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In-situ cloud data from three international flight campaigns is compared to the new Federal Aviation Administration (FAA) Title 14 Code of Federal Regulations Part 33 Appendix D mixed-phase/glaciated environmental envelope (1), and the corresponding identical European Aviation Safety Agency (EASA) CS-25 Appendix P envelope (2) (hereafter "Appendix D/P"). Appendix D/P consists of a temperature-altitude envelope, a 99th percentile total water content (TWC99) envelope at the 17.4 Nm distance scale, a distance factor to estimate TWC99 values at other distance scales, ice crystal median mass diameter (MMD), and recommended liquid water content (LWC) levels in mixed-phase icing conditions. The dataset is from 45 flight missions in 3 tropical locations, with 472 runs in approximately 115 clouds, providing about 29,600 Nm of in-cloud data in deep convection over four targeted temperature intervals: 10, 30, 40, and 50 +5 C. The measurements span altitueds from about 17,000' to 39,000'. The comparisons will serve as a basis for regulatory and industry assessment of the efficacy of Appendix D/P.

Tropical second-growth forests could help mitigate climate change, but the degree to which their carbon potential is achieved will depend on exposure to disturbance. Wind disturbance is common in tropical forests, shaping structure, composition, and function, and influencing successional trajectories. However, little is known about the impacts of extreme winds in fragmented landscapes, though second-growth forests are often located in mosaics of forest, pasture, cropland, and other land cover types. Though indirect evidence suggests that fragmentation increases risk of wind damage, few studies have found such impacts following severe storms. In this study, we ask whether fragmentation and forest type (old vs. second growth) were associated with variation in wind damage after a severe convective storm in a fragmented production landscape in western Amazonia. We applied linear spectral unmixing to Landsat 8 imagery from before and after the storm, and combined it with field observations of damage to map wind effects on forest structure and biomass (Figure 4, 5). We also used Landsat 8 imagery to map land cover with the goals of identifying old- and second-growth forest and characterizing fragmentation. We used these data to assess variation in wind disturbance across 95,596 hectares of forest, distributed over 6,110 patches. We find that fragmentation is significantly associated with wind damage, with damage severity higher at forest edges and in edgier, more isolated patches (Figure 7). Damage was more severe in old-growth than in second-growth forests, but this effect was weaker than that of fragmentation (Figure 8). These results illustrate the importance of considering spatial configuration and landscape context in planning tropical forest restoration and predicting carbon sequestration in second-growth forests. Future research should address the mechanisms behind these results, to minimize wind damage risk in second-growth forests so their carbon potential can be maximally achieved.

Surface skin temperature (T(sub s)) is an important parameter for characterizing the energy exchange at the ground/water-atmosphere interface. The Satellite ClOud and Radiation Property retrieval System (SatCORPS) employs a single-channel thermal-infrared (TIR) method to retrieve T(sub s) over clear-sky land and ocean surfaces from data taken by geostationary Earth orbit (GEO) and low Earth orbit (LEO) satellite imagers. GEO satellites can provide somewhat continuous estimates of T(sub s) over the diurnal cycle in non-polar regions, while polar T(sub s) retrievals from LEO imagers, such as the Advanced Very High Resolution Radiometer (AVHRR), can complement the GEO measurements. The combined global coverage of remotely sensed T(sub s), along with accompanying cloud and surface radiation parameters, produced in near-realtime and from historical satellite data, should be beneficial for both weather and climate applications. For example, near-realtime hourly T(sub s) observations can be assimilated in high-temporal-resolution numerical weather prediction models and historical observations can be used for validation or assimilation of climate models. Key drawbacks to the utility of TIR-derived T(sub s) data include the limitation to clear-sky conditions, the reliance on a particular set of analyses/reanalyses necessary for atmospheric corrections, and the dependence on viewing and illumination angles. Therefore, T(sub s) validation with established references is essential, as is proper evaluation of T(sub s) sensitivity to atmospheric correction source. This article presents improvements on the NASA Langley GEO satellite and AVHRR TIR-based T(sub s) product that is derived using a single-channel technique. The resulting clear-sky skin temperature values are validated with surface references and independent satellite products. Furthermore, an empirically adjusted theoretical model of satellite land surface temperature (LST) angular anisotropy is tested to improve satellite LST retrievals. Application of the anisotropic correction yields reduced mean bias and improved precision of GOES-13 LST relative to independent Moderate-resolution Imaging Spectroradiometer (MYD11_L2) LST and Atmospheric Radiation Measurement Program ground station measurements. It also significantly reduces inter-satellite differences between LSTs retrieved simultaneously from two different imagers. The implementation of these universal corrections into the SatCORPS product can yield significant improvement in near-global-scale, near-realtime, satellite-based LST measurements. The immediate availability and broad coverage of these skin temperature observations should prove valuable to modelers and climate researchers looking for improved forecasts and better understanding of the global climate model.

Consistent cross-sensor Advanced Very High Resolution Radiometer (AVHRR) calibration coefficients are determined using desert, polar ice, and deep convective cloud (DCC) invariant Earth targets. The greatest AVHRR calibration challenge is the slow orbit degradation of the host satellite, which precesses toward a terminator orbit. This issue is solved by characterizing the invariant targets with NOAA-16 AVHRR observed radiances that have been referenced to the Aqua Moderate Resolution Imaging Spectrometer (MODIS) calibration using simultaneous nadir overpass (SNO) observations. Another benefit of the NOAA-16 invariant target-modeled reflectance method is that, because of the similarities among the AVHRR spectral response functions, a smaller spectral band adjustment factor is required than when establishing calibrations relative to a non-AVHRR reference instrument. The sensor- and band-specific calibration uncertainties, with respect to the calibration reference, are, on average, 2 percent and 3 percent for channels 1 and 2, respectively. The uncertainties are smaller for sensors that are in afternoon orbits, have longer records, and spend less time in terminator conditions. The multiple invariant targets referenced to Aqua MODIS (MITRAM) AVHRR calibration coefficients are evaluated for individual target consistency, compared against Aqua MODIS/AVHRR SNOs, and selected published calibration gains. The MITRAM and SNO relative calibration biases mostly agree to within 1 percent for channels 1 and 2, respectively. The individual invariant target and MITRAM sensor relative calibration biases are mostly consistent to within 1 percent and 2 percent for channels 1 and 2, respectively. The differences between the MITRAM and other published calibrations are mostly attributed to the reference instrument calibration differences.

The NASA Langley Satellite ClOud and Radiative Property retrieval System (SatCORPS) is routinely applied to multispectral imagery from several geostationary and polar-orbiting imagers to retrieve cloud properties for weather and climate applications. Validation of the retrievals with independent datasets is continuously ongoing in order to understand differences caused by calibration, spatial resolution, viewing geometry, and other factors. The CALIOP instrument provides a decade of detailed cloud observations which can be used to evaluate passive imager retrievals of cloud boundaries, thermodynamic phase, cloud optical depth, and water path on a global scale. This paper focuses on comparisons of CALIOP retrievals to retrievals from MODIS, VIIRS, AVHRR, GOES, SEVIRI, and MTSAT. CALIOP is particularly skilled at detecting weakly-scattering cirrus clouds with optical depths less than approx. 0.5. These clouds are often undetected by passive imagers and the effect this has on the property retrievals is discussed.

Cloud properties play a critical role in climate change. Monitoring cloud properties over long time periods is needed to detect changes and to validate and constrain models. The Clouds and the Earth's Radiant Energy System (CERES) project has developed several cloud datasets from Aqua and Terra MODIS data to better interpret broadband radiation measurements and improve understanding of the role of clouds in the radiation budget. The algorithms applied to MODIS data have been adapted to utilize various combinations of channels on the Advanced Very High Resolution Radiometer (AVHRR) on the long-term time series of NOAA and MetOp satellites to provide a new cloud climate data record. These datasets can be useful for a variety of studies. This paper presents results of the MODIS and AVHRR analyses covering the period from 1980-2014. Validation and comparisons with other datasets are also given.

Cloud properties determined from satellite imager radiances provide a valuable source of information for nowcasting and weather forecasting. In recent years, it has been shown that assimilation of cloud top temperature, optical depth, and total water path can increase the accuracies of weather analyses and forecasts. Aircraft icing conditions can be accurately diagnosed in near-­‐real time (NRT) retrievals of cloud effective particle size, phase, and water path, providing valuable data for pilots. NRT retrievals of surface skin temperature can also be assimilated in numerical weather prediction models to provide more accurate representations of solar heating and longwave cooling at the surface, where convective initiation. These and other applications are being exploited more frequently as the value of NRT cloud data become recognized. At NASA Langley, cloud properties and surface skin temperature are being retrieved in near-­‐real time globally from both geostationary (GEO) and low-­‐earth orbiting (LEO) satellite imagers for weather model assimilation and nowcasting for hazards such as aircraft icing. Cloud data from GEO satellites over North America are disseminated through NCEP, while those data and global LEO and GEO retrievals are disseminated from a Langley website. This paper presents an overview of the various available datasets, provides examples of their application, and discusses the use of the various datasets downstream. Future challenges and areas of improvement are also presented.

At NASA Langley Research Center, a variety of cloud, clear-sky, and radiation products are being derived at different scales from regional to global using geostationary satellite (GEOSat) and lower Earth-orbiting (LEOSat) imager data. With growing availability, these products are becoming increasingly valuable for weather forecasting and nowcasting. These products include, but are not limited to, cloud-top and base heights, cloud water path and particle size, cloud temperature and phase, surface skin temperature and albedo, and top-of-atmosphere radiation budget. Some of these data products are currently assimilated operationally in a numerical weather prediction model. Others are used unofficially for nowcasting, while testing is underway for other applications. These applications include the use of cloud water path in an NWP model, cloud optical depth for detecting convective initiation in cirrus-filled skies, and aircraft icing condition diagnoses among others. This paper briefly describes a currently operating system that analyzes data from GEOSats around the globe (GOES, Meteosat, MTSAT, FY-2) and LEOSats (AVHRR and MODIS) and makes the products available in near-real time through a variety of media. Current potential future use of these products is discussed.