beth ebert bmrc, melbourne, australia

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Assessment of Tropical Rainfall Potential (TRaP) forecasts during the 2003- 04 Australian tropical cyclone season Beth Ebert BMRC, Melbourne, Australia with thanks to Sheldon Kusselson, Mike Turk, Ralph Ferraro and Bob Kuligowski 2 nd IPWG Meeting, Monterey, 25-28 October 2004

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Assessment of Tropical Rainfall Potential (TRaP) forecasts during the 2003-04 Australian tropical cyclone season. Beth Ebert BMRC, Melbourne, Australia with thanks to Sheldon Kusselson, Mike Turk, Ralph Ferraro and Bob Kuligowski. 2 nd IPWG Meeting, Monterey, 25-28 October 2004. - PowerPoint PPT Presentation

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Page 1: Beth Ebert BMRC, Melbourne, Australia

Assessment of Tropical Rainfall Potential (TRaP) forecasts

during the 2003-04 Australian tropical cyclone season

Beth Ebert

BMRC, Melbourne, Australia

with thanks to Sheldon Kusselson, Mike Turk, Ralph Ferraro and Bob Kuligowski

2nd IPWG Meeting, Monterey, 25-28 October 2004

Page 2: Beth Ebert BMRC, Melbourne, Australia

TRaP - Tropical Rainfall PotentialNESDIS nowcasts of rain in tropical cyclones

Generation of TRaP:

1. Compute areal rain rates from passive microwave sensor (SSM/I, AMSU, or TRMM)

2. Using operational forecast cyclone track, advect rainfall for 24 h, assuming steady state storm structure

3. Analyst vets TRaP prior to public release

TC Craig, 10 March 2003

DARWIN

DARWIN

SSM/I"snapshot"

TRaP

Page 3: Beth Ebert BMRC, Melbourne, Australia

Validation of TRaP over U.S. for 2002 Atlantic hurricane season(Ferraro et al., 2004, Wea. Forecasting, submitted)

SSM/IAMSUTRMM

42 TRaPs verified against Stage IV radar/gauge analyses at 4 km resolution.

TRaP under-estimated rain rate, volume, max.

TRaPs from TRMM performed best, closely followed by AMSU.

TRaP outperformed Eta NWP model forecasts at 50 km resolution.

Sensor (cases)

Rain rate (TRaP /

Stage IV)

Rain volume (TRaP /

Stage IV)

TRaP maximum rain

(mm d-1)

Stage IV maximum rain

(mm d-1)

AMSU (11) 0.59 0.55 90.1 351.8

SSM/I (16) 0.71 0.65 106.1 345.0

TRMM (15) 0.84 0.77 140.5 355.9

Page 4: Beth Ebert BMRC, Melbourne, Australia

Atlantic vs. South Pacific hurricane rainfall

Mean rain rate from TRMM TMI as a function of radial distance from storm center, 1998-2000

Lonfat et al., 2004, Mon. Wea. Rev.

Atlantic

South Pacific

Page 5: Beth Ebert BMRC, Melbourne, Australia

2003-2004 Australian tropical cyclones

Validation strategies: maximum 24 h rain at landfall vs. rain gauge observations ±3h (±12 h) spatial rainfall distribution in 10° box vs. operational 0.25° gauge analysis ±3h contiguous rain area (CRA) bounded by 20 mm d-1 threshold vs.

operational 0.25° gauge analysis ±3h

(25)

(9)

(19)

(29)(3)

Page 6: Beth Ebert BMRC, Melbourne, Australia

Tropical Cyclone Fay (17-28 March 2004)

TRaP too great on most days, especially near landfall Some extreme values for SSM/I and TRMM

* Areal TRaP vs gauge observations not ideal but no radar data available

landfall

Page 7: Beth Ebert BMRC, Melbourne, Australia

Tropical Cyclone Fay (28 March 2004)

Maximum 24 h rain (mm)

Observed159.4

0100 UTC 28 March 2004

AMSU111.6

0233 UTC 28 March 2004

SSM/I478.1

1304 UTC 27 March 2004

TRMM251.0

0156 UTC 28 March 2004

AMSU

SSM/I TRMM

OBS

Page 8: Beth Ebert BMRC, Melbourne, Australia

Maximum rain at landfall

TRaP estimated maximum rain well for some TCs, overestimated for others

AMSU less likely to overestimate

Mea

n

Page 9: Beth Ebert BMRC, Melbourne, Australia

* statistics for land grid boxes only

Spatial validation - TC Fay (28 March 2004)

Page 10: Beth Ebert BMRC, Melbourne, Australia

Aggregated results – all vs. vetted (checked by analyst) TRaPs

Rain area and volume too small by ~50% POD for heavy rain is ~0.2-0.6, FAR is ~0.2-0.6 Vetted TRaPs perform better than all (unvetted + vetted) TRaPs

Page 11: Beth Ebert BMRC, Melbourne, Australia

Aggregated results – sensor intercomparison

SSM/I TRaPs had some large errors, AMSU had smallest errors AMSU TRaPs gave largest rain area AMSU TRaPs showed best performance, then TRMM, then SSM/I

Page 12: Beth Ebert BMRC, Melbourne, Australia

CRA verification method (Ebert and McBride, 2000)

Define entities using threshold (Contiguous Rain Areas)

Location error determined by pattern matching (minimum total squared error, maximum correlation, or

maximum overlap)

external specification using best track data

Verify properties of CRA (size, mean and maximum intensity, etc.)

Error decomposition

MSEtotal = MSEdisplacement + MSEvolume + MSEpattern

Version for pattern matching using correlation:

(r=correlation, s=std.dev.)

ObservedX

ForecastF

2)XF(MSEvolume

)rr(ssMSE optXFntdisplaceme 2

2 12 )ss()r(ssMSE XFoptXFpattern

...track errors

...rain retrieval, no growth/decay

...steady state rain structure

Related to:

Page 13: Beth Ebert BMRC, Melbourne, Australia

CRA validationTC Fay (0303 UTC 25 March 2004)

Page 14: Beth Ebert BMRC, Melbourne, Australia

CRA validationTC Monty (2216 UTC 1 March 2004)

Page 15: Beth Ebert BMRC, Melbourne, Australia

CRA validation results for vetted TRaPs

Pattern error most important, followed by volume error, then displacement error

150

100

50

0

(km) (%) (%) (%)

Page 16: Beth Ebert BMRC, Melbourne, Australia

Comparison to operational NWP

Mesoscale model (mesoLAPS, 12 km resolution) TC-centered mesoscale model (TC-LAPS, 15 km resolution)

24 h rain forecasts for TC Monty, ~00 UTC 2 March 2004

Verification on 0.25° grid consistent with TRaP verification

Page 17: Beth Ebert BMRC, Melbourne, Australia

Comparison to operational NWP

NWP models overestimated rain area and volume Correlations comparable between TRaP and models Threat score best for TC-LAPS

Fairer comparison might use vetted TRaPs but not enough days in common

TRaP

Page 18: Beth Ebert BMRC, Melbourne, Australia

Comparison of Australian and US results (median values for vetted TRaPs)

Australia United States

Maximum rainfall too large by ~1/3 ~ 1/3 of observed

Heavy rain area ~ half of observed ~ 2/3 of observed

Heavy rain volume ~ half of observed ~ 2/3 of observed

Error magnitude RMS error R RMS error R

POD (heavy rain) ~0.45 ~0.50

FAR (heavy rain) ~0.30 ~0.25

Sensor intercomparison

AMSU outperformed SSM/I, not enough

TRMM to judge

TRMM best, then AMSU, then SSM/I

Comparison to NWP Worse in many respectsBetter in almost all

respects

Page 19: Beth Ebert BMRC, Melbourne, Australia

Reasons for differences

Australia United States

Reference data Rain gauge analysis,Stage IV radar-gauge

analysis

Spatial scale ~25 km 4 km

Temporal matching ± 3 h ± 30 min

NWP model High resolution (12-15 km) Low resolution (50 km)

Typical TC size Bigger than average Smaller than average

Atmospheric moisture Drier Moister

Page 20: Beth Ebert BMRC, Melbourne, Australia

CRA validation suggests...

LocationError18%

Volumeerror34%

Patternerror48%

track forecasts

satelliterainretrieval

no growthor decay

steady state rain structure

sources of error related to assumptions in the TRaP formulation...

Improve satellite rain algorithms

Adjust for atmospheric moisture, shear

Orographic enhancement

Include storm rotation

Statistical filter

Improve tracks (multi-model NWP)

that might be improved using a variety of strategies.

Page 21: Beth Ebert BMRC, Melbourne, Australia

Living with uncertainty – Ensemble TRaP

Perturb or vary: Cyclone track Parameters of microwave rain rate retrieval Satellite sensors included in the ensemble, including VIS/IR Sources of TC rain forecasts: R-CLIPER, NWP, ...

TC Monty, 00 UTC 2 March 2004Ensemble of 27 TRaP forecasts (15 AMSU, 8 SSM/I, 4 TRMM) valid within ± 12 hMean includes histogram transformation

Page 22: Beth Ebert BMRC, Melbourne, Australia

Thank you!