9,521 research outputs found
Intercomparison of the northern hemisphere winter mid-latitude atmospheric variability of the IPCC models
We compare, for the overlapping time frame 1962-2000, the estimate of the
northern hemisphere (NH) mid-latitude winter atmospheric variability within the
XX century simulations of 17 global climate models (GCMs) included in the
IPCC-4AR with the NCEP and ECMWF reanalyses. We compute the Hayashi spectra of
the 500hPa geopotential height fields and introduce an integral measure of the
variability observed in the NH on different spectral sub-domains. Only two
high-resolution GCMs have a good agreement with reanalyses. Large biases, in
most cases larger than 20%, are found between the wave climatologies of most
GCMs and the reanalyses, with a relative span of around 50%. The travelling
baroclinic waves are usually overestimated, while the planetary waves are
usually underestimated, in agreement with previous studies performed on global
weather forecasting models. When comparing the results of various versions of
similar GCMs, it is clear that in some cases the vertical resolution of the
atmosphere and, somewhat unexpectedly, of the adopted ocean model seem to be
critical in determining the agreement with the reanalyses. The GCMs ensemble is
biased with respect to the reanalyses but is comparable to the best 5 GCMs.
This study suggests serious caveats with respect to the ability of most of the
presently available GCMs in representing the statistics of the global scale
atmospheric dynamics of the present climate and, a fortiori, in the perspective
of modelling climate change.Comment: 39 pages, 8 figures, 2 table
Preparing for a Northwest Passage: A Workshop on the Role of New England in Navigating the New Arctic
Preparing for a Northwest Passage: A Workshop on the Role of New England in Navigating the New Arctic (March 25 - 27, 2018 -- The University of New Hampshire) paired two of NSF\u27s 10 Big Ideas: Navigating the New Arctic and Growing Convergence Research at NSF. During this event, participants assessed economic, environmental, and social impacts of Arctic change on New England and established convergence research initiatives to prepare for, adapt to, and respond to these effects. Shipping routes through an ice-free Northwest Passage in combination with modifications to ocean circulation and regional climate patterns linked to Arctic ice melt will affect trade, fisheries, tourism, coastal ecology, air and water quality, animal migration, and demographics not only in the Arctic but also in lower latitude coastal regions such as New England. With profound changes on the horizon, this is a critical opportunity for New England to prepare for uncertain yet inevitable economic and environmental impacts of Arctic change
2011 Strategic roadmap for Australian research infrastructure
The 2011 Roadmap articulates the priority research infrastructure areas of a national scale (capability areas) to further develop Australia’s research capacity and improve innovation and
research outcomes over the next five to ten years. The capability areas have been identified through considered analysis of input provided by stakeholders, in conjunction with specialist advice from Expert Working Groups
It is intended the Strategic Framework will provide a high-level policy framework, which will include principles to guide the development of policy advice and the design of programs related to the funding of research infrastructure by the Australian Government. Roadmapping has been identified in the Strategic Framework Discussion Paper as the most appropriate prioritisation mechanism for national, collaborative research infrastructure. The strategic identification of Capability areas through a consultative roadmapping process was also validated in the report of the 2010 NCRIS Evaluation.
The 2011 Roadmap is primarily concerned with medium to large-scale research infrastructure. However, any landmark infrastructure (typically involving an investment in excess of $100 million over five years from the Australian Government) requirements identified in this process will be noted. NRIC has also developed a ‘Process to identify and prioritise Australian Government landmark research infrastructure investments’ which is currently under consideration by the government as part of broader deliberations relating to research infrastructure.
NRIC will have strategic oversight of the development of the 2011 Roadmap as part of its overall policy view of research infrastructure
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community
In recent years, deep learning (DL), a re-branding of neural networks (NNs),
has risen to the top in numerous areas, namely computer vision (CV), speech
recognition, natural language processing, etc. Whereas remote sensing (RS)
possesses a number of unique challenges, primarily related to sensors and
applications, inevitably RS draws from many of the same theories as CV; e.g.,
statistics, fusion, and machine learning, to name a few. This means that the RS
community should be aware of, if not at the leading edge of, of advancements
like DL. Herein, we provide the most comprehensive survey of state-of-the-art
RS DL research. We also review recent new developments in the DL field that can
be used in DL for RS. Namely, we focus on theories, tools and challenges for
the RS community. Specifically, we focus on unsolved challenges and
opportunities as it relates to (i) inadequate data sets, (ii)
human-understandable solutions for modelling physical phenomena, (iii) Big
Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and
learning algorithms for spectral, spatial and temporal data, (vi) transfer
learning, (vii) an improved theoretical understanding of DL systems, (viii)
high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote
Sensin
Using Data in Undergraduate Science Classrooms
Provides pedagogical insight concerning the skill of using data The resource being annotated is: http://www.dlese.org/dds/catalog_DATA-CLASS-000-000-000-007.htm
- …