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This work investigates the sensitivity of wind speed forecasts during wind ramp events to parameters within a numerical weather prediction model boundary layer physics scheme. In a novel way, it explores how these sensitivities vary across 1) equally-likely ensemble members with different initial conditions, 2) different times during the events, 3) different types of ramp-causing events, and 4) different horizontal grid spacing. Previous research using monthly wind speed averages finds that a small number of parameters in the surface layer and boundary layer schemes are responsible for the majority of the forecast uncertainty. These include parameters related to the dissipation of turbulent kinetic energy, the Prandtl number, turbulent length scales, and surface roughness. In this study, the values of parameters within the Mellor-Yamada-Nakahishi-Niino (MYNN) boundary layer scheme and the MM5 surface layer scheme of the Weather Research and Forecast (WRF) model are perturbed in a systematic way to evaluate parametric sensitivity for two types of specific ramp-causing phenomena: marine pushes and stable mix-out events. In addition to examining variations in parametric sensitivity, forecast variance from the physics ensembles is compared to that from an initial condition ensemble to evaluate the relative contributions from initial condition uncertainty and parametric uncertainty. This work supports the second Department of Energy Wind Forecast Improvement Project (WFIP2). Results indicate that during marine push events, wind speed forecasts are more sensitivity to initial conditions than to parameters within the MYNN boundary layer scheme. In the mix-out cases, the sensitivity to initial conditions and parametric variations are comparable. A major finding of this study is that there are large differences in parametric sensitivity between members of the same initial condition ensemble for all cases. These sensitivity differences are the result of variations in the atmospheric state within the initial condition ensemble. Parametric sensitivity changes over the course of each forecast and strong wind ramp events can produce large changes in sensitivity. Finally, parametric sensitivity changes between event type and with model resolution. These conclusions are particularly relevant for future sensitivity studies and efforts at model tuning.

We have identified a clonal complex of Mycobacterium bovis present at high frequency in cattle in population samples from several sub-Saharan west-central African countries. This closely related group of bacteria is defined by a specific chromosomal deletion (RDAf1) and can be identified by the absence of spacer 30 in the standard spoligotype typing scheme. We have named this group of strains the African 1 (Af1) clonal complex and have defined the spoligotype signature of this clonal complex as being the same as the M. bovis BCG vaccine strain but with the deletion of spacer 30. Strains of the Af1 clonal complex were found at high frequency in population samples of M. bovis from cattle in Mali, Cameroon, Nigeria, and Chad, and using a combination of variable-number tandem repeat typing and spoligotyping, we show that the population of M. bovis in each of these countries is distinct, suggesting that the recent mixing of strains between countries is not common in this area of Africa. Strains with the Af1-specific deletion (RDAf1) were not identified in M. bovis isolates from Algeria, Burundi, Ethiopia, Madagascar, Mozambique, South Africa, Tanzania, and Uganda. Furthermore, the spoligotype signature of the Af1 clonal complex has not been identified in population samples of bovine tuberculosis from Europe, Iran, and South America. These observations suggest that the Af1 clonal complex is geographically localized, albeit to several African countries, and we suggest that the dominance of the clonal complex in this region is the result of an original introduction into cows naïve to bovine tuberculosis.