Given the substantial radiative e ects of cirrus clouds and the need to validate cirrus cloud mass in climate models, it is important to measure the global distribution of cirrus properties with satellite remote sensing. Existing cirrus remote sensing techniques, such as solar re ectance methods, measure cirrus ice water path (IWP) rather indirectly and with limited accuracy. Submillimeter-wave radiometry is an independent method of cirrus remote sensing based on ice particles scattering the upwelling radiance emitted by the lower atmosphere. A new aircraft instrument, the Far Infrared Sensor for Cirrus (FIRSC), is described. The FIRSC employs a Fourier Transform Spectrometer (FTS), which measures the upwelling radiance across the whole submillimeter region (0.1 to 1.0 mm wavelength). This wide spectral coverage gives high sensitivity to most cirrus particle sizes and allows accurate determination of the characteristic particle size. Radiative transfer modeling is performed to analyze the capabilities of the submillimeter FTS technique. A linear inversion analysis is done to show that cirrus IWP, particle size, and upper tropospheric temperature and water vapor may be accurately measured. A nonlinear statistical algorithm is developed using a database of 20,000 spectra simulated by randomly varying most relevant cirrus and atmospheric parameters. An empirical orthogonal function analysis reduces the 500 point spectrum (20 to 70 cm ?1) to 15 \pseudo-channels" which are then input to a neural network to retrieve cirrus IWP and median particle diameter. A Monte Carlo accuracy study is performed with simulated spectra having realistic noise. The retrieval errors are low for IWP (rms less than a factor of 1.5) and for particle sizes (rms less than 30%) for IWP greater than 5 g/m 2 and a wide range of median particle sizes. This detailed modeling indicates that there is good potential to accurately measure cirrus properties with a submillimeter FTS.
ContextExamining land cover’s influences on roadkills at single predetermined scales is more common than evaluating multiple scales, but examining land cover at the appropriate scale may be necessary for efficient design of mitigation measures, and that appropriate scale may be difficult to discern a priori. In addition, the taxonomic rank at which data is analysed may influence results and subsequent conclusions concerning mitigation. AimsThe objective of the present study was to assess the influence of variation in spatial scales of land cover explanatory variables and taxonomic rank of response variables in models of wildlife–vehicle collisions (WVCs). Research questions include: (1) do the scales of land cover measurement that produce the highest quality models differ among species; (2) do the factors that influence roadkill events differ within species at different scales of measurement and among species overall; and (3) does the taxonomic rank at which data is analysed influence the selection of explanatory variables? MethodsFour frequent WVC species representing diverse taxonomic classes, i.e. two mammals (Cerdocyon thous and Hydrochaeris hydrochaeris), one reptile (Caiman yacare) and one bird (Caracara plancus), were examined. WVCs were buffered, land cover classes from classified satellite imagery at three buffer radii were extracted, and logistic regression model selection was used. Key resultsThe scale of land cover variables selected for the highest quality models (and the selected variables themselves) may vary among wild fauna. The same explanatory variables and formulae are not always included in the candidate models in all compared scales for a given species. Explanatory variables may differ among taxonomically similar species, e.g. mammals, and pooling species at higher taxonomic ranks can result in models that do not correspond with species-level models of all pooled species. ConclusionsThe most accurate analyses of WVCs will likely be found when species are analysed individually and multiple scales of predictor variable collection are evaluated. ImplicationsMitigating the effects of roadways on wildlife population declines for both common and rare species is resource intensive. Resources spent optimising models for spatially targeting management actions may reduce the amount of resources used and increase the effectiveness of these actions.
Improved techniques for remote sensing of cirrus are needed to obtain global data for assessing the effect of cirrus in climate change models. Model calculations show that the far infrared/sub-millimeter spectral region is well suited for retrieving cirrus Ice Water Path and particle size parameters. Especially useful cirrus information is obtained at frequencies below 60 cm -1 where single particle scattering dominates over thermal emission for ice particles larger than about 50 µm. Earth radiance spectra have been obtained for a range of cloud conditions using an aircraft-based Fourier transform spectrometer. The Far InfraRed Sensor for Cirrus (FIRSC) is a Martin-Puplett interferometer which incorporates a polarizer for the beamsplitter and can be operated in either intensity or linear polarization measurement mode. Two detector channels span 10 to 140 cm -1 with a spectral resolution of 0.1 cm -1 ; achieving a Noise Equivalent Temperature of approximately 1K at 30 cm -1 in a 4 sec scan. Examples are shown of measured and modeled Earth radiance for a range of cloud conditions from 1998 and 1999 flights.
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