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Radiation and fog in complex topography - Research and operational forecasting with NMM

NOAA/NCEP Washington, Aug. 2005. Radiation and fog in complex topography - Research and operational forecasting with NMM. Z.Janjic, D. Scherer & A. Bott. Mathias D. Müller Institute of Meteorology, Climatology & Remote Sensing University of Basel, Switzerland Mathias.mueller@unibas.ch.

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Radiation and fog in complex topography - Research and operational forecasting with NMM

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  1. NOAA/NCEP Washington, Aug. 2005 Radiation and fog in complex topography - Research and operational forecasting with NMM Z.Janjic, D. Scherer & A. Bott Mathias D. Müller Institute of Meteorology, Climatology & Remote Sensing University of Basel, Switzerland Mathias.mueller@unibas.ch

  2. WHAZZZUPP ! The forecast system at the University of Basel A radiation parameterization of topographic effects Towards a 3D fog model Coffee and Cookies :-)

  3. Realtime Forecasts / Nesting GFS 00/12 UTC NMM-22 & ETA-22 00/12 UTC 5 grids 72 h NMM-4 03/15 UTC ETA-4 15 UTC NMM-2 15 UTC 45 h

  4. Hardware Weather Forecast & Post Processing Beowulf Cluster Theoretical Performance 386 Gflops1 Master 2 x 2.4 GHz Xeon Cpu's, 2GB Mem1 Fileserver 1.1TB Disk36 Nodes 2 x 2.4 GHz Xeon Cpu's, 2GB MemGigabit Ethernet and SCI interconnect Visualisation & Preprocessing 2 x 3.0 GHz Xeon CPU‘s, 4 GB Mem, Linux OS IDL, 2 TB Disk

  5. NWP at the University of Basel - Flow chart NOAA/NCEP 3D-VAR / GFS Sea-temperatue Snow RS - Bern NDVI Snowmask Meteoblue MeteoSwiss Observations Meteomedia Observations Visualisation Current forecast Archive short-term Archive Initalization phase 1 Verification Kalman Cronos File-Server / Cluster tasks management Forecast Model on Cronos Cluster Final Initialization Post processing 3D-VAR not implemented! EXTERNAL URZ WS-ETA Synchronized communication (MPI) Co VAR Data

  6. NDVI initialization with NOAA-AVHRR CLIMATOLOGY AND REALTIME NDVI CLIMATOLOGY NDVI REALTIME NDVI AUGUST, 13 2003 AGGREGATED TO 4 KM MODEL RESOLUTION

  7. 22 km NMM 4 km NMM Operational Model Statistics Forecast output of 5 Model Grids: 35 GB / 24h Total number of Images produced: ~ 20‘000 / 24h Programming language for all Graphics: IDL Operating System: LINUX Communication and time synchronisation with forecast Models, running on Cronos Cluster. Computing Time: NMM22 ~ 60 min (critical !) NMM4 ~ 90 min NMM2 ~ 4 hours 2km NMM Computing Time of Models is upper limit for Visualisation time

  8. Forecast Visualisation – key points Efficiency of Visualisation Data and Information Reduction / Aggregation (storage and resolution restrictions) (e.g. Smoothing, Skipping, 24h-precipitation) Conversion / Computation of Variables for visualisation from raw model output (e.g. Vorticity, CAPE, Helicity, Streamlines) Precomputation of domain dependent but constant layers (Topographic Shading, Continent outlines,….) Model Coordinate Systems (Lambert, Eta-Grid, …, Lat/Lon) Internal Coordinate System (Geographical Lat/Lon) Projection / Mapping to different Coordinate Systems for Visualisation Scaling: constant, image optimized, constant-optimized for forecast range

  9. Maps – transparent, constant and content Layers

  10. Point Forecasts – Meteograms

  11. Point Forecasts – Tephigrams

  12. Internet – meteoblue.ch

  13. Pictograms – it‘s for children 3. Web-page compilation: 1. Local field statistics: 2. Decision Tree: no rain rain Rain Cloud Cover Temperature ….. Rain Cloud Cover Temperature ….. clouds Rain Cloud Cover Temperature ….. Rain Cloud Cover Temperature …..

  14. 3D visualisations – nice and useless?

  15. A radiation parameterization of topographic effects Mathias Müller, Dieter Scherer

  16. Parameterization features Radiation fluxes from Mesoscale Model Land-Surface Model of Mesoscale Model Considers: Slope and azimuth angle of absorbing surface Shadows Sky-view restriction Negligible computational costs Computation of radiative mean fluxes for model grid cells based on higher resolution DEM Interface between land-surface model and radiative transfer model Relatively easy to include in other models (sandwich-like) Parameterization features

  17. Shortwave radiation - direct & diffuse Radiation fluxes from Mesoscale Model Land-Surface Model of Mesoscale Model

  18. Longwave radiation Radiation fluxes from Mesoscale Model Land-Surface Model of Mesoscale Model

  19. Effects of slope angle, slope aspect and shadow • affects: • direct shortwave

  20. Effects of slope angle, slope aspect and shadow • affects: • direct shortwave

  21. Effects of slope angle, slope aspect and shadow • affects: • direct shortwave

  22. Effects of slope angle, slope aspect and shadow • affects: • direct shortwave

  23. Sky-view factor 0.5 • affects: • diffuse shortwave • longwave

  24. Case Studies Parameterization was tested in the Nonhydrostatic Mesoscale Model (NMM) of NOAA/NCEP (version: Z. Janjic) 220 Alpine stations

  25. 2m Temperature Differences +2 K Sun facing -3 K shadow

  26. 2m Temperature Differences 0.5 +2 K in Valleys • Skyview affects: • diffuse shortwave • longwave

  27. Verification – Clear sky summer conditions (alpine stations) 22 June 2003 Nighttime warming reduces model cold bias (0.5K) Problem of verification: Stations are located in valleys but radiation affects slopes

  28. Verification – overcast conditions (alpine stations) 24 December 2003 Nighttime warming reduces model cold bias

  29. Benefits from subgrid topography doubles the effect ! 4 km Grid with 1 km subgrid topography

  30. Parallel run intercomparison (4 km grid)

  31. Conclusions Radiation fluxes from Mesoscale Model Land-Surface Model of Mesoscale Model Mean radiation flux based on high resolution DEM Modifies temperature in a full physics NMM run up to +/-3 K Reduces nighttime cold bias by about 0.5-1 K No computational costs during time integration Easy to implement, sandwich-like module for mesoscale models

  32. 3D fog with NMM and PAFOG Mathias Müller, Andreas Bott, Zavisa Janjic

  33. NMM_PAFOG Droplet number concentration Liquid water content NMM dynamical framework PAFOG microphysics Condensation/evaporation in the lowest 1500 m is replaced by PAFOG

  34. PAFOG microphysics Supersat. where S is the Supersaturation Assumption on the droplet size distribution : Log-normal function D droplet Diameter Dc,0 mean value of D σc Standart deviation of the given droplet size distribution (σc=0.2)

  35. GRID of NMM_PAFOG 50 x 50 x 45 27 layers in the lowest 1000 m 11 soil layers Thickness(cm): 0.5 0.75 1.2 1.8 2.7 4.0 6.0 10 30 60 100

  36. Boundary conditions for dNc HEIGHT PAFOG TOP 1000 m σc 1000m PAFOG TOP

  37. Nesting GFS NMM-22 NMM_PAFOG GRID: 50 x 50 x 45 (+11 soil layers) dx: 1 km dt: 2s (dynamics) / 10s (physics) CPU: 40 min/24hr on 9 Pentium-4 (very efficient!) NMM_PAFOG NMM-4 NMM-2 15 UTC

  38. 19:00 MEZ (3 hr forecast) PAFOG STANDARD 27 Nov 2004

  39. 22:00 MEZ (6 hr forecast) PAFOG STANDARD 27 Nov 2004

  40. 02:00 MEZ (10 hr forecast) PAFOG STANDARD Accurate sedimentation in PAFOG due to dNc computation. 28 Nov 2004

  41. 08:00 MEZ (16 hr forecast) PAFOG STANDARD 28 Nov 2004

  42. 10:00 MEZ (18 hr forecast) PAFOG STANDARD 28 Nov 2004

  43. qc at 5m height (01:00 MEZ) PAFOG STANDARD

  44. qc at 5m height (06:00 MEZ) PAFOG STANDARD

  45. Nc at 5m height (06:00 MEZ)

  46. Cold air pooling (05:00 MEZ) too cold !

  47. Cold bias problem Z.Janjic

  48. 19:00 MEZ (3 hr forecast) PAFOG STANDARD 27 Nov 2004

  49. 22:00 MEZ (6 hr forecast) PAFOG STANDARD 27 Nov 2004

  50. 02:00 MEZ (10 hr forecast) PAFOG STANDARD Accurate sedimentation in PAFOG due to dNc computation. 28 Nov 2004

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