No abstract

The strong nonlocal thermal equilibrium (NLTE) emission in the upper atmosphere impedes the usage of Cross-track Infrared Sounder (CrIS) temperature channels near 4.3 μm in the operational data assimilation. This study explores the bias characteristics of those temperature channels near 4.3 μm with and without a fast NLTE model implemented in the Community Radiative Transfer Model (CRTM). It is shown that the biases of those temperature channels can reach up to 12 K and are dramatically reduced to below 4 K during daytime by the fast NLTE model with small differences from the nighttime biases. However, the biases after applying the NLTE correction remain large for CrIS upper atmospheric temperature channels during both daytime and nighttime. A further investigation suggests that the remaining biases in those temperature channels mainly originate from the cold biases in the stratospheric temperature profiles of National Center for Environmental Prediction Global Forecast System (GFS) forecasts as input to CRTM. The cold biases reach the maximum of about 8 K near the tropics at about 1 hPa, decreasing toward higher latitudes and lower altitudes.where fg is the mathematical expectation operator. From the above equation, it is seen that the large differences between x o and x b are correlated to the large sum of μ o and μ b . Since the data assimilation YIN BIAS STATISTICS OF CRIS SW CHANNELS 1248 Key Points: • CrIS can provide the global infrared radiance in improving NWP forecast skills • The NLTE emission can introduce the large biases in CrIS shortwave temperature sounding channels • The NLTE effect on CrIS data bias is assessed by the fast NLTE model in CRTM Correspondence to: M. Yin, my11g@my.fsu.edu Citation: Yin, M. (2016), Bias characterization of CrIS shortwave temperature sounding channels using fast NLTE model and GFS forecast field,

A physically optimized a priori database is developed for Global Precipitation Measurement Microwave Imager (GMI) snow water retrievals over ocean. The initial snow water content profiles are derived from CloudSat Cloud Profiling Radar (CPR) measurements. A radiative transfer model in which the single‐scattering properties of nonspherical snowflakes are based on the discrete dipole approximate results is employed to simulate brightness temperatures and their gradients. Snow water content profiles are then optimized through a one‐dimensional variational (1D‐Var) method. The standard deviations of the difference between observed and simulated brightness temperatures are in a similar magnitude to the observation errors defined for observation error covariance matrix after the 1D‐Var optimization, indicating that this variational method is successful. This optimized database is applied in a Bayesian retrieval snow water algorithm. The retrieval results indicated that the 1D‐Var approach has a positive impact on the GMI retrieved snow water content profiles by improving the physical consistency between snow water content profiles and observed brightness temperatures. Global distribution of snow water contents retrieved from the a priori database is compared with CloudSat CPR estimates. Results showed that the two estimates have a similar pattern of global distribution, and the difference of their global means is small. In addition, we investigate the impact of using physical parameters to subset the database on snow water retrievals. It is shown that using total precipitable water to subset the database with 1D‐Var optimization is beneficial for snow water retrievals.

The Global Precipitation Mission (GPM) Microwave Imager (GMI) has four channels at 166 and 183 GHz that provide critical information on snow precipitation. Since the applications of these high‐frequency microwave channels to snowfall prediction and retrieval are still in a very early stage, it is important to evaluate the biases between observed and simulated brightness temperatures for those channels under snowfall conditions. A radiative transfer model that supports the computation of single‐scattering properties for non‐spherical snow particles is employed to simulate GMI brightness temperatures. Snow water content profiles are first derived from the CloudSat Cloud Profiling Radar (CPR) measurements and then used as input to the radiative transfer model. Results show that the biases of observed minus simulated brightness temperatures for GMI channels averaged over all selected pixels are generally less than 1 K under snowfall conditions, except for the 166 GHz horizontal polarization (166H) channel that has a bias of about 3 K. Larger biases are observed under snowfall conditions when the scene brightness temperatures are low. Further investigation indicates that the GMI remaining biases are often associated with shallow snow cells or deep convective clouds. We hypothesize that in shallow snow cells the errors in retrieved cloud liquid water likely contribute to the large bias for the GMI 166H channel, while in deep convective clouds the attenuation in CPR radar reflectivities and possible sampling bias likely cause the biases for GMI channels under low scene brightness temperatures. This study lays out an applicable configuration in simulating microwave brightness temperatures at high frequency under snowfall conditions, and the explanation provided for the GMI remaining biases at 166 and 183 GHz are useful for future applications of these channels.

No abstract