Cloud mask-related differential linear adjustment model for MODIS infrared water vapor product

Abstract

Water vapor is the primary greenhouse gas of the Earth-atmosphere system and plays a vital role in understanding climate change, if correctly measured from satellites. The Moderate Resolution Imaging Spectroradiometer (MODIS) can monitor water vapor retrievals at near-infrared (nIR) bands in the daytime as well as at infrared (IR) bands in both daytime and night time. However, the accuracy of IR retrievals under confident clear conditions (>99% probability) is much poorer than that of nIR retrievals. Additionally, IR retrievals under unconfident clear conditions (>95%, >66% and ≤66% probabilities) are usually discarded because the possible presence of clouds would further reduce their accuracy. In this study, we develop a cloud mask-related differential linear adjustment model (CDLAM) to adjust IR retrievals under all confident clear conditions. The CDLAM-adjusted IR retrievals are evaluated with the linear least square (LS) adjusted nIR retrievals under confident clear condition and Global Positioning System (GPS) observations under different probabilities of clear conditions. Both case studies in the USA and global (65° S~65° N) evaluation reveal that the CDLAM can significantly reduce uncertainties in IR retrievals at all clear-sky confidence levels. Moreover, the accuracy of the CDLAM-adjusted IR retrievals under unconfident clear conditions is much better than IR retrievals without adjustment under confident clear conditions, highlighting the effectiveness of the CDLAM in enhancing the accuracy of IR retrievals at all clear-sky confidence levels as well as the data availability improvement of IR retrievals after adjustment with the CDLAM (14% during the analyzed time periods). The most likely reason for the efficiency of the CDLAM may be that the deviation of the differential water vapor information derived by the differential process is significantly shrunken after the linear regression analysis in the presented model. Therefore, the CDLAM is a promising tool for effectively adjusting IR retrievals under all probabilities of clear conditions and can improve our knowledge of the water vapor distribution and variation.