bluuebunny/crossref_metadata_2025 huggingface dataset:
The EU weather value chain - past, current and future perspectives
Authors: Gutbrod Karl
Published: June 2021

The EU weather value chain has developed vigorously, with private services growing at a 20% p.a. rate over the past decade. Growth has also happened amongst national services, and the sector of instrument providers.

The study uses a value chain model of five steps : 1. Instrument Supply, 2. Networks, (for operation of Measurements & Observations), 3. Data processing, 4. Products (Mass distribution) and 5. Tailored services.

The study analyses the entire value chain in selected countries, mainly United Kingdom, Netherlands, Germany, Switzerland, Austria, France, Spain, Italy and Czechia, including all steps from instrument to service providers, including national and private services, and quantifies their evolution over the period from 2010 to 2019. It uses annual reports, press articles and other sources to estimate development of value creation, in terms of annual budgets, revenues, profits and employment. It also attempts to identify the sources of uncertainty for the assessments and propose techniques to reduce this uncertainty.

Further, it classifies the value creation by value chain step and market segment, and attempts to differentiate between domestic and export of products and services.

The study further compares the value creation by inhabitant, GDP, area and other social parameters, to create parameters which can be used for characterising the value chain, and developing a better understanding of causal factors enhancing the development of the weather value chain in selected countries. These parameters are then compared with those of more other highly developed value chains in the USA and Japan.

In a final step, the study makes some projections about future evolution of the weather value chain and recommendations for the future development of a more productive and beneficial value chain.
Persistence analysis in convective turbulence 
Authors: Banerjee Tirtha , Chowdhuri Subharthi
Published: June 2021

Persistence is defined as the probability that the local value of a fluctuating field remains at a particular state for a certain amount of time, before being switched to another state. The concept of persistence has been found to have many diverse practical applications, ranging from non-equilibrium statistical mechanics to financial dynamics to distribution of time scales in turbulent flows and many more. In this study, we carry out a detailed analysis of the statistical characteristics of the persistence probability density functions (PDFs) of velocity and temperature fluctuations in the surface layer of a convective boundary layer, using a field-experimental dataset. Our results demonstrate that for the time scales smaller than the integral scales, the persistence PDFs of turbulent velocity and temperature fluctuations display a clear power-law behavior, associated with a self-similar eddy cascading mechanism. Apart from that, we show that the effects of non-Gaussian temperature fluctuations act only at those scales which are larger than the integral scales, where the persistence PDFs deviate from the power-law and drop exponentially.

To advance our knowledge, we also investigate how the turbulent structures associated with velocity and temperature fluctuations interact to produce the emergent flux signatures, a vexing problem but of paramount importance for a plethora of applications, encompassing both engineering and Earth sciences. We discover that the persistence patterns for heat and momentum fluxes are widely different. Moreover, we uncover the power-law scaling and length scales of turbulent motions that cause this behavior. Furthermore, by separating the phases and amplitudes of flux events, we explain the origin and differences between heat and momentum transfer efficiencies in convective turbulence. In summary, our findings provide a new understanding of the connection between flow organization and flux generation mechanisms, two cornerstones of turbulence research.
Investigating long-term variations in cloud base height over Sofia, Bulgaria 
Authors: Levi Viktor , Vladimirov Evgeni , Danchovski Ventsislav
Published: June 2021

 

Clouds have a key role in weather and climate forecasting due to their effect on global radiation and water budget. Clouds change the radiation energy in the Earth-atmosphere system by reducing both incoming and outgoing parts, depending on their macro- and microphysical characteristics such as cloud base height (CBH), optical properties etc. These clouds properties are generally related to cloud types, so the effects in weather and climate caused by various cloud types differ greatly. It is known that high clouds cause the earth's surface to heat up, while low clouds cause cooling. Obviously, cloud radiation forcing is an important source of uncertainty in the numerical weather and climate models, so the registered and expected changes in the properties of clouds due to a warming climate need in-depth studies. But cloud base height is not only important for weather and climate forecasting, but also for airplane traffic safety.  Nowadays, retrieving the CBH is mainly based on satellite and ground-based observations. Satellite-borne instruments provide tempting spatial coverage but uncertainty in CBH estimation should be considered. In contrast, many ground-based observations of the CBH are characterized by higher accuracy. Nowadays, ceilometers - lidars specifically designed to detect CBH, that operate continuously and unattended, providing high vertical and time-resolution data, are reference instrument in CBH measurement. In addition, rawinsondes provide in-situ measurements of temperature, humidity, and pressure, so that the CBH can be evaluated by the lifting condensation level or by threshold value in relative humidity. In areas where only surface measurements are available, a simple adiabatic model of a rising air parcel can be applied in the CBH assessment. In this work, based on ceilometer, rawinsonde and surface measurements, the characteristics of CBH over Sofia, Bulgaria are studied in detail. We start with an intercomparison between CBHs obtained from three types of ground-based observations, considering the individual advantages and disadvantages of the methods by using ceilometer as reference. Finally, the daily, seasonal and interannual variability of CBH over Sofia are interpreted.
Homogenization of Norwegian monthly precipitation series
Authors: Kuya Elinah Khasandi , Gjelten Herdis Motrøen , Tveito Ole Einar
Published: June 2021

Climate normals play an important role in weather and climate studies and therefore require high-quality dataset that is both consistent and homogenous. The Norwegian observation network has changed considerably during the last 20-30 years, introducing non-climatic changes such as automation and relocation. Homogenization was therefore necessary and work has been done at the Norwegian Meteorological Institute to establish a homogeneous precipitation reference dataset for the purpose of calculating the new climatological standard normals for the period 1991-2020. 

The homogenization tool Climatol was applied to detect inhomogeneities in the Norwegian precipitation series, for the period 1961-2018. 370 series (including 44 from Sweden and one from Finland) of monthly precipitation sums, from the ClimNorm precipitation dataset were used in the homogenization analysis. ClimNorm is an international network activity under the Nordic Framework for Climate Services covering six countries in the Nordic region (Denmark, Estonia, Finland, Latvia, Norway and Sweden) with an objective that includes sharing data, methods and experiences in preparing a data basis as good as possible for calculation of new climate normals. 

Results from homogeneity testing found inhomogeneities in 95 (29 %) of the 325 Norwegian precipitation series. However, only 81 (25 %) of the series were classified as inhomogeneous after conferring with metadata and therefore adjusted. Relocation of the precipitation gauge and automation were the main causes of all the inhomogeneities in the Norwegian series, explaining 71 % and 12 % respectively of all detected breaks. All but one of the accepted inhomogeneities could be confirmed with metadata. Inhomogeneities found in the Swedish and Finnish series were adjusted without metadata. Results further showed benefits of incorporating metadata to the automatically detected inhomogeneities. Linear trend analysis showed increasing trends in the period 1961-2018 except in autumn where a decreasing trend was observed. The homogeneity analysis produced a 58-year long homogenous dataset for 325 monthly precipitation sum with regional temporal variability and spatial coherence that was significantly better than that of non-homogenized series. The homogenized dataset is more reliable in explaining the large-scale climate variations and was used to calculate the new climate normals in Norway.
A calibrated and consistent combination of probabilistic forecasts for the exceedance of several precipitation thresholds using neural networks.
Authors: Schaumann Peter , Hess Reinhold , Rempel Martin , Blahak Ulrich , Schmidt Volker
Published: June 2021

In this talk we present a new statistical method for the seamless combination of two different ensemble precipitation forecasts (Nowcasting and NWP) using neural networks (NNs), see [1]. The method generates probabilistic forecasts for the exceedance of a set of predetermined thresholds (from 0.1mm up to 5mm). The aim of the combination model is to produce seamless and calibrated forecasts which outperform both input forecasts for all lead times and which are consistent regarding the considered thresholds. First, the hyper-parameters of the NNs are chosen according to a certain hyper-parameter optimization algorithm (not to be confused with the training of the NNs itself) on a 3-month dataset (dataset A). Then, the resulting NNs are tested via a rolling origin validation scheme on two 3-month datasets (datasets B & C) with different input forecasts each. Datasets A & B contain forecasts of DWD's RadVOR, a radar-based nowcasting system, and Ensemble-MOS, a post-processing system of NWP ensembles made by COSMO-DE-EPS, with a horizontal resolution of 20km, which is a predecessor of ICON-D2-EPS. Ensemble-MOS forecasts were provided for up to +6h, while RadVOR forecasts were available up to +2h. For dataset C, forecasts with a grid size of 2.2km are used from STEPS-DWD, a new implementation of the Short-term Ensemble Prediction System (STEPS) by  DWD, and ICON-D2-EPS as a NWP ensemble system. Forecasts were made up to +6h. In both validation datasets (B & C), the forecasts show the well-known behavior that the nowcasting systems RadVOR & STEPS are superior for short lead times, while NWP forecasts (Ensemble-MOS & ICON-D2-EPS) outperform these systems for later lead times. Based on the comparison of several validation scores (bias, Brier skill score, reliability and reliability diagram) we can show that the combination is indeed calibrated, consistent and outperforms both input forecasts for all lead times. It should be noted that the combination works on dataset C, although the hyper-parameters were chosen based on dataset A, which contains different forecasts for a different grid size.

[1] P. Schaumann, R. Hess, M. Rempel, U. Blahak and V. Schmidt, A calibrated and consistent combination of probabilistic forecasts for the exceedance of several precipitation thresholds using neural networks. Weather and Forecasting (in print)
Multidimensional extremes: joint study of precipitation and temperature time series
Authors: Izsák Beatrix , Szentimrey Tamás , Lakatos Mónika , Pongrácz Rita
Published: June 2021

To study climate change, it is essential to analyze extremes as well. The study of extremes can be done on the one hand by examining the time series of extreme climatic events and on the other hand by examining the extremes of climatic time series. In the latter case, if we analyze a single element, the extreme is the maximum or minimum of the given time series. In the present study, we determine the extreme values of climatic time series by examining several meteorological elements together and thus determining the extremes. In general, the main difficulties are connected with the different probability distribution of the variables and the handling of the stochastic connection between them. The first issue can be solved by the standardization procedures, i.e. to transform the variables into standard normal ones. For example, the Standardized Precipitation Index (SPI) uses precipitation sums assuming gamma distribution, or the standardization of temperature series assumes normal distribution. In case of more variables, the problem of stochastic connection can be solved on the basis of the vector norm of the variables defined by their covariance matrix. According to this methodology we have developed a new index in order to examine the precipitation and temperature variables jointly. We present the new index with the mathematical background, furthermore some examples for spatio-temporal examination of these indices using our software MASH (Multiple Analysis of Series for Homogenization; Szentimrey) and MISH (Meteorological Interpolation based on Surface Homogenized Data Basis; Szentimrey, Bihari). For our study, we used the daily average temperature and precipitation time series in Hungary for the period 1870-2020. First of all, our analyses indicate that even though some years may not be considered extreme if only either precipitation or average temperature is taken in to account, but examining the two elements together these years were extreme years indeed. Based on these, therefore, the study of the extremes of multidimensional climate time series complements and thus makes the study of climate change more efficient compared to examining only one-dimensional time series.
Characterizing Large-Scale Circulations Driving Extreme Precipitation in the Northern French Alps
Authors: Blanc Antoine , Blanchet Juliette , Creutin Jean-Dominique
Published: June 2021

Extreme precipitation in the Northern French Alps are mainly associated with large-scale circulations (LSCs) bringing moist air from the Atlantic Ocean and the Mediterranean Sea - two atmospheric influences that are very frequent in the climatology. In this work, we investigate what characterizes the Atlantic/Mediterranean circulations driving extreme precipitation in the Northern French Alps in comparison to "random" Atlantic/Mediterranean circulations. We focus on extreme 3-day precipitation over two medium size neighboring catchments from 1950 to 2017. Atlantic and Mediterranean circulations are identified using an existing weather pattern classification established for Southern France. Every single LSC is characterized using three atmospheric descriptors based on analogy in geopotential shapes at 500hPa over Western Europe that were introduced in previous works. They are i) the celerity, characterizing the stationary nature of a geopotential shape, and ii) the singularity and relative singularity, characterizing the resemblance of a geopotential shape to its analogs, in other words the way this geopotential shape is closely reproduced in the climatology. We add to these analogy-based descriptors a new (non analogy) descriptor accounting for the strength of the low and high pressure systems. We show that Atlantic/Mediterranean circulations driving extreme 3-day precipitation in the Northern French Alps are the Atlantic/Mediterranean circulations featuring the strongest centers of action as well as the most stationary and the most reproducible geopotential shapes - characteristics that are rare for both atmospheric influences. In the Atlantic case, these characteristics appear to be even more pronounced and rare with regard to the whole climatology, pointing LSC as an important driver of extreme precipitation. In the Mediterranean case, these characteristics appear to be more random with regard to the whole climatology, pointing a more balanced contribution between specific LSC and humidity in driving extreme precipitation.
Coastal-maritime risk and early detection in Basque Country
Authors: Gaztelumendi Santiago , Egaña Joseba , R. Gelpi Ivan , Gomez de Segura Jose Daniel , Aranda Jose Antonio
Published: June 2021

The Basque Country is periodically affected by severe coastal-maritime episodes which, depending on their severity, can significantly alter human activities on the coastal strip, cause considerable material damage or even directly or indirectly result in personal injury.

In particular, in the field of coastal-maritime impact, three types of risk are currently considered in the warning/alert/alarm system operated by the Emergencies and Meteorology Directorate. The first one is associated with wind reversals along the coastline ("galernas") with a particular impact on users of beaches and coastline during the summer season. The second one, associated with bad sea conditions, with an impact on navigation in coastal sea waters (2 miles). The third one, associated with high sea-wave and tide conditions that favour overtopping and flooding in the most exposed areas of the coast.

The process of determining and communicating warnings/warnings/alarms is a complex decision-making operation involving multiple actors analysing different types of information based on a variety of available tools.  In this contribution we include a description of the warning/alert/alarm system, some aspects related to communication and dissemination including an analysis of the warnings issued during the operation of the system. We also provide a brief description of the hazard indicators and the early warning system (EWS) currently in operation at the Basque Meteorological Agency (Euskalmet), which allows monitoring and predicting severe situations and their potential impact in advance.

With regard to the warnings issued, we will present the main characteristics of the warning/alert/alarm system for maritime-coastal risk, including a "historical" perspective and comparing it with the previous warning system. We will analyse the monthly, seasonal and annual distribution of the warnings/alerts/alarms issued in recent years. We will also present the results of the validation process of this system during these last years of operation.

With regard to the early warning system (EWS), a description of the current system operating in Euskalmet is presented, covering the very short, short, medium and long term. Describing its main components that allow estimating the precursor variables of impact in each case. Sharp wind-reversals with wind intensification and propagation along the coast in the case of "Galernas". Wave height, periods and sea state in the case of Navigation, and overtopping indexes in the case of coastal impact. Finally, some conclusions are included regarding its operational performance and future work to be carried out to improve some operational aspects of the system.
Assessment of climate services prototypes on seasonal water management for the Mediterranean regions 
Authors: Dayon Gildas , Besson Francois , Viel Christian , Soubeyroux Jean-Michel , Etchevers Pierre
Published: June 2021

In the framework of the MEDSCOPE project, Météo-France has initiated the development of new prototypes for seasonal water resource management in the Mediterranean region, addressing different scientific and technical challenges essential for a future operationalization of the services . In order to have a replicable result on the Mediterranean area, we decided first to consider the three large watersheds onof the Rhone river in France, the Ebro river in Spain and the Po river in Italy.

Our first challenge was to use a new hydrologic model SURFEX-CTRIP, covering the whole Mediterranean area. Another point was to perfect and evaluate a new downscaling tool named ADAMONT permitting to debiase all seasonal forecast input variables needed for hydrology applications and not only (temperature and, precipitation and 5 other surface meteorological parameters). We decided also to assess the new UERRA hydrological analyse available on these three countries. Lthe last challenge was to identify local end users facing with decision making process at seasonal scale for water resources management and develop decision help products adapted to their needs.

The evaluation of these prototypes, carried out over the period 2019-2020 using the MF Syst 6 and then Syst 7 seasonal forecasting model, has highlighted a significant potential in a future operational application but also difficulties to be overcome.

The communication will present the main results of this work and discuss the lessons to be learnet from this experience

 
Observations of an elevated rotor and precipitation processes decoupled during a mountain wave event in the Eastern Pyrenees (Cerdanya-2017 Field Experiment) 
Authors: Udina Mireia , Bech Joan , Gonzalez Sergi , Paci Alexandre , Trapero Laura , Miró Josep Ramón , Codina Bernat
Published: June 2021

The study documents the formation of a rotor underneath the mountain waves generated the 15 January 2017 over the eastern Pyrenees (near the border between France, Spain and Andorra) during the Cerdanya-2017 field campaign. The event was characterized by strong winds, mountain waves and relevant snow accumulation over the Cerdanya valley and the eastern Pyrenees. The evolution and location of the mountain waves and precipitation structure were studied using high temporal resolution data from a UHF wind-profiler and a vertically pointing K-band Doppler radar, separated a few kilometres in horizontal distance.

A mountain wave was detected in the morning and shortened slightly in the afternoon when a transient rotor was formed disconnected from the surface flow (Udina et al. 2020). A strong turbulence zone was identified at the upper edge of the mountain wave, above the rotor, a feature observed in previous studies. The mountain wave and rotor induced circulation was favoured by the valley shape and the second mountain ridge location, in addition to the weak and variable winds, established during the sunset close to the valley surface. In addition, we find decoupling between precipitation processes and mountain wave induced circulations. During the studied event, mountain wave wind circulations and low-level turbulence do not affect neither the snow crystal riming or aggregation along the vertical column nor the surface particle size distribution of the snow. This study illustrates that precipitation profiles and mountain induced circulations may be decoupled which can be very relevant for either ground-based or spaceborne remote sensing of precipitation (Gonzalez et al 2019). This research is supported by CGL2015-65627-C3-1-R, CGL2015- 65627-C3-2-R (MINECO/FEDER), CGL2016-81828-REDT and RTI2018- 098693-B-C32 (AEI/FEDER).

References:

Gonzalez, S., Bech, J., Udina, M., Codina, B., Paci, A., & Trapero, L. (2019). Decoupling between precipitation processes and mountain wave induced circulations observed with a vertically pointing K-band doppler radar. Remote Sensing, 11(9), 1034.

Udina, M., Bech, J., Gonzalez, S., Soler, M. R., Paci, A., Miró, J. R., Trapero, L., Donier, J.M., Douffet, T., Codina, B., Pineda, N. (2020). Multi-sensor observations of an elevated rotor during a mountain wave event in the Eastern Pyrenees. Atmospheric Research, 234, 104698.
Experiment to quantify the solar radiative temperature error of radiosondes
Authors: von Rohden Christoph , Sommer Michael , Naebert Tatjana , Dirksen Ruud
Published: June 2021

One of the main goals of the GCOS Reference Upper Air Network (GRUAN) is to perform reference observations of profiles of atmospheric temperature and humidity for the purpose of monitoring climate change. Two essential criteria for establishing a reference observation are measurement-traceability and the availability of measurement uncertainties. Radiosoundings have proven valuable in providing in-situ profiles of temperature, humidity and pressure at unmatched vertical resolution. Data products from commercial radiosondes often rely on black-box or proprietary algorithms, which are not disclosed to the scientific user. Furthermore, long-term time-series from these products are frequently hampered by changes in the hardware and/or the data processing. GRUAN data products (GDPs) comply with the above-mentioned criteria for a reference product. Correction algorithms are open-source and well-documented and the data include vertically resolved best-estimates of the uncertainties.

This presentation discusses the quantification and the correction for the temperature error due to solar radiation that is applied in the GRUAN data processing for the Vaisala RS41 radiosonde. Heating of the temperature sensor by solar radiation is the dominant source of error for daytime radiosoundings.

At Lindenberg Observatory a dedicated laboratory set-up was built to quantify the solar temperature error of radiosondes. The setup allows to create conditions that are similar to those encountered during an actual radiosounding, with special emphasis on parameters such as pressure, air flow (ventilation), and illumination conditions. The radiosonde is placed inside a quartz tube that is integrated in a wind tunnel-like construction that can be operated between ambient pressure and 3 hPa. During the measurements the radiosonde is rotated along its longitudinal axis to mimic the spinning during ascent, and the large quartz window makes it possible to illuminate the temperature sensor together with a considerable part of the sensor boom, allowing to assess the contribution of the heat transfer from the sensor boom to the sensor. A parameterization of the heating of the sensor in terms of flux, pressure, ventilation and solar elevation is presented. This parameterization is the basis of the GRUAN correction algorithm, which in addition includes a radiation model and altitude information. In conclusion the GRUAN data product is compared to the manufacturer-processed data.
Can Lidars compete with sonic anemometers? - Comparison of wind measurements from different Doppler Lidar scan strategies to sonic anemometer data
Authors: Wolz Kevin , Beyrich Frank , Steinheuer Julian , Detring Carola , Leinweber Ronny , Mauder Matthias
Published: June 2021

The technological development of ground-based active remote sensing instruments has reached a point where they have the possibility to drastically increase the temporal and spatial data density compared to conventional instruments, which would allow for a better process understanding and is expected to enhance the forecasting skills of numerical weather prediction systems and reduce its uncertainties. To test the measurement uncertainty and feasibility of Doppler Lidar systems we participated in the FESST@MOL 2020 field campaign, organized by the German Meteorological Service (DWD) in Lindenberg, Germany. During this campaign, eight Doppler Lidars were operated at the boundary layer field site (GM) Falkenberg. We evaluated different scanning strategies for the determination of the wind profile in the Atmospheric Boundary Layer (ABL) using multiple different triple Lidar virtual tower (VT) scan patterns including range height indicator (RHI) and step/stare scan modes. We compared these Lidar-based wind measurements with the data from a sonic anemometer on a 99 m tall instrumented tower also located in Falkenberg over a period of four months. The lidar and the sonic anemometer data were processed to 10- and 30- minute averages and compared to each other. The VT measurements underestimated the mean horizontal wind compared to the sonic anemometer by around 0.2 m s‑1. Besides that, we compared the VT data with those from a single fourth nearby Doppler Lidar which was running in a velocity-azimuth display (VAD) mode. The calculated mean horizontal wind values between the two different modes showed a good comparability but differed stronger with increasing height.
Towards establishing an Irish National Soil Moisture Monitoring Network
Authors: Hochstrasser Tamara , Jarmain Caren , Finkele Klara , Murphy Paul , Fenton Owen , Richards Karl , Daly Eve , Saunders Matthew , Cummins Thomas , Whetton Rebecca , O'Loughlin Fiachra
Published: June 2021

The hydrologic cycle is currently being altered due to climate change and the potential impacts are diverse. Long-term monitoring of the components of this cycle will aid our understanding of these changes. Soil moisture is one of the components often neglected, with few long-term datasets available. It is an important variable, regulating the exchange of water and heat energy between the land surface and the atmosphere through evaporation and plant transpiration. Our understanding of soil moisture dynamics is often limited, and an expansion of the current monitoring network holds many benefits. Recently, a process was initiated to establish an effective Irish National Soil Moisture Monitoring Network. This network aims to be research question driven, consistent in its measurement approach and designed to be useful for ongoing research. Here we report on the consultation process that started in early 2020 within the Irish Agmet group, a working group on Agrometeorology. Specifically, we (1) provide background to the consultation process that showed widespread support for a long-term soil moisture monitoring network which led to funding applications to establish this network, (2) review initiatives underway to monitor soil moisture and other components of the hydrologic cycle and highlight the benefits of long-term widespread measurements to various initiatives and sectors, (3) suggest new and novel monitoring technologies that will be investigated and (4) provide an implementation strategy for what is anticipated to be an efficient and successful network. The paper should serve as a starting point to obtaining widespread support for a national hydro-climate monitoring network.
Updated Operational Implementation of the Canadian Forest Fire Weather Index System in Ireland
Authors: Flattery Padraig , Finkele Klara , Downes Paul , O'Leary Ferdia , Nugent Ciaran
Published: June 2021

Since 2006 the Canadian Forest Fire Weather Index System (FWI) has been used operationally at Met Éireann to predict the risk of forest fires in Ireland (Walsh, S, 2006). Although only around 11% or ca 770,000 ha of the total land area of Ireland is afforested, there are also large areas of open mountain and peatlands that are covered in grasses, dwarfshrub and larger woody shrub type vegetation which can provide ready fuel for spring wildfires, when suitable conditions arise. Following winter, much of this vegetation is either dead or has a very low live moisture content, and the flammability of this vegetation can be readily influenced by prevailing weather, most especially following prolonged dry periods. The Department of Agriculture, Food and Marine is the Forest Protection authority in Ireland and issues Fire Danger Notices as part of this work. These notices permit improved preparedness for fire responses and are based on information provided by Met Éireann on the current status of FWI and FWI components using observation data at synoptic stations and the predicted FWI for the next five days ahead based on numerical weather prediction input data.

The FWI is based on

three different types of forest fuel, ie how quickly these dry out/get rewetted. These are the Fine Fuels Moisture Code (FFMC), the Duff Moisture Code (DMC) and the Drought Code (DC).

components based on fire behaviour: the Initial Spread Index (ISI), the Build-up Index (BUI), and the Fire Weather Index (FWI) which represents fire intensity as energy output rate per unit length of fire front. It is then used to determine the Daily Severity Rating (DSR) of the fire danger. 

Of these components, the FFMC and ISI components have been found to provide the most accurate indication of risk under Irish conditions, based on the fuels involved and ignition patterns observed to date.

The DSR was based on a climatology of 1971 to 2005 at the time of operational implantation of the FWI at Met Éireann. An updated climatology based on the new reference period of 1990 to 2020 will be shown as well as the change of the 98 percentiles of extreme rating using this new reference period.  

Walsh, S. “Implementation in Ireland of the Canadian Forest Fire Weather Index System.” In Making Science Work on the Farm. A Workshop on Decision Support Systems for Irish Agriculture, 120–126. Dublin: AGMET, 2007. 
Applying the METRo model for road-condition forecasting in Norway 
Authors: Mayer Stephanie , Andrade Fabio , Lorenz Torge , de Lima Luciano , Hovenburg Anthony , Dahlin Christopher
Published: June 2021

According to the 14th Annual Road Safety Performance Index Report by the European Transport Safety Council, annually more than 100,000 accidents occur on European roads, of which 22,660 people lost their lives in 2019. The factors contributing to road traffic accidents are commonly grouped into three categories: environment, vehicle or driver. The European accident research and safety report 2013 by Volvo states in about 30% of accidents contributing factors could be attributed to weather and environment leading for example to unexpected changes in road friction, such as black ice. In this work, we are developing a solution to forecast road conditions in Norway by applying the Model of the Environment and Temperature of Roads – METRo, which is a surface energy balance model to predict the road surface temperature. In addition, METRo includes modules for water accumulation at the surface (liquid and frozen) and vertical heat dissipation (Crevier and Delage, 2001). The road condition is forecasted for a given pair of latitude, longitude and desired forecast time. Data from the closest road weather station and postprocessed weather forecast are used to initialize METRo and provide boundary conditions to the road weather forecast. The weather forecasts are obtained from the THREDDS service and the road weather station data from the FROST service, both provided by MET Norway. We develop algorithms to obtain the data from these services, process them to match the METRo model input requirements and send them to METRo’s pre-processing algorithms, which combine observations and forecast data to initialize the model. In a case study, we will compare short-term METRo forecasts with observations obtained by road weather stations and with observations retrieved by car-mounted environmental sensors (e.g., road surface temperature). This work is part of the project AutonoWeather - Enabling autonomous driving in winter conditions through optimized road weather interpretation and forecast financed by the Research Council of Norway in 2020. 
Clouds and aerosol effects on solar energy in Cyprus
Authors: Papachristopoulou Kyriakoula , Fountoulakis Ilias , Kosmopoulos Panagiotis , Raptis Panagiotis Ι. , Mamouri Rodanthi-Elisavet , Nisantzi Argyro , Witthuhn Jonas , Bühl Johannes , Gkikas Antonis , Hadjimitsis Diofantos G. , Kontoes Charalampos , Kazadzis Stelios
Published: June 2021

Cyprus focuses on increasing the share of its renewable energy resources from 13.9% in 2020 to 22.9% in 2030, with solar energy exploitation systems to be one of the main pillars of this effort, due to the high solar potential of the island. In this study, we investigated the effect of clouds as well as aerosols, and especially dust, on the downwelling surface solar irradiation in terms of Global Horizontal Irradiation (GHI) and Direct Normal Irradiation (DNI). In order to quantify the effects of clouds, aerosols and dust on different surface solar radiation components, we used the synergy of satellite derived products for clouds, high quality and fine resolution satellite retrievals of aerosols and dust from the newly developed MIDAS dataset, and radiative transfer modeling (RTM). GHI and DNI climatologies have been also developed based on the above information. According to our findings, clouds attenuate ~25 – 30% of annual GHI and 35 – 50% of annual DNI, aerosols attenuate 5 – 10% and 15 – 35% respectively, with dust being responsible for 30 – 50% of the overall attenuation by aerosols. The outcomes of this study are useful for installation planning and for estimating the PV and CSP performance on a short-term future basis, helping towards improved penetration of solar energy exploitation systems in the electric grid of Cyprus. Furthermore, they are strongly linked to Affordable and Clean Energy (SDG 7) which has a central role in national climate plans and requires services in energy meteorology, climate applications of satellite data, and providing high quality wind and radiation data.

 

Acknowledgements

This study was funded by the EuroGEO e-shape (grant agreement No 820852) and EXCELSIOR (grant agreement No 857510)
Historical and future temporal trends in the summer Urban Heat Island of Athens (Greece) 
Authors: van der Schriek Tim , Varotsos Konstantinos V. , Founda Dimitra , Giannakopoulos Christos
Published: June 2021

Historical changes, spanning 1971–2016, in the Athens Urban Heat Island (UHI) over summer were assessed by contrasting two air temperature records from established meteorological stations in urban and rural settings. When contrasting two 20-year historical periods (1976–1995 and 1996–2015), there is a significant difference in summer UHI regimes. The stronger UHI-intensity of the second period (1996–2015) is likely linked to increased pollution and heat input. Observations suggest that the Athens summer UHI characteristics even fluctuate on multi-annual basis. Specifically, the reduction in air pollution during the Greek Economic Recession (2008-2016) probable subtly changed the UHI regime, through lowering the frequencies of extremely hot days (Tmax > 37 °C) and nights (Tmin > 26 °C).

Subsequently, we examined the future temporal trends of two different UHIs in Athens (Greece) under three climate change scenarios. A five-member regional climate model (RCM) sub-ensemble from EURO-CORDEX with a horizontal resolution of 0.11° (~12 × 12 km) simulated air temperature data, spanning the period 1976–2100, for the two station sites. Three future emissions scenarios (RCP2.6, RCP4.5 and RCP8.5) were implanted in the simulations after 2005. The observed daily maximum and minimum air temperature data (Tmax and Tmin) from two historical UHI regimes (1976–1995 and 1996–2015, respectively) were used, separately, to bias-adjust the model simulations thus creating two sets of results.

This novel approach allowed us to assess future temperature developments in Athens under two different UHI intensity regimes. We found that the future frequency of days with Tmax > 37 °C in Athens was only different from rural background values under the intense UHI regime. There is a large increase in the future frequency of nights with Tmin > 26 °C in Athens under all UHI regimes and climate scenarios; these events remain comparatively rare at the rural site.

This study shows a large urban amplification of the frequency of extremely hot days and nights which is likely forced by increasing air pollution and heat input. Consequently, local mitigation policies aimed at decreasing urban atmospheric pollution are expected to be also effective in reducing urban temperatures during extreme heat events in Athens under all future climate change scenarios. Such policies therefore have multiple benefits, including: reducing electricity (energy) needs, improving living quality and decreasing heat- and pollution related illnesses/deaths.

 
Landslide Early Warning in Indonesia: A Gap Analysis
Authors: Satyaningsih Ratna , Jetten Victor , Ettema Janneke , Sopaheluwakan Ardhasena , Nuryanto Danang Eko , Umer Yakob
Published: June 2021

For the last decade, rainfall-triggered landslides have been one of the major hazards in Indonesia. According to the National Agency for Disaster Management (BNPB) reports, from 2010 to 2020, a total of 5822 landslides occurred in Indonesia and caused 1812 casualties, 1627 injured, and 234 missing. More than 75% of those landslides occurred in Java, the most populous island in the region. Settlements alongside agricultural fields often are located in areas that are susceptible to landslides. As relocation would be costly, a landslide early warning system (LEWS) could provide the necessary information for communities susceptible to landslides to prepare for the upcoming hazard. The objective of this study is to map the issues with the existing landslide early warning system in Indonesia and our plan to improve landslide forecasting by tailoring available rainfall forecasts and monitoring.

The United Nations International Strategy for Disaster Reduction (UNISDR) has defined an end-to-end early warning system that essentially comprises knowledge risk, hazard forecasting, alerts dissemination, and community response. In the definition, the UNISDR also highlighted timely and meaningful warning information for appropriate preparedness and action in a sufficient time. Landslide prediction itself is challenging in terms of when and where precisely the landslides occur as different landslide types have different characteristics and trigger mechanisms. Moreover, when rainfall forecast data is used as input for a physically-based hydrological and landslide model, the uncertainty and accuracy of the rainfall will affect the forecast skill.

National LEWS with a longer lead-time is operational, utilizing generic rainfall thresholds derived from 1-day and 3-day cumulative rainfall triggering landslides occurred in Indonesia (mostly in the Java Island) as warning signals. The rainfall thresholds were derived from NASA Tropical Rainfall Measuring Mission (TRMM) rainfall estimates with a spatial resolution of 0.25°×0.25°. Different studies showed that the thresholds derived from that product are lower than those derived from raingauge measurements, potentially leading to more false alerts. These thresholds are applied for all catchments in Indonesia even though the region has different climate regimes and geomorphological characteristics, leading to insufficient accuracy for the local landslide prediction.  As for the forecast, the current LEWS applies rainfall forecast with the same spatial resolution as TRMM, which is not suitable for (sub-)catchment-scale prediction.

This study proposes an approach to tailor rainfall data from various high-resolution sources, like radar, NWP models, and satellite, where historical landslide data are to be used to derive dynamical rainfall thresholds at local scale.
Precipitation associated with extra-tropical cyclones: response to uniform global warming and to polar amplification
Authors: Sinclair Victoria , Catto Jennifer
Published: June 2021

Extra-tropical cyclones constitute a large part of the circulation in the mid-latitudes and can lead to high impact weather. Therefore, it is beneficial to society to determine how these storms and their associated weather may change in the future. We focus on precipitation associated with extra-tropical cyclones (ETCs) and first aim to determine how the relationship between dynamical measures (e.g. maximum relative vorticity) of cyclone intensity and ETC related precipitation will response to climate change. Secondly, because not all ETCs are the same, we investigate whether the relationship between ETC precipitation and ETC intensity depends on the type of cyclone. Finally, we examine whether certain types of ETCs, in terms of their precipitation patterns, are likely to become more or less common in the future. We address these questions using aqua-planet simulations performed using an atmosphere-only model (OpenIFS) with fixed sea surface temperatures (SSTs). The simulations are run at T255 resolution (~ 80 km) and are 10 years long which generates a very large sample size of ETCs (> 14,000). The three simulations differ only in terms of the specific SST distribution: a control simulation is performed with the well-known “QObs” SST distributions, the second simulation has a uniform warming of 4K applied everywhere, and the third simulation is a polar amplification experiment with a 5K warming poleward of 45 degrees. In each experiment, all ETCs are objectively identified and tracked. Different types of cyclones are identified by applying k-means clustering to the precipitation pattern within a 12-degree radius of the cyclone centre. In all three experiments, more dynamically intense ETCs have more precipitation associated with them but there is considerable spread. Uniform warming strengthens this relationship and hence a ETC of a certain dynamical intensity will have more precipitation associated with it in a warmer climate. Clustering identifies 4 distinct types of ETCs in terms of their precipitation patterns: ETCs with most precipitation associated with the warm front; ETCs dominated by cold front precipitation; ETCs dominated by cyclone-centred precipitation; ETCs with very little precipitation. All 4 cyclone types appear in each experiment. Uniform warming causes a notable increase in the number of ETCs with precipitation concentrated on the warm front and a decrease in the number of ETCs with weak precipitation. In contrast, polar warming causes a large increase in the number of ETCs with weak precipitation and ETCs dominated by cold front precipitation decrease in number. These results, and others, will be presented along with dynamical interpretations.
Numerical Analysis of a Hybrid Stochastic Turbulence Model for Stable Stratification
Authors: Boyko Vyacheslav , Vercauteren Nikki
Published: June 2021

We present results on the modeling of intermittent turbulence in the nocturnal boundary
layer using a data-driven approach. In high stratification and weak wind conditions, the
bulk shear can be too weak to sustain continuous turbulence and the sporadic submesoscale
motions trigger the turbulence production.
The main idea is to extend a TKE-based, 1.5 order turbulence closure model by in-
troducing a stochastic differential equation (SDE) for the nondimensional correction of the
mixing length. Such a nonstationary SDE model is built upon the traditional surface-layer
scaling functions, which model the effect of the static stability on the surface-layer profiles
using scaling with the Richardson (Ri) number . The nonstationary parameters of the SDE
equation are determined from data with a model-based clustering approach. Furthermore, it
is found that parameters scale with the local gradient Ri number, resulting in a closed-form
nonstationary stability correction depending only on this local gradient Ri number. Benefi-
cial is the interpretation of the noise term of the SDE. This term is interpreted as an effect
of the submesoscale motions on turbulent mixing. Furthermore, the SDE model provides
a conceptual view on intermittent turbulence, whereby in the noise-free limit, the steady-
state solution converges to the traditional functional scaling. Per construction, the SDE
is readily incorporated in a turbulence closure by modifying the definition of the stability
correction. Details will be provided.
We will present a numerical analysis of such a hybrid model for quasi-steady-state so-
lutions with different model settings. Furthermore, we investigate the regime transitions
between weakly and strongly stable flows under intermittent mixing based on the temper-
ature inversion characteristics.