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).




These satellite-observable signals can be directly compared with the high-resolution satellite L1 observations; therefore the G-SDSU bridges model and satellite remote sensing. For example, modeling community can apply the Goddard SDSU to i) radiance-based model evaluation/development using multi-sensor satellites L1B measurements, or ii) operator of data assimilation system using L1 measurements of multi-sensor satellites. Remote sensing community can utilize the G-SDSU to i) algorithm test/evaluation using atmospheric model simulations, ii) construction of a priori database, or iii) simulating satellite signals for future satellite missions.



Satellite Platforms and Sensors
The G-SDSU is generalized multi-sensor simulators. Thus, it simulates various satellite signals operated at past, current, or in near future:
·       Microwave simulator: SSMI, SSMIS, TMI, GMI, AMSR, AMSU, MSU, etc.
·       Radar simulator: TRMM PR, GPM DPR, CloudSat CPR, etc.
·       Visible IR simulator: AVHRR, MODIS, VIRS, GLI, GOES APR, AIRS, ASTER, etc.
·       LIDAR simulator: CALIPSO CALIOP, CATS ISS, etc.
·       Broadband simulator: ERBE, CERES, etc.
Users must know sensor-channel specifications for simulations. For orbital and sensor scan simulations, users also know the satellite orbital parameters and sensor scanning speed and sampling rate. Since the G-SDSU is the official platform for the Glocal Precipitation Measurement (GPM) simulator, it already has specs of the GPM satellite orbit and GMI/DPR scanning parameters.




Code Structure and Requirements
The initial version of SDSU has been released from HyARC Nagoya University in 2007 (Masunaga et al. 2009). Since then, the SDSU has been reinvented at NASA GSFC. Programming structure becomes object-oriented Fortran90Control parameters and input-output (IO) processes are compiled in a unified module that is hardwired to the different simulator modules. Dynamic allocation allows better memory management for large IO data. Many routine calculations use pre-computed look-up tables that can boost the computational speed (> 10x) without loosing measure accuracy. The G-SDSU can optionally utilize Message Passing Interface (MPI) library for parallel simulations in a multi-core to super computers. Two MPI options are available. First option is input-file decomposition, which has greater advantage when a large number of model files need to be processed. Second option is domain decomposition, which assign different sections of CRM domains for different processors. The second option also involves memory decomposition; thus it has advantageous to process a large-domain model file (or complex bin microphysics). Thus, compilation of the G-SDSU requires 1) Fortran compiler, 2) MPI library (optional), 3) NetCDF library, 4) C-compiler, 5) C-Preprocessor, and 6) Make utility in the standard Unix-flavor machine (UNIX, LINUX, Mac OSX, and etc.). 





Simulator Packages and Principles

  1. 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.
  2. 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.
  3. 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.
  4. 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.



  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)
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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
  9. 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).
  10. 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.



Code Release
The V2 of the G-SDSU CORE module has been released as the NASA Open Release. Various simulators can be embedded on the core modules. The V3 and beyond (most recent code) of the G-SDSU has been released in the packages of the NASA Unified WRF. Please visit the NU-WRF home page, and read the section of the Software Release.
Toshi Matsui (Toshihisa.Matsui-1@nasa.gov)