FY-4闪电资料在厦门强降水监测预警中的应用

FY-4上的闪电成像仪(Lightning Mapping Imager，LMI)从静止轨道平台对视场覆盖范围内的闪电进行连续不间断的观测，闪电资料为监测预警强对流天气提供了重要信息。为研究FY-4闪电资料监测预警强对流的能力，以2018年5月7日厦门暴雨为研究个例，利用FY-4闪电资料、FY-4亮温资料、厦门市自动气象站降水资料以及地面闪电定位网监测数据，研究闪电数据在强降水监测预警中的应用。结果表明：FY-4闪电资料与地基闪电进行数据融合，有效减少了天基与地基闪电产品各自数据的不完整性、不确定性和误差；闪电的移动轨迹与对流云团的移动轨迹相符，且在云团移动轨迹的前方；温度梯度较大的区域和深对流内，闪电强度较强；闪电强度与雨强成正比，且闪电频数峰值多出现在降水峰值前45 min左右。     

FY-4闪电资料在厦门强降水监测预警中的应用

FY-4上的闪电成像仪(Lightning Mapping Imager，LMI)从静止轨道平台对视场覆盖范围内的闪电进行连续不间断的观测，闪电资料为监测预警强对流天气提供了重要信息。为研究FY-4闪电资料监测预警强对流的能力，以2018年5月7日厦门暴雨为研究个例，利用FY-4闪电资料、FY-4亮温资料、厦门市自动气象站降水资料以及地面闪电定位网监测数据，研究闪电数据在强降水监测预警中的应用。结果表明：FY-4闪电资料与地基闪电进行数据融合，有效减少了天基与地基闪电产品各自数据的不完整性、不确定性和误差；闪电的移动轨迹与对流云团的移动轨迹相符，且在云团移动轨迹的前方；温度梯度较大的区域和深对流内，闪电强度较强；闪电强度与雨强成正比，且闪电频数峰值多出现在降水峰值前45 min左右。     

Importance of satellite-retrieved rain/no-rain information on short-range weather predictions

The impact of assimilating rain (satellite-retrieved rainfall is greater than zero) and no-rain (satellite-retrieved rainfall is equal to zero) information retrieved from the Tropical Rainfall Measuring Mission (TRMM) 3B42 precipitation is assessed during Indian summer monsoon 2013 using the weather research and forecasting (WRF) model. Daily three parallel experiments are performed with and without satellite rainfall assimilation for short-range weather forecasts. Additional two experiments are performed daily to evaluate the sensitivity of cumulus parameterization on the WRF model predictions when precipitations are used for assimilation. Precipitation assimilation improves the 48 h low-level temperature, moisture, and winds predictions. Rainfall prediction is also improved over central India when satellite-retrieved rainfall information are assimilated compared to without rainfall assimilation (CNT) experiments. More improvements are seen in moisture forecasts when the Kain–Fritsch (KF) cumulus convection parameterization scheme is used against the Grell–Devenyi ensemble (GD) scheme, whereas for temperature and wind speed forecasts the Grell convection parameterization scheme performed better over the Indian region. Overall, precipitation assimilation improved the WRF model analysis and subsequent model forecasts compared with without precipitation assimilation experiments. Results show that no-rain observations also have a significant positive impact on short-range weather forecasts.     

Lightning,rainfall, AOD,and convection variabilities in the monsoon zone of India

Lightning, rainfall, aerosol optical depth (AOD), and convection variabilities in the monsoon zone of India are studied during the period 2001–2012. Accumulated rain is used to study rainfall and the parameters surface temperature, convective available potential energy (CAPE), and outgoing longwave radiation (OLR) are used to study convection. Principal component analysis (PCA) is performed for the first time in order to understand the variability and interrelations among the parameters lightning (flash rates derived from the Tropical Rainfall Measuring Mission Satellite [TRMM] Lightning Imaging Sensor data products), rainfall, AOD, and convection (surface temperature, CAPE, and OLR), in the monsoon zone of India. The results of PCA show that lightning is very well correlated with CAPE and surface temperature. Lightning is poorly correlated with AOD and accumulated rain. This indicates that buoyancy due to heating of land during daytime is the best predictor for lightning occurrence in the monsoon zone. Very good correlation between AOD and accumulated rain suggests the significance of AOD in the monsoon zone precipitation.     

Development of a coupled land-atmosphere satellite data assimilation system for improved local atmospheric simulations

This study developed a coupled land-atmosphere satellite data assimilation system as a new physical downscaling approach, by coupling a mesoscale atmospheric model with a land data assimilation system (LDAS). The LDAS consists of a land surface scheme as the model operator, a radiative transfer model as the observation operator, and the simulated annealing method for minimizing the difference between the observed and simulated microwave brightness temperature. The atmospheric model produces forcing data for the LDAS, and the LDAS produces better initial surface conditions for the modelling system. This coupled system can take into account land surface heterogeneities through assimilating satellite data for a better precipitation prediction. To assess the effectiveness of the new system, 3-dimensional numerical experiments were carried out in a mesoscale area of the Tibetan Plateau during the wet monsoon season. The results show significant improvement compared with a no assimilation regional atmospheric model simply nested from the global model. The surface soil moisture content and its distribution from the assimilation system were more consistent to in situ observations. These better surface conditions affect the land-atmosphere interactions through convection systems and lead to better atmospheric predictability as confirmed by satellite-based cloud observations and in situ sounding observations. Through the use of satellite brightness temperature, the developed coupled land-atmosphere assimilation system has shown potential ability to provide better initial surface conditions and its inputs to the atmosphere and to improve physical downscaling through regional models.     

Satellite data assimilation and estimation of a 3D coastal sediment transport model using error-subspace emulators

This paper describes sequential assimilation of data into a three-dimensional coastal ocean model using fast and cheap statistical surrogates of the model (emulators). The model simulates resuspension and deposition of fine sediments in a macro-tidal environment of the Fitzroy Estuary and Keppel Bay, North-East Australian coast. The assimilation algorithm was applied first to synthetic observations produced by a twin model run, and then with real data obtained from satellite observation. The latter are derived from remote sensing algorithms customised to the study region. The main objective of simulations was to test the data assimilation scheme using synthetic observations and identify potential issues and challenges when assimilating real data sets. The assimilation algorithm proved capable of substantially reducing a prior uncertainty of the model for both the scenario with the synthetic observations and the scenario with the satellite data. Significant remaining error in western Keppel Bay after assimilating satellite data is diagnostic of an underlying error in the system conceptualisation – in other words, it indicates that the primary source of error is not in the parameter values specified, but in the model structure, in the interpretation of satellite data or in the other input data. The results of our study show the utility of the developed technique for the data assimilation into the three-dimensional sediment transport model of the Fitzroy estuary and Keppel Bay. More research is required to understand the capacity of this technique to generalise to other models and regions.     

Research of Satellite Precipitation Data Assimilation on WRF Model

Weather research and forecasting model and four\|dimensional variational(4Dvar)data assimilation system were used to assimilate Tropical Rainfall Measuring 3B42 precipitation dataset(TRMM 3B42),Global Precipitation Measurement dataset(GPM)and FY\|2G precipitation dataset during 1 July to 4 July 2015.The results showed that：(1)assimilation of the satellite precipitation datasets does improve the forecasting of precipitation,because all assimilation precipitation RMSE are in(0,1),and assimilating GPM dataset is superior than others;(2)the results of 2 m relative humidity from all experiments underestimated real observations,and 2 m relative humidity RMSE(units %)were in(10,50).Moreover,assimilating GPM provides an advantage in estimating various air moisture conditions;(3)Although the impact of assimilating precipitation datasets were complex for simulating 10 m wind speed,results of 10 m wind speed experiments were overestimated\|the real observation and the RMSE were in 1.5~3 m/s.In conclusion,GPM precipitation datasets assimilation was good for simulating precipitation,relative humidity and 10 m wind speed.     

Impact of 3DVAR assimilation of Doppler Weather Radar wind data and IMD observation for the prediction of a tropical cyclone

This study aims to investigate the impact of the Three-Dimensional Variational (3DVAR) assimilation of Doppler Weather Radar (DWR) wind data together with the India Meteorological Department (IMD) upper air and surface data for the prediction of a tropical cyclone, which formed over the Bay of Bengal. The National Centers for Environmental Prediction Final Analyses (NCEP FNL) data are used to produce initial conditions. Three numerical experiments were designed to study the effect of 3DVAR assimilation. For the first experiment, the model integrations were performed without any assimilation of observations. IMD upper air and surface observations were assimilated using 3DVAR for the second experiment and the third experiment assimilated DWR wind data along with IMD observations. The model results are compared with one another and also with the observations. The results of the study indicate that the assimilation of DWR wind data and IMD data have resulted in improvements in the simulation of strong vertical velocity, higher warm core temperature and strong gradients in the horizontal wind speed as well as improved spatial distribution of the precipitation.     

A simple method for integrating a complex model into an ensemble data assimilation system using MPI

This paper details a strategy for modifying the source code of a complex model so that the model may be used in a data assimilation context, and gives the standards for implementing a data assimilation code to use such a model. The strategy relies on keeping the model separate from any data assimilation code, and coupling the two through the use of Message Passing Interface (MPI) functionality. This strategy limits the changes necessary to the model and as such is rapid to program, at the expense of ultimate performance. The implementation technique is applied in different models with state dimension up to .2.7 × 10⁸ The overheads added by using this implementation strategy in a coupled ocean-atmosphere climate model are shown to be an order of magnitude smaller than the addition of correlated stochastic random errors necessary for some nonlinear data assimilation techniques.     

Data assimilation of ozone in the atmospheric transport chemistry model LOTOS

Modelled ozone concentrations often differ from measured concentrations quite substantially, partly due to measurement errors, but mainly due to uncertainties in the model. Modelling studies would therefore benefit highly from more reliable model simulations. One way to achieve this is the application of data assimilation, a technique that uses measurement information within the model simulation in a way that is consistent with the model itself. This aim of this paper is to show that this is indeed one way to go with atmospheric transport chemistry models (ATCMs) by presenting results of data assimilation simulations of ozone with the model LOTOS. The assimilation technique used in this study is the Ensemble Kalman Filter. A simulation for a period of 4 weeks has been performed in which ground-level ozone measurements have been assimilated. The necessary noise input consisted of uncertainties in the emissions of NO_x, SO_x, VOC and CO in 17 groups of countries. The main conclusion is that it is possible to improve ATCM simulations of ozone by data assimilation, but that noise inputs other than emissions only are essential for the reduction of the differences between measured and modelled concentrations to acceptable margins.     

Assimilation of Nearly Turbulent Rayleigh–Bénard Flow Through Vorticity or Local Circulation Measurements: A Computational Study

We introduce a continuous (downscaling) data assimilation algorithm for the 2D Bénard convection problem using vorticity or local circulation measurements only. In this algorithm, a nudging term is added to the vorticity equation to constrain the model. Our numerical results indicate that the approximate solution of the algorithm is converging to the unknown reference solution (vorticity and temperature) corresponding to the measurements of the 2D Bénard convection problem when only spatial coarse-grain measurements of vorticity are assimilated. Moreover, this convergence is realized using data which is much more coarse than the resolution needed to satisfy rigorous analytical estimates.     

OPTICS算法在雷电临近预报中的应用

针对密度分布不均的雷电定位资料,提出了一种基于OPTICS聚类算法的雷电临近预警模型。该模型运用OPTICS算法对雷暴天气连续时段的雷电定位资料进行聚类分析,有效剔除了影响雷暴云分布的稀疏点。在聚类分析结果基础上,利用“膨胀〖CD*2〗侵蚀”算法还原雷暴云真实分布,根据雷暴云的移动趋势进行雷电落区预报。此外,针对传统预测算法运行时间长的缺陷,运用邻接表改进了OPTICS算法,且优化了可达队列更新策略。实验结果表明,基于改进的OPTICS算法所构建的雷电临近预报模型降低了算法运行时间,同时提高了雷电预报模型适应能力及预测的准确率。     

Inner convective system cloud-top wind estimation using multichannel infrared satellite images

Knowledge of deep convective system cloud processes and dynamic structures is a key feature in climate change and nowcasting. However, the horizontal inner structures at the cloud tops of deep convective systems are not well understood due to lack of measurements and the complex processes linked to dynamics and thermodynamics. This study describes a new technique to extract inner cloud-top dynamics using brightness temperature differences. This new information could help clarify ring and U or V shape structures in deep convection and be potentially useful in nowcasting applications. Indeed, the use of high-resolution numerical weather prediction (NWP) models, which now include explicit microphysical processes, requires data assimilation at very high resolution as well. A standard atmospheric motion vector tracking algorithm was applied to a pair of images composed of combinations of Spinning Enhanced Visible and Infra-red Imager (SEVIRI) channels. Several ranges of channel differences were used in the tracking process, such intervals being expected to correspond to specific cloud-top microphysics structures. Various consistent flows of motion vectors with different speeds and/or directions were extracted at the same location depending on the channel difference intervals used. These differences in speed/direction can illustrate local wind shear situations, or correspond to expansion or dissipation of cloud regions that contain high concentrations of specific kinds of ice crystals or droplets. The results from this technique were compared to models and ancillary data to advance our discussion and inter-comparisons. Also, the technique proposed here was evaluated using SEVIRI images simulated by the radiative transfer model RTTOV with input data from the UK Met Office Unified Model. A future application of the new data is exemplified by showing the relationship between wind divergence calculated from the new atmospheric motion vector and convective cloud top intensification.     

Assimilation of SPOT/VEGETATION NDVI data into a sahelian vegetation dynamics model

This paper presents a method to monitor the dynamics of herbaceous vegetation in the Sahel. The approach is based on the assimilation of Normalized Difference Vegetation Index (NDVI) data acquired by the VEGETATION instrument on board SPOT 4/5 into a simple sahelian vegetation dynamics model. The study region is located in the Gourma region of Mali. The vegetation dynamics model is coupled with a radiative transfer model (the SAIL model). First, it is checked that the coupled models allow for a realistic simulation of the seasonal and interannual variability of NDVI over three sampling sites from 1999 to 2004. The data assimilation scheme relies on a parameter identification technique based on an Evolution Strategies algorithm. The simulated above-ground herbage mass resulting from NDVI assimilation is then compared to ground measurements performed over 13 study sites during the period 1999-2004. The assimilation scheme performs well with 404 kg DM/ha of average error (n = 126 points) and a correlation coefficient of r = 0.80 (to be compared to the 463 kg DM/ha and r = 0.60 of the model performance without data assimilation). Finally, the sensitivity of the herbage mass model estimates to the quality of the meteorological forcing (rainfall and net radiation) is analyzed thanks to a stochastic approach.     

Predicting lightning outages of transmission lines using generalized regression neural network

Lightning is the major cause of transmission line outages, which can result in large area blackouts of power systems. One effective method to prevent catastrophic consequences is to predict lightning outages before they occur. The abundance of recorded lightning and lightning outage data in power system makes it possible to predict lightning outages of transmission lines. This paper proposes an artificially intelligent algorithm using general regression neural networks (GRNN) to predict lightning outages of transmission lines. First, the data that can be obtained from the operation and management system of a power company are analyzed, and the features that can be used as input parameters of GRNN are extracted. The prediction model based on GRNN is then built to perform lightning outage prediction. Finally, the effectiveness of the proposed method is validated by comparing it with (Back Propagation) BP and (Radial Basis Function) RBF neural networks using actual lightning data and lightning outage data. The simulation results show that the proposed method provides much better prediction performance.     

Comparative study of adaptive techniques for denoising CN Tower lightning current derivative signals

The lightning current derivative data recorded at the CN Tower during the past 18 years contain different kinds of noise and needs to be denoised for accurately determining the lightning current waveform parameters. It is usually a challenging task to denoise transient signals having large bandwidth without altering their waveshapes or shrinking their amplitudes. This paper deals with denoising the CN Tower lightning current derivative signals using several adaptive techniques. A new adaptive denoising approach (Divide-and-Conquer) has been successfully used to denoise a vast variety of CN Tower lightning current derivative waveshapes. The supremacy of the new technique over the existing ones is outlined for a signal with a poor signal-to-noise ratio (SNR). While keeping the signal amplitude unchanged and preserving its waveshape, the new denoising technique improved its SNR from ?22.93 dB to 71.41 dB.     

伴随时空特性的雷电预测BP-ANN模型研究

为提高雷电预测模型的准确率和学习性能，提出一种基于增量学习和时空特性的雷电预测BP-ANN二项分类器。通过增量方式和依据数据的时空特征进行历史数据的学习，建立多种BP-ANN模型，分别对新的数据进行预测分类，然后采用多数投票方式确定新数据的类别。分别构建基于增量学习的BP-ANN模型、基于时空特性的BP-ANN模型以及结合基于增量学习和时空特性的BP-ANN模型这3种雷电预测模型，并在真实雷电数据集上进行预测准确度和学习性能的测试，结果表明了增量学习、时空特性以及二者结合的优劣。     

基于DBSCAN聚类算法的闪电临近预报模型

针对闪电定位仪中庞大而杂乱的定位数据,提出一种基于改进DBSCAN聚类算法(IDBSCAN)进行闪电聚类分析的方法。该方法依据闪电定位系统中的实时监控数据,搜索闪电密度大于阈值范围的地闪点,建立密度可达最大值的地闪聚类簇,并找到该簇类中的核心地闪点。同时,应用邻接表结构对DBSCAN算法进行改进,使得初始地闪数据的搜索集的建立时间和空间得到大大减少。在聚类分析结果基础上,对核心地闪点的移动路径进行拟合,从而预报下一时刻的核心地闪点位置。实验证明,将IDBSCAN算法应用在闪电临近预报中是有效的。     

Ensemble data assimilation in the presence of cloud

In numerical weather prediction (NWP) data assimilation (DA) methods are used to combine available observations with numerical model estimates. This is done by minimising measures of error on both observations and model estimates with more weight given to data that can be more trusted. For any DA method an estimate of the initial forecast error covariance matrix is required. For convective scale data assimilation, however, the properties of the error covariances are not well understood.An effective way to investigate covariance properties in the presence of convection is to use an ensemble-based method for which an estimate of the error covariance is readily available at each time step. In this work, we investigate the performance of the ensemble square root filter (EnSRF) in the presence of cloud growth applied to an idealised 1D convective column model of the atmosphere. We show that the EnSRF performs well in capturing cloud growth, but the ensemble does not cope well with discontinuities introduced into the system by parameterised rain. The state estimates lose accuracy, and more importantly the ensemble is unable to capture the spread (variance) of the estimates correctly. We also find, counter-intuitively, that by reducing the spatial frequency of observations and/or the accuracy of the observations, the ensemble is able to capture the states and their variability successfully across all regimes.