Aircraft reports are an important source of information for numerical weather prediction (NWP). From March 2020, the COVID‐19 pandemic resulted in a large loss of aircraft data but despite this it is difficult to see any evidence of significant degradation in the forecast skill of global NWP systems. This apparent discrepancy is partly because forecast skill is very variable, showing both day‐to‐day noise and lower frequency dependence on the mean state of the atmosphere. The definitive way to cleanly assess aircraft impact is using a data denial experiment, which shows that the largest impact is in the upper troposphere. The method used by Chen (2020, https://doi.org/10.1029/2020gl088613) to estimate the impact of COVID‐19 is oversimplistic. Chen understates the huge importance of satellite data for modern weather forecasts and raises more alarm than necessary about a drop in forecast accuracy.

Recent developments in numerical weather prediction have led to the use of correlated observation‐error covariance (OEC) information in data assimilation and forecasting systems. However, diagnosed OEC matrices are often ill‐conditioned and may cause convergence problems for variational data assimilation procedures. Reconditioning methods are used to improve the conditioning of covariance matrices while retaining correlation information. In this article, we study the impact of using the “ridge regression” method of reconditioning to assimilate Infrared Atmospheric Sounding Interferometer (IASI) observations in the Met Office 1D‐Var system. This is the first systematic investigation of how changing target condition numbers affects convergence of a 1D‐Var routine. This procedure is used for quality control, and to estimate key variables (skin temperature, cloud‐top pressure, cloud fraction) that are not analysed by the main 4D‐Var data assimilation system. Our new results show that the current (uncorrelated) OEC matrix requires more iterations to reach convergence than any choice of correlated OEC matrix studied. This suggests that using a correlated OEC matrix in the 1D‐Var routine would have computational benefits for IASI observations. Using reconditioned correlated OEC matrices also increases the number of observations that pass quality control. However, the impact on skin temperature, cloud fraction, and cloud‐top pressure is less clear. As the reconditioning parameter is increased, differences between retrieved variables for correlated OEC matrices and the operational diagonal OEC matrix reduce. As correlated choices of OEC matrix yield faster convergence, using stricter convergence criteria along with these matrices may increase efficiency and improve quality control.

This article reports the results of a preliminary mission study to assess the potential of space‐borne laser heterodyne radiometry (LHR) for the remote sensing of temperature for assimilation in a numerical weather prediction (NWP) model. The LHR instruments are low cost and small in size, lending themselves to a wide variety of satellite platforms. The impact of different configurations of an idealized LHR instrument is assessed against the Infrared Atmospheric Sounding Interferometer (IASI), via single‐column linear information content analysis, using inputs consistent with the background errors of the Met Office 4D‐Var assimilation system. Multiplexed configurations give promising results, in particular for sounding of upper‐atmospheric temperatures.

Radiance observations are typically affected by biases that come mainly from instrument error (scanning or calibration) and inaccuracies of the radiative transfer model. These biases need to be removed for successful assimilation, so a bias correction scheme is crucial in the Numerical Weather Prediction (NWP) system. Today, most NWP centres, including the Bureau of Meteorology (hereafter, “the Bureau”), correct the biases through variational bias correction (VarBC) schemes, which were originally developed for global models. However, there are difficulties in estimating the biases in a limited-area model (LAM) domain. As a result, the Bureau’s regional NWP system, ACCESS-C (Australian Community Climate and Earth System Simulator-City), uses variational bias coefficients obtained directly from its global NWP system ACCESS-G (Global). This study investigates independent radiance bias correction in the data assimilation system for ACCESS-C. We assessed the impact of using independent bias correction for the LAM compared with the operational bias coefficients derived in ACCESS-G between February and April 2020. The results from our experiment show no significant difference between the control and test, suggesting a neutral impact on the forecast. Our findings point out that the VarBC-LAM strategy should be further explored with different settings of predictors and adaptivity for a more extended period and over additional domains.

A numerical weather prediction (NWP) system that represents continental Australia at a convection-permitting resolution presents both advantages and challenges. It must represent diverse weather regimes over areas of variable observation coverage, which complicates the land and atmospheric modelling and data assimilation. A single-domain prototype system, ACCESS-A (Australia Community Climate and Earth System Simulator – Australia), has been developed and tested. Compared with the current operational NWP system comprising seven small domains (ACCESS-C), ACCESS-A incorporates improvements to satellite, conventional and radar data assimilation and uses an upgraded model science configuration. ACCESS-A was extensively evaluated over two 3-month periods. Qualitative and quantitative precipitation verification indicates that ACCESS-A reproduces the seasons’ weather patterns and observed behaviour of convective precipitation. Objective verification of defined subdomains shows that forecast skill of near-surface weather is variable across the continent. It is found more skilful in better-observed regions that coincide with areas dominated by more predictable, synoptically driven weather systems. Regions with lower skill, particularly corresponding to areas not covered by ACCESS-C, suggest a focus for future research. A comparison with ACCESS-C confirmed the anticipated improved skill related to the forecast model’s upgraded land and atmospheric physics, and provides confidence in the combined impact of all upgrades implemented in ACCESS-A. ACCESS-A is demonstrated to be ready to prepare for operational NWP and ongoing research at the Australian Bureau of Meteorology.