TOVS GLA MONTHLY GRIDS from NOAA-8 02 (TOVSAMNE) at GES DISCEntry ID: TOVSAMNE_02
Abstract: This dataset (TOVSAMNE) contains the TIROS Operational Vertical Sounder (TOVS) level 3 geophysical parameters derived using data from NOAA-8 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 and each representative of a different averaging time period. 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.
Data Set Citation
Dataset Originator/Creator: Goddard Laboratory for Atmospheres at NASA GSFC
Dataset Title: TOVS GLA MONTHLY GRIDS from NOAA-8 V02
Dataset Series Name: TOVSAMNE
Dataset Release Date: 2018-08-08T00:00:00.000Z
Dataset Release Place: Greenbelt, MD, USA
Dataset Publisher: Goddard Earth Sciences Data and Information Services Center (GES DISC)
Data Presentation Form: Digital Science Data
Dataset DOI: 10.5067/WIJ1G0NBQXDJOnline Resource: https://disc.gsfc.nasa.gov/datacollection/TOVSAMNE_02.html
This description is a member of a collection. The collection is described in
Start Date: 1983-05-01Stop Date: 1984-05-31
ATMOSPHERE > ATMOSPHERIC PRESSURE > SURFACE PRESSURE
ATMOSPHERE > ATMOSPHERIC RADIATION > LONGWAVE RADIATION
ATMOSPHERE > ATMOSPHERIC RADIATION > NET RADIATION
ATMOSPHERE > ATMOSPHERIC TEMPERATURE
ATMOSPHERE > ATMOSPHERIC TEMPERATURE > SURFACE TEMPERATURE
ATMOSPHERE > ATMOSPHERIC TEMPERATURE > SURFACE TEMPERATURE > AIR TEMPERATURE
ATMOSPHERE > ATMOSPHERIC TEMPERATURE > SURFACE TEMPERATURE > SKIN TEMPERATURE
ATMOSPHERE > ATMOSPHERIC TEMPERATURE > SURFACE TEMPERATURE > VIRTUAL TEMPERATURE
ATMOSPHERE > ATMOSPHERIC TEMPERATURE > UPPER AIR TEMPERATURE
ATMOSPHERE > ATMOSPHERIC TEMPERATURE > UPPER AIR TEMPERATURE > TEMPERATURE ANOMALIES
ATMOSPHERE > ATMOSPHERIC TEMPERATURE > UPPER AIR TEMPERATURE > VERTICAL PROFILES
ATMOSPHERE > ATMOSPHERIC TEMPERATURE > UPPER AIR TEMPERATURE > VIRTUAL TEMPERATURE
ATMOSPHERE > ATMOSPHERIC WATER VAPOR
ATMOSPHERE > ATMOSPHERIC WATER VAPOR > WATER VAPOR INDICATORS > TOTAL PRECIPITABLE WATER
ATMOSPHERE > ATMOSPHERIC WATER VAPOR > WATER VAPOR PROFILES
ATMOSPHERE > CLOUDS
ATMOSPHERE > CLOUDS > CLOUD PROPERTIES > CLOUD FRACTION
ATMOSPHERE > CLOUDS > CLOUD PROPERTIES > CLOUD TOP PRESSURE
ATMOSPHERE > CLOUDS > CLOUD PROPERTIES > CLOUD TOP TEMPERATURE
ATMOSPHERE > PRECIPITATION > PRECIPITATION AMOUNT
Quality 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 Niņo 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.
Use Constraints Cite Joel Susskind, NASA Goddard
and the GES DISC NASA Goddard
Data Set Progress
Distribution Media: Online Archive
Distribution Size: 83.0 MB
Distribution Format: netCDF
Role: TECHNICAL CONTACT
Email: joel.susskind-1 at nasa.gov
Goddard Space Flight Center Mailstop 610.0
Province or State: MD
Postal Code: 20771
Role: METADATA AUTHOR
Email: lena.iredell at nasa.gov
GES DISC NASA Goddard Code 610.2
Province or State: MD
Postal Code: 20771
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 (), 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/podguide...
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
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Creation and Review Dates
DIF Creation Date: 2018-08-06
Last DIF Revision Date: 2019-01-25