Transcript 6.4-Studies on Tropical Cyclone Forecasting using TIGGE

Studies on Tropical Cyclone Forecasting
using TIGGE
11th Session of THORPEX GIFS -TIGGE WG Meeting
12-14 June 2013
Met Office Exeter
Munehiko Yamaguchi
Meteorological Research Institute of Japan Meteorological Agency
Outline of the talk
1. Summary of tropical cyclone related papers
using TIGGE
2. Introduction of recent studies on tropical
cyclones using TIGGE
3. Status of Cyclone XML data exchange
4. Summary
Tropical Cyclone Related Papers using TIGGE -1-
Application
(2 papers)
Intercomparison (including
multi-center grand
ensemble) (6 papers)
Dynamics and
Predictability (6 papers)
Statistics based on the TIGGE article website: http://tigge.ecmwf.int/references.html
Tropical Cyclone Related Papers using TIGGE -2Intercomparison (including multi-center grand ensemble)
1.Halperin D. J. and co-authors, 2013: An evaluation of tropical cyclone genesis forecasts from
global numerical models. Weather and Forecasting. (In Press)
2.Magnusson, L., A. Thorpe, M. Bonavita, S. Lang, T. McNally and N. Wedi, 2013: Evaluation
of forecasts for hurricane Sandy, ECMWF Technical Memorandum, 699, 1-28.
3.Yamaguchi, M., T. Nakazawa, and S. Hoshino, 2012: On the relative benefits of a multi-centre
grand ensemble for tropical cyclone track prediction in the western North Pacific. Q. J. Roy.
Meteorol. Soc., doi: 10.1002/qj.1937.
4.Hamill, T.M., J.S. Whitaker, M. Fiorino and S.G. Benjamin, 2011, Global Ensemble redictions
of 2009's Tropical Cyclones Initialized with an Ensemble Kalman Filter, Monthly Weather
Review, 139, 668-688. doi: http://dx.doi.org/10.1175/2010MWR3456.1
5.Keller, J. H., S. C. Jones, J. L. Evans, and P. A. Harr, 2011: Characteristics of the TIGGE
multimodel ensemble prediction system in representing forecast variability associated with
extratropical transition, Geophys. Res. Lett., 38, L12802, doi:10.1029/2011GL047275
6.Majumdar, Sharanya J. and Peter M. Finocchio, 2010: On the ability of global Ensemble
Prediction Systems to predict tropical cyclone track probabilities. Weather and Forecasting, 25, 2,
659-680. http://journals.ametsoc.org/doi/abs/10.1175/2009WAF2222327.1
Tropical Cyclone Related Papers using TIGGE -3Dynamics and Predictability Study
1.Belanger, James I., Peter J. Webster, Judith A. Curry, Mark T. Jelinek, 2012: Extended
prediction of north indian ocean tropical cyclones. Wea. Forecasting, 27, 757–769.
doi: http://dx.doi.org/10.1175/WAF-D-11-00083.1
2.Gombos, Daniel, Ross N. Hoffman, James A. Hansen, 2012, Ensemble statistics for diagnosing
dynamics: Tropical cyclone track forecast sensitivities revealed by ensemble regression,
Monthly Weather Review, e-View. doi: http://dx.doi.org/10.1175/MWR-D-11-00002.1
3.Schumacher, Russ S., Thomas J. Galarneau, Jr., 2012, Moisture transport into midlatitudes
ahead of recurving tropical cyclones and its relevance in two predecessor rain events, Monthly
Weather Review, e-View. doi:http://dx.doi.org/10.1175/MWR-D-11-00307.1
4.Majumdar, S. J., Chen, S.-G. and Wu, C.-C., 2011, Characteristics of Ensemble Transform
Kalman Filter adaptive sampling guidance for tropical cyclones. Q.J.R. Meteorol. Soc., 137,
503-520. doi: 10.1002/qj.746 http://onlinelibrary.wiley.com/doi/10.1002/qj.746/abstract
5.Yamaguchi, Munehiko, David S. Nolan, Mohamed Iskandarani, Sharanya J. Majumdar,
Melinda S. Peng, Carolyn A. Reynolds, 2011, Singular Vectors for Tropical Cyclone–Like
Vortices in a Nondivergent Barotropic Framework, Journal of the Atmospheric Sciences, 68 (10),
2273-2291. doi: http://dx.doi.org/10.1175/2011JAS3727.1
6.Yamaguchi, M. and S. J. Majumdar, 2010: Using TIGGE data to diagnose initial perturbations
and their growth for tropical cyclone ensemble forecasts. Mon. Wea. Rev., 138, 9, 3634-3655.
http://journals.ametsoc.org/doi/abs/10.1175/2010MWR3176.1
Tropical Cyclone Related Papers using TIGGE -4Application
1.Liangbo Qi, Hui Yu, and Peiyan Chen, 2013: Selective Ensemble Mean Technique for Tropical
Cyclone Track Forecast by Using Ensemble Prediction Systems. Q. J. Roy. Meteorol. Soc.
(Accepted)
2.Hsiao-Chung Tsai, Russell L. Elsberry, 2013: Detection of Tropical Cyclone Track Changes
from the ECMWF Ensemble Prediction System. Geophysics Research Letter, doi:
10.1002/grl.50172
Tropical Cyclone Related Papers using TIGGE -5-
Others (Sensitivity
analysis, ET, etc., 4 papers)
Track (8 papers)
Genesis (2 papers)
Few studies on TC intensity
Evaluation of forecasts of Hurricane Sandy
9 days before the landfall
Landfall near Brigantine, New Jersey
Probability (%) of 850 hPa wind speed greater than 38 m/s somewhere inside a radius of
100 km for New York Harbour between 2012-10-29 12z and 2012-10-30 12z.
Magnusson et al. (2013, ECMWF Tech Memo)
Intercomparison of TC track predictions in the western North Pacific
The
ensemble
mean
better
performance
than
the control
Position
errors (km)
of 1- has
to 5-day
TC track
predictions by
the unperturbed
prediction
in (unfilled
generalbars)
andand
theensemble
improvement
rate
is of
relatively
control member
mean (filled
bars)
each SME.
large for the longer
prediction
times.
The circle (hyphen)
mark means
that the difference in the
errors between the control member and ensemble mean is
(not) statistically significant at the 95 % significance level.
Yamaguchi et al. (2012, QJRMS)
Verification result of TC strike probability prediction
Strike prob. is computed at every 1 deg. over the responsibility area of RSMC
Tokyo - Typhoon Center (0∘-60∘N, 100∘E-180∘) based on the same definition as
Van der Grijn (2002). Then the reliability of the probabilistic forecasts is verified.
Reliability Diagram
-Verification for ECMWF EPS-
In an ideal system, the red
line is equal to a line with a
slope of 1 (black dot line).
The number of samples (grid points)
predicting the event is shown by
dashed blue boxes, and the number
of samples that the event actually
happened is shown by dashed green
boxes, corresponding to y axis on
the right.
Benefit of MCGE over SME
Combine 3 SMEs
Reliability is improved, especially in the high-probability range.
MCGE reduces the missing area (see green dash box at a
probability of 0 %).
Typhoon track prediction by MCGE-9 (BOM, CMA, CMC, CPTEC,
ECMWF, JMA, KMA, NCEP, UKMO)
Good example
Bad example
Typhoon Megi initiated at
1200 UTC 25th Oct. 2010
Typhoon Conson initiated at
1200 UTC 12th Jul. 2010
Observed track
There are prediction cases where any SMEs cannot capture the observed track.
=> It would be of great importance to identify the cause of these events and
modify the NWP systems including the EPSs for better probabilistic forecasts.
Global TC track predictions initialized with an EKF
Hamill et la. (2011a, 2011b, MWR)
Evaluation of TC activity in the North Indian Ocean
using ECMWF ensemble
Belanger et al. (2012, WAF)
Case Study for Typhoon SON-TINH (2012)
Black: detected ensemble storms, Blue: Tropical Depression,
Green: Tropical Storm, Yellow: Severe Tropical Storm, Red: Typhoon
Evaluation of TC activity in the east of Philippians
Verified area: 120E-140E and 10N-25N
Verified period is July – October in 2011 and 2012
Storm track procedure: Vitart et al. (2010, MWR)
Day3 –
Day7
Day7 – Day14
(Week 2)
Climatology (based on 0.057
the best track data by
RSMC Tokyo)
0.0849
0.120
ECMWF
0.069
0.124
Prediction window
Day1 –
Day3
0.030
JMA
0.043
0.0845
N/A
NCEP
0.034
0.074
0.130
UKMO
0.041
0.076
0.127
•Probabilities are
calculated at each
grid point (0.5 x 0.5
degree) in the
verified box.
•A threshold
distance of 300 km
is used to
determine whether
observed or
forecasted TCs
affect a grid point.
Numbers in red are for forecasts better than climatology
Verification of TC genesis events in the western North
Pacific using ECMWF 1-mont EPS
OBJECTIVE VERIFICATIONS AND FALSE ALARM ANALYSES OF
WESTERN NORTH PACIFIC TROPICAL CYCLONE EVENT
FORECASTS BY THE ECMWF 32-DAY ENSEMBLE
Tsai et al. (2013, Asia-Pacific JAS)
How well in advance ECMWF EPS predicts the genesis
events of Fiona and Igor.
The number of members with strong vortices (pink) gradually
increases as the forecast time gets shorter in the Igor case while it
increases rapidly in the Fiona case.
Pre-Igor
Pre-Fiona
Courtesy of Will Komaromi (RSMAS, UM)
Probabilistic Verification
• ECMWF ensemble forecasts, Jun 1 – Nov 30, 20102012
–
7-day forecasts, 00 UTC only
• All forecasts up to and including genesis.
• Verification: NHC best track. TC or not TC.
Question: what is the probability that a TC exists at
XX h? (with time tolerance of 1 day).
Courtesy of Sharan Majumdar (RSMAS, UM)
Reliability Diagram: 2010-2 Seasons
Selective Ensemble Mean Technique for
Tropical Cyclone Track Forecast
Qi et al. (2013, QJRMS)
DETECTION OF TRACK CHANGES FROM ECMWF
ENSEMBLE FORECASTS
• Tsai and Elsberry (2013 GRL*) demonstrated that the ECMWF 5day ensemble track forecasts available on the TIGGE website in
near-real time provide information on alternate tracks
– Cluster analysis of historical forecast tracks yielded six track
clusters
– When the ensemble track spread is large, cluster analysis will
indicate the two or more distinct cluster tracks contributing to
that spread
– In bifurcation (two track clusters) situations, the track clusters
with percentages greater than 70% can be reliably selected as
the better choice
* Tsai, H.-C., R. L. Elsberry, 2013: Detection of tropical cyclone track changes from the ECMWF ensemble
prediction system. Geophys. Res. Lett., 40, 797-801, doi: 10.1002/grl.50172.
Courtesy of Russell Elsberry (NPS)
Evaluation of TC track prediction in bifurcation situations
using ECMWF EPS –western North Pacific-
Tsai and Elsberry (2013, GRL)
Cyclone XML (CXML) Homepage
Producing center: CMC, CMA, ECMWF, JMA, KMA, MeteoFrance, STI, UKMO, NCEP (9 centers in total)
Data are used for A WWRP-RDP “North Western Pacific Tropical
Cyclone (TC) Ensemble Forecast Project (NWP-TCEFP),
Severe Weather Forecast Demonstration Project (SWFDP), etc.
Some issues
• Data from STI seems to be unavailable.
• The last date that the TCEFP retrieved the data is October 2010.
• Differences in a coverage and pre-storm tracking as follows:
Center
Coverage
Pre-storm Tracking (TD
Min.
Pressure
Max. Wind
Speed
CMA
NWP only
Named TCs
Yes
No
ECMWF
Globe
All TCs, but need to exist at T+0
Yes
Yes + location
JMA
NWP only
Named TCs
Yes
Yes
MSC
Globe
Named TCs
Yes
Yes
NCEP
Globe
Named TCs
Yes
Yes
UKMO
Globe
All TCs
No
No
The ECMWF monthly forecasting system
Experimental product: Tropical cyclone activity
Weekly Mean Tropical Cyclone Strike Probability. Date: 20100408 0 UTC
t+(264-432)
Probability of a TC passing within 300km radius
< 10%
20°E
40°E
10.. 20
60°E
20.. 30
80°E
100°E
30.. 40
120°E
140°E
40.. 50
160°E
180°E
50.. 60
200°E
60.. 70
220°E
240°E
70.. 80
260°E
280°E
80.. 90
300°E
> 90%
320°E
340°E
80°N
80°N
70°N
70°N
60°N
60°N
50°N
50°N
40°N
40°N
30°N
30°N
20°N
20°N
10°N
10°N
0°N
0°N
10°S
10°S
20°S
20°S
30°S
30°S
40°S
40°S
50°S
50°S
60°S
60°S
70°S
70°S
80°S
80°S
20°E
40°E
60°E
80°E
100°E
120°E
140°E
160°E
180°E
200°E
220°E
240°E
260°E
280°E
300°E
320°E
340°E
Courtesy of Frederic Vitart (ECMWF)
Summary
•
There are 14 tropical cyclone research articles using the
TIGGE data (http://tigge.ecmwf.int/references.html). Eight of
them are studies on TC track forecasting (intercomparison,
benefit of multi-centre grand ensemble, application).
•
Studies on predicting TC genesis (activity) seem to be done
more recently.
•
There are few studies on TC intensity.
•
Extension of CXML may be beneficial in order to enhance
research on TC genesis and intensity as well as TC track.
(discrepancy of the information included in the CXML limits
studies of these kinds)
Verification result of TC strike probability -2-
All SMEs are over-confident (forecasted probability
is larger than observed frequency), especially in the
high-probability range.
Benefit of MCGE over SME -2Best SME (ECMWF)
MCGE-3
(ECMWF+JMA+UKMO)
MCGEs reduce the missing area! The area is reduced by about
1/10 compared with the best SME. Thus the MCGEs would be
more beneficial than the SMEs for those who need to avert
missing TCs and/or assume the worst-case scenario.
MCGE-6
(CMA+CMC+ECMWF+JMA+NCEP+UKMO)
MCGE-9 (All 9 SMEs)
Verification at 3 day predictions
x axis: ensemble spread
y axis: position error of ensemble mean track
prediction
Verification of ensemble spread
Reliability Diagram of Day3-Day7 (T+72 – T+168)