A study on the sensitivities of simulated aerosol optical properties to composition and size distribution using airborne measurements

We present a flexible framework to calculate the optical properties of atmospheric aerosols at a given relative humidity based on their composition and size distribution. The similarity of this framework to climate model parameterisations allows rapid and extensive sensitivity tests of the impact of uncertainties in data or of new measurements on climate relevant aerosol properties. The data collected by the FAAM BAe-146 aircraft during the EUCAARI-LONGREX and VOCALS-REx campaigns have been used in a closure study to analyse the agreement between calculated and measured aerosol optical properties for two very different aerosol types. The agreement achieved for the EUCAARI-LONGREX flights is within the measurement uncertainties for both scattering and absorption. However, there is poor agreement between the calculated and the measured scattering for the VOCALS-REx flights. The high concentration of sulphate, which is a scattering aerosol with no absorption in the visible spectrum, made the absorption measurements during VOCALS-REx unreliable, and thus no closure study was possible for the absorption. The calculated hygroscopic scattering growth factor overestimates the measured values during EUCAARI-LONGREX and VOCALS-REx by ∼30% and ∼20%, respectively. We have also tested the sensitivity of the calculated aerosol optical properties to the uncertainties in the refractive indices, the hygroscopic growth factors and the aerosol size distribution. The largest source of uncertainty in the calculated scattering is the aerosol size distribution (∼35%), followed by the assumed hygroscopic growth factor for organic aerosol (∼15%), while the predominant source of uncertainty in the calculated absorption is the refractive index of organic aerosol (28–60%), although we would expect the refractive index of black carbon to be important for aerosol with a higher black carbon fraction.

Using reanalysis data to quantify extreme wind power generation statistics : a 33 year case study in Great Britain

With a rapidly increasing fraction of electricity generation being sourced from wind, extreme wind power generation events such as prolonged periods of low (or high) generation and ramps in generation, are a growing concern for the efficient and secure operation of national power systems. As extreme events occur infrequently, long and reliable meteorological records are required to accurately estimate their characteristics. Recent publications have begun to investigate the use of global meteorological “reanalysis” data sets for power system applications, many of which focus on long-term average statistics such as monthly-mean generation. Here we demonstrate that reanalysis data can also be used to estimate the frequency of relatively short-lived extreme events (including ramping on sub-daily time scales). Verification against 328 surface observation stations across the United Kingdom suggests that near-surface wind variability over spatiotemporal scales greater than around 300 km and 6 h can be faithfully reproduced using reanalysis, with no need for costly dynamical downscaling. A case study is presented in which a state-of-the-art, 33 year reanalysis data set (MERRA, from NASA-GMAO), is used to construct an hourly time series of nationally-aggregated wind power generation in Great Britain (GB), assuming a fixed, modern distribution of wind farms. The resultant generation estimates are highly correlated with recorded data from National Grid in the recent period, both for instantaneous hourly values and for variability over time intervals greater than around 6 h. This 33 year time series is then used to quantify the frequency with which different extreme GB-wide wind power generation events occur, as well as their seasonal and inter-annual variability. Several novel insights into the nature of extreme wind power generation events are described, including (i) that the number of prolonged low or high generation events is well approximated by a Poission-like random process, and (ii) whilst in general there is large seasonal variability, the magnitude of the most extreme ramps is similar in both summer and winter. An up-to-date version of the GB case study data as well as the underlying model are freely available for download from our website: http://www.met.reading.ac.uk/~energymet/data/Cannon2014/.

Sources, sinks, and transatlantic transport of North African dust aerosol: a multimodel analysis and comparison with remote sensing data

This study evaluates model-simulated dust aerosols over North Africa and the North Atlantic from five global models that participated in the Aerosol Comparison between Observations and Models phase II model experiments. The model results are compared with satellite aerosol optical depth (AOD) data from Moderate Resolution Imaging Spectroradiometer (MODIS), Multiangle Imaging Spectroradiometer (MISR), and Sea-viewing Wide Field-of-view Sensor, dust optical depth (DOD) derived from MODIS and MISR, AOD and coarse-mode AOD (as a proxy of DOD) from ground-based Aerosol Robotic Network Sun photometer measurements, and dust vertical distributions/centroid height from Cloud Aerosol Lidar with Orthogonal Polarization and Atmospheric Infrared Sounder satellite AOD retrievals. We examine the following quantities of AOD and DOD: (1) the magnitudes over land and over ocean in our study domain, (2) the longitudinal gradient from the dust source region over North Africa to the western North Atlantic, (3) seasonal variations at different locations, and (4) the dust vertical profile shape and the AOD centroid height (altitude above or below which half of the AOD is located). The different satellite data show consistent features in most of these aspects; however, the models display large diversity in all of them, with significant differences among the models and between models and observations. By examining dust emission, removal, and mass extinction efficiency in the five models, we also find remarkable differences among the models that all contribute to the discrepancies of model-simulated dust amount and distribution. This study highlights the challenges in simulating the dust physical and optical processes, even in the best known dust environment, and stresses the need for observable quantities to constrain the model processes.

On the cause of a magnetospheric flux transfer event

We present a detailed investigation of a magnetospheric flux transfer event (FTE) seen by the Active Magnetospheric Tracer Explorer (AMPTE) UKS and IRM satellites around 1046 UT on October 28, 1984. This event has been discussed many times previously in the literature and has been cited as support for a variety of theories of FTE formation. We make use of a model developed to reproduce ion precipitations seen in the cusp ionosphere. The analysis confirms that the FTE is well explained as a brief excursion into an open low-latitude boundary layer (LLBL), as predicted by two theories of magnetospheric FTEs: namely, that they are bulges in the open LLBL due to reconnection rate enhancements or that they are indentations of the magnetopause by magnetosheath pressure increases (but in the presence of ongoing steady reconnection). The indentation of the inner edge of the open LLBL that these two models seek to explain is found to be shallow for this event. The ion model reproduces the continuous evolution of the ion distribution function between the sheath-like population at the event center and the surrounding magnetospheric populations; it also provides an explanation of the high-pressure core of the event as comprising field lines that were reconnected considerably earlier than those that are draped over it to give the event boundary layer. The magnetopause transition parameter is used to isolate a field rotation on the boundaries of the core, which is subjected to the tangential stress balance test. The test identifies this to be a convecting structure, which is neither a rotational discontinuity (RD) nor a contact discontinuity, but could possibly be a slow shock. In addition, evidence for ion reflection off a weak RD on the magnetospheric side of this structure is found. The event structure is consistent in many ways with features predicted for the open LLBL by analytic MHD theories and by MHD and hybrid simulations. The de Hoffman-Teller velocity of the structure is significantly different from that of the magnetosheath flow, indicating that it is not an indentation caused by a high-pressure pulse in the sheath but is consistent with the motion of newly opened field lines (different from the sheath flow because of the magnetic tension force) deduced from the best fit to the ion data. However, we cannot here rule out the possibility that the sheath flow pattern has changed in the long interval between the two satellites observing the FTE and subsequently emerging into the magnetosheath; thus this test is not conclusive in this particular case. Analysis of the fitted elapsed time since reconnection shows that the core of the event was reconnected in one pulse and the event boundary layer was reconnected in a subsequent pulse. Between these two pulses is a period of very low (but nonzero) reconnection rate, which lasts about 14 mins. Thus the analysis supports, but does not definitively verify, the concept that the FTE is a partial passage into an open LLBL caused by a traveling bulge in that layer produced by a pulse in reconnection rate.

Location and characteristics of the reconnection X line deduced from low-altitude satellite and ground-based observations: 2. Defense Meteorological Satellite Program and European Incoherent Scatter data

We present an analysis of a cusp ion step, observed by the Defense Meteorological Satellite Program (DMSP) F10 spacecraft, between two poleward moving events of enhanced ionospheric electron temperature, observed by the European Incoherent Scatter (EISCAT) radar. From the ions detected by the satellite, the variation of the reconnection rate is computed for assumed distances along the open-closed field line separatrix from the satellite to the X line, do. Comparison with the onset times of the associated ionospheric events allows this distance to be estimated, but with an uncertainty due to the determination of the low-energy cutoff of the ion velocity distribution function, ƒ(ν). Nevertheless, the reconnection site is shown to be on the dayside magnetopause, consistent with the reconnection model of the cusp during southward interplanetary magnetic field (IMF). Analysis of the time series of distribution function at constant energies, ƒ(ts), shows that the best estimate of the distance do is 14.5±2 RE. This is consistent with various magnetopause observations of the signatures of reconnection for southward IMF. The ion precipitation is used to reconstruct the field-parallel part of the Cowley D ion distribution function injected into the open low-latitude boundary layer in the vicinity of the X line. From this reconstruction, the field-aligned component of the magnetosheath flow is found to be only −55±65 km s−1 near the X line, which means either that the reconnection X line is near the stagnation region at the nose of the magnetosphere, or that it is closely aligned with the magnetosheath flow streamline which is orthogonal to the magnetosheath field, or both. In addition, the sheath Alfvén speed at the X line is found to be 220±45 km s−1, and the speed with which newly opened field lines are ejected from the X line is 165±30 km s−1. We show that the inferred magnetic field, plasma density, and temperature of the sheath near the X line are consistent with a near-subsolar reconnection site and confirm that the magnetosheath field makes a large angle (>58°) with the X line.

Computationally efficient rule-based classification for continuous streaming data

Advances in hardware and software technologies allow to capture streaming data. The area of Data Stream Mining (DSM) is concerned with the analysis of these vast amounts of data as it is generated in real-time. Data stream classification is one of the most important DSM techniques allowing to classify previously unseen data instances. Different to traditional classifiers for static data, data stream classifiers need to adapt to concept changes (concept drift) in the stream in real-time in order to reflect the most recent concept in the data as accurately as possible. A recent addition to the data stream classifier toolbox is eRules which induces and updates a set of expressive rules that can easily be interpreted by humans. However, like most rule-based data stream classifiers, eRules exhibits a poor computational performance when confronted with continuous attributes. In this work, we propose an approach to deal with continuous data effectively and accurately in rule-based classifiers by using the Gaussian distribution as heuristic for building rule terms on continuous attributes. We show on the example of eRules that incorporating our method for continuous attributes indeed speeds up the real-time rule induction process while maintaining a similar level of accuracy compared with the original eRules classifier. We termed this new version of eRules with our approach G-eRules.

The statistical cusp: a flux transfer event model

Traditionally, the cusp has been described in terms of a time-stationary feature of the magnetosphere which allows access of magnetosheath-like plasma to low altitudes. Statistical surveys of data from low-altitude spacecraft have shown the average characteristics and position of the cusp. Recently, however, it has been suggested that the ionospheric footprint of flux transfer events (FTEs) may be identified as variations of the “cusp” on timescales of a few minutes. In this model, the cusp can vary in form between a steady-state feature in one limit and a series of discrete ionospheric FTE signatures in the other limit. If this time-dependent cusp scenario is correct, then the signatures of the transient reconnection events must be able, on average, to reproduce the statistical cusp occurrence previously determined from the satellite observations. In this paper, we predict the precipitation signatures which are associated with transient magnetopause reconnection, following recent observations of the dependence of dayside ionospheric convection on the orientation of the IMF. We then employ a simple model of the longitudinal motion of FTE signatures to show how such events can easily reproduce the local time distribution of cusp occurrence probabilities, as observed by low-altitude satellites. This is true even in the limit where the cusp is a series of discrete events. Furthermore, we investigate the existence of double cusp patches predicted by the simple model and show how these events may be identified in the data.

Midday auroral breakup events and related energy and momentum transfer from the magnetosheath

Combined observations by meridian-scanning photometers, all-sky auroral TV camera and the EISCAT radar permitted a detailed analysis of the temporal and spatial development of the midday auroral breakup phenomenon and the related ionospheric ion flow pattern within the 71°–75° invariant latitude radar field of view. The radar data revealed dominating northward and westward ion drifts, of magnitudes close to the corresponding velocities of the discrete, transient auroral forms, during the two different events reported here, characterized by IMF |BY/BZ| < 1 and > 2, respectively (IMF BZ between −8 and −3 nT and BY > 0). The spatial scales of the discrete optical events were ∼50 km in latitude by ∼500 km in longitude, and their lifetimes were less than 10 min. Electric potential enhancements with peak values in the 30–50 kV range are inferred along the discrete arc in the IMF |BY/BZ| < 1 case from the optical data and across the latitudinal extent of the radar field of view in the |BY/BZ| > 2 case. Joule heat dissipation rates in the maximum phase of the discrete structures of ∼ 100 ergs cm−2 s−1 (0.1 W m−2) are estimated from the photometer intensities and the ion drift data. These observations combined with the additional characteristics of the events, documented here and in several recent studies (i.e., their quasi-periodic nature, their motion pattern relative to the persistent cusp or cleft auroral arc, the strong relationship with the interplanetary magnetic field and the associated ion drift/E field events and ground magnetic signatures), are considered to be strong evidence in favour of a transient, intermittent reconnection process at the dayside magnetopause and associated energy and momentum transfer to the ionosphere in the polar cusp and cleft regions. The filamentary spatial structure and the spectral characteristics of the optical signature indicate associated localized ˜1-kV potential drops between the magnetopause and the ionosphere during the most intense auroral events. The duration of the events compares well with the predicted characteristic times of momentum transfer to the ionosphere associated with the flux transfer event-related current tubes. It is suggested that, after this 2–10 min interval, the sheath particles can no longer reach the ionosphere down the open flux tube, due to the subsequent super-Alfvénic flow along the magnetopause, conductivities are lower and much less momentum is extracted from the solar wind by the ionosphere. The recurrence time (3–15 min) and the local time distribution (∼0900–1500 MLT) of the dayside auroral breakup events, combined with the above information, indicate the important roles of transient magnetopause reconnection and the polar cusp and cleft regions in the transfer of momentum and energy between the solar wind and the magnetosphere.

Analysis of incoherent scatter spectra from non-Maxwellian plasma

Incoherent scatter data from non-thermal F-region ionospheric plasma are analysed, using theoretical spectra predicted by Raman et al. It is found that values of the semi-empirical drift parameter D∗, associated with deviations of the ion velocity distribution from a Maxwellian, and the plasma temperatures can be rigorously deduced (the results being independent of the path of iteration) if the angle between the line-of-sight and the geomagnetic field is larger than about 15–20 degrees. For small aspect angles, the deduced value of the average (or 3-D) ion temperature remains ambiguous and the analysis is restricted to the determination of the line-of-sight temperature because the theoretical spectrum is insensitive to non-thermal effects when the plasma is viewed along directions almost parallel to the magnetic field. This limitation is expected to apply to any realistic model of the ion velocity distribution, and its consequences are discussed. Fit strategies which allow for mixed ion composition are also considered. Examples of fits to data from various EISCAT observing programmes are presented.

On the determination of ion temperature in the auroral F-region ionosphere

Assessment is made of the effect of the assumed form for the ion velocity distribution function on estimates of three-dimensional ion temperature from one-dimensional observations. Incoherent scatter observations by the EISCAT radar at a variety of aspect angles are used to demonstrate features of ion temperature determination and to study the ion velocity distribution function. One form of the distribution function which has recently been widely used In the interpretation of EISCAT measurements, is found to be consistent with the data presented here, in that no deviation from a Maxwellian can be detected for observations along the magnetic field line and that the ion temperature and its anisotropy are accurately predicted. It is shown that theoretical predictions of the anisotropy by Monte Carlo computations are very accurate, the observed value being greater by only a few percent. It is also demonstrated for the case studied that errors of up to 93% are introduced into the ion temperature estimate if the anisotropy is neglected. Observations at an aspect angle of 54.7°, which are not subject to this error, have a much smaller uncertainty (less than 1%) due to the adopted form of the distribution of line-of-sight velocity.

Assessing the sensitivities of a distributed snow model to forcing data resolution

Highly heterogeneous mountain snow distributions strongly affect soil moisture patterns; local ecology; and, ultimately, the timing, magnitude, and chemistry of stream runoff. Capturing these vital heterogeneities in a physically based distributed snow model requires appropriately scaled model structures. This work looks at how model scale—particularly the resolutions at which the forcing processes are represented—affects simulated snow distributions and melt. The research area is in the Reynolds Creek Experimental Watershed in southwestern Idaho. In this region, where there is a negative correlation between snow accumulation and melt rates, overall scale degradation pushed simulated melt to earlier in the season. The processes mainly responsible for snow distribution heterogeneity in this region—wind speed, wind-affected snow accumulations, thermal radiation, and solar radiation—were also independently rescaled to test process-specific spatiotemporal sensitivities. It was found that in order to accurately simulate snowmelt in this catchment, the snow cover needed to be resolved to 100 m. Wind and wind-affected precipitation—the primary influence on snow distribution—required similar resolution. Thermal radiation scaled with the vegetation structure (~100 m), while solar radiation was adequately modeled with 100–250-m resolution. Spatiotemporal sensitivities to model scale were found that allowed for further reductions in computational costs through the winter months with limited losses in accuracy. It was also shown that these modeling-based scale breaks could be associated with physiographic and vegetation structures to aid a priori modeling decisions.

Parametrizing the horizontal inhomogeneity of ice water content using CloudSat data products

In order to calculate unbiased microphysical and radiative quantities in the presence of a cloud, it is necessary to know not only the mean water content but also the distribution of this water content. This article describes a study of the in-cloud horizontal inhomogeneity of ice water content, based on CloudSat data. In particular, by focusing on the relations with variables that are already available in general circulation models (GCMs), a parametrization of inhomogeneity that is suitable for inclusion in GCM simulations is developed. Inhomogeneity is defined in terms of the fractional standard deviation (FSD), which is given by the standard deviation divided by the mean. The FSD of ice water content is found to increase with the horizontal scale over which it is calculated and also with the thickness of the layer. The connection to cloud fraction is more complicated; for small cloud fractions FSD increases as cloud fraction increases while FSD decreases sharply for overcast scenes. The relations to horizontal scale, layer thickness and cloud fraction are parametrized in a relatively simple equation. The performance of this parametrization is tested on an independent set of CloudSat data. The parametrization is shown to be a significant improvement on the assumption of a single-valued global FSD

Prediction of metabolisable energy concentrations of fresh cut grass using digestibility data measured with non-pregnant non-lactating cows

Pasture-based ruminant production systems are common in certain areas of the world, but energy evaluation in grazing cattle is performed with equations developed, in their majority, with sheep or cattle fed total mixed rations. The aim of the current study was to develop predictions of metabolisable energy (ME) concentrations in fresh-cut grass offered to non-pregnant non-lactating cows at maintenance energy level, which may be more suitable for grazing cattle. Data were collected from three digestibility trials performed over consecutive grazing seasons. In order to cover a range of commercial conditions and data availability in pasture-based systems, thirty-eight equations for the prediction of energy concentrations and ratios were developed. An internal validation was performed for all equations and also for existing predictions of grass ME. Prediction error for ME using nutrient digestibility was lowest when gross energy (GE) or organic matter digestibilities were used as sole predictors, while the addition of grass nutrient contents reduced the difference between predicted and actual values, and explained more variation. Addition of N, GE and diethyl ether extract (EE) contents improved accuracy when digestible organic matter in DM was the primary predictor. When digestible energy was the primary explanatory variable, prediction error was relatively low, but addition of water-soluble carbohydrates, EE and acid-detergent fibre contents of grass decreased prediction error. Equations developed in the current study showed lower prediction errors when compared with those of existing equations, and may thus allow for an improved prediction of ME in practice, which is critical for the sustainability of pasture-based systems.

How Pragmatically Odd! Interface Delays and Pronominal Subject Distribution in the L2 Spanish of English Natives

Sorace (2000, 2005) has claimed that while L2 learners can easily acquire properties of L2 narrow syntax they have significant difficulty with regard to interpretation and the discourse distribution of related properties, resulting in so-called residual optionality. However, there is no consensus as to what this difficulty indicates. Is it related to an insurmountable grammatical representational deficit (in the sense of representation deficit approaches; e.g. Beck 1998, Franceschina 2001, Hawkins 2005), is it due to cross-linguistic interference, or is it just a delay due to a greater complexity involved in the acquisition of interface-conditioned properties? In this article, I explore the L2 distribution of null and overt subject pronouns of English speaking learners of L2 Spanish. While intermediate learners clearly have knowledge of the syntax of Spanish null subjects, they do not have target-like pragmatic knowledge of their distribution with overt subjects. The present data demonstrate, however, that this difficulty is overcome at highly advanced stages of L2 development, thus suggesting that properties at the syntax-pragmatics interface are not destined for inevitable fossilization.

Testing projected wild bee distributions in agricultural habitats: predictive power depends on species traits and habitat type

Species distribution models (SDM) are increasingly used to understand the factors that regulate variation in biodiversity patterns and to help plan conservation strategies. However, these models are rarely validated with independently collected data and it is unclear whether SDM performance is maintained across distinct habitats and for species with different functional traits. Highly mobile species, such as bees, can be particularly challenging to model. Here, we use independent sets of occurrence data collected systematically in several agricultural habitats to test how the predictive performance of SDMs for wild bee species depends on species traits, habitat type, and sampling technique. We used a species distribution modeling approach parametrized for the Netherlands, with presence records from 1990 to 2010 for 193 Dutch wild bees. For each species, we built a Maxent model based on 13 climate and landscape variables. We tested the predictive performance of the SDMs with independent datasets collected from orchards and arable fields across the Netherlands from 2010 to 2013, using transect surveys or pan traps. Model predictive performance depended on species traits and habitat type. Occurrence of bee species specialized in habitat and diet was better predicted than generalist bees. Predictions of habitat suitability were also more precise for habitats that are temporally more stable (orchards) than for habitats that suffer regular alterations (arable), particularly for small, solitary bees. As a conservation tool, SDMs are best suited to modeling rarer, specialist species than more generalist and will work best in long-term stable habitats. The variability of complex, short-term habitats is difficult to capture in such models and historical land use generally has low thematic resolution. To improve SDMs’ usefulness, models require explanatory variables and collection data that include detailed landscape characteristics, for example, variability of crops and flower availability. Additionally, testing SDMs with field surveys should involve multiple collection techniques.