Global retrieval of ATSR cloud parameters and evaluation (GRAPE): dataset assessment

The Along-Track Scanning Radiometers (ATSRs) provide a long time-series of measurements suitable for the retrieval of cloud properties. This work evaluates the freely-available Global Retrieval of ATSR Cloud Parameters and Evaluation (GRAPE) dataset (version 3) created from the ATSR-2 (1995�2003) and Advanced ATSR (AATSR; 2002 onwards) records. Users are recommended to consider only retrievals flagged as high-quality, where there is a good consistency between the measurements and the retrieved state (corresponding to about 60% of converged retrievals over sea, and more than 80% over land). Cloud properties are found to be generally free of any significant spurious trends relating to satellite zenith angle. Estimates of the random error on retrieved cloud properties are suggested to be generally appropriate for optically-thick clouds, and up to a factor of two too small for optically-thin cases. The correspondence between ATSR-2 and AATSR cloud properties is high, but a relative calibration difference between the sensors of order 5�10% at 660 nm and 870 nm limits the potential of the current version of the dataset for trend analysis. As ATSR-2 is thought to have the better absolute calibration, the discussion focusses on this portion of the record. Cloud-top heights from GRAPE compare well to ground-based data at four sites, particularly for shallow clouds. Clouds forming in boundary-layer inversions are typically around 1 km too high in GRAPE due to poorly-resolved inversions in the modelled temperature profiles used. Global cloud fields are compared to satellite products derived from the Moderate Resolution Imaging Spectroradiometer (MODIS), Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) measurements, and a climatology of liquid water content derived from satellite microwave radiometers. In all cases the main reasons for differences are linked to differing sensitivity to, and treatment of, multi-layer cloud systems. The correlation coefficient between GRAPE and the two MODIS products considered is generally high (greater than 0.7 for most cloud properties), except for liquid and ice cloud effective radius, which also show biases between the datasets. For liquid clouds, part of the difference is linked to choice of wavelengths used in the retrieval. Total cloud cover is slightly lower in GRAPE (0.64) than the CALIOP dataset (0.66). GRAPE underestimates liquid cloud water path relative to microwave radiometers by up to 100 g m�2 near the Equator and overestimates by around 50 g m�2 in the storm tracks. Finally, potential future improvements to the algorithm are outlined.

Characterising North Atlantic weather patterns for climate-optimal aircraft routing

Daily weather patterns over the North Atlantic are classified into relevant types: typical weather patterns that may characterize the range of climate impacts from aviation in this region, for both summer and winter. The motivation is to provide a set of weather types to facilitate an investigation of climate-optimal aircraft routing of trans-Atlantic flights (minimizing the climate impact on a flight-by-flight basis). Using the New York to London route as an example, the time-optimal route times are shown to vary by over 60 min, to take advantage of strong tailwinds or avoid headwinds, and for eastbound routes latitude correlates well with the latitude of the jet stream. The weather patterns are classified by their similarity to the North Atlantic Oscillation and East Atlantic teleconnection patterns. For winter, five types are defined; in summer, when there is less variation in jet latitude, only three types are defined. The types can be characterized by the jet strength and position, and therefore the location of the time-optimal routes varies by type. Simple proxies for the climate impact of carbon dioxide, ozone, water vapour and contrails are defined, which depend on parameters such as the route time, latitude and season, the time spent flying in the stratosphere, and the distance over which the air is supersaturated with respect to ice. These proxies are then shown to vary between weather types and between eastbound and westbound routes.

Development of colour and firmness in strawberry crops is UV light sensitive, but colour is not a good predictor of several quality parameters

BACKGROUND: Strawberry (Fragaria × ananassa Duchesne var. Elsanta) plants were grown in polytunnels covered with three polythene films that transmitted varying levels of ultraviolet (UV) light. Fruit were harvested under near-commercial conditions and quality and yield were measured. During ripening, changes in the colour parameters of individual fruit were monitored, and the accuracy of using surface colour to predict other quality parameters was determined by analysing the correlation between colour and quality parameters within UV treatments. RESULTS: Higher exposure to UV during growth resulted in the fruit becoming darker at harvest and developing surface colour more quickly; fruit were also firmer at harvest, but shelf life was not consistently affected by the UV regime. Surface colour measurements were poorly correlated to firmness, shelf life or total phenolics, anthocyanins and ellagic acid contents. CONCLUSION: Although surface colour of strawberry fruits was affected by the UV regime during growth, and this parameter is an important factor in consumer perception, we concluded that the surface colour at the time of harvest was, contrary to consumer expectations, a poor indicator of firmness, potential shelf life or anthocyanin content. Copyright © 2011 Society of Chemical Industry

Benchmarking and setting intervention targets for key cell count parameters

Herd Companion uses routine milk‐recording records to generate twelve‐month rolling averages that indicate performance trends. This article looks at Herd Somatic Cell Count (SCC) and four other SCC‐related parameters from 252 National Milk Records (NMR) recorded herds to assess how each parameter correlates with the Herd SCC. The analysis provides evidence for the importance of targeting individual cows with high SCC recordings (>200,000 cells/ml and >500,000 cells/ml) and/or individual cows with repeatedly high SCC recordings (chronic high SCC) and/or cows that begin lactation with a high SCC recording (dry period infection) in order to achieve bulk milk Herd SCC below 200,000 cells/ml.

Wind generation's contribution to supporting peak electricity demand: meteorological insights

Wind generation’s contribution to meeting extreme peaks in electricity demand is a key concern for the integration of wind power. In Great Britain (GB), robustly assessing this contribution directly from power system data (i.e. metered wind-supply and electricity demand) is difficult as extreme peaks occur infrequently (by definition) and measurement records are both short and inhomogeneous. Atmospheric circulation-typing combined with meteorological reanalysis data is proposed as a means to address some of these difficulties, motivated by a case study of the extreme peak demand events in January 2010. A preliminary investigation of the physical and statistical properties of these circulation types suggests that they can be used to identify the conditions that are most likely to be associated with extreme peak demand events. Three broad cases are highlighted as requiring further investigation. The high-over-Britain anticyclone is found to be generally associated with very low winds but relatively moderate temperatures (and therefore moderate peak demands, somewhat in contrast to the classic low-wind cold snap that is sometimes apparent in the literature). In contrast, both longitudinally extended blocking over Scotland/Scandinavia and latitudinally extended troughs over western Europe appear to be more closely linked to the very cold GB temperatures (usually associated with extreme peak demands). In both of these latter situations, wind resource averaged across GB appears to be more moderate.

Spatio-temporal surface soil heat flux estimates from satellite data: results for the AMMA experiment at the Fakara (Niger) supersite

This paper describes a method that employs Earth Observation (EO) data to calculate spatiotemporal estimates of soil heat flux, G, using a physically-based method (the Analytical Method). The method involves a harmonic analysis of land surface temperature (LST) data. It also requires an estimate of near-surface soil thermal inertia; this property depends on soil textural composition and varies as a function of soil moisture content. The EO data needed to drive the model equations, and the ground-based data required to provide verification of the method, were obtained over the Fakara domain within the African Monsoon Multidisciplinary Analysis (AMMA) program. LST estimates (3 km × 3 km, one image 15 min−1) were derived from MSG-SEVIRI data. Soil moisture estimates were obtained from ENVISAT-ASAR data, while estimates of leaf area index, LAI, (to calculate the effect of the canopy on G, largely due to radiation extinction) were obtained from SPOT-HRV images. The variation of these variables over the Fakara domain, and implications for values of G derived from them, were discussed. Results showed that this method provides reliable large-scale spatiotemporal estimates of G. Variations in G could largely be explained by the variability in the model input variables. Furthermore, it was shown that this method is relatively insensitive to model parameters related to the vegetation or soil texture. However, the strong sensitivity of thermal inertia to soil moisture content at low values of relative saturation (<0.2) means that in arid or semi-arid climates accurate estimates of surface soil moisture content are of utmost importance, if reliable estimates of G are to be obtained. This method has the potential to improve large-scale evaporation estimates, to aid land surface model prediction and to advance research that aims to explain failure in energy balance closure of meteorological field studies.

Wide-angle X-ray scattering study of structural parameters in non-crystalline polymers

The paper presents an analysis of WAXS (wide-angle X-ray scattering) data which aids an understanding of the structure of non-crystalline polymers. Experimental results are compared with calculations of scattering from possible models. Evidence is presented which supports the view that the chains in molten PE do not lie parallel but have a conformation in accord with the predictions of energy calculations. However, the evidence indicates that in “molten” PTFE the chains lie parallel over distances well in excess of their diameters. WAXS-based proposals are made for the conformations of a-PMMA and a-PS.

Extracting quantitative structural parameters for disordered polymers from neutron scattering data

The organization of non-crystalline polymeric materials at a local level, namely on a spatial scale between a few and 100 a, is still unclear in many respects. The determination of the local structure in terms of the configuration and conformation of the polymer chain and of the packing characteristics of the chain in the bulk material represents a challenging problem. Data from wide-angle diffraction experiments are very difficult to interpret due to the very large amount of information that they carry, that is the large number of correlations present in the diffraction patterns.We describe new approaches that permit a detailed analysis of the complex neutron diffraction patterns characterizing polymer melts and glasses. The coupling of different computer modelling strategies with neutron scattering data over a wide Q range allows the extraction of detailed quantitative information on the structural arrangements of the materials of interest. Proceeding from modelling routes as diverse as force field calculations, single-chain modelling and reverse Monte Carlo, we show the successes and pitfalls of each approach in describing model systems, which illustrate the need to attack the data analysis problem simultaneously from several fronts.

Direct Computation of Stochastic Flow in Reservoirs with Uncertain Parameters

A direct method is presented for determining the uncertainty in reservoir pressure, flow, and net present value (NPV) using the time-dependent, one phase, two- or three-dimensional equations of flow through a porous medium. The uncertainty in the solution is modelled as a probability distribution function and is computed from given statistical data for input parameters such as permeability. The method generates an expansion for the mean of the pressure about a deterministic solution to the system equations using a perturbation to the mean of the input parameters. Hierarchical equations that define approximations to the mean solution at each point and to the field covariance of the pressure are developed and solved numerically. The procedure is then used to find the statistics of the flow and the risked value of the field, defined by the NPV, for a given development scenario. This method involves only one (albeit complicated) solution of the equations and contrasts with the more usual Monte-Carlo approach where many such solutions are required. The procedure is applied easily to other physical systems modelled by linear or nonlinear partial differential equations with uncertain data.

Data assimilation for state and parameter estimation: application to morphodynamic modelling

Data assimilation is predominantly used for state estimation; combining observational data with model predictions to produce an updated model state that most accurately approximates the true system state whilst keeping the model parameters fixed. This updated model state is then used to initiate the next model forecast. Even with perfect initial data, inaccurate model parameters will lead to the growth of prediction errors. To generate reliable forecasts we need good estimates of both the current system state and the model parameters. This paper presents research into data assimilation methods for morphodynamic model state and parameter estimation. First, we focus on state estimation and describe implementation of a three dimensional variational(3D-Var) data assimilation scheme in a simple 2D morphodynamic model of Morecambe Bay, UK. The assimilation of observations of bathymetry derived from SAR satellite imagery and a ship-borne survey is shown to significantly improve the predictive capability of the model over a 2 year run. Here, the model parameters are set by manual calibration; this is laborious and is found to produce different parameter values depending on the type and coverage of the validation dataset. The second part of this paper considers the problem of model parameter estimation in more detail. We explain how, by employing the technique of state augmentation, it is possible to use data assimilation to estimate uncertain model parameters concurrently with the model state. This approach removes inefficiencies associated with manual calibration and enables more effective use of observational data. We outline the development of a novel hybrid sequential 3D-Var data assimilation algorithm for joint state-parameter estimation and demonstrate its efficacy using an idealised 1D sediment transport model. The results of this study are extremely positive and suggest that there is great potential for the use of data assimilation-based state-parameter estimation in coastal morphodynamic modelling.

Precise calculations of the existence of multiple AMOC equilibria in coupled climate models Part I: equilibrium states

This study examines criteria for the existence of two stable states of the Atlantic Meridional Overturning Circulation (AMOC) using a combination of theory and simulations from a numerical coupled atmosphere–ocean climate model. By formulating a simple collection of state parameters and their relationships, the authors reconstruct the North Atlantic Deep Water (NADW) OFF state behavior under a varying external salt-flux forcing. This part (Part I) of the paper examines the steady-state solution, which gives insight into the mechanisms that sustain the NADW OFF state in this coupled model; Part II deals with the transient behavior predicted by the evolution equation. The nonlinear behavior of the Antarctic Intermediate Water (AAIW) reverse cell is critical to the OFF state. Higher Atlantic salinity leads both to a reduced AAIW reverse cell and to a greater vertical salinity gradient in the South Atlantic. The former tends to reduce Atlantic salt export to the Southern Ocean, while the latter tends to increases it. These competing effects produce a nonlinear response of Atlantic salinity and salt export to salt forcing, and the existence of maxima in these quantities. Thus the authors obtain a natural and accurate analytical saddle-node condition for the maximal surface salt flux for which a NADW OFF state exists. By contrast, the bistability indicator proposed by De Vries and Weber does not generally work in this model. It is applicable only when the effect of the AAIW reverse cell on the Atlantic salt budget is weak.

Profiling dehydrin gene sequence and physiological parameters in drought tolerant and susceptible spring wheat cultivars.

Physiological and yield traits such as stomatal conductance (mmol m-2s-1), Leaf relative water content (RWC %) and grain yield per plant were studied in a separate experiment. Results revealed that five out of sixteen cultivars viz. Anmol, Moomal, Sarsabz, Bhitai and Pavan, appeared to be relatively more drought tolerant. Based on morphophysiological results, studies were continued to look at these cultivars for drought tolerance at molecular level. Initially, four well recognized primers for dehydrin genes (DHNs) responsible for drought induction in T. durum L., T. aestivum L. and O. sativa L. were used for profiling gene sequence of sixteen wheat cultivars. The primers amplified the DHN genes variably like Primer WDHN13 (T. aestivum L.) amplified the DHN gene in only seven cultivars whereas primer TdDHN15 (T. durum L.) amplified all the sixteen cultivars with even different DNA banding patterns some showing second weaker DNA bands. Third primer TdDHN16 (T. durum L.) has shown entirely different PCR amplification prototype, specially showing two strong DNA bands while fourth primer RAB16C (O. sativa L.) failed to amplify DHN gene in any of the cultivars. Examination of DNA sequences revealed several interesting features. First, it identified the two exon/one intron structure of this gene (complete sequences were not shown), a feature not previously described in the two database cDNA sequences available from T. aestivum L. (gi|21850). Secondly, the analysis identified several single nucleotide polymorphisms (SNPs), positions in gene sequence. Although complete gene sequence was not obtained for all the cultivars, yet there were a total of 38 variable positions in exonic (coding region) sequence, from a total gene length of 453 nucleotides. Matrix of SNP shows these 37 positions with individual sequence at positions given for each of the 14 cultivars (sequence of two cultivars was not obtained) included in this analysis. It demonstrated a considerable diversity for this gene with only three cultivars i.e. TJ-83, Marvi and TD-1 being similar to the consensus sequence. All other cultivars showed a unique combination of SNPs. In order to prove a functional link between these polymorphisms and drought tolerance in wheat, it would be necessary to conduct a more detailed study involving directed mutation of this gene and DHN gene expression.

RACORO extended-term, aircraft observations of boundary-layer clouds

A first-of-a-kind, extended-term cloud aircraft campaign was conducted to obtain an in-situ statistical characterization of continental boundary-layer clouds needed to investigate cloud processes and refine retrieval algorithms. Coordinated by the Atmospheric Radiation Measurement (ARM) Aerial Facility (AAF), the Routine AAF Clouds with Low Optical Water Depths (CLOWD) Optical Radiative Observations (RACORO) field campaign operated over the ARM Southern Great Plains (SGP) site from 22 January to 30 June 2009, collecting 260 h of data during 59 research flights. A comprehensive payload aboard the Center for Interdisciplinary Remotely-Piloted Aircraft Studies (CIRPAS) Twin Otter aircraft measured cloud microphysics, solar and thermal radiation, physical aerosol properties, and atmospheric state parameters. Proximity to the SGP's extensive complement of surface measurements provides ancillary data that supports modeling studies and facilitates evaluation of a variety of surface retrieval algorithms. The five-month duration enabled sampling a range of conditions associated with the seasonal transition from winter to summer. Although about two-thirds of the cloud flights occurred in May and June, boundary-layer cloud fields were sampled under a variety of environmental and aerosol conditions, with about 77% of the flights occurring in cumulus and stratocumulus. Preliminary analyses illustrate use of these data to analyze cloud-aerosol relationships, characterize the horizontal variability of cloud radiative impacts, and evaluate surface-based retrievals. We discuss how an extended-term campaign requires a simplified operating paradigm that is different from that used for typical, short-term, intensive aircraft field programs.