Predictability of convective precipitation: Convection permitting ensemble simulations

Transcription

1 Predictability of convective precipitation: Convection permitting ensemble simulations Vera Maurer, Norbert Kalthoff, and Leonhard Gantner Institute for Meteorology and Climate Research () KIT University of the State of Baden-Wuerttemberg and National Research Center of the Helmholtz Association

2 Influence of the land surface on convection Taylor et al. (2011): 1 out of 8 convective systems is initiated over LSTA (land surface temperature anomaly) gradients hetereogeneities can be caused by precipitation patterns by F. Guichard LES simulations by Couvreux et al. (2012): only deep convection, if H 0 > 300 Wm 2 near surface convergence MCS trajectory precipitation initiation 15 UTC density current 17 UTC T sfc C 2

3 Influence of the land surface on convection uncertainty when initializing real data model simulations: initialization: ECMWF soil moisture soil moisture reduced by 35 % Pa s -1 ω and wind vectors in 925 hpa Gantner and Kalthoff (2010) m s -1 3

4 Influence of the atmosphere African Easterly Waves Saharan Heat Low exerted influence by existing convective systems paremeterized processes in a global model: Rio et al.,

5 Motivation and Aims land surface atmospheric influence on convection in West Africa large uncertainties for model forecasts (data, physics, parameters) setup of high resolution ensemble simulations (1) Which is the best ensemble setup? (2) How strong is the influence of the land surface compared to the atmospheric one? 5

6 Overview (1) Ensemble setup 3 ensemble versions (2) Verification and comparison of 3 versions (3) Comparison of 2 ensemble components 6

7 (1) Ensemble setup global ensemble (ECMWF) 11 member ensemble created by climatological EDA optimized soil perturbations: different fields for soilmoisture initialization, including adapted AMSR E soil moisture different soil type distributions physics of soil model single model ensemble (EC) nested into ECMWF ensemble 4 soil perturbations randomly distributed onto 12 members multi analyses ensemble (ANA) 3 global (re) analyses 4 soil perturbations 12 members 2 components! 7

8 (1) Ensemble setup COSMO simulations with and without convection parameterization for 10 days in July 2006 (domains with 14 km and 2.8 km horizontal resolution ) 10 day precipitation sum from 15 W 20 E 25 N TMPA (23 to 31 July 2006) initiation of local MCSs in Sahel zone simulations with daily initialization Mali Niger Nigeria 3 evaluation domains: Sahara Sahel Sudan 2 N 8

9 (2) Verification of precipitation forecasts area averaged precipitation for 3 subdomains (10 day sums): observation (TMPA) Sudan Sahel ANA Sahara EC ECMWF 9

10 (2) Verification of precipitation forecasts ROC diagrams (event: 24 h precipitation sums > 5.5 mm) ANA EC ECMWF ROC area higher for ANA and EC in Sahel and Sahara, for ECMWF in Sudan ECMWF dominated by misses in all domains except in Sudan 10

11 (2) Verification of precipitation forecasts L component for all ensemble members and 24 h precipitation sums L = 0 perfect location of simulated precipitation objects ANA EC ECMWF L = 0.32 L = 0.32 L = 0.42 L component day in July/August 2006 SAL INDEX after Wernli, 2008 No perturbation generates systematically better/worse precipitation forecast Ensemble setup ok 11

12 (3) Comparison of ensemble components temporal evolution of ensemble standard deviation for ensemble subsets of ANA L 0 H 0 T 925 hpa different soil perturbations u 600 hpa IWV v 600 hpa different analyses soil moisture precipitation forecast hour forecast hour 12

13 (3) Comparison of ensemble components T and wind vectors in 925 hpa on 27/07/2006, 1300 UTC and isoline of TB (233 K) at 1800 UTC specific humidity in 925 hpa at 1300 UTC different soils 13 different analyses

14 (3) Comparison of ensemble components local initiation of convection in the vicinity of surface heterogeneities sensible heat flux synthetic satellite image 16/07/2011, 13 UTC (fh 37) 14

15 (3) Comparison of ensemble components local initiation of convection in the vicinity of surface heterogeneities differences generated by atmospheric forcing active system in the morning, less incoming radiation, cooler land surface 29/07/2006, 11 UTC (fh 35) with TB for 13 UTC 15

16 Conclusions Verification area averaged precipitation for ANA and EC clearly too low in Sudan, for ECMWF too low in Sahel and Sahara precipitation systematically displaced to the south in the global forecasts classical scores hint a similar skill for all versions spatial verification shows advantage of ANA and EC over ECMWF multi analyses and single model version show equal skill! Comparison of ensemble components soil perturbations can cause differences in BL temperature and moisture budget that are decisive for the development of deep convection ensemble simulations show that soil perturbations generate equally strong differences in precipitation forecast as atmospheric ones 16