Advanced Very High Resolution Radiometer Channels Research Articles

A 40-Year Time Series of Land Surface Emissivity Derived from AVHRR Sensors: A Fennoscandian Perspective

Accurate land surface temperature (LST) retrieval depends on precise knowledge of the land surface emissivity (LSE). Neglecting or inaccurately estimating the emissivity introduces substantial errors and uncertainty in LST measurements. The emissivity, which varies across different surfaces and land uses, reflects material composition and surface roughness. Satellite data offer a robust means to determine LSE at large scales. This study utilises the Normalised Difference Vegetation Index Threshold Method (NDVITHM) to produce a novel emissivity dataset spanning the last 40 years, specifically tailored for the Fennoscandian region, including Norway, Sweden, and Finland. Leveraging the long and continuous data series from the Advanced Very High Resolution Radiometer (AVHRR) sensors aboard the NOAA and MetOp satellites, an emissivity dataset is generated for 1981–2022. This dataset incorporates snow-cover information, enabling the creation of annual emissivity time series that account for winter conditions. LSE time series were extracted for six 15 × 15 km study sites and compared against the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD11A2 LSE product. The intercomparison reveals that, while both datasets generally align, significant seasonal differences exist. These disparities are attributable to differences in spectral response functions and temporal resolutions, as well as the method considering fixed values employed to calculate the emissivity. This study presents, for the first time, a 40-year time series of the emissivity for AVHRR channels 4 and 5 in Fennoscandia, highlighting the seasonal variability, land-cover influences, and wavelength-dependent emissivity differences. This dataset provides a valuable resource for future research on long-term land surface temperature and emissivity (LST&E) trends, as well as land-cover changes in the region, particularly with the use of Sentinel-3 data and upcoming missions such as EUMETSAT’s MetOp Second Generation, scheduled for launch in 2025.

Direct assimilation of AVHRR satellite radiance measurements in a reanalysis system

AbstractThe Advanced Very‐High‐Resolution Radiometer (AVHRR) is a broad‐band, five‐channel scanner sensing in the visible, near‐infrared and thermal infrared portions of the electromagnetic spectrum. AVHRR instruments onboard polar‐orbiting satellites have data records spanning 30 years. The radiances from the infrared channels of AVHRR have been directly assimilated over the ice‐free ocean in the NASA Goddard Earth Observing System (GEOS) since 2017 to constrain skin sea surface temperature (SST). The GEOS system already uses an advanced bulk SST and a skin SST model which incorporates diurnal warming and cool‐skin temperature on the surface of the ocean. The positive contribution of this effort to the Numerical Weather Prediction (NWP) makes it desirable to extend the skin SST data assimilation procedure to reanalysis applications. The AVHRR data from platforms that were previously unexplored for NWP applications are assimilated at the Global Modeling and Assimilation Office (GMAO) at NASA, for an upcoming reanalysis project. This study uses results from reanalysis experiments to assess the impact of assimilating AVHRR radiances over open ocean on the atmospheric state variables, focusing on the analyzed skin temperatures that include the active skin SST model. It is demonstrated that including the skin temperature variations in atmospheric analysis is generally beneficial. The addition of radiance measurements from AVHRR provides further improvements to the overall reanalysis system performance. by helping to correct the diurnal heating and reducing errors in the background departure of hyperspectral radiance observations, which are an essential component of atmospheric reanalyses.

The Climate Monitoring SAF Outgoing Longwave Radiation from AVHRR

Data from the Advanced Very High Resolution Radiometer (AVHRR) have been used to create several long-duration data records of geophysical variables describing the atmosphere and land and water surfaces. In the Climate Monitoring Satellite Application Facility (CM SAF) project, AVHRR data are used to derive the Cloud, Albedo, and Radiation (CLARA) climate data records of radiation components (i.a., surface albedo) and cloud properties (i.a., cloud cover). This work describes the methodology implemented for the additional estimation of the Outgoing Longwave Radiation (OLR), an important Earth radiation budget component, that is consistent with the other CLARA variables. A first step is the estimation of the instantaneous OLR from the AVHRR observations. This is done by regressions on a large database of collocated observations between AVHRR Channel 4 (10.8 µm) and 5 (12 µm) and the OLR from the Clouds and Earth’s Radiant Energy System (CERES) instruments. We investigate the applicability of this method to the first generation of AVHRR instrument (AVHRR/1) for which no Channel 5 observation is available. A second step concerns the estimation of daily and monthly OLR from the instantaneous AVHRR overpasses. This step is especially important given the changes in the local time of the observations due to the orbital drift of the NOAA satellites. We investigate the use of OLR in the ERA5 reanalysis to estimate the diurnal variation. The developed approach proves to be valuable to model the diurnal change in OLR due to day/night time warming/cooling over clear land. Finally, the resulting monthly mean AVHRR OLR product is intercompared with the CERES monthly mean product. For a typical configuration with one morning and one afternoon AVHRR observation, the Root Mean Square (RMS) difference with CERES monthly mean OLR is about 2 Wm−2 at 1° × 1° resolution. We quantify the degradation of the OLR product when only one AVHRR instrument is available (as is the case for some periods in the 1980s) and also the improvement when more instruments are available (e.g., using METOP-A, NOAA-15, NOAA-18, and NOAA-19 in 2012). The degradation of the OLR product from AVHRR/1 instruments is also quantified, which is done by “masking” the Channel 5 observations.

Narrowband-to-Broadband Conversions for Top-of-Atmosphere Reflectance from the Advanced Very High Resolution Radiometer (AVHRR)

The current lack of a long, 30+ year, global climate data record of reflected shortwave top-of-atmosphere (TOA) radiation could be tackled by relying on existing narrowband records from the Advanced Very High Resolution Radiometer (AVHRR) instruments, and transform these measurements into broadband quantities like provided by the Clouds and the Earth’s Radiant Energy System (CERES). This paper presents the methodology of an AVHRR-to-CERES narrowband-to-broadband conversion for shortwave TOA reflectance, including the ready-to-use results in the form of scene-type dependent regression coefficients, allowing a calculation of CERES-like shortwave broadband reflectance from AVHRR channels 1 and 2. The coefficients are obtained using empirical relations in a large data set of collocated, coangular and simultaneous AVHRR-CERES observations, requiring specific orbital conditions for the AVHRR- and CERES-carrying satellites, from which our data analysis uses all available data for an unprecedented observation matching between both instruments. The multivariate linear regressions were found to be robust and well-fitting, as demonstrated by the regression statistics on the calibration subset (80% of data): adjusted R 2 higher than 0.9 and relative RMS residual mostly below 3%, which is a significant improvement compared to previous regressions. Regression models are validated by applying them on a validation subset (20% of data), indicating a good performance overall, roughly similar to the calibration subset, and a negligible mean bias. A second validation approach uses an expanded data set with global coverage, allowing regional analyses. In the error analysis, instantaneous accuracy is quantified at regional scale between 1.8 Wm − 2 and 2.3 Wm − 2 (resp. clear-sky and overcast conditions) at 1 standard deviation (RMS bias). On daily and monthly time scales, these errors correspond to 0.7 and 0.9 Wm − 2 , which is compliant with the GCOS requirement of 1 Wm − 2 .

Homogeneity criteria from AVHRR information within IASI pixels in a numerical weather prediction context

Abstract. This article focuses on the selection of satellite infrared IASI (Infrared Atmospheric Sounding Interferometer) observations in the global numerical weather prediction (NWP) system ARPEGE (Action de Recherche Petite Echelle Grande Echelle). The observation simulation is performed with the sophisticated radiative transfer model RTTOV-CLD, which takes into account the cloud scattering and the multilayer clouds from atmospheric profiles and cloud microphysical profiles (liquid water content, ice content and cloud fraction). The aim of this work is to select homogeneous scenes by using the information of the collocated Advanced Very High Resolution Radiometer (AVHRR) pixels inside each IASI field of view and to retain the most favourable cases for the assimilation of IASI infrared radiances. Two methods to select homogeneous scenes using homogeneity criteria already proposed in the literature were adapted: the criteria derived from Martinet et al. (2013) for cloudy sky selection in the French mesoscale model AROME (Applications of Research to Operations at MEsoscale) and the criteria from Eresmaa (2014) for clear-sky selection in the global model IFS (Integrated Forecasting System). A comparison between these methods reveals considerable differences, in both the method to compute the criteria and the statistical results. From this comparison a revised method representing a kind of compromise between the different tested methods is proposed and it uses the two infrared AVHRR channels to define the homogeneity criteria in the brightness temperature space. This revised method has a positive impact on the observation minus the simulation statistics, while retaining 36 % of observations for the assimilation. It was then tested in the NWP system ARPEGE for the clear-sky assimilation. These criteria were added to the current data selection based on the McNally and Watts (2003) cloud detection scheme. It appears that the impact on analyses and forecasts is rather neutral.

Retrieval of aerosol optical depth over land surfaces from AVHRR data

Abstract. The Advanced Very High Resolution Radiometer (AVHRR) provides a global, long-term, consistent time series of radiance data in several wavebands which are used for the retrieval of surface spectral reflectance, albedo and surface temperature. Long-term time series of such data products are necessary for studies addressing climate change, sea ice distribution and movement, and ice sheet coastal configuration. AVHRR radiances have also been used to retrieve aerosol properties over ocean and land surfaces. However, the retrieval of aerosol over land is challenging because of the limited information content in the data which renders the inversion problem ill defined. Solving the radiative transfer equations requires additional information to reduce the number of unknowns. In this contribution we utilise an empirical linear relationship between the surface reflectances in the AVHRR channels at wavelengths of 3.75 μm and 2.1 μm, which has been identified in the Moderate Resolution Imaging Spectroradiometer (MODIS) data. Following the MODIS dark target approach, this relationship is used to obtain the surface reflectance at 0.64 μm. The comparison of the estimated surface reflectances with MODIS reflectance products (MOD09) shows a strong correlation. Once this was established, the MODIS "dark-target" aerosol retrieval method was adapted to AVHRR data. A simplified look-up table (LUT) method, adopted from the Bremen AErosol Retrieval (BAER) algorithm, was used in the retrieval. The aerosol optical depth (AOD) values retrieved from AVHRR with this method compare favourably with ground-based measurements, with 71.8% of the points located within ±(0.1 + 0.15τ) (τ is the AOD) of the identity line. This method can be easily applied to other satellite instruments which do not have a 2.1 μm channel, such as those currently planned to be used on geostationary satellites.

Long‐term hydrological changes of the Aral Sea observed by satellites

AbstractThe Aral Sea has been shrinking since the former Soviet Union constructed irrigation projects to divert water from its main rivers in the 1960s. The diminishing of the Aral Sea is “one of the worst environmental disasters in the world” (from United Nations Secretary‐General Ban Ki‐moon). In this study, 33 years of satellite observations from Advanced Very High Resolution Radiometer (AVHRR) and 21 years of satellite altimetry sea level data from TOPEX/Poseidon, Jason‐1, and Jason‐2 are used to quantify the long‐term hydrological changes in the Aral Sea. A simple algorithm with AVHRR channels 1 and 2 albedo is developed to identify and discriminate the water pixels from land and cloud. Thus, monthly water coverage in the region can be reliably generated. The water coverage maps since 1981 show constant decline of the Aral Sea. The coverage dropped from ∼4.7–4.8 × 104 km2 in 1981 to about ¼ of this value in the recent years. In fact, drastic hydrological change was observed in the main Aral Sea during the 2000s. In the South Aral Sea, sea level shows a steady decrease from 35 m above sea level to <26 m since 1993. Total loss of water storage since 1993 is estimated to be ∼2.0 × 102 km3 for the South Aral Sea with a rate of ∼16−20 km3/yr before 2002, and a smaller value after 2002. In 1990, the North Aral Sea was observed to separate from the main Aral Sea. Water coverage, sea level, and total water storage were kept relatively stable for the period between 1993 and 2013 in the North Aral Sea. A water level increase and coverage expansion occurred during the 2005−2006 period when a dam was built in 2005 between the North Aral Sea and the South‐East Aral Sea.

Multi-Sensor Calibration Studies of AVHRR-Heritage Channel Radiances Using the Simultaneous Nadir Observation Approach

The European Space Agency project for studies of cloud properties in the Climate Change Initiative programme (ESA-CLOUD-CCI) aims at compiling the longest possible time series of cloud products from one single multispectral sensor—The five-channel Advanced Very High Resolution Radiometer (AVHRR) instrument. A particular aspect here is to include corresponding products based on other existing (Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Along-Track Scanning Radiometer (AATSR), MEdium Resolution Imaging Spectrometer (MERIS), Visible and Infrared Radiometer Suite (VIIRS)) and future Sea and Land Surface Temperature Radiometer (SLSTR) sensors measuring in similar (AVHRR-heritage) spectral channels. Initial inter-comparisons of the involved AVHRR-heritage channel radiances over a short demonstration period (2007–2009) were performed. Using Aqua-MODIS as reference, AVHRR (NOAA-18), AATSR, and MERIS channel radiances were evaluated using the simultaneous nadir (SNO) approach. Results show generally agreeing radiances within approximately 3% for channels at 0.6 µm and 0.8 µm. Larger deviations (+5%) were found for the corresponding AATSR channel at 0.6 µm. Excessive deviations but with opposite sign were also indicated for AATSR 1.6 µm and MERIS 0.8 µm radiances. Observed differences may largely be attributed to residual temporal and spatial matching differences while excessive AATSR and MERIS deviations are likely partly attributed to incomplete compensation for spectrally varying surface and atmospheric conditions. However, very good agreement was found for all infrared channels among all the studied sensors. Here, deviations were generally less than 0.2% for the measured brightness temperatures with the exception of some remaining non-linear deviations at extreme low and high temperatures.

The Clouds Climate Change Initiative: Assessment of state-of-the-art cloud property retrieval schemes applied to AVHRR heritage measurements

Cloud property retrievals from 3 decades of the Advanced Very High Resolution Radiometer (AVHRR) measurements provide a unique opportunity for a long-term analysis of clouds. In this study, the accuracy of AVHRR-derived cloud properties cloud mask, cloud-top height, cloud phase and cloud liquid water path is assessed using three state-of-the-art retrieval schemes. In addition, the same retrieval schemes are applied to the AVHRR heritage channels of the Moderate Resolution Imaging Spectroradiometer (MODIS) to create AVHRR-like retrievals with higher spatial resolution and based on presumably more accurate spectral calibration. The cloud property retrievals were collocated and inter-compared with observations from CloudSat, CALIPSO and AMSR-E The resulting comparison exhibited good agreement in general. The schemes provide correct cloud detection in 82 to 90% of all cloudy cases. With correct identification of clear-sky in 61 to 85% of all clear areas, the schemes are slightly biased towards cloudy conditions. The evaluation of the cloud phase classification shows correct identification of liquid clouds in 61 to 97% and a correct identification of ice clouds in 68 to 95%, demonstrating a large variability among the schemes. Cloud-top height (CTH) retrievals were of relatively similar quality with standard deviations ranging from 2.1km to 2.7km. Significant negative biases in these retrievals are found in particular for cirrus clouds. The biases decrease if optical depth thresholds are applied to determine the reference CTH measure. Cloud liquid water path (LWP) is also retrieved well with relative low standard deviations (20 to 28g/m2), negative bias and high correlations. Cloud ice water path (IWP) retrievals of AVHRR and MODIS exhibit a relative high uncertainty with standard deviations between 800 and 1400g/m2, which in relative terms exceed 100% when normalized with the mean IWP. However, the global histogram distributions of IWP were similar to the reference dataset.MODIS retrievals are for most comparisons of slightly better quality than AVHRR-based retrievals. Additionally, the choice of different near-infrared channels, 3.7μm as opposed to 1.6μm, can have a significant impact on the retrieval quality, most pronounced for IWP, with better accuracy for the 1.6μm channel setup. This study presents a novel assessment of the quality of cloud properties derived from AVHRR channels, which quantifies the accuracy of the considered retrievals based on common approaches and validation data. Furthermore, it assesses the capabilities of AVHRR-like spectral information for retrieving cloud properties in the light of generating climate data records of cloud properties from three decades of AVHRR measurements.

Derive a MODIS-Based Calibration for the AVHRR Reflective Solar Channels of the NOAA KLM Operational Satellites

The Advanced Very High Resolution Radiometer (AVHRR) has been on board the National Oceanic and Atmospheric Administration (NOAA) polar-orbiting satellites (POSs) beginning with the Television Infrared Observation Satellite N launched in October 1978. Since then, a series of AVHRR sensors have collected over 30 years of continuous daily global observations. Recently, the NOAA K, L, and M POSs have provided an improved monitoring of the Earth's environment. The AVHRR instrument carried on NOAA-15 (NOAA-K) was launched in May 1998. It is followed by NOAA-16 to NOAA-18 (NOAA-L, NOAA-M, and NOAA-N), Metop-A, and NOAA-19 (NOAA-N'). All of them have been operating to date. An accurate and consistent calibration for the AVHRR reflective solar channels has been challenging as there is no onboard calibrator and vicarious calibration often needs to accumulate large number of reliable observations to derive any meaningful long-term trends. In this paper, we use the Committee on Earth Observation Satellites-endorsed calibration/validation Libya-4 (28.55 N, 23.39 W) desert site to track the long-term stability of reflective solar channels of the NOAA KLM AVHRR. The Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Terra and Aqua platforms is used as a reference to recalibrate the NOAA KLM trends. This study is focused on evaluating the calibration accuracy for the visible and near-infrared channels of each AVHRR instrument using MODIS channels 1 (620-670 nm) and 2 (841-876 nm). A site-specific bidirectional reflectance distribution function developed based on observations made by MODIS is used to normalize AVHRR-observed reflectances. Impacts of atmospheric water vapor on AVHRR-to-MODIS reflectance ratios are corrected with measured total water-vapor contents derived using the split-window temperature difference technique. Finally, MODIS-based AVHRR calibration results, which track the AVHRR on-orbit change, are applied on top of the AVHRR prelaunch values and presented in the form of quadratic polynomials as a function of time. All recalibrated AVHRR reflectances are compared with those provided from a Level 1B product. Additional validation is performed using NOAA-17 AVHRR observations acquired over Antarctic Dome Concordia site where the impact due to atmospheric water vapor is believed to be extremely small.

Retrieval of aerosol optical depth and surface reflectance over land from NOAA AVHRR data

An algorithm for the land aerosol and bidirectional reflectance inversion by times series technique (LABITS) is proposed and applied to data from the National Oceanic and Atmospheric Administration (NOAA) advanced very high resolution radiometer (AVHRR). The surface reflectance and aerosol optical depth are inverted from AVHRR Channel 1 data using a model for the earth-atmosphere system which couples an atmospheric radiative transfer model with the Ross–Thick–Li-sparse bidirectional reflectance factor (BRF) model. Basic assumptions are that the surface bidirectional reflective property does not vary over a 2–4day periods and that the aerosol characteristics are uniform within a 0.1°×0.1° window (approximately 10km×10km). The LABITS algorithm is applied to data from AVHRR on the NOAA-15, NOAA-16, and NOAA-18 satellites over four distinct areas, namely, North America, Europe, the Sahara and India, to simultaneously retrieve the aerosol optical depth (AOD), BRF parameters and surface albedo. Preliminary results show that AOD and reflectance retrieved from the three different instruments are in good agreement and that LABITS provides good results over both bright surfaces, e.g. the Sahara, and dark surfaces, e.g. Europe. Evaluation of the AOD versus data from the Aerosol Robotic Network (AERONET) provides a correlation coefficient R2 of 0.88 and a root-mean-square error (RMSE) of approximately 0.07; and the uncertainty is approximately Δτ=±0.05±0.20τ. Comparing our results with the moderate resolution imaging spectroradiometer (MODIS) AOD products, over many areas, provides biases in the range of ±0.05. The surface albedo values calculated from the retrieved BRF parameters are similar to those provided by the MODIS albedo product (MCD43). The robustness and applicability of the LABITS algorithm are demonstrated with the retrieval of AOD over China during August 2008. Daily and monthly averaged results show good agreement with collocated AERONET observations and AQUA MODIS products (MYD04 and MYD08). The AOD uncertainty is estimated as Δτ=±0.05±0.30τ. The preliminary analysis of time series over selected AERONET sites shows that the temporal variations of the AOD values retrieved by application of LABITS to AVHRR data are overall similar to temporal variations of AOD provided by the MODIS and AERONET. The algorithm has the potential to retrieve global AOD over land for long time series of NOAA AVHRR data going back to the 1980s, which are urgently needed for studies on aerosol climatology and global climate change.

Radiometric and spectral characterization and comparison of the Antarctic Dome C and Sonoran Desert sites for the calibration and validation of visible and near-infrared radiometers

Global climate change studies require long-term, radiometrically accurate, and stable observations from a number of satellites. Spatially uniform and radiometrically/spectrally stable vicarious calibration sites can be used to quantify the sensor gain change over time, monitor the instrument performance, and compare measurements from multiple instruments to maintain consistent radiometric calibration. This study uses AQUA moderate resolution imaging spectroradiometer (MODIS), MetOp-A advanced very high resolution radiometer (AVHRR), and Earth Observing-1 Hyperion to analyze the radiometric and spectral characteristics of the Dome C and Sonoran Desert sites and perform intercomparisons. The radiometric stability of both sites over a period of eight years as evaluated using AQUA MODIS is found to be better than 2% in the visible (0.64 μm) and near-infrared regions (0.86 μm), assuming the MODIS calibration is stable. The bidirectional reflectance distribution (BRDF) effect over Dome C is large, with greater than 5% seasonal variation, compared to the Sonoran Desert with less than 2% variation. However, the BRDF impact can be reduced to less than 2% for Dome C after normalization by an appropriate BRDF model. For water vapor absorption channels, such as AVHRR channel 2 (0.86 μm), this study suggests that the Sonoran Desert is largely affected with greater than 6% absorption variability compared to that of Dome C with less than 2%. The study also reveals that the operationally calibrated AVHRR top-of-atmosphere reflectance is lower than that of MODIS by about 8%.

Assessment of MetOp-A Advanced Very High Resolution Radiometer (AVHRR) short-wave infrared channel measurements using Infrared Atmospheric Sounding Interferometer (IASI) observations and line-by-line radiative transfer model simulations

MetOp-A satellite-based hyper-spectral Infrared Atmospheric Sounding Interferometer (IASI) observations are used to evaluate the accuracy of the broadband short-wave infrared (SWIR) atmospheric window channel (channel 3B) centred at 3.74 μm of the Advanced Very High Resolution Radiometer (AVHRR) carried on the same platform. To complement the partial spectral coverage of IASI, line-by-line radiative transfer model (LBLRTM)-simulated IASI spectra are used. The comparisons result in significant negative AVHRR minus IASI bias in radiance (∼–0.04 mW m–2 sr–1 cm–1) with scene temperature dependency in which the absolute value of the bias linearly increases with increasing temperature. It is demonstrated that the negative bias and the scene temperature dependency of the bias are the results of significant absorption in the portion of AVHRR spectral band not seen by IASI, leading to the conclusion that MetOp-A AVHRR channel 3B is not purely an ‘atmospheric window’ channel.

Calibrations for AVHRR channels 1 and 2: review and path towards consensus

The over three-decade-long data record from the Advanced Very High Resolution Radiometer (AVHRR) is ideal for studies of the Earth's changing climate. However, the lack of on-board calibration requires that the solar channels be recalibrated after launch. Numerous calibration studies have been conducted, but significant differences remain among the calibrations. This study is one effort to outline a path towards consensus calibration of the AVHRR solar channels. The characteristics of the polar orbiting satellites bearing the AVHRRs, the AVHRR instruments and data are described as they are related to calibration. A review of past and current calibration studies is also presented and examples of their lack of consensus shown. A list of consensus items is then provided that, if followed by the AVHRR calibration community, should bring the various calibration methods to within the small percent difference required for long-term climate detection.

Validation of the Climate-SAF surface broadband albedo product: Comparisons with in situ observations over Greenland and the ice-covered Arctic Ocean

This paper describes a validation study performed by comparing the Climate-SAF Surface Albedo Product (SAL) to ground truth observations over Greenland and the ice-covered Arctic Ocean. We compare Advanced Very High Resolution Radiometer (AVHRR)-based albedo retrievals to data from the Greenland Climate Network (GCN) weather stations and the floating ice station Tara for polar summer 2007. The AVHRR dataset consists of 2755 overpasses. The overpasses are matched to in situ observations spatially and temporally. The SAL algorithm presented here derives the surface broadband albedo from AVHRR channels 1 and 2 using an atmospheric correction, temporal sampling of an empirical Bidirectional Reflectance Distribution Function (BRDF), and a narrow-to-broadband conversion algorithm. The satellite product contains algorithms for snow, sea ice, vegetation, bare soil, and water albedo. At the Summit and DYE-2 stations on the Greenland ice sheet, instantaneous SAL RMSE is 0.073. The heterogeneous surface conditions at satellite pixel scale over the stations near the Greenland west coast increase RMSE to > 0.12. Over Tara, the instantaneous SAL RMSE is 0.069. The BRDF sampling approach reduces RMSE over the ice sheet to 0.053, and to 0.045 over Tara. Taking into account various sources of uncertainty for both satellite retrievals and in situ observations, we conclude that SAL agrees with in situ observations within their limits of accuracy and spatial representativeness.

Tracking the calibration stability and consistency of the 3.7, 11.0 and 12.0 μm channels of the NOAA-KLM AVHRR with MODIS

The NOAA-KLM satellites (NOAA-15 to 18) are the current polar-orbiting operational environmental satellites (POES) that carry the Advanced Very High Resolution Radiometer (AVHRR). This study examines the calibration stability and consistency of all three infrared channels (3.7, 11.0 and 12.0 μm) of AVHRR onboard NOAA-15 to 18. The short-term stability is examined from variations of the scan-by-scan gain response, while the long-term stability and calibration consistency are examined by tracking the trends of gain response and measured scene brightness temperatures. The relative differences of observed scene brightness temperatures among NOAA-15 to 18 AVHRR are determined using MODIS as a transfer radiometer based on observations from simultaneous nadir overpasses (SNO). Results show that variations of the scan-to-scan gain responses are within 0.10% under normal operational conditions, while long-term gain changes over six years from 2001 to 2006 vary from 2 to 4% depending on channel. Long-term trending results show that total six-year drifts in observed brightness temperature from NOAA-15 to 18 AVHRR are less than 0.5 K for a given scene temperature in the 250 to 270 K range for the 3.7, 11.0 and 12.0 μm channels, respectively. The calibration consistency is examined for a scene temperature range of 220 to 290 K. The temperature biases among NOAA-16 to 18 AVHRR are within ±0.5 K for the 11.0 and 12.0 μm channels. For NOAA-15 AVHRR, biases of –2.0 K at 11.0 μm and –1.5 K at 12.0 μm are found in comparison with others at the low end of the temperature range. For the 3.7 μm channel, relative biases up to a few degrees among NOAA-15 to 18 could be found at low brightness temperatures.

Effect of Out-of-Band Response in NOAA-16 AVHRR Channel 3b on Top-of-Atmosphere Radiances Calculated with the Community Radiative Transfer Model

Abstract The Community Radiative Transfer Model (CRTM) developed at the Joint Center for Satellite Data Assimilation (JCSDA) is used in conjunction with a daily sea surface temperature (SST) and the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) atmospheric data and surface wind to calculate clear-sky top-of-atmosphere (TOA) brightness temperatures (BTs) in three Advanced Very High Resolution Radiometer (AVHRR) thermal infrared channels over global oceans. CRTM calculations are routinely performed by the sea surface temperature team for four AVHRR instruments on board the National Oceanic and Atmospheric Administration (NOAA) satellites NOAA-16, NOAA-17, and NOAA-18 and the Meteorological Operation (MetOp) satellite MetOp-A, and they are compared with clear-sky TOA BTs produced by the operational AVHRR Clear-Sky Processor for Oceans (ACSPO). It was observed that the model minus observation (M−O) bias in the NOAA-16 AVHRR channel 3b (Ch3b) centered at 3.7 μm experienced a discontinuity of ∼0.3 K when a new CRTM version 1.1 (v.1.1) was implemented in ACSPO processing in September 2008. No anomalies occurred in any other AVHRR channel or for any other platform. This study shows that this discontinuity is caused by the out-of-band response in NOAA-16 AVHRR Ch3b and by using a single layer to the NCEP GFS temperature profiles above 10 hPa for the alpha version of CRTM. The problem has been solved in CRTM v.1.1, which uses one of the six standard atmospheres to fill in the missing data above the top pressure level in the input NCEP GFS data. It is found that, because of the out-of-band response, the NOAA-16 AVHRR Ch3b has sensitivity to atmospheric temperature at high altitudes. This analysis also helped to resolve another anomaly in the absorption bands of the High Resolution Infrared Radiation Sounder (HIRS) sensor, whose radiances and Jacobians were affected to a much greater extent by this CRTM inconsistency.

On-Orbit Calibration Assessment of AVHRR Longwave Channels on MetOp-A Using IASI

Accurate and precise satellite radiance measurements are important for data assimilations in numerical weather prediction models and for climate-change detection. After the successful launch of the infrared atmospheric sounding interferometer (IASI), several studies have indicated that the IASI radiance measurements are well calibrated and maintain superb spectral and radiometric calibration accuracy. Owing to its hyperspectral nature and high-quality measurements, the IASI radiance can serve as a relative reference to independently assess the radiance measurements of broad- or narrow-band instruments that share the same spectral region. In this paper, we demonstrate the utility of the IASI radiances to evaluate the Advanced Very High Resolution Radiometer (AVHRR) infrared (IR) channel measurements. The coregistered AVHRR pixels inside each IASI pixel are averaged spatially. We then compared the spatially averaged radiance from AVHRR IR channels with IASI by convolving the IASI-measured spectra with the AVHRR spectral response functions. It was found that, statistically, the temperature observed from AVHRR channels 4 and 5 is slightly warmer than that in IASI for the brightness temperature (BT) range of 200 K-300 K. The mean BT difference (IASI minus AVHRR) is less than 0.4 K with a standard deviation of ~0.3 K for AVHRR channels 4 and 5. The BT difference between IASI and AVHRR IR channels is scene-temperature dependent for both channels 4 and 5, which is probably caused by the nonlinearity of the AVHRR detectors. Both AVHRR channels 4 and 5 show slight scan-dependent bias with maximum differences of approximately ~0.2 K (with AVHRR being warmer than IASI) at both ends of the scan.

NOAA-AVHRR Orbital Drift Correction From Solar Zenithal Angle Data

This paper presents a new method for NOAA's (National Ocean and Atmospheric Administration) orbital drift correction. This method is pixel-based, and in opposition with most methods previously developed, does not need explicit knowledge of land cover. This method is applied to AVHRR (Advanced Very High Resolution Radiometer) channel information, and relies only on the additional knowledge of solar zenithal angle (SZA) and acquisition date information. In a first step, anomalies in SZA and channel time series are retrieved, and screened out for anomalous values. Then, the part of the parameter anomaly which is explained by SZA anomaly is removed from the data, to estimate new parameter anomalies, and this iteratively until the influence of SZA anomalies is totally removed from the parameter data. This correction has been applied to bimonthly AVHRR data provided by the GIMMS group (Global Inventory Modeling and Mapping Studies), covering Africa from November 2000 to December 2006. NDVI and LST (land surface temperature) have been estimated from raw and corrected data, and averaged over homogeneous vegetation classes. Differences between raw and corrected averaged parameters show an improvement in the quality of the data. In order to validate this method, a whole week (10 to 17 July 2004) of METEOSAT SEVIRI (Spinning Enhanced Visible and InfraRed Imager) data have been used, from which LST have been estimated using a similar method to the one used to retrieve LST from AVHRR data. The comparison between both platforms at the same time of acquisition shows good concordance.

Validation of clear-sky radiances over oceans simulated with MODTRAN4.2 and global NCEP GDAS fields against nighttime NOAA15-18 and MetOp-A AVHRR data

An accurate and globally representative forward radiative transfer model (RTM) is needed to explore improvements in sea surface temperature (SST) retrievals from spaceborne infrared observations. This study evaluates the biases in top-of-atmosphere (TOA) brightness temperatures (BT) modeled with the moderate resolution transmission (MODTRAN4.2) band RTM, bounded by a Fresnel's reflective flat sea surface. This model is used to simulate global clear-sky Advanced Very High Resolution Radiometer (AVHRR) nighttime BTs from NOAA-15 through 18 and MetOp-A platforms for one full day of 18 February 2007. Inputs to RTM (SST fields and vertical profiles of atmospheric relative humidity, temperature, pressure, and geopotential height) are specified from the National Centers for Environmental Prediction's (NCEP) Global Data Assimilation System (GDAS) data. Model BTs in AVHRR channels 3B (3.7 μm), 4 (11 μm), and 5 (12 μm) are then compared with their respective measured counterparts, available in the NESDIS operational SST files. Ideally, the RTM should match the observations, but in fact, the modeled BTs are biased high with respect to the AVHRR BTs. The “Model minus Observation” (M − O) bias ranges from about 0 to 2 K, depending upon spectral band, view zenith angle, and sea and atmosphere state at the retrieval point. The bias asymptotically decreases towards confidently clear-sky conditions, but it never vanishes and invariably shows channel-specific dependencies on view zenith angle and geophysical conditions (e.g., column water vapor and sea–air temperature difference). Fuller exploration of the potential of the current RTM (e.g., adding global vertical aerosol profiles) or improvements to its input (NCEP SST and atmospheric profiles) may reduce this bias, but they cannot fully reconcile its spectral and angular structure. The fact that the M − O biases are closely reproducible for five AVHRR sensors flown onboard different platforms adds confidence in the validation approach employed in this study. We emphasize the need for establishing a globally adequate forward RTM for the use in SST modeling and retrievals. A first test of the RTM adequacy is its ability, when used in conjunction with the global fields from the numerical weather prediction models, to reproduce the TOA clear-sky radiances measured by satellite sensors.