After extensive efforts over the course of a decade, convective-scale weather forecasts with horizontal grid spacings of 1–5 km are now operational at national weather services around the world, accompanied by ensemble prediction systems (EPSs). However, though already operational, the capacity of forecasts for this scale is still to be fully exploited by overcoming the fundamental difficulty in prediction: the fully three-dimensional and turbulent nature of the atmosphere. The prediction of this scale is totally different from that of the synoptic scale (103 km), with slowly evolving semigeostrophic dynamics and relatively long predictability on the order of a few days. Even theoretically, very little is understood about the convective scale compared to our extensive knowledge of the synoptic-scale weather regime as a partial differential equation system, as well as in terms of the fluid mechanics, predictability, uncertainties, and stochasticity. Furthermore, there is a requirement for a drastic modification of data assimilation methodologies, physics (e.g., microphysics), and parameterizations, as well as the numerics for use at the convective scale. We need to focus on more fundamental theoretical issues—the Liouville principle and Bayesian probability for probabilistic forecasts—and more fundamental turbulence research to provide robust numerics for the full variety of turbulent flows. The present essay reviews those basic theoretical challenges as comprehensibly as possible. The breadth of the problems that we face is a challenge in itself: an attempt to reduce these into a single critical agenda should be avoided.

A comparison between anelastic and compressible convection-permitting weather forecasts for the Alpine region is presented. This involves mesoscale simulation of a typical westerly flow accompanied by a passage of frontal systems as well as intense airmass convection and orographic convection. The limited-area model employing a 2.2-km horizontal grid length is driven by time-dependent boundary conditions from a coarse-resolution model. The results obtained with the anelastic and the compressible model versions show good agreement. Validations of the 10-m wind, 2-m temperature, 2-m dewpoint temperature, total cloud cover, and surface precipitation against observations for a seven-member forecast ensemble reveal only minor differences between the two configurations. The sensitivity study demonstrates only a small impact of realistic pressure perturbations (about a reference profile) on the solutions. Overall, anelastic approximation proves remarkably accurate in simulating moist mesoscale dynamics. The study has been conducted using a newly developed hybrid limited-area nonhydrostatic version of the Consortium for Small-Scale Modeling (COSMO) model. This version facilitates the use of two alternative dynamical cores: compressible (original) and anelastic (new). The new dynamical core, which is based on the Euler–Lagrangian (EULAG) solver, aims at integrating atmospheric flow equations at resolutions higher than O(1) km for steep orography. A coupler has been developed to merge the EULAG dynamical core with the COSMO numerical weather prediction framework.

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This paper reports on the application of the cloud-resolving convection parameterization (CRCP) to the Community Atmospheric Model (CAM), the atmospheric component of the Community Climate System Model (CCSM). The cornerstone of CRCP is the use of a two-dimensional zonally oriented cloud-system-resolving model to represent processes on mesoscales at the subgrid scale of a climate model. Herein, CRCP is applied at each climate model column over the tropical western Pacific warm pool, in a domain spanning 10°S–10°N, 150°–170°E. Results from the CRCP simulation are compared with CAM in its standard configuration. The CRCP simulation shows significant improvements of the warm pool climate. The cloud condensate distribution is much improved as well as the bias of the tropopause height. More realistic structure of the intertropical convergence zone (ITCZ) during the boreal winter and better representation of the variability of convection are evident. In particular, the diurnal cycle of precipitation has phase and amplitude in good agreement with observations. Also improved is the large-scale organization of the tropical convection, especially superclusters associated with Madden–Julian oscillation (MJO)-like systems. Location and propagation characteristics, as well as lower-tropospheric cyclonic and upper-tropospheric anticyclonic gyres, are more realistic than in the standard CAM. Finally, the simulations support an analytic theory of dynamical coupling between organized convection and equatorial beta-plane vorticity dynamics associated with MJO-like systems.

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by the novel coronavirus. The role of environmental factors in COVID-19 transmission is unclear. This study aimed to analyze the correlation between meteorological conditions (temperature, relative humidity, sunshine duration, wind speed) and dynamics of the COVID-19 pandemic in Poland. Data on a daily number of laboratory-confirmed COVID-19 cases and the number of COVID-19-related deaths were gatheredfrom the official governmental website. Meteorological observations from 55 synoptic stations in Poland were used. Moreover, reports on the movement of people across different categories of places were collected. A cross-correlation function, principal component analysis and random forest were applied. Maximum temperature, sunshine duration, relative humidity and variability of mean daily temperature affected the dynamics of the COVID-19 pandemic. An increase intemperature and sunshine hours decreased the number of confirmed COVID-19 cases. The occurrence of high humidity caused an increase in the number of COVID-19 cases 14 days later. Decreased sunshine duration and increased air humidity had a negative impact on the number of COVID-19-related deaths. Our study provides information that may be used by policymakers to support the decision-making process in nonpharmaceutical interventions against COVID-19.

It has been noted previously that during frontal collapse dynamical processes lead to the formation of potential vorticity (PV) anomalies in the vicinity of the surface front. These processes can either be associated with diffusion in the presence of the tight temperature gradients or with intrusion into the atmosphere of the vanishingly thin layer adjacent to the surface. This paper explores the dynamical consequences of these PV anomalies on the parent baroclinic wave cyclone. The method used is to find the vertical motion attributable to these anomalies. The location and magnitude of this vertical motion is clearly key to the dynamical influence of the anomalies. As we are dealing with a three-dimensional evolving cyclone with an emergent tropopause fold, the calculation of vertical motion needs to be capable of accounting for the role of the highly deformed tropopause. Hence we develop and use a nonlinear balance model approach. The result shows that the PV anomalies near the surface front induce downward vertical motion at the tropopause fold, and thus they act to amplify this feature. This role of diffusion associated with surface frontal collapse is the key finding of this paper. An analysis is made of the role of upper-and lower-level PV anomalies on the overall rate of the wave development.

Data from epidemiological reports on the COVID-19 epidemic in Poland and meteorological parameters from 16 synoptic stations has been used to assess the impact of weather on the dynamics of COVID-19 epidemic in 16 administrative regions (voivodeships) of Poland. We can estimate that 10-12% of COVID-19 cases can be related to the weather events and its impact on the transmission of infectious diseases with a 10-14 days lag. In particular, relative humidity and the maximum daily temperature had the highest impact on the dynamics of the COVID-19 epidemic in each of the 16 voivodeships. The periods with significant weather impact were determined by means of a multiple linear regression model.