This work proposes a novel approach for probabilistic end-to-end all-sky imager-based nowcasting with horizons of up to 30 min using an ImageNet pre-trained deep neural network. The method involves a two-stage approach. First, a backbone model is trained to estimate the irradiance from all-sky imager (ASI) images. The model is then extended and retrained on image and parameter sequences for forecasting. An open access data set is used for training and evaluation. We investigated the impact of simultaneously considering global horizontal (GHI), direct normal (DNI), and diffuse horizontal irradiance (DHI) on training time and forecast performance as well as the effect of adding parameters describing the irradiance variability proposed in the literature. The backbone model estimates current GHI with an RMSE and MAE of 58.06 and 29.33 W m−2, respectively. When extended for forecasting, the model achieves an overall positive skill score reaching 18.6 % compared to a smart persistence forecast. Minor modifications to the deterministic backbone and forecasting models enables the architecture to output an asymmetrical probability distribution and reduces training time while leading to similar errors for the backbone models. Investigating the impact of variability parameters shows that they reduce training time but have no significant impact on the GHI forecasting performance for both deterministic and probabilistic forecasting while simultaneously forecasting GHI, DNI, and DHI reduces the forecast performance.
In order to facilitate offshore wind farm tenders, Deutscher Wetterdienst (DWD, Germany's national meteorological service) provides reanalysis data and quality assessments to Bundesamt für Seeschifffahrt und Hydrographie (BSH, Federal Maritime and Hydrographic Agency). The regional reanalysis COSMO-REA6 is used besides the global reanalysis ERA5. New reanalyses and derived products getting available are (i) the regional reanalysis CERRA (C3S), (ii) COSMO-R6G2, a successor of COSMO-REA6 which is currently produced by DWD and (iii) HoKliSim-De, a convection-permitting climate simulation for Germany with COSMO-CLM as a regional downscaling of ERA5. In the present study, the quality of the different data sets for offshore wind energy application is compared using in-situ measurements of the wind speed and wind direction from the top anemometer and vane of the FINO1 research platform and satellite-based data of the near-surface wind speed from the Copernicus Marine Environment Monitoring Service (CMEMS) and the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF).
Evaluation at FINO1 focuses on the time period prior to the installation of nearby wind farms to avoid wake effects. COSMO-REA6, CERRA and HoKliSim-De show only small biases and resemble the observed distribution of the wind speed at FINO1 whereas ERA5 shows slightly lower values of the wind speed at 100 m. All model-based products tend to slightly underestimate the occurrence of south-westerly wind directions and overestimate wind directions from West to Northwest. Smallest directional biases are analysed for COSMO-REA6. Analysis of the windstorm CHRISTIAN suggests that ensemble information is required for the representation of individual extreme events.Evaluation of the near-surface wind speed using satellite-based data is performed for an area around the German Exclusive Economic Zone (EEZ) of the North Sea. The median bias of ERA5 and COSMO-REA6 is close to zero. CERRA shows a systematic overestimation of the near-surface wind speed compared to the satellite-based reference datasets. By contrast, a slight underestimation is analysed for HoKliSim-De. The bias distribution analysed for a first simulation stream of COSMO-R6G2 is similar to COSMO-REA6 which provides initial indication for the applicability of the new product.The marine Atmospheric Boundary Layer (ABL) over the southern Bulgarian Black Sea coast is studied based on remote sensing measurements with a monostatic Doppler sodar system located at about 400 m inland. Long-term profile data (August 2008–October 2016) with high spatial (10 m) and temporal (20 min running averages at every 10 min) resolution was analysed to reveal the complex vertical structure of the coastal ABL at marine airflow. The processes of air masses transformation due to the sharp change in physical characteristics of the underlying surface lead to Internal Boundary Layer (IBL) formation. Its spatial scales as a sublayer of the coastal ABL depend on the distance from the shore. In the absence of temperature and humidity profile measurements, the turbulent profiles of marine air masses of different fetch over land (400 to 2500 m) were used to examine the characteristics of the IBL. Different fetch or distance passed by the marine airflow before reaching the sodar is considered selecting intervals of wind directions. IBL heights between 60 and 150 m depending on the fetch are obtained.
In this work, we present the most recent updates in the E-OBS gridded data set for daily mean wind speed over Europe. The data set is provided as an ensemble of 20 equally likely realisations. The main improvements of this data set are the use of forward selection linear regression for the monthly background field, as well as a method to ensure the reliability of the ensemble dispersion. In addition, we make a preliminary study into possible causes of the observed terrestrial wind stilling effect, such as local changes in surface roughness length.
In 2021, several weather disasters occurred in which conditions surpassed recorded extremes. Analysis of the performance of warning systems in these disasters by the WWRP HIWeather project shows that in most, but not all cases, there was adequate forewarning of the magnitude of the event, but that lack of preparedness and/or communication failures led to loss of life in particularly vulnerable groups. Using information from the HIWeather value chain database, we present an overview of key aspects of each event – the weather and its impact, the forecasts, the warnings, and the responses – followed by some results of a comparative analysis of warning performance and some conclusions about critical components of a successful warning system. In the light of this analysis we conclude with a checklist of key components in the design of an effective warning system for unprecedented weather events.
It has become a worldwide expectation that the physics curriculum includes everyday knowledge as well. One important field that can make the curriculum more colourful and exciting is the field of meteorology. In our paper, a three-lesson-long curriculum for high schools will be presented on how to teach the connection between fog formation and air quality. The international educational experience of this particular topic will be surveyed, mainly in the countries of the Carpathian Basin, moreover, the measurement processes and education methods used in the GLOBE Program will also be presented. The experimental curriculum consists of three parts. In the first part air humidity and the concepts of absolute and relative humidity are discussed. Through a few specific exercises, the students participating in the program learn to specify relative humidity and become acquainted with fog formation. It is shown via an experiment that air cooling at a saturated state is not enough to form fog because condensational nuclei are needed for the formation of tiny water drops.
In the 2nd lesson, the concept of temperature inversion and its connection to fog and air pollution are discussed. Using Internet websites the students collect information about the formation of smog, its types, occurrence, and the conditions for declaring smog alerts.In the 3rd lesson, the methods of air pollution analysis and different air pollutants are discussed. Websites, where the students can follow the air pollution data of their area, are used. Based on these, problems related to the interpretation of the data will be solved. The information which is available on the website of the European Environment Agency is also touched on.We report on an approach to distributed wind power forecasting, which supports wind energy integration in power grid operation during exceptional and critical situations. Forecasts are generated on-site the wind power plant (WPP) in order to provide blackout-robust data transmission directly from the WPP to the grid operator. An adaptively trained forecasting model uses locally available sensor data to predict the available active power (AAP) signal in a probabilistic fashion. A forecast generated off-site based on numerical weather prediction (NWP) is deposited and combined on-site the WPP with the locally generated forecast. We evaluate the performance of the method in a case study and find that the locally generated forecast significantly improves forecast reliability for a short-term horizon, which is highly relevant for enabling power reserve provision from WPPs.
The weather information value chain provides a framework for characterising the production, communication, and use of information by all stakeholders in an end-to-end warning system covering weather and hazard monitoring, modelling and forecasting, risk assessment, communication and preparedness activities. Warning services are typically developed and provided through a multitude of complex and malleable value chains (networks), often established through co-design, co-creation and co-provision. In November 2020, a 4-year international project under the World Meteorological Organization (WMO) World Weather Research Programme was instigated to explore value chain approaches to describe and evaluate warning systems for high impact weather by integrating physical and social science. It aims to create a framework with guidance and tools for using value chain approaches, and to develop a database of high impact weather warning case studies for scientists and practitioners to review, analyse and learn from previous experience using value chain approaches.
Here we describe a template for high-impact weather event case study collection that provides a tool for scientists and practitioners involved in researching, designing and evaluating weather-related warning systems to review previous experience of high impact weather events and assess their efficacy.As the frequency of extreme heat events in cities is significantly increasing due to climate change, the implementation of adaptation measures is important for urban planning. Microclimate modelling approaches enable scenario analyses and evaluations of adaptation potentials. An ENVI-met microclimate model was setup for a heterogeneous urban study area in Cologne/Germany characterized by closed building structures in the eastern part and an urban park area in the western part. The goal of this paper is to evaluate the model sensitivity and performance to different wind direction forcing and demonstrate the importance of accurate wind forcing data for precise microclimate modelling evaluated with sensor measurements. To this end, we compared simulated air temperatures at 3 m height level using measured wind direction forcing data with simulated air temperatures using constant wind direction forcing from west, north, east and south direction. All other forcing data like wind speed were kept exactly the same as in the reference run. This sensitivity study was performed for a warm summer day in 2022. The model results of all five model runs (reference plus four scenarios) were compared to microclimatological measurements derived from one station of a dense meteorological sensor network located in the study area using the simulated mean air temperatures. We found significant temperature differences between the four sensitivity tests and the reference run as well as to the sensor measurements. Temperature differences between the reference run and the measurements were small and a high statistical model fit could be determined (Nash Sutcliffe Model Efficiency Coefficient/NSE = 0.91). The four model runs with constant wind directions showed significantly larger differences to measurement data and a worse statistical correlation between simulated and observed data (NSE between 0.62 and 0.15). For constant west winds, cooler air temperatures and higher wind speeds were found in the urban park and in the streets and courtyards east of the park. Constant east wind causes warmer air temperatures in the urban park area and lower wind speeds in the street canyons and inner courtyards. This shows that cooling effects in adjacent building blocks due to the greened urban park largely depend on the wind direction. The sensitivity tests show that the wind direction effect can result in local air temperature differences of up to 4 K on average. These results shows that even on summer days with low wind speeds, accurate wind direction data is highly relevant for accurate air temperature simulation. This finding can have important implications for urban planning and the design of green infrastructures in cities, e. g. for the design of fresh air corridors.
Atmospheric boundary layers (ABLs) exhibit transient processes on various time scales that range from a few days down to seconds, with a scale separation of the large-scale forcing and the small-scale turbulent response. One of the standing challenges in modeling and simulation of ABLs is a physically based representation of complex multiscale boundary layer dynamics. In this study, an idealized time-dependent ABL, the so-called Ekman–Stokes boundary layer (ESBL), is considered as a simple model for the near-surface flow in the mid latitudes and polar regions. The ESBL is driven by a prescribed temporal modulation of the bulk–surface velocity difference. A stochastic one-dimensional turbulence (ODT) model is applied to the ESBL as standalone tool that aims to resolve all relevant scales of the flow along a representative vertical coordinate. It is demonstrated by comparison with reference data that ODT is able to capture relevant features of the time-dependent boundary layer flow. The model predicts a parametric enhancement of the bulk–surface coupling in the event of a boundary layer resonance when the flow is forced with the local Coriolis frequency. The latter reproduces leading order effects of the critical latitudes. The model results suggest that the bulk flow decouples from the surface for high forcing frequencies due to a relative increase in detached residual turbulence.
High-resolution weather maps are fundamental components of early warning systems, since they enable the (near) real-time tracking of extreme weather events. In this context, crowd-sourced weather networks producing low-fidelity observations are often the only type of data available at local (e.g. neighborhood) scales. In this work, we demonstrate that we can provide such maps by combining high-fidelity official weather data with low-fidelity crowd-sourced weather data and high-resolution covariate information. Because the crowd-sourced data contains significant bias and noise, we develop an approach to include a bias budget and noise budget in the multi-fidelity Bayesian spatial data analysis. The weights of the different components of these bias and noise budgets are tuned to the data set. We apply this approach to 24 hours of weather data in the Netherlands, for a day that had a “code orange” (i.e. “be prepared for extreme weather with high risk of impact”) weather warning for heavy precipitation. From our analysis, we see a significant – qualitative and quantitative – synergy effect when introducing low-fidelity data and high-resolution covariate information.
We present a comparison between Netatmo hourly precipitation amounts and observations of the same quantity from weather stations managed by national meteorological services, the latter used as reference values. The empirical distributions of the crowdsourced observations in the surroundings of reference stations are used to assess accuracy and precision of crowdsourced data. We found that reference values are typically within the distribution of the crowdsourced data. However, as the amount of precipitation increases, the spread of the crowdsourced distribution increases and the reference values are more and more frequently found towards the right tail of the distribution. These results indicate that accuracy and precision of crowdsourced data change as precipitation increases. We have studied the sensitivity of our results to the size of the neighbourhood chosen around the reference stations and we show that by aggregating the values over those neighbourhoods, crowdsourced data can be trusted in determining precipitation occurrence. We have assessed the variability of precipitation within small neighbourhoods (of radius 1, 3 and 5 km) and we provide estimates on the basis of the precipitation amounts. Our study quantifies the variability of hourly precipitation over small regions, of the size of the so-called “unresolved spatial scales” in limited area models, based on three years of data collected at several places in Scandinavia.
This research produced gridded datasets and maps for use in building design standards to enhance resilience in support of climate change adaptation in Ireland.
The new isothermal maps of return values of maximum and minimum air temperatures at mean sea level for 50, 100 and 120-year return periods based on the generalised extreme value distribution will be crucial to inform the design of buildings and bridges. The warming of the maximum and minimum air temperatures due to climate change has increased the intensity of the highest maximum air temperature while decreasing the intensity of the lowest extreme minimum air temperature of the new isothermal maps compared to previously published maps for a 50-year return period. Specifically, the new extreme isotherms are 32 ∘C for the maximum air temperature and −14 ∘C for the minimum air temperature, whereas the processor maps presented 30 and −16 ∘C, respectively. The geographical distribution of the isotherms for the 120-year return period range from 28 to 34 ∘C for the maximum air temperature and from −6−18 ∘C for the minimum air temperature.For the first time, isothermal maps of return values of the lowest 10 cm soil temperature for 50, 100 and 120-year return periods based on the generalised extreme value distribution have been produced for Ireland. The results presented here will be paramount to supporting the design of building structures. The values of the 120-year return period range from 0 to −2 ∘C. The produced maps represent the worst-case scenario in the current context of climate warming.The new maps of return values of snow loading at 100 m above mean sea level for 50, 100 and 120-year return periods based on the generalised Pareto distribution will be indispensable to support the design of buildings and civil engineering works such as roof patterns or bridges. The values of the 50-year return period map present four classes spread North-East to South-West: < 0.3, 0.3–0.4, 0.4–0.5 and 0.5–0.6 kN m−2, which is more accurate than the previously published map.It is expected that the comprehensive explanation of the methods and the rationale for the new maps presented here as being more accurate than the preceding maps will assist regulators in adopting these new maps in their own jurisdictions. Furthermore, these new maps will be of interest to a diversity of sectors, planners and policymakers to make long, lasting and climate-based sensitive decisions.last mile
Agrometeorological services are a subset of climate services targeted to support farmers' tactical and strategic decisions, with the potential to support farmers' capacity to cope with climate variability and change, as well as strengthen their resilience toward climatic risks. However, the effectiveness of such services is often limited by inadequate and unsuitable means of communication with farmers. Therefore, in recent years, the World Meteorological Organization (WMO) and partners have focussed their efforts on improving communication through these services. At the European Meteorological Society (EMS) Annual Meeting in September 2022, a workshop on effective communication of agrometeorological services was held as a hybrid side event, with the aim of answering the question: “How can we deliver efficient and effective agrometeorological services”? The workshop was a joint endeavour of Met Éireann, the International Society of Biometeorology, the EMS Media and Communication Committee, the Slovenian Environment Agency, the Slovenian Meteorological Society, and the S. W. Tromp Foundation. The aim of this workshop was to advance better communication of services to the agriculture sector as a basis for promoting adaptive strategies for weather and climate change, which would enable sufficient food production at present and in the future. The workshop also provided an opportunity for transdisciplinary discussions between national meteorological and hydrological services, universities, research institutes, private companies, and the WMO. The topics discussed at the workshop included learning about exemplar agrometeorological services at various national hydrometeorological services, strengthening communication of agrometeorological services to end-users, improving data and information sharing, and educating end-users. The workshop resulted in a list of recommendations for the future.
Climate change is set to affect extreme climate and meteorological events. The combination of interacting physical processes (climate drivers) across various spatial and temporal scales resulting to an extreme event is referred to as compound event. The complex geography and topography of Greece forms a variety of regions with different local climate conditions affecting the daily minimum temperature and precipitation distributions and subsequently the distribution of compound events of low temperature and high precipitation values. The aim of our study in this work is to identify these wet–cold events based on observational data from the Hellenic National Meteorological Service (HNMS) stations, which are divided into five different geographical categories, in the period 1980–2004 and coldest months of the year (November-April) on monthly basis. Two available reanalysis products, that of ERA-Interim downscaled with the Weather Research and Forecasting (WRF) model at 5km horizontal resolution (WRF_5), and the coarser resolution (∼30 km) ERA5 Reanalysis dataset from European Centre for Medium-Range Weather Forecasts (ECMWF), are adopted to derive a gridded monthly spatial distribution of wet–cold compound events, after performing a comparison with the observations. The results yield that the monthly maximum HNMS probabilities range from 0.07 % in April to 0.85 % in February, ERA5 range from 0.4 % in April to 2.97 % in February and WRF_5 from 10.4 % in November to 25.04 % in February. The results also displayed that February, January and December, are in this order, the months with the highest WCCEs.
To promote cloud and HPC computing, GRAPEVINE* project objectives include using these tools along with open data sources to provide a reusable IT service. In this service a predictive model based on Machine learning (ML) techniques is created with the aim of preventing and controlling grape vine diseases in the wine cultivation sector. Aside from the predictive ML, meteorological forecasts are crucial input to train the ML models and on a second step to be used as input for the operational prediction of grapevine diseases. To this end, the Weather and Research Forecasting model (WRF) has been deployed in CESGA's HPC infrastructure to produce medium-range and sub-seasonal forecasts for the targeted pilot areas (Greece and Spain). The data assimilation component of WRF – WRFDA – has been also introduced for improving the initial conditions of the WRF model by assimilating observations from weather stations and satellite precipitation products (Integrated Multi-satellitE Retrieval for GPM – IMERG). This methodology for assimilation was developed during STARGATE*
There is a worldwide tendency to approach physics in everyday life. Special attention is being paid to the integration of socio-scientific issues into physics teaching such as saving energy, climate change and its consequences, and other examples. Severe weather conditions and lightning activities, which often cause severe damage and sometimes death, can also be found among the weather-related problems. The accurate description of these topics for high school learners is very complex, but presenting them in a relevant, authentic, and inspiring manner is motivating. Motivation is almost as important as the teaching of the content knowledge because learning physics in public education nowadays seems to be less and less popular among students. This paper aims to share our teaching experiences and some of the ideas concerning lightning strikes and the damages caused by them. In this study, we also cover some of the common beliefs and misconceptions related to lightning flashes and examine our students' thoughts about them.
Small-scale processes in atmospheric boundary layers are typically not resolved due to cost constraints but modeled based on physical relations with the resolved scales, neglecting expensive backscatter. This lack in modeling is addressed in the present study with the aid of the one-dimensional turbulence (ODT) model. ODT is applied as stand-alone column model to numerically investigate stratification effects in long-lived transient Ekman flows as canonical example of polar boundary layers by resolving turbulent winds and fluctuating temperature profiles on all relevant scales of the flow. We first calibrate the adjustable model parameters for neutral cases based on the surface drag law which yields slightly different optimal model set-ups for finite low and moderate Reynolds numbers. For the stably stratified cases, previously calibrated parameters are kept fixed and the model predictions are compared with various reference numerical simulations and also observations by an exploitation of boundary layer similarity. ODT reasonably captures the temporally developing flow for various prescribed stratification profiles, but fails to fully capture the near-surface laminarization by remaining longer in a fully developed turbulent state, which suggests preferential applicability to high-Reynolds-number flow regimes. Nevertheless, the model suggests that large near-surface turbulence scales are primarily affected by the developing stratification due to scale-selective buoyancy damping which agrees with the literature. The variability of the wind-turning angle represented by the ensemble of stratified cases simulated covers a wider range than reference reanalysis data. The present study suggests that the vertical-column ODT formulation that is highly resolved in space and time can help to accurately represent multi-physics boundary-layer and subgrid-scale processes, offering new opportunities for analysis of very stable polar boundary layer and atmospheric chemistry applications.
The intersectoral workshop held in December 2016 among the Ibero-American networks on water, climate change and meteorology, identified the need of downscaled climate change scenarios for Central America. Such scenarios would be developed by National Meteorological and Hydrological Services in the region, based on a common methodology, allowing the assessment of climate change impacts on water resources and extreme hydro-meteorological events. This project was supported by the International and Ibero-American Foundation for Administration and Public Policies of Spain in the framework of the EUROCLIMA+ programme. One final outcome of the project has been a freely accessible web viewer, installed on the Centro Clima webpage (https://centroclima.org/escenarios-cambio-climatico/, last access: 26 September 2022), managed by the Regional Committee on Hydraulic Resources of the Central American Integration System, where all information generated during the project is available for consultation and data downloading by the different sectors of users.
A key element in this project has been to integrate many downscaled projections based on different methods (dynamical and statistical), totalizing 45 different projections, and aiming at estimating the uncertainty coming from different sources in the best possible way. Another essential element has been the strong involvement of the different user sectors through national workshops, first, at the beginning of the project for the identification and definition of viewer features, and then for the presentation of results and planning of its use by prioritized sectors.In a second phase of the project, a regional working group made up of experts from the participating National Meteorological and Hydrological Services will be in charge of viewer maintenance and upgrade, including new sectoral parameters, developed in collaboration with interested users, and computation and addition of new downscaled projections from CMIP6 in collaboration with the State Meteorological Agency of Spain.Impact-based forecasts and warnings (IBFs) are seen as important drivers for adequate anticipation and assessment of potential threats to public safety as they give a better understanding of the weather event's impacts. To prepare for impacts of weather events and prevent weather-related accidents, road maintenance services are actively using weather information in their daily work routine. This paper looks into the requirements that road maintenance services have for IBFs and how weather forecasts are used at the moment. The study is part of an interdisciplinary research project and follows a qualitative social science research approach. Findings show that the following factors are general user requirements: relevance of information, recognition of spatial and temporal requests, acceptability, comprehensibility, and technical demands. These are also applicable to IBFs with the extension to provide a benefit for road maintenance services in situations that rarely occur and where no embodied knowledge in the organization is existent.