A method for identifying hail clouds based on four elements of radar mosaic CR products
Radar is the most effective tool for detecting hail.In the 1960s and 1970s,the widespread use of rain measurement radar(e.g.,Danka 41 in the UK and domestic radars such as 711,713,etc.)enabled the identifica-tion of hail clouds through radar echo features like hook-shaped,finger-shaped,and V-shaped notches.The intro-duction of Doppler weather radar in the late 1980s and early 1990s provided more accurate data,including radial velocity,for effective hail cloud identification.However,four factors affect the accuracy of hail cloud recognition:1)single radar limitations such as detection range,distance attenuation,blind spots,and Earth's curvature;2)the requirement for forecasters to possess high echo analysis skills;3)the variability of classic hail cloud characteristics with orientation,elevation,and distance;4)the limited number of PUP terminals for single radar use,insufficient for county-level forecasters.Radar mosaics can effectively compensate for some of these limita-tions,especially through web-based radar mosaic CR(Combined Reflectivity)products,which are accessible via computers,tablets,and mobile phones for simple and convenient operation.The radar mosaic CR product gathers data from multiple radars simultaneously(within±3 min).The blind spot of one radar is covered by another,and the inter-radar distance of 100-150 km is optimal for detection,min-imizing issues related to angle blind spots,Earth's curvature,and distance attenuation.The four key elements for identifying hail clouds on the CR product chart are:1)echo intensity of 60 dBZ and strong echo core ≥ 65 dBZ,2)strong echo in horizontal and vertical areas,3)strong echo gradient of 30-60 dBZ,and 4)weak echo length formed by cloud anvils.For example,in Jiangxi,hail echo intensity is typically ≥60 dBZ,with larger hail having strong echo nuclei above 65 dBZ.A strong echo area of 60 dBZ should be ≥ 100 km,although smaller hail may represent a smaller area.The vertical thickness of the strong echo(≥6 km)is also significant,though Jiangxi ra-dar mosaics lack CAPPI products for this measurement.The strong echo gradient indicates hail echo walls,with a steep gradient suggesting a shorter distance.The weak echo formed by cloud anvils reflect the high-altitude wind's"pumping"effect.Using the radar mosaic CR product,identifying hail clouds based on these four elements is nearly 100％successful,with a false report rate below 20％,primarily due to seasonal variability in element thresholds.Adding the vertical area of the strong echo can reduce the false alarm rate.Automated identification of hail-inducing echoes based on these four elements involves specific algorithms:1)echo intensity identification through comparison of adjacent points;2)strong echo area identification using clus-tering and scatter contour algorithms;3)strong echo gradient determination by comparing the distance between 30 dBZ and 60 dBZ;4)cloud anvil echo calculation by measuring the 10 dBZ distance from the 30 dBZ edge along the high-altitude wind direction.Results indicate that hail may occur when the radar mosaic CR is ≥60 dBZ,and the Strong Echo Area(SEA)is ≥ 100 km2,the Strong Echo Gradient(SEG)is ≤ 8 km,and the Cloud Anvil Echo(CAE)ratio is between 1∶2 and 1∶3.Most hail in Jiangxi occurs in supercells,though some micro super-cells with SEA=18 km2 may also produce hail under suitable conditions.A method for identifying hail clouds based on these four elements was verified through six hail processes in Jiangxi from 2022 to 2023.The identified hail cloud areas matched actual hail areas,with a false alarm rate of 10％—20％.Future efforts should focus on reducing false alarm rates by incorporating strong echo vertical area and vertical integrated liquid water content elements.This research provides practical experience for simple,fast,and automatic identification of hail weather.
万方数据
维普
