Transcript Evaluation of the GCIP/GAPP Short

Consistent Earth System Data Records for Climate Research: Focus on Shortwave and Longwave Radiative Fluxes
Rachel T. Pinker, Yingtao Ma and Eric Nussbaumer
Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD 20742
Introduction
Environmental satellites are considered as useful tools for providing
information on shortwave (SW) and longwave (LW) radiative fluxes at the
Earth’s surface at various temporal and spatial scales.
We report on results from implementing an improved methodology to
estimate SW radiative fluxes within the atmospheric system at a climatic time
scale (1983-2009) and results from a newly developed inference scheme for
deriving LW fluxes at 0.50 spatial resolution at 3-hourly time scale. This was
done in support of a Making Earth Science Data Records for Use in Research
Environments (MEaSUREs) * project aimed at the development of
capabilities to estimate major components of the terrestrial water cycle at
climatic time scale. Results on SW and LW surface fluxes at global scale as
derived from MODIS observations utilizing updated information on the
atmospheric states and surface properties for a period of about ten years are
also available and will be used in the evaluation.
Upper left: Comparison with independent
satellite results over land; ISCCP-FD,
GEWEX/SRB and CERES (CERESSRBAVG Terra-GEO-MOD_Ed02d ,
monthly time scale during 2000-2005;
Upper right: Evaluation over ocean sites:
Monthly means for 1/2000-12/2005;
Lower left: Evaluation of daily SW over
PIRATA buoys for 2004: a) ISCCP-FD/WHOI
b) UMD/DX_V3.3.3 c)
UMD/DX_V3.3.3_bias_corrected d)
UMD_MODIS

LW , surface

SW , surface
F
F
Monthly Averaged for March 2001
Shortwave Results
SW Model updates
Expanded were land use model types with their spectral representation to
match the IGBP surfaces types in new model simulations of radiative fluxes.
Improved was representation of the global distribution of aerosols .
Independent parameterization was introduced for water and ice clouds.
Incorporated were new Angular Distribution Models (ADM) as developed
under CERES and described in Niu and Pinker (2010).
•Results were evaluated against ground observations from best available data
over land: SURFRAD, DOE/ARM, and BSRN ; over oceans: PIRATA and
TOA/TRITON buoys as well as buoys of opportunity.
•Comparison was done
With: previous version of
inference scheme (v.3.3), with
independent satellite estimates
and with numerical models.
Impact of
Implementation
methodology.
27 land BSRN stations
located globally,
monthly for 2002-2005.
Left: Top: Comparison of
cloud base temperature
calculate from
DSLW/UMD artificial
neural network versus
observed from CPR and
CALIOP data for 2008.
Bottom : Comparison of
cloud base temperature
calculated from the cloud
thickness model from
Wang et al. (2000) versus
observed from CPR and
CALIOP for 2008.
Left: Monthly
averaged RMSE
(W m- ) for: Tropics
(blue), Mid-Latitudes
(green), and Polar
regions (red) during
2003-2007. Black line:
mean RMSE for all
ground stations .
Longwave Results
Longwave model
Method for calculating LW developed from runs of the Rapid Radiative Transfer
Model (RRTM) (Nussbaumer and Pinker, 2012). Atmospheric parameters (i.e.
temperature, humidity) taken from ERA Interim reanalysis data. Validation done
using World Radiation Monitoring Center – Baseline Surface Radiation Network
(WRMC-BSRN) ground station measurements. For cloud contribution to DSLW,
first the cloud based temperature is derived using an artificial neural network trained
using spatially and temporally collocated MODIS and Cloudsat Cloud Profiling
Radar (CPR) and Calipso Cloud-Aerosol Lidar with Orthogonal Polarization
(CALIOP) observations. Applied to ISCCP-DX and as control to MODIS data.
* Work part of MEaSUREs activity titled : “Developing Consistent Earth System Data Records
for the Global Terrestrial Water Cycle”.Team Members:
E. F. Wood (PI)1, T. J Bohn2, J. L Bytheway3, X. Feng4, H. Gao2, P. R.Houser4 (CO-I), C. D
Kummerow3 (CO-I), D. P Lettenmaier2 (CO-I), C. Li5, Y. Ma5, R. F MacCracken4, M. Pan1, R. T
Pinker5 (CO-I), A. K. Sahoo1, J. Sheffield1
(1)
(4)
(2)
(5)
(3)
Left. Daily averaged
DSLW results from
DSLW/UMD model
driven with MODIS and
ISCCP DX cloud products
and four independent
results versus BSRN
observations aggregated
over 2003-2007. The color
scale is given as log10 of
the density.
References / Acknowledgements
Nussbaumer, E. A., and R. T. Pinker, 2012. Estimating surface longwave radiative fluxes from satellites utilizing artificial neural networks. J.
Geophys. Res., 117, D07209.
Wang, J., W. B. Rossow, and Y. Zhang, 2000: Cloud vertical structure and its variations from a 20-yr global rawinsonde dataset. J. Climate,
13, 3041-3056.
This work benefited from support under NASA grant NNX08AN40A titled: Developing Consistent Earth System Data Records for the Global
Terrestrial Water Cycle and NSF grant ATM0631685. Thanks are due to the NASA GES DISC Giovanni for the MODIS data, to the various
MODIS teams that generated parameters used in this study, and to the Baseline Surface Radiation Network for observations used in evaluation.