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

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

Data Assimilation in a Nutshell 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 observations model

Observation (green) & Background (blue) PDFs

Analysis (red) PDF---higher density!

More-Accurate Observation

Less-Accurate Observation

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.

LR Lightning Real- Observation Experiment

Establish geographical domain for Real- observation Experiment Dec 12-24, 2004 Dec 12-24, 2004 –Domain location that encompasses Pessi/Businger previously studied storm Pacific Ocean Pacific Ocean –Low observation density; location of important storm tracks; errors propagate downwind to mainland United States North America 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 Control case –Observation locations from real data –Radiosondes –Surface stations (ASOS, ship, buoy) –ACARS –Cloud drift-winds (no sat radiances) Lightning experiment Lightning experiment –Assimilation of convective rain rate

A Traditional Observation Network ACAR observations Soundings Surface observations

Experiment observations ACARS Obs.

Experiment observations Cloud Track Wind Obs.

Experiment observations Radiosonde Obs Surface Stations

Experiment observations LTNG Obs

Experiment observations Lightning assimilation 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

Real-observation Experiments

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

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

Weather Pattern Sea level pressure Period characterized by extratropical cyclone

Weather Pattern H500 Period with active weather pattern – Trough dominated

Control Experiments Control experiment #1 Control experiment #1 –Not enough variance –Increase inflation factor Control experiment #2 Control experiment #2 –Still low variance –Switching inflation method from constant inflation to Zhang method Control experiment #3 Control experiment #3 –Good variance

Control Experiments Control Experiment #1 – Too low variance

Control Experiments Control Experiment #2– More variance but still too low

Control Experiments Control Experiment #3– Acceptable variance

Control Experiments Control experiment #3 Control experiment #3 –Analysis variance H500mb H500mb T2m, T850mb, T300mb T2m, T850mb, T300mb Y300mb Y300mb SLP, REFL, RAINC etc SLP, REFL, RAINC etc –Observation verification Rank histograms Rank histograms Profile Profile –Other…

Control Experiments

Test Experiments Test Experiments Coding LTNG assimilation into WRF-ENKF 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) Experiment Run (LTNG assimilation - ~1.5 weeks) Comparisons Comparisons

Test Experiments Example of comparison products Test Experiments Example of comparison products Analysis fields Analysis fields –H500, SLP, WINDS, RAINC Forecast fields Forecast fields –All forecast hours

Test Experiments Example of comparison products Test Experiments Example of comparison products

Summary Where we are at… Where we are at… –Data observations gathered from Dec 2002 Cloud track winds Cloud track winds ACARS ACARS Surface Surface Radiosondes Radiosondes LTNG LTNG –Performed “control” runs –Final stages of coding LTNG assimilation code for real observation WRF-ENKF experiments –Ongoing statistical analysis

Summary Future possibilities 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 Real-time lightning data feed into UW-ATMS WRF-ENKF system OSSE DE simulations OSSE DE simulations Robust and flexible OSSE and real observation experiment systems 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 =)