An extensive validation program was conducted after launch to confirm the accuracy of the measurements. The dominant wind field, the first one observed by WINDII, was that of the migrating diurnal tide at the equator. The overall most notable WINDII contribution followed from this: determining the influence of dynamics on the transport of atmospheric species. Currently, nonmigrating tides are being studied in the thermosphere at both equatorial and high latitudes. Other aspects investigated included solar and geomagnetic influences, temperatures from atmospheric-scale heights, nitric oxide concentrations, and the occurrence of polar mesospheric clouds. The results of these observations are reviewed from a perspective of 20 years. A future perspective is then projected, involving more recently developed concepts. It is intended that this description will be helpful for those planning future missions.
[1] The Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) on board the ENVISAT and the Global Positioning System (GPS) receiver on the Challenging Mini-Satellite Payload (CHAMP) provide temperature profiles by limb-viewing midinfrared emission and radio occultation (RO) measurements, respectively. The MIPAS temperatures retrieved at the Institut für Meteorologie und Klimaforschung (IMK) are compared with the GPS-RO/CHAMP observations derived at Jet Propulsion Laboratory (JPL) and GeoForschungsZentrum (GFZ) Potsdam. The three data sets show generally good agreement. The global mean differences averaged between 8 and 30 km in 14 days of September/October 2002 are À0.44 ± 0.02 K and 0.07 ± 0.02 K for MIPAS/GPS-RO JPL and GFZ comparisons, respectively. The MIPAS global mean temperatures below 25 km are slightly lower than those of GPS-RO JPL and GFZ by less than 1 K and 0.2 K, respectively. Above 25 km, the MIPAS temperatures are higher than the JPL and GFZ data, in particular near both poles and the equator, with maxima of 1 K for JPL and 1.5 K for GFZ at 30 km. The standard deviations are $2-4 K. Possible explanations for the observed differences include (1) effect of spatial and temporal mismatch between the correlative measurements on the observed standard deviations, in particular in regions and episodes of enhanced wave activity; (2) a negative bias in GPS-RO/CHAMP temperatures in regions of increased humidity; (3) a mapping of initialization temperature profiles on GPS-RO/CHAMP retrievals at altitudes where low refraction contains no information on air density; and (4) measurement errors of both instruments, particularly the errors due to insufficient knowledge of the instrument line shape and spectroscopy in current MIPAS retrievals.
The TanSat carbon satellite is to be launched at the end of 2016. In order to verify the performance of its instruments, a flight test of TanSat instruments was conducted in Jilin Province in September, 2015. The flight test area covered a total area of about 11,000 km2 and the underlying surface cover included several lakes, forest land, grassland, wetland, farmland, a thermal power plant and numerous cities and villages. We modeled the column-average dry-air mole fraction of atmospheric carbon dioxide (XCO2) surface based on flight test data which measured the near- and short-wave infrared (NIR) reflected solar radiation in the absorption bands at around 760 and 1610 nm. However, it is difficult to directly analyze the spatial distribution of XCO2 in the flight area using the limited flight test data and the approximate surface of XCO2, which was obtained by regression modeling, which is not very accurate either. We therefore used the high accuracy surface modeling (HASM) platform to fill the gaps where there is no information on XCO2 in the flight test area, which takes the approximate surface of XCO2 as its driving field and the XCO2 observations retrieved from the flight test as its optimum control constraints. High accuracy surfaces of XCO2 were constructed with HASM based on the flight’s observations. The results showed that the mean XCO2 in the flight test area is about 400 ppm and that XCO2 over urban areas is much higher than in other places. Compared with OCO-2’s XCO2, the mean difference is 0.7 ppm and the standard deviation is 0.95 ppm. Therefore, the modelling of the XCO2 surface based on the flight test of the TanSat instruments fell within an expected and acceptable range.
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