ALBERT

Description: In support of the Ice, Cloud, and land Elevation Satellite (ICESat)-II mission, this paper studies the bias in surface-elevation measurements caused by undetected thin clouds. The ICESat-II satellite may only have a 1064-nm single-channel lidar onboard. Less sensitive to clouds than the 532-nm channel, the 1064-nm channel tends to miss thin clouds. Previous studies have demonstrated that scattering by cloud particles increases the photon-path length, thus resulting in biases in ice-sheet-elevation measurements from spaceborne lidars. This effect is referred to as atmospheric path delay. This paper complements previous studies in the following ways: First, atmospheric path delay is estimated over the ice sheets based on cloud statistics from the Geoscience Laser Altimeter System onboard ICESat and the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra and Aqua. Second, the effect of cloud particle size and shape is studied with the state-of-the-art phase functions developed for MODIS cirrus- cloud microphysical model. Third, the contribution of various orders of scattering events to the path delay is studied, and an analytical model of the first-order scattering contribution is developed. This paper focuses on the path delay as a function of telescope field of view (FOV). The results show that reducing telescope FOV can significantly reduce the expected path delay. As an example, the average path delays for FOV = 167 microrad (a 100-m-diameter circle on the surface) caused by thin undetected clouds by the 1064-nm channel over Greenland and East Antarctica are illustrated.

Description: African dust can transport across the tropical Atlantic and reach the Amazon basin, exerting far-reaching impacts on climate in downwind regions. The transported dust influences the surface-atmosphere interactions and cloud and precipitation processes through perturbing the surface radiative budget and atmospheric radiative heating and acting as cloud condensation nuclei and ice nuclei. Dust also influences biogeochemical cycle and climate through providing nutrients vital to the productivity of ocean biomass and Amazon forests. Assessing these climate impacts relies on an accurate quantification of dust transport and deposition. Currently model simulations show extremely large diversity, which calls for a need of observational constraints. Kaufman et al. (2005) estimated from MODIS aerosol measurements that about 144 Tg of dust is deposited into the tropical Atlantic and 50 Tg of dust into the Amazon in 2001. This estimated dust import to Amazon is a factor of 3-4 higher than other observations and models. However, several studies have argued that the oversimplified characterization of dust vertical profile in the study would have introduced large uncertainty and very likely a high bias. In this study we quantify the trans-Atlantic dust transport and deposition by using 7 years (2007-2013) observations from CALIPSO lidar. CALIPSO acquires high-resolution aerosol extinction and depolarization profiles in both cloud-free and above-cloud conditions. The unique CALIPSO capability of profiling aerosols above clouds offers an unprecedented opportunity of examining uncertainties associated with the use of MODIS clear-sky data. Dust is separated from other types of aerosols using the depolarization measurements. We estimated that on the basis of 7-year average, 118142 Tg of dust is deposited into the tropical Atlantic and 3860 Tg of dust into the Amazon basin. Substantial interannual variations are observed during the period, with the maximum to minimum ratio of about 1.6 and 2.5 for the deposition to the tropical Atlantic and Amazon, respectively. The MODIS-based estimates appear to fall within the range of CALIPSO-based estimates; and the difference between MODIS and CALIPSO estimates can be largely attributed to the interannual variability, which is corroborated by long-term surface dust concentration observations in the tropical Atlantic. Considering that CALIPSO generally tends to underestimate the aerosol loading, our estimate is likely to represent a low bound for the dust transport and deposition estimate. The finding suggests that models have substantial biases and considerable effort is needed to improve model simulations of dust cycle.

Description: The Greenland and Antarctic ice sheets, which contain enough ice to raise sea level by about 7 and 60 m, respectively, are losing mass at an increasing rate. To acquire continuous information of the cryosphere, after the Ice, Cloud, and land Elevation Satellite (ICESat) (2003-2010), NASA is actively planning for the ICESat-2 mission. Both ICESat and ICESat-2 are space-borne lidar altimetry systems. The systems measure the time of flight of the arriving photons that are reflected by the surface to deduce the elevation of the underlying terrain. As one of NASA's top priority missions, ICESat-2 is scheduled to launch in 2016. One of the major science goals of ICESat-2 is to quantify the ice sheet mass balance to determine its contributions to the sea level change and its impacts on ocean circulation (Abdalati et al. 2010). Compared to ICESat, which operates at 40 Hz and records the reflected laser energy as a waveform, the significantly improved ICESat-2 lidar employs a 532 nm micro-pulse photon counting system that operates at a high frequency of 10kHz with single photon detectability (Yang et al. 2012). To achieve its science goals, ICESat-2 requires the ability of detecting the elevation change with an accuracy of 0.2 cm/year over the entire ice sheet. Since every photon emitted by the lidar system will travel through the atmosphere, clouds can certainly affect the flight time of the arriving photons. Forward scattering by cloud particles increases the photon path length, thus resulting in biases in ice sheet elevation measurements known as atmospheric path delay (Duta et al. 2001, Yang et al. 2010, 2011). To ensure the accuracy of ICESat-2 surface elevation measurements, it is critical to understand how clouds would affect the travel time of arriving photons. In this talk, we will first present a framework that simulates the behavior of a space-borne 532 mn micro-pulse photon counting lidar in cloudy and clear atmospheres. To investigate the process of laser propagation through clouds, a 3-D Monte Carlo radiative transfer model is used to simulate the photon path distribution and the Poisson distribution is adopted for the number of photon returns. Since the photon counting system only registers the time of the first arriving photon within the detector "dead time", the retrieved average surface elevation tends to bias towards higher values. This is known as the first photon bias. With the scenarios simulated here, the first photon bias for clear sky is about 6.5 cm. Clouds affect surface altimetry in two ways: (1) cloud attenuation lowers the average number of arriving photons and hence reduces the first photon bias; (2) cloud forward scattering increases the photon path length and makes the surface appear further away from the satellite. Compared to clear sky, the average surface elevation detected by the photon counting system for cloudy sky with optical depth 1.0 is 4.0 to 6.0 cm lower for the simulations conducted. The effect of surface roughness on the accuracy of elevation retrievals will also discussed.

Description: The separation of cloud and clear scenes is usually one of the first steps in satellite data analysis. Before deriving a geophysical product, almost every satellite mission requires a cloud mask to label a scene as either clear or cloudy through a cloud detection procedure. For clear scenes, products such as surface properties may be retrieved; for cloudy scenes, scientist can focus on studying the cloud properties. Hence the quality of cloud detection directly affects the quality of most satellite operational and research products. This is certainly true for the Ice, Cloud, and land Elevation Satellite-2 (lCESat-2), which is the successor to the ICESat-l. As a top priority mission, ICESat-2 will continue to provide measurements of ice sheets and sea ice elevation on a global scale. Studies have shown that clouds can significantly affect the accuracy of the retrieved results. For example, some of the photons (a photon is a basic unit of light) in the laser beam will be scattered by cloud particles on its way. So instead of traveling in a straight line, these photons are scattered sideways and have traveled a longer path. This will result in biases in ice sheet elevation measurements. Hence cloud screening must be done and be done accurately before the retrievals.

Description: Planned to fly in 2014, the Deep Space Climate Observatory (DSCOVR) would see the whole sunlit half of the Earth from the L 1 Lagrangian point and would provide simultaneous data on cloud and aerosol properties with its Earth Polychromatic Imaging Camera (EPIC). EPIC images the Earth on a 2Kx2K CCD array, which gives a horizontal resolution of about 10 km at nadir. A filter-wheel provides consecutive images in 10 spectral channels ranging from the UV to the near-IR, including the oxygen A and B bands. This paper presents a study of retrieving cloud height with EPIC's oxygen A and B bands. As the first step, we analyzed the effect of cloud optical and geometrical properties, sun-view geometry, and surface type on the cloud height determination. Second, we developed two cloud height retrieval algorithms that are based on the Mixed Lambertian-Equivalent Reflectivity (MLER) concept: one utilizes the absolute radiances at the Oxygen A and B bands and the other uses the radiance ratios between the absorption and reference channels of the two bands. Third, we applied the algorithms to the simulated EPIC data and to the data from SCanning Imaging Absorption SpectroMeter for Atmospheric CartograpHY (SCIAMACHY) observations. Results show that oxygen A and B bands complement each other: A band is better suited for retrievals over ocean, while B band is better over vegetated land due to a much darker surface. Improvements to the MLER model, including corrections to surface contribution and photon path inside clouds, will also be discussed.