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Cloud microphysics

The sensitivity of simulated orographic precipitation to model details other than cloud microphysics Günther Zängl Meteorologisches Institut der Universität München. Cloud microphysics (In nested-domain runs) Convection parameterizations used in the outer model domains

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Cloud microphysics

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

  2. 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 ...) What are the primary sources of uncertainty in high-resolution simulations of orographic precipitation?

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

  4. 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) Set-up of the simulations

  5. 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) Parameterizations used for the reference run and changes for sensitivity tests

  6. 36h-accumulated precipitation in the reference run domain-average precipstation-intp. averageobservation(mm) >47N 11.2 8.12.1 46.6N-47N 47.4 34.718.1 46.2N-46.6N 65.9 42.733.6 <46.2N 83.469.753.7 total 45.5 38.1 26.3 47N 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 v3.5 +20% / +24% +7% / +6%

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

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

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

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

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

  14. 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%) Ranking list of sensitivities (disregarding the obsolete version of the Goddard parameterization)

  15. 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 Which simulation performs best and worst?

  16. 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 Conclusions - Part I The side effects arising from other parameterizations and numerical errors deserve much more attention than they currently receive

  17. ECMWF MAP Reanalysis data instead of operational analyses Reference setup New vertical coordinate -13% / -11% -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 Conclusions - Part II Comparisons between operational analysis and MAP reanalysis can produce misleading results when carried out with one model setup only

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