Polar regions are characterized by their remoteness, making measurements challenging, but an improved knowledge of clouds and radiation is necessary to understand polar climate change. Infrared radiance spectrometers can operate continuously from the surface and have low power requirements relative to active sensors. Here we explore the feasibility of retrieving cloud height with an infrared spectrometer that would be designed for use in remote polar locations. Using a wide variety of simulated spectra of mixed-phase polar clouds at varying instrument resolutions, retrieval accuracy is explored using the CO2 slicing/sorting and the minimum local emissivity variance (MLEV) methods. In the absence of imposed errors and for clouds with optical depths greater than similar to aEuro-0.3, cloud-height retrievals from simulated spectra using CO2 slicing/sorting and MLEV are found to have roughly equivalent high accuracies: at an instrument resolution of 0.5aEuro-cm(-1), mean biases are found to be similar to aEuro-0.2aEuro-km for clouds with bases below 2 and -0.2aEuro-km for higher clouds. Accuracy is found to decrease with coarsening resolution and become worse overall for MLEV than for CO2 slicing/sorting; however, the two methods have differing sensitivity to different sources of error, suggesting an approach that combines them. For expected errors in the atmospheric state as well as both instrument noise and bias of 0.2aEuro-mW/(m(2)aEuro-sraEuro-cm(-1)), at a resolution of 4aEuro-cm(-1), average retrieval errors are found to be less than similar to aEuro-0.5aEuro-km for cloud bases within 1aEuro-km of the surface, increasing to similar to aEuro-1.5aEuro-km at 4aEuro-km. This sensitivity indicates that a portable, surface-based infrared radiance spectrometer could provide an important complement in remote locations to satellite-based measurements, for which retrievals of low-level cloud are challenging.

https://doi.org/10.5194/amt-9-3641-2016

Journal Article

NOAA Cooperative Institutes
Office of Oceanic and Atmospheric Research (OAR)