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.

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.

The environmental conditions of snow particles with different particle sizes and bulk effective densities over the ocean are explored using a coincidence dataset of National Aeronautics and Space Administration (NASA) CloudSat Cloud Profiling Radar (CPR) and Global Precipitation Mission (GPM) Dual-frequency Precipitation Radar (DPR). Observed triple-frequency radar signatures for snow particles over the ocean are firstly derived. Based on modeled triple-frequency signatures for various snow particles, DFR Ku/Ka and the ratio of DFR Ku/Ka to DFR Ku/W from observations are selected to indicate the snow particle size and bulk effective density, respectively. The dependences of two indicators on temperature, relative humidity and cloud liquid water content are presented. The snow particle size range becomes wider at warmer temperatures, higher relative humidities or lower cloud liquid water contents. At cold temperatures, low relative humidities or high cloud liquid water contents, large snow particles are prevalent. At high cloud liquid water contents, the riming process mainly contributes to the increase in snow particle bulk effective density. When supersaturation occurs, a large portion of snow particles have large sizes and low bulk effective densities at cold temperatures. This study can improve the understanding of snow microphysics and demonstrate the potential of spaceborne radar measurements in global snowfall retrievals.

Spaceborne snow water retrievals over oceans are assessed using a multiyear coincident dataset of CloudSat Cloud Profiling Radar (CPR) and Global Precipitation Mission (GPM) Dual-frequency Precipitation Radar (DPR). Various factors contributing to differences in snow water retrievals between CPR and DPR are carefully considered. A set of relationships between radar reflectivity (Z_e) and snow water content (SWC) at Ku- and W-bands is developed using the same microphysical assumptions. It is found that surface snow water contents from CPR are much larger than those from DPR at latitudes above 60°, while surface snow water contents from DPR slightly exceed those from CPR at latitudes below 50°. Coincident snow water content profiles between CPR and DPR are further divided into two conditions. One is that only CPR detects the falling snow. Another is that both CPR and DPR detect the falling snow. The results indicate that about 88% of all snow water content profiles are under the first condition and usually associated with light snowfall events. The remaining snow water content profiles are generally associated with moderate and heavy snowfall events. Moreover, CPR surface snow water contents are larger than DPR ones at high latitudes because most light snowfall events are misdetected by DPR due to its low sensitivity. DPR surface snow water contents exceed CPR ones at low latitudes because CPR may experience a significant reduction in backscattering efficiency of large particles and attenuation in heavy snowfall events. The low sensitivity of DPR also causes a noticeable decrease in detected snow layer depth. The results presented here can help in developing global snowfall retrieval algorithms using multi-radars.