The use of moonlight reflectance for improving cloud parameters at night

Abstract:

The VIIRS “Day/Night Band” (DNB; Lee et al., 2006) channel is capable of measuring extremely low levels visible-band light down to the levels of moonlight reflectance (e.g., on the order of mW m^-2 sr ^-1µm ^-1, several orders of magnitude fainter than conventional daytime visible light measurements) with notable improvements to its predecessor in terms of calibration, radiometric and spatial resolution.

Daytime retrievals of cloud properties have been conducted routinely from visible channels of an assortment of operational and research grade optical sensors for decades. These observations are providing a satellite-based global data record of increasing relevance to climate change monitoring (where clouds are thought to play an integral feedback role). The lack of complete diurnal information regarding these key parameters presents an important shortfall from the MODIS or GOES sensors.

Based on a lunar irradiance and reflectance model for the DNB channel (Miller and Turner, 2009), we have developed a new cloud optical properties retrieval scheme applicable to nighttime observations referred to nighttime lunar cloud optical and microphysical properties (NLCOMP), published in Walther et al. 2013. NLCOMP is implemented in the cloud retrieval scheme inside of the Pathfinder Atmospheres Extended (PATMOS-x) processing system and routine global processing has been demonstrated.

We will present validation studies and multi-months survey of optical cloud properties during night from NLCOMP,and in comparison to the daytime equivalent. We hope that on a long term, NLCOMP can help to extend regional and global ISCCP-like climatologies of cloud optical depth and effective particle size to fill the nighttime and polar winter gap of cloud observations.

The moonlight reflectance also add important input to the current cloud detection and cloud height retrievals at night. We will present the positive impact on retrieval performance by showing case studies and long-term statistics of performance.