Second GPS Radio Occultation Data Users Workshop, 22-24 August, 2005

National Conference Center, Lansdowne, Virginia

Assimilation of GPS RO Data for Severe Weather PredictionIn the Vicinity of Taiwan

1Ching-Yuang Huang

1Department of Atmospheric Sciences, National Central University, Taiwan

Collaborators: 2Ying-Hwa Kuo, 1Shu-Ya Chen, 1Jei-Zu Wang and 2Yong-Run Guo

2National Center for Atmospheric Research, Boulder, USA

Sponsored by NSPO

Case simulations with GPS RO data:2001- Typhoon Lekima2001- Typhoon Nari (2001/09) 2002- Typhoon Nakri (2002/07) 2002- Typhoon Sinlaku (2002/08) 2003- Dujuan Typhoon (2003/08/31/12-09/02/00) 2004- Cold front-1 (2004/02/06/18) 2004- Cold front-2 (2004/02/07/06) 2004- President’s-day front (2004/05/19/12-22/12) 2004- Typhoon Conson (2004/06/07/00-09/00) 2004- Typhoon Mindulle (2004/06/29/06-07/02/06) 2004- Typhoon Aere (2004/08/21/18-25/12) 2004- Typhoon Namadol (2004/12/02-04) 2004- Cold front(2004/12/03 or 06) 2005- Early-March snow event (cold front) WRF2005- Typhoon Haitang (2005/07/17) WRF2005- Typhoon Matsa (2005/08/05)* WRF

6 LEO satellites provide 2500 global daily measurements.

It will offer up to about 50~100 soundings in a regional model domain for a 6-h assimilation time window.

Dujuan Typhoon(2003/08/31/12)

CHAMP

09:00 (4.240 ， 129.910)

10:29 (10.470 ， 102.360)

10:34 (-2.400 ， 133.310)

11:54 (49.050 ， 83.740)

11:57 (42.390 ， 104.010)

13:32 (27.040 ， 86.150)

Cold front-1(2004/02/06/18)

CHAMP

17:01 (16.190 ， 145.940)

18:28 (44.680 ， 147.410)

19:59 (50.880 ， 118.870)

20:05 (21.710 ， 129.010)

20:10 (1.360 ， 102.800)

Cold front-2(2004/02/07/06)

CHAMP

04:48 (39.490 ， 147.130)

06:23 (44.550 ， 151.430)

07:41 (-2.160 ， 107.650)

07:49 (23.600 ， 135.410)

Meiyu Front(2004/05/19/12)

CHAMP

09:57 (50.090, 103.420)

10:00 (45.490,114.790)

10:01 (33.450,139.760)

11:31 (50.560,100.360)

Data assimilated within initial time ± 3 hr

Conson Typhoon(2004/06/07/00)

CHAMP

06:04 (50.650,146.300)

06:09 (28.500,144.440)

06:15 (1.480,143.640)

07:37 (50.040,133.210)

Mindulle Typhoon(2004/06/29/06)

CHAMP

04:19 (9.100,141.050)

07:18 (35.680,97.080)

08:48 (48.790,80.240)

Aere Typhoon(2004/08/21/18)

CHAMP

15:00 (21.180,104.380)

16:32 (25.930,89.000)

16:37 (44.030,87.790)

Nanmadol Typhoon(2004/12/02/12)

CHAMP17:2218:5118:55

18:58

(35.50,112.720)

(47.270,87.850)

(24.320,93.90)

(18.180,92.590)

Cold front/Southwesterly(2004/12/03/12)

CHAMP

17:22

18:51

18:55

18:58

 (35.500, 112.720)

(47.270,87.850)

(24.320,93.900)

(18.180,92.590)

Model Domain and Physics

The model simulations use three nested domains at 45-, 15- and 5-km resolutions.

All the simulations use MM5 version 3.5 or 3.6 with explicit treatments (Goddard’s scheme) for ice/graupel physics in the three domains (1, 2 and 3), Anthes Kuo’s scheme and Grell’s scheme for cumulus parameterization in domain 1 (largest) and domain 2, respectively, and the Blackadar scheme for PBL parameterization in all the domains.

3DVAR was performed for each domain with GPSrf.

MM5/WRF 3DVAR

Cost function defined by

)]}([)]([)(){(2

1 11 xhyOxhyxxBxxJ obsT

obsbT

b

x ： N-component vector of the analysis variable ，

xb ： N-component vector of the background variable (first guess)

yobs ： M-component vector of the observations ，

B ： Forecast-error covariance matrix （ N ×N ），

O ： Observational covariance matrix （ M ×M ），

h ： Transformation operator (converts analysis to observation),

N ： Number of degrees of freedom in analysis,

M ： Number of observations.

Covariance Matrix- O The GPS radio occultation observational covariance matrix is diagonal and thus has assumed no vertical correlations.

This assumption of vertical un-correlation is certainly not supportive of some existing dependence between observations, but in absence of statistical information on those correlations, the assumption insures that the data information is not underestimated in assimilation.

The diagonal elements (variances) are prescribed as a profile exponentially decreasing from 3 N at 100 hPa to 10 N at 1000 hPa. The value of 10 N observational error near the surface is slightly than, but consistent with, the 3% refractivity difference between CHAMP radio-occultations and ECMWF analysis found at 1000 hPa, as reported by Kuo et al. (2004).

Threat Score (TS)

A: the number of the grids on which both

forecast and observation exceed the threshold, F: the number of the grids on which forecast exceeds

the threshold, and

O: the number of the grids on which observation exceeds the threshold.

> 1,500 verification grid points on the island.

AOF

ATS

Cases

Thresholds

NariGTS

NariGPSrf

NariGTS

NariGPSrf

NakriGTS

NakriGPSrf

NakriGTS

NakriGPSrf

0-24 h 0-24 h 24-48 h 24-48 h 0-24 h 0-24 h 24-48 h 24-48 h

0.25 mm 0.545 0.540 0.539 0.536 0.504 0.480 0.505 0.475

0.5 mm 0.544 0.539 0.538 0.535 0.482 0.464 0.498 0.448

1 mm 0.544 0.542 0.532 0.533 0.411 0.425 0.486 0.411

2 mm 0.530 0.524 0.527 0.531 0.376 0.363 0.463 0.378

5 mm 0.489 0.484 0.530 0.528 0.273 0.244 0.321 0.320

10 mm 0.489 0.492 0.527 0.530 0.182 0.174 0.277 0.278

15 mm 0.503 0.496 0.526 0.538 0.149 0.140 0.248 0.258

25 mm 0.524 0.496 0.543 0.531 0.126 0.127 0.233 0.235

50 mm 0.497 0.485 0.462 0.504 0.086 0.088 0.113 0.166

100 mm 0.473 0.444 0.277 0.393 0.009 0.000 0.000 0.129

RMSE (mm) 69.74 71.43 97.84 93.98 90.28 66.55 50.85 37.06

Threat Scores

TS is generally higher for the run with assimilated QuikSCAT data.

(Huang et al., 2005, Weather and Forecasting, August)

Table. Threat scores (TS) and root mean square errors (RMSE) (mm) of the accumulated rainfalls between 0-24 h and 24-48 h for different thresholds for the Lekima (2001) and Sinlaku (2002) simulations with and without GPS refractivity data. (Huang, 2003, NSPO project report)

Lekima Sinlaku

0-24h 24-48h 0-24h 24-48h

No GPS GPS No GPS GPS No GPS GPS No GPS GPS

0.25 mm 0.534 0.534 0.420 0.421 0.353 0.359 0.289 0.290

0.5 mm 0.531 0.531 0.401 0.404 0.350 0.348 0.273 0.269

1 mm 0.523 0.523 0.385 0.380 0.357 0.340 0.253 0.246

2 mm 0.504 0.504 0.361 0.359 0.340 0.328 0.231 0.228

5 mm 0.423 0.423 0.307 0.301 0.317 0.323 0.232 0.233

10 mm 0.373 0.373 0.226 0.233 0.308 0.308 0.240 0.244

15 mm 0.343 0.346 0.167 0.157 0.295 0.297 0.242 0.236

25 mm 0.276 0.282 0.080 0.075 0.254 0.272 0.219 0.207

50 mm 0.241 0.245 0.019 0.029 0.182 0.188 0.127 0.127

100 mm 0.218 0.207 0.000 0.000 0.018 0.032 0.005 0.004

RMSE 80.55 80.86 47.47 44.30 16.57 16.39 37.41 46.80

Max (mm) 470.7 470.6 382.2 382.2 122.82 122.8 103.6 103.6

Thresholds

DUJUAN SIMULATION WITH GPSrf

Episode : 31/08/2003-02/09/2003

Satellite Time Location (Lat, Lon)

CHAMP 09:00 (4.24 ， 129.91)

CHAMP 10:29 (10.47 ， 102.36)

CHAMP 10:34 (-2.40 ， 133.31)

CHAMP 11:54 (49.05 ， 83.74)

CHAMP 11:57 (42.39 ， 104.01)

CHAMP 13:32 (27.04 ， 86.15)

MM5 Version 3.6 with MRF PBL and K-F cumulus parameterization

Initial time (1200UTC 31 August) ± 3 hr

Observed rainfall for (a) 00-1200UTC 1 September 2003 (max: 166.806 mm), (b) 12-2400UTC 1 September 2003 (max: 657.744 mm).。

GTS only

69.3 mm 369 mm

09/01/00-12UTC 09/01/12-24UTC

BOTH (GTS + GPSrf)

81.4 mm 387 mm

09/01/00-12UTC 09/01/12-24UTC

N: No-3DVARB: Both (GTS+GPSrf)G: GTS only+: Best track (CWB)

Dujuan Simulation

MM5 simulated near-surfac

e pressure (mb) and wind

(ms-1) at 24h,36h, 48h,60h.

2004/07 Mindulle Typhoon

a b

c d

No GPSrf No GPSrf24h

60h

48h

36h

No GPSrfNo GPSrf

initial time 2004/06/29/0600UTC

MM5 simulated near-surfac

e pressure (mb) and wind

(ms-1) at 24h,36h, 48h,60h.

2004/07 Mindulle Typhoon

a b

c d

24h

60h

48h

36h

With GPSrf

With GPSrfWith GPSrf

With GPSrf

initial time 2004/06/29/0600UTC

MM5 simulated 24-h accumulated rainfall (mm)First day

First day

0600UTC 29-30 June 2004 (max value: 76.5 mm )

0600UTC 30 June -1 July 2004 (max value: 320.5 mm)

Obs.

Second day

Second day

a b

c d

No GPSrf No GPSrf

With GPSrf With GPSrf

2004/07 Mindulle

initial time 2004/06/29/0600UTC

MM5 simulated near-surfac

e pressure (mb) and wind

(ms-1) at 30h,36h,

2004/12 Nanmadol Typhoon Initial time:2004/12/02/1800UTC

a b

c d

30h

36h

30h

36h

No GPSrf

With GPSrf

No GPSrf

With GPSrf

MM5 simulated 24-h accumulated rainfall (mm)

a

（ a ） max value: 561mm (b ） max value: 554mm 。

b

With GPSrfNo GPSrf

1200UTC 3-4 December 2004 (max value: 528.9mm )

2004/12 Nanmadol Typhoon

Haitang Simulation ongoing with WRF…Episode : 07/17/2005-07/19/20052~4 GPSrf

Conclusions

The simulated results indicate the impact of several GPS refractivity data is only marginally positive for some severe weathers in the vicinity of Taiwan.

More positive impact may be procured by combinations with other unconventional data.

Much more data from COSMIC can be assimilated and might be very helpful for improving weather prediction in Taiwan.

topr

r

o rdrrr

yxdlyxrS

0

20

2

modmodmod

),(2),()(

Modeling of the phase along the straight line;Inverting the modeled phase into a virtual (modeled) refractivity

),(1),( modmod yxyxn

022

0

0modmod

/1)( dr

rr

drdSr

topr

r

2-D model refractive index:

The phase integrated along the straight line:

Inversion of the integrated phase:

rtopr 0r

0

dl(From Sokolovskiy et al. 2004)

Refractivity mapping

Nonlocal operator

local operator

WRF 3DVAR with local and nonlocal operators by 2006.

Comments on Nonlocal Refractivity Operator Assimilation

Errors increase considerably near upper boundary, due to the less cancellation effects in a shorter path. Thus, local refractivity operator may still be recommende

d above the tropopause.

The refractivity mapping normally has even larger errors compared to errors for excess phases, due to the application of Abel inversion in a finite domain with non-negligible local refractivity.

The straightline forward operator is very fast and the assi

milation will fit computationally into real-time operations.

2005/01/01 Taipei 101

End.