ALBERT

Description: Laser beams emitted from the Geoscience Laser Altimeter System (GLAS), as well as other space-borne laser instruments, can only penetrate clouds to a limit of a few optical depths. As a result, only optical depths of thinner clouds (〈 about 3 for GLAS) are retrieved from the reflected lidar signal. This paper presents a comprehensive study of possible retrievals of optical depth of thick clouds using solar background light and treating GLAS as a solar radiometer. To do so we first calibrate the reflected solar radiation received by the photon-counting detectors of GLAS' 532 nm channel, which is the primary channel for atmospheric products. The solar background radiation is regarded as a noise to be subtracted in the retrieval process of the lidar products. However, once calibrated, it becomes a signal that can be used in studying the properties of optically thick clouds. In this paper, three calibration methods are presented: (I) calibration with coincident airborne and GLAS observations; (2) calibration with coincident Geostationary Operational Environmental Satellite (GOES) and GLAS observations of deep convective clouds; (3) calibration from the first principles using optical depth of thin water clouds over ocean retrieved by GLAS active remote sensing. Results from the three methods agree well with each other. Cloud optical depth (COD) is retrieved from the calibrated solar background signal using a one-channel retrieval. Comparison with COD retrieved from GOES during GLAS overpasses shows that the average difference between the two retrievals is 24%. As an example, the COD values retrieved from GLAS solar background are illustrated for a marine stratocumulus cloud field that is too thick to be penetrated by the GLAS laser. Based on this study, optical depths for thick clouds will be provided as a supplementary product to the existing operational GLAS cloud products in future GLAS data releases.

Description: Spatiotemporal data from satellite remote sensing and surface meteorology networks have made it possible to continuously monitor global plant production, and to identify global trends associated with land cover/use and climate change. Gross primary production (GPP) and net primary production (NPP) are routinely derived from the MOderate Resolution Imaging Spectroradiometer (MODIS) onboard satellites Terra and Aqua, and estimates generally agree with independent measurements at validation sites across the globe. However, the accuracy of GPP and NPP estimates in some regions may be limited by the quality of model input variables and heterogeneity at fine spatial scales. We developed new methods for deriving model inputs (i.e., land cover, leaf area, and photosynthetically active radiation absorbed by plant canopies) from airborne laser altimetry (LiDAR) and Quickbird multispectral data at resolutions ranging from about 30 m to 1 km. In addition, LiDAR-derived biomass was used as a means for computing carbon-use efficiency. Spatial variables were used with temporal data from ground-based monitoring stations to compute a six-year GPP and NPP time series for a 3600 ha study site in the Great Lakes region of North America. Model results compared favorably with independent observations from a 400 m flux tower and a process-based ecosystem model (BIOME-BGC), but only after removing vapor pressure deficit as a constraint on photosynthesis from the MODIS global algorithm. Fine resolution inputs captured more of the spatial variability, but estimates were similar to coarse-resolution data when integrated across the entire vegetation structure, composition, and conversion efficiencies were similar to upland plant communities. Plant productivity estimates were noticeably improved using LiDAR-derived variables, while uncertainties associated with land cover generalizations and wetlands in this largely forested landscape were considered less important.

Description: Seasonal forecasts made by coupled atmosphere-ocean general circulation models (GCMs) are increasingly able to provide skillful forecasts of climate anomalies. At some centers, the capabilities of these models are being expanded to represent carbon-climate feedbacks including ocean biogeochemistry (OB), terrestrial biosphere (TB) interactions, and fires. These advances raise the question of whether such models can support skillful forecasts of carbon fluxes.Here, we examine whether land and ocean carbon flux anomalies associated with the 2015-16 El Nino could have been predicted months in advance. This El Nino was noteworthy for the magnitude of the ocean temperature perturbation, the skill with which this perturbation was predicted, and the extensive satellite observations that can be used to track its impact. We explore this topic using NASA's Goddard Earth Observing System (GEOS) model, which routinely produces an ensemble of seasonal climate forecasts, and a suite of offline dynamical and statistical models that estimate carbon flux processes. Using GEOS forecast fields from 2015-16 to force flux model hindcasts shows that these models are able to reproduce significant features observed by satellites. Specifically, OB hindcasts are able to predict anomalies in chlorophyll distributions with lead times of 3-4 months. The ability of TB hindcasts to reproduce NDVI anomalies is driven by the skill of the climate forecast, which is greatest at short lead times over tropical landmasses. Statistical fire forecasts driven by ocean climate indices are able to predict burned area in the tropics with lead times of 3-12 months. We also integrate the ocean and land hindcast fluxes into the GEOS GCM to examine the magnitude of the atmospheric carbon dioxide anomaly and compare with satellite and ground-based observations.While seasonal forecasting remains an active area of research, these results demonstrate that forecasts of carbon flux processes can support a variety of applications, potentially allowing scientists to understand carbon-climate feedbacks as they happen and to capitalize on more flexible satellite technologies that allow areas of interest to be targeted with lead times of weeks to months. We also provide a first glimpse at the spring 2019 carbon forecast using the GEOS-based forecasting system.

Description: A dual-photoelastic-modulator- (PEM-) based spectropolarimetric camera concept is presented as an approach for global aerosol monitoring from space. The most challenging performance objective is to measure degree of linear polarization (DOLP) with an uncertainty of less than 0.5% in multiple spectral bands, at moderately high spatial resolution, over a wide field of view, and for the duration of a multiyear mission. To achieve this, the tandem PEMs are operated as an electro-optic circular retardance modulator within a high-performance reflective imaging system. Operating the PEMs at slightly different resonant frequencies generates a beat signal that modulates the polarized component of the incident light at a much lower heterodyne frequency. The Stokes parameter ratio q = Q/I is obtained from measurements acquired from each pixel during a single frame, providing insensitivity to pixel responsivity drift and minimizing polarization artifacts that conventionally arise when this quantity is derived from differences in the signals from separate detectors. Similarly, u = U/I is obtained from a different pixel; q and u are then combined to form the DOLP. A detailed accuracy and tolerance analysis for this polarimeter is presented.

Description: The future trajectory of the stratospheric ozone recovery will be sensitive to greenhouse gas concentrations through thermal control of chemical loss and via stratospheric circulation changes. The latter in particular is subject to considerable uncertainty meriting continuing monitoring of the evolution of ozone throughout the depth of the stratosphere. Atmospheric reanalyses utilize the data assimilation methodology to obtain comprehensive representations of the state of the atmosphere, including its composition, on multidecadal scales by combining diverse measurements from satellite-borne and conventional data sources. Systematic biases among these various data types pose a challenge for assimilation by introducing spurious discontinuities that affect the utility of reanalyses for studies of long-term variability and trends.In this presentation we will outline an approach, developed at NASA's Global Modeling and Assimilation Office (GMAO), that allows joint assimilation of stratospheric ozone profiles from the Microwave Limb Sounder (MLS) on EOS Aura and the Ozone Mapping and Profiler Suite Limb Profiler (OMPS-LP) currently flying on the Suomi-NPP satellite with future missions projected into the 2030s. We will demonstrate that a simple offline correction significantly reduces biases between MLS and OMPS-LP ozone data providing a strategy for generating a long-term vertically resolved homogenized representation of stratospheric ozone in future reanalyses. One novel element of our approach compared to previous GMAO reanalysis is the use of a version of the Goddard Earth Observing System model with full stratospheric chemistry. We will show selected comparisons of MLS and OMPS-LP assimilation experiments with independent ozonesonde and satellite data as well as two examples of process-based evaluation focused on the 2016 QBO disruption and Arctic winter ozone loss focusing on the relative performance of the MLS and OMPS-LP analyses.

Description: Watersheds draining into the Gulf of Alaska (GoA) experience large seasonal and inter-annual variations of water in the form of rain, snow, and ice, but accurate constraints on these variations have been difficult to obtain. Over larger geographic regions, water variations can be inferred directly from the Gravity Recovery and Climate Experiment (GRACE) data. However, because GoA variations occur over such a small region, the inferred average value of water flux increases as the applied smoothing of the GRACE data decreases. We use this observed scaling together with scaling results obtained from forward models to infer a seasonal amplitude of 115 plus or minus 20 cubic kilometers of water and an average contribution to sea level rise over the two years of data of 0.31 plus or minus 0.09 millimeters per year. These results suggest that accelerated melting that began in the late 1990s, as inferred from altimetry, continues unabated.

Description: No abstract available

Description: The review of optical instrumentation, forward modeling, and inverse problem solution for the polarimetric aerosol remote sensing from space is presented. The special emphasis is given to the description of current airborne and satellite imaging polarimeters and also to modern satellite aerosol retrieval algorithms based on the measurements of the Stokes vector of reflected solar light as detected on a satellite. Various underlying surface reflectance models are discussed and evaluated.

Description: This paper presents the physical basis of the EPIC cloud product algorithms and an initial evaluation of their performance. Since June 2015, EPIC has been providing observations of the sunlit side of the Earth with its 10 spectral channels ranging from the UV to the near-IR. A suite of algorithms has been developed to generate the standard EPIC Level 2 Cloud Products that include cloud mask, cloud effective pressure/height, cloud optical thickness, etc. The EPIC cloud mask adopts the threshold method and utilizes multichannel observations and ratios as tests. Cloud effective pressure/height is derived with observations from the O2 A-band (780 nm and 764 nm), and B-band (680 nm and 688 nm) pairs. The EPIC cloud optical thickness retrieval adopts a single channel approach where the 780 nm and 680 nm channels are used for retrievals over ocean and over land, respectively. Comparison with co-located cloud retrievals from geosynchronous earth orbit (GEO) and low earth orbit (LEO) satellites shows that the EPIC cloud product algorithms are performing well and are consistent with theoretical expectations. These products are publicly available at the Atmospheric Science Data Center at the NASA Langley Research Center for climate studies and for generating other geophysical products that require cloud properties as input.

Description: The propagation of downwelling irradiance at wavelength lambda from surface to a depth (z) in the ocean is governed by the diffuse attenuation coefficient, K(sup -)(sub d)(lambda). There are two standard methods for the derivation of K(sup -)(sub d)(lambda) in remote sensing, which both are based on empirical relationships involving the blue-to-green ratio of ocean color. Recently, a semianalytical method to derive K(sup -)(sub d)(lambda) from reflectance has also been developed. In this study, using K(sup -)(sub d)(490) and K(sup -)(sub d)(443) as examples, we compare the K(sup -)(sub d)(lambda) values derived from the three methods using data collected in three different regions that cover oceanic and coastal waters, with K(sup -)(sub d)(490) ranging from approximately 0.04 to 4.0 per meter. The derived values are compared with the data calculated from in situ measurements of the vertical profiles of downwelling irradiance. The comparisons show that the two standard methods produced satisfactory estimates of K(sup -)(sub d)(lambda) in oceanic waters where attenuation is relatively low but resulted in significant errors in coastal waters. The newly developed semianalytical method appears to have no such limitation as it performed well for both oceanic and coastal waters. For all data in this study the average of absolute percentage difference between the in situ measured and the semianalytically derived K(sup -)(sub d) is approximately 14% for lambda = 490 nm and approximately 11% for lambda = 443 nm.