A Melting-Layer Model for Passive/Active Microwave Remote Sensing Applications. Part I: Model Formulation and Comparison with Observations

2001 ◽  
Vol 40 (7) ◽  
pp. 1145-1163 ◽  
Author(s):  
William S. Olson ◽  
Peter Bauer ◽  
Nicolas F. Viltard ◽  
Daniel E. Johnson ◽  
Wei-Kuo Tao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Microwave Remote Sensing ◽  
Model Formulation ◽  
Layer Model ◽  
Melting Layer ◽  
Sensing Applications ◽  
Remote Sensing Applications ◽  
Active Microwave Remote Sensing

X-ray diffraction and infrared characterization of Oxisols from central and southeastern Brazil

Clay Minerals ◽  
2008 ◽  
Vol 43 (4) ◽  
pp. 549-560 ◽  
Author(s):  
R. P. Nitzsche ◽  
J. B. Percival ◽  
J. K. Torrance ◽  
J. A. R. Stirling ◽  
J. T. Bowen
Keyword(s):  
Remote Sensing ◽  
Near Infrared ◽  
Microwave Remote Sensing ◽  
Short Wave ◽  
Southeastern Brazil ◽  
X Ray Diffraction ◽  
X Ray ◽  
Sensing Applications ◽  
Remote Sensing Applications

AbstractEleven Oxisols with high clay contents, 2.6–59.7 wt.% Fe2O3, and containing hematite, goethite, magnetite and maghemite, from São Paulo, Minas Gerais and Goiás, Brazil, were studied for the purpose of microwave remote sensing applications in the 0.3 to 300 GHz range. Of special interest are: the pseudosand effect caused by Fe-oxide cementation of clusters of soil particles; the mineralogy; and whether the soil magnetic susceptibility affected by ferromagnetic magnetite and maghemite interferes with microwave propagation. Quantitative mineralogical analyses were conducted using X-ray diffraction with Rietveld refinement. Visible, near infrared and short wave infrared spectroscopic analyses were used to characterize the samples qualitatively for comparison with published spectral radiometry results. Quartz (3–88%), hematite (2–36%) and gibbsite (1–40%) occurred in all soils, whereas kaolinite (2–70%) and anatase (2–13%) occurred in nine samples. Ilmenite (1–8%) was found in eight soils and goethite (2–39%) in seven. Of the ferromagnetic minerals, maghemite occurred in seven soils (1–13%) and three contained magnetite (<2%). These results will be applied to the interpretation of the effect of Fe oxides, particularly the ferromagnetic oxides, on microwave interaction with high-Fe soils, with ultimate application to the monitoring of soil water content by microwave remote sensing.

Do the rain microphysics provide information on the overlying ice cloud?

2020 ◽  
Author(s):  
Kamil Mroz ◽  
Alessandro Battaglia ◽  
Stefan Kneifel ◽  
Jose Dias Neto
Keyword(s):  
Remote Sensing ◽  
Size Distribution ◽  
Drop Size ◽  
Microwave Remote Sensing ◽  
Drop Size Distribution ◽  
Radar Signal ◽  
Dual Frequency ◽  
Melting Layer ◽  
Sensing Applications ◽  
Melting Region

&lt;p&gt;This study investigates to what degree the information about the Drop Size Distribution (DSD) of rain can be used to narrow down uncertainty associated with complex ice microphysics. For this purpose, measurements from vertically pointing multi-frequency Doppler radar are thoroughly analysed. Linear Depolarization Ratio information is used to unambiguously separate hydrometeor phases. Within radar volumes where pure rain is identified multi-frequency Doppler spectra are utilised to retrieve a binned DSD with a high degree of confidence (Tridon et al. 2017). By assuming no breakup and negligible interaction between melting particles (Szyrmer and Zawadzki 1999, Olson et al. 2001, Matrosov 2008) the rain drop size distribution closest to the melting region is used to predict the particle size distribution (PSD) in the overlying snow. With these assumptions the resulting shape of the ice PSD depends solely on the hydrodynamical properties of snow that are dictated by its microphysics. &amp;#160;Several ice models are considered in the analysis, ranging from aggregates of columns, dendrites, needles and plates to different stages of rimed snow. Their scattering properties are simulated with Self-Similar-Rayleigh-Gans approximation (Leinonen et al. 2018) whereas falling velocities are modelled after Khvorostyanov and Curry (2005). Doppler spectra are simulated for the predicted ice PSD and compared to the measurements above the melting region. Results suggest that, if appropriate snow model used, the predicted reflectivity differs by less than 3 dB from the measured values as has been tentatively suggested by Fabry and Zawadzki (1995).&lt;/p&gt;&lt;p&gt;Tridon, F., A. Battaglia, E. Luke, P. Kollias, 2017. Rain retrieval from dual-frequency radar Doppler spectra: validation and potential for a midlatitude precipitating case study. Q. J. Roy. Meteorol. Soc. 143, 1364-1380. DOI: 10.1002/qj.3010&lt;/p&gt;&lt;p&gt;Szyrmer, W. and I. Zawadzki, 1999: Modeling of the Melting Layer. Part I: Dynamics and Microphysics. J. Atmos. Sci., 56, 3573&amp;#8211;3592, https://doi.org/10.1175/1520-0469(1999)056&lt;3573:MOTMLP&gt;2.0.CO;2&lt;/p&gt;&lt;p&gt;S. Olson, P. Bauer, N. F. Viltard, D. E. Johnson, W-K. Tao, R. Meneghini, and L. Liao, &amp;#8220;A melting layer model for passive/active microwave remote sensing applications&amp;#8212;Part I: Model formulation and comparisons with observations,&amp;#8221; J. Appl. Meteorol., vol. 40, no. 7, pp. 1145&amp;#8211;1163, Jul. 2001&lt;/p&gt;&lt;p&gt;Y. Matrosov, &quot;Assessment of Radar Signal Attenuation Caused by the Melting Hydrometeor Layer,&quot; in IEEE Transactions on Geoscience and Remote Sensing, vol. 46, no. 4, pp. 1039-1047, April 2008. doi: 10.1109/TGRS.2008.915757&lt;/p&gt;&lt;p&gt;Fabry, F., and I. Zawadzki, 1995: Long-term radar observations of the melting layer of precipitation and their interpretation. J. Atmos. Sci., 52, 838&amp;#8211;851.&lt;/p&gt;&lt;p&gt;Jussi, Leinonen, Kneifel, Stefan, Hogan, Robin J.. Evaluation of the Rayleigh&amp;#8211;Gans approximation for microwave scattering by rimed snowflakes. Q J R Meteorol Soc 2018; 144 ( Suppl. 1): 77&amp;#8211; 88. https://doi.org/10.1002/qj.3093&lt;/p&gt;

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