The LEXIS (Large-scale EXecution for Industry & Society) H2020 project is building an advanced engineering platform taking advantage of HPC, Cloud solutions and Big Data, leveraging existing HPC infrastructures. In the framework of the LEXIS project, CIMA Research Foundation is running a three nested domain WRF Model with European coverage and radar data assimilation over Italy. WRF data is then processed by ITHACA Extreme Rainfall Detection System (ERDS), an early warning system developed for the monitoring of heavy rainfall events. The WRF-ERDS workflow has been applied to the heavy rainfall event that affected Southern Italy, in particular Calabria Region, at the end of November 2020. Rainfall depths obtained using global-scale rainfall datasets and WRF data have been compared both with rain gauge data and with the daily bulletins issued by the Italian Civil Protection Department. The data obtained by running the WRF-ERDS workflow shows as an advanced engineering platform based on HPC and cloud solutions can provide more detailed forecasts to an early warning system like ERDS.
In this work, we describe the integration of Weather and Research Forecasting (WRF) forecasts produced by CIMA Research Foundation within ITHACA Extreme Rainfall Detection System (ERDS) to increase the forecasting skills of the overall early warning system. The entire workflow is applied to the heavy rainfall event that affected the city of Palermo on 15 July 2020, causing urban flooding due to an exceptional rainfall amount of more than 130 mm recorded in about 2.5 h. This rainfall event was not properly forecasted by meteorological models operational at the time of the event, thus not allowing to issue an adequate alert over that area. The results highlight that the improvement in the quantitative precipitation scenario forecast skills, supported by the adoption of the H2020 LEXIS computing facilities and by the assimilation of in situ observations, allowed the ERDS system to improve the prediction of the peak rainfall depths, thus paving the way to the potential issuing of an alert over the Palermo area.
The Extreme Rainfall Detection System (ERDS) is an early warning system (EWS) developed for the monitoring and forecasting of rainfall events on a global scale. Within ERDS the near real-time rainfall monitoring is performed using the Global Precipitation Measurement data, while rainfall forecasts are provided by the Global Forecast System model. Rainfall depths determined on the basis of these data are then compared with a set of rainfall thresholds to evaluate the presence of heavy rainfall events: in places where the rainfall depth is higher than a rainfall threshold, an alert of a severe rainfall event is issued. The information provided by ERDS is accessible through a WebGIS application (http://erds.ithacaweb.org) in the form of maps of rainfall depths and related alerts to provide immediate and intuitive information also for nonspecialized users. This chapter is intended to describe the input data and the extreme rainfall detection methodology currently implemented in ERDS. Furthermore, several case studies (2019 Queensland flood event, 2017 Atlantic hurricane season, and 2017 Eastern Pacific hurricane season) are included to highlight the strengths and weaknesses of this EWS based on global-scale rainfall datasets.
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.
Earth observation satellite systems play an important role in the provision of a wide range of information, especially in data-scarce regions. This role becomes extremely relevant during a disaster and when direct access to the affected area is difficult. This chapter provides an overview of the state of the art remote sensing techniques and tools for the detection and monitoring of hydrometeorological disasters, focusing on tropical cyclones Idai and Kenneth. In a perspective of disaster preparedness, rainfall measurements provided by satellites can enable decision-makers to take urgent measures in the pre-event phase and can be used as input for early warning systems. Data acquired from satellite missions are used for a set of different tools developed to map flood extent, while several satellite-based emergency mapping mechanisms provide timely post-event information by taking advantage of observations provided by satellites (including commercial platforms providing images at very high geometric resolution). Analysis of vegetation dynamics derived from time-series of multispectral imagery is used to assess changes, such as the detection of cyclone-damaged areas, identification of critical conditions in vegetation health/productivity, and land cover change mapping.
A timely identification and monitoring of flood events by means of Earth Observation (EO) data is, nowadays, increasingly feasible thanks to recent advances achieved in remote sensing and hydrological process simulations. Despite the notable progress in these fields, a considerable effort will still be required to reduce the intrinsic inaccuracies of these types of approaches. The coarse spatial and temporal resolution of satellite measurements (compared to the one that characterizes in-situ instruments), in fact, continues to require a local-scale validation. Taking into account pros and cons of the approaches based on remotely-sensed data, this chapter reviews some of the most relevant open-access techniques, products, and services that research and academic institutes are currently providing for the detection and the near real-time monitoring of extreme hydrometeorological events.
LEXIS (Large-scale EXecution for Industry and Society) H2020 project is currently developing an advanced system for Big Data analysis that takes advantage of interacting large-scale geographically-distributed HPC infrastructure and cloud services. More specifically, LEXIS Weather and Climate Large-Scale Pilot workflows ingest data coming from different sources, like global/regional weather models, conventional and unconventional meteorological observations, application models and socio-economic impact models, in order to provide enhanced meteorological information at the European scale. In the framework of LEXIS Weather and Climate Large-scale Pilot, CIMA Research Foundation is running a 7.5 km resolution WRF (Weather Research and Forecasting) model with European coverage, radar assimilation over the Italian area, and daily updates with 48 hours forecast. WRF data is then processed by ITHACA ERDS (Extreme Rainfall Detection System - http://erds.ithacaweb.org), an early warning system for the monitoring and forecasting of heavy rainfall events. The WRF model provides more detailed information compared to GFS (Global Forecast Systems) data, the most widely used source of rainfall forecasts, implemented in ERDS also. The entire WRF - ERDS workflow was applied to two of the most severe heavy rainfall events that affected Italy in 2020. The first case study is related to an intense rainfall event that affected Toscana during the afternoon and the evening of 4th June 2020. In this case, the Italian Civil Protection issued an orange alert for thunderstorms, on a scale from yellow (low) to orange (medium) to red (high). In several locations of the northern part of the Region more than 100 mm of rainfall were recorded in 3 hours, corresponding to an estimated return period equal to or greater than 200 years. As far as the 24-hours time interval concerns, instead, the estimated return period decreases to 10-50 years. Despite the slight underestimation, WRF model was able to properly forecast the spatial distribution of the rainfall pattern. In addition, thanks to WRF data, precise information about the locations that would be affected by the event were available in the early morning, several hours before the event affected these areas. The second case study is instead related to the heavy rainfall event that affected Palermo (Southern Italy) during the afternoon of 15th July 2020. According to SIAS (Servizio Informativo Agrometeorologico Siciliano) more than 130 mm of rain fell in about 2.5 hours, producing widespread damages due to urban flooding phenomena. The event was not properly forecasted by meteorological models operational at the time of the event, and the Italian Civil Protection did not issue an alert on that area (including Palermo). During that day, in fact, only a yellow alert for thunderstorms was issued on northern-central and western Sicily. Within LEXIS, no alert was issued using GFS data due to the severe underestimation of the amount of forecasted rainfall. Conversely, a WRF modelling experiment (three nested domain with 22.5, 7.5 and 2.5 km grid spacing, innermost over Italy) was executed, by assimilating the National radar reflectivity mosaic and in situ weather stations from the Italian Civil Protection Department, and it resulted in the prediction of a peak rainfall depth of about 35 mm in 1 hour and 55 mm in 3 hours, roughly 30 km far apart the actual affected area, thus values supportive at least a yellow alert over the Palermo area. Obtained results highlight how improved rainfall forecast, made available thanks to the use of HPC resources, significantly increases the capabilities of an operational early warning system in the extreme rainfall detection. Global-scale low-resolution rainfall forecasts like GFS one are in fact widely known as good sources of information for the identification of large-scale precipitation patterns but lack precision for local-scale applications.
The LEXIS Weather and Climate Large-scale Pilot will deliver a system for prediction of water-food-energy nexus phenomena and their associated socio-economic impacts. The system will be based on multiple model layers chained together, namely global weather and climate models, high-resolution regional weather models, domain-specific application models (such as hydrological, forest fire risk forecasts), impact models providing information for key decision and policy makers (such as air quality, agriculture crop production, and extreme rainfall detection for flood mapping). This paper will report about the first results of this pilot in terms of serving model output data and products with Cloud and High Performance Data Analytics (HPDA) environments, on top a Weather Climate Data APIs (ECMWF), as well as the porting of models on the LEXIS Infrastructure via different virtualization strategies (virtual machine and containers).
Gli eventi di pioggia intensa sono universalmente riconosciuti come la causa scatenante di molti dei più catastrofici disastri naturali. Negli ultimi decenni, numerosi gruppi di ricerca hanno cercato di sfruttare le potenzialità di alcuni open data resi disponibili in tempo reale per sviluppare sistemi di monitoraggio e allerta per piogge intense e/o eventi alluvionali. Al fine di poter identificare in tempo reale le aree interessate da tali fenomeni, ITHACA ha sviluppato un sistema chiamato Extreme Rainfall Detection System (ERDS) che, utilizzando dati open con copertura spaziale globale, fornisce, per specifici intervalli temporali, sia informazioni relative alle cumulate di pioggia sia allerte di pioggia intensa. Due diversi approcci vengono utilizzati all'interno di tale progetto: il primo prevede l’utilizzo di dati acquisiti da satellite per fornire informazioni in tempo reale sulla quantità di pioggia precipitata mentre il secondo prevede l’utilizzo di un modello di previsione per stimare la pioggia che verrà registrata al suolo nei giorni a venire. Nello specifico, il monitoraggio in tempo reale viene compiuto utilizzando una misura di precipitazione effettuata da satellite fornita della missione NASA/JAXA Global Precipitation Measurement (GPM). Il dato GPM IMERG early run data, disponibile 4 ore dopo l’acquisizione, garantisce al sistema una copertura globale caratterizzata da una risoluzione spaziale di 0.1° ed una risoluzione temporale di 30 minuti. Per quanto riguarda le previsioni di pioggia, il sistema utilizza gli output del modello Global Forecast System (GFS) prodotto dal National Centers for Environmental Prediction (NCEP). Tale dato fornisce una previsione di pioggia con risoluzione spaziale di 0.25°. Entrambe le informazioni vengono cumulate su specifici intervalli di aggregazione (12, 24, 48, 72 e 96 ore) e vengono utilizzate per fornire allerte nei punti in cui la pioggia cumulata supera uno specifico valore di soglia. Tali soglie rappresentano un valore di precipitazione necessario a creare le condizioni scatenanti un'alluvione e sono state calcolate al fine di fornire, per ogni punto della superficie terrestre, allerte alla stessa risoluzione del dato di input. La calibrazione di tali soglie è stata effettuata seguendo un approccio empirico, analizzando eventi di pioggia che nel passato hanno portato a disastri di natura idrometeorologica. Le allerte così identificate possono essere utilizzate per l’identificazione e/o il pre-tasking di immagini satellitari da usare per una rapida valutazione delle aree più colpite. Al fine di rendere operativo tale sistema, una serie di moduli è stata sviluppata ad hoc in Python 3 sfruttando le librerie numpy, h5py, GDAL, datetime, ftplib e urllib. L'intero codice è disponibile su GitHub (https://github.com/ITHACA-org/gpm-accumul e https://github.com/ITHACA-org/gfs-accumul). Gli output prodotti da ERDS sono resi disponibili gratuitamente agli utenti in diversi formati attraverso un'applicazione WebGIS (erds.ithacaweb.org). Nello specifico, i dati vengono prodotti e resi scaricabili in formato Geotiff, garantendo quindi agli utilizzatori di poterli visualizzare in ambiente GIS e di utilizzarli per eseguire ulteriori analisi. Tali dati sono inoltre disponibili attraverso un Web Map Service (WMS) sfruttando GeoServer, rendendoli così consultabili anche da utenti non esperti. Lato client, invece, i raster vengono visualizzati utilizzando Leaflet.
The recent socioeconomic and environmental impacts of extreme flood events occurred in Europe are testimony to the disastrous consequences that such future hazards are likely to pose. Managing inundation risk requires prevention measures in close cooperation with Civil Protection authorities. This topic underlies the TRIBUTE (TRIgger BUffers for inundaTion Events) project (ECHO/SUB/2016/742480/PREV08), whose aim is to help Europe-wide national, regional and local CP authorities answer the following vital question in case of flooding: "Should I initiate an evacuation and how long do I have to evacuate safely?". In the framework of the TRIBUTE project, the technique used for assessing the time that an evacuation should be recommended is the so-called 'trigger buffer'. An evacuation trigger buffer is a pre-established boundary that circumscribes an area in such a way that when floodwaters coming from any direction cross the buffer, an evacuation is recommended. The final aim of the project will be to develop a web service and a mobile application allowing users to select desired sensible points and know related trigger buffers during a specific inundation event. The service will have pan-European coverage. For this purpose, the developed model will be fed, among others, with current estimates of inundation hazards from satellites and information on the vulnerability and coping capacity for the threatened site. Two different activities are of particular interest to these tasks: i) the assessment and mapping of inundation vulnerability indicators for risk evaluation purposes in sensitive areas, and ii) the exploitation of satellite datasets for real-time detection of extreme rainfall events in these areas. The way in which these issues have been addressed will be presented and discussed in this contribution. In particular, the updated version of ITHACA's (www.ithacaweb.org) Extreme Rainfall Detection System (ERDS), a service for the monitoring of exceptional rainfall events, with a nearly global geographic coverage, is presented. This system is able to analyze near real-time rainfall amount for different lead times, with the aim to deliver hourly extreme rainfall alerts. The system uses, as base data, the GPM (Global Precipitation Measurement) mission IMERG (Integrated Multi-satellite Retrievals) products. Additionally, flood vulnerability maps showing the spatial distribution of flood-prone elements, with particular attention to the population and the built environment, are produced using a simplified methodology based on open datasets available at European scale. In the proposed methodology, the population density, the location of vulnerable points of interest, and proper indicators of the complexity and density of the transportation network that fall within the identified trigger buffers are taken into consideration, and, finally, they are combined to create a final vulnerability map with European coverage.
Extreme rainfall may trigger some of the most catastrophic natural disasters, whose consequences may be exacerbated especially in places where an appropriate network of measurement instruments is not available. A combination of remotely sensed data and weather prediction model outputs can often help to obtain information with a global spatial coverage without the limitations that characterize other instruments. In order to achieve this goal, an Extreme Rainfall Detection System (ERDS – erds.ithacaweb.org) was developed and implemented with the aim of monitoring and forecasting exceptional rainfall events. The system was designed with the aim of providing information in an understandable way also for non-specialized users. The NOAA-GFS deterministic weather prediction model is used for the purpose of forecasting extreme rainfall events. Regarding the near real-time rainfall monitoring, the previous version of ERDS was using NASA TRMM TMPA 3-hourly data as input. Due to TRMM instrument shutdown, a different rainfall measurement must be used. NASA GPM IMERG early run half-hourly data proved to be the proper one. A comparison between GPM and rain gauge data allowed to defining the minimum time aggregation intervals to be used for the detection of extreme rainfall events in order to reduce the effects of the bias due to satellite data. The same comparison was also performed using GFS data instead of GPM data. A new extreme rainfall detection methodology was also developed with the aim of increasing system performances. The currently adopted methodology is based on the concept of event-identification threshold. A threshold represents the amount of precipitation needed to trigger a flood event induced by extreme rainfall. Specifically, if for a selected aggregation interval the accumulated precipitation exceeds the threshold, an alert is provided. Obtained results highlighted that the combination of new input data and new threshold methodology allowed one to increase system performances, both in terms of spatial and temporal resolution and in terms of identified events.
Flood events represent some of the most catastrophic natural disasters, especially in localities where appropriate measurement instruments and early warning systems are not available. Remotely sensed data can often help to obtain near real-time rainfall information with a global spatial coverage without the limitations that characterize other instruments. In order to achieve this goal, a freely accessible Extreme Rainfall Detection System (ERDS—erds.ithacaweb.org) was developed and implemented by ITHACA with the aim of monitoring and forecasting exceptional rainfall events and providing information in an understandable way for researchers as well as non-specialized users. The near real-time rainfall monitoring is performed by taking advantage of NASA GPM (Global Precipitation Measurement) IMERG (Integrated Multi-satellite Retrievals for GPM) half-hourly data (one of the most advanced rainfall measurements provided by satellite). This study aims to evaluate ERDS performance in the detection of the extreme rainfall that led to a massive flood event in Queensland (Australia) between January and February 2019. Due to the impressive amount of rainfall that affected the area, Flinders River (one of the longest Australian rivers) overflowed, expanding to a width of tens of kilometers. Several cities were also partially affected and Copernicus Emergency Management Service was activated with the aim of providing an assessment of the impact of the event. In this research, ERDS outputs were validated using both in situ and open source remotely sensed data. Specifically, taking advantage of both NASA MODIS (Moderate-resolution Imaging Spectroradiometer) and Copernicus Sentinel datasets, it was possible to gain a clear look at the full extent of the flood event. GPM data proved to be a reliable source of rainfall information for the evaluation of areas affected by heavy rainfall. By merging these data, it was possible to recreate the dynamics of the event.
Flood events represent some of the most catastrophic natural disasters, especially in localities where appropriate measurement instruments and early warning system are not available. Remotely sensed data can often help to obtain near real-time rainfall information with a global spatial coverage without the limitations that characterize other instruments. In order to achieve this goal, a freely accessible Extreme Rainfall Detection System (ERDS – erds.ithacaweb.org) was developed and implemented by ITHACA with the aim of monitoring and forecasting exceptional rainfall events and providing information in an understandable way also for non-specialized users. The near real-time rainfall monitoring is performed taking advantages of NASA GPM IMERG half-hourly data (one of the most advanced rainfall measurements provided by satellite). This study aims to evaluate ERDS performance in the detection of the extreme rainfall that led to a massive flood event in Queensland (Australia) between January and February 2019. Due to the impressive amount of rainfall that affected the area, Flinders River (one of the longest Australian river) overflowed, expanding to a width of tens of kilometres. Several cities were also partially affected and Copernicus Emergency Management Service was activated with the aim of providing an assessment of the impact of the event. In this research, ERDS outputs were validated using both in-situ and open source remotely sensed data. Specifically, taking advantage of both NASA MODIS (Moderate-resolution Imaging Spectroradiometer) and Copernicus Sentinel datasets it was possible to have a clear look of the full extent of the flood event. GPM data proved to be a reliable source of rainfall information for the evaluation of areas affected by heavy rainfall. By merging these data, it was possible to recreate the dynamics of the event.
Many studies have shown a growing trend in terms of frequency and severity of extreme events. As never before, having tools capable to monitor the amount of rain that reaches the Earth’s surface has become a key point for the identification of areas potentially affected by floods. In order to guarantee an almost global spatial coverage, NASA Global Precipitation Measurement (GPM) IMERG products proved to be the most appropriate source of information for precipitation retrievement by satellite. This study is aimed at defining the IMERG accuracy in representing extreme rainfall events for varying time aggregation intervals. This is performed by comparing the IMERG data with the rain gauge ones. The outcomes demonstrate that precipitation satellite data guarantee good results when the rainfall aggregation interval is equal to or greater than 12 h. More specifically, a 24-h aggregation interval ensures a probability of detection (defined as the number of hits divided by the total number of observed events) greater than 80%. The outcomes of this analysis supported the development of the updated version of the ITHACA Extreme Rainfall Detection System (ERDS: erds.ithacaweb.org). This system is now able to provide near real-time alerts about extreme rainfall events using a threshold methodology based on the mean annual precipitation.
Many studies have shown a growing trend, in terms of number, frequency and severity of extreme events. As never before, having tools capable to monitor the amount of rain that reaches the Earth’s surface has become a focal point for the identification of areas potentially affected by floods. In order to guarantee an almost global spatial coverage, a precipitation evaluation provided by satellite products proved to be the most appropriate source of information. NASA GPM (Global Precipitation Measurement) mission provides since March 2014 different IMERG (Integrated Multi-satellite Retrievals for GPM) products with a spatial coverage of 60°N - 60°S and a spatial resolution of 0,1° x 0,1°. The first part of our study is aimed to compare at the global scale satellite IMERG early and late data and rain gauge precipitation data, in order to evaluate their relative accuracy. The outcomes demonstrate that satellite data guarantees good result when rainfall aggregation interval is equal or greater than 12 hours. More specifically a 24-hours aggregation interval ensures a probability of detection (defined as the number of hits events divided by the total number of observed events) greater than 80% and a bias of -0,1 mm/h. With an aggregation interval of 72 hours a probability of detection greater than 90% is reached. The outcomes of this analysis supported the development of the updated version of the ITHACA Extreme Rainfall Detection System (ERDS - erds.ithacaweb.org). This system is now able to provide hourly near-real time alerts about extreme rainfall events. ERDS is a strategic tool, capable to provide, during the preparedness and response phases of the emergency cycle, immediate and intuitive information about potential flood events. The information is accessible through a WebGIS application, developed in a complete Open Source environment. Results are published on ERDS website by means of standard WMS services. Specifically, this system automatically downloads the most recent GPM IMERG early run half-hourly data and cumulates it according to specific periods (12hr, 24hr, 48hr, 72hr, 96hr). ERDS generates precipitation alerts where and when the precipitation amount is higher than a specific set of thresholds. This set of thresholds has been calculated for every aggregation interval on the basis of the average annual precipitation values evaluated on a 0,1° x 0,1° grid cell basis.
The GPM (Global Precipitation Measurement) mission provides - since March 2014 - different IMERG (Integrated Multi-satellite Retrievals for GPM) products with a spatial coverage of 60°N - 60°S. IMERG products are available in three different versions: early run (with a delay of 6 hours), late run (with a delay of 18 hours) and final run (with a delay of 4 months). Considering the short delay in their availability, IMERG early and late half-hourly data can be used for real-time flood risk monitoring applications. In this study, IMERG early and late data are compared at the global scale with rain gauge precipitation data in order to evaluate their relative accuracy. The results demonstrate that a 24-hours aggregation interval ensures a probability of detection (evaluable as the number of hits events divided by the total number of observed events) greater than 80% and a bias of -0,1 mm/h. With an aggregation interval of 72 hours we reach a probability of detection greater than 90%. The outcomes of this analysis will support the development of the updated version of the ITHACA Extreme Rainfall Detection System (ERDS). This system is able to provide hourly real-time alerts about extreme rainfall events.