Bias adjustment of infrared-based rainfall estimation using Passive Microwave satellite rainfall data

This study explores using Passive Microwave (PMW) rainfall estimation for spatial and temporal adjustment of Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System(PERSIANN-CCS). The PERSIANN-CCS algorithm collects information from infrared images to estimate rainfall. PERSIANN-CCS is one of the algorithms used in the IntegratedMultisatellite Retrievals for GPM (Global Precipitation Mission) estimation for the time period PMW rainfall estimations are limited or not available. Continued improvement of PERSIANN-CCS will support Integrated Multisatellite Retrievals for GPM for current as well as retrospective estimations of global precipitation. This study takes advantage of the high spatial and temporal resolution of GEO-based PERSIANN-CCS estimation and the more effective, but lower sample frequency, PMW estimation. The Probability Matching Method (PMM) was used to adjust the rainfall distribution of GEO-based PERSIANN-CCS toward that of PMW rainfall estimation. The results show that a significant improvement of global PERSIANN-CCS rainfall estimation is obtained

Representations of sources and data: working with exceptions to hierarchy in historical documents

No abstract available

To be or not to Be? - First Evidence for Neutrinoless Double Beta Decay

Double beta decay is indispensable to solve the question of the neutrino mass matrix together with $\nu$ oscillation experiments. Recent analysis of the most sensitive experiment since nine years - the HEIDELBERG-MOSCOW experiment in Gran-Sasso - yields a first indication for the neutrinoless decay mode. This result is the first evidence for lepton number violation and proves the neutrino to be a Majorana particle. We give the present status of the analysis in this report. It excludes several of the neutrino mass scenarios allowed from present neutrino oscillation experiments - only degenerate scenarios and those with inverse mass hierarchy survive. This result allows neutrinos to still play an important role as dark matter in the Universe. To improve the accuracy of the present result, considerably enlarged experiments are required, such as GENIUS. A GENIUS Test Facility has been funded and will come into operation by early 2003.Comment: 16 pages, latex, 10 figures, Talk was presented at International Conference "Neutrinos and Implications for Physics Beyond the Standard Model", Oct. 11-13, 2002, Stony Brook, USA, Proc. (2003) ed. by R. Shrock, also see Home Page of Heidelberg Non-Accelerator Particle Physics Group: http://www.mpi-hd.mpg.de/non_acc

Self-organizing nonlinear output (SONO): A neural network suitable for cloud patch-based rainfall estimation at small scales

Accurate measurement of rainfall distribution at various spatial and temporal scales is crucial for hydrological modeling and water resources management. In the literature of satellite rainfall estimation, many efforts have been made to calibrate a statistical relationship (including threshold, linear, or nonlinear) between cloud infrared (IR) brightness temperatures and surface rain rates (RR). In this study, an automated neural network for cloud patch-based rainfall estimation, entitled self-organizing nonlinear output (SONO) model, is developed to account for the high variability of cloud-rainfall processes at geostationary scales (i.e., 4 km and every 30 min). Instead of calibrating only one IR-RR function for all clouds the SONO classifies varied cloud patches into different clusters and then searches a nonlinear IR-RR mapping function for each cluster. This designed feature enables SONO to generate various rain rates at a given brightness temperature and variable rain/no-rain IR thresholds for different cloud types, which overcomes the one-to-one mapping limitation of a single statistical IR-RR function for the full spectrum of cloud-rainfall conditions. In addition, the computational and modeling strengths of neural network enable SONO to cope with the nonlinearity of cloud-rainfall relationships by fusing multisource data sets. Evaluated at various temporal and spatial scales, SONO shows improvements of estimation accuracy, both in rain intensity and in detection of rain/no-rain pixels. Further examination of the SONO adaptability demonstrates its potentiality as an operational satellite rainfall estimation system that uses the passive microwave rainfall observations from low-orbiting satellites to adjust the IR-based rainfall estimates at the resolution of geostationary satellites. Copyright 2005 by the American Geophysical Union

SpaceNet MVOI: a Multi-View Overhead Imagery Dataset

Detection and segmentation of objects in overheard imagery is a challenging task. The variable density, random orientation, small size, and instance-to-instance heterogeneity of objects in overhead imagery calls for approaches distinct from existing models designed for natural scene datasets. Though new overhead imagery datasets are being developed, they almost universally comprise a single view taken from directly overhead ("at nadir"), failing to address a critical variable: look angle. By contrast, views vary in real-world overhead imagery, particularly in dynamic scenarios such as natural disasters where first looks are often over 40 degrees off-nadir. This represents an important challenge to computer vision methods, as changing view angle adds distortions, alters resolution, and changes lighting. At present, the impact of these perturbations for algorithmic detection and segmentation of objects is untested. To address this problem, we present an open source Multi-View Overhead Imagery dataset, termed SpaceNet MVOI, with 27 unique looks from a broad range of viewing angles (-32.5 degrees to 54.0 degrees). Each of these images cover the same 665 square km geographic extent and are annotated with 126,747 building footprint labels, enabling direct assessment of the impact of viewpoint perturbation on model performance. We benchmark multiple leading segmentation and object detection models on: (1) building detection, (2) generalization to unseen viewing angles and resolutions, and (3) sensitivity of building footprint extraction to changes in resolution. We find that state of the art segmentation and object detection models struggle to identify buildings in off-nadir imagery and generalize poorly to unseen views, presenting an important benchmark to explore the broadly relevant challenge of detecting small, heterogeneous target objects in visually dynamic contexts.Comment: Accepted into IEEE International Conference on Computer Vision (ICCV) 201

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

A scheme for cancelling intercarrier interference using conjugate transmission in multicarrier communication systems

To mitigate intercarrier interference (ICI), a two-path algorithm is developed for multicarrier communication systems, including orthogonal frequency division multiplexing (OFDM) systems. The first path employs the regular OFDM algorithm. The second path uses the conjugate transmission of the first path. The combination of both paths forms a conjugate ICI cancellation scheme at the receiver. This conjugate cancellation (CC) scheme provides (1) a high signal to interference power ratio (SIR) in the presence of small frequency offsets (50 dB and 33 dB higher than that of the regular OFDM and linear self-cancellation algorithms [1], [2], respectively, at ΔfT = 0.1% of subcarrier frequency spacing); (2) better bit error rate (BER) performance in both additive white Gaussian noise (AWGN) and fading channels; (3) backward compatibility with the existing OFDM system; (4) no channel equalization is needed for reducing ICI, a simple low cost receiver without increasing system complexity. Although the two-path transmission reduces bandwidth efficiency, the disadvantage can be balanced by increasing signal alphabet sizes

The Direct Detection of Lyman Continuum Emission from Star-forming Galaxies at z~3

We present the results of rest-frame UV spectroscopic observations of a sample of 14 z ~ 3 star-forming galaxies in the SSA 22a field. These spectra are characterized by unprecedented depth in the Lyman continuum region. For the first time, we have detected escaping ionizing radiation from individual galaxies at high redshift, with 2 of the 14 objects showing significant emission below the Lyman limit. We also measured the ratio of emergent flux density at 1500 Å to that in the Lyman continuum region, for the individual detections (C49 and D3) and the sample average. If a correction for the average IGM opacity is applied to the spectra of the objects C49 and D3, we find f_(1500)/f_(900,corr,C49) = 4.5 and f_(1500)/f_(900,corr,D3) = 2.9. The average emergent flux density ratio in our sample is = 22, implying an escape fraction ~4.5 times lower than inferred from the composite spectrum from Steidel and coworkers. If this new estimate is representative of LBGs, their contribution to the metagalactic ionizing radiation field is J_ν(900) ~ 2.6 × 10^(-22) ergs s^(-1) cm^(-2) Hz^(-1) sr^(-1), comparable to the contribution of optically selected quasars at the same redshift. The sum of the contributions from galaxies and quasars is consistent with recent estimates of the level of the ionizing background at z ~ 3, inferred from the H I Lyα forest optical depth. There is significant variance among the emergent far-UV spectra in our sample, yet the factors controlling the detection or nondetection of Lyman continuum emission from galaxies are not well determined. Because we do not yet understand the source of this variance, significantly larger samples will be required to obtain robust constraints on the galaxy contribution to the ionizing background at z ~ 3 and beyond