Design of a Hybrid Modular Switch

Network Function Virtualization (NFV) shed new light for the design, deployment, and management of cloud networks. Many network functions such as firewalls, load balancers, and intrusion detection systems can be virtualized by servers. However, network operators often have to sacrifice programmability in order to achieve high throughput, especially at networks' edge where complex network functions are required. Here, we design, implement, and evaluate Hybrid Modular Switch (HyMoS). The hybrid hardware/software switch is designed to meet requirements for modern-day NFV applications in providing high-throughput, with a high degree of programmability. HyMoS utilizes P4-compatible Network Interface Cards (NICs), PCI Express interface and CPU to act as line cards, switch fabric, and fabric controller respectively. In our implementation of HyMos, PCI Express interface is turned into a non-blocking switch fabric with a throughput of hundreds of Gigabits per second. Compared to existing NFV infrastructure, HyMoS offers modularity in hardware and software as well as a higher degree of programmability by supporting a superset of P4 language

Apparatus for real-time acoustic imaging of Rayleigh-Benard convection

We have designed and built an apparatus for real-time acoustic imaging of convective flow patterns in optically opaque fluids. This apparatus takes advantage of recent advances in two-dimensional ultrasound transducer array technology; it employs a modified version of a commercially available ultrasound camera, similar to those employed in non-destructive testing of solids. Images of convection patterns are generated by observing the lateral variation of the temperature dependent speed of sound via refraction of acoustic plane waves passing vertically through the fluid layer. The apparatus has been validated by observing convection rolls in both silicone oil and ferrofluid.Comment: 20 pages, 11 figures, submitted to the Review of Scientific Instrument

Impact of Cloud Ice Particle Size Uncertainty in a Climate Model and Implications for Future Satellite Missions

Ice particle size is pivotal to determining ice cloud radiative effect and precipitating rate. However, there is a lack of accurate ice particle effective radius (R_(ei)) observation on the global scale to constrain its representation in climate models. In support of future mission design, here we present a modeling assessment of the sensitivity of climate simulations to R_(ei) and quantify the impact of the proposed mission concept on reducing the uncertainty in climate sensitivity. We perturb the parameters pertaining to ice fall speed parameter and R_(ei) in radiation scheme, respectively, in National Center for Atmospheric Research CESM1 model with a slab ocean configuration. The model sensitivity experiments show that a settling velocity increase due to a larger R_(ei) results in a longwave cooling dominating over a shortwave warming, a global mean surface temperature decrease, and precipitation suppression. A similar competition between longwave and shortwave cloud forcing changes also exists when perturbing R_(ei) in the radiation scheme. Linearity generally holds for the climate response for R_(ei) related parameters. When perturbing falling snow particle size (R_(es)) in a similar way, we find much less sensitivity of climate responses. Our quadrupling CO₂ experiments with different parameter settings reveal that R_(ei) and R_(es) can account for changes in climate sensitivity significantly from +12.3% to −6.2%. By reducing the uncertainty ranges of R_(ei) and R_(es) from a factor of 2 to ±25%, a future satellite mission under design is expected to improve the climate state simulations and reduce the climate sensitivity uncertainty pertaining to ice particle size by approximately 60%. Our results highlight the importance of better observational constraints on R_(ei) by satellite missions

Radiative absorption enhancement of dust mixed with anthropogenic pollution over East Asia

The particle mixing state plays a significant yet poorly quantified role in aerosol radiative forcing, especially for the mixing of dust (mineral absorbing) and anthropogenic pollution (black carbon absorbing) over East Asia. We have investigated the absorption enhancement of mixed-type aerosols over East Asia by using the Aerosol Robotic Network observations and radiative transfer model calculations. The mixed-type aerosols exhibit significantly enhanced absorbing ability than the corresponding unmixed dust and anthropogenic aerosols, as revealed in the spectral behavior of absorbing aerosol optical depth, single scattering albedo, and imaginary refractive index. The aerosol radiative efficiencies for the dust, mixed-type, and anthropogenic aerosols are −101.0, −112.9, and −98.3 Wm⁻²τ⁻¹ at the bottom of the atmosphere (BOA); −42.3, −22.5, and −39.8 Wm⁻²τ⁻¹ at the top of the atmosphere (TOA); and 58.7, 90.3, and 58.5 Wm⁻²τ⁻¹ in the atmosphere (ATM), respectively. The BOA cooling and ATM heating efficiencies of the mixed-type aerosols are significantly higher than those of the unmixed aerosol types over the East Asia region, resulting in atmospheric stabilization. In addition, the mixed-type aerosols correspond to a lower TOA cooling efficiency, indicating that the cooling effect by the corresponding individual aerosol components is partially counteracted. We conclude that the interaction between dust and anthropogenic pollution not only represents a viable aerosol formation pathway but also results in unfavorable dispersion conditions, both exacerbating the regional air pollution in East Asia. Our results highlight the necessity to accurately account for the mixing state of aerosols in atmospheric models over East Asia in order to better understand the formation mechanism for regional air pollution and to assess its impacts on human health, weather, and climate

Impact of Cloud Ice Particle Size Uncertainty in a Climate Model and Implications for Future Satellite Missions

Ice particle size is pivotal to determining ice cloud radiative effect and precipitating rate. However, there is a lack of accurate ice particle effective radius (R_(ei)) observation on the global scale to constrain its representation in climate models. In support of future mission design, here we present a modeling assessment of the sensitivity of climate simulations to R_(ei) and quantify the impact of the proposed mission concept on reducing the uncertainty in climate sensitivity. We perturb the parameters pertaining to ice fall speed parameter and R_(ei) in radiation scheme, respectively, in National Center for Atmospheric Research CESM1 model with a slab ocean configuration. The model sensitivity experiments show that a settling velocity increase due to a larger R_(ei) results in a longwave cooling dominating over a shortwave warming, a global mean surface temperature decrease, and precipitation suppression. A similar competition between longwave and shortwave cloud forcing changes also exists when perturbing R_(ei) in the radiation scheme. Linearity generally holds for the climate response for R_(ei) related parameters. When perturbing falling snow particle size (R_(es)) in a similar way, we find much less sensitivity of climate responses. Our quadrupling CO₂ experiments with different parameter settings reveal that R_(ei) and R_(es) can account for changes in climate sensitivity significantly from +12.3% to −6.2%. By reducing the uncertainty ranges of R_(ei) and R_(es) from a factor of 2 to ±25%, a future satellite mission under design is expected to improve the climate state simulations and reduce the climate sensitivity uncertainty pertaining to ice particle size by approximately 60%. Our results highlight the importance of better observational constraints on R_(ei) by satellite missions