UTH Pathfinder Retrieval
This page contains latest results for applying the UTH algorithm
(Jackson and Bates, 2001, JGR, in press) to the HIRS clear-sky
Pathfinder data.
I first began by applying the algorithm to one month of data, to
two separate satellites, for the month January 1983. I wanted
to look at the bias in UTH when comparing monthly grids that were
constructed by applying the alogorithm to the swath data, pentad
grid data and the monthly mean data.Figure 1 shows the
monthly mean grids when computing the UTH data from swath data and
from pentad grid data for NOAA-6. Both monthly grids show a large
dry region across the Pacific around 20N. This dry region is
associated with the El Nino event of 1982/83. Convective region
just south of the equator has missing data indicating that the
HIRS clear-sky data was not able to establish any clear-sky
observations in this region for the entire month. Missing data
regions in the poleward of 30 degrees are related to the
algorithm's constraint on the temperature profile. The difference
map indicates that the swath-averaged monthly grid is wetter than
the UTH data constructed from the pentad grid data. This bias is
caused by the swath data seeing the transient behavior of UTH and
the exponential relationship between brightness temperature and UTH.Figure 2
gives is the same diagram except for NOAA-7 data. The results are
very similar to NOAA-6.
The same analysis was carried out for the monthly mean data
derived from the swath data to the UTH data derived from monthly
mean data. Figure
3 indicates a significant bias between UTH data derived
from the swath data and month mean data for NOAA-6. The bias is
greater than the pentad data comparison and in the same direction for
virtually all grid cells. Figure 4 for
NOAA-7 gives a similar result.
The spatial distribution of the bias can be explained, in part, by a
large standard deviation of the UTH derived from the swath data. Figure 5 shows how
differences, particularly for the UTH Swath - Monthly data, relates
well with the UTH standard deviation map. Large regions of
agreement occur in the eastern Pacific and eastern north Africa.
The Swath - Pentad does not show significant bias in these large areas
but does show smaller regions at higher latitudes that coincide
with the standard deviation map. Again, N07 results in Figure 6 gives a similar
result to the NOAA-6 data.
A scatter diagram comparing the monthly mean UTH data derived here
quanitifies the bias seen in these diagrams. Figure 7 indicates the
bias between the swath and pentad derived UTH data is 0.8% and
raises to 1.8% for the month-derived UTH. NOAA-7 results in Figure 8 indicate nearly
the same bias but the mean values are wetter by about 2%. The
intersatellite bias can be attributed to both sampling and
instrument bias. RMS error range from about 1% to 1.5% for all
results.
The mean bias seems rather small in the scatter diagrams compared
to the larger regional biases seen in Figures 1-4. Based on these
larger regional biases seen in mainly the UTH derived monthly
grid data and to a lesser degree in the pentad data, I recommend
not using the monthly mean data for deriving UTH. The problem
with using either the pentad grid data or swath data is
intercalibration. Currently, we have intercalibrated only the
monthly mean data. To intercalibrate the pentad data, I would
need to construct all the grid files for all satellites. That
would require construction of about 20 Gbytes of pentad grid data.
Interpolation of the pentad data may also be an issue; however,
this one month study indicates the UTH fields constructed from the
pentad data are reasonable filled. Intercalibration of the swath
data has not been worked out yet; therefore, UTH data constructed
with the swath data would be satellite dependent.
Darren Jackson
Last modified: Tue Aug 7 15:19:22 MDT 2001