Many information processing challenges are difficult to solve with traditional Turing or von Neumann approaches. Implementing unconventional computational methods is therefore essential and optics provides promising opportunities. Here we experimentally demonstrate optical information processing using a nonlinear optoelectronic oscillator subject to delayed feedback. We implement a neuro-inspired concept, called Reservoir Computing, proven to possess universal computational capabilities. We particularly exploit the transient response of a complex dynamical system to an input data stream. We employ spoken digit recognition and time series prediction tasks as benchmarks, achieving competitive processing figures of merit.

Biases in climate model simulations introduce biases in subsequent impact simulations. Therefore, bias correction methods are operationally used to post-process regional climate projections. However, many problems have been identified, and some researchers question the very basis of the approach. Here we demonstrate that a typical cross-validation is unable to identify improper use of bias correction. Several examples show the limited ability of bias correction to correct and to downscale variability, and demonstrate that bias correction can cause implausible climate change signals. Bias correction cannot overcome major model errors, and naive application might result in ill-informed adaptation decisions. We conclude with a list of recommendations and suggestions for future research to reduce, post-process, and cope with climate model biases

Library of Congress Catologing-in-Publication Data Castillo. Enrique. Expert systems and probabilistic network models I Enrique Castillo, Jose Manuel Gutierrez, Ali S. Hadi. p. cm.-(Monographs in computer science) Includes bibliographical references and index.

ABSTRACT:In this paper, we present a new publicly available high-resolution daily precipitation gridded dataset developed for peninsular Spain and the Balearic islands using 2756 quality-controlled stations (this dataset is referred to as Spain02 ). The grid has a regular 0.2°(approx. 20 km) horizontal resolution and spans the period from 1950 to 2003. Different interpolation methods were tested using a cross-validation approach to compare the resulting interpolated values against station data: kriging, angular distance weighting, and thin plane splines. Finally, the grid was produced applying the kriging method in a two-step process. First, the occurrence was interpolated using a binary kriging and, in a second step, the amounts were interpolated by applying ordinary kriging to the occurrence outcomes. This procedure is similar to the interpolation method used to generate the E-OBS gridded data -the state-of-the-art publicly available high-resolution daily dataset for Europe -which was used in this study for comparison purposes. Climatological statistics and extreme value indicators from the resulting grid were compared to those from the 25 km E-OBS dataset using the observed station records as a reference. Spain02 faithfully reproduces climatological features such as annual precipitation occurrence, accumulated amounts and variability, whereas E-OBS has some deficiencies in the southern region. When focusing on upper percentiles and other indicators of extreme precipitation regimes, Spain02 accurately reproduces the amount and spatial distribution of the observed extreme indicators, whereas E-OBS data present serious limitations over Spain due to the sparse data used in this region. As extreme values are more sensitive to interpolation, the dense station coverage of this new data set was crucial to get an accurate reproduction of the extremes.