1. Radar in aLMo Assimilation of Radar Information in the Alpine Model of MeteoSwiss Daniel Leuenberger and Andrea Rossa MeteoSwiss

2. Radar in aLMo Daniel.Leuenberger@MeteoSwiss.ch SRNWP – COST-717 Lisbon, 8.October 2003 1 Introduction Radar information is gaining importance in mesoscale data assimilation Latent Heat Nudging (LHN): Assimilation method for precipitation information Trigger model precipitation where radar detects precipitation (heating), supress it elsewhere (cooling) 4DDA, yet computationally very efficient Conceptionally simple Assimilation of non-prognostic variables not straight forward Heuristic approach of weighting observations and model

3. Radar in aLMo Daniel.Leuenberger@MeteoSwiss.ch SRNWP – COST-717 Lisbon, 8.October 2003 2 Radar Observations Swiss radar network: 3 C-Band Doppler Radars Best estimate of surface rain: preprocessed (e.g. clutter reduction, vertical profile corrections) Resolution: 2 x 2 km 2, 5 min Radar Quality Map

4. Radar in aLMo Daniel.Leuenberger@MeteoSwiss.ch SRNWP – COST-717 Lisbon, 8.October 2003 3 Real Case Study Case System of severe convection over Switzerland Triggered around 22h30 UTC over the Massif Central Development ahead of weak cold front Moderately unstable environment as observed by the Swiss radiosonde Payerne at 00UTC (CAPE ~250 J/kg) Strong wind shear (~ 30 m/s at 6000m) Simulations Operational Alpine Model (aLMo) of MeteoSwiss (  x=7km) Convection Parametrization Started 21.8.00 00 UTC from GME of DWD CTRL (no forcing) LHN during 6h LHN+ during 3h, free forecast afterwards

5. Radar in aLMo Daniel.Leuenberger@MeteoSwiss.ch SRNWP – COST-717 Lisbon, 8.October 2003 4 01 02 03040506 CTRLLHNRadar Case Study of the 21.8.2000 Storm Hourly Sums of Precipitation: Forcing during 6h

6. Radar in aLMo Daniel.Leuenberger@MeteoSwiss.ch SRNWP – COST-717 Lisbon, 8.October 2003 5 01 02 0304 1h Free Forecast 05 2h Free Forecast 06 3h Free Forecast Case Study of the 21.8.2000 Storm Hourly Sums of Precipitation: Forcing during 3h CTRLLHN+Radar

7. Radar in aLMo Daniel.Leuenberger@MeteoSwiss.ch SRNWP – COST-717 Lisbon, 8.October 2003 6 Findings I Model is able to assimilate radar observations Good impact in analysis, sfc winds in line with observations Some impact in free forecast up to 03h Model loses information quickly, i.e. storm dies too early Why is the model not able to maintain storm? Environment not representative? Model resolution ? Try to find reasons by means of idealized simulations

8. Radar in aLMo Daniel.Leuenberger@MeteoSwiss.ch SRNWP – COST-717 Lisbon, 8.October 2003 7 Idealized Simulations Setup Environment from Payerne sounding of 21.8.00 00UTC Fine mesh (  x = 1km), no CPS, no soil model, no radiation Trigger convection with warm bubble: No storm development wind shear Payerne profile KW profile Payerne sounding KW sounding Klemp Wilhelmson Environment Large amount of CAPE (~ 1200 J/kg) Moderate wind shear Favorable for splitting supercell storms

9. Radar in aLMo Daniel.Leuenberger@MeteoSwiss.ch SRNWP – COST-717 Lisbon, 8.October 2003 8 OSSE Setup Reference run Convection initiated with warm bubble Model sfc rain serves as „artificial radar observations“ LHN Analysis Same environment as reference run No warm bubble initiation LHN during 3h (artificial rain rates from reference run) LHN Forecast LHN during first 30, 60 min Free run afterwards

10. Radar in aLMo Daniel.Leuenberger@MeteoSwiss.ch SRNWP – COST-717 Lisbon, 8.October 2003 9 Insertion Frequency of Precipitation Input LHN linearly interpolates between subsequent observations Examine relevance of insertion frequency  t to LHN Analysis  t = 10min linear interpolation  t = 1min

11. Radar in aLMo Daniel.Leuenberger@MeteoSwiss.ch SRNWP – COST-717 Lisbon, 8.October 2003 10 LHN Analysis (LHN during 3h) “OBS“  t = 10min  t = 4min  t = 1min

12. Radar in aLMo Daniel.Leuenberger@MeteoSwiss.ch SRNWP – COST-717 Lisbon, 8.October 2003 11 Domain Sum of LH Nudging Increment

13. Radar in aLMo Daniel.Leuenberger@MeteoSwiss.ch SRNWP – COST-717 Lisbon, 8.October 2003 12 LHN Forecast (  t = 1min) “OBS“ Free forecast after 30 min Analysis (LHN during 3h) Free forecast after 1h

14. Radar in aLMo Daniel.Leuenberger@MeteoSwiss.ch SRNWP – COST-717 Lisbon, 8.October 2003 13 Sensitivity to Horizontal Grid Spacing Analysis (LHN during 3h) Free forecast after 1h  x = 2km  x = 5km

15. Radar in aLMo Daniel.Leuenberger@MeteoSwiss.ch SRNWP – COST-717 Lisbon, 8.October 2003 14 Findings II LHN capable of analysing and initiating supercell storm Good temporal sampling of the observed phenomena is important Representative large-/mesoscale environment important Even a poorly resolved forcing is able to initiate and maintain storm evolution in appropriate environment Supercell storm very stable dynamics: are findings ‚portable‘ to other situations?

16. Radar in aLMo Daniel.Leuenberger@MeteoSwiss.ch SRNWP – COST-717 Lisbon, 8.October 2003 15 Outlook Real-case study Reduction of grid-size to 2km Study impact of errors in radar data More cases Idealized OSSE Sensitivity of vertical forcing distribution Assimilation of model 3D latent heating fields Assimilation of horizontal winds Consider case which is less driven by dynamics

17. Radar in aLMo Daniel.Leuenberger@MeteoSwiss.ch SRNWP – COST-717 Lisbon, 8.October 2003 16 Thank you for your attention !

18. Radar in aLMo Daniel.Leuenberger@MeteoSwiss.ch SRNWP – COST-717 Lisbon, 8.October 2003 17 Latent Heat Nudging Method for the assimilation of precipitation information Satellite-, Radar-estimated precipitation Main idea: force model with heat released/absorbed by precipitation processes Surface precipitation rates Total latent heat for a column ~ proportional to sfc rain rate Main challenge: how to vertically distribute latent heat Common approach: scaling of model latent heating profiles Add nudging term to prognostic temperature equation

19. Radar in aLMo Daniel.Leuenberger@MeteoSwiss.ch SRNWP – COST-717 Lisbon, 8.October 2003 18 Low-level humidity Reference environment q v = 12 g/kg Analysis Free forecast after 1h Drier environment q v = 10 g/kg

20. Radar in aLMo Daniel.Leuenberger@MeteoSwiss.ch SRNWP – COST-717 Lisbon, 8.October 2003 19 Horizontal grid spacing Interpolation of 1km forcing to 2km and 5km mesh Perform LHN runs with coarse mesh  x = 1km  x = 2km  x = 5km

21. Radar in aLMo Daniel.Leuenberger@MeteoSwiss.ch SRNWP – COST-717 Lisbon, 8.October 2003 20 Dynamical Response on Forcing CTRL LHN 10m Wind RH,  and w

22. Radar in aLMo Daniel.Leuenberger@MeteoSwiss.ch SRNWP – COST-717 Lisbon, 8.October 2003 21 Different Forcing (at t = 110 min )  t = 1 min  t = 4 min  t = 10 min Color: Black contours:

23. Radar in aLMo Daniel.Leuenberger@MeteoSwiss.ch SRNWP – COST-717 Lisbon, 8.October 2003 22 Supercell Storm: Conceptual Model Splitting stage Initial stage from Klemp (1987)

24. Radar in aLMo Daniel.Leuenberger@MeteoSwiss.ch SRNWP – COST-717 Lisbon, 8.October 2003 23 LHN Forecast: Cumulated surface rain

25. Radar in aLMo Daniel.Leuenberger@MeteoSwiss.ch SRNWP – COST-717 Lisbon, 8.October 2003 24 Sampling intervals of rain rates (insertion frequency of observations) Mean rain rate from t -  t to t : Linear interpolation between successive rain rates in LHN LHN Experiments with  t = 10, 6, 4, 2, 1 min Insertion Frequency of LHN Input R(t) t

26. Radar in aLMo Daniel.Leuenberger@MeteoSwiss.ch SRNWP – COST-717 Lisbon, 8.October 2003 25 Temperature increment: LHN Temperature Increments Analysed rain rate: Observation weight: Scaling factor f Profile to scale Model fair local profile Model too wet local profile Model too dry near / ideal. profile

27. Radar in aLMo Daniel.Leuenberger@MeteoSwiss.ch SRNWP – COST-717 Lisbon, 8.October 2003 26 Nudging Increment Add nudging increment to prognostic temperature equation:

28. Radar in aLMo Daniel.Leuenberger@MeteoSwiss.ch SRNWP – COST-717 Lisbon, 8.October 2003 27 Findings aLMo is able to assimilate radar observations Good impact in analysis, sfc winds in line with observations. Some impact in forecast up to 03h aLMo loses information quickly, i.e. storm dies too early Why does LHN forecast so rapidly lose radar information? LHN-Scheme (wrong circulation) ? Model resolution ? Environment (humidity) ?

29. Radar in aLMo Daniel.Leuenberger@MeteoSwiss.ch SRNWP – COST-717 Lisbon, 8.October 2003 28 Talk Outline Introduction Latent Heat Nudging Radar Data Real Case Study Idealized Studies Findings Outlook