Showing papers by "Juan B. Valdés published in 1996"
TL;DR: In this paper, the authors examined the current predictive capability of general circulation models linked with macro-scale and landscape-scale hydrologic models that simulate regional and local hydrological regimes under global warming scenarios.
275 citations
TL;DR: In this article, a multidimensional stochastic precipitation model with major emphasis on its spectral structure is proposed, which is based on the autoregressive process considering advection and diffusion, the dominant statistical and physical characteristics of the precipitation field propagation.
Abstract: A multidimensional stochastic precipitation model with major emphasis on its spectral structure is proposed. As a hyperbolic type of stochastic partial differential equation, this model is characterized by a small set of easily estimable parameters. These characteristics are similar to those of the noise-forced diffusive precipitation model, but the representation of the physical and statistical features of the precipitation field is similar to that of the Waymire–Gupta–Rodriguez-Iturbe (WGR) precipitation model. The derivation was based on the autoregressive process considering advection and diffusion, the dominant statistical and physical characteristics of the precipitation field propagation. The model spectrum showed a good match with the Global Atlantic Tropical Experiment spectrum. This model was also compared with the WGR model and the noise-forced diffusive precipitation model both analytically and through applications such as the sampling error estimation from spaceborne sensors and rain gauges. The sampling error from spaceborne sensors based on the proposed model was similar to that of the noise-forced diffusive precipitation model, but much smaller than that of the WGR model. Similar results were also obtained in the estimation of the sampling error from rain gauges.
24 citations
TL;DR: In this article, the analysis of rainfall data based on radar echoes collected in the vicinity of Darwin, Australia, during special observation periods in 1988 was conducted to estimate the scale parameters (such as timescale and length scale) present in the rainfall data, which are important in parameterizing many stochastic rainfall models.
Abstract: This paper presents the analysis of rainfall data based on radar echoes collected in the vicinity of Darwin, Australia, during special observation periods in 1988. The study was conducted to estimate the scale parameters (such as timescale and length scale) present in the rainfall data, which are important in parameterizing many stochastic rainfall models. Another equally important issue addressed here is that of sampling errors in the rainfall observations from space. To address these issues, precipitation analyses were conducted in two and three dimensions. To perform two-dimensional analyses, precipitation fields were averaged along one dimension (i.e., x, y, or t) at a time. Three-dimensional analyses were performed on the complete time series of temporal-hourly averaged spatially distributed observations. Important results obtained from the two-dimensional analyses include isotropy of precipitation fields in space, variations in the rainfall data primarily in time, and length scales of 50 km (for Darwin I) and 52 km (for Darwin II) in both (i.e., north-south and west-east) directions. Length scales were estimated using the results from two-dimensional analyses of the data. Time-domain correlograms obtained for the time series of area-averaged precipitation were used to estimate the timescales (6 hours for Darwin I and 8 hours for Darwin II). These results could be used in simulation studies using various stochastic rainfall models. The estimates of space-time spectra obtained in three-dimensional analyses were used to evaluate the sampling errors. The sampling errors thus estimated using these data sets were quite significant (about 25% to 30% for a 12-hour sampling interval). Sampling errors were as high as 65% for Darwin I and 45% for Darwin II for a 24-hour sampling time interval, which is a possibility if the Defense Meteorology Satellite Program (DMSP) satellite is used. These results are useful in satellite mission planning activities.
7 citations
TL;DR: In this article, a new class of nonlinear control charts which respond quickly to small shifts and jump patterns in tme series is presented, where the underlying disturbance models for the control charts are nonlinear extensions of the IMA(1,1) model.
Abstract: This paper presents a new class of nonlinear control charts which respond quickly to small shifts and jump patterns in tme series. The underlying disturbance models for the control charts are nonlinear extensions of the IMA(1,1) model. The Kalman filtering algorithm generates Bayesian estimates of the process level for the control chart plotting. The single-parameter chart is identical to the EWMA, while the two- and three-parameter designs are much more effective in detecting small shifts mixed with local trends. The nonlinear control charting scheme is also capable of detecting a mean shift in independent observation.
1 citations