A NEW GLOBAL WATER VAPOR DATASET
David L. Randel*, Thomas H. Vonder Haar+, Mark A. Ringerud#,
Graeme L. Stephens+, Thomas J. Greenwald*, Cynthia L. Combs#
* Cooperative Institute for Research in the Atmosphere, Colorado State
University, Ft. Collins, Colorado
+ Department of Atmospheric Science, Colorado State University, Ft. Collins,
# METSAT, Science and Technology Corporation, Ft. Collins, Colorado
Corresponding author address: Dr. David L. Randel, Cooperative Institute
for Research in the Atmosphere, Colorado State University, Ft. Collins,
Bulletin of the AMS (BAMS) - June, 1996 Vol 77, No 6
© Copyright 1996 American Meteorological Society (AMS). Permission
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A comprehensive and accurate global water vapor dataset is critical
to the adequate understanding of water vapor's role in the earth's climate
system. To begin to satisfy this need, the authors have produced a blended
dataset made up of global, five-year (1988-1992), 1 x 1 degree spatial
resolution, atmospheric water vapor (WV) and liquid water path products.
These new products consist of both the daily total column integrated composites
and a multilayered WV product at three layers (1000-700, 700-500, 500-300
mb). The analyses combine WV retrievals from the Television and Infrared
Operational Satellite (TIROS) Operational Vertical Sounder (TOVS), the
Special Sensor Microwave/Imager (SSM/I), and radiosonde observations. The
global vertical layered water vapor dataset was developed by slicing the
blended total column water vapor using layer information from TOVS and
radiosonde. Also produced was a companion, over-oceans only, liquid water
path dataset. Satellite observations of liquid water path are growing in
importance since many of the global climate models are now either incorporating
or contain liquid water as an explicit variable. The complete dataset (all
three products) has been named NVAP, an acronym for National Aeronautics
and Space Administration Water Vapor Project.
This paper provides examples of the new dataset as well as scientific
analysis of the observed annual cycle and the interannual variability of
water vapor at global, hemispheric, and regional scales. A distinct global
annual cycle is shown to be dominated by the Northern Hemisphere observations.
Planetary scale variations are found to relate well to recent independent
estimates of tropospheric temperature variations. Maps of regional interannual
variability in the 5-year period show the effect of the 1992 ENSO and other
There is an urgent need for a comprehensive and accurate global water
vapor dataset to assist many important scientific studies in the atmospheric
sciences. During the next decade, many World Climate Research Programme
(1990) experiments will use present-day and future datasets to improve
our understanding of the role of moisture in climate and its interaction
with other variables such as clouds and radiation. Included in these experiments
are the Global Energy and Water Cycle Experiment (GEWEX) and the GEWEX
Continental-Scale International Project (GCIP). Many aspects of climate
research are dependent on accurate water budget data. These include, but
are not limited to, poleward energy transports, general circulation model
(GCM) verification, regional climate studies, and global change baseline
measurements (Vonder Haar 1994). The new dataset described here is the
first of several new "pilot" datasets to address these needs
(e.g., Chahine and Susskind 1991; Chedin et al. 1994). It does so with
a combination of radiosonde and satellite infrared and satellite microwave
retrievals. This dataset will help build the foundation from which investigators
of future GEWEX-related and Earth Observing System (EOS) related work can
learn and build upon.
Currently, atmospheric water vapor measurements are made from a variety
of sources including radiosondes, aircraft and surface observations, and
in more recent years, by various satellite instruments. Since these individual
sources of data have certain limitations, it follows that a truly global
moisture dataset should be derived from a combination of these measurement
systems (Schubert et al. 1993). For many years, large-scale studies of
atmospheric water vapor have relied wholly upon the analysis of radiosonde
data (Bannon and Steele 1960; Oort 1983). Recently, there have been efforts
to develop algorithms in order to retrieve the global water vapor (WV)
climatology from either infrared or microwave space-based observations
(Prabhakara et al. 1982; Trenberth and Guillemot 1995). Satellite-based
observations are critical to this climatology effort because significant
horizontal gradients in total column water vapor can exist between ground-based
stations (e.g., Chesters et al. 1983). Analyses using only radiosonde data
tend to smooth out these mesoscale gradients, which are important to the
cloudiness, precipitation, and radiation balance fields. These fields are
under special study by many scientists because of their central role in
scale interactions in the climate system.
Large radiosonde data gaps over the oceans, and even over some land
areas (e.g., Africa), limit the ability to define the global water vapor
distribution. The newer data sources, such as those from infrared and microwave
satellite sensors, can greatly enhance the global coverage on a daily basis.
Examples of presently available large-scale WV datasets include satellite
microwave retrievals from the Defense Meteorological Satellite Program
(DMSP) Special Sensor Microwave Imager (SSM/I) data over ocean (Jackson
and Stephens 1995), TOVS infrared retrievals over land and ocean (Rossow
et al. 1991; Wittmeyer and Vonder Haar 1994), upper-tropospheric relative
humidity from geostationary satellites (Schmetz et al. 1995), and a number
of datasets using special radiosonde measurements for research purposes
on limited time and space scales. Also available are model-analyzed 4-D
data assimilated (in many cases operational analysis) global humidity fields
from the European Centre for Medium-Range Weather Forecasts (ECMWF) and
the National Centers for Environmental Prediction (NCEP, formerly the National
Meteorological Center) (Trenberth and Olson 1988).
As noted previously, each of the above datasets has significant limitations.
Radiosonde measurements are made primarily over land with limited spatial
and temporal coverage, infrared satellite techniques are only applicable
in the absence of significant cloud cover, and microwave retrievals are
presently feasible only over oceans. In addition, the satellite datasets
have unobserved geographic areas each day. Figure
1, which shows the single-day plots of available data for July 10, 1989,
illustrates these limitations. A comprehensive, blended WV dataset, accurate
in all meteorological and geographical scenarios, must work within the
limitations of these observing systems while using the advantages of each.
The result is a synergistic effort far better than any single input dataset,
yielding significant improvements in the daily representation of the moisture
fields. Use of a blended WV dataset in climate model studies will greatly
improve our understanding of many of the most difficult phenomena to characterize;
those relying heavily on an accurate description of the moisture field.
The development of a global cloud liquid water product is also important
to the understanding of the earth's climate system. There is a growing
number of scientific studies aimed at characterizing the observed relationship
between physical cloud properties and the Earth Radiation Budget (ERB).
For example, Stephens and Greenwald (1991) investigated the connection
between cloud albedo and cloud liquid water path (LWP), while Greenwald
et al. (1995) examined the climatic characteristics of cloud LWP and also
the relationship between the ERB and cloud LWP using collocated SSM/I and
Earth Radiation Budget Experiment (ERBE) scanner measurements. Also, Zuidema
and Hartmann (1995) investigated the cloud microphysical properties of
marine stratus using a combination of ERBE albedo and SSM/I measurements.
2. INPUT DATA
To address the need for a comprehensive global water vapor dataset,
including cloud liquid water over the oceans, we have produced a blended
global analysis consisting of five years (1988-92), at a 1 degree lat x
1 degree long spatial resolution, with daily, pentad, and monthly temporal
resolutions. This consists of both daily total column integrated composites
and a multi-layered Water Vapor (WV) product at three layers (1000-700,
700-500, 500-300 mb). Radiosonde, SSM/I, and TOVS retrievals have been
combined to produce these global products. The following sections describe
the individual datasets used as input, the products produced from the individual
input datasets, and the quality control required to produce the blended
Historically, upper-air balloon soundings have been the basis for water
vapor statistics used throughout the scientific literature (Oort 1983).
These data have a long history and are still the only global ground truth
available. For this project, radiosonde datasets were examined from a variety
of sources. Differences between datasets are caused primarily by the diverse
quality control applied to the observations, ranging from uncorrected raw
observations to the statistically analyzed, quality controlled set described
in Ross and Elliott (1996). From Ross and Elliott a five-layer WV for all
stations were obtained and used for all five years of the National Aeronautics
and Space Administration Water Vapor Project (NVAP) analysis. The processing
involved using the five layered WV and producing three layers (surface-700,
700-500, 500-300 mb) and the total column WV. If the moisture sounding
did not reach 700 mb, the sounding was discarded These observations were
then inserted into 1 degree lat x 1 degree long global bins. A complete
description of Elliott's data processing and quality control is given in
Ross and Elliott (1996), but the following summarizes the process.
The radiosonde data was transmitted via the Global Telecommunications
System, was decoded at the National Center for Atmospheric Research, and
supplied to the National Oceanic and Atmospheric Administration (NOAA).
Over 1800 stations have reported since 1973; however, the quality control
procedure depends on stations with a long time series, thus limiting the
number of stations to approximately 900. For the mandatory levels, the
quality control involved computing a climatological monthly mean temperature
at each station, and then requiring the observed temperature to fall within
+/- 4 standard deviations of the mean. If a temperature failed a check
at a mandatory level at or below 700 mb, the entire sounding was discarded.
Above 700 mb, the sounding was kept, but data above the last valid level
was discarded. This allows the lower levels, containing the bulk of the
water vapor, to be retained.
A climatological, statistical check on the moisture soundings is more
difficult since synoptic variability can cause both extremely dry and saturated
soundings. Thus, some erroneous moisture soundings would fail to be detected
during the temperature quality control check. For each station both the
00 UTC and 12 UTC soundings were used and the soundings were terminated
at the 300 mb level since humidity measurements are considered unreliable
above that point.
b) Special Sensor Microwave Imager (SSM/I)
The first of the satellite datasets used in the blended product was
derived from the SSM/I, which was flown on the DMSP F8 and F11 satellites.
The SSM/I is a passive dual-polarization instrument (channels at 19.35,
22.235, 37.0, and 85.5 GHz) designed to provide measurements of rainfall,
water vapor, liquid water, and near-surface wind speed over ocean surfaces
and the surface characteristics of sea ice and land. The DMSP F8 satellite
(launched June 19, 1987) was the source of data through 1991 and measurements
from the DMSP F11 (launched November 28, 1991) extended the dataset through
the end of 1992. The F8 and F11 satellites have sun-synchronous equatorial
crossing times of 0615 and 1704 (local time) respectively.
A retrieval scheme based on the physical method employed by Greenwald
et al. (1993), was used to simultaneously retrieve over the oceans, both
total column water vapor and integrated cloud liquid water. This scheme
was an extension of the method of Tjemkes et al. (1991), and is based on
measurements at 19.35 and 37 GHz.
The retrieval model requires several input parameters, the first of
which is sea surface temperature (SST). NVAP used monthly mean SST's produced
by NCEP on a 2 degree lat x 2 degree long global grid (Reynolds 1988).
The next input parameter is the near surface wind speed, which is derived
from the SSM/I brightness temperatures using the Goodberlet et al. (1989)
algorithm. Since the Goodberlet method is less reliable in regions of high
water vapor, the Bates (1991) method is used instead for SST greater than
300 degrees K. A mean cloud temperature is also required in the model and
was specified as the SST minus 6 K, following Greenwald et al. (1993).
One important aspect of the processing involved intercalibrating measurements
from the F8 and F11 satellites in order to provide a consistent dataset
from one satellite to another. Near-coincident (i.e., within about 18 min)
and collocated data from the two satellites were compared in December 1991
and the calibration factors in the model were adjusted accordingly.
There are three significant sources of contamination in the SSM/I retrievals.
The first is land contamination which can result in an overestimation in
the retrieved WV and LWP quantities. A 0.5 degrees lat x 0.5 degrees long
land mask was used to prevent retrievals over land regions. However, land
contamination was evident in certain clusters of small islands that were
not included in the land mask. The final quality control procedure included
manually searching and eliminating gridded data in these specific areas.
The second contamination problem is due to sea ice. Sea ice contamination
was found to be more problematic than land contamination since the surface
characteristics of sea ice are highly variable and since the areal extent
of the ice changes seasonally. Sea ice was detected at the pixel level
using a simple version of the AES/YORK algorithm developed for the SSM/I
Calibration/Validation effort (Hollinger et al. 1991). As with land contamination,
an additional screening of the datasets was done to eliminate erroneous
The final problem was precipitation contamination. For precipitating
clouds, microwave retrievals of water vapor are likely to be overestimated
resulting is a less reliable cloud liquid water retrieval. In this version
of the dataset, quality control measures were implemented to detect and
eliminate precipitation contaminated retrievals. However, caution must
still be observed when using these datasets in areas of heavily precipitating
cloud systems because the method could yield retrieval errors that are
undetected (Greenwald et al. 1993).
c) TIROS Operational Vertical Sounder (TOVS)
Operational satellite-based WV retrievals have been made since 1978
by the NOAA/National Environmental Satellite Data and Information Service
(NESDIS) (Werbowtzki 1981), using raw data collected from the NOAA series
of operational polar-orbiting satellites. Data from NOAA-9, -10, -11, and
-12 satellites were used during the NVAP data period. These satellites
have a near-polar sun-synchronous orbit with a 102-min period and have
local equatorial ascending node crossing times of 1420, 1930, 1340, and
1930 respectively. The TOVS instrument package - for retrieval of atmospheric
temperature, ozone, and humidity - is carried aboard these satellite platforms
and is made up of the second-generation High Resolution Infrared Radiation
Sounder (HIRS/2), the Microwave Sounding Unit (MSU), and the Stratospheric
Sounding Unit. These instruments are cross-track scanners with different
resolutions (ranging from 18.5 - 148 km) and swath widths (1234 - 2230
km) (Kidder and Vonder Haar 1995).
Measurements from all three instruments are used for the retrieval
of vertical temperature and moisture profiles. A different combination
of channels are required depending on clear or clouly sky conditions. The
retrieval scheme is based on the radiance variance approach proposed by
Smith and Woolf (1984), and later modified by Fleming et al. (1986), and
Reale et al. (1989). The three HIRS/2 channels most sensitive to water
vapor are not used in the moisture retrieval. Instead, radiance data from
HIRS/2 and MSU channels, primarily centered on the CO2 and O2 absorption
bands are used to simultaneously generate temperature soundings and layered
moisture in a single solution matrix (Reale et al. 1989). The selected
channels, though not centered on the primary moisture absorbing frequencies,
still contain considerable absorption by water vapor. A statistical eigenvector
regression method was used before 16 September 1988 and was succeeded thereafter
by a physical scheme (used for the majority of the NVAP dataset).
For the NVAP project, the operational TOVS sounding products produced
by NESDIS were used. This quality controlled dataset is available from
other members of the water vapor science community (e.g. John Bates at
NOAA / Cooperative Institute for Research in Environmental Science). The
data included total and three-layered WV for approximately 25 000 retrievals
per day with geographical spacing of approximately 2 degrees. Our local
TOVS processing consisted of gridding the WV retrievals into 1 degree lat
x 1 degree long bins and applying only minimal quality control during the
blending process. Erroneous TOVS data points, identified by comparison
with the other two input datasets, were generally found in desert or coastal
areas and were removed in the quality control screening, as discussed in
There are two problems inherent in all infrared moisture retrievals
that tend to limit the dynamic range of the TOVS data. First, the inability
to perform retrievals in areas of thick clouds can cause a "dry bias"
(Wu et al. 1993). Second, limitations in infrared radiative transfer theory
can cause significant overestimation of WV in regions of large-scale subsidence
(Stephens et al. 1994). For these reasons, SSM/I data are given a higher
total column WV confidence level than TOVS data.
3. DATA QUALITY CONTROL
Quality control is essential in order to produce a quality global data
product. An important aspect of the NVAP dataset was direct human intervention,
therefore, every daily grid was examined by a team of meteorologists. With
each of the three input datasets, there are certain conditions for which
the WV estimates may be inaccurate. Knowledge of these conditions enabled
the NVAP quality control team to focus on potential problem areas and recognize
possible inaccurate values. Other factors such as climate, terrain, and
time consistency were also taken into account.
Use of Elliott's radiosonde dataset greatly reduced the required amount
of manual quality control required. Most of the unusual or erroneous values
had previously been eliminated; however, there were still a few problem
stations. These would appear as stations that reported consistently higher
WV than surrounding radiosonde or satellite retrievals. One possible reason
for such a bias is the inherent limitations of various radiosonde instrumentation
(Elliott and Gaffen 1991). There are approximately a dozen different suppliers
of radiosondes worldwide, with wide differences between humidity sensors.
The Finnish Vaisala, with a thin film capacitive humidity sensor and the
U.S. VIZ, with a carbon hygristor, are considered to have good response
times. These instruments are used predominately by the United States and
European countries and their associates. However, other models have a much
slower response time in humidity measurements, especially at upper levels.
This lag can cause a moist bias in the readings. India uses a lithium chloride
element in their humidity sensors, which is known to have a much slower
response time. Another humidity device called "a goldbeater's skin
hydrometer", used by many Chinese stations, is also known to have
a slower response time. The slower response of these instruments provides
a reasonable explanation for most of the higher values removed in these
As mentioned previously, two primary quality control problems with
SSM/I measurements are land and sea ice contamination, with sea ice being
the most common. For both of these effects, the moisture retrievals are
usually excessively high and this makes the detection of erroneous values
fairly easy. However, high latitude waters such as those adjacent to Antarctica,
Greenland, Siberia, and Japan can have occasional ice contamination not
removed by the ice detection algorithm. Land contamination is a less significant
problem than sea ice since these can be eliminated with a geographical
mask. However, areas containing many small islands or rock outcroppings
can contaminate the retrievals such as the area around South Georgia and
the South Sandwich Islands, southeast of Argentina. Another occasional
difficulty with the SSM/I measurements were erroneous data periods when
the data are mislocated. In the first three years of this project (1988-90),
these periods were documented (e.g., Wentz 1991) and subsequently removed
There were times when TOVS data points needed to be removed after comparisons
with surrounding radiosonde and SSM/I data. Of the few points removed,
the majority tended to be in dry or desert regions. These areas included
central Australia, Namibia, Western Sahara, the coast of Peru, Kazakhstan,
and the Middle East. Such areas are documented to have a "moist bias"
(Stephens et al. 1994).
The procedure for manual quality control involved several steps. For
the SSM/I data, each individual daily grid was visually inspected with
emphasis on checking the known problem areas, as mentioned above. Other
suspicious values, identified either visually, climatologically, or in
a time series (day before or day after), were examined in the individual
input product data grids. If a value was suspiciously higher than the surrounding
values, it was removed. Any values that were questionable were marked for
later examination in the blended product. After the three input sets were
blended, as described in the next section, each daily product was visually
inspected under the same guidelines as before. Suspect values were noted,
then traced to one of the three original datasets. Comparisons were made
between the suspicious value and the surrounding values in all three input
sets. If that value was well above any of the surrounding values in all
three sets, the value was removed from the appropriate input dataset. Once
complete, the modified input files were again blended into the final product
and visually inspected again. A similar process was followed for the layered
4. Blended Data
a) Total column water vapor
To create the blended WV product, the three input datasets were individually
gridded into three separate daily 1 degree lat x 1 degree long global grid
maps. The SSM/I gridded analyses were then checked for missing data over
the oceans and spatially filled using linear interpolation. The total column
WV blended products were created by using a weighting scheme that considered
the radiosonde retrievals to be the most accurate, and the SSM/I retrievals
more accurate than those from TOVS.
The blending process started by assuming the radiosonde points to be
the truth and weighting these values at 100%. Next, the SSM/I and TOVS
grids were combined together using a selected weighting of 10% TOVS and
90% SSM/I for coincident points. This blending information was recorded
in a data source code (DSC) map, numerically ordered by the estimated WV
retrieval error. The DSC map describes the origin of each point in the
blended product with confidence values from 0 to 8. From the highest to
lowest confidence the blended data points are: radiosonde (level 8), combined
TOVS and SSM/I (level 7), SSM/I only (level 6), SSM/I interpolated combined
with TOVS (level 5), SSM/I interpolated (level 4), and TOVS only (level
3). By referencing the DSC maps, which are produced for each day of the
5-year dataset, a NVAP data user has several basic analysis and interpolation
Finally, the total blended WV product was checked for missing data
and missing regions up to 10 degree lat x 10 degree long were spatially
interpolated. This type of grid point value was given a confidence level
of 2. The remaining areas of missing data were filled using a temporal
three-day running average. These results were given the lowest confidence
(level 1) except for missing data (level 0), which occasionally is found
in the south polar latitudes. With the individual data points identified
by the DSC grid map, interpolated points could be easily removed by a user
The NVAP dataset contains daily global WV and liquid water path (LWP)
2 demonstrates the results of the total column WV blending process on April
25, 1988, for the North Pacific. Here we clearly see an intrusion of
moisture from the Tropics into the midlatitudes. These moisture bursts
are most commonly caused by the southwesterly flow in the warm sector of
a mid-latitude cyclonic system, but may also be observed during times when
the subtropical jet has a strong northerly component.
3 shows the global total column WV distribution for July 10,1989. The
monthly average is also shown. The WV maximum in the Tropics and particularly
in the west Pacific is clearly visible, as well as the equator-to-pole
moisture gradients. The largest gradient, in a regional area, is across
the Himalayas from the very moist Indian monsoon region to the dry elevated
b) Layered water vapor
The layered WV is an important part of the NVAP dataset since the vertical
distribution of WV is important to moisture transport and radiation studies.
Two of the three input datasets, radiosonde and TOVS, contain layered information
and were used to create the layered WV dataset. The TOVS data was ordered
into in three layers: surface - 700, 700-500, and 500-300 mb and placed
into a 1 degree lat x 1 degree long grid. The radiosonde retrievals were
processed into identical matching layers. This layered information was
then used to "slice" the blended total column WV product.
In the "slicing" technique, it was necessary to incorporate
two basic assumptions with respect to the global WV distribution. The first
assumes the TOVS total column WV is not as accurate as the SSM/I value,
but that the fraction of total column WV in each layer is relatively correct.
Second, while the total column WV may change rapidly in space and time,
the fraction of the total WV in each layer changes more slowly. Layered
WV from radiosondes and TOVS is divided by the total column WV to derive
the global distribution of the percent-of-total (POT) WV in each layer.
The variability in the POT is a strong function of latitude and season
and does not vary spatially as quickly as the total column WV. These POT
grids were then spatially interpolated to fill in small areas and temporally
interpolated using a 5-day running average to fill in larger gaps. A 5-day
average can be used (versus a 3-day average in the blended total column
WV) because of the slower time variability of the POT. To create the layered
WV products, the blended total WV grids produced earlier are multiplied
by the POT grids. This gives the layered product the advantage of including
SSM/I information along with TOVS and radiosonde data.
A Data Source Code (DSC) map is also provided for each of the daily
layered WV grids (5 to 0, with 5 being the highest confidence and 0 being
the lowest confidence). In order of highest to lowest confidence: radiosonde
only is the highest (level 5); coincident TOVS and radiosonde points are
combined together using a weighted 10% TOVS and 90% radiosonde (level 4);
TOVS only points (level 3); spatially interpolated (level 2); and temporally
filled data (level 1). Remaining missing data is given the lowest confidence
level (level 0). There are increasing areas of missing data with the upper
layers, especially in the polar regions due to persistent cloudiness that
affects the TOVS retrievals.
The NVAP WV for each of the three layers for July 10,1989 are shown
in Figs. 4,
5, and 6 . The monthly averaged layers for July,
1989 is also available. Since the majority of atmospheric water vapor
is in the lower troposphere, it is no surprise the Surface-700 mb geographical
distribution is similar to the total column WV. As we move up in the atmosphere,
Figs. 4 and 5 show the decrease in WV with height. The large isolated maximum
in the middle and upper troposphere in the area of the Indian monsoon is
of special interest. In this area, due to the strong vertical moisture
transport, over 8 mm are present above 500 mb.
In general, results from the layered WV products show that in oceanic
areas, roughly 75-85% of the total WV is in the lowest layer. Depending
on the surface elevation, elevated interior regions have only around 50%
in this layer. In some locations, the surface may be above 700 mb, such
as over the Tibetan highlands, in which case the POT for this layer is
annual cycles of the global and hemispheric daily averages for 1992 (Fig.
7) clearly show the global cycle is dominated by the Northern Hemisphere
(NH) (Wittmeyer and Vonder Haar 1994). It is seen that the time series
of global WV averages are sinusoidal in shape and have a maximum during
June-July-August (JJA). Both hemispheres show a maximum during the summer
months and a minimum in the winter. The differences between the NH and
the Southern Hemisphere (SH) cycles are significant. The annual range of
the NH cycle is twice that of the SH, and the NH summer maximum is much
greater. These differences are related to the following geographical dissimilarities
between the hemispheres. First, the amount of WV the atmosphere can hold
before saturation depends on the temperature (as represented by the Clausius
- Clapeyron relationship). The NH has a greater seasonal temperature change
and the majority of the land surface area, thus allowing greater variability
in the atmospheric WV. Second, the NH has a strong summer convective cloud
maximum, with the intertropical convergence zone being primarily in the
NH. Other factors include the strong summer monsoon season in India (NH)
and the lower water vapor concentrations in the SH contributed by the cold
and elevated Antarctic continent.
We also find the annual cycles are apparent in all atmospheric layers.
Upon examination of the annual cycles of the global and hemispheric averages
(for all five years of the NVAP data), there is some evidence the summer
maximum lags with increasing height. This would suggest a time delay of
the moisture transport from the surface to upper layers is discernible
in the dataset.
c. Liquid water
We include, as part of the NVAP dataset, two atmospheric liquid water
products. These are derived from the SSM/I and are available over oceans
grid points only. Liquid water is currently of special importance since
many GCMs are beginning to include it as an explicit variable. The two
available products are: the LWP, and cloud liquid water (CLW). The LWP
product is the liquid water in any region, averaged during all-sky conditions.
We also include the monthly averages of cloud LWP, expressed as CLW, which
is the liquid water in cloudy-only regions using a specified threshold
of the liquid water retrievals. Extensive discussion and analyses of the
LWP and CLW data are included in several papers noted in the reference
section (e.g., Greenwald et al. 1993, 1995).
5. DATASET ANALYSIS
Table 1 lists the estimates of global and hemispheric averaged WV from
this and previous studies. The present NVAP study includes data for 1988-92,
and the results show slightly higher values than the TOVS observations
but significantly lower than those using ECMWF. It is generally thought
the ECMWF WV results are too high (e.g., Trenberth et al. 1987; Wittmeyer
and Vonder Haar 1994), but these results may change soon since the ECMWF
dataset, along with other operational analyses are being reanalyzed. The
best agreement comparing NVAP with previously published results, occurs
in the NH with the in situ dataset of Rosen et al. (1979).
Table 2 lists the global and hemispheric averages for the NVAP data
from the layered and total column WV (for the time period 1988-92). The
table shows the hemispheric differences in the total and layered WV, most
striking in the total WV where the NH is 10% higher (2.4 mm) than the SH.
Investigator Data Time Period NH SH GL
Present Study 3 sources 1988-1992 25.7 23.3 24.5
Wittmeyer and Vonder Haar (1994) TOVS 1983-89 24.3 22.5 23.4
Wittmeyer (1990) ECMWF 1983-88 28.7 26.1 27.4
Rosen et al. (1979) MIT 1958-63, 68 25.7
Starr et al. (1969) IGY 1957-58 26.0
Trenberth (1991) 6 sources 1957-78 25.3
Trenberth (1987) ECMWF 1978-85 28.6
Table 1. Historic Estimates of Global and Hemispheric Averaged WV
(mm). Modified from Wittmeyer and Vonder Haar (1994).
NVAP 1988-1992 N. Hemis. S. Hemis. Global
500 mb-300 mb 1.5 1.4 1.5
700 mb-500 mb 5.0 4.2 4.6
Surface-700 mb 19.4 18.4 18.9
Total 25.7 23.3 24.5
Table 2. The NVAP Layered and Total Column WV, Global and Hemispheric
Averages (mm) for 1988-92.
Stability of any dataset is necessary for interannual variability studies
and the NVAP processing has been undertaken with this goal in mind. The
Elliott radiosonde dataset identified stations with a long-time series.
The SSM/I retrievals included data from both DMSP F8 and F11 satellites
but these systems were carefully inter-calibrated during a common month
to eliminate any satellite bias. As was previously mentioned, the operational
TOVS retrieval scheme did change in September 1988, but this was found
to have a negligible impact on the global and hemispheric averages.
column WV climatology for the entire five years of the NVAP dataset is
shown in Fig 8. The highly averaged nature of the global distribution
is evident in the smooth WV distribution and agrees well with that published
in Pexioto and Oort ( 1991), which used only radiosondes. Figure
9 presents the annual cycle of the NVAP WV global and hemispheric
averages for the entire five years of the dataset. The interannual
variability of the global averages is less than 2 mm, but this is hemispheric
dependent. The NH's summer and winter months have the greatest variability
near 3 mm with the SH generally less than 2 mm. The first nine months of
1988 are higher than other years, especially when compared to 1992, which
tends to provide the lower bound for the global and NH cycles. While the
largest WV values in the SH also occurs in 1988, 1992 does not provide
the lower bound and is near average.
The global climate of 1988 was indeed much different than 1992. In
1988, the tropical Pacific had La Niña conditions, while 1992 was
a moderate El Niño year (Trenberth and Hoar 1995). The difference
in the WV distribution is shown in Fig.
10 - a plot of 1988 minus 1992 . Most of the world had greater WV in
1988 with the following exceptions: the central Pacific in the area of
warmer than usual sea surface temperatures, off the west coast of the Americas,
off the west coast of Australia, and in SH oceans south of 40 degrees.
In general, the water vapor distribution during the El Niño year
showed drying in the subtropics and midlatitudes, with higher atmospheric
moisture only in areas near the equator.
variability of the WV, after removing annual cycle, is shown in Fig. 11.
The values are expressed as a mean monthly standard deviation. The most
striking high variability areas during the five years, are due to changes
in the tropical circulation patterns caused by the ENSO events. Other variations
over Africa warrant study. The low variability areas are easily seen over
the persistent marine stratus areas and in the U.S. Pacific Northwest.
Christy et al. (1995) have recently reported on the lower troposphere
temperature (LTT) anomalies during the last 15 years from the MSU on board
the NOAA series of operational satellites. The
global and hemispheric temperature anomalies for 1988 - 92 are shown with
the concurrent NVAP WV anomalies in Fig. 12. For the global averages,
the two anomaly time series are well correlated with a correlation coefficient
of 0.75. The first implication of these results, mainly that total atmospheric
WV and temperature apparently vary together as constrained by the Clausius-Clapeyron
relationship (e.g., Stephens 1990; Gutowski et al. 1995), implies the climate
system follows a constant relative humidity rather than a constant absolute
humidity. The high correlation also reinforces the statement that WV is
the principle greenhouse gas. An increase in global WV, therefore, points
to an increase in the greenhouse effect and thus implies a positive feedback.
Of course this is an oversimplification of the greenhouse situation since
WV feedback may also depend of the vertical distribution of WV. In addition,
other greenhouse gases and certain cloud microphysical properties affect
the upper troposphere and stratosphere without being directly tied to atmospheric
Fig. 12 shows us that in 1988 the global LTT was considerably warmer
than 1992. The sharp drop in the global temperature, starting in mid-1991,
was apparently caused by the June eruption of Mt. Pinatubo in the Philippines
and was accompanied by a concurrent drop in global total column water vapor.
The temperature and WV anomalies agree well, with the exception of early
1991 when the SH WV anomaly was strongly negative with no matching negative
temperature anomaly. For March 1991, the NVAP dataset (Fig.
13) indicates that the SH mid-Pacific, Australia, and all regions from
Australia to Indonesia were considerably drier than normal. In these areas,
large-scale subsidence provides the drying mechanism, which also has a
warming effect on the atmosphere. Therefore, while on a global scale, the
atmospheric WV generally correspond to LTT changes, other physical phenomena
and regional dynamic effects can impact significantly on the global relationship
between water vapor and temperature.
An extensive global dataset of water vapor (WV) has been produced from
combining three independent data sources. The dataset includes total column
integrated values, and values for three atmospheric layers for 1988 - 92.
Each of the individual input datasets has significant limitations: microwave
retrievals are presently feasible only over oceans; infrared satellite
techniques only work in the absence of significant cloud cover; and radiosonde
measurements are made primarily over land and are widely spaced, not showing
small-scale WV variations. A comprehensive global dataset should draw upon
the strengths of each of these methods and use the advantages of each for
all meteorological and geographical scenarios. The NASA Water Vapor Project
(NVAP) result is a combined column WV product far better than any single
input dataset. In addition, a single method has been derived using the
layered WV from radiosondes and TOVS retrievals to slice the total column
WV and create the three-layer global WV dataset.
The global-averaged WV was found to be considerably higher during the
1988 La Niña event when compared to the El Niño year of 1992.
The five-year NVAP dataset anomalies match well with the tropospheric temperature
anomalies, and in general confirm the physical principle that a warmer
atmosphere contains more WV than a cooler one. However, these correlations
can be affected by large scale anomalous subsidence when a warmer atmosphere
also becomes a drier one. To fully examine the question of whether the
atmosphere maintains a constant relative or specific humidity, one would
need a more detailed analysis of temperature anomalies at various levels.
This will be pursued in further studies.
There are three primary NVAP products: total column WV, LWP derived
from the SSM/I and available only over the oceans, and layered WV. In addition,
supplemental products such as SSM/I WV, radiosonde WV, TOVS WV, cloud liquid
water, and DSC for total column and layered WV are provided. These products
are available in four possible temporal averaging periods: day, month,
pentad, and annual. The complete five-year dataset along with documentation
and software are available from NASA's Distributed Active Archive Center
- Marshall Space Flight Center . The monthly averages are available electronically
while the daily global grids are be supplied via mail. To order NVAP data,
see the WWW link at the end of this article.
We wish to thank Mr. Donald Reinke for his assistance with the project
organization and processing, and Mr. Ian Wittmeyer for special contributions
to the TOVS processing. We gratefully acknowledge the support and helpful
discussions provided during the course of this work by Dr. James C. Dodge,
NASA Technical Monitor and his colleagues at NASA Headquarters. Special
thanks are extended to Bill Elliott and Becky Ross at the NOAA Air Resources
Laboratory, and to John Bates, NOAA ERL, for discussions and access to
their datasets. Thanks also to the reviewers, who provided many useful
comments. This work has been performed under NASA Contract NASW-4715 to
Science and Technology Corporation and under NASA Contract NAGW-2700 to
Colorado State University.
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To order the NVAP data-set start
up the WWW ordering system.
For more information about the NVAP data-sets, contact:
Dr. David L. Randel (email@example.com)
Colorado State University
Ft. Collins, CO 80523
Tel: (970) 491-8219; Fax: (970) 491-8241
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