bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparison of ERA-5 reanalysis and observed cloud cover in the high Arctic
Authors: Gryning Sven-Erik , Batchvarova Ekaterina , Floors Rogier , Münkel Christoph , Skov Henrik , Sørensen Lise Lotte
Published: June 2021

Knowledge and understanding of Arctic cloud properties is important for climate predictions and weather forecasts but limited because of scarcity of observational data on Arctic clouds in general and especially during the dark winter season. Prediction of clouds is known to be a major challenge in numerical weather forecasts and climate models  and the shortage of observations for use in data-assimilation in the Arctic constitutes a further difficulty.

We present results from an analyses of cloud cover based on profiles of the attenuated backscatter coefficient from an 8-year long data series (July 2011 – April 2019). The observations are carried out in the high Arctic by a ceilometer with a maximum range setting of 7.7 km from the Villum Research Station at Station Nord, Greenland. Results show that the hourly cloud cover turned out to follow a U-shaped rather than Gaussian-like distribution.

Annual and seasonal cloud cover variation is illustrated. The cloud cover is larger during the autumn and winter as compared to summer and spring. The cloud cover exhibits a substantial variation from year to year without a clear trend. The cloud cover during spring is low and decreasing between 2012 and 2017. The cloud cover during the autumn of 2016 is lowest compared to the other years.

The observed cloud cover is compared to the cloud cover provided in the ERA5 reanalysis data-set. The cloud cover for low clouds and medium clouds are combined to represent a total height of 6 km. Both the observed and modelled cloud cover is larger during winter as compared to summer-time cloud cover. The measured reduction in the cloud cover for the autumn of 2016 is present in the reanalysis data as well but the measured low cloud cover during spring is not apparent in the reanalysis data.

The ability of the ERA-5 reanalysis data to predict the observed cloud cover was investigated. Because the cloud cover distribution is U-shaped rather than of a Gaussian nature, standard metrics are not applicable. We apply a generalized skill score that is developed for contingency tables or joint histograms. Three skill scores were calculated. It was found that for all three methods, skills for the predictability of the cloud cover by the ERA5 modelling is better for winter than summer and is poor during the spring.
Agricultural ecosystem services under climate change – Modelling grassland biodiversity
Authors: Moulin Thibault , Calanca Pierluigi
Published: June 2021

European permanent grasslands not only represent a backbone for dairy and meet production, but also are hotspots of biodiversity, providing important ecosystem services to society. Understanding how climate variability and change affect the botanical composition of permanent grasslands is therefore essential for informing adaptation and helping farmers targeting sustainable development goals. It is also a key requirement for gauging climate change effects on forage quality, an aspect often overlooked in impact assessments. In this contribution, we present results of a modelling effort to understand short- and long-term changes in grassland biodiversity in response to climatic variations. We use DynaGraM, a recently developed process-based model for simulating community dynamics in multi-species managed grasslands. Earlier we demonstrated that DynaGraM is capable of representing the composition of permanent grasslands in the French Jura Mountains inferred from floristic relevés. In these earlier investigations, we also showed that the model predicts highest, resp. lowest vegetation diversity for extensive grazing, resp. extensive mowing. We further found that the time scales of responses to external perturbations largely dependent on management, with shorter time scales (of the order of 5 to 10 years) under grazing than under mowing (of the order of 50 years).

Here we apply the model to examine how increasing summer aridity affects the species composition of pastures in the same geographic area. To drive the model, we use a set of climate change scenarios obtained from the CMIP5 repository, which we downscaled with the help of the LARS-WG stochastic weather generator. The results underline that management intensity modulates the impact of summer drought on both yield as well as botanical diversity, with largest changes over time in the latter under extensive grazing. Apart from presenting the results in more detail, we also discuss their practical implications and opportunities to extend in future the scope of this work.
Synoptic classification of atmospheric circulation over the south Eastern Siberia and its relationship with glacier dynamics
Authors: Osipova Olga , Osipov Eduard
Published: June 2021

The energy balance of a glacial surface and its melting is strongly controlled by altering synoptic processes in the lower troposphere. Therefore, classification of the processes of atmospheric circulation over the glaciarized regions is very important for better understanding of long-term trends in glacier changes. The glaciers of the Kodar Ridge (south Eastern Siberia) have shrunk in area by about 60% since the mid-19th century, with the largest decline taking place at the end of the 20th century. We have compiled the daily catalog of the weather types (WTs) from 1970 to 2020 based on the Jenkinson and Collison objective classification applied for the area (47.5–67.5° N, 102.5–132.5° E) centered over the Kodar Ridge. The gridded sea level pressure (SLP) and isobaric 700 hPa data was obtained from the National Center for Environmental Prediction / National Center for Atmospheric Research (NCEP/NCAR) reanalysis. In total, 26 WTs were identified and the frequency of different synoptic types was statistically analyzed. The most frequent group of WTs is advective (40%), followed by anticyclonic (34%) and cyclonic (14%). The unclassified type totally accounts for 13%. We revealed the differences between the frequency of synoptic processes in seasonal cycle and at different atmospheric levels (SLP and 700 hPa). Cyclonic weather types usually prevail in summer, while anticyclonic ones in autumn and winter. At 700 hPa level, the frequency of anticyclonic WTs increases in summer, while the frequency of advective types increases in all seasons. Over the past 50 years, the frequency of anticyclonic types demonstrates decreasing trend, while that of cyclonic and advective weather types increased (SLP data). In the 1980s and early 1990s the frequency of cyclonic WTs decreased, which could lead to a decrease in cloud cover over the Kodar region and an increase in net radiation of glacier surfaces. This study was supported by the Russian Foundation for Basic Research (project No. 19-05-00668).
Using visible satellite images for model evaluation and data assimilation
Authors: Scheck Leonhard , Geiss Stefan , Bach Liselotte , de Lozar Alberto , Weissmann Martin
Published: June 2021

Satellite provide high-resolution information on the state of the atmosphere and thus represent observations are well-suited for data assimilation and model evaluation. So far mainly the thermal infrared channels have been utilized for these purposes. However, there is a rising interest to use also the channels in the solar part of the spectrum, which contain additional, complementary information. Visible channels can provide better information on the water and ice content of clouds than thermal infrared channels, have no problems to detect low clouds and are sensitive to cloud microphysics and the cloud top structure. Moreover, visible reflectances are strongly correlated with the solar irradiation at the surface and thus their assimilation has a clear potential to improve also radiation forecasts.

So far visible satellite images have not been assimilated directly for operational purposes, as multiple scattering dominates in the visible spectral range and makes radiative transfer (RT) computations with standard methods complex and slow. Only recently, we developed a sufficiently fast and accurate forward operator that relies on a compressed reflectance look-up table (LUT) computed with slow standard RT methods. Here we report on using feed-forward neural networks as an alternative to the look-up table and demonstrate that it is possible to achieve higher speed and accuracy. Moreover, both the amount of training data and the memory required by the operator can be reduced by three orders of magnitude. A further advantage is that tangent-linear and adjoint versions can easily be derived for arbitrary network structures and do not have to be changed when the network is trained with different data.

We will also discuss two ways to use the forward operator to improve forecasts. First, we show that observed and synthetic visible  Meteosat SEVIRI images can be used to detect systematic errors in the model clouds that can cause severe problems for data assimilation. Second, based on assimilation experiments using the ICON-D2 model and the local ensemble transformation Kalman filter implemented in DWDs data assimilation coding environment (DACE) we demonstrate for test periods of several weeks that errors in the cloud distribution and the surface radiation can be significantly reduced by assimilating visible SEVIRI images.
Climate change impacts on the Greek tourism sector
Authors: Varotsos Konstantinos V. , Karali Anna , Kitsara Gianna , Giannakopoulos Christos
Published: June 2021

In this study we examine the impacts of climate change on the tourism sector using a number of tailored climate indicators assessing whether climate conditions are suitable for touristic activities such as the Tourism Climate Index -focusing on outdoor activities- and the Beach Climate Index -focusing on beach activities- as well as fire danger indicators such as the Canadian Fire Weather Index, focusing on forest fire risk. To this aim daily or sub-daily data for a number of meteorological variables from a large ensemble member of Regional Climate Models from the EURO-CORDEX data base are used. The data cover the period 1971-2100 under three RCP emissions scenarios, namely RCP2.6, RCP4.5 and RCP8.5. The analysis is performed for three periods, the 1971-2000 which is used as a reference period and two future periods, the 2021-2050 and 2071-2100. The results indicate that the robust warming projected on a seasonal basis, under all three climate scenarios, drives the changes on all indicators examined. Regarding the climate suitability indicators for tourism the results indicate a lengthening of the tourist season suitable climate conditions while for the fire danger indicators, an increase in the number of days with high and very high fire danger conditions is projected. The most pronounced changes are evident towards the end of the century and under the RCP8.5 future emissions scenario. This study is performed in the framework of CLIMPACT, a Greek national funded project which aims to immediate integration, harmonization and optimization of existing climate services and early warning systems for climate change-related natural disasters in Greece, including supportive observations from relevant national infrastructure.

 
Effects of heatwaves on lake composition derived from satellite observations
Authors: Penning de Vries Marloes , Salama Suhyb , Mannaerts Chris , van der Wal Daphne
Published: June 2021

As a consequence of the ever-increasing global temperature, not only the air, and surface, but also lakes are warming up. This is expressed by steadily increasing base temperatures, but also in increases in the frequency and intensity of lake heatwaves. Land-based organisms may adapt to a changing climate by migrating to more suitable habitats, but this is usually not an option for lake-dwellers. Because many livelihoods depend on the ecosystem services of lakes, understanding the effects of heatwaves on lake composition form  an important input for the assessment of climate change impacts and design of adaptation strategies.

Using satellite data of lake temperature and water quality observations, we here investigate the effects of heatwaves on lake composition by studying the relationship between heatwaves and water quality variables of temperature, chlorophyll-a , colored dissolved organic matter, and suspended particulate matter . The latter can be used to infer effects of heat stress on health and populations of phyto- and zooplankton communities and higher aquatic organisms. Satellite-based data sets provided by the Climate Change Initiative of the European Space Agency,  CCI-Lakes (https://climate.esa.int/en/projects/lakes/) are  used in conjunction with the 2SeaColor model to determine depth-dependent attenuation coefficients and water quality variables.These data are complemented with and compared to data from Copernicus Global Land Services (https://land.copernicus.eu/global/products/). 

The co-occurrence of heatwaves and changes in lake composition is investigated using statistical tools, and the causality is examined by comparison with biophysical models. The results from this study are discussed in light of previously published projected changes in heatwave frequency and intensity.
A novel way of correcting between-ensemble-member biases in a lagged S2S ensemble
Authors: Mittermaier Marion , Kolusu Seshagiri Rao , Robbins Joanne
Published: June 2021

The UK Met Office seasonal forecast system, Global Seasonal Forecast System version 5 (GloSea5), is an ensemble forecast prediction system providing sub-seasonal and seasonal forecasts over the globe with ~60 km resolution in the mid-latitudes. GloSea5 also produces hindcasts or historical re-forecasts. The system produces 4 members a day, initialised at 00UTC. Two members run out to 64 days and two run out to 216 days. We use these four members to generate a 40-member lagged ensemble with 10 days of lag time, i.e. for any forecast horizon the oldest members are always 10 days older. Due to this lag and the way these ensemble members are initialised, there is a considerable within-ensemble bias, even for a nominal “day 1” forecast. This within-ensemble bias evolves with increasing lead time horizon.

Traditionally hindcasts are used to correct for the so-called model drift. In this work the idea of using a distribution of daily rainfall amounts from short-lead time forecasts is used using the 2019 Indian monsoon season. Quantile mapping is trialled as a means of removing the “within-ensemble-member” bias to ensure that all ensemble members are drawn from a more consistent underlying distribution. Achieving this would enable the members to be used to drive downstream applications such as hazard or impact models, as such models require individual ensemble members.

The presentation will demonstrate the methodology and the impact it has on ensemble forecast skill, complementing the presentation by Kolusu et al. (same session in conference) which presents an evaluation methodology focusing on patterns for different accumulation lengths and forecast horizons.
GNSS storm nowcasting demonstrator for Bulgaria
Authors: Guerova Guergana , Dousa Jan , Dimitrova Tsvetelina , Václavovic Pavel
Published: June 2021

GNSS is an established atmospheric monitoring technique delivering water vapour data in near-real time with latency 90 minutes for operational Numerical Weather Prediction in Europe within the EGVAP service. However, nowadays with advancement of GNSS processing the quality of real-time GNSS tropospheric products is well comparable to near-real time solution and in addition they can be provided in a temporal resolution of 5 minutes and low latency, suitable for severe weather nowcasting. The aim of the project is to exploit the added value of GNSS tropospheric product for nowcasting of convective storm by building demonstrators in support of public weather and hail suppression services in Bulgaria. In Bulgaria  thunderstorms and hail events are  occur between May and September with a peak in July. The convective Storm Demonstrator (Storm Demo) is based on GNSS tropospheric products and Instability Indices to derive site specific threshold values integrated and updated in real-time on a publicly accessible geoportal. The demonstrator targets development of service centered at GNSS products for two regions with hail suppression operations namely Northwestern and Central Bulgaria.  As a part of the Storm Demo real-time PPP processing will be conducted with G-Nut software for the first time in Southeast Europe for the hail suppression season May-September 2021. Evaluation of the real-time products will be performed using reprocessed GNSS tropospheric products.  The added value of the high temporal resolution of the GNSS tropospheric products will be investigated for selected storm cases.  This service will be unique in Europe and will serve as a prototype for real-time provision of GNSS products for storm nowcasting. 
Towards more effective visualisations in climate services: best practices and recommendations
Authors: Terrado Marta , Calvo Luz , Urquiza Diana , Octenjak Sara , Nicodemou Andria , Bojovic Dragana , Christel Isadora
Published: June 2021

The visual communication of climate information is one of the cornerstones of climate services. Characteristics that make a climate service self-explanatory rely on the type of representation used, e.g. interactive or static maps, charts or infographics, and the visual channels applied for the translation of multi-dimensional data, e.g. combination of colour, size, opacity, shapes and other characteristics.

Climate scientists have traditionally used predetermined types of visualisations to represent climate data, including maps (e.g. flood maps, heat maps, choropleth maps), line graphs, and probability distribution functions (PDFs). However, such a tradition neglects a plethora of stakeholders (e.g. businesses, policy makers, citizens) that are increasingly involved in climate adaptation and that are less familiar with the traditional ways of presenting these data.

Effective visualisations should achieve a balance between the amount of represented data, its robustness (i.e. the representation of scientific confidence and consensus) and saliency (i.e. the relevance of the information to user needs). Choices regarding the representation of uncertainty as well as the terminology and language used in visualisations, can significantly impact the way users interpret climate data. This calls for a standardised approach for the visualisation of climate services, which can benefit from best practices applied in other disciplines, such as user experience, visualisation design, graphic design and cognitive psychology.

We describe the main challenges for the visualisation of climate services identified during a visualisation workshop with representatives from 22 climate service projects involved in the Climateurope network, an EU-funded coordination and support action. In break-out group discussions, participants shared their experiences in the development of climate services visualisations and the lessons learned. Findings show that the chosen representation of uncertainty tends to be case specific and that, in general, there is a preference for interactive visualisations where information is gradually disclosed. Inter- and transdisciplinary approaches and aspects related to terminology and language, which are part of the service co-development, require further attention. The analysis of the obtained results provides a picture of the current practice of the climate services visualisation field in Europe and allows to identify recommendations for the development of the next generation of climate services.
Changes in heat-attributable deaths in Prague, Czech Republic, over 1982–2019
Authors: Urban Aleš , Fonseca-Rodríguez Osvaldo , Di Napoli Claudia , Plavcová Eva , Kyselý Jan
Published: June 2021

Studies projecting the impacts of future climate change on temperature-mortality relationships suggest increasing heat-related mortality in most regions of the world. On the contrary, a reduced risk of heat-related mortality has been observed in many countries over the last decades, suggesting a positive effect of technological development and improved health care systems. However, most of the studies show that the decline in vulnerability of populations to heat has abated in the early 2000s and further decreasing trend is unlikely.

In this study, we analysed temperature-mortality relationships in Prague, Czech Republic during 1982–2019. The study was restricted to five warmest months (May–September). To investigate possible changes in the temperature–mortality relationship, the study period was divided in four decades (1980s to 2010s). Conditional Poisson Regression coupled with the Distributed Lag Non-Linear Model (DLNM) was run separately in each decade, to derive decade-specific temperature–mortality associations. A stratum indicator variable composed of year, month, and day of the week was used to control for long-term, seasonal trends and weekly effects. The DLNM approach was applied in order to analyse delayed effects of temperature on mortality. The attributable number of deaths (AD) and the attributable fraction (AF %) of total May–September deaths on hot days was calculated from the model’s outputs, separately for each decade. Hot days were defined as days with daily mean temperature larger than the 95th percentile of the decade-specific May–September distribution.

We observed a quadratic trend shape in the number of deaths attributable to heat; maximum in the 2010s and minimum in the 1990s. The total number of heat-attributable deaths increased from ≈500 to almost 900 per decade between the 1980s and the 2010s, which corresponds to the fraction of 0.90 and 1.75 %, respectively, of the total number of deaths in a warm season.
Secure and Accurate Road Weather Services - The Belgian SARWS project
Authors: Watelet Sylvain , Van den Bergh Joris , Reyniers Maarten , Casteels Wim , Bogaerts Toon , Mercelis Siegfried , Coopman Tom , Thoen Chris , Hellinckx Peter
Published: June 2021

For the generation of accurate warnings for dangerous road conditions, road weather models typically depend on observations from road weather stations (RWS) at fixed locations along roads and highways. Observations at higher resolution in space and time have the potential to provide more localized, real-time weather warnings. The rise of connected vehicles with onboard sensing capabilities opens up exciting new opportunities in this field. For this purpose, a heterogeneous group of industrial stakeholders and researchers consisting of more than thirty partners from seven countries including Belgium, initiated the CELTIC-NEXT project "Secure and Accurate Road Weather Services" (SARWS). The goal of SARWS is to provide real-time weather services by expanding observational data from traditional RWS sources with data from large-scale vehicle fleets. The Belgian consortium consists of Verhaert New Products & Services, Be-Mobile, Inuits, bpost, imec - IDLab (University of Antwerp) and the Royal Meteorological Institute of Belgium (RMI). Within the Belgian consortium, the focus is on the use of vehicle data to enable real-time warning services for potentially dangerous local weather and road surface conditions. The vehicle fleet consists of cars of the Belgian Post Group (bpost) in the region around Antwerp, and will consist of 15 cars by the end of summer 2021. Data on vehicle dynamics, such as wheel speed, are gathered from the vehicle's CAN bus, while an additional installed sensor box collects air temperature, relative humidity and road surface temperature observations. Data on wipers and fog light activation, and camera images are also collected.

We present the Belgian SARWS setup, data flow, and the developed data distribution platform. We discuss validation results for 2021, comparing car sensor observations to close RWS and weather stations, focusing mainly on air temperature, humidity and road surface temperature, and show the need for calibration and bias correction. We also demonstrate an experimental version of the RMI road weather model that provides short-term road weather forecasts for 50-meter road segments, using car sensor data for initialization, and compare with road weather forecasts at nearby station locations. We also demonstrate machine learning approaches that are explored to detect weather information from the vehicle dynamics.
New flexible retrieval for gusts and mean winds from Doppler wind lidars, tested for various scanning configurations
Authors: Steinheuer Julian , Detring Carola , Beyrich Frank , Löhnert Ulrich , Friederichs Petra , Fiedler Stephanie
Published: June 2021

In the Field Experiment on Sub-Mesoscale Spatio-Temporal Variability in Lindenberg (FESSTVaL, www.fesstval.de) various phenomena in the atmospheric boundary layer are investigated. One goal is to detect wind gusts from measurements of a Doppler wind lidar (DWL). DWL’s allow the determination of wind vector profiles with a high vertical resolution (∼ 30 m) and therefore are an attractive alternative to metorological towers.

However, obtaining wind gusts from DWL measurements is not trivial because a monostatic lidar provides only a radial velocity, i.e., only one component of a three-dimensional vector per individual beam. Measurements in at least three linearly independent directions are therefore necessary to derive the wind vector. These must be performed sequentially, which prolongs the time interval for determining the wind vector and therefore limits the time resolution of the derived wind vector. In order to retrieve wind gusts, wind maxima of a few seconds, one needs to operate the instrument in a quick scanning mode. In this presentation, we show results from different scanning modes and discuss the method for retrieving wind gusts. We tested various configurations with respect to their ability to detect gusts and mean winds at the Boundary Layer Field Site in Falkenberg in autumn 2019. The DWL configurations that measure different lines-of-sight with rapid temporal repetitions have a lower signal-to-noise ratio (SNR) but the highest chance of detecting gusts.

We have developed a new retrieval method that skips prior SNR filtering and instead iteratively removes a fixed number of measurements that do not match a least-squares-fit. The least-squares-fit is then recalculated on the reduced set of measurements, and if necessary, this step is repeated. With appropriate retrieval steps and iteration criteria, our results suggest that prior filtering can be omitted.

We present the results of our new retrieval for eight different DWL configurations consisting of double-beam swinging, step-stare modes, and continuous-scanning modes. The evaluation is done by a comparison of the minimum, maximum and mean wind speed at 90 m a.g.l. against the reference measurements of a sonic anemometer that is located nearby. Ongoing work is addressing further comparison of our retrieved wind variables with unmanned aerial vehicles from the FESSTVaL campaign in summer 2020.
Seasonal variability of trends in regional hot and warm temperature extremes in Europe
Authors: Sulikowska Agnieszka , Wypych Agnieszka , Ustrnul Zbigniew
Published: June 2021

Extremely hot or warm weather over the course of the year may have significant impacts on many aspects of human life, economy, and the natural environment. Until now a comprehensive assessment of changes of extreme heat or warm events in Europe was hindered by the number of metrics employed, time periods examined, and most studies being conducted in the summer season only. This study was designed to systematically investigate long-term trends in the number of extremely hot or warm days in Europe over the course of the year, with a special focus on their spatial extent and intensity. Here, an extreme temperature event (ETE) is defined as a day with an unusually high temperature for a given location and season, even if such a temperature would not be considered extremely high in an absolute sense.

The research was conducted in five spatial domains representing different regions of Europe that together cover a large portion of the continent. The period from spring 1950 to winter 2019/2020 was considered using E-OBS gridded dataset v.21.0e. ETEs were analysed from a spatial perspective and were defined as days when the maximum air temperature exceeds the local percentile-based threshold across at least 10% of the area of the given domain. The severity of each ETE was assessed using the Extremity Index (EI) that combines information on the intensity and spatial range of an event. Given that EI is based on relative measures, it enables the comparison of results obtained in different geographic areas and seasons of the year. The main focus of the analysis was on the evaluation of seasonal EI trends and variability, as well as trends of frequency, spatial range, and intensity of ETEs in five spatial domains.

Climate warming in Europe during the studied 70-year period was accompanied by an increase in the frequency and severity of ETEs expressed in terms of their increasing intensity and spatial range. Yet, as our study demonstrates, every spatial domain is characterized by its own event pattern, and trends across Europe strongly vary geographically and seasonally. Our study highlights that examined trends of temperature extremes are accelerating and in the last 40 years the rate of change has been even more than three times greater than in the entire study period. The greatest changes were noted for summer season in Central Europe and Eastern Europe for the most recent 40-year period.
Sources of predictability over the Mediterranean at seasonal time-scale: building up an empirical forecasting model
Authors: Rodríguez-Guisado Esteban , Rodríguez-Camino Ernesto
Published: June 2021

Although most operational seasonal forecasting systems are based on dynamical models, empirical forecasting systems, built on statistical relationships between present and future at seasonal time horizons conditions of the climate system, provide a feasible and realistic alternative and a source of supplementary information. Here, a new empirical model based on partial least squares regression is presented. Originally designed as a flexible tool, the model can be run with many configurations including different predictands, resolutions, leads and aggregation times. To be able of producing forecast for any selected configuration, the model automatically selects predictors from an initial pool, containing global climate indices and specific predictors for the Mediterranean region unveiled in the frame of the MEDSCOPE project. Additionally, the model explores spatial fields, generating time series based on spatial averages of areas well correlated with the predictand. These time series are added to the initial pool of candidate predictors.  We present here results from a configuration producing probabilistic forecasts of seasonal (3 month averages) temperature and precipitation, their verification and comparison against a selection of state-of-the-art seasonal forecast systems based on dynamical models in a hindcast period (1994-2015). The model is able to produce spatially coherent anomaly patterns, and reach levels of skill comparable to those based on dynamical models. As predictors can be easily removed or incorporated, the model can provide information on the impact of a particular predictor on skill, so it can be used to help in the search and understanding of new sources of predictability. Evaluation of soil moisture impact on summer temperature predictability is shown as an example
Impact of SST on heavy rainfall events on eastern Adriatic during SOP1 of HyMeX
Authors: Tudor Martina , Janeković Ivica , Ivatek-Šahdan Stjepan
Published: June 2021

The season of late summer and autumn is favourable for intensive precipitation events (IPE) in the central Mediterranean. During that period the sea surface is warm and contributes to warming and moistening of the lowest portion of the atmosphere, particularly the planetary boundary layer (PBL). Adriatic sea is surrounded by mountains and the area often receives substantial amounts of precipitation in short time (24 hours). The IPEs are a consequence of convection triggered by topography acting on the southerly flow that has brought the unstable air to the coastline. Improvement in prediction of high impact weather events is one of the goals of The Hydrological cycle in the Mediterranean eXperiment (HyMeX). This study examines how precipitation patterns change in response to different SST forcing. We focus on the IPEs that occurred on the eastern Adriatic coast during the first HyMeX Special observing period (SOP1, 6 September to 5 November 2012). The operational forecast model ALADIN uses the same SST as the global meteorological model (ARPEGE from Meteo France), as well as the forecast lateral boundary conditions (LBCs). First we assess the SST used by the operational atmospheric model ALADIN and compare it to the insitu measurements, ROMS ocean model, OSTIA and MUR analyses. Results of this assessment show that SST in the eastern Adriatic was overestimated by up to 10 K during HyMeX SOP1 period. Then we examine the sensitivity of 8 km and 2 km resolution forecasts of IPEs to the changes in the SST during whole SOP1 with special attention to the intensive precipitation event in Rijeka. Forecast runs in both resolutions are performed for the whole SOP1 using different SST fields prescribed at initial time and kept constant during the model forecast. Categorical verification of 24h accumulated precipitation did not show substantial improvement in verification scores when more realistic SST was used. Furthermore, the results show that the impact of introducing improved SST in the analysis on the precipitation forecast varies for different cases. There is generally a larger sensitivity to the SST in high resolution than in the lower one, although the forecast period of the latter is longer.
Exploitation of GNSS tropospheric gradients for severe weather Monitoring And Prediction (EGMAP): project overview and status
Authors: Zus Florian , Dick Galina , Wickert Jens
Published: June 2021

Global Navigation Satellite Systems (GNSS) have revolutionized positioning, navigation, and timing, becoming a common part of our everyday life.  A geophysical key application is atmospheric water vapor monitoring using GNSS ground station data. GNSS water vapor data, derived from regional ground networks hereby close gaps in the established meteorological observing systems. No other observing system provides data with such high temporal and spatial resolution. The data from European GNSS networks are therefore already widely operationally used to improve regional weather forecasts in several countries. However, the impact of the currently provided data products to the forecast systems is still limited due to the limited atmospheric information content, which is provided by the currently used Zenith Total Delay (ZTD) data.

In this talk we introduce the new project EGMAP (Exploitation of GNSS tropospheric gradients for severe weather Monitoring And Prediction). This project will pioneer the development and usage of next generation data products; tropospheric gradients. The new data products, developed and provided within the project, are expected to improve the impact of the currently provided GNSS data to weather forecast systems. The main innovations, which will be addressed by the project are: (1) Developments to provide high quality ZTDs and tropospheric gradients in near-real-time for the German SAPOS network; (2) Developments to make use of ZTDs and tropospheric gradients in numerical weather prediction, i.e., implement operators in the variational/ensemble data assimilation system of the Weather Research and Forecasting (WRF) model; (3) Impact studies with the state of the art numerical weather model. In this talk we provide an overview and the current status of the project.
Mapping the development and implementation of climate services in the Greater Horn of Africa
Authors: Mittal Neha , Bruno Soares Marta , Venning Mathias , Bremer Scott
Published: June 2021

A plethora of programmes and projects focus on strengthening climate services in the Greater Horn of Africa (GHA); a region of great economic importance in a changing Africa. However, knowledge of how key elements of climate services development and implementation at different stages of the process are addressed in these initiatives is very limited. To overcome this gap, we pursued a systematic review of existing literature under the auspices of the H2020 CONFER project. We scope the academic and grey literature by focusing on initiatives that empirically demonstrate the value added at different stages of climate service development and implementation such as data generation, post-processing, tailoring, and re-packaging of information for communication. Our analysis takes stock of the evolution of climate services in GHA and provides a synoptic overview of the current landscape of climate services at seasonal and sub-seasonal time scales, and across sectors and geographical scales in the region. It highlights the current emphasis on the deployment of climate services in specific countries such as Kenya, Ethiopia and Uganda, while a strong thematic focus on services tailored for the agriculture sector. Our analysis also shows that while there is an increasing effort in tailoring and effectively communicating climate services through greater knowledge co-production, aspects such as accessibility, timeliness, and accuracy of climate services information remain key barriers to the uptake and use of such services. Finally, we identify opportunities for further improvement to the underlying processes of climate services development and implementation as well as future research and policy directions in this region of global importance.
Agroclimatic atlas - prototype
Authors: Jedlička Karel , Hájek Pavel , Andrš Tomáš , Čerba Otakar , Valeš Jiří , Zadražil František
Published: June 2021

Our contribution presents a prototype of Agroclimatic atlas - a web map application, presenting agroclimatic factors: Frost-free period, Water balance, Total precipitation, Total solar radiation, Last date with soil temperature above 10 °C for nitrogen application, Number of days with growing temperatures for a crop, Number of days with optimal growing temperatures for a crop HSU - Heat stress units for a crop, The factors are calculated based on algorithms described in Calculation of Agro-Climatic Factors from Global Climatic Data (Jedlička et al. 2021, doi:  10.3390/app11031245).

The agroclimatic atlas application aims to provide a comprehensive overview of agriculture-related climatic characteristics of an area of interest in a time retrospective.  The application can be used by both an individual farmer or a precision farming expert exploring a wider area.

The principal source of climatic variables (such as temperature, rainfall, evaporation, runoff, and solar radiation) used in the atlas is the ERA5-Land dataset (available as the Copernicus Climate Change Service (C3S) at its Climate Date Store). 

The contemporary version of the Agroclimatic Atlas application is accessible from here https://www.mdpi.com/2076-3417/11/3/1245#. This version is in Czech only and portrays data from Czechia 10 years backward. However, the application is under ongoing development driven by the H2020 projects Stargate, Sieusoil, and Smartagrihubs. Therefore a newer version will be presented at the conference. The first design concepts can be seen in the figure below.

Figure 1. - Mockup of Agroclimatic atlas application, accessible from https://xd.adobe.com/view/65199b72-db2f-420a-aee2-bc90dc83aaea-304a/
Interactive access to climate data from Germany
Authors: Kratzenstein Frank , Kaspar Frank
Published: June 2021

In recent years, the DWD has significantly expanded free access to its climate observations. A first step was a simple FTP site with the possibility to download archives with different data categories, e.g. national and international station-based meteorological data, derived parameters, gridded products, and special categories like phenological data. The data are based on the DWD's observation systems for Germany as well as on the DWD's international activities.

Based on the consistent implementation of OGC standards, an interactive and user-friendly access to the data has been created with the development of the DWD climate portal.

In addition to browsing, previewing, running basic analysis and downloading the data, the available OGC services enable users to set up their own services on the DWD data. Along with the free and extended access to the data and services, the users' demands on the availability, quality, and detail of the metadata also increased significantly. Maintaining and linking metadata to the opendata and services remains a challenge. However, INSPIRE and WIGOS are paving the way to a unified solution and overcoming the problems.

Another challenging requirement was to provide interactive access to long time series from gridded products to the users. To accomplish this, we have moved away from a previously file-based approach to storing the raster data as a georaster in an Oracle database. This design allows us a combined analysis of raster and station data not only in the climate data portal but also in the central climate database.

The presentation will provide a technical and functional overview of the DWD climate data portal.
Temporal and spatial variability of spring precipitation in the Czech Republic
Authors: Pokorná Lucie , Rulfová Zuzana , Kuchyňková Jindřiška
Published: June 2021

Spring precipitation is a key factor determining vegetation growth and strongly affecting soil moisture in spring and summer. Whilst winter precipitation and snow cover as well as summer convective precipitation are frequently researched, only little attention is given to spring precipitation and its temporal variation.

In this contribution we focus on March, April and May daily precipitation at 72 station in the Czechia over 1980-2016. The temporal and spatial variability of precipitation totals, number of wet and dry days and periods is discussed in detail. The altitudes of the stations range from 158 to 1302 m a. s. l. and the mean spring precipitation totals vary from 104 to 327 mm. The highest mean number of dry days (more than 70) is reached at four stations elevated from 241 to 474 m a. s. l.  whilst the lowest number (less than 54) occurs at two stations above 1 km but also at one station in 740 m a. s. l. The duration of severe dry spells is not linked to altitude, either. The longest 43-days dry spell occurred at almost all stations up to 880 m a. s. l. No common trend of precipitation totals as well as dry or wet days exists over the area in studied period.

The analysis indicate that spring precipitation characteristics are rather related to surrounding and position of station with regard to prevailing flow direction than altitude. The hierarchical cluster analysis based on seasonal and monthly precipitation totals, number of wet and dry days, number and duration of wet and dry periods, trends of wet and dry days number and several other characteristics separated individual stations into 4 groups. Except the groups of lowland dry and mountain wet stations, the group associated low elevated wet stations on windward mountain sides and the other one with high elevated dry stations were created.
Assessment of the climatic resource potential of solar radiation as a renewable energy source.
Authors: Zadvornykh Valentina , Oksana Trofimova
Published: June 2021

Due to climate change and the need to reduce greenhouse gas emissions, the trend in the development of global energy is aimed at increasing the share of the introduction of environmentally friendly renewable energy sources. This contributes to ensuring sustainable heat and energy supply to the population and production in the zones of decentralized energy supply, which increases the energy security of the regions and the country as a whole. In addition, the introduction of renewable energy sources is seen as a key tool in adapting to climate change.

The report examines the climatic conditions of the territory of the Russian Federation in order to assess the prospects for the practical use of solar energy.

The basis of solar energy resources in a given point or region is the duration of the solar radiation and the amount of direct and total solar radiation entering the horizontal surface.

The research uses data from observations of the actinometric network of the Russian Federation. In areas where there are no observations of solar radiation, either indirect methods of calculation or access to open and accessible databases were used.

Based on the analysis of the complex of climatic characteristics selected for zoning, 10 radiation-homogeneous regions were identified on the territory of Russia, which were ranked according to the priority of the solar potential. The reliability of the boundaries of the selected regions was confirmed by comparing the spatial and temporal variability of the main radiation indicators. The selected regions can be divided into five groups: the most promising, promising, less promising, unpromising and unpromising.

The most promising regions are the regions south of 50N. The first region occupies the Primorsky Krai, the southern part of the Khabarovsk Region and the Amur Region, and the south-east of Transbaikalia. The second region is the southern part of the European territory of Russia.

The amount of total solar radiation entering the earth's surface for a year in these regions is 1330-1390 kWh/m2, which is the maximum for the territory of Russia. Both regions are characterized by a long duration of sunshine, especially for the first region (about 2400 hours per year). The high solar potential of these regions is indicated by the fact that in the period from April to September, the probability of a day favorable for the use of solar energy is 60%, in July it reaches 90%.

In less promising regions of Eastern Siberia, where there is a large influx of solar radiation in the winter and spring periods, it is advisable to use photovoltaic modules to generate electricity for autonomous consumers in areas of decentralized energy supply.