Search Results

You are looking at 1 - 4 of 4 items for :

  • Author or Editor: Lin Liu x
  • Journal of Atmospheric and Oceanic Technology x
  • All content x
Clear All Modify Search
Ming Li, Jiping Liu, Zhenzhan Wang, Hui Wang, Zhanhai Zhang, Lin Zhang, and Qinghua Yang

Abstract

Reanalysis projects and satellite data analysis have provided surface wind over the global ocean. To assess how well one can reconstruct the variations of surface wind in the data-sparse Southern Ocean, sea surface wind speed data from 1) the National Centers for Environmental Prediction–Department of Energy reanalysis (NCEP–DOE), 2) the European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Re-Analysis (ERA-Interim), 3) National Climate Data Center (NCDC) blended sea winds, and 4) cross-calibrated multiplatform (CCMP) ocean surface velocity are evaluated. First, the accuracy of sea surface wind speed is validated with quality-controlled in situ measurements from research vessels. The results show that the CCMP value is closer to the ship observations than other products, whereas the NCEP–DOE value has the largest systematic positive bias. All four products show large positive biases under weak wind regimes, good agreement with the ship observations under moderate wind regimes, and large negative biases under high wind regimes. Second, the consistency and discrepancy of sea surface wind speed across different products is examined. The intercomparisons suggest that these products show encouraging agreement in the spatial distribution of the annual-mean sea surface wind speed. The largest across-data scatter is found in the central Indian sector of the Antarctic Circumpolar Current, which is comparable to its respective interannual variability. The monthly-mean correlations between pairs of products are high. However, differing from the decadal trends of NCEP–DOE, NCDC, and CCMP that show an increase of sea surface wind speed in the Antarctic Circumpolar region, ERA-Interim has an opposite sign there.

Full access
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.

Restricted access
Paul E. Ciesielski, Wen-Ming Chang, Shao-Chin Huang, Richard H. Johnson, Ben Jong-Dao Jou, Wen-Chau Lee, Po-Hsiung Lin, Ching-Hwang Liu, and Junhong Wang

Abstract

During the Terrain-Influenced Monsoon Rainfall Experiment (TiMREX), which coincided with Taiwan’s Southwesterly Monsoon Experiment—2008 (SoWMEX-08), the upper-air sounding network over the Taiwan region was enhanced by increasing the radiosonde (“sonde”) frequency at its operational sites and by adding several additional sites (three that were land based and two that were ship based) and aircraft dropsondes. During the special observing period of TiMREX (from 15 May to 25 June 2008), 2330 radiosonde observations were successfully taken from the enhanced network. Part of the challenge of processing the data from the 13 upsonde sites is that four different sonde types (Vaisala RS80, Vaisala RS92, Meisei, and Graw) were used. Post–field phase analyses of the sonde data revealed a significant dry bias in many of the sondes—in particular, in the data from the Vaisala RS80 sondes that were used at four sites. In addition, contamination of the sonde data by the ship’s structure resulted in poor-quality low-level thermodynamic data at a key oceanic site. This article examines the methods used to quality control the sonde data and, when possible, to correct them. Particular attention is given to the correction of the humidity field and its impact on various convective measures. Comparison of the corrected sonde humidity data with independent estimates shows good agreement, suggesting that the corrections were effective in removing many of the sonde humidity errors. Examining various measures of convection shows that use of the humidity-corrected sondes gives a much different perspective on the characteristics of convection during TiMREX. For example, at the RS80 sites, use of the corrected humidity data increases the mean CAPE by ∼500 J kg−1, decreases mean convective inhibition (CIN) by 80 J kg−1, and increases the midlevel convective mass flux by greater than 70%. Ultimately, these corrections will provide more accurate moisture fields for diagnostic analyses and modeling studies.

Full access
Yongxiang Hu, David Winker, Mark Vaughan, Bing Lin, Ali Omar, Charles Trepte, David Flittner, Ping Yang, Shaima L. Nasiri, Bryan Baum, Robert Holz, Wenbo Sun, Zhaoyan Liu, Zhien Wang, Stuart Young, Knut Stamnes, Jianping Huang, and Ralph Kuehn

Abstract

The current cloud thermodynamic phase discrimination by Cloud-Aerosol Lidar Pathfinder Satellite Observations (CALIPSO) is based on the depolarization of backscattered light measured by its lidar [Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP)]. It assumes that backscattered light from ice crystals is depolarizing, whereas water clouds, being spherical, result in minimal depolarization. However, because of the relationship between the CALIOP field of view (FOV) and the large distance between the satellite and clouds and because of the frequent presence of oriented ice crystals, there is often a weak correlation between measured depolarization and phase, which thereby creates significant uncertainties in the current CALIOP phase retrieval. For water clouds, the CALIOP-measured depolarization can be large because of multiple scattering, whereas horizontally oriented ice particles depolarize only weakly and behave similarly to water clouds. Because of the nonunique depolarization–cloud phase relationship, more constraints are necessary to uniquely determine cloud phase. Based on theoretical and modeling studies, an improved cloud phase determination algorithm has been developed. Instead of depending primarily on layer-integrated depolarization ratios, this algorithm differentiates cloud phases by using the spatial correlation of layer-integrated attenuated backscatter and layer-integrated particulate depolarization ratio. This approach includes a two-step process: 1) use of a simple two-dimensional threshold method to provide a preliminary identification of ice clouds containing randomly oriented particles, ice clouds with horizontally oriented particles, and possible water clouds and 2) application of a spatial coherence analysis technique to separate water clouds from ice clouds containing horizontally oriented ice particles. Other information, such as temperature, color ratio, and vertical variation of depolarization ratio, is also considered. The algorithm works well for both the 0.3° and 3° off-nadir lidar pointing geometry. When the lidar is pointed at 0.3° off nadir, half of the opaque ice clouds and about one-third of all ice clouds have a significant lidar backscatter contribution from specular reflections from horizontally oriented particles. At 3° off nadir, the lidar backscatter signals for roughly 30% of opaque ice clouds and 20% of all observed ice clouds are contaminated by horizontally oriented crystals.

Full access