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This paper describes the design and construction of the MicroBooNE liquid argon time projection chamber and associated systems. MicroBooNE is the first phase of the Short Baseline Neutrino program, located at Fermilab, and will utilize the capabilities of liquid argon detectors to examine a rich assortment of physics topics. In this document details of design specifications, assembly procedures, and acceptance tests are reported.

As we enter the new era of satellite remote sensing for CO2 and other carbon cyclerelated quantities, advanced modeling and analysis capabilities are required to fully capitalize on the new observations. Model estimates of CO2 surface flux and atmospheric transport are required for initial constraints on inverse analyses, to connect atmospheric observations to the location of surface sources and sinks, and ultimately for future projections of carbon-climate interactions. For application to current, planned, and future remotely sensed CO2 data, it is desirable that these models are accurate and unbiased at time scales from less than daily to multi-annual and at spatial scales from several kilometers or finer to global. Here we focus on simulated CO2 fluxes from terrestrial vegetation and atmospheric transport mutually constrained by analyzed meteorological fields from the Goddard Modeling and Assimilation Office for the period 1998 through 2006. Use of assimilated meteorological data enables direct model comparison to observations across a wide range of scales of variability. The biospheric fluxes are produced by the CASA model at lxi degrees on a monthly mean basis, modulated hourly with analyzed temperature and sunlight. Both physiological and biomass burning fluxes are derived using satellite observations of vegetation, burned area (as in GFED-2), and analyzed meteorology. For the purposes of comparison to CO2 data, fossil fuel and ocean fluxes are also included in the transport simulations. In this presentation we evaluate the model's ability to simulate CO2 flux and mixing ratio variability in comparison to in situ observations at sites in Northern mid latitudes and the continental tropics. The influence of key process representations is inferred. We find that the model can resolve much of the hourly to synoptic variability in the observations, although there are limits imposed by vertical resolution of boundary layer processes. The seasonal cycle and its interannual variations generally respond adequately, but discrepancies in the tropics suggest the need for a refinement of the soil moisture dependence of the respiration flux in CASA. Examples and inferences for interpretation of satellite data will be discussed. In general, the fidelity of these simulations leads us to anticipate incorporation of real-time, highly resolved remote sensing and other observations into quantitative analyses that will reduce uncertainty in the terrestrial CO2 sink and revolutionize our understanding of the key processes controlling atmospheric CO2 and its evolution with time.

We report on a NASA supported modeling effort to reduce uncertainty in carbon cycle processes that create the so-called missing sink of atmospheric CO2. Our overall objective is to improve characterization of CO2 source/sink processes globally with improved formulations for atmospheric transport, terrestrial uptake and release, biomass and fossil fuel burning, and observational data analysis. The motivation for this study follows from the perspective that progress in determining CO2 sources and sinks beyond the current state of the art will rely on utilization of more extensive and intensive CO2 and related observations including those from satellite remote sensing. The major components of this effort are: 1) Continued development of the chemistry and transport model using analyzed meteorological fields from the Goddard Global Modeling and Assimilation Office, with comparison to real time data in both forward and inverse modes; 2) An advanced biosphere model, constrained by remote sensing data, coupled to the global transport model to produce distributions of CO2 fluxes and concentrations that are consistent with actual meteorological variability; 3) Improved remote sensing estimates for biomass burning emission fluxes to better characterize interannual variability in the atmospheric CO2 budget and to better constrain the land use change source; 4) Evaluating the impact of temporally resolved fossil fuel emission distributions on atmospheric CO2 gradients and variability. 5) Testing the impact of existing and planned remote sensing data sources (e.g., AIRS, MODIS, OCO) on inference of CO2 sources and sinks, and use the model to help establish measurement requirements for future remote sensing instruments. The results will help to prepare for the use of OCO and other satellite data in a multi-disciplinary carbon data assimilation system for analysis and prediction of carbon cycle changes and carbodclimate interactions.

A multivariate statistical clustering technique--based on the k-means algorithm of Hartigan has been used to extract patterns of climatological significance from 200 years of general circulation model (GCM) output. Originally developed and implemented on a Beowulf-style parallel computer constructed by Hoffman and Hargrove from surplus commodity desktop PCs, the high performance parallel clustering algorithm was previously applied to the derivation of ecoregions from map stacks of 9 and 25 geophysical conditions or variables for the conterminous U.S. at a resolution of 1 sq km. Now applied both across space and through time, the clustering technique yields temporally-varying climate regimes predicted by transient runs of the Parallel Climate Model (PCM). Using a business-as-usual (BAU) scenario and clustering four fields of significance to the global water cycle (surface temperature, precipitation, soil moisture, and snow depth) from 1871 through 2098, the authors' analysis shows an increase in spatial area occupied by the cluster or climate regime which typifies desert regions (i.e., an increase in desertification) and a decrease in the spatial area occupied by the climate regime typifying winter-time high latitude perma-frost regions. The patterns of cluster changes have been analyzed to understand the predicted variability in the water cycle on global and continental scales. In addition, representative climate regimes were determined by taking three 10-year averages of the fields 100 years apart for northern hemisphere winter (December, January, and February) and summer (June, July, and August). The result is global maps of typical seasonal climate regimes for 100 years in the past, for the present, and for 100 years into the future. Using three-dimensional data or phase space representations of these climate regimes (i.e., the cluster centroids), the authors demonstrate the portion of this phase space occupied by the land surface at all points in space and time. Any single spot on the globe will exist in one of these climate regimes at any single point in time. By incrementing time, that same spot will trace out a trajectory or orbit between and among these climate regimes (or atmospheric states) in phase (or state) space. When a geographic region enters a state it never previously visited, a climatic change is said to have occurred. Tracing out the entire trajectory of a single spot on the globe yields a 'manifold' in state space representing the shape of its predicted climate occupancy. This sort of analysis enables a researcher to more easily grasp the multivariate behavior of the climate system.

The numerical simulation of CO2 transport (and other tracers such as CO, CH4, and biomass burning tracers) in the atmosphere is required to determine the fate of anthropogenic source gases. Estimation of the CO2 exchange between the ocean surface, the terrestrial biosphere, and the atmosphere is of first-order importance to understanding the global carbon cycle and the processes that are most crucial in determining the atmospheric CO2 concentration. Forward transport simulations have been conducted using two-dimensional, time-dependent grids of average surface fluxes (from TRANSCOM) and three-dimensional wind data from a prototype data assimilation system (FV-DAS) run by the Goddard Data Assimilation Office. The objective is to better understand the contribution of meteorological variability to changes in CO2 and other constituents, By accurately accounting for meteorological variability, through use of assimilated winds, we hope to better characterize the distribution of surface sources and sinks (and chemistry where applicable). With assimilated meteorology such chemistry/transport runs provide the basic framework to analyze existing (and proposed) measurement data on a point-by-point, real-time basis. We compare with measured CO2 concentration gradients on a daily, seasonal, regional, and interhemispheric basis to examine the consistency of sources, sinks, and transport formulation. We will also examine the inter-annual variability of atmospheric CO2 due to atmospheric circulation changes using longer runs with assimilated winds.

Mountain-top to mountain-top optical link experiments have been initiated at JPL, in order to perform a systems level evaluation of optical communications. Progress made so far is reported. ne NASA, JPL developed optical communications demonstrator (OCD) is used to transmit a laser signal from Strawberry Peak (SP), located in the San Bernadino mountains of California. This laser beam is received by a 0.6 m aperture telescope at JPL's Table Mountain Facility (TMF), located in Wrightwood, California. The optical link is bi-directional with the TMF telescope transmitting a continuous 4-wave (cw) 780 run beacon and the OCD sending back a 840 nm, 100 - 500 Mbps pseudo noise (PN) modulated, laser beam. The optical link path is at an average altitude of 2 km above sea level, covers a range of 46.8 km and provides an atmospheric channel equivalent to approx. 4 air masses. Average received power measured at either end fall well within the uncertainties predicted by link analysis. The reduction in normalized intensity variance (sigma(sup 2, sub I)) for the 4-beam beacon, compared to each individual beam, at SP, was from approx. 0.68 to 0.22. With some allowance for intra-beam mis-alignment, this is consistent with incoherent averaging. The sigma(sup2, sub I) measured at TMF approx. 0.43 +/- 0.22 exceeded the expected aperture averaged value of less than 0.1, probably because of beam wander. The focused spot sizes of approx. 162 +/- 6 microns at the TMF Coude and approx. 64 +/- 3 microns on the OCD compare to the predicted size range of 52 - 172 microns and 57 - 93 microns, respectively. This is consistent with 4 - 5 arcsec of atmospheric "seeing". The preliminary evaluation of OCD's fine tracking indicates that the uncompensated tracking error is approx. 3.3 micro rad compared to approx. 1.7 micro rad observed in the laboratory. Fine tracking performance was intermittent, primarily due to beacon fades on the OCD tracking sensor. The best bit error rates observed while tracking worked were 1E-5 to 1E-6.

Since the atmospheric deposition of iron has been linked to primary productivity in various oceanic regions, we have conducted an objective study of the correlation of dust deposition and satellite remotely sensed surface ocean chlorophyll concentrations. We present a global analysis of the correlation between atmospheric dust deposition derived from a satellite-based 3-D atmospheric transport model and SeaWiFs estimates of ocean color. We use the monthly mean dust deposition fields of Ginoux et al. which are based on a global model of dust generation and transport. This model is driven by atmospheric circulation from the Data Assimilation Office (DAO) for the period 1995-1998. This global dust model is constrained by several satellite estimates of standard circulation characteristics. We then perform an analysis of the correlation between the dust deposition and the 1998 SeaWIFS ocean color data for each 2.0 deg x 2.5 deg lat/long grid point, for each month of the year. The results are surprisingly robust. The region between 40 S and 60 S has correlation coefficients from 0.6 to 0.95, statistically significant at the 0.05 level. There are swaths of high correlation at the edges of some major ocean current systems. We interpret these correlations as reflecting areas that have shear related turbulence bringing nitrogen and phosphorus from depth into the surface ocean, and the atmospheric supply of iron provides the limiting nutrient and the correlation between iron deposition and surface ocean chlorophyll is high. There is a region in the western North Pacific with high correlation, reflecting the input of Asian dust to that region. The southern hemisphere has an average correlation coefficient of 0.72 compared that in the northern hemisphere of 0.42 consistent with present conceptual models of where atmospheric iron deposition may play a role in surface ocean biogeochemical cycles. The spatial structure of the correlation fields will be discussed within the context of guiding the design of field programs.

The Optical Communications Demonstrator (OCD) is a laboratory based lasercom terminal that was developed to validate several key technologies such as precision beam pointing, high bandwidth beacon tracking and beacon acquisition.

Mountain-top to mountain-top optical link experiments have been initiated at JPL, in order to perform a systems level evaluation of optical communications.

Recent successful demonstrations of laser communications with spacecraft and Earth-orbiting satellites have demonstrated the feasibility of some of the key aspects of this technology.

Alignment of the first Atmospheric Visibility Monitoring Telescope has revealed errors in mount design, mount manufacture, software, and electronics. This article discusses error sources and solutions, and re-alignment results. Alignment requirements for operation are also presented. The telescope now operates with the desired accuracy and repeatability.

Cloud condensation nuclei (CCN) influence cloud albedo through their effect on the cloud droplet size distribution. A number of studies have evaluated the climatic impact of the CCN-cloud albedo feedback, but all have assumed that cloud distributions, cloud thicknesses, and cloud liquid water contents would remain constant as the climate adjusted. This assumption has been tested using the Livermore version of the National Center for Atmospheric Research Community Climate Model. The results indicate that there are no significant compensating changes in cloud properties that would counteract the 1.7 percent global albedo increase resulting from a fourfold increase in marine CCN concentration. Furthermore, when ocean surface temperatures are decreased 4 C in a manner broadly consistent with the enhanced cloud albedos, an increase in cloud fraction of 3.5 percent and a reduction in cloud altitude are predicted, leading to a positive feedback from clouds that would imply a climate impact roughly double that calculated from cloud droplet size distribution change alone.

Techniques for daytime detection of atmospheric transmission and cloud cover to determine the capabilities of future deep-space optical communications links are considered. A modification of the planned nighttime photometry program will provide the best data while minimizing the need for further equipment. Greater degrees of modification will provide increased detection capabilities. Future testing of the equipment will better define the improvement offered by each level of modification. Daytime photometry is favored at certain wavelengths because of higher transmission and lower background noise, thus giving an increased signal-to-noise ratio. A literature search has provided a list of stars brighter than second magnitude at these wavelengths.

Viterbi algorithm for decoding convolutional codes for space channel application

Computer programs for functional analysis, planning and control of space stations and shuttles operation