Assessment of the capacity for flood monitoring and early warning in Enlargement and Eastern/ Southern Neighbourhood countries of the European Union

Flooding is a natural disaster that can damage large areas in the vicinity of rivers, and in the case of flash floods, also in the vicinity of smaller streams. The Global Risks Report 2017 lists extreme weather events, of which flooding is the main risk in most countries, as the risk with the second highest potential impact and the highest likelihood of occurrence. It furthermore seems likely that climate change will aggravate flood impacts in many regions. This report presents an assessment of the capacity for flood monitoring and early flood warning in 17 of the 22 countries which belong to the Eastern and Southern neighbourhood policy of the European Union and the enlargement candidate countries. Many of these receive external funding to improve their systems, but this is often on an ad hoc basis and through individual projects.JRC.E.1-Disaster Risk Managemen

Open weather and climate science in the digital era

The need for open science has been recognized by the communities of meteorology and climate science. While these domains are mature in terms of applying digital technologies, the implementation of open science methodologies is less advanced. In a session on “Weather and Climate Science in the Digital Era” at the 14th IEEE International eScience Conference domain specialists and data and computer scientists discussed the road towards open weather and climate science. Roughly 80 % of the studies presented in the conference session showed the added value of open data and software. These studies included open datasets from disparate sources in their analyses or developed tools and approaches that were made openly available to the research community. Furthermore, shared software is a prerequisite for the studies which presented systems like a model coupling framework or digital collaboration platform. Although these studies showed that sharing code and data is important, the consensus among the participants was that this is not sufficient to achieve open weather and climate science and that there are important issues to address. At the level of technology, the application of the findable, accessible, interoperable, and reusable (FAIR) principles to many datasets used in weathe

Etude du brouillard en zone côtière par modélisation des processus physiques de la couche limite atmosphérique : cas du Grand Casablanca (Maroc)

The prediction of fog remains a challenge due to its time and space variability and to the complex interaction between the numerous physical processes influencing its life cycle. During the first stage of this thesis, the local meteorological and synoptic characteristics of fog occurrence over the Grand Casablanca region (Morocco) are investigated. To achieve this, hourly surface meteorological observations, at two synoptic stations of this coastal region, are used. An objective fog-type classification has been developed in this work and used to discriminate the fog events into the well known types. This fog climatology points out that the fog is often localized and that it is predominantly of advection-radiation type, followed by fog resulting from cloud base lowering and radiation fog. Besides, two different fog types can occur when fog simultaneously concerns the two stations. The advective processes associated with sea breeze circulation during the daylight, followed by the radiative processes often leads to fog formation over this coastal region. Numerical simulations are performed later using the meso-scale non-hydrostatic model Meso-NH. These simulations confirm that the physical processes, governing the life cycle of fog, can be different according to the physiographic features of the area. Moreover, the numerical prediction of coastal fog over heterogeneous area is very sensitive to sea surface temperature, land local topography and land cover. It also depends on the model's ability to reproduce the sea breeze circulation during the daylight followed by the radiative processes early in the night. The systematic numerical simulations of the fog events that occurred during the winter 2013-2014 indicate the Meso-NH's ability to well capture the fog occurrence with a relatively high false alarm rate, particularly over the coastal station.Le brouillard est un phénomène météorologique très difficile à prévoir, même à très courte échéance, en raison de sa grande variabilité spatiale et temporelle qui est due à des interactions complexes entre divers processus physiques. Dans cette thèse, les caractéristiques météorologiques locales et les processus synoptiques favorables aux brouillards sur la région du Grand Casablanca (Maroc) sont examinés à l'aide des observations horaires aux deux stations météorologiques permanentes de cette région côtière. Un algorithme de classification objectif est développé et utilisé pour classer les événements en des types de brouillard les plus rencontrés. Cette étude climatologique a mis en évidence que le brouillard a le plus souvent un caractère localisé et que le type d'advection-rayonnement est le plus fréquent sur la région, suivi des types d'affaissement de stratus et de rayonnement. Quand le brouillard intéresse simultanément les deux stations, la probabilité d'observer deux types différents est assez élevée. Les processus advectifs liés à la circulation de brise de mer au cours de l'après-midi, suivis de ceux radiatifs en début de nuit jouent un rôle important dans la formation du brouillard sur la région. Des simulations numériques à l'aide du modèle Méso-NH sont réalisées. Ces simulations ont confirmé que les processus physiques impliqués dans le cycle de vie du brouillard peuvent être différents selon la nature géographique de la surface. Elles ont aussi mis en évidence que la prévision numérique du brouillard en zone côtière est sensible à la température de la surface de la mer, à la topographie locale, et à l'occupation du sol. De plus, la prévision du brouillard côtier dépend fortement de la capacité du modèle à reproduire correctement la circulation de brise au cours de l'après-midi et les processus radiatifs en début de nuit. Les simulations systématiques des cas de brouillard de l'hiver 2013-2014 a montré la capacité du modèle Méso- NH à reproduire l'occurrence du brouillard avec néanmoins un taux relativement élevé de fausses alarmes, en particulier à la station côtière

Fog study in the coastal areas as through the modeling of the physical processes in the atmospheric boundary layer : case of the Grand Casablanca region, Morocco

Le brouillard est un phénomène météorologique très difficile à prévoir, même à très courte échéance, en raison de sa grande variabilité spatiale et temporelle qui est due à des interactions complexes entre divers processus physiques. Dans cette thèse, les caractéristiques météorologiques locales et les processus synoptiques favorables aux brouillards sur la région du Grand Casablanca (Maroc) sont examinés à l'aide des observations horaires aux deux stations météorologiques permanentes de cette région côtière. Un algorithme de classification objectif est développé et utilisé pour classer les événements en des types de brouillard les plus rencontrés. Cette étude climatologique a mis en évidence que le brouillard a le plus souvent un caractère localisé et que le type d'advection-rayonnement est le plus fréquent sur la région, suivi des types d'affaissement de stratus et de rayonnement. Quand le brouillard intéresse simultanément les deux stations, la probabilité d'observer deux types différents est assez élevée. Les processus advectifs liés à la circulation de brise de mer au cours de l'après-midi, suivis de ceux radiatifs en début de nuit jouent un rôle important dans la formation du brouillard sur la région. Des simulations numériques à l'aide du modèle Méso-NH sont réalisées. Ces simulations ont confirmé que les processus physiques impliqués dans le cycle de vie du brouillard peuvent être différents selon la nature géographique de la surface. Elles ont aussi mis en évidence que la prévision numérique du brouillard en zone côtière est sensible à la température de la surface de la mer, à la topographie locale, et à l'occupation du sol. De plus, la prévision du brouillard côtier dépend fortement de la capacité du modèle à reproduire correctement la circulation de brise au cours de l'après-midi et les processus radiatifs en début de nuit. Les simulations systématiques des cas de brouillard de l'hiver 2013-2014 a montré la capacité du modèle Méso- NH à reproduire l'occurrence du brouillard avec néanmoins un taux relativement élevé de fausses alarmes, en particulier à la station côtière.The prediction of fog remains a challenge due to its time and space variability and to the complex interaction between the numerous physical processes influencing its life cycle. During the first stage of this thesis, the local meteorological and synoptic characteristics of fog occurrence over the Grand Casablanca region (Morocco) are investigated. To achieve this, hourly surface meteorological observations, at two synoptic stations of this coastal region, are used. An objective fog-type classification has been developed in this work and used to discriminate the fog events into the well known types. This fog climatology points out that the fog is often localized and that it is predominantly of advection-radiation type, followed by fog resulting from cloud base lowering and radiation fog. Besides, two different fog types can occur when fog simultaneously concerns the two stations. The advective processes associated with sea breeze circulation during the daylight, followed by the radiative processes often leads to fog formation over this coastal region. Numerical simulations are performed later using the meso-scale non-hydrostatic model Meso-NH. These simulations confirm that the physical processes, governing the life cycle of fog, can be different according to the physiographic features of the area. Moreover, the numerical prediction of coastal fog over heterogeneous area is very sensitive to sea surface temperature, land local topography and land cover. It also depends on the model's ability to reproduce the sea breeze circulation during the daylight followed by the radiative processes early in the night. The systematic numerical simulations of the fog events that occurred during the winter 2013-2014 indicate the Meso-NH's ability to well capture the fog occurrence with a relatively high false alarm rate, particularly over the coastal station

Fog Decision Support Systems: A Review of the Current Perspectives

Accurate and timely fog forecasts are needed to support decision making for various activities which are critically affected by low visibility conditions [...

Analog Ensemble Forecasting System for Low-Visibility Conditions over the Main Airports of Morocco

Low-visibility conditions (LVC) are a common cause of air traffic, road, and sailing fatalities. Forecasting those conditions is an arduous challenge for weather forecasters all over the world. In this work, a new decision support system is developed based on an analog ensemble (AnEn) method to predict LVC over 15 airports of Morocco for 24 forecast hours. Hourly forecasts from the AROME model of eight predictors were used to select the skillful analogs from 2016 to 2018. The verified hourly observations were used as members of the ensemble. The developed ensemble prediction system (EPS) was assessed over 1 year (2019) as a single-value forecast and as a probabilistic forecast. Results analysis shows that AnEn outperforms persistence and its best performances are perceived generally during night and early-morning lead times. From continuous verification analysis, AnEn forecasting errors are found to be location- and lead-time-dependent and become higher for low-visibility cases. AnEn draws an averaged Centered Root Mean Square Error of about 1500 m for all visibilities, 2000 m for fog and 1500 m for mist. As an EPS, AnEn is under-dispersive for all lead times and draws a positive bias for fog and mist events. For probabilistic verification analysis, AnEn visibility forecasts are converted to binary occurrences depending on a set of thresholds from 200 m to 6000 m by a step of 200 m. It is found that the averaged Heidke Skill Score for AnEn is 0.65 for all thresholds. However, AnEn performance generally becomes weaker for fog or mist events prediction

Machine Learning for Fog-and-Low-Stratus Nowcasting from Meteosat SEVIRI Satellite Images

Fog and low stratus (FLS) are meteorological phenomena that have a significant impact on all ways of transportation and public safety. Due to their similarity, they are often grouped together as a single category when viewed from a satellite perspective. The early detection of these phenomena is crucial to reduce the negative effects that they can cause. This paper presents an image-based approach for the short-term nighttime forecasting of FLS during the next 5 h over Morocco, based on geostationary satellite observations (Meteosat SEVIRI). To achieve this, a dataset of hourly night microphysics RGB product was generated from native files covering the nighttime cold season (October to April) of the 5-year period (2016–2020). Two optical flow techniques (sparse and dense) and three deep learning techniques (CNN, Unet and ConvLSTM) were used, and the performance of the developed models was assessed using mean squared error (MSE) and structural similarity index measure (SSIM) metrics. Hourly observations from Meteorological Aviation Routine Weather Reports (METAR) over Morocco were used to qualitatively compare the FLS existence in METAR, where it is also shown by the RGB product. Results analysis show that deep learning techniques outperform the traditional optical flow method with SSIM and MSE of about 0.6 and 0.3, respectively. Deep learning techniques show promising results during the first three hours. However, their performance is highly dependent on the number of filters and the computing resources, while sparse optical flow is found to be very sensitive to mask definition on the target phenomenon

Open weather and climate science in the digital era

The need for open science has been recognized by the communities of meteorology and climate science. While these domains are mature in terms of applying digital technologies, the implementation of open science methodologies is less advanced. In a session on "Weather and Climate Science in the Digital Era"at the 14th IEEE International eScience Conference domain specialists and data and computer scientists discussed the road towards open weather and climate science. Roughly 80ĝ€¯% of the studies presented in the conference session showed the added value of open data and software. These studies included open datasets from disparate sources in their analyses or developed tools and approaches that were made openly available to the research community. Furthermore, shared software is a prerequisite for the studies which presented systems like a model coupling framework or digital collaboration platform. Although these studies showed that sharing code and data is important, the consensus among the participants was that this is not sufficient to achieve open weather and climate science and that there are important issues to address. At the level of technology, the application of the findable, accessible, interoperable, and reusable (FAIR) principles to many datasets used in weather and climate science remains a challenge. This may be due to scalability (in the case of high-resolution climate model data, for example), legal barriers such as those encountered in using weather forecast data, or issues with heterogeneity (for example, when trying to make use of citizen data). In addition, the complexity of current software platforms often limits collaboration between researchers and the optimal use of open science tools and methods. The main challenges we observed, however, were non-technical and impact the practice of science as a whole. There is a need for new roles and responsibilities in the scientific process. People working at the interface of science and digital technology - e.g., data stewards and research software engineers - should collaborate with domain researchers to ensure the optimal use of open science tools and methods. In order to remove legal boundaries on sharing data, non-academic parties such as meteorological institutes should be allowed to act as trusted agents. Besides the creation of these new roles, novel policies regarding open weather and climate science should be developed in an inclusive way in order to engage all stakeholders. Although there is an ongoing debate on open science in the community, the individual aspects are usually discussed in isolation. Our approach in this paper takes the discourse further by focusing on "open science in weather and climate research"as a whole. We consider all aspects of open science and discuss the challenges and opportunities of recent open science developments in data, software, and hardware. We have compiled these into a list of concrete recommendations that could bring us closer to open weather and climate science. We acknowledge that the development of open weather and climate science requires effort to change, but the benefits are large. We have observed these benefits directly in the studies presented in the conference and believe that it leads to much faster progress in understanding our complex world.Water ResourcesAtmospheric Remote Sensin

Open weather and climate science in the digital era

The need for open science has been recognized by the communities of meteorology and climate science. While these domains are mature in terms of applying digital technologies, the implementation of open science methodologies is less advanced. In a session on “Weather and Climate Science in the Digital Era” at the 14th IEEE International eScience Conference domain specialists and data and computer scientists discussed the road towards open weather and climate science. Roughly 80 % of the studies presented in the conference session showed the added value of open data and software. These studies included open datasets from disparate sources in their analyses or developed tools and approaches that were made openly available to the research community. Furthermore, shared software is a prerequisite for the studies which presented systems like a model coupling framework or digital collaboration platform. Although these studies showed that sharing code and data is important, the consensus among the participants was that this is not sufficient to achieve open weather and climate science and that there are important issues to address. At the level of technology, the application of the findable, accessible, interoperable, and reusable (FAIR) principles to many datasets used in weather and climate science remains a challenge. This may be due to scalability (in the case of high-resolution climate model data, for example), legal barriers such as those encountered in using weather forecast data, or issues with heterogeneity (for example, when trying to make use of citizen data). In addition, the complexity of current software platforms often limits collaboration between researchers and the optimal use of open science tools and methods. The main challenges we observed, however, were non-technical and impact the practice of science as a whole. There is a need for new roles and responsibilities in the scientific process. People working at the interface of science and digital technology – e.g., data stewards and research software engineers – should collaborate with domain researchers to ensure the optimal use of op