A method is presented for deriving weather patterns objectively over an area of interest, in this case the UK and surrounding European area. A set of 30 and eight patterns are derived through k-means clustering of daily mean sea level pressure (MSLP) data . These patterns have been designed for the purpose of post-processing forecast output from ensemble prediction systems and understanding how forecast models perform under different circulation types. The 30 weather patterns are designed for use in the medium-range and the eight weather patterns are designed for use in the monthly and seasonal timescales, or when there is low forecast confidence in the medium-range. Weather patterns are numbered according to their annual historic occurrences, with lower numbered patterns occurring most often. Lower numbered patterns occur more in summer (with weak MSLP anomalies) and higher numbered patterns occur more in winter (with strong MSLP anomalies). Weather patterns have been applied in a weather forecasting context, whereby ensemble members are assigned to the closest matching pattern definition. This provides a probabilistic insight into which patterns are most likely within the forecast range and summarises key aspects from the large volumes of data which ensembles provide. Verification of European Centre for Medium-Range Weather Forecasts medium-range ensemble forecasts for the set of eight weather patterns shows small forecast biases annually with some large variations seasonally. The most prominent seasonal variation shows the westerly (NAO+) pattern to over-forecast in summer and under-forecast in winter. Forecast skill was found to be better in winter than summer for most patterns.

It has been 10 years since the ash cloud from the eruption of Eyjafjallajökull caused unprecedented disruption to air traffic across Europe. During this event, the London Volcanic Ash Advisory Centre (VAAC) provided advice and guidance on the expected location of volcanic ash in the atmosphere using observations and the atmospheric dispersion model NAME (Numerical Atmospheric-Dispersion Modelling Environment). Rapid changes in regulatory response and procedures during the eruption introduced the requirement to also provide forecasts of ash concentrations, representing a step-change in the level of interrogation of the dispersion model output. Although disruptive, the longevity of the event afforded the scientific community the opportunity to observe and extensively study the transport and dispersion of a volcanic ash cloud. We present the development of the NAME atmospheric dispersion model and modifications to its application in the London VAAC forecasting system since 2010, based on the lessons learned. Our ability to represent both the vertical and horizontal transport of ash in the atmosphere and its removal have been improved through the introduction of new schemes to represent the sedimentation and wet deposition of volcanic ash, and updated schemes to represent deep moist atmospheric convection and parametrizations for plume spread due to unresolved mesoscale motions. A good simulation of the transport and dispersion of a volcanic ash cloud requires an accurate representation of the source and we have introduced more sophisticated approaches to representing the eruption source parameters, and their uncertainties, used to initialize NAME. Finally, upper air wind field data used by the dispersion model is now more accurate than it was in 2010. These developments have resulted in a more robust modelling system at the London VAAC, ready to provide forecasts and guidance during the next volcanic ash event.

Surface observations of cloud cover for routine verification typically come from manual observations (a human observer makes a visual inspection of the observable sky) or from automated instruments (taking a time-averaged sample of the cloud passing directly overhead). Each observation type has associated limitations. Here a cloud mask field derived from satellite data is examined as an observation type, providing a top-down view of cloud cover. This paper shows that the satellite cloud mask field can be used to assess details of spatial bias differences between regional numerical weather prediction (NWP) models over the United Kingdom (UK). The Structure-Amplitude-Location (SAL) method is used to show how the distribution of cloud within the NWP model domain can be assessed. Finally the satellite cloud mask is used to derive single site observations which can be used to complement the existing surface observation network, or provide total cloud amount guidance in data sparse locations and shows how it could potentially be used to condition surface observational data in the presence of cirrus. 

A method is presented for deriving probabilistic medium‐range (1‐to‐2‐week) weather pattern forecasts for India. This method uses an existing set of 30 objectively derived daily weather patterns, which provide climatological representations for unique states in the large‐scale circulation over India. Weather pattern forecast probabilities are based on the number of ensemble members objectively assigned to each weather pattern. Two summer monsoon case studies illustrate the best use of the forecasting tool within medium‐range guidance, such as highlighting the most likely weather pattern transitions and relating these to the likelihood of weather impacts. Forecast skill is evident out to at least 10–15 days. Winter dry period weather patterns have the highest forecast skill, closely followed by retreating monsoon weather patterns. In contrast, monsoon onset and break monsoon weather patterns have the lowest forecast skill. Finally, a prototype weather pattern forecast climatology application is presented for use in highlighting when extreme rainfall is more likely than normal. This application is based on weather pattern empirical probabilities of threshold exceedances using a high‐resolution regional reanalysis. The transitional pre‐ and post‐monsoon seasons have the greatest variability in rainfall across all possible weather patterns, with a slight dip in variability during the main summer monsoon season. In contrast, very little variability across weather patterns is evident during the relatively dry winter months. This highlights the times of year when a climatology‐based weather pattern forecasting approach may have its greatest benefits over that of a basic daily climatology.

The Met Office in the UK has developed a completely new probabilistic post-processing system called IMPROVER to operate on outputs from its operational Numerical Weather Prediction (NWP) forecasts and precipitation nowcasts. The aim is to improve weather forecast information to the public and other stakeholders whilst better exploiting the current and future generations of underpinning kilometer-scale NWP ensembles. We wish to provide seamless forecasts from nowcasting to medium range, provide consistency between gridded and site-specific forecasts and be able to verify every stage of the processing. The software is written in a modern modular framework that is easy to maintain, develop and share. IMPROVER allows forecast information to be provided with greater spatial and temporal detail and a faster update frequency than previous post-processing. Independent probabilistic processing chains are constructed for each meteorological variable consisting of a series of processing stages that operate on pre-defined grids and blend outputs from several NWP inputs to give a frequently updated, probabilistic forecast solution. Probabilistic information is produced as standard, with the option of extracting a most likely or yes/no outcome if required. Verification can be performed at all stages, although it is only currently switched on for the most significant stages when run in real time. IMPROVER has been producing real-time output since March 2021 and became operational in Spring 2022.

Abstract. The Met Office currently runs two operational ocean forecasting configurations for the North West European Shelf: an eddy-permitting model with a resolution of 7 km (AMM7) and an eddy-resolving model at 1.5 km (AMM15). Whilst qualitative assessments have demonstrated the benefits brought by the increased resolution of AMM15, particularly in the ability to resolve finer-scale features, it has been difficult to show this quantitatively, especially in forecast mode. Applications of typical assessment metrics such as the root mean square error have been inconclusive, as the high-resolution model tends to be penalised more severely, referred to as the double-penalty effect. This effect occurs in point-to-point comparisons whereby features correctly forecast but misplaced with respect to the observations are penalised twice: once for not occurring at the observed location, and secondly for occurring at the forecast location, where they have not been observed. An exploratory assessment of sea surface temperature (SST) has been made at in situ observation locations using a single-observation neighbourhood-forecast (SO-NF) spatial verification method known as the High-Resolution Assessment (HiRA) framework. The primary focus of the assessment was to capture important aspects of methodology to consider when applying the HiRA framework. Forecast grid points within neighbourhoods centred on the observing location are considered as pseudo ensemble members, so that typical ensemble and probabilistic forecast verification metrics such as the continuous ranked probability score (CRPS) can be utilised. It is found that through the application of HiRA it is possible to identify improvements in the higher-resolution model which were not apparent using typical grid-scale assessments. This work suggests that future comparative assessments of ocean models with different resolutions would benefit from using HiRA as part of the evaluation process, as it gives a more equitable and appropriate reflection of model performance at higher resolutions.

RC-Referee Comment AC-Author Comment MC-Manuscript change RC-In this contribution, the authors conduct a skill assessment of two operational ocean models running in the North West European Shelf with different configurations and spatial resolutions. Since the increased spatial resolution might require ad hoc metrics to properly reflect the model performance and reduce the impact of so-called double-penalty effects (occurring when using point-to-point comparisons with features present in the model but misplaced with respect to the observations), the present work is welcomed. It addresses this interesting and essential topic by intercomparing models' performances in overlapping regions to infer their respective strengths and weak-C1

<p>Forecasting the potential for flood-producing precipitation and any subsequent flooding is a challenging task; the process is highly non-linear and inherently uncertain. Acknowledging and accounting for the uncertainty in precipitation and flood forecasts has become increasingly important with the move to risk-based warning and guidance services which combine the likelihood of flooding with the potential impact on society and the environment.</p><p>A standard approach to accounting for uncertainty is to generate ensemble forecasts. Here the national Grid-to-Grid (G2G) model is coupled to a Best Medium Range (BMR) ensemble which consists of three models spanning different time horizons: an ensemble nowcast for the first 6h, which is blended with the short-range 2.2 km Met Office Global Regional Ensemble Prediction System (MOGREPS-UK) ensemble up to 36h and the ~20 km global MOGREPS-G up to day 6. The G2G model is driven by 15-minute accumulations on a 1 km grid.</p><p>16-months of precipitation and river flow ensemble forecasts have been processed to develop and assess a joint verification framework which can facilitate the evaluation of the end-to-end forecasting chain. Analysis concluded the following: (1) daily precipitation accumulations provide the best guidance in terms of rain volume for hydrological impacts. One reason may be because it removes the impact of timing errors at the sub-daily scale. However, sub-daily precipitation can be more closely related to river flow on an ensemble member-by-member basis. (2) Observation uncertainty is important. The same forecasts verified against three different observed precipitation sources (raingauge, radar or merged) can provide markedly different results and interpretations. G2G river flow performance can also be affected, when driven by these datasets rather than forecasts. (3) The change in precipitation-intensity with model is evident and has an impact on downstream modelling and verification. (4) The period used for ensemble verification should be at least two years. The 16-month test period was sufficient for generating enough precipitation threshold-exceedances for the 95th percentile: but insufficient for higher thresholds and for river flow thresholds above half the median annual maximum flood at sub-regional scales. (5) A new method of presenting Time-Window Probabilities (TWPs) has been developed for precipitation thresholds that are hydrologically relevant. Verification of these shows that probabilities are larger, and more reliable so that users can have greater confidence in them. (6) Overall precipitation forecast skill was far more uniform than for river-flow, primarily because the atmosphere is a continuum whilst catchments are finite and subject to external, non-atmospheric factors including antecedent moisture. (7) Though G2G can be sensitive to precipitation outliers, the precipitation ensemble is generally under-spread and spread does not appear to amplify or propagate to enhance the river flow ensemble spread, so spread is reduced rather than increased in the downstream application.</p>