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  1. 3201
    “…We aimed to further study seasonality of respiratory viruses and bacterial pathogens in relation to antibiotic use, as well as meteorological parameters. METHODS: An ecologic study of antibiotic exposure, meteorological parameters, detection of respiratory viruses  and clinical isolates of Clostridioides difficile, Methicillin-resistant Staphylococcus aureus (MRSA), Streptococcus pneumoniae, and Escherichia coli and Klebsiella pneumoniae (grouped together as gram-negative bacteria; GNB) in Rhode Island from 2012 to 2016. …”
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    Seasonality of respiratory viruses and bacterial pathogens
  2. 3202
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    Atmospheric seasonal forecasts of the twentieth century: multi‐decadal variability in predictive skill of the winter North Atlantic Oscillation (NAO) and their potential value for extreme event attribution
  3. 3203
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    Classification of eastward propagating waves on the spherical Earth
  4. 3204
    “…Based on the daily air quality index (AQI) monitoring data in 96 cities from 2014–2016, the spatiotemporal patterns of AQI in North China were investigated, then the influence of meteorological and socio-economic factors on AQI was discussed by statistical analysis and ESDA-GWR (exploratory spatial data analysis-geographically weighted regression) model. …”
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    Understanding the Spatial-Temporal Patterns and Influential Factors on Air Quality Index: The Case of North China
  5. 3205
    por Jing, Jinrui, Li, Qian, Peng, Xuan
    Publicado 2019
    “…Weather radar echo is the data detected by the weather radar sensor and reflects the intensity of meteorological targets. Using the technique of radar echo extrapolation, which is the prediction of future echoes based on historical echo observations, the approaching short-term weather conditions can be forecasted, and warnings can be raised with regard to disastrous weather. …”
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    MLC-LSTM: Exploiting the Spatiotemporal Correlation between Multi-Level Weather Radar Echoes for Echo Sequence Extrapolation
  6. 3206
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    Current state of the global operational aerosol multi‐model ensemble: An update from the International Cooperative for Aerosol Prediction (ICAP)
  7. 3207
    “…We investigate factors influencing European winter (DJFM) air temperatures for the period 1979–2015 with the focus on changes during the recent period of rapid Arctic warming (1998–2015). We employ meteorological reanalyses analysed with a combination of correlation analysis, two pattern clustering techniques, and back‐trajectory airmass identification. …”
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    Effects of the tropospheric large‐scale circulation on European winter temperatures during the period of amplified Arctic warming
  8. 3208
    “…Agricultural activity along with geographical, meteorological and land cover factors are likely to contribute to disease incidence. …”
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    The estimated burden of scrub typhus in Thailand from national surveillance data (2003-2018)
  9. 3209
    “…CONCLUSIONS: In this nationwide observational study, we found an association of incidence of MI with incidence of influenza cases beyond what could be explained by meteorological factors.…”
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    Association of acute myocardial infarction with influenza: A nationwide observational study
  10. 3210
    “…The CEDP model provides better results in terms of accuracy and uncertainty as compared to other ensemble models for all meteorological stations. The performance of the CEDP model indicates that the climate indices are correlated with a weather pattern of four meteorological stations. …”
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    Monthly drought prediction based on ensemble models
  11. 3211
    “…The daily deaths parameter was not considered, because it is not reliable, due to frequent administrative errors. Daily data on meteorological conditions and new cases of COVID-19 were collected for the Lombardy Region (Northern Italy) from 1 March, 2020 to 20 April, 2020. …”
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    Preliminary Analysis of Relationships between COVID19 and Climate, Morphology, and Urbanization in the Lombardy Region (Northern Italy)
  12. 3212
    “…We analyzed relationships between frequency and concentrations of pesticides, land use, meteorological parameters, substance properties, and season. …”
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    Pesticides in ambient air, influenced by surrounding land use and weather, pose a potential threat to biodiversity and humans
  13. 3213
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    Effects of mis‐specified time‐correlated model error in the (ensemble) Kalman Smoother
  14. 3214
    “…Background: We investigated the effect of particulate matter with aerodynamic diameter <2.5 μm (PM2.5) and meteorological conditions on the risk of emergency room visits in patients with atrial fibrillation (AF) in Beijing, which is considered as a monsoon climate region. …”
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    Effect of Air Quality on the Risk of Emergency Room Visits in Patients With Atrial Fibrillation
  15. 3215
    “…Temperature change is an important meteorological indicator reflecting weather stability. …”
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    Short-Term Effect of Temperature Change on Non-Accidental Mortality in Shenzhen, China
  16. 3216
    “…Trace gases such as O(3), NO(x), NO(y), SO(2), CO, and volatile organic compounds (VOCs), photochemical byproducts such as H(2)O(2) and HCHO, aerosol species, and meteorological variables including planetary boundary layer height were simultaneously measured at Olympic Park in Seoul. …”
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    Factors controlling surface ozone in the Seoul Metropolitan Area during the KORUS-AQ campaign
  17. 3217
    “…Multicollinearity of meteorological variables was examined via Spearman correlation, variables with strong correlation (r>0.7) were not included in the model simultaneously. …”
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    The association between tropical cyclones and dengue fever in the Pearl River Delta, China during 2013-2018: A time-stratified case-crossover study
  18. 3218
    “…This study estimated the air temperature in the Yellow River Basin, which spans three different elevation ranges, using multi-source data to address the uneven distribution of regional meteorological stations. The differences in mountain-mass effect for different geomorphic regions at the same altitude were then compared. …”
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    Estimation of air temperature and the mountain-mass effect in the Yellow River Basin using multi-source data
  19. 3219
    “…The relevant management departments should formulate scientific prevention and control measures for the two mosquitoes according to meteorological factors and the risk level of invasion. …”
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    Risk Assessment of Anopheles philippinensis and Anopheles nivipes (Diptera: Culicidae) Invading China under Climate Change
  20. 3220
    “…Materials and Methods: We calculated climbers’ heat balance using the Man-ENvironment heat EXchange model (MENEX-2005) and derived meteorological data from the National Geographic Expedition’s in situ dataset. …”
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    Heat Balance When Climbing Mount Everest
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