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CWB project review: Task#1

Yong-Run Guo

National Center for Atmospheric Research (NCAR)

9 November 2009

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Task#1 Support for the WRFVar component of the CWB operational system (OP1, OP2) and upgrade of WRFVar V3

1.1 Support for the WRFVar component of CWB operational system

Teleconferences between NCAR and CWB (about 10 times)

Major issues discussed and solved:

namelist setting for OP2 (both WRFVar and WRF) has been posted on web Lateral boundary problem for domain3 (5km-resolution) Distribution of the WRF model vertical levels GPSRO data assimilation After reviewing Maxine Chen’s work, the tuned CV3 BES gave better results Minor bugs fixed and shell script

•Staff visiting:From CWB to NCAR,•Staff visiting:From CWB to NCAR,•Staff visiting:From CWB to NCAR,

Staff visiting

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From CWB to NCAR:

Chun-Han Lin from Feb. to August: Satellite data assimilation work with Z.-Q. Liu and H-.C. Lin

Shu-Ya Chen from Mar to Sept.: GPSRO data assimilation work with Y.-R. Guo Maxine Chen from July to Dec.: BES tuning and testing work with Y.-R. Guo Ling-Feng Hsiao from Jan. to Dec.: TC relocation work with Y.-R. Guo C.-T. Fong, J.-S. Hong, and C. Terng from 24 March to 1 April: Discussion on OP2

implementation and related problems for 2009 project C.-T. Fong and J.-S. Hong from 22 September to 1 October: Review the progresses

of 2009 project and reported the problems from OP2.

 From NCAR to CWB: Y.-R. Guo from 17 May to 12 June 2009

Typhoon re-location and new TC bogus Bogus from 2.5o EC data Installation of WRFDAV3.1 in CWB inside firewall IBM ZTD assimilation Seminars and tele-conference Data exchange: EC high-resolution grid analysis data for June and December 2009.

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1.1.1 To provide the consultation on the TC relocation procedure for WRF model

Dr. Terng and Dr. Ling-Feng Hsiao (CWB) worked with Y.-R. Guo (NCAR)

a) TC relocation procedure is directly implemented in WRF_ARW coordinate, not

pressure coordinate, like WRFV3.1 TC bogus scheme;

b) WRF_ARW is a nonhydrostatic model with mass coordinate in vertical.

c) The relocated fields are wind, potential temperature, water vapor mixing ratio,

and surface pressure. The dry hydrostatic surface pressure and geopotentail

height are diagnosed based on the WRF_ARW governing equations (NCAR

Tech. Note/TN-468+STR).

Experiments with 58 cases for 2008 typhoon Jangmi and Sinlaku with (WR) and

without (NR) relocation procedure (Fig. 1)

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0

100

200

300

400

500

0 12 24 36 48 60 72

forecast hours (hr)

trac

k e

rror

s (k

m)

0

10

20

30

40

50

60

percen

tage (%)

WR

NR

(NR-WR)/NR

The relocation scheme effectively improves the TC position error by more than

50% in the initial time and more than 28% in the rest of the forecast periods.

Fig. 1 Mean typhoon track errors for WR (blank bars) and NR (shaded bars) experiments and the percentage of the mean track error reduction by the vortex relocation scheme (solid line) from 58 WRF-ARW forecasts.

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1.1.2 To provide the consultation on the GPS RO data assimilation in WRFVar

Improve the GPSRO assimilation:Adaptive observation error tuning: Desroziers and Ivanov approach;Improve the quality control for GOSRO (Poli et al): Remove the super- and sub-refraction;Consider the vertical thinning for GPSRO soundings.

A series of experiments (June and December 2008)

Basic conclusions:

Observational error tuning would improve the analysis and forecast when verifying against GPS RO soundings (Fig.2)

Vertical data thinning only improve the analysis below ~5 km, and get comparable or bigger bias for the forecast.

New quality control procedure gets slightly improvements for the forecast bias errors.

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QUALITY CONTROL (QC) REFER TO POLI ET AL. (2009)

35km 100

34km 50

22

2

1

qcdz

Nd

qcdz

dN

Quality control existed in WRFVAR v3.1 (Lidia Cucurull 2005)1. Relative Error check(qc=-31,-32,-33)2. Low level check(qc=-31)3. Background check (qc=-3)

Do hope CWB use bfrPrf instead of wetPrf to be consistent with all other operational centers

Consider the vertical correlation of GPSRO data.

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Figure 2, Bias and RMSE errors verifying against the GPS RO data for experiments: QEC QC (tuning with new-QC), QEI QC (no tuning with new-QC), QEC (tuning, no new-QC), QEI (no tuning, no new-QC).

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Compare :

NG: assimilated with GTS only

WG_NT: assimilated with GTS+GPS, without data thinning

WG_WT1: same as WG_NT, but with data thinning before QC

WG_WT2: same as WG_NT, but with data thinning after QC

500 hPa Geopotential Height(June 2008)

Assimilated with GPS RO data would significant impact on the 500 hpa geopotential height (Fig.3).

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2008/06/01_00 (FST=00h)NG WG_NT

WG_WT1 WG_WT2

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2008/06/04_12 (FST=00h)NG WG_NT

WG_WT1 WG_WT2

Tibetan Plateau

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This is the biggest problem with GPSRO assimilation. We also found that

the errors are rather large after several cycles. At 0000 UTC 01 June

2008,with the first guess from NCEP GFS, the errors are comparable to EC

analysis, but after cycling 10 days, at 0000 UTC 10 June 2008, errors are

much larger than both EC and NCEP. It seemed that OP2 forecast has some

kind of drafting. To improve the impact of GPSRO assimilation on the 500

hPa height analysis may be one of the major tasks for 2010 CWB/NCAR

project.

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RMSE BIAS

2008060100Z

Errors are comparable to EC and NCEP

2008061000Z

Errors are much larger than those from EC and NCEP

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1.2 Support of the upgrade of WRFVar V3

1.2.1 To add the CV3 BE option to WRFVar V3

Used the V2.1 as benchmark, modified 17 files in WRFVar V3 and added 13 files to WRFVar V3 for incorporating the CV3 BE with WRFVar code. Now CV3 BE working for both serial and mpp modes in the released code WRFVar V3.1.

1.2.2 Implement the function of outer-loop with multiple CV5 BES tuning factors in WRFVar V3

This work has been completed by Y.-R. Guo and passed to CWB.

Maxine Chen, CWB, has used this code in a series of the experiments.

BE1: OP2+VAR3.1_CV5(default)

BE2: OP2+VAR3.1_CV3(default)

BE3: OP2+VAR3.1_CV3(tuned)

BE4: OP2+VAR3.1_CV3(tuned)+outer loop(3)

BE5: OP2+VER3.1_CV5(tuned)+outer loop(3)

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The basic conclusions from these experiments are

• Tuning finely and using outer loop finely DO help improving analysis and forecast.CV5(tuned)+outer loopCV3(tuned)

• In DEC 2008, CV3 (tuned) is better than CV5 (tuned)+outer loop.

Overall, CWB decided to use CV3 BES option for OP2.1 in end of year 2009, but there are still some works need to do for CV3 BES options:

Need to carefully check the code for CV3 BES option with outer-loops. Logically there is no reason that CV3 BES option with outer-loop will downgrade the performance. Need to fine tune the factors for domain2(15km), domain3(5km), even domain1(45km). We hope that the fine tuning will help to improve the 500 hPa height analysis;The CV3 BES option in WRFVar used the same GSI global BES file but original implemented in WRFVar 6 years ago. We should adopt some recent improvements from GSI system to upgrade our WRFVar system.

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work for tuning the BE (cont.)

Use Hollingsworth&Lonnberg method to get scale-length, background error variance of each BE.

Get background error variance and estimate scale-length from single obs test.

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All of the numbers for AC and RMSE scores are obtained from Eric Chiang, CWB.

To separate the VAR and WRF model, “FULL” cycle experiment was not examined.

Not to compare the regional DA (VAR, COLDF) with the global DA (GSI, NODA).

Only pay attention to the added values of VAR, so the comparison between NODAF (without VAR) and COLDF (with VAR) will be examined

Is there added values with WRFVar in CWB WRFVar/WRF system?

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Summary of compare with ECMWF

Anomaly CorrelationACAC 00hr00hr 24hr24hr 48hr48hr 72hr72hr

hPa FULL NODA NODAF

COLDF

FULL NODA NODAF

COLDF

FULL NODA NODAF

COLDF

FULL NODA NODAF

COLD

F

HH SLPSLP 0.952 0.974 0.970 0.965 0.928 0.930 0.929 0.929 0.889 0.899 0.896 0.895 0.842 0.856 0.852 0.849

500 500 0.938 0.981 0.984 0.948 0.960 0.974 0.972 0.971 0.922 0.944 0.939 0.941 0.857 0.888 0.881 0.883

300 300 0.959 0.992 0.990 0.965 0.976 0.974 0.971 0.972 0.949 0.948 0.942 0.947 0.897 0.901 0.895 0.898

Comparison between NODAF (6-h FSCT) and COLDF (6-h FCST as FG with VAR)(The yellow box is the winner in AC score)

00h analysis: NODAF win all 3 times, and COLDF degraded the AC scores

24, 48, and 72h forecast: COLDF win 5 times AC scores, NODAF win 3 times AC scores, NODAF and COLDF got same AC score 1 time.

Conclusion: For AC scores, VAR has the negative impact on the analysis, but still has

the added values for forecasts.

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Summary of compare with ECMWF

Root Mean Square Error

RMSRMS 00hr00hr 24hr24hr 48hr48hr 72hr72hr

hPa FULL NODA NODAF

COLDF

FULL NODA NODAF

COLD

F

FULL NODA NODAF

COLDF

FULL NODA NODAF

COLD

F

HH 850850 8.283 5.773 5.791 7.632 9.214 9.897 10.09 10.03 11.46 11.56 11.75 11.75 14.52 14.42 14.66 14.69

500 500 12.04 6.678 6.169 11.00 9.728 7.825 8.155 8.295 13.54 11.42 11.81 11.79 18.35 16.14 16.55 16.51

300 300 16.25 7.115 7.855 14.88 12.29 12.75 13.49 13.40 17.90 18.06 18.96 18.78 25.11 24.65 25.32 25.04

TT 850 850 1.235 1.070 1.149 1.056 1.616 1.374 1.400 1.398 1.861 1.592 1.602 1.637 2.056 1.811 1.825 1.864

500 500 0.802 0.606 0.678 0.720 0.883 0.917 0.945 0.937 1.025 1.041 1.069 1.061 1.196 1.217 1.238 1.231

300 300 0.769 0.549 0.643 0.614 0.909 0.882 0.937 0.878 1.143 1.134 1.168 1.136 1.354 1.343 1.372 1.353

UU 850 850 2.311 1.980 2.075 2.022 2.789 2.762 2.807 2.768 3.408 3.376 3.385 3.383 3.938 3.898 3.932 3.925

500 500 2.628 2.207 2.358 2.350 3.019 2.705 2.804 2.717 3.556 3.318 3.387 3.329 4.233 4.066 4.099 4.083

300 300 3.296 2.489 2.760 2.854 3.823 3.502 3.693 3.605 4.793 4.579 4.712 4.623 5.792 5.601 5.707 5.657

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Comparison between NODAF (6-h FSCT) and COLDF (6-h FCST as FG with VAR)

(The yellow box is the winner in RMSE score)

Analysis: NODAF wins 3 for H, 1, for T, 1 for U.

COLDF wins 0 for H, 2, for T, 2 for U.

Total: NODAF wins 5, and COLDF wins 4.

Forecast: 24h: NODAF wins 1 for H, 0 for T, 0 for U.

COLDF wins 2 for H, 3 for T, 3 for U

Total: NODAF wins 1, and COLDF wins 8.

48h: NODAF wins 0 for H, 1 for T, 0 for U.

COLDF wins 2 for H, 2 for T, 3 for U.

Total: NODAF wins 1, and COLDF wins 7, NODAF=COLDF 1.

72h: NODAF wins 1 for H, 0 for T, 0 for U.

COLDF wins 2 for H, 3 for T, 3 for U.

Total: NODAF wins 1, and COLDF wins 8.

Overall, NODAF wins 3, COLDF wins 23, and NPDAF=COLDF 1.

Conclusion: Analysis is comparable, but degraded for H and improve for T and U.

With VAR (COLDF), the forecast performance is significantly improved.

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1.3 Conduct the retrospective runs for the ground-base GPS ZTD data assimilation

1.3.1 determine the period (2008060200 to 2008060500)

1.3.2 prepare the data files

Problems after 25 June 2008 have been found by Yating Tsai (All data rejected). John Braun found that IGS fiducial stations were not contained in the routine processing after 19 June. Site GUKW always tracks 30% fewer satellites.

1.3.3 conduct the retrospective runs (?)

Processing formal error and default observation errors:

PW : 0.2 cmZTD: 1.0 cm

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There are 3 data assimilation experiments have been conducted,

CON: conventional observation onlyPWV: CON+PWZTD: CON+ZTD

The results are very encouraging, especially for heavy rain forecast over Taiwan area.

Thershold = 0.1

00.050.1

0.150.2

0.250.3

0.350.4

0.450.5

0.55

6 12 18 24 30 36fcst time

CON

PWV

ZTD

Thershold = 100

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

6 12 18 24 30 36fcst time

CON

PWV

ZTD

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6 12 18 24 30 36

CON 0.35 0.25 0.26 0.3 0.33 0.08

PWV 0.5 0.17 0.26 0.29 0.36 0.04

ZTD 0.52 0.2 0.25 0.39 0.36 0.08

6 12 18 24 30 36

CON 0.02 004 0 0.01 0 0

PWV 0.01 0.02 0 0.01 0 0.03

ZTD 0.14 0.08 0.01 0.01 0 0

Threat scores, Threshold = 0.1 mm

Threat scores, Threshold = 100 mm

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END

THANK YOU