Issue 7: Profiling of the lower troposphere
Although temperature (and humidity) trends in the upper troposphere and stratosphere are
important, a primary need for long-term monitoring lies in the lower troposphere. Deriving
temperature and humidity trends separately from GPS-RO alone in the lower troposphere,
however, is confounded by the contributions both make to RO soundings there (cf. issues 6 and
9). What role, therefore, should GPS-RO play in a baseline temperature (and/or humidity)
monitoring network for the lower troposphere? To what extent will other sounding systems
(e.g., a reference radiosonde network) still be required, and how should these other networks be
optimally configured in conjunction with GPS-RO?
1. Discussions primarily centered on the ability of GPS/GNSS-RO to profile temperature and
humidity in the lower/mid troposphere rather than the role that GPS-RO can play in the
proposed baseline monitoring network. Factors involving uncertainties and possible errors
associated with deriving geophysical parameters (in particular, temperature and humidity)
from bending angle or refractivity were discussed extensively also in other sections, and the
more detailed issues of best combination with other sounding systems (e.g., radiosonde
network) could not be addressed to clarification. Nonetheless, it is certain that a sustained
constellation of GPS-RO satellites in LEO can provide measurements of bending angle and
refractivity with unprecedented accuracy, precision and temporal stability under all weather
conditions and without the need for external calibration, which contain relevant information
about temperature, pressure, and water vapor in the troposphere. It is thus clear that this
technique has potential for long-term climate monitoring also in the lower/mid troposphere.
What is needed for climate exploitation is 1) a verifiable method to use bending angle or
refractivity as a proxy for temperature and humidity in the troposphere and/or to “sort out”
the temperature/humidity contributions (see item 2 below), and 2) determination of the extent
and quality to which other observations (e.g., accurate external temperature in addition to
bending angle or refractivity) are needed to make use of GPS-RO as part of a benchmark
climate observing system in the troposphere.
2. GPS-RO bending angle or refractivity estimates are directly assimilated into numerical
weather prediction (NWP) models using variational techniques, and work by Ben Ho/UCAR
team and others presented at the workshop has demonstrated the feasibility of retrieving
humidity (and temperature) from GPS-RO measurements using 1-D Var techniques. This is
also relevant to climate use. The role of variational analysis in this context is to allow a
model to “sort out” the relative contributions of T/P (bulk air density) and humidity (water
vapor) to the GPS-RO bending angle or refractivity data. This is accomplished by
minimizing, within errors, the differences between a GPS-RO profile and a corresponding
model background profile (forward modeled from T, P, q to refractivity or bending angle).
In general, the assimilation of GPS-RO observations into NWP models has already resulted
in improved estimates and forecasts of most of the relevant atmospheric state variables,
primarily through the reduction of systematic errors in the model background. The extent to
which variational analysis techniques can be used in climate monitoring was discussed to
some degree and the need for good complementary temperature information in the
troposphere was emphasized.
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Issue 8: Impact of observations scattered across space and time
Most meteorological observations are performed at regular times of day (e.g., 00Z and 12Z) in
specific locations. In situ SST obs are major exception to this rule, but SST changes slowly
compared to the atmosphere. Most satellite observations are for wide areas of the globe. GPSRO is nearly unique because it observes point measurements that change in location and time of
observation in a not quite random way. As a result, utilizing them for climate change analyses
presents new challenges. Past analyses have averaged the observations into large grid boxes and
then into large time segments (e.g., 10x10 degree boxes averaged over a month). An analysis of
the errors this technique causes would be helpful in putting the use of GPS-RO for climate into
perspective. Perhaps the potential impacts of the errors could be quantified by subsampling a
very high resolution model output at the GPS-RO observation locations in time and space (by
latitude band and over land and ocean and elevation) and comparing the results to the full model
field results.
1. The consensus of the workshop was that rather than being an issue, the space-time scattered
observations (including good local time coverage if constellations are set up properly)
constitute actually a major strength of the RO technique. Satellite constellations need to be
designed to minimize temporal and spatial aliasing, and data continuity is to be maintained
by NOAA and/or other operational agencies without gaps over long (multi-decadal) periods.
Issues 9 and 10: Potential aliasing by water vapor (or temperature) changes in GPS-RO
temperature (or water vapor) time series
[Issue 9 question slightly modified/extended to account also for Issue 10]
Changes in water vapor (or temperature) can cause changes in the derived GPS-RO temperature
(or water vapor) retrievals even when there are no changes in the targeted quantity. This effect
needs to be quantified to give confidence in the climatic usefulness over time (potentially many
decades into the future) and to help determine how low in the atmosphere the temperature
retrievals (dry temperature, with water vapor term neglected in refractivity) are accurate (and
below which altitudes water vapor starts to be potentially useful). A study design is outlined that
could help estimate potential long-term aliasing effects more rigorously: Using re-analyses of the
recent past and model output to 2100, GPS RO refractivity or bending angles can be calculated.
The GPS RO (dry) temperature (or water vapor) retrievals can then be calculated using those
“forward modeled” GPS-RO profiles. The difference between the derived and the model
temperatures (or water vapor) would then demonstrate the potential reliability of GPS-RO for
long time series applications and would also show how low (high) in the atmosphere the
retrievals are accurate with regard to potential changes in water vapor (temperature).
Regarding the different issue of ionospheric influences, similar analysis using potential changes
in the ionosphere must be made as well to show that, when based on GPS RO temperature
retrievals, a potential future change in the ionosphere won’t be falsely construed as a change in
temperature of the stratosphere.
1. Given there are no undetected errors (caused the ionosphere) in estimating excess path length
in the neutral (non-dispersive) atmosphere, the current ability to retrieve temperature in the
dry atmosphere using GPS-RO is estimated by workshop participants at ~0.1+/–0.05 K.
Regarding specifically the water vapor impact to temperature (difference of dry temperature
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to actual temperature), it is essentially negligible in the stratosphere (< 0.01 K due to the
small concentration of stratospheric water vapor) but starts to be at the several 0.01 K level
near the tropopause and at the 0.1 K level in the upper troposphere. In the lower/mid
troposphere the temperature aliasing would exceed 1 K but this region is for this reason
treated differently; see Issue 7. In general, the expected level of accuracy and precision is
sufficient to detect small (0.1 K/decade) changes with high confidence. This provides us with
an excellent way to independently validate various climate model predictions. The outlined
study design to more rigorously quantify long-term aliasing effects is found useful and such
study should clearly be undertaken.
2. Regarding ionospheric influences, ways to detect and reduce errors in estimating and
removing the ionospheric delay have been discussed and need to be further developed and
verified. The upcoming (as of 2009) high solar activity phase allows good evaluation of
ionospheric correction themes; also modeling estimates over more than two solar cycles
(2001-2025) are planned. The current estimates are that below 30 km height the ionospheric
residual error effects have ceased to the < 0.1 K level if using adequate ionospheric
correction schemes.
3. If NWP analysis temperature systematic errors in the lower/mid troposphere are sufficiently
low (< 0.5 K), it will be possible to retrieve water vapor profiles with high vertical resolution
and climatically useful accuracy using 1-D variational assimilation techniques (cf. Issue 7).
4. Brief discussion of using geopotential heights (and other variables) retrieved from RO
measurements as a climate signal detection variable (as opposed to using T & q) was also
taking place. Work on best climate change indicators from GPS-RO is on-going (cf. also
Issue 6) and further needed.
Section 4 General Recommendations
[in addition to specific suggestions contained in the Summaries above]
1. Until GPS-RO observations are accepted by the global climate community as sound and
trustworthy, data processing centers should employ climatologists to facilitate the two-way
communication of ideas and concepts between atmospheric/remote sensing scientists and
climate scientists. The burden to do this should initially fall more on the former community
that is offering new solutions to climate problems. Once the soundness and trustworthiness
of GPS-RO has been accepted by the climate community, the burden should certainly be
shared with greater equity.
2. Centers should consider routinely producing, and making readily accessible, climate
monitoring products that can be evaluated by the general climate community. Examples
include: geopotential height and temperature of the tropopause and at standard upper air
pressure levels, height of the moist layer as a function of location and time, height of the
PBL; all products as a function of location and time. The International GNSS Service (IGS),
which provides products produced by the different IGS centers as well as combined products,
might be a good model for this.
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