Meteorological Observatory of Jiangsu Province


Meteorological Observatory of Jiangsu Province

Time filter
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Yang T.,Meteorological Observatory of Jiangsu Province | Lu Z.,Nanjing University of Information Science and Technology
2011 International Conference on Information Science and Technology, ICIST 2011 | Year: 2011

The Kalman filter algorithm can not solve the problem of the time-frequency localization, and it often switches between the low-frequency filter and high-frequency filter by the mobile detector (The changeable dimension filter algorithm) or Markovian transfer probability matrix (VD algorithm), so it has delay and some influence of the mobile detector and Markovian transfer probability matrix. Hence, the filter error of the Kalman filter algorithms is obviously big. This paper brings the new bybrid filter algorithm (WK algorithm) of the 2D Wavelet transform and Kalman Filter, which has the characteristics of the well time-frequency localization and real time. The WK algorithm is used to process the radar information and makes the filter estimated data to approach the true track. © 2011 IEEE.

Zhou Y.-K.,Nanjing University of Information Science and Technology | Zhu B.,Nanjing University of Information Science and Technology | Han Z.-W.,CAS Institute of Atmospheric Physics | Pan C.,Nanjing University of Information Science and Technology | And 3 more authors.
Zhongguo Huanjing Kexue/China Environmental Science | Year: 2016

Based on the meteorological data from 28 observation stations in winter 2013 and 2014, NCEP FNL reanalysis data and ground PM2.5 observations, the characteristic of visibility and its relationship with air pollutants and meteorological conditions in the Yangtze River Delta in winter were analyzed. In winter 2013, the frequency of haze day was 53.4%. 81.6% of the visibility change can be explained by PM2.5 concentration, 10m wind speed, wind shear (500~850 hPa), relative humidity, temperature difference (925~1000 hPa), potential pseudo-equivalent temperature difference (850~925 hPa). The effects of meteorological conditions and air pollutants on visibility were comparable, and the contribution of thermal factor was almost twice that of dynamical factor. The RH impact on visibility was stronger at lower PM2.5 concentration and higher RH (>70%). The visibility in winter 2014 was well reproduced by the nonlinear regression equation. © 2016, Chinese Society for Environmental Sciences. All right reserved.

Liu Z.,Nanjing University | Zhou Y.,Nanjing University | Ju W.,Nanjing University | Gao P.,Meteorological Observatory of Jiangsu Province
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | Year: 2011

Soil water content (SWC) is an important factor which affects the growth of crops and also a valuable parameter required for scientific agricultural water management. Therefore, simulation and prediction of SWC are of significance for agriculture. The aim of the paper was to validate the ability of the mechanistic ecosystem model BEPS to simulate SWC of farm lands in areas with a monsoon climate and to investigate the major factors causing uncertainties in simulated SWC. Simulated SWC was compared with measurements in the growing seasons of winter wheat during 2000 to 2004 at Xuzhou agrometeorological station, Jiangsu province. The results showed that BEPS model was in general able to capture the seasonal and interannual variations of SWC, with R2 in the range from 0.1339 to 0.9225, root mean square error and mean absolute error in the range from 0.026113 to 0.06317 and 0.0232 to 0.0525, respectively. Simulated SWC was sensitive to saturated hydraulic conductivity (K) and the parameter b. The reliability and sensitivity of the simulated results depended on the condition of SWC and prepipitation. Simulated SWC was underestimated by the model and its sensitivity to K and b increased during the period with continuously sparse precipitation and low SWC.

Ju W.,Nanjing University | Gao P.,Meteorological Observatory of Jiangsu Province | Zhou Y.,Nanjing University | Chen J.M.,University of Toronto | And 2 more authors.
International Journal of Remote Sensing | Year: 2010

Yield prediction is important for agricultural management, food security warning and food trade policy. Remote sensing has been a useful tool for predicting crop yields. In this study, a modified daily process-based ecosystem model (the Boreal Ecosystem Productivity Simulator) is employed in conjunction with land cover and leaf area index (LAI) products from MODIS to predict summer grain crop yields in the northern area of the Yangtze River in the Jiangsu Province, China. The model was driven by soil texture, land cover, daily meteorological and MODIS LAI data for 2004-2006. Simulated growing season net primary productivity (NPP) of summer grain crops (November-May) and census data of crop yields in 2004 were used to derive the county-level harvest index, which is then used in conjunction with simulated NPP to predict crop yields in 2005 and 2006. The model captures 89 % and 88 % of variations in crop yields at county-level compared with census data in 2005 and 2006, respectively. The root mean square errors are 265 and 277 kg ha-1 in these two years. The results show the usefulness of a process-based model driven by remote sensing in predicting crop yields. In such predictions, the considerable spatial variability of the harvest index should be taken into consideration. © 2010 Taylor & Francis.

Ju W.,Nanjing University | Gao P.,Meteorological Observatory of Jiangsu Province | Wang J.,Nanjing University | Zhou Y.,Nanjng University | Zhang X.,Meteorological Observatory of Jiangsu Province
Agricultural Water Management | Year: 2010

Soil water is an important factor affecting photosynthesis, transpiration, growth, and yield of crops. Accurate information on soil water content (SWC) is crucial for practical agricultural water management at various scales. In this study, remotely sensed parameters (leaf area index, land cover type, and albedo) and spatial data manipulated using the geographic information system (GIS) technique were assimilated into the boreal ecosystem productivity simulator (BEPS) model to monitor SWC dynamics of croplands in Jiangsu Province, China. The monsoon climate here is characterized by large interannual and seasonal variability of rainfall causing periods of high and low SWC. Model validation was conducted by comparing simulated SWC with measurements by a gravimetric method in the years 2005 and 2006 at nine agro-meteorological stations. The model-to-measurement R2 values ranged from 0.40 to 0.82. Nash-Sutcliffe efficiency values were in the range from 0.10 to 0.80. Root mean square error (RMSE) values ranged from 0.028 to 0.056 m3 m-3. Simulated evapotranspiration (ET) was consistent with ET estimated from pan evaporation measurements. The BEPS model successfully tracked the dynamics and extent of the serious soil water deficit that occurred during September-November 2006. These results demonstrate the applicability of combining process-based models with remote sensing and GIS techniques in monitoring SWC of croplands and improving agricultural water management at regional scales in a monsoon climate. © 2009 Elsevier B.V. All rights reserved.

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