Anhui Meteorological Information Center

Hefei, China

Anhui Meteorological Information Center

Hefei, China
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Fu Y.,Hefei University of Technology | Fu Y.,Anhui Institute of Meteorological science | Pan X.,Hefei University of Technology | Yang Y.,Hefei University of Technology | And 5 more authors.
Journal of Meteorological Research | Year: 2017

Precipitation is an important indicator of climate change and a critical process in the hydrological cycle, on both the global and regional scales. Methods of precipitation observation and associated analyses are of strategic importance in global climate change research. As the first space-based radar, the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) has been in operation for almost 17 years and has acquired a huge amount of cloud and precipitation data that provide a distinctive view to help expose the nature of cloud and precipitation in the tropics and subtropics. In this paper we review recent advances in summer East Asian precipitation climatology studies based on long-term TRMM PR measurements in the following three aspects: (1) the three-dimensional structure of precipitation, (2) the diurnal variation of precipitation, and (3) the recent precipitation trend. Additionally, some important prospects regarding satellite remote sensing of precipitation and its application in the near future are discussed. © 2017, The Chinese Meteorological Society and Springer-Verlag Berlin Heidelberg.


Wang G.,Anhui Meteorological Information Center | Liu X.,Anhui Meteorological Information Center | Qiu K.,Anhui Meteorological Information Center | Wen H.,Anhui Meteorological Information Center
Yaogan Xuebao/Journal of Remote Sensing | Year: 2017

This study adopts the Infrared Atmospheric Sounder of Feng-Yun and the 3rd(B) Weather Satellite(FY-3B/IRAS) brightness temperature to investigate the generalized variational assimilation, which combines the advantages of classical variational assimilation and robust M-estimators. Classical variational assimilation is based on the model variables and satellite observations of the brightness temperature to yield a quadratic functional minimization. The observational errors are needed to follow a Gaussian distribution and subsequently apply the leastsquare principle. The least-squares method is sensitive to outliers; if the analyzed data contain gross errors, the parameter estimation will be inaccurate. The classical variational assimilation consists of two stages. First, an appropriate algorithm is used to identify and address outliers in the data and then, the assimilation. This approach may result in the loss of useful data because the outliers are not always harmful; some outliers may represent new information, such as weather phenomena. At present, the quality control is generally based on a certain threshold value if the subjective uncertainty is too strong. If outliers persist after the quality control, the optimal parametric results that are obtained through classical variational assimilation become meaningless. M-estimators are added to the framework of classical variational assimilation to obtain a generalized variational assimilation, which is coupled with quality control in the process of assimilation. The main idea is to use the weight factor of M-estimators to re-estimate the contribution rate of the observation items to the objective function in each process of objective function minimization based on classical variational assimilation. The cost function consists of M-estimators to guarantee the robustness to outliers. Thus, the assimilation results are improved. Humidity is an important dynamic variable in the NWP model. It does not only determine the occurrence of precipitation but also changes the temperature through evaporation and condensation, and it also influences the wind field by changing the pressure gradient. In addition, the nonlinearity of humidity is stronger than temperature, which causes the humidity to follow a stronger non-Gaussian distribution. Thus, humidity was used as an assimilation experiment effect validation, and the correlation coefficient of humidity was compared with FNL and GDAS, which are assimilated by different M-estimator weights. The specific operation process that is based on the FNL as the background field adopts classical and different weight factors of M-estimators to the variational assimilation of FY-3B/IRAS. In addition, the correlation between the analyzed field, and the GDAS is compared. The correlation between 13 GPS/PWV stations of Anhui province and the integral humidity profile of the relevant field from both GDAS/PWV and FNL/PWV is evaluated. Furthermore, based on the information entropy of freedom degrees, the contribution of IRAS 20 channels were determined to analyze the field for nearly a month. Preliminary results demonstrate the potential application value of the generalized variational assimilation. © 2017, Science Press. All right reserved.


Feng Y.,Nanjing University | Feng Y.,Anhui Institute of Meteorological science | He B.-F.,Anhui Institute of Meteorological science | He B.-F.,Anhui Key Laboratory of Atmospheric Science and Satellite Remote Sensing | And 5 more authors.
Chinese Journal of Ecology | Year: 2012

In order to understand the responses of different types of vegetation in Anhui Provinc of East China to climate elements, this paper analyzed the recent ten years spatiotemporal varia tion patterns of the vegetations and their correlations with air temperature and precipitation, base' on the monthly Normalized Difference Vegetation Index (NDVI) data from Moderate Resolutio Imaging Spectroradiometer (MODIS), the daily temperature/precipitation data from 80 meteoro logical stations, and the land cover data in 2000-2009. The results showed that in recent te years, the vegetation index of different land cover types in the Province had different chang trends. The vegetation index increased significantly in crop planting area and cities, but had les change in other areas. Forestland had the highest average vegetation index, followed by cro planting area, and then, urban area. The monthly variation of the vegetation index presented double-peak pattern in crop planting area, but a single-peak pattern for other land cover types The monthly average NDVI in the Province had a significant positive linear correlation with the monthly mean temperature, and a positive nonlinear correlation with the monthly total precipitation. A threshold value of precipitation existed in its effect on NDVI. There was a weak positive correlation between the NDVI and the inter-annual change of temperature/precipitation. The partial correlation coefficient between vegetation index and air temperature was the maximum in for-estland and the minimum in crop planting area, and was larger in natural vegetation area than in artificial vegetation area. The partial correlation coefficient between vegetation index and precipitation was contrary. In the majority area of middle Huaibei plain and northern Jianghuai (non-irrigable land), the vegetation was co-driven by air temperature and precipitation; in some minority middle Huaibei plain grids and water grids, the vegetation was solely driven by precipitation: and in the other areas, the vegetation was solely driven by air temperature, except in some water grids, it was driven by non-climate factors.


Wang C.,Nanjing University of Information Science and Technology | Shi H.,Nanjing University of Information Science and Technology | Shi H.,CAS Institute of Atmospheric Physics | Shi H.,University of Chinese Academy of Sciences | And 4 more authors.
Particuology | Year: 2015

Boundary-layer height (BLH) under clear, altostratus and low stratus cloud conditions were measured by GPS sounding, wind profiler radar, and micro-pulse lidar during the atmospheric radiation measurement experiment from Sep. to Dec. 2008 in Shouxian, Anhui, China. Results showed that during daytime or nighttime, regardless of cloud conditions, the GPS sounding was the most accurate method for measuring BLH. Unfortunately, because of the long time gap between launchings, sounding data did not capture the diurnal evolution of the BLH. Thus, wind profile radar emerged as a promising instrument for direct and continuous measurement of the mixing height during the daytime, accurately determining BLH using the structure parameter of the electromagnetic refractive index. However, during nighttime, radar was limited by weak signal extraction and did not work well for determining the BLH of the stable boundary layer, often recording the BLH of the residual layer. While micro-pulse lidar recorded the evolution of BLH, it overestimated the BLH of the stable boundary layer. This method also failed to work under cloudy conditions because of the influence of water vapor. Future work needs to develop a method to determine BLH that combines the complimentary features of all three algorithms. © 2015 Chinese Society of Particuology and Institute of Process Engineering, Chinese Academy of Sciences.


Zhang H.,Hefei University of Technology | Zhang H.,Anhui Institute of Environment Science | Liu G.,Hefei University of Technology | Mei J.,Tongling Environment Observatory | And 2 more authors.
Journal of University of Science and Technology of China | Year: 2014

Using the monitoring data of atmospheric pollution and meteorological data from 2007 to 2010, the concentration diurnal variations of atmospheric pollutants (including SO2, NO2 and PM10) in Tongling City were analyzed, and the relationship between the diurnal variations of atmospheric pollutants and meteorological factors was studied. The results show that the diurnal variations of atmospheric pollutant concentrations and meteorological factors can be classified into two types, "single pink and single valley type" and "double pinks and double valleys type". There is no spatial variation in SO2 and PM10 , while NO2 exhibited obvious spatial variation. For areas near the pollution source, pollutant concentration is correlated with atmospheric pressure, relative humidity and temperature, but not with wind speed. For the observation points free from pollutants, the relationship of pollution concentration variation and meteorological factors is similar to the area near the pollution source, and the pollution concentration variation is reversely correlated with wind speed. The other observation points of Tongling City, which are not included in the two, showed little relationship between pollutant concentration variation and meteorological factors.

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