjak  E.  Hoch  S. W.  Fernando  H. J. S.  Dorman  C.  Flagg  D. D.  Krishnamurthy  R.  Wang  Q.  Gaberšek  S.  Creegan  E.  Scantland  N.  Desjardins  S.  Heidinger  A.  Pavolonis  M.  Heymsfield  A. J. 《Boundary-Layer Meteorology》2021,181(2-3):203-226

Boundary-Layer Meteorology - The objective of this work is to evaluate GOES-R (Geostationary Operational Environmental Satellites-R series) data-based fog conditions which occurred during the C-FOG...  相似文献