Ray Chambers Stephen Beare Scott Peak Mohammed Al-Kabani Rainfall Enhancement in Oman~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~E?>$?>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> Ray Chambers Stephen Beare Scott Peak Mohammed Al-Kabani

Introduction Summer rainfall enhancement trials by TIE/ART in the Hajar mountains of Oman, currently entering its fourth year. Ground based ionisation system know as Atlant: Operates in a similar way to ground based silver iodide seeding. Hypothesised to facilitate raindrop coalescence as opposed to providing seed nuclei. Experimental design: balanced randomised operating schedule with dynamically (wind) defined target and control areas. Key elements of the statistical analysis pre-specified.

Main statistical issues Standard temporal and spatial aggregation reduces the high level of spatial and temporal heterogeneity in rainfall data but with significant loss of power. Using gauge level data gives greater power but needs to account for the lack of independence in gauge observations due to spatial and temporal correlation. Correlation is likely to be anisotropic with the direction of greatest correlation at any point in time being being downwind.

Trial Instrumentation - 1

Trial Instrumentation - 2 Network of 119 gauges in 2013, expanded to 149 in 2014, and to 191 in 2015. Two Atlants (H1 & H2) in centre of region in 2013, further two (H3 in north-west, H4 in south-east) in 2014 and another two (H5, south-east, and H6, north-west) in 2015. Vertical sonic wind profilers (SODAR) at H1 and H2 in 2014. Additional SODAR at H5 in 2015. Data from TIE instruments are supplemented by DGMAN AWS data.

Trial Design Trial periods: 2013: 15 May to 31 October (170 days) 2014: 1 June to 18 October (140 days) 2015: 14 June to 18 October (127 days) The 2013 and 2014 trials used a randomised cross-over design. (H1,H3) on/off vs. (H2,H4) off/on in 2014. The 2015 trial (6 sites) used a balanced randomised design, with 3 randomly chosen Atlants operational on a day and the remaining three non-operational. Both designs used blocking to balance against seasonal weather trends during the trial: 4 week blocks in 2013 and 2014 and 20 day blocks in 2015. In all three trials, the operating sequence was specified before commencement of the trial.

Analysis Strategy Statistical models are used to control for the natural variability in rainfall over what can be achieved purely through the randomised experimental design. These models are also used to make a counterfactual prediction of the "natural" rainfall that would have been measured by a gauge located in the downwind footprint of an operational Atlant if it had not been operational. The differences between observed rainfall and these predictions are used as estimates of the change in rainfall (+ve or -ve) caused by Atlant operation. Referred to as the Atlant attribution.

Rainfall Distribution in 2014 Distribution of rainfall was light on the coastal side of the ranges, with frequency and intensity of rainfall highest in the central area where the ranges have the greatest elevations. Bubble plot of the locations of the TIE rain gauges, with gauge rainfall frequency proportional to bubble size and gauge average actual rainfall (i.e. excluding zeros) denoted by the colour scale

Rainfall Distribution in 2015 Distribution of rainfall was relatively even through most the trial area, with heaviest rains recorded along the central and north-eastern parts of the Hajar mountain range. Bubble plot of the locations of the TIE rain gauges, with gauge rainfall frequency proportional to bubble size and gauge average actual rainfall (i.e. excluding zeros) denoted by the colour scale

Gauge-Day Rainfall 2013-15

Defining the Downwind Footprint Distribution of the ion plume should be determined by the upper level wind directions in the free atmosphere (i.e. above the boundary layer). These are the AM 500-700 hPa winds at Muscat International Airport. In the average time it would take an ion plume to reach the free atmosphere, given the average horizontal wind speeds above the site, the plume would drift laterally by: 4.8 km at H2. Footprint model assumes that the plume stays within a 75km long by 30km wide downwind corridor. 30km dynamic downwind corridor footprint model

Statistical Approach - 1 Rainfall modelled using natural logarithms of non-zero daily gauge observations. Upwind model used to construct an instrumental prediction of natural downwind rain using meteorological covariates as well as random day and gauge location effects. Indicator variables used to identify target gauges on the basis of their location downwind of an active Atlant. Estimated attribution defined as the ratio of estimated additional rainfall to estimated natural rainfall.

Statistical Approach - 2 Spatio-temporal block bootstrap used to estimate sampling distribution of estimated attribution. Spread of bootstrap distribution allows assessment of precision of this estimate. We can “read off” confidence interval for true attribution from this distribution. Non-parametric permutation test compares actual estimated attribution based on actual operating schedule to estimated attributions based on random permutations of this schedule. Null hypothesis is that operation of Atlant has no effect on estimated attribution – i.e. observed value is purely due to random spatial and temporal variability. Operation of Atlant has a significant effect on rainfall if estimated attribution is an extreme value for this distribution.

Model Fit 2015 Intercept -0.73 0.18 -4.07 Elevation -0.02 0.11 -0.15 Estimate SE t-value Intercept -0.73 0.18 -4.07 Elevation -0.02 0.11 -0.15 UpwindRain 1.03 5.82 H1 0.02 0.16 0.14 H2 0.35 0.13 2.70 H3 0.19 0.99 H4 0.04 0.15 0.27 H5 0.77 0.21 3.67 H6 -0.34 0.26 -1.29 Significant positive effects at H2 and H5. Positive, but not significant, effects at H1, H3 and H4. Effect at H6 is negative but not significant.

2015 Attribution Distributions

Model Fit 2013-15 Intercept -1.38 0.24 -5.71 Elevation 0.32 1.32 Estimate SE t-value Intercept -1.38 0.24 -5.71 Elevation 0.32 1.32 UpwindRain 1.96 0.25 7.69 Upwind*Elev -0.42 0.16 -2.66 H1 0.40 2.46 H2 0.33 0.15 2.30 H3 0.43 0.13 3.40 H4 -0.17 0.12 H5 0.79 0.23 3.39 H6 -0.40 -1.74 H1*Elev -0.33 0.17 -1.91 H2*Elev -0.30 -2.06 Year effects (+ interactions) included in model, but not significant. H1, H2, H3, H5 significant and positive. H4 and H6 not significant and negative.

2013-15 Attribution Distributions

Attribution Estimates Each year has generated a significant and rather consistent overall attribution effect. High estimate for 2014 can be discounted since it may have been unduly affected by very large rainfall recorded at the H3 site that year. There was no similar H3 effect in 2015. Year Estimated Enhancement Probability of a positive enhancement 2013 18 per cent 99 per cent 2014 33 per cent * 90 per cent 2015 20 per cent 99.9 per cent 2013-14 18.5 per cent 2013-15

What Does an Attribution of 18 Per Cent Across 2013-15 Imply? There were 118 gauges that recorded downwind rainfall in 2014, corresponding to an estimated total of 3619 mm of natural rainfall and an attribution of 724 mm (i.e. 6 mm of extra rain per gauge was estimated as due to Atlant operation). A Voronoi tiling of the area associated with these gauges covers approximately 16,000 sq km. That is, operation of the Atlant mechanisms in 2014 is roughly estimated to have led to 96GL of additional surface water flows. Translating this into effective yield, that is water when and where it is needed, is an issue that needs to be addressed. The rainfall gauge network constructed for these trials should facilitate the development of improved surface and ground water modeling that allows the value of rainfall enhancement in Oman to be properly assessed.

Final Comments - 1 High variability in rainfall data is an issue. Meteorological covariates offer limited control when modelling daily gauge-level rainfall (Rsquare values between 27 and 25 per cent). Combining datasets across years is an effective strategy for increasing the effective sample size and hence identifying significant effects. Correct downwind orientation of target areas is crucial. Random effects reduce the potential for bias from unaccounted daily influences on precipitation in the region and from the physical location of gauges.

Final Comments - 2 Significant effects found for the trial region as a whole each year but not at each individual site, implying unaccounted orographic effects remain important. Access to weather radar data maybe extremely beneficial in helping resolve this issue. Trial results in the Hajar mountains over 2013-15 are very positive. But we continue to be conservative about claiming that similar highly significant results will necessarily be found in other locations and under a different seasonal conditions.