ALBERT

Description: This article presents and assesses an algorithm that constructs 3D distributions of cloud from passive satellite imagery and collocated 2D nadir profiles of cloud properties inferred synergistically from lidar, cloud radar and imager data. It effectively widens the active–passive retrieved cross-section (RXS) of cloud properties, thereby enabling computation of radiative fluxes and radiances that can be compared with measured values in an attempt to perform radiative closure experiments that aim to assess the RXS. For this introductory study, A-train data were used to verify the scene-construction algorithm and only 1D radiative transfer calculations were performed. The construction algorithm fills off-RXS recipient pixels by computing sums of squared differences (a cost function F ) between their spectral radiances and those of potential donor pixels/columns on the RXS. Of the RXS pixels with F lower than a certain value, the one with the smallest Euclidean distance to the recipient pixel is designated as the donor, and its retrieved cloud properties and other attributes such as 1D radiative heating rates are consigned to the recipient. It is shown that both the RXS itself and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery can be reconstructed extremely well using just visible and thermal infrared channels. Suitable donors usually lie within 10 km of the recipient. RXSs and their associated radiative heating profiles are reconstructed best for extensive planar clouds and less reliably for broken convective clouds. Domain-average 1D broadband radiative fluxes at the top of the atmosphere (TOA) for (21 km) 2 domains constructed from MODIS, CloudSat and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) data agree well with coincidental values derived from Clouds and the Earth's Radiant Energy System (CERES) radiances: differences between modelled and measured reflected shortwave fluxes are within ±10 W m −2 for ∼35% of the several hundred domains constructed for eight orbits. Correspondingly, for outgoing longwave radiation ∼65% are within ±10 W m −2 . Copyright © 2011 Royal Meteorological Society and Crown in the right of Canada

Description: The West African summer monsoon (WAM) is an important driver of the global climate and locally provides most of the annual rainfall. A solid climatological knowledge of the complex vertical cloud structure is invaluable to forecasters and modelers to improve the understanding of the WAM. In this paper, 4 years of data from the CloudSat profiling radar and CALIPSO are used to create a composite zonal mean vertical cloud and precipitation structure for the WAM. For the first time, the near-coincident vertical radar and lidar profiles allow for the identification of individual cloud types from optically thin cirrus and shallow cumulus to congestus and deep convection. A clear diurnal signal in zonal mean cloud structure is observed for the WAM, with deep convective activity enhanced at night producing extensive anvil and cirrus, while daytime observations show more shallow cloud and congestus. A layer of altocumulus is frequently observed over the Sahara at night and day, extending southward to the coastline, and the majority of this cloud is shown to contain supercooled liquid in the top. The occurrence of deep convective systems and congestus in relation to the position of the African easterly jet is studied, but only the daytime cumulonimbus distribution indicates some influence of the jet position.

Description: 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 (

Description: [1]  The Ice Particle Size Distribution (PSD) is fundamental to the quantitative description of a cloud. It is also crucial in the development of remote sensing retrieval techniques using radar and/or lidar measurements. The PSD allows one to link characteristics of individual particles (area, mass, scattering properties) to characteristics of an ensemble of particles in a sampling volume (e.g. visible extinction ( σ ), Ice Water Content (IWC), radar reflectivity (Z)). The aim of this study is to describe a normalisation technique to represent the PSD. We update an earlier study by including recent in-situ measurements covering a large variety of ice clouds spanning temperatures ranging between -80 °C and 0 °C. This new data set also includes direct measurements of IWC. We demonstrate that is possible to scale the PSD in size space by the volume weighted diameter D m and in the concentration space by the intercept parameter and obtain the intrinsic shape of the PSD. Therefore, by combining , D m and a modified gamma function representing the normalised PSD shape, we are able to approximate key cloud variables (such as IWC) as well as cloud properties which can be remotely observed (such as Z) with an absolute mean relative error smaller than 20%. The underlying idea is to be able to retrieve the PSD using two independent measurements. We also propose parameterizations for ice cloud key parameters derived from the normalised PSD. We also investigate the effects of uncertainty present in the ice crystal mass-size relationships on the parameterizations and the normalised PSD approach.

Description: Many studies evaluating model boundary-layer schemes focus either on near-surface parameters or on short-term observational campaigns. This reflects the observational datasets that are widely available for use in model evaluation. In this paper we show how surface and long-term Doppler lidar observations, combined in a way to match model representation of the boundary layer as closely as possible, can be used to evaluate the skill of boundary-layer forecasts. We use a 2-year observational dataset from a rural site in the UK to evaluate a climatology of boundary layer type forecast by the UK Met Office Unified Model. In addition, we demonstrate the use of a binary skill score (Symmetric Extremal Dependence Index) to investigate the dependence of forecast skill on season, horizontal resolution and forecast leadtime. A clear diurnal and seasonal cycle can be seen in the climatology of both the model and observations, with the main discrepancies being the model overpredicting cumulus capped and decoupled stratocumulus capped boundary-layers and underpredicting well mixed boundary-layers. Using the SEDI skill score the model is most skillful at predicting the surface stability. The skill of the model in predicting cumulus capped and stratocumulus capped stable boundary layer forecasts is low but greater than a 24 hr persistence forecast. In contrast, the prediction of decoupled boundary-layers and boundary-layers with multiple cloud layers is lower than persistence. This process based evaluation approach has the potential to be applied to other boundary-layer parameterisation schemes with similar decision structures.

Description: We present the results of simulations carried out with the Met Office Unified Model at 12, 4 and 1.5 km resolution for a large region centred on West Africa using several different representations of the convection processes. These span a range of resolutions from much coarser than the size of the convection processes to cloud-system-resolving and thus encompass the intermediate ‘grey zone’. The diurnal cycle in the extent of convective regions in the models is tested against observations from the Geostationary Earth Radiation Budget instrument on Meteosat-8. By this measure, the two best-performing simulations are a 12 km model without convective parametrization, using Smagorinsky-style subgrid-scale mixing in all three dimensions, and a 1.5 km simulation with two-dimensional Smagorinsky mixing. Of these, the 12 km model produces a better match to the magnitude of the total cloud fraction but the 1.5 km one results in better timing for its peak value. The results suggest that the previously reported improvement in the representation of the diurnal cycle of convective organization in the 4 km model compared with the standard 12 km configuration is principally a result of the convection scheme employed rather than the improved resolution per se . The details of this result and implications for high-resolution model simulations are discussed. Copyright © 2013 Royal Meteorological Society

Description: [1]  This paper presents the implementation of a new version of the DARDAR (raDAR liDAR) classification derived from CloudSat and CALIPSO data. The resulting target classification called DARDAR v2 is compared to the first version called DARDAR v1. Overall DARDAR v1 reports more cloud or rain pixels than DARDAR v2. In the low troposphere this is because v1 detects too many liquid cloud pixels, and in the higher troposphere this is because v2 is more restrictive in lidar detection than v1. Nevertheless, the spatial distribution of different types of hydrometeors show similar patterns in both classifications. The French airborne RAdar -LIdar (RALI) platform carries a CloudSat/CALIPSO instrument configuration (Lidar at a wavelength of 532 nm and a 95GHz cloud radar), as well as an EarthCare instrument configuration (High Spectral Resolution Lidar at 355 nm and a 95 GHz Doppler cloud radar). It therefore represents an ideal go-between for A-Train and EarthCare. The DARDAR v2 classification algorithm is adapted to RALI data for A-Train overpasses during dedicated airborne field experiments using the lidar at 532 nm and the radar Doppler measurements. The results from the RALI classification are compared with the DARDAR v2 classification to identify where the classification should still be interpreted with caution. Finally the RALI classification algorithm with lidar at 532 nm is adapted to RALI with High Spectral Resolution lidar data at 355 nm in preparation for EarthCare.