Progress in Radar Assimilation at MeteoSwiss Daniel Leuenberger 1, Marco Stoll 2 and Andrea Rossa 3 1 MeteoSwiss 2 Geographisches Institut, University of Bern 3 Centro meteorologico di Teolo, ARPA Veneto, Italy

1 Motivation Convection often missed in the model model deficiencies improper initial conditions Prerequisites for convection Prefrontal environment (instability,wind) Trigger (frontal pressure disturbance,local low-level convergence) Radar rainfall assimilation provides trigger at the right time and location

2 Latent Heat Nudging refresher Simple, economic 4DDA scheme for radar rainfall Forcing via buoyancy Temperature adjustment given by ratio of radar and model precipitation Vertical distribution given by model Scale nearby or idealised profile if no suitable model profile is available Radar Model Rainrate Diabatic Heating z

3 LHN Experiments aLMo with 7km grid size, diagnostic precipitation 6 summer convection cases over Switzerland of airmass (2), prefrontal (2) and frontal (2) type focus to role of low-level environment and response of model dynamics to radar forcing mostly missed convection in CTRL runs, but one case was well captured 3-6h assimilation duration Best radar estimate of surface precipitation from 3 Swiss radar stations (clutter reduction, vertical profile correction), measurements 5min apart.

4 Observation weight w(x,y,t) Quality function based on visibility of radar Extendable (e.g. clutter maps…)

Case: Missed frontal convection Free forecast Assimilation CTRL LHN RADAR

6 Role of low-level Environment OBS CTRL from aLMo ANA 12UTC LHN from aLMo ANA 12UTC LHN from aLMo ANA 15UTC Free forecast

7 Impact of improved low-level environment 3h sums (+1 to +4 h free forecast) Additional three hours of conventional aLMo assimilation improve environment and thus precip forecast started from LHN! LHN from 12 UTC aLMo ANA LHN from 15 UTC aLMo ANA

8 Response of model dynamics to forcing OBS CTRL

9 Findings LHN is an effective convection trigger Positive impact in QPF up to 5 hours General improvement of postconvective environment (though sometimes locally too strong forcing during assimilation) Weak overestimated precipitation is not sufficiently removed Rapid loss of precipitation signals may be caused by wrong thermodynamical/dynamical PBL structure Need to improve low-level atmosphere, particularly humidity

10 Errors in Radar Data can be a Problem ! 6h cumulated clear sky echo 6h cumulated model response 6h Assimilation of Clear-Sky Echos (CAPE = 800 J/kg)

11 Anaprop Stable stratification (strong inversion) and no rain assimilation of clear-sky echos (6h) no model response (0% rain!) updrafts of 6m/s (for PJC) and 12m/s (for OMC) are induced no errorneous rain, but updrafts could possibly influence larger environment

12 Findings Non-meteorological echos can be drastically amplified by LHN in unstable, moist situations Area of echo seems to be as important as amplitude Wind can drift rain out of forcing area Problem can be reduced by quality control of data and by filtering the input data in the model Effect is reduced in drier or more stable situations

13 Towards operational application LHN promising for very short-range forecasts (up to 12h) rapid update cycle (aLMo/2, 18h forecasts per day, started every 3h) use in concert with other observations, particularly surface observations Extended tests Long periods including different weather situations aLMo/7km and aLMo/2.2km configurations Sensitivity tests Radar quality (ground clutter) Composite size (Swiss Composite vs. Eurocomposit)