机载W波段雷达衰减订正方法的不同云型模拟对比研究

为了探讨适用于机载W波段(94 GHz，3.2 mm)测云雷达的衰减订正算法，根据大气和云的毫米波衰减理论，模拟在5种典型参数情况下的非降水云雷达回波强度，基于回波强度Z与云衰减系数k_c之间的关系，使用解析法等5种衰减订正算法进行回波订正试验，寻求最优k_c-Z关系系数，同时研究改变云液态水含量和云滴数密度(使典型云变成为非典型云)对订正效果的影响。结果表明，W波段k_c-Z关系系数a=5.096 5和b=0.491 9适用的云型接近于典型层积云，而逐库类法订正效果优于解析法和迭代法。在使用最佳k_c-Z关系系数情况下，5种订正算法对典型云的订正效果都较好，即使云厚增加到典型云厚度的2倍，订正误差仍小于5%。对于改变云液态水含量、云滴数密度、云厚度而表示的非典型云，虽然逐库法订正结果略优于逐库近似2法和逐库近似1法，但效果相近，在云不太厚时订正误差均小于10%。如果用“φ≤10%或20%”来评价其适用范围更广些，优于解析法和迭代法。但逐库类法对云液态水含量较敏感，导致“可订正厚度”减小、误差增大。因此，要尽量做好云型和云液态水含量的估算。 

A STUDY ON ATTENUATION CORRECTION TO DIFFERENT CLOUD TYPES AT W BAND OF AIRBORNE RADAR

In order to explore the attenuation correction algorithm for airborne W-band (94GHz, 3.2 mm) cloud radar, radar echoes for five typical non-precipitating clouds and attenuation corrections with five correction algorithms are simulated based on radar equation with atmospheric and cloud attenuation. And the best coefficients in k_c=aZb and the effects of cloud parameters such as cloud water content, droplet number density, height and thickness on correction error are also studied. The results show that the coefficients a=5.096 5 and b=0.491 9 in k_c=aZb for W-band are best suitable for stratocumulus rather than stratus, nimbostratus, and cumulus, and the performance of bin-by-bin algorithms is generally better than the algorithms from Hitschfeld-Bordan and Meneghini. If the best coefficients ink_c=aZb for individual clouds are used, all the five correction algorithms are suitable for either type of clouds and the relative errorsare less than 5% even if the cloud thickness increases to 2 times the typical value. In case that the cloud parameters vary a lot from the typical values, the 3 bin-by-bin algorithms, though R3 is slightly better than the other two(R2 and R1), can still make the relative errorsless than 10% as long as the cloud is not very thick. If the criteria φ≤10% or 20% is used for assessment, the 3 algorithms are most suitable for much different types of clouds as compared with the Hitschfeld-Bordan and Meneghini algorithms. But the bin-by-bin algorithms are sensitive to cloud water content, variation in cloud water content from the typical value leads to a decreased correctable thickness or an increased error. Therefore a good estimation of cloud water content is required in case either of the 3 bin-by-bin algorithms is used for attenuation corrections.