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基于对流云/层状云划分的云分析方法改进

陈锋 董美莹 冀春晓

陈锋, 董美莹, 冀春晓. 2021. 基于对流云/层状云划分的云分析方法改进[J]. 大气科学, 45(2): 315−332 doi: 10.3878/j.issn.1006-9895.2009.19240
引用本文: 陈锋, 董美莹, 冀春晓. 2021. 基于对流云/层状云划分的云分析方法改进[J]. 大气科学, 45(2): 315−332 doi: 10.3878/j.issn.1006-9895.2009.19240
CHEN Feng, DONG Meiying, JI Chunxiao. 2021. Improvement of the Cloud Analysis Method Based on Convective–Stratiform Cloud Partition [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 45(2): 315−332 doi: 10.3878/j.issn.1006-9895.2009.19240
Citation: CHEN Feng, DONG Meiying, JI Chunxiao. 2021. Improvement of the Cloud Analysis Method Based on Convective–Stratiform Cloud Partition [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 45(2): 315−332 doi: 10.3878/j.issn.1006-9895.2009.19240

基于对流云/层状云划分的云分析方法改进

doi: 10.3878/j.issn.1006-9895.2009.19240
基金项目: 国家自然基金重点项目42030610,浙江省基础公益研究计划项目LGF20D050001,浙江省气象科技计划项目2018ZD01、2020ZD07
详细信息
    作者简介:

    陈锋,男,1982年出生,博士,主要从事数值模式及资料同化研究。E-mail: fchen_zj@163.com

  • 中图分类号: P456.7

Improvement of the Cloud Analysis Method Based on Convective–Stratiform Cloud Partition

Funds: Key Projects of National Natural Science Foundation of China (Grant 42030610), Basic Public Welfare Research Program of Zhejiang Province (Grant LGF20D050001), Meteorological Science and Technology Projects of Zhejiang Meteorological Bureau (Grants 2018ZD01, 2020ZD07)
  • 摘要: 本文基于实况融合降水和雷达反射率因子,采用模糊逻辑法提出了一个新的对流云/层状云判别方法,进而改进了GSI(Gridpoint Statistical Interpolation)同化系统中的云分析方案(简称CUST方案)。以2019年6月19日影响浙江的一次梅雨过程为例,利用WRF(Weather and Forecast Research)模式与GSI同化系统开展了逐小时循环同化试验,分析了CUST方案对降水的模拟改进作用和可能影响过程,并与其他方案进行了对比,探讨了CUST方案的应用效果。结果表明:(1)新提出的CUST方案可较为准确地划分对流云和层状云,以此作为判别因子改进GSI同化系统中的云分析方案切实可行。(2)CUST方案在对流区域采用对流云分析方案,在非对流区域采用层云分析方案,减小了单纯对流云方案在非对流区域的空报现象、以及单纯层云方案在强对流区域的漏报现象,有效提升了短时降水的模拟能力。(3)CUST方案对模式起报初期(6 h甚至3 h内)的改进效果较为明显,且对小雨量级的改进幅度要大于大雨量级。(4)与基于地表感热和潜热通量确定的对流尺度速度作为对流判据的混合云分析方案(简称CSW方案)相比,CUST方案基于实况资料划分的对流云/层状云更为合理,模拟的降水结果占优,说明CUST方案方法有较好的应用前景。
  • 图  1  2019年5~7月基于对流云/层状云划分得到的(a)CMPAS降水量、(c)雷达反射率的概率密度,(b)CMPAS降水量、(d)雷达反射率的累积概率密度

    Figure  1.  Probability density function (PDF) of (a) CMPAS (China Meteorological Administration multisource precipitation analysis system) precipitation and (c) radar reflectivity, and accumulated PDF of (b) CMPAS precipitation and (d) radar reflectivity obtained from the separation of the convective and cloud stratiform cloud from May to July 2019

    图  2  基于函数T的识别参数(a)CMPAS降水量、(b)雷达反射率的模糊基函数

    Figure  2.  Fuzzy setting based on function T for the separation parameters: (a) CMPAS precipitation; (b) radar reflectivity

    图  3  浙江快速更新同化系统的模拟区域,彩色阴影表示地形高度(单位:m)

    Figure  3.  Simulation area of Zhejiang WRF-ADAS (Weather and Forecast Research, ARPS (Advanced Regional Prediction System) Data Assimilation System) rapid refresh system, shadings represent terrain height (units: m)

    图  4  循环同化流程示意图

    Figure  4.  Flowchart of the cycling assimilation system

    图  5  2019年6月19日09时根据(a)CMPAS降水方法、(b)雷达反射率方法、(c)模糊逻辑方法划分的对流云占比分布。黑点表示观测降水量大于20 mm h−1的站点

    Figure  5.  Proportions of convective cloud according to (a) CMPAS precipitation method, (b) radar reflectivity method, (c) fuzzy logical method at 0900 BJT (Beijing time) 19 June 2019. Black points denote the observation stations with precipitation exceeding 20 mm h−1

    图  6  不同云分析试验方法分析得到的2019年6月19日09时的云冰、云水含量垂直累积量的水平分布(单位:kg m−2):(a)ST试验;(b)CU试验;(c)CUST试验

    Figure  6.  Horizontal distributions (units: kg m−2) of vertically integrated cloud ice content and cloud water content from different cloud analysis experiments at 0900 BJT 19 June 2019: (a) Experiment ST (stratiform cloud analysis); (b) experiment CU (convective cloud analysis); (c) experiment CUST (improved hybrid cloud analysis)

    图  7  2019年6月19日09时不同云分析试验方法分析得到的格点平均云水(qc)质量混合比和云冰(qi)质量混合比的垂直分布(单位: g kg−1

    Figure  7.  Vertical distributions (units: g kg−1) of grid-averaged cloud ice (qi) mass mixing ratio and cloud water (qc) mass mixing ratio obtained from different cloud analysis methods at 0900 BJT 19 June 2019

    图  8  2019年6月19日10时的1 h累计降水量(单位:mm):(a)ST试验;(b)CU试验;(c)CUST试验;(d)观测;(e)CUST试验与ST试验的差异;(f)CUST试验与CU试验的差异

    Figure  8.  1-h accumulated precipitations (units: mm) at 1000 BJT 19 June 2019: (a) Experiment ST run; (b) experiment CU run; (c) experiment CUST run; (d) observation; (e) experiment CUST run minus experiment ST run; (f) experiment CUST run minus experiment CU run

    图  9  2019年6月19日09~12时ST试验、CU试验、CUST试验模拟的逐10 min累计降水量(a)≥0.1 mm、(b)≥3.0 mm的ETS评分

    Figure  9.  ETS (Equitable threat score) for 10-min accumulated precipitation (a) ≥0.1 mm, (b) ≥3.0 mm from experiments ST, CU, and CUST simulations from 0900 BJT to 1200 BJT 19 June 2019

    图  10  2019年6月19日08~14时的逐小时循环试验中(a)CUST方案分析得到的对流云格点数目占总格点数目的比例,ST、CU、CUST三种方案分析的(b)云水质量混合比、(c)云冰质量混合比垂直累积的模式内层区域平均

    Figure  10.  (a) Percentage of convective cloud grids in total cloud grids obtained from the experiment CUST, vertically integrated (b) cloud water (qc) mass mixing ratio, (c) cloud ice (qi) mass mixing ratio obtained from hourly cycling experiments ST, CU, and CUST averaged in the inner model domain from 0800 BJT to 1400 BJT 19 June 2019

    图  11  2019年6月19日08~14时逐小时循环试验中ST、CU、CUST方案模拟的1 h累计降水量(P)平均的(a)ETS评分(P≥0.1 mm)、(b)空报率(P≥0.1 mm)、(c)ETS评分(P≥3.0 mm)、(d)FAR空报率(P≥3.0 mm)

    Figure  11.  Averaged (a) ETS (1-h accumulated precipitation P≥0.1 mm), (b) FAR (false alarm, P≥0.1 mm), (c) ETS (P≥3.0 mm), (d) false alarm (P≥3.0 mm) obtained from hourly cycling experiments ST, CU, and CUST from 0800 BJT to 1400 BJT 19 June 2019

    图  12  2019年6月19日09时(a)上一次循环模式预报的潜热(单位:W m−2),(b)上一次循环模式预报的感热(单位:W m−2),采用(c)CSW方案、(d)CUST方案分析得到的对流云所占比例分布。图c、d中的黑点表示观测降水量大于20 mm h−1的站点

    Figure  12.  (a) Latent heat fluxes (units: W m−2) from the last cycle, (b) sensible heat fluxes (units: W m−2) from the last cycle, proportions of convective cloud obtained from (c) the experiment CSW (Convective-scale velocity) and (d) the experiment CUST at 0900 BJT 19 June 2019. In Figs. c and d, black points denote observation stations with precipitation exceeding 20 mm h−1

    图  13  2019年6月19日09时(a)雷达反射率(单位:dBZ),(b)基于CSW方案、(c)基于CUST方案分析得到的云水、云冰含量垂直累积的水平分布(单位:kg m−2)。直线ABCD分别表示雷达回波最强区域(直线AB)及其近似垂向区域(直线CD

    Figure  13.  (a) Radar reflectivity (units: dBZ), horizontal distributions (units: kg m−2) of vertically integrated cloud ice and cloud water obtained from (b) the experiment CSW, (c) the experiment CUST at 0900 BJT 19 June 2019. Lines AB and CD represent the strongest radar echo region (line AB) and its approximate vertical region (line CD), respectively

    图  14  2019年6月19日09时沿图13中直线AB(左)和直线CD(右)的垂直剖面:(a、d)观测的雷达反射率因子(填色,单位:dBZ);(b、e)CSW方案、(c、f)CUST方案分析的反射率因子(填色,单位:dBZ)、云水含量(蓝色等值线,单位:g kg−1)、云冰含量(黑色等值线,单位:g kg−1

    Figure  14.  Vertical cross section along the line AB (left) and line CD (right) in Fig. 13 at 0900 BJT 19 June 2019: (a, d) Observed radar reflectivity (shadings, units: dBZ); radar reflectivity (shadings, units: dBZ), cloud water content (blue contours, units: g kg−1), and cloud ice content (black contours, units: g kg−1) obtained from (b, e) the experiment CSW, (c, f) the experiment CUST

    图  15  2019年6月19日10时1 h累计降水量(单位:mm):(a)CSW试验;(b)CUST试验;(c)观测;(d)CUST试验与CSW试验的差异

    Figure  15.  1-hourly accumulated precipitations (units: mm) at 1000 BJT 19 June 2019: (a) Experiment CSW run; (b) experiment CUST run; (c) observation; (d) experiment CUST run minus experiment CSW run

    图  16  2019年6月19日09~12时CSW试验和CUST试验模拟的逐10 min累计降水量(≥0.1 mm)的ETS评分和FAR评分

    Figure  16.  Scores of ETS and FAR (false alarm) for the 10-min accumulated precipitation (≥0.1 mm) of experiment CSW run and experiment CUST run from 0900 BJT to 1200 BJT 19 June 2019

    表  1  数值试验配置方案

    Table  1.   Configuration schemes of experiments

    试验组试验名称云分析方案对流判别方法备注
    第一组STRUC层云
    方案
    对比三个方案,分析改进后的混合云分析方法对模拟结果的影响
    CUARPS对流云
    方案
    CUST混合云分析
    方案
    基于CMPAS降水和雷达反射率的模糊逻辑法
    第二组CSW混合云分析
    方案
    基于地面感热和潜热通量的对流尺度垂直速度法对比两个方案,分析不同判别方法对模拟结果的影响
    CUST混合云分析方案基于CMPAS降水和雷达反射率的模糊逻辑法
    下载: 导出CSV

    表  2  对流云/层状云划分方法检验统计

    Table  2.   Test statistics of the division method for convective cloud and stratiform cloud

    划分方法准确率空报率漏报率
    CMPAS降水方法0.940.000.73
    雷达反射率方法0.920.520.29
    模糊逻辑方法0.950.090.56
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-10-30
  • 录用日期:  2020-09-21
  • 网络出版日期:  2020-09-14
  • 刊出日期:  2021-03-18

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