Heating effect on total phenol, flavonoids, antioxidant activity, and sugar content of six onion varieties has been quantitatively investigated to explore the effect of different temperatures. The onion varieties comprised one red-skinned variety, two white-skinned varieties, and three yellow-skinned varieties. The heating temperature was scanned at 80°C, 100°C, 120°C, and 150°C for 30 minutes each, and quantitative analysis was performed relative to the powdered onion at ambient temperature. Quercetin, glucosides and sugar content were analyzed using high-performance liquid chromatography. The total phenolic and antioxidant content increased in all six varieties. The total flavonoid levels showed a considerable change. On heating the onion samples at 120°C for 30 minutes, the red-skinned variety showed the highest level of total phenolic content [13712.67 ± 1034.85 μg of gallic acid equivalent/g dry weight (μg GAE/g DW)] and total flavonoids [3456.00 ± 185.82 μg of quercetin equivalents/g dry weight (μg Q/g DW)], whereas the content of total phenolics and total flavonoids were 13611.83 ± 341.61 μg GAE/g DW and 3482.87 ± 117.17 μg Q/g DW, respectively, for the yellow-skinned (Sunpower) variety. Quercetin and its glucoside contents increased up to 120°C and then decreased at 150°C, whereas the sugar content continuously decreased with heating. All cultivars showed the same pattern in the heating effect, and the predominant flavonoids were destroyed at higher temperatures. Therefore, it is improper to expose onion powder to a temperature higher than 120°C.

A multiphysics and a stochastic kinetic-energy backscatter scheme are employed to represent model uncertainty in a mesoscale ensemble prediction system using the Weather Research and Forecasting model. Both model-error schemes lead to significant improvements over the control ensemble system that is simply a downscaled global ensemble forecast with the same physics for each ensemble member. The improvements are evident in verification against both observations and analyses, but different in some details. Overall the stochastic kinetic-energy backscatter scheme outperforms the multiphysics scheme, except near the surface. Best results are obtained when both schemes are used simultaneously, indicating that the model error can best be captured by a combination of multiple schemes.

Four model-error schemes for probabilistic forecasts over the contiguous United States with the WRF-ARW mesoscale ensemble system are evaluated in regard to performance. Including a model-error representation leads to significant increases in forecast skill near the surface as measured by the Brier score. Combining multiple model-error schemes results in the best-performing ensemble systems, indicating that current model error is still too complex to be represented by a single scheme alone. To understand the reasons for the improved performance, it is examined whether model-error representations increase skill merely by increasing the reliability and reducing the bias—which could also be achieved by postprocessing—or if they have additional benefits. Removing the bias results overall in the largest skill improvement. Forecasts with model-error schemes continue to have better skill than without, indicating that their benefit goes beyond bias reduction. Decomposing the Brier score into its components reveals that, in addition to the spread-sensitive reliability, the resolution component is significantly improved. This indicates that the benefits of including a model-error representation go beyond increasing reliability. This is further substantiated when all forecasts are calibrated to have similar spread. The calibrated ensembles with model-error schemes consistently outperform the calibrated control ensemble. Including a model-error representation remains beneficial even if the ensemble systems are calibrated and/or debiased. This suggests that the merits of model-error representations go beyond increasing spread and removing the mean error and can account for certain aspects of structural model uncertainty.

A B S T R A C TThis work evaluates several techniques to account for mesoscale initial-condition (IC) and model uncertainty in a short-range ensemble prediction system based on the Weather Research and Forecast (WRF) model. A scientific description and verification of several candidate methods for implementation in the U.S. Air Force Weather Agency mesoscale ensemble is presented. Model perturbation methods tested include multiple parametrization suites, landsurface property perturbations, perturbations to parameters within physics schemes and stochastic 'backscatter' streamfunction perturbations. IC perturbations considered include perturbed observations in 10 independent WRF-3DVar cycles and the ensemble-transform Kalman filter (ETKF). A hybrid of ETKF (for IC perturbations) and WRF-3DVar (to update the ensemble mean) is also tested. Results show that all of the model and IC perturbation methods examined are more skilful than direct dynamical downscaling of the global ensemble. IC perturbations are most helpful during the first 12 h of the forecasts. Physical parametrization diversity appears critical for boundary-layer forecasts. In an effort to reduce system complexity by reducing the number of suites of physical parametrizations, a smaller set of parametrization suites was combined with perturbed parameters and stochastic backscatter, resulting in the most skilful and statistically consistent ensemble predictions.

The impact of assimilating radiance observations from the Advanced Microwave Sounding Unit-A (AMSU-A) on forecasts of several tropical cyclones (TCs) was studied using the Weather Research and Forecasting Model (WRF) and a limited-area ensemble Kalman filter (EnKF). Analysis/forecast cycling experiments with and without AMSU-A radiance assimilation were performed over the Atlantic Ocean for the period 11 August-13 September 2008, when five named storms formed. For convenience, the radiance forward operators and bias-correction coefficients, along with the majority of quality-control decisions, were computed by a separate, preexisting variational assimilation system. The bias-correction coefficients were obtained from 3-month offline statistics and fixed during the EnKF analysis cycles. The vertical location of each radiance observation, which is required for covariance localization in the EnKF, was taken to be the level at which the AMSU-A channels' weighting functions peaked.Deterministic 72-h WRF forecasts initialized from the ensemble-mean analyses were evaluated with a focus on TC prediction. Assimilating AMSU-A radiances produced better depictions of the environmental fields when compared to reanalyses and dropwindsonde observations. Radiance assimilation also resulted in substantial improvement of TC track and intensity forecasts with track-error reduction up to 16% for forecasts beyond 36 h. Additionally, assimilating both radiances and satellite winds gave markedly more benefit for TC track forecasts than solely assimilating radiances.