Correlations between microphysical properties of large-scale semi-transparent cirrus and the state of the atmosphere

By making use of TOVS Path-B satellite retrievals and ECMWF reanalyses, correlations between bulk microphysical properties of large-scale semi-transparent cirrus (visible optical thickness between 0.7 and 3.8) and thermodynamic and dynamic properties of the surrounding atmosphere have been studied on a global scale. These clouds constitute about half of all high clouds. The global averages (from 60°N to 60°S) of mean ice crystal diameter, De, and ice water path (IWP) of these clouds are 55 μm and 30 g m−2, respectively. IWP of these cirrus is slightly increasing with cloud-top temperature, whereas De of cold cirrus does not depend on this parameter. Correlations between De and IWp of large-scale cirrus seem to be different in the midlatitudes and in the tropics. However, we observe in general stronger correlations between De and IWP and atmospheric humidity and winds deduced from the ECMWF reanalyses: De and IWP increase both with increasing atmospheric water vapour. There is also a good distinction between different dynamical situations: In humid situations, IWP is on average about 10 gm−2 larger in regions with strong large-scale vertical updraft only that in regions with strong large-scale horizontal winds only, whereas the mean De of cold large-scale cirrus decreases by about 10 μm if both strong large-scale updraft and horizontal winds are present.

Quantitative analysis of remote gas temperatures and concentrations from their infrared emission spectra

Techniques for obtaining quantitative values of the temperatures and concentrations of remote hot gaseous effluents from their measured passive emission spectra have been examined in laboratory experiments and on field trials. These emission spectra were obtained using an adapted FTIR spectrometer with 0.25 cm-1 spectral resolution. The CO2 and H2O vapour content in the plume from a 55 m smoke stack and the temperature of these gases were obtained by comparing the measured emission spectra with those modelled using the HITRAN atmospheric transmission database. The spatial distributions of CO2, CO and unburnt CH4 in a laboratory methane flame were reconstructed tomographically using a matrix inversion technique.

Remote gas detection and quantitative analysis from infrared emission spectra obtained by Fourier transform infrared spectroscopy

Techniques for obtaining quantitative values of the temperatures and concentrations of remote hot gaseous effluents from their measured passive emission spectra have been examined in laboratory experiments. The high sensitivity of the spectrometer in the vicinity of the 2397 cm-1 band head region of CO2 has allowed the gas temperature to be calculated from the relative intensity of the observed rotational lines. The spatial distribution of the CO2 in a methane flame has been reconstructed tomographically using a matrix inversion technique. The spectrometer has been calibrated against a black body source at different temperatures and a self absorption correction has been applied to the data avoiding the need to measure the transmission directly. Reconstruction artifacts have been reduced by applying a smoothing routine to the inversion matrix.

Translating climate forecasts into agricultural terms: advances and challenges

Seasonal climate prediction offers the potential to anticipate variations in crop production early enough to adjust critical decisions. Until recently, interest in exploiting seasonal forecasts from dynamic climate models (e.g. general circulation models, GCMs) for applications that involve crop simulation models has been hampered by the difference in spatial and temporal scale of GCMs and crop models, and by the dynamic, nonlinear relationship between meteorological variables and crop response. Although GCMs simulate the atmosphere on a sub-daily time step, their coarse spatial resolution and resulting distortion of day-to-day variability limits the use of their daily output. Crop models have used daily GCM output with some success by either calibrating simulated yields or correcting the daily rainfall output of the GCM to approximate the statistical properties of historic observations. Stochastic weather generators are used to disaggregate seasonal forecasts either by adjusting input parameters in a manner that captures the predictable components of climate, or by constraining synthetic weather sequences to match predicted values. Predicting crop yields, simulated with historic weather data, as a statistical function of seasonal climatic predictors, eliminates the need for daily weather data conditioned on the forecast, but must often address poor statistical properties of the crop-climate relationship. Most of the work on using crop simulation with seasonal climate forecasts has employed historic analogs based on categorical ENSO indices. Other methods based on classification of predictors or weather types can provide daily weather inputs to crop models conditioned on forecasts. Advances in climate-based crop forecasting in the coming decade are likely to include more robust evaluation of the methods reviewed here, dynamically embedding crop models within climate models to account for crop influence on regional climate, enhanced use of remote sensing, and research in the emerging area of 'weather within climate'.

Translating climate forecasts into agricultural terms: advances and challenges

Seasonal climate prediction offers the potential to anticipate variations in crop production early enough to adjust critical decisions. Until recently, interest in exploiting seasonal forecasts from dynamic climate models (e.g. general circulation models, GCMs) for applications that involve crop simulation models has been hampered by the difference in spatial and temporal scale of GCMs and crop models, and by the dynamic, nonlinear relationship between meteorological variables and crop response. Although GCMs simulate the atmosphere on a sub-daily time step, their coarse spatial resolution and resulting distortion of day-to-day variability limits the use of their daily output. Crop models have used daily GCM output with some success by either calibrating simulated yields or correcting the daily rainfall output of the GCM to approximate the statistical properties of historic observations. Stochastic weather generators are used to disaggregate seasonal forecasts either by adjusting input parameters in a manner that captures the predictable components of climate, or by constraining synthetic weather sequences to match predicted values. Predicting crop yields, simulated with historic weather data, as a statistical function of seasonal climatic predictors, eliminates the need for daily weather data conditioned on the forecast, but must often address poor statistical properties of the crop-climate relationship. Most of the work on using crop simulation with seasonal climate forecasts has employed historic analogs based on categorical ENSO indices. Other methods based on classification of predictors or weather types can provide daily weather inputs to crop models conditioned on forecasts. Advances in climate-based crop forecasting in the coming decade are likely to include more robust evaluation of the methods reviewed here, dynamically embedding crop models within climate models to account for crop influence on regional climate, enhanced use of remote sensing, and research in the emerging area of 'weather within climate'.

Use of remote sensing to measure change in the extent of habitat for the critically endangered Jerdon's Courser Rhinoptilus bitorquatus in India

Jerdon's Courser Rhinoptilus bitorquatus is one of the most endangered and least understood birds in the world. It is endemic to scrub habitats in southeast India which have been lost and degraded because of human land use. We used satellite images from 1991 and 2000 and two methods for classifying land cover to quantify loss of Jerdon's Courser habitat. The scrub habitats on which this species depends decreased in area by 11-15% during this short period (9.6 years), predominantly as a result of scrub clearance and conversion to agriculture. The remaining scrub patches were smaller and further from human settlements in 2000 than in 1991, implying that much of the scrub loss had occurred close to human population centres. We discuss the implications of our results for the conservation of Jerdon's Courser and the use of remote sensing methods in conservation.

Use of airborne remote sensing to detect riverside Brassica rapa to aid in risk assessment of transgenic crops

High resolution descriptions of plant distribution have utility for many ecological applications but are especially useful for predictive modeling of gene flow from transgenic crops. Difficulty lies in the extrapolation errors that occur when limited ground survey data are scaled up to the landscape or national level. This problem is epitomized by the wide confidence limits generated in a previous attempt to describe the national abundance of riverside Brassica rapa (a wild relative of cultivated rapeseed) across the United Kingdom. Here, we assess the value of airborne remote sensing to locate B. rapa over large areas and so reduce the need for extrapolation. We describe results from flights over the river Nene in England acquired using Airborne Thematic Mapper (ATM) and Compact Airborne Spectrographic Imager (CASI) imagery, together with ground truth data. It proved possible to detect 97% of flowering B. rapa on the basis of spectral profiles. This included all stands of plants that occupied >2m square (>5 plants), which were detected using single-pixel classification. It also included very small populations (<5 flowering plants, 1-2m square) that generated mixed pixels, which were detected using spectral unmixing. The high detection accuracy for flowering B. rapa was coupled with a rather large false positive rate (43%). The latter could be reduced by using the image detections to target fieldwork to confirm species identity, or by acquiring additional remote sensing data such as laser altimetry or multitemporal imagery.

Perfluorodecalin: global warming potential and first detection in the atmosphere

Perfluorodecalin (C10F18) has a range of medical uses that have led to small releases. Recently, it has been proposed as a carrier of vaccines, which could lead to significantly larger emissions. Since its emissions are controlled under the Kyoto Protocol, it is important that values for the global warming potential (GWP) are available. For a 50:50 mixture of the two isomers of perfluorodecalin, laboratory measurements, supplemented by theoretical calculations, give an integrated absorption cross-section of 3.91 x 10(-16) cm(2) molecule(-1) cm(-1) over the spectral region 0-1500 cm(-1); calculations yield a radiative efficiency of 0.56 W m(-2) ppbv(-1) and a 100-year GWP, relative to carbon dioxide, of 7200 assuming a lifetime of 1000 years. We report the first atmospheric measurements of perfluorodecalin, at Bristol, UK and Mace Head, Ireland, where volume mixing ratios are about 1.5 x 10(-15). At these concentrations, it makes a trivial contribution to climate change, but on a per molecule basis it is a potent greenhouse gas, indicating the need for careful assessment of its possible future usage. (c) 2005 Elsevier Ltd. All rights reserved.

The remote distractor effect in saccade programming: channel interactions and lateral inhibition

We explored the dependency of the saccadic remote distractor effect (RDE) on the spatial frequency content of target and distractor Gabor patches. A robust RDE was obtained with low-medium spatial frequency distractors, regardless of the spatial frequency of the tat-get. High spatial frequency distractors interfered to a similar extent when the target was of the same spatial frequency. We developed a quantitative model based on lateral inhibition within an oculomotor decision unit. This lateral inhibition mechanism cannot account for the interaction observed between target and distractor spatial frequency, pointing to the existence of channel interactions at an earlier level. (C) 2004 Elsevier Ltd. All rights reserved.

Low-cost optical tracking for immersive collaboration in the CAVE using the Wii remote

We present a novel way of interacting with an immersive virtual environment which involves inexpensive motion-capture using the Wii Remote®. A software framework is also presented to visualize and share this information across two remote CAVETM-like environments. The resulting applications can be used to assist rehabilitation by sending motion information across remote sites. The application’s software and hardware components are scalable enough to be used on desktop computer when home-based rehabilitation is preferred.