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An all-fiber sensor scheme for simultaneous strain and temperature measurement is presented. The sensing head is formed by serially connecting a polarization maintaining photonic-crystal-fiber-based intermodal interferometer (IMI) with a fiber Bragg grating (FBG). The IMI, exhibiting an opposite strain response as compared to that of the FBG, is highly sensitive to strain, while it is insensitive to temperature. This has potential for improving the strain and temperature measurement resolutions. A sensor resolution of [+ or -] 8.3 [micro][epsilon] in strain and [+ or -] 2[degrees]C in temperature are experimentally achieved within a strain range of 0-957.6 [micro][epsilon] and a temperature range of 24[degrees]C-64[degrees]C, respectively. OCIS codes: 060.2310, 060.2370.

Estimation of evapotranspiration (ET) is important for monitoring crop water stress and for developing decision support systems for irrigation scheduling. Techniques to estimate ET have been available for many years, while more recently remote sensing data have extended ET into a spatially distributed context. However, remote sensing data cannot be easily used in decision systems if they are not available frequently. For many crops ET estimates are needed at intervals of a week or less, but unfortunately due to cost, weather, and sensor availability constraints, high resolution (< 100 m) remote sensing data are usually available no more frequently than 2 weeks. Since resolution of this problem is unlikely to occur soon, a modeling approach has been developed to extrapolate remotely sensed inputs needed to estimate ET. The approach accomplishes this by combining time-series observations from ground-based radiometers and meteorological instruments with episodic visible, near infrared, and thermal infrared remote sensing image data. The key components of the model are a vegetation density predictor and a diurnal land surface temperature disaggregator, both of which supply needed inputs to a surface energy balance model. To illustrate model implementation, remote sensing and ground-based experimental data were collected for cotton grown in 2003 at Maricopa, Ariz. Spatially distributed cotton canopy densities were forecasted for a 22-day interval using vegetation indices from remote sensing and fractional cover from ground-level photography. Spatially distributed canopy and soil surface temperatures were predicted at 15-min time steps for the same interval by scaling diurnal canopy temperatures according to time of day and vegetative cover. Considering that the predictions span a rapid growth phase of the cotton crop, comparison of spatially projected canopy cover with observed cover were reasonably good, with [R.sup.2]=0.65 and a root-mean-squared error (RMSE) of 0.13. Comparison of predicted temperatures also showed fair agreement with RMSE=2.1[degrees]C. These results show that combining episodic remotely sensed data with continuous ground-based radiometric data are a technically feasible way to forecast spatially distributed input data needed for ET modeling over crops. DOI: 10.1061/(ASCE)IR.1943-4774.0000169 CE Database subject headings: Evapotranspiration; Remote sensing; Temperature effects; Arizona. Author keywords: Evapotranspiration.

Width-increased dual-pump enhanced coherent anti-Stokes Raman spectroscopy (WIDECARS) is a technique that is capable of simultaneously measuring temperature and species mole fractions of [N.sub.2], [O.sub.2], [H.sub.2], [C.sub.2][H.sub.4], CO, and C[O.sub.2]. WIDECARS is designed for measurements of all the major species (except water) in supersonic combustion flows fueled with hydrogen and hydrogen/ethylene mixtures. The two lowest rotational energy levels of hydrogen detectable by WIDECARS are [H.sub.2] S(3) and [H.sub.2] S(4). The detection of these lines gives the system the capability to measure temperature and species concentrations in regions of flow containing pure hydrogen fuel at room temperature. OCIS codes: 120.1740, 300.6230.

Pyrometers are commonly used for high temperature measurement, but their accuracy is often limited by uncertainty in the surface emissivity. Radiation heating introduces additional errors due to the extra light reflected off the measured surface. While many types of specialized equipment have been developed for these measurements, this work presents a method for measuring high temperatures using single color pyrometers when the surface emissivity is unknown. It is particularly useful for correcting errors due to reflected light in solar heating applications. The method requires two pyrometers and is most helpful for improving measurement accuracy of low cost commercial instruments. The temperature measurements of two pyrometers operating at different wavelengths are analyzed across a range of sample temperatures to find the surface emissivity values at each wavelength that minimize the difference in temperature measurements between pyrometers. These are taken as the surface emissivity values, and the initial temperature measurements are corrected using the calculated emissivity values to obtain improved estimates of the surface temperature. When applied to temperature data from a solar furnace, the method significantly decreased the difference in the temperature measurements of two single color pyrometers. Simulated temperature data with both random noise and systematic errors are used to demonstrate that the method successfully converges to surface emissivity values and reduces temperature measurement errors even when subjected to significant errors in the model inputs. This method provides a potential low cost solution for pyrometric temperature measurement of solar-heated objects. It is also useful for temperature measurement of objects with unknown emissivity. [DOI: 10.1115/1.4000351] Keywords: pyrometer, solar heating, emissivity, temperature measurement, radiative heating

Temperature depression within the leading edge cavities on a space inducer is measured in Refrigerant 114 using miniature thermocouples mounted on the rotating blades. Time-averaged values of cavity temperature depression are determined all along the descent in cavitation number and correlated with the extent of cavities. In addition to mean values, temperature fluctuations are analyzed with respect to the onset of cavitation instabilities, namely, alternate blade cavitation and supersynchronous rotating cavitation. Temperature spectra relative to a rotating frame of reference are compared with pressure spectra obtained in a fixed frame of reference. Temperature oscillations issued from different blades are compared, and phase shifts between consecutive and opposite blades are evaluated in the case of the supersynchronous instability regime. [DOI: 10.1115/1.4001007]

In the present work, a picosecond lidar system aiming at single-ended combustion diagnostics in full-scale combustion devices with limited optical access, such as power plants, is described. The highest overall range resolution of the system was found to be OCIS codes: 120.1740, 280.3640, 280.1910, 280.6780.

Fiber-optic single-point and quasi-distributed evanescent temperature sensors recoated with a blend of poly(methyl methacrylate) and poly(vinylidene fluoride) are proposed. Solid cladding enables the construction of small-size, low-cost, relatively wide-range and fast-response temperature sensors. The diameter of the sensor does not exceed the dimensions of the original optical fiber, while the response time of the sensor is 7.4 ms. Different mass ratios of polymers in the blend enable fine tuning of the applied cladding's refractive index. This allows the construction of sensors for different temperature ranges, while the application of all-silica graded-index multimode fibers enables the construction of quasi-distributed sensor systems with considerably reduced cross talk. OCIS codes: 060.2370, 120.6780, 120.7000, 160.5470, 280.6780.

A variational method to retrieve profiles of temperature, humidity, and cloud is described, which combines observations from a 12-channel microwave radiometer, an infrared radiometer, and surface sensors with background from short-range numerical weather prediction (NWP) forecasts in an optimal way, accounting for their error characteristics. An analysis is presented of the error budget of the background and observations, including radiometric, modeling, and representativeness errors. Observation errors of some moisture channels are found to be dominated by representativeness, due to their sensitivity to atmospheric variability on smaller scales than the NWP model grid, whereas channels providing information on temperature in the lowest 1 km are dominated by instrument noise. Profiles of temperature and a novel total water control variable are retrieved from synthetic data using Newtonian iteration. An error analysis shows that these are expected to improve mesoscale NWP, retrieving temperature and humidity profiles up to 4 km with uncertainties of < 1 K and < 40% and 2.8 and 1.8 degrees of freedom for signal, respectively, albeit with poor vertical resolution. A cloud classification scheme is introduced to address convergence problems and better constrain the retrievals. This Bayesian retrieval method can be extended to incorporate observations from other instruments to form a basis for future integrated profiling systems. Index Terms--Atmospheric measurements, microwave radiometry, remote sensing, variational methods.

The heat capacity of a calorimeter is determined by measuring the temperature changes that happen when a mass of water at a higher temperature is added to a known mass of water or solution in the calorimeter. An alternate approach is to add a known quantity of electrical energy in measuring the temperature change.