In tropics, Langley calibration is often complicated by abundant cloud cover. The lack of an objective and robust cloud screening algorithm in Langley calibration is often problematic especially for tropical climate sites where short thin cirrus cloud are regular and abundant. Error in this case could be misleading and undetectable unless one scrutinizes the performance of the best fitted line on Langley regression individually. In this work, we introduce a new method to improve the sun photometer calibration beyond the Langley uncertainty over tropical climate. A total of 20 Langley plots were collected using a portable spectrometer over a mid-altitude (1,574 m a.s.l.) tropic site at Kinabalu Park, Sabah. Data collected were plotted in Langley plot on daily and the characteristics of each Langley plot were carefully examined. Our results show that a gradual evolution pattern on the calculated Perez index in time-series was observed for a good Langley plot, but days with poor Langley data basically demonstrate the opposite behavior. Taking this advantage, filtration of possible contaminated data points are performed by calculating the Perez derivative at each distinct airmass until negative value is obtained. At any instances, points that exhibit negative derivative are considered bad data and discarded from Langley regression. The implementation is completely automated and objective where qualitative observation is no longer necessary. The improved Langley plot remarks significant improvement for higher correlation R and lower aerosol optical depth τa. The implication of the proposed method is sensitive enough to identify the occurrence of very short thin cirrus clouds and particularly useful for sunphotometer calibration over tropical climate.