Determining the underlying structures in modelled orographic flow

1. Determining the underlying structures in modelled orographic flow R. R. Burton1, S. B. Vosper2 and S. D. Mobbs1 1 Institute for Atmospheric Science, School of Earth and Environment, University of Leeds, Leeds, UK 2 Met Office, Exeter, UK r Numerical Modelling Motivation – severe turbulence in the Falklands A series of 725 model runs has been completed, initialised with daily radiosonde data from MPA. Of these runs, cases where • The model used to simulate the flow over the Falklands was 3DVOM [2, 3]: • Linear model for turbulent flow over hills • Terrain-following coordinates • Incorporates boundary-layer model • Mixing length scheme for turbulence • Semi-implicit finite difference scheme • Real orography dataset Mount Pleasant Airport (MPA), Falkland Islands, suffers from bouts of extreme turbulence [1]. Any insight into predicting severe weather events would be highly desirable. dirn. the measured average wind direction in the lowest 1km of the atmosphere was from NW’ly to NE’ly were selected, leading to 94 model runs. These are then Identifying the surface pressure patterns associated with severe turbulence and windstorms could be a useful aid in this regard. NUMERICAL DOMAIN FOR THE MODEL RUNS 5th Nov.2000 speed MPA Location of the Falkland Islands and MPA Turbulence and rotor cloud at MPA MPA subjected to a principal components analysis. PCA of model runs Principal Component Analysis (PCA) is an objective method for determining underlying patterns in data. The significant structures are known as empirical orthogonal functions, or EOFs. The first EOF should account for as much of the variability as possible; the second EOF should account for as much of the remaining variability as possible; and so on. Shown below arethe significant structures present in the modelled pressure and 400m vertical velocity fields. EOF 2 (P_SURF) EOF 1 (P_SURF) P_SURF EOF 3 (P_SURF) EOF 4 (P_SURF) Event B 8/9/01 Event A 5/11/00 SURFACE PRESSURE PC score Run # P_SURF W_400m normalised eigenvalue EOF 3 (W_400m) EOF 4 (W_400m) W_400m EOF 1 (W_400m) EOF 2 (W_400m) EOF # A B VERTICAL WINDSPEED AT 400M PC score Run # These EOFs show distinct gravity wave patterns, as shown in both the pressure and vertical velocity structures. EOF1(P) and EOF2(P) show high drag situations, with slightly differing orientations; when either the PC scores of EOF1 and EOF 2, or both, are large and positive, we would expect a very large pressure gradient to exist around the area of MPA. Event A: positive EOF1, negative EOF 2 gives a N-S wave pattern This is indeed the case (see the actual model outputs corresponding to events A and B). Output corresponding to other peaks in the time series (not shown) also display significant effects at the surface. Note that severe turbulence was observed at MPA during event A (see the photo and anemograph trace above.) Event B: positive EOF1, positive EOF 2 gives a NE-SW wave pattern Actual model output: surface pressure and wind vectors Correlation with radiosonde profiles In order to determine a relationship (if any) between the input profile used to drive the model and the output from the model, a similar set of PCA analyses was performed on the radiosonde profiles, using the lowest 2km of each profile. The actual profiles used are derived from the radiosonde data and are produced by the boundary-layer model. The presence of inversion has already been connected with turbulence at MPA [1]. 2.0 1.5 26/02/01 height (km) 05/11/00 1.0 This correlation suggests that an inversion, together with with a strong meridional wind at the inversion level, is linked to turbulent effects at the ground; the stronger the v(dθ/dz) signal, the stronger the response. .5 0.5 -0.5 -0.4 -0.3 -0.2 -0.1 0.0 First PC score for P_SURF Proportion of variance explained explained by the EOFs for v(dθ/dz) and surface pressure EOF value (Ks-1) Best fit: r=0.72 20/08/01 First EOF for dθ/dz It was found that the vertical profile of v(dθ/dz) corresponds very well to the model output, in the sense that the first PC scores for the two cases show a marked degree of correlation (r = 0.72) and the first EOFs explain the same proportion of variance. A strong inversion and large negative (northerly) meridional winds will lead to large v(dθ/dz) A marked correlation between the first EOFs for v(dθ/dz) and surface pressure High values of the first PC scoreforv(dθ/dz) corresponded to actual severe weather at MPA (see anemograph traces to the right). First PC score for v(dθ/dz) Conclusions Acknowledgments Ralph Burton was funded by a NERC grant. Dominant structures have been found: the first three EOFs account for 70% of the variance in the data for both surface pressure and vertical wind speed Approximately the same amount of varianceis contained in the first three EOFs for the vertical profile of v(dθ/dz) derived from the input profile A marked degree of correlationis found between the first EOFs for surface pressure and for v(dθ/dz) This suggests that the vertical profile of v(dθ/dz) may be useful as a diagnostic in the prediction of severe weather events. References [1] Mobbs, S. D., Vosper, S. B., Sheridan, P. F., Cardoso, R., Burton, R. R., Arnold, S. J., Hill, M. K., Horlacher, V. and Gadian, A. M. (2005): “Observations of downslope winds and rotors in the Falkland Islands”, Q. J. Roy. Meteor. Soc.,131, 329-351 [2] Vosper, S.: (2003): “Development and testing of a high resolution mountain-wave forecasting system”, Meteorol. Appl. 10, 75-86 [3] King, J. C., Anderson, P. S., Vaughan, D. G., Mann, G. W., Mobbs, S. D. and Vosper, S. B. (2004): “Wind-borne redistribution of snow across an Antarctic ice rise”, J. Geophys. Res., 109, D11104 For more information about this poster please contact Dr Ralph Burton, School of Earth and Environment, Environment, University of Leeds, Leeds, LS2 9JT Email: ralph@env.leeds.ac.uk