Search

Many radar measurements of the atmosphere can be explained in terms of two scattering mechanisms: incoherent scattering from particles, and coherent scattering from variations in the refractive index of the air, commonly called clear-air or Bragg scattering. Spatial variations in the liquid water content of clouds may also give a coherent contribution to the radar return, but it is commonly believed that this coherent scattering from the droplets is insignificant because variations in humidity have a much larger influence on the refractive index than equal variations in liquid water content. It is argued that the fluctuations in water vapor mixing ratio in clouds can be much smaller than those in liquid water mixing ratio. In this article an expression for the strength of the coherent scattering from particles will be derived for fluctuations caused by turbulent mixing with clean (i.e., particle-free) air, where it will be assumed that the particles follow the flow, that is, their inertia is neglected. It will be shown that the coherent contribution adds to the incoherent contribution, the latter always being present. The coherent particle scattering can be stronger than the incoherent scattering, especially at longer wavelengths and high particle concentrations. Recently published dual-frequency measurements of developing cumulus clouds and smoke show a correlation for which no explanation has been found in terms of incoherent particle scattering and coherent air scattering. Scatterplots of the reflectivity factors at both frequencies show a clustering of points in between the values that correspond to pure clear-air and pure incoherent scattering. Those differences in the radar reflectivity factors could be due to a mixture of Bragg scattering and incoherent particle scattering, but then no correlation is expected, because the origin of the scattering mechanism that dominates at each wavelength is different. However, coherent scattering from the particles can cause the radar reflectivities of dual-wavelength radar measurements to become correlated with each other. It may explain the slopes and the differences seen in the scatterplots of the radar reflectivities of cloud and smoke measurements, with reasonable values of the parameters involved. However, the correlation between the radar reflectivities is very tight near the cloud top and seems to be present in adiabatic cores as well. This is an indication that, apart from mixing with environmental air, the inertia of the droplets could also be important for the creation of small-scale fluctuations in droplet concentration. more...

The COST (European Cooperation in Science and Technology) Action ES0601: Advances in homogenization methods of climate series: an integrated approach (HOME) has executed a blind intercomparison and validation study for monthly homogenization algorithms. Time series of monthly temperature and precipitation were evaluated because of their importance for climate studies and because they represent two important types of statistics (additive and multiplicative). The algorithms were validated against a realistic benchmark dataset. The benchmark contains real inhomogeneous data as well as simulated data with inserted inhomogeneities. Random break-type inhomogeneities were added to the simulated datasets modeled as a Poisson process with normally distributed breakpoint sizes. To approximate real world conditions, breaks were introduced that occur simultaneously in multiple station series within a simulated network of station data. The simulated time series also contained outliers, missing data periods and local station trends. Further, a stochastic nonlinear global (network-wide) trend was added. Participants provided 25 separate homogenized contributions as part of the blind study as well as 22 additional solutions submitted after the details of the imposed inhomogeneities were revealed. These homogenized datasets were assessed by a number of performance metrics including (i) the centered root mean square error relative to the true homogeneous value at various averaging scales, (ii) the error in linear trend estimates and (iii) traditional contingency skill scores. The metrics were computed both using the individual station series as well as the network average regional series. The performance of the contributions depends significantly on the error metric considered. Contingency scores by themselves are not very informative. Although relative homogenization algorithms typically improve the homogeneity of temperature data, only the best ones improve precipitation data. Training was found to be very important. Moreover, state-of-the-art relative homogenization algorithms developed to work with an inhomogeneous reference are shown to perform best. The study showed that currently automatic algorithms can perform as well as manual ones. [ABSTRACT FROM AUTHOR] more...