Estimating the Impact of Assimilating Cirrus Cloud–Contaminated Hyperspectral Infrared Radiances for Numerical Weather Prediction

Abstract

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 observations. Using co-located assimilated observations from the Atmospheric Infrared Sounder (AIRS) and the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), it is found that near 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 532nm (COD532nm) below 0.10 and cloud top temperatures between 240 K and 185 K as expected for cirrus clouds. These contamination statistics are consistent with simulations from the Radiative Transfer for TOVS (RTTOV) radiative transfer 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 K in temperature and 11 K in dew point 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 near 0.11 K in temperature and 0.34 K in dew point are possible, with temperature and dew point tropospheric root-mean-squared-error (RMSD) as large as 0.06 and 0.11 K, respectively. While in isolation these global estimates are not particularly concerning, differences are likely much larger in regions with high cirrus frequency.

The Impact of Assimilating Cirrus‐Effected Infrared Satellite Radiance From the FY‐4A AGRI on Water Vapor Analysis and Rainstorm Forecasting

“…Marquis et al. (2023) also reported that 7.7% of assimilated IR hyperspectral infrared sounder observations are contaminated by cirrus clouds, where large root‐mean‐square errors occur in the temperature and dewpoint after assimilation. Moreover, RTM has a poor effect on cirrus cloud simulations during ASR assimilation, so cloud‐dependent bias correction and quality controls are needed.…”

“…They found that cloud models are not very accurate for high clouds (with improved cloud ice optical properties (Vidot et al, 2015)), which may explain the large observation-minusbackground (O-B) standard deviation and the insufficiently low brightness temperature in the intertropical convergence zone. Marquis et al (2023) also reported that 7.7% of assimilated IR hyperspectral infrared sounder observations are contaminated by cirrus clouds, where large root-mean-square errors occur in the temperature and dewpoint after assimilation. Moreover, RTM has a poor effect on cirrus cloud simulations during ASR assimilation, so cloud-dependent bias correction and quality controls are needed.…”