ARPA-E

About: ARPA-E is a government organization based out in Washington D.C., District of Columbia, United States. It is known for research contribution in the topics: Population & Climate change. The organization has 1161 authors who have published 1267 publications receiving 30049 citations. The organization is also known as: Advanced Research Projects Agency - Energy.

Overview of the first HyMeX Special Observation Period over Italy: observations and model results

Abstract: The Special Observation Period (SOP1), part of the HyMeX campaign (Hydrological cycle in the Mediterranean Experiments, 5 September–6 November 2012), was dedicated to heavy precipitation events and flash floods in the western Mediterranean, and three Italian hydro-meteorological monitoring sites were identified: Liguria–Tuscany, northeastern Italy and central Italy. The extraordinary deployment of advanced instrumentation, including instrumented aircrafts, and the use of several different operational weather forecast models, including hydrological models and marine models, allowed an unprecedented monitoring and analysis of high-impact weather events around the Italian hydro-meteorological sites. This activity has seen strong collaboration between the Italian scientific and operational communities. In this paper an overview of the Italian organization during SOP1 is provided, and selected Intensive Observation Periods (IOPs) are described. A significant event for each Italian target area is chosen for this analysis: IOP2 (12–13 September 2012) in northeastern Italy, IOP13 (15–16 October 2012) in central Italy and IOP19 (3–5 November 2012) in Liguria and Tuscany. For each IOP the meteorological characteristics, together with special observations and weather forecasts, are analyzed with the aim of highlighting strengths and weaknesses of the forecast modeling systems, including the hydrological impacts. The usefulness of having different weather forecast operational chains characterized by different numerical weather prediction models and/or different model set up or initial conditions is finally shown for one of the events (IOP19).

Detection of changes in the summer precipitation time series of the region Emilia-Romagna, Italy

Abstract: The main characteristics of the spatial and temporal variability of summer precipitation observed in 40 rainfall stations of the Emilia-Romagna region in northern Italy, are analysed for the period 1922 to 1995. Non-parametric tests and Empirical Orthogonal Function (EOF) analysis were used as tools in order to achieve the paper’s objective. The Pettitt and Mann-Kendall tests detect shift points and trends in the precipitation time series, respectively, while the EOF analysis reveals the main characteristics of spatial variability. The Standard Normal Homogeneity Test (SNHT) was used to detect the inhomogeneity of the data set.

Climatic effects on vertical mixing and deep-water oxygen content in the subalpine lakes in Italy

Abstract: Deep lakes south of the Alps (DSL: Maggiore, Lugano, Como, Iseo and Garda) are characterised by varying trophic states and dissolved oxygen (DO) concentrations. Some of these lakes experience anoxic conditions in deep waters. We hypothesised that the increase in temperature and water-column stability observed in these lakes during recent decades influenced the deep-water DO concentration. In particular, we tested whether the thermal regime of the lakes and the depth of mixing affect oxygen replenishment during winter–spring turnover. To this aim, we analysed long-term trends and seasonal variability of oxygen levels in the DSL during 1992–2016. We included in our analysis the effects of environmental variables, such as winter air temperature and atmospheric modes of variability. Our results showed a recent decrease in the deep-water oxygen content in lakes Maggiore, Como and Garda and an increase of the extent of anoxic conditions in lakes Lugano and Iseo. Our results suggest that, beside cultural eutrophication, rising environmental pressures, such as global warming, can influence the future trends of the oxygen levels and ecological states of deep lakes.

Evaluation of receptor and chemical transport models for PM10 source apportionment

Abstract: In this study, the performance of two types of source apportionment models was evaluated by assessing the results provided by 40 different groups in the framework of an intercomparison organised by FAIRMODE WG3 (Forum for air quality modelling in Europe, Working Group 3). The evaluation was based on two performance indicators: z-scores and the root mean square error weighted by the reference uncertainty (RMSEu), with pre-established acceptability criteria. By involving models based on completely different and independent input data, such as receptor models (RMs) and chemical transport models (CTMs), the intercomparison provided a unique opportunity for their cross-validation. In addition, comparing the CTM chemical profiles with those measured directly at the source contributed to corroborate the consistency of the tested model results. The most commonly used RM was the US EPA- PMF version 5. RMs showed very good performance for the overall dataset (91% of z-scores accepted) while more difficulties were observed with the source contribution time series (72% of RMSEu accepted). Industrial activities proved to be the most difficult sources to be quantified by RMs, with high variability in the estimated contributions. In the CTMs, the sum of computed source contributions was lower than the measured gravimetric PM10 mass concentrations. The performance tests pointed out the differences between the two CTM approaches used for source apportionment in this study: brute force (or emission reduction impact) and tagged species methods. The sources meeting the z-score and RMSEu acceptability criteria tests were 50% and 86%, respectively. The CTM source contributions to PM10 were in the majority of cases lower than the RM averages for the corresponding source. The CTMs and RMs source contributions for the overall dataset were more comparable (83% of the z-scores accepted) than their time series (successful RMSEu in the range 25% - 34%). The comparability between CTMs and RMs varied depending on the source: traffic/exhaust and industry were the source categories with the best results in the RMSEu tests while the most critical ones were soil dust and road dust. The differences between RMs and CTMs source reconstructions confirmed the importance of cross validating the results of these two families of models. © 2019 The Authors