2. State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, China Meteorological Administration, Beijing 100081
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 andRosenfeld, 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.2.1 Study area and time period
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).
|Station||ID||Latitude (°N)||Longitude (°E)|
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.2.2 Statistical testing method
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.
|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|
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.
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.2.3 Physical testing method
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 km 3 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 3.1 Long-term statistical analysis
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.
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.
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|
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.3.2.1 Weather conditions before cloud seeding
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.3.2.2 Evolution of convection reflectivity
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.3.2.3 Changes in radar parameters
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.
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.
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