FODA CRUZADO

This study made use of CM1, a large eddy simulation meteorological cloud model, to simulate the idealized CS simulation proposed in Anderson et al. (1992). A comparison was made to the full cloud simulation of Orf et al. (2012), Orf et al. (2014) and Oreskovic et al. (2015), to investigate whether an idealized CS simulation can more accurately capture the realism of a cloud model simulation, if the same atmospheric base state conditions are employed. Randomness was added into the CS forcing function in order to better represent the spatial and temporal variability that is present in the thermodynamic cooling in a natural thunderstorm cloud. A number of simulations were performed, one which investigated the

0 0.5 1 1.5 2 2.5 3 3.5 0 2 4 6 8 zm /R0 t/T₀ tg(t)max=200 tg(t)max=300

tg(t)max=inf Orf et al. (2012)

0 1 2 3 4 5 6 0 5 10 15 rm /R0 t/T₀ tg(t)max=200 tg(t)max=300 tg(t)max=inf Orf et al. (2012)

effect of the randomness addition and three others which investigated the temporal dependence of the cooling on the downburst outflow. To the best of the author’ knowledge this study represents the most sophisticated ‘low level’ downburst model that has been utilized to date, with some encouraging results. The following conclusions have been reached:

 The spatial variability around the circumference of the downburst impingement for a CS, which is observed in nature and full cloud simulations, is captured when the imposed atmospheric base state of Brown et al. (1982) is used. Peak circumferential wind speeds are approximately 1.3 that of the circumferential mean values, consistent with that (~2) observed for the full cloud simulation of Orf et al. (2012).

 The addition of randomness into the CS forcing function has little impact on the spatial variability of the downburst outflow, although it does result in a more realistic transition into the turbulent region and aids in achieving computational stability and a grid independent solution.

 The temporal dependence of the CS ramp up function does appear to have a noticeable effect on the decay of the radial wind velocities. Previous CS studies such as Vermeire et al. (2011a), Mason et al. (2009) and Anabor et al. (2011) and IJ studied such as Kim and Hangan (2007) showed an unnatural decay of wind velocities since the source remained ‘on’ throughout the simulation. The tg(t),max=200 simulation of this study showed best agreement to the full cloud simulation data since the radial winds decayed back to the ambient values similarly to how they did in Orf et al. (2014).

 The mean outflow wind velocities of the CS simulation appear to replicate the peak outflow wind velocities of the full cloud simulation. Both vertical circumferentially averaged radial wind speeds fall within 5 m/s of each other, and the heights to the peak winds are located at approximately the same height of 40 m. The later result being unsurprising since both simulations make use of the same numerical model with the same surface treatment options (z0=0.1 m).

 Outflow wind speeds fall within the same range for the current study and the full cloud simulation, although temperature deficits are far larger in the CS simulations (~12 K) than that of the full cloud simulation (~4 K), supporting the concept that

the drag induced by falling precipitation is a large contributor to the magnitude of outflow wind speeds in real events.

 The peak down flow wind velocities in the region around impingement are followed by the peak outflow radial velocities at a location of r=1 km at approximately 1 min - 2 min (depending on how this temporal gap is measured), which is consistent with the full cloud simulation of Orf et al. (2014).

 The scaling procedure of Lundgren et al. (1992) can be applied to non- dimensionalize the temporal and spatial development of the downburst outflow between the CS simulations and the full cloud meteorological simulation. However, this scaling approach appears to be less effective at collapsing peak radial wind speeds, and future studies should investigate the limitations of this scaling method.

 Peak wind speed values of the CS study appear to somewhat overestimate the peak wind speed values observed for the full thunderstorm model. It is suggested for future studies than the cooling rate is lowered from -0.8 K/s to perhaps -0.6 K/s or lower.

The present work shows promising results that indicate that utilizing the simplified CS model in a more sophisticated atmospheric base state results in an outflow wind field that is more comparable to those from a sophisticated meteorological model. The overall goal of this work is to eventually create a computationally less expensive approach, than the full cloud simulations, that effectively captures the complexity of natural downburst events. Future work will involve performing these idealized CS simulations in more atmospheric base state conditions using sounding data from the field in conditions where downbursts have formed. Additionally, including moisture and microphysical effects may be important, as the drag-induced winds contribute a large part to the strength of the down flow from the thunderstorm (Orf et al. 2012). It is also recommended that future studies further investigate the addition of randomness into the cooling forcing function, as the meteorological cloud model shows that variation in the thermodynamic cooling is significant.