Height-resolved Scaling Properties of Tropospheric Water Vapour based on Airborne Lidar Observations

Two-dimensional vertical water vapour cross sections of the free troposphere between altitudes of 2 and 10 km, measured by nadir-viewing airborne differential-absorption lidar with high spatial resolution, were analyzed using structure functions up to the fifth order. We found scale invariance, i.e. a power-law dependency of structure function on length scale, for scales between 5 and 100 km, for the horizontal time series of water vapour mixing ratio. In contrast to one-dimensional in situ measurements, the two-dimensional water vapor lidar observations allow height-resolved analyses of power-law scaling exponents at a vertical resolution of 200 m. The data reveal significantly different scaling properties above and below an air-mass boundary. They stem from three very dissimilar aircraft campaigns: COPS/ETReC over middle and southern Europe in summer 2007, T-PARC around Japan mostly over sea in late summer 2008, and T-IPY around Spitsbergen over sea in winter 2008. After discarding flight segments with low lidar signals or large data gaps, and after averaging horizontally to a resolution of between 1 and 5 km to obtain a high signal to noise ratio, structure functions were computed for 20 flights at various heights, adding up to a length of more than 300,000 km. The power-law scaling exponents of the structure functions do not show significant latitudinal, seasonal or land/sea dependency, but they do differ between air masses influenced by moist convection and air masses aloft, not influenced. A classification of the horizontal water vapour time series into two groups according to whether the series occurred above or below the level of nearby convective cloud tops could be performed by detecting the cloud top height from the lidar backscatter signal in the corresponding flight segment. We found that the scaling exponents can be divided into two groups depending on the respective air mass: The smoothness of the time series, expressed by the first-order scaling exponent, varies from less than 0.5 in the low-level convectively influenced air masses to values greater than 0.5 and most frequently near 0.6 in the higher-level air above the convective cloud tops. The time series’ intermittency, expressed by the variation of the scaling exponent with increasing order, is larger in convectively influenced air masses. These differences in variability strongly suggest that convection provides a source of moisture variability on small scales. Our results show that the high horizontal and vertical resolution of lidar observations allows a characterisation of the scale dependency of the water vapour field at scales close to and smaller than the smallest resolved scales in modern weather and climate models. This provides both a reference for validation of high resolution models and a basis for the design of stochastic or pdf-based parameterisations of clouds and convection

A simple dynamical model of cumulus convection for data assimilation research

A simplified model for cumulus convection has been developed, with the aim of providing a computationally inexpensive, but physically plausible, environment for developing methods for convective-scale data assimilation. Key processes, including gravity waves, conditional instability and precipitation formation, are represented, and parameter values are chosen to reproduce the most important space and time scales of cumulus clouds. The model is shown to reproduce the classic life cycle of an isolated convective storm. When provided with a low amplitude noise source to trigger convection, the model produces a statistically steady state with cloud size and cloud spacing distributions similar to those found in radiative-convective equilibrium simulations using a cloud resolving model. Results are also shown for convection triggered by flow over an orgraphic obstacle, where depending on the wind speed two regimes are found with convection trapped over the mountain, or propagating downstream. The model features prognostic variables for wind and rain that can be used to compute synthetic observations for data assimilation experiments

Bowhead Whale (Balaena Mysticetus) Length Estimations Based on Scapula Measurements

This study presents data and a method for reliably predicting bowhead (Balaena mysticetus) length using one length and one width measurement of the scapula. The bowhead scapula preserves well and is common in coastal arctic archaeological sites. The length measurement is taken as the maximum straight-line measurement along the axis, excluding the scapular cartilage. The width measurement is recorded as a maximum transverse measurement of the ossified portion only. Using a least squares linear regression analysis to evaluate the scapula and whale length relationship, a strong correlation between the width and length measurement and whale length is demonstrated. This technique is useful because estimates of live whale length from complete or near complete scapula can be made to less than one metre.Key words: bowhead whale, Thule, zooarchaeology, taphonomy, arctic archaeology, aboriginal whalingCette étude offre des données et présente une méthode de prédiction fiable de la taille de la baleine boréale (Balaena mysticetus), établie à l'aide d'une mesure de la longueur et de la largeur de l'omoplate. L'omoplate de la baleine boréale se conserve bien et on en trouve de nombreux exemplaires sur les sites archéologiques de la côte arctique. La mesure de la longueur correspond à la distance maximale en ligne droite le long de l'axe, sans tenir compte du cartilage scapulaire. La mesure de la largeur correspond à la distance transversale maximale de la partie ossifiée uniquement. L'utilisation d'une analyse de régression linéaire des moindres carrés pour évaluer le rapport entre la dimension de l'omoplate et celle de la baleine, révèle qu'il existe une forte corrélation entre les mesures de la largeur et de la longueur de l'omoplate et la longueur de la baleine. Cette technique s'avère utile vu qu'on peut déduire la longueur de l'animal vivant à partir d'une omoplate complète ou quasi complète, et ce, avec une marge d'erreur de moins d'un mètre.Mots clés: baleine boréale, Thulé, zooarchéologie, taphonomie, archéologie arctique, pêche à la baleine par les aborigène

Convergence of forecast distributions in a 100,000-member idealised convective-scale ensemble

Many operational weather services use ensembles of forecasts to generate probabilistic predictions. Computational costs generally limit the size of the ensemble to fewer than 100 members, although the large number of degrees of freedom in the forecast model would suggest that a vastly larger ensemble would be required to represent the forecast probability distribution accurately. In this study, we use a computationally efficient idealised model that replicates key properties of the dynamics and statistics of cumulus convection to identify how the sampling uncertainty of statistical quantities converges with ensemble size. Convergence is quantified by computing the width of the 95% confidence interval of the sampling distribution of random variables, using bootstrapping on the ensemble distributions at individual time and grid points. Using ensemble sizes of up to 100,000 members, it was found that for all computed distribution properties, including mean, variance, skew, kurtosis, and several quantiles, the sampling uncertainty scaled as n-1/2 for sufficiently large ensemble size n. This behaviour is expected from the Central Limit Theorem, which further predicts that the magnitude of the uncertainty depends on the distribution shape, with a large uncertainty for statistics that depend on rare events. This prediction was also confirmed, with the additional observation that such statistics also required larger ensemble sizes before entering the asymptotic regime. By considering two methods for evaluating asymptotic behaviour in small ensembles, we show that the large-n theory can be applied usefully for some forecast quantities even for the ensemble sizes in operational use today

Extreme precipitation events over northern Italy. Part I: A systematic classification with machine‐learning techniques

Extreme precipitation events (EPEs) are meteorological phenomena of major concern for society. They can have different characteristics depending on the physical mechanisms responsible for their generation, which in turn depend on the large and mesoscale conditions. This work provides a systematic classification of EPEs over northern–central Italy, one of the regions in Europe with the highest frequency of these events. The EPE statistics have been deduced using the new high‐resolution precipitation dataset ArCIS (Climatological Archive for Central–Northern Italy), that gathers together a very high number of daily, quality‐controlled and homogenized observations from different networks of 11 Italian regions. Gridded precipitation is aggregated over Italian operational warning‐area units (WA). EPEs are defined as events in which daily average precipitation in at least one of the 94 WAs exceeds the 99th percentile with respect to the climate reference 1979–2015. A list of 887 events is compiled, significantly enlarging the database compared to any previous study of EPEs. EPEs are separated into three different dynamical classes: Cat1, events mainly attributable to frontal/orographic uplift; Cat2, events due to frontal uplift with (equilibrium) deep convection embedded; Cat3, events mainly generated by non‐equilibrium deep convection. A preliminary version of this classification is based on fixed thresholds of environmental parameters, but the final version is obtained using a more robust machine‐learning unsupervised K‐means clustering and random forest algorithm. All events are characterized by anomalously high integrated water vapour transport (IVT). This confirms IVT as an important large‐scale predictor, especially for Cat2 events, which is shown to be the most important category in terms of impacts and EPE area extension. Large IVT values are caused by upper‐level waves associated with remotely triggered Rossby wave packets, as shown for two example Cat2 events

Can Artificial Intelligence‐Based Weather Prediction Models Simulate the Butterfly Effect?

We investigate error growth from small-amplitude initial condition perturbations, simulated with a recent artificial intelligence-based weather prediction model. From past simulations with standard physically-based numerical models as well as from theoretical considerations it is expected that such small-amplitude initial condition perturbations would grow very fast initially. This fast growth then sets a fixed and fundamental limit to the predictability of weather, a phenomenon known as the butterfly effect. We find however, that the AI-based model completely fails to reproduce the rapid initial growth rates and hence would incorrectly suggest an unlimited predictability of the atmosphere. In contrast, if the initial perturbations are large and comparable to current uncertainties in the estimation of the initial state, the AI-based model basically agrees with physically-based simulations, although some deficits are still present

Spectroscopy and Dynamics of the Predissociated, Quasi-linear S2 State of Chlorocarbene

In this work, we report on the spectroscopy and dynamics of the quasi-linear S2 state of chlorocarbene, CHCl, and its deuterated isotopologue using optical-optical double resonance (OODR) spectroscopy through selected rovibronic levels of the S1 state. This study, which represents the first observation of the S2 state in CHCl, builds upon our recent examination of the corresponding state in CHF, where pronounced mode specificity was observed in the dynamics, with predissociation rates larger for levels containing bending excitation. In the present work, a total of 14 S2 state vibrational levels with angular momentum ℓ = 1 were observed for CHCl, and 34 levels for CDCl. The range of ℓ in this case was restricted by the pronounced Renner-Teller effect in the low-lying S1 levels, which severely reduces the fluorescence lifetime for levels with Ka \u3e 0. Nonetheless, by exploiting different intermediate S1 levels, we observed progressions involving all three fundamental vibrations. For levels with long predissociation lifetimes, rotational constants were determined by measuring spectra through different intermediate J levels of the S1 state. Plots of the predissociation linewidth (lifetime) vs. energy for various S2 levels show an abrupt onset, which lies near the calculated threshold for elimination to form C(3P) + HCl on the triplet surface. Our experimental results are compared with a series of high level ab initio calculations, which included the use of a dynamically weighted full-valence CASSCF procedure, focusing maximum weight on the state of interest (the singlet and triplet states were computed separately). This was used as the reference for subsequent Davidson-corrected MRCI(+Q) calculations. These calculations reveal the presence of multiple conical intersections in the singlet manifold

Isolation of nine microsatellite loci in Dolichogenidea homoeosomae (Hymenoptera) a parasitoid of the sunflower moth Homoeosoma electellum (Lepidoptera)

Nine microsatellite loci were isolated from the insect Dolichogenidea homoeosomae (Hymenoptera: Braconidae), an important parasitoid of the sunflower moth Homosoeosoma electellum (Lepidoptera: Pyralidae), and assayed for polymorphism. All nine loci were polymorphic within the five populations tested, with two to 14 alleles per locus. Expected and observed heterozygosities ranged from 0.39 to 0.90 and 0.25 to 0.72 respectively. These are the first microsatellite primers developed for D. homeosomae and will be useful for studies of population dynamics and connectivity. © 2006 Blackwell Publishing Ltd

The three-dimensional structure of fronts in mid-latitude weather systems in numerical weather prediction models

Atmospheric fronts are a widely used conceptual model in meteorology, most encountered as two-dimensional (2-D) front lines on surface analysis charts. The three-dimensional (3-D) dynamical structure of fronts has been studied in the literature by means of “standard” 2-D maps and cross-sections and is commonly sketched in 3-D illustrations of idealized weather systems in atmospheric science textbooks. However, only recently has the feasibility of the objective detection and visual analysis of 3-D frontal structures and their dynamics within numerical weather prediction (NWP) data been proposed, and such approaches are not yet widely known in the atmospheric science community. In this article, we investigate the benefit of objective 3-D front detection for case studies of extra-tropical cyclones and for comparison of frontal structures between different NWP models. We build on a recent gradient-based detection approach, combined with modern 3-D interactive visual analysis techniques, and adapt it to handle data from state-of-the-art NWP models including those run at convection-permitting kilometre-scale resolution. The parameters of the detection method (including data smoothing and threshold parameters) are evaluated to yield physically meaningful structures. We illustrate the benefit of the method by presenting two case studies of frontal dynamics within mid-latitude cyclones. Examples include joint interactive visual analysis of 3-D fronts and warm conveyor belt (WCB) trajectories, as well as identification of the 3-D frontal structures characterizing the different stages of a Shapiro–Keyser cyclogenesis event. The 3-D frontal structures show agreement with 2-D fronts from surface analysis charts and augment the surface charts by providing additional pertinent information in the vertical dimension. A second application illustrates the relation between convection and 3-D cold-front structure by comparing data from simulations with parameterized and explicit convection. Finally, we consider “secondary fronts” that commonly appear in UK Met Office surface analysis charts. Examination of a case study shows that for this event the secondary front is not a temperature-dominated but a humidity-dominated feature. We argue that the presented approach has great potential to be beneficial for more complex studies of atmospheric dynamics and for operational weather forecasting

Grist: Grid-based Data Mining for Astronomy

The Grist project is developing a grid-technology based system as a research environment for astronomy with massive and complex datasets. This knowledge extraction system will consist of a library of distributed grid services controlled by a work ow system, compliant with standards emerging from the grid computing, web services, and virtual observatory communities. This new technology is being used to find high redshift quasars, study peculiar variable objects, search for transients in real time, and fit SDSS QSO spectra to measure black hole masses. Grist services are also a component of the "hyperatlas" project to serve high-resolution multi-wavelength imagery over the Internet. In support of these science and outreach objectives, the Grist framework will provide the enabling fabric to tie together distributed grid services in the areas of data access, federation, mining, subsetting, source extraction, image mosaicking, statistics, and visualization