Transcript Initialization and Boundary Conditions for a Limited Area Ensemble

Probabilistic Mesoscale Analyses & Forecasts
Progress & Ideas
Greg Hakim
University of Washington
www.atmos.washington.edu/~hakim
Collaborators:
Brian Ancell, Bonnie Brown, Karin Bumbaco,
Sebastien Dirren, Helga Huntley, Rahul Mahajan,
Cliff Mass, Guillaume Mauger, Phil Mote, Angie Pendergrass,
Chris Snyder, Ryan Torn, & Reid Wolcott.
Plan
1. State estimation & forecasting on the mesoscale.
2. The UW “pseudo-operational” system.
3. Ensemble methods for mining & adapting the “data cube.”
Analysis & prediction is fundamentally probabilistic!
State Estimation
• Limitations of observations.
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Errors.
Sparse in space & time.
Limited info about unobserved fields & locations.
Not usually on a regular grid.
• Limitations of models.
– Errors.
– Often not cast in terms of observations (e.g. radiances)
– Space & time resolution trade off.
• Combine strengths of obs & models…
One-dimensional Examples
Analysis (red) PDF---higher density!
More-Accurate Observation
Less-Accurate Observation
More than one dimension:
Covariance
Relationships between variables (spread obs info)
• Weight to observations and background
• Kalman Filter: propagate the covariance
• Ensemble KF: propagate the square root (sample)
State-dependent Cov Matrices
Cov(Z500,Z500)
“3DVAR”
EnKF
“3DVAR”
EnKF
Cov(Z500,U500)
Mesoscale Example: cov(|V|, qrain)
Sampling Error
Summary of Ensemble Kalman
Filter (EnKF) Algorithm
(1) Ensemble forecast provides background
estimate & statistics (Pb) for new analyses.
(2) Ensemble analysis with new observations.
(3) Ensemble forecast to arbitrary future time.
Real Time Data Assimilation at
the University of Washington
• Operational since 22 December 2004
• 90-member WRF EnKF
• assimilate obs every 6 hours
• 36 km grid over NE Pacific and western NOAM
• Experimental 12 km grid over Pacific Northwest
Transition from research to operations was a
direct result of CSTAR support.
www.atmos.washington.edu/~enkf
System Performance
Winds
UW EnKF
Moisture
GFS
CMC
UKMO
NOGAPS
Applications of Ensemble Data
Example:
Forecast sensitivity and observation impact
• Can rapidly evaluate many metrics & observations
– Allows forecasters to do “what if” experiments.
• cf. adjoint sensitivity:
– new adjoint run for each metric
– Also need adjoint of DA system for obs impact.
Sensitivity to SLP
Analysis difference
(no-buoy – buoy), Shift frontal wave to the southeast
6-hour forecast difference
12-hour forecast difference
18-hour forecast difference
24-hour forecast difference
Predicted Response: 0.63 hPa Actual Response: 0.60 hPa
Observation Impact Example
Typhoon Tokage (2004)
Observation Impact
Squares – rawinsondes
Circles – surface obs.
Diamonds – ACARS
Triangles – cloud winds
Compare forecast where
only this 250 hPa zonal
wind observation is
assimilated to forecast
with no observation
assimilation
F00 Forecast Differences
Sea-level Pressure
500 hPa Height
F24 Forecast Differences
Sea-level Pressure
500 hPa Height
F48 Forecast Differences
Sea-level Pressure
500 hPa Height
Ensemble Opportunities
• Short-term mesoscale probabilistic forecasts
• ensemble population matters (cf. medium range)
• “Hybrid” data assimilation
• flow-dependent covariance in 4dvar cost function.
• Kalman smoother with strong model constraint.
• Observation targeting, thinning, and QC.
• “Adaptive” forecast grids & metrics
• update forecasts on-the-fly with new observations.
• Jim Hansen (NRL)
Summary
• Analysis & prediction is fundamentally probabilistic!
– Future plans should embrace this fact
• Ensembles are not just for prediction & assimilation
– Observations: impact; QC; targeting; thinning
– Models: calibration and adaptation; forget “plug-n-play”
– Data mining: user-defined metrics; “instant updates”