bluuebunny/crossref_metadata_2025 huggingface dataset:
Forced changes in internal variability: an additional uncertainty to deal with
Authors: Topal Daniel , Haszpra Tímea , Herein Mátyás
Published: June 2021

Anthropogenic activities contribute to the rising level of greenhouse gas concentrations in the atmosphere at a rate of approximately 1% per year providing a time-dependent external radiative forcing on the climate. In addition to tangible consequences of anthropogenic forcing affecting the climate system, simultaneous, less apparent changes occurring on low-frequency timescales demand effort to deal with. These include changes in natural internal processes of the climate system due to the non-stationary anthropogenic forcing. This represents additional uncertainty affecting future model projections on top of internal variability, scenario and model uncertainty. Here, with the application of state-of-the-art Single Model Initial-condition Large Ensemble (SMILE) simulations – that account for the chaotic behavior of the climate system with perturbed initial condition runs of the same model – we offer a way forward for new perspectives on externally-forced changes in internal variability. In doing so, we utilize an approach for analyzing SMILEs called the snapshot view, which offers a mathematically exact and elegant formulation and the potential to complement previous, time-series-based diagnostics with ensemble-based statistics. We reveal how the snapshot view allows for surprisingly simple practices to detect anthropogenically forced changes in modes of large-scale internal atmospheric circulation variability (so-called “teleconnection patterns”) as well as coupled modes of atmospheric variability with Arctic sea ice. A crucial message of the snapshot view is that all of the traditional, time series-based methods can be reformulated for ensembles and thus, on the one hand, ambiguous results arising from subjective choices of traditional methods (e.g. length and center of time windows) can be avoided, and on the other hand, new perspectives open for detecting forced changes in internal variability.
How often does the precipitation over 10 minutes exceed 50 mm somewhere on a given network of roads?
Authors: de Valk Cees , Overeem Aart , Fortuin Paul , Garcia Martin Irene
Published: June 2021

For long-term planning of the highway infrastructure, engineers in the Ministry of Infrastructure and Water Management of the Netherlands are considering the trade-offs between the risk posed by extreme precipitation in a changing climate and the cost of measures to reduce this risk for the entire network of highways and its critical elements, such as tunnels. This leads them to questions such as "How often does the precipitation over 10 minutes exceed 50 mm somewhere on a given network of roads?"

Naturally, this frequency is higher than the frequency of exceedance of the same depth at a site; it depends on the size and shape of the domain and on the spatial dependence of extreme precipitation. 

In the present study, statistics describing the spatial dependence of extreme precipitation are estimated from 11 years of gauge-adjusted radar precipitation data collected over the Netherlands.  At each radar pixel, annual maxima of precipitation depth are computed for durations ranging from 15 min to 12h. From these maxima, the values of the extremal coefficient function (ECF) for selected spatial domains are estimated.

From these values, a simple model is derived for converting return values of precipitation depth at a single site to return values of the highest precipitation depth within an arbitrary spatial domain, for durations from 10 min to 12 h. The model describes the duration-dependent statistics of the parameterized footprints of heavy precipitation events.

Confidence intervals are predicted using bootstrapping. The model is checked for fitness for its application to the design and maintenance of the drainage of highways, and the scope for further improvement is discussed. 
Preparing the CAMS Radiation Service for MTG
Authors: Schroedter-Homscheidt Marion , Azam Faiza , Betcke Jethro , Deneke Hartwig , Lefèvre Mireille , Saboret Laurent , Saint-Drenan Yves-Marie , Wey Etienne
Published: June 2021

The Copernicus Atmospheric Monitoring Service (CAMS) provides a surface solar irradiance service which is currently derived from Meteosat Second Generation (MSG). The service combines satellite data products with numerical model output from CAMS on aerosols, water vapour and ozone in order to provide both clear-sky and all-sky radiation time series with availability from 2004 until yesterday. A regular quality control of input parameters, quarterly benchmarking against ground measurements and automatic consistency checks ensures the data quality. To anticipate the increase in resolution that will occur with the commissioning of MTG, it is necessary to enhance methods currently used in CAMS.

The recent development focuses on the assessment and improvement of cloud retrieval products from APOLLO_NG in irradiance retrieval schemes. Such a validation against ground-based surface solar irradiances provides insight into the use of probabilistic cloud masking, specific results for pixels with small COD values below 5, as well as in partly cloudy pixel conditions. Such conditions are often neglected in existing cloud retrieval validation studies due to the expected large uncertainties of cloud properties. But they cannot be omitted in irradiance retrieval schemes for solar energy sector users as complete temporal coverage is required.

Such cloud situations may additionally be better characterized in future with the help of spatially higher resolved channels. Using e.g. SEVIRI’s HRV channel is known to be beneficial in cloud index based irradiance retrieval schemes, but has not been evaluated yet for cloud physical retrieval based irradiance schemes. Results from such a method development for the HRV channel in preparation for MTG, Himawari-8, and GOES-R channels will be presented.
Where are rainfall drops falling in a turbulent wind field ?
Authors: Gires Auguste , Tchiguirinskaia Ioulia , Schertzer Daniel
Published: June 2021

Weather radars measure rainfall in altitude whereas hydro-meteorologists are mainly interested in rainfall at ground level. During their fall, drops are advected by the wind which affects the location of the measured field. In this study, we investigate the fall of rain drops in a turbulent wind field between an height of 1500m and the ground.

The equation governing a rain drop motion relates the acceleration to the forces of gravity and buoyancy along with the drag force. The latter depends non-linearly on the instantaneous relative velocity between the drop and the local wind; which yields to complex behaviour. In this work, the drag force is expressed in a standard way with the help of a drag coefficient, which is itself determined according to a Reynolds number. Corrections accounting for the oblateness of drops greater than 1-2 mm are implemented. Such corrections are validated through comparison of retrieved “terminal fall velocity” (i.e. without wind) with commonly used relationships in the literature.

An explicit numerical scheme is implemented to solve this equation for 3+1D turbulent wind field, and hence analyse the temporal evolution of the velocities and trajectories of rain drops during their fall. Two types of wind inputs are used : (i) Four months of 100 Hz 3D sonic anemometers data. (ii) Numerical simulations of space-time varying wind carried out with the help of Universal Multifractals which are a framework that has been widely used to characterize and simulate geophysical fields extremely variable over a wide range of scales such as wind.

The behaviour of drop velocities is then characterized through temporal multifractal analysis. It notably enables to highlight a scale, depending on the drop size, below which turbulent eddies have a limited impact on their motion. Finally the dispersion on the ground of drops all starting at the same location is quantified and consequences on rainfall remote sensing with radars discussed.

 

Authors acknowledge the RW-Turb project (supported by the French National Research Agency - ANR-19-CE05-0022), for partial financial support.

 
Diverse response of surface ozone to COVID-19 lockdown in China
Authors: Liu Yiming , Wang Tao , Stavrakou Trissevgeni , Elguindi Nellie , Doumbia Thierno , Granier Claire , Bouarar Idir , Gaubert Benjamin , Brasseur Guy P.
Published: June 2021

Ozone (O3) is a key oxidant and pollutant in the lower atmosphere. Significant increases in surface O3 have been reported in many cities during the COVID-19 lockdown. Here we conduct comprehensive observation and modeling analyses of surface O3 across China for periods before and during the lockdown. We find that daytime O3 decreased in the subtropical south, in contrast to increases in most other regions. Meteorological changes and emission reductions both contributed to the O3 changes, with a larger impact from the former especially in central China. The southward-shifted wind with increased temperature, enhanced planetary boundary layer height, decreased cloud fraction and precipitation favored the O3 increase in north and central China, while the northward-shifted wind with decreased temperature and then biogenic volatile organic compounds (VOCs) emissions, increased cloud fraction and precipitation reduced O3 in south China. As for the emission reduction, the drop in nitrogen oxide (NOx) emission contributed to O3 increases in populated regions, whereas the reduction in VOCs contributed to O3 decreases across the country. Due to a decreasing level of NOx saturation from north to south, the emission reduction in NOx (46%) and VOC (32%) contributed to net O3 increases in north China; the opposite effects of NOx decrease (49%) and VOC decrease (24%) balanced out in central China, whereas the comparable decreases (45-55%) in the two precursors contributed to net O3 declines in south China. Our study highlights the complex dependence of O3 on its precursors and the importance of meteorology in the short-term O3 variability.
Towards the perfect warning
Authors: Golding Brian
Published: June 2021

Protection of lives and property from hazardous weather, through the provision of weather warnings, is a core mission of weather services with growing importance as global climate and human changes increase both the exposure and vulnerability of society to weather-related hazards. Exploring how to achieve that most effectively is the aim of the World Weather Research Programme’s High Impact Weather (HIWeather) project. HIWeather brings together physical and social scientists from a wide variety of disciplines and from across the world to study each step of the process from monitoring the weather to making effective protective responses. HIWeather uses a simplified conceptual model of the warning value chain that identifies the roles of key actors and organisations involved in forecasting the weather, the resulting hazard and its socio-economic impacts, in formulating the warning and communicating it to the end-user. HIWeather has drawn on a wide body of research to explore how these different actors can make their expertise contribute more effectively to the desired outcome of reduced death, destruction and disruption. In this talk I shall summarise the results of that research, identifying key principles that should be incorporated in the design of warning systems. In doing so, I shall connect this work with ideas from the design of community-based warning systems, with developments in social media communication, with research on impact-based forecasting, and with progress in convection-permitting and higher resolution NWP models. A key result is that the communication of knowledge is at least as important as its content, and that the creation and nurturing of partnerships between organisations is critical to that. Looking forward, I shall describe a new HIWeather initiative, launched at the end of last year, to gather data on the real-life performance of end-to-end warning systems in specific events for the purpose of analysing how warning outcomes are related to warning system characteristics.
Object-based tracking and nowcasting of gust fronts
Authors: Ritvanen Jenna , Pulkkinen Seppo , Moisseev Dmitri
Published: June 2021

Thunderstorm gust fronts threaten human safety and property, especially in industries such as aviation and construction. The ability to predict the precise time and location of gust front arrivals would mitigate risk and reduce damage. 

Existing methods for nowcasting gust front locations are based on detecting the gust fronts from individual Doppler weather radars or scanning lidars. Even though these methods are locally effective, they have so far not been applied to large-scale radar mosaics to generate forecasts that could benefit society at large. To address this gap, an object-based method is proposed for nowcasting gust fronts by any number of ground-based Doppler weather radars.  

The gust fronts are first detected from the radar measurements and presented as objects consisting of spline curves. Given the one-dimensional geometry of the curves, existing object-based tracking methods, designed for tracking thunderstorms and based on two-dimensional polygons, cannot be applied to the gust front objects. Instead, a tracking method is formulated that matches multiple observations of the same gust front based on the location and length of the curves. The tracking considers possible splitting and merging of the gust front objects. After matching the gust front instances between consecutive timesteps, the location of the gust front is nowcast with a Kalman filter algorithm.  

The methodology is demonstrated with case studies of gust fronts related to mesoscale convective systems (MCS) in Finland. MCSs occur frequently in Finland during summer and cause significant wind and other storm-related damage. Spatially and temporally accurate forecasting of MCS events would aid preparedness and reduce the risk posed to society. The methodology presented in this work can be used to nowcast the gust front trajectory and thus increase preparedness especially for the wind damage related to MCS events. The methodology can also be combined with existing object-based methods for nowcasting convective storm cells, to create comprehensive hazard forecasting systems for thunderstorms.
Virtual co-delivery of international climate services -evolving the way we work with our international partners
Authors: Strachan Jane
Published: June 2021

The Met Office has developed internationally recognised expertise in delivering scientific collaboration, capacity building, training, user-engagement and service development. However, during the last 18-months, as travel restrictions prevented planned in-country delivery of our climate science to services work, our ways of working were forced to evolve so that we could continue meet objectives across a number of international climate service programmes and projects. Successful remote co-delivery of climate services has been possible because of the necessary ground-work put in place through:

A focus on the co-production of climate science and services with a strong stakeholder-led approach;

Strong established international partnerships and networks;

Previous and current innovative international climate services projects, such as the Future Climate for Africa (FCFA) Future Resilience for Africa Cities and Lands (FRACTAL) project, the Weather and Climate Information Services for Africa (WISER) programme, and the Asia Regional Resilience to a Changing Climate (ARRCC) programme.

Delivering international climate science to services work in innovative and thoughtful ways via online platforms, is not just a seen as a temporarily replacement for in-country activity. The examples we showcase will demonstrate how we are evolving our international climate science to services delivery.

During the presentation, we will showcase examples of successful virtual co-delivery of climate services, alongside taking a considered look at the both the opportunities and challenges of virtual collaboration and communication, particularly when working with developing and emerging countries. We will conclude by opening up discussion around others experiences of remote co-delivery of climate services, exploring how a international climate services co-delivery could look in the future.
The full-spectrum correlated-k method to accelerate radiative transfer in NWP models
Authors: Hogan Robin
Published: June 2021

Radiative transfer is frequently cited as the slowest part of an atmospheric model; while perhaps a little unfair (radiation accounting for only 3.5% of the cost of ECMWF's highest resolution operational model), there is no shortage of ideas in the literature for speeding up radiation schemes. In this talk I will describe a flexible tool "ecCKD" for generating gas optics models using the correlated k-distribution (CKD) method, and in particular explore the potential to use the "full-spectrum correlated-k" (FSCK) method in NWP. Via the use of one band for the entire thermal infrared and one for the entire near-infrared, FSCK enables the number of pseudo-monochromatic spectral intervals to be drastically reduced, from over 100 in each of the shortwave and longwave spectra (in the current operational gas optics scheme at ECMWF) to around 25, with a corresponding factor-of-4 speed-up in the radiation scheme. Training against 50 line-by-line test profiles and 34 greenhouse gas scenarios ensures that the resultings gas-optics models are accurate for the full range of terrestrial conditions, plus climate conditions from the last glacial maximum up to 8x pre-industrial CO2 concentrations. Care must be taken to ensure there are sufficient spectral intervals to represent the spectrum of cloud absorption and scattering, as well as surface albedo variations. The reduced number of spectral intervals leads to increased noise in the stochastic McICA solver, but recent optimizations of the Tripleclouds solver make it as fast as McICA but free from stochastic noise. Finally, the new gas optics scheme is demonstrated online in forecasts by the ECMWF model.

 
What are drivers of the tropospheric ozone reduction during spring 2020?
Authors: Gaubert Benjamin , Bouarar Idir , P. Brasseur Guy , Doumbia Thierno , Darras Sabine , Deroubaix Adrien , Granier Claire , Hannigan James , Jo Duseong , Lacey Forrest , Liu Yiming , Müller Jean-François , Ortega Ivan , Shi Xiaoqin , Stavrakou Trissevgeni , Steinbrecht Wolfgang , Tilmes Simone , Wang Tao
Published: June 2021

We use the global Community Earth System Model to investigate the response of secondary pollutants (ozone O3, secondary organic aerosols SOA) in different parts of the world in response to modified emissions of primary pollutants during the COVID‐19 pandemic. We quantify the respective effects of the reductions in anthropogenic emissions and meteorological anomalies, including a discussion on long-term changes from the chemical climatology. We show that the level of NOx has been reduced by typically 40 % in China during February 2020 and by similar amounts in many areas of Europe and North America in mid-March to mid-April 2020. Relative to a situation in which the emission reductions are ignored and despite the calculated increase in hydroxyl and peroxy radicals, the ozone concentration increased only in a few NOx‐saturated regions during the winter months of the pandemic when the titration of this molecule by NOx was reduced. In other regions, where ozone is NOx‐controlled, the concentration of ozone decreased. Zonally averaged ozone concentrations in the free troposphere during Northern Hemisphere spring and summer were 5 to 15 % lower than 19-year climatological values, in good quantitative agreement with observations from ozonesondes and ground-based remote sensing from the Network for the Detection of Atmospheric Composition Change (NDACC). About one third of this anomaly is attributed to the drastic reduction in air traffic during the pandemic, another third to reductions in surface emissions, the remainder to 2020 meteorological conditions, including the exceptional springtime Arctic stratospheric ozone depletion. The overall COVID-19 reduction in mean northern hemisphere tropospheric ozone in June is less than 5 ppb below 400 hPa, but reaches 8 ppb at 250 hPa.
Quantifying the Impact of Assimilating Ground-based GNSS in Operational NWP Models
Authors: Jewell Sharon , Harrison Dawn , Dow Gareth
Published: June 2021

Zenith Total Delay (ZTD) from networks of ground based GNSS receivers has been assimilated in both the Met Office global and high-resolution regional numerical weather predication (NWP) models for over a decade. It is useful to be able to quantify the impact of assimilating these data and compare this with the impact of assimilating other observation types. This helps inform observing network evolution.

Forecast Sensitivity to Observation Impact (FSOI) analysis is an established method for monitoring the collective impact of an observing network on the quality of an NWP forecast. FSOI uses an adjoint-based method to compute the observation sensitivity for each assimilated observation in a global model forecast simultaneously. The sensitivity value represents the change in forecast error for a unit observation innovation; this information is combined with innovation data from the model forecast to provide an estimate for the resulting change in error for the total 24-hour energy norm. The computational efficiency of the FSOI process makes it a good alternative to traditional Observation System Experiments (OSE) when considering the benefits of an observing network.

Unfortunately, the underpinning assumptions in the FSOI methodology mean that the method does not easily translate to higher resolution regional NWP models. For these models, observation impacts can still be determined through OSEs through which observations of specific type or types are denied to the model and the results compared with a Control model run with all types of observations included.

FSOI analysis has previously been used to compare and contrast the benefits associated with different observing method types. This study uses the original FSOI methodology but refines the output to collate impact data at the observing-site level (in relation to a specified observing network).  This enables geographic variations in the impact associated with individual sites within an observing network to be visualised and the tool can be used to assist with decisions on network design and performance.

Results are presented that illustrate the impact of GNSS ZTD data on global NWP model forecasts on a seasonal and annual basis. A number of metrics are used to assess the benefits associated with a GNSS site, including the total impact over a fixed period of time as well as the mean impact per observation on both a global and country scale. The results highlight the significant impact that individual sites in areas of low data density (such as the southern hemisphere) have on model forecasts.

The impact of GNSS ZTD data on the 1.5 km resolution UKV model are also presented. The contrasting impacts in summer and winter, and variation of forecast impact as lead-time increases are explored.
Detection of Alpine Foehn in GNSS-ZWD time series: An innovative application of GNSS Meteorology
Authors: Aichinger-Rosenberger Matthias , Brockmann Elmar , Möller Gregor
Published: June 2021

The atmospheric delay experienced by a signal of the Global Navigation Satellite System (GNSS) is proportional to the water vapour content along the signal path. This fact is typically exploited in GNSS Meteorology by introducing GNSS derived atmospheric parameters like the Zenith Wet Delay (ZWD) in data assimilation schemes. In numerous studies, the positive impact on the (especially precipitation) forecast has been demonstrated. However, while mostly precipitation-related studies represent the current focus of research, other meteorological phenomena might also be investigated by means of GNSS.

The present study represents an initial investigation on the detection of another important meteorological phenomena using GNSS time series: Foehn winds. Foehn denotes a gusty, warm fall wind occurring in mountainous regions worldwide, leading to a relatively mild climate in affected areas. On the other hand, Foehn can also be characterized as severe weather leading to disasters, due to the high wind speeds frequently encountered.

The proposed detection method of Foehn in ZWD time series is based on the significant drying/wetting effects on the lee/luv side of an affected mountain range associated with Foehn. The comparison of ZWD from stations on both sides of the main Alpine ridge reveals characteristic features like distinctive ZWD minima/maxima and significant decrease in correlation between the stations.

In this study we investigate a number of well-documented Foehn events in the Swiss Alps (therefore called Alpine Foehn) using ZWD time series from the Automated GNSS Network Switzerland (AGNES) station network, operated by the Swiss Federal Office of Topography (swisstopo). Based on these case studies, an assessment of the usability of GNSS-ZWD for Foehn detection is presented and possible strengths and weaknesses will be analysed. Finally, an outlook on possible improvements and innovative extensions to the presented approach is given. These range from embedment of ZWD data in operational Foehn classification and the application of Machine-Learning techniques for detection, to the establishment of collocated GNSS/weather stations, which come with a number of scientific benefits - not only for Foehn investigations but GNSS Meteorology in general.
Modelling rain heterogeneities in urban areas
Authors: Boettcher Marita , Burgemeister Finn , Ferner Karolin S. , Schlünzen K. Heinke
Published: June 2021

Urbanisation modifies the local climate and results in the so-called urban climate. Within the urban boundary layer, the average wind speed is reduced, while gustiness is increased. Buildings induce vertical winds. Heterogeneities in the rain pattern around buildings are the consequence. Human discomfort in street canyons may be one result. In addition, sealed urban surfaces lead to large rainwater run-off, which is a cause for flash floods in urban areas.

Increased computational power allows high-resolution modelling in urban areas with a horizontal resolution well below 10 m. The consideration of more meteorological processes like cloud and rain microphysics is possible. This allows us to estimate the impact of rain events, especially heavy rain events and flash floods, to urban neighbourhoods. Nevertheless, the domain size with these high-resolution models is restricted and the cloud and rain development passes the domain without full development of rain. To overcome this challenge, high-resolution information about rain events in urban areas are necessary.

In the area of Hamburg, Germany, measurements of a X-band weather radar at a 100-metre-scale and a vertically pointing micro rain radar are available for several years. These high-resolution measurement data are used to develop a forcing method for the microscale, obstacle resolving transport and stream model MITRAS (Salim et al. 2019). The forcing method samples 2D and 3D information about the rain rate to the model domain. The nudging approach adds the information about the rain rate to the top and the lateral boundaries of the model domain. Model simulations with different synoptic situations evaluate the forcing methodology.

In this contribution, the forcing method will be presented and results from different test cases in a test area in Hamburg will be shown.

Salim M.H, Schlünzen K.H., Grawe D., Boettcher M., Gierisch A.M.U., Fock B.H. (2018): The microscale obstacle-resolving meteorological model MITRAS v2.0: model theory. Geosci. Model Dev., 11, 3427–3445, https://doi.org/10.5194/gmd-11-3427-2018.
Verification as a service to bring more transparency on forecast accuracy to weather services and users
Authors: Müller Evelyn , Hoffmann Jan , Schulze Dennis
Published: June 2021

Actual, continuously available information on the accuracy of forecasts can support both weather services and users of forecasts in quality assurance during operations and identify systematic weaknesses. Comparing the forecast success of different forecasting methods allows decision makers in the weather service and on the user side to evaluate the cost-benefit ratio of available forecasting approaches, be it different models, DMO and post-processing, or different providers. Finally, in addition to on-off experiments for version comparison, the success of developments to the forecast system can be seen in the comparison of time series of verification results against those of other forecasts. 

From the development of the forecasting process to daily operations to the use of forecasts in subsequent industry applications, stakeholders have very different questions about the quality of weather forecasts. From the weather room, there is a particular need for up-to-date information on the previous day's forecast success and rapid access to case verification analyses following unusual events. Especially in B2B, case-specific comparison with the success of other forecasts is also in demand. For management, on the other hand, longer-term trends in forecast quality are the focus of interest. Finally, users often base their choice of a forecasting provider not only on procurement costs and convenience of access, but also take into account the current forecast accuracy of their relevant parameters, in their region, in the forecast horizon relevant to them. Especially weather-sensitive industries such as road weather services, energy production and transmission, but also media often agree with forecast suppliers on continuous monitoring of forecast quality. 

We present different perspectives and questions and show possible answers as use cases in a verification portal.
Taking stock of the socio-economic benefits of weather and climate services in South Asia
Authors: Suckall Natalie , Bruno Soares Marta
Published: June 2021

The use of scientific information about future weather and climate offers enormous potential for society to better manage the risks associated with climate variability and change. There has been significant investment in weather and climate services (WCS) initiatives over the past years, however empirically based evidence of the socio-economic benefits that can be yield from such services is very limited. Furthermore, understanding and capturing the real benefits of WCS is key to ensure continue investment and funding of WCS as well as to enable adaptive management of such services. In this paper, we conduct a review of the literature of WCS evaluations in South Asia. We systematically document and analyse empirical evidence as reported in the academic and grey literature to highlight: (1) the scale and scope of WCS that have been evaluated in the region; (2) the methodological approaches that have been used to monitor and evaluate the benefits of WCS initiatives on the ground; and (3) the socio-economic benefits of WCS categorised under a triple bottom line approach that takes into consideration economic, social and environmental benefits of the WCS under analysis. The paper explores these findings and highlights key areas that warrant further discussion and research. These include, the disconnect between developing and implementing WCS and effectively capturing and recording the benefits that can be yield from WCS; the current emphasis on assessing and capturing economic benefits whilst social and environmental benefits are largely ignored and/or not accounted for; and the need for robust methodologies that enable adequate and holistic evaluations of the benefits that can be generated within the WCS and across the triple bottom line.
Influence of nighttime radiation fog on the development of the daily boundary layer: An large eddy simulation study
Authors: Schwenkel Johannes , Maronga Björn
Published: June 2021

Apart from hazards associated with deep fog, its presence significantly alters the properties of the nocturnal boundary layer (NBL). 
The NBL is typically characterized by a stable stratification resulting in weak or sometimes intermittent turbulence. 
In contrast, the NBL during deep fog is often convective, as for the longwave radiation optical thick fog layer, the net radiative loss takes place at the fog top, destabilizing the atmosphere from above.
Therefore, processes as modified longwave cooling, shortwave absorption, turbulent mixing, reduction of the total water content through droplet settling or modified dewfall, is able to induce differences between the stable NBL (SNBL) and foggy NBL. 
Albeit after sunrise the SNBL is quickly transformed into a convective boundary layer (CBL), properties of the NBL are transferred into the day and affect the CBL. 
Even though fundamental and applied research have significantly improved fog forecasts and contributed to a broader and deeper understanding at the process-level in the last decades, common numerical weather prediction (NWP) models still miss a significant amount of fog events.
A number of complex small-scale processes (such as turbulent mixing, land-atmosphere interactions, aerosol and cloud microphysics and radiation) interacting on different scales have to be correctly resolved or parameterized.
Likewise, the prerequisite formation conditions must be presented precisely as they are highly sensitive to slight changes in temperature, humidity or soil moisture, entailing that even small biases in the forcing data could lead to an incorrect representation of subtle supersaturations and might result in failing to predict fog.

Thus, we will present in this talk results of idealized large eddy simulations pairs (with and without the possibility to form fog) covering the diurnal cycle based on a typical fog event observed in Cabauw considering radiative conditions between February and April. 
As we performed several parameter studies we will demonstrate, that the CBL in cases without fog is warmer and obtain higher inversion heights than in simulations with fog during night.
Further, we show that this temperature deviations are mainly driven by an stronger integrated  longwave cooling during night in the foggy cases.
Moreover, we identified the liquid water path as a crucial parameter determining the strength of the fog impact on CBL development. 
Assessing the value of climate services for the wine sector
Authors: Khosravi Mehri , Bruno Soares Marta , Graça António , Fontes Natacha , Teixeira Marta
Published: June 2021

Climate services involve the production, translation and use of climate information to support users’ decisions towards adapting to climate variability and change. However, the value of climate services to end-users is only truly realised when the information provided by such services is used to support and inform users’ decision-making processes (Bruno Soares et al. 2018). Capturing and assessing the value and benefits of climate services constitutes a critical area of research in the field of climate services and can be informed by a range of epistemological and methodological approaches. 
In this paper, we present the assessment of the socio-economic value and benefits of a climate service developed specifically for the wine sector and implemented under the auspices of the H2020 MED-GOLD project. The assessment was conducted with the end-users during a two-month period where they actively interacted with the climate service and the information it provides. Participatory mixed-methods consisting of a workshop, continuous feedback through survey, and interviews were applied to pursue this assessment.
Our paper describes the process and methods through which the climate service was assessed with the end-users. It then highlights key findings from the study such as typologies of value and benefits yielded by the end-users; usability of information provided by the service across operational and strategic decision-making processes; and key factors influencing use of climate information and the realisation of value. 
In doing so, our paper contributes to current knowledge on what constitutes value to end-users in the wine sector and helps unpack some of the complexity between climate information provision, use and the realisation of its value to end-users. It also contributes to wider ongoing discussions on how to effectively assess the value and benefits of climate services to end-users and how to facilitate the realisation of such value, as well as its assessment, in future climate services initiatives.
The value-added potential of climate services for real estate asset management
Authors: Perrels Adriaan
Published: June 2021

In recent years the financial sector has activated itself regarding the integration of climate change risks in its risk management. This is a slow process and the realization, that also physical risks engendered by climate change should be included, is even more recent (Hamaker-Taylor et al 2018). Within the segment of asset management, notably the management of real estate assets should have particular interest in climate change, as fossil free and efficient energy use, sustainable climate neutral building materials, and minimized exposure to climate change enhanced physical risks merit all sufficient attention. Overall asset oriented climate services will be an important segment (De Bruin et al 2020) This offers a significant scope for climate services for this segment within the financial sector, but both the financial sector as user and the suppliers of climate services are still very much in an exploratory stage of defining, ordering, providing, and using climate services, which are relevant for specific risk management issues within the financial sector (Keenan 2019).

The key issue for the real estate asset manager is how climate change would affect the value of its properties with and without adaptation measures, both as such, as well as in comparison to other property. Furthermore, the disclosure of hitherto not-disclosed risk information of assets will usually affect the prices of these assets, in comparison to similar not-exposed assets (Votsis and Perrels 2016). In this contribution we illustrate on the basis of Finnish cases under what conditions more information on climate change related risks (flooding; forest damage) could entail an economically viable climate service.

References

De Bruin, K., Hubert, R., Evain, J., Clapp, C., Stackpole Dahl, M., Bolt, J., Sillmann, J. (2020). Chapter 8: Physical Climate Risks and the Financial Sector—Synthesis of Investors’ Climate Information Needs, in Filho and Jacobs (eds), Handbook of Climate Services, Springer https://doi.org/10.1007/978-3-030-36875-3

Hamaker-Taylor, R. Perrels, A. Canevari, L., Nurmi, V., Rautio, T. Rycerz, A. Larosa, F. (2018). Results of Explorations of the Climate Services Market for the Financial Sector, EU-MACS Deliverable 2.1, 23.12.2018. http://eu-macs.eu/outputs/#

Keenan, J.J. (2019). Climate Adaptation Finance and Investment in California, Earthscan – Routledge, London/New York, ISBN: 978-0-429-39875-9 (ebk) / ISBN: 978-0-367-02607-3 (hbk)

Votsis, A., Perrels, A. (2016). Housing prices and the public disclosure of flood risk: a difference-in-differences analysis in Finland, Journal of Real Estate Finance and Economics, November 2016, Volume 53, Issue 4, pp 450–471, DOI 10.1007/s11146-015-9530-3
Evaluation of break detection methods for snow data series
Authors: Buchmann Moritz , Begert Michael , Brönnimann Stefan , Resch Gernot , Marty Christoph
Published: June 2021

Measurements of snow depth and snowfall can vary dramatically over small distances. However, it is not clear if this applies to all derived variables and is the same for all seasons. Almost all meteorological time series incorporate some sort of inhomogeneities. Complete metadata and existing “parallel” stations in close proximity are not always available.
First, we analyse the impacts of local-scale variations based on a unique set of parallel manual snow measurements for the Swiss Alps consisting of 30 station pairs with up to 70 years of parallel data. Station pairs are mostly located in the same villages (or within 3km horizontal and 150m vertical distances). 
Seasonal analysis of derived snow climate indicators such as maximum seasonal snow depth, sum of new snow, or days with snow on the ground shows that largest differences occur in spring and the smallest ones are found in DJF and NDJFMA. Relative inter-pair differences (uncertainties) for days with snow on the ground (average snow depth) are below 15% for 90% (30%) .
Second, in view of any homogenization efforts of snow data series, it is paramount to understand the impacts of inhomogeneities. Using state-of-the-art break detection algorithms, we strive to investigate which method works best for detecting breaks in snow data series. The results can then be used on time series with insufficient metadata or no neighbouring stations in order to include them in future homogenization processes.
Furthermore, the knowledge about inhomogeneities and breakpoints paves the way for new applications such as the reliable combination of two parallel series into one single series.
Interannual variability of the GNSS derived precipitable water vapour in the light of tropical climate patterns
Authors: Nykiel Grzegorz , Baldysz Zofia , Latos Beata , Figurski Mariusz
Published: June 2021

Among various greenhouse gases, water vapour is characterized by the single highest positive feedback on the surface temperature and dominates increasing of the Earth’s surface temperature. Hence, long-term changes in its concentration in the atmosphere are one of the indicators for the assessment of the global warming rate. Consequently, monitoring of water vapour interannual variability is an important element in climate observing system, especially considering limitations of the surface technology that is traditionally used for this purpose. In this work, we have used 18 years of global navigation satellite system (GNSS) observations derived from 43 International GNSS Service (IGS) stations located across the global tropics. Based on them, we have estimated zenith tropospheric delay (ZTD) time series by precise point positioning (PPP) approach, and in next step converted them to long-term and homogenous precipitable water vapour (PWV) time series. We have investigated their interannual variability through estimation of non-linear trends and assessment which climate phenomena affect GNSS PWV long-term variability the most. Results have shown that for most of the analysed stations, GNSS PWV time series present distinct analogies to the global and regional climate phenomena such as El Nino Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) or North Pacific Gyro Oscillation (NPGO). Comparative analysis between GNSS PWV non-linear trends and selected climate indices showed strong cross-correlation, that amounted to 0.78. Moreover small-scale weather phenomena, such as local droughts, were clearly distinguishable, thus showing how GNSS PWV time series are sensitive to the combined effect of various weather and climate patterns. 
Understanding the relationship between clouds and surface downward radiation forecast errors with Unsupervised Deep Learning
Authors: Zech Matthias , von Bremen Lueder
Published: June 2021

Cloudiness is a difficult parameter to forecast and has improved relatively little over the last decade in numerical weather prediction models as the EMCWF IFS. However, surface downward solar radiation forecast (ssrd) errors are becoming more important with higher penetration of photovoltaics in Europe as forecasts errors induce power imbalances that might lead to high balancing costs. This study continues recent approaches to better understand clouds using satellite images with Deep Learning. Unlike other studies which focus on shallow trade wind cumulus clouds over the ocean, this study investigates the European land area. To better understand the clouds, we use the daily MODIS optical cloud thickness product which shows both water and ice phase of the cloud. This allows to consider both cloud structure and cloud formation during learning. It is also much easier to distinguish between snow and cloud in contrast to using visible bands. Methodologically, it uses the Unsupervised Learning approach tile2vec to derive a lower dimensional representation of the clouds. Three cloud regions with two similar neighboring tiles and one tile from a different time and location are sampled to learn lower-rank embeddings. In contrast to the initial tile2vec implementation, this study does not sample arbitrarily distant tiles but uses the fractal dimension of the clouds in a pseudo-random sampling fashion to improve model learning.

The usefulness of the cloud segments is shown by applying them in a case study to investigate statistical properties of ssrd forecast errors over Europe which are derived from hourly ECMWF IFS forecasts and ERA5 reanalysis data. This study shows how Unsupervised Learning has high potential despite its relatively low usage compared to Supervised Learning in academia. It further shows, how the generated land cloud product can be used to better characterize ssrd forecast errors over Europe.
Investigating changes in atmospheric circulation patterns connected to extreme precipitation in Norway
Authors: Ødemark Karianne , Müller Malte , Tveito Ole Einar , Palerme Cyril
Published: June 2021

Extreme precipitation events that lead to excess surface water and flood are becoming an amplifying societal cost as a result of both the increasing precipitation amounts in recent years and urbanization. Knowledge about extreme precipitation events is important for the ability to predict them, but also to know how often they occur with various intensities in order to estimate design values for constructions and critical infrastructure. A good description of extreme precipitaton is a challenge since observation networks are often too sparse to describe the spatial structure of precipitation, and the highest amounts are most likely not captured by a precipitation gauge. For the study of extreme precipitation events by means of statistical analysis, long timesteries are required, which is a major challenge when using conventional or new observational data records.  Here, a data set constructed from the numerical seasonal prediction system at ECMWF, SEAS5, has been applied to evaluate mechanisms controlling extreme precipitation events. The construction technique gives the ability to increase the event sample size compared to conventional data sets. We analyze 3-day  maximum precipitation events in the September-October-November season for an area on the west coast of Norway, an area subject to the largest precipitation amounts in Europe. A principal component analysis of the 500 hPa geopotential anomaly has been performed to identify atmospheric circulation patterns related to the extreme precipitation events. We find that two of the EOFs are related to precipitation with high return values for the selected area. These two EOFs have a significant trend over the data period, but with opposing signs. We also investigate the connection between both sea surface temperature (SST) and sea-ice concentration in the Barents-Kara sea and the occurrence of extreme precipitation.