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  • Author or Editor: Wei Yan x
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Shensen Hu, Shuo Ma, Wei Yan, Neil P. Hindley, Kai Xu, and Jun Jiang

Abstract

Atmospheric gravity waves are a kind of mesoscale disturbance, commonly found in the atmospheric system, that plays a key role in a series of mesospheric dynamic processes. When propagating to the upper atmosphere, the gravity waves will disturb the local temperature and density, and then modulate the intensity of the surrounding airglow radiation. As a result, the presence of gravity waves on a moonless night can usually cause the airglow to reveal ripple features in low-light images. In this paper we have applied a two-dimensional Stockwell transform technique (2DST) to airglow measurements from nighttime low-light images of the day–night band on the Suomi National Polar-Orbiting Partnership. To our knowledge this study is the first to measure localized mesospheric gravity wave brightness amplitudes, horizontal wavelengths, and propagation directions using such a method and data. We find that the method can characterize the general shape and amplitude of concentric gravity wave patterns, capturing the dominant features and directions with a good degree of accuracy. The key strength of our 2DST application is that our approach could be tuned and then automated in the future to process tens of thousands of low-light images, globally characterizing gravity wave parameters in this historically poorly studied layer of the atmosphere.

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Shuo Ma, Wei Yan, Yunxian Huang, Jun Jiang, Shensen Hu, and Yingqiang Wang

Abstract

Many quantitative uses of the nighttime imagery provided by low-light sensors, such as the day–night band (DNB) on board the Suomi–National Polar-Orbiting Partnership (SNPP), have emerged recently. Owing to the low nighttime radiance, low-light calibration at night must be investigated in detail. Traditional vicarious calibration methods are based on some targets with nearly invariant surface properties under lunar illumination. However, the relatively stable light emissions may also be used to realize the radiometric calibration under low light. This paper presents a low-light calibration method based on bridge lights, and Visible Infrared Imaging Radiometer Suite (VIIRS) DNB data are used to assess the proposed method. A comparison of DNB high-gain-stage (HGS) radiances over a 2-yr period from August 2012 to July 2014 demonstrates that the predictions are consistent with the observations, and the agreement between the predictions and the observations is on the order of −2.9% with an uncertainty of 9.3% (1σ) for the Hangzhou Bay Bridge and −3.9% with an uncertainty of 7.2% (1σ) for the Donghai Bridge. Such a calibration method based on stable light emissions has a wide application prospect for the calibration of low-light sensors at night.

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Lingsheng Meng, Wei Zhuang, Weiwei Zhang, Angela Ditri, and Xiao-Hai Yan

Abstract

Sea level changes within wide temporal–spatial scales have great influence on oceanic and atmospheric circulations. Efforts have been made to identify long-term sea level trend and regional sea level variations on different time scales. A nonuniform sea level rise in the tropical Pacific and the strengthening of the easterly trade winds from 1993 to 2012 have been widely reported. It is well documented that sea level in the tropical Pacific is associated with the typical climate modes. However, sea level change on interannual and decadal time scales still requires more research. In this study, the Pacific sea level anomaly (SLA) was decomposed into interannual and decadal time scales via an ensemble empirical mode decomposition (EEMD) method. The temporal–spatial features of the SLA variability in the Pacific were examined and were closely associated with climate variability modes. Moreover, decadal SLA oscillations in the Pacific Ocean were identified during 1993–2016, with the phase reversals around 2000, 2004, and 2012. In the tropical Pacific, large sea level variations in the western and central basin were a result of changes in the equatorial wind stress. Moreover, coherent decadal changes could also be seen in wind stress, sea surface temperature (SST), subtropical cells (STCs), and thermocline depth. Our work provided a new way to illustrate the interannual and decadal sea level variations in the Pacific Ocean and suggested a coupled atmosphere–ocean variability on a decadal time scale in the tropical region with two cycles from 1993 to 2016.

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Guomei Wei, Zhigang He, Yanshuang Xie, Shaoping Shang, Hao Dai, Jingyu Wu, Ke Liu, Rui Lin, Yan Wan, Hang Lin, Jinrui Chen, and Yan Li

Abstract

Two Ocean State Monitoring and Analyzing Radar (OSMAR071) (7.8 MHz) high-frequency (HF) radars and four moored ADCPs were operated concurrently in the southwestern Taiwan Strait during January–March 2013. Qualitative and quantitative comparisons of surface currents were conducted between the HF radars and the ADCPs. Except for a location probably affected by shallow water and sand waves on the Taiwan Banks, the HF-radar-derived radial currents (radials) showed good agreement with the ADCP measured results (correlation coefficient: 0.89–0.98; rms difference: 0.07–0.13 m s−1). To provide further insight into the geophysical processes involved, the performance of the HF-radar-derived radials was further evaluated under different sea states (sea states: 2–6). It was found that both the data returns of the radar-derived radials and the differences between the radar-derived radials and the ADCP-derived radials varied with sea state. The HF radar performed best at sea state 4 in terms of data returns. The spatial coverage increased rapidly as the waves increased from sea state 2 to 4. However, it decreased slowly from sea state 4 to 6. Second, the radial differences were relatively high under lower sea states (2 and 3) at the location where the best agreement was obtained between the radar and ADCP radials, whereas the differences increased as the sea states increased at the other three locations. The differences between the radials measured by the HF radars and the ADCPs could be attributed to wave-induced Stokes drift and spatial sampling differences.

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Linghui Cai, Shaoping Shang, Guomei Wei, Zhigang He, Yanshuang Xie, Ke Liu, Tao Zhou, Jinquan Chen, Feng Zhang, and Yan Li

Abstract

Dual high-frequency (HF) radar systems are often used to provide measurements of waves, winds, and currents. In this study, the accuracy of wave measurements using a single HF radar system (OS081H-A) was explored using datasets obtained during 5–27 January 2014 in the southwestern Taiwan Strait. We selected the study region as an area with >90% coverage (i.e., the range was <100 km). Qualitative and quantitative intercomparison of wave measurements (by the radar and five buoys) and wave model products [from the Simulating Wave Nearshore (SWAN) model] were conducted. Intercomparison of the modeled and in situ significant wave height Hs showed that the model-predicted Hs could be considered to be acceptable for use as “sea truth” to evaluate the radar-derived Hs, with mean bias from −0.45 to −0.16 m, mean absolute error (MAE) of 0.24–0.45 m, and root-mean-square error of 0.31–0.54 m. It was found that the MAE of radar-derived Hs was ≤ 1 m for 86% of the sector (except at the edge of sector) when the model-predicted Hs was ≥ 1.5 m. In particular, the MAE was less than 0.6 m for 63% of the sector, which was mainly distributed in the area with a bearing from −50° to +70° and a range of 20–70 km. The results are promising, but more work is needed. We employed a spatial distribution function for the MAE of the radar-derived Hs over the sample duration based on range, bearing, and mean radar-derived Hs.

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Zepei Wu, Shuo Liu, Delong Zhao, Ling Yang, Zixin Xu, Zhipeng Yang, Dantong Liu, Tao Liu, Yan Ding, Wei Zhou, Hui He, Mengyu Huang, Ruijie Li, and Deping Ding

Abstract

Cloud particles have different shapes in the atmosphere. Research on cloud particle shapes plays an important role in analyzing the growth of ice crystals and the cloud microphysics. To achieve an accurate and efficient classification algorithm on ice crystal images, this study uses image-based morphological processing and principal component analysis, to extract features of images and apply intelligent classification algorithms for the Cloud Particle Imager (CPI). Currently, there are mainly two types of ice-crystal classification methods: one is the mode parameterization scheme, and the other is the artificial intelligence model. Combined with data feature extraction, the dataset was tested on ten types of classifiers, and the highest average accuracy was 99.07%. The fastest processing speed of the real-time data processing test was 2,000 images/s. In actual application, the algorithm should consider the processing speed, because the images are in the order of millions. Therefore, a support vector machine (SVM) classifier was used in this study. The SVM-based optimization algorithm can classify ice crystals into nine classes with an average accuracy of 95%, blurred frame accuracy of 100%, with a processing speed of 2,000 images/s. This method has a relatively high accuracy and faster classification processing speed than the classic neural network model. The new method could be also applied in physical parameter analysis of cloud microphysics.

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