The Exploitation of new data sources, data assimilation and ensemble techniques for storm and flood forecasting Project is a NERC Flood Risk for Extreme Events (FREE) Research Programme project (Round 1 - NE/E002137/1 - Duration January 2007 - April 2010) led by Prof AJ Illingworth, University of Reading. This project investigates possible methods of producing ensemble weather forecasts at high-resolution. These ensembles will be used with raingauge and river flow to improve methods of flood forecasting. The dataset includes radiosonde and wind profiles in England and Wales derived using Doppler radar returns from insects. The radial velocity measurements from insects were converted into VAD profiles by fitting a sinusoid to radial velocities at constant range. All measured profiles have been interpolated to the instrument location. Model output files from experiments assimilating radial winds from insects are also available. Floods in the UK are often caused by extreme rainfall events. At present, weather forecasts can give an indication of a threat of severe storms which might cause flash floods, but are unable to say precisely when and where the downpours will occur, due to the complex range of processes and space-time scales involved. The first stage is to predict the air motions leading to convergence and ascent at a certain location where the precipitation will be initiated, then the development of the precipitation needs to be forecast, and hydrological models used to produce accurate, quantitative, probabilistic flood predictions. Data assimilation is a sophisticated mathematical technique that combines observations with model predictions to give an analysis of the current state of the atmosphere. This analysis may be used to initialise a weather forecast. Although precipitation is well observed by weather radar, attempts to assimilate radar data have had little success; by the time the rain develops the forecast model state is too far from the truth and the air motions are inconsistent with the position of the first radar precipitation echo. We propose to overcome this problem by assimilating new types of data from weather radars. These provide information on the evolving humidity fields and air motions in the lower atmosphere so that the model can accurately track the developing storm before precipitation appears. The model used will be a new Met Office model that can be run with a resolution (i.e., grid-spacing) of order 1-4km. This enables storm-cloud motions to be explicitly calculated, rather than treated as a sub-grid-scale effect. Furthermore, current operational forecast models are only updated with observations every few hours; in the new approach the model will be updated much more frequently. This should yield weather forecasts with improved locations (in space-time) for rainfall events. Initialisation errors are not the only cause of inaccuracies in storm-scale weather forecasts. Models are often run only for a small region of the world, and the data on the boundaries of this area provided from a larger-scale model. These data are known as lateral boundary conditions. Errors in these lateral boundary conditions and modelling errors also contribute to the errors in the forecast. Even if these errors were reduced, the nonlinear nature of the storm dynamics ensures that there is a limit, beyond which the value of deterministic forecasts becomes questionable. After that point it becomes important to determine the uncertainties in the forecast precipitation, so an ensemble approach is required. (An ensemble is a collection of perturbed forecasts that may be considered as a statistical sample of the forecast probability distribution.) The appropriate construction of a storm-scale ensemble is an open question. We propose a structured approach where perturbations will be designed on the basis of physical insight into convective forcing mechanisms. The resulting probabilistic rainfall forecasts can be interfaced to hydrological models used for flood forecasting. For the first time, this project will allow different scales of application of these methods to be supported: ranging from localised flash flooding of small catchments, through to indicative first-alert forecasting with UK-coverage and forecasting of river discharges to the sea. The project will also assess the impacts of improvements in numerical weather prediction on flood forecast performance. In this project we anticipate fruitful interactions between the different disciplines of observations and measurement, meteorology and hydrology. Radar assimilation software development and ensemble forecasts will take place using Met Office models, so improvements can be implemented operationally very easily. The use of operational radars makes this project well placed to take advantage of data from any extreme events occurring during the period of the study.

Data from observations made at the Cape Verde Atmospheric Observatory (CVAO) which exists to advance understanding of climatically significant interactions between the atmosphere and ocean and to provide a regional focal point and long-term data. The observatory is based on Calhau Island of São Vicente Cape Verde at 16.848N, 24.871W, in the tropical Eastern North Atlantic Ocean, a region which is data poor but plays a key role in atmosphere-ocean interactions of climate-related and biogeochemical parameters including greenhouse gases. It is an open-ocean site that is representative of a region likely to be sensitive to future climate change, and is minimally influenced by local effects and intermittent continental pollution. The dataset collection contains mixing ratio measurements of Ozone, CO, ethane, propane, iso-butane, acetylene, iso-pentane, and halocarbons. Meteorological measurements (wind speed, wind direction, atmospheric pressure, air temperature, relative humidity, solar radiation, rainfall) and aerosol concentrations are also contained in the data set. The Cape Verde Observatory was previously used during the SOLAS (Surface Ocean / Lower Atmosphere Study) project, from which the present day continuous observations have evolved. As such the earlier SOLAS measurements are also included within this collection. Additionally, back trajectory plots for the site are also within this collection.

This dataset collection contains data from the Stratospheric Photochemistry, Aerosols and Dynamics Expedition (SPADE) which was based at NASA Ames Research Center in California during portions of 1992 and 1993. The data consist of measurements collected onboard the NASA ER-2 aircraft, and selected radiosonde soundings from stations in the region of the experiment. Flights were conducted during October and November of 1992, April and May of 1993, and October of 1993. Theory team products come in two forms: as quantities evaluated along flight tracks and as global or hemispheric fields. Meteorological quantities, such as temperature, geopotential, and potential vorticity are available in both forms. They are based on analyses from both the U.S. National Meteorological Center and from the Assimilation Model of NASA's Goddard Space Flight Center. Other quantities, available along flight tracks only, include visible reflectivity, cloud height, UV reflectivity, and total ozone. The first two are derived from GOES imagery, the last two from the Meteor TOMS sensor. Finally, calculations of mixing ratios of selected chemical species using a photochemical steady state model are available along the flight track.

The Met Office Hadley Centre's sea surface temperature data set, HadSST2, replaces the Met Office Historical Sea Surface Temperature dataset (MOHSST6) and is a monthly global field of SST (Sea Surface Temperature) on a 5 deg latitude by 5 deg longitude grid from 1850 to 2013. The data are neither interpolated nor variance adjusted. The observations that make up this dataset are taken from the International Comprehensive Ocean-Atmosphere DataSet, ICOADS (see http://www.cdc.noaa.gov/coads/), until 1997 and from the NCEP GTS archive thereafter. Individual observations must first pass a series of quality checks (track check, reality check, positional check, climatology check, buddy check, duplicate check). The quality-checked observations in each 1degree longitude X 1degree latitude X pentad gridbox are then averaged using a winsorised average. The pentad climatology is then subtracted from these pentad superobs and the resulting anomalies are averaged to 5degree X 5degree X monthly resolution. The data are then bias-corrected for the use of buckets in the period 1850-1941.

Earth-system modelling data from the UK-Japan Climate Collaboration (UJCC). The project is a joint project between the Hadley Centre (DEFRA) and the NCAS-CGAM (Centre for Global Atmospheric Modelling) at the University of Reading. UJCC makes use of a broad group of models in order to systematically explore the role and value of resolution in climate system research. The dataset comprises of UJCC 30 year simulations from models at resolutions of either (1.25 lat x 1.875 lon) or (0.83 lat x 1.25 lon) with differing degrees of atmosphere-ocean coupling (1 degree ocean or 1/3 degree ocean). The dataset also includes NUGAM (Nihon-UK Global Environmental Model) Atmosphere only simulations and NUGEM Coupled atmosphere and ocean simulations which are both at the same resolution (0.83 lat x 0.56 lon, corresponding to ~60 km in mid-latitudes).

The aim of HITRAN (high-resolution transmission molecular absorption database) was to characterise the amount and wavelength-dependence of absorption by water vapour and other atmospheric species. It was part of the Natural Environment Research Council (NERC) funded Clouds, Water Vapour and Climate (CWVC) program. The dataset contains spectral line parameters derived from laboratory measurements on pure water vapour, and mixtures of water vapour and air. The measurements were made at STFC Rutherford Appleton Laboratory Molecular Spectroscopy Facility, and the line fitting was carried out by the Department of Meteorology at the University of Reading. The spectral line parameters are displayed in HITRAN format. Water vapour lines were fitted to the laboratory data in the spectral range 5037 to 5585 cm-1. These data are public.

This is a copy of The Berlin Stratospheric Data Series provided to the BADC by K. Labitzke and her collaborators (2002) as a CD from the Meteorological Institute, Free University Berlin. This data set contains temperature and geopotential height data on the 100, 50, 30, 10 mb pressure surfaces produced at the Meteorological Institute, Free University of Berlin, from radiosonde data and rocket observations. This data series also contains summer, winter and annual trends and variability of the data, climatological monthly mean temperature and geopotential height at 30 mb, and intercomparisons with other data series. There are also sections on the quasi-biennial oscillation (QBO) and the global signal of the 11-year sunspot cycle in the stratosphere.

The Antarctic Mesoscale Prediction System (AMPS) is an experimental, real-time numerical weather prediction capability that provides support for the United States Antarctic Program, Antarctic science, and international Antarctic efforts. AMPS produces numerical guidance from the Weather Research and Forecasting (WRF) model with twice-daily forecasts covering Antarctica. The effort is sponsored by the National Science Foundation (NSF) Office of Polar Programs and the NSF UCAR and Lower Atmospheric Facilities Oversight Section. It is a collaboration of the National Center for Atmospheric Research and the Byrd Polar Research Center of The Ohio State University.

The Climateprediction.net project is harnessing the spare CPU cycles of tens of thousands of individual users' PCs to run a massive ensemble of climate simulations using the Met Office's Unified Model. A multi-thousand member ensemble of simulation results from the perturbed physics climate sensitivity experiment is available for research purposes.

The Coastal Air Pollution (CAP) field campaigns in 2009 and 2010 (CAP-2009 and CAP-2010 respectively) sought to investigate the impact of local meteorology on coastal air quality and the structure and evolution of the coastal boundary layer. This dataset consists of surface, tower and airbourne measurements of atmospheric chemistry and vertical wind profiles from the Coastal Air Pollution (CAP) field campaign, led by Dr. Claire Reeves (University of East Anglia, UEA). Airborne measurements were made by instrumentation on board the Facility for Airborne Atmospheric Measurements's (FAAM) BAE 146 aircraft, with surface and tower measurements from the Weybourne Atmospheric Observatory (WAO) and the Facility for Ground-based Atmsopheric Measurements's (FGAM) 1290Mhz mobile wind profiling radar providing vertical profiles of winds, signal to noise ratios and spectral width data. These data were used to investigate the impact of local meteorology on coastal air quality and the structure and evolution of the coastal boundary layer. The objectives of the campaign was to: a) characterise the chemical composition of the air above and around WAO in various meteorological conditions to determine how representative the WAO observations are of the coastal region and of the air-mass origin (esp. in the case of maritime/Arctic air); b) determine the local flow patterns that can be established around WAO which may influence the redistribution of pollutants and to aid future identification of such patterns with the more limited vertical data that is routinely collected at WAO; c) identify patterns that decouple polluted layers from the surface; d) characterise the off-shore pollution sources (ship emissions, emissions from off-shore gas platforms) which impact measurements at WAO under maritime conditions; and, e) provide test cases for the one-dimensional MISTRA model of vertical profiles of trace components in the boundary layer and lower free troposphere, especially providing information about vertical exchange.