Issue 9: Potential aliasing by water vapor changes in GPS-RO temperature time series

Changes in water vapor can cause changes in the derived GPS-RO temperature retrieval even when there are no changes in temperature.  This effect needs to be quantified to give confidence in its usefulness over time (potentially many decades into the future) and to help determine how low in the atmosphere the temperature retrievals are accurate.  Using reanalyses of the recent past and model output to 2100, GPS RO bending angles can be calculated.  The GPS RO temperature retrievals can then be calculated using those bending angles.  The difference between the derived and the model temperatures would then demonstrate the potential reliability of GPS RO for long time series applications and would also show how low in the atmosphere the retrievals are accurate with regard to potential changes in water vapor.  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.

 Response from Kevin Trenberth: 

Indeed the potential contamination of lack of knowledge of water vapor can be quantified.  An estimate of water vapor from climatology would be one estimate, but even use of estimates from NWP that take advantage of microwave water vapor channels should produce much more reliable results and keep water vapor uncertainties small from a certain range (my guess is above 6 km).  We should not think of GPSRO alone but in combination with other (cruder) estimates.

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3 Comments

  1. Tom Yunck

    Excellent.

  2. Seth Gutman

    Consolidated Comments by Gutman, Yoe and Reale

    Seth Gutman's Response
    In the absence of ancillary information, the inherent ambiguity associated with parsing total refractivity into its wet and dry components cannot be surmounted. Errors in estimated temperature caused by uncorrected wet refractivity is not potential... it is certain. I have no comments on the estimation of temperature retrieval errors, but I do have a comment on misconstruing changes in the ionosphere caused by errors in estimating temperatures in the stratosphere. The ionosphere is dispersive and the stratosphere is non-dispersive at GPS frequencies. In a dispersive medium, changes in refractivity are related to changes in the electron plasma density and, to a much lesser extent, changes in magnetic field strength and not temperature.

    Jim Yoe's Response
    Yes.

    Tony Reale's Response
    The "parsing" (based on background error for example if NWP is used) is the key aspect attached to the use of any given apriori. For example, in looking at Ben Ho slide 25, he suggests that the reduced scatter in integrated, retrieved COSMIC TPW compared to the original NCEP vs ECMWF TPW values demonstrates that COSMIC TPW is "guess" independent. My "guess (clearly a pun) is that the parsing is highly weighted to change the H20 vapor background, not the T background, that is, the model background error is much lower for T than for H20. Therefore, Ben's results would suggest that the ECMWF and NCEP models respective T are very similar.

    The relevance of the experiment that Dian describes for testing a given parsing approach using long-term reanalysis appears appropriate.

    Perhaps there are other methods to define apriori that are NWP independent, or at least NWP moisture independent (which essentially is what happens using a NWP guess if the error assigned to moisture is much greater that for T). However (and worrisome) is that when I look at individual COSMIC profiles collocated to sondes I see moisture structures that "appear" to be remnants of the NWP background.

  3. Rob Kursinski

    What Kevin says is correct. Let me provide a bit more quantification of the water vapor impact on pressure and temperature. Water vapor contributes to refractivity even in the middle atmospherewhere mixing ratios are a few ppm. For 5 ppm water vapor concentrations, the contribution to refractivity is ~0.01% very small but non-zero. Therefore water vapor should be accounted for in isolating the dry contribution to estimate density, pressure and temperature in the upper troposphere and above.

    Ignoring a constant 5 ppm amount in the stratosphere would cause GPS-derived densities in the stratosphere to be high by ~0.01%. This in turn would cause hydrostatic pressures in the stratosphere to be high by 0.01% (ignoring the contribution of the hydrostatic integral boundary condition error). Interestingly, in this particular case of a ~constant fractional water vapor mixing ratio error, since temperature is proportional to pressure divided by density, the temperature error would be close to 0.

    In the troposphere where mixing ratios typically increase rapidly at lower temperatures, consider the case where the error in assumed water vapor is 50% of the saturation vapor pressure. This error and the resulting density error will grow rapidly with decreasing heights in the troposphere. As a result, the error in the pressure that comes from integrating the density error in the hydrostatic integral is always smaller than the density error at any altitude (the fractional pressure error never "catches up" with the fractional density error because the fractioanl density error keeps growing). Therefore the fractional temperature error is almost as large as the fractional density error. Assuming a lapse rate of 6.5 K/km, a temperature error of 0.1 K occurs at ~214 K, and a 1K error occurs at ~237K. A 1 m geopotential height error due to this water vapor error would occur at 215 K and a 10 m error would occur at 237K. One may be able to estimate upper troposphere water vapor to at least a bit better than a 50% of saturation vapor pressure, in which case these errors may be a bit conservative but upper tropospheric water vapor is notoriously difficult to measure accurately. In terms of errors in climatic trends derived from GPSRO, this class of error would depend on systematic mis-estimates of upper tropospheric water vapor trends.

    Regarding the ionosphere, from the Kursinski et al 1997 estimates of the effects of the ionosphere, the approximate residual daytime solar maximum ionospheric effects are summarized below. These are due to a combination of the residual bending angle errors due to incomplete ionospheric calibration and the error they cause in extrapolating the bending angle profile to altitudes above the maximum altitude of the bending angle profile as needed for the abel transform. These represent the approximate 11 year false signal produced by the solar cycle on daytime measurements

    altitude   fract refractivity error           geopotential error            temperature error

    40 km             0.4% (0.02%)                     100m  (3m)                       3K (0.08K)

    30 km             0.1% (0.02%)                      30m (<1m)                      1K (0.01K)

    20 km             0.03% (0.01%)                    10m (<1m)                     0.3K (<0.01K)

    10 km             0.01% (<0.01%)                  2m  (<1m)                     0.03K (<0.01K)

    Parentheses indicate nighttime solar maximum conditions. These are the signatures predicted using a simple Chapman E and F layer ionospheric model and the dual frequency ionosphere bending angle calibration method. The daytime, solar maximum signature is significant (>0.1K/decade) above 15 km altitude.  This can conceivably be improved upon with a more sophisticated ionosphere calibration and certainly should be improved with a calibration using a third GPS signal frequency.