Mechanisms and prediction of precipitation over complex terrain

Transcription

1 Mechanisms and prediction of precipitation over complex terrain S. Wassermann, M. Kunz, Ch. Kottmeier Institut für Meteorologie und Klimaforschung, Universität Karlsruhe (TH) / International PhD-students and Post-docs meeting on Quantitative Precipitation Forecast (QPF) 5 th -9 th March 2007 Priority Forschungszentrum Program SPP 1167 of Karlsruhe the DFG Quantitative Precipitation Forecast

2 Motivation Problem Goal Precipitation enhancement over low mountain ranges during large scale precipitation events increased potential of danger Forecasting problem: amount / spatial distribution Complex dynamics: Flow around, flow over, waves Improved understanding for relation between inflow characteristics, dynamics and resulting precipitation enhancement Reproduction in COSMO?

3 Strategy / Goals Analyses of real precipitation events: prec. efficiency, spatial distribution Sensitivity studies: Dynamic precipitation efficiency Data analyses: different low mountain ranges Diagnostic Precip. model Kunz (2003,2006) COSMO idealised COSMO real influence of dynamic effects and diabatic heat transitions (destabilisation) on orographic precipitation enhancement relationship between orogr. precipitation and inflow parameters (e.g. Fr) inflow / orogr. induced precipitation in COSMO; uncovering weak points Application for improved predictability of orographic precipitation enhancement.

4 COSMO_ideal Setup COSMO 3.19: Initialisation with radio soundings Hor. grid spacing 2.8 km x 2.8 km Model area 2D: 1000 x 7 x 40 GP wave splitting Model area 3D: 200 x 200 x 40 GP Time step dt = 12 s Cloud microphysics: scheme with prognostic q c, q s, q i (itype_gscp=3)

5 COSMO 3.12 First Simulations with COSMO 3.12 Gaussian dam + artificial vertical profile with constant U, Nd (dry Brunt-Väisälä-Frequency) und Rh x-z-cut at y = 4 (2D sim.) 12:00h wmax = 44.3 m/s 12:00h z in m wmax = 1.2 m/s x-z-cut at y = 4 (2D sim.) N2 = /s, U = 10 m s-1, RF = 99% Nd = /s, U = 10 m s-1, RF = 95% Temporally increasing wave amplitudes! (v.a. only with high humidities)

6 Critical points in idealized simulations Problematic initialisation features: kinks in R h and T profiles (wave reflection) cold tropopause large R h in upper modell layers (slight lifting at boundarys!) (not tested for newer COSMO versions, not always a problem) Crucial modifications in COSMO 3.19: improved Runge-Kutta-scheme T p -Dynamic instead of Tp Buoyancy term completely calculated with fast waves

7 Structure of results Simulation of different experiments from literature (Jiang (2003), Kunz (2003)) -> strongly increasing waves in many cases Sensitivity-Study around Jiang Case (T 0 = -3 C) More realistic: Sensitivity study for T 0 = 10 C -> quite reasonable after some improving efforts Problems with N d < /s and T 0 = 15 C

8 Altitude (km) Altitude (km) Altitude (km) COSMO_ideal compared to ARPS (T 0 = -3 C) N d = /s, U = 10 m/s, R h = 95 %, h m = 1000 m, T 0 = -3 C Jiang (2003) fig. 1 x in km x in km u [m/s] w [m/s] COSMO 3.19 simulation 3D mountain 5.0 [K] q c [g/kg] q s [g/kg] 0.16 q i [g/kg]

9 Constant parameters: N d = /s, U = 10 m/s, R h = 95 %, Exp. of Jiang COSMO sensitivity study (T 0 = -3 C) Rain rate snow rate total prec. rate N U Summary: Sensitivity to properties of the incoming flow can be simulated spatially dependent Some problems left q v

10 Var. of N 2 COSMO sensitivity study (T 0 = -3 C) unexpexted effects Rain rate snow rate total prec. rate Jiang-Fall w max = 0.7 m/s at N = /s! w max = 2.9 m/s at N = /s!

11 Altitude (km) Altitude (km) Altitude (km) COSMO_ideal compared to ARPS (T 0 = 10 C) Jiang (2003), Fig. 1, T 0 = -3 C COSMO, initialized with R h = 95% (5km), T 0 = -10 C u [m/s] w [m/s] 5.0 [K] q c [g/kg] q s [g/kg] 0.16 q i [g/kg] x in km 0.00

12 COSMO sensitivity study (T 0 = 10 C) New: U and N variations cover equal set of Froude numbers Fr 3,3 2,0 1,43 1,11 0,91 0,77 0,67 0,59 0,52 0,45 N d [1/s] 0,003 0,005 0,007 0,009 0,011 0,013 0,015 0,017 0,019 0,022 U [m/s] 36, ,73 12, ,47 7,37 6,49 Profiles for initialisation:

13 COSMO sensitivity study (T 0 = 10 C) Fr = 0.91 Flow characteristic and in this connection state of the atmosphere are conditional for orographic precipitation enhancement and spatial distribution of precipitation expression by nondimensional flow parameters possible (Fr, Scorer..)? reality?

14 Sensitivity to initialisation profile Changes of initialisation profiles between the two sensitivity studies: T 0 = 10 C T 0 = -3 C Humidity profile above 8 km seems to have a strong influence!

15 Spatial distribution of precipitation Fr = 3,3 2,0 7 5 U = 36.6 m/s U = 22.0 m/s y U = 12.2 m/s x 1,43 U = 15.7 m/s N = /s R h = 80 % 1,11 U = 12.2 m/s N = /s U = 22.0 m/s 0,91 U = 10.0 m/s N = /s R h = 90 % 0,77 U = 8.47 m/s N = /s U =7.37 m/s N = /s 0,67 R h = 95 % U = 6.49 m/s N = /s 0,59 0,45 N = /s

16 Froude number dependency of surface precipitation rate grid scale (rain + snow), single point at top of the mountain Fr 3,3 2,0 1,43 1,11 0,91 0,77 0,67 0,59 0,52 0,45 N [1/s] 0,003 0,005 0,007 0,009 0,011 0,013 0,015 0,017 0,019 0,022 U [m/s] 36, ,73 12, ,47 7,37 6,49 Additional: N = /s, U = 10.3 m/s (Fr = 0.6), U = 13.9 m/s (Fr = 0.81), U = 15.5 m/s (Fr = 0.91)); R h = 95 % up to 10 km!!!! Summary: Fr depency of rain rate exists even if cases with waves for N < /s are included. N /s U = 10 m/s depency severely disturbed by interaction of lateral boundary lifting and thick humidity layer

17 Remaining COSMO problems Steady state solution not achievable (compare Doyle et al. (2000), Jiang (2006)), i.e. vertical velocity of buoyancy wave increasing slight uplift at inflow boundary imediately precipitating in case of thick humid layer initialisation, similar problem with WRF reported by Rotunno (pers. communication) x-z-cross section: w in m/s at Y = GP4 N d = /s, U = 10 m/s R h = 95 % h m = 500 m Total maxima

18 H 95% = 5 km H 95% = 12 km Different height of tropopause T 0 = 15 C, N = /s; left: h Tr =5 km, right: h Tr = 12 km t = 9h t = 3h t = 9h

19 Conclusions Idealised simulation results look reasonable from LMK 3.19 and higher new Runge-Kutta-Scheme T p -Dynamic comparable to ARPS; Relationship between inflow characteristics precip. efficiency/pattern exists Relationship to Froude number precip. efficiency exists Problems with high humidity for initialisation above 5 km Problems with N < /s in only with my improved initialisation Problems with Jiang-Case and T 0 = 15 C Other experiments from literature only unrealistic results (e.g. N d = /s, U = 10 m 1/s, R h = 95%, N d = /s, U = 10 m 1/s, R h = 95%)

20 Further steps additional sensitivity studies with idealised model configuration flow effects and precipitation Initialisation with N m instead of N d similarity theory for saturated atmosphere (Fr m ) Investigation of precipitation enhancement and flow pattern for real precipitation events sensitivity studies test hypotheses precipitation flow parameters Comparison with observational data and diagn. model model skill as function of precipitation intensity and/or physical processes Estimation of precipitation enhancement by combinating flow and meteorological parameters, and an diagnostic model approach