Data assimilation of dust aerosol observations for CUACE/Dust forecasting system

Niu, Tianlin; Gong, S. L.; Zhu, Guijie; Liu, H. L.; Hu, Xuegang; Zhou, Chen; Wang, Y. Q.

doi:10.5194/acpd-7-8309-2007

Cited by 32 publications

(41 citation statements)

References 19 publications

(17 reference statements)

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“…Verification results show that without assimilation there is less satisfactory performance in SDS forecasts, with TS of 0.22 for 24 h forecasting, than if the model is used with data assimilation. A detailed comparison of these results is presented in Niu et al (2007). were always associated with high frequency of SDS events in most areas of northern China.…”

Section: Application Of Observation Data In Model Verificationmentioning

confidence: 99%

Surface observation of sand and dust storm in East Asia and its application in CUACE/Dust

et al. 2008

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Abstract. The spatial-temporal distributions and sources of sand and dust storm (SDS) in East Asia from 2001 to 2006 were investigated on the basis of visibility and PM 10 data from the routine SDS and weather monitoring networks run by CMA (China Meteorological Administration). A power functional relationships between PM 10 and visibility was found among various regions generally with a good correlation (r 2 =0.90), especially in Asian SDS source regions. In addition, three SDS occurrence centers, i.e. western China, Mongolia and northern China, were identified with the Mongolia source contributing more dust to the downwind areas including Korea and Japan than other two sources. Generally, high PM 10 concentrations were observed in most areas of northern China. The highest value was obtained in the center of western China with a spring daily mean value of 876 µgm −3 , and the value in other source regions exceeds 200 µgm −3 . These data sets together with the satellite observations in China form the main observation database for the evaluation and data assimilation of CUACE/Dust system -an operational SDS forecasting system for East Asia.

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Section: Application Of Observation Data In Model Verificationmentioning

confidence: 99%

Surface observation of sand and dust storm in East Asia and its application in CUACE/Dust

et al. 2008

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“…Zhang et al (2008) employed a 3D-var system for MODIS AOT to improve sea salt modelling in a global model. Niu et al (2008) and Lin et al (2008) analysed desert dust in a regional model, assimilating either surface visibility and satellite dust loading measurements or PM 10 observations, to improve dust forecasting in China. Niu et al (2008) employed a 3D-var system while Lin et al (2008) chose an ensemble Kalman filter (EnKF).…”

Section: Introductionmentioning

confidence: 99%

“…Niu et al (2008) and Lin et al (2008) analysed desert dust in a regional model, assimilating either surface visibility and satellite dust loading measurements or PM 10 observations, to improve dust forecasting in China. Niu et al (2008) employed a 3D-var system while Lin et al (2008) chose an ensemble Kalman filter (EnKF). Yumimoto et al (2008) used LIDAR extinction by desert dust (identified from its depolarization ratio) to improve dust storms with a 4D-var scheme.…”

Section: Introductionmentioning

confidence: 99%

Applying an ensemble Kalman filter to the assimilation of AERONET observations in a global aerosol transport model

et al. 2010

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Abstract. We present a global aerosol assimilation system based on an Ensemble Kalman filter, which we believe leads to a significant improvement in aerosol fields. The ensemble allows realistic, spatially and temporally variable model covariances (unlike other assimilation schemes). As the analyzed variables are mixing ratios (prognostic variables of the aerosol transport model), there is no need for the extra assumptions required by previous assimilation schemes analyzing aerosol optical thickness (AOT).We describe the implementation of this assimilation system and in particular the construction of the ensemble. This ensemble should represent our estimate of current model uncertainties. Consequently, we construct the ensemble around randomly modified emission scenarios.The system is tested with AERONET observations of AOT and Angström exponent (AE). Particular care is taken in prescribing the observational errors. The assimilated fields (AOT and AE) are validated through independent AERONET, SKYNET and MODIS Aqua observations. We show that, in general, assimilation of AOT observations leads to improved modelling of global AOT, while assimilation of AE only improves modelling when the AOT is high.

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“…In a paper by Hu et al (2008), the detailed methodology to retrieve the dust intensity from the Chinese geostationary FY-2C satellite is given with validations from the ground network observation data. Niu et al (2007) developed the dust assimilation module in CUACE/Dust by combining the satellite and surface network data to form a coherent data set with a method proposed to treat the dust data under clouds where a satellite is unable to detect SDS.…”

Section: Cuace/dust Structurementioning

confidence: 99%

CUACE/Dust – an integrated system of observation and modeling systems for operational dust forecasting in Asia

2008

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Abstract. An integrated sand and dust storm (SDS) forecasting system -CUACE/Dust (Chinese Unified Atmospheric Chemistry Environment for Dust) has been developed, which consists of a comprehensive dust aerosol module with emission, dry/wet depositions and other atmospheric dynamic processes, and a data assimilation system (DAS) using observational data from the CMA (China Meteorological Administration) ground dust monitoring network and retrieved dust information from a Chinese geostationary satellite -FY-2C. This is the first time that a combination of surface network observations and satellite retrievals of the dust aerosol has been successfully used in the real time operational forecasts in East Asia through a DAS. During its application for the operational SDS forecasts in East Asia for spring 2006, this system captured the major 31 SDS episodes observed by both surface and satellite observations. Analysis shows that the seasonal mean threat score (TS) for 0-24 h forecast over the East Asia in spring 2006 increased from 0.22 to 0.31 by using the DAS, a 41% enhancement. The time series of the forecasted dust concentrations for a number of representative stations for the whole spring 2006 were also evaluated against the surface PM 10 monitoring data, showing a very good agreement in terms of the SDS timing and magnitudes near source regions where dust aerosols dominate. This is a summary paper for a special issue of ACP featuring the development and results of the forecasting system.

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Data assimilation of dust aerosol observations for CUACE/Dust forecasting system

Cited by 32 publications

References 19 publications

Surface observation of sand and dust storm in East Asia and its application in CUACE/Dust

Surface observation of sand and dust storm in East Asia and its application in CUACE/Dust

Applying an ensemble Kalman filter to the assimilation of AERONET observations in a global aerosol transport model

CUACE/Dust – an integrated system of observation and modeling systems for operational dust forecasting in Asia

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