Geostationary Operational Environmental Satellite Imager Research Articles

Geostationary Operational Environmental Satellite-R series advanced baseline imagery artifacts

The advanced baseline imager (ABI) on the Geostationary Operational Environmental Satellite (GOES)-R Series is a great improvement compared to the legacy GOES imager. For example, there are more spectral bands at improved spatial resolution and more frequent imagery. The vast majority of the images generated by the ABIs are free of visual defects, well calibrated, and produced in a timely fashion. Yet, there are rare times when visual artifacts, or anomalies, occur. Our study highlights and explains a number of these artifacts, some of which are traditional imagery defects for imagers such as striping and stray light, and colorfully named artifacts such as “caterpillar tracks” and “shark fins.” In addition, multiple resources are presented for more information about image quality and near-real-time image monitoring.

Improving the Heliosat-2 method for surface solar irradiation estimation under cloudy sky areas

The purpose of this study was to improve global horizontal irradiation (GHI) estimates under cloudy sky conditions using the Heliosat-2 method (Rigollier et al., 2004) calibrated with Geostationary Operational Environmental Satellite images (GOES) (Albarelo et al., 2015). The Heliosat-2 method using GOES images as input (GOES_H2) was coupled with a cloudy sky radiative transfer parameterisation (RTP) (RTP: radiative transfer parameterisation) to better account for local cloud properties. The key element of this parameterisation is the cloud absorption coefficient, which was the only element to be computed. The GHI estimates obtained when using GOES_H2 coupled with a RTP (RTP_H2) were compared against GHI measurements from six meteorological stations in French Guiana over four years. We used root mean square error (RMSE), mean bias error (MBE), correlation coefficient, and the Kolmogorov Smirnov test integral to assess GHI estimates accuracy. RTP_H2 obtained estimates were also compared with GOES_H2 estimates. Our results show an improvement in the GHI estimates under cloudy, clear, and all sky conditions. Under cloudy skies, the RMSE and MBE of our method RTP_H2 ranged from 43% to 63% and −2% to −22%, respectively, whereas those of GOES_H2 method ranged from 66% to 87% and −48% to −65%, respectively. Another effect of the new method was the improvement in clear sky GHI estimates: the RMSE and MBE ranged from 16% to 24% and −20% to −8%, respectively, while those of GOES_H2 ranged from 19% to 28% and −23% to −10%, respectively. The improvement in GHI estimates under cloudy sky conditions led to better GHI estimates under all sky conditions: the RMSE and MBE of RTP_H2 ranged from 19% to 26% and −7% to −2%, respectively, while the corresponding values for GOES_H2 ranged from 22% to 32% and −15% to −8%, respectively. The suggested cloudy sky RTP offers the advantages of simple implementation and good computation speed. This method requires only one parameter; users need to configure a cloud absorption coefficient suitable for their local conditions. We conclude that coupling the Heliosat-2 method with a cloudy sky RTP improves GHI estimates in French Guiana and reduces discrepancies between satellite-derived irradiation and ground measurements in areas with high cloudiness.

Impact of assimilating GOES imager clear‐sky radiance with a rapid refresh assimilation system for convection‐permitting forecast over Mexico

AbstractThe Geostationary Operational Environmental Satellite (GOES) imager data could provide a continuous image of the evolutionary pattern of severe weather phenomena with its high spatial and temporal resolution. The capability to assimilate the GOES imager radiances has been developed within the Weather Research and Forecasting model's data assimilation system. Compared to the benchmark experiment with no GOES imager data, the impact of assimilating GOES imager radiances on the analysis and forecast of convective process over Mexico in 7–10 March 2016 was assessed through analysis/forecast cycling experiments using rapid refresh assimilation system with hybrid‐3DEnVar scheme. With GOES imager radiance assimilation, better analyses were obtained in terms of the humidity, temperature, and simulated water vapor channel brightness temperature distribution. Positive forecast impacts from assimilating GOES imager radiance were seen when verified against the Tropospheric Airborne Meteorological Data Reporting observation, GOES imager observation, and Mexico station precipitation data.

Impacts of assimilating all or GOES-like AHI infrared channels radiances on QPFs over Eastern China

The launch of the Japanese Advanced Himawari Imager (AHI) on 7 October 2014 represents a new era of geostationary operational environmental satellite (GOES) imagers, providing many more channels than any previously launched GOES imagers for the first time. In this study, we compare the impacts of assimilating all AHI versus GOES-like infrared channels radiances on regional forecasts over Eastern China. The National Centers for Environmental Prediction (NCEP) Gridpoint Statistical Interpolation (GSI) analysis system and Advanced Research Weather Research and Forecast model are employed. Positive impacts are obtained on quantitative precipitation forecasts (QPFs) associated with a typical summer precipitation case over eastern China in both set-ups, i.e. one assimilating all 10 AHI infrared channels (AHIA) and the other assimilating only four GOES-like AHI channels (AHIG). It is found that a southwest to northeast oriented band of the atmosphere with high water vapor content that was formed and moved inland with time under the influence of a subtropical high and an eastward-propagating middle-latitude trough was responsible for the persistent precipitation in the eastern China of the selected case. The AHIA experiment generated the largest improvement on QPFs due to it generating a wetter atmosphere in the middle and low troposphere over the ocean off the southeast coast of China than the AHIG experiment and a control experiment without assimilating any AHI channel.

Estudo de Caso de Sistemas Convectivos de Mesoescala Intensos Ocorridos entre os Dias 02 e 03 de Janeiro de 2013 no Estado do Rio de Janeiro: Características Estruturais e Termodinâmicas

This paper presents an analysis of the structural and thermodynamic characteristics of intense Mesoscale Convective Systems (MCS) that occurred in the State of Rio de Janeiro between 02 and 03 January 2013. These MCS caused floods and landslides in Metropolitan and Mountain Regions of Rio de Janeiro, with material and economic losses, dozens injured, and two fatal human losses. The analyzes were made using Geostationary Operational Environmental Satellite images in infrared spectrum (channel 4), the Forecasting and Tracking of Active Cloud Clusters method, images from radar at Pico do Couto, meteorological data from surface stations, and reanalyzes from National Center for Environmental Predictions. Results from satellite showed that the studied MCS were stimulated by a cold front that moved over southern of the State of Rio de Janeiro on 02 January. The MCS that reached Metropolitan and Mountain Regions of Rio de Janeiro initiated at 01:15 UTC of 03 January, acquired its own dynamic, and moved eastward with a mean velocity of around 11 m/s, travelling around 150 km between 01:15 UTC and 14:45 UTC of 03 January. This system reached the mountain region of the State of Rio de Janeiro with maximum intensity in the early morning at Xerem, Duque de Caxias. The MCS had a maximum total area of around 23200 km 2 (170 km in extension) and maximum convective area of around 5600 km 2 , very cold cloud tops ( 0,7 (semicircle), radar reflectivities “strong”, and a total rainfall of 224,6 mm in Xerem (from surface stations). Except by the MCS extension, its characteristics were similar to the Mesoscale Convective Complexes (MCC) studied in the literature. The thermodynamic and circulation fields showed strong moisture convergence and atmospheric instabilities over State of Rio de Janeiro, contributing to the formation of this severe storm. We have also observed circulation patterns similar to the ones found in MCC events described in the literature, with a Low Level Jet over Southeast South America and a High Level Jet in the subtropics.

Improved Tropical Storm Forecasts with GOES-13/15 Imager Radiance Assimilation and Asymmetric Vortex Initialization in HWRF

Abstract The Geostationary Operational Environmental Satellite (GOES) imagers provide high temporal- and spatial-resolution data for many applications, such as monitoring severe weather events. In this study, radiance observations of four infrared channels from GOES-13 and GOES-15 imagers are directly assimilated using the National Centers for Environmental Prediction (NCEP) gridpoint statistical interpolation (GSI) analysis system to produce the initial conditions for the Hurricane Weather Research and Forecasting Model (HWRF). Impacts of GOES imager data assimilation on track and intensity forecasts are demonstrated for a landfalling tropical storm that moved across the Gulf of Mexico—Debby (2012). With a higher model top and a warm start, an asymmetric component is also added to the original HWRF symmetric vortex initialization. Two pairs of data assimilation and forecasting experiments are carried out for assessing the impacts of the GOES imager data assimilation on tropical storm forecasts. The first pair employs a symmetric vortex initialization and the second pair includes an asymmetric vortex initialization. Numerical forecast results from these experiments are compared against each other. It is shown that a direct assimilation of GOES-13 and GOES-15 imager radiance observations, which are available at all analysis times, in HWRF results in a consistently positive impact on the track and intensity forecasts of Tropical Storm Debby in the Gulf of Mexico. The largest positive impact on the track and intensity forecasts comes from a combined effect of GOES imager radiance assimilation and an asymmetric vortex initialization.

Geostationary Operational Environmental Satellite Imager infrared channel-to-channel co-registration characterization algorithm and its implementation in the ground system

Channel-to-channel co-registration is an important performance metric for the Geo- stationary Operational Environmental Satellite (GOES) Imager, and large co-registration errors can have a significant impact on the reliability of derived products that rely on combinations of multiple infrared (IR) channels. Affected products include the cloud mask, fog and fire detection. This is especially the case for GOES-13, in which the co-registration error between channels 2 (3.9 μm) and 4 (10.7 μm) can be as large as 1 pixel (or ∼ 4k m) in the east-west direction. The GOES Imager IR channel-to-channel co-registration characterization (GII4C) algorithm is pre- sented, which allows a systematic calculation of the co-registration error between GOES IR channel image pairs. The procedure for determining the co-registration error as a function of time is presented. The algorithm characterizes the co-registration error between corresponding images from two channels by spatially transforming one image using the fast Fourier transfor- mation resampling algorithm and determining the distance of the transformation that yields the maximum correlation in brightness temperature. The GII4C algorithm is an area-based approach which does not depend on a fixed set of control points that may be impacted by the presence of clouds. In fact, clouds are a feature that enhances the correlations. The results presented show very large correlations over the majority of Earth-viewing pixels, with stable algorithm results. Verification of the algorithm output is discussed, and a global spatial-spectral gradient asym- metry parameter is defined. The results show that the spatial-spectral gradient asymmetry is strongly correlated to the co-registration error and can be an effective global metric for the qual- ity of the channel-to-channel co-registration characterization algorithm. Implementation of the algorithm in the GOES ground system is presented. This includes an offline component to deter- mine the time dependence of the co-registration errors and a real-time component to correct the co-registration errors based on the inputs from the offline component. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. (DOI: 10

Intercalibration of GOES Imager visible channels over the Sonoran Desert

AbstractThe Geostationary Operational Environmental Satellites (GOES) have been observing the Western Hemisphere since the late 1970s, providing valuable information for weather forecast and climate change studies. Due to the lack of an onboard calibration device for the visible channel, accurate reflectance of the visible channel data depends on vicarious calibration methods to provide postlaunch calibration coefficients to compensate for the degraded responsivity. In this study, the Sonoran Desert, which can be viewed by both GOES‐East and GOES‐West satellites, is used to intercalibrate the visible channels on board the three‐axis stabilized GOES satellite Imagers traceable to the Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 (C6) calibration standard. It was found that when the anomalous reflectance in 2004 and 2005 are excluded, the Sonoran Desert is radiometrically, spatially, and spectrally stable at the GOES viewing geometries and thus can be considered as a pseudo‐invariant calibration site to develop long‐term GOES Imager visible data set. To characterize the desert target reflectance with the MODIS data, GOES observations over 1 year period are used to convert the MODIS reflectance to the GOES viewing and solar illumination geometries. The spectral band adjustment factor for each GOES Imager visible channel is generated with a set of clear‐sky Hyperion measurements. A trending algorithm, which consists of a polynomial function for the description of instrument degradation performance and two sine terms for the impacts of the seasonal variations of the solar zenith angle and atmospheric components, is applied to fit the time series of prelaunch calibrated reflectance. The combined calibration uncertainty of the desert calibration method is less than 4% at the Aqua MODIS C6 calibration standard. The difference of the postlaunch calibration coefficients between the desert calibration and the current GOES visible operational calibration methods is mainly within 5%.

Fast Fourier transformation resampling algorithm and its application in satellite image processing

The image resampling algorithm, fast Fourier transformation resampling (FFTR), is introduced. The FFTR uses a global function in the Fourier expansion form to represent an image, and the image resampling is achieved by the introduction of a phase shift in the Fourier expansion. The comparison with the cubic spline interpolation approach in the image resampling is presented, which shows that FFTR is more accurate in the satellite image resampling. The FFTR algorithm is also generally reversible, because both the resampled and its original images share the same Fourier spectrum. The resampling for the images with hot spots is discussed. The hot spots in an image are the pixels with the second-order derivatives that are order of magnitude larger than the average value. The images with the hot spots are resampled with the introduction of a local Gaussian function to model the hot spot data, so that the remaining data for the Fourier expansion are continuous. Its application to the infrared channel image of Geostationary Operational Environmental Satellite Imager, to mitigate a diur- nally changing band co-registration, is presented. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. (DOI: 10.1117/1.JRS.8.083683)

Land-surface temperature retrieval at high spatial and temporal resolutions based on multi-sensor fusion

Land-surface temperature (LST) is of great significance for the estimation of radiation and energy budgets associated with land-surface processes. However, the available satellite LST products have either low spatial resolution or low temporal resolution, which constrains their potential applications. This paper proposes a spatiotemporal fusion method for retrieving LST at high spatial and temporal resolutions. One important characteristic of the proposed method is the consideration of the sensor observation differences between different land-cover types. The other main contribution is that the spatial correlations between different pixels are effectively considered by the use of a variation-based model. The method was tested and assessed quantitatively using the different sensors of Landsat TM/ETM+, moderate resolution imaging spectroradiometer and the geostationary operational environmental satellite imager. The validation results indicate that the proposed multisensor fusion method is accurate to about 2.5 K.

Diurnal and Scan Angle Variations in the Calibration of GOES Imager Infrared Channels

The current Geostationary Operational Environmental Satellite (GOES) Imager infrared (IR) channels experience a midnight effect that can result in erroneous instrument responsivity around satellite midnight. An empirical method named the Midnight Blackbody Calibration Correction (MBCC) was developed and implemented in the GOES Imager IR operational calibration, aiming to correct the midnight calibration errors. The main objective of this study is to evaluate the MBCC performance for the GOES-11/-12 Imager IR channels by examining the diurnal variation of the mean brightness temperature (Tb) bias with respect to reference instruments. Two well-calibrated hyperspectral radiometers on low Earth orbits (LEOs), the Atmospheric Infrared Sounder on the Aqua satellite and the Infrared Atmospheric Sounding Interferometer (IASI) on the Metop-A satellite, are used as the reference instruments in this study. However, as the timing of the collocated geostationary–LEO intercalibration data is related to the GOES scan angle, it is then necessary to assess the GOES scan angle calibration variations, which becomes the second objective of this study. Our results show that the applications and performance of the MBCC method varies greatly between the different channels and different times. While it is usually applied with high frequency for about 8 h around satellite midnight for the short-wave channels (Ch2), it may only be intensively used right after satellite midnight or even barely used for the other IR channels. The MBCC method, if applied with high frequency, can reduce the mean day/night calibration difference to less than 0.15 K in almost all the GOES IR channels studied in this paper except for Ch4 (10.7 <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$\mu\hbox{m}$</tex></formula> ). The uncertainty of the nighttime GOES and IASI Tb difference for different scan angles is less than 0.1 K in each IR channel, indicating that there is no apparent systematic variation with the scan angle, and therefore, the estimated diurnal cycles of GOES Imager calibration is not prone to the systematic effects due to scan angle.

Evaluating Added Benefits of Assimilating GOES Imager Radiance Data in GSI for Coastal QPFs

Abstract The Geostationary Operational Environmental Satellites (GOES) provide high-resolution, temporally continuous imager radiance data over the West Coast (GOES-West currently known as GOES-11) and East Coast (GOES-East currently GOES-12) of the United States. Through a real case study, benefits of adding GOES-11/12 imager radiances to the satellite data streams in NWP systems for improved coastal precipitation forecasts are examined. The Community Radiative Transfer Model (CRTM) is employed for GOES imager radiance simulations in the National Centers for Environmental Prediction (NCEP) gridpoint statistical interpolation (GSI) analysis system. The GOES imager radiances are added to conventional data for coastal quantitative precipitation forecast (QPF) experiments near the northern Gulf of Mexico and the derived precipitation threat score was compared with those from six other satellite instruments. It is found that the GOES imager radiance produced better precipitation forecasts than those from any other satellite instrument. However, when GOES imager radiance and six different types of satellite instruments are all assimilated, the score becomes much lower than the individual combination of GOES and any other instrument. Our analysis shows that an elimination of Advance Microwave Sounding Unit-B (AMSU-B)/Microwave Humidity Sounder (MHS) data over areas where GOES detects clouds significantly improved the forecast scores from AMSU-B/MHS data assimilation.

Determining the Flight Icing Threat to Aircraft with Single-Layer Cloud Parameters Derived from Operational Satellite Data

AbstractAn algorithm is developed to determine the flight icing threat to aircraft utilizing quantitative information on clouds derived from meteorological satellite data as input. Algorithm inputs include the satellite-derived cloud-top temperature, thermodynamic phase, water path, and effective droplet size. The icing-top and -base altitude boundaries are estimated from the satellite-derived cloud-top and -base altitudes using the freezing level obtained from numerical weather analyses or a lapse-rate approach. The product is available at the nominal resolution of the satellite pixel. Aircraft pilot reports (PIREPs) over the United States and southern Canada provide direct observations of icing and are used extensively in the algorithm development and validation on the basis of correlations with Geostationary Operational Environmental Satellite imager data. Verification studies using PIREPs, Tropospheric Airborne Meteorological Data Reporting, and NASA Icing Remote Sensing System data indicate that the satellite algorithm performs reasonably well, particularly during the daytime. The algorithm is currently being run routinely using data taken from a variety of satellites across the globe and is providing useful information on icing conditions at high spatial and temporal resolutions that are unavailable from any other source.

A multi-angle aerosol optical depth retrieval algorithm for geostationary satellite data over the United States

Abstract. Aerosol optical depth (AOD) retrievals from geostationary satellites have high temporal resolution compared to the polar orbiting satellites and thus enable us to monitor aerosol motion. However, current Geostationary Operational Environmental Satellites (GOES) have only one visible channel for retrieving aerosols and hence the retrieval accuracy is lower than those from the multichannel polar-orbiting satellite instruments such as the Moderate Resolution Imaging Spectroradiometer (MODIS). The operational GOES AOD retrieval algorithm (GOES Aerosol/Smoke Product, GASP) uses 28-day composite images from the visible channel to derive surface reflectance, which can produce large uncertainties. In this work, we develop a new AOD retrieval algorithm for the GOES imager by applying a modified Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm. The algorithm assumes the surface Bidirectional Reflectance Distribution Function (BRDF) in the channel 1 of GOES is proportional to seasonal average MODIS BRDF in the 2.1 μm channel. The ratios between them are derived through time series analysis of the GOES visible channel images. The results of AOD and surface reflectance retrievals are evaluated through comparisons against those from Aerosol Robotic Network (AERONET), GASP, and MODIS. The AOD retrievals from the new algorithm demonstrate good agreement with AERONET retrievals at several sites across the US with correlation coefficients ranges from 0.71 to 0.85 at five out of six sites. At the two western sites Railroad Valley and UCSB, the MAIAC AOD retrievals have correlations of 0.8 and 0.85 with AERONET AOD, and are more accurate than GASP retrievals, which have correlations of 0.7 and 0.74 with AERONET AOD. At the three eastern sites, the correlations with AERONET AOD are from 0.71 to 0.81, comparable to the GASP retrievals. In the western US where surface reflectance is higher than 0.15, the new algorithm also produces larger AOD retrieval coverage than both GASP and MODIS.

Improved Coastal Precipitation Forecasts with Direct Assimilation of GOES-11/12 Imager Radiances

Abstract The Geostationary Operational Environmental Satellite (GOES) imager provides observations that are of high spatial and temporal resolution and can be applied for effectively monitoring and nowcasting severe weather events. In this study, improved quantitative precipitation forecasts (QPFs) for three coastal storms over the northern Gulf of Mexico and the East Coast is demonstrated by assimilating GOES-11 and GOES-12 imager radiances into the Weather Research and Forecasting (WRF) model. Both the National Centers for Environmental Prediction (NCEP) Gridpoint Statistical Interpolation (GSI) analysis system and the Community Radiative Transfer Model (CRTM) are utilized to ingest GOES IR clear-sky data. Assimilation of GOES imager radiances during a 6–12-h time window prior to convective initiation and/or development could significantly improve the precipitation forecasts near the coast of the northern Gulf of Mexico. The 3-h accumulative precipitation threat scores are increased by about 20% after 6 h of model forecasts and more than 50% after 18–24 h of model forecasts. A detailed diagnosis of analysis fields and model forecast fields is carried out for one of the three convective precipitation events included in this study. It is shown that the assimilation of GOES data in regions of no or little clouds improved the model description of an upstream midlatitude trough and a subtropical high located in the south of the convection. The GOES observations located in the western part of land region covered by GOES within the latitude zone of 18°–37°N near 100°W contributed to a better forecast of the position of the eastward-propagating trough, while GOES observations over the Gulf of Mexico increased the amount of water vapor advection from the south into the convective region by the wind associated with the subtropical high. In the past, GOES imager radiances were not directly used in the GSI system. This study highlights the importance of satellite imagery information observed in the preconvective environment for improved cloud and precipitation forecasts. The developed data assimilation technique will prepare the NWP user community for accelerated use of advanced satellite data from the GOES-R series.

Consistency assessment of Atmospheric Infrared Sounder and Infrared Atmospheric Sounding Interferometer radiances: Double differences versus simultaneous nadir overpasses

[1] Quantifying the radiometric difference and creating a calibration link between Atmospheric Infrared Sounder (AIRS) on Aqua and Infrared Atmospheric Sounding Interferometer (IASI) on MetOp are crucial for creating fundamental climate data records and intercalibrating other narrowband or broadband satellite instruments. This study employs two different methods to assess the AIRS and IASI radiance consistency within the four Geostationary Operational Environmental Satellite (GOES) imager infrared channels (with central wavelengths at 6.7, 10.7, 12.0, and 13.3 μm) through a period of 2 years and 9 months. The first method employs the differences of AIRS and IASI relative to the GOES observations sampled in the tropics to indirectly track the AIRS and IASI radiance differences. The second approach directly compares AIRS and IASI in the polar regions through the simultaneous nadir overpass observations. Both methods reveal that AIRS and IASI radiances are in good agreement with each other both in the tropics and in the polar regions within GOES imager channels used in this study, while AIRS is found to be slightly warmer than IASI by less than 0.1 K.

New GOES imager algorithms for cloud and active fire detection and fire radiative power assessment across North, South and Central America

Vegetation fires are a key global terrestrial disturbance factor and a major source of atmospheric trace gases and aerosols. Therefore, many earth-system science and operational monitoring applications require access to repetitive, frequent and well-characterized information on fire emissions source strengths. Geostationary imagers offer important temporal advantages when studying rapidly changing phenomena such as vegetation fires. Here we present a new algorithm for detecting and characterising active fires burning within the imager footprints of the Geostationary Operational Environmental Satellites (GOES), including consideration of cloud-cover and calculation of fire radiative power (FRP), a metric shown to be strongly related to fuel consumption and smoke emission rates. The approach is based on a set of algorithms now delivering near real time (NRT) operational FRP products from the Meteosat Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) imager (available from http://landsaf.meteo.pt/), and the GOES processing chain presented here is designed to deliver a compatible fire product to complete geostationary coverage of the Western hemisphere. Results from the two GOES imagers are intercompared, and are independently verified against the well regarded MODIS cloud mask and active fire products. We find that the detection of cloud and active fires from GOES matches that of MODIS very well for fire pixels having FRP > 30 MW, when the GOES omission error falls to less than 10%. The FRP of fire clusters detected near simultaneously by both GOES and MODIS have a bias of only 22 MW, and a similar bias is found when comparing near-simultaneous GOES East and GOES West FRP observations. However, many fire pixels having FRP < 30 MW remain undetected by GOES, probably unavoidably since it has a much coarser spatial resolution than MODIS. Adjustment using data from the less frequent but more accurate views obtained from high spatial resolution polar orbiting imagers could be used to bias correct regional FRP totals. Temporal integration of the GOES FRP record indicates that during the summer months, biomass burning combusts thousands of millions of tonnes of fuel daily across the Americas. Comparison of these results to those of the Global Fire Emissions Database (GFEDv2) indicate strong linear relationships ( r² > 0.9), suggesting that the timely FRP data available from a GOES real-time data feed is likely to be a suitable fire emissions source strength term for inclusion in schemes aiming to forecast the concentrations of atmospheric constituents affected by biomass burning.

Comparison of AIRS and IASI Radiances Using GOES Imagers as Transfer Radiometers toward Climate Data Records

Abstract The Atmospheric Infrared Sounder (AIRS) and the Infrared Atmospheric Sounding Interferometer (IASI), together with the future Cross-track Infrared Sounder, will provide long-term hyperspectral measurements of the earth and its atmosphere at ∼10 km spatial resolution. Quantifying the radiometric difference between AIRS and IASI is crucial for creating fundamental climate data records and establishing the space-based infrared calibration standard. Since AIRS and IASI have different local equator crossing times, a direct comparison of these two instruments over the tropical regions is not feasible. Using the Geostationary Operational Environmental Satellite (GOES) imagers as transfer radiometers, this study compares AIRS and IASI over warm scenes in the tropical regions for a time period of 16 months. The double differences between AIRS and IASI radiance biases relative to the GOES-11 and -12 imagers are used to quantify the radiance differences between AIRS and IASI within the GOES imager spectral channels. The results indicate that, at the 95% confidence level, the mean values of the IASI − AIRS brightness temperature differences for warm scenes are very small, that is, −0.0641 ± 0.0074 K, −0.0432 ± 0.0114 K, and −0.0095 ± 0.0151 K for the GOES-11 6.7-, 10.7-, and 12.0-μm channels, respectively, and −0.0490 ± 0.0100 K, −0.0419 ± 0.0224 K, and −0.0884 ± 0.0160 K for the GOES-12 6.5-, 10.7-, and 13.3-μm channels, respectively. The brightness temperature biases between AIRS and IASI within the GOES imager spectral range are less than 0.1 K although the AIRS measurements are slightly warmer than those of IASI.

Developing Algorithm for Operational GOES-R Land Surface Temperature Product

The Geostationary Operational Environmental Satellite (GOES) program is developing the Advanced Baseline Imager (ABI), a new generation sensor to be carried onboard the GEOS-R satellite (launch expected in 2014). Compared to the current GOES Imager, ABI will have significant advantages for retrieving land surface temperature (LST) as well as providing qualitative and quantitative data for a wide range of applications. The infrared bands of the ABI sensor are designed to achieve a spatial resolution of 2 km at nadir and a noise equivalent temperature of 0.1 K. These improve the imager specifications and compare well with those of polar-orbiting sensors (e.g., Advanced Very High Resolution Radiometer and Moderate Resolution Imaging Spectroradiometer). In this paper, we discuss the development of a split window LST algorithm for the ABI sensor. First, we simulated ABI sensor data using the MODTRAN radiative transfer model and NOAA88 atmospheric profiles. To model land conditions, we developed emissivity data for 78 virtual surface types using the surface emissivity library from Snyder Using the simulation results, we performed regression analyses with the candidate LST algorithms. Algorithm coefficients were stratified for dry and moist atmospheres as well as for daytime and nighttime conditions. We estimated the accuracy and sensitivity of each algorithm for different sun-view geometries, emissivity errors, and atmospheric assessments. Finally, we evaluated the most promising algorithm using real data from the GOES-8 Imager and SURFace RADiation Network. The results indicate that the optimized LST algorithm meets the required accuracy (2.3 K) of the GOES-R mission.

Deriving a climate temperature record from satellite data

Temperatures at the Earth’s surface are important for the study of global warming. Typically, global temperature change is assessed by in situ surface air temperature (SAT) measurements at 2m height at weather stations. The diurnal temperature range (DTR) is also an important index of climate change.1–5 Until recently, most information on DTR also came from SATs, based on station observations. Strong diurnal and seasonal cycles are identified in SAT.6, 7 However, weather stations are usually located in relatively densely populated regions where anthropogenic impacts may affect measurements, and thus the temperature record may not be representative of global change. Moreover, station observations are sparse and unevenly distributed, and they suffer from differences in elevation and time of observation.8 The use of satellite-derived data could contribute to a globally consistent measurement.9 Most surface temperature retrievals from satellites are based on polar orbiters.10–15 Surface temperature, especially land surface temperature (LST), has a strong diurnal cycle, which cannot be captured at the temporal resolution (approximately two views per day) of such satellites. Only two points in the cycle are measured, and the sampling is neither continuous nor homogeneous because the exact timing of these two points will drift. The drift in local equatorial crossing time of the US National Oceanic and Atmospheric Association (NOAA) series of polar-orbiting satellites,16 for example, leads to a non-climatic trend in surface temperature, which degrades the usefulness of LSTmeasurements from those satellites. Geostationary satellites, on the other hand, provide good diurnal coverage, making them attractive for deriving information on the diurnal LST cycle.17–19 One of our current projects is to derive long-term climate surface temperature records from the NOAA Geostationary Operational Environmental Satellite (GOES) series. In addition to problems with maintaining consistent calibration over time, changes in GOES imager channels are also a big concern. Figure 1. The GOES-8 (red) and GOES-12 (green) bands.