Estimating the impact of assimilating cirrus cloud–contaminated hyperspectral infrared radiances for numerical weather prediction

The assimilation of hyperspectral infrared sounders (HIS) observations aboard Earth-observing satellites has become vital to numerical weather prediction, yet this assimilation is predicated on the assumption of clear-sky obser-vations. Using collocated assimilated observations from the Atmospheric Infrared Sounder (AIRS) and the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), it is found that nearly 7.7% of HIS observations assimilated by the Naval Research Laboratory Variational Data Assimilation System-Accelerated Representer (NAVDAS-AR) are contaminated by cirrus clouds. These contaminating clouds primarily exhibit visible cloud optical depths at 532 nm (COD532nm) below 0.10 and cloud-top temperatures between 240 and 185 K as expected for cirrus clouds. These contamination statistics are consistent with simulations from the Radiative Transfer for TOVS (RTTOV) model showing a cirrus cloud with a COD532nm of 0.10 imparts brightness temperature differences below typical innovation thresholds used by NAVDAS-AR. Using a one-dimensional variational (1DVar) assimilation system coupled with RTTOV for forward and gradient radiative transfer, the analysis temperature and moisture impact of assimilating cirrus-contaminated HIS observations is estimated. Large differences of 2.5 Kin temperature and 11 Kin dewpoint are possible for a cloud with COD532nm of 0.10 and cloud-top temperature of 210 K. When normalized by the contamination statistics, global differences of nearly 0.11 K in tempera-ture and 0.34 K in dewpoint are possible, with temperature and dewpoint tropospheric root-mean-squared errors (RMSDs) as large as 0.06 and 0.11 K, respectively. While in isolation these global estimates are not particularly concerning, differ-ences are likely much larger in regions with high cirrus frequency.