113 research outputs found
Spatio-temporal bivariate statistical models for atmospheric trace-gas inversion
Atmospheric trace-gas inversion refers to any technique used to predict
spatial and temporal fluxes using mole-fraction measurements and atmospheric
simulations obtained from computer models. Studies to date are most often of a
data-assimilation flavour, which implicitly consider univariate statistical
models with the flux as the variate of interest. This univariate approach
typically assumes that the flux field is either a spatially correlated Gaussian
process or a spatially uncorrelated non-Gaussian process with prior expectation
fixed using flux inventories (e.g., NAEI or EDGAR in Europe). Here, we extend
this approach in three ways. First, we develop a bivariate model for the
mole-fraction field and the flux field. The bivariate approach allows optimal
prediction of both the flux field and the mole-fraction field, and it leads to
significant computational savings over the univariate approach. Second, we
employ a lognormal spatial process for the flux field that captures both the
lognormal characteristics of the flux field (when appropriate) and its spatial
dependence. Third, we propose a new, geostatistical approach to incorporate the
flux inventories in our updates, such that the posterior spatial distribution
of the flux field is predominantly data-driven. The approach is illustrated on
a case study of methane (CH) emissions in the United Kingdom and Ireland.Comment: 39 pages, 8 figure
Inversion of long-lived trace gas emissions using combined Eulerian and Lagrangian chemical transport models
Supplement related to this article is available online at: http://www.atmos-chem-phys.net/11/9887/2011/acp-11-9887-2011-supplement.zip.We present a method for estimating emissions of long-lived trace gases from a sparse global network of high-frequency observatories, using both a global Eulerian chemical transport model and Lagrangian particle dispersion model. Emissions are derived in a single step after determining sensitivities of the observations to initial conditions, the high-resolution emissions field close to observation points, and larger regions further from the measurements. This method has the several advantages over inversions using one type of model alone, in that: high-resolution simulations can be carried out in limited domains close to the measurement sites, with lower resolution being used further from them; the influence of errors due to aggregation of emissions close to the measurement sites can be minimized; assumptions about boundary conditions to the Lagrangian model do not need to be made, since the entire emissions field is estimated; any combination of appropriate models can be used, with no code modification. Because the sensitivity to the entire emissions field is derived, the estimation can be carried out using traditional statistical methods without the need for multiple steps in the inversion. We demonstrate the utility of this approach by determining global SF6 emissions using measurements from the Advanced Global Atmospheric Gases Experiment (AGAGE) between 2007 and 2009. The global total and large-scale patterns of the derived emissions agree well with previous studies, whilst allowing emissions to be determined at higher resolution than has previously been possible, and improving the agreement between the modeled and observed mole fractions at some sites
Estimation of trace gas fluxes with objectively determined basis functions using reversible-jump Markov chain Monte Carlo
Atmospheric trace gas inversions often attempt to attribute fluxes to a
high-dimensional grid using observations. To make this problem
computationally feasible, and to reduce the degree of under-determination,
some form of dimension reduction is usually performed. Here, we present an
objective method for reducing the spatial dimension of the parameter space in
atmospheric trace gas inversions. In addition to solving for a set of
unknowns that govern emissions of a trace gas, we set out a framework that
considers the number of unknowns to itself be an unknown. We rely on the
well-established reversible-jump Markov chain Monte Carlo algorithm to use
the data to determine the dimension of the parameter space. This framework
provides a single-step process that solves for both the resolution of the
inversion grid, as well as the magnitude of fluxes from this grid. Therefore,
the uncertainty that surrounds the choice of aggregation is accounted for in
the posterior parameter distribution. The posterior distribution of this
transdimensional Markov chain provides a naturally smoothed solution, formed
from an ensemble of coarser partitions of the spatial domain. We describe the
form of the reversible-jump algorithm and how it may be applied to trace gas
inversions. We build the system into a hierarchical Bayesian framework in
which other unknown factors, such as the magnitude of the model uncertainty,
can also be explored. A pseudo-data example is used to show the usefulness of
this approach when compared to a subjectively chosen partitioning of a
spatial domain. An inversion using real data is also shown to illustrate the
scales at which the data allow for methane emissions over north-west Europe
to be resolved
A 24-year record of high-frequency, in situ, observations of hydrogen at the Atmospheric Research Station at Mace Head, Ireland
Re-Evaluation of the UK’s HFC-134a Emissions Inventory Based on Atmospheric Observations
Independent
verification of national greenhouse gas inventories
is a vital measure for cross-checking the accuracy of emissions data
submitted to the United Nations Framework Convention on Climate Change
(UNFCCC). We infer annual UK emissions of HFC-134a from 1995 to 2012
using atmospheric observations and an inverse modeling technique,
and compare with the UK’s annual UNFCCC submission. By 2010,
the inventory is almost twice as large as our estimates, with an “emissions
gap” equating to 3.90 (3.20–4.30) Tg CO<sub>2</sub>e.
We evaluate the RAC (Refrigeration and Air-Conditioning) model, a
bottom up model used to quantify UK emissions from refrigeration and
air-conditioning sectors. Within mobile air-conditioning (MAC), the
largest RAC sector and most significant UK source (59%), we find a
number of assumptions that may be considered oversimplistic and conservative;
most notably the unit refill rate. Finally, a Bayesian approach is
used to estimate probable inventory inputs required for minimization
of the emissions discrepancy. Our top-down estimates provide only
a weak constraint on inventory model parameters and consequently,
we are unable to suggest discrete values. However, a significant revision
of the MAC servicing rate, coupled with a reassessment of non-RAC
aerosol emissions, are required if the discrepancy between methods
is to be reduced
Use of semiconductor optical amplifiers in signal processing applications
We describe a 42.6 Gbit/s all-optical pattern recognition system which uses semiconductor optical amplifiers (SOAs). A circuit with three SOA-based logic gates is used to identify the presence of specific port numbers in an optical packet header
42.6 Gbit/s fully integrated all-optical XOR gate
We demonstrate an SOA-based all-optical high-speed Mach-Zehnder interferometer exclusive- OR (XOR) gate fabricated in a silica III-V hybrid-integration technology platform. The device includes integrated time delays for rapid differential operation as well as integrated phase shifters for fine tuning of power splitters and interferometer bias enabling highly optimized XOR gate operation. XOR functionality is verified through inspection of the output pulse sequence and the carrier-suppressed output spectrum. A 2.3 dB penalty for a 42.6 Gb/s RZ-OOK signal at a 10-9 bit error rate is observed
Atmospheric radiocarbon measurements to quantify CO<sub>2</sub> emissions in the UK from 2014 to 2015
Bayesian spatiotemporal inference of trace gas emissions using an integrated nested Laplacian approximation and Gaussian Markov random fields
Long-term trends in ozone in baseline and European regionally-polluted air at Mace Head, Ireland over a 30-year period
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