The Convective Storm Initiation Project (CSIP) is an international project to understand precisely where, when, and how convective clouds form and develop into showers in the mainly maritime environment of southern England. A major aim of CSIP is to compare the results of the very high resolution Met Office weather forecasting model with detailed observations of the early stages of convective clouds and to use the newly gained understanding to improve the predictions of the model. A large array of ground-based instruments plus two instrumented aircraft, from the U.K. National Centre for Atmospheric Science (NCAS) and the German Institute for Meteorology and Climate Research (IMK), Karlsruhe, were deployed in southern England, over an area centered on the meteorological radars at Chilbolton, during the summers of 2004 and 2005. In addition to a variety of ground-based remote-sensing instruments, numerous rawinsondes were released at one- to two-hourly intervals from six closely spaced sites. The Met Office weather radar network and Meteosat satellite imagery were used to provide context for the observations made by the instruments deployed during CSIP. This article presents an overview of the CSIP field campaign and examples from CSIP of the types of convective initiation phenomena that are typical in the United Kingdom. It shows the way in which certain kinds of observational data are able to reveal these phenomena and gives an explanation of how the analyses of data from the field campaign will be used in the development of an improved very high resolution NWP model for operational use.

The link between pollution and health is commonly explored by trying to identify the dominant cause of pollution and its most significant effect on health outcomes. The use of multivariate features to describe exposure is less explored because investigating a large domain of scenarios is theoretically (i.e., interpretation of results) and technically (i.e., computational effort) challenging. In this work we explore the use of Bayesian Networks with a multivariate approach to identify the probabilistic dependence structure of the environment‐health nexus. This consists of environmental factors (topography and climate), exposure levels (concentration of outdoor air pollutants), and health outcomes (mortality rates). The information is collated with regard to a data‐rich study area: the English regions (UK), which incorporate environmental types that are different in character from urban to rural. We implemented a reproducible workflow in the R programming language to collate environment‐health data and analyze almost 50 millions of observations making use of a graphical model (Bayesian Network) and Big Data technologies. Results show that for pollution and weather variables the model tests well in sample but also has good predictive power when tested out of sample. This is facilitated by a training/testing split in the data along time and space dimension and suggests that the model generalizes well to new regions and time periods.

The Republic of Kazakhstan, located in Central Asia, has experienced many years of environmental degradation, largely as a result of the poor management of its significant natural resources. In this survey, data relating to different environmental factors are critically analysed in order to understand the state of the environment. It was found that: warming trends are seen in sensitive areas (e.g. the steppe and near glaciers); drying trends are seen where there is already water stress (e.g. the Aral Sea); air quality has been declining recently (following improvements on the decadal timescale) in major urban centres, particularly Almaty; water quality appears to be improving in some areas (e.g. important lakes in the Aktobe and Zhambyl regions); and levels of exposure to radioactivity are below internationally recommended levels (where data have been found). More generally, there is an issue with data availability and quality, which requires attention if Kazakhstan is going to make the best use of its increasing investment in environmental actions. Current policies are reviewed and recommendations are made for future interventions.

An isolated thunderstorm formed in the southern United Kingdom on 15 June 2005 and moved through the area where a large number of observational instruments were deployed as part of the Convective Storm Initiation Project. Earlier, a convergence line had formed downstream of Devon in the southwest of the United Kingdom in a southwesterly airflow, along which a series of light showers had formed. The depth of these showers was limited by a capping inversion, or lid, at around 2.5 km. The deep thunderstorm convection developed from one of these showers when the convection broke through the lid and ascended up to the next inversion, associated with a tropopause fold at around 6 km. A series of clear-air reflectivity RHIs are used to map the height of the capping inversion and its lifting resulting from the ascent along the convergence line. The origins of the lid are tracked back to some descent from the midtroposphere along dry adiabats. The strength of the lid was weaker along a northwest-to-southeast-oriented region located behind an overrunning upper cold front. The transition from shallow to deep convection occurred where this region with a weaker lid intersected the region with a raised lid, oriented southwest to northeast, downstream of Devon. A very high resolution forecast model that is being developed by the Met Office predicted the isolated thunderstorm successfully. This success depended on the accurate representation of the following two scales: the synoptic-scale and the surface-forced mesoscale convergence line. The interaction between these scales localized the convection sufficiently in space and time for the initiation and subsequent development to be highly predictable despite the relatively poor representation in the model of processes at the cloud scale.

The effect on deep convection of large‐scale potential vorticity (PV) anomalies and their associated tropospheric stable layers is complex and not well understood. This article examines the meteorological events of 9 July 2007 (IOP7b of the Convective and Orographically‐induced Precipitation Study (COPS)), which was dominated by an upper‐level PV anomaly that stretched from the UK to southern France and as far northeast as Denmark. Three precipitation regions were identified from the case: lines of intense storms beneath the PV anomaly; less intense, more widespread convective precipitation to the east of the PV anomaly; and, in between, a region of no precipitation. The latter of these coincided with the high‐resolution measurements and model analyses from COPS. The extensive and varied data analyzed in this investigation show that convective available potential energy (CAPE) was present in this region (the distribution of CAPE and convective inhibition (CIN) is presented via an innovative, pseudo‐3D visualization that allows horizontal and vertical interactions to be considered). However, convection was capped by a complex arrangement of dry layers; the base of the key layer was at 750 hPa. These dry layers descended separately from the upper troposphere, moving around the PV anomaly as it developed from a breaking Rossby wave to the west during the seven days before the IOP. This case adds to other studies that show that descent of complex dry layers is an important mechanism for forming convection‐inhibiting atmospheric lids in Western Europe. A simple conceptual model is developed that synthesizes the effect of large‐scale PV anomalies on deep convection from a series of consistent case studies. This model has significant implications for storm forecasting and projections of storminess in future climates, as it highlights the importance of thin structures that can advect hundreds of km before having an impact. Copyright © 2012 Royal Meteorological Society