Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

IF value: 5.240
IF5.240
IF 5-year value: 5.768
IF 5-year
5.768
CiteScore value: 8.9
CiteScore
8.9
SNIP value: 1.713
SNIP1.713
IPP value: 5.53
IPP5.53
SJR value: 3.18
SJR3.18
Scimago H <br class='widget-line-break'>index value: 71
Scimago H
index
71
h5-index value: 51
h5-index51
Volume 6, issue 6
Geosci. Model Dev., 6, 2049–2062, 2013
https://doi.org/10.5194/gmd-6-2049-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
Geosci. Model Dev., 6, 2049–2062, 2013
https://doi.org/10.5194/gmd-6-2049-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

Development and technical paper 26 Nov 2013

Development and technical paper | 26 Nov 2013

Multi-sensor cloud retrieval simulator and remote sensing from model parameters – Part 1: Synthetic sensor radiance formulation

G. Wind1,2, A. M. da Silva1, P. M. Norris1,3, and S. Platnick1 G. Wind et al.
  • 1NASA Goddard Space Flight Center, 8800 Greenbelt Rd. Greenbelt, Maryland 20771, USA
  • 2SSAI, Inc., 10210 Greenbelt Road, Suite 600, Lanham, Maryland 20706, USA
  • 3Universities Space Research Association, 10211 Wincopin Circle #500, Columbia, Maryland 21044, USA

Abstract. In this paper we describe a general procedure for calculating synthetic sensor radiances from variable output from a global atmospheric forecast model. In order to take proper account of the discrepancies between model resolution and sensor footprint, the algorithm takes explicit account of the model subgrid variability, in particular its description of the probability density function of total water (vapor and cloud condensate.) The simulated sensor radiances are then substituted into an operational remote sensing algorithm processing chain to produce a variety of remote sensing products that would normally be produced from actual sensor output. This output can then be used for a wide variety of purposes such as model parameter verification, remote sensing algorithm validation, testing of new retrieval methods and future sensor studies. We show a specific implementation using the GEOS-5 model, the MODIS instrument and the MODIS Adaptive Processing System (MODAPS) Data Collection 5.1 operational remote sensing cloud algorithm processing chain (including the cloud mask, cloud top properties and cloud optical and microphysical properties products). We focus on clouds because they are very important to model development and improvement.

Publications Copernicus
Citation