A technical way to auto-plot meteorological factors real time distribution map via GrADS

2013 ◽  
pp. 173-177
Author(s):  
J Zhang ◽  
Z Rao ◽  
Q Xie
Jurnal MIPA ◽  
2018 ◽  
Vol 7 (1) ◽  
pp. 16
Author(s):  
Michelle Jenneth Mailoor ◽  
Guntur Pasau ◽  
Maria D. Bobanto

Telah dilakukan penelitian untuk memetakan distribusi petir untuk wilayah Manado berdasarkan data petir tahun 2013 dan 2014. Data real time sambaran petir dari rekaman lightning detector diolah menggunakan beberapa program, yaitu Lightning 2000, Golden Software Surfer 8, Lightning Data Processing, GIS 10.3, Google Earth dan Microsoft Excel. Pada program GIS 10.3 data yang didapatkan kemudian dipetakan menggunakan metode Kriging. Hasil yang diperoleh dalam penelitian ini berupa peta kontur distribusi petir di wilayah Kota Manado. Berdasarkan hasil dari pengolahan data, diperoleh data yang menunjukkan bahwa kejadian petir tertinggi terdapat pada bulan Oktober 2013 yaitu sebanyak 6.540 kejadian dan bulan Mei 2014 yaitu sebanyak 7.330 kejadian petir. Distribusi petir CG+ tertinggi terdapat pada kecamatan Wenang dan tidak ada kejadian petir CG+ di 4 kecamatan yaitu Kecamatan Tikala, Paal Dua, Singkil dan TumintingResearch has been done to make a distribution map for Manado area based on lightning data of year 2013 and 2014. The real time data of lightning strikes from lightning detector processed by using a few program that is Lightning 2000, Golden Software Surfer 8, Lightning Data Processing, GIS 10.3, Google Earth and Microsoft Excel. Data that we got from GIS 10.3 use for mapping with Kriging method. Output from this research is contour map in Manado city area. Based on output from processed data, we got data that the highest lightning event happened in October 2013 that is 6.540 event and in May 2014 that is 7.330 lightning event. Highest CG+ lightning distribution located in Wenang Districts and there is no CG+ lightning event in 4 districts which is Tikala, Paal Dua, Singkil and Tuminting Districts


Author(s):  
Meiping Yun ◽  
Wenwen Qin

Despite the wide application of floating car data (FCD) in urban link travel time estimation, limited efforts have been made to determine the minimum sample size of floating cars appropriate to the requirements for travel time distribution (TTD) estimation. This study develops a framework for seeking the required minimum number of travel time observations generated from FCD for urban link TTD estimation. The basic idea is to test how, with a decreasing the number of observations, the similarities between the distribution of estimated travel time from observations and those from the ground-truth vary. These are measured by employing the Hellinger Distance (HD) and Kolmogorov-Smirnov (KS) tests. Finally, the minimum sample size is determined by the HD value, ensuring that corresponding distribution passes the KS test. The proposed method is validated with the sources of FCD and Radio Frequency Identification Data (RFID) collected from an urban arterial in Nanjing, China. The results indicate that: (1) the average travel times derived from FCD give good estimation accuracy for real-time application; (2) the minimum required sample size range changes with the extent of time-varying fluctuations in traffic flows; (3) the minimum sample size determination is sensitive to whether observations are aggregated near each peak in the multistate distribution; (4) sparse and incomplete observations from FCD in most time periods cannot be used to achieve the minimum sample size. Moreover, this would produce a significant deviation from the ground-truth distributions. Finally, FCD is strongly recommended for better TTD estimation incorporating both historical trends and real-time observations.


2016 ◽  
Vol 13 (10) ◽  
pp. 6967-6973 ◽  
Author(s):  
Yongming He ◽  
Lei He ◽  
Yuan Wang ◽  
Yu Xiao ◽  
Yingwu Chen ◽  
...  

During the observations made by imaging satellites, meteorological factors are likely to change frequently. The vagaries of weather conditions and significant effects on the actual observation results mean that there is an urgent need to apply more intelligence to satellite mission planning. Thus, this paper describes an autonomous replanning method for imaging satellites that considers the real-time weather conditions. Considering the characteristics of different input data, this method replans the low-yield task set and fine-tunes others to improve profitability. Moreover, the proposed method can heuristically select the appropriate adjustment rule to achieve autonomous satellite mission planning. A series of simulations with various task quantities and in different environments shows that the proposed method can respond effectively to real-time weather changes, and can steadily improve the total profits in a variety of weather conditions during Earth observation activities.


2008 ◽  
Vol 59 (3) ◽  
pp. 476-485 ◽  
Author(s):  
A. B. Moradi ◽  
S. E. Oswald ◽  
J. A. Massner ◽  
K. P. Pruessmann ◽  
B. H. Robinson ◽  
...  

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