1. r Determining the underlying structures in modelled orographic flow R. R. Burton 1, S. B. Vosper 2 and S. D. Mobbs 1 1 Institute for Atmospheric Science, School of Earth and Environment, University of Leeds, Leeds, UK 2 Met Office, Exeter, UK 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 PCA of model runs Motivation – severe turbulence in the Falklands Numerical Modelling Location of the Falkland Islands and MPA 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 A series of 725 model runs has been completed, initialised with daily radiosonde data from MPA. Of these runs, cases where the measured average wind direction in the lowest 1km of the atmosphere was from North-Westerly to North-Easterly were selected, leading to 94 model runs. These are then subjected to a principal components analysis. Mount Pleasant Airport ( MPA ), Falkland Islands, suffers from bouts of extreme turbulence [1] and any insight into predicting severe weather events would be highly desirable. Identifying the surface pressure patterns associated with severe turbulence and windstorms could be a useful aid in this regard. SURFACE PRESSURE VERTICAL WINDSPEED AT 400M Turbulence and rotor cloud at MPA Conclusions 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. Acknowledgments EOF # v(dθ/dz)P_SURF 147% 224%17% 310%13% 46%8% Proportion of variance explained by the EOFs for v(dθ/dz) and surface pressure Event A: positive EOF1, negative EOF 2 gives a N-S wave pattern Event B: positive EOF1, positive EOF 2 gives a NE-SW wave pattern Actual model output: surface pressure and wind vectors Event A 5/11/00 Event B 8/9/01 A B 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. dirn speed A strong inversion and large negative (northerly) meridional winds will lead to large v(dθ/dz) First EOF for dθ/dz A marked correlation between the first EOFs for v(dθ/dz) and surface pressure References Ralph Burton was funded by a NERC grant. EOF 1 (P_SURF) EOF 4 (P_SURF)EOF 3 (P_SURF) EOF 1 (W_400m) EOF 2 (P_SURF) EOF 2 (W_400m) EOF 4 (W_400m)EOF 3 (W_400m) EOF # normalised eigenvalue Run # PC score Run # PC score EOF value (Ks -1 ) height (m) First PC score for v(dθ/dz) First PC score for P_SURF Best fit: r=0.72 [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 The presence of inversion has already been connected with turbulence at MPA [1]. 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. 5/11/00 P_SURF W_400m 05/11/00 26/02/01 20/08/01 High values of the first PC score for v(dθ/dz) corresponded to actual severe weather at MPA (see anemograph traces to the right). P_SURF W_400m  A marked degree of correlation is 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.  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 variance is contained in the first three EOFs for the vertical profile of v(dθ/dz) derived from the input profile 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. 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.) 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 are the significant structures present in the modelled pressure and 400m vertical velocity fields. MPA