Forecasting Rainfall exploiting new data Assimilation techniques and Novel observations of Convection (FRANC): Ensemble member output from Unified Model runs as described in Flack et al. (2018): Convective-Scale Perturbation Growth Across the Spectrum of Convective Regimes, Monthly Weather Review, 146, 387-405 The dataset contains ensemble run output from 36 hour long runs under different model set ups (see details below) for 6 case studies (see Flack et al. 2018 for greater detail). The case studies (and model output available in the dataset) chosen related to a spectrum of 'convective adjustment time scales', defined as the ratio between the convective available potential energy (CAPE) and its rate of release at the convective scale. 'control' run files contain large scale rainfall rates and amounts whilst the 'control_multilevel' files contain various parameters on various levels, including mean sea level pressure, zonal, meridional and vertical wind components, specific humidity and temperature. - Case A: 20th April 2012, part of the Dynamical and Microphysical Evolution of Convective Storms (DYMECS) field experiment (Stein et al. 2015), showing typical conditions for scattered showers in the United Kingdom. - Case B: 12 August 2013, for a case where a surface low was situated over Scandinavia and the Azores high was beginning to build, leading to persistent northwesterly flow. - Case C: 23rd July 2013, relating to the fifth intensive observation period (IOP 5) of the Convective Precipitation Experiment (COPE; Leon et al. 2016). A low pressure system was centered to the west of the United Kingdom with several fronts ahead of the main center, which later decayed. - Case D: 2nd August 2013, covering IOP 10 of the COPE field campaign, with convection initiating at 1100 UTC. The synoptic situation shows a low pressure system centered to the west of Scotland, which led to southwesterly winds and a convergence line being set up along the North Cornish coastline (in southwest England). - Case E: 27th July 2013, covers the period of IOP 7 of the COPE field campaign where two mesoscale convective systems (MCS) influenced the U.K.’s weather throughout the forecast period. - Case F: 5th August 2013, was chosen for the complex situation for considering convective-scale perturbation grown and a second case driven by the boundary conditions as seen during IOP 12 of the COPE campaign A brief description of the model run IDs and model setup is given below. The model used to create these ensembles is the Met Office Unified Model (MetUM). The United Kingdom Variable resolution (UKV) configuration is used, and so the data has a grid spacing of approximately 1.5 km. This was run at version 8.2 and run with the MetUM Graphical User Interface (GUI). run ID: xkyib This is the control experiment and everything is kept identical to the operational running of this configuration of the MetUM. run ID: xldef Here the Gaussian potential temperature perturbations are added into the model. Full details of the perturbation method are described in Flack et al. (2018) Convective-Scale Perturbation Growth Across the Spectrum of Convective Regimes, Monthly Weather Review, 146, 387-405, however a brief overview is given below: A Gaussian distribution (defined using random numbers between +/- 1 at each grid point, with the seed determined by the time the model is ran) is created at every grid point in the domain. A superposition is created and rescaled to 0.1 K so as to be an appropriate amplitude for boundary layer noise. Each of the Gaussian distributions have a standard deviation of 9km so as to be added onto an appropriate scale for the model. The perturbations are added in at a model hybrid height of 261.6 m (approximately the 8th model level).

The Forecasting Rainfall exploiting new data Assimilation techniques and Novel observations of Convection (FRANC) project undertook a series of studies to design and test efficient and effective ways of assimilating moisture information from observations that respect the intricate dynamical and physical relationships that operate in the atmosphere. The aim of this work was, if successful, that such new approaches would allow better use of cloud and rain affected observations than previously. Predicting convective rain is made harder by the fact that some events are inherently unpredictable, even with good data assimilation and models, due to their high sensitivity to even small errors in the initial conditions. Studies were also made to look at the dynamical reasons for the low predictability of such events using diagnostics derived from models and observations. To these ends this collection contains data from two of the studies within this project plus helical scan data from the Met Office's Wardon Hill radar utilised by the project team. The two datasets from the project team cover ensemble member output from runs of the Met Office's Unified Model conducted to support the project and Doppler radar radial wind observations and associated observation-minus-model residuals from the Met Office UKV 3D Var assimilation scheme. Please see the individual datasets for additional information. For further details of the FRANC project please also see Dance et al. (2019) article in the online resources linked to from this record: Improvements in Forecasting Intense Rainfall: Results from the FRANC (Forecasting Rainfall Exploiting New Data Assimilation Techniques and Novel Observations of Convection) Project.

This dataset contains ERA5 initial release (ERA5t) surface level analysis parameter data from 10 member ensemble runs. ERA5t is the European Centre for Medium-Range Weather Forecasts (ECWMF) ERA5 reanalysis project initial release available upto 5 days behind the present data. CEDA will maintain a 6 month rolling archive of these data with overlap to the verified ERA5 data - see linked datasets on this record. Ensemble means and spreads were calculated from the ERA5t 10 member ensemble, run at a reduced resolution compared with the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation, for which these data have been produced to provide an uncertainty estimate. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record. See linked datasets for ensemble member and spread data. Note, ensemble standard deviation is often referred to as ensemble spread and is calculated as the standard deviation of the 10-members in the ensemble (i.e., including the control). It is not the sample standard deviation, and thus were calculated by dividing by 10 rather than 9 (N-1). See linked datasets for ensemble mean and ensemble spread data. The ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects. An initial release of ERA5 data (ERA5t) is made roughly 5 days behind the present date. These will be subsequently reviewed and, if required, amended before the full ERA5 release. CEDA holds a 6 month rolling copy of the latest ERA5t data. See related datasets linked to from this record.

This dataset contains ERA5.1 surface level analysis parameter data for the period 2000-2006 from 10 member ensemble runs. ERA5.1 is the European Centre for Medium-Range Weather Forecasts (ECWMF) ERA5 reanalysis project re-run for 2000-2006 to improve upon the cold bias in the lower stratosphere seen in ERA5 (see technical memorandum 859 in the linked documentation section for further details). Ensemble means and spreads are calculated from these 10 member ensemble, run at a reduced resolution compared with the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation, for which these data have been produced to provide an uncertainty estimate. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record. Note, ensemble standard deviation is often referred to as ensemble spread and is calculated as the standard deviation of the 10-members in the ensemble (i.e., including the control). It is not the sample standard deviation, and thus were calculated by dividing by 10 rather than 9 (N-1). See linked datasets for ensemble mean and ensemble spread data. The main ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects. An initial release of ERA5 data, ERA5t, are also available upto 5 days behind the present. A limited selection of data from these runs are also available via CEDA, whilst full access is available via the Copernicus Data Store.

This dataset contains ERA5 surface level analysis parameter data from 10 ensemble runs. ERA5 is the 5th generation reanalysis project from the European Centre for Medium-Range Weather Forecasts (ECWMF) - see linked documentation for further details. The ensemble members were used to derive means and spread data (see linked datasets). Ensemble means and spreads were calculated from the ERA5t 10 member ensemble, run at a reduced resolution compared with the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation, for which these data have been produced to provide an uncertainty estimate. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record. Note, ensemble standard deviation is often referred to as ensemble spread and is calculated as the standard deviation of the 10-members in the ensemble (i.e., including the control). It is not the sample standard deviation, and thus were calculated by dividing by 10 rather than 9 (N-1). See linked datasets for ensemble member and ensemble mean data. The ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects. An initial release of ERA5 data (ERA5t) is made roughly 5 days behind the present date. These will be subsequently reviewed ahead of being released by ECMWF as quality assured data within 3 months. CEDA holds a 6 month rolling copy of the latest ERA5t data. See related datasets linked to from this record. However, for the period 2000-2006 the initial ERA5 release was found to suffer from stratospheric temperature biases and so new runs to address this issue were performed resulting in the ERA5.1 release (see linked datasets). Note, though, that Simmons et al. 2020 (technical memo 859) report that "ERA5.1 is very close to ERA5 in the lower and middle troposphere." but users of data from this period should read the technical memo 859 for further details.