Define the problems of CWB WRF ---performance of 3DVAR

Define the problems of CWB WRF---performance of 3DVAR

Define the problems of CWB WRF---performance of 3DVAR

Impact of observationsImpact of observations

Only Qscat dataBlack line : first guess

Purple line : analysis

Qscat data &

Sound data

Black line : first guess


Qscat data &

Sound data &

Pilot data



Qscat data &

Sound data &

Pilot data &

Airep data

Impact of ACARS Black line : first guess


Qscat data & Sound data & Pilot data & Airep data & Gpsro data

Strange results over indonesia after assimilate the GPSSRO data



Qscat data & Sound data & Pilot data & Airep data & Gpsro data & Satem data & Satob data & Buoy data

Qscat data & Sound data & Pilot data & Airep data & Gpsro data & Satem data & Satob data & Metar data

Qscat data & Sound data & Pilot data & Airep data & Gpsro data & Satem data & Satob data

Qscat data & Sound data & Pilot data & Airep data & Gpsro data & Satem data & Satob data & Ships data

Qscat data & Sound data & Pilot data & Airep data & Gpsro data & Satem data & Satob data & Synop data

AA

All obs data

在Ａ基礎下加入地面資料的分析場比較

Impact of SYNOP

0907050009070500500 hPa

850 hPa 200 hPa

NCEP GFS WRF GPS/RO ․

Data impact of GPSRO

on analysis fieldFocus on

2008062100~2008063012

COLDACV3 & ECNODA & EC COLDACV5 & EC

COLDACV5 (NO GPSRO) & EC

0.936

0.963

0.9530.979 10 day

Anomaly Correlation

拿掉 GPSRO 對分析場有明顯的助益


0.968

2008062100 ~ 2008063012 2008062100 ~ 2008063012

10 day mean analysis co10 day mean analysis compare with EC analysismpare with EC analysis

500 hPa Geopotential Heig500 hPa Geopotential Heightht

Blue : analysis

Red : EC analysis

1. Take out GPSRO improved sub high range and also modified analysis field on Indo-China Peninsula

COLDACV3 & ECNODA & EC COLDACV5 & EC

2008062100 ~ 2008063012 2008062100 ~ 2008063012

10 day mean analysis co10 day mean analysis compare with EC analysismpare with EC analysis

300 hPa Geopotential Heig300 hPa Geopotential Heightht

Blue : analysis

Red : EC analysis

COLDACV5(NO GPSRO) & EC

0.961

0.980

0.9750.991

拿掉 GPSRO 對分析場有明顯的助益


0.982

NODA & EC

0.979

NODA & EC

0.967

NODA & EC

0.930

COLDACV5 & EC

0.936

COLDACV5 & EC

0.966

COLDACV5 & EC

0.932

CYCLEACV5 & EC

0.920

CYCLEACV5 & EC

0.950

CYCLEACV5 & EC

0.908

00hr 24hr 48hr10 day mean10 day meanRed : EC analysis

reduce

Although the poor quality of analysis could be recovered quickly in forecast, however, the direct impact is to contribute the uncertainty in verification and drop off forecaster’s confidence.

We have to conduct a comprehensive OSE to optimal use the observations in 3DVAR. Especially for GPSRO, Synop, and Airep.

Can we not only to remove or eliminate the “question data”, but also find ways to best use the observations?

In addition to improve the data use policy, e.g. the QC or data thinning, is there problems in 3DVAR? How to do a reasonable surface analysis? How to assimilate the surface observations?

Analysis performanceAnalysis performance

00 and 06 hr fcst 03 and 09 hr fcst

06 and 12 hr fcst 15 and 21 hr fcst

03 and 09 hr fcst

12 and 18 hr fcst

IC at 00Z

IC at 06Z

Calculate the difference

2

3

4

5

6

7

8

6 12 18 24 30 36 42 48 54 60 66 72 78

fcst hour

H rm

se a

t 500

hPa

0000 UTC 0600 UTC 1200 UTC 1800 UTC ALL

F12-F6

F18-F12

Analysis increment

The difference for 00Z-18Z and 12Z-06Z should be larger, while 06Z-00Z and 18Z-112Z should be smaller (The observations at 06 and 18Z is not much as 00 and 12Z).For analysis increment, IC at 06 Z (06Z-00Z ) has the largest analysis increment, why?

80 cases

3

3.5

4

4.5

5

5.5

6

6.5

7

7.5

8

0 12 24 36 48 60

forecast hour

500

hPa

Hei

ght

NCEP NODA COLD OP2 OP2DFI CV3 CV3DFI

Calculate over 56 runs at00 and 12 Z

00-12 hr fcst

12-24 hr fcst

24-36 hr fcst

NoDFI

With DFI

NODA

0.3

0.35

0.4

0.45

0.5

0.55

0.6

0.65

0.7

0.75

0.8

0 12 24 36 48 60

forecast hour

850

hPa

TNCEP NODA COLD OP2 OP2DFI CV3 CV3DFI

Calculate over 56 runs at00 and 12 Z

00-12 hr fcst

12-24 hr fcst

24-36 hr fcst

CV3

CV5

0.4

0.5

0.6

0.7

0.8

0.9

1

1.1

1.2

0 12 24 36 48 60

forecast hour

Sur

face

pre

ssur

e

NCEP NODA COLD OP2 OP2DFI CV3 CV3DFI

Calculate over 28 runs at00 Z only00-12 hr fcst

12-24 hr fcst

24-36 hr fcst

WRFVAR COLD

START

CYCLE Background

Error

(CV3) (CV5)

GPSRO DFI

NO DATA

COLDACV3 ˇ̌ ˇ̌ ˇ ˇ ˇ̌

COLDACV5 ˇ̌ ˇ̌ ˇ ˇ ˇ̌

CYCLEACV5

作業設定ˇ̌ ˇ̌ ˇ ˇ ˇ̌

COLDACV3

(no gpsro)ˇ̌ ˇ̌ ˇ ˇ

COLDACV5

(no gpsro)ˇ̌ ˇ̌ ˇ ˇ

CYCLEACV5

DFIˇ̌ ˇ̌ ˇ ˇ ˇ̌ ˇ̌

Experiments design

1. All experiments run one month (2008060100~2008063012) except for “no gpsro” 2. All experiments compare with ECMWF analysis

Analysis Field

Anomaly Correlation of Geopotential HeighthPa NO

DATA

COLD

ACV3

COLDACV5

CYCLECV5

HH SLPSLP 0.974 0.959 0.966 0.952

500 500 0.981 0.961 0.947 0.938

300 300 0.992 0.979 0.967 0.959

Summary of compare with ECMWF

Root Mean Square Error of Geopotential Height

hPa NO

DATA

COLD

ACV3

COLDACV5

CYCLECV5

HH 850850 5.773 7.819 7.777 8.283

500 500 6.678 9.594 11.21 12.04

300 300 7.115 11.84 14.56 16.25

Anomaly Correlation of Geopotential Height

0.80.820.840.860.880.9

0.920.940.960.98

1

SLP 500hPa 300hPa

NODATA

COLDACV3

COLDACV5

CYCLECV5

Root Mean Square of Geopotential Height

02468

101214161820

850hPa 500hPa 300hPa

NODATA

COLDACV3

COLDACV5

CYCLECV5

Big jump

Root Mean Square of TemperaturehPa NO

DATA

COLD

ACV3

COLDACV5

CYCLECV5

TT 850850 1.070 1.152 1.046 1.235

500 500 0.606 0.653 0.679 0.802

300 300 0.549 0.577 0.571 0.769

Summary of compare with ECMWF

Root Mean Square of Temperature

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2


NODATA

COLDACV3

COLDACV5

CYCLECV5

Root Mean Square of WindhPa NO

DATA

COLD

ACV3

COLDACV5

CYCLECV5

UU 850850 1.980 2.216 1.997 2.311

500 500 2.207 2.437 2.282 2.628

300 300 2.489 2.932 2.717 3.296

Root Mean Square of Wind

0

0.5

1

1.5

2

2.5

3

3.5

4


NODATA

COLDACV3

COLDACV5

CYCLECV5

Summary of analysis field1.1. Height Height :

NO DATA > COLDSTART+CV3 >COLDSTART+CV5 > CYCLE+CV52.2. TemperatureTemperature : NO DATA is best ， CYCLE+CV5 is worst3.3. WindWind : NO DATA is best ， CYCLE+CV5 is worst4. There are obvious gaps between NO DATA and experiments with 3DV

AR, especially on height field. The difference in temperature is not so big.

Any problems to derive the height field in 3DVAR?

1. COLDSTART is better than CYCLE !

Forecast Field

Anomaly Correlation of Geopotential Height_SLP

0.8

0.82

0.84

0.86

0.88

0.9

0.92

0.94

0.96

0.98

1

0hr 24hr 48hr 72hr

NODATACOLDACV3COLDACV5CYCLECV5

Anomaly Correlation of Geopotential Height _500hPa

0.8

0.82

0.84

0.86

0.88

0.9

0.92

0.94

0.96

0.98

1

0hr 24hr 48hr 72hr

NODATA

COLDACV3

COLDACV5

CYCLECV5

Anomaly Correlation of Geopotential Height_300hPa

0.8

0.82

0.84

0.86

0.88

0.9

0.92

0.94

0.96

0.98

1

0hr 24hr 48hr 72hr

NODATA

COLDACV3

COLDACV5

CYCLECV5

Geopotential Height -- AC

OP2 is the worst

• The degrade in 3DVAR experiment is reduced in forecast.

• CV5 (cycle) is worst, Is it due to the bad 1st guess, both in analysis and forecast, which is due to the bad analysis?

Root Mean Square of Geopotential Height_850hPa

02468

101214161820222426

0hr 24hr 48hr 72hr

NODATA

COLDACV3

COLDACV5

CYCLECV5


02468

101214161820222426

0hr 24hr 48hr 72hr

NODATA

COLDACV3

COLDACV5

CYCLECV5


02468

101214161820222426

0hr 24hr 48hr 72hr


Geopotential Height -- RMS

• The analysis error is larger than the 24-hr forecast error in 3DVAR cases, which is consistent with Hong’s results.

Root Mean Square of Temperature_850hPa

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

0hr 24hr 48hr 72hr

NODATA

COLDACV3

COLDACV5

CYCLECV5


0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

0hr 24hr 48hr 72hr

NODATA

COLDACV3

COLDACV5

CYCLECV5


00.20.40.60.8

11.21.41.61.8

2

0hr 24hr 48hr 72hr

NODATA

COLDACV3

COLDACV5

CYCLECV5

Temperature -- RMS

1. In analysis, OP2 is worst, in forecast, OP2 is still worst in 850 hPa, no big difference above 500 hPa.

Root Mean Square of Wind_850hPa

00.5

11.5

22.5

33.5

44.5

55.5

6

0hr 24hr 48hr 72hr

NODATA

COLDACV3

COLDACV5

CYCLECV5


00.5

11.5

22.5

33.5

44.5

55.5

6

0hr 24hr 48hr 72hr



00.5

11.5

22.5

33.5

44.5

55.5

6

0hr 24hr 48hr 72hr

NODATA

COLDACV3

COLDACV5

CYCLECV5

1. Base on RMS results ， the reduced difference between four experiments followed the increase of forecast

2. 850hPa : NO DATA is best

Wind -- RMS

Summary

• The 3DVAR degrade the analysis performance– Height is more significant than T, is there any problem to derive t

he height field in 3DVAR?– The degrade in 3DVAR experiment is reduced in forecast.

• Cold start is better than cycle– Is it due to the bad 1st guess, which is from the bad analysis?

• The bottom line is– We can’t give up the mesoscale data assimilation– We need to keep some kind of cyclic run, to take the advantage f

rom firstguess.– The poor performance of analysis is not only hurt model initial co

ndition, but also first guess.

It is shown that there is apparent problems on the 3DVAR performance.

Is there any fundamental problems in 3DVAR? -The performance of multi-variable analysis? -The minimization procedure? -The translation between analysis variables and model variable? -Role of outer loop?

Is it a general feature in regional variational analysis? -Assess the performance of GSI -Assess the performance of EAKF

Impact of DFI on

500 hPa Geopotential HeightCYCLEACV5 & EC

0.920

CYCLEACV5DFI & EC

0.936

DFI improved (smoothed) analysis field of CYCLE+CV5 obviously ， but still can’t similar to NO DATA

Blue : analysis

Red : EC analysis

Use DFI

10 day mean10 day mean

DFI

DFI

00 12 24 36 48 60 72 hoursWithout 21 89 169 235 333 359 307 kmWith 11 106 180 201 277 248 258 kmCases 12 12 10 9 7 2 2

with DFIRelo+new bogus

without DFIRelo+new bogus

Summary

• DFI can improve the poor 3DVAR analysis efficiently, in particular in typhoon initialization.– How to use DFI efficiently and correctly?– The plan to develop the DFI in nest domain.– Any plan to develop the other initialization

scheme?

Define the problems of CWB WRF ---performance of 3DVAR

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