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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.

With the launch of the Deep Space Climate Observatory (DSCOVR), new estimates of the daytime Earth radiation budget can be computed from a combination of measurements from the two Earth-observing sensors onboard the spacecraft, the Earth Polychromatic Imaging Camera (EPIC) and the National Institute of Standards and Technology Advanced Radiometer (NISTAR). Although these instruments can provide accurate top-of-atmosphere (TOA) radiance measurements, they lack sufficient resolution to provide details on small-scale surface and cloud properties. Previous studies have shown that these properties have a strong influence on the anisotropy of the radiation at the TOA, and ignoring such effects can result in large TOA-flux errors. To overcome these effects, high-resolution scene identification is needed for accurate Earth radiation budget estimation. Selected radiance and cloud property data measured and derived from several low earth orbit (LEO, including NASA Terra and Aqua MODIS, NOAA AVHRR) and geosynchronous (GEO, including GOES (east and west), METEOSAT, INSAT-3D, MTSAT-2, and HIMAWARI-8) satellite imagers were collected to create hourly 5-km resolution global composites of data necessary to compute angular distribution models (ADM) for reflected shortwave (SW) and longwave (LW) radiation. The satellite data provide an independent source of radiance measurements and scene identification information necessary to construct ADMs that are used to determine the daytime Earth radiation budget. To optimize spatial matching between EPIC measurements and the high-resolution composite cloud properties, LEO/GEO retrievals within the EPIC fields of view (FOV) are convolved to the EPIC point spread function (PSF) in a similar manner to the Clouds and the Earth's Radiant Energy System (CERES) Single Scanner Footprint TOA/Surface Fluxes and Clouds (SSF) product. Examples of the merged LEO/GEO/EPIC product will be presented, describing the chosen radiance and cloud properties and details of how data from the multi-satellite measurements are selected.

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.

Assimilating high-resolution radar reflectivity and radial velocity into convection-permitting numerical weather prediction models has proven to be an important tool for improving forecast skill of convection. The use of satellite data for the application is much less well understood, only recently receiving significant attention. Since both radar and satellite data provide independent information, combing these two sources of data in a robust manner potentially represents the future of high-resolution data assimilation. This research combines Geostationary Operational Environmental Satellite 13 (GOES-13) cloud water path (CWP) retrievals with Weather Surveillance Radar-1988 Doppler (WSR-88D) reflectivity and radial velocity to examine the impacts of assimilating each for a severe weather event occurring in Oklahoma on 24 May 2011. Data are assimilated into a 3-km model using an ensemble adjustment Kalman filter approach with 36 members over a 2-h assimilation window between 1800 and 2000 UTC. Forecasts are then generated for 90 min at 5-min intervals starting at 1930 and 2000 UTC. Results show that both satellite and radar data are able to initiate convection, but that assimilating both spins up a storm much faster. Assimilating CWP also performs well at suppressing spurious precipitation and cloud cover in the model as well as capturing the anvil characteristics of developed storms. Radar data are most effective at resolving the 3D characteristics of the core convection. Assimilating both satellite and radar data generally resulted in the best model analysis and most skillful forecast for this event.

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.

With geostationary satellite data it is possible to have a continuous record of diurnal cycles of cloud properties for a large portion of the globe. Daytime cloud property retrieval algorithms are typically superior to nighttime algorithms because daytime methods utilize measurements of reflected solar radiation. However, reflected solar radiation is difficult to accurately model for high solar zenith angles where the amount of incident radiation is small. Clear and cloudy scenes can exhibit very small differences in reflected radiation and threshold-based cloud detection methods have more difficulty setting the proper thresholds for accurate cloud detection. Because top-of-atmosphere radiances are typically more accurately modeled outside the terminator region, information from previous scans can help guide cloud detection near the terminator. This paper presents an algorithm that uses cloud fraction and clear and cloudy infrared brightness temperatures from previous satellite scan times to improve the performance of a threshold-based cloud mask near the terminator. Comparisons of daytime, nighttime, and terminator cloud fraction derived from Geostationary Operational Environmental Satellite (GOES) radiance measurements show that the algorithm greatly reduces the number of false cloud detections and smoothes the transition from the daytime to the nighttime clod detection algorithm. Comparisons with the Geoscience Laser Altimeter System (GLAS) data show that using this algorithm decreases the number of false detections by approximately 20 percentage points.

An automated contrail detection algorithm (CDA) is developed to exploit six of the infrared channels on the 1-km MODerate-resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua satellites. The CDA is refined and balanced using visual error analysis. It is applied to MODIS data taken by Terra and Aqua over the United States during 2006 and 2008. The results are consistent with flight track data, but differ markedly from earlier analyses. Contrail coverage is a factor of 4 less than other retrievals and the retrieved contrail optical depths and radiative forcing are smaller by approx.30%. The discrepancies appear to be due to the inability to detect wider, older contrails that comprise a significant amount of the contrail coverage. An example of applying the algorithm to MODIS data over the entire Northern Hemisphere is also presented. Overestimates of contrail coverage are apparent in some tropical regions. Methods for improving the algorithm are discussed and are to be implemented before analyzing large amounts of Northern Hemisphere data. The results should be valuable for guiding and validating climate models seeking to account for aviation effects on climate.

Upper troposphere cloud top heights (CTHs), restricted to cloud top pressures (CTPs) less than 500 hPa, inferred using four satellite retrieval methods applied to Twelfth Geostationary Operational Environmental Satellite (GOES-12) data are evaluated using measurements during the July August 2007 Tropical Composition, Cloud and Climate Coupling Experiment (TC4). The four methods are the single-layer CO2-absorption technique (SCO2AT), a modified CO2-absorption technique (MCO2AT) developed for improving both single-layered and multilayered cloud retrievals, a standard version of the Visible Infrared Solar-infrared Split-window Technique (old VISST), and a new version of VISST (new VISST) recently developed to improve cloud property retrievals. They are evaluated by comparing with ER-2 aircraft-based Cloud Physics Lidar (CPL) data taken during 9 days having extensive upper troposphere cirrus, anvil, and convective clouds. Compared to the 89% coverage by upper tropospheric clouds detected by the CPL, the SCO2AT, MCO2AT, old VISST, and new VISST retrieved CTPs less than 500 hPa in 76, 76, 69, and 74% of the matched pixels, respectively. Most of the differences are due to subvisible and optically thin cirrus clouds occurring near the tropopause that were detected only by the CPL. The mean upper tropospheric CTHs for the 9 days are 14.2 (+/- 2.1) km from the CPL and 10.7 (+/- 2.1), 12.1 (+/- 1.6), 9.7 (+/- 2.9), and 11.4 (+/- 2.8) km from the SCO2AT, MCO2AT, old VISST, and new VISST, respectively. Compared to the CPL, the MCO2AT CTHs had the smallest mean biases for semitransparent high clouds in both single-layered and multilayered situations whereas the new VISST CTHs had the smallest mean biases when upper clouds were opaque and optically thick. The biases for all techniques increased with increasing numbers of cloud layers. The transparency of the upper layer clouds tends to increase with the numbers of cloud layers.

Broadband (BB) shortwave (SW) and longwave (LW) fluxes at TOA (Top of Atmosphere) are crucial parameters in the study of climate and can be monitored over large portions of the Earth's surface using satellites. The VISST (Visible Infrared Solar Split-Window Technique) satellite retrieval algorithm facilitates derivation of these parameters from the Geostationery Operational Environmental Satellites (GOES). However, only narrowband (NB) fluxes are available from GOES, so this derivation requires use of narrowband-to-broadband (NB-BB) conversion coefficients. The accuracy of these coefficients affects the validity of the derived broadband (BB) fluxes. Most recently, NB-BB fits were re-derived using the NB fluxes from VISST/GOES data with BB fluxes observed by the CERES (Clouds and the Earth's Radiant Energy Budget) instrument aboard Terra, a sun-synchronous polar-orbiting satellite that crosses the equator at 10:30 LT. Subsequent comparison with ARM's (Atmospheric Radiation Measurement) BBHRP (Broadband Heating Rate Profile) BB fluxes revealed that while the derived broadband fluxes agreed well with CERES near the Terra overpass times, the accuracy of both LW and SW fluxes decreased farther away from the overpass times. Terra's orbit hampers the ability of the NB-BB fits to capture diurnal variability. To account for this in the LW, seasonal NB-BB fits are derived separately for day and night. Information from hourly SW BB fluxes from the Meteosat-8 Geostationary Earth Radiation Budget (GERB) is employed to include samples over the complete solar zenith angle (SZA) range sampled by Terra. The BB fluxes derived from these improved NB-BB fits are compared to BB fluxes computed with a radiative transfer model.

Both techniques show skill in detecting multilayered clouds, but they disagree more than 50% of the time. BTD method tends to detect more ML clouds than CO2 method and has slightly higher detection accuracy. CO2 method might be better for minimizing false positives, but further study is needed. Neither method as been optimized for GOES data. BTD technique developed on AVHRR, better BTD signals & resolution. CO2 developed on MODIS, better resolution & 4 CO2 channels. Many additional comparisons with ARSCL data will be used to optimize both techniques. A combined technique will be examined using MODIS & Meteosat-8 data. After optimization, the techniques will be implemented in the ARM operational satellite cloud processing.

Cloud and radiation products derived from the MTSAT-1R satellite have been developed for TWP-ICE. These include pixel-level, gridded, and ground site and aircraft matched. These products are available from the Langley website and the ARM data center. As shown in Figs 2, and 4-6, these products compare favorably with in-situ ground and aircraft based measurements. With additional quantitative validation these products can provide valuable information about tropical convection and its impact on the radiation budget and climate. As new algorithm improvements, such as multi-layer cloud detection, are implemented these products will be reprocessed and updated.

Cloud Diameter, C(sub D), offers a simple measure of Marine Boundary Layer (MBL) cloud organization. The diurnal cycle of cloud-physical properties and C(sub D) at Pt Reyes are consistent with previous work. The time series of C(sub D) can be used to identify distinct mesoscale organization regimes within the Pt. Reyes observation period.

At NASA Langley Research Center (LaRC), radiances from multiple satellites are analyzed in near real-time to produce cloud products over many regions on the globe. These data are valuable for many applications such as diagnosing aircraft icing conditions and model validation and assimilation. This paper presents an overview of the multiple products available, summarizes the content of the online database, and details web-based satellite browsers and tools to access satellite imagery and products.

Satellites can provide global coverage of a number of climatically important radiative parameters, including broadband (BB) shortwave (SW) and longwave (LW) fluxes at the top of the atmosphere (TOA) and surface. These parameters can be estimated from narrowband (NB) Geostationary Operational Environmental Satellite (GOES) data, but their accuracy is highly dependent on the validity of the narrowband-to-broadband (NB-BB) conversion formulas that are used to convert the NB fluxes to broadband values. The formula coefficients have historically been derived by regressing matched polarorbiting satellite BB fluxes or radiances with their NB counterparts from GOES (e.g., Minnis et al., 1984). More recently, the coefficients have been based on matched Earth Radiation Budget Experiment (ERBE) and GOES-6 data (Minnis and Smith, 1998). The Clouds and the Earth's Radiant Energy Budget (CERES see Wielicki et al. 1998)) project has recently developed much improved Angular Distribution Models (ADM; Loeb et al., 2003) and has higher resolution data compared to ERBE. A limited set of coefficients was also derived from matched GOES-8 and CERES data taken on Topical Rainfall Measuring Mission (TRMM) satellite (Chakrapani et al., 2003; Doelling et al., 2003). The NB-BB coefficients derived from CERES and the GOES suite should yield more accurate BB fluxes than from ERBE, but are limited spatially and seasonally. With CERES data taken from Terra and Aqua, it is now possible to derive more reliable NB-BB coefficients for any given area. Better TOA fluxes should translate to improved surface radiation fluxes derived using various algorithms. As part of an ongoing effort to provide accurate BB flux estimates for the Atmospheric Radiation Measurement (ARM) Program, this paper documents the derivation of new NB-BB coefficients for the ARM Southern Great Plains (SGP) domain and for the Darwin region of the Tropical Western Pacific (DTWP) domain.

Thin cirrus clouds account for about 20-30% of the total cloud coverage and affect the global radiation budget by increasing the Earth's albedo and reducing infrared emissions. Thin cirrus, however, are often underestimated by traditional satellite cloud detection algorithms. This difficulty is caused by the lack of spectral contrast between optically thin cirrus and the surface in techniques that use visible (0.65 micron ) and infrared (11 micron ) channels. In the Clouds and the Earth s Radiant Energy System (CERES) Aqua Edition 1 (AEd1) and Terra Edition 3 (TEd3) Cloud Masks, thin cirrus detection is significantly improved over both land and ocean using a technique that combines MODIS high-resolution measurements from the 1.38 and 11 micron channels and brightness temperature differences (BTDs) of 11-12, 8.5-11, and 3.7-11 micron channels. To account for humidity and view angle dependencies, empirical relationships were derived with observations from the 1.38 micron reflectance and the 11-12 and 8.5-11 micron BTDs using 70 granules of MODIS data in 2002 and 2003. Another challenge in global cloud detection algorithms occurs near the day/night terminator where information from the visible 0.65 micron channel and the estimated solar component of 3.7 micron channel becomes less reliable. As a result, clouds are often underestimated or misidentified near the terminator over land and ocean. Comparisons between the CLAVR-x (Clouds from Advanced Very High Resolution Radiometer [AVHRR]) cloud coverage and Geoscience Laser Altimeter System (GLAS) measurements north of 60 N indicate significant amounts of missing clouds from CLAVR-x because this part of the world was near the day/night terminator viewed by AVHRR. Comparisons between MODIS cloud products (MOD06) and GLAS in the same region also show similar difficulties with MODIS cloud retrievals. The consistent detection of clouds through out the day is needed to provide reliable cloud and radiation products for CERES and other research efforts involving the modeling of clouds and their interaction with the radiation budget.

SEVIRI data are cross-calibrated against the corresponding Aqua and Terra MODIS channels. Compared to Terra MODIS, no significant trends are evident in the 0.65, 0.86, and 1.6 micron channel gains between May 2004 and May 2006, indicating excellent stability in the solar-channel sensors. On average, the corresponding Terra reflectances are 12, 14, and 1% greater than the their SEVIRI counterparts. The Terra 3.8- micron channel brightness temperatures T are 7 and 4 K greater than their SEVIRI counterparts during day and night, respectively. The average differences between T for MODIS and SEVIRI 8.6, 10.8, 12.0, and 13.3- micron channels are between 0.5 and 2 K. The cloud properties are being derived hourly over Europe and, in initial comparisons, agree well with surface observations. Errors caused by residual calibration uncertainties, terminator conditions, and inaccurate temperature and humidity profiles are still problematic. Future versions will address those errors and the effects of multilayered clouds.

Aircraft icing is one of the most dangerous weather conditions for general aviation. Currently, model forecasts and pilot reports (PIREPS) constitute much of the database available to pilots for assessing the icing conditions in a particular area. Such data are often uncertain or sparsely available. Improvements in the temporal and areal coverage of icing diagnoses and prognoses would mark a substantial enhancement of aircraft safety in regions susceptible to heavy supercooled liquid water clouds. The use of 3.9 microns data from meteorological satellite imagers for diagnosing icing conditions has long been recognized (e.g., Ellrod and Nelson, 1996) but to date, no explicit physically based methods have been implemented. Recent advances in cloud detection and cloud property retrievals using operational satellite imagery open the door for real-time objective applications of those satellite datasets for a variety of weather phenomena. Because aircraft icing is related to cloud macro- and microphysical properties (e.g., Cober et al. 1995), it is logical that the cloud properties from satellite data would be useful for diagnosing icing conditions. This paper describes the a prototype realtime system for detecting aircraft icing from space.

Satellites are ideal for continuous monitoring of aircraft icing conditions in many situations over extensive areas. The satellite imager data are used to diagnose a number of cloud properties that can be used to develop icing intensity indices. Developing and validating these indices requires comparison with objective "cloud truth" data in addition to conventional pilot reports (PIREPS) of icing conditions. Minnis et al. examined the relationships between PIREPS icing and satellite-derived cloud properties. The Atlantic-THORPEX Regional Campaign (ATReC) and the second Alliance Icing Research Study (AIRS-II) field programs were conducted over the northeastern USA and southeastern Canada during late 2003 and early 2004. The aircraft and surface measurements are concerned primarily with the icing characteristics of clouds and, thus, are ideal for providing some validation information for the satellite remote sensing product. This paper starts the process of comparing cloud properties and icing indices derived from the Geostationary Operational Environmental Satellite (GOES) with the aircraft in situ measurements of several cloud properties during campaigns and some of the The comparisons include cloud phase, particle size, icing intensity, base and top altitudes, temperatures, and liquid water path. The results of this study are crucial for developing a more reliable and objective icing product from satellite data. This icing product, currently being derived from GOES data over the USA, is an important complement to more conventional products based on forecasts, and PIREPS.

Contrails, like natural cirrus clouds, can cause a warming of the Earth-atmospheric system by absorbing longwave radiation from the surface and lower troposphere and radiating additional radiation back to the surface. They can also produce some cooling of the surface during the daytime by reflecting some sunlight back to space. Recently, Minnis et al. (2004) determined from surface observations of cirrus cloud cover that the overall impact appears to be a warming that is consistent with theoretical calculations, at least over the United States of America (USA) and surrounding areas. This finding highlights the need to better understand the formation and persistence of contrails and their radiative properties. To better assess the climatic impact of contrails, it is essential to determine the variability of the contrail microphysical properties, their impact on the atmospheric radiation budget, and their relationship to the atmospheric state. To that end, this paper continues the analyses of Advanced Very High Resolution Radiometer (AVHRR) data from the NOAA-15 (N15), NOAA-16 (N16), and NOAA-17 (N17) satellites, Moderate Resolution Imaging Spectroradiometer (MODIS) data from the Terra and Aqua satellites. The combination of these satellites provides a relatively comprehensive coverage of the daily cycle of air traffic. Thus, it should be possible to use these data to help understand the impact of air traffic on the upper tropospheric humidity during the day as well as determine the local-time variability of contrail coverage. The results will be valuable for developing models of contrail effects and methods for mitigating the impact of aviation on climate.

A preliminary new, physically based method for realtime estimation of the probability of icing conditions has been demonstrated using merged GOES-10 and 12 data over the continental United States and southern Canada. The algorithm produces pixel-level cloud and radiation properties as well as an estimate of icing probability with an associated intensity rating Because icing depends on so many different variables, such as aircraft size or air speed, it is not possible to achieve 100% success with this or any other type of approach. This initial algorithm, however, shows great promise for diagnosing aircraft icing and putting it at the correct altitude within 0.5 km most of the time. Much additional research must be completed before it can serve as a reliable input for the operational CIP. The delineation of the icing layer vertical boundaries will need to be improved using either the RUC or balloon soundings or ceilometer data to adjust the cloud base height, a change that would require adjustment of the cloud-top altitude also.

The need for ready access to satellite data and associated physical parameters such as cloud properties has been steadily growing. Air traffic management, weather forecasters, energy producers, and weather and climate researchers among others can utilize more satellite information than in the past. Thus, it is essential that such data are made available in near real-time and as archival products in an easy-access and user friendly environment. A host of Internet web sites currently provide a variety of satellite products for various applications. Each site has a unique contribution with appeal to a particular segment of the public and scientific community. This is no less true for the NASA Langley's Clouds and Radiation (NLCR) website (http://www-pm.larc.nasa.gov) that has been evolving over the past 10 years to support a variety of research projects This website was originally developed to display cloud products derived from the Geostationary Operational Environmental Satellite (GOES) over the Southern Great Plains for the Atmospheric Radiation Measurement (ARM) Program. It has evolved into a site providing a comprehensive database of near real-time and historical satellite products used for meteorological, aviation, and climate studies. To encourage the user community to take advantage of the site, this paper summarizes the various products and projects supported by the website and discusses future options for new datasets.

Imagers on many of the current and future operational meteorological satellites in geostationary Earth orbit (GEO) and lower Earth orbit (LEO) have enough spectral channels to derive cloud microphysical properties useful for a variety of applications. The products include cloud amount, phase, optical depth, temperature, height and pressure, thickness, effective particle size, and ice or liquid water path, shortwave albedo, and outgoing longwave radiation for each imager pixel. Because aircraft icing depends on cloud temperature, droplet size, and liquid water content as well as aircraft variables, it is possible to estimate the potential icing conditions from the cloud phase, temperature, effective droplet size, and liquid water path. A prototype icing index is currently being derived over the contiguous USA in near-real time from Geostationary Operational Environmental Satellite (GOES-10 and 12) data on a half-hourly basis and from NOAA- 16 Advanced Very High Resolution (AVHRR) data when available. Because the threshold-based algorithm is sensitive to small errors and differences in satellite imager and icing is complex process, a new probability based icing diagnosis technique is developed from a limited set of pilot reports. The algorithm produces reasonable patterns of icing probability and intensities when compared with independent model and pilot report data. Methods are discussed for improving the technique for incorporation into operational icing products.

Satellite data have long been used for determining the extent of cloud cover and for estimating the properties at the cloud tops. The derived properties can also be used to estimate aircraft icing potential to improve the safety of air traffic in the region. Currently, cloud properties and icing potential are derived in near-real time over the United States of America (USA) from the Geostationary Operational Environmental Satellite GOES) imagers at 75 W and 135 W. Traditionally, the results have been given in two dimensions because of the lack of knowledge about the vertical extent of clouds and the occurrence of overlapping clouds. Aircraft fly in a three-dimensional space and require vertical as well as horizontal information about clouds, their intensity, and their potential for icing. To improve the vertical component of the derived cloud and icing parameters, this paper explores various methods and datasets for filling in the three-dimensional space over the USA with cloud water.

Contrail-induced cloud cover has been shown to be a significant factor in regional climate change over the United States of America. As air traffic increases, the potential for globally significant impacts also rises. To better understand and predict these potential climatic effects, it is necessary to develop models that can accurately represent contrail properties based on ambient atmospheric variables including temperature, relative humidity and winds. Several high-resolution numerical weather analyses (NWA) including the 20-km Rapid Update Cycle (RUC) and the University of Oklahoma Center for Analysis and Prediction of Storms Advanced Regional Prediction System (ARPS) can provide the temperature, humidity and wind information necessary to diagnose contrail formation. One outstanding problem that must be addressed to achieve a realistic simulation of contrails is the large uncertainties in upper tropospheric relative humidity (UTH) in numerical weather analyses. Current numerical weather analyses tend to underestimate UTH due to large dry biases in the balloon soundings used to construct the analyses. To evaluate each of the models, we match several months of contrail properties derived from satellite and surface observations to the NWA-derived humidity, vertical velocity, wind shear and atmospheric stability. The relationships between several contrail properties (including optical depth and areal contrail coverage) and the NWA-derived statistics will be analyzed to determine under which atmospheric conditions widespread contrail outbreaks are favored.

Condensation trails, or contrails, formed in the wake of high-altitude aircraft have long been suspected of causing the formation of additional cirrus cloud cover. More cirrus is possible because 10 - 20% of the atmosphere at typical commercial flight altitudes is clear but ice-saturated. Since they can affect the radiation budget like natural cirrus clouds of equivalent optical depth and microphysical properties, contrail -generated cirrus clouds are another potential source of anthropogenic influence on climate. Initial estimates of contrail radiative forcing (CRF) were based on linear contrail coverage and optical depths derived from a limited number of satellite observations. Assuming that such estimates are accurate, they can be considered as the minimum possible CRF because contrails often develop into cirrus clouds unrecognizable as contrails. These anthropogenic cirrus are not likely to be identified as contrails from satellites and would, therefore, not contribute to estimates of contrail coverage. The mean lifetime and coverage of spreading contrails relative to linear contrails are needed to fully assess the climatic effect of contrails, but are difficult to measure directly. However, the maximum possible impact can be estimated using the relative trends in cirrus coverage over regions with and without air traffic. In this paper, the upper bound of CRF is derived by first computing the change in cirrus coverage over areas with heavy air traffic relative to that over the remainder of the globe assuming that the difference between the two trends is due solely to contrails. This difference is normalized to the corresponding linear contrail coverage for the same regions to obtain an average spreading factor. The maximum contrail-cirrus coverage, estimated as the product of the spreading factor and the linear contrail coverage, is then used in the radiative model to estimate the maximum potential CRF for current air traffic.