The third version of the Regional Atmosphere and Land (RAL3) science configuration is documented. Developed through international partnerships, RAL configurations define settings for the Unified Model atmosphere and Joint UK Land Environment Simulator (JULES) when applied across timescales with kilometre and sub-kilometre-scale model grids. The RAL3 configuration represents a major advance compared to previous versions by delivering a common science definition suitable for application to tropical and mid-latitude regions. Developments within RAL3 include the introduction of a double-moment microphysics scheme and a bimodal cloud scheme, replacing use of a single-moment scheme and different cloud schemes for mid-latitudes and tropics in previous versions. Updates have been implemented to the boundary layer scheme and a consolidation of land model settings to be more consistent with global atmosphere and land (GAL) science configurations. Physics developments aimed to address priorities for model performance improvement identified by users. This paper documents the RAL3 science configuration, including a series of iterative revisions delivered since its first release, and their characteristics. Evidence is provided from the variety of assessments of RAL3, relative to the previous version (RAL2). Collaborative development and evaluation across organizations have enabled evaluation across a range of domains, grid spacing and timescales. The analysis indicates more realistic precipitation distributions, improved representation of clouds and of visibility, a continued trend to more realistic representation of convection, and reduced near-surface wind speeds but a persistent cold-temperature bias. Overall the convective-scale verification scores and climatological model distributions relative to observations improve for the majority of variables. Ensemble results show improvements to the spread–error relationship. User feedback from subjective assessment activities has also been positive. Differences between RAL3 revisions and RAL2 are further illustrated through a process-based analysis of a convective system over the UK. The latest RAL3 configuration (RAL3.3) is therefore recommended for research, operational numerical weather prediction, and climate production at kilometre and sub-kilometre scales.

In April and May 2023, Southeast Asia (SEA) encountered an exceptional heatwave. The Continental SEA was hardest hit, where all the countries broke their highest temperature records with measurements exceeding 42 °C, and Thailand set the region’s new record of 49 °C. This study provides a comprehensive analysis of this event by investigating its spatiotemporal evolution, physical mechanisms, forecast performance, return period, and extensive impacts. The enhanced high-pressure influenced by tropical waves, moisture deficiency and strong land-atmosphere coupling are considered as the key drivers to this extreme heatwave event. The ECMWF exhibited limited forecast skills for the reduced soil moisture and failed to capture the land-atmosphere coupling, leading to a severe underestimation of the heatwave’s intensity. Although the return period of this heatwave event is 129 years based on the rarity of temperature records, the combination of near-surface drying and soil moisture deficiency that triggered strong positive land-atmosphere feedback and rapid warming was extremely uncommon, with an occurrence probability of just 0.08%. These analyses underscore the exceptional nature of this unparalleled heatwave event and its underlying physical mechanisms, revealing its broad impacts, including significant health repercussions, a marked increase in wildfires, and diminished agricultural yields.

Using multi‐decadal simulations, we investigate the relationship between the quasi‐biennial oscillation (QBO) and the Madden–Julian oscillation (MJO) in the Global Ocean Mixed Layer configuration of the Met Office Unified Model (MetUM‐GOML1) at two horizontal resolutions (approximately 200 and 90 km at the equator). MetUM‐GOML1 produces a weak and insignificant correlation between QBO winds and mean MJO amplitude in boreal winter, in contrast to the significant anti‐correlation in reanalysis. While reanalysis shows the easterly QBO favors stronger Maritime Continent MJO activity, MetUM‐GOML1 displays stronger West Pacific MJO activity. The biased QBO–MJO relationship in MetUM‐GOML1 may be due to weak QBO‐induced temperature anomalies in the tropical tropopause layer, or to errors in MJO vertical structure.

In December 2021, Super Typhoon Rai caused significant devastation to the South Philippines and East Malaysia. In the meantime, an unprecedented flood event occurred in Peninsular Malaysia at 2,000 km west of the typhoon's path, causing comparable socioeconomic impacts as Rai. Record‐breaking 3‐day precipitation was received by Peninsular Malaysia during 16–18 December. Based on the storm tracking results, this study identified two mesoscale convective systems (MCSs) that were directly responsible for the flooding. The two MCSs were directly initiated by a tropical depression and sustained by an elongated easterly water vapor corridor originating from the Super Typhoon Rai. The return period and joint frequency analysis of key drivers indicate that the 3‐day downpour was more severe than a “once‐in‐a‐century” event. Historical records suggest such anomalous moisture channel has become more frequent in Southeast Asia, which alarms heightened attention in forecasting winter flood.

Skillful weather forecasting on sub-seasonal timescales is important to enable users to make cost-effective decisions. Forecast skill can be expected to be mediated by the prediction of atmospheric flow patterns, often known as weather regimes, over the relevant region. Here, we show how the Grosswetterlagen (GWL), a set of 29 European weather regimes, can be modulated by the extra-tropical teleconnection from the Madden-Julian Oscillation (MJO). Together, these GWL regimes represent the large-scale flow characteristics observed in the four North Atlantic-European classical weather regimes (NAE-CWRs), while individually capturing synoptic scale flow details. By matching each GWL regime to the nearest NAE-CWR, we reveal GWL regimes which occur during the transition stages between the NAE-CWRs and show the importance of capturing the added synoptic detail of GWL regimes when determining their teleconnection pattern from the MJO. The occurrence probabilities of certain GWL regimes are significantly changed 10–15 days after certain MJO phases, exhibiting teleconnection patterns similar to their NAE-CWR matches but often with larger occurrence anomalies, over fewer consecutive MJO phases. These changes in occurrence probabilities are likely related to MJO-induced changes in the persistence and transition probabilities. Other GWL regimes are not significantly influenced by the MJO. These findings demonstrate how the MJO can modify the preferred evolution of the NAE atmospheric flow, which is important for sub-seasonal weather forecasting.

The background error covariance matrix plays a vital role in any data assimilation system. Proper specification, which is determined by the forecast system set‐up, is often required. Previous studies have investigated its relevance in various global and regional numerical weather prediction (NWP) systems; however, very few have explored it in tropical NWP systems. Here, we present and evaluate the structures of the background error covariance matrix for a tropical convective‐scale NWP system. A total of 12 background error covariance matrices are modelled using differences between pairs of forecasts of different lengths but valid at the same time, based on the application of the vertical‐first and horizontal‐first transform order formulations on six permutations of the training data. Through pseudo‐single observation tests, we extract and test the sensitivity of their structures to the training data period (seasons), forecast lag and transform order. The structures typically exhibit more dependence on forecast lag and transform order; horizontal‐first transform order covariances had structures with shorter horizontal length‐scales for wind and larger wind background error standard deviations. We also note that some covariances had horizontal and vertical structures with stronger mass–wind coupling, closely resembling an equatorial Kelvin wave. To assess the performance of each of the covariances, 12 month‐long data assimilation trials in May 2018 (characterised by frequent occurrences of localised thunderstorm events) are performed. We show improved short‐range precipitation forecasts in trials using some of the covariances compared to the current operational covariance. These covariances generally have structures with weak mass–wind coupling, shorter horizontal length‐scales for wind and larger wind background error standard deviations, compared to other covariances which led to poorer forecasts. These may be desirable factors when modelling the background error covariance matrix for tropical convective‐scale data assimilation systems.