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The sensitivity of simulated orographic precipitation to model details other than cloud microphysics Günther Zängl Meteorologisches Institut der Universität.

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Presentation on theme: "The sensitivity of simulated orographic precipitation to model details other than cloud microphysics Günther Zängl Meteorologisches Institut der Universität."— Presentation transcript:

1 The sensitivity of simulated orographic precipitation to model details other than cloud microphysics Günther Zängl Meteorologisches Institut der Universität München

2 What are the primary sources of uncertainty in high-resolution simulations of orographic precipitation?  Cloud microphysics  (In nested-domain runs) Convection parameterizations used in the outer model domains  Soil moisture / PBL parameterization  Numerical side effects (vertical coordinate specification, numerical diffusion...)

3 Test strategy  the convection parameterization in the coarse domains  the soil moisture specification  the PBL parameterization  the vertical coordinate formulation  the implementation of horizontal diffusion Compare the spread among five different microphysical parameterizations against the effect of changing

4 Set-up of the simulations  Model: MM5, version 3  4 nested domains, finest horizontal resolution 1.4 km (see figure)  38 model levels in the vertical  Case: MAP-IOP 10 (Oct. 24/25, 1999)  Initial / boundary data: Operational ECMWF analyses  Period of simulation: Oct. 24, 00 UTC - Oct. 25, 18 UTC  Validation against 81 surface stations for Oct. 24, 06 UTC - Oct. 25, 18 UTC (see figure for location)

5 Parameterizations used for the reference run and changes for sensitivity tests  Reisner2 microphysical scheme from MM5 version 3.3 (Reisner1, Goddard v3.3, Goddard v3.5, Reisner2 v3.5)  Grell cumulus parameterization in D1 (37.8 km) and D2 (12.6 km) (Kain-Fritsch in D1 and D2; Grell in D1-D3; Kain-Fritsch in D1-D2 and Grell in D3)  Gayno-Seaman PBL parameterization (Blackadar PBL, MRF PBL)  Modified horizontal diffusion scheme (Zängl 2002, MWR) computes the horizontal diffusion of temperature and the moisture variables truly horizontally rather than along the terrain-following coordinate surfaces (Original diffusion scheme for moisture only / for moisture and temperature)  Smooth-level vertical coordinate system (similar to Schär et al. 2002, MWR)

6 36h-accumulated precipitation in the reference run domain-average precip station-intp. average observation (mm) >47N N-47N N-46.6N <46.2N total N 46.6N 46.2N

7 Difference fields (sensitivity experiment - REF run) Reisner1-scheme new Reisner2-scheme +5% / +4% +2% / +3% Relative difference in domain-average (station-interpolated average)

8 Goddard v3.3 Goddard v % / +24% +7% / +6%

9 Kain-Fritsch instead of Grell in D1 and D2 Grell in D1, D2 and D3 Cumulus parameterizations -10% / -7% -8% / -5%

10 Combination of Kain-Fritsch in D1 / D2 and Grell in D3 -16% / -13%

11 Blackadar PBL Boundary-layer parameterization (reference: Gayno-Seaman PBL) -6% / -6% MRF PBL -16% / -12% Predictive soil moisture scheme instead of fixed soil moisture: <1%

12 Parameterizations as in REF run Kain-Fritsch in D1, D2; Grell in D3 Smooth-level vertical coordinate system -7% / -4% -9%/ -10% (-24%/ -21% w.r.t. REF)

13 Diffusion along sigma-levels Diffusion along sigma-levels for moisture only for moisture and temperature Implementation of horizontal diffusion +9% / +26% +40% / +66%

14 Ranking list of sensitivities (disregarding the obsolete version of the Goddard parameterization) 1. Horizontal diffusion (40% - 65%) 2. Convection scheme (5% - 15%) Boundary-layer parameterization (5% - 15%) 3. Vertical coordinate system (7% - 10%) 4. Cloud microphysics (2% - 7%)

15 Which simulation performs best and worst?  Best simulation: Kain-Fritsch parameterization in D1 and D2, Grell in D3, smooth-level vertical coordinate: bias 3.8 mm (+14%); rms error 12.1 mm  Worst simulation: Original diffusion scheme (i.e. diffusion along sigma-levels for temperature and moisture), parameterizations as in REF: bias 36.9 mm (+140%); rms error 48.0 mm  For comparison: REF run bias 11.8 mm; rms error 19.5 mm

16 Conclusions - Part I  Errors in precipitation forecasts are not necessarily due to errors in the cloud microphysics  Particularly large systematic errors can arise from computing the horizontal diffusion along terrain- following coordinate surfaces  The side effects arising from other parameterizations and numerical errors deserve much more attention than they currently receive

17 Reference setup ECMWF MAP Reanalysis data instead of operational analyses -13% / -11% New vertical coordinate -18% / -21%

18 New vertical coordinate and modified configuration of convection parameterizations (Kain-Fritsch in D1 / D2 and Grell in D3) -3% / -6%

19  The impact of using the Reanalysis data instead of the operational analyses depends sensitively on the model setup  Comparison with observations reveals: Using the Reanalysis data yields a substantial improvement for the standard setup and the standard setup with the new vertical coordinate, but not for the setup which yielded the best results with the operational analysis  Comparisons between operational analysis and MAP reanalysis can produce misleading results when carried out with one model setup only Conclusions - Part II


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