Verification and Diagnostics for Data Assimilation System of Global GRAPES

Liu Yan; Xue Jishan; Zhang Lin; Lu Huijuan

doi:10.11898/1001-7313.20160101

Vol.27, NO.1, 2016

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Article Navigation > Journal of Applied Meteorological Science > 2016 > 27(1): 1-15

Liu Yan, Xue Jishan, Zhang Lin, et al. Verification and diagnostics for data assimilation system of global GRAPES. J Appl Meteor Sci, 2016, 27(1): 1-15. DOI: 10.11898/1001-7313.20160101.

Citation:

Liu Yan, Xue Jishan, Zhang Lin, et al. Verification and diagnostics for data assimilation system of global GRAPES. J Appl Meteor Sci, 2016, 27(1): 1-15. DOI: 10.11898/1001-7313.20160101.

Liu Yan, Xue Jishan, Zhang Lin, et al. Verification and diagnostics for data assimilation system of global GRAPES. J Appl Meteor Sci, 2016, 27(1): 1-15. DOI: 10.11898/1001-7313.20160101.

Citation:

Liu Yan, Xue Jishan, Zhang Lin, et al. Verification and diagnostics for data assimilation system of global GRAPES. J Appl Meteor Sci, 2016, 27(1): 1-15. DOI: 10.11898/1001-7313.20160101.

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Verification and Diagnostics for Data Assimilation System of Global GRAPES

DOI: 10.11898/1001-7313.20160101

1.
Numerical Weather Prediction Center of CMA, Beijing 100081
2.
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081

Received Date: 2015-03-29
Rev Recd Date: 2015-09-04
Publish Date: 2016-01-31

Abstract

Abstract

Numerical Weather Prediction Center of China Meteorological Administration has upgraded the global GRAPES (Global/Regional Assimilation and PrEdiction System) variation data assimilation system. The new data assimilation system employs the same coordinate, grids and atmospheric state variables as those of the GRAPES model. It can reduce analysis errors due to the interpolation and variable transformations, and also provide basics for developing GRAPES four-dimension variation assimilation system. Some key characteristics of the new global GRAPES data assimilation system are discussed, and then the performance is evaluated in detail, by comparing with observations, analysis or reanalysis data from advanced operational numerical weather prediction centers, and the medium-range forecast from background and analysis fields and different forecast models. Some guidelines for further optimizing the system is also given based on diagnosis and quantitatively estimating the impact of observations. Results show that the GRAPES data assimilation system assimilates conventional observations, satellite radiances and radio occultation observations effectively, making analyses closer to the real atmosphere and improving the forecast skill. The analysis of GRAPES are similar to those of European Centre for Medium-Range Weather Forecasts and National Center for Environmental Prediction at the large-scale circulation fields. However, some differences still remains, which actually expose issues of GRAPES. These differences are related to overlarge contributions from radiosonde, surface, ships, aircraft and radio occultation observations, and the relatively weaker influence of satellite radiance observations.There is broad consensus among the global numerical weather prediction centers that these types of observations tend to be the highest-ranked contributors to forecast skill: Microwave temperature sounder, hyper-spectral infrared sounder, radiosondes, aircraft observations, radio occultation and atmospheric motion vectors, although not necessarily uniformly in this order. However, contributions of the microwave temperature sounder and hyper-spectral infrared sounder in GRAPES are not dominant, because GRAPES still uses less radiance data, and on the other hand, the bias correction effect is not so good.Contributions of wind and humidity observation are less in GRAPES. Additionally, biases in regions of the Tibet Plateau, upper levels of the model and the tropics are relatively larger compared to observations and the reanalysis, which are related to the treatment method of topography and upper boundary of model. To gain better analysis and forecast skill, there is a requirement to place more emphasis on the above issues.
- variation data assimilation system;
- performance diagnostics;
- verification in the observation space

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References(26)

Figures and Tables

Fig. 1 Probability of d^a and d^b for TEMP, AIREP, SYNOPS, SHIPS, SATOB, RO and AMSUM-A of NOAA18 satellite

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Fig. 2 Distribution of mean and root mean square of BMO and AMO for radionsonde pressure observations at 500 hPa

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图 3 青藏高原地区探空观测的BMO和AMO的平均值和均方根误差廓线

(a) 气压，(b) 相对湿度, (c)u分量, (d)v分量

Fig. 3 Profile of mean and root mean square of BMO and AMO for the radiosonde observation in the Tibet Plateau for pressure (a), relative humidity (b), u wind (c), v wind (d)

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Fig. 4 Probability d^a and d^b of atmospheric motion vectors observations over the Tibet Plateau

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图 5 G-3DVar与ERA-Interim在850，500 hPa和250 hPa位势高度的相关系数和均方根误差的时间演变

(a) 北半球的相关系数, (b) 南半球的相关系数, (c) 北半球的均方根误差, (d) 南半球的均方根误差

Fig. 5 Time evolution of correlation coefficients and root mean square errors between G-3DVar and ERA-Interim at 850, 500 hPa and 250 hPa for the Northern Hemisphere and the Southern Hemisphere

(a) correlation coefficient of the Northern Hemisphere, (b) correlation coefficient of the Southern Hemiphere, (c) root mean square error of the Northern Hemisphere, (d) root mean square error of the Southern Hemiphere

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Fig. 6 Mean and root mean square error of GRAPES geopertential height, temperature and zonal wind analysis compared to ERA_Interim (GRAPES minus ERA-Interim) in the Northern Hemisphere, the Southern Hemisphere and the tropics

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图 7 G-3DVar与ERA-Interim分析场月平均偏差的纬度-高度剖面图

(a) 位势高度 (单位：gpm)，(b) 温度 (单位：K)，(c) 比湿 (单位：kg·kg^-1)，(d)u分量 (单位：m·s^-1)，(e)v分量 (单位：m·s^-1)

Fig. 7 Analysis comparisons between GRAPES and ERA_Interim for geopertential height (unit:gpm)(a), temperature (unit:K)(b), specific humidity (unit:kg·kg^-1)(c), u wind (unit:m·s^-1)(d) and v wind (unit:m·s^-1)(e)

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Fig. 8 Anomaly correlation coefficient (ACC) of the 500 hPa height for the 8-day forecast in the Northern Hemisphere (a), the Southern Hemisphere (b) and the East Asia (c)

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Table 1 Statistics of mean and variance for innovation and residual

观测	要素	ξ_b	ξ_a	σ_b	σ_a	r_ab
探空	气压/hPa	-0.4014	-0.0217	0.8037	0.4522	0.32
	u分量/(m·s^-1)	-0.006	0.0054	3.7618	2.812	0.56
	v分量/(m·s^-1)	-0.0202	0.014	3.6782	2.7799	0.57
	相对湿度/%	3.0262	-1.0595	22.2519	15.7933	0.50
地面	地表气压/hPa	-0.2361	-0.021	0.941	0.6754	0.52
船舶	地表气压/hPa	-0.1467	0.0107	1.0522	0.7872	0.56
云导风	风的u分量/(m·s^-1)	0.3533	0.1679	3.823	2.8708	0.56
飞机	温度/K	0.4076	0.1766	1.4039	1.1394	0.66
	u分量/(m·s^-1)	0.0121	-0.0123	3.6421	2.7596	0.57
	v分量/(m·s^-1)	-0.1173	-0.0149	3.6005	2.7251	0.57
掩星	折射率/%	-0.00073	-0.00048	0.00013	0.00005	0.15
微波温度计 (NOAA18)	通道5亮温/K	0.1113	0.0755	0.2291	0.2119	0.86
	通道6亮温/K	-0.0567	0.0776	0.1976	0.1612	0.67
	通道7亮温/K	0.0377	0.0179	0.2105	0.1793	0.73
	通道8亮温/K	0.0945	0.0709	0.2319	0.2057	0.79
	通道9亮温/K	0.2925	0.1876	0.2803	0.2126	0.58
	通道10亮温/K	0.4785	0.4873	0.2485	0.2087	0.70
AIRS	通道平均亮温/K	0.1492	0.1117	0.52	0.48	0.85

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Table 2 Statistics information of background check

观测	要素	资料使用率/%	系统指定的α_qc	本文式 (4) 统计的α_qc	质控范围
探空	气压/hPa	98	5.0	2.9	[-2.6, 1.7] (400~850 hPa)
	u, v分量/(m·s^-1)	100	9.0	2.5	[-7.5, 7.5]
	相对湿度/%	97	3.0	1.6	[-41, 45]
船舶	地表气压/hPa	73	5.0	3.7	[-2.1, 2.3]
地面	地表气压/hPa	73	3.0	2.4	[-2.1, 1.7]
云导风	u, v分量/(m·s^-1)	99	5.0	1.5	[-7.3, 8.0]
飞机	u, v分量/(m·s^-1)	99	4.0	1.8	[-7.3, 7.3]
飞机	温度/K	99	4.0	2.2	[-2.4, 3.2]
掩星	折射率/%	90	4.0	2.0	[-1.2, 1.2]
微波温度计	AMSU-A亮温/K	74	2.0	2.0
高光谱	AIRS亮温/K	8	2.0

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