ALBERT

Description: The relative roles of statistical weather preprocessing and streamflow postprocessing in hydrological ensemble forecasting at short- to medium-range forecast lead times (day 1–7) are investigated. For this purpose, a regional hydrologic ensemble prediction system (RHEPS) is developed and implemented. The RHEPS is comprised of the following components: (i) hydrometeorological observations (multisensor precipitation estimates, gridded surface temperature, and gauged streamflow); (ii) weather ensemble forecasts (precipitation and near-surface temperature) from the National Centers for Environmental Prediction 11-member Global Ensemble Forecast System Reforecast version 2 (GEFSRv2); (iii) NOAA's Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM); (iv) heteroscedastic censored logistic regression (HCLR) as the statistical preprocessor; (v) two statistical postprocessors, an autoregressive model with a single exogenous variable (ARX(1,1)) and quantile regression (QR); and (vi) a comprehensive verification strategy. To implement the RHEPS, 1 to 7 days weather forecasts from the GEFSRv2 are used to force HL-RDHM and generate raw ensemble streamflow forecasts. Forecasting experiments are conducted in four nested basins in the US Middle Atlantic region, ranging in size from 381 to 12 362 km2. Results show that the HCLR preprocessed ensemble precipitation forecasts have greater skill than the raw forecasts. These improvements are more noticeable in the warm season at the longer lead times (〉 3 days). Both postprocessors, ARX(1,1) and QR, show gains in skill relative to the raw ensemble streamflow forecasts, particularly in the cool season, but QR outperforms ARX(1,1). The scenarios that implement preprocessing and postprocessing separately tend to perform similarly, although the postprocessing-alone scenario is often more effective. The scenario involving both preprocessing and postprocessing consistently outperforms the other scenarios. In some cases, however, the differences between this scenario and the scenario with postprocessing alone are not as significant. We conclude that implementing both preprocessing and postprocessing ensures the most skill improvements, but postprocessing alone can often be a competitive alternative.

Description: The relative roles of statistical weather preprocessing and streamflow postprocessing in hydrological ensemble forecasting at short- to medium-range forecast lead times (day 1–7) are investigated. For this purpose, a regional hydrologic ensemble prediction system (RHEPS) is developed and implemented. The RHEPS is comprised by the following components: i) hydrometeorological observations (multisensor precipitation estimates, gridded surface temperature, and gauged streamflow); ii) weather ensemble forecasts (precipitation and near-surface temperature) from the National Centers for Environmental Prediction 11-member Global Ensemble Forecast System Reforecast version 2 (GEFSRv2); iii) NOAA’s Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM); iv) heteroscedastic censored logistic regression (HCLR) as the statistical preprocessor; v) two statistical postprocessors, an autoregressive model with a single exogenous variable (ARX(1,1)) and quantile regression (QR); and vi) a comprehensive verification strategy. To implement the RHEPS, 1 to 7 days weather forecasts from the GEFSRv2 are used to force HL-RDHM and generate raw ensemble streamflow forecasts. Forecasting experiments are conducted in four nested basins in the U.S. middle Atlantic region, ranging in size from 381 to 12,362 km2. Results show that the HCLR preprocessed ensemble precipitation forecasts have greater skill than the raw forecasts. These improvements are more noticeable in the warm season at the longer lead times (〉 3 days). Both postprocessors, ARX(1,1) and QR, show gains in skill relative to the raw ensemble flood forecasts but QR outperforms ARX(1,1). Preprocessing alone has little effect on improving the skill of the ensemble flood forecasts. Indeed, postprocessing alone performs similar, in terms of the relative mean error, skill, and reliability, to the more involved scenario that includes both preprocessing and postprocessing. We conclude that statistical preprocessing may not always be a necessary component of the ensemble flood forecasting chain.

Description: Watermelons(Citrullus lanatus)are known to have sufficient amino acid content. In this study, watermelons grown and consumed in Malaysia were investigated for their amino acid content, L-citrulline and L-arginine, by the isocratic RP-HPLC method. Flesh and rind watermelons were juiced, and freeze-dried samples were used for separation and quantification of L-citrulline and L-arginine. Three different mobile phases, 0.7% H3P04, 0.1% H3P04, and 0.7% H3P04 : ACN (90 : 10), were tested on two different columns using Zorbax Eclipse XDB-C18and Gemini C18with a flow rate of 0.5 mL/min and a detection wavelength at 195 nm. Efficient separation with reproducible resolution of L-citrulline and L-arginine was achieved using 0.1% H3P04on the Gemini C18column. The method was validated and good linearity of L-citrulline and L-arginine was obtained withR2= 0.9956,y=0.1664x+2.4142andR2=0.9912,y=0.4100x+3.4850, respectively. L-citrulline content showed the highest concentration in red watermelon of flesh and rind juice extract (43.81 mg/g and 45.02 mg/g), whereas L-arginine concentration was lower than L-citrulline, ranging from 3.39 to 11.14 mg/g. The isocratic RP-HPLC method with 0.1% H3P04on the Gemini C18column proved to be efficient for separation and quantification of L-citrulline and L-arginine in watermelons.

Description: Bricks of low elastic modulus are occasionally used in some developing countries, such as Indonesia and India. Most of the previous research efforts focused on masonry structures built with bricks of considerably high elastic modulus. The objective of this study is to quantify the equivalent elastic modulus of lower-stiffness masonry structures, when the mortar has a higher modulus of elasticity than the bricks, by employing finite element (FE) simulations and adopting the homogenization technique. The reported numerical simulations adopted the two-dimensional representative volume elements (RVEs) using quadrilateral elements with four nodes. The equivalent elastic moduli of composite elements with various bricks and mortar were quantified. The numerically estimated equivalent elastic moduli from the FE simulations were verified using previously established test data. Hence, a new simplified formula for the calculation of the equivalent modulus of elasticity of such masonry structures is proposed in the present study.