1. Meteorologisches Institut der Universität München
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
Compare the spread among five different microphysical parameterizations against the effect of changing
the convection parameterization in the coarse domains
the soil moisture specification
the PBL parameterization
the vertical coordinate formulation
the implementation of horizontal diffusion

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 (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
46.6N-47N
46.2N-46.6N
<46.2N
total
47N
46.6N
46.2N

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

8. Goddard v Goddard v3.5
+20% / +24% % / +6%

9. Cumulus parameterizations
Kain-Fritsch instead of Grell
in D1 and D Grell in D1, D2 and D3
-10% / -7% % / -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% %/ -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% % / +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. 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.  Comparisons between operational analysis and
Conclusions - Part II
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