Can a Multimodel SuperEnsemble technique be used for precipitation forecasts?

The Multimodel SuperEnsemble technique is a postprocessing method for the estimation of weather forecast parameters reducing direct model output errors. It differs from other ensemble analysis techniques by the use of an adequate weighting of the input forecast models in order to obtain a combined estimation of meteorological parameters. Weights are calculated by least-square minimization of the differences between the model and the observed field during a so-called training period. <br><br> Although it can be applied successfully on continuous parameters like temperature, relative humidity, wind speed and mean sea level pressure, the Multimodel SuperEnsemble also gives good results when applied on the precipitation, a parameter quite difficult to handle with standard post-processing methods. Here we present a methodology for the Multimodel precipitation forecasts with a careful ensemble dressing via the precipitation PDF estimation

Can a Multimodel SuperEnsemble technique be used for precipitation forecasts?

Abstract. The Multimodel SuperEnsemble technique is a postprocessing method for the estimation of weather forecast parameters reducing direct model output errors. It differs from other ensemble analysis techniques by the use of an adequate weighting of the input forecast models in order to obtain a combined estimation of meteorological parameters. Weights are calculated by least-square minimization of the differences between the model and the observed field during a so-called training period. Although it can be applied successfully on continuous parameters like temperature, relative humidity, wind speed and mean sea level pressure, the Multimodel SuperEnsemble also gives good results when applied on the precipitation, a parameter quite difficult to handle with standard post-processing methods. Here we present a methodology for the Multimodel precipitation forecasts with a careful ensemble dressing via the precipitation PDF estimation

The hydro-meteorological chain in Piemonte region, North Western Italy - analysis of the HYDROPTIMET test cases

International audienceThe HYDROPTIMET Project, Interreg IIIB EU program, is developed in the framework of the prediction and prevention of natural hazards related to severe hydro-meteorological events and aims to the optimisation of Hydro-Meteorological warning systems by the experimentation of new tools (such as numerical models) to be used operationally for risk assessment. The objects of the research are the mesoscale weather phenomena and the response of watersheds with size ranging from 102 to 103 km2. Non-hydrostatic meteorological models are used to catch such phenomena at a regional level focusing on the Quantitative Precipitation Forecast (QPF). Furthermore hydrological Quantitative Discharge Forecast (QDF) are performed by the simulation of run-off generation and flood propagation in the main rivers of the territory. In this way observed data and QPF are used, in a real-time configuration, for one-way forcing of the hydrological model that works operationally connected to the Piemonte Region Alert System. The main hydro-meteorological events that affected Piemonte Region in the last years are analysed, these are the HYDROPTIMET selected test cases of 14?18 November 2002 and 23?26 November 2002. The results obtained in terms of QPF and QDF offer a basis to evaluate the sensitivity of the whole hydro-meteorological chain to the uncertainties in the numerical simulations. Different configurations of non-hydrostatic meteorological models are also evaluated

The forecaster's added value in QPF

Abstract. To the authors' knowledge there are relatively few studies that try to answer this question: "Are humans able to add value to computer-generated forecasts and warnings?". Moreover, the answers are not always positive. In particular some postprocessing method is competitive or superior to human forecast. Within the alert system of ARPA Piemonte it is possible to study in an objective manner if the human forecaster is able to add value with respect to computer-generated forecasts. Every day the meteorology group of the Centro Funzionale of Regione Piemonte produces the HQPF (Human Quantitative Precipitation Forecast) in terms of an areal average and maximum value for each of the 13 warning areas, which have been created according to meteo-hydrological criteria. This allows the decision makers to produce an evaluation of the expected effects by comparing these HQPFs with predefined rainfall thresholds. Another important ingredient in this study is the very dense non-GTS (Global Telecommunication System) network of rain gauges available that makes possible a high resolution verification. In this work we compare the performances of the latest three years of QPF derived from the meteorological models COSMO-I7 (the Italian version of the COSMO Model, a mesoscale model developed in the framework of the COSMO Consortium) and IFS (the ECMWF global model) with the HQPF. In this analysis it is possible to introduce the hypothesis test developed by Hamill (1999), in which a confidence interval is calculated with the bootstrap method in order to establish the real difference between the skill scores of two competitive forecasts. It is important to underline that the conclusions refer to the analysis of the Piemonte operational alert system, so they cannot be directly taken as universally true. But we think that some of the main lessons that can be derived from this study could be useful for the meteorological community. In details, the main conclusions are the following: – despite the overall improvement in global scale and the fact that the resolution of the limited area models has increased considerably over recent years, the QPF produced by the meteorological models involved in this study has not improved enough to allow its direct use: the subjective HQPF continues to offer the best performance for the period +24 h/+48 h (i.e. the warning period in the Piemonte system); – in the forecast process, the step where humans have the largest added value with respect to mathematical models, is the communication. In fact the human characterization and communication of the forecast uncertainty to end users cannot be replaced by any computer code; – eventually, although there is no novelty in this study, we would like to show that the correct application of appropriated statistical techniques permits a better definition and quantification of the errors and, mostly important, allows a correct (unbiased) communication between forecasters and decision makers

The cases of June 2000, November 2002 and September 2002 as examples of Mediterranean floods

International audienceFour flood events that affected three different countries are here described in terms of meteorological genesis and in terms of consequences on the population and on the territory. Each event is a good representative of a class of phenomena that produce important effects on the urban and extra-urban tissue and that must be taken into account in an optic of civil protection and risk evaluation. This is the subject of the HYDROPTIMET project, part of the Interreg IIIB program, which is collocated in the framework of the prevention of natural hazards and, in particular, those related to severe meteo-hydrological events. This paper aims at being a general introduction of the four events which are the subject of more detailed studies, already published or under submission

Evaluation of the hydro-meteorological chain in Piemonte Region, north western Italy - analysis of two HYDROPTIMET test cases

International audienceThe HYDROPTIMET Project, Interreg IIIB EU program, is developed in the framework of the prediction and prevention of natural hazards related to severe hydro-meteorological events and aims to the optimisation of Hydro-Meteorological warning systems by the experimentation of new tools (such as numerical models) to be used operationally for risk assessment. The object of the research are the Mesoscale weather phenomena and the response of watersheds with size ranging from 102 to 103 km2. Non-hydrostatic meteorological models are used to catch such phenomena at a regional level focusing on the Quantitative Precipitation Forecast (QPF). Furthermore hydrological Quantitative Discharge Forecast (QDF) are performed by the simulation of run-off generation and flood propagation in the main rivers of the interested territory. In this way observed data and QPF are used, in a real-time configuration, for one-way forcing of the hydrological model that works operationally connected to the Piemonte Region Alert System. The main hydro-meteorological events that interested Piemonte Region in the last years are studied, these are the HYDROPTIMET selected test cases of 14-18 November 2002 and 23-26 November 2002. The results obtained in terms of QPF and QDF offer a sound basis to evaluate the sensitivity of the whole hydro-meteorological chain to the uncertainties in the numerical simulations. Different configurations of non-hydrostatic meteorological models are also analysed

Targeted Protein Degradation for Infectious Diseases: from Basic Biology to Drug Discovery

Targeted protein degradation (TPD) is emerging as one of the most innovative strategies to tackle infectious diseases. Particularly, proteolysis-targeting chimera (PROTAC)-mediated protein degradation may offer several benefits over classical anti-infective small-molecule drugs. Because of their peculiar and catalytic mechanism of action, anti-infective PROTACs might be advantageous in terms of efficacy, toxicity, and selectivity. Importantly, PROTACs may also overcome the emergence of antimicrobial resistance. Furthermore, anti-infective PROTACs might have the potential to (i) modulate "undruggable"targets, (ii) "recycle"inhibitors from classical drug discovery approaches, and (iii) open new scenarios for combination therapies. Here, we try to address these points by discussing selected case studies of antiviral PROTACs and the first-in-class antibacterial PROTACs. Finally, we discuss how the field of PROTAC-mediated TPD might be exploited in parasitic diseases. Since no antiparasitic PROTAC has been reported yet, we also describe the parasite proteasome system. While in its infancy and with many challenges ahead, we hope that PROTAC-mediated protein degradation for infectious diseases may lead to the development of next-generation anti-infective drugs

From Monoamine Oxidase Inhibition to Antiproliferative Activity: New Biological Perspectives for Polyamine Analogs

Monoamine oxidases (MAOs) are well-known pharmacological targets in neurological and neurodegenerative diseases. However, recent studies have revealed a new role for MAOs in certain types of cancer such as glioblastoma and prostate cancer, in which they have been found overexpressed. This finding is opening new frontiers for MAO inhibitors as potential antiproliferative agents. In light of our previous studies demonstrating how a polyamine scaffold can act as MAO inhibitor, our aim was to search for novel analogs with greater inhibitory potency for human MAOs and possibly with antiproliferative activity. A small in-house library of polyamine analogs (2-7) was selected to investigate the effect of constrained linkers between the inner amine functions of a polyamine backbone on the inhibitory potency. Compounds 4 and 5, characterized by a dianiline (4) or dianilide (5) moiety, emerged as the most potent, reversible, and mainly competitive MAO inhibitors (Ki &lt; 1 μM). Additionally, they exhibited a high antiproliferative activity in the LN-229 human glioblastoma cell line (GI50 &lt; 1 μM). The scaffold of compound 5 could represent a potential starting point for future development of anticancer agents endowed with MAO inhibitory activity

A Complete Meteo/Hydro/Hydraulic Chain Application to Support Early Warning and Monitoring Systems: The Apollo Medicane Use Case

Because of the ongoing changing climate, extreme rainfall events’ frequency at the global scale is expected to increase, thus resulting in high social and economic impacts. A Meteo/Hydro/Hydraulic forecasting chain combining heterogeneous observational data sources is a crucial component for an Early Warning System and is a fundamental asset for Civil Protection Authorities to correctly predict these events, their effects, and put in place anticipatory actions. During the last week of October 2021 an intense Mediterranean hurricane (Apollo) affected many Mediterranean countries (Tunisia, Algeria, Malta, and Italy) with a death toll of seven people. The CIMA Meteo/Hydro/Hydraulic forecasting chain, including the WRF model, the hydrological model Continuum, the automatic system for water detection (AUTOWADE), and the hydraulic model TELEMAC-2D, was operated in real-time to predict the Apollo weather evolution as well as its hydrological and hydraulic impacts, in support of the early warning activities of the Italian Civil Protection Department. The WRF model assimilating radar data and in situ weather stations showed very good predictive capability for rainfall timing and location over eastern Sicily, thus supporting accurate river flow peak forecasting with the hydrological model Continuum. Based on WRF predictions, the daily automatic system for water detection (AUTOWADE) run using Sentinel 1 data was anticipated with respect to the scheduled timing to quickly produce a flood monitoring map. Ad hoc tasking of the COSMO-SkyMed satellite constellation was also performed to overcome the S1 data latency in eastern Sicily. The resulting automated operational mapping of floods and inland waters was integrated with the subsequent execution of the hydraulic model TELEMAC-2D to have a complete representation of the flooded area with water depth and water velocity