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April 8-10, 2013 Beijing. A method of analogue-based correction of errors in model prediction and its application to 2013 summer climate prediction. REN Hongli , LIU Ying, ZHENG Zhihai BCC/CMA, Beijing, China 10081. Statistical correction of model prediction errors. - PowerPoint PPT Presentation
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A method of analogue-based correction of errors in model prediction and its application to 2013
summer climate prediction
REN Hongli, LIU Ying, ZHENG Zhihai
BCC/CMA, Beijing, China 10081
April 8-10, 2013 Beijing
Systematic Error Correction
Developing statistical methods to utilize historical data information in predictions of climate model .
Statistical correction of model prediction errors
Analogue-based Correction of Errors (ACE)
.ranflowsys EEEE
Constant systematic errors
Flow-dependent errors
If is bounded and is small enough, then
Hypothesis
~~L
tL
t
ELt
~~~EL
t
Basic equation for ACE :
0 L
tNumerical model
Real model of atmosphere
Model satisfied by analogue
~D ~ ~EE
Analogue-based Correction of model prediction Errors (ACE)
Strategy: Improve model prediction through using
abundant historical analogue information.
( Ren and Chou, 2005, 2007)
jj PPPP ~~~ˆSMSM0SM0SM
Application of the ACE in seasonal prediction
Using multi-analogues
m
jjj PPPP
100
~~~ˆ
Seasonal mean of the Eq.
To estimate prediction errors of the current seasonal-mean prediction and realize the error correction.
Based on the ACE method, the Analogue-Dynamical Seasonal Prediction System (ADSPS) has been developed.
( Ren et al, 2006, 2007, 2009; Zheng et al, 2009)
Analogue-Dynamical Seasonal Prediction System( ADSPS)
Basic structure of the ADSPS
Initial conditions Historical analogues
Model hindcasts
Model prediction
Corrected prediction Error characteristics
The schemes for the actual application are developed from this basic structure of the ADSPS.
Applications to spring-summer ENSO prediction
• DATA and model– OBS: monthly HadISST during 1983-2012
• Interpolated into the resolution of BCC model– Model: BCC Coupled GCM 1.0 (BCC_CGCM1) – Hindcasts: monthly SST during 1983-2012
• Initiate month: Feb. • Predict from Mar. to Aug., every year
• Method – BCC model prediction removing the systematic errors – Corrected prediction with the ACE
• Correction of SSTAs in Mar.―Aug. during 1993-2012
1993
1983―1992
1983―1993
1994
1983―2011
2012
…………
Illustrations of the designed scheme
Independent validation
Time series of Nino 3.4 Index
Mar. Apr. May Jun. Jul. Aug.
BCC 0.92 0.85 0.59 0.37 0.26 0.28
ACE 0.9 0.79 0.63 0.44 0.36 0.28
Temporal correlation coefficients of Nino 3.4 indices between OBS and BCC or Correction of BCC during 1993-2012
Mar. Apr. May Jun. Jul. Aug.
BCC 0.6 0.53 0.59 0.68 0.74 0.83
ACE 0.29 0.41 0.45 0.49 0.69 0.78
RMSE ( ) of Nino 3.4 indices (≥ 0.5 ) during 1993-2012℃ ℃
TCC
Predictions of Niño3.4 index and SSTA in 2013
May Jun. Junl. Aug.
Nino3.4I 0.089 0.277 0.284 0.426
A new Warm-Pool El Niño event may be emerging.
Applications to China summer PRCP prediction in recent 4 years 2009 2010
2011 2012
Year
BCC CGCM1
BCC OP Apr
ADSPS March
2009
70 76 72
2010
61 73 69
2011
60 70 75
2012
61 68 67
平均 63.0 71.8 70.8
PS scoresThe same level of skill with BCC operation
2013 China summer PRCP prediction
• Positive PRCP anomalies are located over North China, Northeast China and South China. • Negative PRCP anomalies are located over Northwest China and the Yangtze River basin.
The method of analogue-based correction of errors (ACE) has been introduced to improve seasonal-mean predictions produced by climate model;
This ACE method can reduce the flow-dependent prediction errors besides the constant systematic errors;
Applications of the method in correcting the predictions of ENSO and China precipitation anomalies this summer show encouraging performance;
The predictions for this summer are worthy of expecting.
Summary
Thank you!