Abstract. As part of the international project entitled "African Monsoon Multidisciplinary Analysis (AMMA)", NAMMA (NASA AMMA) aimed to gain a better understanding of the relationship between the African Easterly Waves (AEWs), the Sahara Air Layer (SAL), and tropical cyclogenesis. The NAMMA airborne field campaign was based out of the Cape Verde Islands during the peak of the hurricane season, i.e., August and September 2006. Multiple Sahara dust layers were sampled during 62 encounters in the eastern portion of the hurricane main development region, covering both the eastern North Atlantic Ocean and the western Saharan desert (i.e., 5-22 • N and 10-35 • W). The centers of these layers were located at altitudes between 1.5 and 3.3 km and the layer thickness ranged from 0.5 to 3 km. Detailed dust microphysical and optical properties were characterized using a suite of in-situ instruments aboard the NASA DC-8 that included a particle counter, an Ultra-High Sensitivity Aerosol Spectrometer, an Aerodynamic Particle Sizer, a nephelometer, and a Particle Soot Absorption Photometer. The NAAMA sampling inlet has a size cut (i.e., 50% transmission efficiency size) of approximately 4 µm in diameter for dust particles, which limits the representativeness of the NAMMA observational findings. The NAMMA dust observations showed relatively low particle number densities, Correspondence to: G. Chen (gao.chen@nasa.gov) ranging from 268 to 461 cm −3 , but highly elevated volume density with an average at 45 µm 3 cm −3 . NAMMA dust particle size distributions can be well represented by tri-modal lognormal regressions. The estimated volume median diameter (VMD) is averaged at 2.1 µm with a small range of variation regardless of the vertical and geographical sampling locations. TheÅngström Exponent assessments exhibited strong wavelength dependence for absorption but a weak one for scattering. The single scattering albedo was estimated at 0.97 ± 0.02. The imaginary part of the refractive index for Sahara dust was estimated at 0.0022, with a range from 0.0015 to 0.0044. Closure analysis showed that observed scattering coefficients are highly correlated with those calculated from spherical Mie-Theory and observed dust particle size distributions. These values are generally consistent with literature values reported from studies with similar particle sampling size range.
The 2007 National Research Council (NRC) Decadal Survey on Earth Science and Applications from Space recommended Active Sensing of CO(2) Emissions over Nights, Days, and Seasons (ASCENDS) as a midterm, Tier II, NASA space mission. ITT Exelis, formerly ITT Corp., and NASA Langley Research Center have been working together since 2004 to develop and demonstrate a prototype laser absorption spectrometer for making high-precision, column CO(2) mixing ratio measurements needed for the ASCENDS mission. This instrument, called the multifunctional fiber laser lidar (MFLL), operates in an intensity-modulated, continuous wave mode in the 1.57 μm CO(2) absorption band. Flight experiments have been conducted with the MFLL on a Lear-25, UC-12, and DC-8 aircraft over a variety of different surfaces and under a wide range of atmospheric conditions. Very high-precision CO(2) column measurements resulting from high signal-to-noise ratio (>1300) column optical depth (OD) measurements for a 10 s (~1 km) averaging interval have been achieved. In situ measurements of atmospheric CO(2) profiles were used to derive the expected CO(2) column values, and when compared to the MFLL measurements over desert and vegetated surfaces, the MFLL measurements were found to agree with the in situ-derived CO(2) columns to within an average of 0.17% or ~0.65 ppmv with a standard deviation of 0.44% or ~1.7 ppmv. Initial results demonstrating ranging capability using a swept modulation technique are also presented.
Water vapor mass mixing ratio profiles from NASA’s Lidar Atmospheric Sensing Experiment (LASE) system acquired during the Atmospheric Radiation Measurement (ARM)–First International Satellite Cloud Climatology Project (ISCCP) Regional Experiment (FIRE) Water Vapor Experiment (AFWEX) are used as a reference to characterize upper-troposphere water vapor (UTWV) measured by ground-based Raman lidars, radiosondes, and in situ aircraft sensors over the Department of Energy (DOE) ARM Southern Great Plains (SGP) site in northern Oklahoma. LASE was deployed from the NASA DC-8 aircraft and measured water vapor over the ARM SGP Central Facility (CF) site during seven flights between 27 November and 10 December 2000. Initially, the DOE ARM SGP Cloud and Radiation Testbed (CART) Raman lidar (CARL) UTWV profiles were about 5%–7% wetter than LASE in the upper troposphere, and the Vaisala RS80-H radiosonde profiles were about 10% drier than LASE between 8 and 12 km. Scaling the Vaisala water vapor profiles to match the precipitable water vapor (PWV) measured by the ARM SGP microwave radiometer (MWR) did not change these results significantly. By accounting for an overlap correction of the CARL water vapor profiles and by employing schemes designed to correct the Vaisala RS80-H calibration method and account for the time response of the Vaisala RS80-H water vapor sensor, the average differences between the CARL and Vaisala radiosonde upper-troposphere water vapor profiles are reduced to about 5%, which is within the ARM goal of mean differences of less than 10%. The LASE and DC-8 in situ diode laser hygrometer (DLH) UTWV measurements generally agreed to within about 3%–4%. The DC-8 in situ frost point cryogenic hygrometer and Snow White chilled-mirror measurements were drier than the LASE, Raman lidars, and corrected Vaisala RS80H measurements by about 10%–25% and 10%–15%, respectively. Sippican (formerly VIZ Manufacturing) carbon hygristor radiosondes exhibited large variabilities and poor agreement with the other measurements. PWV derived from the LASE profiles agreed to within about 3% on average with PWV derived from the ARM SGP microwave radiometer. The agreement between the LASE and MWR PWV and the LASE and CARL UTWV measurements supports the hypotheses that MWR measurements of the 22-GHz water vapor line can accurately constrain the total water vapor amount and that the CART Raman lidar, when calibrated using the MWR PWV, can provide an accurate, stable reference for characterizing upper-troposphere water vapor
With nearly 1 million observations of column-mean carbon dioxide concentration (X CO 2 ) per day, the Orbiting Carbon Observatory 2 (OCO-2) presents exciting possibilities for monitoring the global carbon cycle, including the detection of subcontinental column CO 2 variations. While the OCO-2 data set has been shown to achieve target precision and accuracy on a single-sounding level, the validation of X CO 2 spatial gradients on subcontinental scales remains challenging. In this work, we investigate the use of an integrated path differential absorption (IPDA) lidar for evaluation of OCO-2 observations via NASA's Atmospheric Carbon and Transport (ACT)-America project. The project has completed eight clear-sky underflights of OCO-2 with the Multifunctional Fiber Laser Lidar (MFLL)-along with a suite of in situ instruments-giving a precisely colocated, high-resolution validation data set spanning nearly 3,800 km across four seasons. We explore the challenges and opportunities involved in comparing the MFLL and OCO-2 X CO 2 data sets and evaluate their agreement on synoptic and local scales. We find that OCO-2 synoptic-scale gradients generally agree with those derived from the lidar, typically to ±0.1 ppm per degree latitude for gradients ranging in strength from 0 to 1 ppm per degree latitude. CO 2 reanalysis products also typically agree to ±0.25 ppm per degree when compared with an in situ-informed CO 2 "curtain." Real X CO 2 features at local scales, however, remain challenging to observe and validate from space, with correlation coefficients typically below 0.35 between OCO-2 and the MFLL. Even so, ACT-America data have helped investigate interesting local X CO 2 patterns and identify systematic spurious cloud-related features in the OCO-2 data set.
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