High resolution radiosonde data from the British Antarctic Survey's stations Halley and Rothera are available. The data consists of vertical profiles of pressure, temperature, relative humidity, humidity mixing ratio, radiosonde position, wind speed and wind direction. Measurements are taken at 2 second intervals and the ascents extend to heights of approximately 20-30 km. The archive has data from 2001 and generally there is 1 ascent per day from both stations.

High resolution radiosonde data from the British Antarctic Survey's Rothera station. The data consists of vertical profiles of pressure, temperature, relative humidity, humidity mixing ratio, radiosonde position, wind speed and wind direction. Measurements are taken at 2 second intervals and the ascents extend to heights of approximately 20-30 km. The archive has data from 2008 and generally there is 1 ascent per day from the station.

High resolution radiosonde data from the British Antarctic Survey's Halley station. The data consists of vertical profiles of pressure, temperature, relative humidity, humidity mixing ratio, radiosonde position, wind speed and wind direction. Measurements are taken at 2 second intervals and the ascents extend to heights of approximately 20-30 km. The archive has data from 2008 and generally there is 1 ascent per day from the station.

The Greenland Flow Distortion EXperiment investigates the role of Greenland in defining the structure and the predictability of both local and downstream weather systems, through a programme of aircraft-based observation and numerical modelling. The Greenland Flow Distortion Experiment (GFDex) will provide some of the first detailed in situ observations of the intense atmospheric forcing events that are thought to be important in modifying the ocean in this area (but are presently poorly understood): namely tip jets, barrier winds and mesoscale cyclones. The dataset contains radiosonde measurements from various sites around Greenland.

The African Easterly Jet (AEJ) is part of a climatic system which is of critical importance to African and global weather and climate, but is poorly observed and not well represented in model analyses. For the JET2000 project the Met Office Met Research flight (MRF) aircraft performed four flights, involving transects along and across the jet and the baroclinic zone, to make observations of unprecedented resolution for this part of the world. 110 dropsondes were dropped along the fight path. The data includes detailed synoptic observations of the basic state over mainland West Africa, and detailed synoptic observations of the African Easterly Waves (AEW) over mainland tropical West Africa.

Airborne Southern Hemisphere Ozone Experiment and Measurements for Assessing the Effects of Stratospheric Aircraft (ASHOE/MAESA) was a combined experiment which was conducted in four phases between March and November 1994 at NASA Ames Research Center, California; Barbers Point, Hawaii; and Christchurch, New Zealand. This dataset contains radiosonde measurements.

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.

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 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. This dataset contains temperature and pressure measurements from radiosondes.

The dataset contains the data collected by radiosondes, released by IMK, in both Bath and Chilbolton, during the period from the 19th of June 2005 to the 25th of August 2005. The datset contains measurements of: The dataset contains measurements of: Height above sea level (m) Time since midnight (s) Time since midnight (hh:mm:ss) Height above ground (m) Dry-bulb temperature (degree Celsius) Relative Humidity (percentage) Wind speed (m/s) Wind direction (degree true) Pressure (hPa) Potential Temperature (degree Kelvin) Dew-point temperature (degree Celsius) Specific humidity (g/kg) Saturation mixing ratio (g/kg) u-component of wind velocity (m/s) v-component of wind velocity (m/s)