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We describe six experiments set up at four regional locations in Great Britain, in 1993 and 1994, to examine the impact of control treatments on bracken and associated vegetation. Present discussion is limited to the effects of treatments on bracken frond variables (density, length and dry mass). These variables would be used by a land manager to judge the extent of infestation and the efficacy of control methods. Results of statistical analyses are reported for the period 1994 to 1998, inclusive. The treatments showed great variability in effectiveness between both sites and years. Great inter-regional differences were seen, but stands at sites within a short distance of each other also varied in their response to treatment. Meso- and micro-climatic differences are suggested as possible causes, together with stand growth phase and genetic effects. The most effective treatments in the short-term were found to be combinations of cutting and herbicide spraying, applied once. Annual cutting usually gave a better result in the longer term. All treatments had greatly improved effects when combined with a follow-up application of herbicide several years after commencement. A number of recommendations are given for management, such as best methods for short- and long-term results. Systematic monitoring is urged as changes in frond density, for example, may reveal the extent of the problem for control at a particular site.

Presents science project ideas featuring weather, including what makes the wind blow, how a cloud forms, and why there are seasons.

By midcentury, nature was collapsed down to physics, as previously disparate phenomena -- clouds, trade winds, air pressure, thunderstorms -- were linked into a dynamic system in which weather is pumped around the world by a circulatory system of water vapor and heat differentials.

During the presummer rainy season (April–June), southern China often experiences frequent occurrences of extreme rainfall, leading to severe flooding and inundations. To expedite the efforts in improving the quantitative precipitation forecast (QPF) of the presummer rainy season rainfall, the China Meteorological Administration (CMA) initiated a nationally coordinated research project, namely, the Southern China Monsoon Rainfall Experiment (SCMREX) that was endorsed by the World Meteorological Organization (WMO) as a research and development project (RDP) of the World Weather Research Programme (WWRP). The SCMREX RDP (2013–18) consists of four major components: field campaign, database management, studies on physical mechanisms of heavy rainfall events, and convection-permitting numerical experiments including impact of data assimilation, evaluation/improvement of model physics, and ensemble prediction. The pilot field campaigns were carried out from early May to mid-June of 2013–15. This paper: i) describes the scientific objectives, pilot field campaigns, and data sharing of SCMREX; ii) provides an overview of heavy rainfall events during the SCMREX-2014 intensive observing period; and iii) presents examples of preliminary research results and explains future research opportunities.

\"Step-by-step instructions for experiments pertaining to earth science and weather.\"-- Provided by publisher.

Permutation tests based on certain ratio statistics have been used (a) in the analysis of rainfall stimulation experiments and (b) in simulation studies aimed at providing guidance in the planning of such experiments. The paper presents asymptotic approximations to the permutation distributions of these statistics, and also to their power functions under a simple multiplicative model for the seeding effect. Simulation results are presented for comparison with the theoretical approximations.

Ice formation and growth processes play a crucial role in the evolution of cloud systems and the formation of precipitation. However, the initial formation and growth of ice crystals are challenging to study in the real atmosphere resulting in uncertainties in weather forecasts and climate projections. The CLOUDLAB project tackles this problem by using supercooled stratus clouds as a natural laboratory for targeted glaciogenic cloud seeding to advance the understanding of ice processes: Ice nucleating particles are injected from an uncrewed aerial vehicle (UAV) into supercooled stratus clouds to induce ice crystal formation and subsequent growth processes. Microphysical changes induced by seeding are measured 3–15 min downstream of the seeding location using in situ and ground-based remote sensing instrumentation. The novel application of seeding with a multirotor UAV combined with the persistent nature of stratus clouds enables repeated seeding experiments under similar and well-constrained initial conditions. This article describes the scientific goals, experimental design, and first results of CLOUDLAB. First, the seeding plume is characterized by using measurements of a UAV equipped with an optical particle counter. Second, the seeding-induced microphysical changes observed by cloud radars and a tethered balloon system are presented. The seeding signatures were detected by regions of increased radar reflectivity (>−20 dBZ), which were 10–20 dBZ higher than the natural background. Simultaneously, high concentrations of seeding particles and ice crystals (up to 2,000 L−1) were observed. A cloud seeding case was simulated with the numerical weather model ICON to contextualize the findings.

In this study, we present RainNet, a deep convolutional neural network for radar-based precipitation nowcasting. Its design was inspired by the U-Net and SegNet families of deep learning models, which were originally designed for binary segmentation tasks. RainNet was trained to predict continuous precipitation intensities at a lead time of 5 min, using several years of quality-controlled weather radar composites provided by the German Weather Service (DWD). That data set covers Germany with a spatial domain of 900km×900 km and has a resolution of 1 km in space and 5 min in time. Independent verification experiments were carried out on 11 summer precipitation events from 2016 to 2017. In order to achieve a lead time of 1 h, a recursive approach was implemented by using RainNet predictions at 5 min lead times as model inputs for longer lead times. In the verification experiments, trivial Eulerian persistence and a conventional model based on optical flow served as benchmarks. The latter is available in the rainymotion library and had previously been shown to outperform DWD's operational nowcasting model for the same set of verification events.RainNet significantly outperforms the benchmark models at all lead times up to 60 min for the routine verification metrics mean absolute error (MAE) and the critical success index (CSI) at intensity thresholds of 0.125, 1, and 5 mmh-1. However, rainymotion turned out to be superior in predicting the exceedance of higher intensity thresholds (here 10 and 15 mmh-1). The limited ability of RainNet to predict heavy rainfall intensities is an undesirable property which we attribute to a high level of spatial smoothing introduced by the model. At a lead time of 5 min, an analysis of power spectral density confirmed a significant loss of spectral power at length scales of 16 km and below. Obviously, RainNet had learned an optimal level of smoothing to produce a nowcast at 5 min lead time. In that sense, the loss of spectral power at small scales is informative, too, as it reflects the limits of predictability as a function of spatial scale. Beyond the lead time of 5 min, however, the increasing level of smoothing is a mere artifact – an analogue to numerical diffusion – that is not a property of RainNet itself but of its recursive application. In the context of early warning, the smoothing is particularly unfavorable since pronounced features of intense precipitation tend to get lost over longer lead times. Hence, we propose several options to address this issue in prospective research, including an adjustment of the loss function for model training, model training for longer lead times, and the prediction of threshold exceedance in terms of a binary segmentation task. Furthermore, we suggest additional input data that could help to better identify situations with imminent precipitation dynamics. The model code, pretrained weights, and training data are provided in open repositories as an input for such future studies.