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Geosat Data Assimilation with Application to the Eastern North Atlantic

An attempt is made to determine the three-dimensional ocean circulation from satellite altimeter measurements by assimilating Geosat sea surface height data into an eddy-resolving QuasiGeostrophic (QG) model of the eastern North Atlantic Ocean. Results are tested against independent information from hydrographic field observations and moored current meter data collected during the Geosat ERM. The comparison supports the concept of inferring aspects of the three-dimensional flow field from sea surface height observations by combining altimetric measurements with the dynamics of ocean circulation models. A Holland-type QG model with open boundaries is set up on a 2000 km X 2000 km domain of the eastern North Atlantic between 25 deg. and 45 deg. N, 32 deg. and 8 deg. W. By using a simple nudging technique, about two years of Geosat altimeter data are assimilated into the model every five days as space-time objective analyses on the model grid. The error information resulting from the analysis is used during the assimilation procedure to account for data uncertainties. Results show an intense eddy field, which in the surface layer interacts with a meandering Azores Front. Compared to Geosat, the model leads to smoothed fields that follow the observations. Model simulations are significantly correlated with hydrographic data from March 1988 and June 1989, both close to the surface and in the subsurface. Good agreement is also found between the model velocity fields and moored current meter data in the top two model layers. The agreement is visually weak in the bottom layer, although a coherence analysis reveals an agreement between the model simulation and current meter data over the full water column at periods exceeding 80 days.

Stammer, Detlef↗

Construction of a Fluid Flowfield from Discrete Point Data using Machine Learning

Many verification and validation procedures in aerospace engineering involve the comparison of computational fluid dynamics (CFD) data to experimental results from sources like wind tunnel tests. However, an incongruity exists between the data available from these sources: flow visualization is available by default in computational data, whereas in most experimental setups the available data is far more discrete and far more limited: integrated forces and moments, discrete pressure and temperature probes, etc. When differences exist between quantities of interest like lift and drag coefficients, the lack of full-field flow data from the experiments complicates most attempts to reconcile why the different data sources disagree. To this end, a shallow neural network, constrained by certain fluid flow properties, was trained to approximate flow field snapshots given only discrete data like that available in a wind tunnel test. The constructed snapshots, even for complex incompressible fluid flows, were found to agree at the large scales with the true flow fields. With this tool, researchers can more readily and easily understand why quantities of interest differ between their experimental and computational datasets. This in turn improves the resulting data's uncertainty measures.

Yury Lebedev↗

Construction of a Fluid Flowfield from Discrete Point Data using Machine Learning

Many verification and validation procedures in aerospace engineering involve the comparison of computational fluid dynamics (CFD) data to experimental results from sources like wind tunnel tests. However, an incongruity exists between the data available from these sources: flow visualization is available by default in computational data, whereas in most experimental setups the available data is far more discrete and far more limited: integrated forces and moments, discrete pressure and temperature probes, etc. When differences exist between quantities of interest like lift and drag coefficients, the lack of full-field flow data from the experiments complicates most attempts to reconcile why the different data sources disagree. To this end, a shallow neural network, constrained by certain fluid flow properties, was trained to approximate flow field snapshots given only discrete data like that available in a wind tunnel test. The constructed snapshots, even for complex incompressible fluid flows, were found to agree at the large scales with the true flow fields. With this tool, researchers can more readily and easily understand why quantities of interest differ between their experimental and computational datasets. This in turn improves the resulting data's uncertainty measures.

Yury Lebedev↗

Spatial Factors in the Integration of Speed Information

We reported that, for a 21FC task with multiple Gabor patches in each interval, thresholds for speed discrimination decreased with the number of patches, while simply increasing the area of a single patch produced no such effect. This result could be explained by multiple patches reducing spatial uncertainty. However, the fact that thresholds decrease with number even when the patches are in fixed positions argues against this explanation. We therefore performed additional experiments to explore the lack of an area effect. Three observers did a 21FC speed discrimination task with 6 Gabor patches in each interval, and were asked to pick the interval in which the gratings moved faster. The 50% contrast patches were placed on a circle at 4 deg. eccentricity, either equally spaced and maximally separated (hexagonal array), or closely-spaced, in consecutive positions (string of pearls). For the string-of-pearls condition, the grating phases were either random, or consistent with a full-field grating viewed through multiple Gaussian windows. When grating phases were random, the thresholds for the hexagonal and string-of-pearls layouts were indistinguishable. For the string-of-pearls layout, thresholds in the consistent-phase condition were higher by 15 +/- 6% than in the random-phase condition. (Thresholds increased by 57 +/- 7% in going from 6 patches to a single patch of equivalent area.). For random-phase patches, the lower thresholds for 6 patches does not depend on a specific spacing or spatial layout. Multiple, closely-spaced, consistent-phase patches that can be interpreted as a single grating, result in thresholds closer to that produced by a single patch. Together, our results suggest that object segmentation may play a role in the integration of speed information.

Verghese, P.↗

Future ATM Concepts Evaluation Tool (FACET) Interface Control Document

This Interface Control Document (ICD) documents the airspace adaptation and air traffic inputs of NASA's Future ATM Concepts and Evaluation Tool (FACET). Its intended audience is the project manager, project team, development team, and stakeholders interested in interfacing with the system. FACET equips Air Traffic Management (ATM) researchers and service providers with a way to explore, develop and evaluate advanced air transportation concepts before they are field-tested and eventually deployed. FACET is a flexible software tool that is capable of quickly generating and analyzing thousands of aircraft trajectories. It provides researchers with a simulation environment for preliminary testing of advanced ATM concepts. Using aircraft performance profiles, airspace models, weather data, and flight schedules, the tool models trajectories for the climb, cruise, and descent phases of flight for each type of aircraft. An advanced graphical interface displays traffic patterns in two and three dimensions, under various current and projected conditions for specific airspace regions or over the entire continental United States. The system is able to simulate a full day's dynamic national airspace system (NAS) operations, model system uncertainty, measure the impact of different decision-makers in the NAS, and provide analysis of the results in graphical form, including sector, airport, fix, and airway usage statistics. NASA researchers test and analyze the system-wide impact of new traffic flow management algorithms under anticipated air traffic growth projections on the nation's air traffic system. In addition to modeling the airspace system for NASA research, FACET has also successfully transitioned into a valuable tool for operational use. Federal Aviation Administration (FAA) traffic flow managers and commercial airline dispatchers have used FACET technology for real-time operations planning. FACET integrates live air traffic data from FAA radar systems and weather data from the National Weather Service to summarize NAS performance. This information allows system operators to reroute flights around congested airspace and severe weather to maintain safety and minimize delay. FACET also supports the planning and post-operational evaluation of reroute strategies at the national level to maximize system efficiency. For the commercial airline passenger, strategic planning with FACET can result in fewer flight delays and cancellations. The performance capabilities of FACET are largely due to its architecture, which strikes a balance between flexibility and fidelity. FACET is capable of modeling the airspace operations for the continental United States, processing thousands of aircraft on a single computer. FACET was written in Java and C, enabling the portability of its software to a variety of operating systems. In addition, FACET was designed with a modular software architecture to facilitate rapid prototyping of diverse ATM concepts. Several advanced ATM concepts have already been implemented in FACET, including aircraft self-separation, prediction of aircraft demand and sector congestion, system-wide impact assessment of traffic flow management constraints, and wind-optimal routing.

FACET↗

Base Flow Investigation of the Apollo AS-202 Command Module

In recent years, both Europe and the US are developing hypersonic research and operational vehicles. These include (re)entry capsules (both ballistic and lifting) and lifting bodies such as ExoMars, EXPERT, ARV, CEV and IXV. The research programs are meant to enable technology and engineering capabilities to support during the next decade the development of affordable (possibly reusable) space transportation systems as well as hypersonic weapons systems for time critical targets. These programs have a broad range of goals, ranging from the qualification of thermal protection systems, the assessment of RCS performances, the development of GNC algorithms, to the full demonstration of the performance and operability of the integrated vehicles. Since the aerothermodynamic characteristics influence nearly all elements of the vehicle design, the accurate prediction of the aerothermal environment is a prerequisite for the design of efficient hypersonic systems. Significant uncertainties in the prediction of the hypersonic aerodynamic and the aerothermal loads can lead to conservative margins in the design of the vehicle including its Outer Mould Line (OML), thermal protection system, structure, and required control system robustness. The current level of aerothermal prediction uncertainties results therefore in reduced vehicle performances (e.g., sub-optimal payload to mass ratio, increased operational constraints). On the other hand, present computational capabilities enable the simulation of three dimensional flow fields with complex thermo-chemical models over complete trajectories and ease the validation of these tools by, e.g., reconstruction of detailed wind tunnel tests performed under identified and controlled conditions (flow properties and vehicle attitude in particular). These controlled conditions are typically difficult to achieve when performing in flight measurements which in turn results in large associated measurement uncertainties. Similar problems arise when attempting to rebuild measurements performed in "hot" ground facilities, where the difficulty level is increased by the addition of the free-flow characterization itself. The implementation of ever more sophisticated thermochemical models is no obvious cure to the aforementioned problems since their effect is often overwhelmed by the large measurement uncertainties incurred in both flight and ground high enthalpy facilities. Concurrent to the previous considerations, a major contributor to the overall vehicle mass of re-entry vehicles is the afterbody thermal protection system. This is due to the large acreage (equal or bigger than that of the forebody) to be protected. The present predictive capabilities for base flows are comparatively lower than those for windward flowfields and offer therefore a substantial potential for improving the design of future re-entry vehicles. To that end, it is essential to address the accuracy of high fidelity CFD tools exercised in the US and EU, which motivates a thorough investigation of the present status of hypersonic flight afterbody heating. This paper addresses the predictive capabilities of after body flow fields of re-entry vehicles investigated in the frame of the NATO/RTO - RTG-043 Task Group and is structured as follows: First, the verification of base flow topologies on the basis of available wind-tunnel results performed under controlled supersonic conditions (i.e., cold flows devoid of reactive effects) is performed. Such tests address the detailed characterization of the base flow with particular emphasis on separation/reattachment and their relation to Mach number effects. The tests have been performed on an Apollo-like re-entry capsule configuration. Second, the tools validated in the frame of the previous effort are exercised and appraised against flight-test data collected during the Apollo AS-202 re-entry.

Walpot, Louis M. G.↗

Spatiotemporal Methane Emissions from Global Lakes and Reservoirs

Inland aquatic systems, such as lakes and reservoirs, contribute substantially to global methane (CH4) emissions; yet are among the most uncertain components of the total CH4 budget. Lakes and reservoirs have received recent attention as they may generate high CH4 fluxes. Improved quantification of these CH4 fluxes, particularly their spatiotemporal distribution, is key to realistically incorporating them in CH4 modeling and budget studies. Here we report on a new global, gridded (0.25° lat × 0.25° lon) study of lake and reservoir CH4 emissions, accounting for new knowledge regarding lake and reservoir areal extent and distribution, and spatiotemporal emission patterns influenced by diurnal variability, temperature-dependent seasonality, satellite-derived freeze-thaw dynamics, and eco-climatic and physical CH4-centric type classification. The results of this new data set comprise daily CH4 emissions from lake and reservoirs throughout the full annual cycle and are tightly anchored to field observations, in situ measurements, and remote-sensing observations. Results show that reservoirs cover 297 × 103 km2 globally and emit 10.1 Tg CH4 yr-1 from diffusive (1.2 Tg CH4 yr-1) and ebullitive (8.9 Tg CH4 yr-1) emission pathways. On a global scale, CH4 emitting areas of lakes cover 1853 × 103 km2 and emit 37.1 Tg CH4 yr-1 from diffusive (17.1 Tg CH4 yr-1) and ebullitive (23.0 Tg CH4 yr-1) emission pathways and an additional ebullition flux of 5.2 Tg CH4 yr-1 upon ice-melt due to the accumulation of bubbles during the freeze period. This analysis of lakes and reservoir CH4 emission addresses multiple gaps and uncertainties in previous studies and represents an important contribution to studies of the global CH4 budget. The new data sets and methodologies from this study provide a framework to better understand and model the current and future role of lakes and reservoirs in the global CH4 budget and to guide efforts to mitigate inland aquatic system CH4 emissions. This presentation will describe the methodologies applied and the major results of this study focusing on the spatiotemporal distribution of lake and reservoir type classification, processes driving lake and reservoir emission seasonality, and the contribution of individual lake and reservoir types to the annual cycle of inland aquatic CH4 emissions.

Spatiotemporal↗

A Comparison of Perturbed Initial Conditions and Multiphysics Ensembles in a Severe Weather Episode in Spain

Ensembles of numerical model forecasts are of interest to operational early warning forecasters as the spread of the ensemble provides an indication of the uncertainty of the alerts, and the mean value is deemed to outperform the forecasts of the individual models. This paper explores two ensembles on a severe weather episode in Spain, aiming to ascertain the relative usefulness of each one. One ensemble uses sensible choices of physical parameterizations (precipitation microphysics, land surface physics, and cumulus physics) while the other follows a perturbed initial conditions approach. The results show that, depending on the parameterizations, large differences can be expected in terms of storm location, spatial structure of the precipitation field, and rain intensity. It is also found that the spread of the perturbed initial conditions ensemble is smaller than the dispersion due to physical parameterizations. This confirms that in severe weather situations operational forecasts should address moist physics deficiencies to realize the full benefits of the ensemble approach, in addition to optimizing initial conditions. The results also provide insights into differences in simulations arising from ensembles of weather models using several combinations of different physical parameterizations.

severe weather↗

Geodesy at Mercury with MESSENGER

In 2011 the MESSENGER (MErcury Surface, Space ENvironment, GEochemistry, and Ranging) spacecraft will enter Mercury orbit and begin the mapping phase of the mission. As part of its science objectives the MESSENGER mission will determine the shape and gravity field of Mercury. These observations will enable the topography and the crustal thickness to be derived for the planet and will determine the small libration of the planet about its axis, the latter critical to constraining the state of the core. These measurements require very precise positioning of the MESSENGER spacecraft in its eccentric orbit, which has a periapsis altitude as low as 200 km, an apoapsis altitude near 15,000 km, and a closest approach to the surface varying from latitude 60 to about 70 N. The X-band tracking of MESSENGER and the laser altimetry are the primary data that will be used to measure the planetary shape and gravity field. The laser altimeter, which has an expected range of 1000 to 1200 km, is expected to provide significant data only over the northern hemisphere because of MESSENGER's eccentric orbit. For the southern hemisphere, radio occultation measurements obtained as the spacecraft passes behind the planet as seen from Earth and images obtained with the imaging system will be used to provide the long-wavelength shape of the planet. Gravity, derived from the tracking data, will also have greater resolution in the northern hemisphere, but full global models for both topography and gravity will be obtained at low harmonic order and degree. The limiting factor for both gravity and topography is expected to be knowledge of the spacecraft location. Present estimations are that in a combined tracking, altimetry, and occultation solution the spacecraft position uncertainty is likely to be of order 10 m. This accuracy should be adequate for establishing an initial geodetic coordinate system for Mercury that will enable positioning of imaged features on the surface, determination of the planet's obliquity, and detection of the librational motion of the planet about its axis.

Smith, David E.↗

PIV Investigations of the Flow Field in the Volute of a Rotary Blood Pump

A full-size acrylic model of a rotary blood pump was developed in order to utilize Particle Image Velocimetry (PIV) to make measurements of the fluid velocities and turbulent stresses throughout the device. The development of an understanding of the hemodynamics within the blood pump is critical to the development and validation of computational models. A blood analog solution, consisting of sodium iodide solution and glycerin, was developed to match physiological kinematic viscosity. The refractive indecies of the fluid, the pump casing and the impeller were matched to facilitate the use of PIV to make velocity measurements. Velocity measurements made in the volute exit/diffuser region are presented for pumps speeds of 3000-3850 rpm. At each speed data were obtained at a physiological pressure of 90 mmHg and at a maximum flow condition. Four hundred data pairs were used for each resultant mean velocity vector value, representing greater than an order of magnitude more data pairs than reported previously in the literature on similar devices and resulting in velocity uncertainty levels of approximately 2.9%.

Sankovic, John M.↗

PIV Investigations of the Flow Field in the Volute of a Rotary Blood Pump

A full-size acrylic model of a rotary blood pump was developed in order to utilize Particle Image Velocimetry (PIV) to make measurements of the fluid velocities and turbulent stresses throughout the device. The development of an understanding of the hemodynamics within the blood pump is critical to the development and validation of computational models. A blood analog solution, consisting of sodium iodide solution and glycerin, was developed to match physiological kinematic viscosity. The refractive indices of the fluid, the pump casing, and the impeller were matched to facilitate the use of PIV to make velocity measurements. Velocity measurements made in the volute exit/diffuser region are presented for pumps speeds of 3000-3850 rpm. At each speed data were obtained at a physiological pressure of 12 kPa and at a maximum flow condition. Four hundred data pairs were used for each resultant mean velocity vector value, representing greater than an order of magnitude more data pairs than reported previously in the literature on similar devices and resulting in velocity uncertainty levels of approximately 22.9%.

Sankovic, John M.↗

Aerosol-Radiation-Cloud Interactions in the South-East Atlantic: Future Suborbital Activities to Address Knowledge Gaps in Satellite and Model Assessments

Southern Africa produces almost a third of the Earth's biomass burning (BB) aerosol particles. Particles lofted into the mid-troposphere are transported westward over the South-East (SE) Atlantic, home to one of the three permanent subtropical stratocumulus (Sc) cloud decks in the world. The SE Atlantic stratocumulus deck interacts with the dense layers of BB aerosols that initially overlay the cloud deck, but later subside and may mix into the clouds. These interactions include adjustments to aerosol-induced solar heating and microphysical effects, and their global representation in climate models remains one of the largest uncertainties in estimates of future climate. Hence, new observations over the SE Atlantic have significant implications for global climate change scenarios. Our understanding of aerosol-cloud interactions in the SE Atlantic is hindered both by the lack of knowledge on aerosol and cloud properties, as well as the lack of knowledge about detailed physical processes involved. Most notably, we are missing knowledge on the absorptive and cloud nucleating properties of aerosols, including their vertical distribution relative to clouds, on the locations and degree of aerosol mixing into clouds, on the processes that govern cloud property adjustments, and on the importance of aerosol effects on clouds relative to co-varying synoptic scale meteorology. We discuss the current knowledge of aerosol and cloud property distributions based on satellite observations and sparse suborbital sampling. Recent efforts to make full use of A-Train aerosol sensor synergies will be highlighted. We describe planned field campaigns in the region to address the existing knowledge gaps. Specifically, we describe the scientific objectives and implementation of the five synergistic, international research activities aimed at providing some of the key aerosol and cloud properties and a process-level understanding of aerosol-cloud interactions over the SE Atlantic: NASA's ORACLES, the UK Met Office's CLARIFY-2016, the DoE's LASIC, NSF's ONFIRE, and CNRS' AEROCLO-SA.

aerosols↗

Using Advanced Mass Spectrometry Techniques to Fully Characterize Atmospheric Organic Carbon: Current Capabilities and Remaining Gaps

Organic compounds in the atmosphere vary widely in their molecular composition and chemical properties, so no single instrument can reasonably measure the entire range of ambient compounds. Over the past decade, a new generation of in-situ, field-deployable mass spectrometers has dramatically improved our ability to detect, identify, and quantify these organic compounds, but no systematic approach has been developed to assess the extent to which currently available tools capture the entire space of chemical identity and properties that is expected in the atmosphere. Reduced-parameter frameworks that have been developed to describe atmospheric mixtures are exploited here to characterize the range of chemical properties accessed by a suite of instruments. Multiple chemical spaces (e.g. oxidation state of carbon vs. volatility, and oxygen number vs. carbon number) were populated with ions measured by several mass spectrometers, with gas- and particle-phase α-pinene oxidation products serving as the test mixture of organic compounds. Few gaps are observed in the coverage of the parameter spaces by the instruments employed in this work, though the full extent to which comprehensive measurement was achieved is difficult to assess due to uncertainty in the composition of the mixture. Overlaps between individual ions and regions in parameter space were identified, both between gas- and particle-phase measurements, and within each phase. These overlaps were conservatively found to account for little (<10%) of the measured mass. However, challenges in identifying overlaps and in accurately converting molecular formulas into chemical properties (such as volatility or reactivity) highlight a continued need to incorporate structural information into atmospheric measurements.

G. Isaacman-VanWertz↗

The Outer Solar System Origins Survey: Design and First-Quarter Discoveries - I.

We report the discovery, tracking, and detection circumstances for 85 trans-Neptunian objects (TNOs) from the first 42 square degrees of the Outer Solar System Origins Survey. This ongoing r-band solar system survey uses the 0.9 square degree field of view MegaPrime camera on the 3.6 meter Canada-France-Hawaii Telescope. Our orbital elements for these TNOs are precise to a fractional semimajor axis uncertainty of less than 0.1 percent. We achieve this precision in just two oppositions, as compared to the normal three to five oppositions, via a dense observing cadence and innovative astrometric technique. These discoveries are free of ephemeris bias, a first for large trans-Neptunian surveys. We also provide the necessary information to enable models of TNO orbital distributions to be tested against our TNO sample. We confirm the existence of a cold "kernel" of objects within the main cold classical Kuiper Belt and infer the existence of an extension of the "stirred" cold classical Kuiper Belt to at least several au beyond the 2:1 mean motion resonance with Neptune. We find that the population model of Petit et al. remains a plausible representation of the Kuiper Belt. The full survey, to be completed in 2017, will provide an exquisitely characterized sample of important resonant TNO populations, ideal for testing models of giant planet migration during the early history of the solar system.

Bannister, Michele T.↗

Combination of TOPEX/POSEIDON Data with a Hydrographic Inversion for Determination of the Oceanic General Circulation and its Relation to Geoid Accuracy

A global estimate of the absolute oceanic general circulation from a geostrophic inversion of in situ hydrographic data is tested against and then combined with an estimate obtained from TOPEX/POSEIDON altimetric data and a geoid model computed using the JGM-3 gravity-field solution. Within the quantitative uncertainties of both the hydrographic inversion and the geoid estimate, the two estimates derived by very different methods are consistent. When the in situ inversion is combined with the altimetry/geoid scheme using a recursive inverse procedure, a new solution, fully consistent with both hydrography and altimetry, is found. There is, however, little reduction in the uncertainties of the calculated ocean circulation and its mass and heat fluxes because the best available geoid estimate remains noisy relative to the purely oceanographic inferences. The conclusion drawn from this is that the comparatively large errors present in the existing geoid models now limit the ability of satellite altimeter data to improve directly the general ocean circulation models derived from in situ measurements. Because improvements in the geoid could be realized through a dedicated spaceborne gravity recovery mission, the impact of hypothetical much better, future geoid estimates on the circulation uncertainty is also quantified, showing significant hypothetical reductions in the uncertainties of oceanic transport calculations. Full ocean general circulation models could better exploit both existing oceanographic data and future gravity-mission data, but their present use is severely limited by the inability to quantify their error budgets.

Ganachaud, Alexandre↗

Methods and Results for a Global Precipitation Measurement (GPM) Validation Network Prototype

As one component of a ground validation system to meet requirements for the upcoming Global Precipitation Measurement (GPM) mission, a quasi-operational prototype a system to compare satellite- and ground-based radar measurements has been developed. This prototype, the GPM Validation Network (VN), acquires data from the Precipitation Radar (PR) on the Tropical Rainfall Measuring Mission (TRMM) satellite and from ground radar (GR) networks in the continental U.S. and participating international sites. PR data serve as a surrogate for similar observations from the Dual-frequency Precipitation Radar (DPR) to be present on GPM. Primary goals of the VN prototype are to understand and characterize the variability and bias of precipitation retrievals between the PR and GR in various precipitation regimes at large scales, and to improve precipitation retrieval algorithms for the GPM instruments. The current VN capabilities concentrate on comparisons of the base reflectivity observations between the PR and GR, and include support for rain rate comparisons. The VN algorithm resamples PR and GR reflectivity and other 2-D and 3-D data fields to irregular common volumes defined by the geometric intersection of the instrument observations, and performs statistical comparisons of PR and GR reflectivity and estimated rain rates. Algorithmic biases and uncertainties introduced by traditional data analysis techniques are minimized by not performing interpolation or extrapolation of data to a fixed grid. The core VN dataset consists of WSR-88D GR data and matching PR orbit subset data covering 21 sites in the southeastern U. S., from August, 2006 to the present. On average, about 3.5 overpass events per month for these WSR-88D sites meet VN criteria for significant precipitation, and have matching PR and GR data available. This large statistical sample has allowed the relative calibration accuracy and stability of the individual ground radars, and the quality of the PR reflectivity attenuation correction in convective and stratiform precipitation to be evaluated. We will present results of PR-GR reflectivity and rain rate bias comparisons for each OR site, and for different rain types, for the full data set and as time series. The capabilities of the statistical analysis and vertical cross section tools for display and analysis of individual site overpass event data will be described, and examples of the tools' outputs will be shown.

Morris, Kenneth R.↗

4-D Cloud Water Content Fields Derived from Operational Satellite Data

In order to improve operational safety and efficiency, the transportation industry, including aviation, has an urgent need for accurate diagnoses and predictions of clouds and associated weather conditions. Adverse weather accounts for 70% of all air traffic delays within the U.S. National Airspace System. The Federal Aviation Administration has determined that as much as two thirds of weather-related delays are potentially avoidable with better weather information and roughly 20% of all aviation accidents are weather related. Thus, it is recognized that an important factor in meeting the goals of the Next Generation Transportation System (NexGen) vision is the improved integration of weather information. The concept of a 4-D weather cube is being developed to address that need by integrating observed and forecasted weather information into a shared 4-D database, providing an integrated and nationally consistent weather picture for a variety of users and to support operational decision support systems. Weather analyses and forecasts derived using Numerical Weather Prediction (NWP) models are a critical tool that forecasters rely on for guidance and also an important element in current and future decision support systems. For example, the Rapid Update Cycle (RUC) and the recently implemented Rapid Refresh (RR) Weather Research and Forecast (WRF) models provide high frequency forecasts and are key elements of the FAA Aviation Weather Research Program. Because clouds play a crucial role in the dynamics and thermodynamics of the atmosphere, they must be adequately accounted for in NWP models. The RUC, for example, cycles at full resolution five cloud microphysical species (cloud water, cloud ice, rain, snow, and graupel) and has the capability of updating these fields from observations. In order to improve the models initial state and subsequent forecasts, cloud top altitude (or temperature, T(sub c)) derived from operational satellite data, surface observations of cloud base altitude, radar reflectivity, and lightning data are used to help build and remove clouds in the models assimilation system. Despite this advance and the many recent advances made in our understanding of cloud physical processes and radiative effects, many problems remain in adequately representing clouds in models. While the assimilation of cloud top information derived from operational satellite data has merit, other information is available that has not yet been exploited. For example, the vertically integrated cloud water content (CWC) or cloud water path (CWP) and cloud geometric thickness (delta Z) are standard products being derived routinely from operational satellite data. These and other cloud products have been validated under a variety of conditions. Since the uncertainties have generally been found to be less than those found in model analyses and forecasts, the satellite products should be suitable for data assimilation, provided an appropriate strategy can be developed that links the satellite-derived cloud parameters with cloud parameters specified in the model. In this paper, we briefly outline such a strategy and describe a methodology to retrieve cloud water content profiles from operational satellite data. Initial results and future plans are presented. It is expected that the direct assimilation of this new product will provide the most accurate depiction of the vertical distribution of cloud water ever produced at the high spatial and temporal resolution needed for short term weather analyses and forecasts.

Smith, William L., Jr.↗

Data Reduction Pipeline for the CHARIS Integral-Field Spectrograph I: Detector Readout Calibration and Data Cube Extraction

We present the data reduction pipeline for CHARIS, a high-contrast integral-field spectrograph for the Subaru Telescope. The pipeline constructs a ramp from the raw reads using the measured nonlinear pixel response and reconstructs the data cube using one of three extraction algorithms: aperture photometry, optimal extraction, or chi-squared fitting. We measure and apply both a detector flatfield and a lenslet flatfield and reconstruct the wavelength- and position-dependent lenslet point-spread function (PSF) from images taken with a tunable laser. We use these measured PSFs to implement a chi-squared-based extraction of the data cube, with typical residuals of approximately 5 percent due to imperfect models of the under-sampled lenslet PSFs. The full two-dimensional residual of the chi-squared extraction allows us to model and remove correlated read noise, dramatically improving CHARIS's performance. The chi-squared extraction produces a data cube that has been deconvolved with the line-spread function and never performs any interpolations of either the data or the individual lenslet spectra. The extracted data cube also includes uncertainties for each spatial and spectral measurement. CHARIS's software is parallelized, written in Python and Cython, and freely available on github with a separate documentation page. Astrometric and spectrophotometric calibrations of the data cubes and PSF subtraction will be treated in a forthcoming paper.

Brandt, Timothy D.↗