By using a coupled atmosphere-lake model, which consists of the Weather Research and Forecasting (WRF) model and the Princeton Ocean Model (POM), the present study generated realistic lake surface temperature (LST) over Lake Victoria and revealed the prime importance of LST on the precipitation pattern over the Lake Victoria Basin (LVB). A suite of sensitivity experiments was conducted for the selection of an optimal combination of physics options including cumulus, microphysics, and planetary boundary layer schemes for simulating precipitation over the LVB. The WRF-POM coupled system made a great performance on simulating the expected LST, which is featured with eastward temperature gradient as in the real bathymetry of the lake. Under thorough examination of diagnostic analysis, a distinguished diurnal phenomenon has been unveiled. The precipitation mainly occurs during the nocturnal peak between midnight and early in the morning, which is associated with the strong land breeze circulation, when the lake temperature is warmer than the adjacent land. Further exploration of vertical velocity, surface divergence pattern, and maximum radar reflectivity confirms such conjecture. The time-longitude analysis of maximum radar reflectivity over the entire lake also shows a noticeable pattern of dominating westward propagation.

Long-term hurricane predictions have been of acute interest in order to protect the community from the loss of lives, and environmental damage. Such predictions help by providing an early warning guidance for any proper precaution and planning. In this paper, we present a machine learning model capable of making good preseason-prediction of Atlantic hurricane activity. The development of this model entails a judicious and non-linear fusion of various data modalities such as sea-level pressure (SLP), sea surface temperature (SST), and wind. A Convolutional Neural Network (CNN) was utilized as a feature extractor for each data modality. This is followed by a feature level fusion to achieve a proper inference. This highly non-linear model was further shown to have the potential to make skillful predictions up to 18 months in advance.

In this study, nine different statistical models are constructed using different combinations of predictors, including models with and without projected predictors. Multiple machine learning (ML) techniques are employed to optimize the ensemble predictions by selecting the top performing ensemble members and determining the weights for each ensemble member. The ML-Optimized Ensemble (ML-OE) forecasts are evaluated against the Simple-Averaging Ensemble (SAE) forecasts. The results show that for the response variables that are predicted with significant skill by individual ensemble members and SAE, such as Atlantic tropical cyclone counts, the performance of SAE is comparable to the best ML-OE results. However, for response variables that are poorly modeled by individual ensemble members, such as Atlantic and Gulf of Mexico major hurricane counts, ML-OE predictions often show higher skill score than individual model forecasts and the SAE predictions. However, neither SAE nor ML-OE was able to improve the forecasts of the response variables when all models show consistent bias. The results also show that increasing the number of ensemble members does not necessarily lead to better ensemble forecasts. The best ensemble forecasts are from the optimally combined subset of models.

This study presents the climatological characteristics and key environmental features that are conducive to the development of successive tropical cyclone events (STCs) over the North Atlantic Ocean. Composite analyses were conducted to analyze the temporal, spatial, and mean characteristics of the environmental conditions associated with historical STC events during the study period of 1950–2020. The results show that the tropical cyclone (TC)-induced Rossby waves could explain a majority of Atlantic STCs when newly formed TCs develop to the east of the pre-existing TC during the study period. The remaining STCs which could not be explained by the Rossby wave dispersion theory were likely the result of favorable environmental conditions conducive to the occurrence of the successive development of TCs. The composite analysis of the environmental conditions at various time scales reveals that the low-frequency variability of the environmental conditions likely plays a significant role in modulating the STCs over the North Atlantic Ocean.

Buckwheat sprouts can not only effectively reduce the trypsin inhibitor in buckwheat grain, but also improve the content of bioactive ingredients such as flavonoids and other effective components. However, Polyphenol Oxidase (PPO) in buckwheat sprouts causes browning of food, which limits the application of buckwheat sprouts in food. In addressing this problem, studying the characteristics of PPO in buckwheat sprouts is necessary. PPO in buckwheat sprouts was first studied by germinating buckwheat sprouts as raw material for 10 days. Results showed that the optimum temperature and pH of PPO in buckwheat sprouts were 32°C and 7.2, respectively. PPO had specificity to substrates. The catalytic capacity of PPO with pyrogallic acid was higher than that of catechol, which had weak catalytic capacity of hydroquinone and resorcinol and no catalytic capacity to gallic acid. PPO showed strong thermal stability because its relative activity remained 23.1%±1.69% even heating at 90°C for 30 min. Ascorbic acid, L-cysteine, sodium hydrogen sulfite and sodium sulfite all had significant inhibition effect on PPO activity. The best inhibitor was ascorbic acid, whereas citric acid and Ethylenediaminetetraacetic acid disodium salt had no evident effect. The PPO activity was inhibited at a low concentration of Cu²⁺ and Mg²⁺ (0-1 mm) and it can be significantly activated at a higher concentration (5-50 mm) by adding AL³⁺, whereas no significance was observed when adding Ca²⁺. This study can provide reliable theoretical support for avoiding browning of buckwheat sprouts during food processing.

It is a common practice to use a buffer zone to damp out spurious wave growth due to computational error along the lateral boundary of limited-area weather and climate models. Although it is an effective technique to maintain model stability, an unintended side effect of using such buffer zones is the distortion of the data passing through the buffer zone. Various techniques are introduced to enhance the communication between the limited-area model’s inner domain and the outer domain, which provides lateral boundary values for the inner domain. Among them, scale-selective data assimilation (SSDA) and the spectral nudging (SPNU) techniques share similar philosophy, i.e., directly injecting the large-scale components of the atmospheric circulation from the outer model domain into the interior grids of the inner model domain by-passing the lateral boundary and the buffer zone, but the two methods are taking different implementation approaches. SSDA utilizes a 3-dimensional variational data assimilation procedure to accomplish the data injection objective, whereas SPNU uses a nudging process. In the present study, the two approaches are evaluated comparatively for simulating hurricane track and intensity in a pair of cases: Jeanne (2004) and Irma (2017) using the Weather Research and Forecasting (WRF) model. The results indicate that both techniques are effective in improving tropical cyclone intensity and track simulations by reducing the errors of the large-scale circulation in the inner model domain. The SSDA runs produced better simulations of temperature and humidity fields which are not directly nudged. The SSDA runs also produced more accurate storm intensities in both cases and more realistic structure in Hurricane Jeanne’s case than those produced by the SPNU runs. It should be noted, however, that extending these case study results to more general situations requires additional studies covering a large number of additional cases.