Modern multi-sensor satellite observations provide a more complete view of cloud, precipitation, and aerosols processes over globe; meanwhile, it is becoming a challenge for remote sensing and modeling communities to harness these observations. To this end, a comprehensive unified system of multi-sensor simulators, the Goddard Satellite Data Simulator Unit (G-SDSU), has been developed through multi-institutional collaborations (Matsui et al. 2014). The G-SDSU is the end-to-end satellite simulator unit, which can compute satellite-consistent Level-1 (L1) measurements (radiance/brightness temperature or backscatter) from the output of meso- or cloud-scale model simulations through passive microwave simulator (Kummerow 1993, Olson et al. 1996), radar simulator (Masunaga and Kummerrow 2005), passive visible-IR simulator (Nakajima and Tanaka 1986 & 1988), LIDAR simulator, and broadband simulator (Chou and Suarez 1999 & 2001) through rigorous satellite orbit and scan geometry simulations (Matsui 2013).
The G-SDSU has been coupled with various NASA high-resolution atmospheric model outputs, such as the NASA-Unified Weather Research and Forecasting (NU-WRF) model, the WRF with the Spectra Bin Microphysics (WRF-SBM), the Goddard Cumulus Ensemble (GCE) model, and the NASA Multi-Scale Modeling Framework (MMF), and the Goddard Earth Observing System 5 (GEOS5) via NetCDF format. There are choices of variety of microphysics: e.g., one/two-moment bulk (Goddard scheme, RAMS scheme, Morrison scheme) as well as spectra-bin (HUCM scheme) microphysics. Particle size distributions (PSDs) and various hydrometeor classes in these microphysics schemes are consistently treated among different simulators. The GOCART aerosol microphysics is also supported. Currently, single scatters assume sphere-shape via Mie calculation, oblate shape via T-matrix tables, or complex shapes via DDSCAT tables. Single scattering properties are shared among microwave-radar simulators and visible-IR-LIDAR-broadband simulators, which enables utility for multi-sensor satellite observations/simulations. In recent development (Version 2 to Version 3) rigorous satellite orbit and sensor-scan simulation revealed explicit prediction of sensor geometry, footprint shape/sizes, and realistic gain functions. Most of the simulators can be optionally applied to ground-based remote sensing. You may review following website to compare with other simulator packages (https://sites.google.com/site/satellitesimulators/home).
Simulator Packages and Principles
- Matsui, T., J. Santanello, J. J. Shi, W.-K. Tao, D. Wu, C. Peters-Lidard, E. Kemp, M. Chin, D. Starr, M. Sekiguchi, and F. Aires, (2014): Introducing multisensor satellite radiance-based evaluation for regional Earth System modeling, Journal of Geophysical Research, 119, 8450–8475, doi:10.1002/2013JD021424.
- Matsui, T. (2013), Chapter 12. Mesoscale Modeling and Satellite Simulator, Mesoscale Meteorological Modeling. 3rd Edition, R. A. Pielke Sr. Ed. Academic Press, 760 p, ISBN: 9780123852373.
- Matsui, T. T. Iguchi, X. Li, M. Han, W.-K. Tao, W. Petersen, T. L’Ecuyer, R. Meneghini, W. Olson, C. D. Kummerow, A. Y. Hou, M. R. Schwaller, E. F. Stocker, J.Kwiatkowski (2013), GPM satellite simulator over ground validation sites, Bull. Amer. Meteor. Soc., 94, 1653–1660.
- Masunaga, H., Matsui, T., W.-K. Tao, A. Y. Hou, C. Kummerow, T. Nakajima, P. Bauer, W. Olson, M. Sekiguchi, and T. Y. Nakajima (2011), Satellite Data Simulator Unit: Multi-Sensor and Multi–Frequency Satellite Simulator package, Bulletin of American Meteorological Society, 91, 1625–1632. doi: 10.1175/2010BAMS2809.1.
- Han M., S. A. Braun, T. Matsui, C. R. Williams (2012), Impact of cloud microphysics schemes in WRF model on the simulation of a winter storm as compared to radar and radiometer measurements. Journal of Geophysical Research (in press)
- Iguchi T., T. Matsui, J. J. Shi, W.-K. Tao, A. P. Khain, A. Hou, R. Cifelli, A. Heymsfield, and A. Tokay (2012), Numerical analysis using WRF-SBM for the cloud microphysical structures in the C3VP field campaign: Impacts of supercooled droplets and resultant riming on snow microphysics, Journal of Geophyiscal Research, 117, D23206, doi:10.1029/2012JD018101.
- Iguchi, T., T. Matsui, A. Tokay, P. Kollias, and W.-K. Tao (2012), Two distinct modes in one-day rainfall event during MC3E field campaign: Analyses of disdrometer observations and WRF-SBM simulation. Geophysical Research Letters, 39, L24805, doi:10.1029/2012GL053329.
- Li, X., W.-K. Tao, T. Matsui, C. Liu, and H. Masunaga (2010), Improving a spectral bin microphysical scheme using long-term TRMM satellite observations. Quarterly Journal of Royal Metrological Society, 136(647), 382-399.
- Matsui, T., X. Zeng, W.-K. Tao, H. Masunaga, W. Olson, and S. Lang (2009), Evaluation of long-term cloud-resolving model simulations using satellite radiance observations and multifrequency satellite simulators. Journal of Atmospheric and Oceanic Technology, 26, 1261-1274.
- Shi, J. J., W.-K. Tao, T. Matsui, A. Hou, S. Lang, C. Peters-Lidard, G. Jackson, R. Cifelli, S. Rutledge, and W. Petersen (2010), Microphysical Properties of the January 20-22 2007 Snow Events over Canada: Comparison with in-situ and Satellite Observations. Journal of Applied Meteorology and Climatology. 49(11), 2246–2266.
- Tao, W.K., D. Anderson, J. Chern, J. Enstin, A. Hou, P. Houser, R. Kakar, S. Lang, W. Lau, C. Peters-Lidard, X. Li, T. Matsui, M. Rienecker, M.R. Schoeberl, B.-W. Shen, J.J. Shie, and X. Zeng, (2009), Goddard Multi-Scale Modeling Systems with Unified Physics, Annales Geophysicae, 27, 3055-3064.
- Zeng, X., W.-K. Tao, T. Matsui, S. Xie, S. Lang, M. Zhang, D. Starr, and X. Li, (2011), Estimating the Ice Crystal Enhancement Factor in the Tropics. Journal of Atmospheric Science, 68, 1424–1434. doi: 10.1175/2011JAS3550.1
- Zeng, X., W.-K. Tao, S. Powell, R. Houze, Jr., P. Ciesielski, N. Guy, H. Pierce and T. Matsui (2012), A comparison of the water budgets between clouds from AMMA and TWP-ICE. Journal of Atmospheric Science, (in press).
- Zupanski, D., Sara Q. Zhang, Milija Zupanski, Arthur Y. Hou, Samson H. Cheung, 2011: A Prototype WRF-Based Ensemble Data Assimilation System for Dynamically Downscaling Satellite Precipitation Observations. J. Hydrometeor, 12, 118–134.