A novel approach to fog/low stratus detection using Meteosat 8 data

A method is presented for fog and low stratus detection from daytime satellite imagery based on Meteosat 8 SEVIRI (Spinning-Enhanced Visible and Infra-Red Imager) data. With its excellent spatial, spectral and temporal resolutions, this imagery is an ideal basis for operational fog monitoring. The scheme utilizes a range of pixel-based and novel object-oriented techniques to separate fog and low stratus clouds from other cloud types. Fog and low stratus are identified by a number of tests which explicitly and implicitly address fog/low stratus spectral, spatial and microphysical properties. The scheme's performance is evaluated using ground-based measurements of cloud height over Europe. The algorithm is found to detect low clouds very accurately, with probabilities of detection (POD) ranging from 0.632 to 0.834 (for different inter-comparison approaches), and false alarm ratios (FAR) between 0.059 and 0.021. The retrieval of sub-pixel and temporal effects remain issues for further investigation.     

Gap flow measurements during the Mesoscale Alpine Programme

Summary The lowest pass through the Alpine crest, the Brenner Pass, was heavily instrumented with ground-based and air-borne in-situ and remote sensors during the Special Observation Period (SOP) of the Mesoscale Alpine Programme (MAP) in the fall of 1999 to study gap flow. The main objectives were to study the combined effects of changes of terrain height and changes of width in altering the flow characteristics, to investigate the coupling of the gap flow to the flow aloft, and to provide high-density measurements in the along- and cross-gap directions.Gap flows occurred during one third of the 70-day SOP, a frequency above the long term average. Gap flows took place with and without accompanying cross-barrier flow and with and without a capping inversion. A case study demonstrates the hydraulic jump-like features that occurred in gap flow on 30 October 1999 and illustrates the types of data available for further analyses.     

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.     

初始场的同化对短期气候预测的影响

基于已建立的三维变分资料同化系统(3D-VAR),利用大气环流模式(IAP9L2°×2.5°-AGCM),对同化和未同化2种初始场分别进行了17a(1988—2004年)的集合回报试验,并对试验结果进行了相关分析.结果表明:在热带地区,2组初始场下的集合回报结果差别很小,除热带外的中高纬地区差别较大,尤其是东亚地区;另外,大部分物理量场的17a异常空间相关系数的均值在同化后的初始场下也得到了提高,可能是因为同化的初始场包含了一段时间的大气信息,动力模式更加协调.     

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.     

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.     

Mesoscale Meteorological Features Associated with Heavy Precipitation in the Southern Alpine Region

Summary  Several episodes of heavy precipitation, which occurred in the region south of the Alps, have been simulated by means of the mesoscale model BOLAM3. Each case was run at 3 different resolutions, up to 4 km grid interval. The quantitative precipitation “forecast” fields are compared with available rain data. In general, satisfactory results are obtained in terms of spatial distribution and timing of precipitation, except in cases in which pre-frontal convection is dominant. The diagnostics of phenomena involved in orographic precipitation identify the different mesoscale atmospheric features associated with the interaction with topography, like the formation of low level jets, convergence zones, rainbands, and organized convective systems. These appear as “ingredients” common to all the cases considered and are shown to be sensitive to orographic forcing, as well as to the latent heat exchange processes. Received March 2, 1999/Revised June 1, 1999     

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.     

Aircraft icing in visual meterological conditions below low stratus clouds

There are many reports about icing slightly below stratus clouds in visual meteorological conditions (VMC), which obviously occurs very often but not only in polar regions and over the sea. Calculations of the ice shape at the leading edge of aircraft wings and the main rotor blades of helicopters by computers of GMGO (German Military Geophysical Office) and DLR (Deutsche Forschungsanstalt für Luft- and Raumfahrt) show, that this specific ice accretion, which often accumulates to severe icing, depends mostly on two meteorological parameters: liquid water content (LWC) and air temperature, but does not depend on the size of the drizzle droplets in the case of helicopters.     

ATOVS卫星资料同化对三江源暴雨预报影响的试验研究

选取3个三江源的典型降水个例(2018年6月30日、7月5日、8月24日),利用NCEP FNL再分析数据,并加入ATOVS湿度探测器MHS资料同化,基于WRF模式及其三维变分同化系统对三江源区域3次降水过程进行循环同化试验,分析3次事件的模拟状况,并定量分析降水结果。结果表明,3次降水事件在加入MHS资料同化后,1)模拟的水汽增大,在中层体现最明显且更符合实际情况,高空水汽和风模拟较好,地面温度预报欠佳;2)MHS资料对降水预报的影响主要体现在降水区面积和降水量的增大,提高了降水预报水平,但也带来较多空报区域;3)从TS、ETS、POD评分结果来看,有两个降水试验的提升较为明显,其中“0630”试验TS评分结果在0.5～10 mm之间提升了0.05～0.1,ETS在5 mm提升超过0.08,在10～20 mm之间也有少量提升,POD检验在0.5～20 mm之间均有提升,在0.5～10 mm之间提升最为明显,提升了0.1～0.25,“0824”试验TS与ETS在10～20 mm之间提升超过0.1,POD检验在6～20 mm之间提升了0.1～0.4,同化后降水预报有所改善,且在大阈值降水尤为明显;4)MHS资料同化对“0705”试验降水预报改善不明显,说明同化并不是每次都能给结果带来正效应,因此在使用MHS资料时不能过于信赖它,但总体上,同化MHS资料能够提升预报质量。     

GRAPES非静力数值预报模式的三维变分资料同化系统的发展

为了减少分析变量与模式状态变量之间的插值误差,改善业务预报模式的初值质量,在GRAPES等压面三维变分资料同化系统的基础上,研究发展了针对GRAPES区域模式的非静力模式变量三维变分资料同化系统(GRAPES m3DVAR).该资料同化系统的垂直坐标及其分析变量的水平分布格式、垂直跳点方案与GRAPES预报模式保持完全一致.由于垂直坐标的变化和非静力关系,m3DVAR分析系统中设计了求解动力学约束方程的新方案.通过有效的高精度数学方案,避免了地形追随坐标下平衡方程的非线性项造成的复杂计算,有效解决了非静力平衡条件下求解平衡方程中非线性项的切线性方程和伴随方程引起的困难.重新构造各种观测算子,并考虑了质量场和风场之间的平衡约束关系、背景误差协方差结构,实现对探空、地面资料、船舶报等常规观测的同化.理想单点试验和实际资料的多变量资料同化分析结果表明,非静力模式变量三维变分资料同化系统能够正确地描写多变量之间的相互作用以及物理约束关系,分析结果合理,能够有效减少原等压面三维变分资料同化系统的分析与模式变量之间需要相互插值、变换产生的误差,在一定程度上提高了分析场质量,对预报模式的初值具有一定改善.     

Tropical cyclone track and intensity prediction: The generation and assimilation of high-density, satellite-derived data

Summary The impact of recent scientific and technological advances in tropical cyclone track, intensity and structure modeling is discussed. Since the early 1990s, developments have occurred in remote sensing, data assimilation procedures, numerical models and high performance computing. In particular, there is now quasi-continuous high spatial and temporal resolution data coverage over the previously data-sparse oceans where tropical cyclones spend most of their life cycles. There has been a rapid development of data assimilation methodologies capable of using these data to initialize high-resolution prediction models. Model developments have reached a stage of maturity where the representation of many of the physical processes necessary for improved tropical cyclone track and intensity prediction are now included. Finally, available computer power has reached the teraflop range. Most operational centers have high performance computers capable of tropical cyclone modeling at resolutions necessary for skillful track and intensity simulations. This article focuses on combining all of the above developments in a tropical cyclone data analysis and prediction system. The system has produced statistically significant reductions in the mean forecast error statistics for tropical cyclone track predictions and resulted in far more realistic simulations of tropical cyclone intensity and structure. A large number of tropical cyclones have been modeled, with emphasis on those classified as being “difficult” storms to predict accurately. These difficult storms are most responsible for rapidly growing forecast errors. Our results are illustrated by case studies of such tropical cyclones. Received October 9, 2001 Revised December 28, 2001     

Monitoring of Mesoscale Precipitation Systems in the Alps and the Northern Alpine Foreland by Radar and Rain Gauges

Summary.  Mesoscale convective precipitation systems in the Alpine region are studied by analyzing radar and rain gauge data. The data from weather radars in Austria, France, Germany, and Switzerland are combined into a composite. Availability of radar data restricts the study mainly to the northern part of the Alpine region. Mesoscale convective systems (MCS) occur often in this region and are comparable to large systems observed in the USA. Seven precipitation events lasting one to six days from the years 1992–1996 are examined in detail. They all moved west to east and showed no diurnal preference in formation or dissipation. They reach sizes of 2 − 6 · 10⁴ km². MCS with leading-line trailing-stratiform structure tended to be larger and more intense. A 25-year set of rain gauge data indicates that a giant MCS (covering more than 4 · 10⁴ km² with more than 30 mm/day) occurs every 6 years in the northern Alpine region. MCS occur more frequently in the southern Alpine region. Received February 25, 1999/Revised June 29, 1999     

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     

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.     

The use of an objective index in the synoptic routine work in order to forecast low stratus

Summary ¶The cold seasons (October to March) of the years 1960 to 1984 have been investigated for inversions around Vienna. The basic material consists of climate and synoptic data as well as soundings taken at 0000UTC and 1200UTC at Vienna – Hohe Warte.These observations are filtered in three steps. First all days with inversions in the planetary boundary layer are selected which yields 1795 cases. For these cases the sounding data are used to calculate a so called inversion index I. This index has been developed as an objective measure for the intensity and the vertical extent of inversions. It is used as criterion for the information of low stratus. The second filter discards episodes with precipitation leaving 664 cases with dry but cloudy or foggy weather in the low lands. These remaining cases, for which the large scale weather patterns are determined, are again filtered to leave those with clear skies on the mountains, yielding 187 final cases. They are used to define rules for the formation of low stratus around Vienna, comprising rules for the time of onset and the persistency of low stratus. Further the large scale weather patterns leading to such situations are determined and the limiting values of the inversion index associated with persistent low stratus are found for every month of the cold saison of the year. So this work aims at improving the score of forecasting the formation and persistence of low stratus in the region of Vienna. There was made a verification with this inversion index for the cold season 1999/2000. Using the inversion index the results show that the score of low stratus forecasts are improving. In nearly 30% oft the investigated cases (16 out of 57) the forecasts would have been better, in 62% (36 out of 57) the criteria of the inversion index would have supported the forecasts.Received May 31, 2001; revised April 3, 2002; accepted August 12, 2002 Published online: May 8, 2003