Statistical Estimation of a 13.3 micron Channel for VIIRS using Multisensor Data Fusion with Application to Cloud-Top Pressure Estimation

Meteorologists and other scientists rely heavily on remotely sensed data collected from instruments aboard orbiting satellites. The design of such instruments requires technical and economic trade-offs that results in certain desirable data not being directly available. One way to mitigate the lack of availability of this data is to use machine learning techniques to estimate the values that cannot be directly observed. This can be accomplished by exploiting statistical correlation with information in available data sets. By combining the information from multiple other sources it is often possible to create an accurate estimate of the physical parameters which are not directly observed. We apply this idea toward the problem of estimating the 13.3μm band for the Visible Infrared Imaging Radiometer Suite (VIIRS), an instrument aboard NOAA’s operational satellite, Suomi NPP. The radiance from the 13.3μm band is not directly available from VIIRS although this band has important applications such as estimating cloud-top pressure. We demonstrate that a reliable estimate of this band can be made using other VIIRS bands at 4, 9, 11 and 12μm, as well as input from the Cross-track Infrared Sounder (CrIS), which produces data at much finer spectral resolution, making measurements in hundreds of nearby infrared bands, though with lower spatial resolution. We have tested the result as input values to an algorithm which estimates cloud top pressure using data from 11, 12, and 13.3μm bands.