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NASA NTRS1987

Physical mechanisms of heat, momentum and turbulence fluxes

In a qualitative way, the physical mechanisms which generate fluxes of heat, momentum, and turbulence in the atmosphere are discussed. This material is presented to acquaint people with the Earth science aspects of turbulence as important processes in the atmosphere. To attempt to describe turbulent fluxes of heat, momentum, and moisture in precise mathematical detail becomes an intractable problem. It is burdened by an eighth order set of equations involving more variables than equations. It is a closure problem which requires complicated assumptions that are not necessarily always satisfied, variable boundary conditions, and sparse observational data. Therefore, we must approach the problem in a simplified manner to obtain any kind of solution involving the variables of shear, stress, and heat, moisture, and momentum fluxes. There are other problems, of course, in which the inclusion of the planetary boundary layer is extremely important. Air pollution studies, air-sea exchanges, mesoscale models, and so on, must account for the planetary layer in very specific terms. Some of the physical mechanisms that are involved in generating fluxes are described.

Theon, John S.↗

NASA NTRS2012

A VLA (Very Large Array) Search for 5 GHz Radio Transients and Variables at Low Galactic Latitudes

We present the results of a 5GHz survey with the Very Large Array (VLA) and the expanded VLA, designed to search for short-lived (approx < 1 day) transients and to characterize the variability of radio sources at milli-Jansky levels. A total sky area of 2.66 sq. deg, spread over 141 fields at low Galactic latitudes (b approx equals 6 - 8 deg) was observed 16 times with a cadence that was chosen to sample timescales of days, months and years. Most of the data were reduced, analyzed and searched for transients in near real time. Interesting candidates were followed up using visible light telescopes (typical delays of 1 - 2 hr) and the X-Ray Telescope on board the Swift satellite. The final processing of the data revealed a single possible transient with a flux density of f(sub v) approx equals 2.4mJy. This implies a transients sky surface density of kappa(f(sub v) > 1.8mJy) = 0.039(exp +0.13,+0.18) (sub .0.032,.0.038) / sq. deg (1, 2 sigma confidence errors). This areal density is consistent with the sky surface density of transients from the Bower et al. survey extrapolated to 1.8mJy. Our observed transient areal density is consistent with a Neutron Stars (NSs) origin for these events. Furthermore, we use the data to measure the sources variability on days to years time scales, and we present the variability structure function of 5GHz sources. The mean structure function shows a fast increase on approximately 1 day time scale, followed by a slower increase on time scales of up to 10 days. On time scales between 10 - 60 days the structure function is roughly constant. We find that approx > 30% of the unresolved sources brighter than 1.8mJy are variable at the > 4-sigma confidence level, presumably due mainly to refractive scintillation.

Ofek, E. O.↗

NASA NTRS1994

Optimality Of Variable-Length Codes

Report presents analysis of performances of conceptual Rice universal noiseless coders designed to provide efficient compression of data over wide range of source-data entropies. Includes predictive preprocessor that maps source data into sequence of nonnegative integers and variable-length-coding processor, which adapts to varying entropy of source data by selecting whichever one of number of optional codes yields shortest codeword.

Yeh, Pen-Shu↗

NASA NTRS1976

Transition heating rates obtained on a matted and isolated 0.006 scale model (41-OT) space shuttle orbiter and external tank in the NASA/LaRC variable density hypersonic tunnel (IH17)

Model information and data obtained from wind tunnel tests performed on a 0.006 scale model of the Rockwell International space shuttle orbiter and external tank in the 18 inch Variable Density Hypersonic Wind Tunnel (VDHT) at NASA Langley Research Center are presented. Tests were performed at a Mach number of 8.0 over a Reynolds Number range from 0.1 to 10.0 million per foot at 0 deg and -5 deg angle of attack and 0 deg sideslip angle. Transition heating rates were determined using thin skin thermocouples located at various locations on the orbiter and ET. The test was conducted in three stages: orbiter plus external tank (mated configuration); orbiter alone, and external tank alone. The effects of boundary layer trips were also included in the test sequence. The plotted results presented show the effect of configuration interference on the orbiter lower surface and on the ET. Tabulated data are given.

Cummings, J.↗

NASA NTRS1986

Observations and a model of gravity-wave variability in the middle atmosphere

A major goal was to determine what portion of the gravity-wave frequency spectrum accounted for the majority of the momentum flux and divergence, as this has important implications for the middle atmosphere response. It was found that approx. 70% of the total flux and divergence was due to wave motions with observed periods less than 1 hour, consistent with expectations based on the shape of the observed gravity-wave spectrum (FrItts, 1984). This dominance of the momentum flux and divergence by high-frequency motions implies a potential for the modulation of those quantities by large-amplitude motions at lower frequencies. A second, striking aspect of the velocity and momentum flux data is its dramatic diurnal variability, particularly at certain levels. This variability is illustrated with the momentum flux, computed in 8-hr blocks. The dominant contributions here are due to waves with periods less than 1 hr. The variability with height and size of the mean square velocity in the west beam and the momentum flux, energed over the 3-day period. A detailed analysis of the various tidal motions present during this data interval was performed, and it was determined that variations in the zontal wind profile imposed by the diurnal tidal motion are probably responsible for the modulation of the gravity-wave amplitudes and momentum fluxes.

Fritts, D. C.↗

NASA NTRS1974

Liquid rocket combustion computer model with distributed energy release. DER computer program documentation and user's guide, volume 1

A computer program for analyzing rocket engine performance was developed. The program is concerned with the formation, distribution, flow, and combustion of liquid sprays and combustion product gases in conventional rocket combustion chambers. The capabilities of the program to determine the combustion characteristics of the rocket engine are described. Sample data code sheets show the correct sequence and formats for variable values and include notes concerning options to bypass the input of certain data. A seperate list defines the variables and indicates their required dimensions.

Combs, L. P.↗

NASA NTRSDate not supplied

A Neural Network Parametrization of Volumetric Cloud Fraction Profiles Using Satellite Observations and MERRA-2 Reanalysis Meteorological Data

Clouds play a crucial role in regulating the hydrologic cycle and Earth's radiative energy budget, yet they are often poorly represented in global climate models (GCMs). This study applies deep machine learning techniques to develop a physical parameterization of volumetric cloud fraction (VCF), the fraction of a 3-D grid volume occupied by clouds using satellite lidar-radar measurements. The neural network (NN) captures the complicated relationships between observed VCF profiles and collocated meteorological variables from MERRA-2 reanalysis data. Our results show that the NN model, particularly a sequence-to-sequence long short-term memory (LSTM) network with a sixfactor loss function, effectively learns the underlying cloud physical processes. The NN model outperforms MERRA-2 reanalysis in representing low-level clouds in tropical and subtropical regions and low- and middle-level clouds over midlatitude storm-track regions, and also improves VCF histograms. These improvements are reflected in the vertical distributions of zonally, meridionally, and globally averaged VCFs, geographic distributions of low-, middle-, and high-level clouds, and seasonal variations in monthly-mean VCF. Furthermore, the NN predictions effectively capture the El Niño-Southern Oscillation (ENSO) effects and other interannual variations. The NN parameterization is further evaluated through a sensitivity analysis, in which a single predictor is perturbed at a time. This reveals that relative humidity (RH) is the dominant factor influencing variations in globally averaged VCF at low and middle altitudes, followed by temperature. At higher altitudes, temperature becomes the primary driver of VCF through its effect on RH. Changes in wind components had minimal impact on globally averaged VCF.

Shan Zeng↗

NASA NTRS2018

Satellite-Based View of the Aerosol Spatial and Temporal Variability in the Cordoba Region (Argentina) Using over Ten Years of High-Resolution Data

Space-based observations offer a unique opportunity to investigate the atmosphere and its changes over decadal time scales, particularly in regions lacking in situ and/or ground based observations. In this study, we investigate temporal and spatial variability of atmospheric particulate matter (aerosol) over the urban area of Córdoba (central Argentina) using over ten years (2003–2015) of high-resolution (1 km) satellite-based retrievals of aerosol optical depth (AOD). This fine resolution is achieved exploiting the capabilities of a recently developed inversion algorithm (Multiangle implementation of atmospheric correction, MAIAC) applied to the MODIS sensor datasets of the NASA-Terra and -Aqua platforms. Results of this investigation show a clear seasonality of AOD over the investigated area. This is found to be shaped by an intricate superposition of aerosol sources, acting over different spatial scales and affecting the region with different yearly cycles. During late winter and spring (August-October), local as well as near- and long-range transported biomass burning (BB) aerosols enhance the Córdoba aerosol load, and AOD levels reach their maximum values (>0.35 at 0.47 µm). The fine AOD spatial resolution allowed to disclose that, in this period, AOD maxima are found in the rural/agricultural area around the city, reaching up to the city boundaries pinpointing that fires of local and near-range origin play a major role in the AOD enhancement. A reverse spatial AOD gradient is found from December to March, the urban area showing AODs 40–80% higher than in the city surroundings. In fact, during summer, the columnar aerosol load over the Córdoba region is dominated by local (urban and industrial) sources, likely coupled to secondary processes driven by enhanced radiation and mixing effects within a deeper planetary boundary layer (PBL). With the support of modelled AOD data from the Modern-Era Retrospective Analysis for Research and Application (MERRA), we further investigated into the chemical nature of AOD. The results suggest that mineral dust is also an important aerosol component in Córdoba, with maximum impact from November to February. The use of a long-term dataset finally allowed a preliminary assessment of AOD trends over the Córdoba region. For those months in which local sources and secondary processes were found to dominate the AOD (December to March), we found a positive AOD trend in the Córdoba outskirts, mainly in the areas with maximum urbanization/population growth over the investigated decade. Conversely, a negative AOD trend (up to −0.1 per decade) is observed all over the rural area of Córdoba during the BB season, this being attributed to a decrease of fires both at the local and the continental scale.

aerosols↗

NASA NTRS1979

Development of LACIE CCEA-1 weather/wheat yield models

The advantages and disadvantages of the casual (phenological, dynamic, physiological), statistical regression, and analog approaches to modeling for grain yield are examined. Given LACIE's primary goal of estimating wheat production for the large areas of eight major wheat-growing regions, the statistical regression approach of correlating historical yield and climate data offered the Center for Climatic and Environmental Assessment the greatest potential return within the constraints of time and data sources. The basic equation for the first generation wheat-yield model is given. Topics discussed include truncation, trend variable, selection of weather variables, episodic events, strata selection, operational data flow, weighting, and model results.

Strommen, N. D.↗

NASA NTRS2001

Sampling of the Diurnal Cycle of Precipitation using TRMM

We examine the temporal sampling of tropical regions using observations from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and Precipitation Radar (PR). We conclude that PR estimates at any one hour, even using three years of data, are inadequate to describe the diurnal cycle of precipitation over regions smaller than 12 degrees, due to high spatial variability in sampling. We show that the optimum period of accumulation is four hours. Diurnal signatures display half as much sampling error when averaged over four hours of local time. A similar pattern of sampling variability is found in the TMI data, despite the TMI's wider swath and increased sampling. These results are verified using an orbital model. The sensitivity of the sampling to satellite altitude is presented, as well as sampling patterns at the new TRMM altitude of 402.5 km.

Negri, Andrew J.↗

NASA NTRS2015

Deriving Earth Science Data Analytics Requirements

Data Analytics applications have made successful strides in the business world where co-analyzing extremely large sets of independent variables have proven profitable. Today, most data analytics tools and techniques, sometimes applicable to Earth science, have targeted the business industry. In fact, the literature is nearly absent of discussion about Earth science data analytics. Earth science data analytics (ESDA) is the process of examining large amounts of data from a variety of sources to uncover hidden patterns, unknown correlations, and other useful information. ESDA is most often applied to data preparation, data reduction, and data analysis. Co-analysis of increasing number and volume of Earth science data has become more prevalent ushered by the plethora of Earth science data sources generated by US programs, international programs, field experiments, ground stations, and citizen scientists.Through work associated with the Earth Science Information Partners (ESIP) Federation, ESDA types have been defined in terms of data analytics end goals. Goals of which are very different than those in business, requiring different tools and techniques. A sampling of use cases have been collected and analyzed in terms of data analytics end goal types, volume, specialized processing, and other attributes. The goal of collecting these use cases is to be able to better understand and specify requirements for data analytics tools and techniques yet to be implemented. This presentation will describe the attributes and preliminary findings of ESDA use cases, as well as provide early analysis of data analytics toolstechniques requirements that would support specific ESDA type goals. Representative existing data analytics toolstechniques relevant to ESDA will also be addressed.

data analytics↗

NASA NTRS2002

Comparing Satellite Rainfall Estimates with Rain-Gauge Data: Optimal Strategies Suggested by a Spectral Model

Validation of satellite remote-sensing methods for estimating rainfall against rain-gauge data is attractive because of the direct nature of the rain-gauge measurements. Comparisons of satellite estimates to rain-gauge data are difficult, however, because of the extreme variability of rain and the fact that satellites view large areas over a short time while rain gauges monitor small areas continuously. In this paper, a statistical model of rainfall variability developed for studies of sampling error in averages of satellite data is used to examine the impact of spatial and temporal averaging of satellite and gauge data on intercomparison results. The model parameters were derived from radar observations of rain, but the model appears to capture many of the characteristics of rain-gauge data as well. The model predicts that many months of data from areas containing a few gauges are required to validate satellite estimates over the areas, and that the areas should be of the order of several hundred km in diameter. Over gauge arrays of sufficiently high density, the optimal areas and averaging times are reduced. The possibility of using time-weighted averages of gauge data is explored.

Bell, Thomas L.↗

NASA NTRS2006

Does The Nile Reflect Solar Variability?

Historical records of the Nile water level provide a unique opportunity to investigate the possibility that solar variability influences the Earth's climate. Particularly important are the annual records of the water level, which are uninterrupted for the years 622-1470 A.D. These records are non-stationary, so that standard spectral analyses cannot adequately characterize them. Here the Empirical Mode Decomposition technique, which is designed to deal with nonstationary, nonlinear time series, becomes useful. It allows the identification of two characteristic time scales in the water level data that can be linked to solar variability: the 88 year period and a time scale of about 200 years. These time scales are also present in the concurrent aurora data. Auroras are driven by coronal mass ejections and the rate of auroras is an excellent proxy for solar variability. Analysis of auroral data contemporaneous with the Nile data shows peaks at 88 years and about 200 years. This suggests a physical link between solar variability and the low frequency variations of the Nile water level. The link involves the influence of solar variability on the North Annual Mode of atmospheric variability and its North Atlantic and Indian Oceans patterns that affect rainfall over Eastern Equatorial Africa where the Nile originates.

statistical methods↗

NASA NTRS2012

Space Suit Joint Torque Measurement Method Validation

In 2009 and early 2010, a test method was developed and performed to quantify the torque required to manipulate joints in several existing operational and prototype space suits. This was done in an effort to develop joint torque requirements appropriate for a new Constellation Program space suit system. The same test method was levied on the Constellation space suit contractors to verify that their suit design met the requirements. However, because the original test was set up and conducted by a single test operator there was some question as to whether this method was repeatable enough to be considered a standard verification method for Constellation or other future development programs. In order to validate the method itself, a representative subset of the previous test was repeated, using the same information that would be available to space suit contractors, but set up and conducted by someone not familiar with the previous test. The resultant data was compared using graphical and statistical analysis; the results indicated a significant variance in values reported for a subset of the re-tested joints. Potential variables that could have affected the data were identified and a third round of testing was conducted in an attempt to eliminate and/or quantify the effects of these variables. The results of the third test effort will be used to determine whether or not the proposed joint torque methodology can be applied to future space suit development contracts.

Valish, Dana↗

NASA NTRSDate not supplied

Observation of Trace Gases Seasonal Variability in the Marine Boundary Layer over the Atlantic Ocean during the ACTIVATE Field Campaign

High resolution in-situ measurements of carbon monoxide (CO), carbon dioxide (CO2), methane (CH4), and water vapor (H2O) were made onboard the NASA HU-25 aircraft during the ACTIVATE (Aerosol Cloud meteorology Interactions oVer the western Atlantic Experiment) campaign during 2020 and 2021 in different seasons (winter through summer) over the mid-latitude western Atlantic Ocean. As most of the flights focused on the marine boundary layer (MBL) during the campaign, these trace gas observations are an excellent data set to examine seasonal variability of trace gas background values in the MBL without the influence of localized point sources. We will describe the variability of these trace gases in the MBL background by filtering out concentrated point sources using back trajectory analysis along with trace gas ratios. Additionally, the ocean is a significant sink of anthropogenic CO2 capturing about one quarter of total anthropogenic carbon. By looking at the MBL CO2 variation as a function of season, we discuss observed changes in CO2 uptake over the ocean. These high accuracy observations of trace gas backgrounds in the MBL along with characterizing seasonal effects on oceanic sequestering of anthropogenic CO2 will improve the understanding of seasonal variations and change in climate and inverse modelling over the ocean.

Yonghoon Choi↗

NASA NTRS1991

State estimation applications in aircraft flight-data analysis: A user's manual for SMACK

The evolution in the use of state estimation is traced for the analysis of aircraft flight data. A unifying mathematical framework for state estimation is reviewed, and several examples are presented that illustrate a general approach for checking instrument accuracy and data consistency, and for estimating variables that are difficult to measure. Recent applications associated with research aircraft flight tests and airline turbulence upsets are described. A computer program for aircraft state estimation is discussed in some detail. This document is intended to serve as a user's manual for the program called SMACK (SMoothing for AirCraft Kinematics). The diversity of the applications described emphasizes the potential advantages in using SMACK for flight-data analysis.

Bach, Ralph E., Jr.↗

NASA NTRS2023

Pypromice: A Python Package for Processing Automated Weather Station Data

The pypromice Python package is for processing and handling observation datasets from automated weather stations (AWS). It is primarily aimed at users of AWS data from the Geological Survey of Denmark and Greenland (GEUS), which collects and distributes in situ weather station observations to the cryospheric science research community. Functionality in pypromice is primarily handled using two key open-source Python packages, xarray (Hoyer & Hamman, 2017) and pandas (The pandas development team, 2020). A defined processing workflow is included in pypromice for transforming original AWS observations (Level 0, L0) to a usable, CF-convention-compliant dataset (Level 3, L3) (Figure 1). Intermediary processing levels (L1,L2) refer to key stages in the workflow, namely the conversion of variables to physical measurements and variable filtering (L1), cross-variable corrections and user-defined data flagging and fixing (L2), and derived variables (L3). Information regarding the station configuration is needed to perform the processing, such as instrument calibration coefficients and station type (one-boom tripod or two-boom mast station design, for example), which are held in a toml configuration file. Two example configuration files are provided with pypromice , which are also used in the package’s unit tests. More detailed documentation of the AWS design, instrumentation, and processing steps are described in Fausto et al. (2021).

pypromice↗

NASA NTRS1989

Visualizing large data sets in the earth sciences

The authors describe the capabilities of McIDAS, an interactive visualization system that is vastly increasing the ability of earth scientists to manage and analyze data from remote sensing instruments and numerical simulation models. McIDAS provides animated three-dimensionsal images and highly interactive displays. The software can manage, analyze, and visualize large data sets that span many physical variables (such as temperature, pressure, humidity, and wind speed), as well as time and three spatial dimensions. The McIDAS system manages data from at least 100 different sources. The data management tools consist of data structures for storing different data types in files, libraries of routines for accessing these data structures, system commands for performing housekeeping functions on the data files, and reformatting programs for converting external data to the system's data structures. The McIDAS tools for three-dimensional visualization of meteorological data run on an IBM mainframe and can load up to 128-frame animation sequences into the workstations. A highly interactive version of the system can provide an interactive window into data sets containing tens of millions of points produced by numerical models and remote sensing instruments. The visualizations are being used for teaching as well as by scientists.

Hibbard, William↗