Climate-informed stochastic hydrological modeling: Incorporating decadal-scale variability using paleo data

A hierarchical framework for incorporating modes of climate variability into stochastic simulations of hydrological data is developed, termed the climate-informed multi-time scale stochastic (CIMSS) framework. A case study on two catchments in eastern Australia illustrates this framework. To develop an identifiable model characterizing long-term variability for the first level of the hierarchy, paleoclimate proxies, and instrumental indices describing the Interdecadal Pacific Oscillation (IPO) and the Pacific Decadal Oscillation (PDO) are analyzed. A new paleo IPO-PDO time series dating back 440 yr is produced, combining seven IPO-PDO paleo sources using an objective smoothing procedure to fit low-pass filters to individual records. The paleo data analysis indicates that wet/dry IPO-PDO states have a broad range of run lengths, with 90% between 3 and 33 yr and a mean of 15 yr. The Markov chain model, previously used to simulate oscillating wet/dry climate states, is found to underestimate the probability of wet/dry periods >5 yr, and is rejected in favor of a gamma distribution for simulating the run lengths of the wet/dry IPO-PDO states. For the second level of the hierarchy, a seasonal rainfall model is conditioned on the simulated IPO-PDO state. The model is able to replicate observed statistics such as seasonal and multiyear accumulated rainfall distributions and interannual autocorrelations. Mean seasonal rainfall in the IPO-PDO dry states is found to be 15%-28% lower than the wet state at the case study sites. In comparison, an annual lag-one autoregressive model is unable to adequately capture the observed rainfall distribution within separate IPO-PDO states. Copyright © 2011 by the American Geophysical Union.Benjamin J. Henley, Mark A. Thyer, George Kuczera and Stewart W. Frank

Climate-informed stochastic hydrological modeling: Incorporating decadal-scale variability using paleo data

A hierarchical framework for incorporating modes of climate variability into stochastic simulations of hydrological data is developed, termed the climate-informed multi-time scale stochastic (CIMSS) framework. A case study on two catchments in eastern Australia illustrates this framework. To develop an identifiable model characterizing long-term variability for the first level of the hierarchy, paleoclimate proxies, and instrumental indices describing the Interdecadal Pacific Oscillation (IPO) and the Pacific Decadal Oscillation (PDO) are analyzed. A new paleo IPO-PDO time series dating back 440 yr is produced, combining seven IPO-PDO paleo sources using an objective smoothing procedure to fit low-pass filters to individual records. The paleo data analysis indicates that wet/dry IPO-PDO states have a broad range of run lengths, with 90% between 3 and 33 yr and a mean of 15 yr. The Markov chain model, previously used to simulate oscillating wet/dry climate states, is found to underestimate the probability of wet/dry periods >5 yr, and is rejected in favor of a gamma distribution for simulating the run lengths of the wet/dry IPO-PDO states. For the second level of the hierarchy, a seasonal rainfall model is conditioned on the simulated IPO-PDO state. The model is able to replicate observed statistics such as seasonal and multiyear accumulated rainfall distributions and interannual autocorrelations. Mean seasonal rainfall in the IPO-PDO dry states is found to be 15%-28% lower than the wet state at the case study sites. In comparison, an annual lag-one autoregressive model is unable to adequately capture the observed rainfall distribution within separate IPO-PDO states. Copyright © 2011 by the American Geophysical Union.Benjamin J. Henley, Mark A. Thyer, George Kuczera and Stewart W. Frank

Wind Power Forecasting Methods Based on Deep Learning: A Survey

Accurate wind power forecasting in wind farm can effectively reduce the enormous impact on grid operation safety when high permeability intermittent power supply is connected to the power grid. Aiming to provide reference strategies for relevant researchers as well as practical applications, this paper attempts to provide the literature investigation and methods analysis of deep learning, enforcement learning and transfer learning in wind speed and wind power forecasting modeling. Usually, wind speed and wind power forecasting around a wind farm requires the calculation of the next moment of the definite state, which is usually achieved based on the state of the atmosphere that encompasses nearby atmospheric pressure, temperature, roughness, and obstacles. As an effective method of high-dimensional feature extraction, deep neural network can theoretically deal with arbitrary nonlinear transformation through proper structural design, such as adding noise to outputs, evolutionary learning used to optimize hidden layer weights, optimize the objective function so as to save information that can improve the output accuracy while filter out the irrelevant or less affected information for forecasting. The establishment of high-precision wind speed and wind power forecasting models is always a challenge due to the randomness, instantaneity and seasonal characteristics

Data reduction methods for single-mode optical interferometry - Application to the VLTI two-telescopes beam combiner VINCI

The interferometric data processing methods that we describe in this paper use a number of innovative techniques. In particular, the implementation of the wavelet transform allows us to obtain a good immunity of the fringe processing to false detections and large amplitude perturbations by the atmospheric piston effect, through a careful, automated selection of the interferograms. To demonstrate the data reduction procedure, we describe the processing and calibration of a sample of stellar data from the VINCI beam combiner. Starting from the raw data, we derive the angular diameter of the dwarf star Alpha Cen A. Although these methods have been developed specifically for VINCI, they are easily applicable to other single-mode beam combiners, and to spectrally dispersed fringes.Comment: Accepted for publication in Astronomy & Astrophysics, 17 pages, 19 figure

Is MS1054-03 an exceptional cluster? A new investigation of ROSAT/HRI X-ray data

We reanalyzed the ROSAT/HRI observation of MS1054-03, optimizing the channel HRI selection and including a new exposure of 68 ksec. From a wavelet analysis of the HRI image we identify the main cluster component and find evidence for substructure in the west, which might either be a group of galaxies falling onto the cluster or a foreground source. Our 1-D and 2-D analysis of the data show that the cluster can be fitted well by a classical betamodel centered only 20arcsec away from the central cD galaxy. The core radius and beta values derived from the spherical model(beta = 0.96_-0.22^+0.48) and the elliptical model (beta = 0.73+/-0.18) are consistent. We derived the gas mass and total mass of the cluster from the betamodel fit and the previously published ASCA temperature (12.3^{+3.1}_{-2.2} keV). The gas mass fraction at the virial radius is fgas = (14[-3,+2.5]+/-3)% for Omega_0=1, where the errors in brackets come from the uncertainty on the temperature and the remaining errors from the HRI imaging data. The gas mass fraction computed for the best fit ASCA temperature is significantly lower than found for nearby hot clusters, fgas=20.1pm 1.6%. This local value can be matched if the actual virial temperature of MS1054-032 were close to the lower ASCA limit (~10keV) with an even lower value of 8 keV giving the best agreement. Such a bias between the virial and measured temperature could be due to the presence of shock waves in the intracluster medium stemming from recent mergers. Another possibility, that reconciles a high temperature with the local gas mass fraction, is the existence of a non zero cosmological constant.Comment: 12 pages, 5 figures, accepted for publication in Ap

Detecting Features in the Dark Energy Equation of State: A Wavelet Approach

We study the utility of wavelets for detecting the redshift evolution of the dark energy equation of state w(z) from the combination of supernovae, CMB and BAO data. We show that local features in w, such as bumps, can be detected efficiently using wavelets. To demonstrate, we first generate a mock supernovae (SNe) data sample for a SNAP-like survey with a bump feature in w(z) hidden in, then successfully discover it by performing a blind wavelet analysis. We also apply our method to analyze the recently released "Constitution" SNe data, combined with WMAP and BAO from SDSS, and find weak hints of dark energy dynamics. Namely, we find that models with w(z) < -1 for 0.2 < z < 0.5, and w(z)> -1 for 0.5 < z <1, are mildly favored at 95% confidence level. This is in good agreement with several recent studies using other methods, such as redshift binning with principal component analysis (PCA) (e.g. Zhao and Zhang, arXiv:0908.1568)Comment: 8 pages, 6 figures. Minor changes from v1, matches the version published in JCAP

Generative Adversarial Networks Selection Approach for Extremely Imbalanced Fault Diagnosis of Reciprocating Machinery

At present, countless approaches to fault diagnosis in reciprocating machines have been proposed, all considering that the available machinery dataset is in equal proportions for all conditions. However, when the application is closer to reality, the problem of data imbalance is increasingly evident. In this paper, we propose a method for the creation of diagnoses that consider an extreme imbalance in the available data. Our approach first processes the vibration signals of the machine using a wavelet packet transform-based feature-extraction stage. Then, improved generative models are obtained with a dissimilarity-based model selection to artificially balance the dataset. Finally, a Random Forest classifier is created to address the diagnostic task. This methodology provides a considerable improvement with 99% of data imbalance over other approaches reported in the literature, showing performance similar to that obtained with a balanced set of data.National Natural Science Foundation of China, under Grant 51605406National Natural Science Foundation of China under Grant 7180104