Using continuous ground-based radar and lidar measurements for evaluating the representation of clouds in four operational models

The ability of four operational weather forecast models [ECMWF, Action de Recherche Petite Echelle Grande Echelle model (ARPEGE), Regional Atmospheric Climate Model (RACMO), and Met Office] to generate a cloud at the right location and time (the cloud frequency of occurrence) is assessed in the present paper using a two-year time series of observations collected by profiling ground-based active remote sensors (cloud radar and lidar) located at three different sites in western Europe (Cabauw. Netherlands; Chilbolton, United Kingdom; and Palaiseau, France). Particular attention is given to potential biases that may arise from instrumentation differences (especially sensitivity) from one site to another and intermittent sampling. In a second step the statistical properties of the cloud variables involved in most advanced cloud schemes of numerical weather forecast models (ice water content and cloud fraction) are characterized and compared with their counterparts in the models. The two years of observations are first considered as a whole in order to evaluate the accuracy of the statistical representation of the cloud variables in each model. It is shown that all models tend to produce too many high-level clouds, with too-high cloud fraction and ice water content. The midlevel and low-level cloud occurrence is also generally overestimated, with too-low cloud fraction but a correct ice water content. The dataset is then divided into seasons to evaluate the potential of the models to generate different cloud situations in response to different large-scale forcings. Strong variations in cloud occurrence are found in the observations from one season to the same season the following year as well as in the seasonal cycle. Overall, the model biases observed using the whole dataset are still found at seasonal scale, but the models generally manage to well reproduce the observed seasonal variations in cloud occurrence. Overall, models do not generate the same cloud fraction distributions and these distributions do not agree with the observations. Another general conclusion is that the use of continuous ground-based radar and lidar observations is definitely a powerful tool for evaluating model cloud schemes and for a responsive assessment of the benefit achieved by changing or tuning a model cloud

A method for estimating the turbulent kinetic energy dissipation rate from a vertically pointing Doppler lidar, and independent evaluation from balloon-borne in situ measurements

A method of estimating dissipation rates from a vertically pointing Doppler lidar with high temporal and spatial resolution has been evaluated by comparison with independent measurements derived from a balloon-borne sonic anemometer. This method utilizes the variance of the mean Doppler velocity from a number of sequential samples and requires an estimate of the horizontal wind speed. The noise contribution to the variance can be estimated from the observed signal-to-noise ratio and removed where appropriate. The relative size of the noise variance to the observed variance provides a measure of the confidence in the retrieval. Comparison with in situ dissipation rates derived from the balloon-borne sonic anemometer reveal that this particular Doppler lidar is capable of retrieving dissipation rates over a range of at least three orders of magnitude. This method is most suitable for retrieval of dissipation rates within the convective well-mixed boundary layer where the scales of motion that the Doppler lidar probes remain well within the inertial subrange. Caution must be applied when estimating dissipation rates in more quiescent conditions. For the particular Doppler lidar described here, the selection of suitably short integration times will permit this method to be applicable in such situations but at the expense of accuracy in the Doppler velocity estimates. The two case studies presented here suggest that, with profiles every 4 s, reliable estimates of ϵ can be derived to within at least an order of magnitude throughout almost all of the lowest 2 km and, in the convective boundary layer, to within 50%. Increasing the integration time for individual profiles to 30 s can improve the accuracy substantially but potentially confines retrievals to within the convective boundary layer. Therefore, optimization of certain instrument parameters may be required for specific implementations.

Evaluating forecasts of the evolution of the cloudy boundary layer using diurnal composites of radar and lidar observations

Observations of boundary-layer cloud have been made using radar and lidar at Chilbolton, Hampshire, UK. These have been compared with output from 7 different global and regional models. Fifty-five cloudy days have been composited to reveal the mean diurnal variation of cloud top and base heights, cloud thickness and liquid water path of the clouds. To enable like-for-like comparison between model and observations, the observations have been averaged on to the grid of each model. The composites show a distinct diurnal cycle in observed cloud; the cloud height exhibits a sinusoidal variation throughout the day with a maximum at around 1600 and a minimum at around 0700 UTC. This diurnal cycle is captured by six of the seven models analysed, although the models generally under-predict both cloud top and cloud base heights throughout the day. The two worst performing models in terms of cloud boundaries also have biases of around a factor of two in liquid water path; these were the only two models that did not include an explicit formulation for cloud-top entrainment.

Vertical velocity variance and skewness in clear and cloud-topped boundary layers as revealed by Doppler lidar

In this paper, observations by a ground-based vertically pointing Doppler lidar and sonic anemometer are used to investigate the diurnal evolution of boundary-layer turbulence in cloudless, cumulus and stratocumulus conditions. When turbulence is driven primarily by surface heating, such as in cloudless and cumulus-topped boundary layers, both the vertical velocity variance and skewness follow similar profiles, on average, to previous observational studies of turbulence in convective conditions, with a peak skewness of around 0.8 in the upper third of the mixed layer. When the turbulence is driven primarily by cloud-top radiative cooling, such as in the presence of nocturnal stratocumulus, it is found that the skewness is inverted in both sign and height: its minimum value of around −0.9 occurs in the lower third of the mixed layer. The profile of variance is consistent with a cloud-top cooling rate of around 30Wm−2. This is also consistent with the evolution of the thermodynamic profile and the rate of growth of the mixed layer into the stable nocturnal boundary layer from above. In conditions where surface heating occurs simultaneously with cloud-top cooling, the skewness is found to be useful for diagnosing the source of the turbulence, suggesting that long-term Doppler lidar observations would be valuable for evaluating boundary-layer parametrization schemes. Copyright c 2009 Royal Meteorological Society

Threshold-free object and ground point separation in LIDAR data

Light Detection And Ranging (LIDAR) is an important modality in terrain and land surveying for many environmental, engineering and civil applications. This paper presents the framework for a recently developed unsupervised classification algorithm called Skewness Balancing for object and ground point separation in airborne LIDAR data. The main advantages of the algorithm are threshold-freedom and independence from LIDAR data format and resolution, while preserving object and terrain details. The framework for Skewness Balancing has been built in this contribution with a prediction model in which unknown LIDAR tiles can be categorised as “hilly” or “moderate” terrains. Accuracy assessment of the model is carried out using cross-validation with an overall accuracy of 95%. An extension to the algorithm is developed to address the overclassification issue for hilly terrain. For moderate terrain, the results show that from the classified tiles detached objects (buildings and vegetation) and attached objects (bridges and motorway junctions) are separated from bare earth (ground, roads and yards) which makes Skewness Balancing ideal to be integrated into geographic information system (GIS) software packages.

Ribose 2′-O-methylation provides a molecular signature for the distinction of self and non-self mRNA dependent on the RNA sensor Mda5

The 5'-cap-structures of higher eukaryote mRNAs are ribose 2'-O-methylated. Likewise, a number of viruses replicating in the cytoplasm of eukayotes have evolved 2'-O-methyltransferases to modify autonomously their mRNAs. However, a defined biological role of mRNA 2'-O-methylation remains elusive. Here we show that viral mRNA 2'-O-methylation is critically involved in subversion of type-I-interferon (IFN-I) induction. We demonstrate that human and murine coronavirus 2'-O-methyltransferase mutants induce increased IFN-I expression, and are highly IFN-I sensitive. Importantly, IFN-I induction by 2'-O-methyltransferase-deficient viruses is dependent on the cytoplasmic RNA sensor melanoma differentiation-associated gene 5 (MDA5). This link between MDA5-mediated sensing of viral RNA and mRNA 2'-O-methylation suggests that RNA modifications, such as 2'-O-methylation, provide a molecular signature for the discrimination of self and non-self mRNA.

Differential recognition of Staphylococcus aureus quorum-sensing signals depends on both extracellular loops 1 and 2 of the transmembrane sensor AgrC.

Virulence in Staphylococcus aureus is regulated via agr-dependent quorum sensing in which an autoinducing peptide (AIP) activates AgrC, a histidine protein kinase. AIPs are usually thiolactones containing seven to nine amino acid residues in which the thiol of the central cysteine is linked to the alpha-carboxyl of the C-terminal amino acid residue. The staphylococcal agr locus has diverged such that the AIPs of the four different S. aureus agr groups self-activate but cross-inhibit. Consequently, although the agr system is conserved among the staphylococci, it has undergone significant evolutionary divergence whereby to retain functionality, any changes in the AIP-encoding gene (agrD) that modifies AIP structure must be accompanied by corresponding changes in the AgrC receptor. Since AIP-1 and AIP-4 only differ by a single amino acid, we compared the transmembrane topology of AgrC1 and AgrC4 to identify amino acid residues involved in AIP recognition. As only two of the three predicted extracellular loops exhibited amino acid differences, site-specific mutagenesis was used to exchange the key AgrC1 and AgrC4 amino acid residues in each loop either singly or in combination. A novel lux-based agrP3 reporter gene fusion was constructed to evaluate the response of the mutated AgrC receptors. The data obtained revealed that while differential recognition of AIP-1 and AIP-4 depends primarily on three amino acid residues in loop 2, loop 1 is essential for receptor activation by the cognate AIP. Furthermore, a single mutation in the AgrC1 loop 2 resulted in conversion of (Ala5)AIP-1 from a potent antagonist to an activator, essentially resulting in the forced evolution of a new AIP group. Taken together, our data indicate that loop 2 constitutes the predicted hydrophobic pocket that binds the AIP thiolactone ring while the exocyclic amino acid tail interacts with loop 1 to facilitate receptor activation.

Feedback dew-point sensor utilising optimally cut plastic optical fibres

A plastic optical fibre reflectance sensor that makes full use of the critical angle of the fibres is implemented to monitor dew formation on a Peltier-cooled reflector surface. The optical configuration permits isolation of optoelectronic components from the sensing head and better light coupling between the reflector and the detecting fibre, giving a better signal of the onset of dew formation on the reflector. Continuous monitoring of the rate of change in reflectance as well as the absolute reflectance signals, the use of a novel polymethyl-methacrylate-coated hydrophobic film reflector on the Peltier element and the application of feedback around the point of dew formation, further reduces the possibility of contamination of the sensor head. Under closed-loop operation, the sensor is capable of cycling around the point of dew formation at a frequency of 2.5 Hz.

Boundary layer dynamics over London, UK, as observed using Doppler lidar during REPARTEE-II

Urban boundary layers (UBLs) can be highly complex due to the heterogeneous roughness and heating of the surface, particularly at night. Due to a general lack of observations, it is not clear whether canonical models of boundary layer mixing are appropriate in modelling air quality in urban areas. This paper reports Doppler lidar observations of turbulence profiles in the centre of London, UK, as part of the second REPARTEE campaign in autumn 2007. Lidar-measured standard deviation of vertical velocity averaged over 30 min intervals generally compared well with in situ sonic anemometer measurements at 190 m on the BT telecommunications Tower. During calm, nocturnal periods, the lidar underestimated turbulent mixing due mainly to limited sampling rate. Mixing height derived from the turbulence, and aerosol layer height from the backscatter profiles, showed similar diurnal cycles ranging from c. 300 to 800 m, increasing to c. 200 to 850 m under clear skies. The aerosol layer height was sometimes significantly different to the mixing height, particularly at night under clear skies. For convective and neutral cases, the scaled turbulence profiles resembled canonical results; this was less clear for the stable case. Lidar observations clearly showed enhanced mixing beneath stratocumulus clouds reaching down on occasion to approximately half daytime boundary layer depth. On one occasion the nocturnal turbulent structure was consistent with a nocturnal jet, suggesting a stable layer. Given the general agreement between observations and canonical turbulence profiles, mixing timescales were calculated for passive scalars released at street level to reach the BT Tower using existing models of turbulent mixing. It was estimated to take c. 10 min to diffuse up to 190 m, rising to between 20 and 50 min at night, depending on stability. Determination of mixing timescales is important when comparing to physico-chemical processes acting on pollutant species measured simultaneously at both the ground and at the BT Tower during the campaign. From the 3 week autumnal data-set there is evidence for occasional stable layers in central London, effectively decoupling surface emissions from air aloft.

Charge measurements in stratiform cloud from a balloon based sensor

Abstract. The electrification of stratiform clouds has is little investigated in comparison with thunderstorms and fair weather atmospheric electricity. Theory indicates that, at the upper and lower horizontal boundaries of layer clouds, charging will arise from vertical flow of cosmogenic ions in the global atmospheric electric circuit. Charge is transferred to droplets and particles, affecting cloud microphysical processes such as collision and droplet activation. Due to the lack of in-situ measurements, the magnitude and distribution of charge in stratiform clouds is not well known. A sensitive, inexpensive, balloon borne charge sensor has been developed to make in-situ measurements of edge charging in stratiform cloud using a standard meteorological radiosonde system. The charge sensor has now been flown through over 20 stratiform clouds and frequently detected charge up to 200 pC m-3 near cloud edges. These results are compared with measurements from the same sensor used to investigate charge in particle layers, such as volcanic ash from the Eyjafjallajökull eruption, and Saharan dust in the Cape Verde Isles. 1.

A wind-tunnel study of flow distortion at a meteorological sensor on top of the BT Tower, London, UK

High quality wind measurements in cities are needed for numerous applications including wind engineering. Such data-sets are rare and measurement platforms may not be optimal for meteorological observations. Two years' wind data were collected on the BT Tower, London, UK, showing an upward deflection on average for all wind directions. Wind tunnel simulations were performed to investigate flow distortion around two scale models of the Tower. Using a 1:160 scale model it was shown that the Tower causes a small deflection (ca. 0.5°) compared to the lattice on top on which the instruments were placed (ca. 0–4°). These deflections may have been underestimated due to wind tunnel blockage. Using a 1:40 model, the observed flow pattern was consistent with streamwise vortex pairs shed from the upstream lattice edge. Correction factors were derived for different wind directions and reduced deflection in the full-scale data-set by <3°. Instrumental tilt caused a sinusoidal variation in deflection of ca. 2°. The residual deflection (ca. 3°) was attributed to the Tower itself. Correction of the wind-speeds was small (average 1%) therefore it was deduced that flow distortion does not significantly affect the measured wind-speeds and the wind climate statistics are reliable.

Evaluating the structure and magnitude of the ash plume during the initial phase of the 2010 Eyjafjallajökull eruption using lidar observations and NAME simulations

The Eyjafjallajökull volcano in Iceland erupted explosively on 14 April 2010, emitting a plume of ash into the atmosphere. The ash was transported from Iceland toward Europe where mostly cloud-free skies allowed ground-based lidars at Chilbolton in England and Leipzig in Germany to estimate the mass concentration in the ash cloud as it passed overhead. The UK Met Office's Numerical Atmospheric-dispersion Modeling Environment (NAME) has been used to simulate the evolution of the ash cloud from the Eyjafjallajökull volcano during the initial phase of the ash emissions, 14–16 April 2010. NAME captures the timing and sloped structure of the ash layer observed over Leipzig, close to the central axis of the ash cloud. Relatively small errors in the ash cloud position, probably caused by the cumulative effect of errors in the driving meteorology en route, result in a timing error at distances far from the central axis of the ash cloud. Taking the timing error into account, NAME is able to capture the sloped ash layer over the UK. Comparison of the lidar observations and NAME simulations has allowed an estimation of the plume height time series to be made. It is necessary to include in the model input the large variations in plume height in order to accurately predict the ash cloud structure at long range. Quantitative comparison with the mass concentrations at Leipzig and Chilbolton suggest that around 3% of the total emitted mass is transported as far as these sites by small (<100 μm diameter) ash particles.

Visible-near infrared spectroscopy sensor for predicting curd and whey composition during cheese processing

The potential of visible-near infrared spectra, obtained using a light backscatter sensor, in conjunction with chemometrics, to predict curd moisture and whey fat content in a cheese vat was examined. A three-factor (renneting temperature, calcium chloride, cutting time), central composite design was carried out in triplicate. Spectra (300–1,100 nm) of the product in the cheese vat were captured during syneresis using a prototype light backscatter sensor. Stirring followed upon cutting the gel, and samples of curd and whey were removed at 10 min intervals and analyzed for curd moisture and whey fat content. Spectral data were used to develop models for predicting curd moisture and whey fat contents using partial least squares regression. Subjecting the spectral data set to Jack-knifing improved the accuracy of the models. The whey fat models (R = 0.91, 0.95) and curd moisture model (R = 0.86, 0.89) provided good and approximate predictions, respectively. Visible-near infrared spectroscopy was found to have potential for the prediction of important syneresis indices in stirred cheese vats.

Novel on-line sensor technology for continuous monitoring of milk coagulation and whey separation in cheese making

The cheese industry has continually sought a robust method to monitor milk coagulation. Measurement of whey separation is also critical to control cheese moisture content, which affects quality. The objective of this study was to demonstrate that an online optical sensor detecting light backscatter in a vat could be applied to monitor both coagulation and syneresis during cheesemaking. A prototype sensor having a large field of view (LFV) relative to curd particle size was constructed. Temperature, cutting time, and calcium chloride addition were varied to evaluate the response of the sensor over a wide range of coagulation and syneresis rates. The LFV sensor response was related to casein micelle aggregation and curd firming during coagulation and to changes in curd moisture and whey fat contents during syneresis. The LFV sensor has potential as an online, continuous sensor technology for monitoring both coagulation and syneresis during cheesemaking.