scholarly journals Towards realistic simulations of QED cascades: Non-ideal laser and electron seeding effects

2018 ◽  
Vol 25 (8) ◽  
pp. 083104 ◽  
Author(s):  
Archana Sampath ◽  
Matteo Tamburini
Keyword(s):  
Seeding Effects

P3-064: CROSS-SEEDING EFFECTS OF AMYLOID B-PROTEIN AND A-SYNUCLEIN

2014 ◽  
Vol 10 ◽  
pp. P650-P651
Author(s):  
Kenjiro Ono ◽  
Ryoichi Takahashi ◽  
Tokuhei Ikeda ◽  
Masahito Yamada
Keyword(s):  
Seeding Effects

Synthesis of MgAl2O4 spinel: seeding effects on formation temperature

1991 ◽  
Vol 26 (14) ◽  
pp. 3797-3802 ◽  
Author(s):  
J. -F. Pasquier ◽  
S. Komarneni ◽  
R. Roy
Keyword(s):  
Formation Temperature ◽  
Mgal2o4 Spinel ◽  
Seeding Effects

Numerical study of the cloud seeding effects

1990 ◽  
Vol 42 (2) ◽  
pp. 145-164 ◽  
Author(s):  
M. Ćurić ◽  
D. Janc
Keyword(s):  
Numerical Study ◽  
Cloud Seeding ◽  
Seeding Effects

Implementation of a Silver Iodide Cloud-Seeding Parameterization in WRF. Part II: 3D Simulations of Actual Seeding Events and Sensitivity Tests

2013 ◽  
Vol 52 (6) ◽  
pp. 1458-1476 ◽  
Author(s):  
Lulin Xue ◽  
Sarah A. Tessendorf ◽  
Eric Nelson ◽  
Roy Rasmussen ◽  
Daniel Breed ◽  
...  
Keyword(s):  
Silver Iodide ◽  
Winter Season ◽  
Wrf Model ◽  
Reanalysis Data ◽  
Atmospheric Conditions ◽  
Cloud Seeding ◽  
The North ◽  
Cloud Properties ◽  
Regional Reanalysis ◽  
Seeding Effects

AbstractFour cloud-seeding cases over southern Idaho during the 2010/11 winter season have been simulated by the Weather Research and Forecasting (WRF) model using the coupled silver iodide (AgI) cloud-seeding scheme that was described in Part I. The seeding effects of both ground-based and airborne seeding as well as the impacts of model physics, seeding rates, location, timing, and cloud properties on seeding effects have been investigated. The results were compared with those from Part I and showed the following: 1) For the four cases tested in this study, control simulations driven by the Real-Time Four Dimensional Data Assimilation (RTFDDA) WRF forecast data generated more realistic atmospheric conditions and precipitation patterns than those driven by the North America Regional Reanalysis data. Sensitivity experiments therefore used the RTFDDA data. 2) Glaciogenic cloud seeding increased orographic precipitation by less than 1% over the simulation domain, including the Snake River basin, and by up to 5% over the target areas. The local values of the relative precipitation enhancement by seeding were ~20%. Most of the enhancement came from vapor depletion. 3) The seeding effect was inversely related to the natural precipitation efficiency but was positively related to seeding rates. 4) Airborne seeding is generally more efficient than ground-based seeding in terms of targeting, but its efficiency depends on local meteorological conditions. 5) The normalized seeding effects ranged from 0.4 to 1.6 under various conditions for a certain seeding event.

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