Daytime SABER/TIMED observations of water vapor in the mesosphere: retrieval approach and first results
Abstract. This paper describes a methodology for water vapor retrieval in the mesosphere-lower thermosphere (MLT) using 6.6 μm daytime broadband emissions measured by SABER, the limb scanning infrared radiometer on board the TIMED satellite. Particular attention is given to accounting for the non-local thermodynamic equilibrium (non-LTE) nature of the H2O 6.6 μm emission in the MLT. The non-LTE H2O(ν2) vibrational level populations responsible for this emission depend on energy exchange processes within the H2O vibrational system as well as on interactions with vibrationally excited states of the O2, N2, and CO2 molecules. The rate coefficients of these processes are known with large uncertainties that undermines the reliability of the H2O retrieval procedure. We developed a methodology of finding the optimal set of rate coefficients using the nearly coincidental solar occultation H2O density measurements by the ACE-FTS satellite and relying on the better signal-to-noise ratio of SABER daytime 6.6 μm measurements. From this comparison we derived an update to the rate coefficients of the three most important processes that affect the H2O(ν2) populations in the MLT: a) the vibrational-vibrational (V–V) exchange between the H2O and O2 molecules; b) the vibrational-translational (V–T) process of the O2(1) level quenching by collisions with atomic oxygen, and c) the V–T process of the H2O(010) level quenching by collisions with N2, O2, and O. Using the advantages of the daytime retrievals in the MLT, which are more stable and less susceptible to uncertainties of the radiance coming from below, we demonstrate that applying the updated H2O non-LTE model to the SABER daytime radiances makes the retrieved H2O vertical profiles in 50–85 km region consistent with climatological data and model predictions. The H2O retrieval uncertainties in this approach are about 10% at and below 70 km, 20% at 80 km, and 30% at 85 km altitude.
