bluuebunny/crossref_metadata_2025 huggingface dataset:
Assessing the quality of state-of-the-art regional climate information: the case of the UK Climate Projections 2018
Authors: Baldissera Pacchetti Marina , Dessai Suraje , Stainforth David , Bradley Seamus
Published: June 2021

We assess the quality of state-of-the-art regional climate information intended to support adaptation decision-making. We use the UK Climate Projections 2018 (UKCP18) as an example of such information. The probabilistic, global and regional land projections of UKCP18 exemplify some of the key methodologies that are at the forefront of providing regional climate information for decision support in adapting to a changing climate. We assess the quality of the evidence and the methodology used to support their statements about future regional climate derived from these projections along five quality dimensions: transparency, theory, diversity, completeness and adequacy for purpose. The assessment produced two major insights. First, the main issue that taints the quality of UKCP18 is the lack of transparency. The lack of transparency is particularly problematic if the information is directed towards non-expert users, who would need to develop technical skills to evaluate the quality and epistemic reliability of this information. Second, the probabilistic projections are of lower quality than the global projections. This assessment is a consequence of both lack of transparency in the probabilistic projections, and the way the method is used and justified to produce quantified uncertainty estimates about future climate. We suggest how higher quality could be achieved. This can be achieved by improving transparency of evidence and methodology and by better satisfying other dimensions through changes in elements of evidence and methodology. We conclude by recommending further avenues for testing the effectiveness of the framework and highlighting the need for further research in user perspectives on quality.
Progress towards an improved parameterisation of small-scale orographic impacts on the atmospheric boundary layer
Authors: Quimbayo-Duarte Julian , Schmidli Juerg
Published: June 2021

An accurate representation of the momentum budget in numerical models is essential in the quest for reliable weather forecasting, from large scales (climate models) to small scales (numerical weather prediction models, NWP). It is well known that orographic waves play an important role in large-scale circulation. The vertical propagation of such waves is associated with a vertical flux of horizontal momentum, which may be transferred to the mean flow by wave-mean flow interaction and wave-breaking (Sandu et al., 2019). The orography scales inducing such phenomena are often smaller than the model resolution, even for NWP models, leading to the need for parameterisation schemes for orographic drag. Yet, such parameterization in current models is fairly limited (Vosper et al., 2020). The present work aims to contribute to an improved understanding and parameterization of the impact of small-scale orography on the lower atmosphere with a focus on the stable atmospheric boundary layer.

As a first step, an idealized set of experiments has been designed to explore the capabilities of the Icosahedral Nonhydrostatic model in its large eddy simulation mode (ICON-LES, Dipankar et al., 2015) to represent turbulence processes in the stably-stratified atmosphere. Initial experiments testing the model performance over flat terrain (GABLS experiment, Beare et al., 2006), orographic wave generation (shallow bell-shaped topography, Xue et al., 2000) and moderate complex terrain (U-shaped valley, Burns and Chemel 2014) have been conducted. The results demonstrate that ICON-LES adequately represents the boundary layer processes for the investigated cases in comparison to the literature.

In a second step, an idealized set of experiments of atmospheric flow over idealized sinusoidal and multiscale terrain has been designed to study the impact of the orographically-induced gravity waves on the total surface drag and the vertical flux of horizontal momentum. The influence of different atmospheric conditions is assessed by varying the background wind speed and the temperature stratification at the initial time.
Making forecasters SAD: Verification of Scale, Anisotropy and Direction using wavelets.
Authors: Buschow Sebastian , Friederichs Petra
Published: June 2021

Many atmospheric phenomena like fronts, convection and turbulence leave a distinct imprint on the spatial structure of meteorological fields such as precipitation, wind and temperature. Whether or not a forecast model is able to realistically simulate the resulting spatial correlation patterns is therefore a relevant question for model developers, forecasters and end users alike. Highly resolved numerical models have the potential to achieve this goal, but their realism is often difficult to assess objectively due to the sheer amount of data and wide variety of possible error contributions. 

While some existing verification methods measure an overall “structure” error, most of these approaches are limited to precipitation fields and fail to produce specific, interpretable judgements. Here, we introduce a new structural verification technique based on the dual-tree complex wavelet transformation: The SAD-scores explicitly quantify how well the observed spatial Scales, degrees of Anisotropy and preferred Directions are represented by the simulation. Directional aspects in particular have previously often been neglected, but can be important in assessing the realism of predicted fronts, convergence lines and organized convection. 

Unlike many established techniques, SAD is applicable not only to precipitation but to any meteorological field of interest. General verdicts like “the structure was predicted poorly” can be resolved into specific statements like “the modelled convection was too small in scale” or “the simulated front was too linear and rotated by an angle of X degrees”. The localized nature of the wavelets furthermore allows us to conveniently display the structural properties on a map. Lastly, making use of the inverse wavelet transform, we show how the detected structural errors can potentially be corrected, thereby leading the way towards future post-processing applications.
Projected changes in the frequency of thermal comfort conditions at cold and warm cities, based on advanced bioclimatic indices
Authors: Katavoutas Giorgos , Founda Dimitra , Varotsos Konstantinos V. , Giannakopoulos Christos
Published: June 2021

Climate change and global warming affect heat or cold related stress in humans and ecosystems. Human thermal comfort or discomfort conditions in particular, are assessed through the application of simple or rational thermal (or bioclimatic) indices. In contrast to simple, advanced indices like the Universal Thermal Climate Index (UTCI) are based on human energy balance between the human body and thermal environment and involve multiple meteorological parameters, like air temperature, air humidity, wind speed and mean radiant temperature. Accelerating warming is expected to affect both heat and cold related stress conditions in the future, resulting in increased frequency of heat related stress, especially at warmer cities like the cities of Mediterranean, and at the same time reduced frequency of cold related stress at colder cities, like the cities of northern Europe. Asymmetrical changes in the frequency of heat or cold related stress conditions will eventually determine the future changes (increases or decreases) in the frequency of conditions of no thermal stress at cities of different background climate. The study will investigate future changes in ‘thermal comfort’ or ‘favourable’ conditions at cities with different base climate. Simulations by a set of state-of-the-art Regional Climate Models (RCMs) in the frame of EURO-CORDEX (Coordinated Regional Climate Downscaling Experiment) modeling experiment (http://www.euro-cordex.net) with a horizontal resolution of about 12 km (0.11°) downscaled over the areas of interest will be realized for a control period and for two periods in the near and distant future, under the RCP4.5 and RCP8.5 future emissions scenarios. Simulations of meteorological variables for the estimation of UTCI at 3-hourly step will be retrieved for the closest land model grid point to the observation sites, while the performance of the RCMs will be evaluated against results from observations for the control period.
Assimilating visible satellite reflectances in combination with radar data in a pre-operational convective-scale seamless prediction system 
Authors: Bach Liselotte , Deppisch Thomas , Scheck Leonhard , de Lozar Alberto , Welzbacher Christian , Khoshravian Kobra , Blahak Ulrich , Stephan Klaus , Schraff Christoph , Ulbrich Sven , Potthast Roland
Published: June 2021

In the framework of the SINFONY project at Deutscher Wetterdienst (DWD) we have developed data assimilation of visible satellite reflectances of the SEVIRI instrument (MSG) and radar observations in a rapid update cycle (ICON-D2-KENDA-RUC) which will be running in a first 24/7-testsuite starting in spring of this year. Our major goal related to the assimilation of these new observation systems is to improve the positioning of cloud and precipitation systems and their intensities, needed for the seamless transition of radar nowcasting to numerical weather prediction (NWP) in our SINFONY system. We give an overview of the steps undertaken in the course of developing the data assimilation of visible satellite reflectances. This includes quality control, observation error modelling, data reduction and bias correction of the reflectances. Further development and enhancement of the forward operator MFASIS is still ongoing. A major step to allow for a successful assimilation has been the improvement of microphysical consistency between the NWP model and MFASIS both with 1-moment and 2-moment microphysics to reduce the bias of first-guess departures. To further enhance and stabilize the agreement of observations and model climatologies over the course of the year and different weather regimes, an innovative histogram-based bias correction has been developed. We show results of data assimilation experiments combining visible reflectances and radar data in the ICON-D2-KENDA-Rapid Update Cycle using 2-moment microphysics. Further, we discuss the improvement of forecast skill from both observing systems and the way they complement each other – putting special emphasis to the key variable of interest in the SINFONY system, namely radar reflectivity.
Quantifying the Wind Direction Climate over the Baltic States using Principal Component Analysis
Authors: Sile Tija , Pogumirskis Maksims , Seņņikovs Juris , Bethers Uldis
Published: June 2021

Wind direction is an important meteorological parameter, however, its analysis is made difficult by it being a circular variable that cannot easily be averaged. The goal of this study was to identify the main features of wind direction climate over the Baltic States in a methodical way. We used Principal Component Analysis (PCA) for this purpose.

Two data sets were used: UERRA re-analysis with 11 km horizontal resolution and surface wind direction observations from Latvian stations. We used PCA on both of these datasets and analyzed the results together. Such an approach enabled comparison of the wind direction climate of the reanalysis with the observations. However, preliminary results suggested applying PCA also on the subset of UERRA data that corresponds to observation stations. This eliminates effects caused by differences in spatial coverage between  gridded and station datasets.

To verify the quality of the reanalysis independently of the PCA method, Earth Mover’s Distance (EMD) was used to directly compare wind direction distributions at the station grid points with observations.

Results show good correspondence overall between the reanalysis data and the observations. The PCA method identifies SW as the prevailing wind direction, in good agreement with the expectations. The PCA results enable identification of the main wind direction features of the region, such as increased frequency of northern winds during the summer and increased frequency of southern winds during the winter that can be explained by synoptic scale processes. Additionally, the PCA method identifies coast parallel flows created by mesoscale interaction between the Baltic Sea and the dry land, and wind deflection around terrain (hills up to 300 m above sea level).

This approach could be generalized to other regions and help create a more systematic understanding about wind direction climate, as well as assist in quantifying the performance of reanalysis and identify meteorological processes that need to be investigated further.

Corresponding author is grateful to the project “Mathematical modelling of weather processes - development of methodology and applications for Latvia (1.1.1.2/VIAA/2/18/261)” for financial support.
Extremal dependence as given by the tail pairwise dependence matrix inprecipitation and temperature data
Authors: Szemkus Svenja , Friederichs Petra
Published: June 2021

A better understanding of the dynamics and impacts of extreme weather events and their changes due to climate change is the subject of the ClimXtreme project (climxtreme.net) funded by the German Federal Ministry of Education and Research. 
The CoDEx project is investigating how data compression techniques can contribute to a better description and understanding of extremes. Various unsupervised learning approaches, such as clustering or principal component analysis, focusing on extremes have been developed recently and will be investigated and compared within the project. 
We use principal component analysis to study the spatial (co-)occurrence during extreme weather events such as heavy precipitation, heat waves or droughts. The focus on extreme events is done by using the tail pairwise dependence matrix (TPDM), proposed by Cooley and Thibaud (2019) as an analogue to the covariance matrix for extremes. Since the simultaneous occurrence of precipitation deficits and high temperature played an important role, especially in heat waves, we explore how Cooley and Thibaud's concept can be used in this regard. We propose an estimation of the TPDM based on pairwise dependencies of two variables. A singular value decomposition gives us insight into the spatial co-occurrence of extreme spatial patterns, which contributes to the understanding of so-called compound events. 
We use daily precipitation and temperature data, including observational stations and regional reanalyses in Germany and Europe. Using this method, we extract spatial patterns over Germany and Europe based on extreme dependencies. In addition, we identify historical events, and examine them in more detail in this context.
A sequential decomposition of proper scoring rules
Authors: Allen Sam , Ferro Chris , Kwasniok Frank
Published: June 2021

The objective assessment of forecasts plays an integral role in the development of a prediction system. Scoring rules condense all information regarding forecast performance into a single numerical value, providing a convenient framework to objectively rank and compare competing prediction schemes. However, the value of a forecast to its user will depend on how it is to be used, and it is therefore necessary to consider several different characteristics of a forecast’s performance. Although scoring rules provide only a single measure of forecast accuracy, they can often be decomposed into components that each assess a distinct aspect of a forecast, such as its calibration or information content. These decompositions of scores provide additional feedback to the forecaster, which can be used to identify strengths and limitations in the prediction scheme, and, in turn, help to improve future forecasts. But these aspects of forecast quality could themselves depend on several factors, such as the time of the year, the spatial location, or on the value of the forecast itself, and it is therefore useful to evaluate the performance of a forecast under different circumstances; if a forecaster were able to identify situations in which their forecasts perform particularly poorly, then they could more easily develop their forecast strategy to account for these deficiencies. To help forecasters identify such situations, we introduce a novel decomposition of scoring rules that allows for a more rigorous examination of the sources of information in a forecast whilst simultaneously quantifying the magnitude of conditional forecast biases. We apply this decomposition to MeteoSwiss COSMO-2E forecasts for the occurrence of moderately extreme weather events and illustrate how the additional information provided by this decomposition can be used to design more appropriate statistical post-processing techniques.
How do the sea and the land conditions affect the coastal breezes? 20 days analysed from WRF simulations in the Gulf of Cádiz (Iberian Peninsula)
Authors: Román-Cascón Carlos , Mulero-Martínez Roberto , Bruno Miguel , Yagüe Carlos , Lothon Marie , Lohou Fabienne , Álvarez Oscar , Gómez-Enri Jesus , Izquierdo Alfredo , Mañanes Rafael , Adame Jose Antonio
Published: June 2021

Sea breezes are common and recurrent thermally-driven winds formed in coastal areas under conditions of weak synoptic forcing, due to the differential heat capacity of the sea and the land. Their accurate forecast is key because of the impacts on maxima near-surface temperatures, humidity (and then thermal comfort), pollutants distribution, convective-systems formation, etc., being crucial for the wind energy sector and because they develop in areas that are normally densely populated.

Some studies have investigated the impacts of the surface conditions in coastal breezes in different regions around the world. Their findings are diverse, mostly attributed to differences in the marine boundary layer stability, which can favour or inhibit the vertical mixing. This is needed to vertically distribute thermal changes in the land or the sea surfaces to deeper atmospheric layers, and thus to modify the horizontal surface pressure gradients. In this work, we use the Weather Research and Forecasting (WRF) model to investigate how the coastal breezes are affected by changes in the surface representation in the Gulf of Cádiz, in the Atlantic coast of the Southwestern Iberian Peninsula. We focus on artificial and realistic changes in land use, soil moisture and sea surface temperature. The analysis is performed for a case study of 20 days in August 2020, characterised by many coastal-breeze events in the area analysed and by a gradual decrease in the sea surface temperature. The model is evaluated with observational data at different coast locations, inland and on the ocean, as well as using wind speed transects from satellite altimetry.
How much atmospheric dynamics do we need to capture yield reductions from proposed large wind parks in the German Bight?
Authors: Minz Jonathan , Imberger Marc , Badger Jake , Kleidon Axel
Published: June 2021

German energy scenarios expect 50 - 70 GW of wind turbine capacity to be installed in the German Bight by 2050. Such deployments were expected to yield ∼4000 full load hours (FLH) per year, owing to higher wind speeds compared to land. However, a recent reevaluation of these estimates using the Weather Research and Forecasting model with Explicit Wake Parameterization (WRF-EWP) and the Kinetic Energy Budget of the Atmosphere model (KEBA) found that if the proposed deployments are installed, yield per turbine could be as low as 3000 - 3500 FLH per year, although the total yield still increases. Whereas WRF represents a comprehensive physical representation of atmospheric dynamics, KEBA is an simple approximation of complex atmospheric processes. It states that it is the fixed kinetic energy budget of the boundary layer volume encompassing the wind park which determines large wind park (order of104km2) yields rather than just wind speeds. This budget is a function of park geometry and boundary layer heights. Increasing the number of turbines within the wind park removes more kinetic energy from the budget. This leads to slower wind speeds and lower overall yields. The estimates from both approaches were within 20% of each other. Here, we examine these results in greater detail to uncover key atmospheric constraints on the performance of large offshore wind parks. We investigate the role of atmospheric variables like wind direction, atmospheric stability, boundary layer height and surface friction on large scale generation by comparing the estimates of the two modelling approaches. We consider the WRF simulations of large-scale wind power generation and atmospheric circulation as the most realistic available representation, since farms of the scale considered in this study are not yet in operation. We also test the underlying assumptions of KEBA and hence the limits of its applicability. Through a detailed comparison of the two approaches we will provide insights into the effectiveness of KEBA. We posit that estimates of regional wind energy potential need to account for large wind park - atmosphere interactions which may constrain large wind park yields. Our analysis will provide policy makers with a simple yet physically representative tool for making robust predictions of future offshore wind park performance, thereby enabling the design of better energy policies.
Experiences with virtual schools on “Climate data for impact assessments”
Authors: Bessembinder Janette , Klostermann Judith , Dankers Rutger , Djurdjevic Vladimir , Halenka Tomas
Published: June 2021

The provision of climate services to users is a fast developing field. In support of this development, the IS-ENES3 project, funded within the EC Horizon2020 program, organized three schools on “Climate data for impact assessments” in 2020 and 2021. In an Autumn school, a Spring school and a Summer school, climate scientists and impact scientists were brought together. An important aim of the schools was to enhance interaction between Vulnerability-Impact-Adaptation (VIA) researchers, climate services providers and climate researchers. Another aim was to provide an overview of information on climate modeling, climate data, impact modelling and climate services based on the work of the IS-ENE3 project.

In the first three weeks a series of lectures was given, covering topics such as climate data and modelling, impact models, portals for accessing and processing climate data, setting-up impact assessments, and communication of results to stakeholders. In the last three weeks the participants worked in small groups of one climate scientist with one impact scientist on a case study under the guidance of the course lecturers. Impact and climate researchers were combined on purpose to let them experience how they could help each other.

Originally the schools were planned to take place on-site (e.g. in Prague) during one week; however, due to COVID-19 the schools had to be transformed to virtual schools with two weekly sessions during six weeks. Although the virtual set-up had some disadvantages (e.g. less possibilities for networking), there were also some advantages (e.g. the possibility to record the lectures and make them available to a broader audience; more time to explore and work with climate data in between the sessions, no CO2 emissions for travelling). During this presentation we will present the set-up of the schools and the conversion to a virtual school. We will focus on the lessons learnt and the evaluation of the virtual schools by the participants and give some recommendations for similar schools and how to link the climate and VIA research communities .
Application to NWP Models Verification of an Atmospheric Circulation Patterns Classification
Authors: Salerno Raffaele , Bertolani Laura
Published: June 2021

At Meteo Expert, a Italian private organization providing weather and climate services and formerly known as Epson Meteo Centre, we are using the Self Organizing Map (SOM) algorithm to study synoptic circulation over Southern Europe, evaluating the capability of five NWP global models and one multi-model ensemble to predict its variability in order to relate synoptic circulation patterns to temperature and precipitation forecast’s quality over Italy. SOM is an iterative algorithm that ‘learns’ the patterns of the input data vectors and organizes them into nodes within the SOM space, arranging like patterns in neighboring nodes and the most unlike patterns in nodes farthest from each other. Daily observed and predicted weather types from the five NWP global models and the multi-model ensemble were recognized and classified by the SOM. The SOM-based classification built for our purposes produces a 12-weather-type set using daily 500 hPa and 700 hPa geopotential, sea level pressure, 850 hPa temperature and 700 hPa specific humidity. The five global models are GFS from National Centers for Environmental Prediction, IFS from European Centre for Medium-Range Weather (ECMWF), Arpege from Meteo France, GEM from Canadian Meteorological Centre, ICON from Deutscher Wetterdienst, together with MIX, our multi-model ensemble. Here we would like to present some examples of this operational activity in the one-year-period, also showing how much the source of forecast errors may depend on large-scale dynamics rather than model's physical parameterisations. A quality index has been used to quantify the overall ability of models in predicting the circulation patterns, showing that MIX and ECMWF reached the best performance within 96 hours of forecast.
Fog dissipation through ground warming monitored by satellite image : an approach to support regional forecasting 
Authors: Cros Sylvain , Haeffelin Martial , Toledo Felipe , Jean-Charles Dupont , Jordi Badosa
Published: June 2021

By reducing the atmospheric visibility, fog events have strong impacts on several humans activities. Transport security, military operations, air quality forecast and solar energy production are critical activities considering fog dissipation time as a high valuable information.

Fog dissipation occurs through these two following processes. (1) An adiabatic cloud elevation converts the fog into a low stratus, increasing the visibility at ground level while keeping an overcast sky. (2) A radiative warming can break through a large continuous fog deck. Then, the cleared area increases progressively by heating the ground of the neighboured fog covered area.

These two events are particularly difficult to forecast using NWP models as many non-linear local processes at short-time scale are involved. Moreover, current network of fog presence sensors is too scarce to analyse and/or anticipate the phenomena. Subsequent images of geostationary meteorological satellite offer a high temporal resolution that enables to monitor large fog decks and detect punctual clear areas that induce dissipation (case 2). However, fog detection using satellite images suffers from a lack of distinction between fog and very low stratus.

In this work, we explored the potential of MSG SEVIRI radiometer through radiance observations and more advanced cloud products to analyse fog events effectively observed at the SIRTA atmospheric observatory (Palaiseau, France). We assumed that, during these events, pixels classified as “very low cloud” according to SAF-NWC algorithm were covered by fog. We monitored the evolution of these pixels using a cloud index derived from HRV channels, providing a more detailed spatial distribution of cloud cover during day time. We analysed the evolution of brightness temperature spatial gradient from the SEVIRI infrared window channel (IR 10.8µm). We isolated cases where ground warming situation could anticipate an irreversible fog dissipation. Then we deduced some fog dissipation forecasting principles.

This approach has the potential to provide to users information on morning fog sustainability with a higher accuracy and finer temporal resolution than NWP. Ongoing work focuses on characterizing favourable situations for accurate forecasts, while further predictors are investigated using recent products providing a smart distinction between fog and low stratus using SEVIRI images.
Using expert elicitation to strengthen future regional climate information for climate services
Authors: Grainger Sam , Dessai Suraje , Daron Joseph , Taylor Andrea , Ling Siu Yim
Published: June 2021

Climate change knowledge can inform regional and local adaptation decisions. However, estimates of future climate are uncertain and methods for assessing uncertainties typically rely on the results of climate model simulations, which are constrained by the quality of assumptions used in model experiments and the limitations of available models. To strengthen knowledge for adaptation decisions, we use structured expert elicitation to assess future climate change in the Lower Yangtze region in China. We elicit judgements on future changes in temperature and precipitation as well as uncertainty sources, comparing elicited judgements and model outputs from phase 5 of the Couple Model Intercomparison Project (CMIP5). We find high consensus amongst experts that the Lower Yangtze region will be warmer in the coming decades, albeit with differences in the magnitude of change. There is less consensus around the direction and magnitude of change for future precipitation change in the region. When compared with CMIP5 model outputs, experts provide similar or narrower uncertainty ranges for temperature change and diverse ranges for precipitation. Experts considered additional factors (e.g. model credibility, observations, theory and paleo-climatic evidence) and uncertainties not usually represented in conventional modelling approaches. We explore the value in bringing together multiple lines of evidence in the context of climate services, arguing that while decision makers should not rely solely on expert judgements, this information can complement model information to strengthen regional climate change knowledge. These multiple lines of evidence can help in building dialogue between climate experts and regional stakeholders, contributing to the development of climate services. 
Predicting Subseasonal Hydrological Droughts for Swiss Lakes and Large Rivers – Combining Mesoscale Hydrological EPS and Machine Learning Approaches  
Authors: Chang Annie Yuan-Yuan , Jola Simone , Bogner Konrad , Domeisen Daniela I.V. , Zappa Massimiliano
Published: June 2021

Although Switzerland is not commonly associated with the occurrence of droughts, in recent years, Switzerland has experienced several unprecedented drought events. Considering that many sectors in Switzerland depend heavily on its water resources, hydropower production, navigation and transportation, agriculture, and tourism, it is important to establish a reliable warning system for early drought recognition. Drought forecast at subseasonal timescales, particularly the onset of a drought event, remains a challenge which is linked to the limited skill of subseasonal meteorological forecasts especially in Europe. The goal of this research is to develop a model to produce skillful subseasonal prediction of low-flows in large river basins and water levels of the major lakes in Switzerland. The envisaged methodology combines monthly hydro-meteorological forecast outputs from the hydrological model PREVAH (Precipitation-Runoff-Evapotranspiration HRU model) with machine learning algorithms. An operational setup of PREVAH has been previously implemented for Switzerland with meteorological forcing from 51 ensemble members and 32 days lead time from the operational extended-range prediction system of the European Centre for Medium-Range Weather Forecasts (ECMWF). Although the PREVAH forecasts are considered semi-idealized (assuming natural flow conditions) and they do not go through an in-depth calibration process, they provide a robust representation of the hydrological processes at the catchment level. The proposed machine learning model is expected to mimic the flow routing mechanism and match PREVAH forecasts from its 300 catchments with measured streamflow and lake level in river basins. The proof-of-concept will focus on the river Aare until the station of Brügg-Aegerten, downstream of the lake of Biel. The findings of this work will highlight the potential of directly linking mesoscale hydro-meteorological forecasts with streamflow and providing subseasonal low-flow predictions in an operational set-up.
Winter extreme weather in EURO-CORDEX climate models and their links to large-scale atmospheric circulation
Authors: Plavcová Eva , Lhotka Ondřej , Stryhal Jan
Published: June 2021

Regional Climate Models (RCMs) are powerful tools to study changes in the climate system on the regional scale. However, the reliability of their simulations has been considerably limited by the longstanding issue that climate models often fail to reproduce various aspects of the historical climate. In our study, we analyse how RCMs from the EURO-CORDEX project are able to reproduce extreme winter weather. We analyse temporal and spatial characteristics of extreme wind gust, extremely cold temperature, and extreme precipitation. Model outputs are validated against observed data from the European gridded observational database (EOBS) and the novel ERA5 reanalysis. We focus on the Central European domain (defined between 48–52°N and 10–19°E) over the 1979 – 2017 period. We investigate a set of 9 simulations of 3 different RCMs driven by 3 different global climate models which allow us to analyse the influence of driving data on the RCM’s performance. Since local climate elements are relatively tightly linked to a large-scale atmospheric circulation over Europe in winter, we also evaluate the ability of RCMs to reproduce the atmospheric circulation and its links to selected high-impact winter weather in detail. We use the classification of circulation based on the method of Sammon mapping. Investigation of these links can lead to better physical understanding of the climate and to the identification of inadequacies in simulated characteristics of the studied events. All of this is an important step forward in further improving the models and enhancing the credibility of climate change scenarios based on climate model simulations.
Relating Integral Latent Heat Release to Integral Mass Flux in Cumulus Convective Clouds.
Authors: Kogan Yefim
Published: June 2021

Parameters of latent heat release were analyzed using LES model data. The system of trade wind cumulus clouds observed during the RICO field project was simulated over a domain size of a mesoscale model grid. The initialization of simulations were described in detail in the LES model intercomparison study by van Zanten et al 2011. Over 2000 clouds were analyzed focusing on relationship between parameters of latent heat release (phase transition rates) and dynamical/microphysical cloud characteristics.

Thephase transition rates, which in warm tropical clouds are represented by processes of condensation/evaporation, were analyzed by stratifying the clouds by their size/stage of maturity. The analyzed parameters included, among others, integral mass and buoyancy fluxes, cloud and rain water parameters, supersaturation. In addition to phase transition processes, we also analyzed the formation of precipitation and its dependence on cloud dynamical parameters. Of particular interest was the ratio of precipitation to condensation rate, which can be considered as an indicator of cloud “precipitation efficiency” (PE=PR/CR). We found that a critical vertical cloud depth separates clouds where PE is predominantly  < 1, from clouds where precipitation efficiency is mostly larger than one

The investigation of the relationships between phase transion rates and  cloud thermodynamical parameters revealed a remarkably strong correlation  between integral latent heat released in a cloud and its integral mass flux. The anticipated dependence on buoyancy flux was significanly weaker.

The identified latent heat-mass flux dependency and, based upon it, derived simple functional formulation can be important for the development of parameterization of subgrid latent heat release in meso- and large-scale forecast models.
Multi-scale analysis of turbulence data from AIRTEC-CM urban field campaigns in Madrid
Authors: Yagüe Carlos , Román-Cascón Carlos , Ortiz Pablo , Sastre Mariano , Maqueda Gregorio , Serrano Encarna , Artiñano Begoña , Gómez-Moreno Francisco Javier , Díaz-Ramiro Elías , Alonso Elisabeth , Fernández Javier , Borge Rafael , Narros Adolfo , Cordero Jose María , García Ana M. , Núñez Andrés
Published: June 2021

Several urban field campaigns have been carried out in the city of Madrid (Spain) during the years 2020 and 2021 in the frame of the AIRTEC-CM (*) research project (Urban Air Quality and Climate Change Integral Assessment). The analysis of the relation between the turbulence measured close to the surface and pollution concentration (e.g., particle matter of different sizes, NOx, etc.) is a key aspect to achieve the different objectives of the project. In this context, we present some preliminary analyses of the turbulence data measured from sonic anemometers located at different emplacements. We focus on the turbulence differences among two instruments nearby located but at different heights above the street level: 1) at the top of a 22 m-height building, and 2) at the top of a shorter building of 2.5 m of height. Typical turbulent parameters (turbulent kinetic energy (TKE), friction velocity (u*) and sensible heat flux (SH)) are analysed for both sonic anemometers and their differences are statistically compared. An investigation of the main temporal scales involved in the atmospheric diffusion is also performed using the Multi-Resolution Flux Decomposition technique (MRFD), applied over a relatively long period that includes different atmospheric conditions in February 2020. The information obtained from this analysis will be related to the pollution concentration measured in the city, trying to determine the importance of the near-surface turbulence (and the corresponding scales of the main eddies found) in the pollutant’s levels.

 

(*) AIRTEC-CM research project (S2018/EMT-4329) is funded by The Regional Government of Madrid (Spain).
Deep learning for Chinese NOx emission inversion and the integration of in situ observations: a case study on the COVID-19 pandemic
Authors: He Tai-Long , Jones Dylan , Miyazaki Kazuyuki , Bowman Kevin , Jiang Zhe , Li Rui
Published: June 2021

The COVID-19 pandemic led to the lockdown of over one-third of Chinese cities in early 2020. Observations have shown significant reductions of atmospheric abundances of NO2 over China during this period. This change in atmospheric NO2 implies a dramatic change in emission of NOx, which provides a unique opportunity to study the response of the chemistry of the atmospheric to large reductions in anthropogenic emissions. We use a deep learning (DL) model to quantify the change in surface emissions of NOx in China that are associated with the observed changes in atmospheric NO2 during the lockdown period. Compared to conventional data assimilation systems, deep neural networks are free of the potential errors associated with parameterized subgrid-scale processes. Furthermore, they are not susceptible to the chemical errors typically found in atmospheric chemical transport models. The neural-network-based approach also offers a more computationally efficient means of inverse modeling of NOx emissions at high spatial resolutions. Our DL model is trained using meteorological predictors and reanalysis data of surface NO2 from 2005 to 2017. The evaluation is conducted using in-situ measurements of NO2 in 2019 and 2020. The Baidu 'Qianxi' migration data sets are used to evaluate the model's performance in capturing the typical variation in Chinese NOx emissions during the Chinese New Year holidays. The TROPOMI-derived TCR-2 chemical reanalysis is used to evaluate the DL analysis in 2020. We show that the DL-based approach is able to better reproduce the variation in anthropogenic NOx emissions and capture the reduction in Chinese NOx emissions during the period of the COVID-19 pandemic.
Build, measure, understand: Pupils contributing to meteorological measurement campaigns.
Authors: Göber Martin , Rust Henning , Kox Thomas , Wentzel Bianca , Böttcher Christopher , Lehmke Jonas , Trojand Andreas , Fleischhut Nadine
Published: June 2021

Voluntary weather measurements have a long tradition and the opportunities have recently expanded with that the advent of the Internet of Things. Atmospheric measurements are prototypical examples for the maker community and popular means to strengthen interest in STEM subjects. In two projects in Germany  (in Brandenburg, within the FESSTVaL (Field Experiment on submesoscale spatio-temporal variability in Lindenberg) measurement campaign initiated by the Hans-Ertel-Center for Weather Research, and in Bavaria, in the KARE-Citizen Science  project), we use a weather station to be assembled by pupils as a participatory vehicle to increase interest in and understanding of weather and climate, as well as of weather forecasting, and to generate high resolution data for research.

The devices measure e.g. temperature, humidity, radiation, pressure and precipitation in the students' immediate environment. They can be placed in almost any location, since they operate independent of W-LAN and external power supply. The data is visualized directly via a web app. Students report weather impacts, such as observed damage or their own exposure to weather. Due to the pandemic, only a few dozens pupils were able to participate and building their devices had to be done with digital guidance and video support. Further online materials on understanding weather forecasting and its uncertainty were provided.

Understanding of weather risks was surveyed before and after participation to detect any changes. Students were asked questions about thunderstorm, rain and heat events and climatic changes since 1880. The results show a good understanding of weather risks compared to a population of all ages representative study. In online workshops pupils together with the scientists scetched and discussed the influence of the placement of their stations on their measurements. Interesting meteorological phenomena were discovered in the dataset, e.g. a cold pool that can form during a thunderstorm and trigger new ones. Thus, our network of higher spatial and temporal resolution data collected by the pupils has the potential to study these small-scale phenomena in more detail than with professional networks of about 25 km spacing.
Local turbulent kinetic energy modelling based on Lagrangian stochastic approach in CFD and application to wind energy
Authors: Martínez Kerlyns , Bossy Mireille , Jabir Jean-François
Published: June 2021

In order to better integrate the underlying meteorological processes with the developing technologies within wind energy industry, acquiring relevant statistical information of air motion at a local place, and quantifying the subsequent uncertainty of involved parameters in the models, are fundamental tasks. Special emphasis should be made on the growing interest in energy production forecasting and modelling for wind energy developments that rises the issue of accounting for the uncertain nature of the local forecast. Taking this into consideration, we present the construction of an original stochastic model for the instantaneous turbulent kinetic energy at a given point of a flow, and we validate estimator methods on this model with observational data examples from annual historic of a 10 Hz anemometer wind measurements.
More precisely, starting from the viewpoint of Lagrangian modelling of the wind in the boundary layer, we establish a mathematical link between 3D+time computational fluid dynamics (CDF) models for turbulent near-wall flows and stochastic time series models by deriving a family of mean-field dynamics featuring the square norm of the turbulent velocity. Then, by approximating at equilibrium the characteristic nonlinear terms of the dynamics, we recover the so called Cox-Ingersoll-Ross stochastic model, which was previously suggested in the literature for modelling wind speed. Remarkably, our stochastic model for the instantaneous turbulent kinetic energy is parametrised by physical constants in CFD, which provides a more direct link between the stochastic nature of the underlying processes and the classical physics behind these phenomena. Nevertheless, these physical parameters may vary with the flow characteristics and situations, so we consider it relevant to adjust their values while constructing the forecasts. Such tuning of the physical parameters was previously proposed in the literature from a deterministic modelling context with RANS equations. We then propose a two-step procedure for the calibration of the parameters: a training stage where we construct a priori distribution for the parameter vector using direct methods and wind measurements, and a stage of refinement of the uncertainty distribution using Bayesian inference combined with Markov Chain Monte Carlo sample techniques. In particular, we show the accuracy of the calibration method and the performance of the calibrated model in predicting the wind distribution through the quantification of uncertainty.
It’s All About the Weather!  An Activity- and Project-based Atmospheric Science Course for High School Students.
Authors: Walters Eric
Published: June 2021

Activity-based learning “encourages students to actively participate in their own learning experiences through practical activities such as problem solving and independent investigation.  This teaching method encourages students to be independently inquisitive, to think critically and to work collaboratively.”  Project-based learning is a teaching method in which students gain knowledge and skills by responding to authentic, engaging, and complex questions, problems, and challenges.  In both pedagogical scenarios, students “become scientists” with their own questions, data, and results while becoming active participants in the scientific community.

 

Students (and teachers) are able to access a wealth of real-time and climatological data available to students, along with a variety of data collection and analysis tools.  A high school atmospheric science course can be effectively transformed into an activity- and project-based course that leverages these data and tools to address essential questions and enduring understandings by thoughtfully connecting learning objectives and learning outcomes.

 

This paper will provide an overview of Atmospheric Science, a Grade 12 science elective taken by students at Marymount School of New York, which has been offered since 2004 and has evolved as access to data and new technologies has evolved.  Students engage in a variety of learning experiences that require them locate and verify appropriate data sources; collect and analyze real-time weather data; and then consider the best way to represent that data through multi-modal approaches.  Learning activities such as the Neighborhood Air Pollution Data Lab and the Advanced Climate Model Activity help students build agency in key atmospheric science concepts and principles. Projects such as the Weekend Weather Forecast; the Google Earth Drought Project; and the Climate Change Podcast help students extend their knowledge as well as share their work with the global community.  Samples of student work will be shared and strategies for planning and assessment will be discussed.