bluuebunny/crossref_metadata_2025 huggingface dataset:
Long term changes of the sub-daily precipitation extremes in Hungary
Authors: Lakatos Monika , Szentes Olivér
Published: June 2021

The warming climate evokes increasing frequency of extreme precipitation in some region. Analysis of long-term measurements could support the better understanding of the processes that cause extreme precipitation events.

Automatic stations replaced the ombrometer in many places in Hungary, particularly from the late 1990s. The change of the measurement practice do not allow simply merging the data recorded form the registering paper in the past and the recent 10 minutes measurements.   The most intense 5, 10, 20, 30, 60, 180 min sub-totals per rainfall events were recorded from the ombrometer registering paper before atomization, typically until 1993. By contrast, the 10 min precipitation sum from the AWSs are stored in the meteorological database of the Hungarian Meteorological Service from automatization. In order to join together the older and the AWS measurements it was necessary to develop a method to make this possible. Therefore we downscaled the 10 min data in time. The sampling of the AWSs is one minute, although the 1-minute data are available only for some stations in the digital database.  We applied a linear regression model to downscale the 10-miniute data for 1 min. After this, we can derive the most intense sub-totals per events from the AWS data as if they have been measured with the ombrometers.

Thereby a set of sub-daily precipitation indices defined in the INTENSE project (https://research.ncl.ac.uk/intense/aboutintense/ can be computed for longer data series. Some of the indices specified in INTENSE project describes the maximum rainfall totals and timing, the intensity, duration and frequency of heavy precipitation, frequency of rainfall above specific thresholds and some of them is related to diurnal cycle. A few of these indices are analysed for long data series to detect the sub-daily precipitation changes in Hungary.
Troubles with teleconnections
Authors: Huth Radan , Hynčica Martin , Piskala Vladimír , Pokorná Lucie
Published: June 2021

Rotated principal component analysis (RPCA) is a commonly used tool to detect modes of low-frequency atmospheric circulation variability, also referred to as teleconnections. Teleconnections manifest themselves as distant areas of high negative or positive correlations in sea level pressure, geopotential height, or another variable describing atmospheric circulation. For outputs of RPCA to be valid representations of teleconnections, their spatial patterns (loadings) must correspond to underlying correlation / covariance structures, that is, be in agreement with autocorrelation maps.

When comparing teleconnections identified in different datasets (e.g., between reanalyses, between outputs of climate models, between different periods, between different seasons), the spatial similarity of loadings is evaluated and quantified; if it is low, the datasets are said to disagree in the representation of a particular teleconnection. However, things appear to be less straightforward: It may happen that although the loadings pertaining to the same teleconnection differ, the maps of correlations with the action centres (i.e., points with highest positive or negative loadings) are identical. This may suggest that while the autocorrelation structures are the same in the two datasets, they appear with different weight (intensity). This issue appears to be unrelated to uncertainty due to the number of principal components to rotate; it typically occurs for various reasonable numbers of components.

In our contribution, we (i) introduce the above described issue on several examples (RPCA of different reanalyses, of sliding time periods, and of sliding 93-day seasons), (ii) discuss what is a correct interpretation of such cases (should we consider the teleconnections to be equal or different when the autocorrelation maps agree but the loadings disagree?), and (iii) suggest possible ways out of it (to use oblique instead of orthogonal rotation, to return back to autocorrelation maps).
A modified lightning potential index for numerical models with parameterized deep convection
Authors: Schröder Guido
Published: June 2021

A modified lightning potential index (MLPI) for numerical models with parameterized deep convection is presented. It is based on the LPI formula of Lynn and Yair (2010). Following the idea of Lopez (2016), the quantities (e.g. vertical velocity) needed in the LPI formula are derived from the updraft of the Bechtold-Tiedtke parameterization scheme (Bechtold et al., 2014). The formula is further improved by taking into account the vertical equivalent potential temperature gradient.

The LPI and MLPI are tested in ICON with 20km resolution (ICON-20) over central Europe. A key component in the LPI is the vertical velocity. To assess its quality, the vertical velocity of the updraft in the convection scheme in ICON-20 is compared to updrafts in the convection-resolving COSMO model with 2.2 km resolution (COSMO-D2). It is shown that in ICON-20 the extension of the vertical velocity is generally broader with the maximum located in higher altitudes. In the charge separation area where the vertical velocity is relevant, the ICON-20 vertical velocity is less than in COSMO-D2. Consequently, the LPI values in ICON-20 are lower by a factor of 2 compared to COSMO-D2.

The MLPI is verified against LINET lightning data (Betz et al. 2009) over central Europe for summer 2020 and compared to LPI in COSMO-D2. The MLPI is also compared to the LPI and the lightning flash density (LFD,  Lopez, 2016), all computed in ICON-20. For the test period the MLPI outperforms the LPI and LFD. However, the quality of the LPI in COSMO-D2 cannot quite be reached.

 

Bechtold et al. 2014: Representing Equilibrium and Nonequilibrium Convection in Large-Scale Models. J. Atmos. Sci. 71, 734-753.

Betz et al., 2009:  LINET - An international lightning detection network in Europe. Atmos.  Res. 91 564–573.

Lopez, 2016: A Lightning Parameterization for the ECMWF Integrated Forecasting System. Mon. Wea. Rev., 144, 3057-2075.

Lynn and Yair, 2010: Prediction of lightning flash density with the WRF model  Adv. Geosci., 23, 11–16.
Visually Mapping Global Climate Indicators, Risks and the Sustainable Development Goals
Authors: Ransom Claire , Haran Valentine , Baddour Omar
Published: June 2021

There is emerging scientific literature on climate change, risk and policy action. However, the interdisciplinary nature of the research has resulted in difficulties for policy makers to quickly and easily find spatial-temporal and geographic information on climate change as it relates to ecosystems, populations and development. We present a mapping tool that links the seven World Meteorological Organization (WMO) state of the climate indicators to climate and climate change impacts and the United Nations Sustainable Development Goals (SDGs). The seven WMO were chosen for their clarity, relevance for a range of audiences, and ability to be updated using internationally agreed and published methods with open access and high-quality data. Each indicator indicators (greenhouse gas concentration, global mean surface temperature, ocean heat content, sea level rise, sea ice extent, glacial mass extent and ocean acidification) represents key aspects of the climate system linked to various associated risks identified by the Intergovernmental Panel on Climate Change and the academic community. Systemically mapping the relationships between the WMO climate indicators and related risks to show how climate can affect the achievement of specific SDGs, with clear visual representations, provides stakeholders with a new tool to better grasp the interconnected and complex nature of how climate change threatens sustainable development. The presentation will introduce the tool in both a digital and print format and explore how the product can help decision makers and civil society engage with complex climate science. We will then put forward recommendations and plans for evaluating the effectiveness of the tool for future applications and improvements.
Predictability analysis and skillful scale verification of the Lightning Potential Index (LPI) in the COSMO-D2 high resolution ensemble system
Authors: Salmi Michele , Marsigli Chiara , Dorninger Manfred
Published: June 2021

During the last decade, the constant improvement in computational capacity led to the development of the first limited-area, kilometer-scale ensemble prediction systems (L-EPS). The COSMO-D2 EPS (now ICON-D2) is the operational L-EPS at the German weather service (DWD) and has a spatial resolution of around 2km. This grid resolution allows large scale, deep convective processes such as thunderstorms or heavy showers to be handled explicitly, without any physical parametrization necessary. Special parameters involving both clouds (micro-)physics and large scale lifting – such as the Lighting Potential Index, or LPI – have also been developed in order to try and bring the forecasting of deep convection and therefore also of lightning activity to a new level of spatial accuracy. With such high-precision forecasts comes however also a much higher error potential, at least for gridpoint-verification. The use of this high resolution setup in an ensemble prediction system might however bring huge benefits in terms of skill and predictability. This work is a preliminary attempt to apply innovative verification approaches such as the dispersion Fractions Skill Score (dFSS) or the ensemble-SAL (eSAL) to the LPI in the COSMO-D2 EPS. Aim of this work is to assess the relationship between the ensemble error and the ensemble dispersion at different spatial scales. For the summer months 2019 the COSMO-D2 EPS shows a general tendency to underestimate the unpredictability of the lightning events, though the spread-error relationship varies greatly for different forecast lead times. With the help of the dFSS, one can also express this relationship in terms of skillful scales. On average, the system produces a useful forecast during the afternoon hours for horizontal scales of around 200 km. However, the ensemble members show an average horizontal dispersion that lies around half of that value, at more or less 100 km.
Review on modelling tools for maize pests risks in agro-climatic zones of central Europe.
Authors: Gajraj Rudrani , Eitzinger Josef
Published: June 2021

Projections of crop-pest dynamics under climate change (CC) impact negatively throughout the 21st century, suggesting increased pest pressure over central Europe. Variability in environmental conditions significantly affects the spread, abundance, and management of maize pests, such as Western corn rootworm (WCR), European corn borer (ECB) and wireworms, as indicated from the past monitoring data and quantitative pest models. Regional climate change and landscape factors are modifying the development of these thermophile insects and their impact on crops. Several conceptual modelling works related to pest population dynamics, phenology models, spread and risk mapping have been proposed. The challenges become complex for WCR and wireworms due to their soil-dwelling nature, eventually bringing significant site-specific spatial uncertainties to simulate the soil-crop-pest ecosystem. Most of the approaches have been quantitatively standardised, leaving out qualitative factors prioritizing farmer’s behaviour towards pest control measures. In our study we investigate statistical-empirical and process based models and integrated modelling frameworks for future simulation of soil-crop-pest phenology under the CC impact. We identified gaps in the improvements of pest modelling approaches and site-specific agrometeorological indices for four agro-climatic zones. In conclusion we propose a mechanistic physiological based demographic model, including improved population models under defined landscape extent and agrometeorological drivers. For example, temperature, photoperiod, precipitation changes, and topography are the significant drivers for ECB, whereas in addition, WCR and wireworms include soil type, crop rotation, and farmer’s management options. We derive crop damage factors from the quantitative surveys influencing maize grain yield, root damage and pest control measures, including weather conditions, insecticide distribution, pest pressures, and synchrony between pest and crop growth cycle. Agro-climatic zones of central Europe will experience northward shifts in temperature zones, growing season length, by further accelerating climate change. Hence, it is exposed to more vulnerabilities and demands improved alert services and control measures. The improved pest modelling approach integrated with crop models under CC impact provides a reasonable basis for advanced crop protection strategies with reduced pesticide use in maize production. It is used to formulate a regional risk assessment for specific pests to inform an integrated approach of cultivation, biological and chemical measures.
Seamless rainfall prediction skill comparison between GLOSEA5 and NEPS-G ensemble prediction systems
Authors: Kolusu Seshagirirao , Mittermaier Marion , Robbins Joanne , Jones Caroline , Ashrit Raghavendra , Mitra Ashis K
Published: June 2021

The southwest monsoon rains in 2019 were the heaviest over India in a quarter of a century. The 2019 seasonal JJAS precipitation over the whole country was 110 % of its long period average (LPA) of 880mm. Precipitation is a cumulative field driven by many atmospheric processes both within nature and numerical prediction.  It’s a weather variable that impacts everyone’s life and hence is used routinely to assess the skill of modelling systems. In this study, we have analyzed the 2019 JJAS seasonal precipitation forecast skill of two global ensemble models: (1) the UK Met Office GloSea5 and (2) the National Center for Medium Range Weather Forecasting (NCMRWF) Global Ensemble Prediction System (NEPS-G). The Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG-GPM) rainfall and ERA5 winds with high spatial resolution and temporal data are used for verification of the model forecasts across a seamless range of time scales.  In order to compare a seamless range of time scales, we have summed forecast fields over time windows of forecast lead time from 1 day to 2 weeks. We also computed the actual skill and potential skill of the model ensemble forecasts at different lead windows. Our results for both models show large precipitation biases and reduced precipitation skills with forecast lead windows. We also found that the models’ actual and potential skill are sensitive to the number of ensemble members and type of ensemble generation. Moreover, the GloSea5 model actual skill is higher than the NEPS-G model over Indian homogeneous regions. To use the GloSea5 NWP forecast model ensemble members for more quantitative applications in downstream hazard and/or impact-based modelling and applications the between-ensemble-member bias introduced by the lagging needs to be addressed.
Multi-model assessment of the late-winter tropospheric response to El Niño and La Niña
Authors: Mezzina Bianca , García-Serrano Javier , Bladé Ileana , Palmeiro Froila M. , Batté Lauriane , Ardilouze Constantin , Benassi Marianna , Gualdi Silvio
Published: June 2021

El Niño-Southern Oscillation (ENSO) is known to affect the Northern Hemisphere tropospheric circulation in late-winter (January–March), but whether El Niño and La Niña lead to symmetric impacts and with the same underlying dynamics remains unclear, particularly in the North Atlantic. Three state-of-the-art atmospheric models forced by symmetric anomalous sea surface temperature (SST) patterns, mimicking strong ENSO events, are used to robustly diagnose symmetries and asymmetries in the extra-tropical ENSO response. Asymmetries arise in the sea-level pressure (SLP) response over the North Pacific and North Atlantic, as the response to La Niña tends to be weaker and shifted westward with respect to that of El Niño. The difference in amplitude can be traced back to the distinct energy available for the two ENSO phases associated with the non-linear diabatic heating response to the total SST field. The longitudinal shift is embedded into the large-scale Rossby wave train triggered from the tropical Pacific, as its anomalies in the upper troposphere show a similar westward displacement in La Niña compared to El Niño. To fully explain this shift, the response in tropical convection and the related anomalous upper-level divergence have to be considered together with the climatological vorticity gradient of the subtropical jet, i.e. diagnosing the tropical Rossby wave source. In the North Atlantic, the ENSO-forced SLP signal is a well-known dipole between middle and high latitudes, different from the North Atlantic Oscillation, whose asymmetry is not indicative of distinct mechanisms driving the teleconnection for El Niño and La Niña.

The multi-model assessment, with 50 members for each experiment, contributes to the ERA4CS-funded MEDSCOPE project and includes: EC-EARTH/IFS (L91, 0.01hPa), CNRM/ARPEGE (L91, 0.01hPa), CMCC/CAM (L46, 0.3hPa).
Evaluation of the near-surface evolution of Foehn events in COSMO-1
Authors: Tian Yue , Schmidli Juerg , Quimbayo-Duarte Julian
Published: June 2021

Foehn is a downslope wind with a large impact on society due to its gusty nature and the associated high-temperature extremes. It is influenced by and interacts with near-surface processes, such as the cold air pool (CAP), an important phenomenon that is often present in the foehn valleys during the cold season. Therefore, it is challenging to accurately forecast the foehn characteristics, in terms of the onset, strength, and decay as the near-surface evolution is the result of multi-scale interactions between the larger atmosphere and the mountain/local valley topography. From a meso-/micro-scale perspective, this study investigates the skill of the COSMO model (v5.7) at 1.1 km grid spacing in simulating the near-surface foehn evolution for a set of south foehn events, representative of different foehn types around the Alps. The evaluation is based on a comparison to station data from the automatic monitoring network of MeteoSwiss, with a focus on the Rhine Valley. Significant cold and moist biases are found in the model during foehn hours in all the chosen cases. Biases in Foehn duration and spatial extent are also studied. The sensitivity of these biases to several land-surface parameterization choices, e.g., skin layer, bare soil evaporation, and resistance for heat fluxes, are investigated and presented. Simulations for the same foehn events with COSMO at 500 m grid spacing are also evaluated for a better understanding of the model biases. Further studies from the perspective of climatological statistics are needed to establish the relationships between model biases and foehn types.
Requirements for the use of impact-based forecasts by road safety organisations in Germany
Authors: Schmidt Jasmina , Tietze Nikola , Kox Thomas
Published: June 2021

Road transportation is considered the most vulnerable mode of transport in terms of weather impacts (Molarius et al., 2014). Extreme weather events like winter storms, heavy rain or glazed frost can have a significant impact on road quality and hence, public safety. In addition, seasonal and daily changes in traffic volume or commuter flows influence the occurrence of accidents. German road safety organisations like maintenance services receive weather information through a road condition and weather information system (SWIS) that provides  information like precipitation and road surface temperature. Hence, these information give an overview over the weather conditions on the roads - not what the impacts of those conditions can have on traffic. With predictive modelling, it is possible to assess the impacts of weather on road safety as hourly probabilities of weather-related road accidents (Becker et al. 2020). It is assumed that such information on impacts of weather events on road infrastructure can be of value for road maintenance services that are responsible for ensuring road safety.  However, it is still unclear how such statistical information on accident probability would be used in practice. Early warning systems are encouraged to be ‘people-centred’ (UNISDR 2015), allowing users to act in sufficient time and in an appropriate manner. In order to become people-centred, those implementing a warning system must know who their audience is and understand their information requirements for an optimal response (Zhang et al. 2019). This conference contribution will shed some light on the requirements that practitioners of road safety organisations in Germany have for impact-based forecast on weather-related road accidents. The study is part of an interdisciplinary research project in collaboration with Germany's National Meteorological Service DWD and follows a qualitative social science research approach. With the aim of stakeholder engagement in the process of developing predictive weather-related accident models, focus group discussions with managers of highway and road maintenance services as well as representatives of road and transport authorities were carried out and first results will be presented. Findings contribute to the understanding of professional recipients’ responses to weather warnings and weather information for their daily work tasks and hence, provide a deeper insight into weather service requirements from a set of stakeholders that are instrumental to public safety. Road safety organisations use weather information for the preparation of events, e.g. in planning of personnel and adjusting equipment at hand. In the focus groups, interest in further information for decision-making was expressed, while individual experience of the road’s conditions in practice was also highlighted.
The Swiss snow climatology as seen by reanalyses and further gridded datasets
Authors: Goeldi Monika , Gubler Stefanie , Steger Christian , Scherrer Simon C. , Kotlarski Sven
Published: June 2021

Snow cover is a key component of alpine environments and knowledge of its spatiotemporal variability, including long-term trends, is vital for a range of dependent systems like winter tourism, hydropower production, etc. Snow cover retreat during the past decades is considered as an important and illustrative indicator of ongoing climate change. As such, the monitoring of surface snow cover and the projection of its future changes play a key role for climate services in alpine regions.

In Switzerland, a spatially and temporally consistent snow cover climatology that can serve as a reference for both climate monitoring and for future snow cover projections is currently missing. To assess the value and the potential of currently available long term spatial snow data we compare a range of different gridded snow water equivalent (SWE) datasets for the area of Switzerland, including three reanalysis-based products (COSMO-REA6, ERA5, ERA5-Land). The gridded data sets have a horizontal resolution between 1 and 30 km. The performance of the data sets is assessed by comparing them against three reference data sets with different characteristics (station data, a high-resolution 1km snow model that assimilates snow observations, and an optical remote sensing data set). Four different snow indicators are considered (mean SWE, number of snow days, date of maximum SWE, and snow cover extent) in nine different regions of Switzerland and six elevation classes.

The results reveal high temporal correlations between the individual datasets and, in general, a good performance regarding both countrywide and regional estimates of mean SWE. In individual regions, however, larger biases appear. All data sets qualitatively agree on a decreasing trend of mean SWE during the previous decades particularly at low elevations, but substantial differences can exist. Furthermore, all data sets overestimate the snow cover fraction as provided by the remote sensing reference. In general, reanalysis products capture the general characteristics of the Swiss snow climatology but indicate some distinctive deviations – e.g. like a systematic under- respectively overestimation of the mean snow water equivalent.
Implementing the modified potato blight risk forecasting model in the Republic of Ireland – the New Irish Rules
Authors: Flattery Padraig , Finkele Klara , Cucak Mladen , Kildea Steven , Fealy Rowan , Downes Paul
Published: June 2021

 

Potato late blight caused by oomycete Phytophthora infestans (Mont) De Bary, is arguably the most important disease of potato in terms of economic losses and environmental and economic impact of the disease management in Ireland and globally. The pathogen spreads rapidly in humid weather in the foliage of potatoes and tomatoes, causing the foliage to decay and the infection of the potato/tomato. Due to the pathogen’s dependence on weather conditions, a number of forecasting methods have been developed to reduce the input of pesticides and fungicides required to control the disease. Blight is of particular significance to Ireland, following the decimation of the potato crop in the 1850s which, among other colonial factors, contributed to widespread famine, emigration and death. Until recently, blight in Ireland has been forecast using the Irish Rules. The model was developed in the 1950s and calibrated based on the sparse meteorological station coverage at the time and lower risk perception by growers.

 

Since then, the understanding of the pathogen’s development and its relationship to meteorological situation has advanced. This has led to the development of a modified open-source Irish Rules model written in R which facilitates improved forecasting and seasonal re-evaluation of the Irish Rules (Cucak et al., 2019). The new method reduces the threshold for relative humidity from 90% to 88% and the initial (sporulation) period from 12 hours to 10 hours, the analysis also showed thresholds for blight epidemics could be changed from 10°C to 12°C. Though risk estimation has increased compared to the previous rules, estimated chemical usage is still lower compared to standard grower’s practice. The new methodology is now referred to as the New Irish Rules.

 

This research presents the implementation of this new blight forecasting model in Met Éireann’s operational infrastructure, ensuring methods for forecasting blight are as up-to-date as possible and are using industry-wide best-practice.

References: 

Cucak, M., Sparks, A., Moral, R. D. A., Kildea, S., Lambkin, K., & Fealy, R. (2019). Evaluation of the ‘Irish rules’: the potato late blight forecasting model and its operational use in the Republic of Ireland. Agronomy, 9(9), 515.
The development of a regional climate change forum in South Asia 
Authors: Heath Jamie , Salmon Kate , Daron Joseph , Fox Cathryn
Published: June 2021

South Asia is one of the most vulnerable regions in the world to climate change due to its greater exposure to climate hazards, superimposed on the backdrop of limited institutional capacity to adapt and livelihood dependency on climate-sensitive agriculture. The Asia Regional Resilience to a Changing Climate programme (ARRCC) takes a regional approach to climate change by aiming to strengthen the provision and uptake of weather and climate services in some of the most vulnerable South Asian countries, Afghanistan, Bangladesh, Nepal, and Pakistan.  As part of the ARRCC programme, the Climate Analysis for Risk Information and Services in South Asia (CARISSA) project has identified that countries within South Asia would particularly benefit from a platform for knowledge exchange on climate science and services. Alongside the Met Office and FCDO, stakeholders in South Asia identified the most effective methods of knowledge exchange being 1) Establishing an online community of practice on climate change science and services, 2) Incorporating climate change information into the regional , climate outlook forum (RCOF) process, and 3) Establishing a dedicated and regular regional forum on climate change in South Asia. Here, we show the process of co-developing a network of key stakeholders in South Asia who are best placed to contribute to and benefit from, promotion of climate services in the region. We share our experience of co-creating an online community of best practice on climate science and services in South Asia and how this will help to establish greater uptake of climate information in decision-making. Finally, we will discuss how to incorporate regular, in-person meetings into the RCOF process to share climate knowledge and nurture collaborations between stakeholders throughout the region.
Time series homogenization with the ACMANT software
Authors: Domonkos Peter
Published: June 2021

The development of ACMANT homogenization software started during the European COST HOME project, around 2010. Due to its excellent results in method comparison tests, the development of ACMANT has been being continuous since then. While its first version was applicable only to the homogenization of monthly temperature series, the later versions are applicable to a wide range of climatic variables and either for monthly or daily time series.

The operation of ACMANT is fast and automatic, and it is easy to use that even for large size datasets. The method can homogenize together time series of varied lengths, well tolerate data gaps, includes outlier filtering and infilling of data gaps (optional). ACMANT includes modern and effective statistical tools for the detection and removal of inhomogenities, such as step function fitting, bivariate detection for breaks of annual means and seasonal amplitudes (where applicable), ANOVA correction method and ensemble homogenization with varied pre-homogenization of neighbour series. For these properties, ACMANTv4 was the most accurate homogenization method in most method comparison tests of the Spanish MULTITEST project (https://doi.org/10.1175/JCLI-D-20-0611.1). In these tests, one important exception occurred, namely network mean trend errors were removed with significantly higher certainty by the Pairwise Homogenization Algorithm when approximately a half of the time series were affected with quasi synchronous breaks imitating concerted technical changes in the performance of climate observations. The most recent developments aiming the release of ACMANTv5 include the elimination of this drawback of ACMANT.

For ACMANTv5, a new break detection method has been developed, in which the combination of two time series comparison methods is applied. The new method contains both the use of composite reference series and pairwise comparisons, and in the detection with composite reference series the step function fitting is forced to include the breaks detected by pairwise comparisons. Another novelty of ACMANTv5 is that it gives options to use metadata in the homogenization procedure. The default operation mode of ACMANTv5 is still fully automatic, with or without the automatic use of a prepared metadata table. ACMANTv5 uses every date of the metadata list as a break indicator, and they are evaluated together with other indicators obtained by pairwise comparisons. Optionally, ACMANTv5 gives access to users to edit the list of detected breaks based on the pairwise detections of the first homogenization round. In the later steps of ACMANTv5 user intervention is not possible, but metadata may be considered by the automatic procedure also in the final estimation of break positions.     
Cars as next generation mobile weather stations: an initial investigation
Authors: Pagani Giuliano Andrea , Molendijk Marcel , Noteboom Jan Willem
Published: June 2021

Modern automobiles are becoming more and more “computers on the wheels” having lots of digital equipment on board. Such equipment is both for the comfort and entertainment of the passengers and for their safety. Sensors play a key role in measuring several parameters of the car performance (e.g., traction control, anti-lock breaking system) and also environmental  parameters are observed directly (e.g., air temperature) or can be somehow inferred (e.g., precipitation via windscreen wipers activity/speed).

KNMI has been provided air temperature recorded every 10 minutes by thousands of vehicles driving in the Netherlands for the period January-October 2020. We have performed an initial exploratory temporal and spatial analysis to understand the most promising periods of the day and areas where sufficient data is available to perform a more thorough data analysis in the future. Furthermore, we have performed a correlation analysis between the outside temperature measured by cars and air and ground temperature observed by official weather station sensors placed at one location on the Dutch highways. The correlation results for three randomly selected days (with different weather conditions) show a good positive correlation coefficient ranging from 0.93 to 0.76 for car and station air temperature and from 0.91 to 0.67 for car temperature and station ground temperature.

This initial exploration paves the way to the use of (OEM) car data as (mobile) weather stations. We foresee in the future to use a combination of sensed variables from cars such as air temperature, traction control, windscreen wipers activity for example to improve observations of road slipperiness and related warning systems that are not restricted to Dutch highways only.
Evaluation of daily forecasts by the coupled Terrestrial Systems Modelling Platform (TSMP) over a small convection-permitting model domain in Central Europe
Authors: Iakunin Maksim , Wagner Niklas , Graf Alexander , Görgen Klaus , Kollet Stefan
Published: June 2021

In many of today’s resource management and climate change adaptation challenges, versatile and  reliable numerical model simulations are the basis for informed decision making. The integration of multiple compartmental  models into simulation platforms allows us to reproduce interacting geosystem processes and thereby solve a wide range of problems in a variety of applications. The Terrestrial System Modelling Platform (TSMP, https://www.terrsysmp.org) is an integrated regional Earth system model that simulates processes from the groundwater across the land surface to the top of the atmosphere on multiple spatio-temporal scales. TSMP consists of the COSMO (Consortium for Small-scale Modeling) atmospheric model, the CLM (Community Land Model), and the hydrologic model ParFlo, coupled through OASIS3-MCT. TSMP is used in various studies from climate change simulations to near-real time forecasting and monitoring. Here we present the results of the evaluation of the TSMP in a monitoring setup, providing daily forecasts with a lead time of 10days of the atmospheric, surface, and groundwater states and fluxes for a heterogeneous mid mountain-ranges area in Western Germany. The model domain covers an area of 150km x 150km at 1km (atmosphere) and 0.5km (land surface and subsurface) resolution. The simulated data is compared with observations from the TERENO (Terrestrial Environmental Observatories, https://www.tereno.net) Eifel/Lower Rhine Valley network. This TERENO observatory comprises a total area of 2354 km² and provides data from a very dense measurement network of 12 climate stations, 6 eddy covariance stations, 6 lysimeter stations, and 13 cosmic-ray neutron stations. To assess the quality and suitability of the TSMP as a monitoring system of the geosystem’s state and evolution with agricultural applications in mind,  forecasts from July 2019 to October 2020 are analyzed with reference to the observations. Results show that the TSMP can well represent the main subsurface hydrological and relevant meteorological features.
FESSTVaL: Field Experiment on sub-mesoscale spatio-temporal variability in Lindenberg – the campaign 2021 an its predecessors
Authors: Hohenegger Cathy , Ament Felix , Beyrich Frank , Duran Ivan Bastak , Löhnert Ulrich , Göber Martin , Masbou Matthieu , Rust Henning , Sakradzija Mirjana , Schmidli Jürg , Wiesner Sarah
Published: June 2021

Measuring submesoscale variability is the core task of the field campaign FESSTVaL (Field Experiment on Sub-Mesoscale Spatio-Temporal Variability in Lindenberg).  FESSTVaL focuses on three sources of submesoscale variability: cold pools, wind gusts and boundary layer pattern. It took place in the summer months of 2021 at the Meteorological Observatory Lindenberg – Richard-Aßmann-Observatory (MOL-RAO) of the German Weather Service (DWD) near Berlin and was initiated by the Hans-Ertel-Center for Weather Research (HErZ).

In order to capture phenomena at the submesoscale (500 m – 5 km), generally not captured by conventional measurement network, a hierarchical measurement strategy is adopted. This includes wind profiling stations with a coordinated scanning strategy of several Doppler Lidars, two mobile profilers to measure thermodynamic properties and precipitation, more than 100 stations with near-surface measurements of air temperature, pressure and soil moisture, more than 20 automatic weather stations, an X-Band radar, and a number of energy balance stations. This equipment is augmented by the extensive ground-based remote sensing array at the MOL-RAO, operated by DWD and by flights operated by Unmanned Aerial Systems. Complementing to this, the benefit of a citizen-science measurement network is investigated during the campaign with “Internet-of-things” based technology and low-cost sensors built and maintained by citizens. The measurements are supplemented by high-resolution large-eddy simulations (ICON-LES).

Originally planned for the summer 2020, FESSTVaL had to be postponed to 2021 and replaced by three local individual campaigns, conducted in Bayern, Lindenberg and Hamburg in 2020. Those three test campaigns demonstrated the ability of the envisionned measurement strategy and planned instruments to capture submesoscale variability and submesoscale weather phenomean. This talk will give a brief overview on the results of these three campaigns, as a foretaste to FESSTVaL, together with some of the very first measurements taken during FESSTVaL.
Medicane Ianos: a comparative study between WRF model and satellite measurements
Authors: Federico Stefano , Comellas Prat Albert , Torcasio Rosa Claudia , D'Adderio Leo Pio , Dietrich Stefano , Panegrossi Giulia
Published: June 2021

On September 14th, 2020 a depression originated on the Libyan coasts generated a Mediterranean tropical-like cyclone (hereafter referred to as Medicane Ianos), which moved northward until it hit, with its northernmost cloud bands, the southern Italian coasts and finally bent towards Greece, where it made landfall on September 18th, 2020. Heavy precipitation and flash flooding were reported, associated to huge damages to railway, houses, and four casualties.

The correct prediction, as much as possible, of the trajectory and the intensity of these events is fundamental to prevent risks to infrastructures, natural landscapes, and people. One of the ways to evaluate the performances of the numerical weather prediction models is the comparison with satellite measurements. In particular, the mature phase of Medicane Ianos, as predicted by the weather research and forecasting (WRF) model, has been compared, for the first time, with the Global Precipitation Measurement mission Core Observatory (GPM-CO) active and passive measurements. Different microphysics schemes were used in order to investigate which is the most suitable to achieve the best forecast of Medicane Ianos considering different parameters as depression localization, reflectivity, rainfall rate, integrated liquid and ice content. The results show that all the schemes identified the precipitation bands structure of the Medicane overestimating the vertical extent of the convective structures. At the same time, all the schemes predicted an excessive columnar ice water content if compared to the one estimated from satellite measurements. It has to be highlighted that the overestimation is marked on the western precipitation bands of the Medicane eye, while a better agreement is obtained for the northern bands. Similar results are obtained for columnar liquid water content, even if the quantitative estimation is closer to the GPM measurements. Finally, all the schemes located the Medicane circulation center further north-west of its actual position.
FAIR: User-friendly delivery of climate and weather data
Authors: Frank Christopher William , Figura Richard , Fischer Bernd , Dimpfel Frank , Rothstein Ulrich , Eberz Charlotte , Schuchert Marvin , Lübcke Max
Published: June 2021

Climate and weather data play an important role for e.g. identifying actions against climate change and optimizing industries. However, a correct understanding and handling of such data is often difficult for users without a meteorological background. Moreover, specialized software solutions and an infrastructure capable of handling large amounts of data are needed to process and analyze these data. 

The research project FAIR addresses this issue by simplifying the exchange of information and data between the German Meteorological Service (DWD) and stakeholders from industry and public. To fulfill this purpose, microservices for processing, caching, visualizing, and analyzing meteorological data in an efficient way are being developed. Processing comprises, for example, the selection of specific information from model data or the conversion of the result into formats commonly used by the user. The compilation of microservices makes it possible to support different types of applications and at the same time to make data from third parties available to the DWD. To demonstrate the utility of these microservices, three test scenarios are considered: 1) wind farm planning, 2) integration of meteorological data for individual traffic routing, and 3) planning of social events such as festivals. 

In this article, we present the general idea and the current state of the project. The focus is on the challenges that have been identified for the three test scenarios and our technical approaches to address them. Herein we present the developed architecture, the data flow, the FAIR portal and the handling of metadata.
Weather scenarios from AROME-EPS forecasts using autoencoder and clustering methods
Authors: Mounier Arnaud , Raynaud Laure , Rottner Lucie , Plu Matthieu
Published: June 2021

The use of ensemble prediction systems (EPS) is challenging because of the huge information it provides. Forecasts from ensemble prediction systems (EPS) are often summarised by statistical quantities (ie quantiles maps). Although such mathematical representation is efficient for capturing the ensemble distribution, it lacks physical consistency, which raises issues for many applications of EPS in an operational context. In order to provide a physically-consistent synthesis of the French convection-permitting AROME-EPS forecasts, we propose to automatically draw a few scenarios that are representative of the different possible outcomes. Each scenario is a reduced set of EPS members.

To design a scenario synthesis, the procedure can be divided into two parts. A first step aims at extracting relevant features in each EPS member in order to reduce the problem dimensionality. Then, a clustering is done based on these features.

The originality of our work is to leverage the capacities of deep learning for the features extraction. For that purpose, we use a convolutional autoencodeur (CAE) to learn an optimal low-dimensional representation (also called latent space representation) of the input forecast field. In this work, the algorithm is developed to work on 1h-accumulated rainfall from AROME-EPS, with a focus on convective cases.

The CAE is trained on around 150 000 forecasts and its performance is evaluated based on the quality of the reconstructed input fields from the latent space. To examine the reconstruction quality, an object-oriented approach is used. CAE is also compared with the commonly-used principal component analysis (PCA). In a second part, different clustering methods (kmeans, HDBSCAN, …) are applied to EPS members in the latent space and evaluated using subjective and objective diagnostics.
Autonomous driving in adverse winter weather conditions
Authors: Hippi Marjo , Sukuvaara Timo , Mäenpää Kari , Perälä Toni , Stepanova Daria
Published: June 2021

Autonomous driving can be challenging especially in winter conditions when road surface is covered by icy and snow or visibility is low due to precipitation, fog or blowing snow. These harsh weather and road conditions set up very important requirements for the guidance systems of autonomous cars. In the normal conditions autonomous cars can drive without limitations but otherwise the speed must be reduced, and the safety distances increased to ensure safety on the roads. 

Autonomous driving needs very precise and real-time weather and road condition information. Data can be collected from different sources, like (road) weather models, fixed road weather station network, weather radars and vehicle sensors (for example Lidars, radars and dashboard cameras). By combining the all relevant weather and road condition information a weather-based autonomous driving mode system is developed to help and guide autonomous driving. The driving mode system is dividing the driving conditions from perfect conditions to very poor conditions. In between there are several steps with slightly alternate driving modes depending for example snow intensity and friction. In the most challenging weather conditions, automatic driving must be stopped because the sensors guiding the driving are disturbed by for example heavy snowfall or icy road.

Finnish Meteorological Institute is testing autonomous driving in the Arctic vehicular test track in Sodankylä, Northern Finland. The test track is equipped with road weather observation system network including road weather stations, IoT sensors measuring air temperature and humidity along with various communication systems. Also, tailored road weather services are produced to the test track, like precise road weather model calculations and very accurate radar precipitation observations and nowcasting. The developed weather-based autonomous driving system is tested on Sodankylä test track among other arctic autonomous driving testing.

This study presents the Sodankylä Arctic vehicular test track environment and weather-based autonomous driving mode system that is developed at the Finnish Meteorological Institute.
Spatio-temporal analysis of short-term rainfall extremes in Croatia
Authors: Cindric Kalin Ksenija , Nimac Irena , Patalen Leonardo , Pasaric Zoran
Published: June 2021

Extreme rainfall events of short duration and their possible intensification in the future may induce large implications in the existing engineering structures and water risk assessment. In Croatia, the sub-daily time scale (6 hours and 12 hours) is used to select an appropriate threshold value to define yellow, green and red alarming categories in the Meteoalarm rainfall warning system. The study aims i) to provide the general climatology of short-rainfall extremes to analyse regional variability in precipitation extremes, and ii) to update the alarming rainfall thresholds in Croatia and to upgrade the alarming system from regional (presently 8 regions) to the county level (21 counties). The short-term (from 5 minutes to one hour) and sub-daily (from 2 to 24 hours) precipitation amounts are analysed in terms of their spatial and temporal characteristics. The study comprises the ombrographic station network of 56 stations, evenly distributed over Croatia, with datasets covering the period 1961-2019. For each duration, the Generalized Extreme Value (GEV) distribution is fitted to the annual-maxima precipitation amounts, employing both the stationary and non-stationary model. Spatial distribution of the corresponding 2, 5, 10, 25, 50 and 100 years return values shows a clear distinction between the continental and maritime regions for each duration of rainfall with a pronounced maximum in the northern Adriatic (Kvarner) region. The implications on the current rainfall thresholds in the alarming system and new proposals will be discussed. Additionally, the changes in characteristics of short-term precipitation extremes will be presented providing the climatological background for climate change adaptation activities and impact assessment in Croatia.