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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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STMAS:Space-Time Mesoscale Analysis SystemSteve 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

    • 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


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/


MADIS Mesonet Providers 5/1/2004 Meteorological Assimilation Data Ingest System (MADIS)

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


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: to deal with the desert-oasis problemMulti-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)


Multi-scale Analysis using a Recursive Filter to deal with the desert-oasis problem


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


1900 UTC LAPS using LAPS and STMAS to surface and radar observations


2000 UTC LAPS using LAPS and STMAS to surface and radar observations


2100 UTC LAPS using LAPS and STMAS to surface and radar observations


2200 UTC LAPS using LAPS and STMAS to surface and radar observations


1900 UTC STMAS using LAPS and STMAS to surface and radar observations


2000 UTC STMAS using LAPS and STMAS to surface and radar observations


2100 UTC STMAS using LAPS and STMAS to surface and radar observations


2200 UTC STMAS using LAPS and STMAS to surface and radar observations


2155 utc
2155 UTC using LAPS and STMAS to surface and radar observations


Improvements to stmas
Improvements to STMAS using LAPS and STMAS to surface and radar observations

  • 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 using LAPS and STMAS to surface and radar observations

  • 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 using LAPS and STMAS to surface and radar observations

  • 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 using LAPS and STMAS to surface and radar observations

Approaching boundary


Comparison of four different analysis techniques
Comparison of four different analysis techniques using LAPS and STMAS to surface and radar observations

Barnes Analysis

Standard Recursive filter

Telescopic Recursive filter

Wavelet fitting


Kalman filter for surface data
Kalman Filter for Surface Data using LAPS and STMAS to surface and radar observations

  • 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 using LAPS and STMAS to surface and radar observations

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 using LAPS and STMAS to surface and radar observations

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


Kalman Forecast Errors (F) using LAPS and STMAS to surface and radar observations(based on stations reappearing after not reporting for a time interval on x-axis)

Temperature

Dewpoint


More about stmas
More about STMAS using LAPS and STMAS to surface and radar observations

  • 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)


Severe weather event 30 31 may 2004
Severe Weather Event: using LAPS and STMAS to surface and radar observations 30-31 May 2004


2000 UTC using LAPS and STMAS to surface and radar observations


2100 UTC using LAPS and STMAS to surface and radar observations


2200 UTC using LAPS and STMAS to surface and radar observations


2300 UTC using LAPS and STMAS to surface and radar observations


0000 UTC using LAPS and STMAS to surface and radar observations


0100 UTC using LAPS and STMAS to surface and radar observations


0200 UTC using LAPS and STMAS to surface and radar observations


0300 UTC using LAPS and STMAS to surface and radar observations


2000 UTC using LAPS and STMAS to surface and radar observations

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


2100 UTC using LAPS and STMAS to surface and radar observations


2200 UTC using LAPS and STMAS to surface and radar observations


2300 UTC using LAPS and STMAS to surface and radar observations


0000 UTC using LAPS and STMAS to surface and radar observations


2330 UTC using LAPS and STMAS to surface and radar observations


2345 UTC using LAPS and STMAS to surface and radar observations


2315 UTC using LAPS and STMAS to surface and radar observations


0015 UTC using LAPS and STMAS to surface and radar observations


0030 UTC using LAPS and STMAS to surface and radar observations


0045 UTC using LAPS and STMAS to surface and radar observations


0100 UTC using LAPS and STMAS to surface and radar observations


2000 UTC using LAPS and STMAS to surface and radar observations

STMAS analysis of equivalent potential temperature and winds


2100 UTC using LAPS and STMAS to surface and radar observations


2200 UTC using LAPS and STMAS to surface and radar observations


2300 UTC using LAPS and STMAS to surface and radar observations


2315 UTC using LAPS and STMAS to surface and radar observations


2330 UTC using LAPS and STMAS to surface and radar observations


2345 UTC using LAPS and STMAS to surface and radar observations


0000 UTC using LAPS and STMAS to surface and radar observations


0015 UTC using LAPS and STMAS to surface and radar observations


0030 UTC using LAPS and STMAS to surface and radar observations


0045 UTC using LAPS and STMAS to surface and radar observations


0100 UTC using LAPS and STMAS to surface and radar observations


2300 UTC using LAPS and STMAS to surface and radar observations

2300GMT

Zoomed-in analysis of equivalent potential temperature and winds


2315 UTC using LAPS and STMAS to surface and radar observations

2315GMT


2330 UTC using LAPS and STMAS to surface and radar observations

2330GMT


2345 UTC using LAPS and STMAS to surface and radar observations

2345GMT


0000 UTC using LAPS and STMAS to surface and radar observations

0000GMT


0015 UTC using LAPS and STMAS to surface and radar observations

0015GMT


0030 UTC using LAPS and STMAS to surface and radar observations

0030GMT


0045 UTC using LAPS and STMAS to surface and radar observations

0045GMT


0100 UTC using LAPS and STMAS to surface and radar observations

0100GMT


Recusive Analysis- Moist Convergence and Wind : 2300 - 0100 using LAPS and STMAS to surface and radar observations

2300GMT

Zoomed-in analysis of moisture convergence


2315 UTC using LAPS and STMAS to surface and radar observations

2315GMT


2330 UTC using LAPS and STMAS to surface and radar observations

2330GMT


2345 UTC using LAPS and STMAS to surface and radar observations

2345GMT


0000 UTC using LAPS and STMAS to surface and radar observations

0000GMT


0015 UTC using LAPS and STMAS to surface and radar observations

0015GMT


0030 UTC using LAPS and STMAS to surface and radar observations

0030GMT


0045 UTC using LAPS and STMAS to surface and radar observations

0045GMT


0100 UTC using LAPS and STMAS to surface and radar observations

0100GMT


Summary
Summary using LAPS and STMAS to surface and radar observations

  • 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 using LAPS and STMAS to surface and radar observations

  • 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


Goal: A National Automated Boundary Product using LAPS and STMAS to surface and radar observations

0015 UTC


Severe weather event 27 28 may 2004
Severe Weather Event: using LAPS and STMAS to surface and radar observations 27-28 May 2004



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


1900 UTC LAPS using LAPS and STMAS to surface and radar observations


1900 UTC STMAS using LAPS and STMAS to surface and radar observations


2000 UTC LAPS using LAPS and STMAS to surface and radar observations


2000 UTC STMAS using LAPS and STMAS to surface and radar observations


2058 utc
2058 UTC using LAPS and STMAS to surface and radar observations


2100 UTC LAPS using LAPS and STMAS to surface and radar observations


2100 UTC STMAS using LAPS and STMAS to surface and radar observations


2155 utc1
2155 UTC using LAPS and STMAS to surface and radar observations


2200 UTC LAPS using LAPS and STMAS to surface and radar observations


2200 UTC STMAS using LAPS and STMAS to surface and radar observations


Analyses of temperature and winds using stmas 15 min analyses 2200 0000 utc
Analyses of Temperature and Winds using STMAS using LAPS and STMAS to surface and radar observations15-min analyses: 2200 - 0000 UTC


2200 UTC STMAS using LAPS and STMAS to surface and radar observations


2215 UTC STMAS using LAPS and STMAS to surface and radar observations


2230 UTC STMAS using LAPS and STMAS to surface and radar observations


2245 UTC STMAS using LAPS and STMAS to surface and radar observations


2300 UTC STMAS using LAPS and STMAS to surface and radar observations


2315 UTC STMAS using LAPS and STMAS to surface and radar observations


2330 UTC STMAS using LAPS and STMAS to surface and radar observations


2345 UTC STMAS using LAPS and STMAS to surface and radar observations


0000 UTC STMAS using LAPS and STMAS to surface and radar observations


2258 utc
2258 UTC using LAPS and STMAS to surface and radar observations


2359 utc
2359 UTC using LAPS and STMAS to surface and radar observations


Relation of analyzed equivalent potential temperature and moisture convergence fields to radar echoes2100 UTC 27 May - 0200 UTC 28 May


2100 utc
2100 UTC moisture convergence fields to radar echoes


2200 utc
2200 UTC moisture convergence fields to radar echoes


2300 utc
2300 UTC moisture convergence fields to radar echoes


0000 utc
0000 UTC moisture convergence fields to radar echoes


0200 utc
0200 UTC moisture convergence fields to radar echoes


Scaling functions
Scaling functions moisture convergence fields to radar echoes

  • 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 moisture convergence fields to radar echoes

  • 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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