Skip to main content
Skip to main content

COAPS Virtual Library (Publications)

Search within Results:
Display Options:

Select All    Deselect All
 |   | 
Details
   print
  Records Links (up)
Author Hou, T.Y.; Yan, M.P.; Wu, Z. url  doi
openurl 
  Title A Variant Of The Emd Method For Multi-Scale Data Type $loc['typeJournal Article']
  Year 2009 Publication Advances in Adaptive Data Analysis Abbreviated Journal Adv. Adapt. Data Anal.  
  Volume 01 Issue 04 Pages 483-516  
  Keywords Empirical Mode Decomposition (EMD); adaptive data analysis; sparse representation  
  Abstract  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1793-5369 ISBN Medium  
  Area Expedition Conference  
  Funding Approved $loc['no']  
  Call Number COAPS @ mfield @ Serial 670  
Permanent link to this record
 

 
Author Liu, Q.; Tan, Z-M.; Sun, J.; Hou, Y.; Fu, C.; Wu, Z. url  openurl
  Title Changing rapid weather variability increases influenza epidemic risk in a warming climate Type $loc['typeJournal Article']
  Year 2020 Publication Environmental Research Letters Abbreviated Journal Environmental Research Letters  
  Volume 15 Issue 4 Pages  
  Keywords  
  Abstract The continuing change of the Earth's climate is believed to affect the influenza viral activity and transmission in the coming decades. However, a consensus of the severity of the risk of influenza epidemic in a warming climate has not been reached. It was previously reported that the warmer winter can reduce influenza epidemic-caused mortality, but this relation cannot explain the deadly influenza epidemic in many countries over northern mid-latitudes in the winter of 2017-2018, one of the warmest winters in recent decades. Here we reveal that the widely spread 2017-2018 influenza epidemic can be attributed to the abnormally strong rapid weather variability. We demonstrate, from historical data, that the large rapid weather variability in autumn can precondition the deadly influenza epidemic in the subsequent months in highly populated northern mid-latitudes; and the influenza epidemic season of 2017-2018 was a typical case. We further show that climate model projections reach a consensus that the rapid weather variability in autumn will continue to strengthen in some regions of northern mid-latitudes in a warming climate, implying that the risk of influenza epidemic may increase 20% to 50% in some highly populated regions in later 21st century.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Funding Approved $loc['no']  
  Call Number COAPS @ user @ Serial 1070  
Permanent link to this record
 

 
Author Deng, J.; Wu, Z.; Zhang, M.; Huang, N.E.; Wang, S.; Qiao, F. url  doi
openurl 
  Title Using Holo-Hilbert spectral analysis to quantify the modulation of Dansgaard-Oeschger events by obliquity Type $loc['typeJournal Article']
  Year 2018 Publication Quaternary Science Reviews Abbreviated Journal Quaternary Science Reviews  
  Volume 192 Issue Pages 282-299  
  Keywords Pleistocene; Paleoclimatology; Greenland; Antarctica; Data treatment; Data analysis; Dansgaard-oeschger (DO) events; Obliquity forcing; Phase preference; Holo-hilbert spectral analysis; Amplitude modulation; EMPIRICAL MODE DECOMPOSITION; GREENLAND ICE-CORE; NONSTATIONARY TIME-SERIES; ABRUPT CLIMATE-CHANGE; LAST GLACIAL PERIOD; NORTH-ATLANTIC; MILLENNIAL-SCALE; RECORDS; VARIABILITY; CYCLE  
  Abstract Astronomical forcing (obliquity and precession) has been thought to modulate Dansgaard-Oeschger (DO) events, yet the detailed quantification of such modulations has not been examined. In this study, we apply the novel Holo-Hilbert Spectral Analysis (HHSA) to five polar ice core records, quantifying astronomical forcing's time-varying amplitude modulation of DO events and identifying the preferred obliquity phases for large amplitude modulations. The unique advantages of HHSA over the widely used windowed Fourier spectral analysis for quantifying astronomical forcing's nonlinear modulations of DO events is first demonstrated with a synthetic data that closely resembles DO events recorded in Greenland ice cores (NGRIP, GRIP, and GISP2 cores on GICC05 modelext timescale). The analysis of paleoclimatic proxies show that statistically significantly more frequent DO events, with larger amplitude modulation in the Greenland region, tend to occur in the decreasing phase of obliquity, especially from its mean value to its minimum value. In the eastern Antarctic, although statistically significantly more DO events tend to occur in the decreasing obliquity phase in general, the preferred phase of obliquity for large amplitude modulation on DO events is a segment of the increasing phase near the maximum obliquity, implying that the physical mechanisms of DO events may be different for the two polar regions. Additionally, by using cross-spectrum and magnitude-squared analyses, Greenland DO mode at a timescale of about 1400 years leads the Antarctic DO mode at the same timescale by about 1000 years. (C) 2018 Elsevier Ltd. All rights reserved.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0277-3791 ISBN Medium  
  Area Expedition Conference  
  Funding Approved $loc['no']  
  Call Number COAPS @ user @ Serial 971  
Permanent link to this record
 

 
Author Liu, M.; Lin, J.; Wang, Y.; Sun, Y.; Zheng, B.; Shao, J.; Chen, L.; Zheng, Y.; Chen, J.; Fu, T.-M.; Yan, Y.; Zhang, Q.; Wu, Z. url  doi
openurl 
  Title Spatiotemporal variability of NO2 and PM2.5 over Eastern China: observational and model analyses with a novel statistical method Type $loc['typeJournal Article']
  Year 2018 Publication Atmospheric Chemistry and Physics Abbreviated Journal Atmos. Chem. Phys.  
  Volume 18 Issue 17 Pages 12933-12952  
  Keywords TROPOSPHERIC NITROGEN-DIOXIDE; PROVINCIAL CAPITAL CITIES; CRITERIA AIR-POLLUTANTS; BOUNDARY-LAYER; NORTH CHINA; HILBERT SPECTRUM; UNITED-STATES; TIME-SERIES; OZONE; EMISSIONS  
  Abstract Eastern China (27-41 degrees N, 110-123 degrees E) is heavily polluted by nitrogen dioxide (NO2), particulate matter with aerodynamic diameter below 2.5 mu m (PM2.5), and other air pollutants. These pollutants vary on a variety of temporal and spatial scales, with many temporal scales that are nonperiodic and nonstationary, challenging proper quantitative characterization and visualization. This study uses a newly compiled EOF-EEMD analysis visualization package to evaluate the spatiotemporal variability of ground-level NO2, PM2.5, and their associations with meteorological processes over Eastern China in fall-winter 2013. Applying the package to observed hourly pollutant data reveals a primary spatial pattern representing Eastern China synchronous variation in time, which is dominated by diurnal variability with a much weaker day-to-day signal. A secondary spatial mode, representing north-south opposing changes in time with no constant period, is characterized by wind-related dilution or a buildup of pollutants from one day to another.

We further evaluate simulations of nested GEOS-Chem v9-02 and WRF/CMAQ v5.0.1 in capturing the spatiotemporal variability of pollutants. GEOS-Chem underestimates NO2 by about 17 mu g m(-3) and PM2.5 by 35 mu g m(-3 )on average over fall-winter 2013. It reproduces the diurnal variability for both pollutants. For the day-to-day variation, GEOS-Chem reproduces the observed north-south contrasting mode for both pollutants but not the Eastern China synchronous mode (especially for NO2). The model errors are due to a first model layer too thick (about 130 m) to capture the near-surface vertical gradient, deficiencies in the nighttime nitrogen chemistry in the first layer, and missing secondary organic aerosols and anthropogenic dust. CMAQ overestimates the diurnal cycle of pollutants due to too-weak boundary layer mixing, especially in the nighttime, and overestimates NO2 by about 30 mu g m(-3) and PM2.5 by 60 mu g m(-3). For the day-to-day variability, CMAQ reproduces the observed Eastern China synchronous mode but not the north-south opposing mode of NO2. Both models capture the day-to-day variability of PM2.5 better than that of NO2. These results shed light on model improvement. The EOF-EEMD package is freely available for noncommercial uses.
 
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1680-7324 ISBN Medium  
  Area Expedition Conference  
  Funding Approved $loc['no']  
  Call Number COAPS @ user @ Serial 946  
Permanent link to this record
 

 
Author Zou, M.; Xiong, X.; Wu, Z.; Li, S.; Zhang, Y.; Chen, L. url  doi
openurl 
  Title Increase of Atmospheric Methane Observed from Space-Borne and Ground-Based Measurements Type $loc['typeJournal Article']
  Year 2019 Publication Remote Sensing Abbreviated Journal Remote Sensing  
  Volume 11 Issue 8 Pages  
  Keywords Methane increase trend; Boundary layer; Mid-upper troposphere; Satellite; AIRS  
  Abstract It has been found that the concentration of atmospheric methane (CH4) has rapidly increased since 2007 after a decade of nearly constant concentration in the atmosphere. As an important greenhouse gas, such an increase could enhance the threat of global warming. To better quantify this increasing trend, a novel statistic method, i.e. the Ensemble Empirical Mode Decomposition (EEMD) method, was used to analyze the CH4 trends from three different measurements: the mid-upper tropospheric CH4 (MUT) from the space-borne measurements by the Atmospheric Infrared Sounder (AIRS), the CH4 in the marine boundary layer (MBL) from NOAA ground-based in-situ measurements, and the column-averaged CH4 in the atmosphere (X-CH4) from the ground-based up-looking Fourier Transform Spectrometers at Total Carbon Column Observing Network (TCCON) and the Network for the Detection of Atmospheric Composition Change (NDACC). Comparison of the CH4 trends in the mid-upper troposphere, lower troposphere, and the column average from these three data sets shows that, overall, these trends agree well in capturing the abrupt CH4 increase in 2007 (the first peak) and an even faster increase after 2013 (the second peak) over the globe. The increased rates of CH4 in the MUT, as observed by AIRS, are overall smaller than CH4 in MBL and the column-average CH4. During 2009-2011, there was a dip in the increase rate for CH4 in MBL, and the MUT-CH4 increase rate was almost negligible in the mid-high latitude regions. The increase of the column-average CH4 also reached the minimum during 2009-2011 accordingly, suggesting that the trends of CH4 are not only impacted by the surface emission, however that they also may be impacted by other processes like transport and chemical reaction loss associated with [OH]. One advantage of the EEMD analysis is to derive the monthly rate and the results show that the frequency of the variability of CH4 increase rates in the mid-high northern latitude regions is larger than those in the tropics and southern hemisphere.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2072-4292 ISBN Medium  
  Area Expedition Conference  
  Funding Approved $loc['no']  
  Call Number COAPS @ user @ Serial 1055  
Permanent link to this record
Select All    Deselect All
 |   | 
Details
   print

Save Citations:
Export Records:

2000 Levy Avenue
Building A, Suite 292
Tallahassee, FL 32306-2741
Phone: (850) 644-4581
Fax: (850) 644-4841
contact@coaps.fsu.edu

© 2022 Center for Ocean-Atmospheric Prediction Studies (COAPS), Florida State University

Center for Ocean-Atmospheric Prediction Studies (COAPS)