An improved fast radiative transfer model for special sensor microwave imager/sounder upper atmosphere sounding channels

2010 ◽  
Vol 115 (D15) ◽  
Author(s):  
Yong Han ◽  
Paul van Delst ◽  
Fuzhong Weng
2007 ◽  
Vol 112 (D11) ◽  
Author(s):  
Yong Han ◽  
Fuzhong Weng ◽  
Quanhua Liu ◽  
Paul van Delst

2013 ◽  
Vol 141 (10) ◽  
pp. 3314-3330 ◽  
Author(s):  
Karl W. Hoppel ◽  
Stephen D. Eckermann ◽  
Lawrence Coy ◽  
Gerald E. Nedoluha ◽  
Douglas R. Allen ◽  
...  

Abstract Upper atmosphere sounding (UAS) channels of the Special Sensor Microwave Imager/Sounder (SSMIS) were assimilated using a high-altitude version of the Navy Global Environmental Model (NAVGEM) in order to investigate their potential for operational forecasting from the surface to the mesospause. UAS radiances were assimilated into NAVGEM using the new Community Radiative Transfer Model (CRTM) that accounts for Zeeman line splitting by geomagnetic fields. UAS radiance data from April 2010 to March 2011 are shown to be in good agreement with coincident temperature measurements from the Sounding of the Atmosphere Using Broadband Emission Radiometry (SABER) instrument that were used to simulate UAS brightness temperatures. Four NAVGEM experiments were performed during July 2010 that assimilated (i) no mesospheric observations, (ii) UAS data only, (iii) SABER and Microwave Limb Sounder (MLS) mesospheric temperatures only, and (iv) SABER, MLS, and UAS data. Zonal mean temperatures and observation − forecast differences for the UAS-only and SABER+MLS experiments are similar throughout most of the mesosphere, and show large improvements over the experiment assimilating no mesospheric observations, proving that assimilation of UAS radiances can provide a reliable large-scale constraint throughout the mesosphere for operational, high-altitude analysis. This is confirmed by comparison of solar migrating tides and the quasi-two-day wave in the mesospheric analyses. The UAS-only experiment produces realistic tidal and two-day wave amplitudes in the summer mesosphere in agreement with the experiments assimilating MLS and SABER observations, whereas the experiment with no mesospheric observations produces excessively strong mesospheric winds and two-day wave amplitudes.


2012 ◽  
Vol 33 (6) ◽  
pp. 1611-1624 ◽  
Author(s):  
Iñigo Mendikoa ◽  
Santiago Pérez-Hoyos ◽  
Agustín Sánchez-Lavega

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rehman S. Eon ◽  
Charles M. Bachmann

AbstractThe advent of remote sensing from unmanned aerial systems (UAS) has opened the door to more affordable and effective methods of imaging and mapping of surface geophysical properties with many important applications in areas such as coastal zone management, ecology, agriculture, and defense. We describe a study to validate and improve soil moisture content retrieval and mapping from hyperspectral imagery collected by a UAS system. Our approach uses a recently developed model known as the multilayer radiative transfer model of soil reflectance (MARMIT). MARMIT partitions contributions due to water and the sediment surface into equivalent but separate layers and describes these layers using an equivalent slab model formalism. The model water layer thickness along with the fraction of wet surface become parameters that must be optimized in a calibration step, with extinction due to water absorption being applied in the model based on equivalent water layer thickness, while transmission and reflection coefficients follow the Fresnel formalism. In this work, we evaluate the model in both field settings, using UAS hyperspectral imagery, and laboratory settings, using hyperspectral spectra obtained with a goniometer. Sediment samples obtained from four different field sites representing disparate environmental settings comprised the laboratory analysis while field validation used hyperspectral UAS imagery and coordinated ground truth obtained on a barrier island shore during field campaigns in 2018 and 2019. Analysis of the most significant wavelengths for retrieval indicate a number of different wavelengths in the short-wave infra-red (SWIR) that provide accurate fits to measured soil moisture content in the laboratory with normalized root mean square error (NRMSE)< 0.145, while independent evaluation from sequestered test data from the hyperspectral UAS imagery obtained during the field campaign obtained an average NRMSE = 0.169 and median NRMSE = 0.152 in a bootstrap analysis.


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