5 .1 Introduction
5.2 TES Data
Currently the available TES atmospheric data is limited to 25 sols of data around 15 = 200 (early fall). This is 17 orbits (orbits 20 through 36) during the first hiatus period.
The TES data is a series of temperature measurements at fixed pressures obtained by inverting the shape of the 15 J..Lm C02 line. Each spectrum is inverted to produce 10 to 20 temperature measurements in a vertical column. One column is termed a
"profile." During these observations, the spacecraft was in a highly elliptical 35 hour
orbit. Near the periapse of each orbit, the instrument records a very concentrated swath of data (up to 1100 profiles per 6 minute time-step). Figure 5.1 shows the number of profiles per time-step for the full set of data available. Sol 0 is the first sol that contains observations. The timing of the observations is selected so that this is the same Ls as the actual observations and so that the local time of day is correct for the observations. While the footprint of the instrument varies considerably over the orbit, it is significantly smaller than the MGCM grid and therefore the actual footprint is ignored.
During the actual assimilation (as opposed to assimilation of synthetic data), it is necessary to filter the data. This is done by removing any profile where 'ii!j-'I!~
>
50K for at least one point in the profile. Since there appears to be significant observational error correlation within a profile, if any point in the profile is problematic, the rest are also problematic. Points with large differences cause problems because they result in large forcing effects which tend to destabilize the system. A look at the rv 170 profiles that were not assimilated (less than 0.1% of the all the profiles) shows thatC/) Q)
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Figure 5.1: Data Density for Available TES Data
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This indicates the total number of TES profiles available for each 6 minute time-step of the MGCM. The close passes are the locations with the highest density and mark the beginning of each orbit.
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they are either cases where the inversion algorithm used to generate the profiles failed or the instrumental noise was extreme (we cannot actually distinguish between the two). Even though very few profiles are removed, the net effect is large. Without removing those profiles, the assimilation only produces marginal improvements in the analysis model (the process would change the model, but apparently not in a useful manner). Roughly 1/3 of the filtered profiles are in a tight cluster at very high altitudes (surface pressures of"' 1 mbar) on the flank of Olympus Mons. The high altitude profiles probably reflect the fact that the inversion process has problems with very low surface pressures. The other 2/3 of the profiles are scattered around the planet, somewhat grouped near the equator, with no apparent pattern.
The choice of 50K is somewhat arbitrary. It was selected as being small enough to remove the worst of the problematic profiles (as measured by the improvements in the analysis model), but not so small that valid profiles (i.e., ones with significant contributions) are removed. Most of the removed profiles either show the signs of low surface pressure inversion problems or unphysical behavior probably due to in- stabilities in the inversion algorithm (for example, many have very cold temperatures, as much as 30K below the C02 condensation temperature). A few profiles appear fairly reasonable but are either very cold or very warm. Since there are very few of them and they are not particularly clustered, it is not clear if these are intense local phenomena, instrument noise or inversion problems. While the 50K filter catches many of the worst profiles, there are several other profiles that were very likely also problems (often having differences of 40K or larger). Unfortunately, especially during the first few sols of the assimilation, there are some valid large differences, making it difficult to use a smaller filtering value.
Attempts to pre-filter the data, using some simple rejection criteria, were not successful. At best, they performed about as well as the simple 50K cutoff, and were generally a bit worse. The two criteria used were profiles with temperatures more than 5K below the condensation temperature (which removed very few profiles) and those with surface pressures less than 2.5 mbar. The latter category was probably overly broad and removed a significant number of valid profiles, and since they were
concentrated in regions, it was sufficient to hamper the assimilation. We currently hope that work on the inversion side of the process (where error information about the inversion is available) can do a better job of removing profiles and improve the assimilation. At present the only solution would be to manually check all 177,000 profiles.
In order to insure that the MGCM matches the actual observations, the MGCM was run with conditions as near to that of the data as possible. This was done in several ways. First, the season of the initial model states was identical to that of the data. This insures, to the extent that the MGCM seasons are right, that the data and model are similar. The same season was also used to build gains covering the period of the observations. Secondly, the MGCM time-it primarily uses the hour since the cold start-of the observations was selected to insure that the local time of day was correct for each observation. This allows the observations to modify the correct state of the atmosphere. Finally, the opacity (or dustiness) used in the model was twice the 9 J-tm to 10 J-tm infrared opacities estimated from the TES observations [Kieffer et al., 1992]. This results in T = 0.32. While there may have been some spatial and temporal variability of the dust seen by TES [Smith et al., 1998), the MGCM is designed to have a constant mass density of dust.