Abstract
Abstract. A research algorithm is developed for noise evaluation and feature detection of the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) Level 1 (L1) backscatter data with an emphasis on cloud/aerosol features in the upper troposphere and lower stratosphere (UT/LS). CALIOP measurement noise of the version v2.01 and v2.02 L1 backscatter data aggregated to (5 km) horizontal resolution is analyzed with two approaches in this study. One is to compare the observed and modeled molecular scatter profiles by scaling the modeled profile (with a fitted scaling factor α) to the observed clear-sky backscatter profiles. This scaling α value is sensitive to errors in the calibrated backscatter and the atmospheric model used. Most of the nighttime 532-nm α values are close to unity, as expected, but an abrupt drop occurred in October 2008 in the daytime 532-nm α, which is likely indicative of a problem in the v2.02 daytime calibrated data. The 1064-nm night α is generally close to 2 while its day α is ~3. The other approach to evaluate the lidar measurement noise is to use the calibrated lidar backscatter data at altitudes above 19 km. With this method, the 532-nm and 1064-nm measurement noises are analyzed and characterized individually for each profile in terms of the mean (μ) and standard deviation (σ), showing larger σ values in general over landmasses or bright surfaces during day and in radiation-hard regions during night. A significant increasing trend is evident in the nighttime 1064-nm σ, which is likely responsible for the increasing difference between the feature occurrence frequencies (532-nm vs. 1064-nm) derived from this study. For feature detection with the research algorithm, we apply a σ–based method to the aggregated L1 data. The derived morphology of feature occurrence frequency is in general agreement with that obtained from the Level 2 (L2) 05 km_CLAY+05 km_ALAY products at 5 km horizontal resolution. Finally, a normalized probability density function (PDF) method is employed to evaluate the day-night backscatter data in which noise levels are largely different. CALIOP observations reveal a higher probability of daytime cloud/aerosol occurrence than nighttime in the tropical UT/LS region for 532-nm total backscatters >0.01 km−1 sr−1.
Highlights
Cirrus and aerosol properties in the upper troposphere and lower stratosphere (UT/LS) region play an important role in climate-feedback processes (e.g., Jensen et al, 1996; Dessler et al, 2008)
Because the estimated α values are noisy and can be affected by both measurement and model errors, which are non-trivial to distinguish at this point, in the analysis hereafter we ignore the seasonal variations in the fitted α and use α = 1.0 to remove the background molecular backscatter to compare the feature detection from this work with the Level 2 (L2) layer products
Noise characteristics of the calibrated CALIOP 532-nm and 1064-nm attenuated backscatters were investigated with two approaches
Summary
Cirrus and aerosol properties in the upper troposphere and lower stratosphere (UT/LS) region play an important role in climate-feedback processes (e.g., Jensen et al, 1996; Dessler et al, 2008). For unbiased cloud/aerosol detection, one would like to have a sensor with stable noise so that a constant threshold could be applied globally for feature detection It is nearly the case for CloudSat reflectivity noise (Tanelli et al, 2008), but not for the CALIOP noise. Through a better understanding and characterization of the CALIOP measurement noise, we hope to apply our research algorithm for joint analyses of CALIOP and other A-train observations (e.g., Aura MLS) in UT/LS cloud/aerosol studies To achieve this goal, we first reduce the L1 CALIOP data to a manageable size by aggregating them to a coarser spatial resolution, and estimate the measurement noise in terms of mean (μ) and standard deviation (σ ). Some of the initial results for noise evaluation and feature detection with the v2.01 and v2.02 CALIOP L1 and L2 data at 5 km horizontal resolution are presented here
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