A New Algorithm for Estimating Low Cloud Base Height in Southwest China

Abstract The prevalence of low clouds significantly affects flight safety in Southwest China. However, relevant cloud parameters, especially Low Cloud Base Height(LCBH), lack accurate forecasts. Based on the hourly atmospheric vertical profiles of ERA5 from 2008 to 2019, we developed a new algorithm (RHs-CCL) for estimating LCBH by combining relative humidity threshold methods with Convective Condensation Level(CCL). To evaluate the performance of RHs-CCL, we use it to estimate the hourly LCBH of airports in Southwest China and compare the results with those based on the ground-based observations and the ERA5 CBH data. Using the observations as a ground truth, we compare the RHs-CCL algorithm with several existing algorithms with the following findings: (1) The correlation coefficient between RHs-CCL and observations reaches 0.5 on average and the error of RHs-CCL is smaller than those of existing algorithms with the minimum mean absolute error and root mean square error at the four airports studies can reach 243m and 321m,(2) The bias score of RHs-CCL is 0.97 on average and low clouds classification utilizing RHs-CCL attains the highest accuracy up to 86%, (3) The errors of ERA5 CBH are the largest compared with the others, (4) By implementing convective cloud occurrence condition and CCL, RHs-CCL has better applicability in regions of enhanced convective activity. These results suggest the potential of RHs-CCL as an algorithm moving forward for improvement of the LCBH estimates based upon high-resolution reanalysis products and for better predictions of the LCBH utilizing outputs from numerical weather prediction models.