Modeling the Long-Term Frequency Distribution of Regional Ozone Concentrations Using Synthetic Meteorology

2001 ◽  
Vol 35 (18) ◽  
pp. 3718-3726 ◽  
Author(s):  
Darrell A. Winner ◽  
Glen R. Cass
Keyword(s):  
Frequency Distribution ◽  
Term Frequency ◽  
Ozone Concentrations

Temporal patterns and trends of surface ozone concentrations over Portugal

2021 ◽  
Author(s):  
Carla Gama ◽  
Alexandra Monteiro ◽  
Myriam Lopes ◽  
Ana Isabel Miranda
Keyword(s):  
Air Quality ◽  
Iberian Peninsula ◽  
Human Health ◽  
Temporal Variability ◽  
Urban Areas ◽  
Surface Ozone ◽  
Quality Data ◽  
Ozone Concentrations ◽  
Long Term Study

<p>Tropospheric ozone (O<sub>3</sub>) is a critical pollutant over the Mediterranean countries, including Portugal, due to systematic exceedances to the thresholds for the protection of human health. Due to the location of Portugal, on the Atlantic coast at the south-west point of Europe, the observed O<sub>3</sub> concentrations are very much influenced not only by local and regional production but also by northern mid-latitudes background concentrations. Ozone trends in the Iberian Peninsula were previously analysed by Monteiro et al. (2012), based on 10-years of O<sub>3</sub> observations. Nevertheless, only two of the eleven background monitoring stations analysed in that study are located in Portugal and these two stations are located in Porto and Lisbon urban areas. Although during pollution events O<sub>3</sub> levels in urban areas may be high enough to affect human health, the highest concentrations are found in rural locations downwind from the urban and industrialized areas, rather than in cities. This happens because close to the sources (e.g., in urban areas) freshly emitted NO locally scavenges O<sub>3</sub>. A long-term study of the spatial and temporal variability and trends of the ozone concentrations over Portugal is missing, aiming to answer the following questions:</p><p>-           What is the temporal variability of ozone concentrations?</p><p>-           Which trends can we find in observations?</p><p>-           How were the ozone spring maxima concentrations affected by the COVID-19 lockdown during spring 2020?</p><p>In this presentation, these questions will be answered based on the statistical analysis of O<sub>3</sub> concentrations recorded within the national air quality monitoring network between 2005 and 2020 (16 years). The variability of the surface ozone concentrations over Portugal, on the timescales from diurnal to annual, will be presented and discussed, taking into account the physical and chemical processes that control that variability. Using the TheilSen function from the OpenAir package for R (Carslaw and Ropkins 2012), which quantifies monotonic trends and calculates the associated p-value through bootstrap simulations, O<sub>3</sub> concentration long-term trends will be estimated for the different regions and environments (e.g., rural, urban).  Moreover, taking advantage of the unique situation provided by the COVID-19 lockdown during spring 2020, when the government imposed mandatory confinement and citizens movement restriction, leading to a reduction in traffic-related atmospheric emissions, the role of these emissions on ozone levels during the spring period will be studied and presented.</p><p> </p><p>Carslaw and Ropkins, 2012. Openair—an R package for air quality data analysis. Environ. Model. Softw. 27-28,52-61. https://doi.org/10.1016/j.envsoft.2011.09.008</p><p>Monteiro et al., 2012. Trends in ozone concentrations in the Iberian Peninsula by quantile regression and clustering. Atmos. Environ. 56, 184-193. https://doi.org/10.1016/j.atmosenv.2012.03.069</p>

Long-Term Frequency Stabilization with Digital Servo System

2009 ◽  
Vol 36 (4) ◽  
pp. 804-808 ◽  
Author(s):  
姬中华 Ji Zhonghua ◽  
张冉冉 Zhang Ranran ◽  
马杰 Ma Jie ◽  
董磊 Dong Lei ◽  
赵延霆 Zhao Yanting ◽  
...  
Keyword(s):  
Servo System ◽  
Frequency Stabilization ◽  
Term Frequency

Long-Term Frequency Stabilization of Multi-Lasers Based on Wavemeter

2019 ◽  
Vol 46 (2) ◽  
pp. 0211004 ◽  
Author(s):  
钱源 Qian Yuan ◽  
梁世勇 Liang Shiyong ◽  
黄垚 Huang Yao ◽  
管桦 Guan Hua ◽  
高克林 Gao Kelin
Keyword(s):  
Frequency Stabilization ◽  
Term Frequency

scholarly journals Long-term erosion of the Nepal Himalayas by bedrock landsliding: the role of monsoons, earthquakes and giant landslides

2019 ◽  
Vol 7 (1) ◽  
pp. 107-128 ◽  
Author(s):  
Odin Marc ◽  
Robert Behling ◽  
Christoff Andermann ◽  
Jens M. Turowski ◽  
Luc Illien ◽  
...  
Keyword(s):  
Frequency Distribution ◽  
Sampling Time ◽  
Clear Correlation ◽  
Size Frequency Distribution ◽  
Meteorological Forcing ◽  
Erosion Rates ◽  
Size Frequency ◽  
Heavy Tailed ◽  
Mountain Belts

Abstract. In active mountain belts with steep terrain, bedrock landsliding is a major erosional agent. In the Himalayas, landsliding is driven by annual hydro-meteorological forcing due to the summer monsoon and by rarer, exceptional events, such as earthquakes. Independent methods yield erosion rate estimates that appear to increase with sampling time, suggesting that rare, high-magnitude erosion events dominate the erosional budget. Nevertheless, until now, neither the contribution of monsoon and earthquakes to landslide erosion nor the proportion of erosion due to rare, giant landslides have been quantified in the Himalayas. We address these challenges by combining and analysing earthquake- and monsoon-induced landslide inventories across different timescales. With time series of 5 m satellite images over four main valleys in central Nepal, we comprehensively mapped landslides caused by the monsoon from 2010 to 2018. We found no clear correlation between monsoon properties and landsliding and a similar mean landsliding rate for all valleys, except in 2015, where the valleys affected by the earthquake featured ∼5–8 times more landsliding than the pre-earthquake mean rate. The long-term size–frequency distribution of monsoon-induced landsliding (MIL) was derived from these inventories and from an inventory of landslides larger than ∼0.1 km2 that occurred between 1972 and 2014. Using a published landslide inventory for the Gorkha 2015 earthquake, we derive the size–frequency distribution for earthquake-induced landsliding (EQIL). These two distributions are dominated by infrequent, large and giant landslides but under-predict an estimated Holocene frequency of giant landslides (> 1 km3) which we derived from a literature compilation. This discrepancy can be resolved when modelling the effect of a full distribution of earthquakes of variable magnitude and when considering that a shallower earthquake may cause larger landslides. In this case, EQIL and MIL contribute about equally to a total long-term erosion of ∼2±0.75 mm yr−1 in agreement with most thermo-chronological data. Independently of the specific total and relative erosion rates, the heavy-tailed size–frequency distribution from MIL and EQIL and the very large maximal landslide size in the Himalayas indicate that mean landslide erosion rates increase with sampling time, as has been observed for independent erosion estimates. Further, we find that the sampling timescale required to adequately capture the frequency of the largest landslides, which is necessary for deriving long-term mean erosion rates, is often much longer than the averaging time of cosmogenic 10Be methods. This observation presents a strong caveat when interpreting spatial or temporal variability in erosion rates from this method. Thus, in areas where a very large, rare landslide contributes heavily to long-term erosion (as the Himalayas), we recommend 10Be sample in catchments with source areas > 10 000 km2 to reduce the method mean bias to below ∼20 % of the long-term erosion.

scholarly journals Climatology of Cloud Vertical Structures from Long-Term High-Resolution Radiosonde Measurements in Beijing

Atmosphere ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 401
Author(s):  
Qing Zhou ◽  
Yong Zhang ◽  
Shuze Jia ◽  
Junli Jin ◽  
Shanshan Lv ◽  
...  
Keyword(s):  
High Resolution ◽  
Atmospheric Circulation ◽  
Frequency Distribution ◽  
Hydrological Cycle ◽  
Global Radiation ◽  
Circulation Model ◽  
Middle Level ◽  
Radiation Budget ◽  
Cloud Base

Clouds are significant in the global radiation budget, atmospheric circulation, and hydrological cycle. However, knowledge regarding the observed climatology of the cloud vertical structure (CVS) over Beijing is still poor. Based on high-resolution radiosonde observations at Beijing Nanjiao Weather Observatory (BNWO) during the period 2010–2017, the method for identifying CVS depending on height-resolved relative humidity thresholds is improved, and CVS estimation by radiosonde is compared with observations by millimeter-wave cloud radar and ceilometer at the same site. Good consistency is shown between the three instruments. Then, the CVS climatology, including the frequency distribution and seasonal variation, is investigated. Overall, the occurrence frequency (OF) of cloudy cases in Beijing is slightly higher than that of clear-sky cases, and the cloud OF is highest in summer and lowest in winter. Single-layer clouds and middle-level clouds are dominant in Beijing. In addition, the average cloud top height (CTH), cloud base height (CBH), and cloud thickness in Beijing are 6.2 km, 4.0 km, and 2.2 km, respectively, and show the trend of reaching peaks in spring and minimums in winter. In terms of frequency distribution, the CTH basically resides below an altitude of 16 km, and approximately 43% of the CBHs are located at altitudes of 0.5–1.5 km. The cloud OF has only one peak located at altitudes of 4–8 km in spring, whereas it shows a trimodal distribution in other seasons. The height at which the cloud OF reaches its peak is highest in summer and lowest in winter. To the best of our knowledge, the cloud properties analyzed here are the first to elucidate the distribution and temporal variation of the CVS in Beijing from a long-term sounding perspective, and these results will provide a scientific observation basis for improving the atmospheric circulation model, as well as comparisons and verifications for measurements by ground-based remote sensing equipment.

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