Centre d'Enseignement et de Recherche en Environnement Atmosphérique

In this paper, we focus on the retrospective topology correction of surfaces. We propose a technique to accurately correct the spherical topology of cortical surfaces. Specifically, we construct a mapping from the original surface onto the sphere to detect topological defects as minimal nonhomeomorphic regions. The topology of each defect is then corrected by opening and sealing the surface along a set of nonseparating loops that are selected in a Bayesian framework. The proposed method is a wholly self-contained topology correction algorithm, which determines geometrically accurate, topologically correct solutions based on the magnetic resonance imaging (MRI) intensity profile and the expected local curvature. Applied to real data, our method provides topological corrections similar to those made by a trained operator.

This paper is a summary of the "Best Practice Guideline" (BPG) document (Franke et al., 2007) produced in the framework of the European COST Action 732 "Quality assurance and improvement of micro-scale meteorological models", available from the site given in the reference section. The full document provides guidelines for undertaking simulations that are used to evaluate micro-scale obstacle-accommodating meteorological models. This paper provides an overview of the topics covered in the full document without reproducing the specific recommendations.

We commonly refer to state estimation theory in geosciences as data assimilation (DA). This term encompasses the entire sequence of operations that, starting from the observations of a system, and from additional statistical and dynamical information (such as a dynamical evolution model), provides an estimate of its state. DA is standard practice in numerical weather prediction, but its application is becoming widespread in many other areas of climate, atmosphere, ocean, and environment modeling; in all circumstances where one intends to estimate the state of a large dynamical system based on limited information. While the complexity of DA, and of the methods thereof, stands on its interdisciplinary nature across statistics, dynamical systems, and numerical optimization, when applied to geosciences, an additional difficulty arises by the continually increasing sophistication of the environmental models. Thus, in spite of DA being nowadays ubiquitous in geosciences, it has so far remained a topic mostly reserved to experts. We aim this overview article at geoscientists with a background in mathematical and physical modeling, who are interested in the rapid development of DA and its growing domains of application in environmental science, but so far have not delved into its conceptual and methodological complexities. This article is categorized under: Climate Models and Modeling > Knowledge Generation with Models.

A database of 15,617 point measurements of dimethylsulfide (DMS) in surface waters along with lesser amounts of data for aqueous and particulate dimethylsulfoniopropionate concentration, chlorophyll concentration, sea surface salinity and temperature, and wind speed has been assembled. The database was processed to create a series of climatological annual and monthly 1°×1° latitude‐longitude squares of data. The results were compared to published fields of geophysical and biological parameters. No significant correlation was found between DMS and these parameters, and no simple algorithm could be found to create monthly fields of sea surface DMS concentration based on these parameters. Instead, an annual map of sea surface DMS was produced using an algorithm similar to that employed by Conkright et al. [1994]. In this approach, a first‐guess field of DMS sea surface concentration measurements is created and then a correction to this field is generated based on actual measurements. Monthly sea surface grids of DMS were obtained using a similar scheme, but the sparsity of DMS measurements made the method difficult to implement. A scheme was used which projected actual data into months of the year where no data were otherwise present.

Global‐scale emissions of carbonaceous aerosol from fossil fuel usage have been calculated with a resolution of 1° × 1°. Emission factors for black and organic carbon have been gathered from the literature and applied to domestic, transport, and industrial combustion of various fuel types. In addition, allowance has been made for the level of development when calculating emissions from a country. Emissions have been calculated for 185 countries for the domestic, industrial, and transport sectors using a fuel usage database published by the United Nations [1993]. Some inconsistencies were found for a small number of countries with regard to the distribution of fuel usage between the industrial and domestic sectors. Care has been taken to correct for this using data from the fuel use database for the period 1970–1990. Emissions based on total particulate matter (TPM) and submicron emission factors have been calculated. Global emissions for 1984 of black carbon total 6.4 TgC yr −1 and organic carbon emissions of 10.1 TgC yr −1 were found using bulk aerosol emission factors, while global black carbon emissions of 5.1 TgC yr −1 and organic carbon emissions of 7.0 TgC yr −1 were found using submicron emission factors. Use of the database is quite flexible and can be easily updated as emission factor data are updated. There is at least a factor of 2 uncertainty in the derived emissions due to the lack of exactly appropriate emission data. The emission fields have been introduced into the ECHAM4 atmospheric general circulation model and run for 5 model years. Monthly mean model results are compared to measurements in regions influenced by anthropogenic fossil fuel emissions. The resultant aerosol fields have been used to calculate the instantaneous solar radiative forcing at the top of the troposphere due to an external mixture of fossil fuel derived black carbon and organic carbon aerosol. Column burdens of 0.143 mgBC m −2 and 0.170 mgOC m −2 were calculated. Because of secondary production of organic carbon aerosol, it is recommended that the burden of organic carbon aerosol be doubled to 0.341 mgOC m −2 . The resultant forcing when clouds are included is +0.173 W m −2 for black carbon and −0.024 W m −2 for organic carbon (×2) as a global annual average. The results are compared to previous works, and the differences are discussed.

Following Mandelbrot's theory of fractals, the area-perimeter relation is used to investigate the geometry of satellite- and radar-determined cloud and rain areas between 1 and 1.2 x 10(6) square kilometers. The data are well fit by a formula in which the perimeter is given approximately by the square root of the area raised to the power D [See equation in the PDF], where D is interpreted as the fractal dimension of the perimeter. It is concluded that rain and cloud perimeters are fractals-they have no characteristic horizontal length scale between 1 and 1000 kilometers.

The main objective of the COST Action 732 is the improvement and qualityassurance of micro-scale obstacle-accommodating meteorological models and theirapplication to the prediction of flow and transport processes in urban or industrialenvironments. This report contains the full best practice guidelines for undertaking simulations that areused to evaluate microscale obstacle-accommodating meteorological modelsSummaries of this report have been published as the following documents : Franke, J., Hellsten, A., Schlunzen, H. A., & Carissimo, B. (2010). The Best Practise Guideline for the CFD simulation of flows in the urban environment: an outcome of COST 732. In The Fifth International Symposium on Computational Wind Engineering (CWE2010) (pp. 1-10)Franke, J., Hellsten, A., Schlunzen, K. H., & Carissimo, B. (2011). The COST 732 Best Practice Guideline for CFD simulation of flows in the urban environment: a summary. International Journal of Environment and Pollution, 44(1-4), 419-427.

Gas and particle measurements are described for optically thick regional hazes, dominated by aged smoke from biomass burning, in the cerrado and rain forested regions of Brazil. The hazes tended to be evenly mixed from the surface to the trade wind inversion at 3–4 km in altitude. The properties of aged gases and particles in the regional hazes were significantly different from those of young smoke (<4 min old). As the smoke aged, the total amount of carbon in non‐methane hydrocarbon species (C<11) was depleted by about one third due to transformations into CO 2 , CO, and reactive molecules, and removed by dry deposition and/or by conversion to particulate matter. As the smoke particles aged, their sizes increased significantly due to coagulation and mass growth by secondary species (e.g., ammonium, organic acids and sulfate). During aging, condensation and gas‐to‐particle conversion of inorganic and organic vapors increased the aerosol mass by ∼20–40%. One third to one half of this mass growth likely occurred in the first few hours of aging due to the condensation of large organic molecules. The remaining mass growth was probably associated with photochemical and cloud‐processing mechanisms operating over several days. Changes in particle sizes and compositions during aging had a large impact on the optical properties of the aerosol. Over a 2 to 4 day period, the fine particle mass‐scattering efficiency and single‐scattering albedo increased by 1 m 2 g −1 , and ∼0.06, respectively. Conversely, the Angstrom coefficient, backscatter ratio, and mass absorption efficiency decreased significantly with age.

We present laboratory studies and field observations that explore the role of aminium salt formation in atmospheric nanoparticle growth. These measurements were performed using the Thermal Desorption Chemical Ionization Mass Spectrometer (TDCIMS) and Ultrafine Hygroscopicity Tandem Differential Mobility Analyzers. Laboratory measurements of alkylammonium-carboxylate salt nanoparticles show that these particles exhibit lower volatilities and only slightly lower hygroscopicities than ammonium sulfate nanoparticles. TDCIMS measurements of these aminium salts showed that the protonated amines underwent minimal decomposition during analysis, with detection sensitivities comparable to those of organic and inorganic deprotonated acids. TDCIMS observations made of a new particle formation event in an urban site in Tecamac, Mexico, clearly indicate the presence of protonated amines in 8-10 nm diameter particles accounting for about 47% of detected positive ions; 13 nm particles were hygroscopic with an average 90% RH growth factor of 1.42. Observations of a new particle formation event in a remote forested site in Hyytiälä, Finland, show the presence of aminium ions with deprotonated organic acids; 23% of the detected positive ions during this event are attributed to aminium salts while 10 nm particles had an average 90% RH growth factor of 1.27. Similar TDCIMS observations during events in Atlanta and in the vicinity of Boulder, Colorado, show that aminium salts accounted for 10-35% of detected positive ions. We conclude that aminium salts contribute significantly to nanoparticle growth and must be accounted for in models to accurately predict the impact of new particle formation on climate.

A daily analysis of African dust concentrations in the Mediterranean atmosphere has been made between June 1983 and December 1994 using the International Satellite Cloud Climatology Project (ISCCP‐B2) archive of Meteosat visible (VIS) channel images. The ISCCP‐B2 archive of Meteosat infrared (IR) images has also been used to determine the frequencies of dust mobilization over the continent, north of 30°N. Despite a large daily variability, climatological results show a clear seasonal cycle with a maximum during the dry season: dust transport begins over the eastern basin in spring and spreads over the western basin in summer. These patterns are shown to be related to both cyclogenesis over North Africa and rainfall over the Mediterranean Sea. Indeed, the frequency of dust mobilization over the continent and of dust outbreaks over the sea are strongly related to the climatology of depressions affecting North Africa. Precipitations appear to be an important factor explaining both the seasonal east‐west shift in transport location and the south‐north gradients of dust concentrations over the Mediterranean.

Representation, representativity, representativeness error, forward interpolation error, forward model error, observation‐operator error, aggregation error and sampling error are all terms used to refer to components of observation error in the context of data assimilation. This article is an attempt to consolidate the terminology that has been used in the earth sciences literature and was suggested at a European Space Agency workshop held in Reading in April 2014. We review the state of the art and, through examples, motivate the terminology. In addition to a theoretical framework, examples from application areas of satellite data assimilation, ocean reanalysis and atmospheric chemistry data assimilation are provided. Diagnosing representation‐error statistics as well as their use in state‐of‐the‐art data assimilation systems is discussed within a consistent framework.

Abstract The Cévennes–Vivarais Mediterranean Hydrometeorological Observatory (OHM-CV) is a research initiative aimed at improving the understanding and modeling of the Mediterranean intense rain events that frequently result in devastating flash floods in southern France. A primary objective is to bring together the skills of meteorologists and hydrologists, modelers and instrumentalists, researchers and practitioners, to cope with these rather unpredictable events. In line with previously published flash-flood monographs, the present paper aims at documenting the 8–9 September 2002 catastrophic event, which resulted in 24 casualties and an economic damage evaluated at 1.2 billion euros (i.e., about 1 billion U.S. dollars) in the Gard region, France. A description of the synoptic meteorological situation is first given and shows that no particular precursor indicated the imminence of such an extreme event. Then, radar and rain gauge analyses are used to assess the magnitude of the rain event, which was particularly remarkable for its spatial extent with rain amounts greater than 200 mm in 24 h over 5500 km2. The maximum values of 600–700 mm observed locally are among the highest daily records in the region. The preliminary results of the postevent hydrological investigation show that the hydrologic response of the upstream watersheds of the Gard and Vidourle Rivers is consistent with the marked space–time structure of the rain event. It is noteworthy that peak specific discharges were very high over most of the affected areas (5–10 m3 s−1 km−2) and reached locally extraordinary values of more than 20 m3 s−1 km−2. A preliminary analysis indicates contrasting hydrological behaviors that seem to be related to geomorphological factors, notably the influence of karst in part of the region. An overview of the ongoing meteorological and hydrological research projects devoted to this case study within the OHM-CV is finally presented.

Airborne measurements of the emissions of gases and particles from 19 individual forest, cerrado, and pasture fires in Brazil were obtained during the Smoke, Clouds, and Radiation‐Brazil (SCAR‐B) study in August‐September 1995. Emission factors were determined for a number of major and minor gaseous and particulate species, including carbon dioxide, carbon monoxide, sulfur dioxide, nitrogen oxides, methane, nonmethane hydrocarbons, halocarbons, particulate (black and organic) carbon, and particulate ionic species. The magnitude of the emission factors for gaseous species were determined primarily by the relative amounts of flaming and smoldering combustion, rather than differences in vegetation type. Hydrocarbons and halocarbons were well correlated with CO, which is indicative of emissions primarily associated with smoldering combustion. Although there was large variability between fires, higher emission factors for SO 2 and NO χ were associated with an increased ratio of flaming to smoldering combustion; this could be due to variations in the amounts of sulfur and nitrogen in the fuels. Emission factors for particles were not so clearly associated with smoldering combustion as those for hydrocarbons. The emission factors measured in this study are similar to those measured previously in Brazil and Africa. However, particle emission factors from fires in Brazil appear to be roughly 20 to 40% lower than those from North American boreal forest fires.

Abstract. Online coupled mesoscale meteorology atmospheric chemistry models have undergone a rapid evolution in recent years. Although mainly developed by the air quality modelling community, these models are also of interest for numerical weather prediction and regional climate modelling as they can consider not only the effects of meteorology on air quality, but also the potentially important effects of atmospheric composition on weather. Two ways of online coupling can be distinguished: online integrated and online access coupling. Online integrated models simulate meteorology and chemistry over the same grid in one model using one main time step for integration. Online access models use independent meteorology and chemistry modules that might even have different grids, but exchange meteorology and chemistry data on a regular and frequent basis. This article offers a comprehensive review of the current research status of online coupled meteorology and atmospheric chemistry modelling within Europe. Eighteen regional online coupled models developed or being used in Europe are described and compared. Topics discussed include a survey of processes relevant to the interactions between atmospheric physics, dynamics and composition; a brief overview of existing online mesoscale models and European model developments; an analysis on how feedback processes are treated in these models; numerical issues associated with coupled models; and several case studies and model performance evaluation methods. Finally, this article highlights selected scientific issues and emerging challenges that require proper consideration to improve the reliability and usability of these models for the three scientific communities: air quality, numerical meteorology modelling (including weather prediction) and climate modelling. This review will be of particular interest to model developers and users in all three fields as it presents a synthesis of scientific progress and provides recommendations for future research directions and priorities in the development, application and evaluation of online coupled models.

Black carbon mass absorption efficiencies of smoke particles were measured for various types of biomass fires during the Smoke, Clouds, and Radiation‐Brazil (SCAR‐B) experiment using thermal evolution measurements for black carbon and optical absorption methods. The obtained results range between 5.2 and 19.3 m 2 g −1 with an average value of 12.1±4.0 m 2 g −1 . Particle size distributions and optical properties were also measured to provide a full set of physical parameters for modeling calculations. Mie theory was used to model the optical properties of the particles assuming both internal and external mixtures coupling the modeling calculations with the experimental results obtained during the campaign. For internal mixing, a particle model with a layered structure consisting of an absorbing black carbon core, surrounded by a nonabsorbing shell, was assumed. Also, for internal mixing, a discrete dipole approximation code was used to simulate packed soot clusters commonly found in electron microscopy photographs of filters collected during the experiment. The modeled results for layered spheres and packed clusters explain black carbon mass absorption coefficients up to values of about 25 m 2 g −1 , but measurements show even higher values which were correlated with the chemical composition and characteristics of the structure of the particles. Unrealistic high values of black carbon absorption efficiencies were linked to high concentrations of K, which influence the volatilization of black carbon (BC) at lower temperatures than usual, possibly causing artifacts in the determination of BC by thermal technique. The modeling results are compared with nephelometer and light absorption measurements.

Abstract. Data assimilation is used in atmospheric chemistry models to improve air quality forecasts, construct re-analyses of three-dimensional chemical (including aerosol) concentrations and perform inverse modeling of input variables or model parameters (e.g., emissions). Coupled chemistry meteorology models (CCMM) are atmospheric chemistry models that simulate meteorological processes and chemical transformations jointly. They offer the possibility to assimilate both meteorological and chemical data; however, because CCMM are fairly recent, data assimilation in CCMM has been limited to date. We review here the current status of data assimilation in atmospheric chemistry models with a particular focus on future prospects for data assimilation in CCMM. We first review the methods available for data assimilation in atmospheric models, including variational methods, ensemble Kalman filters, and hybrid methods. Next, we review past applications that have included chemical data assimilation in chemical transport models (CTM) and in CCMM. Observational data sets available for chemical data assimilation are described, including surface data, surface-based remote sensing, airborne data, and satellite data. Several case studies of chemical data assimilation in CCMM are presented to highlight the benefits obtained by assimilating chemical data in CCMM. A case study of data assimilation to constrain emissions is also presented. There are few examples to date of joint meteorological and chemical data assimilation in CCMM and potential difficulties associated with data assimilation in CCMM are discussed. As the number of variables being assimilated increases, it is essential to characterize correctly the errors; in particular, the specification of error cross-correlations may be problematic. In some cases, offline diagnostics are necessary to ensure that data assimilation can truly improve model performance. However, the main challenge is likely to be the paucity of chemical data available for assimilation in CCMM.

A new miniaturized glucose oxidase based needle-type glucose microsensor has been developed for subcutaneous glucose monitoring. The sensor is equivalent in shape and size to a 26-guage needle (0.45-mm o.d.) and can be implanted with ease without any incision. The novel configuration greatly facilitates the deposition of enzyme and polymer films so that sensors with characteristics suitable for in vivo use (upper limit of linear range greater than 15 mM, response time less than 5 min, and sensitivity yielding a 5:1 signal-to-background ratio at normal basal glucose levels) can be prepared in high yield (greater than 60%). The sensor response is largely independent of oxygen tension in the normal physiological range. It also exhibits good selectivity against common interferences except for the exogenous drug acetaminophen.

Data assimilation (DA) and uncertainty quantification (UQ) are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics. Typical applications span from computational fluid dynamics (CFD) to geoscience and climate systems. Recently, much effort has been given in combining DA, UQ and machine learning (ML) techniques. These research efforts seek to address some critical challenges in high-dimensional dynamical systems, including but not limited to dynamical system identification, reduced order surro-gate modelling, error covariance specification and model error correction. A large number of developed techniques and methodologies exhibit a broad applicability across numerous domains, resulting in the necessity for a comprehensive guide. This paper provides the first overview of state-of-the-art researches in this interdisciplinary field, covering a wide range of applications. This review is aimed at ML scientists who attempt to apply DA and UQ techniques to improve the accuracy and the interpretability of their models, but also at DA and UQ experts who intend to integrate cutting-edge ML approaches to their systems. Therefore, this article has a special focus on how ML methods can overcome the existing limits of DA and UQ, and vice versa. Some exciting perspectives of this rapidly developing research field are also discussed.

Abstract. A continuous measurement for black carbon (hereafter "BC") in a 40 m shallow ice core retrieved from the East Rongbuk Glacier (hereafter "ERG") in the northeast saddle of Mt. Qomolangma (Everest) provided the first historical record of BC deposition during the past ~50 yrs in the high Himalyas. Apparent increasing trend (smooth average) of BC concentrations was revealed since the mid-1990s. Seasonal variability of BC concentrations in the ice core indicated higher concentrations in monsoon seasons than those in non-monsoon seasons. Backward air trajectory analysis by the HYSPLIT model indicated that South Asia's BC emissions had significant impacts on the BC deposition in the Mt. Qomolangma (Everest) region. The estimated average atmospheric BC concentration in the region was about 80 ng m−3 during 1951–2001. And it was suggested BC emitted from South Asia could penetrate into the Tibetan Plateau by climbing over the elevated Himalayas. A significant increasing trend of the radiative forcing simulated by the SNICAR model appeared since 1990, which even exceeded 4.5 W m−2 in the summer of 2001. It was suggested that this amplitudes of BC concentrations in the atmosphere over the Himalayas and consequently in the ice in the glaciers could not be neglected when assessing the dual warming effects on glacier melting in the Himalayas.

Abstract This review discusses recent advances in geophysical data assimilation beyond Gaussian statistical modeling, in the fields of meteorology, oceanography, as well as atmospheric chemistry. The non-Gaussian features are stressed rather than the nonlinearity of the dynamical models, although both aspects are entangled. Ideas recently proposed to deal with these non-Gaussian issues, in order to improve the state or parameter estimation, are emphasized. The general Bayesian solution to the estimation problem and the techniques to solve it are first presented, as well as the obstacles that hinder their use in high-dimensional and complex systems. Approximations to the Bayesian solution relying on Gaussian, or on second-order moment closure, have been wholly adopted in geophysical data assimilation (e.g., Kalman filters and quadratic variational solutions). Yet, nonlinear and non-Gaussian effects remain. They essentially originate in the nonlinear models and in the non-Gaussian priors. How these effects are handled within algorithms based on Gaussian assumptions is then described. Statistical tools that can diagnose them and measure deviations from Gaussianity are recalled. The following advanced techniques that seek to handle the estimation problem beyond Gaussianity are reviewed: maximum entropy filter, Gaussian anamorphosis, non-Gaussian priors, particle filter with an ensemble Kalman filter as a proposal distribution, maximum entropy on the mean, or strictly Bayesian inferences for large linear models, etc. Several ideas are illustrated with recent or original examples that possess some features of high-dimensional systems. Many of the new approaches are well understood only in special cases and have difficulties that remain to be circumvented. Some of the suggested approaches are quite promising, and sometimes already successful for moderately large though specific geophysical applications. Hints are given as to where progress might come from.