Death Zone Weather Extremes Mountaineers Have Experienced in Successful Ascents

BackgroundFew data are available on mountaineers’ survival prospects in extreme weather above 8000 m (the Death Zone). We aimed to assess Death Zone weather extremes experienced in climbing-season ascents of Everest and K2, all winter ascents of 8000 m peaks (8K) in the Himalayas and Karakoram, environmental records of human survival, and weather extremes experienced with and without oxygen support.Materials and MethodsWe analyzed 528 ascents of 8K peaks: 423 non-winter ascents without supplemental oxygen (Everest–210, K2–213), 76 ascents in winter without oxygen, and 29 in winter with oxygen. We assessed environmental conditions using the ERA5 dataset (1978–2021): barometric pressure (BP), temperature (Temp), wind speed (Wind), wind chill equivalent temperature (WCT), and facial frostbite time (FFT).ResultsThe most extreme conditions that climbers have experienced with and without supplemental oxygen were: BP 320 hPa (winter Everest) vs. 329 hPa (non-winter Everest); Temp –41°C (winter Everest) vs. –45°C (winter Nanga Parbat); Wind 46 m⋅s–1 (winter Everest) vs. 48 m⋅s–1 (winter Kangchenjunga). The most extreme combined conditions of BP ≤ 333 hPa, Temp ≤ −30°C, Wind ≥ 25 m⋅s–1, WCT ≤ −54°C and FFT ≤ 3 min were encountered in 14 ascents of Everest, two without oxygen (late autumn and winter) and 12 oxygen-supported in winter. The average extreme conditions experienced in ascents with and without oxygen were: BP 326 ± 3 hPa (winter Everest) vs. 335 ± 2 hPa (non-winter Everest); Temp −40 ± 0°C (winter K2) vs. −38 ± 5°C (winter low Karakoram 8K peaks); Wind 36 ± 7 m⋅s–1 (winter Everest) vs. 41 ± 9 m⋅s–1 (winter high Himalayan 8K peaks).Conclusions1.The most extreme combined environmental BP, Temp and Wind were experienced in winter and off-season ascents of Everest.2.Mountaineers using supplemental oxygen endured more extreme conditions than climbers without oxygen.3.Climbing-season weather extremes in the Death Zone were more severe on Everest than on K2.4.Extreme wind speed characterized winter ascents of Himalayan peaks, but severely low temperatures marked winter climbs in Karakoram.

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Directional extreme wind speed data for the design of buildings and other structures

10.6028/nbs.bss.160 ◽

1984 ◽

Cited By ~ 3

Author(s):

Michael Changery ◽

Eugene J Dumitriu-Valcea ◽

Emil Simiu

Keyword(s):

Wind Speed ◽

Wind Speed Data ◽

Extreme Wind ◽

Extreme Wind Speed

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

10.21203/rs.3.rs-181418/v1 ◽

2021 ◽

Author(s):

Tianyu Qin ◽

Yu Hao ◽

Juan He

Keyword(s):

Wind Speed ◽

Atmospheric Pressure ◽

Guangdong Province ◽

Maximum Temperature ◽

Maximum Pressure ◽

Weather Factors ◽

Sarima Model ◽

Extreme Wind ◽

Wind Speed Maximum ◽

Extreme Wind Speed

Abstract 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.

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