The inability to predict precipitation extremes under nonstationary climate remains a crucial science gap. Precipitation is not a state-variable within climate models, exhibits space-time heterogeneities, and is subject to thresholds and intermittences. Atmospheric variables in the spatiotemporal neighborhood, like temperature, humidity and updraft velocity, are often better predicted than prec...

Downscaling global weather prediction model outputs to individual locations or local scales is a common practice for operational weather forecast in order to correct the model outputs at subgrid scales. This paper presents an empirical-statistical downscaling method for precipitation prediction which uses a feed-forward multilayer perceptron MLP neural network. The MLP architecture was optimize...

The nature of predictability is examined in a numerical model relevant to the midlatitude atmosphere and oceans. The approach followed is novel and uses new theoretical tools from information theory, namely entropy functionals, as measures of information content and their application to finite ensembles. Particular attention is paid here to the practical application of these methods to the prob...

One of the most effective ways to minimize the area and capacitance overhead caused by through-silicon vias (TSVs) in 3D ICs is to reduce the size of TSVs themselves. Today, the diameter of the smallest TSV available is around 1μm, and it is expected to reach sub-micron dimensions in a few years. This downscaling of TSVs requires research on the impact of nanoscale TSVs on the quality of 3D IC ...

The area-to-point kriging method (ATPK) is an important technology of downscaling without auxiliary information in remote sensing. However, it uses a constant semivariogram to downscale geospatial variables, which ignores the spatial heterogeneity between objects. To deal with this kind heterogeneity, study proposes fuzzy object-based ATPK method, mainly consists three steps: extraction objects...