ALBERT

Description: Detecting a tropospheric ozone trend from sparsely sampled ozonesonde profiles (typically once per week) is challenging due to the short-lived anomalies in the time series resulting from ozone's high temporal variability. To enhance trend detection, we have developed a sophisticated statistical approach that utilizes a geoadditive model to assess ozone variability across a time series of vertical profiles. Treating the profile time series as a set of individual time series on discrete pressure surfaces, a class of smoothing spline ANOVA (analysis of variance) models is used for the purpose of jointly modeling multiple correlated time series (on separate pressure surfaces) by their associated seasonal and interannual variabilities. This integrated fit method filters out the unstructured variation through a statistical regularization (i.e., a roughness penalty) by taking advantage of the additional correlated data points available on the pressure surfaces above and below the surface of interest. We have applied this technique to the trend analysis of the vertically correlated time series of tropospheric ozone observations from (1) IAGOS (In-service Aircraft for a Global Observing System) commercial aircraft profiles above Europe and China throughout 1994–2017 and (2) NOAA GML's (Global Monitoring Laboratory) ozonesonde records at Hilo, Hawaii, (1982–2018) and Trinidad Head, California (1998–2018). We illustrate the ability of this technique to detect a consistent trend estimate and its effectiveness in reducing the associated uncertainty in the profile data due to the low sampling frequency. We also conducted a sensitivity analysis of frequent IAGOS profiles above Europe (approximately 120 profiles per month) to determine how many profiles in a month are required for reliable long-term trend detection. When ignoring the vertical correlation, we found that a typical sampling strategy (i.e. four profiles per month) might result in 7 % of sampled trends falling outside the 2σ uncertainty interval derived from the full dataset with an associated 10 % of mean absolute percentage error. Based on a series of sensitivity studies, we determined optimal sampling frequencies for (1) basic trend detection and (2) accurate quantification of the trend. When applying the integrated fit method, we find that a typical sampling frequency of four profiles per month is adequate for basic trend detection; however, accurate quantification of the trend requires 14 profiles per month. Accurate trend quantification can be achieved with only 10 profiles per month if a regular sampling frequency is applied. In contrast, the standard separated fit method, which ignores the vertical correlation between pressure surfaces, requires 8 profiles per month for basic trend detection and 18 profiles per month for accurate trend quantification. While our method improves trend detection from sparse datasets, the key to substantially reducing the uncertainty is to increase the sampling frequency.

Description: Author(s): Chang-Kai Chiu, Yi-Hsin Chen, Yen-Chun Chen, Ite A. Yu, Ying-Cheng Chen, and Yong-Fan Chen We observed electromagnetically induced transparency-based four-wave mixing (FWM) in the pulsed regime at low light levels. The FWM conversion efficiency of 3.8(9)% was observed in a four-level system of cold 87Rb atoms using a driving laser pulse with a peak intensity of ≈80 μW/cm2, corresponding t... [Phys. Rev. A 89, 023839] Published Mon Feb 24, 2014

Description: Author(s): Chang-Kai Li, Feng Wang, Bin Liao, Xiao-Ping OuYang, and Feng-Shou Zhang We present an ab initio study of the electronic stopping power of protons and helium ions in an insulating material, HfO 2 . The calculations are carried out in channeling conditions with different impact parameters by employing Ehrenfest dynamics and real-time, time-dependent density functional theor... [Phys. Rev. B 96, 094301] Published Fri Sep 01, 2017

Description: Author(s): Chang-kai Li, Fei Mao, Feng Wang, Yan-long Fu, Xiao-ping Ouyang, and Feng-Shou Zhang Nonadiabatic dynamics simulations are performed to investigate the electronic stopping power of a helium ion moving through ZnTe crystalline thin films under channeling conditions. Using ab initio time-dependent density-functional theory, we found by direct simulation that electronic stopping power … [Phys. Rev. A 95, 052706] Published Mon May 22, 2017

Description: We have developed a new statistical approach (M3Fusion) for combining surface ozone observations from thousands of monitoring sites around the world with the output from multiple atmospheric chemistry models to produce a global surface ozone distribution with greater accuracy than can be provided by any individual model. The ozone observations from 4766 monitoring sites were provided by the Tropospheric Ozone Assessment Report (TOAR) surface ozone database which contains the world's largest collection of surface ozone metrics. Output from six models was provided by the participants of the Chemistry-Climate Model Initiative (CCMI) and NASA's Global Modeling and Assimilation Office (GMAO). We analyze the 6-month maximum of the maximum daily 8-hour average ozone value (DMA8) for relevance to ozone health impacts. We interpolate the irregularly-spaced observations onto a fine resolution grid by using integrated nested Laplace approximations, and compare the ozone field to each model in each world region. This method allows us to produce a global surface ozone field based on TOAR observations, which we then use to select the combination of global models with the greatest skill in each of 8 world regions; models with greater skill in a particular region are given higher weight. This blended model product is bias-corrected within two degrees of observation locations to produce the final fused surface ozone product. We show that our fused product has an improved mean squared error compared to the simple multi-model ensemble mean.

Description: We have developed a new statistical approach (M3Fusion) for combining surface ozone observations from thousands of monitoring sites around the world with the output from multiple atmospheric chemistry models to produce a global surface ozone distribution with greater accuracy than can be provided by any individual model. The ozone observations from 4766 monitoring sites were provided by the Tropospheric Ozone Assessment Report (TOAR) surface ozone database, which contains the world's largest collection of surface ozone metrics. Output from six models was provided by the participants of the Chemistry-Climate Model Initiative (CCMI) and NASA's Global Modeling and Assimilation Office (GMAO). We analyze the 6-month maximum of the maximum daily 8 h average ozone value (DMA8) for relevance to ozone health impacts. We interpolate the irregularly spaced observations onto a fine-resolution grid by using integrated nested Laplace approximations and compare the ozone field to each model in each world region. This method allows us to produce a global surface ozone field based on TOAR observations, which we then use to select the combination of global models with the greatest skill in each of eight world regions; models with greater skill in a particular region are given higher weight. This blended model product is bias corrected within 2∘ of observation locations to produce the final fused surface ozone product. We show that our fused product has an improved mean squared error compared to the simple multi-model ensemble mean, which is biased high in most regions of the world.

Description: Negative temperature perturbations (T′) from gravity waves are known to be favorable to tropical tropopause layer (TTL) clouds, and recent studies have further suggested a possible role of dT′/dz in facilitating TTL cloud formation and maintenance. With a focus on exploring the influence of dT′/dz on TTL clouds, this study utilizes radio occultation temperature retrievals and cloud layers from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) to understand how gravity wave perturbations modulate cloud occurrence in the tropics. Cloud populations were evaluated in four phases corresponding to positive or negative T′ and dT′/dz. We find that 55 % of TTL clouds are found where T′ and dT′/dz are both negative. Regions of frequent convection are associated with higher cloud populations in the warm phase T′〉0. We show that the partitioning of cloud population among wave phases exhibits dependence on background relative humidity. In the phase where T′ and dT′/dz are both negative, the mean cloud effective radius is the smallest of all four phases, but the differences are small. It is shown that the strongest mean negative T′ anomaly is centered on the cloud top, resulting in positive dT′/dz above the cloud top and negative dT′/dz below. This negative T′ anomaly propagates downward with time, characteristic of upward propagating gravity waves. Negative (positive) T′ anomalies are associated with increased (decreased) probability of being occupied by clouds. The magnitude of T′ correlates with the increase or decrease in cloud occurrence, giving evidence that the wave amplitude influences the probability of cloud occurrence. While the decrease in cloud occurrence in the warm phase is centered on the altitude of T′ maxima, we show that the increase in cloud occurrence around T′ minima occurs below the minima in height, indicating that cloud formation or maintenance is facilitated mainly inside negative dT′/dz. Together with existing studies, our results suggest that the cold phase of gravity waves is favorable to TTL clouds mainly through the region where dT′/dz is negative.