•24h ahead hourly solar irradiance forecast using Artificial Neural Networks (ANN).•A master optimization process based on ANN ensemble for the Rome site is used.•29% improvement on persistence using ...MOS–ANN model that corrects NWP with local data.
In the paper two models implemented to forecast the hourly solar irradiance with a day in advance are described. The models, based on Artificial Neural Networks (ANN), are generated by a master optimization process that defines the best number of neurons and selects a suitable ensemble of ANN.
The two models consist of a Statistical (ST) model that uses only local measured data and a Model Output Statistics (MOS) that corrects Numerical Weather Prediction (NWP) data. ST and MOS are tested for the University of Rome “Tor Vergata” site. The models are trained and validated using one year data. Through a cross training procedure, the dependence of the models on the training year is also analyzed.
The performance of ST, NWP and MOS models, together with the benchmark Persistence Model (PM), are compared. The ST model and the NWP model exhibit similar results. Nevertheless different sources of forecast errors between ST and NWP models are identified. The MOS model gives the best performance, improving the forecast of approximately 29% with respect to the PM.
Systematic and timely documentation of triggered (i.e. event) landslides is fundamental to build extensive datasets worldwide that may help define and/or validate trends in response to climate ...change. More in general, preparation of landslide inventories is a crucial activity since it provides the basic data for any subsequent analysis. In this work we present an event landslide inventory map (E-LIM) that was prepared through a systematic reconnaissance field survey in about 1 month after an extreme rainfall event hit an area of about 5000 km
in the Marche-Umbria regions (central Italy). The inventory reports evidence of 1687 triggered landslides in an area of ~550 km
. All slope failures were classified according to type of movement and involved material, and documented with field pictures, wherever possible. The database of the inventory described in this paper as well as the collection of selected field pictures associated with each feature is publicly available at figshare.
In the province of Concepción (Chile), floods are considered one of the main natural hazards. One of the most important cities of this area is Talcahuano. During the last years, Talcahuano has been ...affected by a number of flood episodes, as a consequence of an increase in the frequency of extraordinary atmospheric events, along with a higher exposure to the flood risk caused by an intense urban development. On 27 February 2010, an 8.8° earthquake (Richter scale) occurred in central southern Chile and originated the tsunami which flooded a large percentage of the residential area and military base of the Talcahuano city. This flood event affected a population higher than 180,000 people (including 23 casualties and invaluable economic and environmental losses). The objective of this study is to investigate the social perception and knowledge of Talcahuano residents affected by different types of flood, including tsunami, emphasizing which are their risks, vulnerability, resilience and coping capacity concepts. In addition, the kind of measures that have been proposed to improve their capacity to face floods after having suffered the natural disaster will be determined. This social assessment has been carried out based on a survey to permanent residents. Research results reveal that their endogenous and exogenous characteristics have resulted determinant to explain their perception.
•A hybrid model output statistics is developed to refine day ahead GHI forecast.•A new physical algorithm was built to improve the GHI dampening due to humidity.•Great forecast errors reduction was ...achieved using these techniques.
In this paper a new hybrid Model Output Statistics (MOS), named MOS cascade, is developed to refine the day-ahead forecast of the global horizontal irradiance provided by the Weather Research and Forecast (WRF) model. The proposed approach is based on a sequence of two different MOS. The first, called MOSRH, is a new physically based algorithm, built to correct the treatment of humidity in the WRF radiation schemes. The second, called MOSNN, is based on artificial intelligence techniques and aims to correct the main systematic and learnable errors of the Numerical Weather Prediction output. The 1-day and 2-day forecast accuracies are analyzed via direct comparison with irradiance data measured in two sites, Rome and Lugano. The paper shows that a considerable reduction in error was achieved using MOSRH model and MOS cascade. The differences between the two sites are discussed in details. Finally, the results obtained are compared with the benchmark accuracy reached for the data available for the average climate in Southern Spain and Switzerland.
A 1:5,000 scale geological map and 31 geological cross-sections are presented for the surroundings of Amatrice village (central Apennines, Italy), epicentral area of the first damaging earthquake of ...the 2016-2017 seismic sequence. This detailed geological dataset focuses on: (i) the extent, the thickness, and the internal stratigraphic architecture of the Quaternary continental deposits; (ii) the bedding and the thickness of the Miocene substratum; and (iii) the spatial distribution of the main fault systems. The provided dataset would update the available regional geological maps in deciphering the syn-to-post-orogenic history of the Amatrice Basin. Eventually, the accuracy of the geological mapping would represent a basic tool for interpreting and integrating the multidisciplinary dataset deriving from post-seismic activities.
Globally, harmful algal blooms (HABs) are an increasing problem. In the Gulf of Maine and Bay of Fundy, blooms of the toxic dinoflagellate Alexandrium catenella are annually recurrent phenomena. As ...this region is one of the most rapidly warming areas of the global ocean, an improved understanding of the mechanisms driving the initiation of local A. catenella blooms, their interannual variability and the implications of future climate change is critical to local monitoring strategies and marine resources management. A 27-year (1988–2014) time series of weekly A. catenella cell counts from the Bay of Fundy and concurrent satellite-measured sea surface temperature, freshwater discharge from the St. John River and wind-driven turbulence are compared to assess their relationship to variability in bloom phenology metrics. The mean thermal habitat associated with early detection of A. catenella is 6.5 ± 1.6°C, whereas that of bloom initiation averages 9.2 ± 1.5°C. Both thermal habitats for A. catenella are trending earlier over the study period. Bloom initiations that precede the arrival of the thermal habitat mean (occur in colder water) are associated with higher spring freshwater discharge and are generally weaker blooms. Increased spring freshwater discharge is also associated with earlier bloom initiation and earlier maximum concentration dates. No significant relationship was observed with the strength of wind-driven mixing. Removal of the mean thermal seasonal cycle shows that surface temperature anomalies have a strong negative relationship to the bloom phenology metrics and arrival of thermal habitat: warmer years are linked to earlier arrival of thermal habitats (∼12 days °C–1) and earlier detection and bloom initiation dates (∼33 days °C–1). Using these relationships and present trends in Bay of Fundy surface temperature warming over the period 1982–2019, we project the arrival dates of bloom thermal habitat and bloom phenology metrics out to the middle of this century. Based on current rates of sea surface temperature change, bloom phenology metrics (e.g., bloom initiation, early detection), can be expected to shift 1–2 months earlier in the season by mid-century. Such changes in the phenology of A. catenella blooms will need to be incorporated into both monitoring strategies and forecasting models for the region.
This paper is devoted to the important yet unexplored subject of crowding effects on market impact, that we call 'co-impact'. Our analysis is based on a large database of metaorders by institutional ...investors in the U.S. equity market. We find that the market chiefly reacts to the net order flow of ongoing metaorders, without individually distinguishing them. The joint co-impact of multiple contemporaneous metaorders depends on the total number of metaorders and their mutual sign correlation. Using a simple heuristic model calibrated on data, we reproduce very well the different regimes of the empirical market impact curves as a function of volume fraction φ: square-root for large φ, linear for intermediate φ, and a finite intercept
when
. The value of
grows with the sign correlation coefficient. Our study sheds light on an apparent paradox: How can a non-linear impact law survive in the presence of a large number of simultaneously executed metaorders?
PDF
