1. Modeling the Zeeman effect in high-altitude SSMIS channels for numerical weather prediction profiles: comparing a fast model and a line-by-line model
- Author
-
Roger Saunders, Patrick Eriksson, Peter Rayer, Mathias Milz, Richard Larsson, Stephan A. Buehler, Anna Booton, Viju O. John, and William Bell
- Subjects
Physics ,Atmospheric Science ,Zeeman effect ,010504 meteorology & atmospheric sciences ,Meteorology ,lcsh:TA715-787 ,lcsh:Earthwork. Foundations ,0211 other engineering and technologies ,02 engineering and technology ,Numerical weather prediction ,01 natural sciences ,Standard deviation ,lcsh:Environmental engineering ,Computational physics ,symbols.namesake ,Data assimilation ,Atmospheric radiative transfer codes ,SSMIS ,Range (statistics) ,symbols ,Special sensor microwave/imager ,lcsh:TA170-171 ,021101 geological & geomatics engineering ,0105 earth and related environmental sciences - Abstract
We present a comparison of a reference and a fast radiative transfer model using numerical weather prediction profiles for the Zeeman-affected high-altitude Special Sensor Microwave Imager/Sounder channels 19–22. We find that the models agree well for channels 21 and 22 compared to the channels' system noise temperatures (1.9 and 1.3 K, respectively) and the expected profile errors at the affected altitudes (estimated to be around 5 K). For channel 22 there is a 0.5 K average difference between the models, with a standard deviation of 0.24 K for the full set of atmospheric profiles. Concerning the same channel, there is 1.2 K on average between the fast model and the sensor measurement, with 1.4 K standard deviation. For channel 21 there is a 0.9 K average difference between the models, with a standard deviation of 0.56 K. Regarding the same channel, there is 1.3 K on average between the fast model and the sensor measurement, with 2.4 K standard deviation. We consider the relatively small model differences as a validation of the fast Zeeman effect scheme for these channels. Both channels 19 and 20 have smaller average differences between the models (at below 0.2 K) and smaller standard deviations (at below 0.4 K) when both models use a two-dimensional magnetic field profile. However, when the reference model is switched to using a full three-dimensional magnetic field profile, the standard deviation to the fast model is increased to almost 2 K due to viewing geometry dependencies, causing up to ±7 K differences near the equator. The average differences between the two models remain small despite changing magnetic field configurations. We are unable to compare channels 19 and 20 to sensor measurements due to limited altitude range of the numerical weather prediction profiles. We recommended that numerical weather prediction software using the fast model takes the available fast Zeeman scheme into account for data assimilation of the affected sensor channels to better constrain the upper atmospheric temperatures.
- Published
- 2016
- Full Text
- View/download PDF