14th Cyclone Workshop Brian Ancell The University of Washington

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Presentation transcript:

14th Cyclone Workshop Brian Ancell The University of Washington Comparison of Two Operational Ensemble Systems in the Prediction of a Pacific Northwest Windstorm 14th Cyclone Workshop Brian Ancell The University of Washington

The Classic Western WA Windstorm

The Classic Western WA Windstorm

The Classic Western WA Windstorm

The Classic Western WA Windstorm

The Classic Western WA Windstorm

The Classic Western WA Windstorm

A Forecast Challenge Arises at 12 UTC November 16, 2007 University of Washington Mesoscale Ensemble (UWME) - 8 members - MM5 model at 36-km grid spacing, 32 levels - Initial and lateral boundary conditions from global centers - No data assimilation - “Best guess” is mean IC run with mean BC (Centroid) University of Washington Ensemble Kalman Filter (UW EnKF) - 80 members - WRF model at 36-km grid spacing, 37 levels - Initial and lateral boundary conditions drawn from Gaussian distribution - Assimilates surface, aircraft, sounding, and cloud-track wind data - “Best guess” is forecast mean

UWME Centroid 00-hr

UWME Centroid 06-hr

UWME Centroid 12-hr

UWME Centroid 18-hr

UWME Centroid 24-hr

UWME Centroid 30-hr

UW EnKF Forecast Mean 00-hr

UW EnKF Forecast Mean 06-hr

UW EnKF Forecast Mean 12-hr

UW EnKF Forecast Mean 18-hr

UW EnKF Forecast Mean 24-hr

UW EnKF Forecast Mean 30-hr

UW EnKF vs. UWME UW EnKF UWME

Various EnKF Members (30-hr)

Questions Why the difference in cyclone track? Which “best guess” should a forecaster trust? Is there a way to use the EnKF to increase the short-term predictability of such a high-impact event?

Breaking down the differences… Issue #1) Model error?

Breaking down the differences… Issue #1) Model error? - Nope!

Breaking down the differences… Issue #1) Model error? - Nope! Issue #2) Let’s be fair! - Centroid (UWME) vs. Mean (EnKF)

EnKF Centroid 00-hr

EnKF Centroid 06-hr

EnKF Centroid 12-hr

EnKF Centroid 18-hr

EnKF Centroid 24-hr

EnKF Centroid 30-hr

Breaking down the differences… Issue #1) Model error? - Nope! Issue #2) Let’s be fair! - Centroid (UWME) vs. Mean (EnKF) Issue #3) Past performance

(From Torn and Hakim 2008)

Verification 1) Past performance – favors UWME (no windstorm)

Verification 1) Past performance – favors UWME (no windstorm) 2) Suboptimal LBC – favors UWME (no windstorm)

Verification 1) Past performance – favors UWME (no windstorm) 2) Suboptimal LBC – favors UWME (no windstorm) 3) Lack of observations in EnKF – favors UWME (no windstorm)

What actually happened? Verification 1) Past performance – favors UWME (no windstorm) 2) Suboptimal LBC – favors UWME (no windstorm) 3) Lack of observations in EnKF – favors UWME (no windstorm) What actually happened?

What actually happened? Verification 1) Past performance – favors UWME (no windstorm) 2) Suboptimal LBC – favors UWME (no windstorm) 3) Lack of observations in EnKF – favors UWME (no windstorm) What actually happened? No windstorm!

Forecast Tools for Better Predictability EnKF extended forecasts (out to 48-hr) run at 00 and 12 UTC - Is there a way to increase the predictability of potentially serious PNW windstorms in the interim?

Forecast Tools for Better Predictability Key: Compare members to observations at locations where forecast is sensitive to error First choice: Adjoint sensitivity

Forecast Tools for Better Predictability Key: Compare members to observations at locations where forecast is sensitive to error First choice: Adjoint sensitivity Simplified adjoint physics Significant nonlinearity

Forecast Tools for Better Predictability Key: Compare members to observations at locations where forecast is sensitive to error First choice: Adjoint sensitivity Ensemble sensitivity! - Incorporates nonlinearity - Easily calculated Simplified adjoint physics Significant nonlinearity

Ensemble Senstivity 00-hr 500 GPH, Absolute Vorticity Ensemble Sensitivity to 500 GPH mb/m 00-hr Response function = Western Washington SLP at 30-hr

Correlation of P with Forecast Error

Correlation of P with Forecast Error

Correlation of P with Forecast Error Location of projection Error (mb) All 3.9 70% 3.6 50% 2.7 30% 2.2 10% 2.0 Original error all members = 3.8

Summary Forecast troubles arose when two operational ensemble systems suggested diverging cyclone tracks associated with a potential western Washington windstorm Differences were likely due to the limited-area configuration (suboptimal boundary conditions) and fewer observations in the EnKF Very large ensemble sensitivity existed for this case, resulting in nonlinear perturbation evolution and large forecast spread Using an independent forecast as “truth”, results suggest a better subset of EnKF members may be produced using observations and ensemble sensitivity guidance