We have designed and built an instrument to measure and monitor the “nightglow” of the Earth’s atmosphere in the near ultraviolet (NUV). In this paper we describe the design of this instrument, called NIGHTGLOW. NIGHTGLOW is designed to be flown from a high altitude research balloon, and circumnavigate the globe. NIGHTGLOW is a NASA, University of Utah, and New Mexico State University project. A test flight took place from Palestine, Texas on July 5, 2000, lasting about 8 h. The instrument performed well and landed safely in Stiles, Texas with little damage. The resulting measurements of the NUV nightglow are compared with previous measurements from sounding rockets and balloons. 相似文献
The Free-Wilson paradigm is an established and powerful tool for quantitatively relating activity withchemical structure.Current implementations of the paradigm,however,are flawed both conceptually andin execution.As part of an attempt to more fully realize the promise of the paradigm,it was necessaryto examine these limitations in detail.This report introduces a robust,theory-founded Free-Wilson implementation:stepwise principalcomponents regression analysis(SPCRA).SPCRA is computationally superior to previousimplementations but does not in itself correct their conceptual flaws.The development of SPCRA did,however,facilitate derivation of a simple and chemically significantinterpretation of the Free-Wilson structure-activity model.A number of statistical aspects of this modelcommonly misused in previous applications are discussed at length.These discussions provide criticalbackground for the development of an alternative implementation of the Free-Wilson paradigm. 相似文献
As part of the Canadian contribution to the International Polar Year (IPY), several major international research programs have focused on offshore arctic marine ecosystems. The general goal of these projects was to improve our understanding of how the response of arctic marine ecosystems to climate warming will alter food web structure and ecosystem services provided to Northerners. At least four key findings from these projects relating to arctic heterotrophic food web, pelagic-benthic coupling and biodiversity have emerged: (1) Contrary to a long-standing paradigm of dormant ecosystems during the long arctic winter, major food web components showed relatively high level of winter activity, well before the spring release of ice algae and subsequent phytoplankton bloom. Such phenological plasticity among key secondary producers like zooplankton may thus narrow the risks of extreme mismatch between primary production and secondary production in an increasingly variable arctic environment. (2) Tight pelagic-benthic coupling and consequent recycling of nutrients at the seafloor characterize specific regions of the Canadian Arctic, such as the North Water polynya and Lancaster Sound. The latter constitute hot spots of benthic ecosystem functioning compared to regions where zooplankton-mediated processes weaken the pelagic-benthic coupling. (3) In contrast with another widely shared assumption of lower biodiversity, arctic marine biodiversity is comparable to that reported off Atlantic and Pacific coasts of Canada, albeit threatened by the potential colonization of subarctic species. (4) The rapid decrease of summer sea-ice cover allows increasing numbers of killer whales to use the Canadian High Arctic as a hunting ground. The stronger presence of this species, bound to become a new apex predator of arctic seas, will likely affect populations of endemic arctic marine mammals such as the narwhal, bowhead, and beluga whales. 相似文献
A new low-dimensional parameterization based on principal component analysis (PCA) and convolutional neural networks (CNN) is developed to represent complex geological models. The CNN–PCA method is inspired by recent developments in computer vision using deep learning. CNN–PCA can be viewed as a generalization of an existing optimization-based PCA (O-PCA) method. Both CNN–PCA and O-PCA entail post-processing a PCA model to better honor complex geological features. In CNN–PCA, rather than use a histogram-based regularization as in O-PCA, a new regularization involving a set of metrics for multipoint statistics is introduced. The metrics are based on summary statistics of the nonlinear filter responses of geological models to a pre-trained deep CNN. In addition, in the CNN–PCA formulation presented here, a convolutional neural network is trained as an explicit transform function that can post-process PCA models quickly. CNN–PCA is shown to provide both unconditional and conditional realizations that honor the geological features present in reference SGeMS geostatistical realizations for a binary channelized system. Flow statistics obtained through simulation of random CNN–PCA models closely match results for random SGeMS models for a demanding case in which O-PCA models lead to significant discrepancies. Results for history matching are also presented. In this assessment CNN–PCA is applied with derivative-free optimization, and a subspace randomized maximum likelihood method is used to provide multiple posterior models. Data assimilation and significant uncertainty reduction are achieved for existing wells, and physically reasonable predictions are also obtained for new wells. Finally, the CNN–PCA method is extended to a more complex nonstationary bimodal deltaic fan system, and is shown to provide high-quality realizations for this challenging example.
Mitigating and adapting to global changes requires a better understanding of the response of the Biosphere to these environmental variations. Human disturbances and their effects act in the long term (decades to centuries) and consequently, a similar time frame is needed to fully understand the hydrological and biogeochemical functioning of a natural system. To this end, the ‘Centre National de la Recherche Scientifique’ (CNRS) promotes and certifies long-term monitoring tools called national observation services or ‘Service National d'Observation’ (SNO) in a large range of hydrological and biogeochemical systems (e.g., cryosphere, catchments, aquifers). The SNO investigating peatlands, the SNO ‘Tourbières’, was certified in 2011 ( https://www.sno-tourbieres.cnrs.fr/ ). Peatlands are mostly found in the high latitudes of the northern hemisphere and French peatlands are located in the southern part of this area. Thus, they are located in environmental conditions that will occur in northern peatlands in coming decades or centuries and can be considered as sentinels. The SNO Tourbières is composed of four peatlands: La Guette (lowland central France), Landemarais (lowland oceanic western France), Frasne (upland continental eastern France) and Bernadouze (upland southern France). Thirty target variables are monitored to study the hydrological and biogeochemical functioning of the sites. They are grouped into four datasets: hydrology, fluvial export of organic matter, greenhouse gas fluxes and meteorology/soil physics. The data from all sites follow a common processing chain from the sensors to the public repository. The raw data are stored on an FTP server. After operator or automatic processing, data are stored in a database, from which a web application extracts the data to make them available ( https://data-snot.cnrs.fr/data-access/ ). Each year at least, an archive of each dataset is stored in Zenodo, with a digital object identifier (DOI) attribution ( https://zenodo.org/communities/sno_tourbieres_data/ ). 相似文献