In the 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 clouds are regular and abundant. Errors in this case could be misleading and undetectable unless one scrutinizes the performance of the best fitted line on the Langley regression individually. In this work, we introduce a new method to improve the sun photometer calibration past the Langley uncertainty over a tropical climate. A total of 20 Langley plots were collected using a portable spectrometer over a mid-altitude (1,574 m a.s.l.) tropical site at Kinabalu Park, Sabah. Data collected were daily added to Langley plots, and the characteristics of each Langley plot were carefully examined. Our results show that a gradual evolution pattern of the calculated Perez index in a time-series was observable for a good Langley plot, but days with poor Langley data basically demonstrated the opposite behavior. Taking advantage of this fact, the possibly contaminated data points were filtered by calculating the Perez derivative of each distinct air mass until a negative value was obtained. Any points that exhibited a negative derivative were considered bad data and discarded from the Langley regression. The implementation was completely automated and objective, rendering qualitative observation no longer necessary. The improved Langley plot exhibits significant improvement in addressing higher values for correlation, R, and lower values for aerosol optical depth, τa. The proposed method is sensitive enough to identify the occurrence of very short and thin cirrus clouds and is particularly useful for sun-photometer calibration over a tropical climate.