The latitude-energy dispersion of precipitating ions inferred from the Intercosmos-Bulgaria-1300 satellite data

1989 ◽  
Vol 9 (12) ◽  
pp. 187-192
Author(s):  
V.V. Maryin ◽  
L.V. Tverskaya ◽  
M.V. Telsov ◽  
S.I. Shkolnikova
2006 ◽  
Vol 63 (5) ◽  
pp. 1377-1389 ◽  
Author(s):  
Tim Li ◽  
Bing Fu

Abstract The structure and evolution characteristics of Rossby wave trains induced by tropical cyclone (TC) energy dispersion are revealed based on the Quick Scatterometer (QuikSCAT) and Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) data. Among 34 cyclogenesis cases analyzed in the western North Pacific during 2000–01 typhoon seasons, six cases are associated with the Rossby wave energy dispersion of a preexisting TC. The wave trains are oriented in a northwest–southeast direction, with alternating cyclonic and anticyclonic vorticity circulation. A typical wavelength of the wave train is about 2500 km. The TC genesis is observed in the cyclonic circulation region of the wave train, possibly through a scale contraction process. The satellite data analyses reveal that not all TCs have a Rossby wave train in their wakes. The occurrence of the Rossby wave train depends to a certain extent on the TC intensity and the background flow. Whether or not a Rossby wave train can finally lead to cyclogenesis depends on large-scale dynamic and thermodynamic conditions related to both the change of the seasonal mean state and the phase of the tropical intraseasonal oscillation. Stronger low-level convergence and cyclonic vorticity, weaker vertical shear, and greater midtropospheric moisture are among the favorable large-scale conditions. The rebuilding process of a conditional unstable stratification is important in regulating the frequency of TC genesis.


2011 ◽  
Vol 4 (1) ◽  
pp. 500-502
Author(s):  
Md. Fazlul Haque ◽  
◽  
Md. Mostafizur Rahman Akhand ◽  
Dr. Dewan Abdul Quadir

2007 ◽  
Vol 13 (1s) ◽  
pp. 80-85
Author(s):  
E.B. Kudashev ◽  
◽  
A.N. Filonov ◽  

2020 ◽  
Vol 17 (11) ◽  
pp. 219-230
Author(s):  
Yan Zhu ◽  
Min Sheng ◽  
Jiandong Li ◽  
Di Zhou

Author(s):  
Rupali Dhal ◽  
D. P. Satapathy

The dynamic aspects of the reservoir which are water spread, suspended sediment distribution and concentration requires regular and periodical mapping and monitoring. Sedimentation in a reservoir affects the capacity of the reservoir by affecting both life and dead storages. The life of a reservoir depends on the rate of siltation. The various aspects and behavior of the reservoir sedimentation, like the process of sedimentation in the reservoir, sources of sediments, measures to check the sediment and limitations of space technology have been discussed in this report. Multi satellite remote sensing data provide information on elevation contours in the form of water spread area. Any reduction in reservoir water spread area at a specified elevation corresponding to the date of satellite data is an indication of sediment deposition. Thus the quality of sediment load that is settled down over a period of time can be determined by evaluating the change in the aerial spread of the reservoir at various elevations. Salandi reservoir project work was completed in 1982 and the same is taken as the year of first impounding. The original gross and live storages capacities were 565 MCM& 556.50 MCM respectively. In SRS CWC (2009), they found that live storage capacity of the Salandi reservoir is 518.61 MCM witnessing a loss of 37.89 MCM (i.e. 6.81%) in a period of 27 years.The data obtained through satellite enables us to study the aspects on various scales and at different stages. This report comprises of the use of satellite to obtain data for the years 2009-2013 through remote sensing in the sedimentation study of Salandi reservoir. After analysis of the satellite data in the present study(2017), it is found that live capacity of the reservoir of the Salandi reservoir in 2017 is 524.19MCM witnessing a loss of 32.31 MCM (i.e. 5.80%)in a period of 35 years. This accounts for live capacity loss of 0.16 % per annum since 1982. The trap efficiencies of this reservoir evaluated by using Brown’s, Brune’s and Gill’s methods are 94.03%, 98.01and 99.94% respectively. Thus, the average trap efficiency of the Salandi Reservoir is obtained as 97.32%.


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