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The average distribution of convective latent heating, boundary layer sensible heat flux, and vertical velocity are determined for the winter 1976 DST period from GLAS model diagnostics. Key features are the regions of intense latent heating over Brazil, Central Africa, and Indonesia; and the regions of strong sensible heating due to air mass modification over the North Atlantic and North Pacific Oceans.
A physical model for the Io plasma torus is constructed to explain the EUV radiative emission observed by the Voyager UV spectrometer. Electron impact excitation rate coefficients for electronic transitions of S III, S IV, O II and O III are calculated by the method of distorted waves (Davis, Kepple, and Blaha, 1976); these coefficients account for the asymmetric shape of the 686 A feature. It is concluded that the electron gas must have a distribution function with a non-Maxwellian tail. An approximate representation of the distribution function as two temperature components requires a cold component of 3.5-4 eV and density of 2000 per cu cm and a hot component of about 100 eV and density of 50-100 per cu cm to satisfy observational constraints.
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How and where the ocean tides dissipate their energy are longstanding questions that have consequences ranging from the history of the Moon to the mixing of the oceans. Historically, the principal sink of tidal energy has been thought to be bottom friction in shallow seas. There has long been suggestive however, that tidal dissipation also occurs in the open ocean through the scattering by ocean-bottom topography of surface tides into internal waves, but estimates of the magnitude of this possible sink have varied widely. Here we use satellite altimeter data from Topex/Poseidon to map empirically the tidal energy dissipation. We show that approximately 10(exp 12) watts-that is, 1 TW, representing 25-30% of the total dissipation-occurs in the deep ocean, generally near areas of rough topography. Of the estimated 2 TW of mixing energy required to maintain the large-scale thermohaline circulation of the ocean, one-half could therefore be provided by the tides, with the other half coming from action on the surface of the ocean.
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Modeled temperature data from a one-dimensional, time-dependent, initial value, planetary boundary layer model for 16 separate model runs with varying initial values of moisture availability are applied, by the use of a regression equation, to longwave infrared GOES satellite data to infer moisture availability over a regional area in the central U.S. This was done for several days during the summers of 1978 and 1980 where a large gradient in the antecedent precipitation index (API) represented the boundary between a drought area and a region of near normal precipitation. Correlations between satellite derived moisture availability and API were found to exist. Errors from the presence of clouds, water vapor and other spatial inhomogeneities made the use of the measurement for anything except the relative degree of moisture availability dubious.
We present a nonparametric Bayesian framework to infer radial distribution functions from experimental scattering measurements with uncertainty quantification using nonstationary Gaussian processes. The Gaussian process prior mean and kernel functions are designed to mitigate well-known numerical challenges with the Fourier transform, including discrete measurement binning and detector windowing, while encoding fundamental yet minimal physical knowledge of the liquid structure. We demonstrate uncertainty propagation of the Gaussian process posterior to unmeasured quantities of interest. Experimental radial distribution functions of liquid argon and water with uncertainty quantification are provided as both a proof of principle for the method and a benchmark for molecular models.
Spatially explicit and temporally continuous estimates of photosynthesis will be of great importance for increasing our understanding of and ultimately closing the terrestrial carbon cycle. Current capabilities to model photosynthesis, however, are limited by accurate enough representations of the complexity of the underlying biochemical processes and the numerous environmental constraints imposed upon plant primary production. A potentially powerful alternative to model photosynthesis through these indirect observations is the use of multi-angular satellite data to infer light-use efficiency (ε) directly from spectral reflectance properties in connection with canopy shadow fractions. Hall et al. (this issue) introduced a new approach for predicting gross ecosystem production that would allow the use of such observations in a data assimilation mode to obtain spatially explicit variations in ε from infrequent polar-orbiting satellite observations, while meteorological data are used to account for the more dynamic responses of ε to variations in environmental conditions caused by changes in weather and illumination. In this second part of the study we implement and validate the approach of Hall et al. (this issue) across an ecologically diverse array of eight flux-tower sites in North America using data acquired from the Compact High Resolution Imaging Spectroradiometer (CHRIS) and eddy-flux observations. Our results show significantly enhanced estimates of ε and therefore cumulative gross ecosystem production (GEP) over the course of one year at all examined sites. We also demonstrate that ε is greatly heterogeneous even across small study areas. Data assimilation and direct inference of GEP from space using a new, proposed sensor could therefore be a significant step towards closing the terrestrial carbon cycle.
The author has identified the following significant results. Good ecological, classification accuracy (90-95%) can be achieved in areas of rugged relief on a regional basis for Level 1 cover types (coniferous forest, deciduous forest, grassland, cropland, bare rock and soil, and water) using computer-aided analysis techniques on ERTS/MSS data. Cost comparisons showed that a Level 1 cover type map and a table of areal estimates could be obtained for the 443,000 hectare San Juan Mt. test site for less than 0.1 cent per acre, whereas photointerpretation techniques would cost more than 0.4 cent per acre. Results of snow cover mapping have conclusively proven that the areal extent of snow in mountainous terrain can be rapidly and economically mapped by using ERTS/MSS data and computer-aided analysis techniques. A distinct relationship between elevation and time of freeze or thaw was observed, during mountain lake mapping. Basic lithologic units such as igneous, sedimentary, and unconsolidated rock materials were successfully identified. Geomorphic form, which is exhibited through spatial and textual data, can only be inferred from ERTS data. Data collection platform systems can be utilized to produce satisfactory data from extremely inaccessible locations that encounter very adverse weather conditions, as indicated by results obtained from a DCP located at 3,536 meters elevation that encountered minimum temperatures of -25.5 C and wind speeds of up to 40.9m/sec (91 mph), but which still performed very reliably.
The scope of the three LACIE phases is discussed as well as the system efficiencies which had to be implemented to cope with the resulting LANDSAT data load. The methodologies used in system analysis, some of the specific data collected, and the inferences of these data and their implication on future systems are also discussed.
Surface air temperatures observed from stations around Antarctica have indicated predominantly positive trends which are as high as 0.4K per decade along the Antarctic Peninsula. Since the average air temperature during summer in the West Antarctic region is about -6 K, and since most of the stations are near coastal regions, it is important to know whether such trends are due to a local or large scale phenomenon. We analyzed about 19 years of infrared satellite data (1979-1997) to obtain a spatially detailed study of the variability of surface temperatures over the entire Antarctic region. We also analyzed potential impact of temperature changes over the sea ice cover using co-registered and nearly coincident microwave satellite data. The surface temperatures inferred from infrared data are shown to agree well with those observed from the limited number of Antarctic station data with a correlation coefficient of 0.98 and a standard deviation of less than 3K. The 19-year temperature data set shows positive trends of 0.12, 0.68, and 0.41 C per decade in ocean, sea ice and ice sheet surfaces, respectively. The sensitivity of the trend to record length was studied, using long term station data sets, and the results show that the trends start to stabilize at around 2 decades which is about the record length of satellite data. However, interannual fluctuations of the temperatures are large, especially in the ice sheets, and the 95% confidence level for the trends ranges from 0.12 to 1.22 for sea ice and from -0.74 to 1.55 C per decade for the ice sheet. In the sea ice regions, the position of the ice edge in winter with respect to the northernmost position of sea ice during the study period is also shown to be influenced by alternating warm and cold anomalies around the continent. This pattern is similar to that suggested by the presence of the Antarctic Circumpolar Wave (ACW) but the wavenumber appears to be predominantly mode 3 (instead of reported mode 2) for the Antarctic region. Unexpectedly, trends in the sea ice extent are found to be positive in most Antarctic sectors during the same period with the exception of the Bellingshausen and Amundsen Seas region.
Using Viking Orbiter images, detailed photoclinometric profiles were obtained across irregular depressions, fretted fractures, troughs and pits, solitary scarps, and simple grabens in the north Tharsis region of Mars. This data allows inferences to be made on the shallow crustal structure of this region. Topographical data obtained for Mars is discussed in relation to mechanical discontinuities.
Eddy covariance data are critical for inferring ecosystem water use strategies. Yet, such inferences are sensitive to a range of assumptions applied across studies, hindering our understanding of water use strategies within and across eddy covariance sites. A recent analysis across 151 FLUXNET2015 and AmeriFlux-FLUXNET datasets found that poor model performance was the key driver of non-robust inferences of ecosystem water use strategies. Here, we leverage this previous analysis to (i) identify the specific assumptions that improve inference model performance across most sites, (ii) explain the mechanisms behind the performance improvements, and (iii) check whether better performance improves water use inference. We find that the common practice of fitting a model to canopy conductance (G c ) derived from the evapotranspiration (ET) observations, rather than to observed ET itself, artificially amplifies data errors and degrades the model performance. Next, accounting for vegetation dynamics by applying a growing season filter or incorporating satellite LAI data improves performance, but the former practice may remove soil water stress periods. Lastly, using the leaf-to-air vapor pressure deficit (VPD l ) derived from ET observations as a model input may artificially inflate performance. Based on these results, we recommend selecting observed ET (rather than derived G c ) as the response variable, carefully accounting for vegetation dynamics, and avoiding derived VPD l as a model input; these best practices improve model performance by c. 20% and robustness by c. 80% across all eddy covariance sites. Nevertheless, the performance improvements do not always correspond to more robust inference of water use strategies, as model parameter selection and surface energy budget closure corrections still strongly influence the ecosystem water use parameter estimation in a site-specific manner.
Degradation via sintering is an ongoing challenge that impedes the broad commercial success of supported metallic nanoparticle catalysts. To mitigate degradation via informed catalyst design and process operations, here we aim to disambiguate the underlying mechanisms of sintering by combining theory and experiment in a quantitative framework. While mechanistic sintering models exist, they only model a single sintering pathway, even though multiple sintering mechanisms can occur simultaneously or dominate at different stages of the process. Data-driven machine learning models have emerged as a means to represent complex processes through data regression. However, machine learning models have very large data needs and lack mechanistic insights due to their black-box encoding. To develop an interpretive model of catalyst degradation via sintering, we constructed a hybrid model combining mechanistic “physics-based” models and data-driven methods to obtain both reliable predictions and mechanistic insights regarding experimentally observed sintering phenomena. Focusing on nanoparticle sintering in the Rh–TiO 2 catalyst for the reverse water–gas shift (RWGS) reaction, the hybrid model couples a mechanistic term for Ostwald ripening with energy values calculated via density functional theory (DFT) with a parametric, data-driven discrepancy function term for unmodeled mechanisms. The hybrid model is trained using Bayesian inference with data collected from small-angle X-ray scattering (SAXS) in situ experiments wherein average nanoparticle diameter versus time was measured at three relevant operating temperatures. The calibrated hybrid model results show that an Ostwald ripening-only model parameterized with fixed DFT energies does not fully capture the time and temperature dependence of the SAXS-observed sintering kinetics, and that an additional functional contribution, or DFT energy calibration, is required to reconcile simulation and experiment. Analysis of the hybrid-model error confirms that the hybrid model outperforms both the purely mechanistic and purely data-driven alternatives in terms of expected predictive accuracy for time-evolving average particle sizes. Furthermore, the results support the hypothesis that the Ostwald ripening mechanism is less important for explaining the sintering phenomena as operating temperature increases under an assumed fixed DFT parameterization. This could be explained in one of two ways: either latent, unmodeled sintering mechanisms dominate at higher temperatures, or the DFT uncertainty increases with temperature. The proposed modeling approach directly links theory to experiments and simulations via a statistical hybrid modeling framework and can be extended to other catalytic systems to improve predictive models and mechanistic understanding.
GRACE satellite gravity data is used to estimate terrestrial (surface plus ground) water storage within the Congo Basin in Africa for the period of April, 2002 - May, 2006. These estimates exhibit significant seasonal (30 +/- 6 mm of equivalent water thickness) and long-term trends, the latter yielding a total loss of approximately 280 km(exp 3) of water over the 50-month span of data. We also combine GRACE and precipitation data set (CMAP, TRMM) to explore the relative contributions of the source term to the seasonal hydrological balance within the Congo Basin. We find that the seasonal water storage tends to saturate for anomalies greater than 30-44 mm of equivalent water thickness. Furthermore, precipitation contributed roughly three times the peak water storage after anomalously rainy seasons, in early 2003 and 2005, implying an approximately 60-70% loss from runoff and evapotranspiration. Finally, a comparison of residual land water storage (monthly estimates minus best-fitting trends) in the Congo and Amazon Basins shows an anticorrelation, in agreement with the 'see-saw' variability inferred by others from runoff data.
The concentrations in the mesosphere of H2O, CH4, H2CO, H2O2, and HCN were inferred from data on positive ion compositions, obtained from one mid-latitude and four high-latitude rocket flights. The inferred concentrations were found to agree only partially with the ground-based microwave measurements and/or model prediction by Garcia and Solomon (1985). The CH4 concentration was found to vary between 70 and 4 ppb in daytime and 900 and 100 ppbv at night, respectively. Unexpectedly high H2CO concentrations were obtained, with H2CO/H2O ratios between 0.0006 and 0.1, and a mean HCN volume mixing ratio of 6 x 10 to the -10th was inferred.
Iron meteorites analyzed in this study have nitrogen concentrations 70 microg/g or less and delta N-15 from -90 to +150 percent. Although the iron meteorites have a large range of delta N-15, most have values more negative than -50 percent. The nitrogen isotopic compositions were established by cosmochemical processes and were little modified by fractional crystallization or other chemical processes within the parent bodies. The data do not suggest the existence of a well-mixed solar nebular reservoir for nitrogen, as was already inferred from data from stony meteorites. The range of greater than 1100 percent observed for delta N-15 in bulk meteorites is probably too large to be accounted for by physical and chemical mass-dependent fractionation processes in the solar nebula, and thus reflects nebular inhomogeneities of nucleosynthetic origin.