Search Results
You are looking at 1 - 7 of 7 items for
- Author or Editor: Shafiqul Islam x
- Refine by Access: All Content x
Abstract
While there is growing evidence that the main contribution to trends in U.S. precipitation occurs during fall, most studies of seasonal precipitation have focused on winter or summer. Here, the leading mode of fall precipitation variability over North America is isolated from multiple data sources and connected to a hemispheric-scale circulation pattern. Over North America, the leading mode of fall precipitation variability in both station-based and satellite-blended data is a tripole that links fall precipitation anomalies in southern Alaska, the central United States, and eastern Canada. This mode is part of a larger pattern of alternating wet and dry anomalies stretching from the western Pacific to the North Atlantic. Dynamically, the precipitation anomalies are closely associated with changes to regional-scale moisture transport that are, in turn, linked to two independently identified hemispheric-scale wave patterns that are one-quarter wavelength out of phase (i.e., in quadrature) and resemble the circumglobal teleconnection.
Abstract
While there is growing evidence that the main contribution to trends in U.S. precipitation occurs during fall, most studies of seasonal precipitation have focused on winter or summer. Here, the leading mode of fall precipitation variability over North America is isolated from multiple data sources and connected to a hemispheric-scale circulation pattern. Over North America, the leading mode of fall precipitation variability in both station-based and satellite-blended data is a tripole that links fall precipitation anomalies in southern Alaska, the central United States, and eastern Canada. This mode is part of a larger pattern of alternating wet and dry anomalies stretching from the western Pacific to the North Atlantic. Dynamically, the precipitation anomalies are closely associated with changes to regional-scale moisture transport that are, in turn, linked to two independently identified hemispheric-scale wave patterns that are one-quarter wavelength out of phase (i.e., in quadrature) and resemble the circumglobal teleconnection.
Abstract
There have been numerous attempts to detect the presence of deterministic chaos by estimating the correlation dimension. The values of reported correlation dimension for various geophysical time series vary between 1.3 and virtually infinity (i.e., no saturation). It is pointed out that analyzing variables that depend on physical constraints and thresholds, like precipitation, may lead to underestimation of the correlation dimension of the underlying dynamical system.
Abstract
There have been numerous attempts to detect the presence of deterministic chaos by estimating the correlation dimension. The values of reported correlation dimension for various geophysical time series vary between 1.3 and virtually infinity (i.e., no saturation). It is pointed out that analyzing variables that depend on physical constraints and thresholds, like precipitation, may lead to underestimation of the correlation dimension of the underlying dynamical system.
Abstract
The behavior of a numerical cloud model is investigated in terms of its sensitivity to perturbations with two kinds of lateral boundary conditions: 1) with cyclic lateral boundary conditions, the model is sensitive to many aspects of its structure, including a very small potential temperature perturbation at only one grid point, changes in time step, and small changes in parameters such as the autoconversion rate from cloud water to rainwater and the latent heat of vaporization; 2) with prescribed lateral boundary conditions, growth and decay of perturbations are highly dependent on the flow conditions inside the domain. It is shown that under relatively uniform (unidirectional) advection across the domain, the perturbations will decay. On the other hand, convergence, divergence, or, in general, flow patterns with changing directions support error growth. This study shows that it is the flow structure inside the model domain that is important in determining whether the prescribed lateral boundary conditions will result in decaying or growing perturbations. The numerical model is inherently sensitive to initial perturbations, but errors can decay due to advection of information from lateral boundaries across the domain by uniform flow. This result provides one explanation to the reported results in earlier studies showing both error growth and decay.
Abstract
The behavior of a numerical cloud model is investigated in terms of its sensitivity to perturbations with two kinds of lateral boundary conditions: 1) with cyclic lateral boundary conditions, the model is sensitive to many aspects of its structure, including a very small potential temperature perturbation at only one grid point, changes in time step, and small changes in parameters such as the autoconversion rate from cloud water to rainwater and the latent heat of vaporization; 2) with prescribed lateral boundary conditions, growth and decay of perturbations are highly dependent on the flow conditions inside the domain. It is shown that under relatively uniform (unidirectional) advection across the domain, the perturbations will decay. On the other hand, convergence, divergence, or, in general, flow patterns with changing directions support error growth. This study shows that it is the flow structure inside the model domain that is important in determining whether the prescribed lateral boundary conditions will result in decaying or growing perturbations. The numerical model is inherently sensitive to initial perturbations, but errors can decay due to advection of information from lateral boundaries across the domain by uniform flow. This result provides one explanation to the reported results in earlier studies showing both error growth and decay.
Abstract
This study evaluates the issues of soil moisture coupling on the partitioning of surface fluxes at the diurnal timescale over a mesoscale domain from the First International Satellite Land Surface Climatology Project Field Experiment (FIFE) in Kansas. A state-of-the-art atmospheric model (the Fifth-Generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model, or MM5) is used as a control run in which soil moisture is prescribed by a time-invariant as well as time-varying moisture availability function. Then, in a coupled model simulation, the atmospheric model is coupled with a detailed land surface model. Three days are simulated with progressively smaller surface soil moisture conditions to identify the influence of interactive soil moisture on surface fluxes partitioning at the diurnal timescale. Preliminary results suggest that, for days with wetter surface soil moisture conditions and moderately high wind speed, a time-variant interactive soil moisture representation provides a more accurate partitioning of surface fluxes. For drier surface conditions with relatively low wind speed, a constant soil moisture availability function may be adequate.
Abstract
This study evaluates the issues of soil moisture coupling on the partitioning of surface fluxes at the diurnal timescale over a mesoscale domain from the First International Satellite Land Surface Climatology Project Field Experiment (FIFE) in Kansas. A state-of-the-art atmospheric model (the Fifth-Generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model, or MM5) is used as a control run in which soil moisture is prescribed by a time-invariant as well as time-varying moisture availability function. Then, in a coupled model simulation, the atmospheric model is coupled with a detailed land surface model. Three days are simulated with progressively smaller surface soil moisture conditions to identify the influence of interactive soil moisture on surface fluxes partitioning at the diurnal timescale. Preliminary results suggest that, for days with wetter surface soil moisture conditions and moderately high wind speed, a time-variant interactive soil moisture representation provides a more accurate partitioning of surface fluxes. For drier surface conditions with relatively low wind speed, a constant soil moisture availability function may be adequate.
Abstract
In this paper, the authors evaluate the significance of multidecadal trends in seasonal-mean summertime precipitation and precipitation characteristics over the southwestern United States using stochastic, chain-dependent daily rainfall models. Unlike annual-mean precipitation, trends during the summertime monsoon, covering the period 1931–2000, indicate an overall increase in seasonal precipitation, the number of rainfall events, and the coverage of rainfall events in peripheral regions north of the “core” monsoon area of Arizona and western New Mexico. In addition, there is an increasing trend in intense storm activity and a decreasing trend in extreme dry-spell lengths. Over other regions of the domain, there are no discernible trends found in any of the observed characteristics. These trends are robust to the choice of start dates and, in the case of seasonal-mean precipitation, appear to persist into the current century.
Abstract
In this paper, the authors evaluate the significance of multidecadal trends in seasonal-mean summertime precipitation and precipitation characteristics over the southwestern United States using stochastic, chain-dependent daily rainfall models. Unlike annual-mean precipitation, trends during the summertime monsoon, covering the period 1931–2000, indicate an overall increase in seasonal precipitation, the number of rainfall events, and the coverage of rainfall events in peripheral regions north of the “core” monsoon area of Arizona and western New Mexico. In addition, there is an increasing trend in intense storm activity and a decreasing trend in extreme dry-spell lengths. Over other regions of the domain, there are no discernible trends found in any of the observed characteristics. These trends are robust to the choice of start dates and, in the case of seasonal-mean precipitation, appear to persist into the current century.
Abstract
A general framework has been developed to study the predictability of space–time averages of mesoscale rainfall in the tropics. A comparative ratio between the natural variability of the rainfall process and the prediction error is used to define the predictability range. The predictability of the spatial distribution of precipitation is quantified by the cross correlation between the control and the perturbed rainfall fields. An upper limit of prediction error, called normalized variability, has been derived as a function of space–time averaging. Irrespective of the type and amplitude of perturbations, a space–time averaging set of 25 km2–15 min (or larger time averaging) is found to be necessary to limit the error growth up to or below the prescribed large-scale mean rainfall.
Abstract
A general framework has been developed to study the predictability of space–time averages of mesoscale rainfall in the tropics. A comparative ratio between the natural variability of the rainfall process and the prediction error is used to define the predictability range. The predictability of the spatial distribution of precipitation is quantified by the cross correlation between the control and the perturbed rainfall fields. An upper limit of prediction error, called normalized variability, has been derived as a function of space–time averaging. Irrespective of the type and amplitude of perturbations, a space–time averaging set of 25 km2–15 min (or larger time averaging) is found to be necessary to limit the error growth up to or below the prescribed large-scale mean rainfall.
Abstract
A forecasting lead time of 5–10 days is desired to increase the flood response and preparedness for large river basins. Large uncertainty in observed and forecasted rainfall appears to be a key bottleneck in providing reliable flood forecasting. Significant efforts continue to be devoted to developing mechanistic hydrological models and statistical and satellite-driven methods to increase the forecasting lead time without exploring the functional utility of these complicated methods. This paper examines the utility of a data-based modeling framework with requisite simplicity that identifies key variables and processes and develops ways to track their evolution and performance. Findings suggest that models with requisite simplicity—relying on flow persistence, aggregated upstream rainfall, and travel time—can provide reliable flood forecasts comparable to relatively more complicated methods for up to 10 days lead time for the Ganges, Brahmaputra, and upper Meghna (GBM) gauging locations inside Bangladesh. Forecasting accuracy improves further by including weather-model-generated forecasted rainfall into the forecasting scheme. The use of water level in the model provides equally good forecasting accuracy for these rivers. The findings of the study also suggest that large-scale rainfall patterns captured by the satellites or weather models and their “predictive ability” of future rainfall are useful in a data-driven model to obtain skillful flood forecasts up to 10 days for the GBM basins. Ease of operationalization and reliable forecasting accuracy of the proposed framework is of particular importance for large rivers, where access to upstream gauge-measured rainfall and flow data are limited, and detailed modeling approaches are operationally prohibitive and functionally ineffective.
Abstract
A forecasting lead time of 5–10 days is desired to increase the flood response and preparedness for large river basins. Large uncertainty in observed and forecasted rainfall appears to be a key bottleneck in providing reliable flood forecasting. Significant efforts continue to be devoted to developing mechanistic hydrological models and statistical and satellite-driven methods to increase the forecasting lead time without exploring the functional utility of these complicated methods. This paper examines the utility of a data-based modeling framework with requisite simplicity that identifies key variables and processes and develops ways to track their evolution and performance. Findings suggest that models with requisite simplicity—relying on flow persistence, aggregated upstream rainfall, and travel time—can provide reliable flood forecasts comparable to relatively more complicated methods for up to 10 days lead time for the Ganges, Brahmaputra, and upper Meghna (GBM) gauging locations inside Bangladesh. Forecasting accuracy improves further by including weather-model-generated forecasted rainfall into the forecasting scheme. The use of water level in the model provides equally good forecasting accuracy for these rivers. The findings of the study also suggest that large-scale rainfall patterns captured by the satellites or weather models and their “predictive ability” of future rainfall are useful in a data-driven model to obtain skillful flood forecasts up to 10 days for the GBM basins. Ease of operationalization and reliable forecasting accuracy of the proposed framework is of particular importance for large rivers, where access to upstream gauge-measured rainfall and flow data are limited, and detailed modeling approaches are operationally prohibitive and functionally ineffective.
