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Guoyu Ren
,
Hongbin Liu
,
Ziying Chu
,
Li Zhang
,
Xiang Li
,
Weijing Li
,
Yu Chen
,
Ge Gao
, and
Yan Zhang
Full access
Cheng-Dong Xu
,
Jin-Feng Wang
,
Mao-Gui Hu
, and
Qing-Xiang Li

Abstract

A probabilistic spatiotemporal approach based on a spatial regression test (SRT-PS) is proposed for the quality control of climate data. It provides a quantitative probability that represents the uncertainty in each temperature observation. The assumption of SRT-PS is that there might be large uncertainty in the station record if there is a large residual difference between the record estimated in the spatial regression test and the true station record. The result of SRT-PS is expressed as a confidence probability ranging from 0 to 1, where a value closer to 1 indicates less uncertainty. The potential of SRT-PS to estimate quantitatively the uncertainty in temperature observations was demonstrated using an annual temperature dataset for China for the period 1971–2000 with seeded errors. SRT-PS was also applied to assess a real dataset, and was compared with two traditional quality control approaches: biweight mean and biweight standard deviation and SRT. The study provides a new approach to assess quantitatively the uncertainty in temperature observations at meteorological stations.

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Xian-Xiang Li
,
Dennis Y. C. Leung
,
Chun-Ho Liu
, and
K. M. Lam

Abstract

The flow characteristics inside urban street canyons were studied in a laboratory water channel. The approaching flow direction was horizontal and perpendicular to the street axis. The street width was adjusted to form street canyons of aspect ratios 0.5, 1.0, and 2.0. The velocity field and turbulent intensity were measured with a laser Doppler anemometer at various locations within the street canyons, which were used to elucidate the flow pattern inside the street canyons. It was found that the previous numerical modeling results are in good agreement with the current experimental results at most locations. For the street canyon of aspect ratio 0.5, which belongs to the wake interference flow regime, the mean and fluctuating velocity components were more difficult to measure as compared with the other two cases because of its more complicated flow pattern. Some guidelines for numerical modeling were developed based on the measurement results. The data presented in this paper can also be used as a comprehensive database for the validation of numerical models.

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Xiang-Yu Li
,
Axel Brandenburg
,
Gunilla Svensson
,
Nils E. L. Haugen
,
Bernhard Mehlig
, and
Igor Rogachevskii

Abstract

We investigate the effect of turbulence on the collisional growth of micrometer-sized droplets through high-resolution numerical simulations with well-resolved Kolmogorov scales, assuming a collision and coalescence efficiency of unity. The droplet dynamics and collisions are approximated using a superparticle approach. In the absence of gravity, we show that the time evolution of the shape of the droplet-size distribution due to turbulence-induced collisions depends strongly on the turbulent energy-dissipation rate , but only weakly on the Reynolds number. This can be explained through the dependence of the mean collision rate described by the Saffman–Turner collision model. Consistent with the Saffman–Turner collision model and its extensions, the collision rate increases as even when coalescence is invoked. The size distribution exhibits power-law behavior with a slope of −3.7 from a maximum at approximately 10 up to about 40 μm. When gravity is invoked, turbulence is found to dominate the time evolution of an initially monodisperse droplet distribution at early times. At later times, however, gravity takes over and dominates the collisional growth. We find that the formation of large droplets is very sensitive to the turbulent energy dissipation rate. This is because turbulence enhances the collisional growth between similar-sized droplets at the early stage of raindrop formation. The mean collision rate grows exponentially, which is consistent with the theoretical prediction of the continuous collisional growth even when turbulence-generated collisions are invoked. This consistency only reflects the mean effect of turbulence on collisional growth.

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Cheng-Dong Xu
,
Jin-Feng Wang
,
Mao-Gui Hu
, and
Qing-Xiang Li

Abstract

Some climate datasets are incomplete at certain places and times. A novel technique called the point estimation model of Biased Sentinel Hospitals-based Area Disease Estimation (P-BSHADE) is introduced to interpolate missing data in temperature datasets. Effectiveness of the technique was empirically evaluated in terms of an annual temperature dataset from 1950 to 2000 in China. The P-BSHADE technique uses a weighted summation of observed stations to derive unbiased and minimum error variance estimates of missing data. Both the ratio and covariance between stations were used in calculation of these weights. In this way, interpolation of missing data in the temperature dataset was improved, and best linear unbiased estimates (BLUE) were obtained. Using the same dataset, performance of P-BSHADE was compared against three estimators: kriging, inverse distance weighting (IDW), and spatial regression test (SRT). Kriging and IDW assume a homogeneous stochastic field, which may not be the case. SRT employs spatiotemporal data and has the potential to consider temperature nonhomogeneity caused by topographic differences, but has no objective function for the BLUE. Instead, P-BSHADE takes into account geographic spatial autocorrelation and nonhomogeneity, and maximizes an objective function for the BLUE of the target station. In addition to the theoretical advantages of P-BSHADE over the three other methods, case studies for an annual Chinese temperature dataset demonstrate its empirical superiority, except for the SRT from 1950 to 1970.

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Xiang-Yu Li
,
Bernhard Mehlig
,
Gunilla Svensson
,
Axel Brandenburg
, and
Nils E. L. Haugen

Abstract

It was previously shown that the superdroplet algorithm for modeling the collision–coalescence process can faithfully represent mean droplet growth in turbulent clouds. An open question is how accurately the superdroplet algorithm accounts for fluctuations in the collisional aggregation process. Such fluctuations are particularly important in dilute suspensions. Even in the absence of turbulence, Poisson fluctuations of collision times in dilute suspensions may result in substantial variations in the growth process, resulting in a broad distribution of growth times to reach a certain droplet size. We quantify the accuracy of the superdroplet algorithm in describing the fluctuating growth history of a larger droplet that settles under the effect of gravity in a quiescent fluid and collides with a dilute suspension of smaller droplets that were initially randomly distributed in space (“lucky droplet model”). We assess the effect of fluctuations upon the growth history of the lucky droplet and compute the distribution of cumulative collision times. The latter is shown to be sensitive enough to detect the subtle increase of fluctuations associated with collisions between multiple lucky droplets. The superdroplet algorithm incorporates fluctuations in two distinct ways: through the random spatial distribution of superdroplets and through the Monte Carlo collision algorithm involved. Using specifically designed numerical experiments, we show that both on their own give an accurate representation of fluctuations. We conclude that the superdroplet algorithm can faithfully represent fluctuations in the coagulation of droplets driven by gravity.

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Xiang-Yu Li
,
Axel Brandenburg
,
Gunilla Svensson
,
Nils E. L. Haugen
,
Bernhard Mehlig
, and
Igor Rogachevskii

Abstract

We investigate the effect of turbulence on the combined condensational and collisional growth of cloud droplets by means of high-resolution direct numerical simulations of turbulence and a superparticle approximation for droplet dynamics and collisions. The droplets are subject to turbulence as well as gravity, and their collision and coalescence efficiencies are taken to be unity. We solve the thermodynamic equations governing temperature, water vapor mixing ratio, and the resulting supersaturation fields together with the Navier–Stokes equation. We find that the droplet size distribution broadens with increasing Reynolds number and/or mean energy dissipation rate. Turbulence affects the condensational growth directly through supersaturation fluctuations, and it influences collisional growth indirectly through condensation. Our simulations show for the first time that, in the absence of the mean updraft cooling, supersaturation-fluctuation-induced broadening of droplet size distributions enhances the collisional growth. This is contrary to classical (nonturbulent) condensational growth, which leads to a growing mean droplet size, but a narrower droplet size distribution. Our findings, instead, show that condensational growth facilitates collisional growth by broadening the size distribution in the tails at an early stage of rain formation. With increasing Reynolds numbers, evaporation becomes stronger. This counteracts the broadening effect due to condensation at late stages of rain formation. Our conclusions are consistent with results of laboratory experiments and field observations, and show that supersaturation fluctuations are important for precipitation.

Free access
Qian Zhou
,
Lei Chen
,
Wansuo Duan
,
Xu Wang
,
Ziqing Zu
,
Xiang Li
,
Shouwen Zhang
, and
Yunfei Zhang

Abstract

Using the latest operational version of the ENSO forecast system from the National Marine Environmental Forecasting Center (NMEFC) of China, ensemble forecasting experiments are performed for El Niño–Southern Oscillation (ENSO) events that occurred from 1997 to 2017 by generating initial perturbations of the conditional nonlinear optimal perturbation (CNOP) and climatically relevant singular vector (CSV) structures. It is shown that when the initial perturbation of the leading CSV structure in the ensemble forecast of the CSVs scheme is replaced by those of the CNOP structure, the resulted ensemble ENSO forecasts of the CNOP+CSVs scheme tend to possess a larger spread than the forecasts obtained with the CSVs scheme alone, leading to a better match between the root-mean-square error and the ensemble spread, a more reasonable Talagrand diagram, and an improved Brier skill score (BSS). All these results indicate that the ensemble forecasts generated by the CNOP+CSVs scheme can improve both the accuracy of ENSO forecasting and the reliability of the ensemble forecasting system. Therefore, ENSO ensemble forecasting should consider the effect of nonlinearity on the ensemble initial perturbations to achieve a much higher skill. It is expected that fully nonlinear ensemble initial perturbations can be sufficiently yielded to produce ensemble forecasts for ENSO, finally improving the ENSO forecast skill to the greatest possible extent. The CNOP will be a useful method to yield fully nonlinear optimal initial perturbations for ensemble forecasting.

Open access
Shanchuan Xiao
,
Di Cheng
,
Ning Hu
,
Yongwei Wang
,
Huilin Zhang
,
Yuwang Gou
,
Xiang Li
, and
Zhenglin Lv

Abstract

The use of high-albedo roof materials is a simple and effective way to reduce roof temperature, conserve electricity required for air conditioning, and ease power shortages. In this study, three common cooling roof materials, namely, white elastomeric acrylic (AC) paint, a white thermoplastic polyolefin (TPO) membrane, and an aluminum foil composite film–covered styrene–butadiene–styrene bituminous (SBS) membranes, were chosen to conduct a nearly 4-yr experiment in Nanjing, China, to study the difference in surface temperatures (ΔTs ) between the cooling roof materials and concrete. The results showed that even during heatwaves, ΔTs was only 2.1°C (AC), 3.8°C (TPO), and 7.0°C (SBS) on average and 6.9°–18.2°C to the greatest extent, which was far less than those reported by many studies. The intensity of solar radiation where the cooling roof material is used and the roof material’s albedo contribute to the difference in ΔTs . The initial albedo of the AC was 0.53 and dropped to 0.16 due to rapid aging, which is close to that of concrete, in less than 3 months. The albedo of TPO and SBS dropped to 0.16 after 9 and 4.7 years, respectively. Further, SBS is the optimal choice in terms of cost and performance, costing only USD 0.67 m−2 yr−1. However, its albedo exhibits seasonal fluctuations and is significantly affected by air pollution. In particular, particulate matter settles on the surface, thereby decreasing the albedo. Nevertheless, manual cleaning can recover the albedo, extend service life, and further reduce costs.

Open access
Xuejin Wang
,
Baoqing Zhang
,
Feng Li
,
Xiang Li
,
Xuliang Li
,
Yibo Wang
,
Rui Shao
,
Jie Tian
, and
Chansheng He

Abstract

From 1998 to the present, the Chinese government has implemented numerous large-scale ecological programs to restore ecosystems and improve environmental protection in the agro-pastoral ecotone of northern China (APENC). However, it remains unclear how vegetation restoration modulates intraregional moisture cycles and changes regional water balance. To fill this gap, we first investigated the variation in precipitation (P) from the China Meteorological Forcing Dataset and evapotranspiration (ET) estimated using the Priestley–Taylor Jet Propulsion Laboratory model under two scenarios: dynamic vegetation (DV) and no dynamic vegetation (no-DV). We then used the dynamic recycling model to analyze the changes in precipitation recycling ratio (PRR). Finally, we examined how vegetation restoration modulates intraregional moisture recycling to change the regional water cycle in APENC. Results indicate P increased at an average rate of 4.42 mm yr−2 from 1995 to 2015. ET with DV exhibited a significant increase at a rate of 1.57, 3.58, 1.53, and 1.84 mm yr−2 in the four subregions, respectively, compared with no-DV, and the annual mean PRR values were 10.15%, 9.30%, 11.01%, and 12.76% in the four subregions, and significant increasing trends were found in the APENC during 1995–2015. Further analysis of regional moisture recycling shows that vegetation restoration does not increase local P directly, but has an indirect effect by enhancing moisture recycling process to produce more P by increasing PRR. Our findings show that large-scale ecological restoration programs have a positive effect on local moisture cycle and precipitation.

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