The oculometer Summary report

Summarized description of oculometer for computing eye directio

Systematic errors in global air-sea CO2 flux caused by temporal averaging of sea-level pressure

International audienceLong-term temporal averaging of meteorological data, such as wind speed and air pressure, can cause large errors in air-sea carbon flux estimates. Other researchers have already shown that time averaging of wind speed data creates large errors in flux due to the non-linear dependence of the gas transfer velocity on wind speed (Bates and Merlivat, 2001). However, in general, wind speed is negatively correlated with air pressure, and a given fractional change in the pressure of dry air produces an equivalent fractional change in the atmospheric partial pressure of carbon dioxide (<i>p</i>CO_2air). Thus low pressure systems cause a drop in <i>p</i>CO_2air, which together with the associated high winds, promotes outgassing/reduces uptake of CO₂ from the ocean. Here we quantify the errors in global carbon flux estimates caused by using monthly or climatological pressure data to calculate <i>p</i>CO_2air (and thus ignoring the covariance of wind and pressure) over the period 1990?1999, using two common parameterisations for gas transfer velocity (Wanninkhof, 1992 (W92) and Wanninkhof and McGillis, 1999 (WM99)). Results show that on average, compared with estimates made using 6 hourly pressure data, the global oceanic sink is systematically overestimated by 7% (W92) and 10% (WM99) when monthly mean pressure is used, and 9% (W92) and 12% (WM99) when climatological pressure is used

Oculometer for remote tracking of eye movement

Prototype oculometer which tracks lateral eye position and measures the direction of the eyes optical axis, pupil size, and blink occurrence performs measurements on the subject on a real-time basis from a remote location

The use of high altitude aerial photography to inventory wildlife habitat in Kansas: An initial evaluation

The use of aerial photography as a method for determining the wildlife conditions of an area is discussed. Color infrared photography is investigated as the most effective type of remote sensor. The characteristics of the remote sensing systems are described. Examples of the remote sensing operation and the method for reducing the data are presented

Methods for Combining Payload Parameter Variations with Input Environment

Methods are presented for calculating design limit loads compatible with probabilistic structural design criteria. The approach is based on the concept that the desired limit load, defined as the largest load occuring in a mission, is a random variable having a specific probability distribution which may be determined from extreme-value theory. The design limit load, defined as a particular value of this random limit load, is the value conventionally used in structural design. Methods are presented for determining the limit load probability distributions from both time-domain and frequency-domain dynamic load simulations. Numerical demonstrations of the methods are also presented

Electronic filter discriminates between true and false reflections

Electronic filtering system discriminates between true corneal and false reflections, solving the problem of spurious reflections of the CRT light in newly designed oculometer

System design study for an optimal remote oculometer for use in operational aircraft

System design of optimal remote oculometer for use in operational aircraf

Adjusting for desert-dust-related biases in a climate data record of sea surface temperature

Atmospheric desert-dust aerosol, primarily from north Africa, causes negative biases in remotely sensed climate data records of sea surface temperature (SST). Here, large-scale bias adjustments are deduced and applied to the v2 climate data record of SST from the European Space Agency Climate Change Initiative (CCI). Unlike SST from infrared sensors, SST measured in situ is not prone to desert-dust bias. An in-situ-based SST analysis is combined with column dust mass from the Modern-Era Retrospective analysis for Research and Applications, Version 2 to deduce a monthly, large-scale adjustment to CCI analysis SSTs. Having reduced the dust-related biases, a further correction for some periods of anomalous satellite calibration is also derived. The corrections will increase the usability of the v2 CCI SST record for oceanographic and climate applications, such as understanding the role of Arabian Sea SSTs in the Indian monsoon. The corrections will also pave the way for a v3 climate data record with improved error characteristics with respect to atmospheric dust aerosol