52 research outputs found
Lingwistika
L-awtur jagħmel studju komparattiv bejn il-Malti, l-Isqalli u l-Għarbi u b’hekk jagħti tagħrif etimoloġiku fuq xi kliem Malti miġjub mill-Isqalli u mill-Għarbi.peer-reviewe
L-iżvilupp tal-Malti u l-kitba tiegħu
L-awtur jagħti dettall lingwistiku dwar l-iżvilupp tal-Malti u l-kitba tiegħu.peer-reviewe
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
Characterization of uncertainties in atmospheric trace gas inversions using hierarchical Bayesian methods
We present a hierarchical Bayesian method for atmospheric trace gas
inversions. This method is used to estimate emissions of trace gases as well
as "hyper-parameters" that characterize the probability density functions
(PDFs) of the a priori emissions and model-measurement covariances. By
exploring the space of "uncertainties in uncertainties", we show that the
hierarchical method results in a more complete estimation of emissions and
their uncertainties than traditional Bayesian inversions, which rely heavily
on expert judgment. We present an analysis that shows the effect of
including hyper-parameters, which are themselves informed by the data, and
show that this method can serve to reduce the effect of errors in assumptions
made about the a priori emissions and model-measurement uncertainties. We
then apply this method to the estimation of sulfur hexafluoride (SF6)
emissions over 2012 for the regions surrounding four Advanced Global
Atmospheric Gases Experiment (AGAGE) stations. We find that improper
accounting of model representation uncertainties, in particular, can lead to
the derivation of emissions and associated uncertainties that are unrealistic
and show that those derived using the hierarchical method are likely to be
more representative of the true uncertainties in the system. We demonstrate
through this SF6 case study that this method is less sensitive to
outliers in the data and to subjective assumptions about a priori emissions
and model-measurement uncertainties than traditional methods
Mass balance reassessment of glaciers draining into the Abbot and Getz Ice Shelves of West Antarctica
We present a reassessment of input-output method ice mass budget estimates for the Abbot and Getz regions of West Antarctica using CryoSat-2-derived ice thickness estimates. The mass budget is 8 ± 6 Gt yr−1 and 5 ± 17 Gt yr−1 for the Abbot and Getz sectors, respectively, for the period 2006–2008. Over the Abbot region, our results resolve a previous discrepancy with elevation rates from altimetry, due to a previous 30% overestimation of ice thickness. For the Getz sector, our results are at the more positive bound of estimates from other techniques. Grounding line velocity increases up to 20% between 2007 and 2014 alongside mean elevation rates of −0.67 ± 0.13 m yr−1 between 2010 and 2013 indicate the onset of a dynamic thinning signal. Mean snowfall trends of −0.33 m yr−1 water equivalent since 2006 indicate recent mass trends are driven by both ice dynamics and surface processes
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Modelling the growth of atmospheric nitrous oxide using a global hierarchical inversion
Nitrous oxide is a potent greenhouse gas (GHG) and ozone-depleting substance, whose atmospheric abundance has risen throughout the contemporary record. In this work, we carry out the first global hierarchical Bayesian inversion to solve for nitrous oxide emissions, which includes prior emissions with truncated Gaussian distributions and Gaussian model errors, in order to examine the drivers of the atmospheric surface growth rate. We show that both emissions and climatic variability are key drivers of variations in the surface nitrous oxide growth rate between 2011 and 2020. We derive increasing global nitrous oxide emissions, which are mainly driven by emissions between 0 and 30∘ N, with the highest emissions recorded in 2020. Our mean global total emissions for 2011–2020 of 17.2 (16.7–17.7 at the 95 % credible intervals) Tg N yr−1, comprising of 12.0 (11.2–12.8) Tg N yr−1 from land and 5.2 (4.5–5.9) Tg N yr−1 from ocean, agrees well with previous studies, but we find that emissions are poorly constrained for some regions of the world, particularly for the oceans. The prior emissions used in this and other previous work exhibit a seasonal cycle in the extra-tropical Northern Hemisphere that is out of phase with the posterior solution, and there is a substantial zonal redistribution of emissions from the prior to the posterior. Correctly characterizing the uncertainties in the system, for example in the prior emission fields, is crucial for deriving posterior fluxes that are consistent with observations. In this hierarchical inversion, the model-measurement discrepancy and the prior flux uncertainty are informed by the data, rather than solely through “expert judgement”. We show cases where this framework provides different plausible adjustments to the prior fluxes compared to inversions using widely adopted, fixed uncertainty constraints.</p
Modelling the growth of atmospheric nitrous oxide using a global hierarchical inversion
Nitrous oxide is a potent greenhouse gas (GHG) and ozone-depleting substance, whose atmospheric abundance has risen throughout the contemporary record. In this work, we carry out the first global hierarchical Bayesian inversion to solve for nitrous oxide emissions, which includes prior emissions with truncated Gaussian distributions and Gaussian model errors, in order to examine the drivers of the atmospheric surface growth rate. We show that both emissions and climatic variability are key drivers of variations in the surface nitrous oxide growth rate between 2011 and 2020. We derive increasing global nitrous oxide emissions, which are mainly driven by emissions between 0 and 30∘ N, with the highest emissions recorded in 2020. Our mean global total emissions for 2011–2020 of 17.2 (16.7–17.7 at the 95 % credible intervals) Tg N yr−1, comprising of 12.0 (11.2–12.8) Tg N yr−1 from land and 5.2 (4.5–5.9) Tg N yr−1 from ocean, agrees well with previous studies, but we find that emissions are poorly constrained for some regions of the world, particularly for the oceans. The prior emissions used in this and other previous work exhibit a seasonal cycle in the extra-tropical Northern Hemisphere that is out of phase with the posterior solution, and there is a substantial zonal redistribution of emissions from the prior to the posterior. Correctly characterizing the uncertainties in the system, for example in the prior emission fields, is crucial for deriving posterior fluxes that are consistent with observations. In this hierarchical inversion, the model-measurement discrepancy and the prior flux uncertainty are informed by the data, rather than solely through “expert judgement”. We show cases where this framework provides different plausible adjustments to the prior fluxes compared to inversions using widely adopted, fixed uncertainty constraints.</p
Correlations and forecast of death tolls in the Syrian conflict
The Syrian armed conflict has been ongoing since 2011 and has already caused thousands of deaths. The analysis of death tolls helps to understand the dynamics of the conflict and to better allocate resources and aid to the affected areas. In this article, we use information on the daily number of deaths to study temporal and spatial correlations in the data, and exploit this information to forecast events of deaths. We found that the number of violent deaths per day in Syria varies more widely than that in England in which non-violent deaths dominate. We have identified strong positive auto-correlations in Syrian cities and non-trivial cross-correlations across some of them. The results indicate synchronization in the number of deaths at different times and locations, suggesting respectively that local attacks are followed by more attacks at subsequent days and that coordinated attacks may also take place across different locations. Thus the analysis of high temporal resolution data across multiple cities makes it possible to infer attack strategies, warn potential occurrence of future events, and hopefully avoid further deaths
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