Simulating extreme hydro-meteorological events with DRIHM(2US) services

From 1970 to 2012, about 9000 High Impact Weather Events (HIWE) were reported globally: all together, they caused the loss of 1.94 million lives and economic damage of US$ 2.4 trillion (2014 UNISDR report). Storms and floods accounted for 79 per cent of the total number of disasters due to weather, water and climate extremes and caused 55 per cent of lives lost and 86 per cent of economic losses. Predicting high impact weather events (HIWE) is still one of the main challenges of the 21st century, with significant socio-economic implications. At the heart of this challenge, lies the ability to access hydro-meteorological data and models and to facilitate the collaboration between meteorologists, hydrologists, and Earth science experts for accelerated scientific advances in hydro-meteorological research (HMR). The EU funded DRIHM (Distributed Research Infrastructure for Hydro-Meteorology) and DRIHM2US (Distributed Research Infrastructure for Hydro-Meteorology to US) projects developed a prototype e-Science environment to facilitate this collaboration and to provide advanced end-to-end HMR services (models, datasets and post-processing tools). The DRIHM(2US) services will be presented and demonstrated for the Genoa 2014 flash-flood event

Detecting extreme rainfall events using the WRF-ERDS workflow: the 15 July 2020 Palermo case study

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

Heavy Rainfall Identification within the Framework of the LEXIS Project: The Italian Case Study

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

Hindcast high-resolution simulation of the most catastrophic rainfall event in Genoa City (7-8 October 1970): hydro-meteorological and geomorphological analysis

Liguria region is historically affected by severe hydro-meteorological events often resulting in dramatic death tolls and large socio-economic impacts. On 7-8 October 1970, Genoa, region capital city, was struck by the most catastrophic flood event of its history. On the evening of 7 October pre-frontal storms affected the western side of the city (Voltri, Prà and Pegli municipalities), while on 8 October 1970 an anticyclone block generated recurring convective systems that hit Genoa city and above all the Bisagno Valley. The heavy rainfall continued more than 24 h with highs at Bolzaneto rain gauge (Polcevera Valley, northwest of Genoa city center) where over 950 mm of rainfall in 24 hours was measured. Over the city center and the Bisagno Valley, 400 mm in 24 h was recorded. The Bisagno stream channels overflowed, submerging the city center. The 1970 event in Genoa City was also the most dramatic in terms of damage: 44 fatalities occurred and over 2000 individuals were evacuated. This study hindcasts the meteorological evolution of this event at high spatial resolution (1.5 km) and temporal one (1 hour) using the Weather and Research Forecasting (WRF) model by downscaling the ERA5 climatology developed by European Center for Medium-Range Weather Forecast (ECMWF). The weather hindcast scenario is compared with available meteorological observations as well as with recorded geomorphological impacts on Genoa city center and municipalities

2ELDAS comparison of three different algorithms for soil moisture assimilation in the HIRLAM system

Ponencia presentada en: SRNWP/HIRLAM Workshop on Surface Processes, Surface Assimilation and Turbulence celebrado en Norrköping, 15-17 September 2004

Effects of the Representation of Convection on the Modelling of Hurricane Tomas (2010)

The cumulus parameterization is widely recognised as a crucial factor in tropical meteorology: this paper intends to shed further light on the effects of convection parameterization on tropical cyclones’ numerical predictions in the “grey zone” (10–1 km grid spacing). Ten experiments are devised by combining five different convection treatments over the innermost, 5 km grid spacing, domain, and two different global circulation model datasets (IFS and ERA-Interim). All ten experiments are finally analysed and compared to observations provided by the National Hurricane Center’s best track record and multisatellite rainfall measurements. Results manifestly point to the superiority of employing no convective parameterization at the scale of 5 km versus the usage of any of those provided by WRF to reproduce the case study of Hurricane Tomas, which hit the Lesser Antilles and Greater Antilles in late October and early November 2010

Significance of ELDAS soil moisture products for NWP

Ponencia presentada en: ECMWF/ELDAS Workshop on Land Surface Assimilation celebrado los días 8-11 de noviembre de 2004 en Shinfield Park, Reading.The analysis of soil moisture for the initialization of numerical weather prediction (NWP) models is discussed subjected to the constraints imposed by an operational environment. Three different techniques are compared within the HIRLAM forecasting system. The first method is the HIRLAM default option based on optimal interpolation (OI) analysis with optimal coefficients analytically formulated. It makes use of 2-metre temperature and relative humidity errors. A second method is based on a simplified variational approach to assimilate also 2-metre observations of temperature and relative humidity. The estimate of tangent linear of the observation operator is obtained here from an extra integration of the numerical model. Finally, a third method is based on the assimilation of soil moisture by a parent (global) model and a posteriori corrected by the forecasted precipitation error. The soil moisture produced is then imported to the HIRLAM system