科研成果

This study examines the hygroscopicity of newly formed particles (diameters range 25-45 nm) during two atmospheric new particle formation (NPF) events in the German mid-level mountains during the Hill Cap Cloud Thuringia 2010 (HCCT-2010) field experiment. At the end of the NPF event involving clear particle growth, we measured an unusually high soluble particle fraction of 58.5% at 45 nm particle size. The particle growth rate contributed through sulfuric acid condensation only accounts for around 6.5% of the observed growth rate. Estimations showed that sulfuric acid condensation explained, however, only around 10% of that soluble particle fraction. Therefore, the formation of additional water-soluble matter appears imperative to explain the missing soluble fraction. Although direct evidence is missing, we consider water-soluble organics as candidates for this mechanism. For the case with clear growth process, the particle growth rate was determined by two alternative methods based on tracking the mode diameter of the nucleation mode. The mean particle growth rate obtained from the inter-site data comparison using Lagrangian consideration is 3.8 (+/- 2.6) nm h(-1). During the same period, the growth rate calculated based on one site data is 5.0 nm h(-1) using log-normal distribution function method. In light of the fact that considerable uncertainties could be involved in both methods, we consider both estimated growth rates consistent.

Gaseous and particulate species from two prescribed fires were sampled in-situ, to better characterize prescribed burn emissions. Measurements included gaseous and fine particulate matter (PM2.5) species, particle number concentration, particulate organic carbon (POC) speciation, water-soluble organic carbon (WSOC) and water-soluble iron. Major PM2.5 components included OC (∼57%), EC (∼10%), chloride (∼1.6%), potassium (∼0.7%) and nitrate (∼0.9%). Major gaseous species include carbon dioxide, carbon monoxide, methane, ethane, methanol and ethylene. Particulate organic tracers of biomass burning, such as levoglucosan, dehydroabietic acid and retene, increased significantly during the burns. Water-soluble organic carbon (WSOC) also increased significantly during the fire and levels are highly correlated with total potassium (K) (R2 = 0.93) and levoglucosan (R2 = 0.98). The average WSOC/OC ratio was 0.51 ± 0.03 and did not change significantly from background levels. Thus, the WSOC/OC ratio may not be a good indicator of secondary organic aerosol (SOA) in regions that are expected to be impacted by biomass burning. Results using a biomass burning source profile derived from this work further indicate that source apportionment is sensitive to levels of potassium in biomass burning source profiles. This underscores the importance of quantifying local biomass burning source profiles.

Developing highly scalable algorithms for global atmospheric modeling is becoming increasingly important as scientists inquire to understand behaviors of the global atmosphere at extreme scales. Nowadays, heterogeneous architecture based on both processors and accelerators is becoming an important solution for large-scale computing. However, large-scale simulation of the global atmosphere brings a severe challenge to the development of highly scalable algorithms that fit well into state-of-the-art heterogeneous systems. Although successes have been made on GPU-accelerated computing in some top-level applications, studies on fully exploiting heterogeneous architectures in global atmospheric modeling are still very less to be seen, due in large part to both the computational difficulties of the mathematical models and the requirement of high accuracy for long term simulations. In this paper, we propose a peta-scalable hybrid algorithm that is successfully applied in a cubed-sphere shallow-water model in global atmospheric simulations. We employ an adjustable partition between CPUs and GPUs to achieve a balanced utilization of the entire hybrid system, and present a pipe-flow scheme to conduct conflict-free inter-node communication on the cubed-sphere geometry and to maximize communication-computation overlap. Systematic optimizations for multithreading on both GPU and CPU sides are performed to enhance computing throughput and improve memory efficiency. Our experiments demonstrate nearly ideal strong and weak scalabilities on up to 3,750 nodes of the Tianhe-1A. The largest run sustains a performance of 0.8 Pflops in double precision (32% of the peak performance), using 45,000 CPU cores and 3,750 GPUs.