Identification and trend analysis of homogeneous rainfall zones over the East Asia monsoon region

2014 ◽  
Vol 35 (7) ◽  
pp. 1422-1433 ◽  
Author(s):  
Jehangir Ashraf Awan ◽  
Deg-Hyo Bae ◽  
Kyung-Joong Kim
Elem Sci Anth ◽  
2017 ◽  
Vol 5 (0) ◽  
pp. 50 ◽  
Author(s):  
Kai-Lan Chang ◽  
Irina Petropavlovskikh ◽  
Owen R. Copper ◽  
Martin G. Schultz ◽  
Tao Wang

2008 ◽  
Vol 8 (24) ◽  
pp. 7543-7555 ◽  
Author(s):  
Y. J. He ◽  
I. Uno ◽  
Z. F. Wang ◽  
P. Pochanart ◽  
J. Li ◽  
...  

Abstract. The impact of the East Asia monsoon on the seasonal behavior of O3 in the boundary layer of Eastern China and the west Pacific region was analyzed for 2004–2006 by means of full-year nested chemical transport model simulations and continuous observational data obtained from three inland mountain sites in central and eastern China and three oceanic sites in the west Pacific region. The basic common features of O3 seasonal behaviors over all the monitoring sites are the pre- and post-monsoon peaks with a summer trough. Such bimodal seasonal patterns of O3 are predominant over the region with strong summer monsoon penetration, and become weaker or even disappear outside the monsoon region. The seasonal/geographical distribution of the pre-defined monsoon index indicated that the East Asia summer monsoon is responsible for the bimodal seasonal O3 pattern, and also partly account for the differences in the O3 seasonal variations between the inland mountain and oceanic sites. Over the inland mountain sites, the O3 concentration increased gradually from the beginning of the year, reached a maximum in June, decreased rapidly to the summer valley in July or August, and then peaked in September or October, thereafter decreased gradually again. Over the oceanic sites, O3 abundance showed a similar increasing trend beginning in January, but then decreased gradually from the end of March, followed by a wide trough with the minimum in July and August and a small peak in October or November. A sensitivity analysis performed by setting China-emission to zero revealed that the chemically produced O3 from China-emission contributed substantially to the O3 abundance, particularly the pre- and post-monsoon O3 peaks, over China mainland. We found that China-emission contributed more than 40% to total boundary layer O3 during summertime (60–70% in July) and accounted for about 40 ppb of each peak value over the inland region if without considering the effect of the nonlinear chemical productions. In contrast, over the oceanic region in the high monsoon index zone, the contribution of China-emission to total boundary layer O3 was always less than 20% (<10 ppb), and less than 10% in summer.


2021 ◽  
Vol 32 (2) ◽  
pp. 159-169
Author(s):  
Fengnian Wang ◽  
Yuejun Si ◽  
Baosheng Li ◽  
Dongfeng Niu ◽  
Xiaohao Wen ◽  
...  

10.2196/25454 ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. e25454
Author(s):  
Lori Ann Post ◽  
Jasmine S Lin ◽  
Charles B Moss ◽  
Robert Leo Murphy ◽  
Michael G Ison ◽  
...  

Background The COVID-19 pandemic has had a profound global impact on governments, health care systems, economies, and populations around the world. Within the East Asia and Pacific region, some countries have mitigated the spread of the novel coronavirus effectively and largely avoided severe negative consequences, while others still struggle with containment. As the second wave reaches East Asia and the Pacific, it becomes more evident that additional SARS-CoV-2 surveillance is needed to track recent shifts, rates of increase, and persistence associated with the pandemic. Objective The goal of this study is to provide advanced surveillance metrics for COVID-19 transmission that account for speed, acceleration, jerk, persistence, and weekly shifts, to better understand country risk for explosive growth and those countries who are managing the pandemic successfully. Existing surveillance coupled with our dynamic metrics of transmission will inform health policy to control the COVID-19 pandemic until an effective vaccine is developed. We provide novel indicators to measure disease transmission. Methods Using a longitudinal trend analysis study design, we extracted 330 days of COVID-19 data from public health registries. We used an empirical difference equation to measure the daily number of cases in East Asia and the Pacific as a function of the prior number of cases, the level of testing, and weekly shift variables based on a dynamic panel model that was estimated using the generalized method of moments approach by implementing the Arellano-Bond estimator in R. Results The standard surveillance metrics for Indonesia, the Philippines, and Myanmar were concerning as they had the largest new caseloads at 4301, 2588, and 1387, respectively. When looking at the acceleration of new COVID-19 infections, we found that French Polynesia, Malaysia, and the Philippines had rates at 3.17, 0.22, and 0.06 per 100,000. These three countries also ranked highest in terms of jerk at 15.45, 0.10, and 0.04, respectively. Conclusions Two of the most populous countries in East Asia and the Pacific, Indonesia and the Philippines, have alarming surveillance metrics. These two countries rank highest in new infections in the region. The highest rates of speed, acceleration, and positive upwards jerk belong to French Polynesia, Malaysia, and the Philippines, and may result in explosive growth. While all countries in East Asia and the Pacific need to be cautious about reopening their countries since outbreaks are likely to occur in the second wave of COVID-19, the country of greatest concern is the Philippines. Based on standard and enhanced surveillance, the Philippines has not gained control of the COVID-19 epidemic, which is particularly troubling because the country ranks 4th in population in the region. Without extreme and rigid social distancing, quarantines, hygiene, and masking to reverse trends, the Philippines will remain on the global top 5 list of worst COVID-19 outbreaks resulting in high morbidity and mortality. The second wave will only exacerbate existing conditions and increase COVID-19 transmissions.


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