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Tropical cyclone track and intensity prediction skill of GFS model over NIO during 2019&2020

Tropical Cyclone Research and Review 2022年第1期11卷 36-49页

作者： Ch.Sridevi D.R.Pattanaik A.K.Das Akhil Srivastava V.R.Durai C.J.Johny Medha Deshpande P.Suneetha Radhika Kanase India Meteorological Department Numerical Weather Prediction DivisionLodhi RoadNew Delhi110003India Indian Institute of Tropical Meteorology MoESPune411008India Dept of Meteorology and Oceanography Andhra UniversityVisakhapatnamAndhra Pradesh530003India

The Tropical Cyclone(TC)track prediction using different nwp models and its verification is the critical task to provide prior knowledge about the model errors,which is beneficial for giving the model guidance-based real-time cyclone warning advisories.This study has attempted to verify the Global Forecast System(GFS)model forecasted tropical cyclone track and intensity over the North Indian Ocean(NIO)for the years 2019 and 2020.GFS is one of the operational models in the India Meteorological Department(IMD),which provides the medium-range weather forecast up to 10 days.The forecasted tracks from the GFS forecast are obtained using a vortex tracker developed by Geophysical Fluid Dynamics Laboratory(GFDL).A total of 13 tropical cyclones formed over the North Indian Ocean,eight during 2019 and five in 2020 have been considered in this study.The accuracy of the model predicted tracks and intensity is verified for five days forecasts(120 h)at 6-h intervals;the track errors are verified in terms of Direct Position Error(DPE),Along Track Error(ATE)and Cross-Track Error(CTE).The annual mean DPE over NIO during 2019(51–331 km)is lower than 2020(82–359 km),and the DPE is less than 150 km up to 66 h during 2019 and 48 h during 2020.The positive ATE(76–332 km)indicates the predicted track movement is faster than the observed track during both years.The positive CTE values for most forecast lead times suggest that the predicted track is towards the right side of the observed track during both years.The cyclone Intensity forecast for the maximum sustained wind speed(Max WS)and central mean sea level pressure(MSLP)are verified in terms of mean error(ME)and root mean square error(RMSE).The errors are lead time independent.However,most of the time model under-predicted the cyclone intensity during both years.Finally,there is a significant variance in track and intensity errors from the cyclone to cyclone and Bay of Bengal basin to the Arabian Sea basin.

关键词： Tropical cyclone Track prediction GFS nwp Global model GFDL

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A case study of GOES-15 imager bias characterization with a numerical weather prediction model

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Frontiers of Earth Science 2016年第3期10卷 409-418页

作者： Lu REN Department of Climate and Space Sciences and Engineering University of Michigan MI 48109 USA

The infrared imager onboard the Geostationary Operational Environmental Satellite 15 （GOES-15） pro- vides temporally continuous observations over a limited spatial domain. To quantify bias of the GOES-15 imager, observations from four infrared channels （2, 3, 4, and 6） are compared with simulations from the numerical weather prediction model and radiative transfer model. One-day clear-sky infrared observations from the GOES-15 imager over an oceanic domain during nighttime are selected. Two datasets, Global Forecast System （GFS） analysis and ERA- Interim reanalysis, are used as inputs to the radiative transfer model. The results show that magnitudes of biases for the GOES-15 surface channels are approximately 1 K using two datasets, whereas the magnitude of bias for the GOES-15 water vapor channel can reach 5.5 K using the GFS dataset and 2.5 K using the ERA dataset. The GOES- 15 surface channels show positive dependencies on scene temperature, whereas the water vapor channel has a weak dependence on scene temperature. The strong dependence of bias on sensor zenith angle for the GOES-15 water vapor channel using GFS analysis implies large biases might exist in GFS water vapor profiles.

关键词： data assimilation nwp GOES imager bias

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Evaluation of TIGGE Ensemble Forecasts of Precipitation in Distinct Climate Regions in Iran

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Advances in Atmospheric Sciences 2018年第4期35卷 457-468页

作者： Saleh AMINYAVARI Bahram SAGHAFIAN Majid DELAVAR Department of Technical and Engineering Science and Research Branch Islamic Azad University Department of Water Resources Engineering Tarbiat Modares University

The application of numerical weather prediction （nwp） products is increasing dramatically. Existing reports indicate that ensemble predictions have better skill than deterministic forecasts. In this study, numerical ensemble precipitation forecasts in the TIGGE database were evaluated using deterministic, dichotomous （yes/no）, and probabilistic techniques over Iran for the period 2008-16. Thirteen rain gauges spread over eight homogeneous precipitation regimes were selected for evaluation. The Inverse Distance Weighting and Kriging methods were adopted for interpolation of the prediction values, downscaled to the stations at lead times of one to three days. To enhance the forecast quality, nwp values were post-processed via Bayesian Model Averaging. The results showed that ECMWF had better scores than other products. However, products of all centers underestimated precipitation in high precipitation regions while overestimating precipitation in other regions. This points to a systematic bias in forecasts and demands application of bias correction techniques. Based on dichotomous evaluation, NCEP did better at most stations, although all centers overpredicted the number of precipitation events. Compared to those of ECMWF and NCER UKMO yielded higher scores in mountainous regions, but performed poorly at other selected stations. Furthermore, the evaluations showed that all centers had better skill in wet than in dry seasons. The quality of post-processed predictions was better than those of the raw predictions. In conclusion, the accuracy of the nwp predictions made by the selected centers could be classified as medium over Iran, while post-processing of predictions is recommended to improve the quality.

关键词： ensemble forecast nwp TIGGE evaluation post-processing

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基于波动特征提取下云层分型的短期光伏发电功率预测方法

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太阳能学报 2023年

作者：张蕊李安燚刘世岩薛世伟贾清泉巩秦海燕山大学电气工程学院河北能源技术服务有限公司国网河北省电力有限公司电力科学研究院

短期光伏发电功率的预测精度与天气类型紧密相关，云层的无规则运动导致光伏发电功率波动。因此该文通过监测不同天气类型下，云层的运动形态提高预测精度。首先，基于nwp因子将天气划分为5种类型，并通过变分模态分解将光伏发电功率分... 详细信息

短期光伏发电功率的预测精度与天气类型紧密相关，云层的无规则运动导致光伏发电功率波动。因此该文通过监测不同天气类型下，云层的运动形态提高预测精度。首先，基于nwp因子将天气划分为5种类型，并通过变分模态分解将光伏发电功率分为平滑过程与波动过程数据。其次，采用云层灰度值判断云层厚度，由加速鲁棒特征（SURF）监测得到云层特征点，跟踪特征点的移动得到云层的运动速度和方向。然后，提出波动特征参数，结合云层运动状态分析波动形态，从而将云层运动状态与波动形态相关联实现“云层分型”。最后，针对平滑数据和波动过程的数据特征，结合机器学习算法自身的适应性条件，提出基于CNN-LSTM的组合预测算法。实现了基于nwp相关因子，以光伏功率历史平滑数据，历史波动数据为输入、以光伏功率预测值为输出的预测方法，显著提高了光伏发电功率的预测精度。

关键词： nwp 组合预测变分模态分解波动特征SUBF云层监测 CNN-LSTM

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On the development of the GRAPES――A new generation of the national operational nwp system in China

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Chinese Science Bulletin 2008年第22期53卷 3429-3432页

作者： ZHANG RenHe SHEN XueShun State Key Laboratory of Severe Weather Chinese Academy of Meteorological Sciences Beijing 100081 China

Numerical weather prediction (nwp) has become one of the most important means for weather fore-casts in the world. It also mirrors a nation's comprehensive strength in meteorology. In 2000, China Meteorological Administration (CMA) established the National Innovative Base for Meteorological Numerical Prediction in the Chinese Academy of Meteorological Sciences (CAMS), to work on developing a new generation of the national operational nwp system――Global/Regional Assimilation and PrEdiction System (GRAPES), to enhance meteorological services in China in the new century. In recent years, the GRAPES has witnessed a fast development. The GRAPES has been set up as an integration of the model framework, data assimilation, regional and global nwp system, which can be commonly used for both operation and research. In this paper, a brief review is made for illustrating the GRAPES system, including the advanced designs of the GRAPES, its diverse applications in multifields, and efficiencies of the regional and global GRAPES in operational applications based on hindcast results.

关键词：数值天气预报 nwp 气象学天气预测

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An Innovative Bias-Correction Approach to CMA-GD Hourly Quantitative Precipitation Forecasts

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Journal of Tropical Meteorology 2021年第4期27卷 428-436页

作者：刘金卿戴光丰欧小锋 Hunan Meteorological Observatory Changsha 410118 China Guangzhou Institute of Tropical and Marine Meteorology/Guangdong Provincial Key Laboratory of Regional Numerical Weather Prediction CMAGuangzhou 510640 China Heavy Rain and Drought Flood Disasters in Plateau and Basin Key Laboratory of Sichuan Chengdu 610072 China Hunan Key Laboratory of Meteorological Disaster Prevention and Reduction Changsha 410118 China

This paper proposes a simple and powerful optimal integration(OPI)method for improving hourly quantitative precipitation forecasts(QPFs,0-24 h)of a single-model by integrating the benefits of different biascorrected methods using the high-resolution CMA-GD model from the Guangzhou Institute of Tropical and Marine Meteorology of China Meteorological Administration(CMA).Three techniques are used to generate multi-method calibrated members for OPI:deep neural network(DNN),frequency-matching(FM),and optimal threat score(OTS).The results are as follows:(1)The QPF using DNN follows the basic physical patterns of CMA-GD.Despite providing superior improvements for clear-rainy and weak precipitation,DNN cannot improve the predictions for severe precipitation,while OTS can significantly strengthen these predictions.As a result,DNN and OTS are the optimal members to be incorporated into OPI.(2)Our new approach achieves state-of-the-art performances on a single model for all magnitudes of precipitation.Compared with the CMA-GD,OPI improves the TS by 2.5%,5.4%,7.8%,8.3%,and 6.1%for QPFs from clear-rainy to rainstorms in the verification dataset.Moreover,OPI shows good stability in the test dataset.(3)It is also noted that the rainstorm pattern of OPI relies heavily on the original model and that OPI cannot correct for deviations in the location of severe precipitation.Therefore,improvements in predicting severe precipitation using this method should be further realized by improving the numerical model's forecasting capability.

关键词： DNN deep-learning bias-correction post-processing OTS optimal integration nwp

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Multi-Layer Forecast Project of Rain-Induced Debris Flow

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Wuhan University Journal of Natural Sciences 2005年第4期10卷 774-778页

作者： ZHANGJing-hong WEIFang-qiang LIUShu-zhen CUIPeng ZHONGDun-lun LIFa-bin GAOKe-chang InstituteofMountainChineseAcademyofSciences China HazardsandEnvironment Chengdu610041Sichuan／／HainanMeteorologicalandEcologicalAcademyHaikou570203HainanChina InstituteofMountainHazardsandEnvironment ChineseAcademyofSciencesChengdu610041SichuanChina

Based on four kinds of methods—numerical weather prediction model, cloud image of stationary meteorological satellite, echo image of meteorological radar and telemetric rain gauge, multi space-time scale precipitation prediction products have been achieved, and multi-layer project of debris flow forecast is established with different space-time scale to get different forecast precision. The forecast system has the advantages in combination of regions and ravines, rational compounding of time and space scale. The project, which has debris flow forecast models of Sichuan province, Liangshan district and single ravine, can forecast debris flow in 3 layers and meets the demand of hazard mitigation in corresponding layer.

关键词： debris flow forecast precipitation forecast satellite radar telemetric rain gauge nwp

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基于地基云图分类与Stacking模型的光伏发电功率预测

基于地基云图分类与Stacking模型的光伏发电功率预测

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作者：巩秦海燕山大学

学位级别：硕士

光伏发电功率受云层的无规则运动影响而产生波动,且其波动特征与天气类型紧密相关,从而影响短期光伏发电功率预测的预测精度,因此本文通过监测不同天气类型下云层的运动形态,提出了一种基于地基云图分类与Stacking模型的光伏发电功率预... 详细信息

光伏发电功率受云层的无规则运动影响而产生波动,且其波动特征与天气类型紧密相关,从而影响短期光伏发电功率预测的预测精度,因此本文通过监测不同天气类型下云层的运动形态,提出了一种基于地基云图分类与Stacking模型的光伏发电功率预测研究方法。首先,基于数值天气预报(Numerical Weather Prediction,nwp)构建天气分型因子与天气分型向量,将天气类型划分为晴天天气、多云天气、阴天天气、阵雨天气与全雨天气,并通过变分模态分解将各种天气类型下的光伏发电功率分为类晴空平滑过程数据与波动过程数据。其次,对地基云图进行畸变矫正,提取云图中各像素点的灰度值,计算各云图灰度阈值对云图类型进行判定。由加速鲁棒特征监测地基云图中云团的特征移动,计算云团的运动速度和方向。进而区分不同云图类型下云团运动速度的快慢,实现地基云图分类。然后,通过光伏发电功率的波动过程数据提出移动平均线、标准差、峰值比3种波动特征参数,将波动数据划分到不同的地基云图下。根据江苏某光伏电站2017年1月1日～2017年12月31日的光伏发电功率及历史云图数据,构建卷积神经网络(Convolutional Neural Network,CNN)结合长短期记忆网络(Long Short-Term Memory,LSTM)模型对本文提出的各种场景进行仿真,分析天气分型和地基云图分类在光伏发电预测方面的优势。最后,为了提高光伏发电功率的预测精度,结合机器学习算法的适应性条件,提出了一种多模型融合Stacking的预测模型。将CNN-LSTM模型与XGBoost(e Xtreme Gradient Boosting)模型作为Stacking模型的基模型,选取极限学习机(Extreme Learning Machine,ELM)作为Stacking模型的元模型,采用3交叉训练的方法对各种场景下的光伏发电功率进行训练预测。通过对比CNN-LSTM模型、XGBoost模型、Stacking模型的预测误差,结果表明Stacking模型显著地提高了光伏发电功率的预测精度。

关键词： nwp 变分模态分解地基云图灰度值 Stacking模型

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A Four-Dimensional Variational System for Skillful Operational Prediction of Convective Storms

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Advances in Atmospheric Sciences 2016年第10期33卷 1102-1103页

作者： Nathan SNOOK Qinghong ZHANG Center for the Analysis and Prediction of Storms University of Oklahoma Department of Atmospheric & Oceanic Sciences School of PhysicsPeking University

Forecasting convective storms using nwp models is an important goal and a highly active area of ongoing research. Skillful and reliable nwp of convective storms could allow for severe weather warnings with longer lead times, as opera- tional forecasters begin to incorporate convective-scale fore- casts into severe weather forecast operations （Stensrud et al., 2009, 2013）. This would then provide vulnerable individuals and industries with more time to seek shelter and/or mitigate the impact of severe weather hazards.

关键词： VDRAS A Four-Dimensional Variational System for Skillful Operational Prediction of Convective Storms nwp

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ANALYSIS OF THE CYCLONIC VORTEX AND EVALUATION OF THE PERFORMANCE OF THE RADAR INTEGRATED NOWCASTING SYSTEM(RAINS) DURING THE HEAVY RAINFALL EPISODE WHICH CAUSED FLOODING IN PENANG, MALAYSIA ON 5 NOVEMBER 2017

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Tropical Cyclone Research and Review 2018年第4期7卷 217-229页

作者： DIONG JEONG YIK YIP WENG SANG NURSALLEH K.CHANG FADILA JASMIN FAKARDDIN AMBUN DINDANG MUHAMMAD HELMI ABDULLAH Malaysian Meteorological Department

Even though Malaysia is relatively safe from the direct path of tropical cyclones nevertheless the passage of such systems over the neighbouring seas and their tail effect present a unique challenge for forecasters. In rare situations, tropical cyclones had made landfall on Malaysian shores such as Typhoon Vamei in 2001 and Tropical Storm Greg in 1996. Hence it is vital to forecast the severity of the heavy rainfall events associated with low pressure systems to assist the disaster management agencies in decision making. Towards this endeavour, the Malaysian Meteorological Department(MMD) utilises a nowcasting system called Radar Integrated Nowcasting System(RaINS) which uses a combination of radar data and Numerical Weather Prediction(nwp) data. RaINS was adapted from SWIRLS(Short-range Warning of Intense Rainstorms in Localised Systems) developed by the Hong Kong Observatory(HKO) and operationalised in MMD in August 2017. This paper studies the cyclonic vortex and synoptic features during the heavy rainfall event that caused major flooding in Penang, Malaysia on 5 November 2017. This paper also investigates the performance of RaINS in predicting the rain cloud distribution and intensity during that event. It is found that RaINS is capable of forecasting the rain cloud distribution and intensity reasonably well in the very short-term period of 1-3 hours. The forecasts are verified by comparing the RaINS forecast data with observed radar echo.

关键词： cyclonic vortex RaINS nwp SWIRLS Penang

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