1. Evaluation of the Latent heat nudging scheme for the rainfall assimilation at the meso- gamma scale Andrea Rossa* and Daniel Leuenberger MeteoSwiss *current affiliation: ARPA Veneto, Centro Meteo Teolo

2. arossa@arpa.veneto.it WSN05 Toulouse, 5.-9. September 2005 1 Motivation QPF poorest performance area of NWP, especially in summer (convection) Rainfall assimilation to mitigate spinup effect Radar observations for high-resolution NWP models Characteristics of LHN Buoyancy driven Computational efficiency 4DDA, timely assimilation of high-frequency observations This study: reevaluation of Latent Heat Nudging (LHN) within high-res model using idealised and real radar data

3. arossa@arpa.veneto.it WSN05 Toulouse, 5.-9. September 2005 2 Idealised Experiments: Setup and Strategy Model: Local Model (LM) in idealised configuration Unstable environment supportive for supercell storms Reference simulation Surface precipitation for assimilation 4D fields for validation Assimilation simulations Proof of concept with identical twin simulation (CTRL) Sensitivity to uncertainty in observations and environment

4. arossa@arpa.veneto.it WSN05 Toulouse, 5.-9. September 2005 3 Reference and CTRL simulation REF total sfc rain RH, W and  e , W and cold pool xxx y zy CTRL + 5 % xxx y y z

5. arossa@arpa.veneto.it WSN05 Toulouse, 5.-9. September 2005 4 Extrema of w Delay: 12 min

6. arossa@arpa.veneto.it WSN05 Toulouse, 5.-9. September 2005 5 Assimilation increments Transient phase: 45 min

7. arossa@arpa.veneto.it WSN05 Toulouse, 5.-9. September 2005 6 Observation Uncertainty Amplitude Error Factor 0.5 (2) results in 88% (133%) of total precipitation Environment and model dynamics damps error Temporal resolution Combined amplitude and structure error Strong sensitivity Non-Rain Echoes Potential strong sensitivity

8. arossa@arpa.veneto.it WSN05 Toulouse, 5.-9. September 2005 7 Non-Rain Echoes: convective conditions 6h Sum of Radar 6h Sum of Model Precipitation x x yy

9. arossa@arpa.veneto.it WSN05 Toulouse, 5.-9. September 2005 8 Non-Rain Echoes: AP conditions Vertical mixing due to strong, spurious updrafts x z t w

10. arossa@arpa.veneto.it WSN05 Toulouse, 5.-9. September 2005 9 Environment Uncertainty Perfect observations, degraded environment Low-level Humidity -4% (4%) error in PBL humidity  -14% (11%) precip. deviation For slightly drier conditions: convection suppressed For slightly moister conditions: multi-cell development Environmental Wind Can lead to distorted system dynamics

11. arossa@arpa.veneto.it WSN05 Toulouse, 5.-9. September 2005 10 Uncertainty in the Environment: Error in Wind Surface precipitation  T LHN, cloud, precip , W and cold pool Bias in environmental wind: +2m/s; assimilation only x x x y z y

12. arossa@arpa.veneto.it WSN05 Toulouse, 5.-9. September 2005 11 Case study of 8. May 2003 21 UTC 200 km

13. arossa@arpa.veneto.it WSN05 Toulouse, 5.-9. September 2005 12 Simulations with  x = 2.2km, no CPS 4h Forecast 21-22 UTC CTRLRADARLHN Analysis 17-18 UTC

14. arossa@arpa.veneto.it WSN05 Toulouse, 5.-9. September 2005 13 8. May 2003 Storm LHN Analysis RADAR 1h Forecast RADAR

15. arossa@arpa.veneto.it WSN05 Toulouse, 5.-9. September 2005 14 Findings LHN has good potential for triggering convection Excellent results in identical twin simulation Scheme sensitive to observation and environment errors Non-rain echoes pose serious problem  Data quality Case study of real storm LHN triggers storm at right location and intensity Radar data clearly improves QPF in the first hours Outflow located too far downstream  position error Storm kept in model for more than 4h Storm environment important, particularly PBL! Use mesoscale data assimilation to complement rainfall assimilation (AWS, VAD/radial winds, GPS tomography, PBL profilers)

16. arossa@arpa.veneto.it WSN05 Toulouse, 5.-9. September 2005 15 Thank you for your attention !