Signal Processing of Images for Convective Boundary Layer Height Estimation from Radar (SPICER) and multi-instrument verification

Abstract

The study of the planetary boundary layer (PBL) is one of the main topics of the atmospheric community. The current study presents a new algorithm for PBL height determination using a publicly available but unexplored data source, the Weather Service Radar (WSR-88D). The diurnal evolution of the PBL is also known as Convective Boundary Layer (CBL), key in the study of convection and precipitation. This paper presents the Signal Processing of Images for Convective Boundary Layer Height Estimation (SPICER) algorithm that can automatically detect the CBL Height (CBLH) for all of the 159 radar locations across the United States during clear days. The present work is the first step to applying SPICER to a network of Next Generation Radars (NEXRAD) with continuous countrywide coverage. With the possible combination with the Automated Surface Observing System network (ASOS), a source of ceilometer profile data, a validated dataset of CBLH estimates can be expected soon. The algorithm treats averaged differential reflectivity vs range as an image and applies filtering plus Canny edge detection to estimate the CBLH. In addition, another algorithm is presented to automate the detection of the mixing layer height (MLH), a proxy for CBLH from Raman Lidar and a 915 MHz wind profiler. A comparison of CBLH estimates vs widely used methods in meteorology (Radiosondes, Raman Lidar, ceilometer, 915 MHz wind profiler, and Doppler Lidar-based derived Value-Added Product (VAP) ) is performed to validate the NEXRAD detected CBLH using SPICER. The SPICER algorithm shows over 0.9 correlation with radiosonde measurements.