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NASA NTRS2006

Acquiring Comprehensive Observations using an Integrated Sensorweb for Early Warning

As an integrated observing strategy, the concept of sensorweb for Earth observations is appealing in many aspects. For instance, by increasing the spatial and temporal coverage of observations from space and other vantage points, one can eventually aid in increasing the accuracy of the atmospheric models which are precursor to hurricane track prediction, volcanic eruption forecast, and trajectory path of transcontinental transport of dust, harmful nuclear and chemical plumes. In reality, there is little analysis'available in terms of benefits, costs and optimized set of sensors needed to make these necessary observations. This is a complex problem that must be carefully studied and balanced over many boundaries such as science, defense, early warning security, and surveillance. Simplistically, the sensorweb concept from the technological point of view alone has a great appeal in the defense, early warning and security applications. In fact, it can be relatively less expensive in per unit cost as opposed to building and deploying it for the scientific use. However, overall observing approach should not be singled out and aligned somewhat . orthogonally to serve a particular need. On the other hand, the sensorweb should be designed and deployed to serve multiple subject areas and customers simultaneously; and can behave as directed measuring systems for both science and operational entities. Sensorweb can be designed to act as expert systems, and/or also provide a dedicated integrated surveillance network. Today, there is no system in the world that is fully integrated in terms of reporting timely multiple hazards warnings, computing the lass of life and property damage estimates, and is also designed to cater to everyone's needs. It is not an easier problem to undertake and more so is not practically solvable. At this time due to some recent events in the world, the scientific community, social scientists, and operational agencies are more cognizant and getting together to address such colossal problems. Increasing our knowledge of the home planet, via amplified set of observations, is certainly a right step in a right direction. Furthermore, this is a pre-requisite in understanding multiple hazard phenomena's. This paper examines various sensorweb options and observing architectures that can be useful specifically in addressing some of these complex issues. The ultimate goal is to serve the society by providing potential natural hazards information to the decision makers in the most expeditious manner so they can prepare themselves to mitigate potential risks to human life, livestock and property.

Habib, Shahid↗

NASA NTRS2006

Acquiring Comprehensive Observations using an integrated Sensorweb for Early Warning

As an integrated observing strategy, the concept of sensorweb for Earth observations is appealing in many aspects. For instance, by increasing the spatial and temporal coverage of observations from space and other vantage points, one can eventually aid in increasing the accuracy of the atmospheric models which are precursor to hurricane track prediction, volcanic eruption forecast, and trajectory path of transcontinental transport of dust, harmful nuclear and chemical plumes. In reality, there is little analysis'available in terms of benefits, costs and optimized set of sensors needed to make these necessary observations. This is a complex problem that must be carefully studied and balanced over many boundaries such as science, defense, early warning, security, and surveillance. Simplistically, the sensorweb concept from the technological point of view alone has a great appeal in the defense, early warning and security applications. In fact, it can be relatively less expensive in per unit cost as opposed to building and deploying it for the scientific use. However, overall observing approach should not be singled out and aligned somewhat orthogonally to serve a particular need. On the other hand, the sensorweb should be designed and deployed to serve multiple subject areas and customers simultaneously; and can behave as directed measuring systems for both science and operational entities. Sensorweb can be designed to act as expert systems, and/or also provide a dedicated integrated surveillance network. Today, there is no system in the world that is fully integrated in terms of reporting timely multiple hazards warnings, computing the loss of life and property damage estimates, and is also designed to cater to everyone's needs. It is not an easier problem to undertake and more so is not practically solvable. At this time due to some recent events in the world, the scientific community, social scientists, and operational agencies are more cognizant and getting together to address such colossal problems. Increasing our knowledge of the home planet, via amplified set of observations, is certainly a right step in a right direction. Furthermore, this is a pre-requisite in understanding multiple hazard phenomena's. This paper examines various sensorweb options and observing architectures that can be useful specifically in addressing some of these complex issues. The ultimate goal is to serve the society by providing potential natural hazards information to the decision makers in the most expeditious manner so they can prepare themselves to mitigate potential risks to human life, livestock and property.

Habib, Shahid↗

NASA NTRSDate not supplied

MISR Level 2 Surface parameters (MIL2ASLS_V1)

The Land Surface data include bihemispherical and directional-hemispherical reflectances (albedo), hemispherical directional and bidirectional reflectance factors (BRF), BRF model parameters, leaf-area index (LAI), fraction of photosynthetically active radiation (FPAR), and normalized difference vegetation index (NDVI) on a 1.1 km grid. The land surface data include hemispherical directional reflectance factor, bihemispherical reflectance (i.e., albedo), bidirectional reflectance factor, directional hemispherical reflectance, BRF model parameters, FPAR, and terrain-referenced view and illumination angles. [Location=GLOBAL LAND] [Temporal_Coverage: Start_Date=2000-02-24; Stop_Date=] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180] [Data_Resolution: Latitude_Resolution=1.1 km - 17.6 km; Longitude_Resolution=1.1 km - 17.6 km; Horizontal_Resolution_Range=1 km - < 10 km or approximately .01 degree - < .09 degree; Temporal_Resolution=about 15 orbits/day].

DIRECTIONAL HEMISPHERICAL REFLECTANCE↗

NASA NTRSDate not supplied

MISR Level 2 Surface parameters (MIL2ASLS_V2)

BIDIRECTIONAL REFLECTANCE FACTOR↗

NASA NTRS2012

Global CO2 Distributions over Land from the Greenhouse Gases Observing Satellite (GOSAT)

January 2009 saw the successful launch of the first space-based mission specifically designed for measuring greenhouse gases, the Japanese Greenhouse gases Observing SATellite (GOSAT). We present global land maps (Level 3 data) of column-averaged CO2 concentrations (X(sub CO2)) derived using observations from the GOSAT ACOS retrieval algorithm, for July through December 2009. The applied geostatistical mapping approach makes it possible to generate maps at high spatial and temporal resolutions that include uncertainty measures and that are derived directly from the Level 2 observations, without invoking an atmospheric transport model or estimates of CO2 uptake and emissions. As such, they are particularly well suited for comparison studies. Results show that the Level 3 maps for July to December 2009 on a lO x 1.250 grid, at six-day resolution capture much of the synoptic scale and regional variability of X(sub CO2), in addition to its overall seasonality. The uncertainty estimates, which reflect local data coverage, X(sub CO2) variability, and retrieval errors, indicate that the Southern latitudes are relatively well-constrained, while the Sahara Desert and the high Northern latitudes are weakly-constrained. A probabilistic comparison to the PCTM/GEOS-5/CASA-GFED model reveals that the most statistically significant discrepancies occur in South America in July and August, and central Asia in September to December. While still preliminary, these results illustrate the usefulness of a high spatiotemporal resolution, data-driven Level 3 data product for direct interpretation and comparison of satellite observations of highly dynamic parameters such as atmospheric CO2.

Hammerling, Dorit M.↗

NASA NTRSDate not supplied

Observing Supraglacial Lakes Using Deep Learning and PlanetScope Imagery

Supraglacial lakes (SGL)s result from melt water accumulation in topographic depressions on the surface of glaciers. SGLs primarily affect glacial dynamics through a positive feedback loop in which the albedo-lowering effect of SGLs can escalate surface melt leading to increases in lake extent and depth, amplifying the afore mentioned albedo-lowering effect. The implications of accelerated glacial melt include increased sea level rise and modifications to ocean primary productivity. SGLs are critical indicators of surface melt and its downstream impacts and should be monitored efficiently. In situ observations and measurements of SGLs are time consuming, cost-prohibitive and difficult to scale. Earth observation data and machine learning enable scalable monitoring of SGLs through pattern detection and quantification of lake evolution over time [1]. This work presents a model developed by training a convolutional neural network with imagery and labels from NASA Operation IceBridge and predicting SGLs in high temporal and spatial resolution PlanetScope imagery.

Supraglacial lake↗

NASA NTRS2008

The NASA MSFC Earth Global Reference Atmospheric Model-2007 Version

Reference or standard atmospheric models have long been used for design and mission planning of various aerospace systems. The NASA/Marshall Space Flight Center (MSFC) Global Reference Atmospheric Model (GRAM) was developed in response to the need for a design reference atmosphere that provides complete global geographical variability, and complete altitude coverage (surface to orbital altitudes) as well as complete seasonal and monthly variability of the thermodynamic variables and wind components. A unique feature of GRAM is that, addition to providing the geographical, height, and monthly variation of the mean atmospheric state, it includes the ability to simulate spatial and temporal perturbations in these atmospheric parameters (e.g. fluctuations due to turbulence and other atmospheric perturbation phenomena). A summary comparing GRAM features to characteristics and features of other reference or standard atmospheric models, can be found Guide to Reference and Standard Atmosphere Models. The original GRAM has undergone a series of improvements over the years with recent additions and changes. The software program is called Earth-GRAM2007 to distinguish it from similar programs for other bodies (e.g. Mars, Venus, Neptune, and Titan). However, in order to make this Technical Memorandum (TM) more readable, the software will be referred to simply as GRAM07 or GRAM unless additional clarity is needed. Section 1 provides an overview of the basic features of GRAM07 including the newly added features. Section 2 provides a more detailed description of GRAM07 and how the model output generated. Section 3 presents sample results. Appendices A and B describe the Global Upper Air Climatic Atlas (GUACA) data and the Global Gridded Air Statistics (GGUAS) database. Appendix C provides instructions for compiling and running GRAM07. Appendix D gives a description of the required NAMELIST format input. Appendix E gives sample output. Appendix F provides a list of available parameters to enable the user to generate special output. Appendix G gives an example and guidance on incorporating GRAM07 as a subroutine in other programs such as trajectory codes or orbital propagation routines.

Leslie, F.W.↗

NASA NTRSDate not supplied

Airborne High Spectral Resolution Lidar Measurements of Aerosol Distributions and Properties During the NASA CAMP2Ex Mission

The NASA Clouds, Aerosols and Monsoon Processes-Philippines Experiment (CAMP2Ex) occurred during August-October, 2019,primarily over the ocean near the Philippines, including the ocean west of Luzon. CAMP2Ex attempts to characterize the role of anthropogenic and natural aerosol particles in modulating warm and mixed-phase precipitation in the maritime continent during the Southwest Monsoon. This mission is also examining relationships between spatial heterogeneity in the aerosol and cloud fields and radiative heating rates and studying the effects of aerosol concentration and composition on the microphysical properties of shallow cumulus and cumulus congestus clouds. Critical to the successful investigation of these objectives is profiling of aerosol, cloud and meteorological properties throughout the atmospheric column. For the CAMP2Ex experiment, we deployed the second generation NASA Langley airborne High Spectral Resolution Lidar (HSRL-2) on the NASA P-3 aircraft to obtain profile measurements of aerosol and thin cloud extinction and optical thickness via the HSRL technique at 355 and 532 nm, and of backscatter and depolarization at 355, 532, and 1064 nm. The HSRL technique allows for the independent measurement of extinction and backscatter without the need to assume modeled lidar ratios. Additional HSRL-2 data products include aerosol type, mixed layer depth, and range-resolved aerosol microphysical parameters. The HSRL measurements reveal the temporal, spatial, and vertical variability of aerosol optical properties over these locations. The HSRL-2 measured these backscatter, extinction, and depolarization profiles during all nineteen science flights and the transit flights to and from the aircraft operations base at Clark International Airport.In this presentation, we use these HSRL-2 measurements to show the horizontal and vertical distributions of aerosols in the CAMP2Ex operational area as well as provide insight into the optical and physical characteristics of these aerosols. We also discuss how these aerosols are distributed within and above the mixed layer.

aerosol↗

NASA NTRS2012

Using Satellite Remote Sensing and Modelling for Insights into N02 Air Pollution and NO2 Emissions

Nitrogen oxides (NO(x)) are key actors in air quality and climate change. Satellite remote sensing of tropospheric NO2 has developed rapidly with enhanced spatial and temporal resolution since initial observations in 1995. We have developed an improved algorithm and retrieved tropospheric NO2 columns from Ozone Monitoring Instrument. Column observations of tropospheric NO2 from the nadir-viewing satellite sensors contain large contributions from the boundary layer due to strong enhancement of NO2 in the boundary layer. We infer ground-level NO2 concentrations from the OMI satellite instrument which demonstrate significant agreement with in-situ surface measurements. We examine how NO2 columns measured by satellite, ground-level NO2 derived from satellite, and NO(x) emissions obtained from bottom-up inventories relate to world's urban population. We perform inverse modeling analysis of NO2 measurements from OMI to estimate "top-down" surface NO(x) emissions, which are used to evaluate and improve "bottom-up" emission inventories. We use NO2 column observations from OMI and the relationship between NO2 columns and NO(x) emissions from a GEOS-Chem model simulation to estimate the annual change in bottom-up NO(x) emissions. The emission updates offer an improved estimate of NO(x) that are critical to our understanding of air quality, acid deposition, and climate change.

Lamsal, L. N.↗

NASA NTRS1992

The evolution equation for the flame surface density in turbulent premixed combustion

One central ingredient in flamelet models for turbulent premixed combustion is the flame surface density. This quantity conveys most of the effects of the turbulence on the rate of energy release and is obtained via a modeled transport equation, called the Sigma-equation. Past theoretical work has produced a rigorous approach that leads to an exact, but unclosed, formulation for the turbulent Sigma-equation. In this exact Sigma-equation, it appears that the dynamical properties of the flame surface density are determined by a single parameter, namely the turbulent flame stretch. Unfortunately, the flame surface density and the turbulent flame stretch are not available from experiments and, in the absence of experimental data, little is known on the validity of the closure assumptions used in current flamelet models. Direct Numerical Simulation (DNS) is the obvious, complementary approach to get basic information on these fundamental quantities. Three-dimensional DNS of premixed flames in isotropic turbulent flow is used to estimate the different terms appearing in the Sigma-equation. A new methodology is proposed to provide the source and sink terms for the flame surface density, resolved both temporally and spatially throughout the turbulent flame brush. Using this methodology, the effects of the Lewis number on the rate of production of flame surface area are described in great detail and meaningful comparisons with flamelet models can be performed. The analysis reveals in particular the tendency of the models to overpredict flame surface dissipation as well as their inability to reproduce variations due to thermo-diffusive phenomena. Thanks to the detailed information produced by a DNS-based analysis, this type of comparison not only underscores the shortcomings of current models but also suggests ways to improve them.

Trouve, A.↗

NASA NTRS2004

Novel Image Processing Interface to Relate DSB Spatial Distribution from Immunofluorescence Foci Experiments to the State-of-the-Art Models of DNA Breakage

A recently developed software (NASARadiationTrackImage) allows a quick and automatic segmentation of foci that indicate spatial localization of specific proteins that are visualized by immunofluorescence. Of interest are the spatial and temporal distribution of foci such as gammaH2AX, a signal of the phosphorylation of a variant of the histone H2A that has been shown to correspond to DSBs, or proteins involved in DSB processing, such as ATM, Rad51, and p53, following exposures of human cells to high charge and energy (HZE) ion irradiation. Experimental data are recorded as sets of two-dimensional images in color with cells and foci of gammaH2AX, ATM, Rad51 or others shown. Different cells, levels of radiation and timing after radiation were recorded. The software allows us to calculate the number of foci per cell, overall intensity of light in foci and their spatial organization. A simple statistical model allows for testing of foci overlap (eclipse). A more complex statistical model previously known as DNAbreak simulates track structure and random chromosome geometry. It has one adjustable parameter corresponding to an average intensity of DSB creation in cubic micrometers of DNA volume per particle track or unit dose. Its limitation is the low-resolution limit both in physical space and DSB's along DNA. It works adequately on the scale of a cell and provides further insights on how the geometry of tracks and DNA affects genomic damage of the cell and subsequent repair. Future developments of the model for the description of the time evolution of DNA damage response proteins, and more robust track structure models will be discussed.

Ponomarev, A. L.↗

NASA NTRS2002

On the Choice of Variable for Atmospheric Moisture Analysis

The implications of using different control variables for the analysis of moisture observations in a global atmospheric data assimilation system are investigated. A moisture analysis based on either mixing ratio or specific humidity is prone to large extrapolation errors, due to the high variability in space and time of these parameters and to the difficulties in modeling their error covariances. Using the logarithm of specific humidity does not alleviate these problems, and has the further disadvantage that very dry background estimates cannot be effectively corrected by observations. Relative humidity is a better choice from a statistical point of view, because this field is spatially and temporally more coherent and error statistics are therefore easier to obtain. If, however, the analysis is designed to preserve relative humidity in the absence of moisture observations, then the analyzed specific humidity field depends entirely on analyzed temperature changes. If the model has a cool bias in the stratosphere this will lead to an unstable accumulation of excess moisture there. A pseudo-relative humidity can be defined by scaling the mixing ratio by the background saturation mixing ratio. A univariate pseudo-relative humidity analysis will preserve the specific humidity field in the absence of moisture observations. A pseudorelative humidity analysis is shown to be equivalent to a mixing ratio analysis with flow-dependent covariances. In the presence of multivariate (temperature-moisture) observations it produces analyzed relative humidity values that are nearly identical to those produced by a relative humidity analysis. Based on a time series analysis of radiosonde observed-minus-background differences it appears to be more justifiable to neglect specific humidity-temperature correlations (in a univariate pseudo-relative humidity analysis) than to neglect relative humidity-temperature correlations (in a univariate relative humidity analysis). A pseudo-relative humidity analysis is easily implemented in an existing moisture analysis system, by simply scaling observed-minus background moisture residuals prior to solving the analysis equation, and rescaling the analyzed increments afterward.

Dee, Dick P.↗

NASA NTRS2000

Coupled Model Simulation of Snowfall Events Over the Black Hills

Although many long-term simulations of snow accumulation and oblation have been made using stand-alone land surface models and surface models coupled with GCMs, less research has focused on short-term event simulations. Actually, accurate event simulations of snow-related processes are the basis for successful long-term simulation. Three advantages of event simulations of snowfall and snow melting are availability of: (1) intensive observation data from field experiments for validation; (2) more physically-realistic precipitation schemes for use in atmospheric models to simulate snowfall; and (3) a more detailed analysis of the snow melting processes. In addition to the complexities of snow related processes themselves, terrain-induced effects on snowfall/snow melting make simulations of snow events more difficult. Climatological observations indicate that terrain features such as the Black Hills of South Dakota and Wyoming can exert important effects on snow accumulation and snow oblation processes. One of the primary effects is that the orography causes forced uplift of airflow and causes atmospheric waves to form both upwind and downwind of it. Airflow often splits around the obstacle, converging on the lee side. This convergence may lead to precipitation enhancement. It also provides an elevated heat and moisture source that enhances atmospheric instability. During the period of April 5-May 5, 1999, the Upper Missouri River Basin Pilot Project (UMRBPP) made intensive observations on precipitation events occurring in the Black Hills. Two moderate snowfall events were captured during the period. The resulting high temporal and spatial resolution data provides opportunities to investigate terrain effects on snowfall amount, distribution, and melting. Successful simulation of snowfall amount, distribution, and evolution using atmospheric models is important to subsequent modeling of snow melting using snow sub-models in land surface schemes. In this paper, a coupled model system, consisting of an atmosphere model (ARPS) and a land-surface model (revised NCAR LSM), is used to simulate one of these UMRBPP cases.

Wang, Jianzhong↗

NASA NTRS2009

Satellite Observations for Detecting and Tracking Changes in Atmospheric Composition

The satellite observations provide constraints on detailed atmospheric modeling, including emissions inventories, indications of transport, harmonized data over vast areas suitable for trends analysis, and a link between spatial scales ranging from local to global, and temporal scales from diurnal to interannual. 1 The National Oceanic and Atmospheric Administration's (NOAA) long-term commitments help provide these observations in cooperation with international meteorological organizations. NASA s long-term commitments will advance scientifically important observations as part of its Earth Science Program, and will assist the transition of the science measurements to applied analyses through the Applied Science Program. Both NASA and NOAA have begun to provide near realtime data and tools to visualize and analyze satellite data,2 while maintaining data quality, validation, and standards. Consequently, decision-makers can expect satellite data services to support air quality decision making now and in the future. The international scientific community's Integrated Global Atmosphere Chemistry Observation System Report3 outlined a plan for ground-based, airborne and satellite measurements and models to integrate the observations into a four-dimensional representation of the atmosphere (space and time) to support assessment and policy information needs. This plan is being carried out under the Global Earth Observation System of Systems (GEOSS). Demonstrations of such an integrated capability4 provide new understanding of the changing atmosphere and link policy decisions to benefits for society. In this article, we highlight the use of satellite data to constrain biomass burning emissions, to assess oxides of nitrogen (NO(x)) emission reductions, and to contribute to state implementation plans, as examples of the use of satellite observations for detecting and tracking changes in atmospheric composition.

Neil, Doreen O.↗

NASA NTRS1983

Magnetohydrodynamic simulation of the coronal transient associated with the solar limb flare of 1980, June 29, 18:21 UT

The spatial and temporal behavior of excess and depleted density regions of the coronal transient accompanying the west limb solar flare of 18:21 UT on June 29, 1980, is modeled mathematically on the basis of SMM-X-ray-polychrometer data, and the results are compared to observations made with the radio spectrograph at Harvard Radio Astronomy Station at Fort Davis and with the NCAR/HAO Mark III K-coronameter at Mauna Loa. Input data for the model include T(max) = about 20 x 10 to the 6th K, n(max) = about 4 x 10 to the 11th/cu cm, and an assumed ejection velocity of 200 km/sec. Computations using an improved 2D nonplane MHD model are carried out for locally open and closed magnetic topologies. The spatially wide, large-amplitude, temporarily steepened MHD wave predicted by the model for both magnetic topologies is shown to agree well with the observations, except that the predicted density enhancement exceeded observed values by at least 50 percent for the closed field and by a factor of 3 for the open field. This discrepancy is seen as an indication that the mass emitting soft X-rays was confined in closed-field regions near the sun during the obervation period.

Wu, S. T.↗

NASA NTRS2021

First Attempt of Global-scale Assimilation of Subdaily Scale Soil Moisture Estimates from CYGNSS and SMAP into a Land Surface Model

Soil moisture performs a key function in the hydrologic process and understanding the global-scale water cycle. However, estimations of soil moisture taken from current sun-synchronous orbit (SSO) satellites are limited in that they are neither spatially nor temporally continuous. This limitation creates discontinuous soil moisture observation from space and hampers our understanding of the fundamental processes that control the surface hydrologic cycle across both time and space domains. Here, we propose to use frequent soil moisture observations from NASA’s constellation of eight micro-satellites called the Cyclone Global Navigation Satellite System(CYGNSS) together with the Soil Moisture Active Passive (SMAP) to assimilate subdaily-scale soil moisture intoa land surface model(LSM). Our results, which are based on triple collocation analysis(TCA), show how current scientific advances in satellite systems can fill previous gaps in soil moisture observations in subdaily scale bypast observations, and eventually adds value to improvements in global scale soil moisture estimates in LSMs. Overall, TCA-based fractional mean square errors (fMSE) of LSM soil moisture are improved by 61% with the synergetic assimilation of CYGNSS data with SMAP soil moisture observations. However, assimilating satellite-based soil moisture over dense vegetation areas can degrade the performance of LSMs as these areas propagate erroneous soil moisture information to LSMs. To our knowledge, this study isthe first global assimilation of GNSS-based soil moisture observations in land surface models.

Hyunglok Kim↗

NASA NTRS1980

Influence of magnetic pressure on stellar structure: A Mechanism for solar variability

A physical mechanism is proposed that couples the Sun's dynamo magnetic field to its gravitational potential energy. The mechanism involves the isotropic field pressure resulting in a lifting force on the convective envelope, thereby raising its potential energy. Decay of the field due to solar activity allows the envelop to subside and releases this energy, which can augment the otherwise steady solar luminosity. Equations are developed and applied to the Sun for several field configurations. The best estimate model suggests that uniform luminosity variations as large as 0.02% for half a sunspot cycle may occur. Brief temporal variations or the rotation of spatial structures could allow larger excursions in the energy released.

Schatten, K. H.↗

NASA NTRS1986

The effect of the dynamic wet troposphere on VLBI measurements

Calculations using a statistical model of water vapor fluctuations yield the effect of the dynamic wet troposphere on Very Long Baseline Interferometry (VLBI) measurements. The statistical model arises from two primary assumptions: (1) the spatial structure of refractivity fluctuations can be closely approximated by elementary (Kolmogorov) turbulence theory, and (2) temporal fluctuations are caused by spatial patterns which are moved over a site by the wind. The consequences of these assumptions are outlined for the VLBI delay and delay rate observables. For example, wet troposphere induced rms delays for Deep Space Network (DSN) VLBI at 20-deg elevation are about 3 cm of delay per observation, which is smaller, on the average, than other known error sources in the current DSN VLBI data set. At 20-deg elevation for 200-s time intervals, water vapor induces approximately 1.5 x 10 to the minus 13th power s/s in the Allan standard deviation of interferometric delay, which is a measure of the delay rate observable error. In contrast to the delay error, the delay rate measurement error is dominated by water vapor fluctuations. Water vapor induced VLBI parameter errors and correlations are calculated. For the DSN, baseline length parameter errors due to water vapor fluctuations are in the range of 3 to 5 cm. The above physical assumptions also lead to a method for including the water vapor fluctuations in the parameter estimation procedure, which is used to extract baseline and source information from the VLBI observables.

Treuhaft, R. N.↗