We compare atmospheric total precipitable water (TPW) derived from
the
SSM/I (Special Sensor Microwave Imager) and SSMIS (Special Sensor Microwave
Imager/Sounder) radiometers and WindSat to collocated TPW estimates derived
from COSMIC (Constellation System for Meteorology, Ionosphere, and Climate)
radio occultation (RO) under clear and cloudy conditions over the oceans from
June 2006 to December 2013. Results show that the mean microwave (MW)
radiometer – COSMIC TPW differences range from 0.06 to 0.18 mm for clear
skies, from
0.79 to 0.96 mm for cloudy skies, from 0.46 to 0.49 mm for cloudy but non-precipitating
conditions, and from 1.64 to 1.88 mm for precipitating conditions. Because RO
measurements are not significantly affected by clouds and precipitation, the
biases mainly result from MW retrieval uncertainties under cloudy and
precipitating conditions. All COSMIC and MW radiometers detect a positive TPW
trend over these 8 years. The trend using all COSMIC observations
collocated with MW pixels for this data set is 1.79 mm decade
Clouds are important regulators for Earth's radiation and hydrological
balances. Water vapor is a primary variable that affects cloud radiative
effects and hydrological feedbacks. In addition, the three-dimensional
distribution of water vapor is a key factor for cloud formation and
distribution (Soden et al., 2002). Held and Soden (2000) and Soden and Held (2006) illustrated that water vapor amounts will increase in response to
global warming. Climate models predict that the column-integrated amount of
water vapor, or total precipitable water (TPW), will increase by
The TPW depends on temperature (Trenberth and Guillemot, 1998; Trenberth et al., 2005). Global TPW can be derived from satellite visible, infrared, and microwave sensors (i.e., Wentz and Spencer, 1998; Fetzer et al., 2006; John and Soden, 2007; Fetzer et al., 2008; Noël et al., 2004). However, no single remote sensing technique is capable of completely fulfilling the needs for climate studies in terms of spatial and temporal coverage and accuracy. For example, while water vapor retrievals from visible and infrared satellite sensors are limited to clear skies over both land areas and oceans, passive microwave (MW) imagers on satellites can provide all sky water vapor products, but only over oceans. These water vapor products are mainly verified by comparing to reanalyses, radiosonde measurements, or other satellite data (i.e., Soden, and Lanzante, 1996; Sohn and Smith, 2003; Noël et al., 2004; Palm et al., 2010; Sohn and Bennartz, 2008; Wick et al., 2008, hereafter Wick2008; Milz et al., 2009; Prasad and Singh, 2009; Pougatchev et al., 2009; Knuteson et al., 2010; Larar et al., 2010; Wang et al., 2010; Ho et al., 2010a, b). Results from these validation studies show that the quality of water vapor data from different satellite sensors varies under different atmospheric conditions. The change in reanalysis systems and inconsistent calibration among data may also cause uncertainty in long-term stability of water vapor estimates. In addition, it is well known that radiosonde sensor characteristics can be affected by the changing environment (Luers and Eskridge, 1998; Wang and Zhang, 2008). Ho et al. (2010b) demonstrated that the quality of radiosonde humidity measurements varies with sensor types, adding extra difficulties in making a consistent validation of long-term water vapor products.
MW imagers are among the very few satellite instruments that are able to provide long-term (close to 30 years) all-weather time series of water vapor measurements using similar sensors and retrieval techniques (Wentz, 2015). The measured radiances at 19.35, 22.235, and 37.0 GHz from SSMIS and 18.7, 23.8, and 37.0 GHz from WindSat are used to derive TPW, total cloud water (TCW), wind speed, and rainfall rates over oceans (Wentz and Spencer, 1998). These four variables are retrieved by varying their values until the brightness temperatures calculated using a forward model match satellite-observed brightness temperatures. Because MW radiation is significantly affected (absorbed or scattered) by heavy rain, these four variables are only retrieved under conditions of no or light to moderate rain (Schlüssel and Emery, 1990; Elsaesser and Kummerow, 2008; Wentz and Spencer, 1998).
Recently, version 7.0 daily ocean products mapped to a 0.25
Unlike passive MW radiometers and infrared sensors, radio occultation (RO)
is an active remote sensing technique. RO can provide all-weather, high-vertical-resolution (from
Launched in June 2006, COSMIC (Constellation Observing System for Meteorology, Ionosphere, and Climate) RO data have been used to study atmospheric temperature and refractivity trends in the lower stratosphere (Ho et al., 2009a, b, 2012) and modes of variability above, within, and below clouds (Biondi et al., 2012, 2013; Teng et al., 2013; Scherllin-Pirscher et al., 2012; Zeng et al., 2012; Mears et al., 2012). Wick2008 demonstrated the feasibility of using COSMIC-derived TPW to validate SSM/I TPW products over the eastern Pacific Ocean using 1 month of data. Many studies have demonstrated the usefulness of RO-derived water vapor to detect climate signals of El Niño–Southern Oscillation (ENSO; Teng et al., 2013; Scherllin-Pirscher et al., 2012; Huang et al., 2013) and Madden–Julian Oscillation (MJO; Zeng et al., 2012) and improve moisture analysis of atmospheric rivers (Neiman et al., 2008; Ma et al., 2011).
The objective of this study is to use COSMIC RO TPW to characterize the global TPW values and trends derived from multiple MW radiometers over oceans, including under cloudy and precipitating skies. COSMIC TPW from June 2006 to December 2013 is compared to co-located TPW derived from MW radiometers over the same time period. Because RO data are not strongly sensitive to clouds and precipitation, COSMIC TPW estimates can be used to identify possible MW TPW biases under different meteorological conditions. We describe data sets and analysis methods used in the comparisons in Sect. 2. The comparison results under clear skies and cloudy skies are summarized in Sects. 3 and 4, respectively. The time series analysis is in Sect. 5. We conclude this study in Sect. 6.
The RSS version 7.0 ocean products are available for SSM/I, SSMIS, AMSR-E, WindSat, and TMI. The inversion algorithm is mainly based on Wentz and Spencer (1998), in which above a cutoff in the liquid water column (2.45 mm), water vapor is no longer retrieved. The various radiometers from the different satellites have been precisely intercalibrated at the radiance level by analyzing the measurements made by pairs of satellites operating at the same time. This was done for the explicit purpose of producing versions of the data sets that can be used to study decadal-scale changes in TPW, wind, clouds, and precipitation; thus, special attention was focused on interannual variability in instrument calibration. The calibration procedures and physical inversion algorithm used to simultaneously retrieve TPW, surface wind speed (and thereby surface wind stress and surface roughness), and the total liquid water content are summarized in Wentz (2013, 1997). This allows the algorithm to minimize the effect of wind speed, clouds, and rain on the TPW measurement.
The RSS version 7.0 daily data are available on a 0.25
Because COSMIC reprocessed TPW data are only available from June 2006 to
December 2013 (i.e., COSMIC2013), the SSM/I F15, SSMIS F16, SSMIS F17,
and WindSat RSS version 7.01 ocean products covering the same time
period are used in this study. Table 1 summarizes the starting date and end
date for RSS SSM/I F15, SSMIS F16, SSMIS F17, and WindSat data. The all sky
daily RSS ocean products for F15, F16, F17, and WindSat are downloaded from
The atmospheric refractivity
Satellite instruments used in this study.
Typical values of cloud liquid water content range from
For extremely high values of
To resolve the ambiguity of COSMIC refractivity associated with both
temperature and water vapor in the lower troposphere, a 1D-Var algorithm
(
Note that because RO refractivity is very sensitive to water vapor
variations in the troposphere (Ho et al., 2007), and is less sensitive to
temperature errors, the RO-derived water vapor product is of high accuracy (Ho
et al., 2010a, b). It is estimated that 1 K of temperature error will
introduce less than 0.25 g kg
The horizontal footprint of a COSMIC observation is about 200 km in the
lower troposphere and its vertical resolution is about 100 m near the
surface and 1.5 at 40 km. The COSMIC post-processed water vapor profiles
version 2010.2640 collected from the COSMIC Data Analysis and Archive Center
(CDAAC)
(
In this study, only those COSMIC water vapor profiles penetrating lower than
0.1 km are integrated to compute TPW. Approximately 70 to 90 % of COSMIC
profiles reach to within 1 km of the surface (Anthes et al., 2008). Usually
more than 30 % of COSMIC water vapor profiles reach below 0.1 km in the
midlatitudes and higher latitudes and a little bit less than 10 % in the
tropical regions. To compensate for the water vapor amount below the
penetration height, we follow the following procedure:
We assume that the relative humidity below the penetration height is equal to
80 %. This is a good assumption, especially over oceans near the sea
surface (Mears et al., 2015). The temperatures below the penetration height are taken from the ERA-Interim
reanalysis. We compute the water vapor mixing ratio below the penetration
heights. We integrate the TPW using COSMIC water vapor profiles above the penetration
heights with those water vapor profiles below the penetration heights.
The COSMIC TPW estimates are not very sensitive to the assumption of 80 %
relative humidity below 0.1 km (step i above). The assumption of
80 %
Pairs of MW and RO TPW estimates collocated within 50 km and 1 h are
collected. The location of RO observation is defined by the RO tangent point
at 4–5 km altitude. Wick2008 used MW–RO pairs within 25 km and 1 h in time. To evaluate the effect of the
spatial difference on the TPW difference, we also computed TPW differences
for MW–RO pairs within 75, 100, 150, and 200 km. We found that the larger
spatial difference increases the mean TPW biases slightly to
With a 0.25
TPW scatter plots for the COSMIC and RSS version 7.0 pairs under
clear conditions for
The matching pairs of RO and MW observations are not distributed uniformly over the world's oceans. In fact, they are heavily concentrated in middle latitudes, as shown in Fig. 1e. This biased distribution is caused by several factors, including the polar orbits of the satellites, which produce more observations in higher latitudes, and also the failure of many COSMIC RO soundings to penetrate to 0.1 km in the subtropics and tropics (due to super-refraction, which is often present in these regions). Thus, the results presented here, especially the trends, are not representative of global averages. However, the main purpose of this paper is to compare two independent satellite systems for obtaining TPW under varying sky conditions. If the agreement is good, one has confidence in both systems. In this case, SSM/I and WindSat estimates of TPW will be verified and can then be used with confidence globally, including where RO observations are sparse or do not exist.
Mean and standard deviation of differences (MW minus RO) in TPW (mm) between four MW radiometers and COSMIC RO under various sky conditions. The sample numbers for each pair are shown in the third position of each column.
In total there are 26 678 F15–RO pairs, 32 610
F16–RO pairs, 31 291 F17–RO pairs, and 21 996 WindSat–RO pairs from June
2006 to December 2013. Figure 2a–d show scatter plots for F15–COSMIC TPW,
F16–COSMIC TPW, F17–COSMIC TPW, and WindSat–COSMIC TPW under clear skies.
Figure 2a–d show that the MW clear sky TPW values from F15, F16, F17, and
WindSat are all very consistent with those from co-located COSMIC
observations. As summarized in Table 2, under clear conditions where SSM/I
provides high-quality TPW estimates, the mean TPW bias between F16 and COSMIC
(F16–COSMIC) is equal to 0.03 mm with a standard deviation
Figure 3a–c depict the scatter plots for F16–COSMIC pairs under cloudy, cloudy non-precipitating, and precipitating conditions from June 2006 to December 2013 over oceans. While there is a very small bias (0.031 mm) for clear pixels (Fig. 2b), there is a significant positive TPW bias (0.794 mm) under cloudy conditions (Fig. 3a). This may explain the close to 0.45 mm mean TMI gb-GPS TPW biases found by Wentz (2015) in which close to 7 years of data were used. Figure 3c depicts that the large SSM/I TPW biases under cloudy skies are mainly from the pixels with precipitation (mean bias is equal to 1.825 mm) although precipitation pixels are of about less than 6 % of the total F16–COSMIC pairs. Because RO measurements are not significantly affected by clouds and precipitation, the biases mainly result from MW retrieval uncertainty under cloudy conditions. The fact that the MW–COSMIC biases for precipitating conditions (1.825 mm, Fig. 3c, and 1.64–1.88 mm in Table 2) are much larger than those for cloudy but non-precipitating conditions indicates that significant scattering and absorbing effects are present in the passive MW measurements when it rains. The correlation coefficients for F15–RO, F16–RO, F17–RO, and WindSat–RO pairs for all sky conditions are all larger than 0.96 (not shown).
TPW scatter plots for the COSMIC and RSS version 7.0 F16 SSM/I pairs
under
TPW scatter plots for the gb-GPS
and RSS version 7.0 F16 SSM/I pairs from June 2006 to December 2013 under
MW and gb-GPS TPW comparisons show differences similar to the MW–RO differences under different sky conditions. We compared F16 pixels with those from gb-GPS within 50 km and 1 h over the 33 stations studied by Mears et al. (2015) from 2002 to 2013. Figure 4a–d depict the scatter plots for F16 gb-GPS TPW under clear, cloudy, cloudy non-precipitating, and cloudy precipitating conditions, respectively. The F16-gb-GPS mean biases are equal to 0.241 mm (clear skies), 0.614 mm (cloudy skies), 0.543 mm (cloudy non-precipitating), and 1.197 mm (precipitating), which are similar to those estimated from MW–RO comparisons (Table 2).
Mean and standard of the mean for the F16–COSMIC TPW biases varying
with
The results above show that the MW estimates of TPW are biased positively compared to both the RO and the ground-based GPS estimates, which are independent measurements. The biases are smallest for clear skies and largest for precipitating conditions, with cloudy, non-precipitating biases in between. Overall, the results suggest that clouds and especially precipitation contaminate the MW radiometer measurements, which in turn affect the MW TPW retrievals.
Mean and standard of the mean for the F16 gb-GPS TPW biases varying
with
To further examine how rain and cloud droplets affect the MW TPW retrievals,
we show how the F16–RO TPW biases vary under different meteorological
conditions in Fig. 5. The bias dependence on wind speed (Fig. 5a) is
small. Unlike the results from Mears et al. (2015), the mean TPW biases
between F16 and COSMIC are within 0.5 mm with high winds (wind speed larger
than 20 m s
The time series of monthly mean F16 – COSMIC TPW differences under
Mean and standard deviation (SD) of the mean in millimeters of the
monthly time series of differences of MW minus RO TPW under various sky
conditions. The trend of the RO estimates of TPW (mm decade
In Fig. 6 we compare RSS v7.0 F16 MW TPW to the gb-GPS TPW over
various meteorological conditions. The magnitudes of the MW gb-GPS TPW
differences under high rain rate and high total cloud water conditions are
somewhat smaller than those of MW–RO pairs (varying from about 0.5 to 2.0 mm), which may be because most of the MW gb-GPS samples are collected under
low rain rates (less than 1 mm h
To further examine MW TPW long-term stability and trend uncertainty due to
rain and water droplets for different instruments, we compared time series
of the MW and COSMIC monthly mean TPW differences from June 2006 to December
2013. Figure 7a–d show the monthly mean F16–COSMIC TPW differences from
June 2006 to December 2013 for clear, cloudy, cloudy non-precipitating, and
precipitating conditions. In general, the microwave TPW biases under
different atmospheric conditions are positive and stable from June 2006 to
December 2013, as reflected in relatively small standard deviation values
(Table 3). Except for F15, the standard deviations of the monthly mean TPW
anomaly range are less than 0.38 mm (Table 3). In contrast, the F15–COSMIC
monthly mean
The time series of deseasonalized TPW differences (microwave
radiometer – COSMIC) under cloudy skies for
Table 3 also shows the trend in the RO estimates of TPW differences over the
8-year period of study. The trends range from
The deseasonalized time series of monthly mean TPW for all MW and COSMIC observations under all sky conditions. The red and blue dashed lines are the best fit of deseasonalized COSMIC and MW TPW time series, respectively.
Figure 8 depicts the deseasonalized trends of the MW–RO TPW differences for
F15 (Fig. 8a), F16 (Fig. 8b), F17 (Fig. 8c), and WindSat (Fig. 8d)
under cloudy skies. Except for F15, the deseasonalized trends of the MW–RO
TPW differences for the MW radiometers are close to zero, indicating little
change over these 8 years. The trends of the biases associated with F15,
F16, F17, and WindSat under all sky conditions range from
The reason for larger standard deviations of the MW minus RO differences for F15 (Tables 2 and 3 and Fig. 8a) is very likely because the F15 data after August 2006 were corrupted by the rad-cal beacon that was turned on at this time. Adjustments were derived and applied to reduce the effects of the beacon, but the final results still show excess noise relative to uncorrupted measurements (Hilburn and Wentz, 2008). RSS does not recommend using these measurements for studies of long-term change. Thus, we consider the F15 data less reliable during the period of our study.
Figure 9 shows the deseasonalized time series of the monthly mean TPW for all
MW and RO pairs under all sky conditions. The nearly 8-year trends for TPW
estimated from both passive MW radiometers and active COSMIC RO sensors are
positive and very similar in magnitude. The mean trend of all COSMIC RO TPW
is 1.79 mm decade
As discussed earlier, the trend of 1.78 mm decade
The very close agreement between RO and MW observations where they coexist
gives credibility to both observing systems and allows us to use global MW
data to compute global TPW trends over all oceanic regions, including where
RO observations are sparse or absent. Figure 10 shows the global map of TPW
trends over oceans using all F16, F17, and WindSat data from 2006 to 2013.
Figure 10 shows that the positive trends in TPW occur mainly over the
central and northern Pacific, south of China and west of Australia,
southeast
of South America, and east of America. Positive trends also exist in general
over the middle latitudes (40–60
Mears et al. (2017) computed global average (60
The global map of TPW trend in millimeters per decade over oceans using all F16, F17, and WindSat data from 2006 to 2013.
Global mean TPW monthly anomaly (mm) relative to 1981–2010 mean for
ocean regions 60
RSS water vapor products have been widely used for climate research. The
newly available RSS v7.0 data products have been processed using consistent
calibration procedures (Wentz, 2013). This was done for the explicit purpose
of producing versions of the data sets that can be used to study
decadal-scale changes in TPW, wind, clouds, and precipitation. These water
vapor products are mainly verified by comparing to reanalyses, radiosonde
measurements, or other satellite data. However, because the quality of these
data sets may also vary under different atmospheric conditions, the
uncertainty in long-term water vapor estimates may still be large. In this
study, we used TPW estimates derived from COSMIC active RO sensors to
identify TPW uncertainties from four different MW radiometers under clear,
cloudy, cloudy and non-precipitating, and
cloudy and precipitating skies over nearly
8 years (from June 2006 to December 2013). Because RO data have low
sensitivity to clouds and precipitation, RO-derived water vapor products are
useful for identifying the possible TPW biases retrieved from measurements of
passive microwave imagers under different sky conditions. We reach the
following conclusions:
The trends of TPW in our data set, which are heavily biased toward middle
latitudes (40–60
Other studies have suggested that this positive feedback results in a nearly
constant global mean relative humidity (Soden and Held, 2006; Sherwood et
al., 2010). However, it is difficult to directly relate our estimated TPW
trends to a constant RH hypothesis of Earth's atmosphere under global warming.
The global mean surface temperature has been rising at about the rate of 0.2 K decade
The RO data are from the COSMIC Data Analysis and Archive
Center, Constellation Observing System for Meteorology, Ionosphere and
Climate, University Corporation for Atmospheric Research. Atmospheric
profiles are from COSMIC Occultation Data, COSMIC Data Analysis and Archive
(
The authors declare that they have no conflict of interest.
This work is supported by the NSF CAS AGS-1033112. We thank Eric DeWeaver (NSF) and Jack Kaye (NASA) for sponsoring this work. Edited by: Qiang Fu Reviewed by: two anonymous referees