bluuebunny/crossref_metadata_2025 huggingface dataset:
Teaching Climate Education in an Urban University Classroom
Authors: Damas M. Chantale
Published: June 2022

COP26 was a turning point for youth representatives from different countries, who for the first time were included to participate in climate change discussions at all levels. These youth representatives called on their peers to be more meaningfully engaged in the fight towards a better environmentally friendly future for all.  How can we engage students in our classrooms who are not as passionate, and who may or may not care about climate change?   New approaches and strategies are needed in the classroom to engage students in climate education in order to increase climate literacy. Towards this goal, a course on global climate change is created with the aim of increasing university students’ knowledge about climate change, as well as encourage them to be part of the solution to mitigate its impacts on people and resources.  One particular approach used in the course are case studies that engage students in realistic scenarios that contextualize concepts. Case studies, which also encourage critical thinking, active learning, and peer interactions, can be used in various teaching modalities.  This particular course is taught online asynchronous and included several activities to increase learning of major topics in climate change, including planning solutions for risk assessment and management. As a final project, students worked on a placed-based case study to understand the consequences of climate change in an urban city like New York City.  The benefits of using the case studies and other approaches, learning outcomes, as well as challenges and lessons learned developing and teaching this course during the COVID-19 pandemic are discussed.
Peculiarities of urban heat island in Vilnius
Authors: Bukantis Arunas , Urbanavichiute Laura
Published: June 2022

An urban heat island means that an urban area or metropolitan area is significantly warmer than its surrounding rural areas due to human activities. Vilnius is the capital and largest city of Lithuania, with a population of 590 000 as of 2022. The area is 401 km2 and the density is 1392/km2. The aim of the study is to determine temperature differences in Vilnius city and its suburbs based on daily average, maximum and minimum temperature data during 2012‒2019 period. This work also determines the effect that cloud coverage, wind speed and atmospheric circulation has on temperature difference between Vilnius city and its suburbs. In this study data from Vilnius University automatic meteorological station and two automatic stations of Lithuanian Hydrometeorological service in Vilnius city suburbs were collected and analysed, such as daily and monthly temperature averages, maximum and minimum temperature averages, wind speed parameters, cloud coverage and atmospheric circulation data. Vilnius University automatic meteorological station is the only station which represents microclimate of Vilnius Downtown (was opened on March 23rd, 2012). The study found that the average annual air temperature in the center of Vilnius is on average 0.59-0.88 ° C higher than in the suburbs. The strongest heat island (0.91-1.44 ° C) is formed during the warm season (May-September), and the smallest differences in average air temperature are formed in January (0.12-0.21 °C). Based on wind and cloud data, their impact on heat island formation was assessed. The heat island that’s formed in Vilnius city is dependent on wind speed, e.g., weaker winds cause a stronger heat island. Cloud cover does not have such a significant impact on the formation of the heat island in Vilnius, but the trend shows that higher cloud coverage may form a weaker heat island. In 50 % of cases, the strongest heat island is formed by anticyclonic atmospheric circulation, 30 % of cases – by cyclonic atmospheric circulation and 20 % – by small gradient fields.
Triggering and evolution of early morning hail over the Croatian coast – convection-permitting modeling study
Authors: Malečić Barbara , Jelić Damjan , Blašković Lucija , Križanac Anna-Maria , Horvath Kristian , Telišman Prtenjak Maja
Published: June 2022

Gaining a deeper physical understanding of the high-impact weather events repeatedly occurring over the Croatian coast is highly needed to reduce the casualties and economic impacts due to these highly localized and hardly predictable phenomena. Recently obtained national hail climatology in Croatia revealed that parts of the Croatian coast are frequently struck by early morning hailstorms with the local maximum of the daily activity occurring approximately at 08:00 h local time (CET). While the afternoon maximums of hail activity could be attributed to the daily heating cycle over land, the mechanisms responsible for the occurrence of the early morning maximum of hail activity are still unknown. This high uncertainty regarding the mechanisms associated with early morning hail formation stem from the scarcity of high-resolution modeling studies and observations of such events further lowering their forecasting ability.

Benefiting from the advantages associated with the convection-permitting high-resolution numerical modeling, this work aims to inspect the triggering mechanism and evolution of such hailstorms. Moreover, a special focus is on the role that synoptic, mesoscale, but also local conditions such as orography or the shape of the coastline, play in the formation of the convergence zone responsible for severe weather effects. Several cases of early morning hailstorms are selected for simulations using Weather Research and Forecasting (WRF) model alongside HAILCAST and Lightning Potential Index (LPI) parameterizations. HAILCAST is a one-dimensional hail growth model that forecasts the maximum hail diameter at the ground. Similarly, LPI highlights the areas with the potential for developing lightning activity. Using these recently developed tools alongside a highly valuable data set of hail observations from Croatian meteorological stations, Croatian hailpad network, and lightning observations from the LINET network, the ability of HAILCAST and LPI to reproduce the observed hail and lightning activity is assessed.

By utilizing a process-oriented approach for analyzing such high impact events, a deeper understanding of the synoptic, mesoscale and local conditions that benefit the development of hailstorms is obtained. This directly benefits the forecasting ability of early morning hail over the Croatian coast, but also highlights the ability and potential improvements of the high-resolution modeling systems and specific tools to represent such highly localized severe weather phenomena.
Open Data from a mixed on-premise and cloud environment at the Finnish Meteorological Institute
Authors: Visa Mikko
Published: June 2022

Finnish Meteorological Institute has a long history of providing open data. Partly as a result of the INSPIRE directive almost all important data was opened back in 2013. Because of this we have quite a long data usage record and experience on technical solutions and user needs. The presentation will open up the current status and future development keeping in mind the upcoming WMO WIS2 development as well as the Open Data directive with its High Value Dataset proposal.

Data is provided via machine-readable interfaces as well as human usable web interfaces. We use on-premise storage and interfaces and in addition offer cloud-based distribution via Amazon Public Dataset program. The current operational interfaces are based on WFS 2.0, WMS and Amazon S3. Most recently added datasets include weather and flood warnings in Common Alerting Protocol (CAP) format and radar data archive via Amazon S3 in GeoTIFF and HDF5 formats. There is development ongoing for providing data via even more developer-friendly interfaces such as the OGC EDR and OGC Features API. Also new data is being added continuously based on our own and user needs.

An impact study has also been conducted for the usage of data from 2018 which revealed some findings on what data is used and how it impacts the users and their potential businesses. Also valuable information on the future needs of users was gathered and the most important findings of this study will be presented during the session. Based on these findings an impact dashboard is currently being setup to continuously monitor the impact and usage of our open data offering.
Short-term rainfall characteristics dependence on the length of processed period
Authors: Crhova Lenka , Kliegrová Stanislava , Valeriánová Anna
Published: June 2022

Characteristics of short-term rainfalls, especially their intensity and design values, are very important for the technical (e.g., sewage system) and hydrological practice. However, measurement and data processing of these characteristics are rather complicated. The rainfall intensity has been regularly measured in the Czech Republic since 1898 with a few manual pluviographs. Automatic rain gauges started to replace them in the late 1990s. Nowadays, a large part of historical pluviographs records has been digitalized. Both the digitalized pluviographs records and the automatic rain gauge measurement provide rainfall series in temporal resolution of 1 minute. Concerning the joined series of these two types of measurement, the relatively long series of rainfall intensity are available. The series longer than 40 years are available for almost 50 stations of the Czech Hydrometeorological Institute. In addition, the indirect rainfall data of high temporal resolution can be also derived from radar measurement. The radar measurement has been in operation for a relatively short period (since 2002) in the Czech Republic but it provides detailed indirect information on a spatial distribution of rainfall.

In our contribution, characteristics of short-term rainfalls (duration 10 – 360 min) for selected stations with the longest series of measurement are studied with regard to different extent of processed period. Especially, return levels of short-term rainfalls estimated for time series of different lengths and beginning are compared. The sensitivity of short-term rainfalls characteristics to the choice of processed period is analyzed in order to assess a possibility to use relatively short rainfall series (e.g., 20-year or shorter) for estimation of return levels with regard to the return period. Thus, a possibility to use return levels estimated from radar data combined with the estimates from longer station series in order to improve information about their spatial distribution is discussed.
Sensitivity of moving PCA in detecting spatial changes in teleconnections
Authors: Piskala Vladimír , Huth Radan
Published: June 2022

Principal Component Analysis (PCA) is a well-known and widely used technique in climatology to identify modes of low-frequency variability of atmospheric circulation. PCA is also used to examine changes in mode patterns over time. We present temporal variability of modes using moving PCA of winter (DJF) monthly mean 500 hPa height anomalies for 20 to 50-year long moving periods with one year step. We compare modes identified for the ensemble mean and ensemble members of the 20CRv2, CERA20, ERA5 and NCEP/NCAR reanalyses. It shows that PCA is sensitive to the period analysed and results can vary considerably if the period is shifted by even one year. Changes in the pattern of a mode are usually interpreted as real changes in atmospheric circulation. However, these changes can be influenced by many aspects, e.g., data quality, length of the analysed period, or settings of PCA. The sudden changes from period to period are more common whit shorter moving periods, while longer periods give more stable results. Some of these sudden changes appear to be more or less random, while others occur in all ensemble members or even in all reanalyses used in the same period. This suggests that comparisons of modes from just a few periods, or even just two periods, may be affected by the presence of a sudden change. Comparing modes from periods that are shifted by only one year, for example, may then provide different results and hence a different interpretation of the shift in the centres of variability.
Characterizing and correcting Heliosat Surface Solar Radiation bias on intra-day time scales with deep neural networks
Authors: Carpentieri Alberto , Meyer Angela , Wild Martin , Folini Doris
Published: June 2022

Accurate intraday forecasts of surface solar radiation (SSR) are essential for utility companies and electricity grid operators. However, satellite SSR estimates can suffer from surprisingly low accuracies on short observation time scales and from significant spatial and temporal biases when compared to ground-based SSR measurements.

We present a bias assessment of two high-resolution satellite SSR products for intra-day application time scales. The satellite SSRs are retrieved with the Heliosat SARAH-2 and the HelioMont algorithms (Müller et al., 2015; Stöckli, 2013). We investigate intra-day and intra-hour estimates for altitudes from 200 to 3570 m a.s.l. We make use of 133 ground stations of the high-precision monitoring network SwissMetNet for the bias analysis. For solar zenith angle (SZA) lower than 90 degrees, we find that Heliosat SARAH-2 underestimates SSR at high altitudes (over 1000m) with an instantaneous root mean squared deviation (RMSD) of 179 W/m2and a mean bias deviation (MBD) of -68 W/m2 in the winter half year. The bias magnitude is approximately the double w.r.t. low altitude stations, due to difficulties in distinguishing snow-covered surfaces from clouds.

We also present an intra-hour bias correction approach: a deep neural network exploiting time-encoding features to model the bias in the time dimension. Our model achieves up to 30% RMSE reduction in the case of Heliosat SARAH-2, especially in mountainous regions. Moreover, we highlight the importance of the clear-sky index for bias correcting the Heliosat SSR estimates. Including a clear-sky index as a regressor in the bias correction improves the bias correction on average from 15.5% to 21.9% RMSE reduction. We also discuss the relevance of bias correcting satellite-derived SSR maps for short-term forecasting applications of SSR.

 

 

References

 

Müller, R., U. Pfeifroth, C. Träger-Chatterjee, J. Trentmann, and R. Cremer (2015), Digging the METEOSAT Treasure-3 Decades of Solar Surface Radiation, Remote Sensing, 7(6), 8067-8101, doi:10.3390/rs70608067.

Stöckli (2013). The HelioMont Surface Solar Radiation Processing. Scientific Report 93, MeteoSwiss, 122 pp.
The Unmanned Multipurpose Airborne Sensor Carrier MASC-V for Offshore Wind Energy Research
Authors: Weber Ines , Platis Andreas , zum Berge Kjell , Schön Martin , Boventer Jakob , Bamati Matteo , Savvakis Vasileios , Miranda Garcia Gabriela , Sajidi Mosaab , Wang Yongtan , Bange Jens
Published: June 2022

The Multipurpose Airborne Sensor Carrier (MASC) is a fixed-wing unmanned aircraft system (UAS) that has been continuously developed and used for in-situ, high-resolution flight measurements of atmospheric variables such as wind, temperature, humidity as well as trace gas and particle concentrations by the Environmental Physics group at University of Tübingen. The most recent innovation in the MASC-series is the MASC-V type vertical takeoff and landing UAS. It has been designed in cooperation with ElevonX d.o.o.. Compared to its predecessor, MASC-3, it can automatically takeoff and land on small patches of land while carrying an identical atmospheric measurement payload. This capability, complemented by an enhanced safety and operational concept, allows for deployment in offshore applications. Particularily, MASC-V has demonstrated safe operation beyond visual line of sight (BVLOS) from the remote safety pilot in offshore applications within the EUs new legal framework introduced in 2020.

Before its first offshore mission, MASC-V underwent a system validation against a meteorological tower at the German Weather Service (DWD) Observatory site at Falkenberg, Germany. Offshore measurements were conducted from the German offshore island Heligoland at the Testfield for Maritime Technologies in cooperation with the Fraunhofer Institute for Applied Material Science in September 2021. The goal of the Heligoland campaign was to validate the remote sensing of sea surface wind measurements by Synthetic Aperture Radar (SAR) satellites of the Sentinel-1 formation at low flight altitudes (20 m - 30 m). SAR satellites can deliver detailed wind data over large areas such as the German Bight including for example wind farm wake effects. Direct validation of these results is difficult with other in-situ techniques. Buoys and measurement towers or platforms can provide stationary data. Aerial measurements with manned aircraft are only possible at higher altitudes. The new UAS data provide the first aerial in-situ SAR validation measurement at low altitude. Additionally, we have demonstrated the capabilities of VTOL fixed-wing UAS for vertical profiling as well as to operate tens of kilometers away from ground personell over open water.
New daily homogenized database for Catalonia using ACMANTv5 (1950-2021)
Authors: Prohom Marc J. , Cunillera Jordi , Domonkos Peter , Herrero Mònica , Busto Montserrat , Barrera-Escoda Antoni , Reynés Jaume
Published: June 2022

A new data set of quality-controlled and homogenized daily maximum (TX) and minimum (TN) temperature and precipitation (PPT) for Catalonia is presented. For temperature, the dataset includes 26 TX and TN long-term series, covering most of the period 1950–2021, while a subset of 7 additional series is also included as a support, but covering a shorter period. For precipitation, a set of 71 daily PPT series is considered, covering the period 1950-2021. Although the number of series is important enough for a geographical area such as Catalonia (32,000 square kilometres), the Pyrenean region is underrepresented. For this reason, the database incorporates two Andorran series.

First of all, a description of the quality control protocol implemented for the detection of erroneous daily observations is shown. It is based on a combination of both, semiautomatic filters and visual data inspection, and special attention is paid for correction of data displacement error.

Secondly, a short description of the methodology for creating long blended series is shown, based on completeness, continuity and location (both altitude and distance) criteria. Then, time series homogeneity is addressed by means of ACMANTv5, one of the best relative homogenization methods, and firstly used on a climate dataset in this work.

Finally, a description of the results of annual and seasonal trends for the whole dataset is provided. Trends are compared with the previous dataset used in Catalonia, that was homogenized using a combination of two approaches: HOMER (monthly time series) and Vincent (transfer of monthly adjustments to daily values). In addition, some climate extreme indicators based on daily time series are also calculated for Catalonia, as part of the Yearly Bulletin of Climate Indicators released by the Meteorological Service of Catalonia (SMC). The homogenized series will be regularly updated and are already available throughout the webpage of the SMC.
Lessons learnt from implementing a postprocessing suite for probabilistic seamless weather forecasts
Authors: Spirig Christoph , Bhend Jonas , Hemri Stephan , Rajczak Jan , Nerini Daniele , Moret Lionel , Liniger Mark A.
Published: June 2022

MeteoSwiss is currently implementing a new NWP postprocessing suite for providing automated local weather forecasts to the general public. As these forecasts are nowadays mainly accessed via smartphone app, we aimed at global postprocessing approaches, that is, optimizing forecasts not only at observation sites but at any location in Switzerland. The system takes advantage of both regional area and global NWP ensemble models with different forecast horizons for providing seamless probabilistic predictions over two weeks leadtime. Finally, the postprocessing suite also considers operational aspects such as robustness towards missing or delayed input data or the ability to cope with limited reforecasts records for training.

Both ensemble model output statistics (EMOS) and machine learning (ML) methods are applied for postprocessing the target parameters temperature, precipitation, wind and cloud cover. Forecast skill in terms of CRPS improves by up to 30% compared to direct model output, with largest benefits for temperature and wind in areas of complex orography and only marginal gains for precipitation during seasons with a high fraction of convective situations. The postprocessing of multiple NWP sources not only allows seamless forecasts, but also proved more skillful than single-model postprocessing. EMOS postprocessing performed well even in case only short reforecast records were available, but was outperformed by ML approaches given sufficient training data. While this general-purpose postprocessing suite improves forecasts overall, it showed weaknesses in some warning-relevant weather situations. Future developments will aim at extending its applicability to these less frequent situations, target further parameters, and extending the use of ML methods.
Decisions made when updating national climate projections for Norway
Authors: Dyrrdal Anita Verpe , Nilsen Irene Brox , Mayer Stephanie , Hygen Hans Olav , Dobler Andreas , Hanssen-Bauer Inger
Published: June 2022

The Norwegian Centre for Climate Services (NCCS), a national collaboration on the natural scientific knowledge for climate adaptation, has published the national climate assessment reports for Norway (Hanssen-Bauer et al., 2009; Hanssen-Bauer et al., 2015, Hanssen-Bauer et al., 2019). The work on national climate projections and associated products are described in Nilsen et al., 2022. In the wake of IPCC AR6 (CMIP6), the Norwegian Environment Agency has commissioned an update of the report “Climate in Norway 2100”, including new fine-scale climate projections for Norway. The report will present changes in past climate of the last 2000 years, current climate and hydrological normals and projected future changes in climate, hydrology and effects on natural hazards.

A literature review of climate change in the Arctic islands of Svalbard will be included (mainly based on “Climate in Svalbard 2100” (Hanssen-Bauer et al., 2019)). However, Svalbard will not be included in the modeling work due to the large difference in data availability, both measurements and model simulations, between mainland Norway and the Arctic.

Due to limited resources, delays in the international collaboration on climate projections (CMIP and EURO-CORDEX) and expectations from society of updated recommendations in the light of the already launched IPCC report, strict priorities in the modeling group are necessary. Early model selection and ensemble size is particularly urged by hydrological modeling. In this presentation we will share decisions made so far in the project. We anticipate our priorities and considerations as working with the newest generation of climate model simulations to be of interest to the international community and other national climate services. 

The presentation will include considerations around emission scenarios and the GCM-RCM ensemble, where a combination of CMIP5 and CMIP6 simulations will be used. We also present results from the historical climate development, the use of empirical statistical downscaling, convective-permitting simulations for improved understanding of changes in heavy rainfall, bias adjustment methods, improvements in the hydrological model and some early ideas on data distribution and outreach. 

References: 

Hanssen-Bauer et al., 2009: Klima i Norge 2100. Bakgrunnsmateriale til NOU Klimatilpasning (in Norwegian).

Hanssen-Bauer et al., 2015: Klima i Norge 2100. Kunnskapsgrunnlaget for klimatilpasning oppdatert i 2015 (in Norwegian). NCCS report 02/2015.

Hanssen-Bauer et al., 2019: Climate in Svalbard. A knowledge base for climate adaptation. NCCS report 02/2019.

Nilsen et al., 2022: From climate model output to actionable climate information in Norway. Frontiers in Climate, accepted.
Can we detect hail from GNSS observations? Case studies from severe weather events in Switzerland 2021
Authors: Aichinger-Rosenberger Matthias
Published: June 2022

Tropospheric products from Global Navigation Satellite Systems (GNSS) have become a vital data source for Numerical Weather Prediction (NWP) and meteorology in general over the last decades. Commonly derived parameters like path delays and integrated water vapor are utilized for data assimilation and have proven to be beneficial for precipitation forecasts. In recent years, also other meteorological phenomena and parameters, such as soil moisture variations, snow properties or foehn winds, have been studied by means of GNSS remote sensing. However, typical troposphere products only consider the gaseous constituents of water to influence GNSS observations. Therefore, GNSS troposphere products neglect the effect of hydrometeors (liquid and solid particles of water) present in the air. Typically, their contribution to tropospheric delays is small, but becomes significant for severe weather events.

This study investigates the signature of hydrometeors, in particular hail, in time series of GNSS data, aiming to detect precursors of hail formation in the vicinity of GNSS stations. Hail represents one of the most treacherous types of severe weather and, at the same time, can only be detected from radar images. As radar observing systems are cost-intensive and therefore very sparsely distributed, the possibility of hail detection from GNSS observations would be very beneficial, especially for nowcasting applications.

The study presents an analysis of different hail events, e.g. the severe thunderstorm happening over the city of Zurich in the night of 12/13.07.2021, which caused tremendous damages on infrastructure. Therefore, a combination of different GNSS observations (tropospheric delays, signal-to-noise ratio, carrier phase residuals) will be used to investigate potential signs of the hail formation and compared to operational radar observations and event reports. Finally, an outlook on the usability of data-driven methods such as machine-learning-based detection methods for this use case will be given.  
Investigating links between the Arctic and East Asian cold extremes with self-organizing maps
Authors: Chang Chueh-Hsin (Shing) , Johnson Nathaniel , Yoo Changhyun , Nyamjantsan Baljinnyam , We Ching-Hsuan , Li Xichen
Published: June 2022

To investigate the connection between the Arctic and East Asia cold events, we categorize the continuum of Eurasian circulation patterns using self-organizing maps (SOMs). The analysis identifies several circulation patterns associated with East Asian extreme temperature occurrences during winter months from November to January (NDJ) 1979-2020, including the warm Arctic-cold Siberia-like pattern (WACS).

The SOM-derived trend pattern of geopotential height at 500 hPa illuminates a zonal wave train of wavenumber 4 extending from central Eurasia to eastern Pacific. The analysis shows that the observed trend is a result of competing influences from the change of frequency of  several different SOM patterns.  

SOMs reveals that some patterns undergo statistically significant trend over certain time periods, notably a positive trend of WACS-like pattern since late 1990s that has resulted in more frequent cold events in East Asia. The composites based on the WACS-like SOM pattern show that an anomalous Siberia High leads to cold surface air temperature over a broad region in East Asia, which can provide potential sources of predictability.

In the associated SST composite, a cold tongue of SST emerges in the southeast Pacific centered around 15o stretching offshore from the coast of Peru, which corresponds closely to the observed NDJ SST trend pattern. We explore the relationship between the WACS-like SOM pattern and El Niño-Southern Oscillation (ENSO). We also evaluate the subseasonal impacts of WACS on East Asian cold events in winter using a probabilistic forecast model determined through multiple linear regression with WACS index, ENSO, and long-term trend as predictors. 
Direct effect of Aerosols on Surface Solar Radiation (GHI and DNI) for Solar Energy: sensitivity study using CAMS, satellite-based and ground-based measurements 
Authors: Kouklaki Dimitra , Papachristopoulou Kyriakoula , Fountoulakis Ilias , Raptis Ioannis-Panagiotis , Kazadzis Stelios , Eleftheratos Kostas
Published: June 2022

The atmospheric aerosols and their direct radiative effects play an essential role on the variability of solar resources on the earth’s surface and, consequently, solar energy potential production, especially in the proximity of aerosol anthropogenic and natural sources. In this work, we investigate the direct impact of total and dust aerosols on two different downwelling surface solar irradiance (DSSI) components, the Global Horizontal Irradiance (GHI) and Direct Normal Irradiance (DNI), under clear-sky conditions and their implications on solar energy. The study focuses on the broader Mediterranean Basin, over a 18-year time period, between 2003 and 2020. In this framework, various studies using total aerosol and dust optical depth (AOD and DOD, respectively) from the satellite-derived ModIs Dust AeroSol (MIDAS) and the model based from the Copernicus Atmospheric Monitoring Service (CAMS) have been appeared in the literature. In this study we use ground-based measurements from the AErosol RObotic NETwork (AERONET) to assess results from the aforementioned datasets/studies. Based on the retrievals of aerosol optical properties from the ground-based measurements, a sensitivity analysis was performed to determine the dependence of the GHI and DNI from aerosol optical properties variability using different time scales and aerosol inputs/datasets with different spatiotemporal resolution. Finally, deviations of the total aerosols/dust effects on DSSI resulting from three datasets (satellite, CAMS, ground-based) are presented and discussed and the reasons of such deviations are presented. The importance of this research lies in the fact that understanding the sensitivity of solar energy applications to the spatiotemporal variability of aerosols and dust, is vital for a sustainable energy transition, energy planning and the efficiency of current and future solar energy related investments.
Investigation of local carbon fluxes from lakes to the atmosphere from eddy covariance observations and the erroneous contribution from non-local processes
Authors: Esters Leonie , Rutgersson Anna , Nilsson Erik , Shalée Erik
Published: June 2022

Inland freshwater bodies such as lakes provide the largest contribution of natural carbon to the atmosphere. To study this contribution to the atmospheric carbon cycle, eddy-covariance flux measurements at lake sites have become increasingly popular. This method allows to estimate the fluxes from local source processes. The difficulty of such local observations is that large-scale non-local processes as for example entrainment or advection can add erroneous contributions to the eddy covariance flux estimations. Scalar quantities from the free-atmosphere above the measurement tower can be entrained into the boundary layer and to the measurement site. Also, the lake is surrounded by land, from where scalar characteristics can be horizontally advected to the measurement site.

During four years of eddy-covariance measurements of carbon dioxide from Lake Erken, a freshwater lake in mid-Sweden, we found unexpected fluxes of carbon dioxide when the lake was entirely covered with ice. We investigated these unexpected fluxes using a statistical approach, which is based on surface-layer data (van de Boer et al., 2014). This approach reveals that non-local processes produce these erroneous fluxes. Additionally, we found that the strength and occurrence of the non-local processes depend on the fetch (distance between the instrumented tower and upwind shore) and the prevailing wind speed. The combination of fetch and wind speed was defined as the time over water. The smaller the time over water, the higher the contribution of the non-local processes.

We present how we corrected the contribution of the non-local processes to the estimations from the eddy covariance flux tower, not only for the ice-covered periods, but also the periods when the lake was free of ice. We present this analysis approach as an example for Lake Erken and propose that it can be extended to other lakes. This can potentially increase our understanding of the carbon exchange between lakes and the atmosphere.
Moisture origin of the extreme precipitation event in Western Europe in July 2021
Authors: Weijenborg Chris , Benedict Imme , Polak Florian , Vermeulen Thomas , Kalverla Peter , Sodemann Harald
Published: June 2022

From the 12th to the 15th of July 2021, Western Europe was confronted with an abnormal amount of precipitation leading to extreme floods and enormous damage in western Germany, Belgium, Luxembourg, and the Netherlands. Locally, almost thrice as much as the monthly precipitation amount was observed, up to 175 mm in two days. In this study, the atmospheric conditions resulting in this extreme precipitation are investigated, with a focus on understanding the enhanced moisture supply leading to the extreme precipitation amounts. Prior to the event, both the Baltic Sea and the Mediterranean Sea were exceptionally warm, and it was hypothesized that high evaporation rates over these regions could be transported towards western Europe to result in these enormous amount of rain.

To test this hypothesis we apply two state-of-the-art moisture tracking approaches. First, we use the Lagrangian moisture attribution diagnostic WaterSip, based on trajectories computed from ERA5 with the Lagranto trajectory model. Second, we use the Eulerian moisture tracking tool WAM-2layers, applied to ERA5 re-analysis data. Comparison of ERA5 with Eobs shows that the precipitation during the event was captured rather well. The moisture sources are mostly located over the continent, and to a lesser extent over the North and Baltic Seas. There is a height dependency of the atmospheric transport of air and moisture. For the surface layer, the moisture that led to precipitation mainly originated from the region of the Baltic Sea, North Sea, Denmark and Scandinavia. Higher up in the atmosphere, this was changing towards the European continent and parts of the Mediterranean Sea.

In addition, the impact on precipitation by adapting the sea surface temperature (SST) of both the Baltic and the Mediterranean was studied using WRF. This analysis showed that SST changes in the Mediterranean had the largest impact on precipitation in western Europe. Furthermore, our results suggest that the Mediterranean Sea, which had a positive SST anomaly of 2˚C, was the main moisture source preceding the precipitation event.
Daily Precipitation Downscaling Using Deep Learning Techniques: The Impact of Missing Value Imputation Methods
Authors: Jung Hae Soo , Oh Sungmin , Park Seon Ki
Published: June 2022

The need for high-resolution meteorological data to identify and understand abnormal weather phenomena is increasing as extreme events are predicted to become more intense and destructive over the coming decades. This is particularly the case for extreme precipitation events, which have high spatial variability and observational uncertainty. While dynamical and statistical downscaling models are often used to provide finer resolution data, recent studies have focused on deep learning (DL)-based downscaling techniques due to their ability to extract spatial features from large spatio-temporal data. High-quality and high-resolution target data are crucial for the train of DL-based models, however, most of the available data are based on land observations and, therefore, have significant missing values. Here, we investigate the sensitivity of a DL-based downscaling model to the choice of missing value imputation (MVI) methods to improve the accuracy of DL-based downscaling. We use precipitation data over the European continent (44∘N-54∘N×5∘E-15∘E) from 2017 to 2020; ERA5 reanalysis data with 0.5∘ × 0.5∘ spatial resolution and E-OBS observational gridded data with 0.1∘ × 0.1∘ resolution are used as the predictor and target, respectively. To build the DL model, we combine the ResNet and Upsampling features. We find that the DL model describes spatial features of precipitation events better than the simple regridding of the coarser data. Moreover, the model trained with the gap-filled target data using ERA5 regridded values shows improved results compared to that with the gap-filled data by replacing missing values with 0. Our results highlight the importance of MVI methods for the DL-based downscaling and the potential of deep learning for precipitation. Our future work will test a wider range of MVI methods including the method using EM(Expectation-Maximization) algorithm and extending coastal target data, and examine the performance of DL-models focusing on extreme events.
Introducing BitTorrent : a scary but efficient way to disseminate archive and real-time data
Authors: Baldeck Nicolas
Published: June 2022

The amount of data being distributed is growing fast. There are more and more datasets, higher-resolution data and new use-cases bringing new users every days.

Given this increase, producers face the challenge of distributing their data in efficient, cost-effective and timely manner.

Several methods are already well developed or being experimented. HTTP and FTP protocols have been used for decades. Technologies such as OPeNDAP or OGC-compliant web services allow to request subsets of the data. Cloud services offer massive parallel dissemination or on-premise processing. But all those solutions are centralized services with an important drawback : being held by a single entity serving the data and supporting the associated costs.

In the same time, decentralized "peer-to-peer" technologies have been widely studied and used for half a century.

From the RFC n°1 in 1969, Napster in 1999 to the well-known BitTorrent or even the transport layer of the Bitcoin blockchain, the concept of decentralized data sharing is now mature.

Enthusiasts with constrained resources found a way to distribute their "questionable" content. A single individual distributing thousands of copies of the latest movie, with the only source being its domestic broadband connection.

What if, instead of a movie, the content was the latest output of an operational NWP model? How many users could we serve? How long would it take? How easy would it be to implement?

Through large-scale cloud experiments, we demonstrate that not only it is technically possible to use BitTorrent or IPFS for disseminating weather data, but also that this solution can often be more efficient than traditional approaches, with higher download bandwidth, low dissemination latency and better reliability. Another significant advantage is cost reduction, leveraging the barriers for producers to make more data available.

We address social questions such as "isn't peer-to-peer a pirate's thing?" or "are users ready to share their bandwidth?".

And finally, we provide guidelines for easily implementing peer-to-peer technologies alongside existing infrastructures (it's a matter of a few hours).
Observation of turbulence profiles with lidar synergy
Authors: Lange Vega Diego , Späth Florian , Abbas Syed , Behrendt Andreas , Wulfmeyer Volker
Published: June 2022

The energy reaching the earth surface in form of solar radiation during the daytime is partly reflected as outgoing radiation, partly conducted into the ground and partly trans- ported into the atmosphere by turbulent eddies of various scales forming the convective boundary layer (CBL) during the daytime. The latter energy flux partitions into sensible heat flux H and latent heat flux L. The understanding of H and L profiles is decisive for correct atmospheric simulations with models since these profiles rule the heat and water budgets, the distribution of humidity and temperature, and thus the atmospheric stability and furthermore the formation of clouds and precipitation (Behrendt et al. 2020).

In recent years, it has been demonstrated that lidar, is capable of not only determining mean profiles and gradients in the daytime CBL, the interfacial layer, and the lower free troposphere above but also higher-order-moment profiles of turbulent fluctuations for more and more variables, like vertical wind, moisture, temperature, aerosol backscatter, horizontal wind and dissipation rate, taking advantage of the synergy between a Raman lidar (temperature, moisture and aerosol backscatter) and Doppler lidars (vertical and horizontal wind) (Wulfmeyer, et al. 2016).

In this regard, the Institute of Physics and Meteorology of the University of Hohenheim has developed a thermodynamic profiler based on the Raman lidar technique, namely the Atmospheric Raman Temperature and Humidity Sounder (ARTHUS) (Lange et al. 2019). ARTHUS can be operated on ground-based, ship-borne and airborne platforms.

Stable 24/7 operations over long periods were achieved during several field campaigns and at the Land Atmosphere Feedback Observatory (LAFO) at the University of Hohenheim accumulating almost a year of data until now and covering a huge variety of weather conditions. Two collocated Doppler lidars (one in vertically staring mode and a second one in a 6-beam scanning mode) give the horizontal and vertical wind components, needed for H and L calculations, as well as dissipation rate.

ARTHUS has been also deployed during the EUREC4A field campaign (Stevens et al, 2020), on board RV Maria S Merian, to study ocean-atmosphere interaction, (18 Jan to 18 Feb 2020), along with two Doppler lidars.

Between 15th July and 20th September 2021, ARTHUS was deployed at Lindenberg Observatory from the German Weather Service (DWD).

At the conference, L, H and dissipation rate case examples from these campaigns will be presented.

References

Behrendt et al. 2020, https://doi.org/10.5194/amt-13-3221-2020

Lange et al. 2019, https://doi.org/10.1029/2019GL085774

Stevens et al. 2021, https://doi.org/10.5194/essd-2021-18

Wulfmeyer et al. 2016, https://doi.org/10.1175/JAS-D-14-0392.1
What controls the strength of convective circulations in real-case Large-Eddy Simulations during FESSTVaL?
Authors: Sakradzija Mirjana , Dewani Noviana , Beyrich Frank , Klocke Daniel , Bastak Duran Ivan , Schmidli Juerg , Schlemmer Linda
Published: June 2022

Large-Eddy Simulations (LES) are set up to complement the FESSTVaL Field Experiment on sub-mesoscale spatio-temporal variability in Lindenberg using the Icosahedral Nonhydrostatic (ICON) model in a limited-area mode. The LES represent realistic conditions in the area around the observational site and are forced by the ICON-D2 analysis and forecasts. Four nested domains are used of which the innermost one has a horizontal grid resolution of about 75 m. This presentation will focus on the sensitivity of surface turbulent heat fluxes and their spatial variability on soil moisture to explain the discrepancies between the modeled and observed sensible heat flux. In-situ measurements provide the surface heat flux representative of different land cover conditions, crops and forests, while the area-averaged flux is measured along a scintillometer path of several kilometers. Next, a series of experiments will test the sensitivity of the strength of convective circulations on soil moisture. In these experiments, the initial soil moisture is increased, decreased or perturbed in different ways to find what role the variability of soil moisture plays in setting the strength of the convective circulations. The variance of vertical velocity is then compared to the FESSTVaL measurements derived from Doppler Lidar in vertical stare mode to find how realistic the circulations in LES are. This analysis focuses on several "golden days" that include a clear-sky day, three shallow-convective days of which one developed boundary-layer rolls and two precipitating days in June 2021. The complete set of the LES covers the intensive observational period of FESSTVaL from the 7. June to 4. July 2021.
Atmospheric dispersion modelling in the cloud 
Authors: Carion Tristan , Jassens Bart , Delcloo Andy
Published: June 2022

For both military and civilian purposes, the assessment of the impact of a CBRN agent release is crucial. To assess the area of contamination of an agent, atmospheric dispersion models can be used. A web application for assessing the impact of CBRN-type incidents is currently being developed in the frame of a joint project of the Royal Military Academy of Belgium, the Royal Meteorological Institute of Belgium and ECMWF. It is hosted on the European Weather Cloud system, benefiting from the closeness to the ECMWF meteorological data and from the computing power. The application is designed to provide both deterministic and ensemble dispersion modelling capabilities for any worldwide hazardous release. 

The dispersion model programs are wrapped in Julia, a modern technical computing language that runs in the backend of the web application. The Julia package manager offers an efficient and easy way to port third-party libraries, avoiding the sometimes painful installation of scientific softwares. For example, the Lagrangian dispersion model FLEXPART can be installed with a one-liner, which makes it very easy to use on the cloud once Julia is installed.

The main objective of the application is to provide a quick and reliable decision-making input for non-experts in dispersion modelling in case of CBRN-incidents. Response models will also be implemented using event driven simulation to account for external data (population density, topography etc.). Besides, the application can also be used as a user-friendly interface to multiple dispersion models, making it easier to run, visualize and execute simulations for more experienced users.
DWD Geoportal – A central hub for Open Data, API and communication
Authors: Reetz Björn , Riede Hella , Fuchs Dirk , Jerg Matthias , Hagedorn Renate
Published: June 2022

In November 2021, DWD launched the pilot phase of the new DWD Geoportal at dwd-geoportal.de. It offers user-friendly exploration of DWD's Open Data, combining access to data, metadata, and documentation along with fulltext search and interactive previews powered by OGC WMS. This new front end covers datasets hosted on opendata.dwd.de as well as other DWD Open Data sources, for instance the Climate Data Centre and the DWD GeoServer.

The contribution will outline the latest version of the new DWD Geoportal and discuss current development and features that are planned for integration in the near future.

Open Data has been a part of the DWD data distribution strategy since 2017, starting with a small selection of meteorological products, but the number of available datasets has grown continuously over the last years. Since the start, users can download file-based meteorological products without registration. Free access and the variety of products have been welcomed by the general public as well as private met service providers. However, the more datasets were provided in a directory structure, the more tedious it became to find and select among all available data. Also, metadata and documentation were available, but on separate DWD websites. The DWD Geoportal adressed these trends, especially having new users of DWD's open data in mind.

Cloud technology is a suitable way forward for hosting the geoportal in it's operational state along with the data. Benefits are expected for the easy integration of rich APIs with the DWD Geoportal, and the flexible and fast deployment and scaling of optional or prototypical services such as WMS-based previews. DWD focuses on cloud technology for new development projects.