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This study focuses on the fusion of models and real observations in atmospheric sciences, emphasizing the need for error statistics and spreading observational information. The ensemble Kalman filter method is utilized to improve forecast accuracy. Real-time observations are assimilated, including radiosondes, surface stations, ACARS, and convective rain rate measurements. Control and test experiments are conducted to assess the impact of different assimilation techniques. The results show improved forecast variance and analysis fields.
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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
Data Assimilation • Fusion of models & observations. • Need error statistics! • Spreads observational information. • Analysis: • smaller error than observations. • smaller error than model estimate of obs.
prob of current state given all current and past observations prob of obs given current state prob of current state given all past observations. Cyclic algorithm given new Data Assimilation in a Nutshell observations model
Ensemble Kalman Filter Crux: use an ensembleof fully non-linear forecasts tomodel 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.
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
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
A Traditional Observation Network2004100118 ACAR observations Soundings Surface observations
Experiment observations • Radiosonde Obs • Surface Stations
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
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
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
Weather Pattern Sea level pressurePeriod characterized by extratropical cyclone
Weather Pattern H500Period with active weather pattern – Trough dominated
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
Control ExperimentsControl Experiment #2– More variance but still too low
Control ExperimentsControl Experiment #3– Acceptable variance
Control ExperimentsControl Experiment #3– Acceptable variance
Control ExperimentsControl Experiment #3– Acceptable variance
Control ExperimentsControl Experiment #3– Acceptable variance
Control Experiments • Control experiment #3 • Analysis variance • H500mb • T2m, T850mb, T300mb • Y300mb • SLP, REFL, RAINC etc • Observation verification • Rank histograms • Profile • Other…
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
Test ExperimentsExample of comparison products • Analysis fields • H500, SLP, WINDS, RAINC • Forecast fields • All forecast hours
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
Summary • Future possibilities • Alternative assimilation fields • In-house rain rate/LTNG rate relationship • Different domains • Other sample storms
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 =)
