1. Developing STMAS in the LAPS frameworkBy
Steve Albers
August 2006

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
LAPS Flow Diagram

4. STMAS LAPS Grid LAPS Analysis Grid 15-min Time Cycle
Horizontal Resolution = 5 km
Size: 721x539

5. Data Acquisition and Quality Control

6. LAPS Data Sources
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
Rough Climatological Estimates
Station Blacklist
Dynamical Models
Use of large-scale background (first-guess)
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. Analysis Strategies Modified Barnes Recursive Filter
Uses successive correction
“Default” LAPS method
Used in STMAS only for comparison purposes
Recursive Filter
Method used in STMAS
Improved temporal continuity
Described later in presentation by Yuanfu Xie

12. 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

13. 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 )

14. STMAS (Modified Barnes)

15. STMAS (Recursive Filter)

16. The End
Questions?

17. X-sect T / Wind

18. LAPS Wind Analysis

19. Products Derived from Wind Analysis

20. Doppler and Other Wind Obs

21. LAPS radar ingest

22. 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

23. 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

24. 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

25. Horizontal Filter/Analysis
Before
After

26. Radar Mosaic

27. LAPS cloud analysis
METAR
METAR
METAR

28. Cloud Schematic

29. Cloud Isosurfaces

30. 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

31. Cloud Analysis Flow Chart

32. Cloud & Radar X-sect (Taiwan)

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

34. Derived cloud products flow chart

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

36. Visible Satellite Impact

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

38. Storm-Total Precipitation (wideband mosaic)

39. 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

40. 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 hPa, and above 500
Saturates where there are analyzed clouds
Performs final QC against supersaturation

41. 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

42. Cloud vertical motions

43. Balance scheme tuned

44. 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 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

45. Sources of LAPS Information
The Taiwan LAPS homepage

46. Analysis Information LAPS analysis discussions are near the bottom of:
Especially noteworthy are the links for
Satellite Meteorology
Analyses: Temperature, Wind, and Clouds/Precip.
Modeling and Visualization
A Collection of Case Studies

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
1km (169*151)
1368 km ( 153 points)
1260 km ( 141 points)
151 pts
9km
3km
CPUs 42
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)
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
Time-Step
27 secs
9 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 B.C.
Upper Radiative Condition
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
CWB Hot Start Physics

51. Kalman Flow Chart

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

53. An example of the use of LAPS in convective event
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
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. Why Run Models in the Weather Office? (cont.)
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

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

100. LAPS Summary You can see more about our local modeling efforts at
What else in the future? (hopefully a more complete input data stream to AWIPS LAPS analysis)

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

114. Gauge Radar Analysis

115. Selected references
Albers, S., 1995: The LAPS wind analysis. Wea. and Forecasting, 10,
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,
Birkenheuer, D., B.L. Shaw, S. Albers, E. Szoke, 2001: Evaluation of local-scale forecasts for severe weather of July 20, 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, 15th 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