bluuebunny/crossref_metadata_2025 huggingface dataset:
Open Access to Five Years of Calibrated Measurements of the Polarimetric X-band Weather Radar in Bonn (BoXPol)
Authors: Pejcic Velibor , Soderholm Joshua , Mühlbauer Kai , Louf Valentin , Trömel Silke
Published: June 2022

Compared to conventional weather radars, dual-polarimetric radar systems can provide more precise information about precipitation amounts, types, shapes, concentration, drop size distributions, and precipitation processes. The wealth of information is exploited to improve our understanding on precipitation generating processes, to evaluate and improve numerical weather prediction models and provide more accurate (short-term) forecasts and severe weather warnings. As support for this research we provide and describe a five years (2014 - 2019) open-access dataset measured with the dual-pol Doppler X-band (9.3 GHz) weather radar in Bonn (BoXPol), western Germany. The BoXPol data set is archived by the German Climate Computing Centre (DKRZ) as daily NetCDF files consisting of all important polarimetric variables for each of the ten plan position indicator scans. Furthermore, supporting notebooks for processing and visualization of the polarimetric data are provided with the data set. The radar scanning strategy, the technical radar information and the best practice for radar data processing, like e.g. differential phase processing, derivation of specific differential phase, partial beam blockage detection and attenuation correction, are described as well. We compare the ground-based radar reflectivity measurements with the satellite observations of the Dual- frequency Precipitation Radar, operating on the core satellite of the Global Precipitation Mission, to provide absolute calibration offsets with the data set.  Stable calibration periods are identified with the Relative Calibration Adjustment technique using reflectivity statistics generated from local stable clutter.  Absolute calibration for the differential reflectivity measurements is determined using the vertically pointing scan (birdbath scan) and provided as well.
Observed changes in heavy precipitation over the Nordic-Baltic region
Authors: Dyrrdal Anita Verpe , Olsson Jonas , Médus Erika , Arnbjerg-Nielsen Karsten , Post Piia , Aņiskeviča Svetlana , Thorndahl Søren , Førland Eirik , Wern Lennart , Mačiulytė Viktorija , Mäkelä Antti , Södling Johan
Published: June 2022

The Nordic-Baltic region has experienced numerous flooding episodes resulting from heavy rainfall. Such events are costly and may potentially threaten the safety of the population. As part of the cooperation in the Nordic Framework for Climate Services (NFCS), we here present results from two separate analyses of heavy precipitation in a large region covering Fennoscandia and the Baltics (Dyrrdal et al., 2021; Olsson et al., 2022).

In Dyrrdal et al. (2021), long-term (1901–2020) changes of annual maximum daily precipitation from 138 stations and short-term (1969–2020) changes from 724 stations were analyzed, along with their date of occurrence. Results show a majority of positive trends in daily annual maxima and the 5-year return level, with hotspots in southeast Norway, southern Sweden and southwest of Finland. Almost all stations with a statistically significant trend showed an increase in the intensity of annual maximum 1-day precipitation during the last 50 years. In the region as a whole, annual maximum precipitation events occur somewhat later in the year now compared to the beginning the last century

For urban flooding, the occurrence of short-duration rainfall is of special importance. In Olsson et al. (2022), sub-daily rainfall observations from 543 meteorological stations in the Nordic-Baltic region were collected, quality controlled and consistently analyzed in terms of records, return levels, geographical and climatic dependencies, time of occurrence of maxima and trends. The results reflect the highly heterogeneous rainfall climate in the region, with longitudinal and latitudinal gradients as well as local variability, and overall agree with previous national investigations. Trend analyses for Norway and Denmark indicated predominantly positive trends in the period 1980-2018. 

 

References:

Dyrrdal, A.V., Olsson, J., Médus, E., Arnbjerg-Nielsen, K., Post, P., Aņiskeviča, S., Thorndahl, S., Førland, E., Wern, L, Mačiulytė, V. and Mäkelä, A., 2021: Observed changes in heavy daily precipitation over the Nordic-Baltic region, Journal of Hydrology: Regional Studies, 38, 100965, ISSN 2214-5818, https://doi.org/10.1016/j.ejrh.2021.100965.

Olsson, J., Dyrrdal, A.V., Médus, E., Södling, J., Aniskeviča, S., Arnbjerg-Nielsen, K., Førland, E., Mačiulytė, V., Mäkelä, A., Post, P., Thorndahl, S.L., and L. Wern (2022) Sub-daily rainfall extremes in the Nordic-Baltic region, Hydrol. Res., in press.

 
A very simple gust estimation algorithm for wind energy applications
Authors: Schicker Irene , Meirold-Mautner Ingo
Published: June 2022

Rapid changes of wind speed, wind gusts, can harm the structural safety of wind turbines, reduce their energy output, lead to a shorter rotor blade life cycle due to fatigue, and reduce the lifetime of other wind turbine components. Besides damages to infrastructure, the oscillations and / or ramping events can result in a fast fluctuation of the grid voltage and add additional burden to the power grid.

Thus, accurate forecasting of wind gusts and gustiness for power grid balancing, trading, can become a crucial factor. However, often no wind gust measurements are available from the wind farm operators (SCADA, mast if available) as only 10-minute wind speed averages can be used. Often, the environmental conditions surrounding a wind farm, e.g. roughness length, are unknown, too. Thus, a very simple gust estimation algorithm based on the 10-minute SCADA wind speed averages is needed.

In the past, several attempts have been made to estimate the wind gusts based on very simple assumptions (Wieringa, 1973, Cvitan, 2004, Harper et al., 2010). However, those algorithms all have their flaws.

To account for gustiness a very simple gust estimation algorithm was developed and tested for different heights above ground and locations (flat, mountainous), and observation qualities such as standard synop-sites, mast measurements, and SCADA data. The algorithm was developed in a way that it can be used in a deterministic and probabilistic setup. A verification of the algorithm against measurements showed that it is able to outperform other, similarly simple algorithms and that it is able to provide a good estimate of gustiness for those locations.
Co-produced storylines of climate hazard uncertainty for the heritage sector 
Authors: Goddard Christopher , Fung Fai
Published: June 2022

Climate storylines, or narratives, are gaining traction in climate science and services as a means of producing and communicating application relevant information. We have performed case studies of co-produced storylines to assess their usefulness and usability as a process of producing information, as well as communicating it in a final stakeholder product. This aims to add to the limited pool of examples of the real-world application of these concepts. 

We present an example of working with the heritage sector in the UK to co-produce storylines of summer temperature and winter rainfall hazards. This is a sector with a variety of assets and operations spread over countries, clearly exposed to weather and climate hazards, and organisations are beginning to perform risk assessments and adaptation planning. The stakeholder, and a wider focus group of sector representatives from around Europe, provided feedback and suggestions which add to the limited body of evidence assessing storylines as both a process and product directly with users. A particular focus was to expose the uncertainty in climate projection products and uncover the application relevance of this. We showcase how a weather forecasting product, DECIDER weather patterns, can be used on climate timescales to help understand hazards and the role of different types of uncertainty.  

A major finding was that the value of the co-production process was highlighted, which may be even more important than the storylines themselves for building credibility and confidence in the climate projection information. Framing projection information using past events and explaining uncertainty and regional variations using the weather patterns were found to be key aspects of the information for the stakeholder. Working through the process of tailoring the information to an application relevant range also proved to be an effective mechanism for enabling discussion about the risk appetite and uncertainty budget of the related planning and decisions.  
Global verification of the 24-hour precipitation forecast of numerical weather prediction models
Authors: Schlögl Sebastian , Huonder Ursula , Müller Mathias
Published: June 2022

Verification of precipitation forecasts are rarely published in scientific literature. This study deals with a verification of the 24-hour-precipitation forecast of the global numerical weather prediction models GFS, ICON, ARPEGE and NEMS, as well as the reanalysis model ERA5.

For model comparison more than 10’000 precipitation measurements worldwide from the measurement network METAR were used in hourly resolution. Annual, monthly, and daily precipitation sums of the global prediction models were compared with quality-controlled measurements for the year 2020. Continuous error metrics (e.g., mean absolute errors (MAE), mean bias errors (MBE)) as well as categorical error metrics (e.g., probability of detection, false alarm rate, Heidke Skill Score) are calculated for each measurement location separately, as well as averaged by a 2 x 2 degrees grid to account for the unequal global distribution of the measurements.

In general, the model errors are large in equatorial regions, areas close to the coast and in mountainous areas, where the annual precipitation amount is typically large. The best model performance was analysed for the reanalysis model ERA5 with a mean absolute error of 234 mm and a small mean bias error of -6 mm. ICON showed the lowest MAE of the operational weather forecast models (MAE = 253 mm) followed by GFS (MAE = 270 mm), ARPEGE (MAE = 281 mm) and NEMS (MAE = 296 mm). NEMS and ICON tend to underestimate annual precipitation amounts, whereas GFS and ARPEGE tend to overestimate annual precipitation amounts. Daily precipitation events larger than 1 mm were detected most accurate with ICON (HSS = 0.46) and ERA5 (HSS = 0.45), followed by GFS, ARPEGE and NEMS.

For each country, the model with the highest accuracy was determined based on the MAE of annual precipitation sums and based on the accuracy of daily precipitation events. For more than 80 % of all countries worldwide ERA5 or ICON showed the best model performance.
High-resolution temperature downscaling for global cities based on satellite imagery, weather station data and NWP model data
Authors: Bader Nico , Schlögl Sebastian , Gutbrod Karl
Published: June 2022

The growth of urban areas in combination with an increased number of heatwaves worldwide caused by the anthropogenic climate change can make cities more vulnerable. Increasing number of buildings and sealed surfaces are changing the energy budget in urban areas towards higher longwave radiation fluxes due to the greater heat storage capacity.

Since WMO stations are typically located outside the city, where air temperatures normally are lower than in the city center, initial conditions of NWP models do not accurately represent the air temperatures in urban areas. Hence, NWP models tend to underestimate the air temperature in urban areas since NWP models cannot fully resolve the urban heat island effect. Without any post-processing the MAE is 1.7 K and the MBE is -1 K. This study focuses on an analysis of 17 different European cities in the year 2020. It quantifies the improvement of the statistical downscaling model over an NWP model by a) including dense air temperature measurements in the urban and rural areas, b) including satellite derived variables as model input and c) including both dense air temperature measurements and satellite derived variables.

Dense air temperature networks in cities help to better understand the micro-scale air temperature field in an urban environment. These air temperature data train a statistical high-resolution air temperature downscaling model for urban environments in 10 m horizontal resolution. Including official measurement stations, the statistical model can be transferred to other cities for an operational use to calculate micro-scale air temperatures on an hourly basis. The model is further forced by surface texture parameters from the high-resolution satellites Sentinel-2 and Landsat-8, as well as digital elevation models, and raw model output from meso-scale NWP models.

With a dense air temperature network (a), the urban heat island effect can be resolved, resulting in a reduced bias to almost 0 K. Including satellite derived variables as model input (b) the downscaling approach ensures to decrease the MAE by 0.4 K and to better represent the inner-city temperature variability. To better take dynamic processes into account, the downscaling approach can be extended with a dense measurement network (c) which also further reduces the MAE.

The statistical model approach enables to resolve high-resolution temperature fields in the past, making it possible to calculate high-resolution urban heat island maps. Furthermore, real-time temperature fields can help to significantly enhance the initial conditions for NWP models, thus improving forecast models in urban areas. A statistical downscaling of the numerical weather forecast can help decision-makers improving the heat wave management in cities.
Evaluation of a Multi-Model Ensemble Prediction System 
Authors: Shin Hyuncheol , Kim Eun Jung , Yun Sug-gyeong , Kang Hyun-Suk , Kwon Young Cheol , Park Jong-Kil
Published: June 2022

A multi-model ensemble prediction system that integrates UM (Unified Model) global and ensemble models, ECMWF global and ensemble models, and KIM (Korean Integrated Model) global model was established and its performance was evaluated. KIM is KMA’s new generation model which is based on the cubed sphere grid system and has 12km horizontal resolution and 91 vertical levels. It was developed over 9 years from 2011 to 2019 and operationally launched in April 2020.
The optimal mean field was produced by weighted average of 5 models, where the weights were given inversely proportional to the error of each model.
As a result of comparing RMSE (Root Mean Square Error) of the multi-model ensemble with 5 member models, a multiple-model ensemble showed better performance than any other member model. It was better than ECMWF ensemble model which showed the best performance among member models. In RMSE score, Mean Surface pressure was improved by 3.1% and 500hPa geopotential height was improved by 2.1% against ECMWF ensemble model. 
The performance of a multi-model ensemble by various weight calculation methods which are used for weighted averaging was compared. It was found that horizontally domain-averaged weight, having the different value for the forecast time gave the best performance. Giving the detailed and complex weights differently for each forecast time and each grid point rather degraded the performance of a multi-model ensemble.
In BIAS verification, it was found that the BIAS of the multi-model ensemble showed the value between the maximum BIAS and the minimum BIAS of member models. Therefore, it was found that when the BIAS of the member models was distributed in a balanced positive and negative directions without being biased in one direction, the multi-model ensemble showed the smallest BIAS.
Synoptic analysis of Cyclone Ianos via surface, satellite and reanalysis data
Authors: Hérincs Dávid , Dezső Zsuzsanna
Published: June 2022

Mediterranean Tropical-like Cyclones, or commonly named as medicanes are a special type of cyclone over the Mediterranean Sea. These cyclones are quite similar to tropical cyclones, although smaller. Their development process is the same as that of subtropical or tropical cyclones forming over the higher latitude subtropical areas of the Atlantic Ocean: they mainly originate from extratropical lows that cut off from the main westerly flow and gradually acquire subtropical and then tropical characteristics if they remain long enough over relatively warm waters. This type of cyclones is officially classified in the Atlantic region by the National Hurricane Center (USA), and nowadays some meteorological services, for example the UK Met Office already admit the medicanes as subtropical or tropical cyclones, too.

In September 2020, a unique cyclone developed over the central Mediterranean Sea, named 'Ianos' after the Greek Meteorological Service. The genesis occurred over warm water of 26-27 °C from a larger cluster of thunderstorms and it did not have extratropical precursor. Later, the cyclone gradually strengthened and built up a well-defined tropical-like structure with sustained deep convection and occasionally with an eye. In our work, we investigated this cyclone based on available surface and satellite-based measurements and the ECMWF ERA-5 reanalysis data with 0.75° resolution to determine whether it is really a tropical cyclone. We analysed the low-level, vertical and high-level structure of the cyclone and compared reanalysis parameters that typically differ between tropical and extratropical cyclones. The results show that Ianos had more tropical than subtropical characteristics and did not show any extratropical sign, so it could be classified as a tropical cyclone. Furthermore, surface measurements confirmed that the cyclone reached at least the middle part of the Category 1 intensity interval on the Saffir-Simpson hurricane wind scale before it made landfall in Greece, and it is also possible that it had been a Category 1 hurricane for a short period the previous day, too.
Sensing the Wind with Hot-air Balloons and their Application in NWP Models
Authors: de Bruijn Cisco , de Haan Siebren , Bosveld Fred , Marseille Gert-Jan , Holtslag Bert
Published: June 2022

Numerical Weather Prediction (NWP) models with a horizontal grid of 2 km or finer need detailed information for estimating the initial state of the atmosphere. Ground-based remote-sensing instruments like Sodars, Doppler lidars and Profilers provide already meteorological information of the Atmospheric Boundary Layer (ABL). Although observational networks have been extended over the years, there are still gaps in data gathering particular on the finer scales. Therefore we have commenced research to investigate data from third parties. Here we focus on wind-information in the ABL from recreational Hot-air Balloon (HaB) flights. In the basic equipment of a HaB pilot there is a professional navigation device, which is compulsory for safety reasons. Similarly to routinely launched weather balloons, the Global Navigation Satellite System (GNSS)-data from consecutive positions and the elapsed time are the basis of the calculation of the horizontal wind vector. On a yearly basis about 6000 flights are taken place in the Netherlands, mainly during the morning- and evening transition. As soon as the surface is covered with snow and when convection is strongly reduced, flights may also take place during the day. The HaB data are validated with observations from the meteorological site of Cabauw and we compare the HaB winds with mast data and other available observations like a RASS wind profiler and a wind lidar. We also compare the HaB data with the results of an NWP model and we will report about a first attempt to assimilate the HaB data in a NWP model. To explore the possibilities of this new type of wind observations in more complex terrain, we will present also the results of an intriguing HaB flight in Austria showing a typical mountain-valley circulation.
A computationally efficient parameterization for 3D radiative effects at the surface in large-eddy simulations
Authors: Tijhuis Mirjam , van Stratum Bart , van Heerwaarden Chiel
Published: June 2022

The amount of solar radiation that reaches the Earth’s surface is strongly influenced by clouds and aerosols. This results in a complex pattern of radiation at the surface with cloud shadows and regions with enhanced solar radiation. Cloud enhancements can cause surface irradiances up to 25% higher than under clear sky conditions. Insight into these peaks is valuable to operators of the electricity grid, as these peaks can cause grid voltages to exceed the safety limits. Furthermore, the radiation at the surface impacts the surface fluxes, boundary layer turbulence and clouds. Therefore, proper modelling of surface radiation is important for predictions of solar energy production and for detailed weather forecasts. Current 1D calculations of radiative transfer cannot capture the complex pattern of surface radiation, whereas 3D calculations are very costly. In this study, we developed a simple method to account for the 3D radiative effects at the surface in LES. The horizontal spreading of the diffuse radiation is accounted for by applying a spatial filter to the surface diffuse radiation field. With this filter, we add only little extra complexity to the existing 1D calculation. Therefore, our method is computationally efficient. The filtering of the diffuse radiation is applied to the results of a Large Eddy Simulation for a summer day in Cabauw, the Netherlands, on which shallow cumulus clouds formed during the day. The results of the LES simulation are compared to detailed high-quality observations (1Hz). Without the filtering, the cloud enhancements are not captured at all, and the probability distribution of global radiation is unimodal, whereas the observed distribution is bimodal. After filtering, our results closely match with the observations. The probability distribution of global radiation is now bimodal and cloud enhancements are simulated. We found that small changes in the filter width do not strongly influence the results. Furthermore, we showed that the width of the filter can be parameterized as a linear function of one variable related to the cloud field, e.g. the maximum cloud size or the cloud base height. Hence, this work presents a proof-of-concept for our method to come to more realistic surface irradiances by filtering diffuse radiation at the surface.
GWL-REA: An improved method for classifying Hess-Brezowksy Grosswetterlagen based on pattern correlations in combination with Lamb Weather Type statistics
Authors: James Paul , Ostermöller Jennifer
Published: June 2022

The Hess-Brezowsky Grosswetterlagen (GWL) are a widely used set of 29 synoptic weather patterns, focussed on Central Europe. Previous algorithmic methods for classifying GWLs, based on multi-parameter correlations with respective climatological GWL patterns, yielded acceptable results in terms of the larger-scale synoptic character. However, synoptic patterns did not always correspond well to the specific nomenclature of the respective chosen GWL type in terms of central (anti)cyclonicity or location of the primary steering High (Low). To overcome this problem, the original method has been expanded by introducing statistics from the well-known Lamb Weather Types (LWT) system. LWTs centred over specific European locations allow a direct determination of e.g. local cyclonicity, allowing biases to be placed on the pattern correlations to improve the nomenclature correspondence of the results. The pattern correlations themselves have also been refined by introducing a many-to-one mapping, in which each GWL is represented by several different synoptic patterns, spanning a much wider range of the within-type variability in the multi-variable phase-space than was previously possible. This new method (GWL-REA), designed within the context of climate research at DWD, produces a much-improved GWL classification of reanalysis data with a potentially wide-range of applications. A version of the new method (GWL-EPS) is also deployed in operational forecasting at DWD for classifying medium-range ensemble forecasts (ECMWF-EPS15). The 51 ensemble runs are each classified and the likely alternative GWL developments are shown as a structure of GWL-branches, resulting in an efficient summary of the expected synoptic developments over the next two weeks.
Modelling climate statistical parameters by MISH interpolation procedure
Authors: Izsak Beatrix , Szentes Olivér , Bihari Zita
Published: June 2022

The MISH (Meteorological Interpolation based on Surface Homogenized Data Basis; Szentimrey and Bihari) software was developed at the Climate Department of Hungarian Meteorological Service (OMSZ) specifically for interpolation of climate elements. At OMSZ the MISH is currently using to interpolate temperature, precipitation, wind, global radiation, pressure and humidity data. The software consists of two major parts: the modelling of climate statistical parameters and the interpolation subsystem. A good quality modelling requires long homogenized data sets, but the representativity of the station system is also very important. Therefore, as many measurements as possible should be considered in the modelling process.

Interpolation in MISHv1.03 is based on statistical parameters that are determined in advance in the modelling part by using climate data series. It means that this modelling has to be done only once, before the interpolation. If the modelling is made from a homogenised data set of many stations, it is possible to interpolate well with few predictors. At the Hungarian Meteorological Service, archived data are continuously recorded and new meteorological stations are being installed. The previous MISH modelling procedure for temperature data series was done using 58 stations, but this has been renewed and, recently we estimate climate statistical parameters using data of 112 stations.

In this presentation, we present the results of the new modelling for mean, minimum and maximum temperature values. In all three cases, our results show a significant improvement on previous results. We can say that we can provide more accurate estimates with few predictors for locations in Hungary where measurements are not available. The new modelling also gives us a better understanding of the climate of the past centuries, since we can make more accurate estimates from fewer measurements. It also allows us to report climate change in more detail.
Improving the urban surface representation in high-resolution reanalyses frameworks
Authors: Valmassoi Arianna , Keller Jan D.
Published: June 2022

In this work, we aim to include a computationally inexpensive enhanced urban and land surface representation in a reanalyses framework. First, we update the land use representation used in the ICON-LAM numerical weather prediction by employing the 100m CORINE data set with three urban land use classes instead of the older 300m GLOBCOVER with only one urban category. Then, we include an energetic surface modification for complex urban fabric as an increase in the heat capacity and area sensible heat flux.

Further, we use the Copernicus Land Surface Temperature (LST) satellite product available at 5-km 1-hour resolution in the context of data assimilation to correct surface temperatures. Specifically, we investigate the sensitivity on parameter settings within the LETKF-KENDA data assimilation scheme, i.e., horizontal localization length, adaptive inflation, coarsening factor, thinning, and different observational errors.
The latter two are of particular importance since we deal with spatially dense data, whose errors are spatially correlated. Thus, we include the observation error correlation in the KENDA scheme and compare the results to the standard version.

The results show that the LST assimilation improves the land temperature biases drastically, e.g. down from over 7K to 2.5K for the Berlin area. At the same time, it also seems to cause a degradation in 2-meter temperature biases. However, this effect seems to be related to their model equivalent calculation (especially during daytime) rather than the observation impact itself. The analysis increments do not exhibit a similar behavior across various cities in the Central European area.

The urban correction improves the 2-meter temperature representation, especially in the morning hours. We do not find the combined changes in surface temperatures due to LST assimilation and urban correction to drastically alter the Urban Heat Island representation for the two case study cities (Berlin and Cologne).
Measuring the solar wind with Vigil: solar-terrestrial relations and space-weather forecasting
Authors: Verscharen Daniel
Published: June 2022

The Vigil mission is ESA's flagship operational space-weather mission, planned to be launched in 2027/28. It will combine remote-sensing and in-situ measurements to enable accurate space-weather predictions from its unique vantage point at the fifth Sun-Earth Lagrange point. This location will allow Vigil to observe remotely space-weather events that propagate along the Sun-Earth line. In addition, Vigil will observe the source regions of the solar wind and of other space-weather events about 4.5 days before these source regions will point at Earth. Likewise, in-situ measurements of the solar-wind plasma at the fifth Sun-Earth Lagrange point will sample solar-wind streams that point at Earth about 4.5 days later.

The Plasma Analyser (PLA) instrument onboard Vigil will measure the solar wind at the location of the spacecraft over a wide dynamic range of plasma parameters. The data from PLA will predict high-speed solar wind streams headed towards Earth. These high-speed streams can drive relativistic (>MeV) electron flux enhancements in the Earth's radiation belts. Although these events are less spectacular than coronal mass ejections and the associated geomagnetic storms, they are potentially more damaging to our infrastructure due to their longer duration. The associated flux enhancements especially pose a threat to spacecraft in Medium Earth Orbits and Geostationary Equatorial Orbits. PLA measurements will also support work to improve and validate models for solar-terrestrial relations in the inner heliosphere. These models can be used, for example, to provide better predictions of the background plasma conditions through which Earth-directed coronal mass ejections propagate.

PLA is currently designed and built at the University College London's Mullard Space Science Laboratory (UCL/MSSL). The instrument is an electrostatic plasma analyser that relies largely on the flight heritage of previous scientific instruments designed by UCL/MSSL. I will give a status update of the PLA development and an outlook into the space-weather prediction capabilities enabled by PLA and the other Vigil payload.
Teal: a visualisation tool for non-expert users to explore climate data
Authors: Boorman Penny , Troccoli Alberto , Bertocco Elena , Boyer William , Dubus Laurent , Cordeddu Stefano , Contreras Beatriz , Correa Nathalia , Hibbert Sian , Nielsen Kristian , Obahoundje Salomon , Saravanan Sittukala , Turrisi Giuseppe , Walker Shelbie
Published: June 2022

The Teal tool enables a non-expert user to visualise and explore climate variables and carbon emissions. A global map shows climate data by country and sub-country, for temporal resolutions from annual to daily. Currently Teal shows data produced from ERA5 reanalysis from 1950, it will shortly be expanded to include additional data sources such as observations and projections of future climate, with other developments planned.  Documentation and guidance are included for users, with functionality and data periodically revised.  

Teal was built on previous Copernicus Climate Change Service (C3S) demonstration tools: the European Climatic Energy Mixes (ECEM, 2015-2018) demo and the C3S Climate & Energy Educational Demo (Edu Demo, 2019-2020). The Edu Demo won the 2021 C3S Gala award for 'Creating impactful visualization and communication material and promoting C3S to user communities’. Teal considerably evolved under the recently completed (October 2021) H2020 SECLI-FIRM project, and is being further developed under ongoing projects such as the H2020 FOCUS-Africa. 

Teal was originally designed to raise awareness and provide data and visualisation about climate change and related energy issues. A key objective was to provide a good user experience which allowed students and educators to easily navigate options on the interface. Based on users’ feedback, it was realised that Teal is attractive and functional for both business and educational users alike. As a result, Teal is available both for public dissemination (free access) and for commercial climate services. 

Teal has been developed by the World Energy and Meteorology Council (WEMC), WEMC was established in 2015 and is an organisation based at the University of East Anglia in Norwich, UK. WEMC works with stakeholders around the world to promote and enhance interaction between the energy industry and the weather, climate and wider environmental sciences communities. 
Assessment of new DWD and Copernicus regional reanalysis products R6G2 and CERRA with focus on near surface wind speed 
Authors: Niermann Deborah , Spangehl Thomas , Borsche Michael , Kaspar Frank , Schimanke Semjon
Published: June 2022

Reanalyses are enjoying an ever-increasing number of users and interest groups, especially due to steadily increasing spatial and temporal resolutions. However, new applications also require a comprehensive quality assessment of the used data set, taking into account different time scales, parameters, and regions. Within the framework of the joint BMBF project ClimXtreme (https://www.climxtreme.net/), we evaluate the quality of DWD’s current reanalysis product COSMO-REA6 and its follow-up product R6G2 (COSMO-REA6 Generation 2) concerning the representation of extreme events like heat waves, windstorms and heavy precipitation.

Reanalyses enable a comprehensive assessment of extreme events and their relevant processes. Here we are aiming at an evaluation approach of extreme wind events and comparisons for mean near surface wind speed, analyzing sub-daily, daily and seasonal patterns, as well as regional differences. The comparisons (using station measurements and gridded observations as reference as well as known reanalyses such as ERA5 as benchmark) also integrate brand new data from the Copernicus European Regional Re-Analysis (CERRA). Both regional reanalyses, CERRA and R6G2 are available for the complete EURO-CORDEX domain, at an almost similar spatial resolution of 5.5 km for CERRA and about 6 km for R6G2. The evaluation here, concentrate on the German region and current events from the years 2018, like winter storm Friederike.

For evaluation the platform MAVIS (Meteorological Analysis and Visualization System), based on Freva (http://doi.org/10.5334/jors.253) is used. Freva is a scientific software framework supporting scientific cooperation on high performance computing environments, equipped with a standardized model database, a programming interface and a history of evaluations.
QPF and QPE performance during the July 2021 flood event
Authors: Verkade Jan , Bouaziz Laurène , van Heeringen Klaas-Jan , Weerts Albrecht
Published: June 2022

Last July, the Ardennes/Eifel regions and the wider Moselle, Meuse and Rhine basins were hit by severe flooding. Large precipitation depths were forecast as of the weekend prior to the flooding, some 5 days in advance. Post the event, however, it turned out that precipitation forecasts were significantly lower than the observed precipitation depths. This caused various hydrological forecasts to initially underestimate river flow. That, in turn, had all sorts of fall-out, including a significant additional level of incredulity on the part of flood responders, who were already taken by surprise due to the unusual timing of the flood. 

The analyses show the extent to which various quantitative precipitation forecast (QPF) products (including DWD-ICON and ECMWF deterministic and ensemble products) coincide and deviate from the ‘observational’ quantitative precipitation estimates (QPE), both in terms of quantity, timing and location. The analysis shows that the true uncertainty in the weather forecasts was significantly larger than what the ensemble forecasts suggested. 

The analyses also show that the various observational and reanalysis products (including ERA5, E-OBS, HYRAS, RADOLAN and those originating from various operational hydrological forecasting systems) greatly vary in their estimate of actual/true/observed precipitation amounts. 

The analysis is conducted both on the level of the original grids as well as on the level of various Meuse and Rhine tributaries including the Vesdre and Ahr river basins. Also, the effect of the different QPE and QPF estimates on the simulated and forecasted discharge is shown. 

The present case study provides anecdotal evidence of the performance of QPF originating from numerical weather prediction products in severe flood events. Additional emphasis will be given to the propagation of these uncertainties to river flow forecasts produced by the Dutch national fluvial forecasting service, where the lead author was acting as flood duty officer in the week preceding the floods. 
CLIMADA: A beginner-friendly tool for weather and climate risk assessment
Authors: Riedel Lukas , Kam Pui Man , Röösli Thomas , Kropf Chahan , Bresch David N.
Published: June 2022

CLIMADA (from CLIMate ADAptation) is a free and open-source software framework for weather and climate risk modelling written in Python. It uses an event-based, probabilistic approach to integrate multiple hazards, exposure, and vulnerability into a risk assessment for impact forecasts on regional, national, and global scales. In line with the Economics of Climate Adaptation (ECA) methodology, recent versions of CLIMADA support cost-benefit analysis for climate risks in face of a warming climate. Explicitly incorporating adaptation measures and strategies, CLIMADA aims to support stakeholders and decision makers in handling climate-related impacts on infrastructure, societies, and ecosystems.

Although CLIMADA is a primarily scientific tool, it is easy to learn and use. CLIMADA offers numerous tutorials with step-by-step guidance provided as Jupyter Notebooks. These and the extensive documentation are written for both Python beginners and advanced users. For quick and intuitive visual feedback of results, CLIMADA offers pre-defined plotting functions generating forecast and impact maps. Combining open-source software with open data, it allows users to explore and assess the numerous facets of climate risk. Recent versions of CLIMADA include a data API which automatically downloads required data like storm tracks and exposed assets. The available data on a global scale serves as a demonstration, and users can always read in custom datasets reflecting their particular use case. The combination of these features makes CLIMADA an efficient toolset for both research and education.

In this contribution we will present CLIMADA and demonstrate its capabilities. We invite participants to join the session interactively and will provide ready-to-use examples and exercises.
The morphology of convective cold pools in a dense station network during FESSTVaL
Authors: Kirsch Bastian , Hohenegger Cathy , Ament Felix
Published: June 2022

Cold pools are crucial for understanding the organization of atmospheric convection. However, basic properties like their size, shape and temperature structure remain obscured by the insufficient resolution of operational station networks. In summer 2021 the FESSTVaL field experiment aimed to address this observational gap and shed light on the structure and life cycle of cold pools on the sub-mesoscale (100 m to 10 km). The experiment took place in the rural surrounding of the Meteorological Observatory Lindenberg (south-east of Berlin, Germany) and featured a dense network of custom-designed low-cost measurement stations covering an area of 30-km diameter. The instrumental setup included 80 novel APOLLO stations sampling air temperature and pressure at 1-s resolution and 19 supplementary weather stations. A X-band rain radar also provided highly resolved information on rainfall.

The FESSTVaL network recorded 42 cold pool events of different strength and size during the three-month measurement phase. Based on the spatial interpolation of the network observations, morphological properties of more than 1200 identified cold pool objects are derived. The analysis reveals a median cold pool diameter of 8.4 km. According to average aspect ratios between 1.5 and 1.6, the sampled cold pools are not round, independent of their size and strength. Moreover, large cold pools tend to be stronger and more heterogeneous than small ones. As FESSTVaL is especially suited to study young cold pools, we further analyse the growth phase of selected events initiated inside the station network. Remarkably, their area scales very linearly with the accumulated rainfall amount suggesting that rainfall is the main driver for the growth of cold pools, both directly by evaporative cooling and indirectly by downward transport of upper air masses.
IVINE: a crop model for simulating pheno-physiological processes on vineyards, also from a climatic point of view
Authors: Cassardo Claudio , Andreoli Valentina , Spanna Federico , Traversa Martina
Published: June 2022

Several factors inﬂuence grapevine's quality and productivity; among them, there are weather and climate. Temperature, solar radiation, precipitation and soil moisture have relevant impacts on crop growth and yield. Recently crop growth models were used as tools to assess climate variability and change in crop yields and quality. Crop growth models are currently employed at a regional scale for agricultural needs, environmental applications, and/or for supporting the process of decision-making and planning in agriculture. Some crop growth models are adaptable to various crops and can simulate crop growth and plant development, as well as water and nitrogen balances. Speciﬁc crop growth models have also been developed to simulate grapevine growth and development. Generally, crop growth models include speciﬁc modules calculating the occurrence of phenological stages, that can also be used as stand-alone routines. Finally, grapevine phenology has recently been studied in connection with climate change by means of grape harvest dates used to reconstruct past climate and, more rarely, future climate. The numerical crop growth model IVINE (Italian Vineyard Integrated Numerical model for Estimating physiological values) was developed at our dept. to simulate grapevine phenological and physiological processes. The boundary conditions required by IVINE are hourly meteorological data related to some air and soil values (among which the more relevant are air temperature and soil moisture), while other inputs are initial conditions and parameters characterizing geography, soil texture, grapevine variety, and cultivation interventions. The main IVINE outputs are: the phenological stages (e.g. dormancy exit, bud-break, fruit set, veraison, and harvest), the leaf development, the yield, the berry sugar concentration, and the leaf water potential. The IVINE requires some experimental parameters depending on the cultivar, which have been evaluated through some calibration experiments for some popular Italian varieties. IVINE includes theoretical physically based equations for processes such as water balance and photosynthesis, and empirical equations for others. Long-term simulations carried out in Italian territory show, in the period 1980-2010, significant trends in almost all physio-phenological variables, as well as a reduction in interannual variability, correlated with the climate change still ongoing.
IVINE model performances depend on the quality of input data: the use of experimental data measured not far from the vineyard could improve the quality of the simulation, even if the model seems able to account for the interannual variability of the meteorological conditions, which reflects in the pheno-physiological trends interannual variability. During the presentation, the model will be introduced, and a sketch of the main results obtained will be discussed.
Aviation Turbulence Forecasting over the Portuguese Flight Information Regions: Objective Verification and case study
Authors: Belo-Pereira Margarida
Published: June 2022

Aviation turbulence remains one of the leading causes of weather-related aviation accidents. Therefore, turbulence prediction is a major concern of aviation forecasters.

The performance of several turbulence diagnostics derived from ECMWF forecasts is evaluated over Portuguese Flight Information Regions (FIR) and surrounding areas, for the period February 2020 to April 2021, excluding May and June. In addition, the algorithm developed and used operationally by aviation meteorologists at IPMA to forecast moderate and severe turbulence over Portuguese FIRs is also discussed. The forecasts were compared with turbulence observations from special air reports and DEVG data from AMDARs received at the Portuguese MWO.

The objective verification approach in this paper uses not only the Relative Operating Characteristic curves but also novel measures such as the recently proposed Symmetric Extreme Dependence Index (SEDI) and Symmetric Extreme Dependence Index (SEDS). These measures are particularly suitable for assessing the forecasting skill of rare, such as moderate or greater turbulence, which accounts for 1.6% of the total data.

The vertical wind shear (VWS), DUTTON, Brown and Ellrod indices outperform the other turbulence diagnostics. In addition, VWS performs the best in terms of all verification measures. It has been found that adding a Richardson number function to these five turbulence diagnostics improves the performance of aviation turbulence forecasting. Consequently, the operational index combines these five diagnostics with the Richardson number.

Prediction of moderate and severe turbulence depends on the choice of the optimal threshold. However, this optimal threshold varies with the verification measure used. The results show that TSS and SEDI achieve a higher value for lower thresholds compared to SEDS. This is because when the contingency table becomes dominated by the correct predictions of non-events, both TSS and SEDI penalize under-prediction more than over-prediction.

Finally, the performance of the operational turbulence index and compared with the WAFS product is also illustrated for two turbulence episodes. 