bluuebunny/crossref_metadata_2025 huggingface dataset:
Extreme precipitation and temperature events in the past decades and its future impacts in the São Paulo Macro Metropolis.
Authors: de Oliveira Brandão Campos Thamiris Luisa , Dias de Freitas Edmilson
Published: June 2021

The São Paulo Macro Metropolis (SPMM) is composed of five metropolitan regions (i.e., São Paulo, Campinas, Paraíba Valley and North Coast, Sorocaba, and Santos), two urban agglomerations (i.e., Piracicaba and Jundiaí) and a microregion of Bragança. With an estimated 33 million inhabitants over an area 53,000 km2. Urban areas take up 11,000 km2 of this area, represented by 174 municipalities that account for nearly 73.9% of the state’s total population. The SPMM is already experiencing the impacts of severe climate events. It is situated along a transition climatic region between humid subtropical and tropical climates. Meteorological systems such as cold fronts, the South Atlantic Convergence Zone (ZCAS), sea breezes, urban heat islands, and other local circulations associated with topography favor the occurrence of severe weather at different time scales. The study aims at understanding climate change effects on the intensity and frequency of temperature and precipitation extremes in the SPMM, contributing to the establishment of policies to reduce the existing socioenvironmental risks in urban areas. For this, the ability of observation systems, reanalysis (ERA5, ERA40, NCEP/NCAR, and Terra Climate), and remote sensing (CMAP and CPC) were analyzed and used to show the decadal evolution of extreme events in SPMM. We also investigated the potential of CMIP5 models to simulate the extreme precipitation and temperature events in the SPMM that occurred between the 1950s and 2005. Future climate changes in the SPMM were analyzed based on CMIP5 models that best simulate past extreme events in the SPMM, considering the different carbon emission scenarios. This study is important to develop adaptive strategies to deal with climate change impacts on urban areas, such as droughts and floods.
Improving weather forecasts by means of HPC solutions: the LEXIS approach in the 2020 Bitti flood event
Authors: Mazzoglio Paola , Pasquali Paolo , Parodi Andrea , Parodi Antonio
Published: June 2021

In the framework of LEXIS (Large-scale EXecution for Industry & Society) H2020 project, CIMA Research Foundation is running a 3 nested domain WRF (Weather Research and Forecasting) model with European coverage and weather radar data assimilation over Italy. Forecasts up to 48 hours characterized by a 7.5 km resolution are then processed by ITHACA ERDS (Extreme Rainfall Detection System), an early warning system for the heavy rainfall monitoring and forecasting. This type of information is currently managed by ERDS together with two global-scale datasets. The first one is provided by NASA/JAXA GPM (Global Precipitation Measurement) Mission through the IMERG (Integrated Multi-satellitE Retrievals for GPM) Early run data, a near real-time rainfall information with hourly updates, 0.1° spatial resolution and a 4 hours latency. The second one is instead provided by GFS (Global Forecast System) at a 0.25° spatial resolution.
The entire WRF-ERDS workflow has been tested and validated on the heavy rainfall event that affected the Sardinia region between 27 and 29 November 2020. This convective event significantly impacted the southern and eastern areas of the island, with a daily rainfall depth of 500.6 mm recorded at Oliena and 328.6 mm recorded at Bitti. During the 28th, the town of Bitti (Nuoro province) was hit by a severe flood event.
Near real-time information provided by GPM data allowed us to issue alerts starting from the late morning of the 28th. The first alert over Sardinia based on GFS data was provided in the late afternoon of the 27th, about 40 km far from Bitti. In the early morning of the 28th, a new and more precise alert was issued over Bitti. The first alert based on WRF data was instead provided in the morning of the 27th and the system continued to issue alerts until the evening of the 29th, confirming that, for this type of event, precise forecasts are needed to provide timely alerts.
Obtained results show how, taking advantage of HPC resources to perform finer weather forecast experiments, it is possible to significantly improve the capabilities of early warning systems. By using WRF data, ERDS was able to provide heavy rainfall alerts one day before than with the other data.
The integration within the LEXIS platform will help with the automatization by data-aware orchestration of our workflow together with easy control of data and workflow steps through a user-friendly web interface.
Eliciting mental models to understand how different individuals affected by disaster risk understand science, and scientific uncertainty
Authors: Hudson-Doyle Emma , Harrison Sara , Hill Stephen , Williams Matt , Paton Douglas , Bostrom Ann , Becker Julia
Published: June 2021

Communicating the uncertainty associated with forecasts is crucial to effective crisis response management, but it is particularly challenging when time frames are too short to articulate the complexities of the information. However, not communicating uncertainties is problematic. For technical experts, interdependencies amongst event characteristics over time creates evolving uncertainties that may eclipse those associated with modelled outcomes. For the public and emergency decision-makers, the lack of uncertainty awareness may result in future alternative courses of action not being identified and assessed, reducing the efficacy of decisions and action plans. Furthermore, revealing uncertainty can both increase or decrease the credibility and trustworthiness of the communicator.  Some individuals will devalue a message when uncertainty is communicated, while others may devalue the message when they expect uncertainty and it has not been communicated. If we are to develop effective ways to communicate uncertainty in a crisis, research needs to understand the reasons for these differences. 

Key influences include how perceptions of science, its uncertainty, and the scientific process, act as a lens through which scientific information is interpreted. This lens can warp communicated information, particularly when uncertainty is high: during a crisis, people may not take appropriate safety actions based upon scientific advice if the message contradicts or fails to accommodate, their existing perceptions of the science. Forecasts, warnings, and other communication products must address these existing perceptions if they are to be effective. These perceptions are represented in people’s mental models of how they think the world works, including their model of scientific processes, motivations, beliefs, and values, which vary across disciplines and organizations due to epistemic differences. We will report on the initial findings from a study that  a) identifies the appropriate methodology to elicit mental models of science in the public and professional populations, and b) uses this to explore how mental models of scientific uncertainty are held by the public, emergency managers, scientists, engineers, and key decision-makers involved in hazard response. Our aim is to identify the shared concepts underlying these mental models, so forecast messaging can be effectively crafted to include uncertainty in a way that aligns with  individuals’ mental models. Through this we offer strategies to enhance individual decision-making under uncertainty in ways that develop the trust that the public and decision-makers have in forecasts.
The benefits of using TV-meteorologists as climate change communicators
Authors: Sivle Anders Doksæter , Berger Anniken Celine , Svehagen Mai-Linn Finstad , Hygen Hans Olav , Jeuring Jelmer
Published: June 2021

User surveys indicate that the Norwegian Meteorological Institute (MET Norway) enjoys a very high level of trust in the Norwegian population, being the state agency with the best reputation in Norway 15 years in a row. In our latest annual polling close to 85 percent say they have a high degree of trust in our climate research, while 82 percent have a high degree of trust in our TV meteorologists as climate change communicators. The combined climate science and communication expertise within MET Norway can capitalize on these high levels of trust to advance climate communication for Norwegian society. This is the backdrop for the project TV meteorologists as climate communicators initiated in spring 2019, inspired by similar projects in other countries, especially Climate Matters in the USA.

MET Norway supplies meteorologists to forecast the weather at NRK, the Norwegian national public broadcaster. The TV-meteorologist's assets is that they use language that people understand, and that they can talk about climate change from a local point of view and in relation to the weather situation at a given time. The main objective of this project was therefore to regularly include climate information in the weather forecasts on NRK radio and television. 

In 2019 and 2020 we had 40 stories in the weather forecasts on NRK television. Most of the stories have been related to temperature changes, but also about Arctic sea-ice, flash-floods, and changes in snow cover. The TV-meteorologists involved in the project report that they mainly get positive feedback from the audience. The majority of the stories have also been shared through social media and through press releases to reach other audiences than those that watch TV. Most of these stories have been shared (e.g. retweeted) by multiple users, and we estimate a reach of about 20 percent of Norway's population as realistic.

The TV-meteorologists have also received training in both climate research and climate communication on a monthly basis since the project started. After the project ended in december 2020, 40 percent of the TV-meteorologists said that they feel more comfortable to communicate climate information than before the project started. 

The project is now operationalized in a climate editorial desk on MET, consisting of several climate researchers, TV-meteorologists and communication advisors that meets on a weekly basis. 
Generating automatic warning proposals
Authors: Mahlstein Irina , Nerini Daniele
Published: June 2021

A warning system is a complex chain, which builds on different applications leading to a customer friendly product. The goal of the product is to deliver useful information to the end-user, giving indication of the severity of the event and what best to do in order to avoid damages and/or injuries/fatalities. In-between the different production steps are a number of processes, which can be altered to improve the products; for example by including probabilistic information or by producing impact-oriented warnings.

As MeteoSwiss is renewing its warning system, it opens up the possibility to include the above-mentioned information. Furthermore, it also offers the option to automatize the warning generation chain. One key part of this process are the automatically generated first guesses of warning regions. These regions display the danger level of any given hazard based only on the meteorological situation; hence, no predefined regions will be used to generate the warning products. As of now, MeteoSwiss used a set of predefined regions on which the danger level was indicated. These regions were not necessarily defined to best represent weather phenomena but rather often municipal boundaries.

However, how to produce meaningful regions is not trivial and it requires discussions with the forecasters as there are a number of parameters to tune. Tuning the regions is needed as no forecasting system is perfect and ideally, the automatically generated first guesses compensate for these short-comings. However, realistically speaking, before achieving a fully automatic warning system, there will be an intermediate phase when first guesses will likely have to be manually adjusted by the forecasters.

We will present our work and first results of automatic warning proposals based on COSMO-2E and feedbacks thereof we got from discussions with the forecasters.
Rapid changes of Alpine glaciers in Austria tracked by ALS surveys 
Authors: Helfricht Kay , Hartl Lea , Stocker-Waldhuber Martin , Seiser Bernd , Fischer Andrea
Published: June 2021

Unprecedented glacier changes are reported for many mountain regions on earth based on surveys with different spatial resolution and repeat intervals. Eastern Alpine glaciers have been receding since the LIA maximum, with increasing relative volume loss at the beginning of the 21st century. New high-resolution data of surface elevation from ALS surveys enable the analysis of most recent glacier changes at three mountain ranges in western Austria as an impact of climate change.

Surface elevation change rates between 2007 and 2018 increased again in comparison to former periods. Volume loss takes place even in the highest elevation zones, and most of the glaciers are out of an equilibrium state, such that consolidation of mass balance towards zero appears impossible under present climate conditions. The disintegration of low lying glacier tongues and a strong depletion of the firn cover are further signs of rapid glacier changes. The frequency distributions of surface elevation change throughout the area of each glacier show distinct shifts in peak ice thickness change and patterns of surface change distribution that suggest ongoing processes of glacier disintegration. Combining recent surface elevation changes and estimations of the spatial distribution of ice thickness in Austria shows that most of glaciers will vanish in 50 years or less. Only glaciers currently larger than 5 km² can be expected to exist longer at reduced size. At current rates of mass loss, glaciers are projected to retreat entirely to above 2800m in the Ötztal and Stubai ranges by 2050. Further concerns arise regarding methods of tracking the future development of the remaining ice bodies. In particular, in the Silvretta mountain range, the majority of glacier margins have to be delineated in debris-covered glacier zones. It is debatable whether some of the smallest glaciogenic features should still be accounted for in glacier inventories or moved to an inventory of transient cryogenic landforms.
Doppler wind lidar activities at Cabauw
Authors: Knoop Steven , Bosveld Fred , de Haij Marijn , Apituley Arnoud
Published: June 2021

Atmospheric motion and turbulence are essential parameters for weather and topics related to air quality. Therefore, wind profile measurements play an important role in atmospheric research and meteorology. One source of wind profile data are Doppler wind lidars, which are laser-based remote sensing instruments that measure wind speed and wind direction up to a few hundred meters or even a few kilometers. Commercial wind lidars use the laser wavelength of 1.5 µm and therefore backscatter is mainly from aerosols while clear air backscatter is minimal, limiting the range to the boundary layer typically.

We have carried out a two-year intercomparison of the ZephIR 300M (ZX Lidars) short-range wind lidar and tall mast wind measurements at Cabauw [1]. We have focused on the (height-dependent) data availability of the wind lidar under various meteorological conditions and the data quality through a comparison with in situ wind measurements at several levels in the 213m tall meteorological mast. We have found an overall availability of quality-controlled wind lidar data of 97% to 98 %, where the missing part is mainly due to precipitation events exceeding 1 mm/h or fog or low clouds below 100 m. The mean bias in the horizontal wind speed is within 0.1 m/s with a high correlation between the mast and wind lidar measurements, although under some specific conditions (very high wind speed, fog or low clouds) larger deviations are observed. This instrument is being deployed within North Sea wind farms.

Recently, a scanning long-range wind lidar Windcube 200S (Leosphere/Vaisala) has been installed at Cabauw, as part of the Ruisdael Observatory program [2]. The scanning Doppler wind lidars will provide detailed measurements of the wind field, aerosols and clouds around the Cabauw site, in coordination with other instruments, such as the cloud radar.

[1] Knoop, S., Bosveld, F. C., de Haij, M. J., and Apituley, A.: A 2-year intercomparison of continuous-wave focusing wind lidar and tall mast wind measurements at Cabauw, Atmos. Meas. Tech., 14, 2219–2235, 2021

[2] https://ruisdael-observatory.nl/
Skill assessment of sub-seasonal 2-m temperature forecasts for Helsinki, Finland
Authors: Rantanen Mika , Kämäräinen Matti , Hyvärinen Otto , Vajda Andrea
Published: June 2021

Sub-seasonal to seasonal scale forecasts provide useful information for city authorities for operational planning, preparedness and maintenance costs optimization. In the EU H2020 E-SHAPE project the Finnish Meteorological Institute aims at developing an operational service providing user-oriented sub-seasonal and seasonal forecast products for the City of Helsinki tailored for winter maintenance activities. To be able to provide skilful sub-seasonal to seasonal forecasts products, bias adjustment and evaluation of the used weather parameters, i.e. temperature and snow is crucial. 

In this study, we focus on the skill assessment of sub-seasonal temperature forecasts in Helsinki, Finland, experimenting with various methods to adjust the bias from the raw temperature forecasts. Due to its coastal location, skilful forecasting of temperatures for Helsinki is challenging. The temperature gradient on the coastline is especially strong during spring when inland areas warm considerably faster than the coastline. Therefore, raw point forecasts for Helsinki suffer from cold bias during the March-July period.

We use the 2 m temperature extended-range reforecasts obtained from the ECMWF S2S database and apply two bias adjustment techniques: removing the mean bias and the quantile mapping method. Reforecasts for a 20-years period, 2000-2019 with 10 ensemble members, run twice a week for 46 days ahead were calibrated and evaluated. Two datasets are used as reference, observations from Helsinki Kaisaniemi weather station and gridded ERA5 reanalysis data. Thus, these combinations yield in total five sets of forecasts which are evaluated against the observations.

The results of the experiments and the potential added value of bias correction will be presented for discussion. Based on the preliminary results, especially the cold bias in spring and early summer can be improved with the bias-correction methods. The bias-adjusted extended-range temperature forecasts are used in the development of sub-seasonal winter forecast products tailored for the needs of city maintenance.
Evaluation of multi-parameter dependencies in weather and climate simulations
Authors: Wahl Sabrina , Keller Jan D.
Published: June 2021

Weather and climate simulations based on numerical models provide 4-dimensional reconstructions of multiple meteorological parameters describing the atmopsheric state. Yet, the vast majority of evaluation studies focus on the evaluation of single parameters in time and space without looking at the statistical multivariate dependence between the parameters. This, however, is necessary especially with respect to specific events where two or more parameters are involved, i.e., so called compound events. Previous studies have investigated the representation of natural hazards such as wildfires, heat stress, droughts by evaluating corresponding indices based on two or more parameters. Thereby the evaluation process stays in a single parameter framework with well established verification methods at hand. 

In this work, we present a more sophisticated and generalized approach to investigate physical dependencies between parameters by employing copula theory. With this method, we aim at evaluating the multivariate statistical dependence between two parameters (i.e. the copula) separately from their marginal distributions. This separation enables a more detailed investigation of compound indices (CI) based on the involved parameters. The differences in CI derived from model simulations and observations can now be related to deficiencies of the numerical model due to (i) a misrepresentation of the marginal distributions of the contributing variables, (ii) a misrepresentation of the statistical dependence between the parameters (the copula), (iii) or both. While the method is applicable to all combinations of two parameters, we will present the results of a specific joint copula-based evaluation of temperature and humidity which are the basis for natural hazards mentioned above.
Using Value Chain Approaches to Evaluate End-to-End Warning Systems
Authors: Ebert Elizabeth , Hoffmann David , Mooney Carla
Published: June 2021

The  weather information value chain provides a framework for characterising the production, communication, and use of information by all stakeholders in an end-to-end warning system. Since the generation of weather warning and climate services has become more complex, both technically and organizationally, the  value chain concept has become a popular tool for describing and assessing the production, use and  benefits of such services.

The end-to-end warning system for high impact weather brings together hazard monitoring, modelling and forecasting, risk assessment, communication and preparedness activities and systems and processes which enable timely action to reduce risks. Weather and associated warning services are typically developed and provided through a multitude of complex and malleable value chains (networks), often established through co-design, co-creation and co-provision.  

A new international project under the WMO World Weather Research Programme is using value chain approaches to describe and evaluate the end-to-end warning system for high impact weather. Its aims are

(1) To review value chain approaches used to describe weather, warning and climate services to assess and provide guidance on how they can be best applied in a high impact weather warning context that involves multiple users and partnerships;

(2) To generate an easily accessible means (an End-to-End Warning Chain Database) for scientists and practitioners involved in researching, designing and evaluating weather-related warning systems to review previous experience of high impact weather events and assess their efficacy using value chain approaches.

We encourage the research and operational community to participate in this project by contributing case studies of high impact events and collaborating in their analysis. Integration of the physical and social sciences in this project will lead to new insights that we hope will ultimately improve the effectiveness of warnings for high impact weather.
Experiences in using low-cost sensors to observe road weather
Authors: Karsisto Virve , Latva Lasse , Miettinen Janne , Hippi Marjo , Mäenpää Kari , Siili Tero , Posio Pentti , Sukuvaara Timo
Published: June 2021

Road weather information is essential for keeping the roads well maintained and safe during wintertime. Main source of road weather observations are road weather stations, but IoT (Internet of things) sensor technology provides new ways to observe road weather. Finnish Meteorological Institute (FMI) and Fintraffic Road are studying whether such IoT technology could help increase spatial density and/or improve coverage in the observation network and whether these additional observations could also be used to improve road weather forecasts. Around 100 autonomous battery-operated low-cost IoT sensors based on LoRaWAN communication technology were installed into the roadside area of a motorway in southern Finland and at the Sodankylä airport test track during winter 2020. Most of the sensors were of the types UC11-T1 from Ursalink and ELT-2 from ELSYS AB, but there were a few MCF-LW12TERWP sensors from MCF88 as well. All sensors measure air temperature and humidity and the MCF sensors also measure air pressure. Some of the sensors were installed at a weather station and some at road weather stations to enable data comparison with reference stations. During wintertime the IoT sensors’ air temperature measurements correspond rather well to the reference measurements. However, during other times of the year the solar radiation often causes warm bias to the measurements. The bias is reduced when the sensors are installed inside radiation shields. However, the reliability of the IoT devices needs improvement, as several sensors stopped working during the measurement campaign. This was probably caused by a firmware bug, that led to excess power consumption and emptying of batteries in some of the devices.

The FMI road weather model uses surface temperature observations in the model initialization to improve the forecasts. As the model surface temperature is forced to the observed surface temperature, the air temperature measurements don’t have that much effect in the initialization. When there are no surface temperature observations available at the forecast location, the model uses values interpolated from road weather station observations. The interpolation is done with the universal kriging method, where elevation is used as an explanatory variable. In this project we studied whether air temperature observations from IoT sensors could be used as explanatory variable as well. The results thus far show that use of air temperature observations from road weather stations improves the interpolated surface temperature values at least in some situations. However, this is rather location dependent. Initial results suggest that IoT observations would be useful this way as well. According to the results, IoT observations show potential to improve road weather monitoring and forecasting, but more studies are still needed.
Impact of Snow Representation in Seasonal Forecast Systems
Authors: Risto Danny , Fröhlich Kristina , Ahrens Bodo
Published: June 2021

Current seasonal forecast systems have difficulties predicting temperature over continental regions, whereas for some regions with maritime influence their performance is better. The main driver for better skill in maritime regions is related to the ocean and its memory effect. For continental regions, the land surface can become a more important source of predictability on (sub-)seasonal time scales. Besides soil moisture, snow is a crucial component of the land surface as it stores an extensive amount of water and modulates the earth’s radiation budget each winter season. A snow-covered land surface leads to local temperature decreases in the overlying air (snow-albedo effect and high emissivity) and melting snow cools the surface air and contributes to soil moisture and river water. We compare the snow representation in seasonal forecast systems from four European weather/climate services provided by the Copernicus Climate Change Service (C3S) and their performance in predicting snow, temperature and precipitation. The goal is to identify the impact of the snow initialisation and snow modelling from the four forecasts systems. The first results show that the predicted anomalies of 2m temperature over continental regions correlate with reanalyses only for the first forecasted month, whereas anomalies in snow water equivalent can be predicted up to several months. While the biases among the forecast systems differ, the correlation skills are similar for the same variable, with precipitation having the lowest correlation skills. Furthermore, we will investigate the causal relationships between snow and 2m temperature with time-lagged correlation or similar methods and will consider the whole ensembles of the hindcasts.
Assessing urban climate model results with crowd-sourced data
Authors: Hahn Claudia , Oswald Sandro , Hollosi Brigitta , Goler Robert , Kainz Astrid , Zuvela-Aloise Maja
Published: June 2021

Climate change impacts are amplified in cities due to the urban heat island effect and the high population density. Information about the intra-urban temperature patterns is therefore crucial to support resilient city planning. Within the ACRP funded project LUCRETIA, the intra-urban temperature patterns in Vienna, Austria, are investigated using urban climate models (MUKLIMO_3, PALM-4U) and data from citizen weather stations.

While the density of conventional weather station networks is usually too low to capture the temperature patterns in cities and to assess urban climate model results, citizen weather stations provide a dense monitoring network, especially in cities. In Vienna, more than 1000 citizen weather stations from the company Netatmo are available for our study period in August 2018, after the quality control. First investigations showed, that air temperature measurements from citizen weather stations are in good agreement with measurements from conventional stations. The observed differences are attributed to the different locations of the stations and micro-scale effects. A preliminary comparison of citizen weather station data with urban climate model results from MUKLIMO_3 for Vienna revealed for some of the stations similar patterns as the comparison between conventional stations and model results: a reasonably good agreement during the day, after model initialization, and a temperature overestimation at night. Within LUCRETIA we are assessing in more detail the model results (MUKLIMO_3, PALM-4U) for a three day period in August 2018, thereby looking at the effect of the different land-use classes within the city. In addition, we will investigate whether similar spatial temperature patterns are identified when using urban climate models and data from citizen weather stations.
Assessment of the planetary boundary layer height by means of machine learning techniques using ceilometer signals
Authors: Barragan Ruben , Molero Francisco , Artiñano Begoña
Published: June 2021

It is well known that anthropogenic aerosols deteriorate air quality increasing public health risk. Therefore their characterization must be one of the main objectives in atmosphere studies, although the heterogeneous distribution of the aerosols in the atmosphere hampers it. Anthropogenic aerosols are mostly concentrated within the planetary boundary layer (PBL) that extends from the surface up to a variable height that usually coincides with the presence of a temperature inversion. The PBL height, then, is affected by the radiation emitted by the surface causing turbulence and evolving along the day and in this way limiting the vertical mixing of the air pollutants generated near the surface. Therefore, it can be assumed that the lower the PBL height, the higher the aerosol concentration from local sources. Lidars have demonstrated their capabilities to study the aerosol vertical distribution and their spatio-temporal evolution can provide very complete information on both aerosol spatial distribution and their characterization. Their wavelength dependence of the backscatter and extinction coefficients allows for a more detailed discrimination of aerosol types. On the other hand, ceilometers are capable of providing continuous aerosol vertical profiles with good spatial resolution and a large range, besides ceilometers operating at 1064nm can provide backscatter and extinction coefficients as Lidar instruments. The present work has been carried out in the Madrid metropolitan area located in the center of the Iberian Peninsula, which counts with a population of nearly 6 million inhabitants and a car fleet of almost 3 million vehicles. Its main objective is the assessment of the planetary boundary layer height by means of machine learning techniques using ceilometer signals and also its validation by using multiwavelength lidar measurements and radiosoundings. Typical techniques as the wavelet and the gradient methods are unable to detect the PBL in cases with presence of low clouds or residual layers. For that purpose, several profiles stored in the Madrid database, covering different synoptic situations as long-range transport of aerosols and clean-atmosphere situations are used. These profiles have been performed by the CHM15k Nimbus ceilometer deployed next to the MDR-CIEMAT ACTRIS station (40.4565ºN, 3.7257ºW, 663 m a.s.l.), equipped with a Lidar-Raman instrument (integrated in EARLINET-ACTRIS) and located in the Madrid North-West city outskirts.

 

Acknowledgements

This work was supported by H2020 programme from the European Union (grant 654109, ACTRIS-2 project), the Spanish Ministry of Economy and Competitivity (CRISOL, CGL2017-85344-R) and Madrid Regional Government (TIGAS-CM, Y2018/EMT-5177).  
Possible contribution of passive radiometers to the analysis of the evolution of fog episodes
Authors: Ferrario Massimo Enrico , Maria Sansone
Published: June 2021

The Regional Agency for the Environment of Veneto (ARPAV - North East Italy) through its Meteorological Center, has been managing some passive radiometers since 2005, one located in the Po Valley in the historic center of Padua (210,000 inh.) and the other in a wide pre-Alpine valley (Val Belluna) in the municipality of Feltre (20,000 inh.). Both instruments are located on the roof of the host building and scan the atmosphere with a thermal profile every 5 min. In the Po Valley and in Val Belluna there are frequent episodes of fog, especially in the autumn / winter season, which can sometimes persist throughout the day.

Both radiometers are MTP-5 HE produced by Attex and are able to obtain the thermal profile up to 1 km, with an interpolated value every 50 m, in almost all weather conditions, using a single channel centered on the absorption of the molecular oxygen microwave at 60 GHz. This simple type of radiometer, very useful for studying the characteristics of thermal inversion, or super-adiabatic heating of the first layers of the atmosphere, is widely used to characterize the PBL (Planetary Boundary Layer) in terms of atmospheric stability.

The proposed study seeks to explore the possibility of using thermal profile data from passive radiometers to study fog evolution only at the Padua site, where other meteorological information is also available, such as professional weather stations, webcams and visibilimeters. A statistic of the phenomenon will be presented, with the help of satellite images, from data series of over 10 years, together with some case studies, which will try to highlight the limits and effectiveness of this new approach.
High-Resolution Radar Echo Prediction with Machine Learning
Authors: Choma Matej , Bartel Jakub , Šimánek Petr , Rybář Vojtěch
Published: June 2021

The standard for weather radar nowcasting in the Central Europe region is the COTREC extrapolation method. We propose a recurrent neural network based on the PredRNN architecture, which outperforms the COTREC 60 minutes predictions by a significant margin.

Nowcasting, as a complement to numerical weather predictions, is a well-known concept. However, the increasing speed of information flow in our society today creates an opportunity for its effective implementation. Methods currently used for these predictions are primarily based on the optical flow and are struggling in the prediction of the development of the echo shape and intensity.

In this work, we are benefiting from a data-driven approach and building on the advances in the capabilities of neural networks for computer vision. We define the prediction task as an extrapolation of sequences of the latest weather radar echo measurements. To capture the spatiotemporal behaviour of rainfall and storms correctly, we propose the use of a recurrent neural network using a combination of long short term memory (LSTM) techniques with convolutional neural networks (CNN). Our approach is applicable to any geographical area, radar network resolution and refresh rate.

We conducted the experiments comparing predictions for 10 to 60 minutes into the future with the Critical Success Index, which evaluates the spatial accuracy of the predicted echo and Mean Squared Error. Our neural network model has been trained with three years of rainfall data captured by weather radars over the Czech Republic. Results for our bordered testing domain show that our method achieves comparable or better scores than both COTREC and optical flow extrapolation methods available in the open-source pySTEPS and rainymotion libraries.

With our work, we aim to contribute to the nowcasting research in general and create another source of short-time predictions for both experts and the general public.
Observational evidence of urban heat island intensification during heatwaves in European cities
Authors: Possega Marco , Aragão Leonardo , Ruggieri Paolo , Santo Marco Antonio , Di Sabatino Silvana
Published: June 2021

Heat waves (HWs) are extreme weather conditions characterized by persistent high temperatures with considerable impacts on society in terms of
mortality, thermal stress and energy demand of the population. One of the most interesting aspects of HWs concerns the interaction with the phenomenon
of urban heat island (UHI). The UHI is the tendency of urbanized areas to have warmer temperatures than the surrounding rural areas, mainly due to the thermal
properties of materials forming urban environment and the heat produced by human activities. Some studies analyzed the behavior of UHI during periods of
extreme heat, showing an amplification of the gradient of temperature between urban and rural areas in HW conditions, but results are often limited to case
studies with a single HW and/or a specific city. Other papers dealt with the same topic by examining events on various cities using outputs of global models,
but with resolution insufficient to include in detail urban-scale processes and therefore to take into account specific properties of the cities investigated. The
approach of this work consisted in providing observational evidence and extending the aforementioned results, studying the effect of HWs on UHI in 41 European cities
with different characteristics (geography, topography, urban planning) through the analysis of daily maximum / minimum temperatures data measured by
meteorological stations for the summers of period 2000-2019. In particular, the intensity of UHI was assessed through the computation of a Composite UHI Index
(UHII), defined as the difference between averaged urban and non-urban values. The different behavior of UHII during HWs compared to "normal" summer days
(NO) in selected European cities was investigated, detecting an intensification of index values regarding periods of extreme heat for the majority of examined
locations. More specifically, the analysis of temporal evolution of UHII was conducted, revealing an average increase of this index during the occurrence of
HW events due to higher urban than rural temperatures. This work provides an indication of how European urban areas respond to severe hot periods and could
be useful to validate numerical model simulations for more detailed analysis, for example regarding mitigation strategies. Finally, the emergence of some outliers,
namely cities whose UHI manifested a different reaction to HWs, may deserve dedicated studies in the future.

 
Energy cascade for highly anisotropic turbulence in the Stable Boundary Layer
Authors: Gucci Federica , Giovannini Lorenzo , Zardi Dino , Vercauteren Nikki
Published: June 2021

The broad variety of phenomena occurring on multiple scales under stably stratified conditions and their complex interactions make it difficult to get a full description of the Stable Boundary Layer (SBL). Near-surface turbulence may be intermittent and highly anisotropic even at small scales. By studying the invariants of the anisotropy Reynolds stress tensor, it is possible to analyse the eddy kinetic energy distribution over the three components of the flow. Recent analyses of SBL turbulence data highlighted a prevalence of one-component limiting state of anisotropy. The causes of this particular limiting state are not fully understood, but there is evidence that submeso activity influences turbulence topology. 

This open question motivated the present work, that addresses the issue from the point of view of space dimensionality. In large-scale atmospheric and oceanic dynamics it is well known that turbulent motions may transfer energy both to the large and to the small scales, according to density stratification and rotation. These two properties act as constraints on the flow, giving it a 2D structure, and leading turbulence to be more complex than the homogeneous and isotropic case. For a SBL in low-wind speed conditions, atmospheric stratification might be very strong and we investigate if some of the peculiar characteristics of this regime might be related to a quasi-2D dynamics, with the occurrence of an inverse energy cascade, typical of 2D-like turbulence.

Energy exchanges across larger and smaller scales are studied by analysing the direction of the momentum flux with different methods, including a coarse-graining approach based on Large Eddy Simulation (LES) theory. The SnoHATS dataset was used to this purpose, where two vertically-separated horizontal arrays of sonic anemometers over the Plaine Morte Glacier (Switzerland) allowed the computation of the full three-dimensional velocity gradient. In order to fully characterize the energy exchanges according to different states of turbulence anisotropy, energy conversion processes between eddy kinetic and potential energy have also been considered and analysed at different heights. To this purpose, the dataset FLOSSII was used, providing turbulence measurements up to 30 m above a flat grass surface, often covered by snow. 

Results seem to suggest that turbulent kinetic energy in the SBL is distributed mainly in one component more as a consequence of wave-turbulence interactions than of development of 2D-like turbulence. This gives insights on mechanisms driving turbulence anisotropy that might be used to improve turbulence parameterizations in the SBL.
The NSRDB PV Resource Product: Spectral Solar Radiation Data on Inclined Surfaces 
Authors: Xie Yu , Sengupta Manajit
Published: June 2021

Global horizontal irradiance (GHI) and direct normal irradiance (DNI), representing broadband solar radiation measured on a horizontal surface and those received in a narrow beam along the direction of the incoming sunlight, respectively, are the most frequently used quantities to assess the solar resource in specific locations or large-scale areas. However, GHI and DNI indirectly, and often deficiently, represent the amount of radiation that is converted into electric power by photovoltaic (PV) panels that are usually installed at tilt angles or on solar tracking systems to maximize the power output. Spectral distribution of surface radiation, affects the PV performance due to the spectral response of semiconductor materials and the solar cell designs to split spectral radiation, but this information is not directly informed from GHI and DNI data. To address this issue, we developed a Fast All-sky Radiation Model for Solar applications with Narrowband Irradiances on Tilted surfaces (FARMS-NIT) to simultaneously compute spectral radiation over horizontal and inclined surfaces using the physical properties that can be inferred by surface- or satellite-based radiometers. It utilizes the optical properties of aerosols and a pre-computed lookup table of cloud transmittance to efficiently solve spectral radiances that can be spectrally and angularly weighted to directly match the PV response. This new model has been implemented in the National Solar Radiation Data Base (NSRDB) to quantify the solar resource that is available for conversion by a PV plant. The data are referred to as the NSRDB PV Resource product to be distinguished from the routine solar resource product from satellite observations. This study will review the input data and mechanics to develop the PV Resource product as well as the procedures to access the product. To provide a critical reference in the data applications, we will evaluate the PV Resource data using surface observations from stations operated by the National Renewable Energy Laboratory (NREL), University of Oregon, and the First Solar, Inc.
Investigating the relationship between outdoor heat events upon sleep quality in Ankara 
Authors: Ahan Merve , Nouri Andre
Published: June 2021

Within the existing literature, it is well established that thermal comfort thresholds play an integral part in sleep quality. For this reason and within consolidated urban environments that are continually witnessing increased vulnerability to heat stress as a result of urban densification and climate change, impediments upon sleep quality due to excess physiological heat stress is a growing concern. Such risk factors are particularly the case for older traditional building methods that depend on natural ventilation.  

Focused upon the capital city of Ankara, and through the application and modification of the WMO’s Expert Team on Climate Change Detection Monitoring Indices (ETCCDMI) to determine the effects of sleep quality during different heat stress events, three different conditions were respectively examined, during a: (1) typical summer day; (2) very hot day; and lastly, (3) heat wave event. Within this case study, the relationship between the indoor thermal thresholds and sleep quality shall be undertaken through the use of: (i) the Physiologically Equivalent Temperature (PET) calculated from climatic variables retrieved from the residential units at a 10 minute temporal resolution; (ii) questionnaires to acquire qualitative responses from local residents; and, (iii) local urban climatic data, retrieved from Ankara’s meteorological station (#17130) at an hourly temporal resolution.  

The outputs of the study address the growing need to identify and address the impact that urban augmenting temperatures have upon the sleep quality of occupants in more vulnerable construction typologies. Given that such vulnerabilities in urban settings are continually observing more frequent and intense heatwaves and hot days, the study highlights the need to strengthen the understanding between such outdoor events and sleep quality patterns in warming urban frameworks. 
Evaluating the post-processing of the European Flood Awareness System’s continental scale streamflow forecasts
Authors: Matthews Gwyneth , Cloke Hannah , Dance Sarah , Prudhomme Christel
Published: June 2021

Floods are the most common and disastrous natural hazards and due to climate change and socio-economic growth floods are becoming more destructive. Early warning systems are one of the best ways to decrease the effect of floods by increasing preparedness. However, uncertainties are inevitably introduced throughout streamflow forecasting systems and untreated they can limit the value of the forecasts to end-users. In recent decades several post-processing methods have been introduced and shown to improve forecast skill through bias and spread correction. The European Flood Awareness System, part of the European Commission's Copernicus Emergency Management Service, post-processes its medium-range ensemble flood forecasts at over a thousand stations across Europe where historic and near real-time observations are available. A combination of different techniques, namely the Model Conditional Processor, Ensemble Model Output Statistics, and the Kalman Filter are used to account for hydrological and meteorological uncertainties using recent observations and forecasts. Evaluation of the post-processing method is performed using two years of twice-weekly reforecasts. In general, the skill of the forecasts is improved, but the magnitude of this improvement decreases at longer lead-times as recent observations become less impactful. The improvement from post-processing is found to vary substantially between the stations and the continuous ranked probability skill score is used to investigate the impact of different station characteristics. Low-lying large catchments are shown to have the greatest increase in skill from post-processing whereas small high-elevation catchments are harder to correct. However, it was found that the flood magnitudes observed in the historic record are of greater importance than the length of the record itself for determining the effectiveness of the post-processing method.

 