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Patterns of precipitation: Fine-scale rain dynamics in the South of England
No full textThe consensus in the climate change community is that one of the (many) effects of climate change will be that the
nature of rain events will change, and in all likelihood, they will become more extreme. Currently, most long-term
rain rate data sets are hourly (or longer) rain accumulations, so investigating the rain events that occur for less than
0.01% (52.5 minutes) of a year is not possible. Rain datasets do exist with smaller temporal resolution, but these are
either not continuous, or simply have not been in operation long enough to investigate any trends in climate change.
The Chilbolton Observatory in the south of England is one of the world’s most advanced meteorological
radar experimental facilities, and is home to the world’s largest fully steerable meteorological radar, the Chilbolton
Advanced Meteorological Radar (CAMRa). It also hosts a wide range of meteorological and atmospheric sensing
instruments, including cameras, lidars, radiometers and a wide selection of different types of rain gauges. The UK
atmospheric science, hydrology and Earth Observation communities use the instruments located at Chilbolton to
conduct research in weather, flooding and climate. This often involves observations of meteorological phenomena
operating below the current resolution of (forecasting and climate) models and work on their effective parameterisation.
The Chilbolton datasets contain a continuous drop counting rain gauge time series at 10 seconds integration
time, spanning from January 2001 to the present. Though the length of the time series is not sufficient to
confidently identify any effects of climate change, the time resolution is sufficient to investigate the differences in
the extreme values of rain events over the nine years of the dataset, characterising the inter-annual and seasonal
variability. Changes in the occurrence of different rain events have also been investigated by looking at event and
inter-event durations to determine if there is any change in the relative number of stratiform and convective events
over the time period.
Knowledge of the fine scale variability of rain (both in the spatial and temporal domains) is important for
the development of accurate models for small-scale forecasting, as well as models for the implementation and
operation of rain affected systems, such as microwave radio communications and flood mitigation. As the rain
gauge measurements made at Chilbolton will continue for the foreseeable future, these datasets will become
increasingly valuable, as they provide a “ground-truth” that can be compared with the results of climate and other
models
Institute of Physics, Environmental Physics Group newsletter (42), March 2010
No full textThis file contains the newsletters of the Environmental Physics Group at the Institute of Physics. The fundamental aim of the Group is to promote physics within the context of the environmental sciences. In achieving this aim we provide a forum for the discussion of physics as it applies to the environment and encourage the development and application of physical methods to environmental research. The Group also encourages the education and training of physicists in the environmental sciences through meetings and contacts with educationalists at all levels. Because of the broad nature of environmental physics the Group is involved in co-operative meetings with other professional organisations with interests in the environment. These newsletters are an archive of our activities since the formation of the Group. For more information about the Environmental Physics Group, see http://www.iop.org/activity/groups/subject/env/index.htm
Metafor: Common metadata for climate modelling digital repositories
No full textA poster about common metadata for climate modelling repositories.
More people than ever now have a need to discover the results of climate models in order to prepare for and mitigate against the potentially severe impacts of global climate change. But climate modelling is a complex process, which requires accurate and complete metadata (data describing data) in order to identify, assess and use the climate data stored in digital repositories.
Simulations have a key role in constructing understanding and producing predictions in climate science. But it can be difficult to discriminate between two simulations, even when you were responsible for producing them! Existing documentation currently revolves around (at best) the runtime, but not the scientific detail and relevance of the model components. There is little or no documentation of the “simulation context” (the whys and wherefores and issues associated with any particular simulation)
POLAR DATA HEADER FORMAT
No full textThis document refers to the file format for polar reflectivity, velocity moments and dual polarisation parameters collected by the processing systems at radar sites around the UK, and the single parameters data files created by the central processing system at the UK Met Office headquarters
NERC ARSF Data Management Plan
No full textThe document is an agreed record of the data management needs and issues of the NERC Airborne Research and Survey Facility. It defines who is responsible for data management activities. It includes conditions of use and deposit to clearly express the ownership and rights associated with the data
IT Infrastructure at the Cape Verde Remote observatory
No full textThis poster is an overview of the work carried out by Dan Walker and James Groves at the Cape verde remote observatory. The objective of the visit was to put in place a computer network to help retrieve data back from the site
Selection of presentations from the Aura Science Team Meeting - HIRDLS - October 2010
No full textHigh Resolution Dynamics Limb Sounder (HIRDLS) instrument
FAAM Website (prior to 03.2010)
No full textThis document is a mirror of the FAAM website in the version that was live between 2004 and 03.2010.
FAAM designed a new website which went live in 03.2010, the content of which would not exactly mirror the content of the preceding version.
The mirror was created as a means of providing access to that bits of information that are not present anymore in the new version
Evolution of Climate Science Modelling Language within international standards frameworks
No full textThe Climate Science Modelling Language (CSML) was originally developed as part of the NERC Data Grid
(NDG) project in the UK. It was one of the first Geography Markup Language (GML) application schemas
describing complex feature types for the metocean domain. CSML feature types can be used to describe typical
climate products such as model runs or atmospheric profiles. CSML has been successfully used within NDG
to provide harmonised access to a number of different data sources. For example, meteorological observations
held in heterogeneous databases by the British Atmospheric Data Centre (BADC) and Centre for Ecology and
Hydrology (CEH) were served uniformly as CSML features via Web Feature Service.
CSML has now been substantially revised to harmonise it with the latest developments in OGC and ISO
conceptual modelling for geographic information. In particular, CSML is now aligned with the near-final ISO
19156 Observations & Measurements (O&M) standard. CSML combines the O&M concept of ’sampling features’
together with an observation result based on the coverage model (ISO 19123). This general pattern is specialised
for particular data types of interest, classified on the basis of sampling geometry and topology.
In parallel work, the OGC Met Ocean Domain Working Group has established a conceptual modelling ac-
tivity. This is a cross-organisational effort aimed at reaching consensus on a common core data model that could
be re-used in a number of met-related application areas: operational meteorology, aviation meteorology, climate
studies, and the research community. It is significant to note that this group has also identified sampling geometry
and topology as a key classification axis for data types.
Using the Model Driven Architecture (MDA) approach as adopted by INSPIRE we demonstrate how the
CSML application schema is derived from a formal UML conceptual model based on the ISO TC211 framework.
By employing MDA tools which map consistently between UML and GML we can treat the formal UML model
as the primary governed artefact and automatically produce the GML schema as a secondary output.
Finally we describe how increased convergence between CSML and Scientific Feature Types in the Unidata
Commmon Data Model may assist with bridging the implementation gap between OGC/ISO services and the
CF-NetCDF binary data management community. This improved agreement at the conceptual (feature type) level
is important to enable better interoperability at the data exchange and service levels