The Extra-Area Effect in 71 Cloud Seeding Operations during Winters of 2008–14 over Jiangxi Province, East China

+ Author Affiliations + Find other works by these authors
  • Corresponding author: Zhanyu YAO, yaozy@cma.gov.cn
  • Funds:

    Supported by the National Natural Science Foundation of China (41775139 and 41375135), Key Project of Strategic International Scientific and Technological Innovation Cooperation Program of the Ministry of Science and Technology (2016YFE0201900), and China Meteorological Administration Special Public Welfare Research Fund (GYHY201406033)

  • doi: 10.1007/s13351-019-8122-1

PDF

  • Effects of weather modification operations on precipitation in target areas have been widely reported, but little is specifically known about the downwind (extra-area) effects in China. We estimated the extra-area effect of an operational winter (November–February) aircraft cloud-seeding project in northern Jiangxi Province in eastern China by using a revised historical target/control regression analysis method based on the precipitation data in winter. The results showed that the overall seasonal average rainfall at the downwind stations increased by 21.67% (p = 0.0013). This enhancement effect was detected as far as 120 km away from the target area. Physical testing was used to compare the cloud characteristics before and after seeding on 29 November 2014. A posteriori analysis with respect to the characteristics of cloud units derived from operational weather radar data in Jiangxi was performed by tracking cloud units. Radar features in the target unit were enhanced relative to the control unit for more than two hours after the operational cloud seeding, which is indicative of the extra-area seeding effect. The findings could be used to help relieve water shortages in China.
  • 加载中
  • Fig. 1.  Locations of stations used to assess the extra-area effect of operational cloud seeding in Jiangxi Province in winter months (November–February) during 2008–14. The daily mean wind field at 600 hPa for a total of 71 cloud-seeded days is also shown. The blue, yellow, and green dots donate the weather stations in the seeded (target) region, weather stations in the downwind area, and control stations, respectively. The areas outlined by black and blue dash-dotted lines are the range of operational seeding and identified target region, respectively. The radar is located at station Nanchang (28.59°N, 115.9°E). Heights are given in meters above the mean sea level. All stations are described in Table 1.

    Fig. 2.  Scatterplots of the rainfall fitted by linear regression vs. the observed rainfall at the 12 downwind stations during 1978–97. Blue lines represent the 1:1 line. R2 is the determination coefficient.

    Fig. 3.  Interannual variations in the average rainfall enhancement ratio [(observed – predicted)/predicted] for the (a) 9 stations in the target area and (b) 12 stations in the downwind area during winters of 2008–14.

    Fig. 4.  Spatial distribution of the radar composite reflectivity (dBZ) for the target unit over northern Jiangxi Province at 1436 BT 29 November 2014 when the unit was seeded by an aircraft. Radar echoes were derived from volume scanned radar data deployed at Nanchang (28.59°N, 115.9°E). Units with echoes larger than the threshold of 30 dBZ were tracked and displayed. Control units were selected around the seeding period (1408–1505 BT). Six units were tracked as potential control units with labels of #12, #19, #29, #45, #49, and #51 (not marked here).

    Fig. 5.  (a) 500-hPa geopotential height field (dagpm) and 850-hPa geopotential wind field (m s−1) at 0800 BT 29 November 2014 and (b) water vapor TBB (K) derived from FY-2 satellite data at 1400 BT 29 November 2014. Dark lines are the 500-hPa contour lines and arrows represent the wind direction and speed at 850 hPa.

    Fig. 6.  Temporal and spatial evolutions of the composite reflectivity of the target (#8 in blue) and control (#19 in red) units after cloud seeding at (a) 1436, (b) 1500, (c) 1530, (d) 1600, (e) 1630, and (f) 1642 BT. Note that the seeding was performed during 1430–1436 BT.

    Fig. 7.  Temporal evolutions of (a) echo-top height, (b) echo volume, (c) maximum reflectivity, (d) vertically integrated liquid water content (VILWC), and (e) precipitation flux derived from the S-band volume-scan radar data deployed at Nanchang (28.59°N, 115.9°E). The seeding period (1430–1436 BT) is shaded in pink during which the aircraft was passing through and seeding the target unit. The blue (#8), red (#19), and gray (#12, #29, #45, #49, and #51) lines denote the radar variables of the target unit, most suitable control unit, and alternative units selected during the matching process, respectively.

    Table 1.  Information of the control, target, and downwind National Automatic Stations used in this study

    Station ID Latitude (°N) Longitude (°E)
    Control
    Pingxiang (PG) 57786 27.63 113.85
    Lianhua (LH) 57789 27.13 113.95
    Anfu (AF) 57798 27.40 114.60
    Yongxin (YOX) 57891 26.93 114.25
    Jinggangshan (JGS) 57894 26.58 114.17
    Target
    Yongxiu (YX) 58509 29.05 115.82
    Duchang (DC) 58517 29.27 116.20
    Gaoan (GA) 58605 28.42 115.38
    Nanchang (NC) 58607 28.55 115.95
    Zhangshu (ZS) 58608 28.07 115.55
    Jiujiang (JJ) 58502 29.73 116.00
    Jinxian (JX) 58614 28.38 116.27
    Chongren (CR) 58710 27.77 116.05
    Xinyu (XY) 57796 27.80 114.93
    Downwind
    Wannian (WN) 58615 28.68 117.08
    Yujiang (YJ) 58616 28.20 116.82
    Leping (LP) 58620 28.97 117.13
    Dongxiang (DX) 58622 28.95 117.58
    Shangrao (SR) 58623 28.47 117.92
    Yiyang (YY) 58624 28.40 117.43
    Hengfeng (HF) 58625 28.42 117.60
    Guixi (GX) 58626 28.30 117.23
    Qianshan(QX) 58629 28.32 117.70
    Jinxi (JIX) 58712 27.92 116.78
    Zixi (ZX) 58713 27.72 117.07
    Wuyuan (WY) 58529 29.27 117.85
    Download: Download as CSV

    Table 2.  Correlations of precipitation among the five control sites and the group of downwind sites. The one-tailed p-values were obtained by using the Student’s t-test assuming unequal variances. The same method was also applied to test the correlations among the five control sites and group of target sites, and similar results were obtained (not listed)

    Station Correlation p-value
    PG 0.89 6.91E–08
    LH 0.88 1.88E–07
    AF 0.91 1.88E–08
    YOX 0.84 2.27E–06
    JGS 0.83 2.99E–06
    Download: Download as CSV

    Table 3.  Linear regression equation for winter rainfall (mm) between the control group (represented by x) and each downwind station (represented by y)

    StationRegression equation
    WY y = 1.1012x – 16.1339
    LP y = 1.1403x – 30.4658
    DX y = 1.116x – 14.130
    WN y = 1.134x – 19.785
    YY y = 1.1551x – 22.4279
    HF y = 1.1201x – 15.8786
    SR y = 1.1176x – 7.2871
    YJ y = 1.1616x – 23.54239
    GX y = 1.1370x – 20.6839
    QX y = 1.0396x + 5.3449
    JIX y = 1.0029x + 44.0250
    ZX y = 0.96337x + 40.06729
    Download: Download as CSV

    Table 4.  Statistics for the enhancement ratio over the target and downwind regions. The one-tailed p-values were obtained by using the Student’s t-test assuming unequal variances

    Region Enhancement ratio p-value
    Target 17.30% 0.25
    Downwind 21.67% 0.0013
    Download: Download as CSV

    Table 5.  Winter seeding effects at each of the 12 downwind stations and p-values calculated by the Student’s t-test after cloud seeding during 2008–14. Distance between the target stations and each individual downwind station is also given

    Station Distance from the target (km) Seeding effect (mm) Ratio (%) p-value
    2008 2009 2010 2011 2012 2013 2014
    YJ 20 34.63 60.89 64.46 10.01 29.10 70.13 –17.51 15.05 0.013
    JIX 25 –12.98 49.79 72.50 96.35 119.86 74.23 –12.05 27.32 0.014
    WN 35 58.26 97.90 116.55 31.60 53.95 16.82 42.47 26.09 0.002
    GX 40 46.95 72.13 94.50 35.40 33.61 74.79 35.73 21.78 0.000
    LP 44 88.97 86.48 58.24 –11.08 47.23 –9.64 246.41 29.02 0.035
    ZX 73 –28.01 14.98 112.48 59.56 66.16 87.14 –13.25 30.48 0.038
    YY 77 61.02 94.23 140.71 28.89 84.67 114.52 38.06 23.20 0.001
    DX 80 99.87 137.87 117.60 –10.03 61.54 20.75 74.40 12.97 0.006
    HF 90 72.92 76.20 177.45 39.23 90.89 108.17 20.25 20.51 0.002
    WY 104 45.18 86.58 88.30 –37.33 –0.03 –2.64 103.42 21.45 0.049
    QX 108 14.37 65.47 133.34 28.35 60.56 67.24 43.07 18.23 0.003
    SR 126 21.25 60.67 160.15 61.06 58.94 72.67 23.57 14.75 0.005
    Download: Download as CSV
  • [1]

    Biondini, R., J. Simpson, and W. Woodley, 1977: Empirical predictors for natural and seeded rainfall in the Florida area cumulus experiment (FACE), 1970–1975. J. Appl. Meteor., 16, 585–594, doi: 10.1175/1520-0450(1977)016<0585:EPFNAS>2.0.CO;2.
    [2]

    Boe, B. A., J. A. Jr. Heimbach, T. W. Krauss, et al., 2014: The dispersion of silver iodide particles from ground-based generators over complex terrain. Part I: Observations with acoustic ice nucleus counters. J. Appl. Meteor. Climatol., 53, 1325–1341. doi:  10.1175/JAMC-D-13-0240.1.
    [3]

    Breed, D., R. Rasmussen, C. Weeks, et al., 2014: Evaluating winter orographic cloud seeding: Design of the Wyoming Weather Modification Pilot Project (WWMPP). J. Appl. Meteor. Climatol., 53, 282–299. doi:  10.1175/JAMC-D-13-0128.1.
    [4]

    Chu, X., L. L. Xue, B. Geerts, et al., 2014: A case study of radar observations and WRF LES simulations of the impact of ground-based glaciogenic seeding on orographic clouds and precipitation. Part I: Observations and model validations. J. Appl. Meteor. Climatol., 53, 2264–2286. doi:  10.1175/JAMC-D-14-0017.1.
    [5]

    Ćurić, M., D. Janc, and V. Vučković, 2008: Precipitation change from a cumulonimbus cloud downwind of a seeded target area. J. Geophys. Res. Atmos., 113, D11215. doi:  10.1029/2007JD009483.
    [6]

    DeFelice, T. P., J. Golden, D. Griffith, et al., 2014: Extra area effects of cloud seeding—An updated assessment. Atmos. Res., 135–136, 193–203. doi:  10.1016/j.atmosres.2013.08.014.
    [7]

    Dennis, A. S., 1980: Weather modification by cloud seeding. Inter. Geophy. Seri, 24, 140–142.
    [8]

    Dixon, M., and G. Wiener, 1993: TITAN: Thunderstorm identification, tracking, analysis, and nowcasting—A radar-based methodology. J. Atmos. Ocean. Technol., 10, 785–797. doi:  10.1175/1520-0426(1993)010<0785:TTITAA>2.0.CO;2.
    [9]

    Elliott, R. D., and K. J. Brown, 1971: The Santa Barbara II project—Downwind effects. Proceedings of International Conference on Weather Modification, Australian Academy of Science, Canberra, 6–11.
    [10]

    Gabriel, K. R., 1999: Ratio statistics for randomized experiments in precipitation stimulation. J. Appl. Meteor., 38, 290–301. doi:  10.1175/1520-0450(1999)038<0290:RSFREI>2.0.CO;2.
    [11]

    Griffith, D. A., M. E. Solak, R. D. Almy, et al., 2005: The santa barbara cloud seeding project in coastal southern California, summary of results and their implications. J. Wea. Modif., 37, 21–27.
    [12]

    Han, L., S. X. Fu, L. F. Zhao, et al., 2009: 3D convective storm identification, tracking, and forecasting—An enhanced TITAN algorithm. J. Atmos. Oceanic Technol., 26, 719–732. doi:  10.1175/2008JTECHA1084.1.
    [13]

    Hobbs, P. V., and L. F. Radke, 1973: Redistribution of snowfall across a mountain range by artificial seeding: A case study. Science, 181, 1043–1045. doi:  10.1126/science.181.4104.1043.
    [14]

    Jin, D. C., Z. Y. Guan, and W. Y. Tang, 2013: The extreme drought event during winter–spring of 2011 in East China: Combined influences of teleconnection in midhigh latitudes and thermal forcing in maritime continent region. J. Climate, 26, 8210–8222. doi:  10.1175/JCLI-D-12-00652.1.
    [15]

    Jing, X. Q., B. Geerts, and B. Boe, 2016: The extra-area effect of orographic cloud seeding: Observational evidence of precipitation enhancement downwind of the target mountain. J. Appl. Meteor. Climatol., 55, 1409–1424. doi:  10.1175/JAMC-D-15-0188.1.
    [16]

    Kessinger, C., S. Ellis, J. Vanandel, et al., 2003: The AP clutter mitigation scheme for the WSR-88D. 31st Conf. on Radar Meteorology, Amer. Meteor. Soc., Seattle, WA.
    [17]

    Liu, L. P., L. L. Wu, and Y. M. Yang, 2007: Development of fuzzy-logical two-step ground clutter detection algorithm. Acta Meteor. Sinica, 65, 252–260. (in Chinese) doi:  10.11676/qxxb2007.024.
    [18]

    Long, A. B., 2001: Review of downwind extra-area effects of precipitation enhancement. J. Wea. Modif., 33, 24–45.
    [19]

    Maier, D., R. Bertram, D. Klimm, et al., 2009: Influence of the atmosphere on the growth of LiYF4 single crystal fibers by the micro-pulling-down method. Cryst. Res. Technol., 44, 137–140. doi:  10.1002/crat.200800400.
    [20]

    Marshall, J. S., and W. M. K. Palmer, 1948: The distribution of raindrops with size. J. Meteor., 5, 165–166. doi:  10.1175/1520-0469(1948)005<0165:TDORWS>2.0.CO;2.
    [21]

    Nirel, R., and D. Rosenfeld, 1995: Estimation of the effect of operational seeding on rain amounts in Israel. J. Appl. Meteor., 34, 2220–2229. doi:  10.1175/1520-0450(1995)034<2220:EOTEOO>2.0.CO;2.
    [22]

    Pokharel, B., B. Geerts, and X. Q. Jing, 2015: The impact of ground-based glaciogenic seeding on clouds and precipitation over mountains: A case study of a shallow orographic cloud with large supercooled droplets. J. Geophys. Res. Atmos., 120, 6056–6079. doi:  10.1002/2014JD022693.
    [23]

    Silverman, B. A., 2001: A critical assessment of glaciogenic seeding of convective clouds for rainfall enhancement. Bull. Amer. Meteor. Soc., 82, 903–924. doi:  10.1175/1520-0477(2001)082<0903:ACAOGS>2.3.CO;2.
    [24]

    Solak, M. E., D. P. Yorty, and D. A. Griffith, 2003: Estimations of downwind cloud seeding effects in Utah. J. Wea. Modif., 35, 52–58.
    [25]

    Wise, E. A., 2005: Precipitation evaluation of the North Dakota Cloud Modification Project (NDCMP). Master dissertation, University North Dakota, Grand Forks, ND, 63.
    [26]

    Woodley, W. L., and D. Rosenfeld, 2004: The development and testing of a new method to evaluate the operational cloud-seeding programs in Texas. J. Appl. Meteor., 43, 249–263. doi:  10.1175/1520-0450(2004)043<0249:TDATOA>2.0.CO;2.
    [27]

    Woodley, W. L., D. Rosenfeld, and B. A. Silverman, 2003a: Results of on-top glaciogenic cloud seeding in Thailand. Part I: The demonstration experiment. J. Appl. Meteor., 42, 920–938. doi:  10.1175/1520-0450(2003)042<0920:ROOGCS>2.0.CO;2.
    [28]

    Woodley, W. L., D. Rosenfeld, and B. A. Silverman, 2003b: Results of on-top glaciogenic cloud seeding in Thailand. Part II: Exploratory analyses. J. Appl. Meteor., 42, 939–951. doi:  10.1175/1520-0450(2003)042<0939:ROOGCS>2.0.CO;2.
    [29]

    Xue, L. L., X. Chu, R. Rasmussen, et al., 2014: The dispersion of silver iodide particles from ground-based generators over complex terrain. Part II: WRF large-eddy simulations versus observations. J. Appl. Meteor. Climatol., 53, 1342–1361. doi:  10.1175/JAMC-D-13-0241.1.
    [30]

    Yao, Z. Y., 2006: Review of weather modification research in Chinese Academy of Meteorological Sciences. J. Appl. Meteor. Sci., 17, 786–795. (in Chinese) doi:  10.3969/j.issn.1001-7313.2006.06.016.
    [31]

    Zhao, Z., and H. C. Lei, 2010: Numerical simulation of seeding extra-area effects of precipitation using a three-dimensional mesoscale model. Atmos. Ocean. Sci. Lett., 3, 19–24. doi:  10.1080/16742834.2010.11446838.
  • 20190627161630.pdf

  • 加载中
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

The Extra-Area Effect in 71 Cloud Seeding Operations during Winters of 2008–14 over Jiangxi Province, East China

    Corresponding author: Zhanyu YAO, yaozy@cma.gov.cn
  • 1. Key Laboratory for Cloud Physics of China Meteorological Administration, Chinese Academy of Meteorological Sciences, China Meteorological Administration, Beijing 100081
  • 2. State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, China Meteorological Administration, Beijing 100081
Funds: Supported by the National Natural Science Foundation of China (41775139 and 41375135), Key Project of Strategic International Scientific and Technological Innovation Cooperation Program of the Ministry of Science and Technology (2016YFE0201900), and China Meteorological Administration Special Public Welfare Research Fund (GYHY201406033)

Abstract: Effects of weather modification operations on precipitation in target areas have been widely reported, but little is specifically known about the downwind (extra-area) effects in China. We estimated the extra-area effect of an operational winter (November–February) aircraft cloud-seeding project in northern Jiangxi Province in eastern China by using a revised historical target/control regression analysis method based on the precipitation data in winter. The results showed that the overall seasonal average rainfall at the downwind stations increased by 21.67% (p = 0.0013). This enhancement effect was detected as far as 120 km away from the target area. Physical testing was used to compare the cloud characteristics before and after seeding on 29 November 2014. A posteriori analysis with respect to the characteristics of cloud units derived from operational weather radar data in Jiangxi was performed by tracking cloud units. Radar features in the target unit were enhanced relative to the control unit for more than two hours after the operational cloud seeding, which is indicative of the extra-area seeding effect. The findings could be used to help relieve water shortages in China.

    • Effects of cloud seeding on precipitation and cloud microphysics in target areas have been investigated since the start of attempts to modify weather for both the social benefit and scientific research (Biondini et al., 1977; Nirel and Rosenfeld, 1995; Gabriel, 1999; Silverman, 2001; Woodley et al., 2003a, b; Woodley and Rosenfeld, 2004; Yao, 2006; Pokharel et al., 2015). However, the increase in precipitation induced by cloud seeding in target areas is generally thought to decrease the amount of precipitation falling in the downwind areas, possibly leading to droughts over long periods of time. It is therefore important to determine whether cloud seeding affects the areas downwind of the intended target area.

      Many seeding activities aimed at increasing precipitation in specific target areas have been found to be accompanied by an increase in precipitation outside the intended area (e.g., Hobbs and Radke, 1973; Long, 2001; So-lak et al., 2003; Wise, 2005; Griffith et al., 2005; Ćurić et al., 2008; DeFelice et al., 2014; Jing et al., 2016). The phenomenon is often termed the extra-area effect (DeFelice et al., 2014). Evidence for the increase in precipitation in the “extra area” has been provided by both statistical and observational studies.

      Elliott and Brown (1971) explored downwind seeding effects in the California Santa Barbara project. Precipitation was measured by using 168 rain gauges. The precipitation from seeded convective cloud bands was compared with that from unseeded cloud bands. The precipitation caused by seeding was clearly greater when the cloud-top temperature was warmer than average. The effects were observed 150–200 km downwind of the seeding site.

      A common method used to quantify the effects of seeding is a posteriori historical target/control regression analysis (Dennis, 1980). An adaptation to the historical regression analysis was done by Solak et al. (2003) to estimate the downwind effects on precipitation during a long-standing winter (December–March) operational snow enhancement project in Utah, USA. They established a linear regression equation between each downwind station and the control group that provided the highest correlation of precipitation with the downwind station. The extra-area seeding effect was demonstrated by comparing the observed downwind precipitation during the seeding period with the natural downwind precipitation predicted by the regression equation. The 17 downwind sites had an average observed to predicted ratio of 1.08, indicating an increase of 8% in extra-area precipitation. This positive effect extended for more than 150 miles, and this distance is consistent with observations of the transport distance of silver iodide (AgI) plumes downwind from sources of the seeding material (e.g., Boe et al., 2014).

      The statistical methodology inevitably has some limitations. The topography, climatology, and other factors may induce uncertainties in the statistical results. More advanced equipments and in situ observations have been introduced into physical evaluations in recent years, which can be used to overcome shortcomings in statistical evaluations.

      As part of the Wyoming weather modification pilot project, high concentrations of ice nucleus (IN) were observed 100 km downwind of the AgI generating region after the winter orographic cloud-seeding operations (Boe et al., 2014). The high concentrations of IN remained in the atmosphere for more than two hours after cloud seeding. To evaluate the unintended impact of ground-based seeding over foothills about 50 km downwind of the target mountain range, Jing et al. (2016) compared the radar reflectivity during treated and untreated periods for two areas, including the target area covered by the AgI plume and the control area.

      Radar data from the X-band Doppler on Wheels collected during seven storms in the 2012 AgI Seeding Cloud Impact Investigation (ACSII-12) campaign in Wyoming indicated that AgI nuclei were able to disperse by up to 80 km across two mountain ranges. The reflectivity of the target area during the seeding period was higher than that of control area during the same period. Some mechanisms for the vertical mixing of AgI nuclei that may account for this result were proposed, including the boundary layer mixing, convection, and a lee-side hydraulic jump (Jing et al., 2016).

      Since it is impossible to quantify the precipitation amount released by the unseeded cloud if it is already seeded, in recent years, modeling simulations have become one of the most efficient tools in exploring cloud microphysics and seeding effects. The simulations show that the seeding material is often transported several hundred kilometers downwind of the target (Zhao and Lei, 2010; Chu et al., 2014; Xue et al., 2014), consistent with the observational results. Ćurić et al. (2008) used a three-dimensional mesoscale cloud-resolving model to simulate a seeding experiment that led to a change in cumulative precipitation far from the initial seeding area. Their results showed that precipitation was enhanced by about 50% in an area 110 km downwind of the initial seeding site.

      Most of these investigations have been conducted in countries other than China. If these long-range seeding effects can be repeated, they could be used to reduce droughts more effectively in China and could contribute to more economic benefits. This study is to estimate the so-called extra-area effect caused by cloud seeding by applying an adapted historical target/control regression analysis method (Solak et al., 2003) to a long-term winter (November–February) cloud-seeding operation in northern Jiangxi Province in eastern China during 2008–14. To corroborate the statistical results, a case study was conducted by using physical testing to compare the characteristics of clouds between the target and control areas on 29 November 2014. Relevant concepts, terms, and methods used in this paper are defined in Appendix.

    2.   Data and methods
    • An aircraft cloud-seeding program was conducted in northern Jiangxi Province in eastern China during 2008–14. Supercooled clouds were seeded at their base and/or top with AgI. Seeding at the cloud-top level was normally initiated in the temperature range (−7 to −12°C). Base seeding with flares was carried out when the clouds were mature and precipitating, whereas top seeding was usually performed before the clouds began to precipitate. Each individual seeding operation released 80–120 g of AgI.

    • The seeding period was selected as the winter months (November–February) during 2008–14. Little or no seeding was conducted during the winter months from 1978 to 1997 and therefore it was selected as the control period. A total of 71 operational aircraft cloud-seeding campaigns were carried out during the seeding period. The downwind effect of seeding is inextricably associated with the motion of upper-level winds and cloud systems during the seeding period. Mean wind fields for the seeding period were therefore determined by obtaining the 700-, 600-, and 500-hPa pressure level data from the ECMWF reanalysis dataset (Fig. 1). The wind flow during the seeding period was mainly from the west and the winds had an average speed of more than 15 m s−1. The weather stations used in this study are all National Automatic Stations (Table 1).

      Figure 1.  Locations of stations used to assess the extra-area effect of operational cloud seeding in Jiangxi Province in winter months (November–February) during 2008–14. The daily mean wind field at 600 hPa for a total of 71 cloud-seeded days is also shown. The blue, yellow, and green dots donate the weather stations in the seeded (target) region, weather stations in the downwind area, and control stations, respectively. The areas outlined by black and blue dash-dotted lines are the range of operational seeding and identified target region, respectively. The radar is located at station Nanchang (28.59°N, 115.9°E). Heights are given in meters above the mean sea level. All stations are described in Table 1.

      Station ID Latitude (°N) Longitude (°E)
      Control
      Pingxiang (PG) 57786 27.63 113.85
      Lianhua (LH) 57789 27.13 113.95
      Anfu (AF) 57798 27.40 114.60
      Yongxin (YOX) 57891 26.93 114.25
      Jinggangshan (JGS) 57894 26.58 114.17
      Target
      Yongxiu (YX) 58509 29.05 115.82
      Duchang (DC) 58517 29.27 116.20
      Gaoan (GA) 58605 28.42 115.38
      Nanchang (NC) 58607 28.55 115.95
      Zhangshu (ZS) 58608 28.07 115.55
      Jiujiang (JJ) 58502 29.73 116.00
      Jinxian (JX) 58614 28.38 116.27
      Chongren (CR) 58710 27.77 116.05
      Xinyu (XY) 57796 27.80 114.93
      Downwind
      Wannian (WN) 58615 28.68 117.08
      Yujiang (YJ) 58616 28.20 116.82
      Leping (LP) 58620 28.97 117.13
      Dongxiang (DX) 58622 28.95 117.58
      Shangrao (SR) 58623 28.47 117.92
      Yiyang (YY) 58624 28.40 117.43
      Hengfeng (HF) 58625 28.42 117.60
      Guixi (GX) 58626 28.30 117.23
      Qianshan(QX) 58629 28.32 117.70
      Jinxi (JIX) 58712 27.92 116.78
      Zixi (ZX) 58713 27.72 117.07
      Wuyuan (WY) 58529 29.27 117.85

      Table 1.  Information of the control, target, and downwind National Automatic Stations used in this study

      In addition to the weather stations in the target region, a number of stations downwind of the target area were selected to explore the changes in precipitation in the downwind area caused by cloud seeding (see Fig. 1 and Table 1 for locations of these stations). The stations are representative and were not contaminated by other cloud-seeding activities during the selected time periods.

    • To quantify the impact of aircraft seeding on seasonal precipitation in Jiangxi Province, we adapted the commonly used historical regression method for evaluating the effect of cloud seeding (Dennis, 1980). This regression equation is based on the historical relationship between variables in the designated target area and those in the selected control area. Records of variables to be tested are acquired for a historical period of over 20 years (Griffith et al., 2005). Following the well-established practices (e.g., Solak et al., 2003), the seasonal accumulated precipitation was taken into account as an important input variable. Five stations in northwestern Jiangxi and upwind of the target area were selected as the control group (Table 1). Among all the stations, the seasonal average precipitation at these five stations had the best correlation with the downwind group (Table 2). Linear regression equations were then established between the control group and each downwind station (Table 3) for the seeding period.

      Station Correlation p-value
      PG 0.89 6.91E–08
      LH 0.88 1.88E–07
      AF 0.91 1.88E–08
      YOX 0.84 2.27E–06
      JGS 0.83 2.99E–06

      Table 2.  Correlations of precipitation among the five control sites and the group of downwind sites. The one-tailed p-values were obtained by using the Student’s t-test assuming unequal variances. The same method was also applied to test the correlations among the five control sites and group of target sites, and similar results were obtained (not listed)

      StationRegression equation
      WY y = 1.1012x – 16.1339
      LP y = 1.1403x – 30.4658
      DX y = 1.116x – 14.130
      WN y = 1.134x – 19.785
      YY y = 1.1551x – 22.4279
      HF y = 1.1201x – 15.8786
      SR y = 1.1176x – 7.2871
      YJ y = 1.1616x – 23.54239
      GX y = 1.1370x – 20.6839
      QX y = 1.0396x + 5.3449
      JIX y = 1.0029x + 44.0250
      ZX y = 0.96337x + 40.06729

      Table 3.  Linear regression equation for winter rainfall (mm) between the control group (represented by x) and each downwind station (represented by y)

      The goodness of fit of the regression model was evaluated by using scatterplots between the predicted (calculated by using the relationship in Table 3) and observed precipitation in the downwind region during 1978–97. The determination coefficient R2 of each regression equation in Table 3 is shown in Fig. 2. The line 1:1 indicates good agreement between the predicted and observed rainfall, and the determination coefficients show that this model performs well.

      Figure 2.  Scatterplots of the rainfall fitted by linear regression vs. the observed rainfall at the 12 downwind stations during 1978–97. Blue lines represent the 1:1 line. R2 is the determination coefficient.

      Equations were also developed between the control group and each of the nine stations in the target region by using the same method. These regression equations were used to predict the natural precipitation at the target and downwind stations. The observed rainfall and its prediction in the seeded seasons were compared and the effects of seeding were tested. The statistical method remains uncertain because only the precipitation falling on the ground is evaluated. In addition to the amount of precipitation, other factors such as the cloud height, cloud liquid water content, cloud base temperature, and boundary layer depth, may also contribute to the observed differences in precipitation processes due to cloud seeding. It is therefore necessary to establish the relationship between the statistical results and physical variations in clouds.

    • Radar-based evaluation methods have become more generally used in the evaluation of cloud-seeding experiments during the past 20 years by either tracking a reflectivity maximum or advecting a circle around the seeding target (Maier et al., 2009). The physical testing in this study was based on a seeding case study using radar data from a station at Nanchang (28.59°N, 115.9°E) in Jiangxi Province, which is part of the New Generation Weather Radar System (CINRAD) of China. The temporal resolution was 6 minutes and a monitoring radius of 200 km was achieved by this radar system. A fuzzy logic algorithm was used to remove anomalous propagation echoes (Kessinger et al., 2003; Liu et al., 2007).

      A sophisticated algorithm similar to the Thunderstorm Identification, Tracking, Analysis, and Nowcasting (TITAN) algorithm (Dixon and Wiener, 1993; Han et al., 2009) was applied to track the seeded cloud unit (target unit) under the investigation and to search for alternative control units. The most objectively comparable control unit was then defined and tracked by applying the following steps.

      (1) The target area was determined by using the seeding trajectory information combined with the dispersion of the seeding agent. The target unit was defined when an echo first reached a threshold of 30 dBZ within a volume > 10 km3 in this area. Other units outside the target area that satisfied the same tracking criteria were also tracked as reserved units.

      (2) All units were tracked before and after the initiation of seeding. Although their complete life history could be tracked, the pre-seeding history was defined as about 30 min prior to the initiation of seeding, and tracking was terminated when echoes larger than the threshold of 30 dBZ faded away.

      (3) The meteorological and topographical similarity is required while selecting control units to match the target units. Potential control units should not be contaminated by the cloud-seeding activities.

      (4) The echo-top height (km), echo volume (km3), maximum reflectivity (dBZ), vertically integrated liquid water content (VILWC; kg m−2), and precipitation flux (m3 s−1) calculated from the maximum radar reflectivity factor, were recorded (Marshall and Palmer, 1948). Multiple potential units with a history similar to the pre-seeding history of the target unit were defined automatically. The most probable unit was determined as the control unit by using our assessment system. Evolution of the control unit after the certain history stage was regarded as natural development of the target unit.

      Variations in these parameters between the target and control units were analyzed over time. The seeding operation is only considered to be effective if these parameters increase after cloud seeding.

    3.   Results and discussion
    • Table 4 lists the average winter rainfall enhancement ratio [(observed rainfall – predicted rainfall)/predicted rainfall] for the 9-station target and 12-station downwind groups during 2008–14, calculated by using the regression analysis described in Section 2.2.

      Region Enhancement ratio p-value
      Target 17.30% 0.25
      Downwind 21.67% 0.0013

      Table 4.  Statistics for the enhancement ratio over the target and downwind regions. The one-tailed p-values were obtained by using the Student’s t-test assuming unequal variances

      Figure 3 shows that the average rainfall enhancement ratio for the 9-station target group is 0.17, suggesting an increase of about 17.3% in the amount of rainfall during the 7-yr cloud-seeding operations. Most of the 9 stations had a positive ratio during 2008–14, except for 2011. Values mostly varied between 0 and 0.4. Previous studies (e.g., Jin et al., 2013) have shown that the observed winter rainfall in 2011 was clearly less than that in other years, which might, in part, explain the negative value. The increase in rainfall has a relatively large p-value (0.25), which is not significant.

      Figure 3.  Interannual variations in the average rainfall enhancement ratio [(observed – predicted)/predicted] for the (a) 9 stations in the target area and (b) 12 stations in the downwind area during winters of 2008–14.

      Similarly, the average rainfall enhancement ratio of the 12-station downwind group is 0.21 (p = 0.0013), suggesting a clear increase in rainfall of about 21.67% after the 71 cloud-seeding operations. Table 5 lists the statistical effects with distance. The results show that winter rainfall in the downwind area has a fair chance of increasing by about 21.67% relative to the predicted rainfall after cloud-seeding operations. Values varied in the range 0–0.4. The enhancement ratio of the downwind stations was larger than that of target stations and did not decrease with distance from the target as expected. This may be because (1) dose of the catalyst released by the seeding aircraft as well as the seeding time were insufficient during this period since the aircraft seeding trajectory was not exactly scientific under some conditions, or (2) the wind speed was so high (> 15 m s−1; Fig. 1) that activation time of the catalyst was not sufficiently stable in the target area and the catalyst was probably transported to the downstream domain before the full activation occurred. Other environmental factors, including the effects of Poyang Lake, wind shear, humidity, vertical velocity, and moisture flux convergence, could not be excluded.

      Station Distance from the target (km) Seeding effect (mm) Ratio (%) p-value
      2008 2009 2010 2011 2012 2013 2014
      YJ 20 34.63 60.89 64.46 10.01 29.10 70.13 –17.51 15.05 0.013
      JIX 25 –12.98 49.79 72.50 96.35 119.86 74.23 –12.05 27.32 0.014
      WN 35 58.26 97.90 116.55 31.60 53.95 16.82 42.47 26.09 0.002
      GX 40 46.95 72.13 94.50 35.40 33.61 74.79 35.73 21.78 0.000
      LP 44 88.97 86.48 58.24 –11.08 47.23 –9.64 246.41 29.02 0.035
      ZX 73 –28.01 14.98 112.48 59.56 66.16 87.14 –13.25 30.48 0.038
      YY 77 61.02 94.23 140.71 28.89 84.67 114.52 38.06 23.20 0.001
      DX 80 99.87 137.87 117.60 –10.03 61.54 20.75 74.40 12.97 0.006
      HF 90 72.92 76.20 177.45 39.23 90.89 108.17 20.25 20.51 0.002
      WY 104 45.18 86.58 88.30 –37.33 –0.03 –2.64 103.42 21.45 0.049
      QX 108 14.37 65.47 133.34 28.35 60.56 67.24 43.07 18.23 0.003
      SR 126 21.25 60.67 160.15 61.06 58.94 72.67 23.57 14.75 0.005

      Table 5.  Winter seeding effects at each of the 12 downwind stations and p-values calculated by the Student’s t-test after cloud seeding during 2008–14. Distance between the target stations and each individual downwind station is also given

    • The seeded day (29 November 2014) was selected as a case for a-posteriori research. The cloud in this case was cumulus embedded stratus. The seeding was initiated at 1408 Beijing Time (BT) and terminated at 1505 BT. Figure 4 shows the target unit on the seeding trajectory, which is labeled as #8 in the tracking system. A total of 6 units were tracked as potential control units and are labeled as #12, #19, #29, #45, #49, and #51 in the tracking system.

      Figure 4.  Spatial distribution of the radar composite reflectivity (dBZ) for the target unit over northern Jiangxi Province at 1436 BT 29 November 2014 when the unit was seeded by an aircraft. Radar echoes were derived from volume scanned radar data deployed at Nanchang (28.59°N, 115.9°E). Units with echoes larger than the threshold of 30 dBZ were tracked and displayed. Control units were selected around the seeding period (1408–1505 BT). Six units were tracked as potential control units with labels of #12, #19, #29, #45, #49, and #51 (not marked here).

    • Weather conditions before the seeding were examined to verify the rationality of the seeding treatment. Figure 5 shows the 500-hPa geopotential height fields and 850-hPa wind fields at 0800 BT on the seeded day (Fig. 5a) and the water vapor image at 1400 BT 29 November 2014 (Fig. 5c). Most of southern China (including Jiangxi Province) was controlled by a southwesterly trough on this day (Fig. 5a). Figure 5a also shows the low-level jet at 850 hPa, which favors the transport of water vapor to the target area from southern oceans. Figure 5b shows that the low TBB clouds covered northern Jiangxi, indicating a higher water vapor content over the target area. This combination of prevailing southwesterly wind plus sufficient water vapor means that water vapor could be easily transported to northern Jiangxi at the time of seeding, favoring the cloud-seeding operation.

      Figure 5.  (a) 500-hPa geopotential height field (dagpm) and 850-hPa geopotential wind field (m s−1) at 0800 BT 29 November 2014 and (b) water vapor TBB (K) derived from FY-2 satellite data at 1400 BT 29 November 2014. Dark lines are the 500-hPa contour lines and arrows represent the wind direction and speed at 850 hPa.

    • Figure 6 shows the composite reflectivity of the target and control units after the ignition of seeding, allowing the evolution of clouds to be tracked over time. The echo of the target unit strengthened over time. There was a clear change in the size of the echo area and intensity from one hour after the initiation of seeding in the target unit, whereas the control unit disappeared in the Plan Position Indicator (PPI) display. It is shown that the control unit was decaying and eventually missed the recognition criteria of the tracking system. Lifetime of the target unit was therefore extended relative to that of the control unit as a result of seeding. This increasing trend persisted for almost two hours, indicating that the development stage of clouds (with echoes > 30 dBZ) was sufficiently extended to produce much more rainfall in the downwind region.

      Figure 6.  Temporal and spatial evolutions of the composite reflectivity of the target (#8 in blue) and control (#19 in red) units after cloud seeding at (a) 1436, (b) 1500, (c) 1530, (d) 1600, (e) 1630, and (f) 1642 BT. Note that the seeding was performed during 1430–1436 BT.

    • By using the 30 dBZ threshold, the target unit (#8) was tracked and 6 alternative control units (#12, #19, #29, #45, #49, and #51) were defined.

      Figure 7 shows the variations in five physical parameters of the target unit and six control units derived from radar data before and after cloud seeding. Among the control units, #19 was found to be the most suitable control unit with which to observe the evolution of clouds before starting to seed the target unit. A clear difference in the lifetime—that is, the duration of echoes > 30 dBZ—of the target and control units was observed. Lifetime of the control units after seeding was 30 min, whereas for the target unit it was almost 2 h. The results indicated that the powerful development stage (with echoes > 30 dBZ) of the cloud was sufficiently extended for much more rainfall to occur. These parameters did not change that much in the control unit after cloud seeding. By contrast, these parameters did not increase so much in the first 30 min after seeding, but clearly increased 40–50 min after seeding, and this trend persisted for almost 2 h. The most sensitive radar parameters during seeding were the echo volume (Fig. 7b) and precipitation flux (Fig. 7e), suggesting that a larger precipitation potential was induced by cloud seeding.

      Figure 7.  Temporal evolutions of (a) echo-top height, (b) echo volume, (c) maximum reflectivity, (d) vertically integrated liquid water content (VILWC), and (e) precipitation flux derived from the S-band volume-scan radar data deployed at Nanchang (28.59°N, 115.9°E). The seeding period (1430–1436 BT) is shaded in pink during which the aircraft was passing through and seeding the target unit. The blue (#8), red (#19), and gray (#12, #29, #45, #49, and #51) lines denote the radar variables of the target unit, most suitable control unit, and alternative units selected during the matching process, respectively.

      Values of these parameters were larger in the target unit than those in the control unit after seeding, resulting in a positive effect. Most AgI agents are activated 40–50 min after the initiation of seeding. The results showed that the effect of seeding persisted for two hours, leading to a longer period of positive effects. The seeded cloud might move out from the fixed target area, given that the cloud units moved at a speed of over 15 m s−1. Part of the increased precipitation induced by cloud seeding would fall into the extra area. This case study may therefore provide proof of a positive extra-area effect, assuming that the same technique is used on other seeding days with favorable synoptic conditions.

    4.   Summary
    • This study examined the possibility of the extra-area seeding effect downwind of a target area in northern Jiangxi Province in eastern China during the operational winter (November–February) aircraft cloud-seeding project between 2008 and 2014. A posteriori analysis of the historical target/control regression approach was used. A group of 5 control stations providing the highest correlation of the average seasonal precipitation with the selected 12 downwind stations was selected, and a linear regression equation was established between the control group and each downwind station. To compensate for limitations of the statistical method, physical testing was used to compare the cloud characteristics before and after seeding on 29 November 2014. A sophisticated cloud-tracking algorithm was used to compare the target and control units based on cloud characteristics derived from operational weather radar data in Jiangxi.

      The average winter rainfall increased by 17.3% (p = 0.25) and 21.0% (p = 0.0013) for the target and downwind domain, respectively, indicating that there were pronounced positive effects during the 7-yr operational cloud seeding. This supports that there is little evidence that the enhancement of precipitation in a target area will reduce precipitation downwind. A positive seeding effect was detected as far as 150 km downwind of the target region, and the enhancement ratio of the downwind stations was greater than that of the target stations, which is counter-intuitive. The larger enhancement effects detected at the downwind stations may be due to other environmental factors, including the poor operational design, wind shear, humidity, vertical velocity, and moisture flux convergence.

      The five radar-derived physical parameters (echo-top height, echo volume, maximum reflectivity, vertically integrated liquid water content, and precipitation flux) were systematically enhanced relative to the control unit before and after cloud seeding. Lifetime of the target unit was extended and the radar-measured reflectivity was much stronger after the cloud seeding period. As a result of the longer than expected enhancement period (more than two hours), the seeded cloud was able to move away from the fixed target area. This increased enhancement period might, at least in part, explain why the rainfall increased in the extra area, while taking the local wind speed into account.

      Although these findings are not new, they have rarely been evaluated in China for either research or operational purposes. These results have implications for relieving water shortages in some regions of China. Further observational studies that stabilize an experimental area, increase the sample size, and use more advanced observational apparatus, in combination with explicit model simulations, are warranted in the follow-up studies.

    • Acknowledgments. The Jiangxi Weather Modification Office conducted the cloud-seeding experiments and is greatly appreciated. We would also like to thank the editor and anonymous reviewers for their constructive comments.

    Appendix
    • Terms used in this article:

      A posteriori analysis. Analysis conducted after cloud-seeding operations rather than specified in advance.

      Control area/region. The area selected to predict the natural precipitation of the target area by using the historical regression method to estimate the seeding effect.

      Control unit. The cloud selected to match with the target unit and used to predict the natural process of evolution of the target unit after seeding. It allows a comparison of the radar-derived rainfall characteristics between the predicted target unit without seeding and the actual seeded target unit.

      Extra-area effect. The seeding-induced effect outside the boundary of the target area.

      Historical target/control regression analysis. A commonly used method for the comparison of target and control units (Dennis, 1980). Records of the selected variable (e.g., precipitation), which might be influenced by seeding, are obtained for a historical (unseeded) period of many years’ duration (preferably > 20 yr) in both the target and control areas. The precipitation datasets for the target and control areas for the unseeded seasons are used to establish a regression equation that estimates the precipitation of the target based on data observed in the control area. This equation is then applied to the seeded period to estimate the precipitation in the target area without cloud seeding. The potential difference in precipitation caused by seeding is determined by comparing the predicted natural precipitation in the target area and actual precipitation during cloud-seeding operations.

      Radar-derived rainfall characteristics. These characteristics include the echo-top height, echo volume, maximum reflectivity, vertically integrated liquid water content, and precipitation flux.

      Target area/region. The area in which the effects of intentional cloud-seeding operations are expected to appear. This area is within 50 km of the location of seeding in this study (e.g., Breed et al., 2014).

      Target unit. The objective cloud receiving AgI treatment in the target area.

      Unit lifetime. The period from the time when echoes of the unit first reach the fixed threshold of 30 dBZ to the time when there is no echo larger than this threshold.

Reference (31)

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return