The Havemann-Taylor Fast Radiative Transfer Code (HT-FRTC) and its applications

The Havemann-Taylor Fast Radiative Transfer Code (HT-FRTC) is a component of the Met Office NEON Tactical Decision Aid (TDA). Within NEON, the HT-FRTC has for a number of years been used to predict the IR apparent thermal contrasts between different surface types as observed by an airborne sensor. To achieve this, the HT-FRTC is supplied with the inherent temperatures and spectral properties of these surfaces (i.e. ground target(s) and background). A key strength of the HT-FRTC is its ability to take into account the detailed properties of the atmosphere, which in the context of NEON tend to be provided by a Numerical Weather Prediction (NWP) forecast model. While water vapour and ozone are generally the most important gases, additional trace gases are now being incorporated into the HT-FRTC. The HT-FRTC also includes an exact treatment of atmospheric scattering based on spherical harmonics. This allows the treatment of several different aerosol species and of liquid and ice clouds. Recent developments can even account for rain and falling snow. The HT-FRTC works in Principal Component (PC) space and is trained on a wide variety of atmospheric and surface conditions, which significantly reduces the computational requirements regarding memory and time. One clear-sky simulation takes approximately one millisecond. Recent developments allow the training to be completely general and sensor independent. This is significant as the user of the code can add new sensors and new surfaces/targets by simply supplying extra files which contain their (possibly classified) spectral properties. The HT-FRTC has been extended to cover the spectral range of Photopic and NVG sensors. One aim here is to give guidance on the expected, directionally resolved sky brightness, especially at night, again taking the actual or forecast atmospheric conditions into account. Recent developments include light level predictions during the period of twilight.

[1]  Clive D Rodgers,et al.  Inverse Methods for Atmospheric Sounding: Theory and Practice , 2000 .

[2]  Shepard A. Clough,et al.  Atmospheric radiative transfer modeling: a summary of the AER codes , 2005 .

[3]  M. Iacono,et al.  Line-by-Line Calculations of Atmospheric Fluxes and Cooling Rates: Application to Water Vapor , 1992 .

[4]  Anthony J. Baran,et al.  A review of the light scattering properties of cirrus , 2009 .

[5]  Anthony J. Baran,et al.  Testing an ensemble model of cirrus ice crystals using midlatitude in situ estimates of ice water content, volume extinction coefficient and the total solar optical depth , 2009 .

[6]  S. J. Revell,et al.  MONIM: the new Met Office Night Illumination Model , 2004 .

[7]  W. C. Snyder,et al.  Classification-based emissivity for land surface temperature measurement from space , 1998 .

[8]  Stephan Havemann,et al.  Atmospheric correction of short-wave hyperspectral imagery using a fast, full-scattering 1DVar retrieval scheme , 2012, Defense + Commercial Sensing.

[9]  E. Forgy,et al.  Cluster analysis of multivariate data : efficiency versus interpretability of classifications , 1965 .

[10]  Marco Matricardi,et al.  A principal component based version of the RTTOV fast radiative transfer model , 2010 .

[11]  Xu Liu,et al.  Principal component-based radiative transfer model for hyperspectral sensors: theoretical concept. , 2006, Applied optics.

[12]  Stephan Havemann,et al.  The Havemann‐Taylor Fast Radiative Transfer Code: Exact fast radiative transfer for scattering atmospheres using Principal Components (PCs) , 2009 .

[13]  Helmut T. Zwahlen,et al.  Visual Target Detection Models for Civil Twilight and Night Driving Conditions , 1999 .

[14]  Stephan Havemann The development of a fast radiative transfer model based on an empirical orthogonal functions (EOF) technique , 2006, SPIE Asia-Pacific Remote Sensing.

[15]  Stephan Havemann,et al.  Hyperspectral retrieval of land surface emissivities using ARIES , 2009 .

[16]  S. Hook,et al.  The ASTER spectral library version 2.0 , 2009 .

[17]  A. Kokhanovsky,et al.  SCIATRAN 2.0 – A new radiative transfer model for geophysical applications in the 175–2400 nm spectral region , 2004 .