Problems in RMSE-based wave model validations

In order to evaluate the reliability of numerical simulations in geophysical applications it is necessary to pay attention when using the root mean square error (RMSE) and two other indicators derived from it (the normalized root mean square error (NRMSE), and the scatter index (SI)). In the present work, in fact, we show on a general basis that, in conditions of constant correlation coefficient, the RMSE index and its variants tend to be systematically smaller (hence identifying better performances of numerical models) for simulations affected by negative bias. Through a geometrical decomposition of RMSE in its components related to the average error and the scatter error it can be shown that the above mentioned behavior is triggered by a quasi-linear dependency between these components in the neighborhood of null bias. This result suggests that smaller values of RMSE, NRMSE and SI do not always identify the best performances of numerical simulations, and that these indicators are not always reliable to assess the accuracy of numerical models. In the present contribution we employ the corrected indicator proposed by Hanna and Heinold (1985) to develop a reliability analysis of wave generation and propagation in the Mediterranean Sea by means of the numerical model WAVEWATCH III®, showing that the best values of the indicator are obtained for simulations unaffected by bias. Evidences suggest that this indicator provides a more reliable information about the accuracy of the results of numerical models. © 2013 Elsevier Ltd

Constraining the ship contribution to the aerosol of the Central Mediterranean

Abstract. Particulate matter with aerodynamic diameters lower than 10 µm, (PM10) aerosol samples were collected during summer 2013 within the framework of the Chemistry and Aerosol Mediterranean Experiment (ChArMEx) at two sites located north (Capo Granitola) and south (Lampedusa Island), respectively, of the main Mediterranean shipping route in the Straight of Sicily. The PM10 samples were collected with 12 h time resolutions at both sites. Selected metals, main anions, cations and elemental and organic carbon were determined. The evolution of soluble V and Ni concentrations (typical markers of heavy fuel oil combustion) was related to meteorology and ship traffic intensity in the Straight of Sicily, using a high-resolution regional model for calculation of back trajectories. Elevated concentration of V and Ni at Capo Granitola and Lampedusa are found to correspond with air masses from the Straight of Sicily and coincidences between trajectories and positions of large ships; the vertical structure of the planetary boundary layer also appears to play a role, with high V values associated with strong inversions and a stable boundary layer. The V concentration was generally lower at Lampedusa than at Capo Granitola V, where it reached a peak value of 40 ng m−3. Concentrations of rare earth elements (REEs), La and Ce in particular, were used to identify possible contributions from refineries, whose emissions are also characterized by elevated V and Ni amounts; refinery emissions are expected to display high La ∕ Ce and La ∕ V ratios due to the use of La in the fluid catalytic converter systems. In general, low La ∕ Ce and La ∕ V ratios were observed in the PM samples. The combination of the analyses based on chemical markers, air mass trajectories and ship routes allows us to unambiguously identify the large role of the ship source in the Straight of Sicily. Based on the sampled aerosols, ratios of the main aerosol species arising from ship emission with respect to V were estimated with the aim of deriving a lower limit for the total ship contribution to PM10. The estimated minimum ship emission contributions to PM10 were 2.0 µg m−3 at Lampedusa and 3.0 µg m−3 at Capo Granitola, corresponding with 11 and 8.6 % of PM10, respectively

Evaluation of the prognostic value of impaired renal function on clinical progression in a large cohort of HIV-infected people seen for care in Italy

Whilst renal dysfunction, especially mild impairment (60 die;ve (Icona) Foundation Study collected between January 2000 and February 2014 with at least two creatinine values available. eGFR (CKD-epi) and renal dysfunction defined using a priori cut-offs of 60 (severely impaired) and 90 ml/min/1.73m2 (mildly impaired). Characteristics of patients were described after stratification in these groups and compared using chi-square test (categorical variables) or Kruskal Wallis test comparing median values. Follow-up accrued from baseline up to the date of the CCVD or AIDS related events or death or last available visit. Kaplan Meier curves were used to estimate the cumulative probability of occurrence of the events over time. Adjusted analysis was performed using a proportional hazards Cox regression model. We included 7,385 patients, observed for a median follow-up of 43 months (interquartile range [IQR]: 21-93 months). Over this time, 130 cerebro-cardiovascular events (including 11 deaths due to CCVD) and 311 AIDS-related events (including 45 deaths) were observed. The rate of CCVD events among patients with eGFR >90, 60-89, <60 ml/min, was 2.91 (95% CI 2.30-3.67), 4.63 (95% CI 3.51-6.11) and 11.9 (95% CI 6.19-22.85) per 1,000 PYFU respectively, with an unadjusted hazard ratio (HR) of 4.14 (95%CI 2.07-8.29) for patients with eGFR <60 ml/min and 1.58 (95%CI 1.10-2.27) for eGFR 60-89 compared to those with eGFR ≥90. Of note, these estimates are adjusted for traditional cardiovascular risk factors (e.g. smoking, diabetes, hypertension, dyslipidemia). Incidence of AIDS-related events was 9.51 (95%CI 8.35-10.83), 6.04 (95%CI 4.74-7.71) and 25.0 (95% CI 15.96-39.22) per 1,000 PYFU, among patients with eGFR >90, 60-89, <60 ml/min, respectively, with an unadjusted HR of 2.49 (95%CI 1.56-3.97) for patients with eGFR <60 ml/min and 0.68 (95%CI 0.52-0.90) for eGFR 60-89. The risk of AIDS events was significantly lower in mild renal dysfunction group even after adjustment for HIV-related characteristics. Our data confirm that impaired renal function is an important risk marker for CCVD events in the HIV-population; importantly, even those with mild renal impairment (90<60)&gt

Numerical simulations of Mediterranean heavy precipitation events with the WRF model: A verification exercise using different approaches

Abstract An intercomparison of eight different microphysics parameterization schemes available in the Weather Research and Forecasting (WRF) model and an analysis of the sensitivity of predicted precipitation to horizontal resolution are presented in this paper. Three different case studies, corresponding to severe rainfall events occurred over the Liguria region (Italy) between October 2010 and November 2011, have been considered. In all the selected cases, the formation of a quasi-stationary mesoscale convective system over the Ligurian Sea interacting with local dynamical effects (orographically-induced low-level wind and temperature gradients) played a crucial role in the generation of severe precipitations. The data set used to evaluate model performances has been extracted from the official regional network, composed of about 150 professional WMO-compliant stations. Two different strategies have been exploited to assess the model skill in forecasting precipitation: a traditional approach, where forecasts and observations are matched on a point-by-point basis, and an object-based method where model success is based on the correct localization and intensity of precipitation patterns. This last method overcomes the known fictitious models performance degradation for increasing spatial resolution. As remarkable results of this analysis, a clear role of horizontal resolution on the model performances accompanied by the identification of a set of best-performing parameterization schemes emerge. The outcomes presented here offer important suggestions for operational weather prediction systems under potentially dangerous heavy precipitations triggered by the mechanisms discussed throughout the paper

Why NRMSE is not completely reliable for forecast/hindcast model test performances

We investigate the reliability of the statistical error indicator NRMSE (Nor- malized Root Mean Square Error) as an index of the performances of numerical simulation for wave forecasting. This widespread indicator, also known as Scat- ter Index, is defined as (S i 12 O i ) 2 O i 2 NRMSE = (1) where O i are observed values and S i are simulated values. A small value of NRMSE identifies a numerical simulation in good agreement with the field ob- servations. We show that NRMSE tends to be systematically smaller (better) for simulations affected by negative bias. This behaviour is verified comparying forty three different parameterizations of the source terms within a process of validation of the wave model Wavewatch III (WWIII) in the Mediterranean sea. In our analysis we show that NRMSE can be orthogonally decomposed in two contributions related to the average bias and to the scatter of simulated values around the observed values, respectively. Results form numercial sim- ulations show how these contributions appear statistically dependent on each other because positive or negative amplifications of the simulation average gen- erally involve a corresponding amplification in the scatter of simulated values. An almost linear dependency between bias and scatter can be found and it implies that minimum NRMSE simulation and unbiased simulation do not co- incide, since minimum NRMSE requires a minimum squared sum of its bias and scatter contributions. This finding suggests that a lower value of NRMSE is not always associated to the best results, and that this indicator is not always reliable if used to find the best simulation. This flaw of NRMSE indicator is already known in literature, and some authors proposed the usage of corrected indicators to overcome it (e.g. Hanna and Heinolds, 1985[1])

The role of the sea on the flash floods events over Liguria (northwestern Italy)

The sensitivity to sea surface temperature (SST) of small-scale, flood-causing convective systems in Mediterranean coastal areas is analyzed by means of mesoscale numerical simulations. Two different SST initializations are considered: a coarse field provided by a global atmospheric model and a high-resolution multisatellite analysis. Quantitative precipitation forecasts are evaluated for a number of recent severe rainfall episodes in Liguria (northwestern Italy). In several cases, using a higher-resolution SST leads to more realistic precipitation estimates in the forecasting range 36-48 h. In the shorter range, the satellite SST has a limited, or even negative, impact, due to the relatively slow adjustment of initial atmospheric fields. In one case, the satellite SST is beneficial for the only run forced with accurate large-scale initial conditions. The results of this work suggest that a potentially significant improvement in severe precipitation forecasting in the Mediterranean could be achieved using realistic small-scale SST fields

Impact of Model Resolution and Initial/Boundary Conditions in Forecasting Flood-Causing Precipitations

In late summer and autumn Mediterranean coastal regions are quite regularly affected by small-scale, flood-producing convective systems. The complexity of mesoscale triggering mechanisms, related to low-level temperature gradients, moisture convergence, and topographic effects contributes to limit the predictability of such phenomena. In the present work, a severe convection episode associated to a flash flood occurred in Cannes (southern France) in October 2015, is investigated by means of numerical simulations with a state-of-the-art nonhydrostatic mesoscale model. In the modelling configuration operational at the University of Genoa precipitation maxima were underestimated and misplaced. The impact of model resolution as well as initial and boundary conditions on the quantitative precipitation forecasts is analyzed and discussed. In particular, the effect of ingesting a high-resolution satellite-derived sea surface temperature field is proven to be beneficial in terms of precipitation intensity and localization, especially when also associated with the most accurate lateral boundary conditions

An integrated PM2.5 source apportionment study: Positive Matrix Factorisation vs. the Chemical Transport Model CAMx

none8Receptor and Chemical Transport Models are commonly used tools in source apportionment studies, even if different expertise is required. We describe an experiment using both approaches to apportion the PM2.5 (i.e., particulate matter with aerodynamic diameters below 2.5 m) sources in the city of Genoa (Italy). A sampling campaign was carried out to collect PM2.5 samples daily for approximately six month during 2011 in three sites. The subsequent compositional analyses included the speciation of elements, major ions and both organic and elemental carbon; these data produced a large database for receptor modelling through Positive Matrix Factorisation (PMF). In the same period, a meteorological and air quality modelling system was implemented based on the mesoscale numerical weather prediction model WRF and the chemical transport model CAMx to obtain meteorological and pollutant concentrations up to a resolution of 1.1 km. The source apportionment was evaluated by CAMx over the same period that was used for the monitoring campaign using the Particulate Source Apportionment Technology tool. Even if the source categorisations were changed (i.e., groups of time-correlated compounds in PMF vs. activity categories in CAMx), the PM2.5 source apportionment by PMF and CAMx produced comparable results. The different information provided by the two approaches (e.g., real-world factor profile by PMF and apportionment of a secondary aerosol by CAMx) was used jointly to elucidate the composition and origin of PM2.5 and to develop a more general methodology. When studying the primary and secondary components of PM, the main anthropogenic sources in the area were road transportation, energy production/industry and maritime emissions, accounting for 40% - 50%, 20% - 30% and 10% - 15%, of PM2.5, respectively.M. C. Bove; P. Brotto; F. Cassola; E. Cuccia; D. Massabò; A. Mazzino; A. Piazzalunga; P. PratiBove, MARIA CHIARA; Brotto, Paolo; Cassola, Federico; Cuccia, ELEONORA SIMONA; Massabo', Dario; Mazzino, Andrea; A., Piazzalunga; Prati, Paol