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Meng F.,University of Maryland University College | Meng F.,Shandong Jianzhu University | Cao C.Y.,College Park | Shao X.,University of Maryland University College | Shi Y.G.,Shandong Meteorological Bureau
International Journal of Remote Sensing | Year: 2014

Spatial and temporal variations in regional aerosol optical thickness (AOT) at 550 nm over Shandong Province, China, based on one year’s Visible Infrared Imaging Radiometer Suite (VIIRS) data were investigated. The regional forest background (FB) annual mean AOT was 0.467 with a standard deviation of 0.339, which was much higher than the North America background continental AOT level of 0.10. Higher AOT values for the study region were mainly found in the spring and summer, especially from May to August, while the lowest mean values were seen in November and December. Urban areas all have obviously higher mean AOT values than those of the rural areas resulting from intense anthropogenic sources. Given that FB AOT represents the natural background level, we estimated that anthropogenic emissions and secondary aerosol generation contribute approximately 0.352 to aerosol loading in this region. Additionally, strong regional imbalance of AOT was found to be distributed over the study area. The maximum annual average AOT values occurred in inland cities, while coastal cities usually had lower AOT values. © 2014, © 2014 Taylor & Francis. Source


Sun Z.,Qingdao Meteorological Bureau | Yang Y.-Q.,Qingdao Meteorological Bureau | Xu X.-L.,Qingdao Meteorological Bureau | Sheng C.-Y.,Shandong Meteorological Bureau | And 2 more authors.
Huanjing Kexue/Environmental Science | Year: 2010

Based on weather data and data obtained by Particle Sizer GRIMM180 set up in Qingdao, aerosol was qualitively classified into sea-fog aerosol, refreshing aerosol and suspended dust aerosol. Analysis of mass concentration and number concentration of three different kinds of aerosols was conducted, and the results are shown as below: (1) total mass concentration of different kinds of aerosol is obviously different; (2) sea-fog aerosol primarily includes particles of which size ranging from 1 μm to 2.5 μm, and refreshing aerosol, includes particles of which size less than 1 μm, and suspended dust aerosol, includes particles of which size ranging from 2.5 μm to 10 μm. (3) precipitation has important role on decreasing larger particle concentration and increasing tiny particle concentration. (4) those tiny particles of which size is less than 1 μm, especially less than 0.6 μm, show an activation phenomenon when they located before the surface weather systems, where air humidity is considerable high. (5) trend of number concentration variability of different particles has different characteristic modes when aerosol property has been changed. Source


Liu H.,Chengdu University of Information Technology | Xu L.,Chengdu University of Information Technology | Fang Y.,Shandong Meteorological Bureau | Zhang Y.,Shandong Meteorological Bureau | And 3 more authors.
IASP 10 - 2010 International Conference on Image Analysis and Signal Processing | Year: 2010

A neural network-based algorithm for retrieving precipltable water vapor (PWV) using the Advanced Very High Resolution Radiometer (AVHRR) data is proposed. The neural network (NN) model is combined with the radiative transfer calculations using MODTRAN 4.0 with the latest global assimilated data. The selected NN is a multilayer feed-forward neural network. The input variables are the top-of-atmosphere (TOA) brightness temperatures in AVHRR channels 4 and 5 and the sea surface temperature (SST), and the output variable is the PWV. In order to evaluate the performance of the trained neural network, simulations are carried out for the mid-latitude summer (MS) model atmosphere, with a RMSE of 0.33 g/cm2. Furthermore, the new approach is validated with the AVHRR data of the Pacific Ocean. The water vapor contents derived from AVHRR image are compared with that derived by radiosonde data, with a difference of 0.12 g/cm2. The advantages of the proposed algorithm are discussed briefly. The preliminary results show that the new algorithm is able to provide an accurate estimation of PWV from AVHRR data. © 2010 IEEE. Source


Du Y.,Chengdu University of Information Technology | Xu L.,Chengdu University of Information Technology | Fang Y.,Shandong Meteorological Bureau | Zhang Y.,Shandong Meteorological Bureau | And 3 more authors.
2010 2nd IITA International Conference on Geoscience and Remote Sensing, IITA-GRS 2010 | Year: 2010

Some retrieval methods for estimating the atmospheric precipitable water (PW) were reviewed in the study. Then, an improved physically algorithm for the retrieval of PW over cloud-free land surfaces based on the split-window covariance-variance ratio technique (SWCVR) was proposed. The thermal infrared split window channels at 11 and 12 urn of the Advanced Very High Resolution Radiometer(AVHRR) instrument were used. The improved method for estimating the PW is simplified, which is based on a linear relationship between the PW and the ratio of the two split-window channel transmittances (τ/tau;11) The new PW retrieval algorithm was tested with the comparisons from both AVHRR and radiosonde observations in some places of South China. The preliminary results show that the algorithm proposed for estimating PW is acceptable since a RMSE of 0.44 cm is obtained. © 2010 IEEE. Source


Li X.,Chengdu University of Information Technology | Xu L.,Chengdu University of Information Technology | Fang Y.,Shandong Meteorological Bureau | Zhang Y.,Shandong Meteorological Bureau | And 3 more authors.
2010 2nd IITA International Conference on Geoscience and Remote Sensing, IITA-GRS 2010 | Year: 2010

Information about the amount and spatial structure of atmospheric water vapor is essential in understanding weather forecasting and climate change. This paper mainly discuss the principles and methods to deduce the atmospheric Precipitable Water Vapor (PWV) from ground-based GPS observations by using a high accuracy GPS processing software package-GAMIT/GLOBK version 10.34. A GPS-network including 6 ground-based GPS receiving stations in China was employed to study the estimations of PWV, which were compared with the corresponding PWV data measured by the radiosondes. The RMSE of the GPS-PWV derived by our calculations is 4.3 mm. The results of the present study show that there is a good coherence between the GPS-PWV and the Radiosonde-PWV, indicating that the GPS algorithm for deriving PWV is effective. © 2010 IEEE. Source

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