星载Ku,Ka和W三频雷达探测云雨三维结构模拟仿真
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摘要
对星载Ku, Ka和W波段微波雷达联合观测中纬度陆地气旋、热带台风和热带洋面气旋个例中云和降水的三维结构进行了模拟仿真.首先利用Weather Research and Forecasting(WRF)云模式模拟了个例中各种水凝物的时空分布,并利用Aqua卫星MODIS观测结果直接检验了中纬度陆地气旋个例模拟结果;然后将模拟结果作为输入,利用星载雷达模拟器计算了相应的雷达回波反射率因子,并利用CloudsSat卫星的W波段云雷达CPR实测信号对之进行了验证;随后利用该模拟数据研究了不同粒子雷达回波反射率的特点.最后假设Ku,Ka和W波段雷达的灵敏度分别为15, 5和-35 dBZ,定量研究了这3个波段在探测云顶高度、云底高度上的优缺点和误差大小.模拟结果证实随着频率的增高,水凝物粒子的雷达回波反射率因子减小.非降水云水和云冰粒子回波明显弱于降水和降雪粒子,一般很难被Ku和Ka波段星载雷达观测到.研究发现W波段雷达对云顶的探测误差一般很小(不到30 m),而Ku,Ka雷达对云顶的探测误差可达数千米.对云底探测而言,W波段雷达可以有效穿透低层液态水含量低的天气系统,但对强降水天气系统云底探测误差较大;Ka波段雷达在台风眼壁云墙附近的强降水区也会出现较大探测误差;而Ku波段雷达云底的探测误差都较小.
The capability of satellite radar working at Ku, Ka and W bands measuring the three dimensional structure of cloud and precipitation was studied by model simulations for three typical weather cases including a mid-latitude cyclone over land(MCL), a tropical typhoon(TT), and a tropical cyclone over ocean(TCO). First, cloud resolving model of Weather Research and Forecasting(WRF) was utilized to simulate the distribution of all types of hydrometeors in these cases. The cloud water path and cloud top temperature from the MCL simulation were validated with real satellite observations from Moderate Resolution Imaging Spectrometer(MODIS) on Aqua satellite. The WRF simulation correctly captured the main features of the storm including the spatial pattern, the geolocation, and the cloud top temperature, etc. Then, based on a satellite radar simulator developed by this study, the radar reflectivities at the Ku, Ka and W bands of those cases were calculated, and the radar reflectivity simulation of the MCL was also evaluated with real satellite measurements from W-band Cloud Profiling Radar(CPR) on CloudSat satellite. The vertical structure of radar reflectivity of the storm in the simulation was very close to the real measurements from CPR. Next, the characteristics of radar reflectivities at the three bands for different cloud and precipitation hydrometeors in the three cases were investigated. It was confirmed that the radar reflectivity factor decreases with the increasing frequencies(i.e. Ku, Ka and W bands). The radar echoes from non-precipitating hydrometers in both liquid and ice phase were much weaker than those from precipitating sized hydrometers, and normally cannot be detected by Ku and Ka band space-born radar. Regarding the capability of penetrating deep storm, the signal of W band radar can penetrate most clouds in the case of MCL and reach the surface very well. However, it started to be saturated from the height 6-9 km in areas with heavy liquid water content in the two cases of TT and TCO. The Ka band radar can detect the vertical distribution of hydrometeors in most areas of the three cases, but was saturated below 4-5 km in the eyewall area and the convection core areas of the cases of TT and TCO. The Ku band radar can penetrate all storms in this study. Moreover, the detecting errors of cloud top height and cloud bottom height for the three bands were quantified, assuming the detecting thresholds of Ku, Ka and W radars are 15, 5 and-35 dBZ. The results showed that W band radar had the smallest error(<30 m) of detecting cloud top height, while for Ku and Ka radars, the errors were as high as several thousand meters particularly in the area with thin clouds due to their misdetection of small particles. In the convection core area with large and dense hydrometeors at the top layers, the errors for Ku and Ka significantly decreased to less than 100 m. Regarding cloud bottom detection, the penetrability of all radars not only depends on cloud geometrical thickness, but also on liquid water content(LWC). In MCL with low LWC the detecting errors for cloud bottom height from W band radar were only a few hundred meters, while reaching several thousand meters for TT and TCO with high LWC. The detecting error for cloud bottom for Ka radar was generally low(less than 300 m) but also increased greatly in strong precipitation areas. For Ku radar, the detecting errors of cloud bottom height were small in all cases except small area near the eyewall for TT. The results of this study provided some unique information to improve the understanding of the detecting capability of tri-frequencies satellite borne cloud and precipitation radar, particularly the differences among different types of storms. And the results can be used as references for designing associated parameters in radar system development.
The capability of satellite radar working at Ku, Ka and W bands measuring the three dimensional structure of cloud and precipitation was studied by model simulations for three typical weather cases including a mid-latitude cyclone over land(MCL), a tropical typhoon(TT), and a tropical cyclone over ocean(TCO). First, cloud resolving model of Weather Research and Forecasting(WRF) was utilized to simulate the distribution of all types of hydrometeors in these cases. The cloud water path and cloud top temperature from the MCL simulation were validated with real satellite observations from Moderate Resolution Imaging Spectrometer(MODIS) on Aqua satellite. The WRF simulation correctly captured the main features of the storm including the spatial pattern, the geolocation, and the cloud top temperature, etc. Then, based on a satellite radar simulator developed by this study, the radar reflectivities at the Ku, Ka and W bands of those cases were calculated, and the radar reflectivity simulation of the MCL was also evaluated with real satellite measurements from W-band Cloud Profiling Radar(CPR) on CloudSat satellite. The vertical structure of radar reflectivity of the storm in the simulation was very close to the real measurements from CPR. Next, the characteristics of radar reflectivities at the three bands for different cloud and precipitation hydrometeors in the three cases were investigated. It was confirmed that the radar reflectivity factor decreases with the increasing frequencies(i.e. Ku, Ka and W bands). The radar echoes from non-precipitating hydrometers in both liquid and ice phase were much weaker than those from precipitating sized hydrometers, and normally cannot be detected by Ku and Ka band space-born radar. Regarding the capability of penetrating deep storm, the signal of W band radar can penetrate most clouds in the case of MCL and reach the surface very well. However, it started to be saturated from the height 6-9 km in areas with heavy liquid water content in the two cases of TT and TCO. The Ka band radar can detect the vertical distribution of hydrometeors in most areas of the three cases, but was saturated below 4-5 km in the eyewall area and the convection core areas of the cases of TT and TCO. The Ku band radar can penetrate all storms in this study. Moreover, the detecting errors of cloud top height and cloud bottom height for the three bands were quantified, assuming the detecting thresholds of Ku, Ka and W radars are 15, 5 and-35 dBZ. The results showed that W band radar had the smallest error(<30 m) of detecting cloud top height, while for Ku and Ka radars, the errors were as high as several thousand meters particularly in the area with thin clouds due to their misdetection of small particles. In the convection core area with large and dense hydrometeors at the top layers, the errors for Ku and Ka significantly decreased to less than 100 m. Regarding cloud bottom detection, the penetrability of all radars not only depends on cloud geometrical thickness, but also on liquid water content(LWC). In MCL with low LWC the detecting errors for cloud bottom height from W band radar were only a few hundred meters, while reaching several thousand meters for TT and TCO with high LWC. The detecting error for cloud bottom for Ka radar was generally low(less than 300 m) but also increased greatly in strong precipitation areas. For Ku radar, the detecting errors of cloud bottom height were small in all cases except small area near the eyewall for TT. The results of this study provided some unique information to improve the understanding of the detecting capability of tri-frequencies satellite borne cloud and precipitation radar, particularly the differences among different types of storms. And the results can be used as references for designing associated parameters in radar system development.
引文
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2 Zhao C F, Liu L P, Wang Q Q, et al. MMCR-based characteristic properties of non-precipitating cloud liquid droplets at Naqu site over Tibetan Plateau in July 2014. Atmos Res, 2017, 190:68–76
3 Zhao C F, Liu L P, Wang Q Q, et al. Toward understanding the properties of high Ice Clouds at the Naqu Site on the Tibetan Plateau using ground-based active remote sensing measurements obtained during a short period in July 2014. J Appl Meteorol Clim, 2016, 55:2493–2507
4 Zhao C F, Wang Y Z, Wang Q Q, et al. A new cloud and aerosol layer detection method based on micropulse lidar measurements. J Geophys Res-Atmos, 2014, 119:6788–6802
5 Garrett T J, Zhao C F. Ground-based remote sensing of thin clouds in the Arctic. Atmos Meas Tech, 2013, 6:1227–1243
6 Zhao C F, Garrett T J. Ground-based remote sensing of precipitation in the Arctic. J Geophys Res-Atmos, 2008, 113:1–10
7 Kummerow C, Barnes W. The Tropical Rainfall Measuring Mission(TRMM)sensor package. J Atmos Ocean Tech, 1998, 15:809–817
8 Kummerow C. The status of the Tropical Rainfall Measuring Mission(TRMM)after two years in orbit. J Appl Meteorol, 2000, 39:1965–1982
9 Iguchi T, Kozu T, Meneghini R, et al. Rain profiling algorithm for the TRMM precipitation radar. J Appl Meteorol, 1997, 39:2038–2052
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11 Li R, Min Q, Fu Y. 1997/98 El Ni?o-induced changes in rainfall vertical structure in the East Pacific. J Clim, 2011, 24:6373–6391
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13 Stephens G L, Vane D G, Boain R J, et al. The Cloudsat mission and the A-Train. Bull Am Meteorol Soc, 2002, 83:1771–1790
14 WangY,LiuGS,SeoEK,etal.Liquidwaterinsnowingclouds:Implicationsforsatelliteremotesensingofsnowfall.AtmosRes,2013, 131:60–72
15 Rose C R, Chandrasekar V. A GPM dual-frequency retrieval algorithm:DSD profile-optimization method. J Atmos Ocean Tech, 2005,23 :1372–1383
16 Hou A Y, Kakar R K, Neeck S, et al. The global precipitation measurement mission. Bull Am Meteorol Soc, 2014, 95:701–722
17 LiaoL,MeneghiniR,TokayA.UncertaintiesofGPMDPRrainestimatescausedbyDSDparameterizations.JApplMeteorolClim,2014, 53:2524–2537
18 Mlawer E J, Taubman S J, Brown P D, et al. Radiative transfer for inhomogeneous atmospheres:RRTM, a validated correlated—K model for the longwave. J Geophys Res-Atmos, 1997, 102:16663–16682
19 Dudhia J. Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. J Atmos Sci, 1989, 46:3077–3107
20 Hong S Y, Noh Y, Dudhia J. A new vertical diffusion package with an explicit treatment of entrainment processes. Mon Weather Rev,2006, 134:2318
21 Kain J S, Fritsch J M. Convective parameterization for Mesoscale models:The Kain-Fritsch scheme. Meteorol Mon, 1993, 24:165–170
22 LiuGS.ADatabaseofmicrowavesingle-scatteringpropertiesfornonsphericaliceparticles.BullAmMeteorolSoc,2008,89:1563–1570
23 Menzel W P, Frey R A, Zhang H, et al. MODIS global cloud-top pressure and amount estimation:Algorithm description and results. J Appl Meteorol Clim, 2008, 47:1175–1198
24 MaceG.Level2GEOPROFproductprocessdescriptionandinterfacecontroldocumentalgorithmversion5.3.NASAJetPropulsion Laboratory, 2007
25 LiR,DongX,GuoJC,etal.Theimplicationsofdusticenucleieffectoncloudtoptemperatureinacomplexmesoscaleconvective system. Sci Rep, 2017, 7:13826
26 Skofronick J G, Stephens G L, Vane D, et al. A cloud-precipitation mission concept for studying cloud and precipitation processes with application to climate models. AGU Fall Meeting, 2013. 1–15
27 Spence T, Cooley M, Stenger P, et al. Concept design of a multi-band shared aperture reflectarray/reflector antenna. IEEE International Symposium on Phased Array Systems and Technology, 2016. 1–6
28 Sánchez-Barbetty M, Sadowy G, Tanelli S, et al. MASTR, the multi-application smallsat tri-band radar. Earth Science Technology Forum, 2017. 1–12
2 Zhao C F, Liu L P, Wang Q Q, et al. MMCR-based characteristic properties of non-precipitating cloud liquid droplets at Naqu site over Tibetan Plateau in July 2014. Atmos Res, 2017, 190:68–76
3 Zhao C F, Liu L P, Wang Q Q, et al. Toward understanding the properties of high Ice Clouds at the Naqu Site on the Tibetan Plateau using ground-based active remote sensing measurements obtained during a short period in July 2014. J Appl Meteorol Clim, 2016, 55:2493–2507
4 Zhao C F, Wang Y Z, Wang Q Q, et al. A new cloud and aerosol layer detection method based on micropulse lidar measurements. J Geophys Res-Atmos, 2014, 119:6788–6802
5 Garrett T J, Zhao C F. Ground-based remote sensing of thin clouds in the Arctic. Atmos Meas Tech, 2013, 6:1227–1243
6 Zhao C F, Garrett T J. Ground-based remote sensing of precipitation in the Arctic. J Geophys Res-Atmos, 2008, 113:1–10
7 Kummerow C, Barnes W. The Tropical Rainfall Measuring Mission(TRMM)sensor package. J Atmos Ocean Tech, 1998, 15:809–817
8 Kummerow C. The status of the Tropical Rainfall Measuring Mission(TRMM)after two years in orbit. J Appl Meteorol, 2000, 39:1965–1982
9 Iguchi T, Kozu T, Meneghini R, et al. Rain profiling algorithm for the TRMM precipitation radar. J Appl Meteorol, 1997, 39:2038–2052
10 Li R, Fu Y F, Zhao P. Characteristics of rainfall structure over the tropical Pacific during the later period of 1997/1998 El Ni?o derived from TRMM PR observations(in Chinese). Chin J Atmos Sci, 2005, 29:225–235[李锐,傅云飞,赵萍.利用热带测雨卫星的测雨雷达资料对1997/1998年El Ni?o后期热带太平洋降水结构的研究.大气科学, 2005, 29:225–235]
11 Li R, Min Q, Fu Y. 1997/98 El Ni?o-induced changes in rainfall vertical structure in the East Pacific. J Clim, 2011, 24:6373–6391
12 LiuP,WangY,FengS,etal.Climatologicalcharacteristicsofovershootingconvectiveprecipitationinsummerandwinteroverthe tropical and subtropical regions(in Chinese). Chin J Atmos Sci, 2012, 36:579–589[刘鹏,王雨,冯沙,等.冬、夏季热带及副热带穿透性对流气候特征分析.大气科学, 2012, 36:579–589]
13 Stephens G L, Vane D G, Boain R J, et al. The Cloudsat mission and the A-Train. Bull Am Meteorol Soc, 2002, 83:1771–1790
14 WangY,LiuGS,SeoEK,etal.Liquidwaterinsnowingclouds:Implicationsforsatelliteremotesensingofsnowfall.AtmosRes,2013, 131:60–72
15 Rose C R, Chandrasekar V. A GPM dual-frequency retrieval algorithm:DSD profile-optimization method. J Atmos Ocean Tech, 2005,23 :1372–1383
16 Hou A Y, Kakar R K, Neeck S, et al. The global precipitation measurement mission. Bull Am Meteorol Soc, 2014, 95:701–722
17 LiaoL,MeneghiniR,TokayA.UncertaintiesofGPMDPRrainestimatescausedbyDSDparameterizations.JApplMeteorolClim,2014, 53:2524–2537
18 Mlawer E J, Taubman S J, Brown P D, et al. Radiative transfer for inhomogeneous atmospheres:RRTM, a validated correlated—K model for the longwave. J Geophys Res-Atmos, 1997, 102:16663–16682
19 Dudhia J. Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. J Atmos Sci, 1989, 46:3077–3107
20 Hong S Y, Noh Y, Dudhia J. A new vertical diffusion package with an explicit treatment of entrainment processes. Mon Weather Rev,2006, 134:2318
21 Kain J S, Fritsch J M. Convective parameterization for Mesoscale models:The Kain-Fritsch scheme. Meteorol Mon, 1993, 24:165–170
22 LiuGS.ADatabaseofmicrowavesingle-scatteringpropertiesfornonsphericaliceparticles.BullAmMeteorolSoc,2008,89:1563–1570
23 Menzel W P, Frey R A, Zhang H, et al. MODIS global cloud-top pressure and amount estimation:Algorithm description and results. J Appl Meteorol Clim, 2008, 47:1175–1198
24 MaceG.Level2GEOPROFproductprocessdescriptionandinterfacecontroldocumentalgorithmversion5.3.NASAJetPropulsion Laboratory, 2007
25 LiR,DongX,GuoJC,etal.Theimplicationsofdusticenucleieffectoncloudtoptemperatureinacomplexmesoscaleconvective system. Sci Rep, 2017, 7:13826
26 Skofronick J G, Stephens G L, Vane D, et al. A cloud-precipitation mission concept for studying cloud and precipitation processes with application to climate models. AGU Fall Meeting, 2013. 1–15
27 Spence T, Cooley M, Stenger P, et al. Concept design of a multi-band shared aperture reflectarray/reflector antenna. IEEE International Symposium on Phased Array Systems and Technology, 2016. 1–6
28 Sánchez-Barbetty M, Sadowy G, Tanelli S, et al. MASTR, the multi-application smallsat tri-band radar. Earth Science Technology Forum, 2017. 1–12