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Vaisala/University of Washington Real-observation Experiments

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Vaisala/University of Washington Real-observation Experiments Clifford Mass, Gregory Hakim, Phil Regulski, Ryan Torn, Jennifer Fletcher Department of Atmospheric Sciences – PowerPoint PPT presentation

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Title: Vaisala/University of Washington Real-observation Experiments


1
Vaisala/University of WashingtonReal-observation
Experiments
  • Clifford Mass, Gregory Hakim,
  • Phil Regulski, Ryan Torn,
  • Jennifer Fletcher
  • Department of Atmospheric Sciences
  • University of Washington
  • October 2006

2
Data Assimilation
  • Fusion of models observations.
  • Need error statistics!
  • Spreads observational information.
  • Analysis
  • smaller error than observations.
  • smaller error than model estimate of obs.

3
Data Assimilation in a Nutshell
observations model
4
Observation (green) Background (blue) PDFs
5
Analysis (red) PDF---higher density!
6
More-Accurate Observation
7
Less-Accurate Observation
8
Ensemble Kalman Filter
Crux use an ensemble of fully non-linear
forecasts to model the statistics of the
background (expected value and covariance matrix).
Advantages
  • No à priori assumption about covariance
    state-dependent corrections.
  • Ensemble forecasts proceed immediately without
    perturbations.

9
LR Lightning Real-Observation Experiment
10
Establish geographical domain for
Real-observation Experiment
  • Dec 12-24, 2004
  • Domain location that encompasses Pessi/Businger
    previously studied storm
  • Pacific Ocean
  • Low observation density location of important
    storm tracks errors propagate downwind to
    mainland United States
  • North America
  • High observation density forecast improvement
    interest area included to see the impact of
    regions of low and high observation densities

11
Real time observations
  • Control case
  • Observation locations from real data
  • Radiosondes
  • Surface stations (ASOS, ship, buoy)
  • ACARS
  • Cloud drift-winds (no sat radiances)
  • Lightning experiment
  • Assimilation of convective rain rate

12
A Traditional Observation Network2004100118
ACAR observations Soundings Surface observations
13
Experiment observationsACARS Obs.
14
Experiment observationsCloud Track Wind Obs.
15
Experiment observations
  • Radiosonde Obs
  • Surface Stations

16
Experiment observationsLTNG Obs
17
Experiment observationsLTNG Obs
18
Experiment observations
  • Lightning assimilation
  • Real LR LTNG strike is identified
  • WRF-ENKF locates LTNG and feeds the experimental
    run the convective precipitation from the Pessi
    convective rain rate/LTNG rate relationship at
    the LTNG coordinates

19
Real-observation Experiments
20
2-Week Experiment
  • 100 by 86 grid points
  • 45-km resolution
  • 33 vertical levels
  • 48 ensemble members
  • Assimilation every 6 hours
  • Forecasts 6, 12, 18, 24, 30, 36, 42 and 48 hours

21
2-Week Experiment
  • WRF ensemble Kalman filter settings
  • Square root filter (Whitaker and Hamill, 2002)
  • Horizontal localization Gaspari and Cohn 5th
    order piecewise
  • Fixed covariance perturbations to lateral
    boundaries
  • Constant uniform covariance inflation method
  • Localization radius 2000km

22
Weather Pattern Sea level pressurePeriod
characterized by extratropical cyclone
23
Weather Pattern H500 Period with active weather
pattern Trough dominated
24
Control Experiments
  • Control experiment 1
  • Not enough variance
  • Increase inflation factor
  • Control experiment 2
  • Still low variance
  • Switching inflation method from constant
    inflation to Zhang method
  • Control experiment 3
  • Good variance

25
Control ExperimentsControl Experiment 1 Too
low variance
26
Control ExperimentsControl Experiment 1 Too
low variance
27
Control ExperimentsControl Experiment 2 More
variance but still too low
28
Control ExperimentsControl Experiment 3
Acceptable variance
29
Control ExperimentsControl Experiment 3
Acceptable variance
30
Control ExperimentsControl Experiment 3
Acceptable variance
31
Control ExperimentsControl Experiment 3
Acceptable variance
32
Control Experiments
  • Control experiment 3
  • Analysis variance
  • H500mb
  • T2m, T850mb, T300mb
  • Y300mb
  • SLP, REFL, RAINC etc
  • Observation verification
  • Rank histograms
  • Profile
  • Other

33
Control Experiments
34
Control Experiments
35
Control Experiments
36
Control Experiments
37
Control Experiments
38
Control Experiments
39
Test Experiments
  • Coding LTNG assimilation into WRF-ENKF
  • Assimilated LTNG rate
  • Transformed LTNG rate into convective rain rate
  • Final coding
  • Testing
  • Experiment Run (LTNG assimilation - 1.5 weeks)
  • Comparisons

40
Test ExperimentsExample of comparison products
  • Analysis fields
  • H500, SLP, WINDS, RAINC
  • Forecast fields
  • All forecast hours

41
Test ExperimentsExample of comparison products
42
Test ExperimentsExample of comparison products
43
Test ExperimentsExample of comparison products
44
Summary
  • Where we are at
  • Data observations gathered from Dec 2002
  • Cloud track winds
  • ACARS
  • Surface
  • Radiosondes
  • LTNG
  • Performed control runs
  • Final stages of coding LTNG assimilation code for
    real observation WRF-ENKF experiments
  • Ongoing statistical analysis

45
Summary
  • Future possibilities
  • Alternative assimilation fields
  • In-house rain rate/LTNG rate relationship
  • Different domains
  • Other sample storms

46
6-month goals
  • Real-time lightning data feed into UW-ATMS
    WRF-ENKF system
  • OSSE DE simulations
  • Robust and flexible OSSE and real observation
    experiment systems
  • Creation of flexible LTNG assimilation modules so
    new experiments can be quickly altered in
    parameter file
  • Other suggestions and comments )
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