643 research outputs found
Holarchical Development: Discovering and Applying Missing Drives from Ken Wilber’s Twenty Tenets
Ken Wilber’s AQAL model offers a way to synthesize the partial truths of many theories
across various fields of knowledge such as evolutionary biology and sociology, developmental
psychology, and perennial and contemporary philosophy to name only a few. Despite its
reconciling power and influence, the model has been validly criticized for its static nature
and its overemphasis on the ascendant, versus descendant, path of development. This
paper points out areas of Wilber’s writing that suggest a way to overcome these criticisms.
Doing so allows for the refinement of AQAL’s Twenty Tenets for an extension of its formal,
dynamic features. This is accomplished first by relating Wilber’s original dynamic drives to
the quadrants and levels enabling the quadrants and levels to then predict additional drives
not specified by Wilber. The full set of drives then suggests clarifications of assumptions and
applications of the model regarding transcendence and inclusion in order for the refined
model to be internally consistent. The result helps correct for AQAL’s ascending bias, a bias
which overemphasizes a linear path from lower to higher stages of development. Instead,
more possibilities emerge such as those in which ascending development is overly dependent
on a higher capacity with inclusion of only basic, lower core capacities. This is in contrast
to more fully realizing the potential for development of individuals or societies in the more
fundamental, lower levels, through deeper inclusion within higher capacities. Also, given
the other horizontal drives that are predicted by the model, further possibilities are explored
for differing directions of, and emphasis in, development
Content in the Context of 4D-Var Data Assimilation. II: Application to Global Ozone Assimilation
Data assimilation obtains improved estimates of the state of a physical system
by combining imperfect model results with sparse and noisy observations of reality.
Not all observations used in data assimilation are equally valuable. The ability to
characterize the usefulness of different data points is important for analyzing the
effectiveness of the assimilation system, for data pruning, and for the design of future
sensor systems.
In the companion paper [Sandu et al.(2011)] we derived an ensemble-based computational
procedure to estimate the information content of various observations in
the context of 4D-Var. Here we apply this methodology to quantify two information
metrics (the signal and degrees of freedom for signal) for satellite observations
used in a global chemical data assimilation problem with the GEOS-Chem chemical
transport model. The assimilation of a subset of data points characterized by the
highest information content, gives analyses that are comparable in quality with the
one obtained using the entire data set
A Practical Method to Estimate Information Content in the Context of 4D-Var Data Assimilation. I: Methodology
Data assimilation obtains improved estimates of the state of a physical system
by combining imperfect model results with sparse and noisy observations of reality.
Not all observations used in data assimilation are equally valuable. The ability to
characterize the usefulness of different data points is important for analyzing the
effectiveness of the assimilation system, for data pruning, and for the design of future
sensor systems.
This paper focuses on the four dimensional variational (4D-Var) data assimilation
framework. Metrics from information theory are used to quantify the contribution
of observations to decreasing the uncertainty with which the system state is known.
We establish an interesting relationship between different information-theoretic metrics
and the variational cost function/gradient under Gaussian linear assumptions.
Based on this insight we derive an ensemble-based computational procedure to estimate
the information content of various observations in the context of 4D-Var. The
approach is illustrated on linear and nonlinear test problems. In the companion paper
[Singh et al.(2011)] the methodology is applied to a global chemical data assimilation
problem
A Practical Method to Estimate Information Content in the Context of 4D-Var Data Assimilation. II: Application to Global Ozone Assimilation
Data assimilation obtains improved estimates of the state of a physical system by combining imperfect
model results with sparse and noisy observations of reality. Not all observations used in data assimilation
are equally valuable. The ability to characterize the usefulness of different data points is important
for analyzing the effectiveness of the assimilation system, for data pruning, and for the design of future
sensor systems.
In the companion paper (Sandu et al., 2012) we derive an ensemble-based computational procedure
to estimate the information content of various observations in the context of 4D-Var. Here we apply
this methodology to quantify the signal and degrees of freedom for signal information metrics of satellite observations used in a global chemical data assimilation problem with the GEOS-Chem chemical
transport model. The assimilation of a subset of data points characterized by the highest information
content yields an analysis comparable in quality with the one obtained using the entire data set
Geostationary Coastal and Air Pollution Events (GEO-CAPE) Sensitivity Analysis Experiment
Geostationary Coastal and Air pollution Events (GEO-CAPE) is a NASA decadal survey mission to be designed to provide surface reflectance at high spectral, spatial, and temporal resolutions from a geostationary orbit necessary for studying regional-scale air quality issues and their impact on global atmospheric composition processes. GEO-CAPE's Atmospheric Science Questions explore the influence of both gases and particles on air quality, atmospheric composition, and climate. The objective of the GEO-CAPE Observing System Simulation Experiment (OSSE) is to analyze the sensitivity of ozone to the global and regional NOx emissions and improve the science impact of GEO-CAPE with respect to the global air quality. The GEO-CAPE OSSE team at Jet propulsion Laboratory has developed a comprehensive OSSE framework that can perform adjoint-sensitivity analysis for a wide range of observation scenarios and measurement qualities. This report discusses the OSSE framework and presents the sensitivity analysis results obtained from the GEO-CAPE OSSE framework for seven observation scenarios and three instrument systems
Empirical validation of dynamic thermal computer models of buildings
A methodology for the validation of dynamic thermal models of buildings has been presented. The three techniques, analytical verification, inter-model comparisons and empirical validation have been described and their relative merits assessed by reference to previous validation work on ESP, SERIR'S, DEROB and BLAST. Previous empirical validation work on these models has been reviewed. This research has shown that numerous sources of error have existed in previous studies leading to uncertainty in model predictions. The effects of these errors has meant that none of the previous empirical validation studies would have produced conclusive evidence of internal errors in the models themselves. An approach towards developing tests to empirically validate dynamic thermal models is given
A hierarchical statistical framework for emergent constraints: application to snow-albedo feedback
Emergent constraints use relationships between future and current climate
states to constrain projections of climate response. Here, we introduce a
statistical, hierarchical emergent constraint (HEC) framework in order to link
future and current climate with observations. Under Gaussian assumptions, the
mean and variance of the future state is shown analytically to be a function of
the signal-to-noise (SNR) ratio between data-model error and current-climate
uncertainty, and the correlation between future and current climate states. We
apply the HEC to the climate-change, snow-albedo feedback, which is related to
the seasonal cycle in the Northern Hemisphere. We obtain a snow-albedo-feedback
prediction interval of \%. The critical dependence on
SNR and correlation shows that neglecting these terms can lead to bias and
under-estimated uncertainty in constrained projections. The flexibility of
using HEC under general assumptions throughout the Earth System is discussed.Comment: 19 pages, 5 Figure
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