TOVSAMNG
02
TOVSAMNG_02 DOI:10.5067/O44UZ58U4LBZ Data reformatted from HDF to netCDF and added an additional group of data adjusting the temperature and OLR fields to what an observation time of 7:30AM would have produced
TOVSAMNG_01 (NO DOI)
TOVS GLA MONTHLY GRIDS from NOAA-10 02 (TOVSAMNG) at GES DISC
Goddard Laboratory for Atmospheres at NASA GSFC
TOVS GLA MONTHLY GRIDS from NOAA-10 V02
TOVSAMNG
2018-08-08T00:00:00.000Z
Greenbelt, MD, USA
Goddard Earth Sciences Data and Information Services Center (GES DISC)
02
Digital Science Data
DOI
10.5067/O44UZ58U4LBZ
https://disc.gsfc.nasa.gov/datacollection/TOVSAMNG_02.html
TECHNICAL CONTACT
JOEL
SUSSKIND
Goddard Space Flight Center
Mailstop 610.0
Greenbelt
MD
20771
USA
301-286-7210
Telephone
joel.susskind-1@nasa.gov
METADATA AUTHOR
Lena
Iredell
GES DISC NASA Goddard Code 610.2
Greenbelt
MD
20771
USA
301-286-9773
Telephone
lena.iredell@nasa.gov
EARTH SCIENCE
ATMOSPHERE
ATMOSPHERIC PRESSURE
SURFACE PRESSURE
EARTH SCIENCE
ATMOSPHERE
ATMOSPHERIC RADIATION
LONGWAVE RADIATION
EARTH SCIENCE
ATMOSPHERE
ATMOSPHERIC RADIATION
NET RADIATION
EARTH SCIENCE
ATMOSPHERE
ATMOSPHERIC RADIATION
OUTGOING LONGWAVE RADIATION
EARTH SCIENCE
ATMOSPHERE
ATMOSPHERIC TEMPERATURE
EARTH SCIENCE
ATMOSPHERE
ATMOSPHERIC TEMPERATURE
SURFACE TEMPERATURE
EARTH SCIENCE
ATMOSPHERE
ATMOSPHERIC TEMPERATURE
SURFACE TEMPERATURE
AIR TEMPERATURE
EARTH SCIENCE
ATMOSPHERE
ATMOSPHERIC TEMPERATURE
SURFACE TEMPERATURE
SKIN TEMPERATURE
EARTH SCIENCE
ATMOSPHERE
ATMOSPHERIC TEMPERATURE
SURFACE TEMPERATURE
VIRTUAL TEMPERATURE
EARTH SCIENCE
ATMOSPHERE
ATMOSPHERIC TEMPERATURE
UPPER AIR TEMPERATURE
EARTH SCIENCE
ATMOSPHERE
ATMOSPHERIC TEMPERATURE
UPPER AIR TEMPERATURE
TEMPERATURE ANOMALIES
EARTH SCIENCE
ATMOSPHERE
ATMOSPHERIC TEMPERATURE
UPPER AIR TEMPERATURE
VERTICAL PROFILES
EARTH SCIENCE
ATMOSPHERE
ATMOSPHERIC TEMPERATURE
UPPER AIR TEMPERATURE
VIRTUAL TEMPERATURE
EARTH SCIENCE
ATMOSPHERE
ATMOSPHERIC WATER VAPOR
EARTH SCIENCE
ATMOSPHERE
ATMOSPHERIC WATER VAPOR
WATER VAPOR INDICATORS
LAYERED PRECIPITABLE WATER
EARTH SCIENCE
ATMOSPHERE
ATMOSPHERIC WATER VAPOR
WATER VAPOR INDICATORS
TOTAL PRECIPITABLE WATER
EARTH SCIENCE
ATMOSPHERE
ATMOSPHERIC WATER VAPOR
WATER VAPOR PROFILES
EARTH SCIENCE
ATMOSPHERE
CLOUDS
EARTH SCIENCE
ATMOSPHERE
CLOUDS
CLOUD PROPERTIES
CLOUD FRACTION
EARTH SCIENCE
ATMOSPHERE
CLOUDS
CLOUD PROPERTIES
CLOUD TOP PRESSURE
EARTH SCIENCE
ATMOSPHERE
CLOUDS
CLOUD PROPERTIES
CLOUD TOP TEMPERATURE
EARTH SCIENCE
ATMOSPHERE
PRECIPITATION
PRECIPITATION AMOUNT
CLIMATOLOGY/METEOROLOGY/ATMOSPHERE
Monthly
Climate Change
Cloud Fraction
EOSDIS
GRID DATA
Ground Temperature
Hydrologic Cycle
Radiation
Radiation Budget
sea level pressure
OLR
sea surface temperature
surface skin temperature
surface air temperature
upper air
Earth Observation Satellites
NOAA-10
National Oceanic & Atmospheric Administration-10
TOVS
TIROS Operational Vertical Sounder
HIRS/2
High Resolution Infrared Radiation Sounder/2
MSU
Microwave Sounding Unit
false
1987-01-01T00:00:00.000Z
1991-08-31T23:59:59.999Z
COMPLETE
CARTESIAN
CARTESIAN
-90
90
-180
180
Not provided
GEOGRAPHIC REGION
GLOBAL
TOVS Pathfinder
TOVS Path A
CWIC
CEOS WGISS Integrated Catalog
Temperatures: Coarse layer temperatures are better defined by the TOVS radiances than point temperatures and therefore the results should be less method dependent provided effects of clouds on the radiances and sources of systematic errors are handled appropriately. The coarse
layer temperatures are best determined in the order starting from the lower troposphere, with quantitative accuracy decreasing with increasing height. Interannual differences of monthly mean surface to 500 mb layer mean temperatures have high quantitative accuracy (better than 0.1 degree C) compared to radiosonde reports and spatial correlations greater than 0.95. They are therefore useful for global and regional trend studies as well as climate variability studies, such as spatial and temporal correlations between interannual differences of lower tropospheric temperature with those of other layer mean temperatures, surface skin temperature, water vapor distribution, clouds, and precipitation. Other layer mean temperatures are potentially less precise. They are best used for interannual variability studies and should be used for precise trend studies with care. As in all other parameters, retrievals over polar regions are more difficult for a number of reasons and expected error bars are larger, perhaps by a factor of 2, than elsewhere.
Point temperatures are less quantitative and should not be used for detailed trend studies. They are potentially useful in climate variability studies and also provide the basic information going into the computation of the layer mean temperatures and OLR.
Surface skin temperatures have high precision over ocean but cannot be directly validated over land. They can potentially be used for trend studies. The most important use may be the relationship of interannual differences of surface skin temperature to that of atmospheric quantities, including the effects of El Niño on tropical and extra tropical circulation. There is also a very strong correlation between interannual differences of surface skin temperature with lower tropospheric temperature over extra-tropical land.
Water vapor is difficult to measure quantitatively for a number of reasons, one of the major ones being there is no accurate data source to use to determine and remove systematic errors from the retrieved moisture parameters. Radiosonde collocations were used to remove systematic errors from retrieved water vapor. Radiosondes have poor sampling (most are in extra-tropical land) and have known moist bias in dry cases. In the methodology used to process the benchmark period, separate systematic error correction coefficients were derived for land and ocean cases. This was deemed to be consistent with different potential sources of error, such as unknown surface emissivity over land. In hindsight, this was an ill conceived idea because the bias correction errors were found to be substantially different in tropical land and ocean areas, giving apparent moisture discontinuities in the tropical fields. Nevertheless, comparisons of interannual differences of monthly mean layer integrated precipitable water with collocated radiosondes showed high spatial correlations of the order of 0.8 for total precipitable water and 0.6 for precipitable water above 500 mb. This means the data should be useful to study interannual variability. Of more significance was the finding that tropical upper tropospheric water vapor was highly correlated in space and time with tropical precipitation. Specific humidities at mandatory levels are even harder to measure quantitatively but are potentially useful in terms of interannual variability. They also are part of the information entering the OLR calculation and can be used to explain some of the variability of OLR.
The main cloud products retrieved are cloud top pressure and effective cloud fraction, given by the product of the fractional cloud cover times the cloud emissivity at 11 mm. Because cloud emissivities are less than 1, especially for cirrus clouds, our global mean effective cloud fraction, which is of the order of 40%, is lower than other commonly quoted values closer to 50%. The methodology of solution attempts to find a cloud fraction and cloud top pressure most consistent with the observations in five IR channels. There is often a modest range of cloud top pressures and corresponding cloud fractions (the higher the cloud top in altitude, the lower the cloud fraction) which give reasonable solutions to the radiative transfer equations. Therefore, cloud top pressures in individual cases may be uncertain up to 100 mb or more, but monthly mean pressures are probably better than 50 mb. The cloud parameters cannot be directly validated but form an important contribution to the calculations of OLR. Clouds are most difficult to determine in polar cases with low thermal contrast between clouds and the surface. Under these conditions, the TOVS IR radiances do not depend appreciably on cloud parameters. Path A and Path B clouds were compared to each other and found to differ significantly from each other, especially over polar regions. For this reason, both sets of clouds were labeled as experimental pending further validation studies. While individual cloud parameters should not be used for quantitative trend studies, they provide valuable quantitative information about interannual variability and response of cloud parameters to sea and land surface temperatures.
Precipitation amounts can be estimated from the cloud parameters and relative humidities retrieved from the TOVS data. The method is based on empirical coefficients derived from collocations with monthly mean rain gauge measurements. While patterns are qualitatively good, the method will tend to underestimate heavy precipitation and potentially give light rain in some cases where no precipitation exists, or it does not reach the ground. The main use of this data should be to study interannual variability of precipitation and its relationship with variability of surface temperature, atmospheric temperature, and water vapor.
OLR is computed from the retrieved products using the radiative transfer equation. Agreement of monthly mean OLR with that derived from Earth Radiation Balance Experiment (ERBE) data is very good, with global mean differences of the order of 1 W/m2 and global standard deviations about 5 W/m2 on a 1 degree by 1 degree grid. This tends to validate all the TOVS products, including the cloud products. However, it should be remembered that a smaller (larger) amount of higher (lower) clouds could result in very similar values of OLR. This product is important for understanding interannual variability of OLR in terms of the variability of its key components: temperature, water vapor, and clouds. One important limitation of the data set is that it assumes a constant CO2 mixing ratio of 350 ppm and therefore does not reflect possible small changes due to changes of CO2 (about 3 ppm/year) over the time period. Longwave cloud radiative forcing (LCRF) is another important indicator of climate variability. Like OLR, LCRF is a calculated quantity, based on the difference of OLR calculated using the retrieved clouds, and clear sky OLR calculated with otherwise the same profiles and ground temperature, but with no clouds present. It should be borne in mind that this is not the quantity determined by the ERBE science team, which determines clear sky OLR by observations under clear conditions. These conditions tend to have warmer temperatures, and possibly drier conditions, than those under cloudy conditions.
For long term measurement of trends, or even climate variability studies, it is important to be able to analyze data from different satellites without having appreciable intersatellite biases. There are two potential problems involved: different instrumentation and different time of day. It is expected that the Path A methodology of systematic error correction for temperature, moisture, and ozone will be accurate enough to account for inter-satellite instrumentation differences. Differences in time of day are not accounted for directly in the original retrieval system. This primarily affects land surface skin temperatures and cloud parameters. In interpretation of these comparisons, time of day sampling differences should be borne in mind. It is either up to the user to account for time sampling differences in their interpretation of the data or to use the temperature and OLR data products found in the data group “adjusted to 730 am”, where the products were adjusted to an observing time of 7:30 AM.
As an error was found in the ozone retrieval processing, the ozone data in not included in this version of the TOVS Pathfinder Path-A data.
Cite Joel Susskind, NASA Goddard and the GES DISC NASA Goddard
The Earth Observing System Data and Information System (EOSDIS) data use policy for NASA data can be accessed at https://earthdata.nasa.gov/earth-observation-data/data-use-policy. For information on how to properly cite and acknowledge data from the NASA GES DISC, refer to https://disc.gsfc.nasa.gov/information/documents?title=data-policy.
English
ARCHIVER
NASA/GSFC/SED/ESD/GCDC/GESDISC
Goddard Earth Sciences Data and Information Services Center (formerly Goddard DAAC), Global Change Data Center, Earth Sciences Division, Science and Exploration Directorate, Goddard Space Flight Center, NASA
https://disc.gsfc.nasa.gov/
DATA CENTER CONTACT
GES DISC HELP DESK SUPPORT GROUP
Goddard Earth Sciences Data and Information Services Center
Code 610.2
NASA Goddard Space Flight Center
Greenbelt
MD
20771
USA
301-614-5224
Telephone
301-614-5268
Fax
gsfc-dl-help-disc@mail.nasa.gov
Online Archive
netCDF
0.00
Susskind, J., Piraino. P., Rokke, L., Iredell, L., and Mehta, A.
1997-01-01T00:00:00.000Z
haracteristics of the TOVS Pathfinder Path A Dataset
Bull. Amer. Meteor. Soc.
78
1449-1472
DOI
https://doi.org/10.1175/1520-0477(1997)078<1449:COTTPP>2.0.CO;2
Susskind, J., J. Rosenfield, and D. Reuter
1983-01-01T00:00:00.000Z
An accurate radiative transfer model for use in the direct physical inversion of HIRS2 and MSU temperature sounding data
JGR
88
8550-8568
Baker. W.E.
1983-01-01T00:00:00.000Z
Objective Analysis and Assimilation of Observational Data from FGGE
Monthly Weather Review
111
328-342
Chahine, M.T. and J. Susskind
1970-01-01T00:00:00.000Z
Fundamentals of the GLA physical retrieval method
1
271-300
Report on the Joint ECMWF/EUMETSAT Workshop on the Use of Satellite Data in Operational Weather Prediction: 1989-1993. Vol. 1, 271-300. T. Hollingsworth, Editor.
Chahine, M.T.
1968-01-01T00:00:00.000Z
Determination of the Temperature Profile in an Atmosphere from its Outgoing Radiances
J. Opt. Soc. Am.
58
1634-1637
Kalnay, E., Balgovind, R, Chao, W., Edelmann, D, Pfaendtner, J., Takacs, L, and Takano, K
1983-01-01T00:00:00.000Z
Documentation of the GLAS Fourth Order General Circulation Model, Volume 1: Model Documentation
NASA Technical Memorandum
1
86064
Kidwell, K
2003-01-01T00:00:00.000Z
NOAA Polar Orbiter Data User's Guide
https://www1.ncdc.noaa.gov/pub/data/satellite/publications/podguides/TIROS-N%20thru%20N-14/
Takacs, L., A. Molod, and T. Wang
1994-01-01T00:00:00.000Z
Documentation of the Goddard Earth Observing System (GEOS) General Circulation Model Version 1
NASA Technical Memorandum
104606
This dataset (TOVSAMNG) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from NOAA-10 and the physical retrieval method of Susskind et al. (1984) and processed by the Satellite Data Utilization Office of the Goddard Laboratory for Atmospheres at NASA/GSFC. This method, which is hydrodynamic model- and a priori data-dependent, is designated as the so-called Path A scheme by the TOVS Pathfinder Science Working Group. The 20 channel High resolution Infrared Radiation Sounder 2 (HIRS2) and the 4 channel Microwave Sounding Unit (MSU) aboard the NOAA-xx series of Polar Orbiting Satellites are used to produce global fields of the 3-dimensional temperature-moisture structure of the atmosphere. In addition to profiles of temperature and moisture, the HIRS2/MSU data are used to derive important quantities such as land and sea surface temperature, outgoing longwave radiation, cloud fraction, cloudtop height, total ozone overburden and precipitation estimates.
The Path A system steps through an interactive forecast-retrieval-analysis cycle. In each 6 hour synoptic period, a 2nd order General Circulation Model (Takacs et al., 1994) is used to generate the 6 hour forecast fields of temperature and humidity. These global fields are used as the first guess for all soundings occurring within a 6 hour time window centered upon the forecast time. These retrievals are then assimilated with all available insitu measurements (such as radiosonde and ship reports) in the 6 hour interval using an Optimal Interpolation (OI) analysis scheme developed by the Data Assimilation Office of the Goddard Laboratory for Atmospheres. This analysis is then used to specify the initial conditions for the next 6 hour forecast, thus completing the cycle.
The retrieval algorithm itself is a physical method based on the iterative relaxation technique originally proposed by Chahine (1968). The basic approach consists of modifying the temperature profile from the previous iteration by an amount proportional to the difference between the observed brightness temperatures and the brightness temperatures computed from the trial parameters using the full radiative transfer equation applied at the observed satellite zenith angle. For the case of the temperature profile, the updated layer mean temperatures are given as a linear combination of multichannel brightness temperature differences with the coefficients given by the channel weighting functions. Constraints are imposed upon the solution in order to ensure stability and convergence of the iterative process. For more details see Susskind et al (1984).
These Level 3 monthly mean products are in the netCDF format. Each data set is representative of a different monthly average time period and for one of nine satellites. All files contain the same number of geophysical parameter arrays with the AM and PM portions of the orbits treated separately. All data are mapped to a 1 degree longitude by 1 degree latitude global grid.
GET RELATED VISUALIZATION
https://docserver.gesdisc.eosdis.nasa.gov/public/project/Images/TOVSAMNG.V2.Dec.1988.500mb.day.temp.png
DATA SET LANDING PAGE
https://disc.gsfc.nasa.gov/datacollection/TOVSAMNG_02.html
Access the dataset landing page from the GES DISC website.
VIEW RELATED INFORMATION
USER'S GUIDE
https://acdisc.gesdisc.eosdis.nasa.gov/data//TOVS/TOVSAMNG.02/doc/README.TOVS.Pathfinder.PathA.v2.pdf
product README file
VIEW RELATED INFORMATION
READ-ME
https://acdisc.gesdisc.eosdis.nasa.gov/data/TOVS/TOVSAMNG.02/doc/README.TOVS.Pathfinder.PathA.v2.pdf
README information for the netCDF level 3 files
GET DATA
DATA TREE
https://acdisc.gesdisc.eosdis.nasa.gov/data/TOVS/TOVSAMNG.02
Access the data via HTTPS
USE SERVICE API
OPENDAP DATA
https://acdisc.gesdisc.eosdis.nasa.gov/opendap/TOVS/TOVSAMNG.02
Access the data via the OPeNDAP protocol
GET DATA
Earthdata Search
https://search.earthdata.nasa.gov/search?q=TOVSAMNG+002
Use the Earthdata Search to find and retrieve data sets across multiple data centers.
TOVSAMNG
02
Science Associated
NASA TOVS Pathfinder Path-A
USA/NASA
CEOS IDN DIF
VERSION 10.2
2018-08-06
2019-10-30
1990-06-04T00:00:00.000Z
2018-08-06T00:00:00.000Z
3
SCIENCE_QUALITY