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Olla P.,CNR Institute of atmospheric Sciences and Climate
Physical Review E - Statistical, Nonlinear, and Soft Matter Physics | Year: 2010

The concept of preferential concentration in the transport of inertial particles by random velocity fields is extended to account for the possibility of zero correlation time and compressibility of the velocity field. It is shown that, in the case of an uncorrelated in time random velocity field, preferential concentration takes the form of a condition on the field history leading to the current particle positions. This generalized form of preferential concentration appears to be a necessary condition for clustering in the uncorrelated in time case. The standard interpretation of preferential concentration is recovered considering local time averages of the velocity field. In the compressible case, preferential concentration occurs in regions of negative divergence of the field. In the incompressible case, it occurs in regions of simultaneously high strain and negative field skewness. © 2010 The American Physical Society.

Olla P.,CNR Institute of atmospheric Sciences and Climate
Physical Review E - Statistical, Nonlinear, and Soft Matter Physics | Year: 2010

The possibility of microscopic swimming by extraction of energy from an external flow is discussed, focusing on the migration of a simple trimer across a linear shear flow. The geometric properties of swimming, together with the possible generalization to the case of a vesicle, are analyzed. The mechanism of energy extraction from the flow appears to be the generalization to a discrete swimmer of the tank-treading regime of a vesicle. The swimmer takes advantage of the external flow by both extracting energy for swimming and "sailing" through it. The migration velocity is found to scale linearly in the stroke amplitude, and not quadratically as in a quiescent fluid. This effect turns out to be connected with the nonapplicability of the scallop theorem in the presence of external flow fields. © 2010 The American Physical Society.

Nardelli B.B.,CNR Institute of atmospheric Sciences and Climate
Journal of Atmospheric and Oceanic Technology | Year: 2012

A novel technique for the high-resolution interpolation of in situ sea surface salinity (SSS) observations is developed and tested. The method is based on an optimal interpolation (OI) algorithm that includes satellite sea surface temperature (SST) in the covariance estimation. The covariance function parameters (i.e., spatial, temporal, and thermal decorrelation scales) and the noise-to-signal ratio are determined empirically, by minimizing the root-mean-square error and mean error with respect to fully independent validation datasets. Both in situ observations and simulated data extracted from a numerical model output are used to run these tests. Different filters are applied to sea surface temperature data in order to remove the large-scale variability associated with air-sea interaction, because a high correlation between SST and SSS is expected only at small scales. In the tests performed on in situ observations, the lowest errors are obtained by selecting covariance decorrelation scales of 400 km, 6 days, and 2.758C, respectively, a noise-to-signal ratio of 0.01 and filtering the scales longer than 1000 km in the SST time series. This results in a root-mean-square error of;0.11 g kg21 and a mean error of;0.01 g kg21, that is, reducing the errors by;25% and;60%, respectively, with respect to the first guess. © 2012 American Meteorological Society.

Palazzi E.,CNR Institute of atmospheric Sciences and Climate | Von Hardenberg J.,CNR Institute of atmospheric Sciences and Climate | Provenzale A.,CNR Institute of atmospheric Sciences and Climate
Journal of Geophysical Research: Atmospheres | Year: 2013

We study the properties of precipitation in the Hindu-Kush Karakoram Himalaya (HKKH) region using currently available data sets. We consider satellite rainfall estimates (Tropical Rainfall Measuring Mission), reanalyses (ERA-Interim), gridded in situ rain gauge data (Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources, Climate Research Unit, and Global Precipitation Climatology Centre), and a merged satellite and rain gauge climatology (Global Precipitation Climatology Project). The data are compared with simulation results from the global climate model EC-Earth. All data sets, despite having different resolutions, coherently reproduce the mean annual cycle of precipitation in the western and eastern stretches of the HKKH. While for the Himalaya only a strong summer precipitation signal is present, associated with the monsoon, the data indicate that the Hindu-Kush Karakoram, which is exposed to midlatitude "western weather patterns", receives water inputs in winter. Time series of seasonal precipitation confirm that the various data sets provide a consistent measurement of interannual variability for the HKKH. The longest observational data sets indicate a statistically significant decreasing trend in Himalaya during summer. None of the data sets gives statistically significant precipitation trends in Hindu-Kush Karakoram during winter. Precipitation data from EC-Earth are in good agreement with the climatology of the observations (rainfall distribution and seasonality). The evolution of precipitation under two different future scenarios (RCP 4.5 and RCP 8.5) reveals an increasing trend over the Himalaya during summer, associated with an increase in wet extremes and daily intensity and a decrease in the number of rainy days. Unlike the observations, the model shows an increasing precipitation trend also in the period 1950-2009, possibly as a result of the poor representation of aerosols in this type of GCMs. Key Points Precipitation Hydrological cycle Himalaya EC-Earth. © 2012 American Geophysical Union. All Rights Reserved.

Marullo S.,ENEA | Artale V.,ENEA | Santoleri R.,CNR Institute of atmospheric Sciences and Climate
Journal of Climate | Year: 2011

Two sea surface temperature (SST) time series, the Extended Reconstructed SST version 3 (ERSST.v3) and the Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST), are used to investigate SST multidecadal variability in the Mediterranean Sea and to explore possible connections with other regions of the global ocean. The consistency between these two time series and the original International Comprehensive Ocean-Atmosphere Dataset version 2.5 (ICOADS 2.5) over the Mediterranean Sea is investigated, evaluating differences from monthly to multidecadal scales. From annual to longer time scales, the two time series consistently describe the same trends and multidecadal oscillations and agree with Mediterranean ICOADS SSTs. At monthly time scales the two time series are less consistent with each other because of the evident annual cycle that characterizes their difference. The subsequent analysis of the Mediterranean annual SST time series, based on lagged-correlation analysis, multitaper method (MTM), and singular spectral analysis (SSA), revealed the presence of a significant oscillation with a period of about 70 yr, very close to that of the Atlantic multidecadal oscillation (AMO). An extension of the analysis to other World Ocean regions confirmed that the predominance of this multidecadal signal with respect to longer period trends is a unique feature of the Mediterranean and North Atlantic Ocean, where it reaches its maximum at subpolar latitudes. Signatures of multidecadal oscillations are also found in the global SST time series after removing centennial and longer-term components. The analysis also reveals that Mediterranean SST and North Atlantic indices are significantly correlated and coherent for periods longer than about 40 yr. For time scales in the range 40-55 yr the coherence between the Mediterranean and subpolar gyre temperatures is higher than the coherence between the Mediterranean SST and North Atlantic Oscillation (NAO) or AMO. Finally, the results of the analysis are discussed in the light of possible climate mechanisms that can couple the Mediterranean Sea with the North Atlantic and the Global Ocean. © 2011 American Meteorological Society.

Federico S.,CNR Institute of atmospheric Sciences and Climate
Natural Hazards and Earth System Science | Year: 2011

Since 2005, one-hour temperature forecasts for the Calabria region (southern Italy), modelled by the Regional Atmospheric Modeling System (RAMS), have been issued by CRATI/ISAC-CNR (Consortium for Research and Application of Innovative Technologies/Institute for Atmospheric and Climate Sciences of the National Research Council) and are available online at meteo.crati.it/ previsioni.html (every six hours). Beginning in June 2008, the horizontal resolution was enhanced to 2.5 km. In the present paper, forecast skill and accuracy are evaluated out to four days for the 2008 summer season (from 6 June to 30 September, 112 runs). For this purpose, gridded high horizontal resolution forecasts of minimum, mean, and maximum temperatures are evaluated against gridded analyses at the same horizontal resolution (2.5 km). Gridded analysis is based on Optimal Interpolation (OI) and uses the RAMS first-day temperature forecast as the background field. Observations from 87 thermometers are used in the analysis system. The analysis error is introduced to quantify the effect of using the RAMS first-day forecast as the background field in the OI analyses and to define the forecast error unambiguously, while spatial interpolation (SI) analysis is considered to quantify the statistics' sensitivity to the verifying analysis and to show the quality of the OI analyses for different background fields. Two case studies, the first one with a low (less than the 10th percentile) root mean square error (RMSE) in the OI analysis, the second with the largest RMSE of the whole period in the OI analysis, are discussed to show the forecast performance under two different conditions. Cumulative statistics are used to quantify forecast errors out to four days. Results show that maximum temperature has the largest RMSE, while minimum and mean temperature errors are similar. For the period considered, the OI analysis RMSEs for minimum, mean, and maximum temperatures vary from 1.8, 1.6, and 2.0 °C, respectively, for the first-day forecast, to 2.0, 1.9, and 2.6 °C, respectively, for the fourth-day forecast. Cumulative statistics are computed using both SI and OI analysis as reference. Although SI statistics likely overestimate the forecast error because they ignore the observational error, the study shows that the difference between OI and SI statistics is less than the analysis error. The forecast skill is compared with that of the persistence forecast. The Anomaly Correlation Coefficient (ACC) shows that the model forecast is useful for all days and parameters considered here, and it is able to capture day-to-day weather variability. The model forecast issued for the fourth day is still better than the first-day forecast of a 24-h persistence forecast, at least for mean and maximum temperature. The impact of using the RAMS first-day forecast as the background field in the OI analysis is quantified by comparing statistics computed with OI and SI analyses. Minimum temperature is more sensitive to the change in the analysis dataset as a consequence of its larger representative error. © Author(s) 2011.

Paparella F.,University of Salento | Von Hardenberg J.,CNR Institute of atmospheric Sciences and Climate
Physical Review Letters | Year: 2012

We report on high-resolution, three-dimensional, high Rayleigh number, and low density ratio numerical simulations of fingering convection. We observe a previously unreported phenomenon of self-organization of fingers that cluster together to form larger-scale coherent structures. The flow ultimately forms density staircases, alternating well-mixed regions with fingering convective zones. We give evidence that the mechanical mixing induced by the clusters forms the staircases with a mechanism analogous to staircase formation in a stably stratified, nonconvective, stirred fluid. © 2012 American Physical Society.

Olla P.,CNR Institute of atmospheric Sciences and Climate
Physical Review E - Statistical, Nonlinear, and Soft Matter Physics | Year: 2012

The phenomenon of spatial clustering induced by death and reproduction in a population of anomalously diffusing individuals is studied analytically. The possibility of social behaviors affecting the migration strategies has been taken into exam, in the case that anomalous diffusion is produced by means of a continuous time random walk (CTRW). In the case of independently diffusing individuals, the dynamics appears to coincide with that of (dying and reproducing) Brownian walkers. In the strongly social case, the dynamics coincides with that of nonmigrating individuals. In both limits, the growth rate of the fluctuations becomes independent of the Hurst exponent of the CTRW. The social behaviors that arise when transport in a population is induced by a spatial distribution of random traps have been analyzed. © 2012 American Physical Society.

Olla P.,CNR Institute of atmospheric Sciences and Climate
Physical Review E - Statistical, Nonlinear, and Soft Matter Physics | Year: 2014

The problem of optimal microscopic swimming in a noisy environment is analyzed. A simplified model in which propulsion is generated by the relative motion of three spheres connected by immaterial links has been considered. We show that an optimized noisy microswimmer requires less power for propulsion (on average) than an optimal noiseless counterpart migrating with identical mean velocity and swimming stroke amplitude. We also show that noise can be used to overcome some of the limitations of the scallop theorem and have a swimmer that is able to propel itself with control over just one degree of freedom. © 2014 American Physical Society.

Federico S.,CNR Institute of atmospheric Sciences and Climate
Atmospheric Measurement Techniques | Year: 2013

This paper presents the current status of development of a three-dimensional variational data assimilation system (3D-Var). The system can be used with different numerical weather prediction models, but it is mainly designed to be coupled with the Regional Atmospheric Modelling System (RAMS). Analyses are given for the following parameters: zonal and meridional wind components, temperature, relative humidity, and geopotential height.

Important features of the data assimilation system are the use of incremental formulation of the cost function, and the representation of the background error by recursive filters and the eigenmodes of the vertical component of the background error covariance matrix. This matrix is estimated by the National Meteorological Center (NMC) method.

The data assimilation and forecasting system is applied to the real context of atmospheric profiling data assimilation, and in particular to the short-term wind prediction. The analyses are produced at 20 km horizontal resolution over central Europe and extend over the whole troposphere. Assimilated data are vertical soundings of wind, temperature, and relative humidity from radiosondes, and wind measurements of the European wind profiler network.

Results show the validity of the analyses because they are closer to the observations (lower root mean square error (RMSE)) compared to the background (higher RMSE), and the differences of the RMSEs are in agreement with the data assimilation settings.

To quantify the impact of improved initial conditions on the short-term forecast, the analyses are used as initial conditions of three-hours forecasts of the RAMS model. In particular two sets of forecasts are produced: (a) the first uses the ECMWF analysis/forecast cycle as initial and boundary conditions; (b) the second uses the analyses produced by the 3D-Var as initial conditions, then it is driven by the ECMWF forecast.

The improvement is quantified by considering the horizontal components of the wind, which are measured at asynoptic times by the European wind profiler network. The results show that the RMSE is effectively reduced at the short range. The results are in agreement with the set-up of the numerical experiment. © 2013 Author(s).

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