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  • Author or Editor: F. Valero x
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E. Hernández, F. Martín, and F. Valero


Two different aspect concerning the state-space modeling for atmospheric pollution are dealt with separately in this paper: (i) the treatment of the advection-diffusion equation and (ii) the use of time series analysis.

A method for forecasting the pollutant concentration is proposed. It is based on discretizing the rectified advection-diffusion (RAD) equation by means of a finite-differences scheme and transforming the resultant numerical algorithm into a state-space form. The state-space model uses an optimum estimator algorithm called the Kalman filter to forecast the air pollutant spatial distribution. The state-space modeling defines two basic equations: system state and measurement equations.

With regard to the second aspect, state-space methodology is applied to forecast atmospheric aerosol lead (Pb) concentration including wind speed and wind direction as exogenous variables of the models. Data of daily aerosol Pb concentration, wind speed, and wind direction are available for a single site in a semiurban area of Madrid. Previously, wind direction data are scored by applying the direct gradient method related to aerosol Pb concentrations. The lowest scores are those of the west, northwest and north sectors and the score of the calm day is the highest. An adaptive space-state model is selected as the best predictive model of the stochastic models proposed in this paper. One-day-lagged wind speed influences strongly the time variation of aerosol Pb concentration.

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K. N. Liou, S. C. Ou, Y. Takano, F. P. J. Valero, and T. P. Ackerman


A dual-channel retrieval technique involving the water vapor band at 6.5 μm and the window region at 10.5 gm has been developed to infer the temperature and emissivity of tropical anvils. This technique has been applied to data obtained from the ER-2 narrow field-of-view radiometers during two flights in the field observation of the Stratosphere-Troposphere Exchange Project (STEP) near Damn, Australia, January-February 1987. The retrieved cloud temperatures are between 190 and 240 K, while the cloud emissivities derived from the retrieval algorithm range from about 0.2 to 1. Moreover, the visible optical depths have been obtained from the cloud emissivity through a theoretical parameterization with values of 0.5-10. A significant portion of tropical cirrus clouds are found to have optical depths greater than about 6. Because of the parameterization, the present technique is unable to precisely determine the optical depth values for optically thick cirrus clouds.

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P. A. Jiménez, E. García-Bustamante, J. F. González-Rouco, F. Valero, J. P. Montávez, and J. Navarro


Daily wind variability in the Comunidad Foral de Navarra in northern Spain was studied using wind observations at 35 locations to derive subregions with homogeneous temporal variability. Two different methodologies based on principal component analysis were used to regionalize: 1) cluster analysis and 2) the rotation of the selected principal components. Both methodologies produce similar results and lead to regions that are in general agreement with the topographic features of the terrain. The meridional wind variability is similar in all subregions, whereas zonal wind variability is responsible for differences between them. The spectral analysis of wind variability within each subregion reveals a dominant annual cycle and the varying presence of higher-frequency contributions in the subregions. The valley subregions tend to present more variability at high frequencies than do higher-altitude sites. Last, the influence of large-scale dynamics on regional wind variability is explored by studying connections between wind in each subregion and sea level pressure fields. The results of this work contribute to the characterization of wind variability in a complex terrain region and constitute a framework for the validation of mesoscale model wind simulations over the region.

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