Comparisons of Three-Dimensional Variational Data Assimilation and Model Output Statistics in Improving Atmospheric Chemistry Forecasts

Atmospheric chemistry models usually perform badly in forecasting wintertime air pollution because of their uncertainties. Generally, such uncertainties can be decreased effectively by techniques such as data assimilation（DA） and model output statistics（MOS）. However, the relative importance and combined effects of the two techniques have not been clarified. Here,a one-month air quality forecast with the Weather Research and Forecasting-Chemistry（WRF-Chem） model was carried out in a virtually operational setup focusing on Hebei Province, China. Meanwhile, three-dimensional variational（3 DVar） DA and MOS based on one-dimensional Kalman filtering were implemented separately and simultaneously to investigate their performance in improving the model forecast. Comparison with observations shows that the chemistry forecast with MOS outperforms that with 3 DVar DA, which could be seen in all the species tested over the whole 72 forecast hours. Combined use of both techniques does not guarantee a better forecast than MOS only, with the improvements and degradations being small and appearing rather randomly. Results indicate that the implementation of MOS is more suitable than 3 DVar DA in improving the operational forecasting ability of WRF-Chem.

IMPACT OF VERTICAL RESOLUTION, MODEL TOP AND DATA ASSIMILATION ON WEATHER FORECASTING——A CASE STUDY

The impacts of stratospheric initial conditions and vertical resolution on the stratosphere by raising the model top,refining the vertical resolution,and the assimilation of operationally available observations,including conventional and satellite observations,on continental U.S.winter short-range weather forecasting,were investigated in this study.The initial and predicted wind and temperature profiles were analyzed against conventional observations.Generally,the initial wind and temperature bias profiles were better adjusted when a higher model top and refined vertical resolution were used.Negative impacts were also observed in both the initial wind and temperature profiles,over the lower troposphere.Different from the results by only raising the model top,the assimilation of operationally available observations led to significant improvements in both the troposphere and stratosphere initial conditions when a higher top was used.Predictions made with the adjusted stratospheric initial conditions and refined vertical resolutions showed generally better forecasting skill.The major improvements caused by raising the model top with refined vertical resolution,as well as those caused by data assimilation,were in both cases located in the tropopause and lower stratosphere.Negative impacts were also observed in the predicted near surface wind and lower-tropospheric temperature.These negative impacts were related to the uncertainties caused by more stratospheric information,as well as to some physical processes.A case study shows that when we raise the model top,put more vertical layers in stratosphere and apply data assimilation,the precipitation scores can be slightly improved.However,more analysis is needed due to uncertainties brought by data assimilation.

SeisGuard: A Software Platform to Establish Automatically an Earthquake Forecasting Model

SeisGuard, a system for analyzing earthquake precursory data, is a software platform to search for earthquake precursory information by processing geophysical data from different sources to establish automatically an earthquake forecasting model. The main function of this system is to analyze and process the deformation, fluid, electromagnetic and other geophysical field observing data from ground-based observation, as well as space-based observation. Combined station and earthquake distributions, geological structure and other information, this system can provide a basic software platform for earthquake forecasting research based on spatiotemporal fusion. The hierarchical station tree for data sifting and the interaction mode have been innovatively developed in this SeisGuard system to improve users’ working efficiency. The data storage framework designed according to the characteristics of different time series can unify the interfaces of different data sources, provide the support of data flow, simplify the management and usage of data, and provide foundation for analysis of big data. The final aim of this development is to establish an effective earthquake forecasting model combined all available information from ground-based observations to space-based observations.

Application of an Error Statistics Estimation Method to the PSAS Forecast Error Covariance Model

In atmospheric data assimilation systems, the forecast error covariance model is an important component. However, the paralneters required by a forecast error covariance model are difficult to obtain due to the absence of the truth. This study applies an error statistics estimation method to the Pfiysical-space Statistical Analysis System （PSAS） height-wind forecast error covariance model. This method consists of two components： the first component computes the error statistics by using the National Meteorological Center （NMC） method, which is a lagged-forecast difference approach, within the framework of the PSAS height-wind forecast error covariance model; the second obtains a calibration formula to rescale the error standard deviations provided by the NMC method. The calibration is against the error statistics estimated by using a maximum-likelihood estimation （MLE） with rawindsonde height observed-minus-forecast residuals. A complete set of formulas for estimating the error statistics and for the calibration is applied to a one-month-long dataset generated by a general circulation model of the Global Model and Assimilation Office （GMAO）, NASA. There is a clear constant relationship between the error statistics estimates of the NMC-method and MLE. The final product provides a full set of 6-hour error statistics required by the PSAS height-wind forecast error covariance model over the globe. The features of these error statistics are examined and discussed.

Validation of WRF model on simulating forcing data for Heihe River Basin

The research of coupling WRF （Weather Research and Forecasting Model） with a land surface model is enhanced to explore the interaction of the atmosphere and land surface; however, regional applicability of WRF model is questioned. In order to do the validation of WRF model on simulating forcing data for the Heihe River Basin, daily meteorological observation data from 15 stations of CMA （China Meteorological Administration） and hourly meteorological observation data from seven sites of WATER （Watershed Airborne Telemetry Experimental Research） are used to compare with WRF simulations, with a time range of a whole year for 2008. Results show that the average MBE （Mean Bias Error） of daily 2-m surface temperature, surface pressure, 2-m relative humidity and 10-m wind speed were -0.19 ℃, -4.49 hPa, 4.08% and 0.92 m/s, the average RMSE （Root Mean Square Error） of them were 2.11 ℃, 5.37 hPa, 9.55% and 1.73 m/s, and the average R （correlation coefficient） of them were 0.99, 0.98, 0.80 and 0.55, respectively. The average MBE of hourly 2-m surface temperature, surface pressure, 2-m relative humidity, 10-m wind speed, downward shortwave radiation and downward longwave were-0.16 ℃,-6.62 hPa,-5.14%, 0.26 m/s, 33.0 W/m^2 and-6.44 W/m^2, the average RMSE of them were 2.62 ℃, 17.10 hPa, 20.71%, 2.46 m/s, 152.9 W/m^2 and 53.5 W/m^2, and the average R of them were 0.96, 0.97, 0.70, 0.26, 0.91 and 0.60, respectively. Thus, the following conclusions were obtained： （1） regardless of daily or hourly validation, WRF model simulations of 2-m surface temperature, surface pressure and relative humidity are more reliable, especially for 2-m surface air temperature and surface pressure, the values of MBE were small and R were more than 0.96; （2） the WRF simulating downward shortwave radiation was relatively good, the average R between WRF simulation and hourly observation data was above 0.9, and the average R of downward longwave radiation was 0.6; （3） both wind speed and rainfall simulated from WRF model did not agree well with observation data.

Development of a fine-resolution atmosphere-wave-ocean coupled forecasting model for the South China Sea and its adjacent seas

A 72-h fine-resolution atmosphere-wave-ocean coupled forecasting system was developed for the South China Sea and its adjacent seas. The forecasting model domain covers from from 15°S to 45°N in latitude and 99°E to135°E in longitude including the Bohai Sea, the Yellow Sea, the East China Sea, the South China Sea and the Indonesian seas. To get precise initial conditions for the coupled forecasting model, the forecasting system conducts a 24-h hindcast simulation with data assimilation before forecasting. The Ensemble Adjustment Kalman Filter(EAKF) data assimilation method was adopted for the wave model MASNUM with assimilating Jason-2 significant wave height(SWH) data. The EAKF data assimilation method was also introduced to the ROMS model with assimilating sea surface temperature(SST), mean absolute dynamic topography(MADT) and Argo profiles data. To improve simulation of the structure of temperature and salinity, the vertical mixing scheme of the ocean model was improved by considering the surface wave induced vertical mixing and internal wave induced vertical mixing. The wave and current models were integrated from January 2014 to October 2015 driven by the ECMWF reanalysis 6 hourly mean dataset with data assimilation. Then the coupled atmosphere-wave-ocean forecasting system was carried out 14 months operational running since November 2015. The forecasting outputs include atmospheric forecast products, wave forecast products and ocean forecast products. A series of observation data are used to evaluate the coupled forecasting results, including the wind, SHW, ocean temperature and velocity.The forecasting results are in good agreement with observation data. The prediction practice for more than one year indicates that the coupled forecasting system performs stably and predict relatively accurate, which can support the shipping safety, the fisheries and the oil exploitation.

A baroclinic typhoon model with a moving multi-nested grid andvariational adjustment initializationII. Forecast experiments

A 3-dimensional baroclinic typhoon model with a moving multi-nested grid and its initialization are described first. Prediction results are improved by using a simple but effective data assimilation method in which the initial field is adjusted by the sixth hour's typhoon report and the weak-constraint variational principle. Finally someforecast examples made by this typhoon model are given.

基于集合卡尔曼滤波的水文模型状态变量反馈校正方法

集合卡尔曼滤波已应用于水文模型初始状态误差校正。如何选择校正变量、是否同步校正模型参数、如何优选滤波超参数是其应用难点。为此,以綦江流域GR5J模型为原型,同化实测流量反馈校正模型状态,通过合成实验和滚动预报实验,分析状态变量选择、模型参数扰动、滤波超参数对预报精度的影响。结果表明:准确识别有偏初始状态,集合卡尔曼滤波能提高预报精度;若难以识别,建议同步校正产流汇流状态变量,减少过校正。模型参数有偏时,应先识别模型参数,再校正模型状态。增加集合数和预热时长能提高校正精度,模型和观测噪声的影响具有非单调性;滤波校正效果随预见期延长而衰减,但要优于模型预热技术。该发现可作为作业预报应用状态校正法的参考。

顾及有效角动量与IGS超快解数据的极移预报方法

极移高精度预报对卫星实时定轨、深空探测器导航等应用至关重要.本文提出了一种联合有效角动量(Effective Angular Momentum,EAM)与国际全球卫星导航系统服务组织(International GNSS Service,IGS)提供的超快解数据进行极移预报的方法.该方法基于IGS超快解数据得到的极移第1天预报值,对引入EAM得到的极移预报结果进行校正,获得联合预报值.首先,基于LS(Least Squares)+AR(Auto-Regressive)模型实现了引入EAM的极移预报,相对国际地球自转与参考系统服务组织(International Earth Rotation and Reference Systems Service,IERS)提供的公报A数据,在超短期(第1~10天)预报跨度可以得到更高精度的极移预报结果,其中大气和海洋角动量发挥了主要作用.其次,鉴于IGS超快解数据精度高、更新快的特点,以IGS超快解为基础数据,基于LS+AR模型可以得到极移第1天预报值,其精度显著优于IERS公报A的极移第1天预报值.最后,利用第1天预报值对顾及有效角动量的预报结果进行校正获得了联合预报值,进一步提高了超短期极移预报精度(尤其是第1~5天).2020年7月24日—2022年1月30日间的联合预报结果表明:第1~20天的预报值总体优于IERS公报A的预报值.其中,第1~10天的预报精度显著提升,在预报第1天,X、Y方向预报值相对公报A预报值的精度提升分别可达39.5%~62.3%和24.5%~51.9%;在预报第10天,相对公报A预报值的精度提升分别可达28.0%~28.9%和21.9%~23.4%.

基于网络搜索数据的GDP组合预测研究

网络搜索数据(Web Search Data, WSD)是研究宏观经济现象的重要微观信息依据。从需求、供给与政策环境等方面选取和筛选关键词来合成网络搜索指数,采用金枪鱼群(Tuna Swarm Optimization, TSO)算法优化的最小二乘支持向量回归(Least Squares Support Vector Regression, LSSVR)模型,对GDP进行预测。结果表明,网络搜索指数与GDP具有强相关性,合成的网络搜索指数能较好地反映GDP的波动走势;网络搜索数据的加入使得预测结果呈现出强时效性,预测效果及预测精度都取决于对最优模型的选择,引入参数智能优化算法可提高模型的预测性能。提出的TSO-LSSVR&WSD模型充分利用网络搜索数据及组合预测优势,提高了GDP的预测精度和时效性,可应用于宏观经济指标预测中。

基于SPSS Modeler的气象数据分析

随着信息化的普及,气象信息化的程度日益提高。气象部门积累了大量的气象数据,如何充分利用这些数据,获取其中蕴藏的价值,已经成为大数据时代面临的主要任务。基于SPSS Modeler对某站点的气象数据进行分析,介绍了数据加载、数据抽取、离群值极值处理、数据分析、数据挖掘等步骤。

上海城区动态洪水风险图应用系统及典型暴雨内涝分析

【目的】为了提高沿海超大城市对洪涝灾害的先知先觉能力,快速预测分析洪涝潮组合影响下的淹没风险时空分布,【方法】以水文-水动力暴雨洪水分析模型为核心模块,通过与外部的气象精细化降雨预报数据库、自动雨量站实时监测数据库及实时水情、闸泵运行数据库相关联,研发了上海城区动态洪水风险图应用系统。采用由雨量站点到气象格网和水力网格的降雨二级空间融合技术,实现了模型与气象预报降雨数据的有效耦合。利用数据挖掘提取和GIS空间分析技术构建了包含10项洪水风险要素要点的城市内涝预报专报自动生成方法,实现了对内涝风险的快速一站式概览。利用系统可以对城市暴雨、河道洪水和风暴潮等单一发生或遭遇组合引起的淹没分布进行快速实时预报计算,模拟和预测城市洪涝潮灾害的有关淹没特征数据和淹没动态过程。【结果】利用系统分析了2023年6月23—24日暴雨的内涝风险分布,将模型模拟的积水区域与积水监测站、灾情直报和热线灾报的积水点进行对比,结果显示在150处对比积水点中,有129处误差不超过20 cm,占86%。模型模拟的积水空间分布与实际情况基本接近。【结论】结果表明:系统满足汛期常态化、业务化运行需求,能够为城市洪涝风险的实时动态分析和防汛指挥决策提供重要工具。

数字孪生水网赋能多业务场景应用与实践

目前,水利部数字孪生流域先行先试工作已经接近尾声。数字孪生太湖地区典型水网工程项目作为江苏省4个先行先试项目之一,包括10个子项,各子项综合考虑太湖地区水网纲、目、结特征,以河、湖、库、城市、圩区为主要试点对象。围绕共建共享的理念,文章从新形势、新思路、新标准、新平台4个方面阐述了数字孪生水网赋能多业务场景应用的方式,包括太湖流域“点—线—面”融合的多层级数据底板、江苏省太湖地区精细化河网模型及服务、“2+N”多场景“四预”业务应用,相关建设成果能够为江苏数字孪生水利建设提供经验,有效支撑未来我国智慧水利体系建设。

基于GRU门控单元网络的电力负荷预测研究

准确预测电力负荷,有利于提高电力系统供需平衡,为提高电力负荷预测精度,提出一种基于迁移学习的电力负荷预测模型。该模型以门控循环单元模型(GRU)为基础模型,通过设定最大均值差异算法阈值,从而选择迁移学习的模型,最终实现电力负荷雨预测。仿真结果表明,所提模型可准确预测电力负荷数据,相较于BPNN模型和LSTM模型,所提出模型的MAPE值更低,为17.5%,分别降低了15%和7.5%,具有更高的预测准确度,可用于电力负荷预测实际应用中。

电力系统负荷预测精度提升策略分析

旨在提出提升电力系统负荷预测精度的策略。综合考虑数据预处理与清洗、特征工程与选择、建模算法与模型融合等多方面因素,采取具体可行的实施方案。通过精细化数据处理、优化特征提取与选择,并运用先进的建模算法和模型融合技术,有效提高了负荷预测模型的准确性和可靠性。这一工作为电力系统运行与规划提供了有力支持,有助于提高电力系统的运行效率和稳定性,为未来能源规划与管理提供了重要参考。

基于互信息及人工神经网络的降雨-径流预报方法

输入变量空间信息的提取、筛选与输入优化是提高数据驱动水文模型性能的关键环节。为提高数据驱动短期径流预报模型效果,以全国第二届水科学数值模拟创新大赛题目为算例,基于互信息及人工神经网络方法构建降雨-径流水文模型,进行小时尺度径流预报,探讨互信息对人工神经网络模型在降雨-径流预报精度提高上的作用。结果表明,基于互信息及人工神经网络的降雨-径流预报模型模拟精度高,适用性较好,验证期纳什效率系数和相关系数分别为0.94、0.96。互信息方法能够实现径流预报模型的输入优化,避免数据冗余,可为空间信息缺乏流域径流预报因子选择提供新思路。

基于数据增广的区域供热系统热力站负荷预测模型准确率提升方法研究

开展了热力站数据生成模型研究,基于生成对抗网络和去噪扩散概率模型建立了数据生成模型,通过学习气象、室温、热力站运行数据的联合分布,对原始训练数据进行增广,为预测模型训练提供充足的数据支撑,从而提高预测模型的准确率。在北京市某热力站进行了验证和实际测试,结果表明:该方法可以将热力站一次侧电动调节阀开度和二次网供水温度的预测误差分别降低约7%和11%;同时,应用准确率提升后的负荷预测值进行供热量调节得到的预计室温与室温目标值之间的偏差可进一步降低5.44%。基于生成对抗网络的生成模型能够扩展预测模型的预测范围,基于去噪扩散概率模型的生成模型能够在原预测范围内提高预测模型的准确率。本文研究可为进一步提高区域供热系统热力站负荷预测能力与按需精准调控水平提供支撑。

基于大数据的物资需求预测与采购决策模型研究

本文首先分析了物资需求预测的现状和挑战;然后提出了基于大数据的物资需求预测模型的优化与验证方法,包括模型框架选择、优化和性能评估等;接着构建了基于预测结果的优化物资采购决策模型,并通过模拟实验验证了其效果;最后提出了保障性措施,包括数字化环境保障、专业模型架构人才保障和制度保障,以确保物资需求预测与采购决策模型的有效发挥。

基于WRF模拟的中国西北河谷城市夏季的大气边界层特征

为提高河谷地形气象场的模拟效果,利用第5代再分析资料(ERA5)和全球再分析资料(FNL)作为初始场,以天水市为研究对象,驱动中尺度天气预报模式比较对西北河谷城市边界层模拟的适用性,分析西北河谷城市夏季的大气边界层特征.结果表明, ERA5模拟的天水市主城区近地面温度、近地面风速、风向以及相对湿度与观测值的相关性更好,尤其是近地面风速和风向,分别比FNL模拟的结果提升25.4%和70.0%.天水市主城区的气象场空间分布呈明显的城市热岛效应和山谷风环流,相对开阔的麦积区城市热岛效应更强;白天发生的降水会弱化谷风环流和热岛效应,河谷内及周边风速均较小.天水市主城区夏季近地面温度与风速呈正相关,与相对湿度呈负相关,大气边界层高度呈现明显的日变化,大气层结稳定.

基于需求分析的MRI设备配置规划预测研究

目的探讨新的政策背景下,如何科学合理地进行磁共振成像(Magnetic Resonance Imaging,MRI)设备的配置。方法以山东省16个地市MRI设备的配置现状作为研究对象,对山东省各地市的医疗机构MRI设备的拥有量及其分布现状、经济性评价、有效性评价等角度展开回顾性研究分析,并基于大型医疗设备配置预测模型,对各地市的配置规划数量进行预测分析。结果各地市单台MRI设备年均利润率大多大于130%;单台MRI设备日均检查或治疗人数均值为30.30人,单台MRI设备检查阳性率均值为83.08%,患者平均等待时间均值为0.86 d。山东省各地市均具有比较大的对MRI设备的需求,预测配置数量排名前3的城市为济南市、临沂市和青岛市。结论本研究结果将为山东省各地市合理有效地进行配置规划提供参考意见,同时也将为其他研究学者对如何进行MRI设备的配置预测规划提供方法学参考依据。