How significant is the impact of irrigation on the local hydroclimate in Californias Central Valley? Comparison of model results with ground and remote-sensing data

The effect of irrigation on regional climate has been studied over the years. However, in most studies, the model was usually set at coarse resolution, and the soil moisture was set to field capacity at each time step. We reinvestigated this issue over the Central Valley of California's agricultural area by: (1) using the regional climate model at different resolutions down to the finest resolution of 4 km for the most inner domain, covering California's Central Valley, the central coast, the Sierra Nevada Mountains, and water; (2) using a more realistic irrigation scheme in the model through the use of different allowable soil water depletion configurations; and (3) evaluating the simulated results against satellite and in situ observations available through the California Irrigation Management Information System (CIMIS). The simulation results with fine model resolution and with the more realistic irrigation scheme indicate that the surface meteorological fields are noticeably improved when compared with observations from the CIMIS network and Moderate Resolution Imaging Spectroradiometer data. Our results also indicate that irrigation has significant impacts on local meteorological fields by decreasing temperature by 3°-7°C and increasing relative humidity by 9-20%, depending on model resolutions and allowable soil water depletion configurations. More significantly, our results using the improved model show that the effects of irrigation on weather and climate do not extend very far into nonirrigated regions. Copyright 2011 by the American Geophysical Union

Digital image correlation (DIC) analysis of the 3 December 2013 Montescaglioso landslide (Basilicata, Southern Italy). Results from a multi-dataset investigation

Image correlation remote sensing monitoring techniques are becoming key tools for providing effective qualitative and quantitative information suitable for natural hazard assessments, specifically for landslide investigation and monitoring. In recent years, these techniques have been successfully integrated and shown to be complementary and competitive with more standard remote sensing techniques, such as satellite or terrestrial Synthetic Aperture Radar interferometry. The objective of this article is to apply the proposed in-depth calibration and validation analysis, referred to as the Digital Image Correlation technique, to measure landslide displacement. The availability of a multi-dataset for the 3 December 2013 Montescaglioso landslide, characterized by different types of imagery, such as LANDSAT 8 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor), high-resolution airborne optical orthophotos, Digital Terrain Models and COSMO-SkyMed Synthetic Aperture Radar, allows for the retrieval of the actual landslide displacement field at values ranging from a few meters (2–3 m in the north-eastern sector of the landslide) to 20–21 m (local peaks on the central body of the landslide). Furthermore, comprehensive sensitivity analyses and statistics-based processing approaches are used to identify the role of the background noise that affects the whole dataset. This noise has a directly proportional relationship to the different geometric and temporal resolutions of the processed imagery. Moreover, the accuracy of the environmental-instrumental background noise evaluation allowed the actual displacement measurements to be correctly calibrated and validated, thereby leading to a better definition of the threshold values of the maximum Digital Image Correlation sub-pixel accuracy and reliability (ranging from 1/10 to 8/10 pixel) for each processed dataset

Predictions for the heavy-ion programme at the Large Hadron Collider

I review the main predictions for the heavy-ion programme at the Large Hadron Collider (LHC) at CERN, as available in early April 2009. I begin by remembering the standard claims made in view of the experimental data measured at the Super Proton Synchrotron (SPS) at CERN and at the Relativistic Heavy Ion Collider (RHIC) at the BNL. These claims will be used for later discussion of the new opportunities at the LHC. Next I review the generic, qualitative expectations for the LHC. Then I turn to quantitative predictions: First I analyze observables which characterize directly the medium produced in the collisions - bulk observables or soft probes -: multiplicities, collective flow, hadrochemistry at low transverse momentum, correlations and fluctuations. Second, I move to calibrated probes of the medium i.e. typically those whose expectation in the absence of any medium can be described in Quantum Chromodynamics (QCD) using perturbative techniques (pQCD), usually called hard probes. I discuss particle production at large transverse momentum and jets, heavy-quark and quarkonium production, and photons and dileptons. Finally, after a brief review of pA collisions, I end with a summary and a discussion about the potentiality of the measurements at the LHC - particularly those made during the first run - to further substantiate or, on the contrary, disproof the picture of the medium that has arisen from the confrontation between the SPS and RHIC data, and theoretical models.Comment: 64 pages, 40 figures, 7 tables; invited review for "Quark-Gluon Plasma 4"; v2: small changes, some predictions and references added, final versio

A high resolution coupled hydrologic–hydraulic model (HiResFlood-UCI) for flash flood modeling

HiResFlood-UCI was developed by coupling the NWS's hydrologic model (HL-RDHM) with the hydraulic model (BreZo) for flash flood modeling at decameter resolutions. The coupled model uses HL-RDHM as a rainfall-runoff generator and replaces the routing scheme of HL-RDHM with the 2D hydraulic model (BreZo) in order to predict localized flood depths and velocities. A semi-automated technique of unstructured mesh generation was developed to cluster an adequate density of computational cells along river channels such that numerical errors are negligible compared with other sources of error, while ensuring that computational costs of the hydraulic model are kept to a bare minimum. HiResFlood-UCI was implemented for a watershed (ELDO2) in the DMIP2 experiment domain in Oklahoma. Using synthetic precipitation input, the model was tested for various components including HL-RDHM parameters (a priori versus calibrated), channel and floodplain Manning n values, DEM resolution (10 m versus 30 m) and computation mesh resolution (10 m+ versus 30 m+). Simulations with calibrated versus a priori parameters of HL-RDHM show that HiResFlood-UCI produces reasonable results with the a priori parameters from NWS. Sensitivities to hydraulic model resistance parameters, mesh resolution and DEM resolution are also identified, pointing to the importance of model calibration and validation for accurate prediction of localized flood intensities. HiResFlood-UCI performance was examined using 6 measured precipitation events as model input for model calibration and validation of the streamflow at the outlet. The Nash–Sutcliffe Efficiency (NSE) obtained ranges from 0.588 to 0.905. The model was also validated for the flooded map using USGS observed water level at an interior point. The predicted flood stage error is 0.82 m or less, based on a comparison to measured stage. Validation of stage and discharge predictions builds confidence in model predictions of flood extent and localized velocities, which are fundamental to reliable flash flood warning

Unsupervised Diverse Colorization via Generative Adversarial Networks

Colorization of grayscale images has been a hot topic in computer vision. Previous research mainly focuses on producing a colored image to match the original one. However, since many colors share the same gray value, an input grayscale image could be diversely colored while maintaining its reality. In this paper, we design a novel solution for unsupervised diverse colorization. Specifically, we leverage conditional generative adversarial networks to model the distribution of real-world item colors, in which we develop a fully convolutional generator with multi-layer noise to enhance diversity, with multi-layer condition concatenation to maintain reality, and with stride 1 to keep spatial information. With such a novel network architecture, the model yields highly competitive performance on the open LSUN bedroom dataset. The Turing test of 80 humans further indicates our generated color schemes are highly convincible

In-Flight CCD Distortion Calibration for Pushbroom Satellites Based on Subpixel Correlation

We describe a method that allows for accurate inflight calibration of the interior orientation of any pushbroom camera and that in particular solves the problem of modeling the distortions induced by charge coupled device (CCD) misalignments. The distortion induced on the ground by each CCD is measured using subpixel correlation between the orthorectified image to be calibrated and an orthorectified reference image that is assumed distortion free. Distortions are modeled as camera defects, which are assumed constant over time. Our results show that in-flight interior orientation calibration reduces internal camera biases by one order of magnitude. In particular, we fully characterize and model the Satellite Pour l'Observation de la Terre (SPOT) 4-HRV1 sensor, and we conjecture that distortions mostly result from the mechanical strain produced when the satellite was launched rather than from effects of on-orbit thermal variations or aging. The derived calibration models have been integrated to the software package Coregistration of Optically Sensed Images and Correlation (COSI-Corr), freely available from the Caltech Tectonics Observatory website. Such calibration models are particularly useful in reducing biases in digital elevation models (DEMs) generated from stereo matching and in improving the accuracy of change detection algorithms

Bias adjustment of satellite-based precipitation estimation using gauge observations: A case study in Chile

Satellite-based precipitation estimates (SPEs) are promising alternative precipitation data for climatic and hydrological applications, especially for regions where ground-based observations are limited. However, existing satellite-based rainfall estimations are subject to systematic biases. This study aims to adjust the biases in the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Cloud Classification System (PERSIANN-CCS) rainfall data over Chile, using gauge observations as reference. A novel bias adjustment framework, termed QM-GW, is proposed based on the nonparametric quantile mapping approach and a Gaussian weighting interpolation scheme. The PERSIANN-CCS precipitation estimates (daily, 0.04°×0.04°) over Chile are adjusted for the period of 2009–2014. The historical data (satellite and gauge) for 2009–2013 are used to calibrate the methodology; nonparametric cumulative distribution functions of satellite and gauge observations are estimated at every 1°×1° box region. One year (2014) of gauge data was used for validation. The results show that the biases of the PERSIANN-CCS precipitation data are effectively reduced. The spatial patterns of adjusted satellite rainfall show high consistency to the gauge observations, with reduced root-mean-square errors and mean biases. The systematic biases of the PERSIANN-CCS precipitation time series, at both monthly and daily scales, are removed. The extended validation also verifies that the proposed approach can be applied to adjust SPEs into the future, without further need for ground-based measurements. This study serves as a valuable reference for the bias adjustment of existing SPEs using gauge observations worldwide