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The dataset is provided to support the publication 'Diagnosing Observation Error Correlations for Doppler Radar Radial Winds in the Met Office UKV Model Using Observation-Minus-Background and Observation-Minus-Analysis Statistics' by Waller et al (2016). The dataset was created as part of the NERC Flooding from Intense Rainfall (FRANC) project in order to study the observation uncertainties associated with Doppler radar radial wind observations assimilated in to the Met Office UK variable resolution model. The dataset is processed output of the Met Office UKV 3D var assimilation scheme for June, July and August 2013 for four different experimental scenarios. Full details and equations are given in Waller et al (2016) but the four different experimental cases are summarised as follows: - Case 1: Control experiment using standard UKV settings in place in January 2014 - Case 2: As Case 1, but with a different background error covariance matrix used in the data assimilation - Case 3: As Case 1, but with raw Doppler radial wind observations rather than superobservations - Case 4: As Case 3, but with an improved observation operator. For each case the dataset consists of the radial wind observations assimilated at each assimilation cycle valid between 01/06/2013 and 31/08/2016 along with the associated observation-minus-background and observation-minus-analysis residuals. Each observation also has metadata that describes the location of the observation (both in latitude/longitude co-ordinates, and co-ordinates relative to the radar station) , the assimilation cycle at which it was assimilated and the observation error variance that the observation was assigned in the data assimilation scheme. These data are published under the Open Government License (http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/) © Crown Copyright, 2020, Met Office”.
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.