Flying phase mask for the printing of long submicron-period stitchingless gratings

International audienceLong and stitchingless gratings are printed by means of a read/write head comprising a phase mask illuminated by an intensity modulated laser beam and a reference grating displacement sensor which dictates the modulation period real time. A nearly perfect grating copying is achieved by fixing the sensor grating scale and the written grating substrate on a long platform sliding under the read/write hea

Active and Passive Radiative Transfer Simulations for GPM-Related Field Campaigns

Using a three-dimensional radiative transfer model combined with cloud-resolving model output, we simulate active and passive sensor observations of clouds and precipitaiton. This combination of tools allows us to diagnose the contributions of various hydrometeor types. Radar multiple scattering is most closely associated with the presence of graupel. At Wband, massive amounts multiple scattering in deep convection can decorrelate the reflectivity profile from the vertical structure, but for less intense events, multiple scattering could be a useful indicator of riming. For passive sensors, polarization differences at 166 GHz indicate the presence of horizontally aligned frozen particles with pronounced aspect ratios, while high concentrations of more isotropic aggregates and graupel dampen the polarization difference while also contributing to the lowest brightness temperature depressions. The insights into remote sensing measurements will facilitate the development of improved algorithms and advanced sensors

What Role Does Hydrological Science Play in the Age of Machine Learning?

ABSTRACT: This paper is derived from a keynote talk given at the Google's 2020 Flood Forecasting Meets Machine Learning Workshop. Recent experiments applying deep learning to rainfall‐runoff simulation indicate that there is significantly more information in large‐scale hydrological data sets than hydrologists have been able to translate into theory or models. While there is a growing interest in machine learning in the hydrological sciences community, in many ways, our community still holds deeply subjective and nonevidence‐based preferences for models based on a certain type of “process understanding” that has historically not translated into accurate theory, models, or predictions. This commentary is a call to action for the hydrology community to focus on developing a quantitative understanding of where and when hydrological process understanding is valuable in a modeling discipline increasingly dominated by machine learning. We offer some potential perspectives and preliminary examples about how this might be accomplished

Design and construction of a Cherenkov imager for charge measurement of nuclear cosmic rays

A proximity focusing Cherenkov imager called CHERCAM, has been built for the charge measurement of nuclear cosmic rays with the CREAM instrument. It consists of a silica aerogel radiator plane across from a detector plane equipped with 1,600 1" diameter photomultipliers. The two planes are separated by a ring expansion gap. The Cherenkov light yield is proportional to the charge squared of the incident particle. The expected relative light collection accuracy is in the few percents range. It leads to an expected single element separation over the range of nuclear charge Z of main interest 1 < Z < 26. CHERCAM is designed to fly with the CREAM balloon experiment. The design of the instrument and the implemented technical solutions allowing its safe operation in high altitude conditions (radiations, low pressure, cold) are presented.Comment: 24 pages, 19 figure

K pi scattering for isospin 1/2 and 3/2 in lattice QCD

We simulate K pi scattering in s-wave and p-wave for both isospins I=1/2, 3/2 using quark-antiquark and meson-meson interpolating fields. We extract the elastic phase shifts delta at several values of the K-pi relative momenta. The resulting phases exhibit qualitative agreement with the experimental phases in all four channels. We express the s-wave phase shifts near threshold in terms of the scattering length and the effective range. Our K pi system has zero total momentum and is simulated on a single ensemble with two dynamical quarks, so results apply for mpi=266 MeV and mK=552 MeV in our simulation. The backtracking contractions in both I=1/2 channels are handled by the use of Laplacian-Heavyside smeared quarks within the distillation method. Elastic phases are extracted from the energy levels using Luscher's relations. In all four channels we observe the expected K(n)pi(-n) scattering states, which are shifted due to the interaction. In both attractive I=1/2 channels we observe additional states that are related to resonances; we attribute them to K_0^*(1430) in s-wave and K*(892), K*(1410) and K*(1680) in p-wave.Comment: 17 pages, 7 figures, version published in PR

Pattern and process in Amazon tree turnover, 1976-2001

Previous work has shown that tree turnover, tree biomass and large liana densities have increased in mature tropical forest plots in the late twentieth century. These results point to a concerted shift in forest ecological processes that may already be having significant impacts on terrestrial carbon stocks, fluxes and biodiversity. However, the findings have proved controversial, partly because a rather limited number of permanent plots have been monitored for rather short periods. The aim of this paper is to characterize regional-scale patterns of 'tree turnover' (the rate with which trees die and recruit into a population) by using improved datasets now available for Amazonia that span the past 25 years. Specifically, we assess whether concerted changes in turnover are occurring, and if so whether they are general throughout the Amazon or restricted to one region or environmental zone. In addition, we ask whether they are driven by changes in recruitment, mortality or both. We find that: (i) trees 10 cm or more in diameter recruit and die twice as fast on the richer soils of southern and western Amazonia than on the poorer soils of eastern and central Amazonia; (ii) turnover rates have increased throughout Amazonia over the past two decades; (iii) mortality and recruitment rates have both increased significantly in every region and environmental zone, with the exception of mortality in eastern Amazonia; (iv) recruitment rates have consistently exceeded mortality rates; (v) absolute increases in recruitment and mortality rates are greatest in western Amazonian sites; and (vi) mortality appears to be lagging recruitment at regional scales. These spatial patterns and temporal trends are not caused by obvious artefacts in the data or the analyses. The trends cannot be directly driven by a mortality driver (such as increased drought or fragmentation-related death) because the biomass in these forests has simultaneously increased. Our findings therefore indicate that long-acting and widespread environmental changes are stimulating the growth and productivity of Amazon forests

Impact of foot-and-mouth disease on mastitis and culling on a large-scale dairy farm in Kenya

Foot and mouth disease (FMD) is a highly transmissible viral infection of cloven hooved animals associated with severe economic losses when introduced into FMD-free countries. Information on the impact of the disease in FMDV-endemic countries is poorly characterised yet essential for the prioritisation of scarce resources for disease control programmes. A FMD (virus serotype SAT2) outbreak on a large-scale dairy farm in Nakuru County, Kenya provided an opportunity to evaluate the impact of FMD on clinical mastitis and culling rate. A cohort approach followed animals over a 12-month period after the commencement of the outbreak. For culling, all animals were included; for mastitis, those over 18 months of age. FMD was recorded in 400/644 cattle over a 29-day period. During the follow-up period 76 animals were culled or died whilst in the over 18 month old cohort 63 developed clinical mastitis. Hazard ratios (HR) were generated using Cox regression accounting for non-proportional hazards by inclusion of time-varying effects. Univariable analysis showed FMD cases were culled sooner but there was no effect on clinical mastitis. After adjusting for possible confounders and inclusion of time-varying effects there was weak evidence to support an effect of FMD on culling (HR = 1.7, 95% confidence intervals [CI] 0.88-3.1, P = 0.12). For mastitis, there was stronger evidence of an increased rate in the first month after the onset of the outbreak (HR = 2.9, 95%CI 0.97-8.9, P = 0.057)

Three-Dimensional Sensor Forward Modeling of Clouds and Precipitation in the Multi-Instrument Inverse Solver Testbed (MIIST)

Sensor forward models are an essential tool for interpreting remote sensing observations and performing quantitative estimates of geophysical parameters. Our three-dimensional forward modeling and retrieval framework allows us to perform detailed analyses of NASA field campaign datasets for a deeper understanding of the remote sensing of clouds and precipitation. This presentation details the componenets of this radiative transfer model used to simulate active (radar) and passive (microwave radiometer) observations, and we give some relevant examples based on both model precipitation systems and actual observations

Active and Passive Radiative Transfer Simulations for GPM-Related Field Campaigns

Radiative transfer modeling is an important tool for interpreting remote sensing observations. It allows us to determine how sensor characteristics will impact observations, and it gives us a framework for us to test assumptions about the phenomena we are attempting to observe. In this work, we use cloud simulations for precipitation events observed during various GPM-related field campaigns. The simulations show how various properties of clouds and precipitation affect the measurements