Abstract
Operational satellite radiometers, such SMMR, SSM/I, SSMIS, and AMSR-E, provide a multi-decadal time series of observations of the globe that can support studies of climate change. Unfortunately, spatial resolution and sampling characteristics differ between sensors, which complicate compiling a single climate record. Resolution concerns can be ameliorated by reconstructing radiometer brightness temperature measurement (Tb) data onto daily-averaged compatible grids. We consider and contrast two widely used methods for image reconstruction: a radiometer version of the scatterometer image reconstruction (SIR) algorithm and Backus-Gilbert (BG). Both require the spatial response function (antenna gain pattern) and the sampling geometry. We discuss considerations for an optimum gridding scheme based on the EASE-Grid 2.0 map projection. The EASE-Grid 2.0 simplifies the application of the Tb images in derived products since the reconstruction for each radiometer channel is implement on the same grid. This has the effect of optimally interpolating low-resolution measurements to locations of the highest resolution measurements. By employing reconstruction techniques rather than traditional “drop in the bucket” (dib) gridding, the effective resolution of the images is spatially enhanced compared to dib images, at the expense of additional computation required for the reconstruction processing. We evaluate the sensitivity of the radiometric accuracy of the resulting Tb images to uncertainties in the antenna gain pattern as well as variations in local-time-of-day. We briefly consider a number of applications of reconstructed Tb images. As part of the NASA-MEASUREs project “An improved, enhanced-resolution, gridded passive microwave ESDR for monitoring cryospheric and hydrologic time series” we are processing all available satellite radiometer data to generate a consistently calibrated and processed time series of gridded images spanning from the 1970’s to the present that will be available from the National Snow and Ice Data Center starting later this year.
A Climate Record of Enhanced Spatial Resolution Microwave Radiometer Data
Operational satellite radiometers, such SMMR, SSM/I, SSMIS, and AMSR-E, provide a multi-decadal time series of observations of the globe that can support studies of climate change. Unfortunately, spatial resolution and sampling characteristics differ between sensors, which complicate compiling a single climate record. Resolution concerns can be ameliorated by reconstructing radiometer brightness temperature measurement (Tb) data onto daily-averaged compatible grids. We consider and contrast two widely used methods for image reconstruction: a radiometer version of the scatterometer image reconstruction (SIR) algorithm and Backus-Gilbert (BG). Both require the spatial response function (antenna gain pattern) and the sampling geometry. We discuss considerations for an optimum gridding scheme based on the EASE-Grid 2.0 map projection. The EASE-Grid 2.0 simplifies the application of the Tb images in derived products since the reconstruction for each radiometer channel is implement on the same grid. This has the effect of optimally interpolating low-resolution measurements to locations of the highest resolution measurements. By employing reconstruction techniques rather than traditional “drop in the bucket” (dib) gridding, the effective resolution of the images is spatially enhanced compared to dib images, at the expense of additional computation required for the reconstruction processing. We evaluate the sensitivity of the radiometric accuracy of the resulting Tb images to uncertainties in the antenna gain pattern as well as variations in local-time-of-day. We briefly consider a number of applications of reconstructed Tb images. As part of the NASA-MEASUREs project “An improved, enhanced-resolution, gridded passive microwave ESDR for monitoring cryospheric and hydrologic time series” we are processing all available satellite radiometer data to generate a consistently calibrated and processed time series of gridded images spanning from the 1970’s to the present that will be available from the National Snow and Ice Data Center starting later this year.