Titlebook: Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. II); Seon Ki Park,Liang Xu Book 2013 Springer-Verlag Berlin H

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Observation Influence Diagnostic of a Data Assimilation System,s due to the assimilated observations, and the complementary 82 % is the influence of the prior (background) information, a short-range forecast containing information from earlier assimilated observations. About 20 % of the observational information is currently provided by surface-based observing

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HIKE

Background Error Correlation Modeling with Diffusion Operators,l and requires appropriate renormalization by rescaling. The exact computation of the rescaling factors (diagonal elements of .) is a computationally expensive procedure, therefore an efficient numerical approximation is needed. Under the assumption of local homogeneity of ., a heuristic method for

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accordance

Entropic Balance Theory and Radar Observation for Prospective Tornado Data Assimilation,ty, the vertical in-phase superimposition between upper and lower mesocyclones, and sudden transition from supercell, mesocyclones totornado. In the application, new variables DZ (temporal difference of radar reflectivity) and DZ. (temporal difference of differential reflectivity) are introduced to

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Peak-Bone-Mass

Arthropathy

Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. II)978-3-642-35088-7