Estimating cloud base height from Himawari-8 based on a random forest algorithm

Abstract

ABSTRACT Estimating cloud-base height (CBH) from satellite-based passive imaging radiometers is very challenging because the conventional visible and infrared radiance measured by satellite cannot be employed to profile clouds from top to base. The Advanced Himawari Imager (AHI) aboard Himawari-8 is the first of Japanese new-generation visible-infrared imaging spectroradiometer with an improved temporal and spatial resolution. A CBH estimation algorithm for Himawari-8 is imminently needed for aviation safety and climate research. This study presents a machine learning methodology for CBH estimation from Himawari-8 data using a random forest (RF) algorithm, which was conducted for the daytime focusing on single-layer clouds. About 2,876,230 matchups in the period from August 2015 to July 2016 are used in both RF training and validation processes for the purpose of learning the relationship between inputs from Himawari-8 Level-2 cloud products together with geolocation information and target CBH from CloudSat/CALIPSO combined measurements. With the trained RF model, the CBH prediction can be independently generated using Himawari-8 upstream products. Data of ground-based Ka radar in Beijing during 2017 and 2019 are used for independent validation. The results indicate that the Himawari-8 CBH estimation agrees well with those of the Ka radar. The CBH estimations for single-layer nonprecipitation clouds show the best performance with a mean difference of −0.2 ± 1.7 km. The presence of multi-layer clouds mostly leads to overestimated CBHs relative to the radar values, particularly for situations that cirrus clouds overlapping clouds at low-level. Since the ability of the Ka radar is limited to retrieve CBH in precipitation cases, significant overestimation also occurs for precipitation clouds with mean difference of 1.7 ± 2.4 km.