243 research outputs found
Le sentiment de puissance. Une approche anthropologique du fait religieux
The aim of this paper is to explain the critical analysis of religion that Nietzsche sets up in relation to his concept of the feeling of power. Faith can help human beings feel powerful even when they are not, although it can also provide them with some kind of effective power. The feeling of power can therefore be used as a tool to better understand various religious attitudes and practices.
 El objetivo de este artĂculo es explicar el anĂĄlisis crĂtico de la religiĂłn que Nietzsche realiza gracias a su concepto de sentimiento de poder. Por un lado, la fe puede ayudar a los seres humanos a sentirse poderosos, aunque no necesariamente sean, por otro lado, ese proceso puede proporcionarles algĂșn tipo de poder efectivo. El sentimiento de poder es, por tanto, una herramienta para comprender mejor numerosas actitudes y prĂĄcticas religiosas
Besançon â Ălot Pasteur
Un projet de restructuration de lâĂźlot est Ă lâorigine de cette intervention archĂ©ologique qui associe une Ă©tude archivistique et constitue lâun des volets de la faisabilitĂ© de ce projet urbanistique de 6 000 m2. Deux sondages totalisant 12 m2 ont Ă©tĂ© implantĂ©s en cĆur dâĂźlot et atteignent la profondeur de 5 m. PĂ©riodes moderne Ă mĂ©diĂ©vale Dâimportantes couches de remblais couvrant les niveaux de lâAntiquitĂ© ont Ă©tĂ© reconnues ainsi quâune portion de rue pavĂ©e aujourdâhui disparue et datĂ©e du ..
Diagnosing horizontal and inter-channel observation error correlations for SEVIRI observations using observation-minus-background and observation-minus-analysis statistics
It has been common practice in data assimilation to treat observation errors as uncorrelated; however, meteorological centres are beginning to use correlated inter-channel observation errors in their operational assimilation systems. In this work, we are the first to characterise inter-channel and spatial error correlations for Spinning Enhanced Visible and Infrared Imager (SEVIRI) observations that are assimilated into the Met Office high-resolution model. The errors are calculated using a diagnostic that calculates statistical averages of observation-minus-background and observation-minus-analysis residuals. This diagnostic is sensitive to the background and observation error statistics used in the assimilation, although, with careful interpretation of the results, it can still provide useful information. We find that the diagnosed SEVIRI error variances are as low as one-tenth of those currently used in the operational system. The water vapour channels have significantly correlated inter-channel errors, as do the surface channels. The surface channels have larger observation error variances and inter-channel correlations in coastal areas of the domain; this is the result of assimilating mixed pixel (land-sea) observations. The horizontal observation error correlations range between 30 km and 80 km, which is larger than the operational thinning distance of 24 km. We also find that estimates from the diagnostics are unaffected by biased observations, provided that the observation-minus-background and observation-minus-analysis residual means are subtracted
Measuring theoretical and actual observation influence in the Met Office UKV: application to Doppler radial winds
In numerical weather prediction it is important to objectively measure the value of the observations assimilated. However, methods such as the forecast sensitivity to observation impact and observing system experiments are dfficult to apply to convective scale data assimilation (DA) systems such as the Met Office's UKV. We develop a new method to estimate the influence of the observations on the analysis, acknowledging that the influence depends not only on the uncertainty in the observations and prior, but how well these are prescribed in the assimilation. Monitoring both the actual and theoretical observation influence can
ag observations that are being assimilated incorrectly and quantify the harm caused to the analysis. By applying these new estimates of the observation influence to the assimilation of Doppler Radial Winds in the UKV system, we demonstrate their ability, along with expert knowledge, to inform the optimisation of both the observation network and DA system
Observation error statistics for Doppler radar radial wind superobservations assimilated into the DWD COSMO-KENDA system
Currently in operational numerical weather prediction (NWP) the density of high-resolution observations, such as Doppler radar radial winds (DRWs), is severely reduced in part to avoid violating the assumption of uncorrelated observation errors. To improve the quantity of observations used and the impact that they have on the forecast requires an accurate specification of the observation uncertainties. Observation uncertainties can be estimated using a simple diagnostic that utilises the statistical averages of observation-minus-background and observation-minus-analysis residuals. We are the first to use a modified form of the diagnostic to estimate spatial correlations for observations used in an operational ensemble data assimilation system. The uncertainties for DRW superobservations assimilated into the Deutscher Wetterdienst convection-permitting NWP model are estimated and compared to previous uncertainty estimates for DRWs. The new results show that most diagnosed standard deviations are smaller than those used in the assimilation, hence it may be feasible assimilate DRWs using reduced error standard deviations. However, some of the estimated standard deviations are considerably larger than those used in the assimilation; these large errors highlight areas where the observation processing system may be improved. The error correlation length scales are larger than the observation separation distance and influenced by both the superobbing procedure and observation operator. This is supported by comparing these results to our previous study using Met Office data. Our results suggest that DRW error correlations may be reduced by improving the superobbing procedure and observation operator; however, any remaining correlations should be accounted for in the assimilation
A pragmatic strategy for implementing spatially correlated observation errors in an operational system: an application to Doppler radial winds
Recent research has shown that high resolution observations, such as Doppler radar radial winds, exhibit spatial correlations. High resolution observations are routinely assimilated into convection permitting numerical weather prediction models assuming their errors are uncorrelated. To avoid violating this assumption observation density is severely reduced. To improve the quantity of observations used and the impact that they have on the forecast requires the introduction of full, correlated, error statistics. Some operational centres have introduced satellite inter-channel observation error correlations and obtained improved analysisâ accuracy and forecast skill scores.
Here we present a strategy for implementing spatially correlated observation errors in an operational system. We then provide the first demonstration of the practical feasibility of incorporating spatially correlated Doppler radial wind error statistics in the Met Office numerical weather prediction system.
Inclusion of correlated Doppler radial winds error statistics has little impact on the computation cost of the data assimilation system, even with a four-fold increase in the number of Doppler radial winds observations assimilated. Using the correlated observation error statistics with denser observations produces increments with shorter length scales than the control. Initial forecast trials show a neutral to positive impact on forecast skill overall, notably for quantitative precipitation forecasts. There is potential to improve forecast skill by optimising the use of Doppler radial winds and applying the technique to other observation types
On methods for assessment of the influence and impact of observations in convection-permitting numerical weather prediction
In numerical weather prediction (NWP), a large number of observations are
used to create initial conditions for weather forecasting through a process
known as data assimilation. An assessment of the value of these observations
for NWP can guide us in the design of future observation networks, help us to
identify problems with the assimilation system, and allow us to assess changes
to the assimilation system. However, the assessment can be challenging in
convection-permitting NWP. First, the strong nonlinearity in the forecast model
limits the methods available for the assessment. Second, convection-permitting
NWP typically uses a limited area model and provides short forecasts, giving
problems with verification and our ability to gather sufficient statistics.
Third, convection-permitting NWP often makes use of novel observations, which
can be difficult to simulate in an observing system simulation experiment
(OSSE). We compare methods that can be used to assess the value of observations
in convection-permitting NWP and discuss operational considerations when using
these methods. We focus on their applicability to ensemble forecasting systems,
as these systems are becoming increasingly dominant for convection-permitting
NWP. We also identify several future research directions: comparison of
forecast validation using analyses and observations, the effect of ensemble
size on assessing the value of observations, flow-dependent covariance
localization, and generation and validation of the nature run in an OSSE.Comment: 35 page
Comparing diagnosed observation uncertainties with independent estimates: a case study using aircraftâbased observations and a convectionâpermitting data assimilation system
Aircraft can report in situ observations of the ambient temperature by using aircraft meteorological data relay (AMDAR) or these can be derived using modeâselect enhanced tracking data (ModeâS EHS). These observations may be assimilated into numerical weather prediction models to improve the initial conditions for forecasts. The assimilation process weights the observation according to the expected uncertainty in its measurement and representation. The goal of this paper is to compare observation uncertainties diagnosed from data assimilation statistics with independent estimates. To quantify these independent estimates, we use metrological comparisons, made with inâsitu researchâgrade instruments, as well as previous studies using collocation methods between aircraft (mostly AMDAR reports) and other observing systems such as radiosondes. In this study, we diagnose a new estimate of the vertical structure of the uncertainty variances using observationâminusâbackground and observationâminusâanalysis statistics from a Met Office limited area threeâdimensional variational data assimilation system (3âkm horizontal gridâlength, 3âhourly cycle). This approach for uncertainty estimation is simple to compute but has several limitations. Nevertheless, the resulting diagnosed variances have a vertical structure that is like that provided by the independent estimates of uncertainty. This provides confidence in the uncertainty estimation method, and in the diagnosed uncertainty estimates themselves. In the future, our methodology, along with other results, could provide ways to estimate the uncertainty for the assimilation of aircraftâbased temperature observations
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