Potential of EUMETSAT MTG-IRS hyperspectral sounder for improving nowcasting and very short range forecast atmospheric models

Obiettivo delle attività di ricerca descritte in questa tesi è lo studio dell’utilizzo dei dati iperspettrali IR per la diagnosi dell’instabilità atmosferica ed il rilevamento anticipato di sistemi convettivi. Lo studio è stato condotto nell’ambito del progetto MTG-IRS Near Real Time, concepito e coordinato da EUMETSAT per potenziare la preparazione degli utenti sulle potenzialità dello strumento IRS a supporto della meteorologia ed in particolare delle attività di previsioni a brevissima scadenza. In dettaglio, i prodotti iperspettrali di levello 2 di IRS, generati a partire da dati reali di IASI e CrIS e distribuiti da EUMETSAT, sono stati processati in quasi tempo reale insieme a dati ausiliari geograficamente co-localizzati ed indipendenti al fine di valutare la correlazione tra il segnale (cioè il contenuto informativo dei prodotti di livello 2) ed il fenomeno meteorologico (l’instabilità convettiva). Lo studio comprende anche il riprocessamento di una serie di casi di studio significativi sull’Italia. I risultati della ricerca mostrano che lo sfruttamento dei dati iperspettrali nel settore delle previsioni a brevissima scadenza è in grado di potenziare la capacità e la prontezza a livello utente dei moderni Servizi Meteorologici operativi per quanto riguarda il rilevamento in anticipo dei fenomeni intensi.In this thesis the research activities aiming at the investigation on the use of hyperspectral IR data for the diagnosis of atmospheric instability and the early detection of convective systems are shown. The study was carried out in the framework of MTG-IRS Near Real Time Demonstration Project, conceived and leaded by EUMETSAT to enhance the user awareness on the potential of the IRS instrument in support to the meteorology and in particular to the nowcasting activities. In detail, the proxy IRS hyperspectral level 2 products, generated from real IASI and CrIS data and distributed by EUMETSAT, were processed in near real time together with auxiliary colocated and independent datasets to assess the correlation between the signal (i.e. the information content of level 2 products) and the weather phenomenon (convective instability). The reprocess of a set of significant case studies over Italy was also included in the study. Research results show that the exploitation of hyperspectral data in the field of nowcasting applications could enhance the capacity and user-readiness of modern, operational Meteorological Services with respect to the early detection of severe weather

Role of Remote Sensing in Disaster Management

The objective of this report is to review the existing satellites monitoring Earth’s resources and natural disasters. Each satellite has different repeat pass frequency and spatial resolution (unless it belongs to the same series of satellites for the purpose of continuation of data flow with same specifications). Similarly, different satellites have different types of sensors on-board, such as, panchromatic, multispectral, infrared and thermal. All these sensors have applications in disaster mitigation, though depending on the electromagnetic characteristics of the objects on Earth and the nature of disaster itself. With a review of the satellites in orbit and their sensors the present work provides an insight to suitability of satellites and sensors to different natural disasters. For example, thermal sensors capture fire hazards, infrared sensors are more suitable for floods and microwave sensors can record soil moisture. Several kinds of disasters, such as, earthquake, volcano, tsunami, forest fire, hurricane and floods are considered for the purpose of disaster mitigation studies in this report. However, flood phenomenon has been emphasized upon in this study with more detailed account of remote sensing and GIS (Geographic Information Systems) applicability. Examples of flood forecasting and flood mapping presented in this report illustrate the capability of remote sensing and GIS technology in delineating flood risk areas and assessing the damages after the flood recedes. With the help of a case study of the Upper Thames River watershed the use of remote sensing and GIS has been illustrated for better understanding. The case study enables the professionals and planning authorities to realize the impact of urbanization on river flows. As the urban sprawl increases with the increase of population, the rainfall and snow melt reaches the river channels at a faster rate with higher intensity. In other words it can be inferred that through careful land use planning flood disasters can be mitigated.https://ir.lib.uwo.ca/wrrr/1002/thumbnail.jp

Effective Cloud Detection and Segmentation using a Gradient-Based Algorithm for Satellite Imagery; Application to improve PERSIANN-CCS

Being able to effectively identify clouds and monitor their evolution is one important step toward more accurate quantitative precipitation estimation and forecast. In this study, a new gradient-based cloud-image segmentation technique is developed using tools from image processing techniques. This method integrates morphological image gradient magnitudes to separable cloud systems and patches boundaries. A varying scale-kernel is implemented to reduce the sensitivity of image segmentation to noise and capture objects with various finenesses of the edges in remote-sensing images. The proposed method is flexible and extendable from single- to multi-spectral imagery. Case studies were carried out to validate the algorithm by applying the proposed segmentation algorithm to synthetic radiances for channels of the Geostationary Operational Environmental Satellites (GOES-R) simulated by a high-resolution weather prediction model. The proposed method compares favorably with the existing cloud-patch-based segmentation technique implemented in the PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network - Cloud Classification System) rainfall retrieval algorithm. Evaluation of event-based images indicates that the proposed algorithm has potential to improve rain detection and estimation skills with an average of more than 45% gain comparing to the segmentation technique used in PERSIANN-CCS and identifying cloud regions as objects with accuracy rates up to 98%

Remote sensing of vegetation fires and its contribution to a fire management information system

In the last decade, research has proven that remote sensing can provide very useful support to fire managers. This chapter provides an overview of the type of information remote sensing can provide to the fire community. It considers first fire management information needs in the context of fire management information system. An introduction to remote sensing then precedes the description of fire information obtainable from remote sensing data (such as vegetation status, active fire detection and burned areas assessment). Finally, operational examples in five African countries illustrate how the information can be used in practice

Towards nowcasting in Europe in 2030

The increasing impact of severe weather over Europe on lives and weathersensitive economies can be mitigated by accurate 0–6 h forecasts (nowcasts), supporting a vital ‘last line of defence’ for civil protection and many other applications. Recognizing lack of skill in some complex situations, often at convective and local sub-kilometre scales and associated with rare events, we identify seven recommendations with the aim to improve nowcasting in Europe by the national meteorological and hydrological services (NMHSs) by 2030. These recommendations are based on a review of user needs, the state of the observing system, techniques based on observations and high-resolution numerical weather models, as well as tools, data and infrastructure supporting the nowcasting community in Europe. Denser and more accurate observations are necessary particularly in the boundary layer to better characterize the ingredients of severe storms. A key driver for improvement is next-generation European satellite data becoming available as of 2023. Seamless ensemble prediction methods to produce enhanced weather forecasts with 0–24 h lead times and probabilistic products require further development. Such products need to be understood and interpreted by skilled forecasters operating in an evolving forecasting context

Cloud-based automation for satellite data processing

Οι δορυφόροι Meteosat παρέχουν μεγάλη ποικιλία σε προϊόντα δεδομένων τα οποία αφορούν την έρευνα και την παρακολούθηση του κλίματος. Η δεύτερη γενιά δορυφόρων Meteosat (Meteosat Second Generation) είναι αυτή τη στιγμή πλήρως επιχειρησιακοί, ενώ ο πρώτος δορυφόρος της τρίτης γενιάς εκτοξεύτηκε τον Δεκέμβριο του 2022 και ο οποίος μεταδίδει ήδη δεδομένα ενώ βρίσκεται στη λειτουργικη του φάση. Με την επιστήμη δεδομένων να γίνεται ταχέως ένα βασικό στοιχείο τόσο ακαδημαϊκά όσο και στη βιομηχανία, και με τον όγκο των διαθέσιμων δεδομένων να γίνεται ολοένα και μεγαλύτερος, η αποτελεσματική διαχείριση και επεξεργασία δεδομένων είναι ουσιώδεις για την πρόοδο όλων των σχετιζόμενων πεδίων. Για αυτό το σκοπό, η διπλωματική αυτή έχει ως στόχο να συνεισφέρει με την κατασκευή ενός pipeline το οποίο μπορεί εν δυνάμει να αποτελέσει τη βάση για μία πιο ευρεία μεθοδολογία σχετικά με τα δορυφορικά δεδομένα. Σε αυτό το πλαίσιο, χρησιμοποιούνται ανεπεξέργαστα .txt αρχεία τα οποία αντιστοιχούν σε τροχιές νεφών και τα οποία παράγονται από έναν αλγόριθμο που ανιχνεύει και παρακολουθεί Mesoscale Conventive Systems σε εικόνες Meteosat. Αρχικά, γίνεται μία προ-επεξεργασία και εξαγωγή χαρακτηριστικών, με στόχο να εισαχθούν τα επεξεργασμένα δεδομένα σε μία SQL βάση δεδομένων. Παράλληλα, αυτοματοποιείται η διαδικασία ενσωμάτωσης νέων δεδομένων στα ήδη υπάρχοντα, αφού όλη η διεργασία έχει στηθεί στο cloud, εξασφαλίζοντας με αυτόν τον τρόπο τη διαθεσιμότητα, την ασφάλεια και την επεκτασιμότητα των δεδομένων. Επιπλέον, αναπτύσσεται μία διαδικτυακή εφαρμογή (web application), στην οποία ο χρήστης μπορεί να επιλέξει και να φιλτράρει τα διαθέσιμα δεδομένα βάσει κάποιων κριτηρίων, χωρίς να απαιτείται γνώση της γλώσσας SQL, κάνοντας έτσι τη διαδικασία πιο προσιτή. Η παρούσα εργασία μπορεί να ενσωματωθεί σε ένα ευρύτερο πλαίσιο όσον αφορά την επεξεργασία όχι μόνο δορυφορικών δεδομένων, αλλά και δεδομένων που προέρχονται από άλλες πηγές, ενώ ταυτόχρονα απευθύνεται τόσο στον επιστημονικό όσο και στο βιομηχανικό τομέα. Η παρούσα διπλωματική αποτελεί από μόνη της μία συνεισφορά στην κοινότητα ανοιχτού λογισμικού, αφού ο πλήρης κώδικας είναι διαθέσιμος δωρεάν μέσω ανοικτών αποθετηρίων (repositories), επιτρέποντας στον οποιονδήποτε να τον τρέξει στο cloud μέσα σε μερικά λεπτά.Meteosat satellites provide a large variety of data products concerning climate monitoring and research. The Meteosat Second Generation Satellites are now fully operational, with the first satellite of the Third Generation deployed in December 2022 and already transmitting data in its commissioning phase. With data science rapidly becoming a key component in both science and the industry, and with the volume of available data ever expanding, efficient data management and processing are essential to the progress of all the relevant fields. To this end, this Thesis aims to contribute by constructing a pipeline that can potentially be the base of a broader methodology for working with satellite data. In this context, raw .txt data files that represent cloud trajectories are utilized, generated from an algorithm that detects and tracks Mesoscale Convective Systems in Meteosat images. An initial preprocessing and feature extraction procedure is carried out, intending to utilize the processed data in order to populate a SQL database. Meanwhile, the process of incorporating new data to the existing dataset is automated, since everything is hosted on the cloud, ensuring data availability, scalability and security. Additionally, to achieve a user-friendly experience, a web application is developed, where users can select and filter the available data based on certain criteria, as well as download the corresponding raw files for further individual use, without the need for SQL knowledge. This project can be incorporated in a broader framework for processing data originating not only from satellites, but also from other sources, while being addressed to both the science and the industry sector. The present Thesis itself is a contribution to the open source community, since the full codebase is freely distributed and made available through open repositories, thus allowing anyone to deploy it to the cloud within minutes

Tobac 1.2: Towards a flexible framework for tracking and analysis of clouds in diverse datasets

We introduce tobac (Tracking and Object-Based Analysis of Clouds), a newly developed framework for tracking and analysing individual clouds in different types of datasets, such as cloud-resolving model simulations and geostationary satellite retrievals. The software has been designed to be used flexibly with any two-or three-dimensional timevarying input. The application of high-level data formats, such as Iris cubes or xarray arrays, for input and output allows for convenient use of metadata in the tracking analysis and visualisation. Comprehensive analysis routines are provided to derive properties like cloud lifetimes or statistics of cloud properties along with tools to visualise the results in a convenient way. The application of tobac is presented in two examples. We first track and analyse scattered deep convective cells based on maximum vertical velocity and the threedimensional condensate mixing ratio field in cloud-resolving model simulations. We also investigate the performance of the tracking algorithm for different choices of time resolution of the model output. In the second application, we show how the framework can be used to effectively combine information from two different types of datasets by simultaneously tracking convective clouds in model simulations and in geostationary satellite images based on outgoing longwave radiation. The tobac framework provides a flexible new way to include the evolution of the characteristics of individual clouds in a range of important analyses like model intercomparison studies or model assessment based on observational data. © 2019 Author(s)

Development of a Short-Term Forecast System for Solar Surface Irradiance Based on Satellite Imagery and NWP Data

The increasing use of renewable energies as a source of electricity has lead to a fundamental transition of the power supply system. The integration of fluctuating weather-dependent energy sources into the grid already has a major impact on its load flows and associated with this economic effects. As a result, the interest in forecasting wind and solar radiation with a sufficient accuracy over short time periods (0-4 h) has grown. In this study, a novel approach for forecasting solar surface irradiance is developed which is based on the optical flow of the effective cloud albedo and SPECMAGIC NOW. This short-term forecast is combined seamlessly with the numerical weather prediction (NWP) to expand the forecast horizon up to 12 h. The optical flow method utilized here is TV-L1 from the open source library OpenCV. This method uses a multi-scale approach to capture cloud motions on various spatial scales. After the clouds are displaced by extrapolating the optical flow into the future, the solar surface radiation will be calculated with SPECMAGIC NOW, which computes the global irradiation spectrally resolved from satellite imagery. Due to the high temporal and spatial resolution of satellite measurements, the effective cloud albedo and thus solar radiation can be forecasted from 15 min up to 4 h with a resolution of 0.05°. The combination of the displacement of clouds by TV-L1 and the calculation of solar surface irradiance by SPECMAGIC NOW is innovative and promising. Finally, a procedure for a seamless blending between a NWP model and the presented nowcasting is developed. For this purpose the software tool ANAKLIM++ is utilized which was originally designed for the efficient assimilation of two-dimensional data sets using variational approach. ANAKLIM++ blends the nowcasting, ICON and IFS between 1-5 h in such a way that the combined forecast delivers a smaller forecast error than the individual forecasts for each lead time

Introduction to Remote Sensing for Monitoring Rainfall, Temperature, Vegetation and Water Bodies

A discussion of remote sensing technology and how it can be used to collect information about the Earth and its atmosphere

Globally Gridded Satellite (GridSat) Observations for Climate Studies

Geostationary satellites have provided routine, high temporal resolution Earth observations since the 1970s. Despite the long period of record, use of these data in climate studies has been limited for numerous reasons, among them: there is no central archive of geostationary data for all international satellites, full temporal and spatial resolution data are voluminous, and diverse calibration and navigation formats encumber the uniform processing needed for multi-satellite climate studies. The International Satellite Cloud Climatology Project set the stage for overcoming these issues by archiving a subset of the full resolution geostationary data at approx.10 km resolution at 3 hourly intervals since 1983. Recent efforts at NOAA s National Climatic Data Center to provide convenient access to these data include remapping the data to a standard map projection, recalibrating the data to optimize temporal homogeneity, extending the record of observations back to 1980, and reformatting the data for broad public distribution. The Gridded Satellite (GridSat) dataset includes observations from the visible, infrared window, and infrared water vapor channels. Data are stored in the netCDF format using standards that permit a wide variety of tools and libraries to quickly and easily process the data. A novel data layering approach, together with appropriate satellite and file metadata, allows users to access GridSat data at varying levels of complexity based on their needs. The result is a climate data record already in use by the meteorological community. Examples include reanalysis of tropical cyclones, studies of global precipitation, and detection and tracking of the intertropical convergence zone