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We are very pleased to bring you an updated special issue from our editors that brings together articles discussing the need for high resolution observations and the use of opportunistic data in numerical weather prediction (NWP).

The issue was first assembled to complement the Royal Meteorological Society's ‘Big data assimilation workshop’ held in September 2019 and has now been updated to include an editorial from Dr Joanne Waller. Joanne works in the Assimilation of Surface-based Observations group at the Met Office Reading Unit at the Department of Meteorology, University of Reading. Jo was this year’s recipient of the L F Richardson Prize for her series of innovative papers reporting significant advances in understanding and implementing new observation uncertainty techniques, alongside significant improvements in Numerical Weather Prediction.

Over recent decades advances in computing techniques and power have allowed the resolution of NWP models to increase. As a result, convection‐permitting NWP is now feasible and commonplace at most operational forecasting centres. The first article in the collection, Clark et al. (2016), provides a summary of operational convection‐permitting NWP models and their benefits.

The accuracy of any forecast is inherently linked with the accuracy of the initial conditions used to start the forecast. Therefore, it is important that the initial conditions are kept close to reality by updating the previous forecast with appropriate observations that capture the most recent atmospheric state (Gustafsson et al., 2018).

The current conventional network does provide some observations at sufficiently high resolution for convective scale NWP. Until recently only a fraction of these high-resolution observations were assimilated, although recent work has improved their use in assimilation (e.g. Geer et al., 2019; Simonin et al., 2019). Despite such improvements, as we move to even higher resolution models, the resolution of the observations will also need to increase.

It may be possible to derive observations from information collected for non‐meteorological purposes. Information can be provided by the general public, either through targeted campaigns or collected without the routine input of the collector, for example via private automatic weather stations (AWSs) or a smartphone app. The remaining papers included in this virtual issue discuss different opportunistic datasets, their quality, processing that can be implemented to improve the data, and the potential benefits of the data for NWP.

The papers in this virtual issue both highlight the need for very high-resolution observations and show that unconventional data, either crowd‐sourced or obtained from non‐meteorological sources, can provide such information.

You can view this special virtual issue here

Meteorological Applications is an open access journal of interest to applied meteorologists, forecasters and users of meteorological services, publishing papers on all aspects of meteorological science including:

• Applications of meteorological, climatological, analytical and forecasting data, and their socio-economic benefits;
• Forecasting, warning and service delivery techniques and methods;
• Weather hazards, their analysis and prediction;
• Performance, verification and value of numerical models and forecasting services;
• Practical applications of ocean and climate models