Hydrological Evaluation with SWAT Model and Numerical Weather Prediction for Flash Flood Warning System in Thailand

Flash floods are a natural disaster that occurs annually, especially in the mountainous terrain and steep slopes of northern Thailand. The current flood forecasting systems and tools are available but have low accuracy and efficiency. The numbers of rainfall and runoff stations are less, because the access to the station area is difficult. Additionally, the operation and maintenance costs are high. Hydrological modeling of a SWAT （Soil and Water Assessment Tool） was used in this study with the application of three days weather forecast from the NWP （numerical weather prediction）, which provided temperature, relative humidity, rainfall, sunshine and wind speed. The data from NWP and SWAT were used to simulate the runoff from the Nan River in the last 10 years （2000-2010）. It was found that the simulated flow rate for the main streams using data from NWP were higher than the observations. At the N64 and Nl stations, the ratios of the maximum simulated flow rate to the observations were equal to 108% and 118%, respectively. However, for the tributaries, it was found that the simulated flow rate using NWP data was lower than the observations, but, it was still within the acceptable range of not greater than 20%,6. At N65, D090201 and D090203 stations, the ratio of the maximum simulated flow rate were 90.0%, 83.0% and 86.0%, respectively. This was due to the rainfall from the NWP model being greater than the measured rainfall. The NWP rainfall was distributed all over the area while the rainfall data from the measurements were obtained from specific points. Therefore, the rain from the NWP model is very useful especially for the watershed areas without rain gauge stations. In summary, the data from the NWP can be used with the SWAT model and provides relatively sound results despite the value for the main river being slightly higher than the observed data. Consequently, the output can be used to create a flood map for flash flood warning in the area.

Analogue correction method of errors and its application to numerical weather prediction

In this paper, an analogue correction method of errors （ACE） based on a complicated atmospheric model is further developed and applied to numerical weather prediction （NWP）. The analysis shows that the ACE can effectively reduce model errors by combining the statistical analogue method with the dynamical model together in order that the information of plenty of historical data is utilized in the current complicated NWP model, Furthermore, in the ACE, the differences of the similarities between different historical analogues and the current initial state are considered as the weights for estimating model errors. The results of daily, decad and monthly prediction experiments on a complicated T63 atmospheric model show that the performance of the ACE by correcting model errors based on the estimation of the errors of 4 historical analogue predictions is not only better than that of the scheme of only introducing the correction of the errors of every single analogue prediction, but is also better than that of the T63 model.

An approach to estimating and extrapolating model error based on inverse problem methods:towards accurate numerical weather prediction

Model error is one of the key factors restricting the accuracy of numerical weather prediction （NWP）. Considering the continuous evolution of the atmosphere, the observed data （ignoring the measurement error） can be viewed as a series of solutions of an accurate model governing the actual atmosphere. Model error is represented as an unknown term in the accurate model, thus NWP can be considered as an inverse problem to uncover the unknown error term. The inverse problem models can absorb long periods of observed data to generate model error correction procedures. They thus resolve the deficiency and faultiness of the NWP schemes employing only the initial-time data. In this study we construct two inverse problem models to estimate and extrapolate the time-varying and spatial-varying model errors in both the historical and forecast periods by using recent observations and analogue phenomena of the atmosphere. Numerical experiment on Burgers＇ equation has illustrated the substantial forecast improvement using inverse problem algorithms. The proposed inverse problem methods of suppressing NWP errors will be useful in future high accuracy applications of NWP.

The Predictability Problems in Numerical Weather and Climate Prediction

The uncertainties caused by the errors of the initial states and the parameters in the numerical model are investigated. Three problems of predictability in numerical weather and climate prediction are proposed, which are related to the maximum predictable time, the maximum prediction error, and the maximum admissible errors of the initial values and the parameters in the model respectively. The three problems are then formulated into nonlinear optimization problems. Effective approaches to deal with these nonlinear optimization problems are provided. The Lorenz’ model is employed to demonstrate how to use these ideas in dealing with these three problems.

A Forecast Error Correction Method in Numerical Weather Prediction by Using Recent Multiple-time Evolution Data

The initial value error and the imperfect numerical model are usually considered as error sources of numerical weather prediction （NWP）. By using past multi-time observations and model output, this study proposes a method to estimate imperfect numerical model error. This method can be inversely estimated through expressing the model error as a Lagrange interpolation polynomial, while the coefficients of polyno- mial are determined by past model performance. However, for practical application in the full NWP model, it is necessary to determine the following criteria： （1） the length of past data sufficient for estimation of the model errors, （2） a proper method of estimating the term ＂model integration with the exact solution＂ when solving the inverse problem, and （3） the extent to which this scheme is sensitive to the observational errors. In this study, such issues are resolved using a simple linear model, and an advection diffusion model is applied to discuss the sensitivity of the method to an artificial error source. The results indicate that the forecast errors can be largely reduced using the proposed method if the proper length of past data is chosen. To address the three problems, it is determined that （1） a few data limited by the order of the corrector can be used, （2） trapezoidal approximation can be employed to estimate the ＂term＂ in this study; however, a more accurate method should be explored for an operational NWP model, and （3） the correction is sensitive to observational error.

Evaluation of Unified Model Microphysics in High-resolution NWP Simulations Using Polarimetric Radar Observations

The UK Met Office Unified Model（UM） is employed by many weather forecasting agencies around the globe. This model is designed to run across spatial and time scales and known to produce skillful predictions for large-scale weather systems. However, the model has only recently begun running operationally at horizontal grid spacings of ～1.5 km [e.g.,at the UK Met Office and the Korea Meteorological Administration（KMA）]. As its microphysics scheme was originally designed and tuned for large-scale precipitation systems, we investigate the performance of UM microphysics to determine potential inherent biases or weaknesses. Two rainfall cases from the KMA forecasting system are considered in this study： a Changma（quasi-stationary） front, and Typhoon Sanba（2012）. The UM output is compared to polarimetric radar observations in terms of simulated polarimetric radar variables. Results show that the UM generally underpredicts median reflectivity in stratiform rain, producing high reflectivity cores and precipitation gaps between them. This is partially due to the diagnostic rain intercept parameter formulation used in the one-moment microphysics scheme. Model drop size is generally both underand overpredicted compared to observations. UM frozen hydrometeors favor generic ice（crystals and snow） rather than graupel, which is reasonable for Changma and typhoon cases. The model performed best with the typhoon case in terms of simulated precipitation coverage.

Statistical Modeling of Energy Production by Photovoltaic Farms

This paper introduces several alternative statistical approaches to modeling and prediction of electric energy generated by photovoltaic farms. The statistical models use outputs of a numerical weather prediction model as their inputs. Presented statistical models allow for easy-to-compute predictions, both in temporal sense and for out-of-sample individual farms. Model performance is illustrated on a sample of real photovoltaic farms located in the Czech Republic.

Research and Operational Development of Numerical Weather Prediction in China

Numerical weather prediction(NWP) is a core technology in weather forecast and disaster mitigation. China’s NWP research and operational applications have been attached great importance by the meteorological community.Fundamental achievements have been made in the theories, methods, and NWP model development in China, which are of certain international impacts. In this paper, the scientific and technological progress of NWP in China since1949 is summarized. The current status and recent progress of the domestically developed NWP system-GRAPES(Global/Regional Assimilation and Pr Ediction System) are presented. Through independent research and development in the past 10 years, the operational GRAPES system has been established, which includes both regional and global deterministic and ensemble prediction models, with resolutions of 3-10 km for regional and 25-50 km for global forecasts. Major improvements include establishment of a new non-hydrostatic dynamic core, setup of four-dimensional variational data assimilation, and development of associated satellite application. As members of the GRAPES system, prediction models for atmospheric chemistry and air pollution, tropical cyclones, and ocean waves have also been developed and put into operational use. The GRAPES system has been an important milestone in NWP science and technology in China.

THE IMPACT OF DIFFERENT PHYSICAL PROCESSES AND THEIR PARAMETERIZATIONS ON FORECAST OF A HEAVY RAINFALL IN SOUTH CHINA IN ANNUALLY FIRST RAINING SEASON

An ensemble prediction system based on the GRAPES model, using multi-physics, is used to discuss the influence of different physical processes in numerical models on forecast of heavy rainfall in South China in the annually first raining season(AFRS). Pattern, magnitude and area of precipitation, evolution of synoptic situation, as well as apparent heat source and apparent moisture sink between different ensemble members are comparatively analyzed. The choice of parameterization scheme for land-surface processes gives rise to the largest influence on the precipitation prediction. The influences of cumulus-convection and cloud-microphysics processes are mainly focused on heavy rainfall;the use of cumulus-convection parameterization tends to produce large-area and light rainfall. Change in parameterization schemes for land-surface and cumulus-convection processes both will cause prominent change in forecast of both dynamic and thermodynamic variables, while change in cloud-microphysics processes show primary impact on dynamic variables. Comparing simplified Arakawa-Schubert and Kain-Fritsch with Betts-Miller-Janjic schemes, SLAB with NOAH schemes, as well as both WRF single moment 6-class and NCEP 3-class with simplified explicit schemes of phase-mixed cloud and precipitation shows that the former predicts stronger low-level jets and high humidity concentration, more convective rainfall and local heavy rainfall, and have better performance in precipitation forecast. Appropriate parameterization schemes can reasonably describe the physical process related to heavy rainfall in South China in the AFRS, such as low-level convergence, latent heat release, vertical transport of heat and water vapor, thereby depicting the multi-scale interactions of low-level jet and meso-scale convective systems in heavy rainfall suitably, and improving the prediction of heavy rainfall in South China in the AFRS as a result.

A Comparison of GPS- and NWP-derived PW Data over the Korean Peninsula

Precipitable Water （PW） derived from Global Positioning System （GPS） measurements and numerical weather prediction （NWP） model analysis data were compared to further evaluate the effcacy of applying GPS-derived PW to the NWP model. The spatial and temporal variations of GPS-derived PW during a rainfall event were also examined. GPS-derived PW measurements show good agreement with the behavior of water vapor at a high spatial resolution during the analysis period. Temporal anomalies of GPS-derived PW moving along with the front are successfully detected by the GPS array. Large positive anomalies of GPS-derived PW are indicated immediately before a rainfall event, and the intensity of these positive anomalies do not seem to decrease significantly as the precipitation system passes. These results indicate that the Korean GPS network may have great potential as a PW sensor over the Korean Peninsula. In contrast with GPS-derived PW, NWP-derived PW shows negative biases. These biases appear to stem mainly from the differences between modeled and actual GPS site elevations, as GPS sites were generally located at elevations lower than those employed by the NWP model. However, there still exists a discernable dry bias after a PW correction is applied to NWP-derived PW. GPS-derived PW better reflects the spatial and temporal moisture variations of precipitation systems, as compared to NWP-derived PW. These results provide entirely new information for improving the regional NWP system, since GPS-derived PW produced with data from the Korean GPS network may be incorporated into the NWP model to improve rainfall forecasts.

ConvLSTM Based Temperature Forecast Modification Model for North China

The correction of model forecast is an important step in evaluating weather forecast results.In recent years,post-processing models based on deep learning have become prominent.In this paper,a deep learning model named EDConvLSTM based on encoder-decoder structure and ConvLSTM is developed,which appears to be able to effectively correct numerical weather forecasts.Compared with traditional post-processing methods and convolutional neural networks,ED-ConvLSTM has strong collaborative extraction ability to effectively extract the temporal and spatial features of numerical weather forecasts and fit the complex nonlinear relationship between forecast field and observation field.In this paper,the post-processing method of ED-ConvLSTM for 2 m temperature prediction is tested using The International Grand Global Ensemble dataset and ERA5-Land data from the European Centre for Medium-Range Weather Forecasts(ECMWF).Root mean square error and temperature prediction accuracy are used as evaluation indexes to compare ED-ConvLSTM with the method of model output statistics,convolutional neural network postprocessing methods,and the original prediction by the ECMWF.The results show that the correction effect of EDConvLSTM is better than that of the other two postprocessing methods in terms of the two indexes,especially in the long forecast time.

Surface Soil Moisture Simulation for a Typical Torrential Event with a Modified Noah LSM Coupling to the NWP Model

Surface soil moisture has great impact on both meso-and microscale atmospheric processes,especially on severe local convection processes and on the dynamics of short-lived torrential rains.To promote the performance of the land surface model (LSM) in surface soil moisture simulations,a hybrid hydrologic runoff parameterization scheme based upon the essential modeling theories of the Xin'anjiang model and Topography based hydrological Model (TOPMODEL) was developed in preference to the simple water balance model (SWB) in the Noah LSM.Using a strategy for coupling and integrating this modified Noah LSM to the Global/Regional Assimilation and Prediction System (GRAPES) analogous to that used with the standard Noah LSM,a simulation of atmosphere-land surface interactions for a torrential event during 2007 in Shandong was attempted.The results suggested that the surface,10-cm depth soil moisture simulated by GRAPES using the modified hydrologic approach agrees well with the observations.Improvements from the simulated results were found,especially over eastern Shandong.The simulated results,compared with the products of the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) soil moisture datasets,indicated a consistent spatial pattern over all of China.The temporal variation of surface soil moisture was validated with the data at an observation station,also demonstrated that GRAPES with modified Noah LSM exhibits a more reasonable response to precipitation events,even though biases and systematic trends may still exist.

A Local Ensemble Transform Kalman Filter Data Assimilation System for the Global FSU Atmospheric Model

Data assimilation is the process by which measurements and model predictions are combined to obtain an accurate representation of the state of the modeled system. We implemented a data assimilation scheme called LETKF （local ensemble transform Kalman filter） with FSUGSM （Florida State University Global Spectral Model） and made an experiment to evaluate the initial condition generated to numerical weather prediction to FSUGSM model. The LETKF analysis carries out independently at each grid point with the use of ＂local＂ observations. An ensemble of estimates in state space represents uncertainty. The FSUGSM is a multilevel （27 vertical levels） spectral primitive equation model, where the variables are expanded horizontally in a truncated series of spherical harmonic functions （at resolution T63） and a transform technique is applied to calculate the physical processes in real space The assimilation cycle runs on the period 01/01/2001 to 31/01/2001 at （00, 06, 12 and 18 GMT） for each day. We examined the atmospheric fields during the period and the OMF （observation-minus-forecast） and the OMA （observation-minus-analysis） statistics to verify the analysis quality comparing with forecasts and observations. The analyses present stability and show suitable to initiate the weather predictions.

安徽省2021年梅雨期降水预报检验分析

对安徽省2021年梅雨期(6月10日—7月10日)6个客观模式和1个主观订正预报产品进行了检验分析,其中包含了3个区域模式数值预报(中国气象局中尺度天气数值预报系统“CMA-MESO”、中国气象局上海数值预报模式系统“CMA-SH9”、安徽WRF,3个全球模式数值预报(中国气象局全球同化预报系统“CMA-GFS”、欧洲中期天气预报中心确定性预报模式“ECMWF”、美国国家环境预报中心全球预报系统“NCEP-GFS”)和安徽智能网格主观订正预报的降水产品,结果表明:传统检验中安徽智能网格和区域模式对晴雨准确率的预报效果优于全球模式,又以CMA-MESO最优;在暴雨及以上量级的强降水预报中,传统检验表明安徽智能网格预报的得分最高(23.83),ECMWF模式则是客观模式预报中效果最好的(20.12),CMA-SH9次之(19.34);通过对除安徽智能网格以外的各个客观数值模式进行的MODE空间检验可知,不同数值模式间暴雨预报误差原因不尽相同,ECMWF与各区域数值模式主要是由雨区位置的预报偏差,尤其是纬度偏差导致的,NCEP-GFS全球模式对降水强度和雨区面积的预报偏弱偏小比较明显,CMA-GFS在强降水方面的预报可参考性较差;各个主客观预报暴雨及以上量级预报,整体表现出较明显的日变化特征,在午夜前后、上午时段TS评分较高,而午后到傍晚评分较低,这个现象或许是梅雨期的午后降水多以地表太阳加热引起的短历时热对流降水为主造成的。

融合精细化气象因素与物理约束的深度学习模型在短期风电功率预测中的应用

现有基于深度学习方法的风电功率预测是一种以气象数据为输入的间接预测,其预测精度依赖于气象预报的准确率,然而现有气象预报资料普遍存在分辨率低,预报模式不稳定的问题。同时,深度学习模型完全依赖数据驱动,缺乏物理规律的指导,预测精度难以进一步提升。因此,提出一种精细化气象因素与物理深度学习相结合的方法。首先,通过降尺度与多模式集成技术,对数值天气预报数据进行处理,改善气象预报产品的低分辨率和准确率问题;其次,基于风电场尾流效应和功率曲线两种物理模型,一方面将物理模型嵌入神经网络损失函数作为正则化项,引入物理约束指导学习过程,以构建物理深度学习网络;另一方面,利用物理模型产生预训练样本,解决观测数据不足的情况,构建预训练模型,为后续有监督学习任务提供支持。最后,通过对某市近海风电场的实际数据进行仿真分析,验证了所提出方法的有效性和优越性。

利用机器学习模拟湿物理参数化方案

数值天气预报模式的湿物理参数化方案对降水预报有很大影响。常规湿物理参数化方案计算复杂、计算量大,且存在较大不确定性。文中采用4种机器学习算法即基于决策树的梯度提升算法(LightGBM)、全连接神经网络(FC)、卷积神经网络(CNN)和卷积块注意力模块(CBAM)提取数值预报模式变量网格点周围的局部信息建模。针对一次中国南海台风过程开展湿物理参数化方案模拟试验,试验表明,4种机器学习模型均能较好地模拟湿物理参数化方案的温、湿效应,能够刻画台风对流活动产生的热源和水汽汇的螺旋结构。位温倾向在对流层中层误差较大,比湿倾向在对流层低层误差较大,随着预报时效延长模型的模拟能力有所降低。

区域高分辨率数值预报检验评估系统

近年来我国区域高分辨率数值预报业务发展迅速,目前传统检验方法已不能满足高分辨率模式降水检验评估需求。区域高分辨率数值预报检验评估系统在吸收降水传统检验方法优势的同时,融入基于降水发展演变过程的检验评估方法,旨在建立一套适用于高时空分辨率观测资料的精细化降水检验评估系统,为促进区域模式改进和高分辨率数值预报产品的偏差理解提供了重要参考,也为理解区域数值预报的模拟能力及其偏差提供了新的视角。

基于网格化数值天气预报的区域光伏发电多输出功率预测方法

区域光伏的短期功率预测是省级及以上电网调控中心制定发电计划、提高光伏消纳率的重要基础之一。光伏短期功率预测本质上是构建数值天气预报与实际功率之间的映射模型,为了实现预测精度的提升,利用网格化的数值天气预报,采用残差网络建立区域光伏的多输出预测模型,充分挖掘区域光伏所属空间的气象资源分布与各光伏电站功率的关联关系,实现以网格化数值天气预报为输入的区域各光伏电站的功率预测。以实际运行数据进行仿真,结果表明,本文方法在各光伏电站的功率和总功率2个方面的预测结果均优于现有成熟方法。

基于累加气候概率的FSS检验方法对多模式短时暴雨预报的评估

为深入认识数值天气模式强降水精细化预报性能,本文以短时强降水多发的浙江省2019年5到10月降水为例,采用分数技巧评分(Fractions Skill Score,简称FSS)邻域检验方法,评估了6个业务模式短时降水预报准确性,重点探讨了各模式短时暴雨预报能力及天气背景的影响。结果表明:(1)基于站点降水的累加气候概率,确定了短时小雨、中雨、大雨、暴雨和大暴雨的预报技巧评分阈值各为0.583、0.522、0.506、0.502和0.500,改进并实现了FSS方法对长时间序列各等级降水预报技巧尺度的综合评估。(2)只有上海中尺度区域数值预报业务系统和浙江中尺度区域数值预报业务系统的暴雨预报平均评分达到预报技巧,相应技巧尺度为159、159和183 km;这3个产品共有约6成预报达到技巧评分,其技巧尺度累积频率从3 km至183 km可增幅近50%,这种尺度选择性评价可为不同尺度下产品应用提供参考。(3)不同天气背景下各模式预报性能差异明显。台风类、梅雨类和弱天气尺度强迫类短时暴雨预报的最优模式分别是欧洲中期天气预报中心全球预报模式、上海中尺度区域数值预报业务系统和浙江中尺度区域数值预报业务系统,各技巧尺度为27、99和135 km,模式产品使用中需分类区别对待。

基于机器学习的NWP ZTD长短期预测模型

对流层延迟是影响全球卫星导航系统(GNSS)定位精度的主要误差源之一,利用数值天气预报(NWP)模型估计天顶对流层延迟(ZTD)是常用的方法之一,但NWP模型预报资料估计的ZTD精度有限;NWP模型再分析资料估计的ZTD不能用于GNSS实时定位,且目前大多数文献未能对ZTD长短期预测分别进行研究。因此,利用欧洲中期天气预报中心(ECMWF)的第五代全球气候再分析资料数据集(ERA5)和国际GNSS服务组织(IGS)的高精度ZTD数据,研究基于反向传播(BP)神经网络、支持向量机和长短期记忆网络3种机器学习算法构建以年为时间窗口的ZTD长期预测模型和以24h为时间窗口的ZTD短期预测模型的可行性。实验结果表明:构建的ZTD长期预测模型和短期预测模型可以有效提高预测ZTD的精度。