bluuebunny/crossref_metadata_2025 huggingface dataset:
Meteorological thunderstorm types in Europe
Authors: Morgenstern Deborah , Stucke Isabell , Simon Thorsten , Mayr Georg J. , Zeileis Achim
Published: June 2022

Lightning in western Europe follows mostly a distinct annual cycle with the majority of lightning occurring in summer or fall reflecting the importance of CAPE thunderstorms (thunderstorms accompanied by high CAPE and moisture values) or, in the Mediterranean, the influence of high sea surface temperatures. But lightning occurs also without the typical favorable conditions so other meteorological processes become relevant. These rare thunderstorm types can be very destructive as the high hit rate to elevated infrastructure in winter shows: even in regions with a pronounced annual lightning cycle, tower striking lightning has no seasonality. Neglecting rare thunderstorm types can lead to insufficient lightning risk assessments, especially concerning elevated infrastructure such as radio towers and wind turbines. A proper understanding of the various thunderstorm types as presented in this study helps to find good places for wind farms and to construct more resilient wind turbines.

We use lightning observations from the EUCLID network along dozens of ERA5 variables with a resolution of 0.25 degree longitude/latitude and one hour. Each found thunderstorm type is described by its driving ERA5 variables, which are in turn categorized based on their meteorological characteristics: Mass/temperature field variables, wind field variables, cloud physics variables, moisture variables, and surface exchange variables. As some thunderstorm types may only occur in specific regions, we exert our analysis on several rather homogeneous subdomains in Europe representing the sea, as well as flat and mountainous domains. Further, we assure that lightning from each season is equally represented in our data set to better capture rare lightning conditions in unfavorable seasons.

This comprehensive data-driven description of various thunderstorm types in Europe is based on unsupervised learning methods. Cluster analysis groups observations with and without lightning based on their accompanying meteorological values working out various thunderstorm groups and clearly distinct from them groups without lightning. The thunderstorm groups are then meteorologically described using their cluster means, principal component analysis, and their prevalent weather patterns. 

First results show that in addition to the well-known CAPE thunderstorm type there are at least cloud-physics thunderstorms and wind-field thunderstorms. Cloud-physics thunderstorms are associated with large amounts of cloud particles, strong updrafts, thick clouds, and high precipitation amounts. Wind-field thunderstorms occur together with large wind speeds and high wind shear and in the absence of CAPE.

The presented description of various thunderstorm types in Europe improves the conceptual understanding of thunderstorms and provides a basis to better evaluate lightning risk in Europe.
An exhaustive global climate model performance assessment based on Lamb weather types
Authors: Brands Swen
Published: June 2022

Sixty global climate model configurations contributing historical experiments to the Coupled Model Intercomparison Project Phase 5 and 6 are evaluated in terms of their capacity to reproduce the observed climatological frequencies of the Lamb weather types in the northern and southern hemisphere, as represented by reanalysis data. Large performance differences are obtained from one model family to another, a few of them yielding favourable results in almost the entire study area. The finer atmospheric resolution of the model versions used in CMIP6 is associated with better model performance and the obtained spatial error patterns are remarkably similar, even for some nominally different model families, which points to unexpected model dependencies. By comparison of the results for the two hemispheres, possible model tuning issues are addressed as well. Since more complete representations of the climate system are in principle preferable to simpler ones and also produce unique scenario features, a score describing GCM complexity in terms of prescribed and interactive climate system components is introduced as additional model selection criterion. In spite of the increasing uncertainty sources, the more complex models do generally not perform worse than the less complex ones. The study comes with an extensive model metadata archive based on a survey involving the model development teams themselves. This archive includes the names and versions of all considered climate system component models, integer codes describing the complexity of the coupled model configurations and other relevant metadata. It helps to avoid “black box” use of the GCMs and can be retrieved from https://github.com/SwenBrands/gcm-metadata-for-cmip/blob/main/get_historical_metadata.py
A stochastic model of mixed-phase cloud micro-physics
Authors: Gomes Albuquerque Daniel , Coelho Abade Gustavo
Published: June 2022

Mixed-phase clouds, i.e., clouds that contain both super-cooled water droplets and ice crystals, are ubiquitous in the atmosphere and play an important role in the climate system. The mixture of liquid and solid water in sub-zero temperatures leads to a condensational instability, in which ice particles tend to grow at the expense of droplet  evaporation. Nonetheless, mixed-phase clouds are unexpectedly long-lived. Earlier mean-field stochastic models are based on the picture of turbulence-induced large-scale dynamical forcing of cloud parcels to explain the longevity of mixed-phase clouds. We claim that small-scale turbulence is key to explain the persistence of such systems. Due to limited computational resources, weather simulation on a global scale is limited to coarse grids with a resolution of kilometers at best. On the other hand, a typical  turbulent flow inside a cloud will display an intricate structure of eddies down to the scale of millimeters. A recent study using the linear eddy model showed that small  scale turbulence does play a role in slowing down cloud glaciation. We propose a more computationally tractable Lagrangian stochastic micro-physical scheme to account for sub-grid fluctuations in velocity, temperature and water vapor fields. The impact of our scheme on phase partitioning is tested in a synthetic, turbulent-like flow that mimics an Arctic mixed-phase stratocumulus (AMPS) cloud. Results are confronted with idealized reference simulations that use Eulerian bulk micro-physics based on an assumed (temperature-dependent) phase partitioning function. Our study suggests that accounting for local variability in a turbulent cloud is important for reproducing steady-state mixed-phase conditions.
Predicting Central European summer heatwaves with Machine Learning
Authors: Weirich Benet Elizabeth , Pyrina Maria , Jiménez Esteve Bernat , Fraenkel Ernest , Cohen Judah , Domeisen Daniela
Published: June 2022

Heatwaves are extreme near-surface temperature events that can have substantial impacts on society and biodiversity. Moreover, the intensity, duration, and frequency of heatwaves are increasing at an accelerating rate as a consequence of climate change. Early Warning Systems can help to reduce the impact of heatwaves, as part of climate adaptation programs. However, state-of-the-art prediction systems can often not make accurate predictions of heatwaves more than two weeks in advance, which is required to take action and mitigate the impact of heatwaves. Here, we investigate central European forecasting of summer heatwaves on sub-seasonal timescales of several weeks using statistical and machine learning methods. As a first step, we select a set of atmospheric and surface predictors which are thought to have the largest impact on heatwave prediction based on previous studies and supported by a correlation analysis. Our findings show that at short lead times (1 week) near-surface temperature, 500hPa geopotential, precipitation, and surface soil moisture in central Europe are the most important predictors. At longer lead times (2—6 weeks), Mediterranean and North Atlantic sea surface temperatures, and the North Atlantic jet stream become the most relevant predictors. Secondly, we apply machine learning methods based on these predictors to forecast (1) summer temperature anomalies and (2) the probability of heat waves for 1—6 weeks lead time at weekly resolution. For each of these two types of forecasts (1) and (2) we use both a linear model and a Random Forest model. The performance of these models decays with lead time, as expected, but outperforms persistence and climatology at all lead times. Our machine learning models beat the European Centre for Medium-Range Weather Forecasts (ECMWF) model for lead times of 3 weeks and longer. We show that machine learning models can help extend the forecasting lead time of summer temperature anomalies and heat waves to sub-seasonal scales.
Simulating cold pools with ICON during the FESSTVaL period
Authors: Ahlgrimm Maike , Kirsch Bastian , Sakradzija Mirjana
Published: June 2022

During the Field Experiment on Submesoscale Spatio-Temporal Variability in Lindenberg (FESSTVaL) just under forty distinct cold pool cases were observed within the area covered by the observational network (see contribution by B. Kirsch on observed cold pool morphology in UP1.5). Here, we investigate to what degree the Icosahedral Nonhydrostatic (ICON) model is able to reproduce the observed cold pool properties.
ICON is run on a limited area domain centered around the observational facilities used during FESSTVaL and covers most of eastern Germany and western Poland. Similar to the operational “D2” setup, the horizontal resolution is approximately 2km such that deep convective transport is largely resolved, while shallow convection remains a subgrid-scale, parameterized process. The model is tested with two options for the shallow convection scheme (Tiedtke-Bechtold with/without stochastic perturbations) and an optional cold pool perturbation (CPP) scheme aimed at improving convection triggering along cold pool boundaries.
The cold pools produced by the model share many qualitative similarities with the observations: ICON successfully produces cold pools on days when they are observed. As in the observations, the growth of the cold pool area appears to be directly associated with precipitation: When precipitation ceases, the cold pool area stops growing. The intensity of the cold pool (measured as the 2m temperature decrease) however tends to reach its peak before precipitation and area growth cease. It appears that the model may lose the cold pool signature rather more quickly than is observed.
As per design, the CPP scheme enhances the vertical velocity along cold pool boundaries, which leads to more intense precipitation associated with the cold pool forming convection as well as enhanced cold pool intensity.
Recent hot and dry summers in Germany in comparison to climate projections 
Authors: Stanley Kelly , Leps Nora , Walter Andreas
Published: June 2022

This study investigates whether the recent extremely hot and dry summers (2003, 2015, 2018 and 2019) in Germany will be normal summers under future climate change conditions from climate projections. Abnormally persistent high-pressure systems during these recent German summers maintained clear skies and dry conditions on the ground, resulting in record-breaking heat and drought conditions which had major socio-economic impacts, e.g. reduced crop yields or impacts on human health and increased mortality. Heat and drought indices are calculated from the German Meteorological Service's (DWD) climate model reference-ensembles for periods 2031-2060 and 2071-2100 under greenhouse gas emission scenarios RCP 2.6 (11 ensemble members) and RCP 8.5 (21 ensemble members). These results are compared with the same indicators calculated from three observational datasets (station data and gridded datasets: HYRAS and DWD Climate Monitoring Grids). The order of recent extreme summers in Germany from hottest and driest is: 2018, 2003, 2019, 2015. The climate projections show increasing summer heat conditions for Germany throughout this century, especially for the RCP 8.5 scenario, but no clear future summer precipitation anomaly trend. These recent extreme summers would be considered extremely hot summers for RCP 2.6 in 2031-2060, 2071-2100 and the RCP 8.5 2031-2060 scenarios, but would be considered normal or even below normal hot summers under the RCP 8.5 scenario in 2071-2100. Due to the combination of extreme heat and strong precipitation deficits, the climatic water balance and derived meteorological drought indicator values of these summers often exceeds the considered likely ranges projected for all future summer scenarios and are only matched by the most anomalous hot and dry summers in the model ensembles, even for the high-emission RCP 8.5 scenario in 2071-2100.
The bicycle as a weather vehicle: transects of the urban CO2 and weather measurements on a bicycle-based atmospheric sensor
Authors: Mazon Jordi
Published: June 2022

It is well known that the CO2 is one of the main greenhouse gases. The traffic of non-electric vehicles is the main contribution to the emissions of this gas in the cities, enhancing the urban heat island effect and increasing the dangers of global warming. The changes in the amount of CO2 across the city occur on a small urban scale, easily measured by a person moving around the city, whether it be walking or cycling. A handheld CO2 sensor capable of recording this gas, temperature, atmospheric pressure, and relative humidity has been mounted on a bicycle to analyse local variations of this gas in a couple of cycling route transects in Barcelona city and some of municipality in its metropolitan area. The results presented here show a local CO2 variability, with some changes of this gas in 90 ppm over distances of few hundreds of meters (less than 1 km). The influence of parks, urban forests, and gardens in absorbing the gas has also been identified. The measurements obtained with this technique demonstrate that using a bicycle as a scientific vehicle is a viable method to analyse the C02, temperature, humidity and pressure distribution in urban and periurban areas, which could be used as a scientific citizen project helping to monitoring the amount of CO2 in urban areas. Many cities and municipalities worldwide are making an effort to adapt their infrastructures by including bike lanes, with the purpose of promoting bicycles as a common mobility vehicle. Bicycles are spreading out throughout cities as a useful and efficient vehicle. Thousands of cyclists moving through urban areas is also an opportunity to use the bicycle as a scientific vehicle to measure and helping monitoring some atmospheric variables (such as temperature, relative humidity, or CO2 values), thus inciting citizens to collaborate with scientists in quantifying the evolution of this gas in urban areas.
Annual cycle of modes of low-frequency circulation variability
Authors: Huth Radan , Pokorná Lucie
Published: June 2022

The modes of low-frequency variability of atmospheric circulation (teleconnections) have been studied mostly in winter. Of the four modes dominating over the North Atlantic – European sector (North Atlantic Oscillation (NAO), and East Atlantic (EA) and two Eurasian (EU1, EU2) patterns), only the NAO has been investigated in other seasons. The objective of this study is to describe the development of the North Atlantic – Europen circulation modes throughout the year, that is, to characterize their annual cycle.

The modes are identified in 500 hPa heights in the Northern Extratropics (north of 20°N inclusive) for period 1948-2016. The data source is the NCEP/NCAR reanalysis. The analysis tool is rotated principal component analysis (using covariance matrix and varimax rotation). The key ingredience of our methodology is that the analysis is based on sliding seasons 90 days long, which are moved with a step of 5 days. That is, the entire year is covered by 73 (=365:5) sliding seasons. The number of rotated components (that is, of detected modes) varies from 9 in winter to 13 in summer.

The four standard modes listed above are detected unambiguously from December to March. They change their position and spatial structure in spring, when also other modes appear. Specifically, the centres of the NAO shift westward in spring and northward in summer. The EA pattern loses its zonal character in the warm half year; its southern centre shifts towards Europe and weakens. The behaviour of the Eurasian modes is more complex. Since they describe a moving wave, being in quadrature one with another, their geographical position is to some extent random and their attribution in successive sliding seasons is hardly possible in spring and autumn. The shape of the modes stabilizes in May and remains fairly stable until October when the circulation returns to the winter character. The results document the changes in circulation action centres and their connectivity and show the obvious asymmetry of annual course of modes. One point correlation maps are employed to support the physical realism of the modes detected by principal component analysis.
Preparing for the unprecedented
Authors: Golding Brian , Potter Sally , Ebert Elizabeth , Hoffmann David
Published: June 2022

In an era of climate change, extremes relative to the historical record are expected to occur more frequently. In 2021, several weather disasters occurred in which conditions surpassed recorded extremes. These included floods and associated impacts in the USA, Europe, China and Indonesia, heat waves and wildfires in southern Europe and northwestern North America, and winter weather in Spain and the southern USA. Analysis of the performance of warning systems in these disasters by the WWRP HIWeather project shows that in most, but not all, cases there was adequate forewarning of the nature and magnitude of the event, but that lack of preparedness and/or communication failures led to loss of life in particular vulnerable groups.  Using information gathered for the HIWeather value chain database, I will present an overview of key aspects of each event – the weather and its impact, the forecasts, the warnings, and the responses. In the flood cases, a common feature was that limitations in the spatial resolution of the forecasts limited the ability of hydrological prediction systems to translate the rainfall forecast into a realistic flood forecast. In the case of the winter weather and heat waves, a lack of preparedness at both official levels and in the at-risk population led to failures of response. A comparative analysis of warning performance shows that communication failures were often distributed along the warning chain. Drawing on material from our recently published book, Towards the Perfect Weather Warning, I will draw some conclusions about critical components that are necessary for a successful warning system.
Cardiovascular and respiratory hospital admissions during different outdoor thermal conditions in urban areas – A case study of Novi Sad (Serbia)
Authors: Savić Stevan , Lužanin Zorana , Arsenović Daniela , Milošević Dragan , Dunjić Jelena
Published: June 2022

Extreme temperature events driven by climate change have been recognized as an important concern for the public health, and these threats have been particularly identified in rapidly growing urban settlements around the world. The impact of extremely hot and cold weather, as well as seasonal air temperature changes are associated with increased mortality and hospitalization, especially due to cardiovascular and respiratory diseases.

This study aimed to examine the relationship between different biometeorology conditions, using outdoor theramal comfort (OTC) indices and cardiovascular/respiratory hospital admissions over a multi-year period in Novi Sad, Serbia. We used the following datasets: a) three-year data of OTC indices (Physiological Equivalent Temperature - PET and Mean Radiant Temperature - Tmrt) calculated on the basis of meteorological datasets of the Novi Sad Urban Network (NSUNET); b) cardiovascular/respiratory hospital admissions for 2016-2017; and c) emergency cardiovascular admissions for the 2015-2017 period.

The main goal of our research is to point out that the OTC indices can more comprehensively represent thermal risks to human health or quality of life because in addition to air temperature, they include other meteorological elements (humidity, wind, radiation), which directly affect people. Therefore, OTC indices should be part of urban monitoring networks and prediction and early warning systems that provide heat risk and hazard assessment. Furtheremore, the study outcomes show that hospital admissions pick during summer heat waves, with a time lag from 0 to 3 days. Finally, during very intensive heat waves, the emergency cardiovascular admission picks and reaches about 27% higher value compared to summer season average admission value.

Acknowledgement: The research was supported by the project (number 142-451-2557/2021) financed by the Autonomous Province of Vojvodina (regional government).
How do large-scale circulation type statistics change after downscaling in the Euro-CORDEX ensemble?
Authors: Røste Julie , Landgren Oskar
Published: June 2022

We present results from atmospheric circulation type classification of climate simulations over Europe using an ensemble of 57 regional simulations from Euro-CORDEX, their 11 global boundary models from CMIP5 as well as the ERA5 reanalysis. We compared frequencies of the different circulation types in the simulations with ERA5 and found that the regional models add value in the summer season. We applied three different classification methods (the subjective Grosswettertypes and the two optimisation algorithms SANDRA and distributed k-means clustering) from the cost733class software and found that the results are not particularly sensitive to choice of circulation classification method.

There are large differences between models. Simulations based on MIROC-MIROC5 and CNRM-CERFACS-CNRM-CM5 show an over-representation of easterly flow and an under-representation of westerly. Comparing the same day in the global and regional models, the downscaled results retain the large-scale circulation from the global model most days, but especially the regional model IPSL-WRF381P changes the circulation more often, which increases the error relative to ERA5. Simulations based on ICHEC-EC-EARTH and MPI-M-MPI-ESM-LR show consistently smaller errors relative to ERA5 in all seasons. The ensemble spread is largest in summer and smallest in winter.

Under the future RCP8.5 scenario, the circulation changes in the summer season, with more than half of the ensemble showing a decrease in frequency of the Central-Eastern European high, the Scandinavian low as well as south-southeasterly flow. There is in general a strong agreement in the sign of the change between the regional simulations and the data from the corresponding global model.

We hope that evaluations like these can provide useful insight for example when selecting regional model simulations for use in climate services.
Comparison of land surface temperature of local climate zones in Croatia and estimation of the vulnerable population heat risk
Authors: Žgela Matej , Herceg Bulić Ivana , Lozuk Jakov , Jureša Patrik
Published: June 2022

Cities are not homogeneous, but areas with similar microclimatic characteristics can be singled out based on land surface features, type of material, human activity, etc. These areas are called local climate zones (LCZ) and they are extremely important in the field of urban climatology. In this study, for the first time, we present LCZs for nine Croatian cities and their wider surroundings: Zagreb, Split, Rijeka, Osijek, Pula, Varaždin, Slavonski Brod, Zadar and Dubrovnik. LCZs were classified using the Local Climate Zone Generator web application. To analyse the thermal characteristics of LCZs in selected cities, we calculated the average land surface temperature (LST) and normalized difference vegetation index (NDVI) for the summer seasons 2017 - 2021 using Google Earth Engine, a cloud-based geospatial analysis platform. An approach for the heat risk estimation based on LCZ, LST and NDVI data is proposed and calculated for the locations of the facilities used by the vulnerable population. The heat risk classes for nursing homes and kindergartens were calculated for two largest Croatian cities, Zagreb and Split.

The results show high classification accuracy among nine cities, with an average of 83 %. Continental cities have higher accuracy as they historically expanded more evenly. On the other hand, coastal cities show slightly lower classification accuracy, which can be attributed to the confined expansion of the cities in the narrow coastal zone and to karst topography. Statistical analysis shows significant differences of LST among LCZs. In all cities, the highest LST values ​are associated with dense built-up parts (i.e. compact LCZs) but also with industrial zones. Out of natural LCZs, the highest LST is found in the karst hinterlands of the coastal cities, i.e. for LCZ E (Bare rock). Except for LCZ G (Water), the lowest values ​​of LST are obtained for forested areas outside the city and in city parks with dense trees, where LST is significantly lower compared to compact LCZs.

Estimation of the heat risk of the vulnerable population of Zagreb and Split shows that facilities with high heat risk are located in most built-up parts of the cities connected with high LST values, but a small share of vegetation. This analysis highlights the importance of the position of such facilities in the city from the aspect of reducing the heat load and improving the standard of life of the vulnerable population.
The Korean Integrated Model (KIM) coupled with atmospheric chemistry model: plans and preliminary results
Authors: Park Shin-Young , Bae Soo Ya
Published: June 2022

The next-generation a global numerical weather prediction model (NWP) based on the Korean Integrated Model (KIM), an operational model of the Korea Meteorological Administration, is being developed to seamlessly predict from short-term scale to climate fluctuations a month by KIAPS. One of the strategic targets for development of next-generation NWP model is to establish the coupled modeling system by adopting various Earth system components, which are LAND, OCEAN, WAVE, Atmospheric Chemistry, etc. The other is to support the air quality forecasting using the coupled NWP model system. It is essential to develop coupled model by reflecting the complex interaction between the atmosphere and atmospheric chemistry. To improve forecasting skill of long-term simulation, the chemistry model is need to consider tropospheric chemistry mechanism as well as stratospheric chemistry. GEOS-Chem, the atmospheric chemistry model of the Goddard Earth Observing System (GEOS), was selected as the chemistry model to be coupled with KIM according to selection criteria. The GEOS-Chem consists of transport, emission, chemistry, convective mixing, planetary boundary layer mixing, and deposition processes. The KIM coupled with chemistry model was developed excluding the advection (transport) process. The emission data used to simulate KIM-chemistry model is the latest and high-resolution global data. The results of simulation used an initial version of coupled model were analyzed and compared with those of the reference model. 

This work was carried out through the R&D project "Development of a Next-Generation Numerical Weather Prediction Model by the Korea Institute of Atmospheric Prediction Systems (KIAPS)", funded by the Korea Meteorological Administration (KMA2020-02212).
Open Innovation and Open Development of the Unified Forecast System - An EPIC Approach
Authors: Huang Maoyi
Published: June 2022

The National Oceanic and Atmospheric Administration (NOAA) established the Earth Prediction Innovation Center to be the catalyst for community research and modeling focusing on informing and accelerating advances in United States' operational NWP forecast modeling systems. Initially, EPIC focuses on the Unified Forecast System (UFS). The Unified Forecast System (UFS) is a community-based, coupled, comprehensive Earth modeling system. The UFS numerical applications span local to global domains and predictive time scales from sub-hourly analyses to seasonal predictions. It is designed to support the Weather Enterprise and to be the source system for NOAA‘s operational numerical weather prediction applications. UFS includes all model elements needed for numerical weather prediction: data assimilation, ensemble design, coupling, physical components and metadata protocols, tools, and algorithms to facilitate postprocessing of model outputs.  EPIC applies an open-innovation and open-development framework that embraces open-source code repositories integrated with automated Continuous Integration/Continuous Deployment (CI/CD) pipelines on cloud and on-prem HPCs. This framework allows community developers to track the status of their contributions, and facilitate rapid incorporation of innovation by implementing consistent and transparent, and standardized and community-driven validation and verification tests. 

In this presentation, I will demonstrate the EPIC approach using the UFS Short-range Weather (SRW) Application as an example including the following steps:

Release the SRW application in the cloud with documentation and necessary data for testing

Compile a library of test cases for challenging forecast environments

Provide Training for SRW users and developers

Streamline benchmarking against baseline cases in skill and computation on cloud HPCs , and

Enable seamless transition to operations by harmonizing research and operational CI/CD pipelines
Graph neural networks for solar energy nowcasting and intra-day prediction in Central Europe
Authors: Schicker Irene , Papazek Petrina
Published: June 2022

With the transition towards an increased use of renewable energy system, especially in solar energy, accurate predictions are essential to maintain and secure grid stability; grid feed-in scheduling of PV production as well as in intra-day and day-ahead trading. To provide forecasts in the nowcasting and intra-day range a combination of different heterogeneous data sources such as satellite data, observations, power productions, and numerical weather prediction data is needed.

In this study, a graph neural network model (GNN) with adapted loss function is evaluated against more “classical” deep learning and statistical approaches such as a ConvLSTM architecture for nowcasting and intra-day prediction of PV production. Besides the GNN and the ConvLSTM a baseline forecast using an observation-based analog method is implemented. The analog method includes a Euclidean distance measure and dimensionality reduction to include not only temporal analogs but also spatial analogs surrounding the target site. For selected sites, which are included in the NWP domain, a simple NWP to PV forecast is used as baesline using the Python library PVLIB. The target region for the models is a belt in Central Europe stretching from Luxembourg to Austria with PV sites in different altitude and climatic regions. Heterogeneous data sources with varying temporal availability and spatial resolution are used as inputData pre-processing and careful feature selection are needed to not overfit the different methods.

Results show that the graph neural network is able to outperform the standard and baseline methods as well as the other two methods for most of the selected cases.
Multi-resolution postprocessing for precipitation
Authors: Bhend Jonas , Spirig Christoph , Moret Lionel , Liniger Mark A.
Published: June 2022

Automated forecasting provides the basis for everyday forecast products used by a wide range of users. Continued progress in numerical weather prediction allows to produce local forecasts with considerable accuracy. To further reduce systematic errors and thereby render such local forecasts more beneficial to users, statistical postprocessing can be employed. While statistical postprocessing can readily be optimized for specific applications, optimization is less straight forward for general-purpose forecasts that are used across a diversity of applications and decisions. This issue is illustrated with ensemble postprocessing for automated precipitation forecasts.

While medium-range precipitation forecasts are often communicated with hourly granularity, beyond the nowcasting range most applications are likely less affected by the precise timing of precipitation. In contrast, (sub-)daily aggregated precipitation  may be a more relevant quantity. In addition, predictability of hourly precipitation is generally very limited days in advance and statistical postprocessing for hourly precipitation forecasts will therefore be strongly affected by the regression-to-the-mean problem (i.e. statistical postprocessing resorts to issuing climatological forecasts in the absence of predictability). To overcome the above issues, we propose a combined postprocessing approach operating on daily aggregated precipitation and hourly fractions of daily precipitation. We present results for a simple disaggregation according to the NWP precipitation and disaggregation according to a separate postprocessing of the hourly fraction of daily totals. The latter approach allows us to correct systematic biases in the diurnal cycle of precipitation occurrence particularly relevant for convective situations in complex topography as is the case in Switzerland. The same approach can be used to extend daily precipitation forecasts into the sub-seasonal to seasonal range.
The State-of-the-art Remapping Process for the Korean Integrated Model (KIM)
Authors: Kim Sang-Wook , Shim Taehyoun , Kim Junghan
Published: June 2022

This study introduces the remapping process of the state-of-the-art Numerical Weather Prediction (NWP) model, the Korean Integrated Model (KIM) system, developed by the Korea Institute of Atmospheric Prediction Systems (KIAPS). The KIM employs the quasi-uniform and pole singularity-free spherical geometry, a cubed-sphere grid, essentially requiring the grid transformation in the pre- and post-process. The remapping process of the KIM consists of two parts: (1) figuring out reference points from source grid points and weights at each reference point and (2) calculating the values at destination points by multiplying the values at reference points and appropriate weights. The KIM uses the linked-cell mapping algorithm to search the reference points around the destination point. The KIM provides various interpolation methods including the alternative-bilinear algorithm (Kim et al., 2019), which can obtain the weights from irregular grid points without assuming a rectangular shape. We are developing an advanced pre- and post-process that downscales cubed-sphere grids to a higher resolution using neural network techniques as an ongoing project. We also plan to share our results that improved the efficiency of the remapping process, which requires a massive resource compared to the simplicity of its calculation. For example, only the address and weights of reference grid points required for the interpolation are stored in a remap matrix (pruning) as an efficient storage managing strategy. Moreover, the advantage of parallelization, evaluated in the Korean 5th super-computer, will also be presented. Through parallelizing the remapping process, the speed improves up to about twelve times, and a more stable calculation speed is expected.
Impact of low ice area in Arctic on atmospheric circulation and climate extremes in mid to high latitudes of the Northern Hemisphere
Authors: Jędruszkiewicz Joanna , Piotrowski Piotr , Wibig Joanna
Published: June 2022

The Arctic region has undergone a substantial climatic changes in recent years with dramatic sea-ice loss, evident particularly in late summer and early autumn. According to many findings the near-surface temperature in high latitudes of the Northern Hemisphere has been rising at rates double that of the lower latitudes and was amplified by rapid loss of sea ice in the Arctic through the snow and ice albedo feedback. Open, free of ice water has a smaller albedo than ice, absorbs more energy incoming from the Sun, resulting in much faster increase in temperature than other areas. The abrupt acceleration in Arctic warming well seen since the middle 1990s coincides with changes in climate in the mid-latitudes. The aim of this paper is to analyse the changes in atmospheric circulation in the mid to high latitudes in the response to low extent of ice cover  in Arctic in the preceding months.

To analyze the variability of sea ice cover in Arctic region we used daily sea-ice fraction data which are defined as the fraction of a grid box which is covered by sea ice. These data were multiplied by field of each grid box. After summing up all recalculated grid points, a new parameter named – area ice cover (AIC) was defined. We analyzed the AIC variability in the whole Artcic and in three sectors Atlantic - from 75°W to 60°E, Asian - from 60.25°E to 189.75°W and American - from 170°W to 75.25°W.

Then AIC index was correlated to selected northern hemisphere teleconnection: North Atlantic Oscillation (NAO), the Scandinavian pattern (SCAND), East Atlantic (EA), Polar/Eurasia index (POL). The Monthly NAO, SCAND, EA and POL values were provided by U.S. National Centers for Environmental Prediction (NCEP). The AIC preceded circulation indices by 1 to 12 months.

In regions and periods of significant correlation, composite maps of temperature anomalies, sea level pressure and geopotential altitude of 100 and 200 hPa in the years with low icing were created and analysed.

The analysis showed that the greatest changes in AIC are observed in Asian sector of Arctic and they have the strongest impact on the atmospheric circulation. AIC changes modulate circulation in both winter and summer period. However the nature of the impact is clearly different. A sensitive regions turns to be Barents and Kara Seas and Novaya Zemlya.
Classifying Synoptic Patterns and Identifying Sensitive Areas for Targeted Observations to Improve Air Quality Prediction over South Korea
Authors: Lee Seungyeon , Qin Xiaohao , Yoon Ji Won , Lee Ebony , Lim Sujeong , Park Seon Ki
Published: June 2022

In air quality prediction, initial conditions, based on both atmospheric and aerosol/chemistry observations, are essentially required for a coupled atmosphere-chemistry model. In general, we have more accurate model results with a higher amount and quality of observations; however, forecast errors in a region of interest may grow from an initial error in a specific upstream region, primarily due to the lack of observations therein. Therefore, finding the upstream areas from which a small initial error can grow into significant forecast errors in the region of interest is essential for the strategic enhancement of observations to improve numerical air quality prediction. The conditional nonlinear optimal perturbation for initial conditions (CNOP-I) represents the initial error that can lead to the most significant error at the forecast time, which enables us to identify the sensitive regions as a considerable initial error value. Thus, CNOP-I is a suitable tool for targeted (adaptive) observations. The goal of this study is twofold: 1) to classify the synoptic patterns for sand dust storm cases by using the principal component analysis (PCA), and 2) to identify the sensitive areas in targeted observations for air quality prediction via CNOP-I. We focus on the Asian dust storm events that occurred over South Korea during the last 32 years (1990—2021). We expect to improve air quality forecasts by classifying the synoptic situations that bring about severe dust storm outbreaks in South Korea and by identifying the upstream areas for targeted observations to which we can potentially enhance observations through international collaborations.
Characteristics of sub-seasonal prediction performance revealed on hindcast of the Korean Integrated Model
Authors: Shim Taehyoun , Kim Shin-Woo , Kim Sang-Wook , Kim Junghan
Published: June 2022

This study introduces the prediction performance of the Korean Integrated Model (KIM) associated with medium-long range or sub-seasonal timescales. KIM has been developed as the Korea Meteorological Administration’s operational numerical weather prediction (NWP) system by the Korea Institute of Atmospheric Prediction Systems (KIAPS). KIM is a newly introduced global atmospheric model system, consisting of a spectral-element non-hydrostatic dynamical core on a cubed sphere grid and an advanced physics parameterization package (Kim et al., 2021; Hong et al., 2018). We’ll conduct hindcast sets and assess the general characteristics and systematic biases of KIM focused on a sub-seasonal timescale.

Through analyzing KIM’s hindcast experiments, we try to examine the prediction performance and predictability in the lead time range between 2 weeks and sub-seasonal timescales. This timescale is one of the important issues in terms of a seamless forecast that links NWP and climate prediction. A seamless forecast means bridging discrete short-term weather forecasts valid at a specific time and time-averaged forecast at longer periods. Sub-seasonal predictions span this time range and this transition period determines forecasting skills.

We plan to perform KIM’s ensemble reforecast simulation for the boreal winter and summer cases for the period of 2001 – 2020 (20 years). The hindcast results are compared with reanalysis data (ERA5). In order to evaluate KIM’s sub-seasonal performance, several skill scores are calculated, which are Root Mean Square Error (RMSE), Anomaly Correlation Coefficient (ACC), Ratio of Predictable Components (RPC), and Root Mean Square Skill Score (RMSSS). Finally, it is expected that these multi-year simulations will contribute to improving sub-seasonal and seasonal predictabilities.
An Evaluation of Algebraic Turbulence Length Scale Formulations
Authors: Reilly Stephanie , Bastak Duran Ivan , Theethai-Jacob Anurose , Schmidli Juerg
Published: June 2022

The most frequently used turbulence parameterizations in numerical weather prediction (NWP) and general circulation (GC) models are turbulence kinetic energy (TKE) schemes. These turbulence schemes are strongly dependent on a key component, the turbulence length scale. The turbulence length scale is used to parameterize the molecular dissipation of TKE and is also required for calculating the turbulence coefficients. Traditionally, the turbulence length scale formulations do not take into account the transfer of TKE across scales, as they are designed for scales above the energy production range of the turbulence spectra. However, with computational power growing, it has become increasingly possible to run numerical models at scales that are within the gray zone of turbulence. At resolutions within this gray zone, the cross-scale transfer of TKE needs to be taken into account in order to accurately represent the turbulence. For this purpose, a turbulence length scale diagnostic was developed. This is achieved by calculating the turbulence length scale from the so-called effective dissipation rate, which is a combination of the cross-scale TKE transfer and the dissipation rate. The effective dissipation rate is estimated from the budget of the TKE using large-eddy simulation (LES) data. A similar approach is used to calculate the turbulence length scale from the budgets of the scalar variances. This study thus makes use of three different turbulence length scale diagnostics based on: the TKE, the variance of the total specific water content, and the variance of the liquid water potential temperature. Using the turbulence length scale diagnostics as a reference, a series of five exisiting algebraic turbulence length scale formulas are evaluated. The objective evaluation is carried out in terms of a local root mean square error and a non-local three-component technique. The algebraic formulations are evaluated for a set of five idealized LES cases, simulated using the MicroHH model. These cases represent different boundary layer conditions. Based on the evaluation, the length scales proposed by Nakanishi and Niino and Honnert et al. are found to be most representative of the turbulence length scale diagnostics.
Temperature and precipitation long range forecasts over Europe at a monthly and shorter temporal resolution
Authors: Kliegrova Stanislava , Belda Michal , Benacek Patrik , Metelka Ladislav , Stepanek Petr
Published: June 2022

Long range forecasts provide information about expected future atmospheric and oceanic conditions averaged over periods of one to three months and are attractive for many sectors. They have made considerable progress in recent years, but seasonal predictability remains a problem in many regions (for example in Europe). Demand is also for long range forecasts, which would predict shorter periods than months (for example 3 decades in each month).

This study considers four seasonal forecasting systems available in the Copernicus Climate Change Service (C3S) archive which provide near-surface air temperature and precipitation data at 1°by 1°spatial resolution: European Centre for Medium-range Weather Forecast System SEAS5 (ECMWF), Météo – France System 8 (MF), Deutscher Wetterdienst GCFS 2.1 (DWD) and Centro Euro-Mediterraneo sui Cambiamenti Climatici SPSv3.5 (CMCC). It quantifies their value in predicting temperature and precipitation at monthly and shorter (3 decades in each month) temporal resolution over Europe. There are two starting dates, May 1st, and November 1st, and forecasts at lead times up to 3 months for each year in the period 1993–2016 (the longest period of hindcasts common to all systems).

We focus on 2 domains: larger (Europe, latitude 42-55°N, longitude 2-30°E) and smaller (the Czech Republic, latitude 47-52°N, longitude 11-20°E). E-OBS daily gridded observational datasets for precipitation and temperature at 0.25°spatial resolution are used as a reference.

Several statistical measures such as mean bias and root mean square error are presented for temperature and precipitation at a monthly and shorter (3 decades in each month) temporal resolution over Europe and for the Czech Republic.