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David Small, Shafiqul Islam, and Mathew Barlow


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.

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Dyi-Huey Chang, Le Jiang, and Shafiqul Islam


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.

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Wahid Palash, Yudan Jiang, Ali S. Akanda, David L. Small, Amin Nozari, and Shafiqul Islam


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.

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