LAPS Technical Overview

1. LAPS Technical Overview

2. LAPS Mission A system designed to: Exploit all available data sources Create analyzed and forecast grids with analysis systems and numerical models Build products for specific forecast applications Provide reliable forecast guidance Use advanced display technology …All within a local weather office, forward site, or in fully deployed mode

3. LAPS Basic Structure Univariate Analysis Analysis Merging/ Balancing NWP Model Initialization/ Prediction Data Very diverse Force geometric, Reconcile gridded Generate forecasts data sets smoothing constraints fields; force and user-specific to interpolate data to consistency based products high resolution grids on atmospheric scale

4. LAPS Components - Stage 1 Data Acquisition and Quality Control Univariate Analysis of the Following Fields Temperature Winds Water Vapor Clouds Microphysical variables Vertical motions

5. Data Acquisition and Quality ControlLAPS supports a wide range of data types

6. LAPS data ingest strategy

7. Multi-layered Quality Control Gross Error Checks RoughClimatologicalEstimates Station Blacklist Dynamical Models Use of background and mesoscale models Standard Deviation Check Statistical Models Buddy Checking

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

9. LAPS Radar Ingest

10. 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

11. Radar Mosaic

12. Analyzed Reflectivity (800 hPa)

13. Surface Precipitation Accumulation Algorithm similar to NEXRAD PPS, but runs in Cartesian space Rain / Liquid Equivalent Z = 200 R ^ 1.6 Snow case: use rain/snow ratio dependent on column maximum temperature Reflectivity limit helps reduce bright band effect

14. Storm-Total Precipitation (wideband mosaic)

15. Storm-Total Precipitation

16. Storm-Total Precipitation (RCWF narrowband)

17. Precip type and snow cover

18. The LAPS Analysis

19. Three-dimensional Analysis Looking for a function that is the best fit of weather through backgrounds and observations in 3D. Data assimilation techniques: Barnes, 3DVar, 4DVAR, KF. Differences….Barnes is a point-wise fitting; 3DVAR, 4DVar. KF are global fitting schemes.

20. Focus on the mesoscale - here model error covariances are poorly known - 3-D var is not an optimum approach Let data define structure Use successive corrections (Barnes) exponential weight with collapsing radius of influence Blend with model background Generate smooth fields that will be reconciled in stage 2 Ensure rapid computation LAPS Analysis Philosophy

22. LAPS Analysis Process and Data Structure LAPSPRD Directory LSX Surface Fields LT1 3-D Temp L1S Prcp Accum LQ3 3-DHumid LAPS Inter. Data Files LW3 Wind LC3 Cloud LCP Derived Pds LM1 Soil

23. Sfc ToverTaiwan

24. CAPE

25. 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)

26. LAPS Wind Analysis

27. X-sect T / Wind

28. 700 hPa Winds and Geopotential - Stage 1

29. Products Derived from Wind Analysis

30. 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 all appropriate LAPS upper air data Assimilates boundary layer moisture from LAPS Sfc Td analysis

31. 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

32. Total Preciptiable Water: Taiwan

33. 3-D Clouds Preliminary analysis from vertical “cloud 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

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

35. CloudSchematic

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

37. Cloud cover (fraction) with surface stations only

38. Cloud fraction with surface stations and radar

39. Cloud fraction with surface stations, radar, and IR

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

41. Cloud Isosurfaces

42. Cloud/precip cross section

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

44. Some dependence on cloud type, Updraft goes to top of cloud CS Strongest updrafts in regions of high reflectivity CB Downdrafts below freezing level Randomness in broad convective regions 0C Updated CWB/ FSL scheme (cloud derive subr)

45. Case Study Example: How LAPS is used in the National Weather Service Utility of LAPS analysis-only for nowcasting A Convective Event on 14 May 1999 Location: DEN-BOU WFO

46. Case Study Example On 14 May, moisture is in place. A line of storms develops along the foothills around noon LT (1800 UTC) and moves east. LAPS used to diagnose potential for severe development. A Tornado Watch issued by ~1900 UTC for portions of eastern CO and nearby areas. A brief tornado did form in far eastern CO west of GLD around 0000 UTC the 15th. Other tornadoes occurred later near GLD.

47. NOWRAD and METARS with LAPS surface CIN 2100 UTC

48. Examine soundings near CAPE max at points B, E and F 2100 UTC

49. Soundings near CAPE max at B, E and F 2100 UTC

50. CIN minimum in area of CAPE max 2200 UTC