Predictability Issues Associated Predictability Issues Associated with Explosive Cyclogenesis in with Explosive Cyclogenesis in

the North-West Pacificthe North-West PacificEdmund K.M. ChangEdmund K.M. Chang

School of Marine and Atmospheric School of Marine and Atmospheric SciencesSciences

Stony Brook UniversityStony Brook University

Third THORPEX International Science Symposium

Collaborators: Kevin Raeder, Nancy Collins and Jeff Andersen (DAReS, NCAR)

Why do we care?Why do we care?– Local weatherLocal weather– Downstream impactsDownstream impacts

Taken from THORPEX International Science Plan (Shapiro and Thorpe, 2004)

Tim

e

Taken from U.S. PARC science plan. Adopted from Hakim (2005)

Initial analysis error structure

12-hr forecast uncertainty

24-hr forecast uncertainty

An example from winter TPARC

Target: T+60Verify: T+144

Tim

e

Verification

Target

Hoskins and Hodges (2002)

Dashed: 250 hPa Trough Tracks Solid: 850 hPa Tracks Cyclogenesis over W. Pacific often triggered by waves propagating out from Asia (Chang and Yu, 1999; Hoskins and Hodges, 2002)

Question:– Is cyclogenesis triggered by upstream waves

more predictable?

Current StudyCurrent Study Case Selection (based on Chang 2005):Case Selection (based on Chang 2005):

– Explosive cyclogenesis over W. Pacific (Day 0)Explosive cyclogenesis over W. Pacific (Day 0)– Upstream wave packet over Asia at Day -3Upstream wave packet over Asia at Day -3

MethodologyMethodology– Ensemble forecasts and sensitivity analysesEnsemble forecasts and sensitivity analyses

CAM3 at T85, 80-member ensembleCAM3 at T85, 80-member ensemble Ensemble assimilation using DART at NCAREnsemble assimilation using DART at NCAR

– OBS: Radiosondes, aircrafts, and SAT windsOBS: Radiosondes, aircrafts, and SAT winds– Kevin Reader, Nancy Collins and Jeff Anderson at NCARKevin Reader, Nancy Collins and Jeff Anderson at NCAR

– Feature based sensitivity analysesFeature based sensitivity analyses Preliminary studies using dry model (Chang 2006)Preliminary studies using dry model (Chang 2006)

Up to now, several cases have been Up to now, several cases have been examinedexamined– Here, results from 1 quite predictable case, and Here, results from 1 quite predictable case, and

1 not so predictable case will be presented1 not so predictable case will be presented

An example of explosive cyclogenesis 3 days after N packet

ERA40 MSLP (contour interval 5 hPa)

“Predictable” Case

ERA40 Z500 (contour interval 60 m)

ERA40 80-member Ensemble mean from Day -3

Ensemble mean from Day -5 Ensemble mean from Day -6

Between Day -1 and Day 0:Between Day -1 and Day 0:– ERA40: cyclone deepened by 28.3 hPaERA40: cyclone deepened by 28.3 hPa– Ensemble forecast from Day -5:Ensemble forecast from Day -5:

Average deepening of 23.3 hPaAverage deepening of 23.3 hPa 60 of 80 members give deepening > 1 Bergeron60 of 80 members give deepening > 1 Bergeron RMS cyclone position error of 533 km at day 0RMS cyclone position error of 533 km at day 0 Average cyclone MSLP bias of +2.9 hPa at day 0Average cyclone MSLP bias of +2.9 hPa at day 0

Feature Based Sensitivity Analysis using dry modelControl Remove upstream waves (10-90E)

Retain only upstream waves (10-90E)(Remove waves in 90E-10E)

Remove all waves (15-day mean)

Forecast from Day -5

2nd example of explosive cyclogenesis 3 days after N packet

“Not so Predictable” Case

ERA40 MSLP (contour interval 5 hPa)

ERA40 Z500 (contour interval 60 m)

ERA40 Ensemble mean from Day -3

Ensemble mean from Day -4 Ensemble mean from Day -5

Case 1 apparently much more predictable Case 1 apparently much more predictable than case 2. Why?than case 2. Why?– Some speculations:Some speculations:

Upstream wave packet appears stronger in case 1: Upstream wave packet appears stronger in case 1: stronger dynamical forcingstronger dynamical forcing

Structure of cyclone much simpler in case 1, but Structure of cyclone much simpler in case 1, but much more complex in case 2much more complex in case 2

Cyclone development in case 2 apparently quite Cyclone development in case 2 apparently quite dependent on diabatic effectsdependent on diabatic effects

– Case 1 qualitatively similar results when CAM is run in Case 1 qualitatively similar results when CAM is run in “adiabatic” mode, or when water (vapor, liquid, and ice) “adiabatic” mode, or when water (vapor, liquid, and ice) quantities are all reset to 0 every 12 hoursquantities are all reset to 0 every 12 hours

ERA40 Control forecast from day -2

Moisture reset to 0 every 12 hoursCAM run in adiabatic mode from day -2

CASE 2

Speculations:Speculations:– Strongly dynamically forced cases are more predictableStrongly dynamically forced cases are more predictable– Cases in which diabatic processes are important are Cases in which diabatic processes are important are

less predictableless predictable

– How general are these results?How general are these results? Are strongly forced cases sensitive to existence of near surface Are strongly forced cases sensitive to existence of near surface

diabatically generated vortices?diabatically generated vortices?

Further work: How do these developments affect Further work: How do these developments affect downstream cyclone events and weather?downstream cyclone events and weather?

Third THORPEX International Science Symposium

ERA40 Control forecast from day -2

Moisture reset to 0 every 12 hoursCAM run in adiabatic mode from day -2

CASE 1

Shaded: 95% significant From Chang (2005)