Satellite Zenith Angle Research Articles

Hourly sea surface temperature retrieval using the Japanese Geostationary Satellite, Multi-Functional Transport Satellite (MTSAT)

An algorithm has been developed for retrieving sea surface temperature (SST) from hourly data transmitted from the Japanese Advanced Meteorological Imager (JAMI) aboard a Japanese geostationary satellite, Multi-functional Transport Satellite (MTSAT)-1R. Threshold tests screening cloudy pixels are empirically adjusted to cases of daytime with/without sun glitter, and nighttime. The Non-Linear SST (NLSST) equation, including several new additional terms, is used to calculate MTSAT SST. The estimated SST is compared with drifting and moored buoy measurements, with the result that the bias of the MTSAT SST is nearly 0.0°K. The root mean square (rms) error is about 0.8°K, and it is 0.7°K under the condition that the satellite zenith angle is less than 50°. It is demonstrated that the hourly MTSAT SST produced by the algorithm developed here captures diurnal SST variations in the equatorial sea in mid-November 2006.

Improvement of AVNIR-2 Radiometric Calibration by Comparison of Cross-Calibration and Onboard Lamp Calibration

The Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) on-orbit radiometric performance has been improved through the comparison of cross-calibration and onboard lamp calibration. We proposed a new cross-calibration scheme which uses top-of-atmosphere reflectance functions of satellite zenith angle at temporally and spatially stable ground sites. Each function is made from Moderate Resolution Imaging Spectroradiometer (MODIS) 500-m resolution data over 16 days, which includes AVNIR-2 observation dates. The results showed that the radiances of AVNIR-2 bands 1 (463 nm), 2 (560 nm), and 3 (652 nm) agreed with the radiances of Aqua and Terra MODIS within 3% accuracy (standard deviation of 2%). AVNIR-2 band 4 (821 nm) had a difference of about 7% (AVNIR-2< MODIS) due to water-vapor absorption which could explain more than half of the 7%. Using many samples from this scheme, we found dependences of radiometric calibration errors within the field of view (FOV) and for different gain modes. The lamp calibration system onboard AVNIR-2 unveiled these dependences over the FOV and time. Furthermore, for the gain mode, consistent results could be retrieved using the cross-calibration scheme. The retrieved radiometric correction factors (over the FOV and gain modes) have been applied to the Japan Aerospace Exploration Agency AVNIR-2 processing scheme. The subsequent validation of the correction showed, for polar snow areas, an improved radiometric performance over the entire FOV.

An automatic method for synthetically preprocessing multi‐source NOAA‐AVHRR series and FY‐1D 1A.5 data

Polar orbiting meteorological satellites have been used successfully in many fields in which applications, such as dynamic monitoring, need multi‐temporal remote sensing data imaged in the same region but at different times and with different sensors. The basis of these quantitative applications is automatic and synthetic preprocessing of different satellite data. In order to ensure the direct comparability of multi‐source satellite National Oceanic & Atmospheric Administration‐Advanced Very High Resolution Radiometer (NOAA‐AVHRR) series and FY‐1D 1A.5 data, based on its imaging mechanisms, the paper discusses radiant and geometric preprocessing methods used for visual and near‐infrared and thermal infrared channels. This is performed by analysing the technical parameters of the satellites and the sensors, theoretical numerical simulation and image data comparison. For synthetic radiance preprocessing, radiance calibration, spectral calibration for different sensors and calibration of the Sun elevation angles or the satellite zenith angles at the imaging time are included. Based on normalization of spatial resolution in scan‐line directions, rough geometric correction and precise registration are sequentially processed for geometric preprocessing with a higher precision. Combining the detailed steps introduced in the paper, automatic preprocessing of polar orbiting meteorological satellites can be realized.

Sea surface temperature from a geostationary satellite by optimal estimation

Optimal estimation (OE) is applied as a technique for retrieving sea surface temperature (SST) from thermal imagery obtained by the Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) on Meteosat 9. OE requires simulation of observations as part of the retrieval process, and this is done here using numerical weather prediction fields and a fast radiative transfer model. Bias correction of the simulated brightness temperatures (BTs) is found to be a necessary step before retrieval, and is achieved by filtered averaging of simulations minus observations over a time period of 20 days and spatial scale of 2.5° in latitude and longitude. Throughout this study, BT observations are clear-sky averages over cells of size 0.5° in latitude and longitude. Results for the OE SST are compared to results using a traditional non-linear retrieval algorithm (“NLSST”), both validated against a set of 30108 night-time matches with drifting buoy observations. For the OE SST the mean difference with respect to drifter SSTs is − 0.01 K and the standard deviation is 0.47 K, compared to − 0.38 K and 0.70 K respectively for the NLSST algorithm. Perhaps more importantly, systematic biases in NLSST with respect to geographical location, atmospheric water vapour and satellite zenith angle are greatly reduced for the OE SST. However, the OE SST is calculated to have a lower sensitivity of retrieved SST to true SST variations than the NLSST. This feature would be a disadvantage for observing SST fronts and diurnal variability, and raises questions as to how best to exploit OE techniques at SEVIRI's full spatial resolution.

Error characterization of infrared and microwave satellite sea surface temperature products for merging and analysis

The complementary nature of infrared and microwave sea surface temperature (SST) products provides opportunities for merging the data, but significant differences among the products must first be understood and characterized. This paper presents a method for specifying the uncertainty in existing infrared and microwave SST products in terms of available satellite‐derived parameters, explores which environmental and sensor parameters contribute most to the errors, and determines if the derived uncertainty estimates can enable significant reductions in the differences among the SST products. Collocations among the satellite retrievals and SST measurements from buoys are used to derive the error estimates. Simple multidimensional lookup tables expressing the expected bias and standard deviation of individual retrievals as functions of various combinations of parameters provide an effective characterization of the uncertainty suitable for operational applications. Bias adjustments derived from the satellite zenith angle, 11–12 μm brightness temperature difference, and SST for the infrared, and wind speed, water vapor content, and SST for the microwave products significantly reduce the variability of differences both between the satellite products and with buoy observations. Further improvements are possible through inclusion of additional independent corrections based on the climatological SST anomaly and aerosol optical depth. These corrections reduce the monthly rms difference between the products by as much as 42% and reduce differences with independent buoys by as much as 10%. The largest individual corrections are to the infrared data in regions of low SST, but the microwave corrections are more geographically distributed and affect more retrievals.

Note on the effect of horizontal gradients for nadir-viewing microwave and infrared sounders

Passive microwave and infrared nadir sounders such as the Advanced Microwave Sounding Unit A (AMSU-A) and the Atmospheric InfraRed Sounder (AIRS), both flying on NASA s EOS Aqua satellite, provide information about vertical temperature and humidity structure that is used in data assimilation systems for numerical weather prediction and climate applications. These instruments scan cross track so that at the satellite swath edges, the satellite zenith angles can reach approx. 60 deg. The emission path through the atmosphere as observed by the satellite is therefore slanted with respect to the satellite footprint s zenith. Although radiative transfer codes currently in use at operational centers use the appropriate satellite zenith angle to compute brightness temperature, the input atmospheric fields are those from the vertical profile above the center of the satellite footprint. If horizontal gradients are present in the atmospheric fields, the use of a vertical atmospheric profile may produce an error. This note attempts to quantify the effects of horizontal gradients on AIRS and AMSU-A channels by computing brightness temperatures with accurate slanted atmospheric profiles. We use slanted temperature, water vapor, and ozone fields from data assimilation systems. We compare the calculated slanted and vertical brightness temperatures with AIRS and AMSU-A observations. We show that the effects of horizontal gradients on these sounders are generally small and below instrument noise. However, there are cases where the effects are greater than the instrument noise and may produce erroneous increments in an assimilation system. The majority of the affected channels have weighting functions that peak in the upper troposphere (water vapor sensitive channels) and above (temperature sensitive channels) and are unlikely t o significantly impact tropospheric numerical weather prediction. However, the errors could be significant for other applications such as stratospheric analysis. Gradients in ozone and tropospheric temperature appear to be well captured by the analyses. In contrast, gradients in upper stratospheric and mesospheric temperature as well as upper tropospheric humidity are less well captured. This is likely due in part to a lack of data to specify these fields accurately in the analyses. Advanced new sounders, like AIRS, may help to better specify these fields in the future.

Advantageous GOES IR results for ash mapping at high latitudes: Cleveland eruptions 2001

The February 2001 eruption of Cleveland Volcano, Alaska allowed for comparisons of volcanic ash detection using two‐band thermal infrared (10–12 μm) remote sensing from MODIS, AVHRR, and GOES 10. Results show that high latitude GOES volcanic cloud sensing the range of about 50 to 65°N is significantly enhanced. For the Cleveland volcanic clouds the MODIS and AVHRR data have zenith angles 6–65 degrees and the GOES has zenith angles that are around 70 degrees. The enhancements are explained by distortion in the satellite view of the cloud's lateral extent because the satellite zenith angles result in a “side‐looking” aspect and longer path lengths through the volcanic cloud. The shape of the cloud with respect to the GOES look angle also influences the results. The MODIS and AVHRR data give consistent retrievals of the ash cloud evolution over time and are good corrections for the GOES data.

Systematic errors inherent in the current modeling of the reflected downward flux term used by remote sensing models.

An underlying assumption of satellite data assimilation systems is that the radiative transfer model used to simulate observed satellite radiances has no errors. For practical reasons a fast-forward radiative transfer model is used instead of a highly accurate line-by-line model. The fast model usually replaces the spectral integration of spectral quantities with their monochromatic equivalents, and the errors due to these approximations are assumed to be negligible. The reflected downward flux term contains many approximations of this nature, which are shown to introduce systematic errors. In addition, many fast-forward radiative transfer models simulate the downward flux as the downward radiance along a path defined by the secant of the mean emergent angle, the diffusivity factor. The diffusivity factor is commonly set to 1.66 or to the secant of the satellite zenith angle. Neither case takes into account that the diffusivity factor varies with optical depth, which introduces further errors. I review the two most commonly used methods for simulating reflected downward flux by fast-forward radiative transfer models and point out their inadequacies and limitations. An alternate method of simulating the reflected downward flux is proposed. This method transforms the surface-to-satellite transmittance profile to a transmittance profile suitable for simulating the reflected downward flux by raising the former transmittance to the power of kappa, where kappa itself is a function of channel, surface pressure, and satellite zenith angle. It is demonstrated that this method reduces the fast-forward model error for low to moderate reflectivities.

Retrieval of Ocean and Lake Surface Temperatures from Hyperspectral Radiance Observations

Abstract This paper advances hyperspectral infrared (IR) radiative transfer techniques for retrieving water (ocean and lake) surface skin temperature from clear-sky radiance observations obtained within the longwave atmospheric window region (800–1000 cm−1). High spectral resolution has optimal potential for multispectral algorithms because of the capability to resolve, and thus avoid, gas absorption lines that otherwise obscure the surface signal in conventional narrowband radiometers. A hyperspectral radiative transfer model (RTM) is developed for varying satellite zenith angles, atmospheric profiles (cloud and aerosol free), surface wind speeds and skin temperatures, with atmospheric column transmittance spectra computed from fast models. Wind speed variations in surface emissivity and quasi-specular reflection are both rigorously accounted for. The RTM is then used for deriving retrieval algorithms based upon statistical and physical methodologies. The statistical method is based upon linear regressio...

Estimating bidirectional angles in NOAA AVHRR images

In studies concerning the surface bidirectional reflectance distribution function (BRDF) and thermal-infrared multiangular emissions, Sun-sensor geometry must be known. This Letter provides a potential and simple method for NOAA Advanced Very High Resolution Radiometer (AVHRR) users to estimate the imaging configuration of each pixel in a geometrically corrected image. Our formulas were tested with example AVHRR data and their precision was shown to be comparatively high with a maximum error of either the satellite zenith or azimuth angle less than 4°. The standard deviation for the zenith is 2.07° and azimuth is 2.47°.

AVHRR Pixel Level Clear-Sky Classification Using Dynamic Thresholds (CLAVR-3)

Abstract Clear-sky classifications from the Clouds from the Advanced Very High Resolution Radiometer (AVHRR)-Phase 1 (CLAVR-1) program are used to create an 8-day rotating clear-sky radiation dataset. This dataset is used to create satellite zenith angle dependent, dynamic, cloud/no-cloud albedo and temperature thresholds for ocean and six different vegetation index groups in 10° latitude intervals over the globe. Individual pixels from ambiguously classified 2 × 2 pixel arrays from CLAVR-1 (MIXED and RESTORED-CLEAR) are reexamined at the individual pixel level, using these dynamic thresholds, and reclassified as CLEAR, MIXED, or CLOUDY. This methodology is referred to as the CLAVR-3 algorithm. It is found that many of the MIXED (partially cloudy or mixed overcast) pixels from CLAVR-1 are cloud free after using the dynamic thresholds. A smaller number of RESTORED-CLEAR pixels are found to be CLEAR, while many are classified MIXED. Poleward of about 80°N and 60°S, the CLAVR-1 algorithm does not provide suf...

Estimating bidirectional angles in NOAA AVHRR images

In studies concerning the surface bidirectional reflectance distribution function (BRDF) and thermal-infrared multiangular emissions, Sun-sensor geometry must be known. This Letter provides a potential and simple method for NOAA Advanced Very High Resolution Radiometer (AVHRR) users to estimate the imaging configuration of each pixel in a geometrically corrected image. Our formulas were tested with example AVHRR data and their precision was shown to be comparatively high with a maximum error of either the satellite zenith or azimuth angle less than 4°. The standard deviation for the zenith is 2.07° and azimuth is 2.47°.

An Improved Algorithm for Generating Global Window Brightness Temperatures from Multiple Satellite Infrared Imagery

Abstract An improved algorithm for the generation of gridded window brightness temperatures is presented. The primary data source is the International Satellite Cloud Climatology Project, level B3 data, covering the period from July 1983 to the present. The algorithm takes window brightness, temperatures from multiple satellites, both geostationary and polar orbiting, which have already been navigated and normalized radiometrically to the National Oceanic and Atmospheric Administration’s Advanced Very High Resolution Radiometer, and generates 3-hourly global images on a 0.5° by 0.5° latitude–longitude grid. The gridding uses a hierarchical scheme based on spherical kernel estimators. As part of the gridding procedure, the geostationary data are corrected for limb effects using a simple empirical correction to the radiances, from which the corrected temperatures are computed. This is in addition to the application of satellite zenith angle weighting to downweight limb pixels in preference to nearer-nadir p...

Application of nonlinear multi-channel algorithms for estimating sea surface temperature with NOAA-14 AVHRR data

NOAA global operational NOAA/AVHRR Nonlinear Sea Surface Temperature (NLSST) retrieval algorithms were used to generate Global Area Coverage (GAC) sea surface temperature (SST) measurements in the global ocean in 1998. The accuracy of SST retrieved from daytime split window NLSST algorithm and nighttime triple window NLSST algorithm for NOAA-14 AVHRR data was investigated in this study. A matchup dataset of drifting buoys and NOAA-14 satellite measurements in the global ocean was generated to validate these operational split window and triple window algorithms For NOAA-14 in 1998, we had 14095 and 22643 satellite and buoy matchups that matched within 25 km and 4 hours for daytime, respectively. The satellite derived SST had a bias of less than 0.1°C and standard deviation of about 0.5°C. This study also showed that the NLSST algorithm provided the same order of SST accuracy in different time of the year and under a wide range of satellite zenith angle and water vapor represented by the channel 4 and 5 brightness temperature difference. Therefore NLSST algorithms are usually independent of season, geographic location, or atmospheric moisture content. Comparison between the low resolution AVHRR GAC data accuracy and high resolution Local Area Coverage (LAC) data accuracy is also discussed.

An empirical model of the OI FUV dayglow from DE-1 images

A refined empirical model of the Dynamics Explorer-1 far-ultraviolet (FUV) imaging photometer’s response to Earth’s quiet time FUV dayglow has been developed for thermospheric studies. The mean photometer response is based upon FUV observations in 156 images obtained during the first five months of imager operations (September 1981–January 1982) and is determined as a function of solar and satellite zenith angles, observational azimuth and solar clock angles, and solar radio flux. Variations with each parameter are characterized and, where possible, fitted with an appropriate function. The fitted response, based on the n-th power of the cosine of the solar zenith angle, is within 10% of actual mean values at all observed solar and satellite zenith angles and is consistent with the results of a first-principles calculation. Subtraction of the model background from other DE-1 images indirectly reveals the enhancement or diminution of thermospheric O/N 2 column density ratios due to transport and Joule heating effects. An analysis of summer storm-time images from the Southern Hemisphere demonstrates the use of the model in revealing these effects. The technique developed here is readily applicable to other FUV data sets.

Improved Estimates of Wide-Ranging Sea Surface Temperature from GMS S-VISSR Data

We have developed an algorithm to estimate the wide-ranging Sea Surface Temperature (SST) data from the GMS-5 (Geostationary Meteorological Satellite) S-VISSR (Stretched-Visible Infrared Spin Scan Radiometer). Better SST estimates are realized by averaging the temporal variation of the VISSR calibration table and decreasing noise of the split-window terms using a spatial filter. The effects of the satellite zenith angle were examined in detail for better estimates, and VISSR-derived SSTs with root-mean-square (rms) error of 0.8 K were achieved using a new algorithm. The accuracy of SST estimates has been improved by using the temporal-spatial average of the split-window terms. Using the new techniques, we demonstrate that the hourly wide-ranging SST image data can be used to study the daily variations of SSTs in the Northern and Southern Pacific Oceans.

Compositing Criteria for Burned Area Assessment Using Multitemporal Low Resolution Satellite Data

Five different multitemporal image compositing criteria were compared for the specific aim of monitoring burned areas using AVHRR-GAC 5 km data. Two of them were based on a classical maximum value technique: maximum value composite of NDVI (MVCN) and of surface temperature (MVCT S). Three others were based on a minimum value technique: minimum value composite of Channel 1 (M iVCB1), of Channel 2 (M iVCB2) and of albedo (M iVCA). The compositing period used was 1 week, and four output channels suitable for identifying burned areas were generated: AVHRR Channel 2 (Ch2), normalized difference vegetation index (NDVI), surface temperature (T S), and global environment monitoring index (GEMI). The different compositing criteria were assessed based on their ability to retain burned areas and also on their choice of close to nadir pixels. The separability of burned and unburned areas for each of the compositing criteria was assessed for each of the individual output channels. The criterion that showed best results in terms of separability was M iVCA followed very closely by M iVCB2; the MVCN criterion clearly showed the least merit of all. An overall analysis of the satellite zenith angles showed that the M iVCB2 criterion selected the smallest angles or closer to nadir pixels followed by M iVCA, MVCN, MVCT s, and M iVCB1. Although impossible to choose a single best compositing criteria in absolute terms, M iVCA proved to be the best compromise because it chose near to nadir pixels and performed best in terms of separability between burned and unburned spectral signatures. Future work will determine how to combine different channels (e.g., GEMI, Ch2, T s, NDVI) for burned area detection, once the M iVCA compositing has been performed.

Angular dependent split-window method for the determination of land surface temperature

The split-window method is used to retrieve land surface temperature from the multispectral measurements of the Advanced Very High Resolution Radiometer (AVHRR) on board the NOAA polar orbiting satellite. Several split-window equations have been developed to retrieve the land surface temperature for a constant satellite zenith angle. A new split-window equation has been developed, in which the coefficients of the equation depend on the viewing angle. An accurate radiative transfer model developed by P.Schlüssel has been used to study the angular effect in the NOAA-11 and 14 AVHRR brightness temperatures. The angular dependence of the split-window equation has been determined from regression analysis of radiative transfer simulations. The results showed that the dependence of the temperature from the viewing angle was eliminated in most cases.

Improved Estimates of Tropical and Subtropical Precipitation Using the GOES Precipitation Index

Abstract Nine years (1986–94) of tropical and subtropical precipitation estimates based on the GOES precipitation index (GPI) are examined. The GPI, based on the results of studies relating fractional coverage of cold cloud to convective rainfall, uses IR observations gathered by geostationary and polar-orbiting satellites. Longitudinal discontinuities in mean GPI coincident with the boundaries of satellite coverage led to a comparison of GPI derived from each geostationary satellite in overlap regions. This study revealed both intersatellite calibration differences and satellite zenith angle dependence. Its goals are to remove these sources of systematic error within the GPI, investigate the climatology of the corrected GPI, and compare against other estimated rainfall datasets. To correct calibration differences, Global Precipitation Climatology Project geostationary satellite IR data are standardized to one satellite by temperature adjustments deduced by the International Satellite Cloud Climatology Pr...

Sea Surface Temperatures from theGOES-8Geostationary Satellite

The introduction of the 10-bit, five-band, multispectral visible and thermal infrared scanner on the National Oceanic and Atmospheric Administration's GOES-8 satellite in 1994 offers an opportunity to estimate sea surface temperatures from a geostationary satellite. The advantage of the Geostationary Operational Environmental Satellite (GOES) over the traditional Advanced Very High Resolution Radiometer is the 30-min interval between images, which can increase the daily quantity of cloud-free ocean observations. Linear regression coefficients are estimated for GOES-8 by using the sea surface temperatures derived from the NOAA-14 polar-orbiting satellite as the dependent variable and the GOES infrared split window channels and the satellite zenith angle as independent variables. The standard error between the polar and geostationary sea surface temperature is 0.35°C. Since the polar satellite sea surface temperature is estimated within 0.5°C relative to drifting buoy near-surface measurements, this implies...