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The roles of chaos seeding and multiple perturbations, including model perturbations and topographic perturbations, in convection-permitting ensemble forecasting, are assessed. Six comparison experiments were conducted for 14 heavy rainfall events over southern China. Chaos seeding was run as a benchmark experiment to compare their effects to the intended perturbations. The results first reveal the chaos seeding phenomenon. That is, the tiny and local perturbations of the skin soil moisture propagate into the whole analysis domain within an hour and expand to every prognostic variable, and the perturbations derived from chaos seeding develop when moist convection is active. Second, the chaos seeding has a statistically significant difference from our intended perturbations for the ensemble spread magnitudes of precipitation and the spread–skill relationships and probabilistic forecast skills of dynamical variables. Additionally, for the probabilistic forecasts of precipitation, initial and lateral boundary perturbations and model perturbations can yield statistically larger FSS and AROC scores than chaos seeding; topographic perturbations can only improve FSS and AROC scores a little. The different performances may be related to the different degrees of the real dynamical influence of our intended perturbations. Finally, model perturbations can increase the ensemble spreads of precipitation, and improve FSS and AROC scores of precipitation and the consistency of mid- and low-level dynamical variables. However, the effects of topographic perturbations are small. [ABSTRACT FROM AUTHOR]

Using a 3-km regional ensemble prediction system (EPS), this study tested a three-dimensional (3D) rescaling mask for initial condition (IC) perturbation. Whether the 3D mask-based EPS improves ensemble forecasts over current two-dimensional (2D) mask-based EPS has been evaluated in three aspects: ensemble mean, spread, and probability. The forecasts of wind, temperature, geopotential height, sea level pressure, and precipitation were examined for a summer month (1–28 July 2018) and a winter month (1–27 February 2019) over a region in North China. The EPS was run twice per day (initiated at 0000 and 1200 UTC) to 36 h in forecast length, providing 56 warm-season forecast cases and 54 cold-season cases for verification. The warm and cold seasons are verified separately for comparison. The study found the following: 1) The vertical profile of IC perturbation becomes closer to that of analysis uncertainty with the 3D rescaling mask. 2) Ensemble performance is significantly improved in all three aspects. The biggest improvement is in the ensemble spread, followed by the probabilistic forecast, and the least improvement is in the ensemble mean forecast. Larger improvements are seen in the warm season than in the cold season. 3) More improvement is in the shorter time range (<24 h) than in the longer range. 4) Surface and lower-level variables are improved more than upper-level ones. 5) The underlying mechanism for the improvement has been investigated. Convective instability is found to be responsible for the spread increment and, thus, overall ensemble forecast improvement. Therefore, using a 3D rescaling mask is recommended for an EPS to increase its utility especially for shorter time range and surface weather elements. Significant Statement: A weather prediction model is a complex system that consists of nonlinear differential equations. Small errors in either its inputs or model itself will grow with time during model integration, which will contaminate a forecast. To quantify such contamination ("uncertainty") of a forecast, the ensemble forecasting technique is used. An ensemble of forecasts is a multiple of model runs at the same time but with slightly "perturbed" inputs or model versions. These small perturbations are supposed to represent true "uncertainty" in inputs or model representation. This study proposed a technique that makes a perturbation's vertical structure more resemble real uncertainty (intrinsic error) in input data and confirmed that it can significantly improve ensemble forecast quality especially for a shorter time range and lower-level weather elements. It is found that convective instability is responsible for the improvement. [ABSTRACT FROM AUTHOR]

Background: Metabolic syndrome (MetS) is characterized by a cluster of signs of metabolic disturbance and has caused a huge burden on the health system. The study aims to explore the prevalence and characteristics of MetS defined by different criteria in the Chinese population.Methods: Using the data of the China Hypertension Survey (CHS), a nationally representative cross-sectional study from October 2012 to December 2015, a total of 28,717 participants aged 35 years and above were included in the analysis. The MetS definitions of the International Diabetes Federation (IDF), the updated US National Cholesterol Education Program Adult Treatment Panel III (the revised ATP III), and the Joint Committee for Developing Chinese Guidelines (JCDCG) on Prevention and Treatment of Dyslipidemia in Adults were used. Multivariable logistic regression was used to identify factors associated with MetS.Results: The prevalence of MetS diagnosed according to the definitions of IDF, the revised ATP III, and JCCDS was 26.4%, 32.3%, and 21.5%, respectively. The MetS prevalence in men was lower than in women by IDF definition (22.2% vs. 30.3%) and by the revised ATP III definition (29.2% vs. 35.4%), but the opposite was true by JCDCG (24.4%vs 18.5%) definition. The consistency between the three definitions for men and the revised ATP III definition and IDF definition for women was relatively good, with kappa values ranging from 0.77 to 0.89, but the consistency between the JCDCG definition and IDF definition (kappa = 0.58) and revised ATP III definition (kappa = 0.58) was poor. Multivariable logistic regression showed that although the impact and correlation intensity varied with gender and definition, area, age, education, smoking, alcohol use, and family history of cardiovascular disease were factors related to MetS.Conclusions: The prevalence and characteristics of the MetS vary with the definition used in the Chinese population. The three MetS definitions are more consistent in men but relatively poor in women. On the other hand, even if estimated according to the definition of the lowest prevalence, MetS is common in China. [ABSTRACT FROM AUTHOR]

The effects of small‐scale topographic perturbations on precipitation forecasts are investigated using the China Meteorological Administration's Convection‐Permitting Ensemble Prediction System. Southern China is chosen as the study domain because complex terrain is located there and heavy rainfall frequently occurs. The investigation comprises seven cases including strong and weak forcings on the synoptic scale in terms of convective adjustment time‐scale and synoptic conditions. To construct the topographic perturbation scheme, the standard deviations of small‐scale topographic heights and random fields were first introduced, and sensitivity runs with different topographic perturbation magnitudes were performed at a 3‐km horizontal grid spacing. The results indicate that the model runs with a single topographic perturbation show small changes in precipitation forecasts on strong‐forcing cases and more important impacts for weak‐forcing events, but the effect is also restricted to the control forecast skills. In addition, the topographic perturbation impacts the precipitation forecasts through dynamic processes, and the impact areas are mainly found at locations with rainstorms and heavy rainstorms in the observations. Moreover, the precipitation forecasts react sensitively to the topographic perturbation magnitude, with a larger topographic perturbation magnitude leading to larger amplitudes and spatial gain of precipitation ensemble spread, as well as a better Brier score. However, the selection of the perturbation magnitude is also influenced by the standard deviations of the small‐scale topographic heights. [ABSTRACT FROM AUTHOR]

To more comprehensively and accurately address model uncertainties in the East Asia monsoon region, a single-physics suite, where each ensemble member uses the same set of physics parameterizations as the control member in combination with multiple stochastic schemes, is developed to investigate if the multistochastic schemes that combine different stochastic schemes together can be an alternative to a multiphysics suite, where each ensemble member uses a different set of physics parameterizations (e.g., cumulus convection, boundary layer, surface layer, microphysics, and shortwave and longwave radiation). For this purpose, two experiments are performed for a summer monsoon month over China: one with a multiphysics suite and the other with a single-physics suite combined with multistochastic schemes. Three stochastic schemes are applied: the stochastically perturbed parameterizations (SPP) scheme, consisting of temporally and spatially varying perturbations of 18 parameters in the microphysics, convection, boundary layer, and surface layer parameterization schemes; the stochastically perturbed parameterization tendencies (SPPT) scheme; and the stochastic kinetic energy backscatter (SKEB) scheme. The combination of the three stochastic schemes is compared with the multiphysics suite in the Global and Regional Assimilation and Prediction Enhanced System–Regional Ensemble Prediction System with a horizontal grid spacing of 15 km. Verification results show that, overall, a single-physics suite that combines SPP, SPPT, and SKEB outperforms the multiphysics suite in precipitation verification and verification for upper-air weather variables, 10-m zonal wind, and 2-m temperature in the East Asian monsoon region. The indication is that a single-physics suite combining SPP, SPPT, and SKEB may be an appropriate alternative to a multiphysics suite. This finding lays a foundation for the development and design of future regional and global ensembles. [ABSTRACT FROM AUTHOR]

In this study, a new parameter optimization method was used to investigate the expansion of conditional nonlinear optimal perturbation (CNOP) in a land surface model (LSM) using long-term enhanced field observations at Tongyu station in Jilin Province, China, combined with a sophisticated LSM (common land model, CoLM). Tongyu station is a reference site of the international Coordinated Energy and Water Cycle Observations Project (CEOP) that has studied semiarid regions that have undergone desertification, salination, and degradation since late 1960s. In this study, three key land-surface parameters, namely, soil color, proportion of sand or clay in soil, and leaf-area index were chosen as parameters to be optimized. Our study comprised three experiments: First, a single-parameter optimization was performed, while the second and third experiments performed triple- and six-parameter optimizations, respectively. Notable improvements in simulating sensible heat flux (SH), latent heat flux (LH), soil temperature (TS), and moisture (MS) at shallow layers were achieved using the optimized parameters. The multiple-parameter optimization experiments performed better than the single-parameter experminent. All results demonstrate that the CNOP method can be used to optimize expanded parameters in an LSM. Moreover, clear mathematical meaning, simple design structure, and rapid computability give this method great potential for further application to parameter optimization in LSMs. [ABSTRACT FROM AUTHOR]

The discovery of a fossil that might be the oldest flowering plant will continue the debate on the origin and structure of ancestral flowering plants., Competing Interests: DT, HL No competing interests declared, (© 2018, Taylor and Li.)