Tropical Cyclone Damage Assessment using a Projection Pursuit Dynamic Cluster Model

Using data from 62 tropical cyclones (TCs) that landed in Guangdong Province in China between 2000 and 2019, we calculated six indices—minimum central pressure, maximum wind speed, maximum rainstorm ratio, cumulative surface rainfall, cyclone track length and lifetime—and constructed a projection pursuit dynamic cluster (PPDC) model to assess TC damage risk. Although a single index may provide correct information on the intensity of certain types of damage, a comprehensive damage risk assessment cannot be obtained from individual indices alone. The PPDC model is a stable tool for TC damage risk assessment, especially in terms of economic loss, agricultural disaster area and disaster-affected population. Model validation improved the correlation of each of the indices. Output from the PPDC model for disaster-affected population and agricultural disaster-affected area also improved after model validation. We examined the limitations of the single indices using data from three TCs. Output from the PPDC model can closely reflect the intensity of the damage caused by the cyclones. Projection pursuit dynamic clustering is a new and objective method for typhoon damage risk assessment, and provides the scientific basis to support disaster prevention and mitigation.

A Model-Based Climatology of Low-Level Jets in the Weddell Sea Region of the Antarctic

Low-level jets (LLJs) are climatological features in polar regions. It is well known that katabatic winds over the slopes of the Antarctic ice sheet are associated with strong LLJs. Barrier winds occurring, e.g., along the Antarctic Peninsula may also show LLJ structures. A few observational studies show that LLJs occur over sea ice regions. We present a model-based climatology of the wind field, of low-level inversions and of LLJs in the Weddell Sea region of the Antarctic for the period 2002–2016. The sensitivity of the LLJ detection on the selection of the wind speed maximum is investigated. The common criterion of an anomaly of at least 2 m/s is extended to a relative criterion of wind speed decrease above and below the LLJ. The frequencies of LLJs are sensitive to the choice of the relative criterion, i.e., if the value for the relative decrease exceeds 15%. The LLJs are evaluated with respect to the frequency distributions of height, speed, directional shear and stability for different regions. LLJs are most frequent in the katabatic wind regime over the ice sheet and in barrier wind regions. During winter, katabatic LLJs occur with frequencies of more than 70% in many areas. Katabatic LLJs show a narrow range of heights (mostly below 200 m) and speeds (typically 10–20 m/s), while LLJs over the sea ice cover a broad range of speeds and heights. LLJs are associated with surface inversions or low-level lifted inversions. LLJs in the katabatic wind and barrier wind regions can last several days during winter. The duration of LLJs is sensitive to the LLJ definition criteria. We propose to use only the absolute criterion for model studies.

Electricity production and comparative analysis for wind availability power potential assessment at four sites in Egypt

This research represents the first wind energy potential assessment that covers major provinces in Egypt. The paper investigates a realistic study technically and economically of wind energy as a talented renewable source for electricity production of various regions in Egypt. More accurate prediction and measurement of wind speed and direction allow wind plants to supply clean, renewable power to businesses, and homeowners at lower costs. Wind resource assessments must be precise in order for wind farms to be built successfully. Wind resource assessments have been carried out in this study. Wind resources evaluation and precise assessment of wind capacity for the four selected sites in Egypt’s provinces from 2017 for 3 years at 10, 50 m above ground level (AGL): Hurgada, Aswan, Alexandria, and the capital of Egypt (Cairo). The wind speed data is taken from NASA for different sites in Egypt. The average annual wind speed was estimated to be 4.44, 4.31, 4.91, and 3.9 m/s at 10 m height, respectively. The economical factors such as NPC and COE in the selected regions are estimated. The optimum location for wind assessment in Egypt is Alexandria which gives maximum wind speed, maximum annual energy, minimum levelized cost of energy, and highest capacity factor. The proposed wind assessment will generated 20,1729 kWh of electricity per year and electricity generation cost per kWh/$ is 0.0818844. This planned cost of wind electric generation is compatible with the local electricity tariff. Also, Feasibility of Construction small wind turbine in this site is investigated. In addition, a criterion of wind farm site selection is presented here with Environmental Impact Assessment (EIA) study through Birds Migration aspect that decreases with increase turbine tower length and short blade length. Through reducing Egypt’s domestic fossil fuel consumption, this work will potentially save tons of carbon emissions each year.

Decision tree-based detection of blowing snow events in the European Alps

Blowing snow processes are crucial in shaping the strongly heterogeneous spatiotemporal distribution of snow and in regulating subsequent snowpack evolution in mountainous terrain. Although empirical formulae and constant threshold wind speeds have been widely used to estimate the occurrence of blowing snow in regions with sparse observations, the scarcity of in situ observations in mountainous regions contrasts with the demands of models for reliable observations at high spatiotemporal resolution. Therefore, these methods struggle to accurately capture the high local variability of blowing snow. This study investigated the potential capability of the decision tree model (DTM) to detect blowing snow in the European Alps. The DTMs were constructed based on routine meteorological observations (mean wind speed, maximum wind speed, air temperature and relative humidity) and snow measurements (including in situ snow depth observations and satellite-derived products). Twenty repetitions of a random sub-sampling validation test with an optimal size ratio (0.8) between the training and validation subsets were applied to train and assess the DTMs. Results show that the maximum wind speed contributes most to the classification accuracy, and the inclusion of more predictor variables improves the overall accuracy. However, the spatiotemporal transferability of the DTM might be limited if the divergent distribution of wind speed exists between stations. Although both the site-specific DTMs and site-independent DTM show great ability in detecting blowing snow occurrence and are superior to commonly used empirical parameterizations, specific assessment indicators varied between stations and surface conditions. Events for which blowing snow and snowfall occurred simultaneously were detected the most reliably. Although models failed to fully reproduce the high frequency of local blowing snow events, they have been demonstrated to be a promising approach requiring limited meteorological variables and have the potential to scale to multiple stations across different regions.

Assessment of the Relationship between Cerebrospinal Meningitis and Climate Variables in Kaduna State, Nigeria

This study examines the relationship between Meningitis and weather parameters (air temperature, maximum temperature, relative humidity, and rainfall) in Kaduna state, Nigeria on a weekly basis from 2007–2019. Meningitis data was acquired weekly from Nigeria Centre for Disease Control (NCDC), Bureau of Statistics and weather parameters were sourced from daily satellite data set National Oceanic and Atmospheric Administration (NOAA), International Research Institute for Climate and Society (IRI). The daily data were aggregated weekly to suit the study. The data were analysed using linear trend and Pearson correlation for relationship. The linear trend results revealed a weekly decline in Cerebro Spinal Meningitis (CSM), wind speed, maximum and air temperature and an increase in relative humidity and rainfall. Generally, results reveal that the most important explanatory weather variables influencing CSM amongst the five (5) are the weekly maximum temperature and air temperature with a positive correlation of 0.768 and 0.773. This study recommends that keen interest be placed on temperature as they play an essential role in the transmission of this disease and most times aggravate the patients' condition.

Predicting the dust events frequency around a degraded ecosystem and determining the contribution of their controlling factors using gradient boosting-based approaches and Game theory

In this study, for the first time, an attempt was made to evaluate the performance of Gradient Boosting Machine (GBM) and extreme gradient boosting (XGB) models with linear, tree, and Dart boosters to predict monthly DEF (MDEF) around a degraded wetland in southwestern Iran. The monthly required data were obtained through observational data recorded at ground stations and satellite imagery from 1988 to 2018. The best predictor variables were selected among the eighteen climatic, terrestrial, and hydrological variables based on the multi-collinearity test (MCT) and Boruta algorithm. The models' performance was evaluated using the Taylor diagram. Game theory (i.e., SHAP values: SHV) was then used to determine the contribution of factors controlling MDEF in different seasons. Mean wind speed, maximum wind speed, rainfall, standardized precipitation evapotranspiration index (SPEI), soil moisture, erosive winds frequency, vapor pressure, vegetation area, water body area, and dried bed area of the wetland were confirmed as the best predictive variables. The XGB-linear and XGB-tree showed a higher capability in predicting the MDEF variations in summer and spring seasons. However, the XGB-Dart yielded a better than other study models in forecasting the MDEF in the autumn and winter seasons. The results also showed that the rainfall (SHV = 1.6), surface water discharge (SHV = 2.4), mean wind speed (SHV = 10.1), and erosive winds frequency (SHV = 1.6) had the largest contribution in the variability of MDEF in winter, spring, summer, and autumn, respectively. The results can be useful to provide different scenarios for combating hazards caused by wind erosion events around degraded wetlands.

Decision tree-based detection of blowing snow events in the European Alps

Blowing snow processes are crucial in shaping the strongly heterogeneous spatiotemporal distribution of snow, and in regulating subsequent snowpack evolution in mountainous terrain. Although empirical formulae and a constant threshold wind speed have been widely used to estimate the occurrence of blowing snow in regions with sparse observations, the scarcity of in-situ observations in mountainous regions contrasts with the demands of models for reliable observations at high spatiotemporal resolution. Therefore, these methods struggle to accurately capture the high local variability of blowing snow. This study investigated the potential capability of the decision tree model (DTM) to detect blowing snow in the European Alps. The DTMs were constructed based on routine meteorological observations (mean wind speed, maximum wind speed, air temperature and relative humidity). Twenty repetitions of random sub-sampling validation test with an optimal size ratio (0.8) between the training and validation subset were applied to train and assess the DTMs. Results show that the maximum wind speed contributes most to the classification accuracy, and the inclusion of more predictor variables improves the overall accuracy. However, the spatiotemporal transferability of the DTM might be limited if divergent distributions exist between stations. Although both the site-specific DTMs and site-independent DTM show strong performance for accurately detecting blowing snow, specific assessment indicators varied between stations and surface conditions. Events for which blowing snow and snowfall occurred simultaneously were detected the most reliably. Although models failed to fully reproduce the high frequency of local blowing snow events, they have been demonstrated a promising approach requiring limited meteorological variables and have the potential to scale to multiple stations across different regions.

Time Series Analysis on Incidence of Pulmonary Tuberculosis With Weather Factors During 2004-2017 in Guangdong Province, China

Background: Although the occurrence of some infectious diseases including TB was found to be associated with specific weather factors, few studies have incorporated weather factors into the model to predict the incidence of tuberculosis (TB). We aimed to establish an accurate forecasting model using TB data in Guangdong Province, incorporating local weather factors.Methods: Data of sixteen meteorological variables (2003-2016) and the TB incidence data (2004-2016) of Guangdong were collected. Seasonal autoregressive integrated moving average (SARIMA) model was constructed based on the data. SARIMA model with weather factors as explanatory variables (SARIMAX) was performed to fit and predict TB incidence in 2017. Results: Maximum temperature, maximum daily rainfall, minimum relative humidity, mean vapor pressure, extreme wind speed, maximum atmospheric pressure, mean atmospheric pressure and illumination duration were significantly associated with log(TB incidence). After fitting the SARIMAX model, maximum pressure at lag 6 (β= -0.007, P < 0.05, 95% confidence interval (CI): -0.011, -0.002, mean square error (MSE): 0.279) was negatively associated with log(TB incidence), while extreme wind speed at lag 5 (β=0.009, P < 0.05, 95% CI: 0.005, 0.013, MSE: 0.143) was positively associated. SARIMAX (1, 1, 1) (0, 1, 1)12 with extreme wind speed at lag 5 was the best predictive model with lower Akaike information criterion (AIC) and MSE. The predicted monthly TB incidence all fall within the confidence intervals using this model. Conclusions: Weather factors have different effects on TB incidence in Guangdong. Incorporating meteorological factors into the model increased the accuracy of prediction.

Analysing the decision making for agricultural risk assessment: An application of extreme value theory

As the most contributed sectors in agriculture, rice farming is facing various risks, namely uncertainty such as crop failure caused by climate change, including air temperature, weather, rainfall and others. Indonesia is categorised as an agricultural country with a tropical climate. By this season, the farmers can plant the rice. Rice farming is currently an inseparable part of most agricultural societies in Indonesia, especially in West Java. However, changes in air temperature, weather and annual rainfall, can increase the uncertainty and upward the risk of crop failure. Thus, the current study seeks to investigate the decision making for agricultural risk assessment (climate variable) through the formulation of a risk model for agricultural insurance in Indonesia. This study utilised the climate variables, which consist of air temperature, wind speed, maximum and minimum temperatures, and rainfall. For determining the magnitude of risk, we applied the Block Maxima method and Peak Over Threshold. The results of this study found that the highest risk of losses occurred in November, December, January, February and March with a value of 0.17485.

Advanced method of managing soil conservation works in Smart Farms

The paper presents the results obtained in the SMART farm by using an advanced method of managing soil conservation works. These works involve, first of all, the collection and storage of data on the spot regarding the following parameters in which the plants will grow: disease climate (air temperature, dew point), growth climate (air temperature, solar radiation, deficiency vapours pressure, relative air humidity, precipitation, wind speed, maximum wind speed, daily evaporation) and soil monitoring (soil moisture, precipitation, soil salinity, soil temperature). Data collection is done through intelligent sensors from a wireless weather station and the reception of this information in real-time on a computer/smartphone by the farmer. Therefore, the farmer will be able to make instant decisions on soil conservation work, thusly saving time and workforce for additional on farm inspections.