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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS1973

Interhemispheric comparison of atmospheric circulation features as evaluated from Nimbus satellite data

Atmospheric structure derived from satellite, multichannel radiance data is used to calculate zonally averaged vertical motions in the wintertime stratosphere of both hemispheres using a heat budget approach. The Northern Hemisphere calculations based on the satellite data are shown to compare favorably with a computation carried out with conventional data, and with results of previous studies. The mean Southern Hemisphere pattern for the month of July 1969 indicates a two-celled structure with the high latitude cell centered at 60-65S. The axis of sinking motion in this cell is at approximately 50S, while the rising motion is centered at 70S. Thus the Antarctic stratospheric jet stream is associated with an indirect cell. Two individual ten-day periods from July 1969 are examined to compare the mean meridional circulation and eddy heat flux patterns in the Southern Hemisphere during a minor midwinter warming and during a quiet period.

Reiter, E. R.↗
NASA NTRS1979

Operational aspects of satellite data collection systems

Operational aspects of satellite data collection systems (DCS) are discussed with consideration given to a cooperative program between the United States and France. The Tiros-N DCS is described which is a random access system providing operational capability for position location and/or data collection of 4000 to 16,000 moving and/or fixed platforms. The platform transmissions and processing of the data is designed to conform with the user needs. The position location is obtained through ground processing of Doppler measurements made by the data collection instrument on board the spacecraft.

Morakis, J. C.↗
NASA NTRS1981

Advanced technology for satellite data collection systems

Technological developments in satellite data collection are aimed at relieving constraints of existing systems to permit expanded capability at lower costs in future operations. Constraints imposed by the limited electromagnetic spectrum available in the UHF band and the cost of user equipment are principal targets for improvement through technology. This paper describes ongoing developmental activities in system and component areas which will become available for the next generation of operations.

Cote, C. E.↗
NASA NTRS1981

A general interactive system for compositing digital radar and satellite data

Reynolds and Smith (1979) have considered the combined use of digital weather radar and satellite data in interactive systems for case study analysis and forecasting. Satellites view the top of clouds, whereas radar is capable of observing the detailed internal structure of clouds. The considered approach requires the use of a common coordinate system. In the present investigation, it was decided to use the satellite coordinate system as the base system in order to maintain the fullest resolution of the satellite data. The investigation is concerned with the development of a general interactive software system called RADPAK for remapping and analyzing conventional and Doppler radar data. RADPAK is implemented as a part of a minicomputer-based image processing system, called Atmospheric and Oceanographic Image Processing System. Attention is given to a general description of the RADPAK system, remapping methodology, and an example of satellite remapping.

Ghosh, K. K.↗
NASA NTRS1998

Variations in Atmospheric Water Vapor as Seen in Satellite Data and Model Reanalysis Fields

New data sets used to describe components of the hydrologic and energy cycles of the Earth system are currently being produced and disseminated through the NASA EOS DAACs and several data assimilation centers supported by such organizations as NASA and NOAA. These data sets incorporate hybrid data analysis schemes and portray satellite and radiosonde data combined in a diagnostic sense and in a forecast mode. There is a need to develop a better understanding of the accuracy and utility of these global, relatively long-term, datasets to describe components of the hydrologic cycle and to understand atmospheric moisture variability and its relation to climatological significant events Much progress has been made in the last ten years in the development of global atmospheric models and analysis of satellite data for global studies. The atmospheric models have improved in their ability to predict both short term and longer term weather events. This has been possible through better understanding of atmosphere dynamics and very rapid advances in computer technology. Over the same period, data assimilation methods have advanced and unconventional data sources such as aircraft and satellite data, drifting buoys, etc., can be assimilated at non-synaptic times. As a result of these improvements, comprehensive global atmospheric fields based on four-dimensional data assimilation methods now provide one of the most reliable methods for studying dynamical and physical behavior in the atmosphere.

Jedlovec, Gary J.↗
NASA NTRS2010

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.↗
NASA NTRS1987

Use of satellite data in volcano monitoring

It is argued that Total Ozone Mapping Spectrometer (TOMS) data, especially data on sulfur dioxide detection in volcanic clouds, and weather satellite data complement each other. TOMS data is most useful for discovering previously unknown eruptions and indicating a minimum volume of SO sub 2 produced by a given eruption. Once an eruption has been reported, weather satellite data can be used to accurately monitor its progress. To be used effectively, these data need to be analyzed jointly and in real time. Toward this end, it is hoped that full and timely utilization can be made of existing TOMS data, a polar orbiting TOMS can be launched in the near future, and that TOMS type instruments can be included on future geostationary satellites.

Mcclelland, Lindsay↗
NASA NTRS1985

Application of satellite data to tropic/subtropic moisture coupling

The objective is to develop analysis tools for use of satellite data to interpret synoptic-scale systems in data-void regions. Interim goals are to: (1) quantify the synoptic information content of satellite data; and (2) utilize these data in the diagnosis of moisture bursts in the eastern tropical Pacific Ocean. Researchers developed and implemented a statistical procedure for using TIROS N microwave data to infer infrared channel data for overcast conditions; they used the same procedure for deducing full TIROS N channel radiance profiles from NOAA 5 VTPR channel data over regions where the TIROS N data are missing. An empirical orthogonal function analysis of twice-daily channel radiance fields over the tropical eastern Pacific was completed. The vertically oriented eigenfunctions were interpreted in terms of typical meteorological events. The horizontal distribution of the eigenfunction amplitudes relates these meteorological signals to moisture bursts. A pair of moisture burst climatologies is complete: one of four years using infrared imagery (including the highly anomalous 1982 to 83 cold season); the other implementing 850 to 200 mb wind analyses in conjunction with GOES imagery. A number of different evaluations of the synoptic evolution of moisture fields (enhanced infrared imagery, moisture channel data, FGGE humidity analysis, and in situ station and sounding observations) are compared. All have limitations; all can be utilized together; all together are still less than adequate in the tropical Pacific.

Mcguirk, J. P.↗
DOE OSTI2022

Deep learning-based spatio-temporal estimate of greenhouse gas emissions using satellite data

Accurate estimation of greenhouse gases (GHGs) emissions is very important for developing mitigation strategies to climate change by controlling and reducing GHG emissions. This project aims to develop multiple deep learning approaches to estimate anthropogenic greenhouse gas emissions using multiple types of satellite data. NO2 concentration is chosen as an example of GHGs to evaluate the proposed approach. Two sentinel satellites (sentinel-2 and sentinel-5P) provide multiscale observations of GHGs from 10-60m resolution (sentinel-2) to ~kilometer scale resolution (sentinel-5P). Among multiple deep learning (DL) architectures evaluated, two best DL models demonstrate that key features of spatio-temporal satellite data and additional information (e.g., observation times and/or coordinates of ground stations) can be extracted using convolutional neural networks and feed forward neural networks, respectively. In particular, irregular time series data from different NO 2 observation stations limit the flexibility of long short-term memory architecture, requiring zero-padding to fill in missing data. However, deep neural operator (DNO) architecture can stack time-series data as input, providing the flexibility of input structure without zero-padding. As a result, the DNO outperformed other deep learning architectures to account for time-varying features. Overall, temporal patterns with smooth seasonal variations were predicted very well, while frequent fluctuation patterns were not predicted well. In addition, uncertainty quantification using conformal inference method is performed to account for prediction ranges. Overall, this research will lead to a new groundwork for estimating greenhouse gas concentrations using multiple satellite data to enhance our capability of tracking the cause of climate change and developing mitigation strategies.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2001

Searching for the Beginning of the Ozone Turnaround Using a 22-Year Merged Satellite Data Set

We have used the data from six satellite instruments that measure the total column amount of ozone to construct a consistent merged data set extending from late 1978 into 2000. The keys to constructing a merged data set are to minimize potential drift of individual instruments and to accurately establish instrument-to-instrument offsets. We have used the short-wavelength D-pair measurements (306nm-313nm) of the SBUV and SBUV/2 instruments near the equator to establish a relatively drift-free record for these instruments. We have then used their overlap with the Nimbus 7 and EP TOMS instruments to establish the relative calibration of the various instruments. We have evaluated the drift uncertainty in our merged ozone data (MOD) set by examining both the individual instrument drift uncertainty and the uncertainty in establishing the instrument- to-instrument differences. We conclude that the instrumental drift uncertainty over the 22-year data record is 0.9 %/decade (2-sigma). We have compared our MOD record with 37 ground stations that have a continuous record over that time period. We have a mean drift with respect to the stations of +0.3 %/decade which is within 1-sigma of our uncertainty estimate. Using the satellite record as a transfer standard, we can estimate the capability of the ground instruments to establish satellite calibration. Adding the statistical variability of the station drifts with respect to the satellite to an estimate of the overall drift uncertainty of the world standard instrument, we conclude that the stations should be able to be used to establish the drift of the satellite data record to within and uncertainty of 0.6 %/decade (2-sigma). Adding to this an uncertainty due to the-incomplete global coverage of the stations, we conclude that the station data should be able to establish the global trend with an uncertainty of about 0.7 %/decade, slightly better than for the satellite record. We conclude that merging the two records together gives only a slight improvement in the uncertainty. Keeping them separate gives the greater confidence of two independent measures of the ozone trend and potential recovery. We fit the trend in our MOD record through May of 1991 and then extrapolated forward to see if the data at the end of the record was above the statistical model as a measure of ozone recovery as was done in the last WMO/UNEP assessment report. Because our data set drifts with respect to the ground-stations through May of 1991, we calculated a smaller global trend (-1.1 %/decade) than in the WMO/UNEP report. Our data in 1998 and 1999 was, on average 2 DU above the extrapolated statistical model with a 2-sigma uncertainty of 6 DU. For the combined mid-latitudes of the northern and southern hemispheres, the data was 5 DU above the extrapolated statistical model with a 2-sigma uncertainty of 10 DU. These may be signs of recovery, but they are still statistically insignificant.

Stolarski, Richard S.↗
NASA NTRS1986

Evaluation of a multivariate variational assimilation of conventional and satellite data for the diagnosis of cyclone systems

A variational data assimilation method for the study of cyclone-scale weather systems is described. The variational data assimilation method is to incorporate primitive equations for a moist, convectively unstable atmosphere and the radiative transfer equation. The variables to be adjusted include the three-dimensional vector wind, height, temperature, and moisture from rawinsonde data, and cloud-wind vectors, moisture, and radiance from satellite data. The development of variational model 1 which contains two nonlinear horizontal momentum equations, an integrated continuity equation, and a hydrostatic equation is examined. Examples applying the assimilation model to rawinsonde and satellite data are presented.

Achtemeier, Gary L.↗
NASA NTRS2003

Gravity Models from CHAMP and other Satellite Data

CHAMP spacecraft is the first of a series of new spacecraft missions that are revolutionizing our ability to model the Earth's geopotential. We report on the analysis of over 100 days of CHAMP data in 2001 and 2002, merged with tracking data of other satellites such as Jason, Topex, GFO, Starlette, Stella, Spot-2, as well as satellite altimetry. We find that the CHAMP-only component of these solutions is a significant improvement over pre-CHAMP satellite only models with respect to the high degree information expressed by the geopotential model coefficients. For example, the variance of the differences with altimeter-derived anomalies through degree 70 is 2.80 mGal(sup 2) for the CHAMP-only solution based on 87 days of data vs. 10.19 mGal(sup 2) for EGM96S. Nonetheless, in order to model properly the various resonances to which different satellites are sensitive, we must include other satellite data. We evaluate the performance of these new CHAMP derived solutions with EGM96 and the EIGEN series of solutions. We review carefully the performance of these models for altimetric satellites.

Lemoime, Frank G.↗
NASA NTRS1977

Reduction and treatment of magnetic anomalies of crustal origin in satellite data

The problem of proper reduction and treatment of the residual total magnetic field observed on satellite orbits is studied. The reduction procedure used for Pogo satellite data is reviewed, and a procedure is presented for reducing the residual total field observed on satellite orbits to a spherical surface. Several examples based on selected models are provided to demonstrate the accuracy of the formulas developed for continuation of the satellite data from an irregular to a spherical surface. This procedure is tested on a set of Pogo data covering the area that contains the Bangui magnetic anomaly in central Africa. A technique is also given for determining the field components on a spherical surface and calculating the total field in any fixed direction of the geomagnetic field.

Bhattacharyya, B. K.↗
NASA NTRS2001

Modeling UV-B Effects on Primary Production Throughout the Southern Ocean Using Multi-Sensor Satellite Data

This study has used a combination of ocean color, backscattered ultraviolet, and passive microwave satellite data to investigate the impact of the springtime Antarctic ozone depletion on the base of the Antarctic marine food web - primary production by phytoplankton. Spectral ultraviolet (UV) radiation fields derived from the satellite data are propagated into the water column where they force physiologically-based numerical models of phytoplankton growth. This large-scale study has been divided into two components: (1) the use of Total Ozone Mapping Spectrometer (TOMS) and Special Sensor Microwave Imager (SSM/I) data in conjunction with radiative transfer theory to derive the surface spectral UV irradiance throughout the Southern Ocean; and (2) the merging of these UV irradiances with the climatology of chlorophyll derived from SeaWiFS data to specify the input data for the physiological models.

Lubin, Dan↗
NASA NTRS1981

Monitoring the Chesapeake Bay using satellite data for Superflux III

The TIROS-N and NOAA-6, and GOES visible infrared satellite data were used to identify and locate surface oceanographic thermal fronts for the purpose of issuing daily and premission advisory briefings in support of the Superflux 3 in situ and remote sensing experiment in the Chesapeake Bay region. Satellite data were collected for the period 1 - 22 October 1980. A summary of that data is presented.

Vukovich, F. M.↗
NASA NTRS1982

Methods of editing cloud and atmospheric layer affected pixels from satellite data

Practical methods of computer screening cloud-contaminated pixels from data of various satellite systems are proposed. Examples are given of the location of clouds and representative landscape features in HCMM spectral space of reflectance (VIS) vs emission (IR). Methods of screening out cloud affected HCMM are discussed. The character of subvisible absorbing-emitting atmospheric layers (subvisible cirrus or SCi) in HCMM data is considered and radiosonde soundings are examined in relation to the presence of SCi. The statistical characteristics of multispectral meteorological satellite data in clear and SCi affected areas are discussed. Examples in TIROS-N and NOAA-7 data from several states and Mexico are presented. The VIS-IR cluster screening method for removing clouds is applied to a 262, 144 pixel HCMM scene from south Texas and northeast Mexico. The SCi that remain after cluster screening are sited out by applying a statistically determined IR limit.

Nixon, P. R.↗
NASA NTRSDate not supplied

NASA GLOBE Clouds: Matching your sky observations to multiple satellite data

Have you ever wondered how your view of the sky compares to satellite data? The NASA GLOBE Clouds team at NASA Langley Research Center in Hampton, VA, matches ground-cloud observations taken by citizen scientists through the GLOBE Program to geostationary satellites as well as to Terra, Aqua, and CALIPSO satellites. The result is the NASA GLOBE Clouds satellite match table sent in a NASA personalized email summarizing both ground-cloud and satellite observations. The table contains ground-cloud and satellite values of total cloud cover and cloud cover by height (low, mid, high), cloud opacity and cloud phase. The satellite match table also includes images from the respective satellites centered on the latitude and longitude of the ground-cloud report entered through the GLOBE Program. Participants can receive satellite flyover notifications for their location through the GLOBE Observer app or through the satellite flyover schedule. This presentation will engage the audience through a live demonstration showing how to access GLOBE Clouds data visualization tools and view corresponding satellite imagery and data. We will also discuss best practices suggested by classroom teachers and undergraduate professors in incorporating active learning and satellite data analysis.

Marilé Colón Robles↗