bluuebunny/crossref_metadata_2025 huggingface dataset:
On Lagrangian stochastic condensation models in turbulent cloud simulations
Authors: Abade Gustavo , Waclawczyk Marta , Grabowski Wojciech , Pawlowska Hanna
Published: June 2022

Turbulent clouds are challenging to model and simulate due to uncertainties in microphysical processes occurring at unresolved subgrid scales (SGS). These processes include the transport of cloud particles, supersaturation fluctuations, turbulent mixing, and the resulting stochastic droplet activation and growth by condensation. In this work, we apply two different Lagrangian stochastic schemes to model SGS of cloud microphysics. Collision and coalescence of droplets are not considered. Cloud droplets and unactivated cloud condensation nuclei (CCN) are described by Lagrangian particles (superdroplets). The first microphysical scheme directly models the supersaturation fluctuations experienced by each Lagrangian superdroplet as it moves with the air flow. Supersaturation fluctuations are driven by turbulent fluctuations of the droplet vertical velocity through the adiabatic cooling/warming effect. A second more elaborate scheme uses both temperature and vapor mixing ratio as stochastic attributes attached to each superdroplet. It is based on the probability density function formalism that provides a consistent Eulerian-Lagrangian formulation of scalar transport in a turbulent flow. Both stochastic microphysical schemes are tested in a synthetic turbulent-like cloud flow that mimics a stratocumulus topped boundary layer. It is shown that SGS turbulence plays a key role in broadening the droplet-size distribution towards larger sizes. Also, the feedback on water vapor of stochastically activated droplets buffers the variations of the mean supersaturation driven the resolved transport. This extends the distance over which entrained CNN are activated inside the cloud layer and produces multimodal droplet-size distributions. Finally, our simulations suggest that stochastic Lagrangian SGS models may expand the ability of Large Eddy Simulations to represent cloud-top entrainment and associated microphysical details at cloud top.
Climate maps and data to support building design standards in Ireland
Authors: Mateus Carla , Lambkin Keith , Coonan Barry , Griffin Seánie , McGovern Jonathan , McGuinness Simon , Murphy Ken , Eames Matthew , Coyle Orla
Published: June 2022

The climate of Ireland is changing. Consequently, climate maps to support building design standards are being updated to inform national Technical Guidance Documents. Additionally, weather files, comprising test reference years and design summer years, are being produced for locations in Ireland to assess the risk of overheating in buildings. This project is carried out by Met Éireann under its strategy ‘Making Ireland Weather and Climate Prepared’ and funded by the Department of Housing, Local Government and Heritage. The outputs of this project are part of Ireland’s Climate Action Plan 2021 under action 197 – develop specific climate maps and data for use in building design to enhance resilience in support of climate change adaptation. The climate services produced in this project will support a wide range of stakeholders, currently collaborating with Met Éireann, which include the National Standards Authority of Ireland, ARUP, Transport Infrastructure Ireland, Office of Public Works, Sustainable Energy Authority of Ireland, Kavanagh Mansfield & Partners, and the Department of Housing, Local Government and Heritage. These climate services will also inform policy in delivering key national infrastructure, including housing, drainage, transport networks and building renovation.

 

The presentation will focus on the importance, data, methodology and outputs of the project’s work packages:

Work package 1: Maps of driving rain intensity (maps of spell index following two orientations: N as per ISO 15927-3:2009 and SW as the predominant wind direction in Ireland).

Work package 2: Maps of isotherms of the highest maximum shade air temperature (50, 100 and 120 years return periods). Maps of isotherms of the lowest minimum shade air temperature (50, 100 and 120 years return periods).

Work package 3: Maps and gridded dataset of estimation of point rainfall frequencies based on the depth-duration-frequency model with durations varying from 1 to 25 days, 24 hours to 15 minutes, and shorter than 15 minutes (50, 100 and 120 years return periods).

Work package 4: Maps of isotherms of the lowest 10cm soil temperature (50, 100 and 120 years return periods). Maps of snow loads (50, 100 and 120 years return periods).

Work package 5: Current and future weather data sets (Test Reference Years and Design Summer Years) to assess overheating risk in buildings.

 

The produced climate services will be made available open-access through the Met Éireann and the Department of Housing, Local Government and Heritage websites.
An alert system for faults critical conditions on underground distribution lines for Milan urban area
Authors: Bonanno Riccardo , Lacavalla Matteo , Sperati Simone
Published: June 2022

This study shows the potential of an alert system addressed to the prediction of failures for underground distribution lines for the Milan urban area. In the summer season, one of the most widespread causes of failure in urban areas is due to a degradation of the insulating material that constitutes the joints between the underground cables. Causes are due both to the age of the joint and to the repeated stress conditions to which the electrical component can be subjected related to the abnormal and frequent increases in cable temperature during summer heat waves. In particular, the high electrical load for cooling, the high soil temperatures and the low moisture content prevent the heat dispersion of the cable (soil drying-out phenomenon). The predictors of such a system are atmospheric and soil meteorological variables that show a clear correlation with the daily failure rate. Another important variable in this forecasting system is the electrical load which represents a considerable stress source for underground power lines. In this alert system, weather forecasts come from the ECMWF IFS model, while load and fault prediction systems are based on a Random Forest machine learning method. These systems are trained with forecast predictors related to atmospheric and soil variables and using as predictands the time series of electrical load and failure provided by the distribution company of Milan. The warning system for the city of Milan is therefore defined by linking together the weather forecast to the load and fault forecasting systems. The performance of the alert system was evaluated by means of a k-fold cross validation, training the forecast system on the time series excluding one year at a time and using it each time as a test set. The system demonstrates the ability to identify quite satisfactorily the main failure events associated with summer heat waves for the period 2010-2019, albeit with an underestimation of failure peaks. This alert system can be a valuable support for the management of the distribution network in urban areas. In fact, the reliability of the electricity service in the perspective of resilience requires planner tools but also operational strategies to face this problem. This tool makes it possible to prepare sufficiently in advance all the measures necessary to mitigate the effects and reduce the time necessary to restore the energy supply service.
ISOBAR SESAR - Artificial intelligence solutions to meteo-based air traffic imbalances for network operations planning
Authors: Ponzano Matteo , Raynaud Laure , Sanchez Cidoncha Marta , Gawinowski Gilles , Soler Arnedo Manuel Fernando , Jardines Aniel , Simarro Juan , Zheng Danlin , Xu Yan , Bastie Elodie , Arnould Berangère , Rey Florenci , Khassiba Ahmed , Peuaud Aurelie , Carré Marie
Published: June 2022

Flight delays are one of the major concerns in air traffic management. The impact of flight delays represents financial and time losses and may derive in loss of reputation of the air traffic business. On average weather accounts for roughly one-third of ATFM (Air Traffic Flow Management) delays (25% for en-route and ~50% for the airports). Examining only the top ten days with highest delays due to weather regulations from the first half of 2018, strong convective activity throughout Europe was the principal cause, with estimated cost due to airport and en-route delays reaching almost €130 million (roughly 10% of the weather delay in 2018 concentrated in only 10 days). Given these large cost figures, even minor improvement in prediction and performance of ATFM operations during significant convective weather events will yield to substantial yearly savings for the ATM (Air Traffic Management) system. Designing an efficient value chain for ATFM, that propagates weather forecasts into a series of tools to select mitigation measures at local and network levels in a collaborative ATFM operations paradigm, requires a multidisciplinary approach to gather the different stakeholders. Such an approach has been developed in the SESAR ISOBAR project, whose aim is to integrate enhanced convective weather forecasts for predicting imbalances between air traffic capacity and demand (requests to fly by airspace users, mainly airlines) and to select appropriate mitigation measures. The value chain developed in the framework of ISOBAR leverages the power of Artificial Intelligence (AI) in the different stages. AI engine is trained using a dataset of selected convective events in summer 2019, which includes forecasts from high resolution ensemble prediction systems (IFS, γ-SREPS and AROME-EPS), declared capacity in air traffic flow and initial air traffic demand. The value chain produces a solution for tactical (day 0) and pre-tactical (day -1) ATFM operations. A validation exercise was organised at EUROCONTROL Innovation Hub in March 2022 with the collaboration of ATC (Air Traffic Control) operational staff and Air Traffic Controllers from Spain, France and Europe air traffic network management. AI Engine was run for some high convective situations over Europe, which were characterised by high delays due to weather regulations. Offline simulations highlighted the added value of the solutions assessed by ATC experts. The predicted air traffic delay has been drastically cut by up 75%.
Projected changes in atmospheric circulation types inducing high intensity of the urban heat island in Iasi city 
Authors: Sfica Lucian , Hritac Robert , Amihaesei Vlad-Alexandru , Ichim Pavel
Published: June 2022

Iași, the largest urban center in northeastern Romania, with approximatively 400000 inhabitants  living in the city and its vicinity, experiences a recent development that raises concerns on the increasing summer heat stress as a result of the combined effect of urban heat island and the increase in air temperature indicated by climate scenarios. Trying to tackle this concern, in our study we wanted (1) to estimate  if  the intervals with intense UHI will be more/less frequent in the future and (2) to assess how much the UHI will exacerbate the expected increase in air temperature.

In the study we used (1) air temperature data, collected from the monitoring network of the urban climate conditions with hourly resolution, to define the intensity of UHI, (2) daily sea level pressure data for 1980-2021 from ERA-5 Land, as input data for historical GWT classification over the study region, (3) daily sea level pressure data for 2023-2100 from EUROCORDEX, as input data for projected GWT classification for RCP4.5 and RCP8.5 climate scenarios and also (4) daily minimum, mean and maximum air temperature data for 2023-2100 from RoClib dataset, to describe the projected changes in air temperature in the study region.     

As resulted from urban climate monitoring Iași city was assessed to have an urban heat island (UHI) with a mean annual intensity approaching 1°C. Beyond this annual mean intensity of UHI, the hourly UHI intensity surrpasses 3°C during 10% of the interval between 2013 and 2021, most of these intervals with high UHI intensity being concentrated during summer nights. Especially during these intervals of high intensity, the UHI will exacerbate the expected increase in air temperature towards the end of the century.

Therefore, our proposed study is designed firstly to identify, through the use of Grosswetter Typen (GWT) classification derived from COST-733 software, those atmospheric circulation types commonly associated to the maximum intensity of UHI of Iași in the past (2013-2021). Secondly, the GWT was conceived using EUROCORDEX data for 2023-2100, aiming to identify the projected variability/trends in those types leading to intense UHI nowadays. After that, using climate scenarios projections regarding air temperature, the air temperature conditions associated with weather patterns characterized by intense UHI are described.

The results indicate that in the future, on one hand the changes in the frequency of those weather patterns that are historicaly associated with intense UHI and summer night heat stress will be marginal, but on the other hand the thresholds of intense UHI and heat stress will be overpassed by other weather patterns not known before to generate heat stress.
Weather and War
Authors: Tsalova Stanislava
Published: June 2022

In my TV station in Bulgaria, when the war in Ukraine started, we started issuing weather forecasts for Ukraine as well. In the first days of the conflict, there were many war fugitives , including Bulgarian citizens. We had to cover weather in Ukraine, Roumania and Moldova as many people travelled that route to come to Bulgaria.

There were many questions then: what is classified in weather when there is war, when weather is helping a fight and when it prevents it.

I will tell you my experience.
Towards spatio-temporal turbulence measurements in the atmospheric boundary layer with a fleet of UAS
Authors: Wildmann Norman , Wetz Tamino
Published: June 2022

Exchange and transport processes in the atmospheric boundary layer (ABL) are driven by turbulence on a wide range of scales. In heterogenous and complex terrain, the common simplification of turbulence to one-dimensional statistical models does not necessarily hold. Coherent structures such as convective cells, secondary circulations, gusts, slope and valley flows can be summarized to sub-mesoscale structures which are not well represented in models. Part of the reason for the lack of understanding of these flow features is the challenge to adequately sample their three-dimensional, spatio-temporal structure and their contribution to the energy budget of the ABL.
We present a system to achieve simultaneous spatial measurements with a fleet of multirotor unmanned aircraft systems (UAS). The major benefit of this approach is, that true simultaneous measurements can be obtained without the need of expensive infrastructure such as masts or lidar instruments. The SWUF-3D (Simultaneous Wind measurement with Unmanned Flight systems in 3D)  fleet was first deployed at the Meteorological Observatory Lindenberg - Richard Aßmann-Observatory (MOL-RAO) of DWD in association to the FESSTVaL campaign. With more than 1000 single flights, the system was validated against sonic anemometers at the 99-m mast in 2020 and 2021 and was able to provide reliable measurements of the wind vector with a RMSE below 0.3 m/s. We showed that turbulent eddies can be resolved with a time resolution of up to 2~Hz, unless the overall TKE level is below the noise threshold of the UAS measurements, which can be the case in stable ABL conditions. We show that fluctuations of the vertical wind component can be captured with the system, if calibrated motor thrust data is used for the estimation. This allows to calculate TKE, momentum fluxes and friction velocity in neutral and convective atmospheric stratification. Additionally to the wind vector estimation, which is done with avionic data of the autopilot, pressure, temperature and humidity sensors are carried by each UAS.
The highlights of the two-week long campaign during FESSTVaL were spatial measurements of a gust front associated to a thunderstorm and a 4.5-hour long continuous vertical profiling of the atmosphere during a convective boundary layer morning transition period. Results of these measurements will be presented. 
Within the project ESTABLIS-UAS (Exposing Spatio-temporal structures of Turbulence in the ABL with In-Situe Measurements by UAS, funded by the European Union), the SWUF-3D fleet will be enhanced to allow operation in larger areas and in complex terrain. A fleet of up to 100 UAS shall be deployed during TEAMx (Multi-scale transport and exchange processes in the atmosphere over mountains – programme and experiment) in 2024.
Sub-seasonal prediction of the year-round Atlantic-European weather regimes
Authors: Osman Marisol , Grams Christian M. , Beerli Remo
Published: June 2022

The prediction skill of sub-seasonal forecast models is evaluated for  seven year-round weather regimes in the Atlantic-European region, with a focus on regime onsets and transitions which modulate surface weather in a way that is particularly relevant for the European energy system. Re-forecasts using models from three prediction centers (the European Centre for Medium-Range Weather Forecasts, the National Center for Environmental Prediction and the UK Met Office) for the 2000-2015 period are considered and compared against weather regimes obtained from ERA Interim reanalysis over the same period. We first evaluate their ability to reproduce weather regime life-cycle characteristics, such as their frequency, length, number and transitions. Then, we focus on the assessment of skill, placing emphasis on the differences in the performance for each weather regime depending on the time of the year. Finally, we consider the year-to-year evolution of skill and the role of interannual variability of the atmosphere in this skill.

Results show that the largest biases in frequency are obtained for Scandinavian Blocking in summer due to an underestimation of the number of life cycles for this regime. The ECMWF model shows the highest skill for most of the weather regimes and seasons, followed closely by the NCEP model. The average regime skill horizon is 3 days longer for ECMWF and NCEP models than for the UKMO model, mainly due to the differences in skill in winter. Greenland Blocking tends to have the longest year-round skill horizon for the three models driven by their performance in winter, which is skillful into week 3 of the forecast period. On the other hand, the skill is lowest for the European Blocking regime for the three models, followed by Scandinavian Blocking. These results demonstrate that weather regime forecasts have the potential to identify periods that may exhibit enhanced forecast skill at sub-seasonal timescales, while at the same time skill depends upon the specific regime.
Direct Numerical Simulation of the Aerodynamically Rough Atmospheric Boundary Layer
Authors: Kostelecky Jonathan , Ansorge Cedrick
Published: June 2022

Direct numerical simulation (DNS) of the atmospheric boundary layer (ABL) is becoming more and more popular for its conceptual simplicity and increasing degree of realism: domain sizes and simulation durations can be attained that allow for extrapolation of results to the geophysical limit. Geophysical flows predominantly occur over rough surfaces, which significantly affects drag, mixing and transport properties of the flow. At the same time, the representation of roughness surface-layer modelling is generally based on bulk representations of roughness related to the roughness parameters for scalar and momentum exchange z0H and z0M. Here, we circumvent surface-layer similarity by directly imposing the intricate mechanical boundary condition resulting from a rough wall, while maintaining the efficient and tuned numerical methods for Cartesian meshes by an immersed boundary method (IBM); three-dimensional roughness elements are fully resolved at the bottom wall of a direct numerical simulation. We follow a spline-based approach for a partially staggered arrangement that was introduced by Laizet and Lamballais (J. Comp. Phys 2009, Vol 228, p.5989-6015). By this approach, the flow boundary conditions at roughness objects are fulfilled exactly which allows for a straightforward treatment of roughness effects in the scalar field. We apply this implementation to investigate the effect of fully resolved three-dimensional roughness elements in a turbulent Ekman boundary layer and obtain a priori estimations of scalar and momentum roughness parameters for canonical roughness configurations.

* This work is funded by the ERC Starting Grant ”Turbulence-Resolving Approaches of the Intermittently Turbulent Atmospheric Boundary Layer [trainABL]” of the European Research Council (funding ID 851347).
Validation of a Spanish regional high-resolution database for wind energy applications 
Authors: Jiménez-Garrote Antonio , Santos-Alamillos Francisco , López-Cuesta Miguel , Ruiz-Arias José Antonio , Sánchez-Hernández Guadalupe , Pozo-Vazquez David
Published: June 2022

The feasibility low carbon power systems (LCPSs) is challenged under high penetration rates of renewables because the inherent variability of renewable sources increases the system’s operation and transmission costs. Optimizing the operation of the systems to make them efficient and profitable requires adapting their design to the regional solar and wind patterns. The MET4LOWCAR Spanish project aims at demonstrating the benefits of the design of the LCPSs that accounts for the regional climatic patterns of both solar and wind renewable resources, using the Spanish territory as a testbed. To that aim, a 30-yr weather integration is being performed with the Weather Research and Forecasting (WRF) model over the Iberian Peninsula and the Balearic Islands throughout a 5-km spatial grid and a 10-min temporal grid (Regional reanalysis). The ultimate goal is to compound a climatic data base of solar- and wind-related variables to simulate the performance of a high-penetration LCPS in multiple scenarios, from long runs to extreme weather events.

 

In this work, the results of the assessment of the wind speed and wind power estimates derived from this regional reanalysis are presented and discussed. Different validation studies were conducted. Firstly, the wind estimates at 10 and 40 m a.g.l. were evaluated based on wind measurement collected at 44 surface meteorological stations and two masts located at wind farms. This study was conducted for years 2019 and 2020. In a second study, regional wind power models for 42 continental Spanish provinces were built and evaluated. Models were obtained using reference power curve and the regional installed capacities, elaborated based on information from wind farms at 506 locations collected from public databases. This study was carried out for the year 2020 at hourly resolution and the model estimates were evaluated based on actual power generation values provided by the Spanish TSO. Finally, wind and wind power estimates derived from the ERA5 reanalysis were also obtained and used for benchmarking purposes.

Results of the wind speed validation show a superior performance of the regional reanalysis for complex topography locations (where most of the Spanish wind farms are located). For instance, differences in the wind speed RMSE values greater than 2 m/s (4.27 vs 2.14 m/s) were found for one mast. Nevertheless, the overall performance of both reanalysis are similar (1.6 m/s mean RMSE value for surface stations). Results of the wind power modeling show a similar overall performance of both reanalysis. Nevertheless, wind power estimates derived from the regional reanalysis were able to better reproduce the wind power intra-day variability.
Cloud Mask Nowcasting over Germany Using Deep Learning
Authors: Jungersen Mads Emil Marker , Rasmussen Thomas Lykke , Nielsen Andreas Holm , Karstoft Henrik
Published: June 2022

The transition to renewable energy sources such as solar energy has increased the interest in predicting cloud masks from remote sensing data. Even though deep learning methods have achieved great success on multiple meteorological tasks, only limited research has been conducted on nowcasting cloud masks based on high temporal and spatial resolution satellite data.

This study investigates forecasting cloud masks over Germany six frames into the future based on satellite images. We compare predictions between three deep learning architectures (ConvLSTM, U-Net, and MetNet) relative to two baseline models (optical flow and persistence). We train and evaluate our models using two years of the ICARE SAFNWC Cloud Mask dataset1 , with a temporal resolution of 15 minutes per frame and a spatial resolution of 3×3 km per pixel. In our experiments we use a larger area of 256×256 pixels to predict the target area of size 128×128 pixels, leading to overall better performance compared to using an input size equal to the output size. Besides comparing different network architectures, we also investigate the effect of varying the temporal input size and output size for ConvLSTM. Finally, we examine the effect of adding more features (land/sea mask and elevation map) and changing the loss function.

In summary, we have performed a comprehensive study investigating cloud mask nowcasting using 70,000 spatially and temporally aligned data frames, examining three loss functions, six evaluation metrics, and three deep learning models.
During the presentation, we will highlight the main results from the study and present details of the model architectures, datasets, and how space and time affect the performance of the models.

1) Kniffka, Stengel, and Hollmann, “SEVIRI Cloud Mask Dataset - Edition 1.”
Pareto optical flow solutions for ensemble, satellite-based forecasts of irradiance and PV 
Authors: Good Garrett
Published: June 2022

 

The sustainable electric grid of the future will rely on comprehensive measurements and forecasts of its millions of components. For PV, such high-resolution forecasts will benefit from the near real-time data and detail provided by satellite observations. Nowcasting or forecasting into the future from detailed satellite images based on e.g. root mean squared error optimization, (e.g. machine learning), however inevitably promotes smoothing and removes detail from the forecast, calling into question the definition of forecast quality. Probabilistic forecasting in the form of ensemble solutions offers an answer, allowing for the detail from satellite images without the expectation of deterministic pixel point accuracy. (Ensemble numerical weather predictions exist on high-resolution grids, but also present smoothed predictions of clouds). 

This study creates ensemble forecasts using a new version of the optical-flow-based nowcasting system presented in past sessions that solves for the global cloud motion using Taylor-approximated streamlines. The optimized flow field is physically constrained through a combination of mass and angular momentum conservation. The errors for the motion of different structures in the image are discerned as secondary objectives to the overall optimization. The optical flow algorithm uses ant-colony, multi-objective optimization, following many solutions before arriving at a Pareto optimum. 

The experiments test the viability of using other cloud motion solutions in the Pareto front to generate ensemble forecasts of the cloud cover and subsequently of irradiance maps and regional PV power. Where regions are fully cloudy or clear, the ensemble solutions should be uniform, while moving regions of variable cloudiness aim for realistic ensemble distributions. The reliability and potential of such forecasts are evaluated and compared to numerical ensemble weather predictions using continual ranked probability scores (CRPS). 
On the extreme East Asian dust outbreak in March 2021 - dust-source attribution, dynamical assessment, and air-quality impact
Authors: Mu Feifei , Reyers Mark , Knarr Leon , Fiedler Stephanie
Published: June 2022

In mid-March 2021, one of the strongest dust storms of the last decades hit East Asia with adverse impacts on socio-economic activities, and loss of life. The dust storm was associated with a Mongolian extra-tropical cyclone. In this study, we assess the atmospheric dynamics involved in the temporal evolution of this event and quantify contributions from the main dust source regions, namely the Gobi Desert and the Taklamakan Desert. We use the ground-based observations from Chinese observational networks, satellite images from MODIS satellite data, and model data from ERA5 and CAMS of ECMWF. Our results highlight that dust aerosols advected far towards the East primarily originated in the Gobi Desert, where anomalously strong dust-emitting winds were associated with the passage of a cold front embedded in the extra-tropical cyclone over Mongolia. The subsequently formed dust cloud of several hundred kilometers in extent led to sharp increases in the concentration of particulate matter that cause severe deterioration of the air quality across Chinese cities. Also, the Taklamakan Desert contributed to the event, but to a lesser degree than the Gobi, measured by the spatial influence across China. In the Taklamakan Desert, strong low-level easterly winds occurred with the cold air intrusion building up a substantial regional dust burden through the morning breakdown of nocturnal low-level jets. The subsequent directional change in synoptic-scale winds allowed an eastward transport of the dust-laden air from the Taklamakan Desert and increased the dust burden over Western China. Indeed the largest dust burden was seen in the West indicating that the Taklamakan Desert contributed to the dust outbreak, although dust from the Gobi Desert affected larger areas. The dust outbreak has led to record-high hourly concentrations of particulate matter in both the western and eastern cities in China based on data of the past seven years. Our results provide the first evidence for the relative contributions of dust aerosols from different East Asian dust source regions to this unusual event. It remains to be investigated how such dust outbreaks change in a warming world.
Investigating the wind conditions in the North Sea for wind turbines at sites N-3.5, N-3.6 and N-7.2
Authors: Brast Maren , Gehrke Michael , Hahn Johannes , Hüttl-Kabus Sabine , Möller Thomas , Outzen Olaf , Spangehl Thomas
Published: June 2022

The Offshore Wind Energy Act (Windenergie-auf-See-Gesetz - WindSeeG) legally requires annual tender procedures for sites in the North Sea in the Exclusive Economic Zone of Germany for the construction of wind turbines. The Federal Maritime and Hydrographic Agency (Bundesamt für Seeschifffahrt und Hydrographie - BSH) conducts so called preliminary investigation of sites (PIS), pursuant to §10 WindSeeG. These include investigations of the wind conditions at the site, to give the interested firms information for calculating a bid. The preliminary investigation of the wind conditions is conducted together with the German Weather Service (Deutscher Wetterdienst) and is based on three elements: a measurement campaign at the site for one year, an evaluation of reanalysis data by the German Weather Service, and a report which brings together all available information about the wind conditions. These reports are published together with the measurement and model data and the results of all other preliminary investigations via the PIS-Data Hub https://pinta.bsh.de as part of the tendering process.

The sites that are dedicated for the construction of wind turbines are defined in the Site Development Plan (Flächenentwicklungsplan), together with the year of the intended tendering process. The first PIS have been successfully completed for two sites in the southeastern North Sea and for one site in the Baltic Sea in March 2021. In the years 2022 and 2023, a call for tender for sites N-3.5, N-3.6 and N-7.2 will be published. These sites are located in the southwestern part of the German Exclusive Economic Zone in the North Sea. At or near these sites, meteorological measurements have been conducted, using a floating lidar which was installed on a buoy (site N-7.2) as well as scanning lidars installed on the transition pieces of wind turbines (N-3.5, N-3.6). These measurements resulted in time series of vertical profiles of wind direction and speed, covering one year each, and giving information about the wind conditions at the sites at relevant heights of future wind turbines. In addition, temperature, relative humidity, air pressure, and sea surface temperature were measured.
Which Sudden Stratospheric  Warming Events are Most Predictable?
Authors: Garfinkel Chaim , Chwat Dvir
Published: June 2022

The predictability of sudden stratospheric  warming (SSW) events are considered in 10 subseasonal to seasonal (S2S) forecast models for 16 SSWs over the period 1998 - 2021.  The four factors that most succinctly distinguish those SSWs with above average predictability are an active Madden-Julian Oscillation  with enhanced convection in the West Pacific, the Quasi-Biennial Oscillation phase with easterlies in the lower stratosphere,  a strong pulse of wave activity   before the event, and the morphology (displacement more predictable). However none of these effects are statistically significant at the 95\% level using a two-tailed t-test due to  the relatively small sample size. Strong events are more predictable if one focuses on the question of whether the models predict a SSW, though not if one focuses on the absolute error of the anomalous stratospheric easterlies. Other factors, such as El Nino and  stratospheric preconditioning are comparatively less important. 
Follow the sun? The effects of national solar capacity targets on renewable generation and security of supply
Authors: Czock Berit , Keutz Julian , Fiedler Stephanie
Published: June 2022

As Europe strives for a decarbonization of electricity supply, many countries proclaim ambitious targets for capacity additions of wind and photovoltaic (PV) power. By increasing renewable capacities, countries seek to reduce their dependency on fossil fired electricity generation. For instance, Germany recently announced new capacity targets that mean a triplication of wind and PV power capacities while at the same time aiming for a full coal exit until 2030. However, the potential of renewable energies is not equally distributed over the European continent. Due to higher irradiance levels with decreasing latitude, PV power achieves higher full load hours and thus higher generation per installed capacity in southern Europe.  Wind power reaches higher capacity factors close to shorelines, where near-surface wind speeds are higher.

In light of the unequal distribution of PV and wind power generation potentials, national capacity targets may not lead to optimal utilization of capacities, i.e. maximum renewable generation. We analyse the effects of such national capacity targets for the case of PV generation. To do so, we compare two scenarios: A scenario with PV generation capacity distributed over Europe according to national political targets and a scenario with a distribution of newly installed capacities chosen such that it optimizes the renewable power potentials. Next to PV generation potentials, our assessment considers the European power grid, because line capacities limit the transmission and hence the trading between different countries. Line limits can have a negative impact on the overall power generation since renewable generation has to be curtailed if power can neither be locally used nor transmitted to another region.  We employ a linear optimization-based electricity market model with inter-European transmission constraints and hourly resolution weather data for 35 historic weather years (1982-2016). We analyse the scenarios if terms of differences in power generation and curtailment, greenhouse gas emissions, and system operational cost. We pay special attention to extreme weather situations and the effects of a sub-optimal PV power allocation on security of supply.
Evaluation of the Solar Energy Nowcasting System (SENSE) using Multiple Ground Based Measurements
Authors: Raptis Ioannis-Panagiotis , Eleftheratos Kostas , Kazadzis Stelios , Kosmopoulos Panagiotis , Gierens Klaus , Fountoulakis Ilias , Papachristopoulou Kyriakoula , Kouklaki Dimitra , Kazantzidis Andreas , Benetatos Charilaos , Psiloglou Vassilis
Published: June 2022

Solar energy nowcasting is a  valuable asset in managing energy loads and having real-time information on solar irradiation availability. SENSE is an operational-ready system which exploits satellite retrieved images from the EUMETSAT’s Meteosat Second Generation for cloud microphysics information above a certain location, modelled aerosol optical properties data from the Copernicus Atmospheric Monitoring Service (CAMS), in conjunction with Radiative Transfer Model (RTM) simulations in order to retrieve the Global Horizontal Irradiance (GHI) and the Direct Normal Irradiance (DNI) spectrally (280 - 2800 nm). For a full year period (December 2020-December 2021) a campaign was held in Athens (Greece) in the framework of the “Atmospheric parameters affecting Spectral solar Irradiance and solar Energy” (ASPIRE) project. For the needs of the campaign a number of ground-based instruments were operating, including two pyranometers, a pyrheliometer, a cloud camera, a CIMEL sunphotometer, a BREWER and a PANDORA spectroradiometer and a Precision Spectral Radiometer (PSR).

In this study, we evaluate the integrated outputs of SENSE in W/m2, at 15-minute frequency against the ground-based recordings, for the period of the campaign. A separate assessment of the SENSE’s inputs (cloud and aerosol optical properties) is performed in respect to ground-based retrievals. Finally, deviations between SENSE and measurements are linked to deviations between climatological/satellite-based atmospheric parameters and those retrieved from the ground-based measurements. The detailed evaluation of SENSE inputs and outputs using detailed ground-based data advances our present-day knowledge about the uncertainties of SENSE products, thus improving our understanding of the renewable energy merit in the total energy use for Greece. This effort is important for SENSE users such as the Greek Independent Power Transmission Operator and the Public Power Corporation Renewable of Greece.
Machine learning operations for weather applications
Authors: Nerini Daniele , Aznar Gabriela , Bhend Jonas
Published: June 2022

In weather applications, machine learning is emerging as an innovative technology with the potential to address many of the shortcomings of traditional modelling procedures. The trend is fostered by the growing availability of observational data, computational resources, and high-level software libraries. However, to ensure that machine learning can deliver on its promises, build trust, and eventually transition to become a reliable technology for production, it is also important to consider the technical and engineering challenges that arise when introducing machine learning in an operational environment.

MLOps is the set of practices that aims at deploying and maintaining machine-learning models in production continuously, reliably, and efficiently. At the core of MLOps is the idea that models can easily change, while the underlying workflows remain. In this sense, the emphasis is shifted from training a specific ML model to building an integrated ML system and to continuously operate it in production.

We will present our first experiences with MLOps for weather forecasting applications at MeteoSwiss. As an example, we will use a ML-based model for postprocessing NWP surface wind forecasts, as it covers the most common and relevant challenges, including the need for efficient data loading and manipulation, the monitoring and visualization of prediction quality, and the automation of model training and deployment pipelines. In this contribution, we aim at sharing our endeavor for MLOps best practices in the applied context of a national meteorological service with the hope to foster discussion and exchanges on the topic of machine learning operations for meteorological applications and their transition to the cloud.
Climate-related operational permafrost monitoring in Svalbard and Norway
Authors: Isaksen Ketil , Lutz Julia , Eastwood Steinar , Godøy Øystein , Aaboe Signe , Sørensen Atle , Ferrighi Lara
Published: June 2022

The web portal for cryospheric information of the Norwegian Meteorological Institute (MET Norway), https://cryo.met.no, provides access to the latest operational data and products, as well as the current state of sea ice, snow, and permafrost in Norway, the Arctic, and the Antarctic. This contribution focuses on the operational permafrost monitoring at MET Norway and the new permafrost monitoring products on cryo.met.no.

Systematic long-term monitoring of permafrost on Svalbard and in Norway essentially began 23 years ago under the European Union-funded Permafrost and Climate in Europe (PACE) project, with the installation of ground temperature measurements in deep boreholes. Since then, more than 35 additional instrumented boreholes have been drilled in Norway and on Svalbard. In recent years, five new permafrost boreholes have been established at remote locations on Svalbard.

Here we present methods for visualising real-time permafrost temperature data from eight operational monitoring sites on Svalbard and in Norway. The most recent permafrost temperatures are compared to the climatology generated from the station's data record, which includes median, confidence intervals, extremes, and trends. There are additional operational weather stations with extended measurement programs at these locations. The collocated monitoring provides daily updated data to study and monitor the current state, trends, and the effects of e.g. extreme climate events on permafrost temperatures. The operational monitoring provides information faster than ever before, potentially assisting in the early detection of e.g. record-high active layer thickness, pronounced permafrost temperature increases, and in early warning systems for natural hazards associated with permafrost warming and degradation. Currently, data and metadata are submitted manually to the international Global Terrestrial Network for Permafrost. Work is in progress to develop operational permafrost data services through the WMO Global Telecommunication System to support e.g. the WMO Global Cryosphere Watch datastream.
Validation of Doppler wind LiDARs of the German Weather Service (DWD) using small unmanned aerial systems ( UAS )
Authors: Boventer Jakob , Weber Ines , Schön Martin , zum Berge Kjell , Platis Andreas , Bange Jens , Beyrich Frank , Detring Carola , Päschke Eileen
Published: June 2022

Doppler wind lidar systems are an indispensable tool for the observation of the atmospheric boundary layer. With varying atmospheric conditions in the boundary layer the performance and availability of these systems varies. In order to ensure their quality measurements are needed to validate the lidar measurements.

During the FESSTVaL field measurement campaign in summer 2021 airborne meteorological measurements in the atmospheric boundary layer above the measuring field Falkenberg of the German Weather Service measurements were recorded for this purpose.

The focus was on the validation of Doppler lidar measurements of the wind speed, wind direction and turbulence kinetic energy in the altitude range from 90 m to 600 m above ground. The obtained data will be used to show in how far the lidar data quality is depending on the altitude the measurements have been taken from.

The validation data were collected with an unmanned aerial system (UAS) of type MASC-3 (Multipurpose Airborne SensorCarrier 3) operated by the University of tuebingen.

The UAS MASC-3 is used for in-situ meteorological measurements of turbulent variables (three-dimensional wind vector, temperature, humidity and turbulence) as well as aerosol particles in the lower atmosphere. [1]

With the help of our UAS measurements, the quality, spatial resolution, and significance of lidar data is investigated and will be assessed in different measurement configurations and under different atmospheric conditions, such as thermal stratification, water vapor content, concentration of aerosol particles and aerosol particle size distribution.

Suitable scanning strategies for the lidar systems can thus be determined, characterized, and the measurement error as well as the representativeness and availability of the lidar wind and turbulence data will be quantified.

The result of the assessment will help to determine the initially mentioned performance and availability of lidar more accurately, as well as to better integrate remote sensing instrumentation into an operational measurement network.

[1] A. Rautenberg et al., MDPI Sensors doi:10.3390/s19102292 (2019)
Tuning the precipitation phase discriminator in IMPROVER
Authors: Moseley Stephen
Published: June 2022

IMPROVER (Integrated Model Post-Processing and Verification) has been developed by the Met Office as an open-source probability-based post-processing system to fully exploit our convection permitting, hourly cycling ensemble forecasts. Post-processed MOGREPS-UK model forecasts are blended with deterministic UKV model forecasts and data from the coarser resolution global ensemble, MOGREPS-G, to produce seamless probabilistic forecasts from now out to 7 days ahead. For precipitation, an extrapolation nowcast is also blended in at the start. Forecasts are converted to probabilities at the start, and all initial stages of post-processing are performed on gridded data, with site-specific forecasts extracted as a final step, helping to ensure consistency. Data are processed on a 10km global grid and on a 2km UK-centred grid. An overview of IMPROVER will be given in a separate talk.

 

The IMPROVER post-processing system includes a precipitation phase discriminator to determine the phase of precipitation after accounting for the change in orography from the modelled grid to the standardised, unsmoothed grid so that forecasts from different resolution models can be blended together seamlessly. The method calculates the melting experienced by precipitation as a product of positive wet-bulb temperature and atmospheric depth. This discriminator includes two empirical thresholds for where snow starts to melt and where melting is complete which are used to separate falling snow, mixed-phase precipitation, and falling rain. Where liquid precipitation falls onto a frozen surface, freezing rain is diagnosed.

This talk describes how the phase discrimination is performed, and how the empirical thresholds have been tuned by comparing spot forecasts with current weather SYNOP observations.