CIMSS Forward Model Capability to Support GOES-R Measurement Simulations

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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 AM University
Introduction
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.
Model Descriptions
Process Flow
WRF Model Simulation
Recipe for ice clouds

• Gas absorption model (PLOD)
• - Regression based
• - Predicts polychromatic level-to-space gas
  transmittances at fixed
• pressure levels
• - RMS errors lt 0.2 K (brightness temperature)
  across spectrum

Dmax lt 60 microns100 droxtals60 microns lt
Dmax lt 1000 microns15 3D bullet rosettes50
solid columns35 plates 1000 microns lt Dmax lt
2500 microns45 hollow columns45 solid
columns10 aggregates2500 microns lt Dmax lt
9500 microns97 3D bullet rosettes3 aggregates
Atmospheric gases
PLOD
HES 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
• gt No need to classify properties
  according to cloud type or region
• gt Size distribution
  characteristics are consistent with field
  measurements
• - Liquid particles lookup tables from Mie
  calculations (587-2350 cm-1)

FIRTM2
Atmospheric temperature

• 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)

Convolution
Cloud R/T Tables
Cloud
Clouds/precipitation
ABI thermal bands
Cloud
FIRTM2 model configuration
An Example

• Code development
• - Written in Fortran 95
• - Modular structure
• - Allows for integration into the Joint
  Center for Data Assimilation CRTM (Community
  Radiative
• Transfer Model)

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
Simulated spectra
668.8627cm-1
900.0391cm-1
Brightness temperature (K)
Forward Model Performance
1411.7249 cm-1




Cloud ice (orange) Snow (blue) Graupel
(purple) Cloud liquid (yellow) Rain (not shown)

• 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)
• 587-1180 cm-1 lt 0.5 K when
  optical depth lt 5
• 1180-2229 cm-1 lt 0.8 K except
  for very high clouds with small particles

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Summary
References
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,
1885-1895. 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,
4699-4718. 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, 1159-1169.

• 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)

Acknowledgments
This work was supported by NOAA cooperative
agreement NA07EC0676.
Contact Tom Greenwald tomg_at_ssec.wisc.edu