Impact of 4DVAR assimilation of rainfall data on the simulation of mesoscale precipitation systems in a mei-yu heavy rainfall event

The multi-scale weather systems associated with a mei-yu front and the corresponding heavy precipitation during a particular heavy rainfall event that occurred on 4 5 July 2003 in east China were successfully simulated through rainfall assimilation using the PSU/NCAR non-hydrostatic, mesoscale, numerical model （MM5） and its four-dimensional, variational, data assimilation （4DVAR） system. For this case, the improvement of the process via the 4DVAR rainfall assimilation into the simulation of mesoscale precipitation systems is investigated. With the rainfall assimilation, the convection is triggered at the right location and time, and the evolution and spatial distribution of the mesoscale convective systems （MCSs） are also more correctly simulated. Through the interactions between MCSs and the weather systems at different scales, including the low-level jet and mei-yu front, the simulation of the entire mei-yu weather system is significantly improved, both during the data assimilation window and the subsequent 12-h period. The results suggest that the rainfall assimilation first provides positive impact at the convective scale and the influences are then propagated upscale to the meso- and sub-synoptic scales.
Through a set of sensitive experiments designed to evaluate the impact of different initial variables on the simulation of mei-yu heavy rainfall, it was found that the moisture field and meridional wind had the strongest effect during the convection initialization stage, however, after the convection was fully triggered, all of the variables at the initial condition seemed to have comparable importance.     

Observation and Numerical Simulations with Radar and Surface Data Assimilation for Heavy Rainfall over Central Korea

This study investigated the impact of multiple-Doppler radar data and surface data assimilation on forecasts of heavy rainfall over the central Korean Peninsula;the Weather Research and Forecasting(WRF) model and its three-dimensional variational data assimilation system(3DVAR) were used for this purpose. During data assimilation,the WRF 3DVAR cycling mode with incremental analysis updates(IAU) was used. A maximum rainfall of 335.0 mm occurred during a 12-h period from 2100 UTC 11 July 2006 to 0900 UTC 12 July 2006.Doppler radar data showed that the heavy rainfall was due to the back-building formation of mesoscale convective systems(MCSs).New convective cells were continuously formed in the upstream region,which was characterized by a strong southwesterly low-level jet(LLJ).The LLJ also facilitated strong convergence due to horizontal wind shear,which resulted in maintenance of the storms.The assimilation of both multiple-Doppler radar and surface data improved the accuracy of precipitation forecasts and had a more positive impact on quantitative forecasting(QPF) than the assimilation of either radar data or surface data only.The back-building characteristic was successfully forecasted when the multiple-Doppler radar data and surface data were assimilated.In data assimilation experiments,the radar data helped forecast the development of convective storms responsible for heavy rainfall,and the surface data contributed to the occurrence of intensified low-level winds.The surface data played a significant role in enhancing the thermal gradient and modulating the planetary boundary layer of the model,which resulted in favorable conditions for convection.     

ON THE OPTIMAL BACKGROUND ERROR COVARIANCES: DIFFERENT SCALE ERRORS’ CONTRIBUTION

The large-scale and small-scale errors could affect background error covariances for a regional numerical model with the specified grid resolution.Based on the different background error covariances influenced by different scale errors,this study tries to construct a so-called"optimal background error covariances"to consider the interactions among different scale errors.For this purpose,a linear combination of the forecast differences influenced by information of errors at different scales is used to construct the new forecast differences for estimating optimal background error covariances.By adjusting the relative weight of the forecast differences influenced by information of smaller-scale errors,the relative influence of different scale errors on optimal background error covariances can be changed.For a heavy rainfall case,the corresponding optimal background error covariances can be estimated through choosing proper weighting factor for forecast differences influenced by information of smaller-scale errors.The data assimilation and forecast with these optimal covariances show that,the corresponding analyses and forecasts can lead to superior quality,compared with those using covariances that just introduce influences of larger-or smallerscale errors.Due to the interactions among different scale errors included in optimal background error covariances,relevant analysis increments can properly describe weather systems(processes)at different scales,such as dynamic lifting,thermodynamic instability and advection of moisture at large scale,high-level and low-level jet at synoptic scale,and convective systems at mesoscale and small scale,as well as their interactions.As a result,the corresponding forecasts can be improved.     

Radar Data Assimilation for the Simulation of Mesoscale Convective Systems

A heavy rainfall case related to Mesoscale Convective Systems (MCSs) over the Korean Peninsula was selected to investigate the impact of radar data assimilation on a heavy rainfall forecast. The Weather Research and Forecasting (WRF) three-dimensional variational (3DVAR) data assimilation system with tuning of the length scale of the background error covariance and observation error parameters was used to assimilate radar radial velocity and reflectivity data. The radar data used in the assimilation experiments were preprocessed using quality-control procedures and interpolated/thinned into Cartesian coordinates by the SPRINT/CEDRIC packages. Sensitivity experiments were carried out in order to determine the optimal values of the assimilation window length and the update frequency used for the rapid update cycle and incremental analysis update experiments. The assimilation of radar data has a positive influence on the heavy rainfall forecast. Quantitative features of the heavy rainfall case, such as the maximum rainfall amount and Root Mean Squared Differences (RMSDs) of zonal/meridional wind components, were improved by tuning of the length scale and observation error parameters. Qualitative features of the case, such as the maximum rainfall position and time series of hourly rainfall, were enhanced by an incremental analysis update technique. The positive effects of the radar data assimilation and the tuning of the length scale and observation error parameters were clearly shown by the 3DVAR increment.     

Four-dimensional variational data assimilation experiments for a heavy rain case during the 2002 IOP in China

A heavy rainfall event along the mei-yu front during 22-23 June 2002 was chosen for this study. To assess the impact of the routine and additional IOP (intensive observation period) radiosonde observations on the mesoscale heavy rainfall forecast, a series of four-dimensional variational (4DVAR) data assimilation and model simulation experiments was conducted using nonhydrostatic mesoscale model MM5 and the MM5 4DVAR system. The effects of the intensive observations in the different areas on the heavy rainfall forecast were also investigated. The results showed that improvement of the forecast skill for mesoscale heavy rainfall intensity was possible from the assimilation of the IOP radiosonde observations. However,the impact of the IOP observations on the forecast of the rainfall pattern was not significant. Initial conditions obtained through the 4DVAR experiments with a 12-h assimilation window were capable of improving the 24-h forecast. The simulated results after the assimilation showed that it would be best to perform the intensive radiosonde observations in the upstream of the rainfall area and in the moisture passageway area at the same time. Initial conditions created by the 4DVAR led to the low-level moisture convergence over the rainfall area, enhanced frontogenesis and upward motion within the mei-yu front,and intensified middle- and high-level unstable stratification in front of the mei-yu front. Consequently,the heavy rainfall forecast was improved.     

Variational Assimilation of GPS Precipitable Water Vapor and Hourly Rainfall Observations for a Meso-β Scale Heavy Precipitation Event During the 2002 Mei-Yu Season

Recent advances in Global Positioning System （GPS） remote sensing technology allow for a direct estimation of the precipitable water vapor （PWV） from delayed signals transmitted by GPS satellites, which can be assimilated into numerical models with four-dimensional variational （4DVAR） data assimilation. A mesoscale model and its 4DVAR system are used to access the impacts of assimilating GPS-PWV and hourly rainfall observations on the short-range prediction of a heavy rainfall event on 20 June 2002. The heavy precipitation was induced by a sequence of meso-β-scale convective systems （MCS） along the mei-yu front in China. The experiments with GPS-PWV assimilation cluster and also eliminated the erroneous rainfall successfully simulated the evolution of the observed MCS systems found in the experiment without 4DVAR assimilation. Experiments with hourly rainfall assimilation performed similarly both on the prediction of MCS initiation and the elimination of erroneous systems, however the MCS dissipated much sooner than it did in observations. It is found that the assimilation-induced moisture perturbation and mesoscale low-level jet are helpful for the MCS generation and development. It is also discovered that spurious gravity waves may post serious limitations for the current 4DVAR algorithm, which would degrade the assimilation efficiency, especially for rainfall data. Sensitivity experiments with different observations, assimilation windows and observation weightings suggest that assimilating GPS-PWV can be quite effective, even with the assimilation window as short as 1 h. On the other hand, assimilating rainfall observations requires extreme cautions on the selection of observation weightings and the control of spurious gravity waves.     

ANALYSIS OF THE EFFECT OF 3DVAR AND ENSRF DIRECT ASSIMILATION OF RADAR DATA ON THE FORECAST OF A HEAVY RAINFALL EVENT

The present study designs experiments on the direct assimilation of radial velocity and reflectivity data collected by an S-band Doppler weather radar (CINRAD WSR-98D) at the Hefei Station and the reanalysis data produced by the United States National Centers for Environmental Prediction using the Weather Research and Forecasting (WRF) model, the WRF model with a three-dimensional variational (3DVAR) data assimilation system and the WRF model with an ensemble square root filter (EnSRF) data assimilation system. In addition, the present study analyzes a Meiyu front heavy rainfall process that occurred in the Yangtze -Huaihe River Basin from July 4 to July 5, 2003, through numerical simulation. The results show the following. (1) The assimilation of the radar radial velocity data can increase the perturbations in the low-altitude atmosphere over the heavy rainfall region, enhance the convective activities and reduce excessive simulated precipitation. (2) The 3DVAR assimilation method significantly adjusts the horizontal wind field. The assimilation of the reflectivity data improves the microphysical quantities and dynamic fields in the model. In addition, the assimilation of the radial velocity and reflectivity data can better adjust the wind fields and improve the intensity and location of the simulated radar echo bands. (3) The EnSRF assimilation method can assimilate more small-scale wind field information into the model. The assimilation of the reflectivity data alone can relatively accurately forecast the rainfall centers. In addition, the assimilation of the radial velocity and reflectivity data can improve the location of the simulated radar echo bands. (4) The use of the 3DVAR and EnSRF assimilation methods to assimilate the radar radial velocity and reflectivity data can improve the forecast of precipitation, rain-band areal coverage and the center location and intensity of precipitation.     

Simulation of heavy rainfall events over Indian monsoon region using WRF-3DVAR data assimilation system

We present the results of the impact of the 3D variational data assimilation (3DVAR) system within the Weather Research and Forecasting (WRF) model to simulate three heavy rainfall events (25–28 June 2005, 29–31 July 2004, and 7–9 August 2002) over the Indian monsoon region. For each event, two numerical experiments were performed. In the first experiment, namely the control simulation (CNTL), the low-resolution global analyses are used as the initial and boundary conditions of the model. In the second experiment (3DV-ANA), the model integration was carried out by inserting additional observations in the model’s initial conditions using the 3DVAR scheme. The 3DVAR used surface weather stations, buoy, ship, radiosonde/rawinsonde, and satellite (oceanic surface wind, cloud motion wind, and cloud top temperature) observations obtained from the India Meteorological Department (IMD). After the successful inclusion of additional observational data using the 3DVAR data assimilation technique, the resulting reanalysis was able to successfully reproduce the structure of convective organization as well as prominent synoptic features associated with the mid-tropospheric cyclones (MTC). The location and intensity of the MTC were better simulated in the 3DV-ANA as compared to the CNTL. The results demonstrate that the improved initial conditions of the mesoscale model using 3DVAR enhanced the location and amount of rainfall over the Indian monsoon region. Model verification and statistical skill were assessed with the help of available upper-air sounding data. The objective verification further highlighted the efficiency of the data assimilation system. The improvements in the 3DVAR run are uniformly better as compared to the CNTL run for all the three cases. The mesoscale 3DVAR data assimilation system is not operational in the weather forecasting centers in India and a significant finding in this study is that the assimilation of Indian conventional and non-conventional observation datasets into numerical weather forecast models can help improve the simulation accuracy of meso-convective activities over the Indian monsoon region. Results from the control experiments also highlight that weather and regional climate model simulations with coarse analysis have high uncertainty in simulating heavy rain events over the Indian monsoon region and assimilation approaches, such as the 3DVAR can help reduce this uncertainty.     

Variational Assimilation of GPS Precipitable Water Vapor and Hourly Rainfall Observations for a Meso-β Scale Heavy Precipitation Event During the 2002 Mei-Yu Season

Recent advances in Global Positioning System (GPS) remote sensing technology allow for a direct estimation of the precipitable water vapor (PWV) from delayed signals transmitted by GPS satellites, which can be assimilated into numerical models with four-dimensional variational (4DVAR) data assimilation. A mesoscale model and its 4DVAR system are used to access the impacts of assimilating GPS-PWV and hourly rainfall observations on the short-range prediction of a heavy rainfall event on 20 June 2002. The heavy precipitation was induced by a sequence of meso-β-scale convective systems (MCS) along the mei-yu front in China.The experiments with GPS-PWV assimilation successfully simulated the evolution of the observed MCS cluster and also eliminated the erroneous rainfall systems found in the experiment without 4DVAR assimilation. Experiments with hourly rainfall assimilation performed similarly both on the prediction of MCS initiation and the elimination of erroneous systems, however the MCS dissipated much sooner than it did in observations. It is found that the assimilation-induced moisture perturbation and mesoscale low-level jet are helpful for the MCS generation and development. It is also discovered that spurious gravity waves may post serious limitations for the current 4DVAR algorithm, which would degrade the assimilation efficiency, especially for rainfall data. Sensitivity experiments with different observations, assimilation windows and observation weightings suggest that assimilating GPS-PWV can be quite effective, even with the assimilation window as short as 1 h. On the other hand, assimilating rainfall observations requires extreme cautions on the selection of observation weightings and the control of spurious gravity waves.     

多普勒雷达资料在冷涡强对流天气中的同化应用试验

应用WRF模式的三维变分同化系统(WRF-3DVAR),对沈阳多普勒天气雷达资料在东北冷涡暴雨个例中的同化应用进行了试验.研制了多普勒雷达资料质量控制系统,实现了对径向风和反射率因子的直接同化,不但可以反演中尺度三维气象要素场,而且可以为模式提供初始场.以天气尺度资料为背景场同化多普勒雷达资料,WRF-3DVAR可以较好地反演冷涡中尺度对流系统的三维结构,反演的地面强对流辐散气流及在对流层中层涡旋都符合中尺度系统概念模型,通过与实际地面探测资料进行了对比,风场环流基本接近,同化了雷达资料的气象要素场可为预报业务提供较好的包含中小尺度系统的实时三维分析场.通过冷涡个例同化试验,应用WRF-3DVAR同化雷达资料后,中尺度模式对对流降水的预报总体有正的影响,对强对流中的一些中小尺度雨团的预报也略有改善.     

多普勒雷达资料同化的稀疏化方式对暴雨过程的影响研究

利用中尺度非静力WRF(Weather Research and Forecasting)模式及其三维变分同化系统,对2007年7月淮河流域的一次强降雨过程进行多普勒雷达径向速度资料的三维变分同化试验,重点考察雷达资料的不同稀疏化方式对同化结果以及对暴雨数值模拟的影响。结果表明:同化多普勒雷达径向速度资料使得模式初始风场包含了更丰富的中尺度特征信息,有效调整了初始场的环流结构,能够改善模式对暴雨过程的模拟效果;以不同的稀疏化处理方式同化多普勒雷达径向速度资料对分析场会产生不同的影响,进而影响模式的降水预报效果,本次试验中当极坐标网格径向分辨率取10 km的时候降水过程的预报效果最好。     

降水资料同化在GRAPES-MESO模式中应用试验研究

利用国家气象中心中尺度业务数值预报模式GRAPES-MESO v3.0,以2010年6月1～30日为例,开展地面降水率1DVAR(one-dimensional variational assimilation)同化方案在GRAPES-3DVAR(three-dimensional variational assimilation)同化系统中的应用试验研究(ASSI试验),并以未加降水资料同化的试验为对照试验(CNTL试验),以评估全国1h加密雨量资料在模式中同化应用的效果。结果表明:1)在相对湿度背景误差和降水率观测误差范围内,1DVAR同化方案能够对湿度廓线进行有意义的调整,使分析降水向观测降水靠近;ASSI试验对初始温、压、湿、风场的修正主要为正效果;2)对2010年6月17～21日江南、华南连续性降水过程进行了分析,整体而言ASSI试验对逐日及逐时降水强度的预报普遍强于CNTL试验,与实况更加接近;3)ASSI试验对2010年6月1～30日08时起报的0～24 h模式预报的小雨、中雨、大雨、暴雨、大暴雨各个降水量级TS评分及ETS评分相比CNTL试验均有较明显提高,预报偏差也更接近于1;4)ASSI试验较CNTL试验能更好地模拟雨带的分布、雨带演变特征和降水强度的变化;5)对降水所做的典型个例和统计检验分析从不同角度说明了地面降水资料1DVAR同化方案在GRAPES-3DVAR系统中的应用改善了GRAPES-MESO v3.0的降水模拟效果。     

The Impact of Assimilating Radar-estimated Rain Rates on Simulation of Precipitation in the 17--18 July 1996 Chicago Floods

Rainfall prediction remains one of the most challenging problems in weather forecasting. In order to improve high-resolution quantitative precipitation forecasts (QPF), a new procedure for assimilating rainfall rate derived from radar composite reflectivity has been proposed and tested in a numerical simulation of the Chicago floods of 17–18 July 1996. The methodology is based on the one-dimensional variation scheme (1DVAR) assimilation approach introduced by Fillion and Errico but applied here using the Ka...     

贵州西部极端对流性暴雨预报失败案例剖析

2020年6月1—2日,贵州西部发生了1次局地性的对流性暴雨过程,预报员和各数值模式对此次过程的预报量级显著偏小,对暴雨范围的低估,造成了特大暴雨、大暴雨的漏报。该文利用常规地面、高空资料,加密自动站观测资料,多普勒天气雷达资料,卫星资料及业务中常用的数值预报产品等对此次暴雨漏报案例进行剖析,结果表明：在弱天气尺度强迫背景下,高温高湿的环境中,未能准确判断对流的触发条件,未分析出露点锋、偏西风和偏南风的辐合、冷池和地面辐合线等的存在及其对强降水的影响,加之难以在短时间内对风场的发展演变进行精细分析,导致此次暴雨过程漏报;对于发生在暖湿气团中的对流性降水的预报,需考虑高温高湿环境下露点锋、辐合区、冷池、地面辐合线的相互作用触发对流并使其组织化发展,从而导致局地性、对流性强降水的产生;基于地面加密自动站资料和雷达资料等的短时临近预报可以帮助捕捉中小尺度系统,从而提高对此类暴雨的预报准确率。     

复杂地形下地面观测资料同化I. 模式地形与观测站地形高度差异对地面资料同化的影响研究

中国地形复杂，模式地形与实际观测地形存在一定高度差异，因此设计合理的复杂地形下地面观测资料的同化方案有利于使我国目前仅用作探测手段的地面观测资料（常规地面观测站和地面自动站）在中尺度数值模式中得到充分利用。作者在MM5_3DVAR同化系统中利用近地层相似理论将地面观测资料进行直接三维变分同化分析，并对地面资料同化方案设计中是否需要考虑模式与实际观测站地形高度差异进行探讨研究。研究结果表明：通过近地层相似理论将地面观测资料同化到数值模式能起到一定的作用，并且地面观测资料（温度、 湿度、 风场、 地面气压）中各物理量同化到数值模式都能影响24小时降水数值结果，但各物理量起的作用大小不一样，其中影响最大的是温度，其次为湿度；地面观测资料同化方案设计有必要考虑模式地形与实际观测站地形高度差异，适当考虑这种高度差异能取得较好的结果。     

Four-dimensional variational data assimilation for mesoscale and storm-scale applications

Summary ?The status and progress of the four-dimensional variational data assimilation (4DVAR) are briefly reviewed focusing on application to prediction of mesoscale/storm-scale atmospheric phenomena. Theoretical background is provided for each important component of the 4DVAR system – forecast and adjoint models, observations, background, cost function, preconditioning, and minimization. An overview of practical issues specific for mesoscale/storm-scale 4DVAR is then presented in terms of high-resolution observations, nonlinearity and discontinuity problem, model error, errors from lateral boundary condition, and precipitation assimilation. Practical strategies for efficient and simplified 4DVAR are also introduced, e.g., incremental 4DVAR, poor man’s 4DVAR, and inverse 3DVAR. A new concept on hybrid approach is proposed to combine an efficient 4DVAR scheme and the standard 4DVAR scheme aiming at reducing computational demand required by the standard 4DVAR while improving the accuracy of the simplified 4DVAR. Applications to both hydrostatic and nonhydrostatic models are illustrated and our vision on opportunities and directions for future research is provided. Received March 12, 2001; revised July 24, 2001; accepted September 5, 2001     

三维变分系统在我国夏季降雨预测中的应用试验

采用MM5模式及其三维变分系统(MM5/3DVAR)对我国夏季降雨进行了一个月的连续预测试验,并对试验结果进行评估.试验中首先采用"National Meteorological Center(NMC)"方法,将2005年8月的MM5模式的预测结果形成与试验区域和水平分辨率相匹配的背景误差场,并将其与全球背景误差场进行了对比分析,结果表明,采用2005年8月MM5模式预报结果生成的背景误差场的基本特征与系统提供的全球背景误差场相似,且长度尺度随着水平分辨率的提高而减小.之后,分别利用NCEP再分析资料(NCEP试验)、NCEP再分析资料基础上采用CRESSMAN方法分析观测资料(LITT试验)和NCEP再分析资料基础上采用3DVAR系统同化观测资料(3DVAR试验)形成模式预报初始场,再次对2005年8月降雨进行逐日连续预报.逐日降雨预报结果表明,相对NCEP试验,LITT试验中1 和10 mm的预报评分有明显提高,但 25 和 50 mm的预报评分却有所下降,而3DVAR试验的预报评分在10 mm以上均有明显提高.对于降雨期间的形势场预报,3个试验中,除温度场和湿度场外,其他变量场的均方根误差随高度增加而增加,但相比而言,3DVAR试验的均方根误差小于其他2个试验.3DVAR试验对降雨的明显改进,可能是因为其对与背景场信息差别比较大的反应中尺度系统的观测资料的分析结果比较靠近观测资料.     

实时天气背景依赖的反射率因子间接同化及多暴雨个例试验

为避免直接同化时反射率非线性观测算子线性化带来的线性近似误差问题,目前许多研究和业务中还常采用间接同化方式来同化雷达反射率因子,其通过背景场温度判定水凝物类型及比例。基于一种实时天气背景依赖的雷达反射率因子间接同化方案,进行了4次暴雨过程（2次强对流,2次锋面）的循环同化及预报试验。结果表明:对于强对流暴雨个例,相对于传统温度判定方案,天气背景依赖方案的温度预报误差更小、降水预报评分更高,而对于锋面过程区别不明显;进一步机理分析表明,对于强对流暴雨个例,由于背景依赖方案在同化反射率因子时引入了实时天气背景信息,使得分析场水凝物结构能够更好表征实际对流特征且与其它模式变量更为协调,进而改善了模式预报的热、动力及水汽条件,从而改善了降雨预报效果;而锋面暴雨由浅对流过程占主导,水凝物以低层的雨水为主导,冰相水凝物对于该过程的影响较小,由于两种方案反演的雨水结构和量级均相似,因此降雨预报差异较小。     

江苏一次突发暴雨的数值模拟

采用中尺度WRF模式（WRF3．4）和WRF-3DVAR系统,同化GTS常规观测资料和GPS反演的探空廓线资料,对2012年8月10日发生在江苏东北部地区的一次突发性特大暴雨进行数值模拟,并利用高分辨率的模式输出数据对此次暴雨过程进行多尺度分析和对流不稳定性研究。结果表明：模拟结果较好地再现了此次过程;台风低压和中纬度槽脊系统相互作用,中低层弱冷空气扩散和高、低空急流耦合为此次暴雨过程提供了有利的大尺度背景;地面中尺度辐合线和迅速发展的β中尺度对流系统（M8CS）是直接导致此次局地突发性暴雨的中尺度系统,地面中尺度辐合线的位置和暴雨的落区基本一致,M8CS的强烈发展对应于暴雨的最强阶段;暴雨发生前和暴雨发生时,对流系统中等日。线密集而陡峭,不稳定能量的突然性释放造成αθ／Oz迅速减小,口。垂直剖面呈伸向低空的“漏斗状”分布,有利于垂直涡度和对流不稳定发展。     

An Overview of Research and Forecasting on Rainfall Associated with Landfalling Tropical Cyclones

The ability to forecast heavy rainfall associated with landfalling tropical cyclones (LTCs) can be improved with a better understanding of the mechanism of rainfall rates and distributions of LTCs. Research in the area of LTCs has shown that associated heavy rainfall is related closely to mechanisms such as moisture transport, extratropical transition (ET), interaction with monsoon surge, land surface processes or topographic effects, mesoscale convective system activities within the LTC, and boundary layer energy transfer etc.. LTCs interacting with environmental weather systems, especially the westerly trough and mei-yu front, could change the rainfall rate and distribution associated with these mid-latitude weather systems. Recently improved technologies have contributed to advancements within the areas of quantitative precipitation estimation (QPE) and quantitative precipitation forecasting (QPF). More specifically, progress has been due primarily to remote sensing observations and mesoscale numerical models which incorporate advanced assimilation techniques. Such progress may provide the tools necessary to improve rainfall forecasting techniques associated with LTCs in the future.