Atmosphere data assimilation system for the Siberian region with the WRF-ARW model and three-dimensional variational analysis WRF 3D-Var

The system of the cyclic assimilation of data on atmospheric conditions used in the West Siberian Administration for Hydrometeorology and Environmental Monitoring is described. It is based on the WRF-ARW mesoscale atmospheric model and on the WRF 3D-Var system of the three-dimensional variational analysis of data. The system is verified when the first approximation data (6-hour forecast) and WRF-ARW forecasts with the lead time up to 24 hours are compared with the observational data. The problems of assimilation of observations from the AMSU-A and AIRS satellite instruments are considered. The effect of using AMSU-A and AIRS for the analysis in the Novosibirsk region is estimated. The experiments demonstrated that the cyclic data assimilation system operates successfully. The AMSU-A observations improve the quality of analyses and forecasts in winter. In summer the impact of satellite observations on the forecast skill scores is ambiguous. Good short-term forecasts are provided by the initial conditions obtained using the system of detailing of the NCEP large-scale analysis.     

Interannual-to-decadal variability of the North Atlantic from an ocean data assimilation system

An ocean analysis, assimilating both surface and subsurface hydrographic temperature data into a global ocean model, has been produced for the period 1958–2000, and used to study the time and space variations of North Atlantic upper ocean heat content (HC). Observational evidence is presented for interannual-to-decadal variability of upper ocean thermal fluctuations in the North Atlantic related to the North Atlantic Oscillation (NAO) variability over the last 40 years. The assimilation scheme used in the ocean analysis is a univariate, variational optimum interpolation of temperature. The first guess is produced by an eddy permitting global ocean general circulation forced by atmospheric reanalysis from the National Center for Environmental Prediction (NCEP). The validation of the ocean analysis has been done through the comparison with objectively analyzed observations and independent data sets. The method is able to compensate for the model systematic error to reproduce a realistic vertical thermal structure of the region and to improve consistently the model estimation of the time variability of the upper ocean temperature. Empirical orthogonal function (EOF) analysis shows that an important mode of variability of the wintertime upper ocean climate over the North Atlantic during the period of study is characterized by a tripole pattern both for SST and upper ocean HC. A similar mode is found for summer HC anomalies but not for summer SST. Over the whole period, HC variations in the subtropics show a general warming trend while the tropical and north eastern part of the basin have an opposite cooling tendency. Superimposed on this linear trend, the HC variability explained by the first EOF both in winter and summer conditions reveals quasi-decadal oscillations correlated with changes in the NAO index. On the other hand, there is no evidence of correlation in time between the NAO index and the upper ocean HC averaged over the whole North Atlantic which exhibits a substantial and monotonic warming trend during the last two decades of the analysis period. The maximum correlation is found between the leading principal component of winter HC anomalies and NAO index at 1 year lag with NAO leading. For SST anomalies significant correlation is found only for winter conditions. In contrast, for HC anomalies high correlations are found also in the summer suggesting that the summer HC keeps a memory of winter conditions.     

ENSO in a hybrid coupled model. Part II: prediction with piggyback data assimilation

A hybrid coupled model (HCM) for the tropical Pacific ocean-atmosphere system is employed for ENSO prediction. The HCM consists of the Geophysical Fluid Dynamics Laboratory ocean general circulation model and an empirical atmospheric model. In hindcast experiments, a correlation skill competitive to other prediction models is obtained, so we use this system to examine the effects of several initialization schemes on ENSO prediction. Initialization with wind stress data and initialization with wind stress reconstructed from SST using the atmospheric model give comparable skill levels. In re-estimating the atmospheric model in order to prevent hindcast-period wind information from entering through empirical atmospheric model, we note some sensitivity to the estimation data set, but this is considered to have limited impact for ENSO prediction purposes. Examination of subsurface heat content anomalies in these cases and a case forced only by the difference between observed and reconstructed winds suggests that at the current level of prediction skill, the crucial wind components for initialization are those associated with the slow ENSO mode, rather than with atmospheric internal variability. A “piggyback” suboptimal data assimilation is tested in which the Climate Prediction Center data assimilation product from a related ocean model is used to correct the ocean initial thermal field. This yields improved skill, suggesting that not all ENSO prediction systems need to invest in costly data assimilation efforts, provided the prediction and assimilation models are sufficiently close. Received: 17 April 1998 / Accepted: 22 July 1999     

Intercomparison of the performance of MM5/WRF with and without satellite data assimilation in short-range forecast applications over the Indian region

The present study is conducted to verify the short-range forecasts from mesoscale model version5 (MM5)/weather research and forecasting (WRF) model over the Indian region and to examine the impact of assimilation of quick scatterometer (QSCAT) near surface winds, spectral sensor microwave imager (SSM/I) wind speed and total precipitable water (TPW) on the forecasts by these models using their three-dimensional variational (3D-Var) data assimilation scheme for a 1-month period during July 2006. The control (without satellite data assimilation) as well as 3D-Var sensitivity experiments (with assimilating satellite data) using MM5/WRF were made for 48 h starting daily at 0000 UTC July 2006. The control run is analyzed for the intercomparison of MM5/WRF short-range forecasts and is also used as a baseline for assessing the MM5/WRF 3D-Var satellite data sensitivity experiments. As compared to the observation, the MM5 (WRF) control simulations strengthened (weakened) the cross equatorial flow over southern Arabian sea near peninsular India. The forecasts from MM5 and WRF showed a warm and moist bias at lower and upper levels with a cold bias at the middle level, which shows that the convective schemes of these models may be too active during the simulation. The forecast errors in predicted wind, temperature and humidity at different levels are lesser in WRF as compared to MM5, except the temperature prediction at lower level. The rainfall pattern and prediction skill from day 1 and day 2 forecasts by WRF is superior to MM5. The spatial distribution of forecast impact for wind, temperature, and humidity from 1-month assimilation experiments during July 2006 demonstrated that on average, for 24 and 48-h forecasts, the satellite data improved the MM5/WRF initial condition, so that model errors in predicted meteorological fields got reduced. Among the experiments, MM5/WRF wind speed prediction is most benefited from QSCAT surface wind and SSM/I TPW assimilation while temperature and humidity prediction is mostly improved due to latter. The largest improvement in MM5/WRF rainfall prediction is due to the assimilation of SSM/I TPW. The assimilation of SSM/I wind speed alone in MM5/WRF degraded the humidity and rainfall prediction. In summary the assimilation of satellite data showed similar impact on MM5/WRF prediction; largest improvement due to SSM/I TPW and degradation due to SSM/I wind speed.     

ＷＲＦ-ＥｎＳＲＦ同化系统的效果检验及其应用

利用自主构建的针对风暴尺度资料同化的WRF-EnSRF同化系统同化多普勒天气雷达资料,检验其在2003年一次梅雨锋暴雨以及2009年一次强对流天气过程的同化性能.结果显示,在两个例中该同化系统均表现出有效的同化能力,经过60 min同化的分析场和以该分析场集合做初值的30 min的集合预报结果都比较接近实际观测.在同化过程中,径向速度和反射率因子的观测增量均方差分别达到3～4 m/s和9-11 dBz.本文考察了初始扰动时全场扰动与对流区域局部扰动,以及扰动环境风场与否对同化效果的影响.试验结果表明,对流区局部扰动相对于全场扰动并没有提高同化效果;对于尺度较大的梅雨锋暴雨来说,扰动环境风场时同化效果较好.为了考察分析场在降水预报中的表现,在暴雨个例中,以分析场为初值做6 h降水预报,经过同化的集合预报和确定性预报结果都比没有经过同化的控制试验预报结果准确.     

Real-data assimilation experiment with a joint data assimilation system:assimilating carbon dioxide mole fraction measurements from the Greenhouse gases Observing Satellite

Tan-Tracker碳卫星资料联合数据同化系统是针对即将发射的中国的碳卫星TANSat,并基于先进数据同化算法PODEn4DVar,同时采用了联合数据同化的策略,紧扣碳卫星资料同化的这一国际前沿开发的面向科研和应用的碳卫星资料同化系统。本文设计运行了Tan-Tracker碳卫星资料联合数据同化系统同化GOSAT卫星CO_2浓度观测的真实同化实验,并评估了Tan-Tracker碳同化系统的可行性和有效性。经过同化后的结果与模式模拟结果同观测对比,均方根误差有了明显的下降(约30%),特别是在地面观测严重缺失的非洲北部、印度半岛、南部非洲、美国南部、澳大利亚西部等地;同时与观测的相关系数并无很大的差异。总体而言,Tan-Tracker碳同化系统成功完成真实数据同化并有较好的结果。     

A land data assimilation system using the MODIS-derived land data and its application to numerical weather prediction in East Asia

Land Data Assimilation Systems have been developed to generate the surface initial conditions such as soil moisture and temperature for better prediction of weather and climate. We have constructed Korea Land Data Assimilation System (KLDAS) based on an uncoupled land surface modeling framework that integrates high-resolution in-situ observation, satellite data, land surface information from the WRF Preprocessing System (WPS) and the MODIS land products over the East Asia. To present better surface conditions, the KLDAS is driven by atmospheric forcing data from the in-situ rainfall gauges and satellite. In this study, we 1) briefly introduce the KLDAS, 2) evaluate the meteorological states near the surface and the surface fluxes reproduced by the KLDAS against the in-situ observation, and then 3) examine the performance of the mesoscale model initialized by the KLDAS. We have generated a 5-year, 10 km, hourly atmospheric forcing dataset for use in KLDAS operating across East Asia. The KLDAS has effectively reproduced the observed patterns of soil moisture, soil temperature, and surface fluxes. Further scrutiny reveals that the numerical simulations incorporating the KLDAS outputs show better agreement in both the simulated near-surface conditions and rainfall distribution over the Korean Peninsula, compared to those without the KLDAS.     

Using satellite-derived Atmospheric Motion Vector (AMV) observations in the ensemble data assimilation system

The use of global Atmospheric Motion Vectors (AMV) satellite observations in the meteorological data assimilation system based on Local Ensemble Transform Kalman Filter (LETKF) algorithm is considered. The height assignment is the most crucial error source for AMV observations. To reduce its impact, the AMV height reassignment method is implemented; it is based on the consistency coefficient bet ween the observed and the background winds. The other way to improve the analysis quality is a more accurate specification of AMV observation errors. This necessitates the use of the nondiagonal observation-error covariance matrix R in the data assimilation scheme. The first results of these studies are presented. It is demonstrated that the use of AMV observations in the data assimilation system reduces the errors of forecasts computed from the initial data of this system.     

A global ocean temperature and altimeter data assimilation system for studies of climate variability

 An ocean data assimilation (ODA) system which can assimilate both temperature and altimeter observations has been applied to the global ocean and tested between January 1993–October 1996. A statistical method has been used to convert sea surface height (SSH) anomalies observations from TOPEX/POSEIDON into synthetic temperature profiles. The innovative aspect of this method is the introduction of time dependency in the correlations used to transform the altimeter observations into temperature corrections. The assimilation system is based on a univariate variational optimal interpolation scheme applied to assimilate both in situ and synthetic temperature profiles. In addition, a longer global analysis for the upper-ocean temperature starting from January 1979 and ending November 1997, has been produced to examine the skill of sea temperature assimilation with a rather simple and practical method. The temperature analysis shows encouraging improvement over a corresponding ocean simulation when compared to independent (not assimilated) temperature data both at seasonal and interannual time scales. However, the univariate data assimilation of hydrographic data does not result in an improvement of the velocity field. In fact the assimilation of sparse in situ data can introduce unrealistic spatial variability in the temperature field which affects the velocity field in a negative way. This deficiency is partially overcome when we also assimilate altimeter observations since the coverage is complete and uniform for this data. In particular, our study shows that temperature corrections due to the altimeter signal have a positive impact on the current system in the tropical Pacific. Received: 28 May 2000 / Accepted: 6 November 2000     

Fast data assimilation using a nonlinear Kalman filter and a model surrogate: An application to the Columbia River estuary

A fast and adjoint-free nonlinear data assimilation (DA) system was developed to simulate 3D baroclinic circulation in estuaries, leveraging two recently developed technologies: (1) a nonlinear model surrogate that executes forward simulation three orders of magnitude faster than a forward numerical circulation code and (2) a nonlinear extension to the reduced-dimension Kalman filter that estimates the state of the model surrogate. The noise sources in the Kalman filter were calibrated using empirical cross-validation and accounted for errors in model and model forcing.The DA system was applied to assimilate in situ measurements of water levels, salinities, and temperatures in simulations of the Columbia River estuary. To validate the DA results, we used a combination of cross-validation studies, process-oriented studies, and tests of statistical and dynamical consistency. The validation studies showed that DA improved the representation of several important processes in the estuary, including nonlinear tidal propagation, salinity intrusion, estuarine residual circulation, heat balance, and response of the estuary to coastal winds.     

Mesoscale data assimilation and prediction of low stratus in the Alpine region

Summary A number of problems related to mesoscale numerical prediction of low stratus in the Alpine region are formulated, and addressed in a series of experiments for two wintertime cases. These problems include modelling aspects and issues of data assimilation which are relevant particularly in relation to the observation nudging technique. A focus is on the influence of orography.A comparison of operational optimum interpolation, and nudging of routine rawinsonde and surface-level data reveals that nudging often yields better analyses and forecasts of low stratus, and notably of the sharp vertical temperature and humidity gradients. However, the humidity advection scheme of the model and, near steep terrain, particularly the horizontal diffusion along the model's -levels are identified to contribute to spurious vertical smoothing which can result in erroneous cloud dissipation. On occasions, forecasts succeeding a nudging period are more sensitive to this process due to the sharper initial vertical gradients.Specific problems of representiveness arise when low-level rawinsonde information is spread laterally along the sloping -levels from low to high terrain. A new concept for -layer models is introduced by speading the observational information along isentropic surfaces, and this tends to improve the low stratus prediction over steep and even moderate orography. A partly successful attempt to take advantage of the steep Alpine orography is made by applying this concept to surface-level humidity data from a high-resolution network of Alpine surface stations which are distributed relatively uniformly in the vertical.With 19 Figures     

Progress and future prospects of decadal prediction and data assimilation:A review

年代际预测,也称为\"近期气候预测\",旨在预测未来1-10年内的气候变化,是气候预测和气候变化研究领域的一个新关注点.它位于季节至年际预测和长期气候变化预测之间,结合了初值问题和外部强迫问题的两个方面.年代际预测的核心技术在于用于模式初始化的同化方法的准确性和效率,其目标是为模式提供准确的初始条件,其中包含观测到的气候系统内部变率.年代际预测的初始化通常涉及在耦合框架内同化海洋观测,其中观测到的信号通过耦合过程传递到其他分量,如大气和海冰.然而,最近的研究越来越关注在海洋-大气耦合模式中探索耦合数据同化(CDA),有人认为CDA有潜力显著提高年代际预测技巧.本文综合评述了该领域的三个方面的研究现状:初始化方法,年代际气候预测的可预测性和预测技巧,以及年代际预测的未来发展和挑战.     

广西对流尺度数值预报数据传输系统的设计与实现

数值天气预报在防灾减灾工作中发挥了越来越重要的作用,为了保障广西对流尺度数值预报产品的传输质量与时效,搭建了广西对流尺度数值预报数据传输系统.文中阐述了该系统的设计思路,并对网络部署、服务器搭建、数据自动收发、传输过程的实时监控等关键技术进行详细介绍.系统的业务运行分析评估结果表明,该系统GIF和Grib数据的整体传输...     

Incorporating circulation statistics in bias correction of GCM ensembles: hydrological application for the Rhine basin

An adapted statistical bias correction method is introduced to incorporate circulation-dependence of the model precipitation bias, and its influence on estimated discharges for the Rhine basin is analyzed for a historical period. The bias correction method is tailored to time scales relevant to flooding events in the basin. Large-scale circulation patterns (CPs) are obtained through Maximum Covariance Analysis using reanalysis sea level pressure and high-resolution precipitation observations. A bias correction using these CPs is applied to winter and summer separately, acknowledging the seasonal variability of the circulation regimes in North Europe and their correlation with regional precipitation rates over the Rhine basin. Two different climate model ensemble outputs are explored: ESSENCE and CMIP5. The results of the CP-method are then compared to observations and uncorrected model outputs. Results from a simple bias correction based on a delta factor (NoCP-method) are also used for comparison. For both summer and winter, the CP-method offers a statistically significant improvement of precipitation statistics for subsets of data dominated by particular circulation regimes, demonstrating the circulation-dependence of the precipitation bias. Uncorrected, CP and NoCP corrected model outputs were used as forcing to a hydrological model to simulate river discharges. The CP-method leads to a larger improvement in simulated discharge in the Alpine area in winter than in summer due to a stronger dependence of Rhine precipitation on atmospheric circulation in winter. However, the NoCP-method, in comparison to the CP-method, improves the discharge estimations over the entire Rhine basin.     

Estimation of air temperature reproduction quality by the MM5 model in the Urals in July

The quality estimation of diurnal air temperature course reproduction by the hydrodynamical MM5 model in the Urals in July is carried out by means of the comparison with the station observational data. The MM5 model reproduces the temperature maximums better than the minimums which are considerably overestimated by the model. The highest accuracy of air temperature reproduction is demonstrated by the model at 03:00, 06:00, 09:00, 12:00, and 24:00, whereas the lowest one, at 15:00, 18:00, and 21:00. The correlation coefficients between the model and actual data vary from 0.74 at 21:00 to 0.87 at 09:00 and 12:00. The autocorrelation functions computed for the actual and model data indicate that the MM5 model reproduces the temperature field with higher quality than the technique based on the inertial properties of the atmosphere.     

Impact of ocean data assimilation on the seasonal forecast of the 2014/15 marine heatwave in the Northeast Pacific Ocean

2013年底至2016年初,东北太平洋上发生了历史上罕见的极端海洋热浪事件(称为\"Blob\"事件),形成了从海表延伸至海洋深处300m的强烈且持续的海温暖异常.本文利用南京信息工程大学全球气候预测系统1.0版本(NUIST-CFS 1.0),采用两种海洋资料同化方案,探究海洋资料同化差异对这一极端海洋热浪事件季节预测的影响.本文采用的一种同化方案为仅同化海表面温度(Surface sea temperature,SST)的SST-nudging方案,而另一种方案为在前一种方案的基础上加入确定性集合卡尔曼滤波(Deterministic Ensemble Kalman Filter,DEnKF),同化更多海洋观测数据的EnKF方案.主要结论为,利用EnKF方案可显著提高对\"Blob\"期间次表层温度异常预测的准确性,这主要源于EnKF方案在预测次表层的水平和垂直热传输方面表现出色.该研究有助于更好地理解海洋热浪事件潜在物理机制及其季节预测水平.     

The impact of radar data assimilation on atmosphere state analysis in the Moscow region

The results of numerical experiments with the data assimilation system including the WRF-ARW mesoscale atmospheric model and WRFDA analysis package in the 3D-Var mode are considered. The focus is on the impact of Doppler weather radar data on the quality of short-range weather forecasting. The maps of weather events and cloud top constructed from the modeling by GIMET-2010 software package are analyzed using the web-GIS METEORAD. The experiments in the Moscow region demonstrated that if only radial wind is assimilated, the intensity of simulated cloud and precipitation formation is the closest to the observed one; however, the spatial mismatches of simulated and observed cloud systems are possible. If Doppler radar data on radial wind and reflectivity are assimilated, the general pattern and location of clouds and precipitation are simulated more accurately, but there is a certain overestimation of convection intensity which leads to the overestimation of the number of thunderstorms and rainfall rate.     

On the assimilation of satellite sounder data in cloudy skies in numerical weather prediction models

Satellite measurements are an important source of global observations in support of numerical weather prediction (NWP). The assimilation of satellite radiances under clear skies has greatly improved NWP forecast scores. However, the application of radiances in cloudy skies remains a significant challenge. In order to better assimilate radiances in cloudy skies, it is very important to detect any clear field-of-view (FOV) accurately and assimilate cloudy radiances appropriately. Research progress on both clear FOV detection methodologies and cloudy radiance assimilation techniques are reviewed in this paper. Overview on approaches being implemented in the operational centers and studied by the satellite data assimilation research community is presented. Challenges and future directions for satellite sounder radiance assimilation in cloudy skies in NWP models are also discussed.