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Comparisons of Two Cloud-Detection Schemes for Infrared Radiance Observations 被引量:1

Comparisons of Two Cloud-Detection Schemes for Infrared Radiance Observations
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摘要 The cloud-detection procedure developed by McNally and Watts(MW03) was added to the Weather Research and Forecasting Data Assimilation System. To provide some guidelines for setting up cloud-detection schemes, this study compares the MW03 scheme to the Multivariate and Minimum Residual(MMR) scheme for both simulated and real Advanced Infrared Sounder(AIRS) radiances. Results show that there is a high level of consistency between the results from simulated and real AIRS data. As expected, both cloud-detection schemes perform well in finding the cloud-contaminated channels based on the channels' peak levels. The clouddetection results from MW03 are sensitive to the prescribed brightness temperature innovation threshold and brightness temperature gradient threshold. When increasing the brightness temperature innovation threshold for MW03 to roughly eight times the default threshold, the two cloud-detection schemes produce consistent data rejection distributions overall for high channels. MMR generally retains more data for long-wave channels. For both cloud-detection schemes, there is a high level of consistency between the cloud-free pixels and the visible/near-IR(Vis/NIR) cloud mask. The cloud-detection procedure developed by McNally and Watts (MW03) was added to the Weather Research and Forecasting Data Assimilation System. To provide some guidelines for setting up cloud-detection schemes, this study compares the MW03 scheme to the Multivariate and Minimum Residual (MMR) scheme for both simulated and real Advanced Infrared Sounder (AIRS) radiances. Results show that there is a high level of consistency between the results from simulated and real AIRS data. As expected, both cloud-detection schemes perform well in finding the cloud-contaminated channels based on the channels' peak levels. The cloud- detection results from MW03 are sensitive to the prescribed brightness temperature innovation threshold and brightness temperature gradient threshold. When increasing the brightness temperature innovation threshold for MW03 to roughly eight times the default threshold, the two cloud-detection schemes produce consistent data rejection distributions overall for high channels. MMR generally retains more data for long-wave channels. For both cloud-detection schemes, there is a high level of consistency between the cloud-free pixels and the visible/near-IR (Vis/NIR) cloud mask.
出处 《Atmospheric and Oceanic Science Letters》 CSCD 2014年第4期358-363,共6页 大气和海洋科学快报(英文版)
基金 sponsored by the National Basic Research Program of China (973 Program, 2013CB430102) the Program of Scientific Innovation Research of College Graduate in Jiangsu Province (Grant Nos. CXZZ12-0490 and CXZZ11-0606) The National Center for Atmospheric Research is sponsored by the National Science Foundation
关键词 AIRS WRF data assimilation system cloud detection brightness temperature departure 红外探测器 辐射观测 云检测 近红外光谱 梯度阈值 亮度温度 资料同化 检测过程
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  • 1Ackerman, S. A., K. I. Strabala, W. R Menzel, et al., 1998: Discriminating clear sky from clouds with MODIS, J. Geophys. Res., 103, 32141-32157.
  • 2Aulignr, T., 2007: Variational Assimilation of Infrared Hyperspectral Sounders Data: Bias Correction and Cloud Detection, PhD's thesis, University Paul Sabatier, 222pp.
  • 3Aulignr, T., 2014a: Multivariate minimum residual method for cloud retrieval. Part I: Theoretical aspects and simulated observation experiments, Mon. Wea. Rev., in press.
  • 4Auligne, T., 2014b: Multivariate minimum residual method for cloud retrieval. Part II: Real observations experiments, Mon. Wea. Rev., in press, doi:http://dx.doi.org/10.1175/MWR-D-13- 00173.1.
  • 5Aumann, H. H., M. T. Chahine, C. Gautier, et al., 2003: AIRS/ AMSU/HSB on the Aqua mission: Design, science objectives, data products, and processing systems, IEEE Trans. Geosei. Remote Sensing, 41,253-264.
  • 6Barker, D. M., W. Huang, Y.-R. Guo, et al., 2004: A three-dimensional variational data assimilation system for MM5: Implemen- tation and initial results, Mon. Wea. Rev., 132, 897-914.
  • 7Barker, D. M., X.-Y Huang, Z. Liu, et al., 2012: The weather research and forecasting (WRF) model's community variational/ensemble data assimilation system, WRFDA, Bull. Amer. Meteor. Soe., 93, 831-843.
  • 8Collard, A. D., and A. E, McNally, 2009: The assimilation of infra-red atmospheric sounding interferometer radiances at ECMWF, Quart. J. Roy. Meteor Soc., 135, 1044-1058.
  • 9Eyre, J. R., and W. R Menzel, 1989: Retrieval of cloud parameters from satellite sounder data: A simulation study, J. Appl. Meteor, 28, 267-275.
  • 10Gilbert, J. C., and C. Lemarrchal, 1989: Some numerial experiments with variable-storage quasi-Newton algorithms, Math. Progr, 45, 407-435.

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