Optical properties of apple skin and flesh in the wavelength range from 350 to 2200 nm

Optical measurement of fruit quality is challenging due to the presence of a skin around the fruit flesh and the multiple scattering by the structured tissues. To gain insight in the light-tissue interaction, the optical properties of apple skin and flesh tissue are estimated in the 350-2200nm range for three cultivars. For this purpose, single integrating sphere measurements are combined with inverse adding- doubling. The observed absorption coefficient spectra are dominated by water in the near infrared and by pigments and chlorophyll in the visible region, whose concentrations are much higher in skin tissue. The scattering coefficient spectra show the monotonic decrease with increasing wavelength typical for biological tissues with skin tissue being approximately three times more scattering than flesh tissue. Comparison to the values from time-resolved spectroscopy reported in literature showed comparable profiles for the optical properties, but overestimation of the absorption coefficient values, due to light losses

Trend in ice moistening the stratosphere – constraints from isotope data of water and methane

Water plays a major role in the chemistry and radiative budget of the stratosphere. Air enters the stratosphere predominantly in the tropics, where the very low temperatures around the tropopause constrain water vapour mixing ratios to a few parts per million. Observations of stratospheric water vapour show a large positive long-term trend, which can not be explained by change in tropopause temperatures. Trends in the partitioning between vapour and ice of water entering the stratosphere have been suggested to resolve this conundrum. We present measurements of stratospheric H_(2)O, HDO, CH_4 and CH_(3)D in the period 1991–2007 to evaluate this hypothesis. Because of fractionation processes during phase changes, the hydrogen isotopic composition of H_(2)O is a sensitive indicator of changes in the partitioning of vapour and ice. We find that the seasonal variations of H_(2)O are mirrored in the variation of the ratio of HDO to H_(2)O with a slope of the correlation consistent with water entering the stratosphere mainly as vapour. The variability in the fractionation over the entire observation period is well explained by variations in H_(2)O. The isotopic data allow concluding that the trend in ice arising from particulate water is no more than (0.01±0.13) ppmv/decade in the observation period. Our observations suggest that between 1991 and 2007 the contribution from changes in particulate water transported through the tropopause plays only a minor role in altering in the amount of water entering the stratosphere

Single event effects in static and dynamic registers in a 0.25−μ−m0.25-\mu-m CMOS technology

We have studied single event effects in static and dynamic registers designed in a quarter micron CMOS process. In our design, we systematically used guardrings and enclosed (edgeless) transistor geometry to improve the total dose tolerance. This design technique improved both the SEL and SEU sensitivity of the circuits. Using SPICE simulations, the measured smooth transition of the cross-section curve between LET threshold and saturation has been traced to the presence of four different upset modes, each corresponding to a different critical charge and sensitive area. A new architecture to protect the content of storage cells has been developed, and a threshold LET around 89 MeV cm/sup 2/ mg/sup -1/ has been measured for this cell at a power supply voltage of 2 V

Towards space based verification of CO₂ emissions from strong localized sources: fossil fuel power plant emissions as seen by a CarbonSat constellation

Carbon dioxide (CO₂) is the most important man-made greenhouse gas (GHG) that cause global warming. With electricity generation through fossil-fuel power plants now being the economic sector with the largest source of CO₂, power plant emissions monitoring has become more important than ever in the fight against global warming. In a previous study done by Bovensmann et al. (2010), random and systematic errors of power plant CO₂ emissions have been quantified using a single overpass from a proposed CarbonSat instrument. In this study, we quantify errors of power plant annual emission estimates from a hypothetical CarbonSat and constellations of several CarbonSats while taking into account that power plant CO₂ emissions are time-dependent. Our focus is on estimating systematic errors arising from the sparse temporal sampling as well as random errors that are primarily dependent on wind speeds. We used hourly emissions data from the US Environmental Protection Agency (EPA) combined with assimilated and re-analyzed meteorological fields from the National Centers of Environmental Prediction (NCEP). CarbonSat orbits were simulated as a sun-synchronous low-earth orbiting satellite (LEO) with an 828-km orbit height, local time ascending node (LTAN) of 13:30 (01:30 p.m. LT) and achieves global coverage after 5 days. We show, that despite the variability of the power plant emissions and the limited satellite overpasses, one CarbonSat has the potential to verify reported US annual CO₂ emissions from large power plants (≥5 Mt CO₂ yr⁻¹) with a systematic error of less than ~4.9% and a random error of less than ~6.7% for 50% of all the power plants. For 90% of all the power plants, the systematic error was less than ~12.4% and the random error was less than ~13%. We additionally investigated two different satellite configurations using a combination of 5 CarbonSats. One achieves global coverage everyday but only samples the targets at fixed local times. The other configuration samples the targets five times at two-hour intervals approximately every 6th day but only achieves global coverage after 5 days. From the statistical analyses, we found, as expected, that the random errors improve by approximately a factor of two if 5 satellites are used. On the other hand, more satellites do not result in a large reduction of the systematic error. The systematic error is somewhat smaller for the CarbonSat constellation configuration achieving global coverage everyday. Therefore, we recommend the CarbonSat constellation configuration that achieves daily global coverage

Atmospheric greenhouse gases retrieved from SCIAMACHY: comparison to ground-based FTS measurements and model results

SCIAMACHY onboard ENVISAT (launched in 2002) enables the retrieval of global long-term column-averaged dry air mole fractions of the two most important anthropogenic greenhouse gases carbon dioxide and methane (denoted XCO_2 and XCH_4). In order to assess the quality of the greenhouse gas data obtained with the recently introduced v2 of the scientific retrieval algorithm WFM-DOAS, we present validations with ground-based Fourier Transform Spectrometer (FTS) measurements and comparisons with model results at eight Total Carbon Column Observing Network (TCCON) sites providing realistic error estimates of the satellite data. Such validation is a prerequisite to assess the suitability of data sets for their use in inverse modelling. It is shown that there are generally no significant differences between the carbon dioxide annual increases of SCIAMACHY and the assimilation system CarbonTracker (2.00 ± 0.16 ppm yr^(−1) compared to 1.94 ± 0.03 ppm yr−1 on global average). The XCO_2 seasonal cycle amplitudes derived from SCIAMACHY are typically larger than those from TCCON which are in turn larger than those from CarbonTracker. The absolute values of the northern hemispheric TCCON seasonal cycle amplitudes are closer to SCIAMACHY than to CarbonTracker and the corresponding differences are not significant when compared with SCIAMACHY, whereas they can be significant for a subset of the analysed TCCON sites when compared with CarbonTracker. At Darwin we find discrepancies of the seasonal cycle derived from SCIAMACHY compared to the other data sets which can probably be ascribed to occurrences of undetected thin clouds. Based on the comparison with the reference data, we conclude that the carbon dioxide data set can be characterised by a regional relative precision (mean standard deviation of the differences) of about 2.2 ppm and a relative accuracy (standard deviation of the mean differences) of 1.1–1.2 ppm for monthly average composites within a radius of 500 km. For methane, prior to November 2005, the regional relative precision amounts to 12 ppb and the relative accuracy is about 3 ppb for monthly composite averages within the same radius. The loss of some spectral detector pixels results in a degradation of performance thereafter in the spectral range currently used for the methane column retrieval. This leads to larger scatter and lower XCH_4 values are retrieved in the tropics for the subsequent time period degrading the relative accuracy. As a result, the overall relative precision is estimated to be 17 ppb and the relative accuracy is in the range of about 10–20 ppb for monthly averages within a radius of 500 km. The derived estimates show that the SCIAMACHY XCH_4 data set before November 2005 is suitable for regional source/sink determination and regional-scale flux uncertainty reduction via inverse modelling worldwide. In addition, the XCO2 monthly data potentially provide valuable information in continental regions, where there is sparse sampling by surface flask measurements

Validation and data characteristics of methane and nitrous oxide profiles observed by MIPAS and processed with Version 4.61 algorithm

The ENVISAT validation programme for the atmospheric instruments MIPAS, SCIAMACHY and GOMOS is based on a number of balloon-borne, aircraft, satellite and ground-based correlative measurements. In particular the activities of validation scientists were coordinated by ESA within the ENVISAT Stratospheric Aircraft and Balloon Campaign or ESABC. As part of a series of similar papers on other species [this issue] and in parallel to the contribution of the individual validation teams, the present paper provides a synthesis of comparisons performed between MIPAS CH4 and N2O profiles produced by the current ESA operational software (Instrument Processing Facility version 4.61 or IPF v4.61, full resolution MIPAS data covering the period 9 July 2002 to 26 March 2004) and correlative measurements obtained from balloon and aircraft experiments as well as from satellite sensors or from ground-based instruments. In the middle stratosphere, no significant bias is observed between MIPAS and correlative measurements, and MIPAS is providing a very consistent and global picture of the distribution of CH4 and N2O in this region. In average, the MIPAS CH4 values show a small positive bias in the lower stratosphere of about 5%. A similar situation is observed for N2O with a positive bias of 4%. In the lower stratosphere/upper troposphere (UT/LS) the individual used MIPAS data version 4.61 still exhibits some unphysical oscillations in individual CH4 and N2O profiles caused by the processing algorithm (with almost no regularization). Taking these problems into account, the MIPAS CH4 and N2O profiles are behaving as expected from the internal error estimation of IPF v4.61 and the estimated errors of the correlative measurements

Drivers of column-average CO_2 variability at Southern Hemispheric Total Carbon Column Observing Network sites

We investigate factors that drive the variability in total column CO_2 at the Total Carbon Column Observing Network sites in the Southern Hemisphere using fluxes tagged by process and by source region from the CarbonTracker analysed product as well as the Simple Biosphere model. We show that the terrestrial biosphere is the largest driver of variability in the Southern Hemisphere column CO_2. However, it does not dominate in the same fashion as in the Northern Hemisphere. Local- and hemispheric-scale biomass burning can also play an important role, particularly at the tropical site, Darwin. The magnitude of seasonal variability in the column-average dry-air mole fraction of CO_2, X_CO_2, is also much smaller in the Southern Hemisphere and comparable in magnitude to the annual increase. Comparison of measurements to the model simulations highlights that there is some discrepancy between the two time series, especially in the early part of the Darwin data record. We show that this mismatch is most likely due to erroneously estimated local fluxes in the Australian tropical region, which are associated with enhanced photosynthesis caused by early rainfall during the tropical monsoon season

A geostatistical framework for quantifying the imprint of mesoscale atmospheric transport on satellite trace gas retrievals

National Aeronautics and Space Administration's Orbiting Carbon Observatory‐2 (OCO‐2) satellite provides observations of total column‐averaged CO2 mole fractions (X_(CO₂)) at high spatial resolution that may enable novel constraints on surface‐atmosphere carbon fluxes. Atmospheric inverse modeling provides an approach to optimize surface fluxes at regional scales, but the accuracy of the fluxes from inversion frameworks depends on key inputs, including spatially and temporally dense CO₂ observations and reliable representations of atmospheric transport. Since X_(CO₂) observations are sensitive to both synoptic and mesoscale variations within the free troposphere, horizontal atmospheric transport imparts substantial variations in these data and must be either resolved explicitly by the atmospheric transport model or accounted for within the error covariance budget provided to inverse frameworks. Here, we used geostatistical techniques to quantify the imprint of atmospheric transport in along‐track OCO‐2 soundings. We compare high‐pass‐filtered (<250 km, spatial scales that primarily isolate mesoscale or finer‐scale variations) along‐track spatial variability in X_(CO₂) and X_(H₂O) from OCO‐2 tracks to temporal synoptic and mesoscale variability from ground‐based X_(CO₂) and X_(H₂O) observed by nearby Total Carbon Column Observing Network sites. Mesoscale atmospheric transport is found to be the primary driver of along‐track, high‐frequency variability for OCO‐2 X_(H₂O). For X_(CO₂), both mesoscale transport variability and spatially coherent bias associated with other elements of the OCO‐2 retrieval state vector are important drivers of the along‐track variance budget