This study investigates the impacts of different physics schemes on hurricane forecasting across different applications of the unified forecast system (UFS) limited area model (LAM), including the UFS short-range weather (SRW) application, the hurricane analysis and forecast system (HAFS), and the UFS single-column model (SCM). The physics schemes compared here encompass cloud microphysics schemes, planetary boundary layer (PBL) parameterizations, surface layer schemes, gravity wave schemes, and land surface models. All these schemes are implemented through the common community physics package (CCPP) framework. Six experiments, each employing different combinations of physics schemes, were designed to explore their influences on hurricane forecasting. The simulated results from the UFS LAM applications are compared with the observed results from stations, radar, and satellite data. The impacts of various physics schemes on hurricane intensity, track, timing, location, and precipitation are investigated through case studies of different hurricanes, such as Barry, Lorenzo, and Ian. All experiments exhibit a right-of-track bias for hurricanes Barry and Lorenzo. In contrast, the forecasts for hurricane Ian have a left-of-track bias. The results show that the moist turbulence kinetic energy (TKE)-based eddy-diffusivity mass-flux (EDMF) PBL scheme can enhance the hurricane intensity through the moist process. The aerosol-aware (AA) Thompson microphysics scheme appears to provide better precipitation forecasts compared to those from the GFDL microphysics scheme. The combination of these updated physics schemes gives a better track comparing with the result from other old physics settings for hurricane Barry, and it also has an improved intensity in the forecast of hurricane Ian. Sensitivity tests conducted using the UFS SRW application indicate that increasing horizontal resolution and adding vertical levels can enhance model performance. Experiments also revealed that the PBL scheme significantly affects hurricane tracks, a finding further supported by SCM results. Additionally, sensitivity tests demonstrate that improved initial conditions through data assimilation can lead to better simulations of both the hurricane track and intensity, as well as more accurate predictions of hurricane landfall timing and location.

Accurate and reliable wind speed prediction plays a significant role in ensuring the reasonable scheduling of wind power resources. However, wind speed sequences often exhibit complex characteristics such as instability and volatility, which create substantial challenges for prediction. In order to cope with these challenges, a multi-step wind speed prediction method based on secondary decomposition (SD) techniques and deep learning prediction models is proposed in this paper. First, the original signal was decomposed into multiple sequences by using two signal decomposition techniques, multi-scale wavelet power spectrum analysis (MWPSA) and variational mode decomposition (VMD). Second, a model was constructed by combining convolutional neural networks (CNNs), bidirectional long short-term memory (BiLSTM) networks, and attention mechanism to perform multi-step wind speed predicting for each sequence, and the model parameters were optimized by the particle swarm optimization (PSO) algorithm. Ultimately, the results from all sequences were combined to generate the final wind speed prediction. The predictive performance of the proposed method was evaluated using real wind speed data collected from a wind farm in China. Experimental results show that the proposed method significantly outperforms other comparison models in multi-step wind speed prediction, which highlights its accuracy and reliability.

Downbursts are a significant meteorological hazard responsible for power line failures. However, because they often occur in areas with sparse meteorological observations, attributing the cause of damage after an event is challenging. To overcome this challenge, this study proposes a diagnostic method to identify destructive downbursts and quantify their associated wind speeds, independent of near-field observations. This method was applied to a case study of a high-voltage transmission line failure in Brazil, combining ERA5 high-resolution reanalysis data, meteorological principles, and physical conservation models. The results indicate that atmospheric circulation at both high and low altitudes created conditions favorable for the development of strong convection. The surface-based convective available potential energy and downdraft convective available potential energy and both exceeded the trigger thresholds for a downburst, confirming that the necessary conditions were met. The maximum instantaneous wind speed estimated by the physical model was $49.05-58.91 \mathrm{m} / \mathrm{s}$. This result aligns closely with the transmission tower's critical failure wind speed (49.18 $\mathrm{m} / \mathrm{s}$), indicating that the wind speeds generated by the downburst were the direct cause of the failure. This study demonstrates that by analyzing the macro-scale meteorological environment, a quantitative link can be established with micro-scale engineering damage. This provides a feasible technical approach for scientific attribution and risk assessment of meteorological disasters affecting power systems, particularly in data-sparse regions.

The National Weather Service (NWS) Office of Water Prediction (OWP), in conjunction with the National Center for Atmospheric Research and the NWS National Centers for Environmental Prediction (NCEP) implemented version 2.1 of the National Water Model (NWM) into operations in April of 2021. As with the initial version implemented in 2016, NWM v2.1 is an hourly cycling analysis and forecast system that provides streamflow guidance for millions of river reaches and other hydrologic information on high-resolution grids. The NWM provides complementary hydrologic guidance at current NWS river forecast locations and significantly expands guidance coverage and water budget information in underserved locations. It produces a full range of hydrologic fields, which can be leveraged by a broad cross section of stakeholders ranging from the emergency responder and water resource communities, to transportation, energy, recreation and agriculture interests, to other water-oriented applications in the government, academic and private sectors. Version 2.1 of the NWM represents the fifth major version upgrade and more than doubles simulation skill with respect to hourly streamflow correlation, Nash Sutcliffe Efficiency, and bias reduction, over its original inception in 2016. This paper will discuss the driving factors underpinning the creation of the NWM, provide a brief overview of the model configuration and performance, and discuss future efforts to improve NWM components and services.

Wind forecast is an essential part of both weather and renewable energy predictions. This study investigated the performances of the weather research and forecasting (WRF) model real-time four-dimensional data assimilation (RTFDDA) and forecasting system for surface wind forecast over China. The system has been running operationally since 2016 with an analysis/forecast cycle every 3 h. The surface wind forecasting skill of the system was evaluated on the 3 km output with the evaluation period of one year from June 2017 to May 2018. The model outputs were validated against observations from the stations over China objectively. The statistics of the system performance was calculated for both station-by-station and the domain average, which include bias, root mean square errors, mean absolute errors, and the correlation between observation and model outputs. The error statistics show that the high-resolution model has advantages in forecasting the detailed structures of weather features and adding values with rapidly refreshing forecast cycles. The verification results demonstrate that WRF tends to forecast surface winds with positive bias for weak wind regimes and negative for high wind regimes. On the other hand, it tends to produce positive bias for lower topography regions and negative for high topography areas. This feature does not change with seasons although the magnitude of wind bias varies with different seasons. The wind forecast bias has the largest diurnal changes in summer, with positive bias close to the coastal areas and the downstream of Yangtze river.

The integrity of wind turbine nacelle wind speed data is of great value for wind farm maintenance and wind power prediction. However, for many reasons the nacelle wind speed data are missed from one time to another. It is difficult to design an appropriate interpolation model to accurately fill in missing wind speed data because the wind evolutions are highly nonlinear and non-stationary. In this paper, an innovative statistical approach, Robust Particle Swarm Optimized Generalized Regression Neural Network (RPSO-GRNN) algorithm is proposed to achieve the high-accuracy regeneration of missing nacelle wind speed data. Firstly, the Dynamic Time Warping (DTW) method, Pearson's Correlation Coefficients (PCC) method, and Nearest Neighbor (NN) method are applied to evaluate the similarity of wind speed data between the wind turbine that contains missing wind speed data and other available turbines, constructing three candidate member models based on GRNN. Secondly, the RPSO algorithm is applied to optimize the GRNN's parameters. Lastly, two superior ones of the three candidate member models are selected to construct an entropy weight-based ensemble estimation model. The experimental results with the dataset from a large wind farm in the Midwest region of the United States show that: (a) DTW is superior to the PCC method and the NN method in dealing with the nonlinear similarity of wind speed data; (b) The RPSO algorithm yields more practical and accurate structure and parameters of GRNN; (c) The ensemble model with entropy weight has a sound theoretical basis, achieving best estimation and stability.

A modeling architecture that facilitates coupling of multiple hydrological process representations together. WRF-Hydro is both a stand-alone hydrological modeling architecture as well as a coupling architecture for coupling of hydrological models with atmospheric models.

A modeling architecture that facilitates coupling of multiple hydrological process representations together. WRF-Hydro is both a stand-alone hydrological modeling architecture as well as a coupling architecture for coupling of hydrological models with atmospheric models.

Radar data assimilation is an important method for short-term convection forecasting or nowcasting. To improve the short-term (mainly 0-3 h) precipitation forecasts for severe convective storms, an analysis nudging (Newtonian relaxation) based hydrometeor and latent heat nudging (HLHN) technique was developed to effectively assimilate radar reflectivity data in a Weather Research and Forecasting (WRF)-based real time four-dimensional data assimilation and short-term forecasting system (RTFDDA). The purpose of this study is to investigate the performance of the RTFDDA system with radar data assimilation (RTFDDA-RDA) with rapid cycling forecasting applications for Shenzhen, a subtropical coastal metropolis in southern China. The RTFDDA-RDA system was run to produce hindcasts for ten severe convective storm events occurred in Guangdong region during the 2017 rainy season. Results show that, through nudging cloud hydrometeors retrieved from radar reflectivity and the associated latent heat release, RTFDDA-RDA is able to produce the meso- and convective scale features of the convective storms in a good accuracy and improve the short-term precipitation forecasting of the convective storms. Subjective and statistical evaluation results demonstrate that RTFDDA-RDA presents a reasonable capability for forecasting convective systems with improving the initial conditions and resulting in significant improvements of precipitation forecasting skills, especially for the 0-3-h nowcasting range. The sensitivity experiments on different latent heating schemes show that, the convective-stratiform separated heating scheme has the best performance of forecasts. Finally, intercomparison of different radar data assimilation approaches will be conducted in future.

This paper investigates the sensitivities of the Weather Research and Forecasting (WRF) model simulations to different parameterization schemes (atmospheric boundary layer, microphysics, cumulus, longwave and shortwave radiations and other model configuration parameters) on a domain centered over the inter-mountain western United States (U.S.). Sensitivities are evaluated through a multi-model, multi-physics and multi-perturbation operational ensemble system based on the real-time four-dimensional data assimilation (RTFDDA) forecasting scheme, which was developed at the National Center for Atmospheric Research (NCAR) in the United States. The modeling system has three nested domains with horizontal grid intervals of 30 km, 10 km and 3.3 km. Each member of the ensemble system is treated as one of 48 sensitivity experiments. Validation with station observations is done with simulations on a 3.3-km domain from a cold period (January) and a warm period (July). Analyses and forecasts were run every 6 h during one week in each period. Performance metrics, calculated station-by-station and as a grid-wide average, are the bias, root mean square error (RMSE), mean absolute error (MAE), normalized standard deviation and the correlation between the observation and model. Across all members, the 2-m temperature has domain-average biases of −1.5–0.8 K; the 2-m specific humidity has biases from −0.5–−0.05 g/kg; and the 10-m wind speed and wind direction have biases from 0.2–1.18 m/s and −0.5–4 degrees, respectively. Surface temperature is most sensitive to the microphysics and atmospheric boundary layer schemes, which can also produce significant differences in surface wind speed and direction. All examined variables are sensitive to data assimilation.

This study aims to explore the interdecadal variation of South Asian High (SAH) and its relationship with SST (Sea surface temperature) of the tropical and subtropical regions by using the NCEP/NCAR monthly reanalysis data from 1948 to 2012, based on the NCAR CAM 3.0 general circulation model. The results show that: 1) the intensity of SAH represents a remarkable interdecadal variation characteristic, the intensity of SAH experienced from weak to strong at the late 1970s, and after the late 1970s , its strength is enhanced and the area is expanded in the east-west direction. The expansion degree is greater westward than eastward, while it is opposite in summer. 2) Corresponding to the interdecadal variation of SAH intensity, after the late 1970s, the divergent component of wind field has two ascending and three descending areas. Of the two ascending areas, one is located in the East Pacific, the other location varies with the season from the Indian Ocean in winter to the South China Sea and West Pacific in summer. Three descending areas are located in the north-central Africa, the East Asia and the Middle Pacific region respectively. 3) Corresponding to the interdecadal variation of SAH intensity, the rotational component of wind field at the lower level is an anomalous cyclone over the South China Sea and West Pacific in summer, while in winter, it is an anomalous cyclone over the Indian Ocean, and an anomalous anticyclone over the equatorial Middle Pacific. 4) Numerical simulations show that the interdecadal variation of SAH is closely related to the SST of the tropical and subtropical regions. The SST of Indian Ocean plays an important role in winter, while in summer, the SST of the South China Sea and West Pacific plays an important role, and the SST of the East Pacific also plays a certain role.

Since 2007, meteorologists of the U.S. Army Test and Evaluation Command (ATEC) at Dugway Proving Ground (DPG), Utah, have relied on a mesoscale ensemble prediction system (EPS) known as the Ensemble Four-Dimensional Weather System (E-4DWX). This article describes E-4DWX and the innovative way in which it is calibrated, how it performs, why it was developed, and how meteorologists at DPGuse it. E-4DWX has 30 operational members, each configured to produce forecasts of 48 h every 6 h on a 272-processor high performance computer (HPC) at DPG. The ensemble's members differ from one another in initial-, lateral-, and lower-boundary conditions; in methods of data assimilation; and in physical parameterizations. The predictive core of all members is the Advanced Research core of the Weather Research and Forecasting (WRF) Model. Numerical predictions of the most useful near-surface variables are dynamically calibrated through algorithms that combine logistic regression and quantile regression, generating statistically realistic probabilistic depictions of the atmosphere's future state at DPG's observing sites. Army meteorologists view E-4DWX's output via customized figures posted to a restricted website. Some of these figures summarize collective results-for example, through means, standard deviations, or fractions of the ensemble exceeding thresholds. Other figures show each forecast, individually or grouped-for example, through spaghetti diagrams and time series. This article presents examples of each type of figure.

By using a four-dimensional data assimilation technique based on the nudging method,a gridded meteorological dataset with a resolution of 1 km is established in Shenzhen.The comparison between the model data and the observational data shows that the gridded data can fairly well describe the characteristics of the local climate in Shenzhen,for there have been much observational data with high resolution from multi-sources assimilated in the dataset.Based on the gridded meteorological dataset,the Shenzhen fine-gridded climate information service platform is established,and many fine-scale products on local climate are provided through the platform,i.e.fine-scale wind roses and gridded wind energy distribution.The dataset has already successfully applied in practice such as gridded air temperature prediction,site-selection of wind energy projects and ventilation assessment on detailed urban planning.The current study shows that the establishment of the gridded meteorological dataset can be expected to provide services to gridded municipal management and construction.

采用1948-2012年NCEP/NCAR月平均再分析资料和CAM3.0大气环流模式,探讨了南亚高压(SAH)强度年代际变化及其与热带、副热带海温的关系.(1) SAH呈显著年代际变化,以1970年代末期为界,之前强度偏弱;之后强度增强、面积扩大、东西扩展,冬季西侧扩展程度大于东侧,夏季则相反.(2)与SAH强度年代际变化相对应,1970年代末期以后,热带、副热带辐散风分量表现为显著的两个上升区和三个下沉区.两个上升区一个位于东太平洋,另一个随季节变化位置有所改变,冬季位于印度洋,夏季位于南海-西太平洋海域;三个主要下沉区分别位于非洲中北部、亚洲东部和中太平洋地区.(3)与SAH强度年代际变化相对应,夏季低层涡旋风分量在南海-西太平洋地区表现为异常气旋性环流,冬季低层涡旋风分量在印度洋表现为异常气旋性环流,而在赤道中太平洋则呈现异常反气旋性环流.(4)数值试验表明:SAH年代际变化与热带、副热带海温关系密切,冬季印度洋海温起作用较大,夏季则是南海-西太平洋海温起作用较大,另外东太平洋海温也起了一定作用.

This paper documents the performance of the fully coupled WRF-Chem model at 21.6 km and 7.2 km resolution over Saudi Arabia in simulating a severe dust storm event that occurred in March 2009. The comparisons between the model simulations and the observed AOD at the Solar Village AERONET site and the MODIS measurements show that WRF-Chem satisfactorily resolves the arrival, evolution and spatial distributions of the dust storm over Saudi Arabia especially for the fine domain at 7.2 km resolution. The model simulated surface meteorological variables at Riyadh Airport, Hafr Al-Batin Airport, Dammam Airport and Gassim Airport follow the observations in terms of magnitude and temporal evolution although model biases such as deficiencies in simulating the amplitude of diurnal cycles are noted. Higher resolution and shorter initialization time improve the model performance in aerosol optical depth but for surface variables shorter initialization time improves correlation while higher horizontal resolution improves mean biases to some extent. The simulated dust plume is mainly confined between the surface and the 5-km height, with the peak concentrations located in the lowest 500 m. The vertical extent of the dust plume shows gradual decreases during the simulation period when averaged over the entire fine domain and an area centered around Solar Village, and also varies in accordance with the development and decay of the boundary layer.(C) 2015 Elsevier Ltd. All rights reserved.

This study investigates the impact of four-dimensional data assimilation (FDDA) on urban climate analysis, which employs the NCAR (National Center for Atmospheric Research) WRF (the weather research and forecasting model) based on climate FDDA (CFDDA) technology to develop an urban-scale microclimatology database for the Shenzhen area, a rapidly developing metropolitan located along the southern coast of China, where uniquely high-density observations, including ultrahigh-resolution surface AWS (automatic weather station) network, radio sounding, wind profilers, radiometers, and other weather observation platforms, have been installed. CFDDA is an innovative dynamical downscaling regional climate analysis system that assimilates diverse regional observations; and has been employed to produce a 5 year multiscale high-resolution microclimate analysis by assimilating high-density observations at Shenzhen area. The CFDDA system was configured with four nested-grid domains at grid sizes of 27, 9, 3, and 1 km, respectively. This research evaluates the impact of assimilating high-resolution observation data on reproducing the refining features of urban-scale circulations. Two experiments were conducted with a 5 year run using CFSR (climate forecast system reanalysis) as boundary and initial conditions: one with CFDDA and the other without. The comparisons of these two experiments with observations indicate that CFDDA greatly reduces the model analysis error and is able to realistically analyze the microscale features such as urban-rural-coastal circulation, land/sea breezes, and local-hilly terrain thermal circulations. It is demonstrated that the urbanization can produce 2.5 k differences in 2 m temperatures, delays/speeds up the land/sea breeze development, and interacts with local mountain-valley circulations.

针对风电场内邻近多台风机测量风速同时发生缺损的工况,提出基于小波神经网络的组合填充算法。首先,分别采用空间邻点法、Pearson相关系数法和动态时间规整算法对风电场内两两风机的测量风速相似性进行分析;其次,提取与缺损测量风速风机在缺损时刻前后风速演化最相似的若干台风机的测量风速,构建小波神经网络,进行单个模型的填充方法研究;最后,提出基于熵权的组合填充模型。实验结果表明,在进行非线性风速相似性度量时,动态时间规整算法优于Pearson相关系数法;基于相似性风速时序构建的神经网络,提高了模型的学习和泛化性能;组合填充模型的精度和平稳性优于单个模型。对风电场内每台风机进行模拟实验增加了模型的普适性。

MicroRNAs (miRNAs) play crucial roles in multiple stages of plant development and regulate gene expression at posttranscriptional and translational levels. In this study, we first identified 238 conserved miRNAs in date palm (Phoenix dactylifera) based on a high-quality genome assembly and defined 78 fruit-development-associated (FDA) miRNAs, whose expression profiles are variable at different fruit development stages. Using experimental data, we subsequently detected 276 novel P. dactylifera-specific FDA miRNAs and predicted their targets. We also revealed that FDA miRNAs function mainly in regulating genes involved in starch/sucrose metabolisms and other carbon metabolic pathways; among them, 221 FDA miRNAs exhibit negative correlation with their corresponding targets, which suggests their direct regulatory roles on mRNA targets. Our data define a comprehensive set of conserved and novel FDA miRNAs along with their expression profiles, which provide a basis for further experimentation in assigning discrete functions of these miRNAs in P. dactylifera fruit development.

Ovary development is a complex process involving numerous genes. A well-developed ovary is essential for females to keep fertility and reproduce offspring. In order to gain a better insight into the molecular mechanisms related to the process of mammalian ovary development, we performed a comparative transcriptomic analysis on ovaries isolated from infant and adult mice by using next-generation sequencing technology (SOLiD). We identified 15,454 and 16,646 transcriptionally active genes at the infant and adult stage, respectively. Among these genes, we also identified 7021 differentially expressed genes. Our analysis suggests that, in general, the adult ovary has a higher level of transcriptomic activity. However, it appears that genes related to primordial follicle development, such as those encoding Figla and Nobox, are more active in the infant ovary, whereas expression of genes vital for follicle development, such as Gdf9, Bmp4 and Bmp15, is upregulated in the adult. These data suggest a dynamic shift in gene expression during ovary development and it is apparent that these changes function to facilitate follicle maturation, when additional functional gene studies are considered. Furthermore, our investigation has also revealed several important functional pathways, such as apoptosis, MAPK and steroid biosynthesis, that appear to be much more active in the adult ovary compared to those of the infant. These findings will provide a solid foundation for future studies on ovary development in mice and other mammals and help to expand our understanding of the complex molecular and cellular events that occur during postnatal ovary development.