1. DATA ASSIMILATION AND NWP IMPROVEMENTS CARPE DIEM AREA 1 Magnus Lindskog on behalf of Nils Gustafsson (AREA 1 Scientific Rapporteur) and AREA 1 colleagues

2. Project organisation

3. WP 2 Extraction of information from Doppler winds De-aliasing Radar radial wind super-observations Dual Doppler retrieval Clear air retrievals

4. De-aliasing of raw radial wind signal

5. De-aliasing algorithm Linear wind model: Map the measurements onto the surface of a torus

6. Case study (illustration de-aliasing) Vantaa (Finland): 4 December 1999, 12:00 UTC observed velocityde-aliased velocity

7. Generating radial wind superobservations Horizontal filtering: Time filtering of superobservations: unfiltered and filtered observationsfiltered observations and model

8. Terrain analysis DUAL DOPPLER RETRIEVAL Po Valley-radars and terrain analysis

9. Data Gridding Radar data –Polar data –4 elevation angles(deg) 0.5, 1.4, 2.3, 3.2 –ray resolutions 0.25km x 440 bins, beam width: 0.9 deg ● Gridded data ● Cartesian data ● 4 layer altitudes (km) ~ 0.5, 1.4, 2.3, 3.2 ● horizontal resolutions cell spacing: 0.5 km, no. of cells: 60 x 60 e.g. Doppler Vel.

10. Calculating wind field Three Fundamental Equations: –Radial Velocities (from each radar) –Mass Continuity

11. Numerical procedures Iterative method: – horizontal components – vertical component Boundary conditions: – zero vertical velocity on ground – zero horizontal velocity gradient on ground (optional: simplify computation w/o loss of accuracy)

12. Courtesy: K. Y. Goh Radar: Gattatico Date: 17 Dec 2002 Elevation:1.4° Field:V Threshold:0 dBZ away towards

13. Wind travelling to the East-North-East at the centre Vel. (m/s)

14. Velocity-Azimuth Display (VAD) Gattatico: minimum ~ 60 deg wind travel to East-North-East gat window spc window

15. WP 3 Data assimilation Observation operator for radial winds in variational data assimilation. Impact studies of radial winds on limited area NWP and assessment of suitability of radial wind measurements for use in operational NWP. Compare assimilation of SO,VAD and Dual Doppler Implementation of 4-dimensional continous assimilation based on IAU using radar satellite as well as surface and radiosonde observations.

16. Cost function: where Background error covariances Observation error covariances Observation operator Background state The HIRLAM 3D-Var

17. The Doppler radar radial wind observation operator Interpolation of the model wind vector to the observation location. Projection of the wind vector on the slanted direction of the radar beam. Broadening of the radar beam: Gaussian averaging kernel. Bending of the radar beam: Snell's law.

18. One-month (January 2002) comparison of Swedish radial winds and HIRLAM model equivalents ●Figure: rms difference as a function of measurement range. ●Rms difference for thinned raw data is significantly higher than for SOs. ●Method used to deter- mine optimal averaging length scales 10 km for 22 km model)

19. 10 day Radar wind assimilation experiment Integration area and radar sites Three parallel runs CRL:conv. obs. RWD:conv. obs+rwd VAD:conv. obs.+VAD 10 day experiment (1-10 Dec., 1999) RMS of +24 h wind forecasts at 850 hPa

20. Radar wind assimilation experiment (case study) Integration area radar sites Four parallel runs: CRL:conv. obs. RWD:conv. obs+rwd VAD:conv. obs.+VAD DUAL: conv obs.+ DUAL 17 Dec. 2002, 18 UTC- 18 Dec. 2004, 00 UTC Radars, Dual Doppler area and model grid-points

21. 17 Dec. 2002 18 UTC radar wind assimilation VAD Radars, Dual Doppler area and model grid-points RWD DUAL 925 hPa wind analyses- increments

22. WP 3: Data assimilation University of Barcelona contribution to WP 3: Continuous data assimilation in the mesoscale MASS model using incremental analysis updates (IAU). IR satellite and radar data are used to enhance the 3D relative humidity field. IAU assimilation cycle. FIRST GUESS + CONVENTIONAL DATA ANALYSIS + RADAR & SATELLITE DATA OI RH ENHANCEMENT MODIFIED ANALYSIS SUBTRACTING FIRST GUESS ANALYSIS INCREMENTS Determination of the analysis increments.

23. WP 3: Data assimilation Results for a test case: 021210. Forecasted precipitation field at 13 UTC. No IAUIAU

24. WP 3: Data assimilation Comparison experiment between nudging and IAU using the MASS model: Testing 2 assimilating frequencies: 6-h and 3-h. Different combinations of assimilated data used. Applied to 10 different cases. Methodology.

25. WP 3: Data assimilation Results: 3-hourly assimilation frequency minimizes the RMSE. Sfc-500 mean relative humidity RMSE for 2 cases (all the variables assimilated).

26. WP 3: Data assimilation Results: IAU overestimates the total amount of precipitation while nudging gives a bias closer to zero. 24-h accumulated precipitation mean error (all the variables assimilated). CaseCNTLIAU6NUD6IAU3NUD3 021210-5.60.6-3.72.1-2.7 0301061.73.9-0.34.10.2 030213-0.20.80.11.10.3 0302203.22.90.33.50.3 030227-0.84.71.15.61.5 030328-2.34.5-0.85.30.0 0304091.61.00.50.80.5 0305061.67.02.17.52.6 0308173.31.2-0.61.6-1.1 0308311.81.70.70.80.4

27. WP 3: Data assimilation Results: assimilating the combination of at least wind and humidity produces the best impact on the precipitation field. Sfc-500 hPa mean relative humidity RMSE for different combinations of assimilated variables. Case 030213. a) 3-h IAU, b) 3-h nudging.

28. WP 4 Assessment of NWP model uncertainity including model errors Software modules for ML/SKF approach Software modules for KF/IIP Report on benefits from improved data assimilation

29. On-line estimation of error covariances Covariances of innovation vectors (v=y-Hx b ): where Innovation covariance model: Tuning of error covariance matrix: Estimated from set of innovations by applying Kriging and Maximum Likelihood techniques. Pre-scribed. (In HIRLAM case error stat const. in time)

30. Krieging and Maximum Likelihood Based on Kriging, in observation space we estimate the covariance of the innovations with a covariogram, V: Parameters p, w, d are estimated with ML tecnique to find the maximum of the following pdf: { (n-number of innovations)

31. Application with HIRLAM 3D-Var Input from HIRLAM (innovations from each assimilation cycle during a 14 day period) (pre-scribed, assumed static HIRLAM observation error and background error model) On-line estimation software module utilising Kriging/ML Alpha parameter for each assimilation cycle, guiding when the pre-scribed -background errors should be increased/decreased. From that a time dependent scaling factor was calculated.

32. Assimilation and forecast experiment Integration area Two parallel runs CRL:static background error INV: time dependent background error 14 day experiment (1-14 Jan., 2002) RMS of 48 hour MSLP forecasts (unit: hPa) as function of assimilation cycle during the 14 day period.

33. Extension to include spatial variations Sub-division of model-domain Example of alpha parameters for each sub-domain

34. WP 5 Assessment of improvements in NWP Analysis of severe weather situations Set up of VSRF procedure Verification of forecasted field coming from VSRF procedure Model inter-comparison experiment

35. System Architecture GTS data High resolution surface networks LAM output Radar data Fast varying satellite products Slow varying satellite products Weather analysis module Weather features identification module Advection module VSRF module OUTPUT

36. Assimilation Cycle Based on Nudging Radar DataMSGData LAPS Analysis CONVENTIONAL DATA: from GTS network SURFACE DATA: from local (high density)networks and OTHER data AOF (Analysis Observations File): 1-DVAR Temperature and Humidity Profiles retrieval Background from Model run LAPS- Pseudo-observations Retrieved Profiles Boundary Conditions from GCM or from Coarser LM CONTINUOS ASSIMILATION CYCLE

37. 0 +1+2+3+12+24 time LAPS analisys 0 LM BC 0BC 1 0+24 LAMI Boundary Condition BC 2 BC 12BC 24 LM LM very short range forecast +12 Assimilation Cycle (nudging) LAPS analisys 1 LAPS analisys 2 LAPS analisys 11 LAPS analisys 12 LAPS background Start LM run Stop LM run

38. Ingestion of satellite data into the Local Analysis and Prediction System (LAPS) In Bologna is done using METEOSAT data via cloud cover analysis from VIS-IR channel water vapor content from the WV channel Severe weather CASE study

39. METEOSAT WV LAPS RAOBLAPS SATELLITE LAPS background RH (%) 400 hPa 18 June 97 1200 UTC

40. LAPS BACKGROUND LAPS RAOBLAPS SATELLITE Total Precipitable Water (mm)

41. With METEOSAT (IR/VIS) data Without satellite data

42. Synoptic Analysis: IOP15 (5-9 nov) Day 5 november is very clear a blocking situation that will caracterize whole observing period. Mediterranean cut-off low start to move SE as an intense Atlantic short wave approaches Italy. Day 6 we observe a very intense developement, with thunderstorms activity in Adriatic sea and East Alps. The following days the centre of surface cyclone interest central and meridional Italy and from day 9 a new cut-off low carring Nord Sea cold air reach NE Alps. CARPE DIEM Meeting 15-16 december 2003

44. Concluding Remarks A lot of knowledge exchange and co-operation between different institutes and expertises within AREA1. Almost all deliverables within AREA 1 have proceeded according to plans and a few extra rather extensive deliverables have been added. Comments from TSC report I and II valuable and are taken into account.