CIMSS Forward Model Capability to Support GOES-R Measurement Simulations Tom Greenwald, Hung-Lung (Allen) Huang, Dave Tobin, Ping Yang*, Leslie Moy, Erik Olson, Jason Otkin, Bryan Baum, Hal Woolf and Xuanji Xu Cooperative Institute for Meteorological Satellite Studies (CIMSS), University of Wisconsin-Madison * Department of Atmospheric Sciences, Texas A&M University Process Flow Introduction Summary References Acknowledgments This work was supported by NOAA cooperative agreement NA07EC0676. Baum, B. A., A. J. Heymsfield, P. Yang, and S. T. Bedka, 2005: Bulk scattering properties for the remote sensing of clouds. Part I: Microphysical Data and Models. J. Appl. Meteor., 44, Niu, J., P. Yang, H.-L. Huang, J. E. Davies, and J. Li, 2006: A fast infrared radiative transfer model for overlapping clouds, JQSRT, in press. Yang, P., K. N. Liou, K. Wyser, and D. Mitchell, 2000: Parameterization of the scattering and absorption properties of individual ice crystals. J. Geophys. Res., 105, Yang, P., B. Baum, A. J. Heymsfield, Y. X. Hu, H.-L. Huang, S.-C. Tsay, and S. Ackerman, 2003: Single scattering properties of droxtals. J. Quant. Spectrosc. Radiat. Transfer, 79-80, A critical part of planning for GOES-R implementation is developing forward radiative transfer (RT) models to compute top-of-atmosphere (TOA) radiances in an end-to-end system. Generating these radiances are important in developing new products and algorithms (such as atmospheric profile retrievals, cloud and aerosol property retrievals, and wind retrievals) and are essential in the preparation of GOES-R data for data assimilation. Toward this effort, the CIMSS has built a forward RT modeling system for computing 2-D TOA thermal radiances from WRF model simulations in all weather conditions for the Hyperspectral Environmental Suite (HES) and thermal channels on the Advanced Baseline Imager (ABI). Capabilities of the RT model system is summarized along with its current performance and planned improvements. D max < 60 microns: 100% droxtals 60 microns < D max < 1000 microns: 15% 3D bullet rosettes 50% solid columns 35% plates 1000 microns < D max < 2500 microns: 45% hollow columns 45% solid columns 10% aggregates 2500 microns < D max < 9500 microns 97% 3D bullet rosettes 3% aggregates PLOD Cloud R/T Tables FIRTM2 Forward Model Performance Requires ~250 MB of RAM Takes ~0.3 sec per profile (clear or cloudy) on a Pentium-4 system running at 2.5 GHz TOA brightness temperature errors under cloudy conditions (not including errors due to PLOD and due to reducing multiple cloud layers to one or two layers) cm-1: < 0.5 K when optical depth < cm-1: < 0.8 K except for very high clouds with small particles Model Descriptions Gas absorption model (PLOD) - Regression based - Predicts polychromatic level-to-space gas transmittances at fixed pressure levels - RMS errors < 0.2 K (brightness temperature) across spectrum Cloud scattering properties - Ice particles: lookup tables for combined habits from 100 to 3250 cm -1 (Baum et al. 2005) based on rigorous scattering calculations (Yang et al. 2000, 2003) Advantages: > No need to classify properties according to cloud type or region > Size distribution characteristics are consistent with field measurements - Liquid particles: lookup tables from Mie calculations ( cm -1 ) Radiative transfer model (FIRTM2) - 5-layer model allows for two cloud layers (Niu et al. 2006) - Assumes clouds scatter radiation isotropically and with no cloud- to-surface and cloud-to-cloud interactions - Cloud layer reflection/transmission determined from lookup tables; parameterized in terms of wavenumber, zenith angle, visible optical depth and effective particle diameter - Computes thermal radiances in both clear and cloudy sky conditions (surface emissivity specified from seasonal database) WRF Model Simulation HES spectrum Atmospheric gases Clouds/precipitation Atmospheric temperature FIRTM2 model configuration Recipe for ice clouds Cloud Code development - Written in Fortran 95 - Modular structure - Allows for integration into the Joint Center for Data Assimilation CRTM (Community Radiative Transfer Model) ABI thermal bands Convolution An Example Cloud ice (orange) Snow (blue) Graupel (purple) Cloud liquid (yellow) Rain (not shown) WRF model 24-hr simulation of deep convection over the upper Midwest 00 UTC 25 June 2003 Horizontal grid spacing: 4 km Sub-domain size: 512 km x 512 km N A system has been developed to routinely produce high quality simulated datasets of the HES and thermal ABI channels from NWP model runs Key features include utilizing the latest ice scattering properties and having portable and modular Fortran 95 code Further work: - Add solar component for the longest wavelengths - Explore other ways to improve accuracy (e.g., include higher order cloud interactions and consider non-isotropic scattering) Simulated spectra: cm cm -1 Brightness temperature (K) Contact: Tom Greenwald cm -1 N