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Wanchen Wu
,
Wei Huang
,
Lin Deng
, and
Chong Wu

Abstract

This study uses the Weather Research and Forecasting (WRF) Model to investigate the performance of hail parameterizations of the WRF double-moment 7-class (WDM7), aerosol-aware Thompson (AAT), and National Taiwan University triple-moment (NTU3M) bulk microphysics schemes (BMSs) on a real case of a hailstorm initiated in Shandong Province, China. The maximum hail size is particularly evaluated because it is crucial to hail severity prediction, along with areal coverage and intensity of the 24-h solid precipitation during the simulations. Compared with the radar-derived maximum hail size, the objective analysis shows that the NTU3M scheme has the best score in the forecast skill of hail-fall coverage and size, while two BMSs with single-moment rimed ice species overestimate hail diameters aloft but underpredict the coverage at the surface. A deeper investigation suggests that the derived size tendencies from the three BMSs are comparable to the benchmark solutions from the detailed hailstone growth and melting models. The NTU3M scheme displays the most consistent size tendency of the maximum diameter with the benchmark solution in the growth processes. The behaviors of melted diameter by parameterizations are highly related to the treatments of number concentration, which are consistent with the predicted hail severity and coverage. Finally, the sensitivity study shows that increasing the model resolution does not improve the forecast of the maximum hail size, given the biases in the hail mass budget equations and the parameterization of particle size distribution, with single-moment rimed ice species of the AAT scheme.

Significance Statement

Improving hail-forecasting skill, including the size, severity, and the spatial and temporal coverage of hail fall, has become an important subject for numerical weather prediction models as the model resolution increases. The objective of this study is to investigate the fundamental differences in hail parameterizations of three bulk microphysics schemes that lead to differences in the prediction of severe hail events and the spatial coverage of hail fall, hopefully providing insights into hail prediction with a regional numerical weather prediction model in the future.

Free access
Chong Wei
,
H. G. Leighton
, and
R. R. Rogers

Abstract

Using radiometer data collected during the Canadian Atlantic Storms Program, we have investigated five different methods of estimating the path-integrated, or columnar, cloud liquid water. The methods consist of one- and two-channel physical retrievals, the standard method of linear statistical inversion using two channels, and two statistical methods that proceed from an initial determination of several empirical regressions between measured and computed quantities. Though differing in details and complexity, the methods gave estimates of cloud liquid that did not deviate greatly from one another. We assessed the accuracy of the methods by simulation. Using hypothetical profiles of cloud liquid in archival soundings, we calculated the atmospheric emission and thus the brightness temperatures that would be measured in the two channels of the radiometer. These values were taken as data for the five methods, and the amount of liquid was calculated. Results showed that the three statistical methods were more accurate than the physical methods, but no one of the three was significantly better than the others. In the four methods requiring measurements in two channels, the columnar water vapor is computed as part of the retrieval procedure. A comparison of the computed with the actual vapor amounts showed that one of the statistical methods employing empirical regressions was the most accurate for vapor retrieval. For this optimum method, the rms deviation of the measured columnar liquid from its actual value was 0.159 mm and the rms deviation of the columnar vapor was 0.867 mm. As fractions of the overall average liquid and vapor in the simulations, these deviations amount to 37% and 8.7% respectively. If cases are excluded in which the liquid amount is small or nonexistent, the fractional deviation of the liquid estimates decreases and that of the vapor increases.

Full access
Wei Li
,
Jie Chen
,
Lu Li
,
Hua Chen
,
Bingyi Liu
,
Chong-Yu Xu
, and
Xiangquan Li

Abstract

Subseasonal to seasonal (S2S) weather forecasting has made significant advances and several products have been made available. However, to date few studies utilize these products to extend the hydrological forecast time range. This study evaluates S2S precipitation from eight model ensembles in the hydrological simulation of extreme events at the catchment scale. A superior bias correction method is used to correct the bias of S2S precipitation for hydrological forecasts, and the results are compared with direct bias correction of hydrological forecasts using raw precipitation forecasts as input. The study shows that the S2S models can skillfully forecast daily precipitation within a lead time of 11 days. The S2S precipitation data from the European Centre for Medium-Range Weather Forecasts (ECMWF), Korea Meteorological Administration (KMA), and United Kingdom’s Met Office (UKMO) models present lower mean error than that of other models and have higher correlation coefficients with observations. Precipitation data from the ECMWF, KMA, and UKMO models also perform better than that of other models in simulating multiple-day precipitation processes. The bias correction method effectively reduces the mean error of daily S2S precipitation for all models while also improving the correlation with observations. Moreover, this study found that the bias correction procedure can apply to either precipitation or streamflow simulations for improving the hydrological forecasts, even though the degree of improvement is dependent on the hydrological variables. Overall, S2S precipitation has a potential to be applied for hydrological forecasts, and a superior bias correction method can increase the forecasts’ reliability, although further studies are still needed to confirm its effect.

Full access
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.

Full access
Husi Letu
,
Run Ma
,
Takashi Y. Nakajima
,
Chong Shi
,
Makiko Hashimoto
,
Takashi M. Nagao
,
Anthony J. Baran
,
Teruyuki Nakajima
,
Jian Xu
,
Tianxing Wang
,
Gegen Tana
,
Sude Bilige
,
Huazhe Shang
,
Liangfu Chen
,
Dabin Ji
,
Yonghui Lei
,
Lesi Wei
,
Peng Zhang
,
Jun Li
,
Lei Li
,
Yu Zheng
,
Pradeep Khatri
, and
Jiancheng Shi

Abstract

Surface downward solar radiation compositions (SSRC), including photosynthetically active radiation (PAR), ultraviolet-A (UVA), ultraviolet-B (UVB), and shortwave radiation (SWR), with high spatial–temporal resolutions and precision are essential for applications including solar power, vegetation photosynthesis, and environmental health. In this study, an optimal algorithm was developed to calculate SSRC, including their direct and diffuse components. Key features of the algorithm include combining the radiative transfer model with machine learning techniques, including full consideration of the effects of aerosol types, cloud phases, and gas components. A near-real-time monitoring system was developed based on this algorithm, with SSRC products generated from Himawari-8/9 and Fengyun-4 series data. Validation with ground-based data shows that the accuracy of the SWR and PAR compositions (daily mean RMSEs of 19.7 and 9.2 W m−2, respectively) are significantly better than those of state-of-the-art products from CERES, ERA5, and GLASS. The accuracy of UVA and UVB measurements is comparable with CERES. Characteristics of aerosols, clouds, gases, and their impacts on SSRC are investigated before, during, and post COVID-19; in particular, significant SSRC variations due to the reduction of aerosols and increase of ozone are identified in the Chinese central and eastern areas during that period. The spatial–temporal resolution of data products [up to 0.05° (10 min)−1 for the full-disk region] is one of the most important advantages. Data for the East Asia–Pacific region during 2016–20 is available from the CARE home page (www.slrss.cn/care/sp/pc/).

Open access