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Passive ocean color images have provided a sustained synoptic view of the distribution of ocean optical properties and color and biogeochemical parameters for the past 20-plus years. These images have revolutionized our view of the ocean. Remote sensing of ocean color has relied on measurements of the radiance emerging at the top of the atmosphere, thus neglecting the polarization and the vertical components. Ocean color remote sensing utilizes the intensity and spectral variation of visible light scattered upward from beneath the ocean surface to derive concentrations of biogeochemical constituents and inherent optical properties within the ocean surface layer. However, these measurements have some limitations. Specifically, the measured property is a weighted-integrated value over a relatively shallow depth, it provides no information during the night and retrievals are compromised by clouds, absorbing aerosols, and low Sun zenithal angles. In addition, ocean color data provide limited information on the morphology and size distribution of marine particles. Major advances in our understanding of global ocean ecosystems will require measurements from new technologies, specifically lidar and polarimetry. These new techniques have been widely used for atmospheric applications but have not had as much as interest from the ocean color community. This is due to many factors including limited access to in-situ instruments and/or space-borne sensors and lack of attention in university courses and ocean science summer schools curricula. However, lidar and polarimetry technology will complement standard ocean color products by providing depth-resolved values of attenuation and scattering parameters and additional information about particle morphology and chemical composition. This review aims at presenting the basics of these techniques, examples of applications and at advocating for the development of in-situ and space-borne sensors. Recommendations are provided on actions that would foster the embrace of lidar and polarimetry as powerful remote sensing tools by the ocean science community.
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Intensive cloud lidar observations have been made by NASA Langley Research Center during the two observation phases of the ECLIPS project. Less intensive but longer term observations have been conducted as part of the FIRE extended time observation (ETO) program since 1987. We present a preliminary analysis of the vertical distribution of clouds based on these observations. A mean cirrus thickness of just under 1 km has been observed with a mean altitude of about 80 percent of the tropopause height. Based on the lidar data, cirrus coverage was estimated to be just under 20 percent, representing roughly 50 percent of all clouds studied. Cirrus was observed to have less seasonal variation than lower clouds. Mid-level clouds are found to occur primarily in association with frontal activity.
Lidar and passive radiance sensing data on cirrus clouds gathered in instrumented high altitude aircraft flights are analyzed, with emphasis on cirrus emittance characteristics. The data were collected with a U-2 aircraft flying at about 19 km altitude. Lidar data defined the vertical structure of the cloud which, when combined with independent atmospheric temperature data, permitted radiance to be derived from thermal radiance data. Sample return lidar images are provided of cirrus and cirrostratus clouds, along with the calculated emittance data.
Lidar backscatter and wind retrievals of the planetary boundary layer height (PBLH) are assimilated into 22 hourly forecasts from the NASA Unified - Weather and Research Forecast (NU-WRF) model during the Plains Elevated Convection Convection at Night (PECAN) campaign on July 11, 2015 in Greensburg, Kansas, using error statistics collected from the model profiles to compute the necessary covariance matrices. Two separate forecast runs using different PBL physics schemes were employed, and comparisons with 6 independent radiosonde profiles were made for each run. Both of the forecast runs accurately predicted the PBLH and the state variable profiles within the planetary boundary layer during the early morning, and the assimilation had a small impact during this time. In the late afternoon, the forecast runs showed decreased accuracy as the convective boundary layer developed. However, assimilation of the Doppler lidar PBLH observations were found to improve the temperature and V velocity profiles relative to independent radiosonde profiles. Water vapor was overcorrected, leading to increased differences with independent data. Errors in the U velocity were made slightly larger. The computed forecast error covariances between the PBLH and state variables were found to rise in the late afternoon, leading to the larger improvements in the afternoon. This work represents the first effort to assimilate PBLH into forecast states using ensemble methods.
Lidar observations obtained from January 24 to February 2, 1989, during the Airborne Arctic Stratospheric expedition (AASE) mission further support the existence of two distinct classes (Types 1 and 2) of polar stratospheric clouds (PSCs). Most of the Type 1 PSCs observed were formed by rapid adiabatic cooling and exhibited very low depolarization ratios and low-to-intermediate scattering ratios. Type 2 PSCs were observed in regions of lowest temperature and showed much larger depolarization and scattering ratios, as would be expected from larger ice crystals. PSCs with low scattering ratios but moderate depolarization ratios were observed near the center of the vortex on one flight. These may have been either sparse Type 2 PSCs or Type 1 PSCs formed by less rapid cooling.
Lidar is an optical radar technique employing laser energy. Variations in signal intensity as a function of range provide information on atmospheric constituents, even when these are too tenuous to be normally visible. The theoretical and technical basis of the technique is described and typical values of the atmospheric optical parameters given. The significance of these parameters to atmospheric and meteorological problems is discussed. While the basic technique can provide valuable information about clouds and other material in the atmosphere, it is not possible to determine particle size and number concentrations precisely. There are also inherent difficulties in evaluating lidar observations. Nevertheless, lidar can provide much useful information as is shown by illustrations. These include lidar observations of: cirrus cloud, showing mountain wave motions; stratification in clear air due to the thermal profile near the ground; determinations of low cloud and visibility along an air-field approach path; and finally the motion and internal structure of clouds of tracer materials (insecticide spray and explosion-caused dust) which demonstrate the use of lidar for studying transport and diffusion processes.
Observational constraints on the change in the radiative energy budget caused by the presence of aerosols, i.e. the aerosol direct radiative effect (DRE), have recently been made using observations from the Cloud- Aerosol Lidar and Infrared Pathfinder Satellite (CALIPSO). CALIPSO observations have the potential to provide improved global estimates of aerosol DRE compared to passive sensor-derived estimates due to CALIPSO's ability to perform vertically-resolved aerosol retrievals over all surface types and over cloud. In this study we estimate the uncertainties in CALIPSO-inferred aerosol DRE using multiple years of observations from the Atmospheric Radiation Measurement (ARM) program's Raman lidars (RL) at mid-latitude and tropical sites. Examined are assumptions about the ratio of extinction-to-backscatter (i.e. the lidar ratio) made by the CALIPSO retrievals, which are needed to retrieve the aerosol extinction profile. The lidar ratio is shown to introduce minimal error in the mean aerosol DRE at the top-of-atmosphere and surface. It is also shown that CALIPSO is unable to detect all radiatively-significant aerosol, resulting in an underestimate in the magnitude of the aerosol DRE by 30â€"50%. Therefore, global estimates of the aerosol DRE inferred from CALIPSO observations are likely too weak.