Application of user-oriented measure of effectiveness to transport and dispersion model predictions of the European tracer experiment

2004 ◽  
Vol 38 (39) ◽  
pp. 6789-6801 ◽  
Author(s):  
Steve Warner ◽  
Nathan Platt ◽  
James F. Heagy
2021 ◽  
Vol 244 ◽  
pp. 117717
Author(s):  
Simon Gant ◽  
Graham Tickle ◽  
Adrian Kelsey ◽  
Harvey Tucker

Atmosphere ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 200 ◽  
Author(s):  
Helen N. Webster ◽  
Benjamin J. Devenish ◽  
Larry G. Mastin ◽  
David J. Thomson ◽  
Alexa R. Van Eaton

Large explosive eruptions can result in the formation of an umbrella cloud which rapidly expands, spreading ash out radially from the volcano. The lateral spread by the intrusive gravity current dominates the transport of the ash cloud. Hence, to accurately forecast the transport of ash from large eruptions, lateral spread of umbrella clouds needs to be represented within volcanic ash transport and dispersion models. Here, we describe an umbrella cloud parameterisation which has been implemented into an operational Lagrangian model and consider how it may be used during an eruption when information concerning the eruption is limited and model runtime is key. We examine different relations for the volume flow rate into the umbrella, and the rate of spreading within the cloud. The scheme is validated against historic eruptions of differing scales (Pinatubo 1991, Kelud 2014, Calbuco 2015 and Eyjafjallajökull 2010) by comparing model predictions with satellite observations. Reasonable predictions of umbrella cloud spread are achieved using an estimated volume flow rate from the empirical equation by Bursik et al. and the observed eruption height. We show how model predictions can be refined during an ongoing eruption as further information and observations become available.


2015 ◽  
Vol 36 (4) ◽  
pp. 3-24 ◽  
Author(s):  
Wilfried Roetzel ◽  
Chakkrit Na Ranong

Abstract An evaluation method is developed for single blow experiments with liquids on heat exchangers. The method is based on the unity Mach number dispersion model. The evaluation of one experiment yields merely one equation for the two unknowns, the number of transfer units and the dispersive Peclet number. Calculations on an example confirm that one single blow test alone cannot provide reliable values of the unknowns. A second test with a liquid of differing heat capacity is required, or a tracer experiment for the measurement of the Peclet number. A modified method is developed for gases. One experiment yields the effective number of transfer units and approximate values of the two unknowns. The numerical evaluation of calculated experiments demonstrates the applicability of the evaluation methods.


2022 ◽  
Vol 22 (1) ◽  
pp. 577-596
Author(s):  
Susan J. Leadbetter ◽  
Andrew R. Jones ◽  
Matthew C. Hort

Abstract. Atmospheric dispersion model output is frequently used to provide advice to decision makers, for example, about the likely location of volcanic ash erupted from a volcano or the location of deposits of radioactive material released during a nuclear accident. Increasingly, scientists and decision makers are requesting information on the uncertainty of these dispersion model predictions. One source of uncertainty is in the meteorology used to drive the dispersion model, and in this study ensemble meteorology from the Met Office ensemble prediction system is used to provide meteorological uncertainty to dispersion model predictions. Two hypothetical scenarios, one volcanological and one radiological, are repeated every 12 h over a period of 4 months. The scenarios are simulated using ensemble meteorology and deterministic forecast meteorology and compared to output from simulations using analysis meteorology using the Brier skill score. Adopting the practice commonly used in evaluating numerical weather prediction (NWP) models where observations are sparse or non-existent, we consider output from simulations using analysis NWP data to be truth. The results show that on average the ensemble simulations perform better than the deterministic simulations, although not all individual ensemble simulations outperform their deterministic counterpart. The results also show that greater skill scores are achieved by the ensemble simulation for later time steps rather than earlier time steps. In addition there is a greater increase in skill score over time for deposition than for air concentration. For the volcanic ash scenarios it is shown that the performance of the ensemble at one flight level can be different to that at a different flight level; e.g. a negative skill score might be obtained for FL350-550 and a positive skill score for FL200-350. This study does not take into account any source term uncertainty, but it does take the first steps towards demonstrating the value of ensemble dispersion model predictions.


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