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 DiagramLAPS 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. 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. LAPS Data Sources

7. LAPS Surface Analysis

8. Multi-layered Quality Control Gross Error Checks –Rough Climatological Estimates 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. 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 Kalman QC Scheme

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 W o = e -(d/r) 2 / err o 2 –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 (u o ) –Background (with zero increment) has weight at each grid point –Background weight proportional to inverse square of estimated error w b = 1 / err b 2 –For each iteration, analyzed increment (u) is as follows: u i,j,k = (u o w o ) / ( (w o )+ w b )

16. X-sect T / 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 BeforeAfter

25. Radar Mosaic

26. LAPS cloud analysis METAR

27. Cloud Schematic

28. Cloud Isosurfaces

29. 3-D Clouds Preliminary analysis from vertical “soundings” derived from METARS, PIREPS, and CO 2 Slicing IR used to determine cloud top (using temperature field) Radar data inserted (3-D if available) Visible satellite can be used

30. Cloud Analysis Flow Chart

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 dataWith 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: 1. 1.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. LAPS analysis discussions are near the bottom of: http://laps.fsl.noaa.gov/presentations/presentations.h tml Especially noteworthy are the links for Satellite Meteorology Analyses: Temperature, Wind, and Clouds/Precip. Modeling and Visualization –A Collection of Case Studies Analysis Information

46. The End

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

48. Forecast domains & computational requirements Forecast domains & Computational requirement 1km (169*151) 1368 km ( 153 points) 1260 km ( 141 points) 151 pts 9km 3km CPUs42 compaq 833 MHz Need 1.5hrs for 24hrs fcst 0.00 0.05 0.10 0.15 0.20 0.40 0.35 0.30 0.25 0.71 0.68 0.65 0.62 0.58 0.54 0.50 0.45 0.92 0.90 0.88 0.86 0.83 0.80 0.77 0.74 0.99 0.98 0.97 0.96 0.94 1.00 30 Vertical layers ( σ levels)

49. CWB Hot-Start MM5 Model Configuration Domain1Domain2 Grid-points153*141*30151*151*30 Horizontal Resolution 9 km3 km Time-Step27 secs 9 secs NestingTwo-way feedback between nests Lateral B.C.Relaxation/inflow-outflow (from CWB/NFS) Lower B.C.Daily SST and LAPS surface analysis Upper B.C. Upper Radiative Condition CWB Hot-Start MM5 Model Configuration

50. CWB Hot Start Physics CWB Hot-Start MM5 Model Physics Initial FieldFrom LAPS and Diabatic Initialization MicrophysicsSchultz scheme PBL schemeMRF PBL Surface scheme5-layer Soil Model RadiationRRTM scheme Shallow Convection YES Cumulus Parameterization NO

51. Kalman Flow Chart

52. Cloud Coverage without/with visible data No vis dataWith vis data

53. Case Study Example An example of the use of LAPS in convective event 14 May 1999 Location: DEN-BOU WFO

54. 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.

55. NOWRAD and METARS with LAPS surface CAPE 2100 UTC

56. NOWRAD and METARS with LAPS surface CIN 2100 UTC

57. Dewpoint max appears near CAPE max, but between METARS 2100 UTC

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

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

60. RUC also has dewpoint max near point E 2100 UTC

61. LAPS & RUC sounding comparison at point E (CAPE Max) 2100 UTC

62. CAPE Maximum persists in same area 2200 UTC

63. CIN minimum in area of CAPE max 2200 UTC

64. Point E, CAPE has increased to 2674 J/kg 2200 UTC

65. Convergence and Equivalent Potential Temperature are co-located 2100 UTC

66. How does LAPS sfc divergence compare to that of the RUC? Similar over the plains. 2100 UTC

67. LAPS winds every 10 km, RUC winds every 80 km 2100 UTC

68. Case Study Example (cont.) The next images show a series of LAPS soundings from near LBF illustrating some dramatic changes in the moisture aloft. Why does this occur?

69. LAPS sounding near LBF 1600 UTC

70. LAPS sounding near LBF 1700 UTC

71. LAPS sounding near LBF 1800 UTC

72. LAPS sounding near LBF 2100 UTC

73. Case Study Example (cont.) Now we will examine some LAPS cross- sections to investigate the changes in moisture, interspersed with a sequence of satellite images showing the location of the cross-section, C-C` (from WSW to ENE across DEN)

74. Visible image with LAPS 700 mb temp and wind and METARS 1500 UTC Note the strong thermal gradient aloft from NW-S (snowing in southern WY) and the LL moisture gradient across eastern CO.

75. LAPS Analysis at 1500 UTC, Generated with Volume Browser

76. Visible image 1600 UTC

77. Visible image 1700 UTC

78. LAPS cross- section 1700 UTC

79. LAPS cross- section 1800 UTC

80. LAPS cross- section 1900 UTC

81. Case Study Example (cont.) The cross-sections show some fairly substantial changes in mid-level RH. Some of this is related to LAPS diagnosis of clouds, but the other changes must be caused by the satellite moisture analysis between cloudy areas. It is not clear how believable some of these are in this case.

82. Case Study Example (cont.) Another field that can be monitored with LAPS is helicity. A description of LAPS helicity is at http://laps.fsl.noaa.gov/frd/laps/LAPB/AWIPS_WFO_page.htm A storm motion is derived from the mean wind (sfc-300 mb) with an off mean wind motion determined by a vector addition of 0.15 x Shear vector, set to perpendicular to the mean storm motion Next we’ll examine some helicity images for this case. Combining CAPE and minimum CIN with helicity agreed with the path of the supercell storm that produced the CO tornado.

83. NOWRAD with METARS and LAPS surface helicity 1900 UTC

84. NOWRAD with METARS and LAPS surface helicity 2000 UTC

85. NOWRAD with METARS and LAPS surface helicity 2100 UTC

86. NOWRAD with METARS and LAPS surface helicity 2200 UTC

87. NOWRAD with METARS and LAPS surface helicity 2300 UTC

88. Case Study Example (cont.) Now we’ll show some other LAPS fields that might be useful (and some that might not…)

89. Divergence compares favorably with the RUC

90. The omega field has considerable detail (which is highly influenced by topography

91. LAPS Topography

92. Vorticity is a smooth field in LAPS

93. Comparison with the Eta does show some differences. Are they real?

94. Stay Away from DivQ at 10 km

95. Why Run Models in the Weather Office? Diagnose local weather features having mesoscale forcing –sea/mountain breezes –modulation of synoptic scale features Take advantage of high resolution terrain data to downscale national model forecasts –orography is a data source!

96. Take advantage of unique local data –radar –surface mesonets Have an NWP tool under local control for scheduled and special support Take advantage of powerful/cheap computers Why Run Models in the Weather Office? (cont.)

99. SFM forecast showing details of the orographic precipitation, as well as capturing the Longmont anticyclone flow on the plains

100. You can see more about our local modeling efforts at http://laps.fsl.noaa.gov/szoke/lapsreview/start.ht ml What else in the future? (hopefully a more complete input data stream to AWIPS LAPS analysis) LAPS Summary

104. Reflectivity (800 hPa)

105. Derived products flow chart

106. Cloud/precip cross section

107. Precip type and snow cover

108. 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

109. Storm-Total Precipitation

110. Storm-Total Precipitation (RCWF narrowband)

111. Future Cloud / Radar analysis efforts Account for evaporation of radar echoes in dry air –Sub-cloud base for NOWRAD –Below the radar horizon for full volume reflectivity Continue adding multiple radars and radar types –Evaluate Ground Clutter / AP rejection

112. Future Cloud/Radar analysis efforts (cont) Consider Terrain Obstructions Improve Z-R Relationship –Convective vs. Stratiform Precipitation Analysis –Improve Sfc Precip coupling to 3D hydrometeors –Combine radar with other data sources Model First Guess Rain Gauges Satellite Precip Estimates (e.g. GOES/TRMM)

113. Gauge Radar Analysis

115. Selected references Albers, S., 1995: The LAPS wind analysis. Wea. and Forecasting, 10, 342-352. Albers, S., J. McGinley, D. Birkenheuer, and J. Smart, 1996: The Local Analysis and prediction System (LAPS): Analyses of clouds, precipitation and temperature. Wea. and Forecasting, 11, 273-287. Birkenheuer, D., B.L. Shaw, S. Albers, E. Szoke, 2001: Evaluation of local-scale forecasts for severe weather of July 20, 2000. Preprints, 14th Conf on Numerical Wea. Prediction, Ft. Lauderdale, FL, Amer. Meteor. Soc. Cram, J.M.,Albers, S., and D. Devenyi, 1996: Application of a Two-Dimensional Variational Scheme to a Meso-beta scale wind analysis. Preprints, 15 th Conf on Wea. Analysis and Forecasting, Norfolk, VA, Amer. Meteor. Soc. McGinley, J., S. Albers, D. Birkenheuer, B. Shaw, and P. Schultz, 2000: The LAPS water in all phases analysis: the approach and impacts on numerical prediction. Presented at the 5th International Symposium on Tropospheric Profiling, Adelaide, Australia. Schultz, P. and S. Albers, 2001: The use of three-dimensional analyses of cloud attributes for diabatic initialization of mesoscale models. Preprints, 14th Conf on Numerical Wea. Prediction, Ft. Lauderdale, FL, Amer. Meteor. Soc.

116. The End

117. Future LAPS analysis work Surface obs QC –Operational use of Kalman filter (with time-space conversion) –Handling of surface stations with known bias Improved use of radar data for AWIPS –Multiple radars –Wide-band full volume scans –Use of Doppler velocities Obtain observation increments just outside of domain –Implies software restructuring Add SST to surface analysis Stability indices –Wet bulb zero, K index, total totals, Showalter, LCL (AWIPS) –LI/CAPE/CIN with different parcels in boundary layer –new (SPC) method for computing storm motions feeding to helicity determination More-generalized vertical coordinate?

118. Recent analysis improvements More generalized 2-D/3-D successive correction algorithm –Utilized on 3-D wind/temperature, most surface fields –Helps with clustered data having varying error characteristics –More efficient for numerous observations –Tested with SMS Gridded analyses feed into variational balancing package Cloud/Radar analysis –Mixture of 2D (NEXRAD/NOWRAD low-level) and 3D (wide-band volume radar) –Missing radar data vs “no echo” handling –Horizontal radar interpolation between radials –Improved use of model first guess RH &cloud liq/ice

119. Cloud type diagnosis Cloud type is derived as a function of temperature and stability

120. LAPS data ingest strategy

121. Dummy Image