Probabilistic Mesoscale Analyses & Forecasts Progress & Ideas Greg Hakim University of Washington 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. Collaborators:

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. –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…

Fusing Models and Observations State estimation (“data assimilation”). –combine obs & model estimate of obs. Benefits of ‘fusion’ –Better state estimates. –Observations influence other (unobserved) fields. –Can use observations to improve models. –Observing network design. –Adaptive control.

One-dimensional Examples

Scalar One-dimensional Example less error than obs and model!

Observation (green) & Background (blue) PDFs

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 EnKF“3DVAR” Cov(Z 500,Z 500) Cov(Z 500,U 500 ) “3DVAR” EnKF

Ensemble Covariances 3D-VAR covarianceensemble covariance temperature-temperature covariance

Mesoscale Example: cov(|V|, q rain )

Sampling Error

Summary of Ensemble Kalman Filter (EnKF) Algorithm (1)Ensemble forecast provides background estimate & statistics (P b ) 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 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.

UW EnKF System Weather Research and Forecasting model, (WRF) 45 km resolution, 33 vertical levels 90 ensemble members 6 hour analysis cycle ensemble forecasts to t+24 hrs at 00 and 12 UTC assimilate rawinsonde, ACARS, cloud drift winds, ASOS, buoy and ship data

System Performance WindsMoisture UW EnKF GFS CMC UKMO NOGAPS

No Assimilation Member WindsMoisture WRF EnKFNo Assimilation Member

GFS Initialized Member WindsMoisture WRF EnKFGFS Initialized Member

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 Pressure500 hPa Height

F24 Forecast Differences Sea-level Pressure500 hPa Height

F48 Forecast Differences Sea-level Pressure500 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”