This article is published with the permission of the Controller of HMSO and the Queen's Printer for Scotland.Hybrid four-dimensional ensemble-variational (4DEnVar) data assimilation is a method which avoids using a linear and adjoint model by relying on an input ensemble to propagate analysis increments in time. Previous studies have shown that hybrid 4DEnVar performs worse than hybrid four-dimensional variational (4D-Var) assimilation. Given hybrid 4DEnVar's heavy reliance on the ensemble, this comparison may be affected by the quality of the input ensemble. Here we investigate how improvements to the ensemble system affect hybrid 4D-Var and how they affect the comparison with hybrid 4DEnVar.Using the Met Office's operational ensemble generation scheme (the ensemble transform Kalman filter, ETKF) it is found that hybrid 4D-Var gains little benefit from using an enlarged ensemble as input (176 as opposed to 23 members). By contrast, hybrid 4DEnVar benefits more from the increased ensemble size, and it benefits further when the weighting given to the ensemble covariance is increased. Both data assimilation methods benefit when the input ensemble is changed from using the ETKF to using an ensemble of 4DEnVars. Both schemes also show further benefit when a large ensemble (200 members) of 4DEnVars is used, and when a large weight is given to the covariance information from this ensemble. Thus, improving the ensemble covariance used in assimilation (ensemble generation method and ensemble size) and increasing its weight can have substantial benefits.Given that both hybrid 4D-Var and hybrid 4DEnVar benefit from improvements to the input ensemble, the relative performance is largely unaffected by the ensemble changes and hybrid 4D-Var performs better than hybrid 4DEnVar for all input ensembles.

Precipitation is a critical aspect of climate. In Europe, extreme precipitation events are costly and represent a threat to life. Evidence suggests the frequency and intensity of these events is increasing in Europe and therefore long-period datasets that can represent such events accurately are required. Precipitation is challenging to represent in gridded models and therefore is not well trusted in relatively low-resolution global reanalyses. For the first time, the European Reanalyses and Observations For Monitoring (EURO4M) project has produced high-resolution atmospheric regional reanalyses of Europe that improve representation of precipitation. These are based on operational forecast systems at the Met Office and the Swedish Meteorological and Hydrological Institute (HIRLAM). The improvement of quality of precipitation in the regional reanalysis datasets over their parent global reanalysis is demonstrated here, together with a discussion on the difficulties of validation of gridded precipitation. It is shown that regional reanalyses show particular improvement in representing high-threshold events. It is also shown that higher resolution, time-varying data assimilation and direct assimilation of precipitation all contribute to improving representation of precipitation. Resolution is of particular importance when representing extreme events.

The Indian Monsoon Data Assimilation and Analysis (IMDAA) is a regional high‐resolution atmospheric reanalysis over the Indian subcontinent. This regional reanalysis over India is the first of its kind and is produced by the National Centre for Medium Range Weather Forecasting and Met Office, UK, in collaboration with the India Meteorological Department under the National Monsoon Mission project of the Ministry of Earth Sciences, Government of India. The reanalysis runs from 1979 to 2018, to span the era of modern meteorological satellites. This article briefly describes the IMDAA system and discusses the performance of the IMDAA during summer monsoon (June–September). This study provides evidence for substantial improvements seen in IMDAA compared to the ERA‐Interim reanalysis fields over India. The evaluation is carried out for the period of 1979–1993 for all major features associated with the Indian Monsoon to highlight improvements compared to ERA‐Interim and to document the biases. The study also demonstrates the potential use of the IMDAA data for applications such as wind resource assessment over India.

A high resolution, long‐term regional reanalysis over the Indian subcontinent has been developed and is currently in production. The regional reanalysis has been produced as part of the Indian Monsoon Data Assimilation and Analysis (IMDAA) project and is the outcome of a collaboration between the Met Office (MO), the National Centre for Medium Range Weather Forecasting (NCMRWF) and the India Meteorological Department (IMD). The reanalysis will produce a consistent data set of high‐resolution fields for a wide range of atmospheric variables available from 1979 to 2016. Production runs started in 2017, and computations for 10 years have been completed as of May 2017. The entire production will be completed in early 2018. This article introduces the IMDAA regional reanalysis, describes the forecast model, data assimilation method, and input data sets used to produce the reanalysis. The performance of the system from a pilot study run for 2008–2009 are presented indicating that the regional reanalysis is able to capture major monsoon features—a key phenomenon in the Indian subcontinent.