The Use of GPS Radio Occultation Data for Tropical Cyclone Prediction
Bill Kuo and Hui LiuUCAR
Outline• Prediction of tropical cyclones:
– Current forecast skills– Comparison of operational model performance for 2011
• The NOAA Hurricane Forecast Improvement Project• Impact of assimilation approaches on NCEP GFS hurricane forecast
skills• GPS radio occultation (RO) and COSMIC• Assimilation of GPS RO data• Impact on the tropical cyclone analysis and prediction
– Hurricane Ernesto (2006)– Typhoon Morakot (2009)
• Outlook
Current capability of NHC
Good track forecast improvements. Errors cut in half in 15 years. Skill of 48h forecast as good as 24-h forecast 10 years ago.No improvement in intensity forecast. Off by 2 categories 5-10% of the time.
77 69 59 61 65 62 62 56
197219
163
210
166 177149 142 132 120
93 101 99 103 110 97
380396
297
417
311342
271243 237
215
170196
157191 200
179
353 356326
279316
254
324 310 309
0
50
100
150
200
250
300
350
400
450
500
Y1994 Y1995 Y1997 Y1998 Y1999 Y2000 Y2001 Y2002 Y2003 Y2004 Y2005 Y2006 Y2007 Y2008 Y2009 Y2010
時 間 年
誤差值
公里
12小時預報 24小時預報
48小時預報 72小時預報
48小時預報趨勢分析線 24小時預報趨勢分析線
Current capability of CWB in (km)
Good improvement from 1994 to 1005. 24 hour forecast errors reduced by half in 10 years. However, the skill becomes somewhat stagnant after 2005. Does not keep track of intensity forecast.
2011 Preliminary NHC Verifications
ECMWF and GFS did well (again).
ECMWF beat consensus at longer ranges.
TVCA beat FSSE.
AEMI not as good as GFSI, even at 5 days.
GFDL and HWRF middle of the pack.
NGPS, CMC, UKMET trailed.
Summary of 2011 Results and Implications
• Deterministic ECMWF (~16km) and GFS (~21 km) performed very well for 2011 (similar to that of 2010). Resolution matters.
• ECMWF beat multi-model consensus at longer ranges. High resolution deterministic global forecast is very useful.
• Multi-model consensus (TVCA) beat the FSU super ensemble (FSSE).
• Lower-resolution GSF global ensemble (AEMI, ~63km) did not perform not as well as high-resolution deterministic GFS (~21km), even at 5 days. Again, resolution matters.
• Performance of operational regional models (GFDL, 48/16/8 km, and HWRF, 27/9 km) was not as good as ECMWF and GFS global models. They are in the middle of the pack. Why didn’t regional model do better? What should they be used for?
• NGPS, CMC, UKMET global models trailed the other global and regional models. Hurricane forecast skill is different from general synoptic forecast skill (e.g., 500 mb height).
NOAA Hurricane Forecast Improvement Project (HFIP)
• 10-Year Goals:– Reduce average track error by 50% for day 1 through 5– Reduce average intensity error by 50% for day 1 through 5– Increase the probability of detection (POD) for rapid intensity
change to 90% at Day 1 decreasing linearly to 60% at day 5, and decrease the false alarm ratio (FAR) for rapid intensity change to 10% for day 1 increasing linearly to 30% at day 5. The focus on rapid intensity change is the highest forecast challenge indentified by the National Hurricane Center.
– Extend the lead time for hurricane forecasts out to Day 7 (with accuracy of Day 5 forecasts in 2003).
• 5-Year Goals:– Reduce track and intensity forecast errors by 20%
HFIP Hurricane Research Priorities
• Advancement in data assimilation (DA) technique– Technique to maximize the usefulness of observations– Identification and analyses of the sources of model error
• Physics advances– Air-sea and BL processes– Microphysical/aerosol/radiation processes
• Advanced model diagnostic techniques– Analyses and forecast of vortex low-order wavenumber evolution– Analyses and forecast of large-scale and hurricane environment evolution (e.g.
shear/track)• Development of high resolution ensembles
– Identify techniques to utilize ensemble members for improved intensity guidance
Pros Cons
3D-Var • Inexpensive• Practical approach for operational purposes
• Static (fixed) error covariance• Limited performance
4D-Var
• Use of observations at appropriate time• Flow-dependent error covariance• Suitably balanced analysis due to explicit use of model dynamics & physics• Treats observations simultaneously• Can handle a wide range of scales simultaneously• Can use all observations from frequently reporting stations (high temporal resolution)• Demonstrated capability (better than 3D-Var)
• Expensive• 4D-Var iterations are sequential – difficult to make full use of current computers & likely impossible in foreseeable future.• Require TL/AD models – difficult to maintain up-to-date code
EnKF
• Simple to design and code• No AD or minimization is needed• Provide estimates of error covariance (but noisy)• Allows for the full history of the evolution of forecast errors.• Can use the full model, including non-differentiable and stochastic terms.• Explicit estimate of errors.• Facilitates ensemble forecasting.
• Expensive• Treats one observation at a time, cost increases with data volume• Can handle limited range of scales (because of localization)• Can only estimate mean and covariances from a small sample, assuming Gaussian distributions• Is still experimental (comparable or slightly better than 3D-Var)
Hybrid• “Error of the day” for 3D-Var or 4D-Var can be obtained through ensemble• Easy to implement• Considered to be an attractive compromise
• In experimental phase• Still expensive
Data Assimilation Methods
Comparison of EnKF vs 3D-VarSame GFS model at T574L64 (~21km at 25N)
EnKF: T254L64 (~47km at 25N)
GSI (3D-Var): T574L64(~21km at 25N)
Use the same operational observational data stream
2010 North Hemisphere tropical cyclones
Hamill et al (2011) MWR
Comparison of EnKF, 3D-Var, and Hybrid
Deep layer (850-200 mb) vector wind, 26 July – 17 Sep, 2010, 20N-20S. Hamill et al. (2011)
Comparison between ECMWF (T639L62, ~28km at 25N) and GFS/EnKF (T254L64, ~47km at 25N) ensemble prediction of tropical cyclones.
ECMWF has significantly better performance over Western Pacific.
The GFS/EnKF has larger errors over the Western Pacific than Eastern Pacific or Atlantic.
Hamill et al. (2011)
ECMWF and GFS Ensemble Forecast Comparison
Differences between ECMWF and GFS/EnKF for 200 mb wind, averaged from 7/26 – 9/27, 2010. From Hamill et al (2011). Larger difference over Western Pacific is noted.
Radio Occultation Technique
COSMIC – Six-Satellite constellation mission, launched in April 2006
Characteristics of GPS RO Data• Limb sounding geometry complementary to ground and space nadir viewing
instruments• Global coverage• Profiles ionosphere, stratosphere and troposphere• High accuracy (equivalent to <1 K; average accuracy <0.1 K)• High precision (0.02-0.05 K)• High vertical resolution (0.1 km near surface – 1 km tropopause)• Only system from space to observe atmospheric boundary layer• All weather-minimally affected by aerosols, clouds or precipitation• Independent height and pressure• Requires no first guess sounding• No calibration required• Independent of processing center• Independent of mission• No instrument drift• No satellite-to-satellite bias• Compact sensor, low power, low cost
All of these characteristicshave been demonstrated inpeer-reviewed literature—many were proven by COSMIC
GPS RO observations advantages for weather analysis and prediction
• There are considerable uncertainties in global analyses over data void regions (e.g., where there are few or no radiosondes), despite the fact that most global analyses now make use of satellite observations.
• GPS RO missions (such as COSMIC) can be designed to have globally uniform distribution (not limited by oceans, or high topography).
• The accuracy of GPS RO is compatible or better than radiosonde, and can be used to calibrate other observing systems.
• GPS RO observations are of high vertical resolution and high accuracy. • GPS RO is an active sensor, and provides information that other satellite
observing systems could not provide• GSP RO provide valuable information on the 3D distribution of moisture
over the tropics, which is important for typhoon prediction.
Problems of using GPS RO data in weather models
• GPS RO data (e.g., phase, amplitude, bending angles, refractivity) are non-traditional meteorological measurements (e.g., wind, temperature, moisture, pressure), advanced assimilation systems are needed to effectively assimilate such observations.
• The limb-viewing geometry makes their measurement characteristics very different from in situ point measurements (e.g., radiosonde) or the nadir-viewing passive microwave/IR measurements. Advanced observation operators are needed.
• The GPS RO measurements are not perfect and subject to various sources of error (e.g., measurement noise, tracking errors, uncalibrated ionospheric effects, super-refraction,…etc).
• Model configuration and issues (e.g., domain size, model top, parallelization, regional/global, ionosphere, model errors) affect the choices of assimilation strategies and outcome.
Φ1, Φ2, a1, a2
α1, α2
α
N
GPS RO measurements & processing
T, e, P
Phase and amplitude of L1 and L2
Bending angles of L1 and L2
Bending angle (Neutral A.)
Refractivity
Ionospheric correction
Bending angle optimization & Abel inversion
A priori meteorological data & 1D-Var
Radio holographic methods, taking multipaths into accountGeometrical (single ray) w/o amplitudes
Satellites orbits & Spherical symmetry
Observation type and operators
Obs Type Obs Operator
N. A.
Retrieved T, e, P Interpolation in time & space
Refractivity Local refractivity
Refractivity Nonlocal refractivity
Refractivity Nonlocal excess phase
Bending angle Local bending angle
Bending angle 2D/3D Ray tracer
Phase + Ray shooting (orbits)
L1,L2Bending angle + Ionospheric &
Plasmaspheric modelPhase, amplitude
Com
plex
ity o
fO
pera
tor
Err
or C
hara
cter
istic
s
Neu
tral
atm
osph
eric
A Priori information is neededError structure is too complicated to properly characterize
Possible choices
Operator is too complicated for now
Pros Cons
3D-Var • Inexpensive• Practical approach for operational purposes
• Static (fixed) error covariance• Limited performance
4D-Var
• Use of observations at appropriate time• Flow-dependent error covariance• Suitably balanced analysis due to explicit use of model dynamics & physics• Treats observations simultaneously• Can handle a wide range of scales simultaneously• Can use all observations from frequently reporting stations (high temporal resolution)• Demonstrated capability (better than 3D-Var)
• Expensive• 4D-Var iterations are sequential – difficult to make full use of current computers & likely impossible in foreseeable future.• Require TL/AD models – difficult to maintain up-to-date code
EnKF
• Simple to design and code• No AD or minimization is needed• Provide estimates of error covariance (but noisy)• Allows for the full history of the evolution of forecast errors.• Can use the full model, including non-differentiable and stochastic terms.• Explicit estimate of errors.• Facilitates ensemble forecasting.
• Expensive• Treats one observation at a time, cost increases with data volume• Can handle limited range of scales (because of localization)• Can only estimate mean and covariances from a small sample, assuming Gaussian distributions• Is still experimental (comparable or slightly better than 3D-Var)
Hybrid• “Error of the day” for 3D-Var or 4D-Var can be obtained through ensemble• Easy to implement• Considered to be an attractive compromise
• In experimental phase• Still expensive
Data Assimilation Methods
Current DA use of GPS RO data3D-Var 4D-Var EnKF Hybrid
2D Bending angle SSI ECMWF
Local bending angle
GSI WRF-Var
ECMWF UKMO
Meteo France
Nonlocal excess phase
WFR-VarGSI* WRF-DART
Nonlocal refractivity JMA
Local refractivityNCEP
WFR-VarCWB
Env. CanadaJMA, CMA WRF-DART WRF-DA
Under test
*Ma et al. (2011) atmospheric river study, using regional GSI + ARW.
Impact of COSMIC on Hurricane Ernesto (2006)
Hurrican Ernesto:
Formed: 25 August 2006
Reached Hurricane strength: 27 August
Dissipated: 1 September 2006
15:50 UTC 27 August 2006
Picture taken by MODIS, 250 m resolution
Forecast experiments
• No GPS: initialized from AVN/GFS analysis at 2006-08-23-06Z
• GPS all: assimilate all 15 GPS profiles at 2006-08-23-06Z, followed by a 5-day forecast
• GPS 1 : only assimilate 1 GPS profile at 2006-08-23-06Z, followed by a 5-day forecast
WRF/DART EnKF system
36-km, 32 member ensemble
Use non-local excess phase observation operator
Low-level moisture change by assimilating GPS
Assimilation of 1 GPS RO sounding in the vicinity of Hurricane Ernesto caused a 1.8g/kg increase in moisture at 1.5 km. This was enough to get the hurricane genesis going.
NCAR 4-Day Ernesto Forecasts
Y.-H. Kuo (NCAR), 2007
Forecast with GPS Forecast without GPSThe Actual Storm
How does GPS RO data improve hurricane forecast?
WRF/DART ensemble assimilation of COSMIC GPSRO soundings
• WRF/DART ensemble Kalman filter data assimilation system• 36-km, 32-members, 5-day assimilation• Assimilation of 178 COSMIC GPSRO soundings (with nonlocal obs
operator, Sokolovskiey et al) plus satellite cloud-drift winds• Independent verification by ~100 dropsondes.
178 COSMIC GPSRO soundings during 21-26 August 2006
From Liu et al. (2011)
Distribution of GPS RO data
Experiment DesignName Conventional data GPS RONODA No No Continuous forecastONLY No Yes GPS onlyONLY6km No Yes above 6km GPS only above 6kmCTRL Yes No ConvectionalRO Yes Yes Conventional + GPSRO6km Yes Yes above 6 km Convectional + GPS6km
> The first three experiments try to identify the impact of GPS data in a clean setting (without the use of any other data)> The second three experiments assess the impact of GPS RO data in a more realistic operational setting.> Conventional data includes radiosondes, surface, aircraft reports, satellite cloud track winds, no radiance.
Analysis of 850 hPa Wind and Total Column Cloud Water(06Z August 23, 36 hours before Ernesto’s genesis, GPSonly run)
Convergence and convection in Ernesto’s genesis area.
Where Ernesto formed at 0000 UTC 25 August
Daily Analysis Increments of Q and T (GPSonly run)(850 hPa, August 23, 2 days before Ernesto’s genesis)
2-hour Forecast Difference of GPSonly-NODA (700 hPa)
00Z 24 August
Water vapor 06Z 23 August Wind
00Z 25 August Ernesto’s genesis time
Daily Analysis Increments of Wind(August 23, 2 days before Ernesto’s genesis, m/s)
GPSonly>6km run
700 hPa
GPSonly run 250 hPa
Strong correlation between GPS in the lower troposphere and winds at all levels.
2-hour Wind Forecast Differences (250 hPa) GPSonly-FCST 06Z 23 August GPSonly>6km-FCST
00Z 24 August
00Z 25 August Ernesto’s genesis time
2-hour Forecasts RMS fit to Radiosondes and dropsondes(Averaged over tropical Atlantic, 21-26 August, 2006)
Assimilation Experiments of RO and Conventional Data
• CTRL (NO GPS) run: Assimilate conventional data.• RO6km run: Add RO refractivity data only higher than 6km.• RO run: Add RO refractivity data of all levels.
• Non-local RO refractivity operator is used.
Analyses of SLP and 1000 hPa Vorticity (00UTC 25 August)
CTRL(NOGPS)
RO6km
RO
48h-forecast of Track errors and SLP IntensityCTRL
GPS>6km
GPS
Ensemble mean of 48-hour forecasts of Ernesto’s central sea level pressure (hPa, top) and track error (km, bottom) initialized from the analyses at 0000 UTC 25 August 2006
Total Integrated Water Vapor of 48-hour Forecasts(00UTC 25 August)
RO CTRLIR image
24h FCST
48h FCST
48-hour Forecasts of Ernesto (2006) with Assimilation of GPS and Conventional Observations
RO RO6kmIR Image
24h FCST
48h FCST
Assimilation of GPS > 6km shows less positive impact
Summary of Ernesto Study
• Assimilation of GPS RO data adds moisture in the lower troposphere, and produces noticeable wind increments, consistent with the convective environment.
• GPS RO data in the lower troposphere is crucial for creating a favorable environment for hurricane genesis
• Assimilation of GPS RO data produced improved analysis and subsequent forecasts both in terms of track and intensity.
Impact of GPS RO data on Typhoon Morakot (2009)
Assimilation experiments for Morakot
• Assimilation from 00Z August 3 to 18Z August 8, 2009 with 2-hourly cycling.
• 36km analysis grid with 35 levels• 48h Forecast with nest grids 36km/12km/4km starting
at August 7 00Z.• CWB observations are used• CTL run: Assimilate CWB conventional observations• GPS run: CTL run + COSMIC data.
Track analyses (August 3 06Z - 8 18Z)
NOGPS
GPS
Ensemble mean
Observed
SLP intensity analyses (August 3 06Z - 8 18Z)
NOGPS
GPS
Ensemble mean
Observed
24h Rain forecast (August 7 - 8 00Z)
NOGPS
GPS
Ensemble mean
Observed
48h Rain forecast (August 8 - 9 00Z)
NOGPS
GPS
Ensemble mean
Observed
Rain Probability Forecast (August 7-8 00Z)
Ensemble mean
Observed
Rain Probability Forecast (August 8 - 9 00Z)
Ensemble mean
Observed
WRF/DART Analyses and Forecasts for Morakot (2009)
• Control (with GPS RO): CWB operational observations including TC BOGUS data, 6-hourly analysis cycle.
• NOGPS: remove the GPS RO data from the control run.• Assimilations started 12UTC Aug. 3, 2009. 45km grid only
for both assimilation and forecasts.• CWB TWRF is also full cycled for the same period.• Cost of the 16 ensemble forecasts is equivalent to one
nested (45/15/3km) deterministic forecast.
Forecast initialized at 12 UTC 5 AugustNo GPS With GPS
Forecasts initialized at 12 UTC 6 AugustNo GPS With GPS
NO GPS
GPS
Difference
Two week assimilation, 1-14 June 2007, 850 mb wind field.
Summary of Morakot Study
• Assimilation of the GPS data:– Improve analysis and prediction of typhoon track– Improve rainfall prediction– Enhanced upper-level divergence, increased low-
level moisture flux, and intensity 500 mb height• Performance of WRF/DART is superior to
WRF-Var• Positive impact of GPSRO is also seen in
WRF-Var
48-h of track forecast errors for Typhoon Jangmi (2008) with and without COSMIC.
Typhoon Prediction During T-PARC
Assimilation of GPSRO data from COSMIC improved the quality of regional analysis and typhoon track errors. For four storms in T-PARC 2008, the average improvement is 13%.
From H. Liu and Jeff Anderson
COSMIC-II SoundingsGeographic Coverage
(6@72°, 6@24°)
1 hour
6 hour
3 hour
24 hour
Summary• GPSRO observations are not affected by clouds and precipitation• GPSRO observations provide three-dimensional water vapor
information over the tropics, which are crucial for:– Tropical cyclone genesis– Tropical cyclone intensity forecast– Tropical cyclone track forecast
• Current FORMOSAT-3/COSMIC sounding distribution is NOT optimal for tropical prediction. The data density is the lowest over the tropics (0.4 sounding over 500 km x 500 km in one day).
• FORMOSAT-7/COSMIC-2, with 10 times more soundings will significantly improve the skills of tropical cyclone prediction:– Improve track, intensity and rainfall forecast for typhoons affecting Taiwan– Directly contribute to all the goals of NOAA’s Hurricane Forecast
Improvement Project (HFIP)
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