马占山,主要从事气溶胶、云和降水物理过程参数化研究.E-mail:
国家气象中心GRAPES区域业务模式对2019年11月29—30日在华北地区降雪过程的预报出现显著高估现象,针对该模式中采用的WSM6云微物理方案进行了深入分析,并与Liu-Ma云微物理方案以及ERA5再分析数据进行比较,探究其可能存在的原因。主要结论如下:冰晶和雪的沉降是WSM6方案在本次地面降雪形成的最主要贡献,Liu-Ma方案则是以大粒子雪和霰的沉降为主,冰晶产生的贡献较少。WSM6方案严重低估了大气中的液态水含量,冰相粒子构成中以冰晶含量为最多,雪含量次之,这些特征都与ERA5资料和Liu-Ma方案有显著的不同,后两者具有较好的一致性。与Liu-Ma方案相比,WSM6方案在模式低层冰晶含量更高、冰晶平均落速更大,二者共同作用使冰晶沉降在本次降水形成中具有重要贡献;WSM6方案中雪的平均落速大于Liu-Ma方案,这是其雪的柱积分总量小而雪的沉降降水多于Liu-Ma方案的直接原因。在WSM6方案中冰晶的凝华/升华过程在冰相微物理过程中占据主导地位,致使雪和霰的凝华过程以及云水凝结过程都明显不足,这是该方案冰晶偏多、雪偏少、液水明显偏少的主要原因。针对冰晶凝华/升华过程(SVI)的敏感性试验发现,SVI转化率与地面降水呈正相关关系、与液水柱积分总量呈“跷跷板”关系,当降低SVI的转化率,地面降雪将显著减少,而柱积分液水总量则会明显增多。
The GRAPES regional operational model in the National Meteorological Centre significantly overestimated the snowfall amount over North China that occurred in 29-30 November 2019. In this paper, the simulated results from the operational WSM6 cloud microphysics scheme are compared with those from Liu-Ma scheme and the ERA5 reanalysis data to investigate the possible reasons. The results show that during this snowfall, the sedimentations of ice crystals and snow were the main contribution in WSM6 scheme, while the precipitation of Liu-Ma scheme was mainly through the sedimentations of snow and graupel, and ice crystals produced less precipitation. The WSM6 scheme evidently underestimated the liquid water content in the atmosphere and the ice crystal content was the largest composition of the ice-phase particles, followed by the snow content. These features were significantly different from the ERA5 data and the Liu-Ma scheme, and the latter two were in good agreement. Compared with the Liu-Ma scheme, the WSM6 scheme had a higher ice crystal content in the lower layer of the model and a larger average ice crystal falling speed, and their combination made ice crystal precipitation become an important contribution to the formation of this snowfall case. The average snow falling speed in the WSM6 scheme was greater than that of Liu-Ma scheme, which was the main reason why the column snow content was small and the precipitation of snow was more than that of the Liu-Ma scheme. In the WSM6 scheme, the deposition/sublimation process of ice crystals dominated the ice-phase microphysical processes so that the sublimation processes of snow and graupel and the condensation process of cloud water were obviously insufficient. This was the main reason for more ice crystals, less snow and cloud water in WSM6 scheme. The sensitivity test for the ice crystal deposition/sublimation process (SVI) revealed that the SVI conversion rate was positively correlated to surface precipitation, and took on a "seesaw" relationship with the column cloud water content. When the SVI conversion rate was reduced, the ground snowfall tended to be significantly reduced and the column cloud water content increased significantly.
降雪是中国北方地区冬季最重要的天气现象之一(
数值天气预报模式是当今进行天气预报主要依赖的工具和手段之一,数值模式的预报精度直接影响天气预报的准确率。在诸多气象要素中,定量降水预报是数值模式中的难点之一,不同模式以及同一模式不同的云物理方案对降水预报均有较大差异(
针对数值天气预报模式中云微物理方案对降雪预报性能的评估、诊断和优化,国外已经有较多的研究工作。
近些年,中国气象局数值预报中心GRAPES模式预报体系不断完善、模式预报性能稳步提升(
2019年11月29—30日,我国西北地区东部、华北等地出现一次降雪天气过程。河北中北部、天津、辽宁西部、内蒙古中西部和东南部、山西、陕西中北部、宁夏南部、甘肃大部、青海东部等地出现小到中雪,其中河北西北部、内蒙古中部、山西北部、甘肃南部和东部局地大雪。
北京地区出现明显降雪天气,降雪出现在29日傍晚到30日早晨,入夜后降雪增强。北京大部地区出现中雪,北部大雪,延庆和昌平局地暴雪,全市平均降水量为3.9 mm,城区平均为3.1 mm,最大降雪出现在延庆海子口站,降水量达13.7 mm。北京城区和东部地区积雪深度为1~3 cm,西部山区和北部地区为2~5 cm,延庆站最大为11 cm。
降雪发生前,在28日新疆北部有低涡低槽,此高空槽逐渐东移并携带低层冷空气东移。700 hPa、850 hPa高空槽配合低层切变系统,随着系统的东移,槽前西南急流加强,水汽条件增强。29日白天,高空槽引导西路冷空气东移,同时槽前的西南暖湿气流向华北地区输送。此时,地面冷锋位于河套和内蒙古中部地区,北京位于地面冷锋东侧。29日下午至傍晚,随着高空槽的移近,北京降雪开始,30日早晨随着高空槽过境,北京降雪结束。综上所述,2019年11月29日北京的降雪天气是在西来槽并且配合中层有利水汽条件的共同作用下产生的(图略)。
本研究中采用的WSM6云微物理方案为全国区GRAPES-3 km业务模式中所使用的云方案,该方案是从中尺度数值天气模式WRF移植而来。WSM6云方案是由
对于液相水凝物的处理,两个云微物理方案都考虑了云水的凝结和蒸发过程、云水自动转化成雨水、雨水碰并云水的过程;WSM6考虑雨水的凝结和蒸发过程,而Liu-Ma方案则只考虑雨水的蒸发。两个方案对冰相微物理过程的处理差异显著,二者都考虑冰晶的初始核化过程、冰相粒子(冰晶、雪和霰)的凝华和升华过程,对于这些过程除了参数化方案本身存有差异外,在水汽倾向更新处理方面也存在不同。另外,两个方案在冰相粒子间的碰并过程、冰相粒子与液相碰并过程以及融化和冻结方面都存在一定差异。两个云方案详细的微物理转化示意图,可详见
本研究所采用的中尺度数值模式为国家气象中心全国区GRAPES-3 km高分辨率区域业务模式4.4版本,GRAPES区域模式主要特征已有较多介绍(
GRAPES模式模拟的水平区域范围
Horizontal domain for GRAPES simulation
2019年11月29日00时到30日00时地面观测(a), WSM6方案(b)和Liu-Ma方案(c)模拟的24 h降水量
Observed (a) and simulated (b) WSM6 scheme, (c) Liu-Ma scheme 24 h accumulated precipitation from 00 UTC 29 to 00 UTC 30 November 2019
2019年11月29日00时到30日00时北京观象台站地面观测以及WSM6方案和Liu-Ma方案模拟的逐小时降水量的时间演变
Time evolution of the observed at Beijing Gauge Station and simulated by WSM6 scheme and Liu-Ma scheme hourly precipitation from 00 UTC 29 to 00 UTC 30 November 2019
云微物理方案中,地面降水的形成是由有落速的液相和冰相水凝物粒子沉降到地面累积而成。在本次降水过程中,由于主要降雪区域内(35°~47°N、108°~124°E,下同)的地面温度都在0℃以下,且大气无逆温现象,液相水凝物粒子对降水的贡献非常小(图略)。为分析WSM6方案中不同冰相粒子对降水的贡献特征以及与Liu-Ma方案的差异,
2019年11月29日00时到30日00时WSM6方案(空心线)和Liu-Ma方案(实心线) 中冰晶、雪和霰三种水凝物各自产生的累计降水量和三者总的累计降水量的区域平均随预报时间的演变
Time evolution of the domain average accumulated precipitation by ice crystal, snow, and graupel and their total in WSM6 scheme (hollow line) and Liu-Ma scheme (solid line) from 00 UTC 29 to 00 UTC 30 November 2019
如前文所述,一些学者在采用不同云微物理方案对降水进行模拟对比研究时发现WSM6方案预报的云水含量存在明显偏少的现象(
ERA5再分析数据(a,b)以及WSM6方案(c,d)和Liu-Ma方案(e,f)模拟的2019年11月29日00时到30日00时24 h平均的液态水(a,c,e)和固态水(b,d,f)的柱积分总量分布
The 24 h averaged liquid water path (LWP) (a, c, e) and ice water path (IWP) (b, d, f) by ERA5 data (a, b) and simulated by WSM6 scheme (c, d) and Liu-Ma scheme (e, f) from 00 UTC 29 to 00 UTC 30 November 2019
由
ERA5再分析数据(a,e)以及WSM6方案(b,d,f)和Liu-Ma方案(c,g,h) 模拟的2019年11月29日00时到30日00时24 h平均的冰晶(a, b, c)、雪(e, f, g)和霰(d, h)的柱积分总量分布
The 24 h average ice crystal (a, b, c), snow (e, f, g) and graupel (d, h) water path from ERA5 data (a, e) and simulated by WSM6 scheme (b, d, f) and Liu-Ma scheme (c, g, h) from 00 UTC 29 to 00 UTC 30 November 2019
降雪区域范围内ERA5再分析数据(a)、WSM6方案(b)和Liu-Ma方案(c)模拟的2019年11月29日00时到30日00时24 h平均的云水(Qc)、雨水(Qr)、冰晶(Qi)、雪(Qs)、霰(Qg)、固态水凝物(Solid)和总水凝物(All)含量的垂直分布
The 24 h averaged vertical profiles of water content of cloud water (Qc), rain water (Qr), ice crystal (Qi), snow (Qs), graupel (Qg), ice phase hydraometer (Solid) and total hydrometer (All) form ERA5 data (a) and simulated by WSM6 scheme (b) and Liu-Ma scheme (c) in snowfall region from 00 UTC 29 to 00 UTC 30 November 2019
综上所述,WSM6方案严重低估了液态水含量,其预报的冰晶较ERA5和Liu-Ma方案垂直分布深厚、量值偏大显著,冰相粒子主要由冰晶和雪组成,二者共同决定了地面降水量的预报;与ERA5相比,WSM6方案的冰相水凝物的垂直分布和量值皆不如Liu-Ma方案合理。
地面降水量是由水凝物含量和下落速度共同决定的,在3.2节分析了WSM6方案中水凝物含量的主要特征,本节重点分析其预报的粒子落速与Liu-Ma方案的差异。由于本次降雪过程主要是由冰相粒子沉降产生,并且WSM6方案预报的霰粒子很少,在此只分析该方案中冰晶和雪的下落速度与Liu-Ma方案的异同(
WSM6方案(a,c)和Liu-Ma方案(b,d)从2019年11月29日00时起报的第7~18小时内冰晶(a,b) 和雪(c,d)的平均下落速度沿纬度41°N的垂直剖面
Vertical cross-sections of average terminal velocity of ice crystal (a, b) and snow (c, d) simulated by WSM6 scheme (a, c) and Liu-Ma scheme (b, d) along 41°N in the lead time of 7-18 h from 00 UTC 29 November 2019
式中:
同
Same as
此处需要说明的是,在WSM6方案和Liu-Ma方案中都是先计算粒子落速和沉降过程,之后再计算水物质之间的微物理转换过程,在模式输出中未兼顾含水量输出(在微物理过程计算结束后输出)和落速输出(在计算沉降时输出)的同步性,因此在
在微物理方案中对水凝物含水量而言,除了水凝物沉降过程为其汇项外,与水汽相关的转化过程则是其唯一的源项和汇项。在上述分析中,我们不难发现WSM6方案在本次降雪过程中具有冰晶含量偏多、液水含量明显偏少的现象。为此,本部分首先分析了WSM6方案和Liu-Ma方案中凝结和蒸发、初始核化、凝华和升华等与水汽相关的微物理转化量所具有的特征。同时,根据
WSM6方案和Liu-Ma方案中与水汽相关过程微物理过程的转化率在降雪区域范围内从2019年11月29日00时起报的第7~18小时平均的垂直廓线
Vertical profiles of average tendency of transform processes related to water vapor simulated by WSM6 scheme and Liu-Ma scheme in snowfall regions in the lead time of 7-18 h from 00 UTC 29 November 2019
在冰相转化过程中(
WSM6方案和Liu-Ma方案7~18 h内主要降雪区域标准化后的微物理转化率的平均值以及
The mean normalized values (unit: %) of microphysical transform tendency related to water vapor simulated by WSM6 scheme and Liu-Ma scheme in snowfall regions in the lead time of 7-18 h and the corresponding values in Colle et al (2015)
微物理转化过程 | WSM6方案 | Liu-Ma方案 | ||
云水 | 凝结 | 0.04 | 34.62 | 71.21 |
蒸发 | 0.03 | 36.46 | 23.36 | |
雨水 | 凝结 | 0.00 | — | — |
蒸发 | 0.34 | 0.12 | 1.68 | |
冰晶 | 凝华 | 85.20 | 10.42 | 2.50 |
升华 | 45.0 | 0.16 | 0.13 | |
初始核化 | 12.82 | 0.05 | 0.03 | |
雪 | 凝华 | 1.78 | 36.01 | 24.05 |
升华 | 9.23 | 2.00 | 0.29 | |
霰 | 凝华 | 0.18 | 18.89 | 0.21 |
升华 | 0.89 | 4.34 | 0.07 |
从上述分析中不难发现WSM6方案最突出的问题为云水蒸发和凝结过程太少,而冰晶凝华和升华过程则太强。针对这些现象对其产生的原因进行分析。在WSM6方案中,冰晶的凝华和升华过程为冰相粒子初始形成的最主要过程,虽然冰晶会经过碰并和自动转化过程形成更大粒子,但其含量仍显著多于ERA5和Liu-Ma方案,这与该过程太强是有直接关系的。另外,由于WSM6方案在计算与水汽相关的过程中,先计算冰晶凝华和升华过程,在扣除相应水汽后,再计算雪的凝华和升华,依次类推至霰的凝华和升华以及冰晶的初始核化过程,这样会保证冰相过程总的凝华量(升华量)不会超过该时步内水汽与冰面饱和比湿之间的差值。但由于该方案中计算的冰晶凝华和升华率过大,其也势必影响之后计算的雪和霰的凝华和升华量,这也是上述分析结果中显示在该方案中雪和霰的凝华率不高的原因。在WSM6方案中,云水的凝结和蒸发过程则是在其他所有微物理过程倾向并行更新完毕后再进行计算的,在这种情况下,冷区(0℃以下)的水汽和温度在经过倾向更新后,二者的平衡态基本是以冰面饱和为基准进行调整的,同时由于同温度下液面饱和比湿要大于冰面饱和比湿,这就使得在冷区内云水的凝结过程则变得更加困难。这也可以解释为什么在夏天对流过程的冷区,WSM6方案模拟出现液态水偏少的现象(
针对上述分析,我们对WSM6方案冰晶凝华/升华过程(svi)太强的现象进行简单的敏感性试验,即在不改变其参数化公式的基础上,人为地调整该过程的转化率。本部分设置了两组试验,在模式其他设置不变的基础上,仅在原有转化率基础上乘以0.5和0.2(试验名称分别为SVI05和SVI02;原方案为SVI10, 即svi×1.0),以分析该过程对降雪偏多和云水偏少现象的影响。
WSM6方案中冰晶凝华/升华过程(svi)对降雪预报影响的敏感性试验(a)SVI10,(b)SVI05,(c)SVI02模拟的2019年11月29日00时到30日00时累计降水量的水平分布
Sensitivity test for the impact of ice crystal deposition/sublimation rate (svi) on surface precipitation forecast in WSM6 scheme from 00 UTC 29 to 00 UTC 30 November 2019 (a) SVI10, (b) SVI05, (c) SVI02
在3.5节的分析中我们认为,WSM6方案对液水含量存在的明显低估现象可能与冷区的svi过程太强有关。为了确认二者是否存在这种影响关系,在
冰晶凝华/升华过程(svi)敏感性试验(a,b)SVI10,(c,d)SVI05,(e,f)SVI02对2019年11月29日00时到30日00时24 h平均的(a,c,e)液水和(b,d,f)冰水柱积分总量的影响
Sensitivity test for the impact of ice crystal deposition/sublimation rate (svi) on 24 h mean total liquid water path (a, c, e) and total ice water content (b, d, f) from 00 UTC 29 to 00 UTC 30 November 2019 (a, b) SVI10, (c, d) SVI05, (e, f) SVI02
与ERA5相比(
本文针对GRAPES区域业务模式中的WSM6云微物理方案对2019年11月29—30日的一次华北降雪过程预报偏强的原因进行了研究,通过对其预报的降水和云宏观、微观特征以及水汽相关微物理转化过程等与Liu-Ma方案以及ERA5再分析数据进行了比较,并针对发现的问题开展了敏感性试验,得到的主要结论如下:
(1) WSM6方案明显高估了本次过程的降水量,在很多区域预报的降雪甚至达到暴雪量级,Liu-Ma方案预报降水量与实况具有较好的一致性;冰晶和雪的沉降为WSM6方案在地面降水形成的最主要过程,而Liu-Ma方案则是以大粒子雪和霰的沉降为主要贡献。
(2) 与ERA5数据和Liu-Ma方案相比,WSM6方案严重低估了液水柱积分总量,在水凝物的垂直廓线分布中也无法显示云水的存在;在其冰相粒子构成中,WSM6方案是以冰晶含量为最多,雪含量次之,这与前二者中以雪为主、冰晶次之的分配特征不一致。
(3) WSM6方案在模式低层有更多的冰晶存在,其冰晶平均落速大于Liu-Ma方案,二者共同作用使冰晶沉降在本次降水形成中具有重要贡献。WSM6方案中雪的平均落速大于Liu-Ma方案,结合雪的柱积分含量少于Liu-Ma方案,而雪沉降产生的降水却多于Liu-Ma方案,这较大可能是与雪的落速偏大有关。
(4) 与Liu-Ma方案和
(5) 通过对WSM6方案中svi的敏感性试验表明,svi转化率与地面降水呈正相关关系、与液水柱积分总量呈现“跷跷板”关系,当降低svi的转化率,地面降雪会显著减少,而液水柱积分总量则明显增多。
在本文的研究中发现,WSM6方案预报的水凝物含量和降雪量与该方案中svi具有很强的敏感性,由于该过程主要影响的是冷区的云微物理过程,在夏季的降水中是否仍具有重要作用,这需要选取夏季降雨个例加以验证。对于WSM6方案的优化工作,未来将尝试采用多种冰晶凝华/升华的参数化公式进行试验,分析该微物理过程对不同季节云和降水预报的影响。
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