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  • Author or Editor: Wei Wang x
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Bradfield Lyon, Michael A. Bell, Michael K. Tippett, Arun Kumar, Martin P. Hoerling, Xiao-Wei Quan, and Hui Wang

Abstract

The inherent persistence characteristics of various drought indicators are quantified to extract predictive information that can improve drought early warning. Predictive skill is evaluated as a function of the seasonal cycle for regions within North America. The study serves to establish a set of baseline probabilities for drought across multiple indicators amenable to direct comparison with drought indicator forecast probabilities obtained when incorporating dynamical climate model forecasts. The emphasis is on the standardized precipitation index (SPI), but the method can easily be applied to any other meteorological drought indicator, and some additional examples are provided. Monte Carlo resampling of observational data generates two sets of synthetic time series of monthly precipitation that include, and exclude, the annual cycle while removing serial correlation. For the case of no seasonality, the autocorrelation (AC) of the SPI (and seasonal precipitation percentiles, moving monthly averages of precipitation) decays linearly with increasing lag. It is shown that seasonality in the variance of accumulated precipitation serves to enhance or diminish the persistence characteristics (AC) of the SPI and related drought indicators, and the seasonal cycle can thereby provide an appreciable source of drought predictability at regional scales. The AC is used to obtain a parametric probability density function of the future state of the SPI that is based solely on its inherent persistence characteristics. In addition, a method is presented for determining the optimal persistence of the SPI for the case of no serial correlation in precipitation (again, the baseline case). The optimized, baseline probabilities are being incorporated into Internet-based tools for the display of current and forecast drought conditions in near–real time.

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Yang Shi, Jiahua Wei, Yan Ren, Zhen Qiao, Qiong Li, Xiaomei Zhu, Beiming Kang, Peichong Pan, Jiongwei Cao, Jun Qiu, Tiejian Li, and Guangqian Wang

Abstract

Acoustic agglomerations have increasingly attracted widespread attention as a cost-effective and environmentally friendly approach for fog removal and weather modification. In this study, research on precipitation interference and the agglomeration performance of droplet aerosols under large-scale acoustic waves was presented. In total, 49 field experiments in the source region of the Yellow River in the summer of 2019 were performed to reveal the influences of acoustic waves on precipitation, such as the radar reflectivity factor Z, rain rate R, and raindrop size distribution (DSD). A monitoring system that consisted of rain gauges and raindrop spectrometers was employed to monitor near-ground rainfall within a 5-km radius of the field site. The ground-based observations showed that acoustic waves could significantly affect the rainfall distribution and microstructure of precipitation particles. The average values of rainfall increased by 18.98%, 10.61%, and 8.74% within 2, 3, and 5 km, respectively, of the operation center with acoustic application. The changing trend of microphysical parameters of precipitation was roughly in line with variation of acoustic waves for stratiform cloud. Moreover, there was a good quadratic relationship between the spectral parameters λ and μ. Raindrop kinetic energy e K and the radar reflectivity factor Z both exhibited a power function relationship with R.

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Chong Shen, Xiaoyang Chen, Wei Dai, Xiaohui Li, Jie Wu, Qi Fan, Xuemei Wang, Liye Zhu, Pakwai Chan, Jian Hang, Shaojia Fan, and Weibiao Li

Abstract

On urban scales, the detailed characteristics of land-use information and building properties are vital to improving the meteorological model. The WRF Model with high-spatial-resolution urban fraction (UF) and urban morphology (UM) is used to study the impacts of these urban canopy parameters (UCPs) on dynamical and thermal meteorological fields in two representative seasons in Guangzhou. The results of two seasons are similar and as follows. 1) The impacts of updated UF and UM are obvious on wind speed but minor on temperature and humidity. In the urban environment, the results with updated UF and UM are more consistent with observations compared with the default UCPs, which means the performance of the model has been improved. 2) The dynamical factors associated with wind speed are analyzed. Turbulent kinetic energy (TKE) is significantly affected by UM but little by UF. And both UF and UM are found to influence friction velocity U*. The UM and greater UF attained larger U*. 3) In addition, the thermal fields are analyzed. The UM and increased UF induce higher surface skin temperature (TSK) and ground heat flux in the daytime, indicating that more heat is transported from the surface to the soil. At night, more heat is transported from the soil to the surface, producing higher TSK. For sensible heat flux (HFX), greater UF induces larger HFX during the daytime. But the effects of UM are complex, which makes HFX decrease during the daytime and increase at night. Finally, larger UF attains lower latent heat in the daytime.

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William S. Olson, Christian D. Kummerow, Song Yang, Grant W. Petty, Wei-Kuo Tao, Thomas L. Bell, Scott A. Braun, Yansen Wang, Stephen E. Lang, Daniel E. Johnson, and Christine Chiu

Abstract

A revised Bayesian algorithm for estimating surface rain rate, convective rain proportion, and latent heating profiles from satellite-borne passive microwave radiometer observations over ocean backgrounds is described. The algorithm searches a large database of cloud-radiative model simulations to find cloud profiles that are radiatively consistent with a given set of microwave radiance measurements. The properties of these radiatively consistent profiles are then composited to obtain best estimates of the observed properties. The revised algorithm is supported by an expanded and more physically consistent database of cloud-radiative model simulations. The algorithm also features a better quantification of the convective and nonconvective contributions to total rainfall, a new geographic database, and an improved representation of background radiances in rain-free regions. Bias and random error estimates are derived from applications of the algorithm to synthetic radiance data, based upon a subset of cloud-resolving model simulations, and from the Bayesian formulation itself. Synthetic rain-rate and latent heating estimates exhibit a trend of high (low) bias for low (high) retrieved values. The Bayesian estimates of random error are propagated to represent errors at coarser time and space resolutions, based upon applications of the algorithm to TRMM Microwave Imager (TMI) data. Errors in TMI instantaneous rain-rate estimates at 0.5°-resolution range from approximately 50% at 1 mm h−1 to 20% at 14 mm h−1. Errors in collocated spaceborne radar rain-rate estimates are roughly 50%–80% of the TMI errors at this resolution. The estimated algorithm random error in TMI rain rates at monthly, 2.5° resolution is relatively small (less than 6% at 5 mm day−1) in comparison with the random error resulting from infrequent satellite temporal sampling (8%–35% at the same rain rate). Percentage errors resulting from sampling decrease with increasing rain rate, and sampling errors in latent heating rates follow the same trend. Averaging over 3 months reduces sampling errors in rain rates to 6%–15% at 5 mm day−1, with proportionate reductions in latent heating sampling errors.

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