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One of the most crucial tasks of measuring top-of-atmosphere (TOA) radiative flux is to understand the relationships between radiances and fluxes, particularly for the reflected shortwave (SW) fluxes. The radiance-to-flux conversion is accomplished by constructing angular distribution models (ADMs). This conversion depends on solar-viewing geometries as well as the scene types within the field of view. To date, the most comprehensive observation-based ADMs are developed using the Clouds and the Earth’s Radiant Energy System (CERES) observations. These ADMs are used to derive TOA SW fluxes from CERES and other Earth radiation budget instruments which observe the Earth mostly from side-scattering angles. The Earth Polychromatic Imaging Camera (EPIC) onboard Deep Space Climate Observatory observes the Earth at the Lagrange-1 point in the near-backscattering directions and offers a testbed for the CERES ADMs. As the EPIC relative azimuth angles change from 168◦ to 178◦, the global daytime mean SW radiances can increase by as much as 10% though no notable cloud changes are observed. The global daytime mean SW fluxes derived after considering the radiance anisotropies at relative azimuth angles of 168◦ and 178◦ show much smaller differences (−2 with root-mean-square errors less than 3.0 Wm−2. Consistency between SW fluxes from EPIC and CERES inverted from very different viewing geometries indicates that the CERES ADMs accurately quantify the radiance anisotropy and can be used for flux inversion from different viewing perspectives.

The NASA Clouds and the Earth’s Radiant Energy System (CERES) project relies on top-of-atmosphere (TOA) broadband fluxes derived from geostationary (GEO) satellite imagery to account for the diurnal flux variations between the CERES observation intervals, and thereby produce a synoptic gridded (SYN1deg) product based on continuous temporal observations. Consistent broadband flux derivation depends on accurate radiative property measurements and cloud retrievals, which largely determine the radiance-to-flux conversion process. Therefore, it is important to ensure a high quality of cloud property input in order to maintain a reliable broadband flux record. In Edition 4 of the CERES SYN1deg product, a robust automated image anomaly detection algorithm based on inter-line and inter-pixel differences, spatial variance, and 2-D Fourier analysis has been successful in identifying imagery with linear artifacts, but the line-by-line inspection and cleaning process must still be performed by a human. Therefore, further automation of this quality assurance process is warranted, especially considering the excessive amount of additional cleaning necessitated by the GOES-17 Advance Baseline Imager (ABI) cooling system anomaly. As such, this article highlights advancement of the CERES GEO image artifact cleaning approach based on a convolutional neural network (CNN) for classification of bad scanlines. Once trained, the CNN approach is a computationally inexpensive means to ensure greater consistency in cloud retrievals, and therefore broadband flux derivation, based on GOES-17 measurements.

Remote sensing observations, especially those from ground-based radars, have been used extensively to discriminate between severe and nonsevere storms. Recent upgrades to operational remote sensing networks in the United States have provided unprecedented spatial and temporal sampling to study such storms. These networks help forecasters subjectively identify storms capable of producing severe weather at the ground; however, uncertainties remain in how to objectively identify severe thunderstorms using the same data. Here, three large-area datasets (geostationary satellite, ground-based radar, and ground-based lightning detection) are used over 28 recent events in an attempt to objectively discriminate between severe and nonsevere storms, with an additional focus on severe storms that produce tornadoes. Among these datasets, radar observations, specifically those at mid- and upper levels (altitudes at and above 4 km), are shown to provide the greatest objective discrimination. Physical and kinematic storm characteristics from all analyzed datasets imply that significantly severe [≥2-in. (5.08 cm) hail and/or ≥65-kt (33.4 m s−1) straight-line winds] and tornadic storms have stronger upward motion and rotation than nonsevere and less severe storms. In addition, these metrics are greatest in tornadic storms during the time in which tornadoes occur.

Satellites routinely observe deep convective clouds across the world. The cirrus outflow from deep convection, commonly referred to as anvil cloud, has a ubiquitous appearance in visible and infrared (IR) wavelength imagery. Anvil clouds appear as broad areas of highly reflective and cold pixels relative to the darker and warmer clear sky background, often with embedded textured and colder pixels that indicate updrafts and gravity waves. These characteristics would suggest that creating automated anvil cloud detection products useful for weather forecasting and research should be straightforward, yet in practice such product development can be challenging. Some anvil detection methods have used reflectance or temperature thresholding, but anvil reflectance varies significantly throughout a day as a function of combined solar illumination and satellite viewing geometry, and anvil cloud top temperature varies as a function of convective equilibrium level and tropopause height. This paper highlights a technique for facilitating anvil cloud detection based on visible observations that relies on comparative analysis with expected cloud reflectance for a given set of angles, thereby addressing limitations of previous methods. A one-year database of anvil-identified pixels, as determined from IR observations, from several geostationary satellites was used to construct a bi-directional reflectance distribution function (BRDF) model to quantify typical anvil reflectance across almost all expected viewing, solar, and azimuth angle configurations, in addition to the reflectance uncertainty for each angular bin. Application of the BRDF model for cloud optical depth retrieval in deep convection is described as well.

The Touch And Go Camera System (TAGCAMS) is a three-camera-head instrument onboard NASA’s OSIRIS-REx asteroid sample return mission spacecraft. The purpose of TAGCAMS is to facilitate navigation to the target asteroid, (101955) Bennu; confirm acquisition of the asteroid sample; document asteroid sample stowage; and provide supplementary imaging for OSIRIS-REx science investigations. During the almost two-year OSIRIS-REx outbound cruise phase we pursued nine TAGCAMS imaging campaigns to check, calibrate and characterize the camera system’s performance before asteroid arrival and proximity operations began in late 2018. The TAGCAMS in-flight calibration dataset provides the relevant information to enable the three cameras to complete their primary observation goals during asteroid operations. The key performance parameters that we investigated in flight included: linearity, responsivity (both point source and extended body), dark current, hot pixels, pointing, image geometry transformation, image quality and stray light. Analyses of the in-flight performance either confirmed the continued applicability of the TAGCAMS ground test results or substantially improved upon the ground test knowledge. In addition, the TAGCAMS calibration observations identified the source of a spacecraft outgassing feature that guided successful remediation efforts prior to asteroid arrival.

The Earth-viewed images acquired by the space probe OSIRIS-REx during its Earth gravity assist flyby maneuver on 22 September 2017 provided an opportunity to radiometrically calibrate the onboard NavCam imagers. Spatially-, temporally-, and angularly-matched radiances from the Earth viewing GOES-15 and DSCOVR-EPIC imagers were used as references for deriving the calibration gain of the NavCam sensors. An optimized all-sky tropical ocean ray-matching (ATO-RM) calibration approach that accounts for the spectral band differences, navigation errors, and angular geometry differences between NavCam and the reference imagers is formulated in this paper. Prior to ray-matching, the GOES-15 and EPIC pixel level radiances were mapped into the NavCam field of view. The NavCam 1 ATO-RM gain is found to be 9.874 &times, 10&minus, 2 Wm&minus, 2sr&minus, 1&micro, m&minus, 1DN&minus, 1 with an uncertainty of 3.7%. The ATO-RM approach predicted an offset of 164, which is close to the true space DN of 170. The pre-launch NavCam 1 and 2 gains were compared with the ATO-RM gain and were found to be within 2.1% and 2.8%, respectively, suggesting that sensor performance is stable in space. The ATO-RM calibration was found to be consistent within 3.9% over a factor of &plusmn, 2 NavCam 2 exposure times. This approach can easily be adapted to inter-calibrate other space probe cameras given the current constellation of geostationary imagers.

For accurate cloud ceiling information, a data fusion approach is proposed that utilizes satellite data to extend surface station information to much wider areas. Cloud base height (CBH) retrieved from satellite observations provides for much larger spatial coverage and higher resolution. The direct comparison of GOES-16 CBH with surface station ceiling yields a local bias that has to be corrected for in the initial GOES-16 cloud base information. This sparsely sampled bias correction presents an irregular 2D mesh of control points, which is then interpolated by constructing a continuous smooth field using polyharmonic splines. The influence of remote stations is restricted by grouping the control points into clusters depending on an effective distance. This cluster-based approach allows for constructing separate spline surfaces corresponding to physically different clouds. The obtained continuous bias correction function is then applied to the entire GOES-16 pixel level CBH except for areas far away from surface stations in data sparse regions such as offshore. The described method is currently being tested using daytime-only observations over the central and eastern United States. Overall, this approach has potential to provide more accurate, high spatial resolution cloud ceiling information for the aviation community.

The National Institute of Standards and Technology Advanced Radiometer (NISTAR) onboard the Deep Space Climate Observatory (DSCOVR) provides continuous full-disk global broadband irradiance measurements over most of the sunlit side of the Earth. The three active cavity radiometers measure the total radiant energy from the sunlit side of the Earth in shortwave (SW; 0.2–4 µm), total (0.4–100 µm), and near-infrared (NIR; 0.7–4 µm) channels. The Level 1 NISTAR dataset provides the filtered radiances (the ratio between irradiance and solid angle). To determine the daytime top-of-atmosphere (TOA) shortwave and longwave radiative fluxes, the NISTAR-measured shortwave radiances must be unfiltered first. An unfiltering algorithm was developed for the NISTAR SW and NIR channels using a spectral radiance database calculated for typical Earth scenes. The resulting unfiltered NISTAR radiances are then converted to full-disk daytime SW and LW flux by accounting for the anisotropic characteristics of the Earth-reflected and emitted radiances. The anisotropy factors are determined using scene identifications determined from multiple low-Earth orbit and geostationary satellites as well as the angular distribution models (ADMs) developed using data collected by the Clouds and the Earth's Radiant Energy System (CERES). Global annual daytime mean SW fluxes from NISTAR are about 6 % greater than those from CERES, and both show strong diurnal variations with daily maximum–minimum differences as great as 20 Wm−2 depending on the conditions of the sunlit portion of the Earth. They are also highly correlated, having correlation coefficients of 0.89, indicating that they both capture the diurnal variation. Global annual daytime mean LW fluxes from NISTAR are 3 % greater than those from CERES, but the correlation between them is only about 0.38.

Linear contrail coverage, optical property, and radiative forcing data over the Northern Hemisphere (NH) are derived from a year (2012) of Terra and Aqua Moderate-resolution Imaging Spectroradiometer (MODIS) imagery and compared with previously published 2006 results (Duda et al., 2013; Bedka et al., 2013; Spangenberg et al., 2013) using a consistent retrieval methodology. Differences in the observed Terra-minus-Aqua screened contrail coverage and patterns in the 2012 annual-mean air traffic estimated with respect to satellite overpass time suggest that most contrails detected by the contrail detection algorithm (CDA) form approximately 2 h before overpass time. The 2012 screened NH contrail coverage (Mask B) shows a relative 3 % increase compared to 2006 data for Terra and increases by almost 7 % for Aqua, although the differences are not expected to be statistically significant. A new post-processing algorithm added to the contrail mask processing estimated that the total contrail cirrus coverage visible in the MODIS imagery may be 3 to 4 times larger than the linear contrail coverage detected by the CDA. This estimate is similar in magnitude to the spreading factor estimated by Minnis et al. (2013). Contrail property retrievals of the 2012 data indicate that both contrail optical depth and contrail effective diameter decreased approximately 10 % between 2006 and 2012. The decreases may be attributed to better background cloudiness characterization, changes in the waypoint screening, or changes in contrail temperature. The total mean contrail radiative forcings (TCRFs) for all 2012 Terra observations were −6.3, 14.3, and 8.0 mW m−2 for the shortwave (SWCRF), longwave (LWCRF), and net forcings, respectively. These values are approximately 20 % less than the corresponding 2006 Terra estimates. The decline in TCRF results from the decrease in normalized CRF, partially offset by the 3 % increase in overall contrail coverage in 2012. The TCRFs for 2012 Aqua are similar, −6.4, 15.5, and 9.0 mW m−2 for shortwave, longwave, and net radiative forcing. The strong correlation between the relative changes in both total SWCRF and LWCRF between 2006 and 2012 and the corresponding relative changes in screened contrail coverage over each air traffic region suggests that regional changes in TCRF from year to year are dominated by year-to-year changes in contrail coverage over each area.

Deep convective updrafts often penetrate through the surrounding cirrus anvil and into the lower stratosphere. Cross-tropopause transport of ice, water vapor, and chemicals occurs within these “overshooting tops” (OTs) along with a variety of hazardous weather conditions. OTs are readily apparent in satellite imagery, and, given the importance of OTs for weather and climate, a number of automated satellite-based detection methods have been developed. Some of these methods have proven to be relatively reliable, and their products are used in diverse Earth science applications. Nevertheless, analysis of these methods and feedback from product users indicate that use of fixed infrared temperature–based detection criteria often induces biases that can limit their utility for weather and climate analysis. This paper describes a new multispectral OT detection approach that improves upon those previously developed by minimizing use of fixed criteria and incorporating pattern recognition analyses to arrive at an OT probability product. The product is developed and validated using OT and non-OT anvil regions identified by a human within MODIS imagery. The product offered high skill for discriminating between OTs and anvils and matched 69% of human OT identifications for a particular probability threshold with a false-detection rate of 18%, outperforming previously existing methods. The false-detection rate drops to 1% when OT-induced texture detected within visible imagery is used to constrain the IR-based OT probability product. The OT probability product is also shown to improve severe-storm detection over the United States by 20% relative to the best existing method.

Snow and ice are important hydrological resources. Their minimum spatial extent over land, here referred to as annual minimum snow/ice (MSI) cover, plays a very important role as an indicator of long-term changes and baseline capacity for surface water storage. Data from Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra satellite for the period of 2000–2014 were utilized in this study. The level-2 MODIS swath imagery for bands B1 to B7 was employed and the 500-m bands B3–B7 were spatially downscaled to a 250-m swath grid. The imagery is available daily with multiple overpasses. This allows for more accurate identification of annual minimum in comparison to high-resolution imagery (e.g., Landsat, ASTER, etc.) available at much coarser temporal rates. Atmospherically corrected 10-day clear-sky composites converted into normalized surface reflectance over the warm season (April 1 to September 20) were employed to identify persistent snow and ice presence. Results were compared with our...

Recent studies have found that flight through deep convective storms and ingestion of high mass concentrations of ice crystals, also known as high ice water content (HIWC), into aircraft engines can adversely impact aircraft engine performance. These aircraft engine icing events caused by HIWC have been documented during flight in weak reflectivity regions near convective updraft regions that do not appear threatening in onboard weather radar data. Three airborne field campaigns were conducted in 2014 and 2015 to better understand how HIWC is distributed in deep convection, both as a function of altitude and proximity to convective updraft regions, and to facilitate development of new methods for detecting HIWC conditions, in addition to many other research and regulatory goals. This paper describes a prototype method for detecting HIWC conditions using geostationary (GEO) satellite imager data coupled with in-situ total water content (TWC) observations collected during the flight campaigns. Three satellite-derived parameters were determined to be most useful for determining HIWC probability: 1) the horizontal proximity of the aircraft to the nearest overshooting convective updraft or textured anvil cloud, 2) tropopause-relative infrared brightness temperature, and 3) daytime-only cloud optical depth. Statistical fits between collocated TWC and GEO satellite parameters were used to determine the membership functions for the fuzzy logic derivation of HIWC probability. The products were demonstrated using data from several campaign flights and validated using a subset of the satellite-aircraft collocation database. The daytime HIWC probability was found to agree quite well with TWC time trends and identified extreme TWC events with high probability. Discrimination of HIWC was more challenging at night with IR-only information. The products show the greatest capability for discriminating TWC ≥ 0.5 g m−3. Product validation remains challenging due to vertical TWC uncertainties and the typically coarse spatio-temporal resolution of the GEO data.

[1] The properties of contrail cirrus clouds are retrieved through analysis of Terra and Aqua Moderate Resolution Imaging Spectroradiometer data for 21 cases of spreading linear contrails. For these cases, contrail cirrus enhanced the linear contrail coverage by factors of 2.4–7.6 depending on the contrail mask sensitivity. In dense air traffic areas, linear contrail detection sensitivity is apparently reduced when older contrails overlap and thus is likely diminished during the afternoon. The mean optical depths and effective particle sizes of the contrail cirrus were 2–3 times and 20% greater, respectively, than the corresponding values retrieved for the adjacent linear contrails. When contrails form below, in, or above existing cirrus clouds, the column cloud optical depth is increased and particle size is decreased. Thus, even without increased cirrus coverage, contrails will affect the radiation balance. These results should be valuable for refining model characterizations of contrail cirrus needed to fully assess the climate impacts of contrails.

Identification of clear-sky snow and ice is an important step in the production of cryosphere radiation budget products, which are used in the derivation of long-term data series for climate research. In this paper, a new method of clear-sky snow/ice identification for Moderate Resolution Imaging Spectroradiometer (MODIS) is presented. The algorithm’s goal is to enhance the identification of snow and ice within the Clouds and the Earth’s Radiant Energy System (CERES) data after application of the standard CERES scene identification scheme. The input of the algorithm uses spectral radiances from five MODIS bands and surface skin temperature available in the CERES Single Scanner Footprint (SSF) product. The algorithm produces a cryosphere rating from an aggregated test: a higher rating corresponds to a more certain identification of the clear-sky snow/ice-covered scene. Empirical analysis of regions of interest representing distinctive targets such as snow, ice, ice and water clouds, open waters, and snow-free land selected from a number of MODIS images shows that the cryosphere rating of snow/ice targets falls into 95% confidence intervals lying above the same confidence intervals of all other targets. This enables recognition of clear-sky cryosphere by using a single threshold applied to the rating, which makes this technique different from traditional branching techniques based on multiple thresholds. Limited tests show that the established threshold clearly separates the cryosphere rating values computed for the cryosphere from those computed for noncryosphere scenes, whereas individual tests applied consequently cannot reliably identify the cryosphere for complex scenes.

[1] A modified automated contrail detection algorithm (CDA) using five infrared channels available from the Moderate Resolution Imaging Spectrometer onboard the Aqua satellite is used to determine linear contrail coverage over the Northern Hemisphere during 2006. Commercial aircraft flight data are employed to filter false contrail detections by the CDA. The Northern Hemisphere annual mean linear contrail coverage ranges from 0.07% to 0.40% for three different CDA sensitivities. Based on visual analyses, the medium sensitivity CDA provides the best estimate of linear contrail coverage, which averages 0.13%. If scaled to the Southern Hemisphere, the global mean coverage would be 0.07%. Coverage is greatest during winter and least during the summer with maximum coverage over the North Atlantic. Less coverage is observed over heavy European and American traffic areas, likely as a result of difficulties in detecting linear contrails that overlap with each other and with older contrail cirrus. These results are valuable for evaluating the representation of contrails and contrail cirrus within global climate models and for retrieving contrail optical properties and radiative forcing.

The Clouds and the Earth’s Radiant Energy System (CERES) has incorporated imagery from 16 individual geostationary (GEO) satellites across five contiguous domains since March 2000. In order to derive broadband fluxes uniform across satellite platforms it is important to ensure a good quality of the input raw count data. GEO data obtained by older GOES imagers (such as MTSAT-1, Meteosat-5, Meteosat-7, GMS-5, and GOES-9) are known to frequently contain various types of noise caused by transmission errors, sync errors, stray light contamination, and others. This work presents an image processing methodology designed to detect most kinds of noise and corrupt data in all bands of raw imagery from modern and historic GEO satellites. The algorithm is based on a set of different approaches to detect abnormal image patterns, including inter-line and inter-pixel differences within a scanline, correlation between scanlines, analysis of spatial variance, and also a 2D Fourier analysis of the image spatial frequencies. In spite of computational complexity, the described method is highly optimized for performance to facilitate volume processing of multi-year data and runs in fully automated mode. Reliability of this noise detection technique has been assessed by human supervision for each GEO dataset obtained during selected time periods in 2005 and 2006. This assessment has demonstrated the overall detection accuracy of over 99.5% and the false alarm rate of under 0.3%. The described noise detection routine is currently used in volume processing of historical GEO imagery for subsequent production of global gridded data products and for cross-platform calibration.

Time series of the Normalized Difference Vegetation Index (NDVI) derived from satellite observations provide important information on the state of terrestrial vegetation over a wide range of spatiotemporal scales. For understanding long-term changes in terrestrial ecosystems (post-1981), data collected by the Advanced Very High Resolution Radiometer (AVHRR) on board the satellites of National Oceanic and Atmospheric Administration (NOAA) series is a unique source of information. In this paper, we describe a new processing methodology for a comprehensive AVHRR data set at 1 km spatial resolution acquired over Canada, the northern United States and Greenland post-1981. The methodology incorporates a pre-processing algorithm, Canadian AVHRR Processing System (CAPS), recently developed by the Canada Centre of Remote Sensing (CCRS), which enables highly accurate geolocation and ortho-rectification at efficiency rates of > 90%. Once image navigation is completed, our approach consists of five key steps: first, two clear-sky composites for each 10 day interval are generated from the forward or backward scattering hemisphere; second, AVHRR Channel 1 and 2 reflectances are normalized to the AVHRR/3 on board NOAA-17 to account for differences in the spectral response function among the AVHRR sensors; third, atmospheric correction is performed using the Simplified Method for Atmospheric correction (SMAC) algorithm, using standard meteorological data sets (water vapor, surface level air pressure, ozone); fourth, NDVI is calculated based on atmospherically corrected Channel 1 and 2 reflectances; and finally, the NDVI is adjusted for directional effects based on the Ross-Thick Li-Sparse Bidirectional Reflectance Distribution Function (BRDF) model. The processed NDVI data are compared to an equivalent spatially and temporally overlapping MODIS NDVI data set from 2001 to 2005 for validation. Results at continental scale indicate that time series of MODIS and AVHRR were similar for a wide range of biomes and generalized ecoregions. Analysis stratified by land cover indicated that the correlation was strongest for homogeneous land cover types, such as cropland, when compared to structurally more diverse classes, such as deciduous broadleaf forests. The comparison of the NDVI at the local scale at seven sites of the Fluxnet Canada Research Network resulted in the correlation coefficient r = 0.95. Given confidence in the processing approach, this NDVI data set can be a valuable source of information for climate and vegetation-related studies over Canada and the northern United States.

Topography and accuracy of image geometric registration significantly affect the quality of satellite data, since pixels are displaced depending on surface elevation and viewing geometry. This effect should be corrected for through the process of accurate image navigation and orthorectification in order to meet the geolocation accuracy for systematic observations specified by the Global Climate Observing System (GCOS) requirements for satellite climate data records. We investigated the impact of orthorectification on the accuracy of maximum Normalized Difference Vegetation Index (NDVI) composite data for a mountain region in north-western Canada at various spatial resolutions (1 km, 4 km, 5 km, and 8 km). Data from AVHRR on board NOAA-11 (1989 and 1990) and NOAA-16 (2001, 2002, and 2003) processed using a system called CAPS (Canadian AVHRR Processing System) for the month of August were considered. Results demonstrate the significant impact of orthorectification on the quality of composite NDVI data in mountainous terrain. Differences between orthorectified and non-orthorectified NDVI composites (ΔNDVI) adopted both large positive and negative values, with the 1% and 99% percentiles of ΔNDVI at 1 km resolution spanning values between − 0.16

The first spatially enhanced Moderate Resolution Imaging Spectroradiometer (MODIS) clear-sky mosaic for the Arctic circumpolar zone (9000 km×9000 km) is presented, as a contribution to the Canadian component of the International Polar Year (IPY) Programme. The imagery was obtained by fusion of MODIS bands B1-B2 observed at 250 m spatial resolution with bands B3-B7 observed at 500 m spatial resolution to satisfy the Global Climate Observing System (GCOS) requirement for a spatial resolution of 250 m for satellite-based products for climate. The fusion method used adaptive regression and normalization to preserve the image radiometric properties. A new cloud and cloud shadow detection method and a clear-sky compositing scheme were used for the 250 m multispectral data. By the end of the IPY in 2009, a decade-long (2000-2009) time series of these data documenting the state and variability of the Arctic region at fine spatial (250 m) and temporal (10-day) resolution will be produced if MODIS continues to operate until the end of this period. The product is generated in the Lambert Azimuthal Equal-Area (LAEA) projection centred over the North Pole. The major intended application of the new data is mapping the surface albedo at 250 m spatial resolution. This product in turn can be used as an input for generating several other Essential Climate Variables (ECVs) as defined by the GCOS.

A new technology was developed at the Canada Centre for Remote Sensing (CCRS) for generating Canada-wide and North America continental scale clear-sky composites at 250 m spatial resolution for all seven MODIS land spectral bands (B1–B7). The MODIS Level 1B (MOD02) swath level data are used as input to circumvent the problems with image distortion in the mid latitude and polar regions inherent to the global sinusoidal (SIN) projection utilized for the standard MODIS data products. The MODIS 500 m land bands B3 to B7 are first downscaled to 250 m resolution using an adaptive regression and normalization scheme for compatibility with the 250 m bands B1 and B2. A new method has been developed to produce the mask of clear-sky, cloud and cloud shadow at 250 m resolution. It shows substantial advantages in comparison with the MODIS 250 m standard cloud masks. The testing of new cloud mask showed that it is in reasonable agreement with the MODIS 1-km standard product once it is aggregated to 1-km scale, while the cloud shadow detection looks more reliable with the new methodology. Nevertheless, more quantitative analyses of the presented scene identification technique are required to understand its performance over the range of input scenes in various seasons. The new clear-sky compositing scheme employs a scene-dependent decision matrix. It is demonstrated that this new scheme provides better results than any others based on a single compositing criterion, such as maximum NDVI or minimum visible reflectance. To account for surface bi-directional properties, two clear-sky composites for the same time period are produced by separating backward scattering and forward scattering geometries, which separate pixels with the sun-satellite relative azimuth angles within 90°–270° and outside of this range. Comparison with Landsat imagery and with MODIS standard composite products demonstrated the advantage of the new technique for screening cloud and cloud shadow, and generating high spatial resolution MODIS clear-sky composites. The new data products are mapped in the Lambert Conformal Conic (LCC) projection for Canada and the Lambert Azimuthal Equal-Area (LAEA) projection for North America. Presently this activity is limited to MODIS/TERRA due to known problems with band-to-band registration and noisy SWIR channels on MODIS/AQUA.

Multispectral surface albedo and bidirectional properties are required for accurate determination of the surface and atmosphere solar radiation budget. A method is developed here to obtain time series of these surface characteristics consistent with the Moderate Resolution Imaging Spectroradiometer (MODIS) using historical satellite observations with limited spectral coverage available from NOAA Advanced Very High Resolution Radiometer (AVHRR) and VEGETATION/Satellite pour l’Observation de la Terre (SPOT). A nonlinear regression model was developed that relates retrievals from four spectral channels of VEGETATION/SPOT or three spectral channels of NOAA AVHRR with retrieval from each of the seven MODIS channels designed for land applications. The model also takes into account the surface land cover type, the normalized difference vegetation index, and the seasonal cycle. It was applied to generate surface albedo and bidirectional parameters of the seven MODIS-like spectral channels at a 10-day interval for the 1995–2004 period over the U.S. southern Great Plains. The relative retrieval accuracy for the MODIS channels replicated from AVHRR or VEGETATION/SPOT data was typically better than 5%. Correlation coefficients between replicated and original data varied from 0.92 to 0.98 for all channels except MODIS channel 5, where it was lower (0.77–0.84). The developed method provides valuable information for parameterization of spectral albedo in global climate models and can be extended to generate global multispectral data compatible with MODIS from historical AVHRR and VEGETATION/SPOT observations for the pre-MODIS era.

The identification of clear-sky and cloudy pixels is a key step in the processing of satellite observations. This is equally important for surface and cloud–atmosphere applications. In this paper, the Separation of Pixels Using Aggregated Rating over Canada (SPARC) algorithm is presented, a new method of pixel identification for image data from the Advanced Very High Resolution Radiometer (AVHRR) on board the NOAA satellites. The SPARC algorithm separates image pixels into clear-sky and cloudy categories based on a specially designed rating scheme. A mask depicting snow/ice and cloud shadows is also generated. The SPARC algorithm has been designed to work year-round (day and night) over the temperate and polar regions of North America, for current and historical AVHRR/NOAA High-Resolution Picture Transmission (HRPT) and Local Area Coverage (LAC) data with original 1-km spatial resolution. The algorithm was tested and applied to data from the AVHRR sensors flown on board NOAA-6 to NOAA-18. The method was employed in generating historical clear-sky composites for the 1982–2005 period at daily, 10-day, and monthly time scales at 1-km resolution for an area of 5700 km × 4800 km centered over Canada. This region also covers the northern part of the United States, including Alaska, as well as Greenland and the surrounding oceans. The SPARC algorithm is designed to produce an aggregated rating that accumulates the results of several tests. The magnitude of the rating serves as an indicator of the probability for a pixel to belong to the clear-sky, partly cloudy, or overcast categories. The individual tests employ the spectral properties of five AVHRR channels, as well as surface skin temperature maps from the North American Regional Reanalysis (NARR) dataset. These temperature fields are available at 32 km × 32 km spatial resolution and at 3-h time intervals. Combining all test results into one final rating for each pixel is beneficial for the generation of multiscene clear-sky composites. The selection of the best pixel to be used in the final clear-sky product is based on the magnitude of the rating. This provides much-improved results relative to other approaches or “yes/no” decision methods. The SPARC method has been compared to the results of supervised classification for a number of AVHRR scenes representing various seasons (snow-free summer, winter with snow/ice coverage, and transition seasons). The results show an overall agreement between the automated (SPARC) and the supervised classification at the level of 80% to 91%.

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.

Among all trace gases, the carbon dioxide and methane provide the largest contribution to the climate radiative forcing and together with carbon monoxide also to the global atmospheric carbon budget. New Micro Earth Observation Satellite (MEOS) mission is proposed to obtain information about these gases along with some other mission's objectives related to studying cloud and aerosol interactions. The miniature suit of instruments is proposed to make measurements with reduced spectral resolution (1.2nm) over wide NIR range 0.9μm to 2.45μm and with high spectral resolution (0.03nm) for three selected regions: oxygen A-band, 1.5μm-1.7μm band and 2.2μm-2.4μm band. It is also planned to supplement the spectrometer measurements with high spatial resolution imager for detailed characterization of cloud and surface albedo distribution within spectrometer field of view. The approaches for cloud/clear-sky identification and column retrievals of above trace gases are based on differential absorption technique and employ the combination of coarse and high-resolution spectral data. The combination of high and coarse resolution spectral data is beneficial for better characterization of surface spectral albedo and aerosol effects. An additional capability for retrieval of the vertical distribution amounts is obtained from the combination of nadir and limb measurements. Oxygen A-band path length will be used for normalization of trace gas retrievals.

The Moderate Resolution Imaging Spectroradiometer (MODIS) is a unique source of reach spectral information useful for many applications. It provides observations in 36 spectral bands ranging in wavelengths from 0.4μm to 14.4μm with a spatial resolution from 250m to 1km. The standard MODIS data processing system and products cover the basic operational needs for a number of products and applications. Implemented globally they, however, cannot always make the best use of MODIS 250m and 500m land channels required for terrestrial monitoring and climate change applications. To address the need of regional users in enhanced MODIS data, especially in terms of spatial resolution, an independent technology for processing MODIS imagery has been developed. It uses MODIS level 1B top of the atmosphere swath data as input. The system includes the following steps: 1) fusion (downscaling) of MODIS 500m land channels B3-B7 with 250m bands B1-B2 to obtain consistent 250m imagery for all seven bands B1-B7; 2) re-projection of 250m bands into standard geographic projection; 3) scene identification at 250m spatial resolution to obtain mask of clear-sky, cloud and cloud shadows; 4) compositing clear-sky pixels over 10-day intervals; 5) atmospheric correction; 6) landcover-based BRDF fitting procedure. The fusion technique is designed to work with MODIS/TERRA data due to known problems with band-to-band registration accuracy on MODIS/AQUA. The developed method is applied to generate MODIS clear-sky land products in the Lambert Conformal Conical (LCC) projection for Canada and the Lambert Azimuthal Equal-Area (LAEA) projection for the North America and the Arctic circumpolar zone. The novel clear-sky compositing approach is proposed that significantly reduces impact of BRDF effect on raw composites by separation of pixels into two ranges of relative azimuth angle within 90°-270° and outside of this interval.

A new technology has been developed at the Canada Centre for Remote Sensing (CCRS) for generating North America continental scale clear-sky composites at 250 m spatial resolution of all seven MODIS land spectral bands (B1-B7). The MODIS Level 1B (MOD02) swath level data were used as input to circumvent the problems with image distortion in the mid-latitude and polar regions inherent to the sinusoidal (SIN) projection utilized for the standard MODIS data products. The new data products are stored in the Lambert Conformal Conical (LCC) projection for Canada and the Lambert Azimuthal Equal-Area (LAEA) projection for North America. The MODIS 500m data (B3-B7) were downscaled to 250m resolution using an adaptive regression algorithm. The clear-sky composites are generated using scene identification information produced at 250m resolution and multi-criteria selection which depends on pixel identification. Cloud shadows were also identified and removed from output product. It is demonstrated that new approach provides better results than any scheme based on a single compositing criterion, such as maximum NDVI, minimum visible reflectance, or combination of them. To account for surface bi-directional properties, two clear-sky composites for same time period are produced for the relative azimuth angles within 90°-270° and outside of this interval. Comparison with Landsat imagery and MODIS standard composite products demonstrated advantages of new technique for screening cloud and cloud shadow and providing the high spatial resolution. The final composites were produced for every 10-day intervals since March 2000. The composite products have been used for mapping albedo and vegetation properties as well as for land cover and change detections applications at 250m scale.

[1] The surface albedos simulated by seventeen climate models over the northern latitudes of the Western Hemisphere were compared with satellite-derived albedo products provided by the International Satellite Cloud Climatology Project (ISCCP). Model simulations were conducted in support of the International Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). Results show the following: (1) Annual albedo of the region averaged for all models is fairly close to that provided by the ISCCP (0.351 versus 0.334). The difference between model average and ISCCP albedos is well below the standard deviation in albedo among models. (2) Most models simulated seasonal variations in regional albedo reasonably well. In summer, the models systematically overestimated albedo relative to the ISCCP data by as much as 0.05. In winter, large differences were detected among the climate models. (3) The spatial correlations among models, and between models and ISCCP, depend on geographic location, season and surface type. In general, the spatial correlation coefficients between individual models and the ISCCP data were highest for the land surface in midsummer and for the ocean surface in spring. Model bias was smaller for the ocean surface than for the land surface, and smaller in summer than in winter. (4) Unlike the modeling results, the satellite data showed large interannual variations in albedo and a systematic decreasing trend over the 16 year period of 1984–1999. Depending on season, the standard deviation of albedo interannual variation ranged from 0.036 to 0.074, and the linear regression slope of the decreasing trend ranged from −0.02 to −0.05 per decade according to ISCCP results. The large interannual variation and decreasing trend are not reflected in model simulations. Additional efforts are still required to improve surface albedo simulations in GCMs and its mapping from satellite.

A method is proposed to derive spatially enhanced imagery for all seven Moderate Imaging Spectroradiometer (MODIS) land spectral bands at 250 m spatial resolution. Originally, only bands B1 and B2 [visible (VIS) at 0.65 μm, and near-infrared (NIR) at 0.85 μm] are available from MODIS at 250 m spatial resolution. The remaining five land channels (bands B3 to B7) are observed at 500 m resolution. The adaptive regression is constructed for each individual MODIS L1B granule of 500 m spatial resolution by splitting the area into smaller blocks and generating nonlinear regression between bands B3 to B7 and B1, B2 and NDVI. Once a set of regression coefficients is generated based on 500 m image, it is then applied to 250 m data containing only channels B1 and B2 to produce five intermediate synthetic channels (B3 to B7) at 250 m spatial resolution. The final step involves normalizing the generated 250 m images to original 500 m images to preserve radiometric consistency. It is achieved in two stages and ensures that downscaled results are unbiased relative to original observations. The developed method was applied to generate Canada-wide clear-sky composites containing all seven MODIS land spectral channels at 250 m spatial resolution over the area of North America 5700 km by 4800 km.

A novel algorithm to address the reprojection of MODIS level 1B imagery is proposed. The method is based on the simultaneous 2D search of latitude and longitude fields using local gradients. In the case of MODIS, the gradient search is realized in two steps: inter-segment and intra-segment search, which helps to resolve the discontinuity of the latitude/longitude fields caused by overlap between consecutively scanned MODIS multi-detector image segments. It can also be applied for reprojection of imagery obtained by single-detector scanning systems, like AVHRR, or push-broom systems, like MERIS. The structure of the algorithm allows equal efficiency with either the nearest-neighbor or the bilinear interpolation modes.

Generating historical AVHRR 1 km baseline satellite data records over Canada suitable for climate change studies Rasim Latifovic, Alexander P. Trishchenko, Ji Chen, William B. Park, Konstantin V. Khlopenkov, Richard Fernandes, Darren Pouliot, Calin Ungureanu, Yi Luo, Shusen Wang, Andrew Davidson, and Josef Cihlar Pages 324-346 Abstract. Satellite data are an important component of the global climate observing system (GCOS). To serve the purpose of climate change monitoring, these data should satisfy certain criteria in terms of the length of observations and the continuity and consistency between different missions and instruments. Despite the great potential and obvious advantages of satellite observations, such as frequent repeat cycles and global coverage, their use in climate studies is hindered by substantial difficulties arising from large data volumes, complicated processing, and significant computer resources required for archiving and analysis. Successful examples of satellite earth observation (...