We have observed weather clutter containing targets (ships) using an S-band radar with a frequency 3.05 GHz, a beam width 1.8°, and a pulsewidth 0.5 μs. To investigate the weather clutter amplitude statistics, w...We have observed weather clutter containing targets (ships) using an S-band radar with a frequency 3.05 GHz, a beam width 1.8°, and a pulsewidth 0.5 μs. To investigate the weather clutter amplitude statistics, we introduce the Akaike Information Criterion (AIC). We have found that the weather clutter amplitudes obey the log-normal, Weibull, and log-Weibull distributions with the shape parameters of 0.308 to 0.470, 4.42 to 4.51, and 15.91 to 16.44, respectively, for small data within the beam width of an antenna. We have proposed the log-normal/CFAR circuit modified a Cell-Averaging (CA) LOG/CFAR circuit. It is found that weather clutter is suppressed with improvement of 51.58 dB by log-normal/CFAR. As a result, we have showed that weather clutter observed by S-band radar does not obey the Rayleigh distribution and our log-normal/CFAR circuit has an effect on suppression of clutter and detection of target, while conventional LOG/CFAR circuit does not. In addition, if our circuit can be realized, we will have an advantage economically.展开更多
冰雹是一种致灾性较强的强对流天气,但在气象业务工作中对其进行快捷、准确的预警和预报仍有一定的难度。本文基于C波段雷达回波资料,构建并应用随机森林模型对冰雹及其伴随强对流天气进行了分类识别及预报。结果发现,随机森林模型对训...冰雹是一种致灾性较强的强对流天气,但在气象业务工作中对其进行快捷、准确的预警和预报仍有一定的难度。本文基于C波段雷达回波资料,构建并应用随机森林模型对冰雹及其伴随强对流天气进行了分类识别及预报。结果发现,随机森林模型对训练集(2008-2017年)中四类冰雹天气(冰雹、冰雹大风、冰雹短强、冰雹大风短强)的平均命中率(Probability of Detection,POD)为90.2%,平均空报比率(False Alarm Ratio,FAR)为11.1%。对于2018-2019年的独立样本测试集,模型的平均POD和FAR则分别为72.8%和34.7%。因此,本文构建的随机森林模型较为理想。应用模型和风暴单体识别与跟踪产品(Strom Cell Identification and Tracking,SCIT)对未来15~60 min的强对流天气进行预报,结果表明四类冰雹天气的平均POD为74.8%,平均临界成功指数为60.8%,平均FAR为24.4%。因此,利用C波段雷达产品,随机森林模型能高效、自动化且较为准确地分类预警、预报冰雹及其伴随强对流天气,可应用于天气预报业务工作。展开更多
文摘We have observed weather clutter containing targets (ships) using an S-band radar with a frequency 3.05 GHz, a beam width 1.8°, and a pulsewidth 0.5 μs. To investigate the weather clutter amplitude statistics, we introduce the Akaike Information Criterion (AIC). We have found that the weather clutter amplitudes obey the log-normal, Weibull, and log-Weibull distributions with the shape parameters of 0.308 to 0.470, 4.42 to 4.51, and 15.91 to 16.44, respectively, for small data within the beam width of an antenna. We have proposed the log-normal/CFAR circuit modified a Cell-Averaging (CA) LOG/CFAR circuit. It is found that weather clutter is suppressed with improvement of 51.58 dB by log-normal/CFAR. As a result, we have showed that weather clutter observed by S-band radar does not obey the Rayleigh distribution and our log-normal/CFAR circuit has an effect on suppression of clutter and detection of target, while conventional LOG/CFAR circuit does not. In addition, if our circuit can be realized, we will have an advantage economically.
文摘冰雹是一种致灾性较强的强对流天气,但在气象业务工作中对其进行快捷、准确的预警和预报仍有一定的难度。本文基于C波段雷达回波资料,构建并应用随机森林模型对冰雹及其伴随强对流天气进行了分类识别及预报。结果发现,随机森林模型对训练集(2008-2017年)中四类冰雹天气(冰雹、冰雹大风、冰雹短强、冰雹大风短强)的平均命中率(Probability of Detection,POD)为90.2%,平均空报比率(False Alarm Ratio,FAR)为11.1%。对于2018-2019年的独立样本测试集,模型的平均POD和FAR则分别为72.8%和34.7%。因此,本文构建的随机森林模型较为理想。应用模型和风暴单体识别与跟踪产品(Strom Cell Identification and Tracking,SCIT)对未来15~60 min的强对流天气进行预报,结果表明四类冰雹天气的平均POD为74.8%,平均临界成功指数为60.8%,平均FAR为24.4%。因此,利用C波段雷达产品,随机森林模型能高效、自动化且较为准确地分类预警、预报冰雹及其伴随强对流天气,可应用于天气预报业务工作。
