Abstract. We compared CALIPSO column aerosol optical depths at 0.532 µm to measurements at 147 AERONET sites, synchronized to within 30 min of satellite overpass times during a 3-yr period. We found 677 suitable overpasses, and a CALIPSO bias of −13 % relative to AERONET for the entire data set; the corresponding absolute bias is −0.029, and the standard deviation of the mean (SDOM) is 0.014. Consequently, the null hypothesis is rejected at the 97 % confidence level, indicating a statistically significant difference between the datasets. However, if we omit CALIPSO columns that contain dust from our analysis, the relative and absolute biases are reduced to −3 % and −0.005 with a standard error of 0.016 for 449 overpasses, and the statistical confidence level for the null hypothesis rejection is reduced to 27 %. We also analyzed the results according to the six CALIPSO aerosol subtypes and found relative and absolute biases of −29 % and −0.1 for atmospheric columns that contain the dust subtype exclusively, but with a relatively high correlation coefficient of R = 0.58; this indicates the possibility that the assumed lidar ratio (40 sr) for the CALIPSO dust retrievals is too low. Hence, we used the AERONET size distributions, refractive indices, percent spheres, and forward optics code for spheres and spheroids to compute a lidar ratio climatology for AERONET sites located in the dust belt. The highest lidar ratios of our analysis occur in the non-Sahel regions of Northern Africa, where the median lidar ratio at 0.532 µm is 55.4 sr for 229 retrievals. Lidar ratios are somewhat lower in the African Sahel (49.7 sr for 929 retrievals), the Middle East (42.6 sr for 489 retrievals), and Kanpur, India (43.8 sr for 67 retrievals). We attribute this regional variability in the lidar ratio to the regional variability of the real refractive index of dust, as these two parameters are highly anti-correlated (correlation coefficients range from −0.51 to −0.85 for the various regions). The AERONET refractive index variability is consistent with the variability of illite concentration in dust across the dust belt.
We compared CALIPSO column aerosol optical depths at 0.532 μm to measurements at 147 AERONET sites, synchronized to within 30 min of satellite overpass times during a 3-yr period. We found 677 suitable overpasses, and a CALIPSO bias of −13% relative to AERONET for the entire data set; the corresponding absolute bias is −0.029, and the standard deviation of the mean (SDOM) is 0.014. Consequently, the null hypothesis is rejected at the 97% confidence level, indicating a statistically significant difference between the datasets. However, if we omit CALIPSO columns that contain dust from our analysis, the relative and absolute biases are reduced to −3% and −0.005 with a standard error of 0.016 for 449 overpasses, and the statistical confidence level for the null hypothesis rejection is reduced to 27%. We also analyzed the results according to the six CALIPSO aerosol subtypes and found relative and absolute biases of −29% and −0.1 for atmospheric columns that contain the dust subtype exclusively, but with a relatively high correlation coefficient of <i>R</i> = 0.58; this indicates the possibility that the assumed lidar ratio (40 sr) for the CALIPSO dust retrievals is too low. Hence, we used the AERONET size distributions, refractive indices, percent spheres, and forward optics code for spheres and spheroids to compute a lidar ratio climatology for AERONET sites located in the dust belt. The highest lidar ratios of our analysis occur in the non-Sahel regions of Northern Africa, where the median lidar ratio at 0.532 μm is 55.4 sr for 229 retrievals. Lidar ratios are somewhat lower in the African Sahel (49.7 sr for 929 retrievals), the Middle East (42.6 sr for 489 retrievals), and Kanpur, India (43.8 sr for 67 retrievals). We attribute this regional variability in the lidar ratio to the regional variability of the real refractive index of dust, as these two parameters are highly anti-correlated (correlation coefficients range from −0.51 to −0.85 for the various regions). The AERONET refractive index variability is consistent with the variability of illite concentration in dust across the dust belt
A technique we refer to as Elevation Information in Tail (EIT) has been developed to provide improved lidar altimetry from CALIPSO lidar data. The EIT technique is demonstrated using CALIPSO data and is applicable to other similar lidar systems with low-pass filters. The technique relies on an observed relation between the shape of the surface return signals (peak shape) and the detector photo-multiplier tube transient response (transient response tail). Application of the EIT to CALIPSO data resulted in an order of magnitude or better improvement in the CALIPSO land surface 30-meter elevation measurements. The results of EIT compared very well with the National Elevation Database (NED) high resolution elevation maps, and with the elevation measurements from the Shuttle Radar Topography Mission (SRTM).
As the potential impacts of global climate change become more clear [1], the need to determine the accuracy of climate prediction over decade-to-century time scales has become an urgent and critical challenge. The most critical tests of climate model predictions will occur using observations of decadal changes in climate forcing, response, and feedback variables. Many of these key climate variables are observed by remotely sensing the global distribution of reflected solar spectral and broadband radiance. These "reflected solar" variables include aerosols, clouds, radiative fluxes, snow, ice, vegetation, ocean color, and land cover. Achieving sufficient satellite instrument accuracy, stability, and overlap to rigorously observe decadal change signals has proven very difficult in most cases and has not yet been achieved in others [2].One of the earliest efforts to make climate quality observations was for Earth Radiation Budget: Nimbus 6/7 in the late 1970s, ERBE in the 1980s/90s, and CERES in 2000s are examples of the most complete global records. The recent CERES data products have carried out the most extensive intercomparisons because if the need to merge data from up to 11 instruments (CERES, MODIS, geostationary imagers) on 7 spacecraft (Terra, Aqua, and 5 geostationary) for any given month. In order to achieve climate calibration for cloud feedbacks, the radiative effect of clear-sky, all-sky, and cloud radiative effect must all be made with very high stability and accuracy. For shortwave solar reflected flux, even the 1% CERES broadband absolute accuracy (1-σ confidence bound) is not sufficient to allow gaps in the radiation record for decadal climate change. Typical absolute accuracy for the best narrowband sensors like SeaWiFS, MISR, and MODIS range from 2 to 4% (1-σ). IPCC greenhouse gas radiative forcing is ~ 0.6 Wm -2 per decade or 0.6% of the global mean shortwave reflected flux, so that a 50% cloud feedback would change the global reflected flux by ~ 0.3 Wm -2 or 0.3% per decade in broadband SW calibration change. Recent results comparing CERES reflected flux changes with MODIS, MISR, and SeaWiFS narrowband changes concluded that only SeaWiFS and CERES were approaching sufficient stability in calibration for decadal climate change [3].Results using deep convective clouds in the optically thick limit as a stability target may prove very effective for improving past data sets like ISCCP. Results for intercalibration of geostationary imagers to CERES using an entire month of regional nearly coincident data demonstrates new approaches to constraining the calibration of current geostationary imagers.The new Decadal Survey Mission CLARREO is examining future approaches to a "NIST-in-Orbit" approach of very high absolute accuracy reference radiometers that cover the full solar and infrared spectrum at high spectral resolution but at low spatial resolution. Sampling studies have shown that a precessing CLARREO mission could calibrate other geo and leo reflected solar radiation and thermal infrared ...
Sunlight contamination dominates the backscatter noise in space-based lidar measurements during daytime. The background scattered sunlight is highly variable and dependent upon the surface and atmospheric albedo. The scattered sunlight contribution to noise increases over land and snow surfaces where surface albedos are high and thus overwhelm lidar backscatter from optically thin atmospheric constituents like aerosols and thin clouds. In this work, we developed a novel lidar remote sensing concept that potentially can eliminate sunlight induced noise. The new lidar concept requires: (1) a transmitted laser light that carries orbital angular momentum (OAM); and (2) a photon sieve (PS) diffractive filter that separates scattered sunlight from laser light backscattered from the atmosphere, ocean and solid surfaces. The method is based on numerical modeling of the focusing of Laguerre-Gaussian (LG) laser beam and plane-wave light by a PS. The model results show that after passing through a PS, laser light that carries the OAM is focused on a ring (called "focal ring" here) on the focal plane of the PS filter, very little energy arrives at the center of the focal plane. However, scattered sunlight, as a plane wave without the OAM, focuses at the center of the focal plane and thus can be effectively blocked or ducted out. We also find that the radius of the "focal ring" increases with the increase of azimuthal mode (L) of LG laser light, thus increasing L can more effectively separate the lidar signal away from the sunlight noise.
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