scholarly journals WRF model parameterization around the highland Titicaca Lake

2021 ◽  
Author(s):  
Rober Mamani ◽  
Patrick Hendrick
Keyword(s):  
Wrf Model ◽  
Model Parameterization

scholarly journals Downscaling of seasonal ensemble forecasts to the convection‐permitting scale over the Horn of Africa using the WRF model

2020 ◽  
Author(s):  
Paolo Mori ◽  
Thomas Schwitalla ◽  
Markos Budusa Ware ◽  
Kirsten Warrach‐Sagi ◽  
Volker Wulfmeyer
Keyword(s):  
Wrf Model ◽  
Horn Of Africa ◽  
Ensemble Forecasts

Effects of incomplete parameterization on full-wavefield viscoelastic seismic data for petrophysical reservoir properties

Geophysics ◽  
2007 ◽  
Vol 72 (3) ◽  
pp. O9-O17 ◽  
Author(s):  
Upendra K. Tiwari ◽  
George A. McMechan
Keyword(s):  
Seismic Data ◽  
Input Data ◽  
Elastic Model ◽  
Quality Factors ◽  
Noise Levels ◽  
Reservoir Properties ◽  
Estimated Parameters ◽  
Model Parameterization ◽  
The Earth ◽  
Incomplete Model

In inversion of viscoelastic full-wavefield seismic data, the choice of model parameterization influences the uncertainties and biases in estimating seismic and petrophysical parameters. Using an incomplete model parameterization results in solutions in which the effects of missing parameters are attributed erroneously to the parameters that are included. Incompleteness in this context means assuming the earth is elastic rather than viscoelastic. The inclusion of compressional and shear-wave quality factors [Formula: see text] and [Formula: see text] in inversion gives better estimates of reservoir properties than the less complete (elastic) model parameterization. [Formula: see text] and [Formula: see text] are sensitive primarily to fluid types and saturations. The parameter correlations are sensitive also to the model parameterization. As noise increases in the viscoelastic input data, the resolution of the estimated parameters decreases, but the parameter correlations are relatively unaffected by modest noise levels.

Random-forest based adjusting method for wind forecast of WRF model

2021 ◽  
pp. 104842
Author(s):  
Anxi Wang ◽  
Libing Xu ◽  
Yi Li ◽  
Jianyong Xing ◽  
Xingrong Chen ◽  
...  
Keyword(s):  
Random Forest ◽  
Wrf Model
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