The status of current efforts to assimilate cloud-and precipitation-affected satellite data is summarised with special focus on infrared and microwave radiance data obtained from operational Earth observation satellites. All global centres pursue efforts to enhance infrared radiance data usage due to the limited availability of temperature observations in cloudy regions where forecast skill is estimated to strongly depend on the initial conditions. Most systems focus on the sharpening of weighting functions at cloud top providing high vertical resolution temperature increments to the analysis, mainly in areas of persistent high and low cloud cover. Microwave radiance assimilation produces impact on the deeper atmospheric moisture structures as well as cloud microphysics and, through control variable and background-error formulation, also on temperature but to lesser extent than infrared data. Examples of how the impacts of these two observation types are combined are shown for subtropical low-level cloud regimes. The overall impact of assimilating such data on forecast skill is measurably positive despite the fact that the employed assimilation systems have been constructed and optimized for clear-sky data. This leads to the conclusion that a better understanding and modelling of model processes in cloud-affected areas and data assimilation system enhancements through inclusion of moist processes and their error characterization will contribute substantially to future forecast improvement.

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A high resolution regional reanalysis of the Indian Monsoon Data Assimilation and Analysis (IMDAA) project is made available to researchers for deeper understanding of the Indian monsoon and its variability. This 12 km resolution reanalysis covering the satellite-era from 1979 to 2018 using 4D-Var data assimilation method and the UK Met Unified Model is presently the highest resolution atmospheric reanalysis carried out for the Indian monsoon region. Conventional and satellite observations from different sources are used, including Indian surface and upper air observations, of which some were not used in any previous reanalyses. Various aspects of this reanalysis, like quality control and bias correction of observations, data assimilation system, land surface analysis, and verification of reanalysis products, are presented in this paper. Representation of important weather phenomena of each season over India in the IMDAA reanalysis verifies reasonably well against India Meteorological Department (IMD) observations and compares closely with ERA5. Salient features of the Indian summer monsoon are found to be well represented in the IMDAA reanalysis. Characteristics of major semi-permanent summer monsoon features (e.g., Low-level Jet and Tropical Easterly Jet) in IMDAA reanalysis are consistent with ERA5. The IMDAA reanalysis has captured the mean, inter-annual, and intra-seasonal variability of summer monsoon rainfall fairly well. IMDAA produces a slightly cooler winter and a hotter summer than the observations; the reverse for ERA5. IMDAA captured the fine-scale features associated with a notable heavy rainfall episode over complex terrain. In this study, the fine grid spacing nature of IMDAA is compromised due to the lack of comparable resolution observations for verification.

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.