Calibration to Improve Forward Model Simulation of Microwave Emissivity at GPM Frequencies Over the U.S. Southern Great Plains

Abstract

Better estimation of land surface microwave emissivity promises to improve over-land precipitation retrievals in the GPM era. Forward models of land microwave emissivity are available but have suffered from poor parameter specification and limited testing. Here, forward models are calibrated and the accompanying change in predictive power is evaluated. With inputs (e.g., soil moisture) from the Noah land surface model and applying MODIS LAI data, two microwave emissivity models are tested, the Community Radiative Transfer Model (CRTM) and Community Microwave Emission Model (CMEM). The calibration is conducted with the NASA Land Information System (LIS) parameter estimation subsystem using AMSR-E based emissivity retrievals for the calibration dataset. The extent of agreement between the modeled and retrieved estimates is evaluated using the AMSR-E retrievals for a separate 7-year validation period. Results indicate that calibration can significantly improve the agreement, simulating emissivity with an across-channel average root-mean-square-difference (RMSD) of about 0.013, or about 20% lower than if relying on daily estimates based on climatology. The results also indicate that calibration of the microwave emissivity model alone, as was done in prior studies, results in as much as 12% higher across-channel average RMSD, as compared to joint calibration of the land surface and microwave emissivity models. It remains as future work to assess the extent to which the improvements in emissivity estimation translate into improvements in precipitation retrieval accuracy.

An examination of methods for estimating land surface microwave emissivity

“…The patterns of improvements are consistent after the model parameters are calibrated. As the results are based on the calibration of Harrison et al [] that solely focused on emissivity simulation during snow‐free conditions, the results here, while broadly consistent with their results, are degraded. The two lower frequencies benefitted most, especially 11H, which saw an error reduction of about 20% (Figure and Table ).…”

“…The northeast quarter poses a persistent challenge at 89 GHz, with large errors both before and after calibration (cf. Figures and ), which is attributed to snow/frozen ground conditions as these features are not apparent in the snow‐free evaluations of Harrison et al []. Spatially averaged, the error ranges from 1.43% (equivalent of ~4°K Tb ) at 11 V to 3.72% (~11°K Tb ) at 89H for CRTM2_uncal and 1.25% (~3.8°K Tb ) at 11 V and 3.74% (~11°K Tb ) at 89H for CRTM2_uncal.…”

“…The northeast corner was even more troublesome for CMEM3 than for CRTM2 and CLIM, again attributable to snow and frozen ground conditions. The degradation at higher frequencies, which was also observed in the snow‐free evaluation of Harrison et al [], is understandable as CMEM3 was developed primarily for simulations lower than 20 GHz [ de Rosnay et al , ].…”

“…These include, for example, Ferraro et al [2013], Ringerud et al [2014], and Prigent et al [2015]. The current study takes advantage of the recent work of Harrison et al [2015] to include the same models but with calibrated parameters. Evaluating uncalibrated and calibrated models, together with other methods (below), under the same framework, gives us a broader understanding of the strengths and weaknesses of the various methods.…”

“…Helpful discussions with S. Joseph Munchak, Mehmet Kurum, Ed Kim, Alicia Joseph, Peggy O'Neil, and Scott Rheingrover are appreciated. All data for this paper are properly cited and referred to in the reference list [Aires et al, 2011;Hansen et al, 2000;Knowles et al, 2006;Ringerud et al, 2014;Ringerud et al, 2015;Yang et al, 2006] and Harrison et al [2015].…”