Ground-Based Temperature and Humidity Profiling Using Spectral Infrared and Microwave Observations. Part I: Simulated Retrieval Performance in Clear-Sky Conditions

Two independent ground-based passive remote sensing methods are used to retrieve lower-tropospheric temperature and humidity profiles in clear-sky cases. A simulation study for two distinctly different climatic zones is performed to evaluate the accuracies of a standard microwave profiler [humidity and temperature profiler (HATPRO)] and an infrared spectrometer [Atmospheric Emitted Radiance Interferometer (AERI)] by applying a unified optimal estimation scheme to each instrument. Different measurement modes for each instrument are also evaluated in which the retrieval uses different spectral channels and observational view angles. In addition, both instruments have been combined into the same physically consistent retrieval scheme to evaluate the differences between a combined retrieval relative to the single-instrument retrievals. In general, retrievals derived from only infrared measurements yield superior RMS error and bias to retrievals derived only from microwave measurements. The AERI retrievals show high potential, especially for retrieving humidity in the boundary layer, where accuracies are on the order of 0.25–0.5 g m for a central European climate. In the lowest 500 m the retrieval accuracies for temperature from elevationscanning microwave measurements and spectral infrared measurements are very similar (0.2–0.6 K). Above this level the accuracies of the AERI retrieval are significantly more accurate (,1 K RMSE below 4 km). The inclusion of microwave measurements to the spectral infrared measurements within a unified physical retrieval scheme only results in improvements in the high-humidity tropical climate. However, relative to the HATPRO retrieval, the accuracy of the AERI retrieval is more sensitive to changes in the measurement uncertainty. The discussed results are drawn from a subset of ‘‘pristine’’ clear-sky cases: in the general case in which clouds and aerosols are present, the combined HATPRO–AERI retrieval algorithm is expected to yield much more beneficial results.