Ensemble representation of uncertainty in Lagrangian satellite rainfall estimates

A new algorithm called Lagrangian Simulation (LSIM) has been developed that enables the interpolation uncertainty present in Lagrangian satellite rainfall algorithms such as the Climate Prediction Center (CPC) morphing technique (CMORPH) to be characterized using an ensemble product. The new algorithm generates ensemble sequences of rainfall fields conditioned on multiplatform multisensor microwave satellite data, demonstrating a conditional simulation approach that overcomes the problem of discontinuous uncertainty fields inherent in this type of product. Each ensemble member is consistent with the information present in the satellite data, while variation between members is indicative of uncertainty in the rainfall retrievals. LSIM is based on the combination of a Markov weather generator, conditioned on both previous and subsequent microwave measurements, and a global optimization procedure that uses simulated annealing to constrain the generated rainfall fields to display appropriate spatial structures. The new algorithm has been validated over a region of the continental United States and has been shown to provide reliable estimates of both point uncertainty distributions and wider spatiotemporal structures

Detecting abrupt changes in the spectra of high-energy astrophysical sources

Variable-intensity astronomical sources are the result of complex and often extreme physical processes. Abrupt changes in source intensity are typically accompanied by equally sudden spectral shifts, that is, sudden changes in the wavelength distribution of the emission. This article develops a method for modeling photon counts collected from observation of such sources. We embed change points into a marked Poisson process, where photon wavelengths are regarded as marks and both the Poisson intensity parameter and the distribution of the marks are allowed to change. To the best of our knowledge, this is the first effort to embed change points into a marked Poisson process. Between the change points, the spectrum is modeled nonparametrically using a mixture of a smooth radial basis expansion and a number of local deviations from the smooth term representing spectral emission lines. Because the model is over-parameterized, we employ an ℓ1ℓ1 penalty. The tuning parameter in the penalty and the number of change points are determined via the minimum description length principle. Our method is validated via a series of simulation studies and its practical utility is illustrated in the analysis of the ultra-fast rotating yellow giant star known as FK Com

Investment incentives in endogenously growing economies

En este trabajo se presenta un modelo estocastico de crecimiento endogeno con costes de ajuste. El modelo es utilizado para analizar teorica y numericamente la incidencia de cambios en la tasa del impuesto sobre sociedades y en los creditos fiscales a la inversion. Asi, se obtiene que los costes de ajuste moderan sensiblemente los efectos distorsionadores de estos instrumentos fiscales sobre la tasa de crecimiento de la economia. Por su parte, la existencia de efectos inducidos sobre la tasa de crecimiento de los cambios en la imposicion incrementa la influencia negativa de aumentos en el impuesto sobre sociedades y reducciones en los subsidios a la inversion sobre el valor de mercado de la empresa por unidad de capital. Este resultado, junto con los efectos de la imposicion sobre la tasa de crecimiento del valor de la empresa, contradice los resultados encontrados en los modelos neoclasicos convencionales donde los cambios que la imposicion produce sobre las decisiones optimas de inversion no afectan al ratio valor de mercado-valor de reposicio