The Characterization of Deep Convective Cloud Albedo as a Calibration Target Using MODIS Reflectances

There are over 25 years of historical satellite data available to climate analysis. The historical satellite data needs to be well calibrated, especially in the visible, where there is no onboard calibration on operational satellites. The key to the vicarious calibration of historical satellites relies on invariant targets, such as the moon, Dome C, and deserts. Deep convective clouds (DCC) also show promise of being a stable invariant or predictable target viewable by all satellites, since they behave as solar diffusers. However DCC have not been well characterized for calibration. Ten years of well-calibrated MODIS is now available. DCC can easily be identified using IR thresholds, where the IR calibration can be traced to the onboard black-bodies. The natural variability of DCC albedo will be analyzed geographically and seasonally, especially difference of convection initiated over land or ocean. Functionality between particle size and ozone absorption with DCC albedo will be examined. Although DCC clouds are nearly Lambertion, the angular distribution of reflectances will be sampled and compared with theoretical models. Both Aqua and Terra MODIS DCC angular models will be compared for consistency. Normalizing angular geostationary DCC reflectances, which were calibrated against MODIS, with SCIAMACHY spectral reflectances and comparing them to MODIS DCC reflectances will inspect the usage of DCC albedos as an absolute calibration target

Characterization of Deep Convective Clouds as Absolute Calibration Targets for Visible Sensors

Uniform radiometric calibration of satellite sensors is vital to the scientists and researchers around the world that use the satellite retrieved properties to study the Earth’s climate. In order to consistently calibrate sensors that have varying degrees of onboard calibration over time, an invariant target approach is needed. Fortunately, all satellite sensors are able to view deep convective clouds (DCC), which are bright, nearly-isotropic solar diffusers that can be easily identified via a simple infrared (IR) brightness temperature threshold. Also, most sensors have a window channel, which are historically well-calibrated using onboard black-bodies. If the DCC reflectivity is predictable in space and time, then the DCC can be used as an absolute reference for past, present, and future operational sensors. This study focuses on analyzing and optimizing the DCC identification criteria in order to derive the most consistent DCC reflectance fields over the tropics, while maintaining the greatest regional temporal stability. The DCC reflectances were characterized using the well-calibrated Aqua-MODIS 0.65 micron channel radiances. The DCC calibration technique revealed that the Aqua-MODIS 0.65µm radiances are temporally stable to within 0.5%/decade based on 9-years of data over the entire tropics. Replacing the DCC bidirectional reflectance distribution function (BRDF) model with a Lambertian BRDF model increased the Aqua-MODIS temporally stability to 0.7%, verifying that the DCC reflectance were nearly isotropic. It is known that the seasonal and diurnal distribution of DCC varies geographically over tropics. However, the inter-annual variability of the distribution is small and very predictable. The DCC reflectance was found to be dependent on the IR threshold criteria, indicating that a comparable IR temperature threshold is necessary to transfer the absolute visible calibration across visible sensors. It was also found that the lowest DCC reflectances were found over the tropical western pacific but the brightest DCC were not always found exclusively over land. These differences can easily be accounted for when using DCC as an invariant Earth target, allowing for a uniform and consistent calibration target for visible sensors

Using Hyper-Spectral SCIAMACHY Radiances to Uniformly Calibrate Contemporary Geostationary Visible Sensors

The goal of the Global Space-Based Inter-Calibration System (GSICS) and the future Climate Absolute Radiance and Refractivity Observatory (CLARREO) missions are to provide consistent calibration coefficients across satellite platforms in order to provide reliable, climate-quality retrievals. A calibration transfer that ties hyper-spectral radiances to an absolute, or traceable, calibration reference is preferred. Hyper-spectral data can be used to easily account for the varying spectral bands of operational sensors. Currently, GSICS uses the well-calibrated Infrared Atmospheric Sounding Interferometer (IASA) hyper-spectral radiances to calibrate geostationary satellite (GEO) IR radiances. To date, no such hyper-spectral instrument has been used as a direct GEO visible calibration reference. The ENVISAT Scanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY) hyper-spectral data have the potential to function as a transfer medium for the absolute calibration reference of the MODerate-resolution Imaging Spectroradiometer (MODIS) and GEO imagers. Additionally, SCIAMACHY can be used to adjust for imager spectral response function (SRF) differences. The SCIAMACHY absolute calibration and stability are evaluated by cross-calibrating SCIAMACHY with Aqua-MODIS at the ground track intersects near the poles, thereby serving as a calibration transfer standard. Preliminary results indicate excellent calibration transfer given that direct comparisons of Aqua-MODIS and SCIAMACHY-convolved-with-Aqua-MODIS-SRF radiances have a monthly temporal uncertainty of 0.44%, and a relative trend of -0.2% per decade. The number of ray-matched, or bore-sighted, SCIAMACHY and GEO radiance pairs is limited by the size of the SCIAMACHY footprint, the number of footprints along the cross-track, and the nadir/limb view duty cycle. Relaxing the ray-matching criteria allows sufficient sampling for seasonal inter-calibration without detriment to the calibration transfer. This calibration approach reveals that the SCIAMACHY-to-Meteosat-9 0.65-µm calibration transfer is within 1% of other Meteosat-9 calibration techniques. Furthermore, this method yields one of the lowest temporal uncertainties of all approaches, most likely because no SRF adjustment is required

Retrieving Clear-Sky Surface Skin Temperature for Numerical Weather Prediction Applications from Geostationary Satellite Data

Atmospheric models rely on high-accuracy, high-resolution initial radiometric and surface conditions for better short-term meteorological forecasts, as well as improved evaluation of global climate models. Remote sensing of the Earth’s energy budget, particularly with instruments flown on geostationary satellites, allows for near-real-time evaluation of cloud and surface radiation properties. The persistence and coverage of geostationary remote sensing instruments grant the frequent retrieval of near-instantaneous quasi-global skin temperature. Among other cloud and clear-sky retrieval parameters, NASA Langley provides a non-polar, high-resolution land and ocean skin temperature dataset for atmospheric modelers by applying an inverted correlated k-distribution method to clear-pixel values of top-of-atmosphere infrared temperature. The present paper shows that this method yields clear-sky skin temperature values that are, for the most part, within 2 K of measurements from ground-site instruments, like the Southern Great Plains Atmospheric Radiation Measurement (ARM) Infrared Thermometer and the National Climatic Data Center Apogee Precision Infrared Thermocouple Sensor. The level of accuracy relative to the ARM site is comparable to that of the Moderate-resolution Imaging Spectroradiometer (MODIS) with the benefit of an increased number of daily measurements without added bias or increased error. Additionally, matched comparisons of the high-resolution skin temperature product with MODIS land surface temperature reveal a level of accuracy well within 1 K for both day and night. This confidence will help in characterizing the diurnal and seasonal biases and root-mean-square differences between the retrievals and modeled values from the NASA Goddard Earth Observing System Version 5 (GEOS-5) in preparation for assimilation of the retrievals into GEOS-5. Modelers should find the immediate availability and broad coverage of these skin temperature observations valuable, which can lead to improved forecasting and more advanced global climate models