Comments on:
Comparing GPS RO Temperatures and Lapse Rates within Clouds with ECMWF and
NCEP Analysis
General Comments:
This is a nice piece of work. To my knowledge, this is the first paper trying to retrieve
temperature within clouds using a combination of GPS RO observation and global
analysis. This is a very challenging task, and it is interesting to find and document
systematic differences between temperatures inside the clouds from various different
sources.
Specific comments:
1. Comparison of different types of measurements:
In this study, various different types of measurements are compared. It is important to
recognize their differences in resolutions, and measurement characteristics:
a.
b.
c.
d.
NCEP – This is a low resolution global reanalysis T62L28.
ECMWF – This is EC’s operational analysis at T799L91
CloudSat – The resolution is on the order of 1.4 km x 2.5 km
GPS RO – The averaging length is on the order of 300 km along the ray, and
about 1 km cross the ray.
Global analyses are providing “averaged” information within the grid point. The GPS RO
measurements are affected by the azimuth angles of the measurements. Also in the
vicinity of clouds, there will be significant horizontal refractivity gradients (due to
moisture variations). Spherical symmetry assumptions may not work well. In other
words, if the ray goes through clouds, the retrieved GPS refractivity does not necessarily
a good representation of the “point” value. It would be an averaged value including both
cloudy and clear-sky.
It is clear that NCEP/NCAR reanalysis has the lowest resolution, and it also suffers a few
disadvantages: old data assimilation system (3D-Var, SSI), do not use all available data
(including COSMIC). The reviewer may ask: Why didn’t you use the FNL (NCEP GFS
final analysis)? It would have been more compatible with the ECMWF operational
analysis. Of course, EC uses 4D-Var, and has a much higher horizontal and vertical
resolution. Another point is that the vertical interpolation (from the relatively low L28
resolution to the GPS vertical resolution) may induce some errors (or uncertainties).
Again, using the NCEP FNL at native grids would have been better.
When performing a comparison between GPS RO and the CloudSat measurements, it
would be useful to keep in mind the azimuth angles of the GPS RO ray path. For
example, I would expect different types of bias depending on whether the GPS RO ray
path is parallel or perpendicular to the direction of the granule. In Fig. 16, you showed
results for 10 positively biased sounding and 29 negatively biased soundings. Question is:
Why some are positively biased, and some are negatively biased? Are there significant
differences in their azimuth angle relative to the track of the CloudSat?
Another question is that since the footprint of the CloudSat is so small, one may wonder
“how representative” are these measurements? Can they really be used to represent area
averages?
2. Retrieval of Cloudy temperature
It is an interesting attempt to estimate the temperature within clouds, based on ECMWF
global analyses and GPS RO measurements. I have been wondering, “What does this
retrieved temperature represent?” “What is the applicable horizontal resolution for the
retrieved ‘cloud temperature’ profile?”
CloudSat is providing estimates of cloud top and cloud base, and other cloud properties
based on a very small footprint.
GPS RO is providing estimated “mean” refractivity along a ray path that has a scale of
300 km by 1 km.
ECMWF is providing a box average value of about 25 km x 25 km.
Would the new GPS RO Cloudy Retrieval algorithm produce significant changes in the
GPS RO refractivity? Or, is the GPS RO refractivity kept constant? This point is not
made clear. GPS RO derived refractivity is a quantity obtained following the retrieval
procedures under various assumptions. All the real atmospheric structure along the ray
path would influence the final derived refractivity. The “wet-retrieved” temperature
assumes that the GPS RO refractivity is representative of a “point value”, and proceeds to
derive the temperature under such assumption. In an inhomegeneous environment, the
GPS refractivity can deviate significantly from the local refractivity. Therefore, the
derived temperature would not be representative of the local value. On the other hand, the
GPS RO refractivity can still be a robust “measurement”, we just need to recognize of
“what it really represents”.
Hope these comments are useful for your revision of the paper.
Bill K.