Evaluation of AIRS Cloud Phase Classification over the Arctic Ocean Against Combined CloudSat-CALIPSO Observations

Cloud phase retrievals from the Atmospheric Infrared Sounder (AIRS) are evaluated against combined CloudSat-CALIPSO (CCL) observations using four years of data over the Arctic Ocean. AIRS cloud phase is evaluated over sea ice and open ocean separately using collocated CCL and AIRS fields of view (FOVs). Additionally, AIRS and CCL cloud phase occurrences are evaluated seasonally, zonally, and with respect to total column water vapor (TCWV) and lapse rate (Γ). Lastly, collocated MODIS cloud information is implemented in a one-month case study to assess the relationship between AIRS phase decisions, cloud cover and cloud phase throughout the AIRS FOV. Depending on the surface type, AIRS classification skill for single-layer ice and liquid-phase clouds is between 85%-95% and 22%-32%, respectively. Most unknown and liquid AIRS phase classifications correspond to mixed-phase clouds. AIRS ice- phase relative occurrence is biased low compared to CCL. However, the liquid-phase relative occurrence is similar between the two instruments. Compared to the CCL climatology, AIRS accurately represents the seasonal cycle of liquid and ice cloud phase across the Arctic as well as the relationship between cloud phase and TCWV and Γ regime in some cases. When MODIS cloud cover or cloud phase within the AIRS FOV is more heterogeneous, it tends to lead the unknown-phase classifications by the AIRS algorithm.

利用北冰洋上空四年的数据，将大气红外探测器（Atmospheric Infrared Sounder, AIRS）的云相态反演结果与CloudSat-CALIPSO联合观测（CCL）结果进行对比评估。通过匹配的CCL与AIRS视场（field of view, FOV），分别评估了海冰和开阔洋面上空的AIRS云相态。此外，还从季节、纬向维度，以及总柱水汽（total column water vapor, TCWV）和 lapse rate（Γ）的角度，评估了AIRS与CCL的云相态出现频率。最后，在为期一个月的案例研究中结合匹配的MODIS云信息，评估了AIRS视场内AIRS相态判定结果与云量、云相态之间的关系。根据下垫面类型的不同，AIRS对单层冰云与液相云的分类精度分别介于85%-95%和22%-32%之间。AIRS的大多数未知相态与液相分类结果对应混合相云。与CCL相比，AIRS冰相的相对出现频率存在偏低偏差；但两者的液相相对出现频率相近。与CCL气候态相比，AIRS能准确反映北极地区液相与冰相云的季节循环，在部分情况下还能准确反映云相态与TCWV及Γ状态之间的关系。当AIRS视场内MODIS云量或云相态的异质性较强时，AIRS算法更易输出未知相态分类结果。