bluuebunny/crossref_metadata_2025 huggingface dataset:
Long-term Tropospheric ozone column data record from GOME to TROPOMI using CCD algorithm
Authors: Heue Klaus-Peter , Loyola Diego , Coldewey-Egbers Melanie , Lerot Christophe , van Roozendael Michel
Published: June 2022

long-term tropospheric ozone time series has been generated for the tropical band (20°S to 20°N) based on convective cloud differential algorithm (CCD). Tropical tropospheric ozone columns were retrieved from several European sensors starting with observations by GOME in 1995 and including data from SCIAMACHY, OMI, GOME-2A and GOME-2B. The algorithm was developed within the ESA CCI project has been updated regularly.The time series has now been extended by DLR with data from GOME-2C and TROPOMI and now encompasses 25 years. The tropospheric ozone retrieval for all data sets is based on the total columns retrieved with the GODFIT algorithm and associated cloud products.

There are however some differences between the different tropospheric columns from the different sensors which have to be corrected for. The operational TROPOMI tropical tropospheric ozone data have a much higher spatial and temporal resolution. Therefore the data are averaged to 1.25° x 2.5° and 1 month to match the resolution of the other sensors. For the CCD time series, we used SCIAMACHY data as reference and fitted an offset and a trend correction to the data of the other sensors. For the sensors that do not have a temporal overlap with SCIAMACHY we used the harmonized version of GOME-2A and OMI as reference. We estimated the trend based on the merged long-term time series. For the tropics an overall trend of +0.9 DU/decade was found in the data set until 2021, varying locally between -0.5 and 1.8 DU/decade. We also looked for seasonal trends, although the uncertainty in the trend analysis increases. The strongest increase in tropospheric ozone was found for March-April-May and the smallest trends occurred during Oct-Dec. Besides the long term trend the data might also be used to investigate the influence of single events like extreme bio mass burning seasons.
Bringing ensembles to the heart of Met Office operations
Authors: Mylne Ken , Roberts Nigel
Published: June 2022

“2022 marks the 30th anniversary of the first operational ensemble forecasts at ECMWF and NCEP (USA), and also 10 years since the Met Office started running the convective-scale MOGREPS-UK ensemble for the UK. Ensembles are now central to the NWP strategies of many centres. There is extensive scientific evidence for the greater skill of probabilistic forecasts based on ensembles and the Met Office plans for its new supercomputer have high resolution ensembles as the core operational systems, with higher resolution deterministic models used for experimental purposes. We have already changed our Key Performance Indicators for forecast accuracy to measure ensemble performance. The IMPROVER post-processing system (see presentation by Moseley and Mylne) is designed to fully exploit convective-scale ensembles and provides a seamless blended probabilistic supply of forecast data to underpin multiple products and services.  Despite many years development and the scientific evidence for greater skill, fully exploiting the benefits of ensembles remains a major challenge, both for operational meteorologists (forecasters) and in products and services for the public and professional users. There have been some notable successes, but many forecasts remain highly deterministic for understandable reasons. To address this, alongside our ensemble-driven NWP approach, the Met Office is seeking to make much wider use of ensembles and probabilistic forecasts throughout its operations.  This will involve close collaboration between scientists, operational staff and services, for example in operational testbed experiments where scientists and forecasters work alongside each other to test new capabilities and examine potentially new working practices. This talk will review early progress in this approach and plans for future research and development.”
Evaluation of the interpolation scheme and order of calculation for extreme value indices based on the updated GPCC Full Data Daily v2022
Authors: Rustemeier Elke , Ziese Markus , Schneider Udo , Finger Peter , Hänsel Stephanie
Published: June 2022

Precipitation is a globally fundamental parameter that has an influence in many fields and determines the hydro-meteorological cycle.

This year, the Global Precipitation Climatology Centre (GPCC) updates the daily in-situ precipitation product Full Data Daily (FDD). The FDD is a reliable raster product, based on global land-surface precipitation totals. The database includes data provided by national meteorological and hydrological services, regional and global data collections as well as WMO GTS-data. The FDD is characterized in particular by the detailed quality control of the input data.

The new FDD v2022 covers a period of 39 years from Jan 1982 - Dec 2020 with daily temporal resolution and 1° spatial resolution based on the modified SPHEREMAP interpolation scheme.

This paper introduces a test study on the methodological decisions for FDD and its effects on the extreme value indices (ETCCDI). For this purpose, we tested different methods for a selected time period in conjunction with the update of the GPCC FDD v2022. The study aims to analyze 1) the impact of the interpolation method on the ETCCDI. We recalculated a sub-period of FDD using modified Spheremap or krigging as interpolation scheme. Two two global data sets are generated each with a different interpolation method. 2) We examine the influence of the computing order on the ETCCDI (1°x1° resolution) by a) calculating the extreme value indices ETCCDI station based first and interpolating later and b) interpolating first and then calculating the ETCCDI. An existing difference is expected, as these are both answers to different questions. However, the quantification of this difference is intended to answer frequently asked questions regarding the data set.

This gives us the opportunity to estimate and quantify the influence of the methodical decisions on the ETCCDI and to improve the understanding of the FDD.
Comparative studies of model perturbations for ICON-D2-EPS.
Authors: Marsigli Chiara , Gebhardt Christoph
Published: June 2022

Convection-permitting ensemble forecasting is well established as a tool to support the prediction of severe weather and high-impact weather in several meteorological centres. At the Deutscher Wetterdienst, ICON-D2-EPS is the limited-area high-resolution component of the ICON modeling system, running as an ensemble of 20 members at 2 km horizontal resolution over Germany and surrounding areas. Several products to support operational forecasting and warning issuing are regularly issued. The perturbed initial conditions are provided by the km-scale ensemble data assimilation system KENDA, run at the same resolution, assimilating a wide range of observations, included radar-derived radar volumes. Boundary conditions are provided by ICON-EPS, the global ensemble with a refinement at 20 km on Europe. Simple model perturbations are applied by Parameter Perturbation, where a set of parameters of the physics parametrisation schemes are perturbed with respect to the default value. In this work, the effect of the model perturbation is studied, by assessing how it contributes to generate diversity between the members for case studies, as compared to a standard operational verification carried out on periods, and how it influences the ensemble spread, in addition to the initial and boundary condition perturbations. The extent to which the ensemble spread is able to represent forecast uncertainty is discussed for near-surface weather parameters. The effect of increasing the amplitude of the parameter perturbation is also addressed. Finally, the impact of the parameter perturbation is discussed in comparison with physically-based perturbation methods which have been implmented and tested in ICON thanks to collaborative works.
Investigating the drivers for crop yield changes during heatwaves and droughts
Authors: Valmassoi Arianna , Esters Leonie T. , Friederichs Petra , Keller Jan D.
Published: June 2022

The relationship between plant yield, food production, and climate conditions is becoming crucial, especially when climate extremes are considered.
Atmospheric numerical models and reanalyses generate valuable weather and climate information. They have been evaluated and used in the past to assess extreme climate events representation. However, they can also be used to force models from other research fields, such as plant production model used in agriculture. This approach allows to create scenarios and/or ensembles to assess the compound uncertainty deriving from the climatic and plant production aspect, especially under hydrometeorological extremes such as heatwaves and droughts.

In this work, we assess the response of the plant yield computed using the AquaCrop model to a prolonged heatwave and drought condition. We focus on the Po Valley, norther Italy, for the summer 2015 period due to its high dependency of irrigation.
The two atmospheric products used are a set of free simulations at 3km resolution using the Weather Research and Forecasting (WRF-ARW) model, which are used as scenarios for the irrigation water requirements, and the 6-km COSMO-REA6 reanalysis, which provides the best reference dataset within the atmospheric reanalyses. The AquaCrop model is forced only with the cropland gridpoints in the Po Valley, and we test the sensitivity of the crop model to parameters such as initial soil moisture, irrigation management, soil, and crop type. Further, we are going to investigate the relationship between plant state and CO2, by using in-situ observations.

Preliminary results show that for wheat, the yield response depends on the meteorological input data, the COSMO-REA6 yields are higher than the ones obtained with WRF-ARW, and the clay content in the soil.  Further, classical and machine learning clustering techniques in parameter space are used to understand the dependency of the yield.
The association between precipitation and the spread of shiga toxin-producing Escherichia coli infection in Lombardy, North of Italy
Authors: Novaro Andrea , Baronetti Alice , Ardissino Gianluigi , Acquaotta Fiorella
Published: June 2022

The HUS (Hemolytic-Uremic Syndrome) is a disease that mainly affects children, it is a complication of an infection by shiga toxin produced by Eschirichia-coli (STEC) infection. It shows a seasonal nature with lower incidence in the cold period, it is endemic in central Europe and North America but fairly rare. In the high incidence period, from April to October, cases appear in time related clusters, usually not epidemic, in fact they are unrelated for spatially distance or caused by different serotypes. It suggests the role of the weather conditions  as a hot gun.  Attempting to predict by weather conditions the temporal and spatial distribution of the illness could prevent its severity and reduce the damage like kidney failure, disability or worse. 

Past climate study (Acquaotta, et al. 2017) suggests a correlation between the heat waves and infection clusters, for example three or more very hot consecutive days increase the risk of disease. Also the epidemiological study of Biggeri and his Collaborations (personal communication) have analyzed the spatial aggregation of illness in Lombardy (North Italian region) to show the areas with a higher or lower risk of infection. 

In this research the study area is the Lombardy plain with 249 selected cases from 2010 to 2020, 11 years. Two are the main goals: the first is to perform a climate characterisation of the high risk zone in Lombardy, highlighted by Biggeri and his Collaborations. The second is to identify the relationship between infection and extreme precipitation events. 

To analyse the climate peculiarities of Lombardy we used 88 weather series from ARPA (Regional Environmental Protection Agency) Lombardy with an homogeneous distribution in the area. On the daily weather series a quality control was carried out to identify some error in the data. Then a hierarchical cluster analysis by mean of the Ward.d2 method was applied to identify the climate areas. On the daily data, climate indices created by the Expert Team on Climate Change Detection and Indices (ETCCDI) were calculated: daily precipitation intensity (SDII), consecutive dry days (CDD), very wet days (R95p) and standard precipitation index (SPI) to show the drought period.

The distributed lag non-linear model (DLNM) was used to examine the relationship between precipitation series and daily illness during January 2010 to December 2020 (Bai et al. 2014;) with a maximum lag of 30 days in order to ensure a greater coverage. The infection dataset was also compared with four climate indices in order to highlight. The linear regression between indices and infections was then calculated. The trends were computed using the TheilSen approach (TSA) (Sen 1968).
Open-data licenses for meteorological datasets - a detailled overview
Authors: Baldeck Nicolas
Published: June 2022

The license is the fundamental contract of open-data. It defines the rights and obligations of both the producers and the re-users. But, more than that, a carefully chosen license can be a powerful tool to develop and enrich a dataset.

Too often, the choice of the license is neglected. A wrong, but really popular approach is  : "We need a license. Creative commons is trendy, let's choose that one!"

We propose a small workshop around the matter of licensing, intended both for producers, re-users and policy-makers. This intervention is suitable for the curious and novice as well as for legal experts.

First, we will define what a license is and what makes a license "open", according to the OKF Open Definition.

Then, we will explain the most common concepts and clauses of open-data licenses.

We will have a quick overview of popular open-licenses and their characteristics, applied to software, hardware, creative contents and datasets (GPL, LGPL, MIT, BSD, CC-BY, CC-BY-SA, ODbL and more...).

We will analyze licenses used for institutional meteorological data, such as Germany's GeoNutzV, France's Licence Ouverte or even USA's public domain notice.

We will study how a license can be a powerful tool to make the product improve by itself, with the examples of Wikipedia, OpenStreetMap, Arduino and Blitzortung.

And finally, we will compile and sort all those licenses, concepts and clauses to focus on our field : dissemination and build-up of open meteorological datasets. The conclusion is that a lot of common licenses aren't really fitted for our need, and the selection must be wise. And the second conclusion is that public institutions owns valuable data. And by using the right clauses and right licensing schemes those datasets can be leveraged to be even more valuable for all users in order to push private sector to contribute to them opening their own datasets.
Model study on the influence of aerosol-cloud interaction processes on precipitation in urban areas
Authors: Bär Friederike , Petrik Ronny , Heinold Bernd , Quante Markus
Published: June 2022

Urban influence on cloud and precipitation processes can appear via two mechanisms: (I) roughness and thermal effects induced by the urban morphology influence dynamical processes and (II) high urban particle emissions effect cloud microphysical processes. Changes in dynamics and cloud microphysics can trigger large-scale changes in storm morphology and precipitation patterns and thus play a significant role in precipitation formation in the vicinity of urban areas. The modifications by the urban area can lead to intensification, delay, or even suppression of convective precipitation. The strength and nature of urban influence is dependent on the prevailing synoptic conditions.

To address the issues of the aerosol-cloud-precipitation interactions in the presence of a city, we use the COSMO-DCEP-MUSCAT model system. The model system is composed of the regional atmospheric model COSMO coupled with the multi-scale chemistry-aerosol transport model MUSCAT and the urban parameterization DCEP - an extension of Martilli's BEP scheme. We apply a 3-way nesting strategy for the simulations, where the highest resolution area is centered over the Leipzig metropolitan area with a 500m grid spacing.

Within this paper, we present several sensitivity studies in order to investigate the influence of urban areas on precipitation. For this purpose, two different synoptic background conditions, based on real weather events, were simulated with the model system: a convergence line and a system moving slowly over the city. This allows us to estimate the influence of the city on the weather system. In addition, a variation of urban emissions shows how sensitive the simulated precipitation is to changing aerosol concentrations.

Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany‘s Excellence Strategy – EXC 2037 'CLICCS - Climate, Climatic Change, and Society' – Project Number: 390683824
Open data foster innovation and facilitate public-private engagement to create socio-economic value
Authors: Ramshorn Christoph , Müller Mathias D. , Gutbrod Karl G.
Published: June 2022

The international meteorological community is on a journey to embrace open data policies. Using our experiences from meteoblue, we give examples of how open data policies have fostered innovation, created private sector capabilities, and can “jump-start” meteorological value chains. These examples are typical and variations of them can be found in many companies and countries that implement effective open data policies.

meteoblue as a company wouldn't have been created and might not be sustainable without the benefit of open data. Already prior to forming the company the availability of WRF, GDAS assimilations, and GFS global model output enabled an initial NWP chain. Over time, the evolving processing chain provided an improved, easily accessible forecast for central Europe, in particular the Alps. Additional accuracy was achieved with running models at higher resolutions and tuning them. The development also led to an automatic post-processing and web-based visualisation chain with numerous innovative diagrams and maps.

The increased availability of open NWP data from national weather models and their further improvement allowed meteoblue to automatically evaluate an increasing number of forecasts for a given location, compare them to weather station data, compute a consensus forecast, and quantify the uncertainties of local forecasts. All information could be made available to end users in straightforward diagrams that in part of the world are used by illiterate farmers.

Availability of open weather station data allowed meteoblue to devise learning methods and further improve local forecast accuracy. Verification results are publicly available and allow users to assess how valuable the forecasts can be to them. Open radar data form the basis of a high accuracy nowcast and short term rain forecasts.

With its multi-model capability the processing chain is open to ingest additional models as well as precipitation radar and weather station data, giving their providers (and their users) instant access to all meteoblue post-processing capabilities. The multi-model processing chain is highly resilient against individual model or other data feed failures. Together, these capabilities allow partners, e.g. from small national weather services to both provide immediate access to their local models and service their communities and customers without interruptions. Therefore, meteorological value chains can be started up very quickly.
Risk assessment of frost occurrence in a changing climate
Authors: Žnidaršič Zala , Sušnik Andreja , Gregorič Gregor , Pogačar Tjaša
Published: June 2022

Frost events that occur after the beginning of the growing season pose great risk for fruit trees, especially after budburst. There have been attempts to model frost risk projections for the 21th century in Slovenia, however the results have shown no change in frost risk due to climate change, despite observations for years 2019 and 2020 showing otherwise. Therefore, an alternative climatological analysis of frost event probability is of great importance, especially in combination with phenological data from Slovenian fruit tree orchards and vineyards. An assessment of the risk of tardive frosts for a series of Slovenian fruit tree orchards and vineyards throughout the 21st century was made from temperature data of climate model projections, specifically from regionally downscaled projections data. The analysis bases on two pheonological models for calculation of budburst - a classic GDD model and a two-phased BRIN model. Afterwards the approach consists of a comparison of the statistical probability of two events, the last frost event probability (day of the year) and a budburst event probability, as simulated by the different phenological models. We discovered that the probability of a tardive frost, i.e. frost events occurring after grapevine budburst, in Slovenian grapevine varieties varies throughout the 20th and 21th century, for example, the probability of a tardive frost increases towards the end of the 21th century for Chardonnay (Eastern Slovenia with continental climate) and Riesling (Northeastern Slovenia with continental climate) varieties but decreases for Merlot (Southwestern Slovenia with Mediterranean climate). An analysis of tardive frost projections for cherry, apple, olive and other fruit tree varieties grown in Slovenia is under way. Some preliminary results of the project will be represented.
Measuring Hailstone Trajectories with the HailSonde
Authors: Soderholm Joshua , Kumjian Matthew , Peterson Anders , Brook Jordan , Protat Alain
Published: June 2022

The size, shape and ground-impact location of each hailstone is characterised by its trajectory through the parent hailstorm. This trajectory determines whether the hailstone passes through regions of the storm that are more favorable for growth or even miss out entirely. Recent simulation-based studies have demonstrated the diversity of trajectories and how certain pathways exist in response to storm processes. Hail trajectories can also be simulated from radar observations, and this has been shown to significantly improve the accuracy of the estimated ground hail swath for case studies. Operational hail analysis techniques currently do not consider trajectories, leaving a degree of uncertainty when estimating ground impact.  The lack of robust observational datasets to verify trajectories is one factor that limits the transition of this new science into operations.

This talk will introduce an innovative approach to measuring trajectories within a hailstorm using hailstone-shaped probes called “HailSondes”. Improvements in low-energy radio, energy storage and electronics miniaturization are combined to make this new sensor possible, which, until recently, was the realm of fantasy for meteorologists. HailSonde measurements will provide critical validation for the practical application radar-derived trajectories for hailstorm analysis and nowcasting, supporting the transition to future hail services and benefiting a wide range of sectors from aviation, risk management, transport and public safety. The design challenges, simulations, prototype development and deployment of HailSondes within field experiments are discussed.
Interactive detection and visual analysis of 3-D fronts in NWP data
Authors: Beckert Andreas , Eisenstein Lea , Hewson Timothy , Oertel Annika , Craig George , Rautenhaus Marc
Published: June 2022

Atmospheric fronts are a widely used conceptual model in meteorology, most encountered as two-dimensional (2-D) front lines, e.g., on surface analysis charts. The three-dimensional (3-D) dynamical structure of fronts is commonly sketched in 3-D illustrations of idealized weather systems in atmospheric science textbooks. Only recently the feasibility of objective detection and visual analysis of “real” 3-D frontal structures within numerical weather prediction (NWP) data has been proposed, and such approaches are not yet widely known in the atmospheric community. In our work, we investigate the benefit of objective 3-D front analysis for case studies of atmospheric dynamics and forecasting. Our technique builds on a recent gradient-based detection approach, combined with modern 3-D interactive visual analysis techniques, all integrated into the open-source meteorological visualization framework Met.3D. Comparison of detected 3-D frontal structures with 2-D fronts from surface analysis charts of weather services show agreement and augment the surface charts by additional vertical information. In our presentation, we show case studies of extratropical cyclones and their frontal dynamics. Examples include joint interactive visual analysis of 3-D fronts and warm conveyor belt trajectories, and development of the 3-D frontal structure of the characteristic stages of a Shapiro-Keyser cyclone. We also demonstrate the benefit of our technique for comparative analysis of frontal dynamics in different numerical weather prediction model simulations, e.g., of different resolution and simulations with parameterised and permitted convection. We argue that the presented approach has large potential to be beneficial for complex studies of atmospheric dynamics and for operational weather forecasting.
Analysis of local-scale transport in a deep dry boundary layer over the Tibetan Plateau in boreal winter.
Authors: Juerg Schmidli , Quimbayo-Durate Julian , Rajput Neelam
Published: June 2022

The Tibetan Plateau (TiP) is the highest and largest plateau in the world. During the boreal winter and spring, a deep atmospheric boundary layer (up to 5 km above the plateau level) is frequently observed in the area. The ultimate goal of the present work is to assess the impact of small-scale topography and thermal circulations on the structure of the dry-season deep boundary layer over the TiP and on tracer exchange with the upper troposphere (UT). The deep boundary layer is responsible for the tracer transport from atmospheric boundary layer (ABL) to upper troposphere and lower stratosphere (UTLS). To this end, we performed a set of idealized large-eddy simulations (LES) resolving the turbulent transport from the surface to the UT using the CM1 model. The initial conditions for the first simulated dry case are based on a very deep boundary layer over TiP (at Gerze station) on 4 March 2008 documented in Chen et al. (2016). The high resolution LES performed for the winter case during boreal winter using the CM1 model. The simulations use a grid spacing of 50 m to ensure that the turbulence and thermal circulations are adequately resolved. Tracer transport and exchange with the UT are investigated using passive tracers released at different height levels. To assess the impact of small-scale topography, the plateau site simulated with flat terrain, idealized small-scale topography, and real topography. The results are generalized by a systematic investigation of the impact of turbulence and thermal circulations on boundary layer depth simulated in an idealized setup using the CM1 model.
Atmospheric parameters affecting SPectral solar IRradiance and solar Energy (ASPIRE)
Authors: Eleftheratos Kostas , Raptis Ioannis-Panagiotis , Kouklaki Dimitra , Kazadzis Stelios , Psiloglou Basil , Founda Dimitra , Kosmopoulos Panagiotis , Fountoulakis Ilias , Benetatos Charis , Gierens Klaus , Kazantzidis Andreas , Richter Andreas
Published: June 2022

The ASPIRE project aims to contribute to the scientific knowledge of interdisciplinary aspects that are related with solar radiation by investigating the effect of various atmospheric parameters such as clouds, aerosols, water vapor and absorbing trace gases to the Spectral Solar Irradiance (SSI) reaching the Earth’s surface. Such aspects deal with solar energy research and technology (e.g. Photovoltaic Systems, PV), impact on health (melanoma, skin cancer and Vitamin D efficiency), agriculture (photosynthetically active radiation, PAR, and crop production) and the complexity of the SSI determination through an atmosphere with various spectral absorbing, scattering and reflecting atmospheric constituents.

The project has four specific objectives: (a) To investigate the effect of atmospheric composition in different solar spectral regions, (b) To assess the impact of atmospheric composition on UV Index, Vitamin D and PAR, (c) To improve PV efficiency based on spectral solar data for various atmospheric composition cases, (d) To evaluate the performance of the Solar Energy Nowcasting SystEm (SENSE) using real solar spectra. The means to fulfil these objectives is a sophisticated atmospheric field experiment that has been held in the city of Athens, Greece, with a unique set of instrumentation and a synergistic approach on the retrieved datasets. Atmospheric composition and solar radiation related measurements and models are coordinated in ASPIRE in order to contribute to the following scientific advancements:

Accuracy of public awareness solar radiation related factors, such as erythemal (UV Index), eye cataract, DNA damage, Vitamin D production and agricultural (PAR) related indexes, could be essentially improved using the proposed measurements’ dataset.

PV efficiency will be assessed and improvements will be suggested, with the use of detailed spectral information that will be available. In addition, atmospheric variables that attenuate SSI will be linked with possible deviations of the PV output. ASPIRE will provide to PV module users and constructors the know-how to link individual atmospheric solar attenuators (e.g. aerosols and trace gases) to such deviations.

Solar energy nowcasting is a crucial aspect for national and private power transmission and distribution system operators worldwide. Detailed evaluation and improvement of such a developed system for Greece is an important aspect of the project, given the fact that the proposed SENSE system has been used by the national energy transmission operator.

 
Grain production and climate change in south-eastern Norway
Authors: Skaland Reidun Gangstø , Hanssen-Bauer Inger , Hygen Hans Olav
Published: June 2022

The extreme weather in 2017 and 2018 served as a wake-up call for the Norwegian agriculture sector. The growth season of 2017 was very wet, whereas 2018 was extraordinarily dry and warm. The weather had major consequences for yields. Many asked themselves how climate change will affect agriculture moving forward, and how we can maintain food production in the face of climate change? 

In a case study lead by Telemarkforskning and financed by Oslofondet, we investigated how climate change may impact the grain production in the Vestfold and Telemark counties, a main grain production area located in the south-eastern part of Norway. 

Climate indices were defined in collaboration with local farmers and calculated for historical and projected future climate change in Vestfold and Telemark. The results show that summer temperatures are rising, and that the growth season is expected to increase by about a month towards the middle of the century, compared to the period 1971–2000. The number of growth degree days increases, too. Precipitation predictions are more uncertain. Towards the middle of the century, a small increase in precipitation is expected in the summer half-year (April to September) in most of the area. However, in grain producing areas most models project reduced precipitation in the summer half-year. Comparing the periods April–May and August–September projections indicate that Spring/early Summer will be wetter, and the late Summer drier, moving forward. The number of dry days is projected to decrease in April–May and increase in August, and opposite for the number of wet days. No matter the development in precipitation, drought risk will increase in the future, because increased temperatures lead to increased evaporation.

Further analyses based on this study are needed to gain knowledge about future climate change and measures for a more climate robust food production within all productions and regions of the country. 
Evolution of the Copernicus Arctic Regional Reanalysis
Authors: Schyberg Harald , Yang Xiaohua , Støylen Eivind , Dahlgren Per , S. Madsen Marianne , Køltzow Morten , Olesen Martin
Published: June 2022

A 30-year Copernicus Arctic Regional Reanalysis (CARRA) for the period of 1991-2021 has recently been finalized within the framework of the Copernicus Climate Change Service (C3S), which is a service implemented by ECMWF on behalf of the European Commission. The reanalysis is performed on European Arctic regions covering Greenland, Iceland, Barents Sea, Svalbard and Northern Scandinavia, with a 2.5 km grid resolution reanalysis based on the HARMONIE-AROME NWP system.  A near-real time extension service of the reanalysis, CARRA-Timely Updates is now being set up with a targeted monthly update with a 2-month delay.  In addition, plans are under discussion to set up a system for the next generation of a CARRA reanalysis which will cover the whole pan-Arctic region with similar resolution for a somewhat further extended reanalysis time period. These efforts are led by MET Norway with the other Nordic and the French national meteorological services as partners. Due to the Arctic climate warming being on average faster than the rest of the globe, there is enhanced user focus on change processes in this area. Through the Copernicus Climate Data Store (CDS), the CARRA dataset is now available for the climate research community. In this presentation we will focus on how the data set verifies and compares to other data sets. We demonstrate how the Arctic reanalysis adds value versus the global reanalysis ERA5 both statistically and for special weather events. This value addition is obtained both by using a higher-resolution model and by using fine-scale regional input data not used in the global system. 

 
Parameterizing the impact of heterogeneous urban canopies on local climate using nudging
Authors: Cheng Ge , Grawe David , Schlünzen K. Heinke
Published: June 2022

A multi-layer urban canopy parameterization using a nudging approach has been developed and integrated into the mesoscale atmospheric model METRAS to improve the urban surface representation. Through modifying the existing nudging equation in METRAS, urban canopy effects on local climate such as reduction of mean wind speeds, enhanced aerodynamic turbulent fluxes as well as urban heat island phenomenon are represented. The nudging technique has the advantages of simple implementation and reasonable model results, moreover, it is widely used as a data assimilation approach in global-scale models. One of the objectives of such a canopy parameterization using nudging is thus to prepare for its direct usage in the global-scale models with a typical resolution of 1 km so as to improve meteorological variables calculation for heterogeneous urban canopies.

In this work, the city Hamburg with heterogeneous surfaces was selected as the study area. Urban canopy information for Hamburg such as building height and building surface fraction (i.e., the ratio of the surface area occupied by buildings to the total plan area) were obtained from the 3D city model of Hamburg LoD1 (Level of Detail 1). We also used the Local Climate Zone map, which defines a range of values of the urban canopy parameters for each urban class. Sensitivity studies using these two types of canopy data are performed to gain a better understanding of how detailed should urban canopy data be to serve as input for an urban canopy parameterization. Model simulations are made for summer 2020 with particularly focusing on the maximum urban heat island phenomena. Simulation results for different meteorological situations are presented and the impacts of model resolution will be discussed.
Estimating return values for precipitation in Norway by combining a large ensemble data set with gridded observations to re-parameterize the extreme value distribution
Authors: Ødemark Karianne , Ellingsbø Tveito Ole Einar
Published: June 2022

Extreme precipitation events that lead to excess surface water and flood are becoming an amplifying societal cost as a result of both the increasing precipitation amounts in recent years and urbanization. Knowledge about extreme precipitation events is important for the ability to predict them, but also to know how often they occur with various intensities in order to estimate design values for constructions and critical infrastructure. 

To study extreme precipitation events by applying statistical analysis requires long timesteries, which often is a challenge when using conventional or new observational data records. 

In the present study, a data set constructed from the numerical seasonal prediction system at ECMWF, SEAS5, has been applied in order to increase the event sample size compared to conventional observational or re-analysis data sets. The data are analyzed by fitting them to a GEV-distribution. This distribution is compared to an equivalent GEV-distribution for the gridded observational data set SeNorge. While this data set has a smaller sample size, the fine scale horizontal resolution allows for more spatial heterogeneity in the data set. In this study we propose a method to estimate return values by combining the two datasets, and in that way exploiting the advantages of both data sources: sample size from SEAS5 and spatial distribution from SeNorge. The combination is done by using a normalized “growth curve” from both data sets. The large sample size is important for the shape and scale parameters of the fit to the GEV and is thus used from the SEAS5 data set. These parameters will define the shape of the curve. Location from SeNorge is then used to get the correct level of the curve.
Spatio-temporal changes in the surface urban heat island extent between 1986-2021 in the polycentric agglomeration, southern Poland based on Landsat satellite images
Authors: Renc Aleksandra , Łupikasza Ewa
Published: June 2022

Currently, more than 50% of the world's population lives in cities, but it is estimated that this number will increase, directly affecting the development of urban infrastructure. An increase in impervious surfaces in the city causes the intensification of the surface urban heat island (SUHI), manifesting itself in an increased temperature in a city compared to the surroundings. In summer, the SUHI is considered a hazardous phenomenon on a local scale for city inhabitants. Although the Górnośląsko-Zagłębiowska Metropolis (GZM) is one of the most urbanised and populated areas in Poland, the issue of SUHI has not yet been addressed for the entire metropolis.

This study discusses spatial and temporal changes in the extent and the risk of SUHI in GZM between 1986 to 2021. Based on LANDSAT data recorded in summer, the land surface temperature was estimated (LST) for twenty-one satellite images. The extents of surface heat island (SHI) and SUHI were marked out based on mean LST and standard deviation. The SHI occupied from 11.5 to 18.5% of the GZM. Variability in the vegetation of the agricultural areas (before and after the harvest) significantly contributed to intraseasonal variability in SHI extent. In contrast to SHI, when determining the SUHI extent, only urban areas (artificial surfaces), distinguished based on the Corine Land Cover (CLC) classification, were considered. Therefore, SUHI extent (4.2 to 13.8%, depending on date) was generally smaller than SHI distinguished only based on the thermal criterion. However, in both cases, there was an upward trend in their extents between 1986 and 2021. Additionally, the SUHI risk indicator, distinguishing the areas most exposed to SUHI based on land cover and demographic data, revealed that the number of districts exposed to a high risk of SUHI increased in the research period. Since SUHI extent depends on the contribution of various types of land cover, temporal changes in the percentage of land cover types in the GZM were also analysed. For this purpose, the land cover types in CLC classification existing for 1990, 2000, 2006, 2012, and 2018  were reorganised into the following five main types: (i) impervious surfaces; (ii) mines, dumps, and construction sites; (iii) agricultural areas; (iv) vegetation areas; (v) water areas. In the research period,  the most significant changes concerned the area of impervious surfaces (increase by 4.8%) and agricultural areas (loss by - 3.7%) that were transformed into built-up areas. The urban area extent increased from 26.9% in 1990 to 30.6% in 2018. Moreover, significant relationships were found between the SUHI area and inhabitant number and population density in GZM districts.
Impact of combined uncertainties on convective precipitation during different synoptic control
Authors: Matsunobu Takumi , Keil Christian , Barthlott Christian
Published: June 2022

Subgrid-scale uncertainty for convective-scale forecasts and its sensitivity to synoptic conditions remain unclear. In this study, the relative importance of individual and combined perturbations representing various sources of uncertainties are addressed by simulating real case studies stratified into weak and strong synoptic convective-forcing situations using the quasi-operational setup of ICON-D2 model with the double moment microphysics scheme. We quantified and intercompared the impact given by three uncertainties, microphysics parameters (cloud condensation nuclei concentrations and shape parameters of cloud droplet size distribution), initial and boundary conditions (IBC), and physically-based stochastic perturbation scheme (PSP). The impact on two aspects of precipitation forecasts are assessed: daily accumulated precipitation amount averaged over Germany and temporal evolution of spatial variability of hourly precipitation. 
The relative impact on 24h precipitation is quantified against the mean of sub-ensembles, in which different parameters are perturbed. Microphysics perturbations and PSP show a minor impact compared to the impact of IBC perturbations, but combining IBC and microphysics perturbations extends the tails of the forecast distribution.  The responses to all the perturbations are relatively insensitive for strong-forcing situations.
Visual inspection of hourly precipitation fields reveals that microphysics perturbations slightly shift convective cells but vary precipitation intensities while IBC perturbations completely deform the spatial convective structure. We attempted to quantify the typical scales of modification using Fractions Skill Scores and found that microphysics perturbations have an explicit impact on the position of individual convective cells. On the other hand, PSP initiates convection earlier and affect the lifetime of cloud and convective precipitation, resulting in earlier and faster growth of spatial variability of hourly precipitation than microphysics. 
In the talk, additional comparisons against radar observations and benefits of practical forecast skills will be also presented. 
The Multi-sensor Approach for Satellite Hail Advection (MASHA): a new technique for nowcasting applications
Authors: Laviola Sante , Vermi Federico , Guarascio Massimo , Monte Giulio , Folino Gianluigi , Levizzani Vincenzo
Published: June 2022

The Multi-sensor Approach for Satellite Hail Advection (MASHA) is a new satellite hybrid technique conceived for the real time detection and advection of hail clouds. MASHA is based on a machine learning algorithm able to identify hail clouds from satellite measurements and predict the evolution of hail-bearing systems every 5 min. The machine Learning techniques represent a valuable tool to address this problem. In particular, the use of deep learning model allows to automatically combine low level data and providing accurate predictions. Operationally, MASHA combines the strengths of the MWCC-H method to detect hail through the whole GPM constellation (Laviola et al., 2020a-b) with the high temporal rate of the Meteosat Rapid Scan Service (MSG-RSS). The novelty of this approach is offering the unprecedented possibility to advect hail-bearing systems in real-time and at very high spatial resolution. This opens the way to the operational applications of MASHA method by offering an unprecedented support to the nowcasting of hailstorms and to regional numerical weather predictions. Recent applications experimented the ingestion of lightning strikes and radar hail indices in order to improve the reconstruction of hail fields when the GPM-C overpasses are missing. The result is a near-real time, more consistent, high-resolution hail map.

References

Laviola S., V. Levizzani, R. R. Ferraro, and J. Beauchamp: Hailstorm Detection by Satellite Microwave Radiometers. Remote Sens. 2020a, 12(4), 621; https://doi.org/10.3390/rs12040621

Laviola S., G. Monte, V. Levizzani, R. R. Ferraro, and J. Beauchamp: A new method for hail detection from the GPM constellation. A prospective for a global hailstorm climatology. Remote Sens. 2020b, 12(21), 3553; https://doi.org/10.3390/rs12213553
On hand available predictors for operational satellite-based forecast
Authors: Cros Sylvain , Badosa Jordi , Szantaï André , Haeffelin Martial
Published: June 2022

Photovoltaic production is highly variable with a limited predictability due to stochastic variation of cloud attenuation of surface solar irradiance. Forecasting this irradiance helps to manage the safe and stable operation of the grid and thus enables to increase solar electricity penetration for a lower carbonated energy mix. In particular, intraday horizon forecasts (typically up to 6 h) are more and more required for the grid power reserve management, the increase of intra-day auctions in the electricity market and the current worldwide development of micro-grids.

Forecast irradiance using images from geostationary meteorological satellite is particularly appropriate for intraday horizon. It gives better performance than NWP models, does not require instrumentation, or costly computing resource. However, the accuracy of such methods is very sensitive to the cloud cover state and its short-term evolution. For instance, methods based on the temporal extrapolation of cloud motion present better results for passing cloud events than for sudden cloud appearance or disappearance. In several studies, reliability predictors have been identified for satellite-based irradiance forecast. They showed clear signals when uncertainty is computed as a function of season, solar zenith angle, cloud albedo and more recently synoptic weather regimes. This anticipation of error range would help grid managers to prepare the sizing of storage capacity or ancillary electricity resource.

A pertinent predictor must have a significant influence on forecast error. It also must be easy to obtain in operational forecast conditions. In this work, we propose to exploit the only observation source required in satellite-based forecast: the satellite image itself. Using 5 years (2017-2021) of image-derived forecasts at 15 min time step over Palaiseau (France), we computed the forecast uncertainties as a function of multiple parameters derived from the HRV channel of Meteosat Second Generation satellite. We highlighted the influence of cloud albedo spatial variance and cloud motion vector field spatial-temporal features in the forecast uncertainties. In addition to the error range provided to users, this work can help forecasters to better characterize their sources of error and to select new predictors for machine-learning approaches.