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Aircraft observations derived from Mode‐Select Enhanced Surveillance (Mode‐S EHS) reports are a valuable, high temporo‐spatial resolution, source of upper‐air information that can be assimilated into numerical weather prediction models. At present temperature and wind Mode‐S EHS observations are assimilated into the Met Office's convection‐permitting model, the UKV. These observations are obtained from two different sources, an inhouse set of receivers and via the European Meteorological Aircraft Derived Data Centre (EMADDC). Currently, Mode‐S EHS data are assimilated using the same observation error standard deviation profiles as AMDAR data; however, differing observation processing is anticipated to result in differing error profiles for the Met Office and EMADDC data and for the AMDAR data. Therefore, we estimate new observation error statistics, including error correlations for the two types of Mode‐S EHS data. We also consider the impact of the different aircraft data on the UKV analysis. We find that the observation error standard deviation profiles for wind and temperature are dependent on observation type and season and differ from the current profiles used in the assimilation. Additionally, the Mode‐S EHS observation errors have a considerable spatial correlation that increases with height and is much longer than the spatial thinning distance. The estimated observation influence shows that Mode‐S EHS data are not optimally assimilated, and that the use of updated, observation‐type specific, error profiles is expected to improve the assimilation. The assimilation may be further optimized by modifying the observation thinning distance or including the correlated observation errors in the assimilation. Aircraft observations are a valuable source of information that can be assimilated into numerical weather prediction models. Using data from the Met Office regional system we assess the observation uncertainty and assimilation impact of aircraft observations. Our new results suggest that the current observation uncertainties used in the assimilation are not correct and, as shown by the calculated assimilation impact metrics, the data are not optimally assimilated. Assimilation of aircraft data may be improved by assigning updated observation error profiles and by modifying the observation thinning distance or accounting for correlated observation error statistics.

The skill of numerical weather forecasts strongly depends on the quality of the initial conditions (analyses), which are created by assimilating observations into previous short‐range model forecasts. Therefore, it is important to carefully assess the influence of different observations on the analysis. The degrees of freedom for signal (DFS) is a useful metric for quantifying this influence. While DFS has long been used in variational data assimilation (DA) systems, its application in ensemble‐based DA systems remains limited. In this study, we propose two novel approaches for estimating the DFS in ensemble‐based systems. One approach uses the weighting vector calculated in ensemble transform Kalman filters, while the other uses the innovation vector and observation‐space increment vector. We also propose a new strategy for implementing the DFS approaches in the presence of domain localization, which first estimates DFS locally and then aggregates the results to derive a global DFS value for each observation. Our numerical results show that the DFS per observation decreases as the localization radius increases. More generally, the proposed DFS approaches and implementation strategy have the potential to be used in practice to inform the optimization of observation networks and DA systems. Plain Language Summary Our daily weather forecasts rely on the use of weather observations (e.g., those from weather stations, satellites and radar). A better forecast can be achieved by improvements to the observation network and better use of the observations. To this end, we need to understand which types of observations are more valuable and whether they are being used optimally. Therefore, we propose new approaches for quantifying the value of different observations for weather forecasting. These approaches are specifically used in ensemble forecasting systems, which are widely used to predict high‐impact weather events such as heavy precipitation. Key Points We propose new approaches for estimating the degrees of freedom for signal in ensemble‐based data assimilation systems We present a general strategy for implementing these approaches in the presence of domain localization Numerical results show that the proposed approaches and strategy accurately estimate observation influence

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.

Aircraft can report in situ observations of the ambient temperature by using aircraft meteorological data relay (AMDAR) or these can be derived using mode‐select enhanced tracking data (Mode‐S 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 in‐situ research‐grade 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 observation‐minus‐background and observation‐minus‐analysis statistics from a Met Office limited area three‐dimensional variational data assimilation system (3 km horizontal grid‐length, 3‐hourly 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 aircraft‐based temperature observations. The data assimilation process weights observations according to the expected uncertainty in its measurement and representation. We compare observation uncertainties diagnosed from data assimilation statistics with independent estimates. To quantify these independent estimates, we use metrological comparisons made with in situ research grade instruments; previous studies using collocation methods between aircraft and radiosonde or other nearby aircraft. The resulting diagnosed variances have a vertical structure that is like the independent estimates of uncertainty. This provides confidence in the diagnosed uncertainty estimation method.

The FRANC project (Forecasting Rainfall exploiting new data Assimilation techniques and Novel observations of Convection) has researched improvements in numerical weather prediction of convective rainfall via the reduction of initial condition uncertainty. This article provides an overview of the project’s achievements. We highlight new radar techniques: correcting for attenuation of the radar return; correction for beams that are over 90% blocked by trees or towers close to the radar; and direct assimilation of radar reflectivity and refractivity. We discuss the treatment of uncertainty in data assimilation: new methods for estimation of observation uncertainties with novel applications to Doppler radar winds, Atmospheric Motion Vectors, and satellite radiances; a new algorithm for implementation of spatially-correlated observation error statistics in operational data assimilation; and innovative treatment of moist processes in the background error covariance model. We present results indicating a link between the spatial predictability of convection and convective regimes, with potential to allow improved forecast interpretation. The research was carried out as a partnership between University researchers and the Met Office (UK). We discuss the benefits of this approach and the impact of our research, which has helped to improve operational forecasts for convective rainfall events.

El objetivo de este artículo es explicar el análisis crítico de la religión 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 algún tipo de poder efectivo. El sentimiento de poder es, por tanto, una herramienta para comprender mejor numerosas actitudes y prácticas religiosas.

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 utilizes 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 to 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.

The Convective Precipitation Experiment (COPE) was a joint U.K.–U.S. field campaign held during the summer of 2013 in the southwest peninsula of England, designed to study convective clouds that produce heavy rain leading to flash floods. The clouds form along convergence lines that develop regularly as a result of the topography. Major flash floods have occurred in the past, most famously at Boscastle in 2004. It has been suggested that much of the rain was produced by warm rain processes, similar to some flash floods that have occurred in the United States. The overarching goal of COPE is to improve quantitative convective precipitation forecasting by understanding the interactions of the cloud microphysics and dynamics and thereby to improve numerical weather prediction (NWP) model skill for forecasts of flash floods. Two research aircraft, the University of Wyoming King Air and the U.K. BAe 146, obtained detailed in situ and remote sensing measurements in, around, and below storms on several days. A new fast-scanning X-band dual-polarization Doppler radar made 360° volume scans over 10 elevation angles approximately every 5 min and was augmented by two Met Office C-band radars and the Chilbolton S-band radar. Detailed aerosol measurements were made on the aircraft and on the ground. This paper i) provides an overview of the COPE field campaign and the resulting dataset, ii) presents examples of heavy convective rainfall in clouds containing ice and also in relatively shallow clouds through the warm rain process alone, and iii) explains how COPE data will be used to improve high-resolution NWP models for operational use.

Religioso incluso en su ateísmo, Nietzsche no solamente da que pensar en materia de religión, sino que constituye un momento crítico en la tradición filosófica que se apodera de la cuestión. Las contribuciones a este monográfico pretenden dar una aproximación perspectivista, si no exhaustiva, de lo que la filosofía de Nietzsche debe a la religión como tal, del lugar que ocupa en su pensamiento, así como de la actualidad de estas reflexiones para el lector contemporáneo que, más que nunca, vive a la sombra de Dios.