Abstract While contemporary numerical weather prediction models represent the large‐scale structure of moist atmospheric processes reasonably well, they often struggle to maintain accurate forecasts small‐scale features such as convective rainfall. Even though high‐resolution resolve more flow, and are therefore arguably accurate, flow becomes increasingly nonlinear dynamically unstable. Importantly, models' initial conditions typically sub‐optimal, leaving scope improve accuracy with...

Abstract In November 2021, the Royal Meteorological Society Data Assimilation (DA) Special Interest Group and University of Reading hosted a virtual meeting on topic DA for convection‐permitting numerical weather prediction. The goal was to discuss recent developments review challenges including methodological progress in making best use observations. took place over two half days 10 12 November, consisted six talks panel discussion. scientific presentations highlighted some work from Europe...

Abstract A new multiscale, ensemble-based data assimilation (DA) method, multiscale local gain form ensemble transform Kalman filter (MLGETKF), is introduced. MLGETKF allows simultaneous update of multiple scales for both the mean and perturbations through assimilating all observations at once. performs DA in independent volumes, which lends algorithm a high degree computational scalability. The analysis enabled rapid creation many pseudoensemble via modulation procedure. that used to raw...

Abstract There has been a recent wave of attention given to atmospheric bores in order understand how they evolve and initiate maintain convection during the night. This surge is attributable data collected 2015 Plains Elevated Convection at Night (PECAN) field campaign. A salient aspect PECAN project its focus on using multiple observational platforms better convective outflow boundaries that intrude into stable boundary layer induce development bores. The intent this article threefold: 1)...

Data assimilation plays a pivotal role in understanding and predicting turbulent systems within geoscience weather forecasting, where data is used to address three fundamental challenges, i.e., high-dimensionality, nonlinearity, partial observations. Recent advances machine learning (ML)-based methods have demonstrated encouraging results. In this work, we develop an ensemble score filter (EnSF) that integrates image inpainting solve the problems with The EnSF method exploits exclusively...

Abstract The majority of data assimilation (DA) methods in the geosciences are based on Gaussian assumptions. While such approximations facilitate efficient algorithms, they cause analysis biases and subsequent forecast degradations. Non-parametric, particle-based DA algorithms have superior accuracy, but their application to high-dimensional models still poses operational challenges. Drawing inspiration from recent advances fields measure transport generative artificial intelligence, this...

Abstract The intersection between classical data assimilation methods and novel machine learning techniques has attracted significant interest in recent years. Here we explore another promising solution which diffusion models are used to formulate a robust nonlinear ensemble filter for sequential assimilation. Unlike standard methods, the proposed Ensemble Score Filter (EnSF) is completely training-free can efficiently generate set of analysis members. In this study, apply EnSF surface...

Abstract Using data from the 6 July 2015 PECAN case study, this paper provides first objective assessment of how assimilation ground-based remote sensing profilers affects forecasts bore-driven convection. To account for multiscale nature phenomenon, impacts are examined separately with respect to (i) bore environment, (ii) explicitly resolved bore, and (iii) bore-initiated The findings work suggest that profiling instruments provide considerable advantages over conventional in situ...

Abstract A novel object-based algorithm capable of identifying and tracking convective outflow boundaries in convection-allowing numerical models is presented this study. The most distinct feature the proposed its ability to seamlessly analyze numerically simulated density currents bores, both which play an important role dynamics nocturnal systems. unified identification classification these morphologically different phenomena achieved through a multivariate approach combined with...

Abstract There is a growing interest in the use of ground-based remote sensors for Numerical Weather Prediction (NWP), which sparked by their potential to address currently existing observation gap within Planetary Boundary Layer (PBL). Nevertheless, open questions still exist regarding relative importance and synergy among various instrument types. To shed light on these important questions, present study examines forecast benefits associated with several different profiling networks using...

A long-lived supercell developed in Northwest Bulgaria on 15 May 2018 and inflicted widespread damage along its track. The first part of this article presents a detailed overview the observed storm evolution. Doppler radar observations reveal that acquired typical supercellular signatures maintained reflectivity values excess 63 dBZ for more than 4 h. thunderstorm was also analyzed through lightning highlighted important characteristics overall dynamics. In second part, study investigates...

The intersection between classical data assimilation methods and novel machine learning techniques has attracted significant interest in recent years. Here we explore another promising solution which diffusion models are used to formulate a robust nonlinear ensemble filter for sequential assimilation. Unlike standard methods, the proposed \textit{Ensemble Score Filter (EnSF)} is completely training-free can efficiently generate set of analysis members. In this study, apply EnSF surface...

Recent work has demonstrated that convective-scale model parameters, such as those related to cloud microphysical schemes, are nonlinearly dynamic/thermodynamic variables in forecasts and observations. This leads errors when data assimilation (DA) schemes based on linear-Gaussian assumptions used estimate the uncertain parameters. Nonlinear modifications standard ensemble Kalman filter (EnKF) have been shown perform better for systems governed by convective dynamics, recent algorithms...

The majority of data assimilation (DA) methods in the geosciences are based on Gaussian assumptions. While these assumptions facilitate efficient algorithms, they cause analysis biases and subsequent forecast degradations. Non-parametric, particle-based DA algorithms have superior accuracy, but their application to high-dimensional models still poses operational challenges. Drawing inspiration from recent advances field generative artificial intelligence (AI), this article introduces a new...

<p>Recent years have seen active efforts within the geophysical community to combine traditional Data Assimilation (DA) methods with emerging Machine Learning (ML) techniques. However, most of this past theoretical work has been centered on variational DA approaches due their similarity ML in terms how underlying optimization problem is formulated and solved. Here I will present a new completely general nonlinear estimation theory that retains flexibility advanced...