Search

AIM-North is a proposed satellite mission that would provide observations of unprecedented frequency and density for monitoring northern greenhouse gases (GHGs), air quality (AQ) and vegeta...

The Visible Infrared Imaging Radiometer Suite (VIIRS) represents a new generation of satellite imagers for global operational observations. In many aspects, it is comparable to the Moderate...

The solar eclipse is a relatively rare event, but it could cause significant impact on satellite products if not accounted for properly. The solar eclipse effect on daily and 10-day clear-sky composites is described here. It is shown that the standard scene identification and compositing algorithm developed at the Canada Centre for Remote Sensing (CCRS) for Moderate Resolution Imaging Spectroradiometer (MODIS) imagery fails to identify pixels affected by a solar eclipse. As a result, the solar-eclipse-affected imagery is almost entirely included in the composite product. A similar effect is observed in the standard NASA MOD09/MYD09 daily composite reflectance products. We describe here the details of the eclipse-affected imagery, the reason why clear-sky compositing criteria failed, and propose some modifications to the standard algorithm, such as reflectance normalization and scene identification procedures, to overcome the solar eclipse contamination problem in multiscene image composites.

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...

There is a well-recognized spatiotemporal meteorological observation gap at latitudes higher than 55°, especially in the region 55°–70°. A possible solution to address this issue is a constellation of four satellites in a highly elliptical orbit (HEO), that is, two satellites for each polar region. An important satellite product to support weather prediction is atmospheric motion wind vectors (AMVs). This study uses observing system simulation experiments (OSSEs) to evaluate the benefit to forecasts resulting from the assimilation of HEO AMVs covering one or both polar regions. The OSSE employs the operational global data assimilation system of the Canadian Meteorological Center. HEO AMVs are assimilated north of 50°N and south of 50°S. From 2-month assimilation cycles, the study examines the following three issues: 1) the impact of AMV assimilation in the real system, and how this compares to the impact seen in the simulated system, 2) the added value of HEO AMVs in the Arctic on top of what is currently available, and 3) the relative impact of HEO AMVs in the Arctic and Antarctic in comparison with no AMVs. Although the simulated impact of currently available AMVs is somewhat higher than the real impact, a firm conclusion is that the added value of Arctic HEO AMVs is substantial, improving predictability at days 3–5 by a few hours in terms of 500-hPa geopotential height. The impact of HEO AMVs is relatively stronger in the Southern Hemisphere. Forecast validation of atmospheric profiles against the simulated “true” state and against analyses generated within the assimilation cycles yields very similar results beyond 48 h.

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.

There has been a significant increase of interest in the building of a comprehensive Arctic observing system in recent years to properly and timely track the environmental and climate processes in this vast region. In this regard, a satellite observing system on highly elliptical orbit (HEO) with 12-h period (Molniya type) is of particular interest, because it enables continuous coverage of the entire Arctic region (58°–90°N) from a constellation of two satellites. Canada is currently proposing to operate such a constellation by 2017. Extending the pioneering study of S. Q. Kidder and T. H. Vonder Haar, this paper presents in-depth analysis of spatiotemporal sampling properties of the imagery from this system. This paper also discusses challenges and advantages of this orbit for various applications that require high temporal resolution and angular sampling.

Precise geolocation is one of the fundamental requirements for satellite imagery to be suitable for climate applications. The Global Climate Observing System and the Committee on Earth Observing Satellites identified the requirement for the accuracy of geolocation of satellite data for climate applications as 1/3 field of view (FOV). This requirement for the series of the Advanced Very High Resolution Radiometer (AVHRR) on the National Oceanic and Atmospheric Administration platforms cannot be met without implementing the ground control point (GCP) correction, particularly for historical data, because of limited accuracy of orbit modeling and knowledge of satellite attitude angles. This paper presents a new method for precise georeferencing of the AVHRR imagery developed as part of the new Canadian AVHRR processing system (CAPS) designed for generating high-quality AVHRR satellite climate data record at 1-km spatial resolution. The method works in swath projection and uses the following: 1) the reference monthly images from Moderate Resolution Imaging Spectroradiometer at 250-m resolution; 2) orthorectification to correct for surface elevation; and 3) a novel image matching technique in swath projection to achieve the subpixel resolution. The method is designed for processing daytime data as it intensively employs observations from optical solar bands, the near-infrared channel in particular. The application of the developed processing system showed that the algorithm achieved better than 1/3 FOV geolocation accuracy for AVHRR 1-km scenes. It has very high efficiency rate (> 97%) due to the dense and uniform GCP coverage of the study area (5700 × 4800 km2 ), covering the entire Canada, the Northern U.S., Alaska, Greenland, and surrounding oceans.

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.

This work extends the previous study of Trishchenko et al. [Trishchenko, A. P., Cihlar, J., & Li, Z. (2002). Effects of spectral response function on surface reflectance and NDVI measured with moderate resolution satellite sensors. Remote Sensing of Environment 81 (1), 1–18] that analyzed the spectral response function (SRF) effect for the Advanced Very High Resolution Radiometer (AVHRR) onboard the NOAA satellites NOAA-6 to NOAA-16 as well as the Moderate Resolution Imaging Spectroradiometer (MODIS), the VEGETATION sensor (VGT) and the Global Imager (GLI). The developed approach is now applied to cover three new AVHRR sensors launched in recent years on NOAA-17, 18, and METOP-A platforms. As in the previous study, the results are provided relative to the reference sensor AVHRR NOAA-9. The differences in reflectance among these three radiometers relative to the AVHRR NOAA-9 are similar to each other and range from − 0.015 to 0.015 (− 20% to + 2% relative) for visible (red) channel, and from − 0.03 to 0.02 (− 5% to 5%) for the near infrared (NIR) channel. The absolute change in the Normalized Difference Vegetation Index (NDVI) ranged from − 0.03 to + 0.06. Due to systematic biases of the visible channels toward smaller values and the NIR channels toward slightly larger values, the overall systematic biases for NDVI are positive. The polynomial approximations are provided for the bulk spectral correction with respect to the AVHRR NOAA-9 for consistency with previous study. Analysis was also conducted for the SRF effect only among the AVHRR-3 type of radiometer on NOAA-15, 16, 17, 18 and METOP-A using AVHRR NOAA-18 as a reference. The results show more consistency between sensors with typical correction being under 5% (or 0.01 in absolute values). The AVHRR METOP-A reveals the most different behavior among the AVHRR-3 group with generally positive bias for visible channel (up to + 5%, relative), slightly negative bias for the NIR channel (1%–2% relative), and negative NDVI bias (− 0.02 to + 0.005). Polynomial corrections are also suggested for normalization of AVHRR on NOAA-15, 16, 17 and METOP-A to AVHRR NOAA-18.

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%.

Satellite observations in the shortwave infrared (SWIR) part of spectrum between 3.5 and 4.0 μm deliver critically important information for many applications. The satellite signal in this spectral band consists of solar-reflected radiation and thermal radiation emitted by surface, clouds, and atmosphere. Accurate retrievals require precise knowledge of solar irradiance values within a channel's bandwidth. The magnitudes of solar irradiance for shortwave infrared channels (3.7–3.9 μm) for the Advanced Very High Resolution Radiometer (AVHRR) on board the National Oceanic and Atmospheric Administration-7 (NOAA-7) to NOAA-18 satellites and the Geostationary Operational Environmental Satellite-8 (GOES-8) to GOES-12 are considered in this paper. Four recent solar reference spectra [those of Kurucz, Gueymard, the American Society for Testing and Materials (ASTM), and Wehrli] are analyzed to determine uncertainties in the knowledge of solar irradiance values for SWIR channels of the listed sensors. Because thermal radiation is frequently converted to effective blackbody temperature for analysis, computations, and calibration purposes, it is proposed here to express band-limited solar irradiance values in terms of brightness temperature as well. It is shown that band-limited solar irradiance for AVHRR radiometers expressed in terms of blackbody equivalent brightness temperature correspond to the range 355–360 K, and vary around 345 K for the SWIR channels of the GOES imagers. The values of band-limited solar irradiance and brightness temperatures are provided for various reference solar spectra. The relative differences in band-limited solar irradiance computed for the considered reference solar spectra are between 0% and 2.5%. Differences expressed in terms of brightness temperatures may reach 0.8 K. The results for the ASTM and the Kurucz reference spectra agree within 0.1% relative difference. Parameters of linear fits relating effective brightness temperatures and spectral radiance equivalent temperatures are also determined for all sensors. They are required for precise radiance–temperature and temperature–radiance conversion through Planck's functions in the case of the finite spectral response of real sensors.

We report the results of a modeling study on the sensitivity of normalized difference vegetation index (NDVI) and surface reflectance to differences in instrument spectral response functions (SRF) for various Advanced Very High Resolution Radiometers (AVHRR) onboard the National Oceanic and Atmospheric Administration's (NOAA) satellites NOAA-6–16 as well as the Moderate Resolution Imaging Spectroradiometer (MODIS), the Vegetation sensor (VGT), and the Global Imager (GLI). Modeling results were validated against real satellite observations employing AVHRR/NOAA-14 and -15 and MODIS, with a very good agreement. It is shown that for identical atmospheric state and similar surface spectral reflectance, the NDVI and spectral reflectances are sensitive to the sensor's SRF. Relative to a reference SRF for AVHRR/NOAA-9, the differences in reflectance among the AVHRRs range from −25% to 12% for visible channel (red) and from −2% to 4% for near-infrared (NIR) channel. Absolute change in NDVI among various AVHRRs ranged from −0.02 to 0.06. The most significant difference was observed for the AVHRR/3. Consistent results were obtained with the AVHRR sensors aboard the following afternoon satellites: NOAA-9, -11, and -12, whereas important discrepancies were found for other AVHRRs aboard NOAA-6 and -10 and especially those launched more recently (NOAA-15 and -16). Reflectance and NDVI measured by MODIS channels 1 and 2 also exhibit significant differences (up to 30–40%) relative to AVHRR. GLI and VGT have some specific features that should be taken into account when intercomparing surface or top of the atmosphere (TOA) reflectance as well as NDVI. Sensitivity of the SRF effect to variable atmospheric state (water vapor, aerosol, and ozone) was also investigated. Polynomial approximations are provided for bulk spectral correction with respect to AVHRR/NOAA-9.

This chapter presents the approach and results of mapping surface albedo and bi-directional reflectance distribution function (BRDF) properties over the Mackenzie River Basin (MRB). Satellite observations from three types of sensors were used: (1) the Advanced Very High Resolution Radiometer (AVHRR) sensor onboard the NOAA platforms, (2) the VEGETATION (VGT) sensor onboard the SPOT platforms, and (3) the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the TERRA platform. The data collected using these sensors span the period of 1985 to 2004. Seasonal and interannual variability of spectral and broadband albedo over the MRB was analyzed. Broadband albedo averaged over the region changed from 0.11±0.03 in summer to 0.4-0.55 in winter. No substantial long-term systematic trends in surface albedo could be detected over the study period, mainly due to large interannual variability, uncertainties in sensors properties, atmospheric correction, and retrieval procedure.

Clouds and wildfire aerosols are both important in our understanding of the radiation budgets in the Mackenzie River Basin (MRB). The smoke aerosols produced by wildfires are predominately aerosols that can absorb incoming solar radiation in the atmosphere. Both the cloud and wildfire aerosol radiative forcing at the top of atmosphere (TOA) and at the surface in the MRB are investigated. Second-order polynomial relationships are found between forcing ( Δ F) and aerosol optical thickness (τ) for the reflected solar fluxes at the TOA, the outgoing longwave (LW) fluxes at the TOA, and the net surface solar fluxes. The monthlymean basin-average cloud shortwave forcing at the TOA and at the surface between June and September are between 150 and 200Wm-2 and -160 and -220 W M-2, respectively, while the basin-averaged outgoing LW cloud forcing at the TOA is around -40 W m-2. Overall, the cloud forcing could impact the shortwave and longwave radiation budgets by 30–50% both at the TOA and at the surface for the MRB region.

The accurate atmospheric correction of historical satellite long-term data is required to make them suitable for climate change application. The correction of satellite data over land for aerosol effect constitutes the most challenging part of the processing. In this paper we explored an opportunity to use inland water bodies over Canada to retrieve aerosol optical depth. Because of large water fraction this approach looks quite promising over Canada territory. The details of water surface reflection model and retrieval scheme are presented. The comparison with AERONET ground-based sun photometer retrievals looks quite encouraging. This opens an opportunity for improved atmospheric correction of historical AVHRR 1-km data over Canada's landmass.

[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.

[1] Ground-based radiation measurements are frequently used for validating the performance of a model in simulating clouds. Such important questions are often raised as: (1) How well do the measurements represent model grid mean values?; (2) How much of model-observation differences can be attributed to inherent sampling errors?; and (3) What scale does modeling need to be performed in order to capture the cloud variation? We attempt to address these questions using surface solar irradiance data retrieved from the Geostationary Operational Environmental Satellite (GOES) and measured at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site. The satellite retrievals are used to mimic ground measurements with various spatial densities and temporal frequencies, from which the sampling errors of the ground observations are quantified and characterized. Most of the differences between point-specific measurements and area-mean satellite retrievals originate from ground sampling errors. We quantify these errors for different months, model grid sizes, and integration intervals. In March 2000, for example, the sampling error is 16 W m−2 for instantaneous irradiances averaged over an area of 10 × 10 km2. It increases to 46 and 64 W m−2 if the model grid size is enlarged to 200 × 200 km2 and 400 × 400 km2, respectively. The sampling uncertainties decrease rapidly as the time-averaging interval increases up to 24 hours and then level off to a relatively small and stable value. Averaging over periods greater than 5 days reduces the error to a magnitude of less than 15 W m−2 over all grid sizes. The sampling error also decreases as the number of ground stations increases inside a grid, but the most substantial reduction occurs as the number of ground sites increases from 1 to 2 or 3 for a grid size of 200 × 200 km2. This means that for computing grid-mean surface solar irradiance, there is no need for an overly dense network of observation stations.

Surface bi-directional reflectance distribution function (BRDF) and albedo properties are retrieved over the Atmospheric Radiation Measurement (ARM) Program Southern Great Plains (SGP) area. A landcover-based fitting approach is employed by using a newly developed landcover classification map and the MODIS 10-day surface reflectance product (MOD09). The surface albedo derived by this method is validated against other satellite systems (e.g. Landsat-7 and MISR) and ground measurements made by an ASD spectroradiometer. Our results show good agreements between the datasets in general. The advantages of this method include the ability to capture rapid changes in surface properties and an improved performance over other methods under a frequent presence of clouds. Results indicate that the developed landcover-based fitting methodology is valuable for generating spatially and temporally complete surface albedo and BRDF maps using MODIS observations.

While existing satellite Earth Observing (EO) systems provide many baseline observations, they are lacking an important combination of capabilities in terms of angular sampling, spectral coverage, and spatial resolution. This has an impact on the accuracy of retrievals and the capability to provide accurate regular operational monitoring of surface and atmospheric properties on a large scale (global or continental). The concept of Advanced Multiangular MEdium Resolution System (AMMERS), that addresses the above issues and provides unique capability presently unavailable from other space observing systems, is proposed here. The mission's unique feature is a combination of medium resolution (400m), multi-spectral observations (13 spectral bands in visible, NIR, SWIR, IR, SW and LW), and multi-angular capabilities (7 angles). AMMERS's multiangular features and swath width allow bi-directional angular sampling close to the solar principal plane and in the perpendicular plane. These capabilities are critically important for accurate estimation of surface albedo, vegetation structure, and forest parameters. AMMERS will also be superior to many other missions in retrieving SST, aerosol, clouds parameters, snow, and wild fire mapping.

[1] Satellite measurements from the infrared (IR) channels of the Advanced Very High Resolution Radiometer (AVHRR)/NOAA have been used to derive many important atmospheric, cloud, and surface parameters for weather prediction, climate modelling, and a variety of environmental studies. Calibration accuracy of the satellite data directly affects accuracies of the derived parameters. So far, very limited attention has been given to the calibration uncertainties of the IR channels. In this study, we analyzed the calibration data of AVHRR radiometers onboard polar orbiting satellites NOAA-9 to NOAA-16. We utilized Global Area Coverage (GAC) data, approximately one orbit per month throughout the lifetime of the instruments, available from the NOAA Satellite Active Archive (SAA). AVHRR IR channels 3B, 4, and 5 are calibrated in-flight. Calibration coefficients are derived from measurements of radiance emitted from an internal calibration target (ICT) and deep-space (SP). The overall budget of uncertainties has been evaluated using an in-flight calibration system that includes four thermal platinum resistance thermometers (PRTs) to monitor the ICT temperature. The measurement noise (NEΔT) was found to vary from 0.03 K to 0.3 K at 300 K depending on the channel and radiometer, and it increases significantly as temperature decreases. Systematic degradation of the radiometric sensitivity of the IR detectors was observed during the lifetime of a radiometer, although the annual rate of degradation is rather small (typically below 1% per year). A significant correlation between the calibration gain and temperature of a radiometer is often observed. The degradation of a sensor's radiometric sensitivity reduces the radiometric resolution of the AVHRR measurements and expands the upper limit of the measured brightness temperature. PRT measurements are subject to significant orbital variation (up to 7 K) and inconsistency for some AVHRR radiometers. The inconsistency was especially large for the AVHRR onboard NOAA-12 (up to 4 K) and NOAA-14 (up to 3 K), but it is less than 0.5 K for NOAA-15 and -16. The inconsistency may signify the presence of a thermal gradient across the ICT. Some systematic differences between PRT measurements may also indicate inaccurate characterization of the PRT sensors, for example for AVHRR/NOAA-11 and -14. The impact of the varying thermal state of the AVHRR environment on the accuracy of AVHRR in-flight thermal calibration was assessed. We found this impact to be significant (up to 0.5 K and more), and proposed a physical model to explain it. We recommend this model for AVHRR operational in-flight calibration, especially during solar radiative contamination events. Estimates of the PRT thermal response time constant were derived and found to vary between 0.5 and 1.5 min among AVHRR radiometers. Overall, we found somewhat higher uncertainties in AVHRR thermal measurements than were assumed previously.

Since the satellites provide frequent and global observations of atmospheric and terrestrial environment, attempts have been made to use satellite data for long-term monitoring of land reflectances, vegetation indices and clouds properties. Although the construction and characteristics of spaceborne instruments may be quite similar, they are not identical among all missions, even for the same type of instrument like AVHRR. Consequently, the effect of varying spectral response may create an artificial noise imposed upon a subtle natural variability. We report the results of a study on the sensitivity of Normalized Difference Vegetation Index (NDVI), surface and cloud reflectance to differences in instrument spectral response functions (SRF) for various satellite sensors. They include AVHRR radiometers onboard NOAA satellites NOAA-6 - NOAA-16, the Moderate Resolution Imaging Spectroradiometer (MODIS), the VEGETATION sensor (VGT) and the Global Imager (GLI). We also analyzed the SRF effects for several geostationary satellites used for cloud studies, such as GOES-8 - 12, METEOSAT-2 - 7, GMS -1 - 5. The results obtained here demonstrate that the effect of instrument spectral response function cannot be ignored in long-term monitoring studies that employ space observations from different sensors. The SRF effect introduces differences in observed reflectances and retrieved quantities that may be comparable or exceed the range of natural variability and possible systematic trends, the contribution from the calibration, atmospheric and other corrections. Some modeling results were validated against real satellite observations with good agreement.

Data from the ScaRaB radiometer flown on board the Meteor-3/7 satellite were first employed for validating and correcting a TOA Earth radiation budget product generated from GOES-7 and the latter was then combined with ground radiation measurements for addressing the effect of clouds on atmospheric absorption of solar radiation. By virtue of comparison between coincident and collocated radiative quantities derived from ScaRaB and GOES sensors, it was found that GOES calibrations for both visible and infrared window channels appear to be adequate, but narrow to broad-band conversion of short-wave measurements suffers systematic errors. After correcting this problem, the cloud radiative forcing at the top of the atmosphere (TOA) and at the surface were derived from space- and ground-based measurements made during the US Atmospheric Radiation Measurement (ARM). The ratio of the two forcing terms is in excellent agreement with that determined by radiative transfer models, in contradiction to the recent claim of cloud absorption anomaly.

This work is mainly focused on the application of the line-by-line (LBL) radiative parameters databases in processing satellite low-resolution measurements of outgoing solar and IR radiation associated with two space instruments such as the scanner of radiation budget (ScaRab on Meteor-3/7 experiment) and ISTOK-1 (PRIRODA-MIR mission). Details of the efficient computer techniques for routine but accurate calculations of solar and IR radiative parameters in order to develop the benchmark databases are briefly described. Some practical results are presented that involve the utilization of line-by-line benchmarks in the ScaRaB measurement validation. The narrow-band model designed for the ISTOK-1 measurement processing and developed on the basis of the LBL precalculations is presented.

Narrowband (NB) to broadband (BB) conversion is a common practice to acquire radiation budget data from operational imagery data. This study attempts to gain further insights into the relationship between NB visible (VIS) albedo and BB shortwave (SW) albedo by means of observational analysis and radiative transfer modeling. Multiple observation datasets were employed including Scanner for Radiation Budget (ScaRaB) satellite measurements, National Centers for Environmental Prediction–National Center for Atmospheric Research reanalysis of precipitable water and temperature profiles, Total Ozone Mapping Spectrometer ozone amount, etc. Radiative transfer modeling was done with an adding–doubling model of high spectral resolution for a range of surface, atmospheric, and cloud conditions. ScaRaB provided calibrated synergistic measurements of VIS and SW albedos. The two types of albedos were found to be linearly correlated with much higher correlation coefficients than previously obtained from other in...

The concept of cloud radiative forcing (CRF) has been widely employed in studying the effects of clouds on the earth’s radiation budget and climate. CRF denotes, in principle, the net influence of cloud alone on the radiation budget of a system. In practice, however, observational determination of CRF is fraught with uncertainties due to factors other than cloud that induce changes in atmospheric background conditions. The most notable variables include aerosol, water vapor, and the data sampling scheme. The impact of these factors on the derivation of CRF and cloud absorption is investigated here by means of modeling and analysis of multiple datasets. Improved estimation of CRF is attempted at the top of the atmosphere (TOA) and at the surface from spatially and temporally collocated ground and satellite measurements for broadband shortwave fluxes. Satellite data employed include pixel measurements from ERBE (1988‐90), ScaRaB (1994‐95), and CERES (1998), as well as surface data acquired across the Canadian radiation network, the ARM Central Facility site in Oklahoma, the US/NOAA SURFRAD networks, and the world BSRN (WMO) networks. It is found that surface CRF is much more susceptible to the variability in background conditions than TOA CRF. Selection of overly clear sky conditions often leads to significant overestimation of surface CRF, but TOA CRF remains intact or only slightly affected. As a result, the ratio of CRF at the surface and TOA is prone to overestimation. With careful treatments of these effects, the CRF ratio turns out to vary mostly between 0.9 and 1.1, implying approximately the same magnitude of atmospheric absorption under clear-sky and cloudy-sky conditions.

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 (...