Error Evaluation and Correction for GRAPES Global Forecasts

Ling TONG; Xindong PENG; Guangzhou FAN; Jun CHANG

doi:10.3878/j.issn.1006-9895.1608.16115

Chinese Journal of Atmospheric Sciences > 2017 > 41(2): 333-344. > DOI: 10.3878/j.issn.1006-9895.1608.16115

Ling TONG, Xindong PENG, Guangzhou FAN, Jun CHANG. 2017: Error Evaluation and Correction for GRAPES Global Forecasts. Chinese Journal of Atmospheric Sciences, 41(2): 333-344. DOI: 10.3878/j.issn.1006-9895.1608.16115

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Error Evaluation and Correction for GRAPES Global Forecasts

1.
Key Laboratory of Sichuan Province of Plateau Atmosphere and Environment & Institute of Atmosphere Science, Chengdu University of Information Technology, Chengdu 610225
2.
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081
3.
Sichuan Branch, China Meteorological Administration Training Centre, Chengdu 610027

Funds:

National Basic Research Program of China (973 Program) 2012CB417204

National Natural Science Foundation of China 41175095

National Natural Science Foundation of China 41575103

1990 "Five-second" National Science and Technology Support Program 2012BAC22B01

More Information

Received Date: January 12, 2016
Available Online: September 22, 2020
Published Date: March 14, 2017

Graphical Abstract

Abstract

Abstract

Using the ERA-interim reanalysis data of ECMWF as the reference, numerical errors of the GRAPES global model are evaluated first. A numerical correction of the systematic model errors is then performed based on the historical observation data by using the Anomaly Numerical-correction with Observations (ANO) method. The effect of the ANO on the global forecast is tested by several case studies, and significant improvements on the global forecasting quality are confirmed. The ANO application in numerical results from 15 to 24 July during 1984-2014 shows significant positive effects in the circulation forecasts compared to those without the ANO application. For example, the potential height and temperature forecasts have been improved in various regions. Analysis of the geopotential height forecasts at 200 hPa shows that the anomalous correlation coefficient (ACC) increases by 0.05 and the root mean square error (RMSE) decreases by 12 gpm on average for all the 31 cases. Similar results can be found at other levels. The above results verify the validity of the ANO method in the improvement of 10-day numerical weather forecasting skill of the GRAPE global model. Compared with the MOS method, the ANO method is more efficient and maneuverable for application in the operational forecast.

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References (24)

References

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