Water Vapor Motion Signal Extraction from FY-2E Longwave Infrared Window Images for Cloud-free Regions: The Temporal Difference Technique

YANG Lu; WANG Zhenhui; CHU Yanli; ZHAO Hang; TANG Min

doi:10.1007/s00376-014-3165-9

Abstract:

The aim of this study is to calculate the low-level atmospheric motion vectors (AMVs) in clear areas with FY-2E IR2 window (11.59-12.79 m) channel imagery, where the traditional cloud motion wind technique fails. A new tracer selection procedure, which we call the temporal difference technique, is demonstrated in this paper. This technique makes it possible to infer low-level wind by tracking features in the moisture pattern that appear as brightness temperature (TB) differences between consecutive sequences of 30-min-interval FY-2E IR2 images over cloud-free regions. The TB difference corresponding to a 10% change in water vapor density is computed with the Moderate Resolution Atmospheric Transmission (MODTRAN4) radiative transfer model. The total contribution from each of the 10 layers is analyzed under four typical atmospheric conditions: tropical, midlatitude summer, U.S. standard, and midlatitude winter. The peak level of the water vapor weighting function for the four typical atmospheres is assigned as a specific height to the TB wind. This technique is valid over cloud-free ocean areas. The proposed algorithm exhibits encouraging statistical results in terms of vector difference (VD), speed bias (BIAS), mean vector difference (MVD), standard deviation (SD), and root-mean-square error (RMSE), when compared with the wind field of NCEP reanalysis data and rawinsonde observations.

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Water Vapor Motion Signal Extraction from FY-2E Longwave Infrared Window Images for Cloud-free Regions: The Temporal Difference Technique

Corresponding author: YANG Lu, yanglu_19@sina.com;

1. Beijing Meteorological Office (Da Xing), Beijing 102600;

2. Collaborative Innovation Center on the Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044;

3. School of Atmospheric Physics, Nanjing University of Information Science & Technology, Nanjing 210044;

4. Institute of Urban Meteorology, China Meteorological Administration, Beijing 100089

Manuscript received: 2013-09-05

Manuscript revised: 2014-04-05

Fund Project: This work was jointly supported by the National Natural Science Foundation of China (Grant Nos. 41175035 and 41005005), the National Basic Research Program of China (Grant No. 2009CB421502), and a project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). The authors are thankful to the National Satellite Meteorological Center of China for providing the FY-2E IR images. The authors also thank the National Centers for Environmental Prediction for providing the NCEP reanalysis datasets.

Abstract: The aim of this study is to calculate the low-level atmospheric motion vectors (AMVs) in clear areas with FY-2E IR2 window (11.59-12.79 m) channel imagery, where the traditional cloud motion wind technique fails. A new tracer selection procedure, which we call the temporal difference technique, is demonstrated in this paper. This technique makes it possible to infer low-level wind by tracking features in the moisture pattern that appear as brightness temperature (TB) differences between consecutive sequences of 30-min-interval FY-2E IR2 images over cloud-free regions. The TB difference corresponding to a 10% change in water vapor density is computed with the Moderate Resolution Atmospheric Transmission (MODTRAN4) radiative transfer model. The total contribution from each of the 10 layers is analyzed under four typical atmospheric conditions: tropical, midlatitude summer, U.S. standard, and midlatitude winter. The peak level of the water vapor weighting function for the four typical atmospheres is assigned as a specific height to the TB wind. This technique is valid over cloud-free ocean areas. The proposed algorithm exhibits encouraging statistical results in terms of vector difference (VD), speed bias (BIAS), mean vector difference (MVD), standard deviation (SD), and root-mean-square error (RMSE), when compared with the wind field of NCEP reanalysis data and rawinsonde observations.

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Water Vapor Motion Signal Extraction from FY-2E Longwave Infrared Window Images for Cloud-free Regions: The Temporal Difference Technique

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