A case study of GOES-15 imager bias characterization with a numerical weather prediction model

Abstract

The infrared imager onboard the Geostationary Operational Environmental Satellite 15 (GOES-15) provides temporally continuous observations over a limited spatial domain. To quantify bias of the GOES-15 imager, observations from four infrared channels (2, 3, 4, and 6) are compared with simulations from the numerical weather prediction model and radiative transfer model. One-day clear-sky infrared observations from the GOES-15 imager over an oceanic domain during nighttime are selected. Two datasets, Global Forecast System (GFS) analysis and ERAInterim reanalysis, are used as inputs to the radiative transfer model. The results show that magnitudes of biases for the GOES-15 surface channels are approximately 1 K using two datasets, whereas the magnitude of bias for the GOES-15 water vapor channel can reach 5.5 K using the GFS dataset and 2.5 K using the ERA dataset. The GOES- 15 surface channels show positive dependencies on scene temperature, whereas the water vapor channel has a weak dependence on scene temperature. The strong dependence of bias on sensor zenith angle for the GOES-15 water vapor channel using GFS analysis implies large biases might exist in GFS water vapor profiles.