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.

Stochastic simulation of reference rainfall scenarios for hydrological applications using a universal multifractal approach

Hydrological applications such as storm-water management or flood design usually deal with and are driven by region-specific reference rainfall regulations or guidelines based on Intensity-Duration-Frequency (IDF) curves. IDF curves are usually obtained via frequency analysis of rainfall data using which the exceedance probability of rain intensity for different durations are determined. It is also rather common for reference rainfall to be expressed in terms of precipitation P, accumulated in a duration D (related to rainfall intensity ), with a return period T (inverse of exceedance probability). Meteorological modules of hydro-meteorological models used for the aforementioned applications therefore need to be capable of simulating such reference rainfall scenarios. The multifractal cascade framework, since it incorporates physically realistic properties of rainfall processes (non-homogeneity or intermittency, scale invariance and extremal statistics) seems to suit this purpose. Here we propose a discrete-in-scale universal multifractal (UM) cascade based approach. Daily, Hourly and six-minute rainfall time series datasets (with lengths ranging from 100 to 15 years) over three regions (Paris, Nantes, and Aix-en-Provence) in France that are characterized by different climates are analyzed to identify scaling regimes and estimate corresponding UM parameters (α, C1) required by the UM cascade model. Suitable renormalization constants that correspond to the P, D, T values of reference rainfall are used to simulate an ensemble of reference rainfall scenarios, and the simulations are finally compared with datasets. Although only purely temporal simulations are considered here, this approach could possibly be generalized to higher spatial dimensions as well.

A three-dimensional atmospheric dispersion model for Mars

Atmospheric local-to-regional dispersion models are widely used on Earth to predict and study the effects of chemical species emitted into the atmosphere and to contextualize sparse data acquired at particular locations and/or times. However, to date, no local-to-regional dispersion models for Mars have been developed; only mesoscale/microscale meteorological models have some dispersion and chemical capabilities, but they do not offer the versatility of a dedicated atmospheric dispersion model when studying the dispersion of chemical species in the atmosphere, as it is performed on Earth. Here, a new three-dimensional local-to-regional-scale Eulerian atmospheric dispersion model for Mars (DISVERMAR) that can simulate emissions to the Martian atmosphere from particular locations or regions including chemical loss and predefined deposition rates, is presented. The model can deal with topography and non-uniform grids. As a case study, the model is applied to the simulation of methane spikes as detected by NASA’s Mars Science Laboratory (MSL); this choice is made given the strong interest in and controversy regarding the detection and variability of this chemical species on Mars.

Performances of Limited Area Models for the WORKLIMATE Heat–Health Warning System to Protect Worker’s Health and Productivity in Italy

Outdoor workers are particularly exposed to climate conditions, and in particular, the increase of environmental temperature directly affects their health and productivity. For these reasons, in recent years, heat-health warning systems have been developed for workers generally using heat stress indicators obtained by the combination of meteorological parameters to describe the thermal stress induced by the outdoor environment on the human body. There are several studies on the verification of the parameters predicted by meteorological models, but very few relating to the validation of heat stress indicators. This study aims to verify the performance of two limited area models, with different spatial resolution, potentially applicable in the occupational heat health warning system developed within the WORKLIMATE project for the Italian territory. A comparison between the Wet Bulb Globe Temperature predicted by the models and that obtained by data from 28 weather stations was carried out over about three summer seasons in different daily time slots, using the most common skill of performance. The two meteorological models were overall comparable for much of the Italian explored territory, while major limits have emerged in areas with complex topography. This study demonstrated the applicability of limited area models in occupational heat health warning systems.

Modeling evaporation from northern waterbodies, the case of an 85-km2 reservoir

<p>Freshwater bodies represent 9% of Canada’s total land area, with more than half of these having a surface area smaller than 100 km<sup>2</sup>. Taking into account the interactions between lakes and the atmosphere in meteorological models is crucial, considering the marked differences with the surrounding land masses (low albedo, unlimited source of water, high thermal capacity, etc.). Open water evaporation, in particular, is often a challenge because of its intangible nature and the scarcity of direct observations. This project focuses on the modeling of the surface energy budget of a reservoir located in the boreal biome of eastern Canada, with an emphasis on the evaporation. Observations are available for the 85-km<sup>2</sup> La Romaine 2 hydroelectric reservoir (50.7°N, 63.2°W), where two micrometeorological towers were deployed: one operated yearlong on the shore and one operated on a floating deck during ice-free conditions. Modeling resorts to the Canadian Small Lake Model (CSLM), a one-dimensional land surface model designed to integrate the lake-atmosphere fluxes into meteorological models. The model also simulates the thermal regime of the water body, including ice formation. Lastly, the model can be used for climate and weather prediction, which may be a useful for reservoir management. Comparison of field observations and simulations confirms the CSLM ability to reproduce the turbulent fluxes and the temperature behavior of the reservoir except for some specific periods, in particular the ice breakups and freeze-ups. The model somehow underestimates the water temperature resulting in a premature depletion of the lake heat storage in autumn. It also overreacts to high wind episodes.</p>

Suivi atmosphérique des émissions de CO2 de la région parisienne

Les avancées scientifiques permettent un suivi des émissions des villes à partir de mesures des concentrations atmosphériques de CO2 sur un réseau de stations et de méthodes d'inversion fondées sur des modèles de météorologie et de transport atmosphérique à méso-échelle. Nous prenons pour exemple l'agglomération de Paris. Les mesures atmosphériques collectées par un réseau de stations urbaines et périurbaines sont présentées, ainsi que les résultats d'une inversion des émissions et les directions de recherche pour affiner ces estimations. Enfin, la signature du premier confinement lié à la Covid-19 pendant le printemps 2020 sur les mesures atmosphériques de CO2 est présentée et suggère une forte réduction des émissions. Scientific advances enable the monitoring of urban CO2 emissions from in situ measurements of atmospheric CO2 concentrations and atmospheric inversion methods based on mesoscale meteorological models. We use the Paris urban area as an example. Atmospheric measurements collected at a network of urban and suburban stations are presented, as well as the results of an atmospheric inversion of the city emissions, and research directions to refine these estimates. The signature of the Covid-19 lockdown measures in the spring 2020 on the urban atmospheric CO2 signals is presented, indicative of a strong reduction of emissions.