Global tree-ring response and inferred climate variation following the mid-thirteenth century Samalas eruption

The largest explosive volcanic eruption of the Common Era in terms of estimated sulphur yield to the stratosphere was identified in glaciochemical records 40 years ago, and dates to the mid-thirteenth century. Despite eventual attribution to the Samalas (Rinjani) volcano in Indonesia, the eruption date remains uncertain, and the climate response only partially understood. Seeking a more global perspective on summer surface temperature and hydroclimate change following the eruption, we present an analysis of 249 tree-ring chronologies spanning the thirteenth century and representing all continents except Antarctica. Of the 170 predominantly temperature sensitive high-frequency chronologies, the earliest hints of boreal summer cooling are the growth depressions found at sites in the western US and Canada in 1257 CE. If this response is a result of Samalas, it would be consistent with an eruption window of circa May–July 1257 CE. More widespread summer cooling across the mid-latitudes of North America and Eurasia is pronounced in 1258, while records from Scandinavia and Siberia reveal peak cooling in 1259. In contrast to the marked post-Samalas temperature response at high-elevation sites in the Northern Hemisphere, no strong hydroclimatic anomalies emerge from the 79 precipitation-sensitive chronologies. Although our findings remain spatially biased towards the western US and central Europe, and growth-climate response patterns are not always dominated by a single meteorological factor, this study offers a global proxy framework for the evaluation of paleoclimate model simulations.

Sea Level Prediction Using Machine Learning

Sea level prediction is essential for the design of coastal structures and harbor operations. This study presents a methodology to predict sea level changes using sea level height and meteorological factor observations at a tide gauge in Antalya Harbor, Turkey. To this end, two different scenarios were established to explore the most feasible input combinations for sea level prediction. These scenarios use lagged sea level observations (SC1), and both lagged sea level and meteorological factor observations (SC2) as the input for predictive modeling. Cross-correlation analysis was conducted to determine the optimum input combination for each scenario. Then, several predictive models were developed using linear regressions (MLR) and adaptive neuro-fuzzy inference system (ANFIS) techniques. The performance of the developed models was evaluated in terms of root mean squared error (RMSE), mean absolute error (MAE), scatter index (SI), and Nash Sutcliffe Efficiency (NSE) indices. The results showed that adding meteorological factors as input parameters increases the performance accuracy of the MLR models up to 33% for short-term sea level predictions. Moreover, the results contributed a more precise understanding that ANFIS is superior to MLR for sea level prediction using SC1- and SC2-based input combinations.

Performance of RegCM4.5 in Simulating the Regional Climate of Western Tianshan Mountains in Xinjiang, China

The western Tianshan Mountains region in China has a complex topography where basins, mountains and glaciers co-exist. It is of great significance to study the sensitivity of meteorological factors in this region to different parameterization schemes of climate models. In this paper, the regional climate model RegCM4.5 is used to simulate the meteorological factor (mean temperature, maximum temperature, minimum temperature, precipitation and wind speed) occurring in the western Tianshan Mountains region from 2012 to 2016, so as to investigate the effects of different cumulus convective schemes (Grell, Tiedtke and Emanuel), including land cumulus convective schemes (LCCs) and ocean convective schemes (OCCs) on annual and seasonal simulations of meteorological factor by using the schemes of RUN1 (Grell for LCC and Tiedtke for OCC), RUN2 (Tiedtke for LCC and Emanuel for OCC), RUN3 (Grell for LCC and Emanuel for OCC) and ENS (the ensemble of RUN1, RUN2 and RUN3). The results show that the simulations of annual and seasonal meteorological factors are not significantly sensitive to the combination of LCCs and OCCs. In the annual simulations, RUN2 scheme has the best simulation performance for the maximum, average and minimum temperatures. However, other schemes of precipitation simulation outperform RUN2 scheme, and there is no difference among the four schemes for wind speed simulation. In the seasonal simulations, RUN2 scheme still performs well in the simulation of the average, maximum and minimum temperatures for four seasons, except for the simulation of the average temperature in spring and summer. For the simulation of the maximum temperature in summer, RUN2 scheme performs the same as ENS. For the simulation of other seasons, different meteorological factors have different performances in four seasons. Overall, the results show that different combinations of cumulus convection schemes can improve the simulation performance of meteorological factors in the western Tianshan Mountains of Xinjiang.

Estimation of the spatial distribution of maximum PM10 and PM2.5 concentration in Bandung City and surrounding countries using WRF-Chem Model (case study in July and October 2018)

Bandung is one of big cities in Indonesia with high activities on industrial and transportation that will increase the air pollutant emission and causes adversely affect the public health. Based on that matter, monitoring of air pollutant concentration is urgently needed to predict the direction of pollutant dispersion and to analyze which locations are vulnerable to maximum exposure of the pollutant. Field monitoring of air pollutant concentration needs much time and high cost, but modeling could help for this. One of the models that can be used to predict the direction of pollutant distribution is the Weather Research Forecasting/Chemistry (WRF-Chem) model, which is a model that combines meteorological models with air quality models. The output of the WRF-Chem running model on July and October 2018, which has been analyzed visually, showed the dispersion pattern of PM10 and PM2.5 is spread mostly to the west, northwest, and north following the wind direction. According to the output of the WRF-Chem model, Bandung Kulon is the most polluted subdistrict by PM10 and PM2.5 with an exposure frequency of 22 hours (PM10), 24 hours (PM2.5) on July 2018 and 19 Hours (PM10), 14 hours (PM2.5) on October 2018. The correlation value for meteorological parameters is quite high in July 2018 (R = 0.9 for wind speed and R = 0.82 for air temperature). So based on the meteorological factor, WRF-Chem model can be used to predict the direction of pollutant distribution.

Research on Photovoltaic power prediction technology Based on Machine Learning

In recent years, the photovoltaic power generation has obvious intermittent, randomness and volatility, and high permeability photovoltaic will have a huge impact on the safety and stability of the grid. The prediction of photovoltaic power generation is to improve the quality of photovoltaic grid, optimize grid scheduling, and ensure the basic technology of power grid safety and stability. In order to improve the prediction accuracy of photovoltaic power generation, this article will comprehensively carding and compare from 3 dimensions: photovoltaic power generation and meteorological factor correlation analysis, similar day selection, prediction method based on machine learning, and summarize the advantages and disadvantages of various methods. Further research has been put forward accordingly.

Influence of meteorological factors on the occurrence of bleeding from varicose veins of the esophagus

BACKGROUND: The problem of liver cirrhosis and associated bleedings from the varicose veins of the esophagus, unfortunately, remains highly relevant for Russia. Undoubtedly, it is important to identify factors that provoke an episode of bleeding. From a practical point of view, the most interesting ones are those that have an easily detectable nature and the maximum possible predictability. One of these factors, which is constantly varying throughout the year, is meteorological conditions. AIM: To evaluate the influence of meteorological factors on the occurrence of bleeding from varicose veins of the esophagus and the possibility of using this information to improve the schemes of primary prevention. MATERIALS AND METHODS: The material for the study was the results of examination and treatment of 86 patients hospitalized in 2016–2018 at the Emergency Hospital of Ryazan with a diagnosis of bleeding from esophageal varices. All these patients underwent fibroesophago-gastroduodenoscopy upon admission. The analysis of meteorological observations for 2016–2018 was carried out based on information from archived summaries of weather data. The results obtained were statistically processed using a standard set of programs. RESULTS: When analyzing the number of hospitalizations of patients with bleeding from varicose veins of the esophagus in different months of the year, a pronounced unevenness of admissions is revealed, the coefficient of variability is 53.65%. After excluding extreme values from the calculations, it was found that the incidence of the pathology increases in spring and autumn, decreases in summer and winter. The maximum incidence occurs in April and September, and the minimum in July and February. To determine the effect on patients of specific meteorological factors, a meteorological profile for a number of months was created from the archived weather data. In the course of statistical processing of the data, it was found that the only meteorological factor demonstrating a stable strong direct correlation with the number of hospitalizations of patients with bleeding from varicose veins of the esophagus (Pearson's correlation coefficient 0.9449) is the average atmospheric pressure gradient. This fact proves the ability of sudden changes in atmospheric pressure to provoke episodes of bleeding from the veins of the esophagus. CONCLUSIONS: When planning a course of primary pharmacological prevention of bleeding emerging the varicose veins of the esophagus, it is necessary to take into account the morbidity peaks occurring in April and September, as well as the ability of sudden changes of the atmospheric pressure to trigger the bleeding episodes.

Short- and long-term exposure to air pollution increases the risk of stroke

Objective Many epidemiological studies have observed the association of air pollutant exposure with the onset, progression, and mortality of stroke. The aim of this study was to investigate the associations of air pollutants, including SO2, NO2, O3, CO, and PM10, with stroke according to exposure duration. Methods Data from the Korean National Health Insurance Service-Health Screening Cohort from 2002 to 2015 were obtained. The 21,240 patients who were admitted for or died due to stroke were 1:4 matched for age, sex, income, and region of residence with 84,960 control participants. The meteorological factors of mean, highest, and lowest temperatures; relative humidity; ambient atmospheric pressure; and air pollutant concentrations (SO2, NO2, O3, CO, and PM10) were analyzed to determine their associations with stroke. The odds ratios for stroke after exposure to each meteorological factor and air pollutant at 7 and 30 days were calculated in the stroke and control groups. Subgroup analyses were conducted according to age, sex, income, and region of residence. Results The odds ratio associated with seven days of exposure to CO was 1.16 (95% CI = 1.04–1.31) in stroke patients. For 30 days of exposure, the odds ratio associated with CO was 1.16 (95% CI = 1.02–1.32) in stroke patients. Seven and 30 days of NO2 exposure were inversely associated with stroke. The odds ratio associated with seven days of exposure to O3 was 1.16 (95% CI = 1.01–1.32) in ischemic stroke patients. Both ischemic and hemorrhagic stroke had negative associations with 7 and 30 days of NO2 exposure. Conclusion Both short- and long-term exposure to CO were related to stroke.

Variations and Sensitivity Analysis of Reference Evapotranspiration During 2010-2019 in the Zhangye Farmland Oasis, Northwest China

Reference evapotranspiration (ET0) is an important parameter for agricultural water management in the arid Zhangye farmland oasis. However, the ET0 variations in this oasis over the last decade and meteorological forcings of these variations are unknown. This study investigated the ET0 variations during 2010-2019 in this oasis using the FAO-56 Penman-Monteith (PM) and Hargreaves equations. Results showed that the ET0 features daily and monthly variations with peak values in mid-July and an annual cycle. Although the estimated ET0 series based on the two equations have high correlations in the time domain, the Hargreaves equation always underestimates the ET0 compared to the PM equation. The yearly ET0 showed statistically significant increasing trends (90% significance level) during 2010-2019, while statistically significant increasing trends in monthly ET0 are found only in March and November. Increasing trends reflected in monthly and yearly ET0 are mainly attributed to the increasing maximum temperature and sunshine duration and decreasing relative humidity. Sensitivity analysis demonstrated that the meteorological factor to which the ET0 is most sensitive varies with time scale and equation. Moreover, regression equations used to correct the underestimation associated with the Hargreaves equation for estimating ET0 in the Zhangye farmland oasis also were constructed.

Lagged meteorological impacts on COVID-19 incidence among high-risk counties in the United States—a spatiotemporal analysis

Background The associations between meteorological factors and coronavirus disease 2019 (COVID-19) have been discussed globally; however, because of short study periods, the lack of considering lagged effects, and different study areas, results from the literature were diverse and even contradictory. Objective The primary purpose of this study is to conduct more reliable research to evaluate the lagged meteorological impacts on COVID-19 incidence by considering a relatively long study period and diversified high-risk areas in the United States. Methods This study adopted the distributed lagged nonlinear model with a spatial function to analyze COVID-19 incidence predicted by multiple meteorological measures from March to October of 2020 across 203 high-risk counties in the United States. The estimated spatial function was further smoothed within the entire continental United States by the biharmonic spline interpolation. Results Our findings suggest that the maximum temperature, minimum relative humidity, and precipitation were the best meteorological predictors. Most significantly positive associations were found from 3 to 11 lagged days in lower levels of each selected meteorological factor. In particular, a significantly positive association appeared in minimum relative humidity higher than 88.36% at 5-day lag. The spatial analysis also shows excessive risks in the north-central United States. Significance The research findings can contribute to the implementation of early warning surveillance of COVID-19 by using weather forecasting for up to two weeks in high-risk counties.

DINAMIKA KUALITAS UDARA AMBIEN SELAMA MASA PANDEMI COVID-19 DI KAWASAN INDONESIA TOURISM DEVELOPMENT CORPORATION NUSA DUA BALI

The Covid-19 pandemic has increased the improvement of air quality in various countries in the world, such as China, Italy, New York, India, Spain and Korea. This study aims to compare ambient air quality during the Covid-19 pandemic with new normal and normal periods, assess the effect of meteorological factors on ambient air quality, and map the spatial distribution of ambient air quality during the normal, Covid-19 pandemic and new normal in the ITDC Nusa Dua area. Air concentration parameter data and meteorological factors were collected using the midget impinger and direct reading method in 2019 (normal period), March and May 2020 (Covid-19 pandemic period) and July, September, and November 2020 (new normal period). Furthermore, comparing air quality using the Anova test, assessing the effect of meteorological factors on air quality using a linear regression test, and mapping the distribution of ambient air using the ArcGis 10.8 application. The analysis showed that the air quality during the Covid-19 pandemic and the new normal was significantly different from the normal period. The concentrations of SO2, NO2, NH3, CO, TSP and H2S during the Covid-19 pandemic and normal just decreased while the O3 concentration increased compared to the normal period. The meteorological factor that affects air quality is the wind speed, the higher the wind speed the lower the O3 concentration. Map of the distribution of spatial concentrations of SO2, NO2, NH3, CO, O3 and H2S in the normal, Covid-19 pandemic and new normal, lowest at the coast point of the peninsula and the highest distribution at the ITDC roundabout, bima statue or influence TSP is the highest spatial concentration of normal distribution at the ITDC roundabout and the bima statue, while the Covid-19 pandemic and normal are only at the coast point of the peninsula beach.Keywords: ambient air quality; Covid-19; pandemic; tourism.