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STMAS : Space-Time Mesoscale Analysis System Steve Koch, John McGinley, Yuanfu Xie, Steve Albers, Ning Wang, Patty Miller. STMAS Goal. Create a mesoscale analysis that: Assimilates all available surface data at high time frequency Performs data quality control

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slide1
STMAS:Space-Time Mesoscale Analysis SystemSteve Koch, John McGinley, Yuanfu Xie, Steve Albers, Ning Wang, Patty Miller
slide2

STMAS Goal

  • Create a mesoscale analysis that:
    • Assimilates all available surface data at high time frequency
    • Performs data quality control
    • Has a very rapid product cycle (< 15 minutes)
    • Can sustain features with typical mesoscale structure (gust fronts, gravity waves, bores, sea breezes, etc)
    • Can be used for boundary identification and monitoring (SPC)
      • Candidate for automated processing
    • Is compatible with current workstation technology
slide3

STMAS utilizes surface data available through the FSL Meteorological Assimilation Data Ingest System (MADIS)

  • Surface Observations
    • Meteorological Aviation Reports (METARs)
    • Coastal Marine Automated Network (C-MAN)
    • Surface Aviation Observations (SAOs)
    • Modernized Cooperative Observer Program (COOP)
    • Many mesonetworks (constantly growing)
  • MADIS offers automated Quality Control
    • Gross validity checks
    • Temporal consistency checks
    • Internal (physical) consistency checks
    • Spatial (“buddy”) checks
  • MADIS Home Page: www-sdd.fsl.noaa.gov/MADIS
  • Real-Time Display: www-frd.fsl.noaa.gov/mesonet/
slide4

MADIS Mesonet Providers 5/1/2004

Mesonet DescriptionProvider NameNo. of SitesCoverage

U.S. Army Aberdeen Proving Grounds

Citizen Weather Observers Program

AWS Convergence Technologies, Inc.

Anything Weather Network

Colorado Department of Transportation

Florida Mesonet

Ft. Collins Utilities

Goodland WFO Miscellaneous

Gulf of Maine Ocean Observing System

FSL Ground-Based GPS

Hydrometeorological Automated Data System

Iowa Department of Transportation

Iowa Environmental Mesonet

Boulder WFO Miscellaneous

Kansas Department of Transportation

Multi-Agency Profiler Surface Observations

Cooperative Mesonets in the Western U.S.

Minnesota Department of Transportation

National Ocean Service Physical Oceanographic Real-Time System

National Weather Service Cooperative Observer Program

Oklahoma Mesonet

Remote Automated Weather Stations

Radiometer

Denver Urban Drainage & Flood Control Dist.

Weather for You

APG

APRSWXNET

AWS

AWX

CODOT

FL - Meso

FTCOLLINS

GLDNWS

GoMOOS

GPSMET

HADS

IADOT

IEM

INTERNET

KSDOT

MAP

MesoWest

MNDOT

NOS – PORTS

NWS – COOP

OK - Meso

RAWS

RDMTR

UDFCD

WXforYou

5

2195

5600

64

107

39

5

15

10

340

60

50

88

13

41

12

2552

92

34

100

116

1777

2

17

414

Maryland

Global

U.S.

CONUS

Colorado

Florida

Colorado

CO/KS/NE

Gulf of Maine

U.S.

New England

Iowa

Iowa

Colorado

Kansas

CONUS

West CONUS

Minnesota

CONUS

New England

Oklahoma

U.S.

U.S.

Colorado

U.S.

Total = 13,748

slide5

MADIS collects data from over 13,000 sites presently (and still growing). Still, the data are largely distributed like “oases and deserts”.

why conventional objective analysis schemes are inadequate to deal with the desert oasis problem
Why conventional objective analysis schemes are inadequate to deal with the desert-oasis problem

Successive correction (SC) schemes (e.g., Barnes used in LAPS) and optimum interpolation (OI) schemes (e.g., as used in MADIS RSAS/MSAS) suffer from common problems:

  • Inhomogeneous station distributions cause problems for a fixed value of the final weighting function or the covariance function scale
  • SC and OI schemes will introduce noise in the deserts if forced to try to show details that are resolvable in the data oases everywhere
  • None of the SC or OI schemes include the high-resolution time information explicitly, with the exception of the modified Barnes scheme of Koch and Saleeby (2001), which required assumptions about the advection vectors in time-to-space conversion approach
stmas solution multi scale space and time analysis capability
STMAS solution: Multi-scale space and time analysis capability
  • Ability to represent large scales resolvable by the data distribution characteristic of the desert regions
  • Two schemes tested: telescopic recursive filter and wavelet fitting
  • Recursive filter uses residual remaining after removal of the large-scale component for telescopic analysis:
    • Compute residual  reduce the filter scale  do analysis of residuals at this next smaller scale  repeat N times until analysis error falls below the expected error in observations (N = 3-6)
    • Include temporal weight in similar manner for the recursive filter
    • Variational cost function assures fit to observations
  • Wavelet fitting technique provides for locally variable levels of detail, non-isotropic searching, and temporal weighting (still under development, though tested on analytic functions)
slide9

Comparisons of hourly analyses of temperature and winds using LAPS and STMAS to surface and radar observationsHourly analyses: 1900 - 2200 UTC

improvements to stmas
Improvements to STMAS
  • Use of Spline Wavelets
    • Accommodate common meteorological structures
    • Improve analysis in data rich and data sparse areas
  • Data Quality Control Using a Kalman Scheme
    • Operate in observation space
    • Provide data projections for future cycles
    • Optimum model for each station
scattered data fitting using spline wavelets
Scattered data fitting using Spline Wavelets
  • Basis functions: second order spline wavelets on bounded interval (by Chui and Quak)
  • Penalty function in variational formulation: a weighted combination of least square error and magnitude of the high order derivatives
  • Inner scaling functions control dilation and translation of the cardinal B-splines:
  • Boundary scaling functions control dilation of the cardinal B-spline with multiple knots at the endpoints
wavelet functions
Wavelet functions
  • Inner wavelet functions: dilation and translation of the cardinal B-wavelets
  • Boundary wavelet functions: dilation of the special B-wavelets derived from cardinal B-splines and boundary scaling functions
scaling and wavelet functions
Scaling and wavelet functions

Approaching boundary

comparison of four different analysis techniques
Comparison of four different analysis techniques

Barnes Analysis

Standard Recursive filter

Telescopic Recursive filter

Wavelet fitting

kalman filter for surface data
Kalman Filter for Surface Data
  • Provides a continuous station estimate of observation based on how a forecaster would perform observation projection: self trend, buddy trends, and NWP – use for quality checking
  • With missing obs – maintain constant station count

Kalman ob

Possible bad ob

Station

value

Kalman continuous

model

Allowable Obs

error

Time

Product

time

simulated temp traces
Simulated Temp Traces

Station 1-

regular

Station 2-

occasional

Station 3-

synoptic

Station 4-

mesonet

Station 5-

data bursts

Station 6-

QC problem

Time

Needed Analysis Product Time

aurora nebraska

44

40

36

No data

Temperature and Dewpoint observations and as derived from Kalman Filter for 22 Mar 2001

Aurora, Nebraska

Enid, Oklahoma

70

60

50

40

No data

slide28
Kalman Forecast Errors (F)(based on stations reappearing after not reporting for a time interval on x-axis)

Temperature

Dewpoint

more about stmas
More about STMAS
  • Ability to use background fields from a model (e.g., RUC) or a previous analysis (these features were adapted from LAPS and are important to have in the data-void desert regions)
  • Background fields are modified to account for very detailed terrain (another useful feature borrowed from LAPS)
  • Background field includes lake and sea surface temperatures and a land-weighting scheme to prevent situations such as warm land grid points having an influence on cooler water areas (via LAPS)
  • Currently, STMAS compares observations to background for its QC method. Kalman filter will provide both a much more sophisticated QC and the ability to fully utilize temporal detail in the data.
  • Reduced pressure calculation for a given reference height, as in LAPS (may see perturbation pressure sometime in the future)
  • Value of STMAS is being measured relative to the LAPS analysis
  • Analyses currently conducted over CIWS domain every 15 minutes on a 5-km grid (a variety of grid product fields are computed)
slide39

2000 UTC

STMAS analysis of temperature and winds: 20 UTC 30 May - 01 UTC 31 May 2004

slide51

2000 UTC

STMAS analysis of equivalent potential temperature and winds

slide63

2300 UTC

2300GMT

Zoomed-in analysis of equivalent potential temperature and winds

slide64

2315 UTC

2315GMT

slide65

2330 UTC

2330GMT

slide66

2345 UTC

2345GMT

slide67

0000 UTC

0000GMT

slide68

0015 UTC

0015GMT

slide69

0030 UTC

0030GMT

slide70

0045 UTC

0045GMT

slide71

0100 UTC

0100GMT

slide72

Recusive Analysis- Moist Convergence and Wind : 2300 - 0100

2300GMT

Zoomed-in analysis of moisture convergence

slide73

2315 UTC

2315GMT

slide74

2330 UTC

2330GMT

slide75

2345 UTC

2345GMT

slide76

0000 UTC

0000GMT

slide77

0015 UTC

0015GMT

slide78

0030 UTC

0030GMT

slide79

0045 UTC

0045GMT

slide80

0100 UTC

0100GMT

summary
Summary
  • STMAS is capable of high time and space resolution of important parameters for mesoscale weather
  • Shows good time continuity
  • Features correlate well with independent observations such as radar
  • Need to enhance analysis with wavelet scheme
  • Need to get robust Kalman QC into scheme
future
Future
  • Ensure compatibility with AWIPS
  • Work in more model background options (RUC and Eta)
  • Utilize STMAS fields for automated boundary diagnostics
  • Work toward a spatial 3-D scheme
slide86

Comparisons of hourly analyses of temperature and winds using LAPS and STMAS to surface and radar observationsHourly analyses: 1900 - 2200 UTC

slide109
Relation of analyzed equivalent potential temperature and moisture convergence fields to radar echoes2100 UTC 27 May - 0200 UTC 28 May
scaling functions
Scaling functions
  • Inner scaling functions: dilation and translation of the cardinal B-splines:
  • Boundary scaling functions: dilation of the cardinal B-spline with multiple knots at the endpoints
wavelet functions1
Wavelet functions
  • Inner wavelet functions: dilation and translation of the cardinal B-wavelets:
  • Boundary wavelet functions: dilation of the special B-wavelets derived from cardinal B-splines and boundary scaling functions
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