1 / 121

Using LAPS as a CWB Nowcasting Tool

Using LAPS as a CWB Nowcasting Tool. By Steve Albers December 2002. Local Analysis and Prediction System (LAPS). A system designed to: Exploit all available data sources Create analyzed and forecast grids Build products for specific forecast applications Use advanced display technology

alyssa
Télécharger la présentation

Using LAPS as a CWB Nowcasting Tool

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Using LAPS as a CWB Nowcasting Tool By Steve Albers December 2002

  2. Local Analysis and Prediction System (LAPS) A system designed to: • Exploit all available data sources • Create analyzed and forecast grids • Build products for specific forecast applications • Use advanced display technology …All within the local weather office

  3. LAPS Flow Diagram

  4. CWB LAPS Grid • LAPS Analysis Grid • Hourly Time Cycle • Horizontal Resolution = 5 km • Vertical Resolution = 50 mb • Size: 199 x 247 x 21

  5. Data Acquisition and Quality Control

  6. LAPSDataSources The blue colored data are currently used in AWIPS LAPS. The other data are used in the "full-blown" LAPS and can potentially be added to AWIPS/LAPS if the data becomes available.

  7. LAPS Surface Analysis

  8. Multi-layered Quality Control • Gross Error Checks • RoughClimatologicalEstimates • Station Blacklist • Dynamical Models • Use of meso-beta models • Standard Deviation Check • Statistical Models (Kalman Filter) • Buddy Checking

  9. Standard Deviation Check • Compute Standard Deviation of observations-background • Remove outliers • Now adjustable via namelist

  10. Kalman QC Scheme FUTURE Upgrade to AWIPS/LAPS QC • Adaptable to small workstations • Accommodates models of varying complexity • Model error is a dynamic quantity within the filter, thus the scheme adjusts as model skill varies

  11. Sfc T

  12. CAPE

  13. 3-D Temperature • First guess from background model • Insert RAOB, RASS, and ACARS if available • 3-Dimensional weighting used • Insert surface temperature and blend upward • depending on stability and elevation • Surface temperature analysis depends on • METARS, Buoys, and Mesonets (LDAD)

  14. Successive correction analysis strategy • 3-D weighting • Successive correction with Barnes weighting • Distance weight e-(d/r)2 applied in 3-dimensions • Instrument error reflected in observation weight • Wo = e-(d/r)2 / erro2 • Each analysis iteration becomes the background for the next iteration • Decreasing radius of influence (r) with each iteration • Each iteration improves fit and adds finer scale structure • Works well with strongly clustered observations • Iterations stop when fine scale structure & fit to obs become commensurate with observation spacing and instrument error

  15. Successive correction analysis strategy (cont) • Smooth blending with Background First Guess • Background subtracted to yield observation increments (uo) • Background (with zero increment) has weight at each grid point • Background weight proportional to inverse square of estimated error • wb = 1 / errb2 • For each iteration, analyzed increment (u) is as follows: • ui,j,k = (uowo) / ( (w o )+ wb )

  16. X-sectT / Wind

  17. LAPS Wind Analysis

  18. Products Derived from Wind Analysis

  19. Doppler and Other Wind Obs

  20. LAPS radar ingest

  21. Remapping Strategy • Polar to Cartesian • 2D or 3D result (narrowband / wideband) • Average Z,V of all gates directly illuminating each grid box • QC checks applied • Typically produces sparse arrays at this stage

  22. Remapping Strategy (reflectivity) • Horizontal Analysis/Filter (Reflectivity) • Needed for medium/high resolutions (<5km) at distant ranges • Replace unilluminated points with average of immediate grid neighbors (from neighboring radials) • Equivalent to Barnes weighting at medium resolutions (~5km) • Extensible to Barnes for high resolutions (~1km) • Vertical Gap Filling (Reflectivity) • Linear interpolation to fill gaps up to 2km • Fills in below radar horizon & visible echo

  23. Mosaicing Strategy (reflectivity) • Nearest radar with valid data used • +/- 10 minute time window • Final 3D reflectivity field produced within cloud analysis • Wideband is combined with Level-III (NOWRAD/NEXRAD) • Non-radar data contributes vertical info with narrowband • QC checks including satellite • Help reduce AP and ground clutter

  24. Horizontal Filter/Analysis Before After

  25. Radar Mosaic

  26. LAPS cloud analysis METAR METAR METAR

  27. CloudSchematic

  28. Cloud Isosurfaces

  29. 3-D Clouds • Preliminary analysis from vertical “soundings” derived from METARS, PIREPS, and CO2 Slicing • IR used to determine cloud top (using temperature field) • Radar data inserted (3-D if available) • Visible satellite can be used

  30. CloudAnalysisFlowChart

  31. Cloud & Radar X-sect (Taiwan)

  32. Cloud & Radar X-sect (wide/narrow band)

  33. Derived cloud products flow chart

  34. Cloud/Satellite Analysis Data • 11 micron IR • 3.9 micron data • Visible (with terrain albedo) • CO2-Slicing method (cloud-top pressure)

  35. Visible Satellite Impact

  36. Cloud Coverage without/with visible data No vis data With vis data

  37. Storm-Total Precipitation (wideband mosaic)

  38. LAPS 3-D Water Vapor (Specific Humidity) Analysis • Interpolates background field from synoptic-scale model forecast • QCs against LAPS temperature field (eliminates possible supersaturation) • Assimilates RAOB data • Assimilates boundary layer moisture from LAPS Sfc Td analysis

  39. LAPS 3-D Water Vapor (Specific Humidity) Analysis [continued] • Scales moisture profile (entire profile excluding boundary layer) to agree with derived GOES TPW (processed at NESDIS) • Scales moisture profile at two levels to agree with GOES sounder radiances (channels 10, 11, 12). The levels are 700-500 hPa, and above 500 • Saturates where there are analyzed clouds • Performs final QC against supersaturation

  40. Adjustments to cloud and moisture scheme • Originally cloud water and ice estimated from Smith-Feddes parcel • Model – this tended to produce too much moisture and ice • Adjustments: • Scale vertical motion by diagnosed cloud amount, extend below cloud base • 2. Reduced cloud liquid consistent with 10% supersaturation of diagnosed water vapor and autoconversion rates from Schultz

  41. Cloud vertical motions

  42. Balance scheme tuned

  43. Proposed Tasks for IA#15 • Transfer existing LAPS/MM5 Hot-Start system to CWB • LAPS build on LINUX • Expand satellite and radar data used for cloud diagnosis • Adapt to GOES 9 (visible + 3.9 micron) • Radar data compression needed? • CWB/NFS as background • Continued tuning for tropics • Add thermodynamic constraint to balance package to correct for bad background fields • Add a verification package to the LAPS/MM5 system – State variables and QPF • Continue regular upgrades CWB software

  44. Sources of LAPS Information • The Taiwan LAPS homepage • http://laps.fsl.noaa.gov/taiwan/taiwan_home.html

  45. Analysis Information LAPS analysis discussions are near the bottom of: http://laps.fsl.noaa.gov/presentations/presentations.html Especially noteworthy are the links for • Satellite Meteorology • Analyses: Temperature, Wind, and Clouds/Precip. • Modeling and Visualization • A Collection of Case Studies

  46. The End

  47. LAPS (Local Analysis and PredictionSystem) Diabatic Initialization technique Hot-Start MM5 Taiwan Short-Term Forecast System Taiwan Short Term Forecast System

  48. Forecast domains & Computational requirement 0.00 0.05 0.10 0.15 0.20 1km (169*151) 0.25 0.30 0.35 0.40 0.45 0.50 0.54 0.58 1260 km ( 141 points) 151 pts 0.62 30 Vertical layers (σ levels) 0.65 0.68 0.71 0.74 0.77 0.80 3km 151 pts 0.83 0.86 0.88 0.90 0.92 0.94 0.96 9km 0.97 0.98 1368 km ( 153 points) 0.99 1.00 CPUs 42 Need 1.5hrsfor 24hrs fcst compaq 833 MHz Forecast domains & computational requirements

  49. CWB Hot-Start MM5 Model Configuration Domain1 Domain2 Grid-points 153*141*30 151*151*30 Horizontal Resolution 9 km 3 km 9 secs Time-Step 27 secs Nesting Two-way feedback between nests Lateral B.C. Relaxation/inflow-outflow (from CWB/NFS) Lower B.C. Daily SST and LAPS surface analysis Upper Radiative Condition Upper B.C. CWB Hot-Start MM5 Model Configuration

  50. CWB Hot Start Physics CWB Hot-Start MM5 Model Physics Initial Field From LAPS and Diabatic Initialization Microphysics Schultz scheme PBL scheme MRF PBL Surface scheme 5-layer Soil Model Radiation RRTM scheme Shallow Convection YES Cumulus Parameterization NO

More Related