An index to indicate precipitation probability and to investigate effects of sub-grid-scale surface parameterizations on model performance

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1 An index to indicate precipitation probability and to investigate effects of sub-grid-scale surface parameteriations on model performance Sylvia Bohnenstengel (1,2), K. Heinke Schlünen (2) (1) Max-Planck Institute for Meteorology, Hamburg (2) Meteorological Institute, University of Hamburg QPF-Workshop in Bad Herrenalb

2 Outline Motivation Character of precipitation at Lindenberg (Germany) Introduce locality index to describe meteorological situations Establish relationship between locality index and precipitation probability Simulations for different meteorological situations (characteried by index values)

3 Parameteriation of sub-grid-scale land-use Model performance Main flow parameteriation heterogeneity resolution meteorological situations H averaged ( ) u H * θ forest * forest ( u ) * θ ( u ) H sea * sea H * θ sand * sand grid cell

4 Main land-use Latent heat flux Main land-use ( u ) H * θ forest * forest grid cell

5 Parameter averaging Latent heat flux Parameter averaging (,, ) u* water sand forest ( ) H θ*, water, sand forest water sand forest Grid cell

6 flux aggregation with blending height Latent heat flux Flux aggregation ( H, H H ) H, water sand forest ( ) u H * θ forest * forest ( u ) * θ ( u ) H water * water H * θ Sand * Sand Grid cell

7 Problems Problem Aggregation effect : Neclegting the non-linear effects, which contribute to box-averaged flux, especially important in very heterogeneous regions. Evaluation showed Performance of parameteriation depends on resolution and meteorological situation. Open Question? Which is the best parameteriation for which meteorological situation?

8 Motivation amount probability precipitation Model area representativity Contribution? index strong vertical exchange Contribution! low vertical exchange latent heat flux sensible heat flux surface processes turbulent exchange

Model area y (km) 16 12 13 14 15 16 17 12 8 53 4-4

Land-use information in grey figure is based on

9 Model area y (km) x(km) -2 Water Flats Sand mix.veg Grass Heath Bushes mix.for. con.for. Urban Lindenberg area, 1D simulations. Land-use information in grey figure is based on CORINE Land Cover data for Germany (CORINE Land Cover, 24).

10 Precipitation in Lindenberg area PLUVIO network 384 mm/year (23) spatial measuring network (2x2km²) 1 minutes averages Synop station MOL 384,9 mm/year (23) 723,1 mm/year (22) single station reconstructed 6 hour values from 6 hour and 12 hour values

11 Scatter diagramm PLUVIO,MOL 6 hour integrals MOL_6 [mm] MOL_6 [mm] PLUVIO_av6 [mm] PLUVIO_av6 [mm]

12 Equality of precipitation amounts (MOL_6, PLUVIO_av6) Fraction of all freqeuncy precipitation data 1% 9% 8% 1% 7% 1% 6% 5% 4% 3% 2% 1% % duration [day] Integration time [days] Lindenberg area MOL_6 PLUVIO_6

13 Diurnal cycle of PLUVIO precipitation amount [mm] hour of day skewed distribution peaks in late night and late afternoon median mean

14 Duration of episodes percent 7% 6% 5% 4% 3% 2% 1% % 6 hours duration of episodes [hour] Hourly precipitation amount Amount dependent on duration Frequency distribution of episodes averaged hourly precipitation amount [mm] Episodes with short duration dominate 94 % of episodes within 6 hours annual amount: 52 % within 6 hours 6 % last 1 hour and cover 1.5 % of annual amount rare, but intense long lasting episodes ~ 48 % of annual amount Characteristic time of a precipitating system ~ 6 hours

15 Summary: Precipitation in Lindenberg area Skewed distribution with low median and high mean Precipitation amount is not representative for short timescale Characteristic timescale for precipitation episode 6 hours: ~ 5 % of amount and 94 % of episodes

16 Character of precipitation is known. How to describe the local meteorological situation near the surface?

17 Variables describing the local meteorological situation (from Etling, 1987)

18 Calculation of surface fluxes Assumption: Stationarity Fluxes constant with height Homogeneity of wind, temperature and humidity fields Scaling parameters near the surface (non-convective): u *, θ *, q * θ w u θ q w u q u w u v w u 2 2

19 Calculation of scaling values ( ) 1 ln L ψ κu u m ( ) ( ) ( ) 1 ln L χ θ θ ψ θ θ κ θ ( ) ( ) ( ) 1 ln L q q q q q χ ψ κ 3 1/ v* * v * h θ u θ g w Describe local meteorological situation near the surface

20 Parameter Averaging: Flux Averaging: ( ) ( ) ( ) ( ) n i i q qi i m i qi i n i i i i L L q q f U l u q f l u q l * * 21 * * 21 ln ln ψ ψ κ ρ ρ ρ ( ) ( ) ( ) ( ) * * 21 ln ln L f L f q q U l u q l q n i qi i m n i i i q ψ ψ κ ρ ρ Sub-grid-scale land-use scheme (latent heat flux)

21 Definition of indices Index with advection impact I adv T T T T adv diff diff adv L s *, Lx < U > with s * max Index with diffusion impact ( u,w ) * * short timescales: very fast & efficient processes. resulting path fast process slow process Figure: characteristic timescales Index with humidity impact I lt T T T T lt lt diff diff 6 hours L s *, with s 94 % of episodes & ~5 % of amount * max ( u,w ) * * I I q rh I I lt *q lt *rh amount probability

Calculation of indices Observational data: 6 hour large-scale profiles geostrophic wind potential temperature specific humidity Observational Precipitation data MOL PLUVIO relationship 1D-METRAS

22 Calculation of indices Observational data: 6 hour large-scale profiles geostrophic wind potential temperature specific humidity Observational Precipitation data MOL PLUVIO relationship 1D-METRAS Index values u * w θ * v* 9 i i 1 1 u f g θ ( u ) 9 *i v i 1 9 f u i * i 1 2 ( u θ ) *i *i θ v*i v*i i Horiontal homogeneous 1D profiles every 6 hours Index J F M A M J J A S O N month I_adv I_lt

23 Indices - classwise I adv I lt PLUVIO 23 MOL 23 MOL 22 8% 8% 7% 7% 6% 6% percent [%] 5% 4% 3% 2% percent [%] 5% 4% 3% 2% 1% 1% % > % > I adv I lt I rh I q percent [% ] 8% 7% 6% 5% 4% 3% percent [% ] 8% 7% 6% 5% 4% 3% 2% 2% 1% 1% % I rh % > I q

24 Indices - accumulated I adv Iadv_sit22 Iadv_sit23 Iadv_precDWD22 Iadv_precDWD23 Iadv_precPLUVIO23 1% 9% 8% 7% I lt Ilt_sit22 Ilt_sit23 Ilt_precDWD22 Ilt_precDWD23 Ilt_precPLUVIO23 1% 9% 8% 7% 6% 5% 4% 3% percentage 6% 5% 4% 3% percentage Situations 22 2% 2% I adv 4 2 1% % I lt 2 1% % Situations 23 I rh Irh_sit22 Irh_sit23 Irh_precDWD22 Irh_precDWD23 Irh_precPLUVIO23 1% 9% 8% I q Iq_sit22 Iq_sit23 Iq_precDWD22 Iq_precDWD23 Iq_precPLUVIO23 1% 9% 8% DWD 22 7% 6% 5% 4% 3% percentage 7% 6% 5% 4% 3% percentage DWD 23 PLUVIO 23 2% 2% 1% 1% % % I rh I q

25 I q and precipitation amount? Idea: Include specific humidity q -> hint on the precipitation amount in the next 6 hours? 12 Iq22 Iq23 Iq23PLUVIO precipitation amount per precipitation event [mm] I q Fig: Precipitation amount per event above an I q threshold. No relationship with amount: Reason: locality of precipitation amounts (shown in the beginning).

26 Summary I lt and I rh indicate the probability for precipitation in the following 6 hours. I rh shows best results. High index situations are rare, but connected with a high probability for precipitation good parameteriation needed. Relative humidity refines I lt and might be applied for other (dry?) regions. I adv shows no relation to precipitation. I q similar to I lt, but decreases for high index situations. Relationship to precipitation amount can not be established.

27 Sensitivity studies y (km) Land-use 1 km resolution Simulations with different I rh and precipitation x(km) -2 Water Flats Sand mix.veg Grass Heath Bushes mix.for. con.for. Urban

28 METRAS model properties Exchange coefficient Convective: countergradient formulation Stable: mixing length approach (Lüpkes, Schlünen, 1996) Coordinate system Terrain following η ( ) ( ) t s t s Lowest grid level Depth of lowest model level Model grid Initialiation Initial soil moisture Nesting Surface energy & humidity budget 1 m 2 m Arakawa C 1d: Average sounding 3d: background profile and 3d analysed data Prescribed dependent on land use and wetness (empirical) Nudging applied Force restore method (1 layer)

29 Calculating Hitrates H m 1 1 for Pi Oi A ni, with ni m i 1 for Pi Oi > A Variable Accuracy A desired Threshold values: minimum to maximum Temperature ( C) Dew point temperature ( C) Wind speed (m s -1 ) Wind direction Pressure (hpa) ± 2 ± 2 ± 1 ± 3 ± 1.7 to 45-1 to 45 1 to 8 to 36, wind speed above 1 m s to 11

30 6 5 Whole model area [%] dd ff te td -2-3 bc_16km bc_8km bc_4km pa_8km pa_4km Changes in hitrates: compared to pa_16km theoretically worst case

31 Berlin area [%] dd ff te td bc_16km bc_8km bc_4km pa_8km pa_4km Changes in hitrates: compared to pa_16km theoretically worst case

32 Differences in hitrates: Berlin rural areas bc_16km bc_8km bc_4km pa_8km pa_4km dd ff te td

33 Differences for urban and rural areas Wind speed rural area performs better than urban area. Temperature rural area performs better than urban area. Dew point high I lt : rural area performs better than urban. low I lt : urban area performs better than rural. Wind direction high and average I lt : rural area performs better than urban. very low I lt : urban area performs better than rural.

34 Performance urban vs. rural areas Difference: temperature - dew point T-TD [ C] T-TD [ C] ILT ILT3 time [UTC] time [UTC] (pa4) rural (fl4) rural T-TD [ C] T-TD [ C] (OBS) rural (pa4) Berlin ILT ILT1 time [UTC] time [UTC] (fl4) Berlin (OBS) Berlin

35 Conclusions urban vs. rural areas Humidity differences between urban and rural areas In general METRAS more humid than measurements except wet situation Ilt2 initialisation of soil moisture needs to be checked Observation: pronounced offset (urban area mostly less moist than rural) METRAS: smaller differences between urban/rural areas BC has a more pronounced daily cycle than PA Urban (heat) dryness island? Observation: urban area dryer in most cases dryness maximum later in the afternoon in urban than rural area. METRAS: difference urban/rural much smaller than observation too few sealed surfaces? dryness maximum as in rural area re-check land use data for the urban.

36 Conclusions for whole model area In general, for the same resolution model results with blending height concept are better than with parameter averaging. Increasing the resolution does not necessarily improve model results (independent of applied parameterisation). Wind is less sensitive than temperature and dew point to parameteriation and resolution. For wind forecast, parameteriation and resolution are more relevant for higher than for lower I lt values. For temperature and dew point forecast, a general I lt relation was not found. The used parameteriation and resolution are important for meteorological situations with low rh. Additional simulations using I rh shall lead to (hopefully) a more general conclusion on model perfomance.

37 Outlook Simulations depending on I rh, since inclusion of relative humidity improved index. Correlation between Hit rates and I rh. Nesting in ECMWF-Analysis data. Check of land-use data. Improvement of soil water initialisation (dependent on previous dryness/wetness period).

38 Acknowledgements Many thanks to Frank Beyrich (DWD) for preparing and providing data and helpful discussions. Data were kindly provided by the Deutscher Wetterdienst (DWD) and BADC. This project is partly funded by SPP 1167.

Developments at DWD: Integrated water vapour (IWV) from ground-based GPS

1 Working Group on Data Assimilation 2 Developments at DWD: Integrated water vapour (IWV) from ground-based Christoph Schraff, Maria Tomassini, and Klaus Stephan Deutscher Wetterdienst, Frankfurter Strasse