共查询到20条相似文献，搜索用时 62 毫秒
1.
2.
A low pressure system that formed on 21 September 2006 over eastern India/Bay of Bengal intensified into a monsoon depression
resulting in copious rainfall over north-eastern and central parts of India. Four numerical experiments are performed to examine
the performance of assimilation schemes in simulating this monsoon depression using the Fifth Generation Mesoscale Model (MM5).
Forecasts from a base simulation (with no data assimilation), a four-dimensional data assimilation (FDDA) system, a simple
surface data assimilation (SDA) system coupled with FDDA, and a flux-adjusting SDA system (FASDAS) coupled with FDDA are compared
with each other and with observations. The model is initialized with Global Forecast System (GFS) forecast fields starting
from 19 September 2006, with assimilation being done for the first 24 hours using conventional observations, sounding and
surface data of temperature and moisture from Advanced TIROS Operational Vertical Sounder satellite and surface wind data
over the ocean from QuikSCAT. Forecasts are then made from these assimilated states. In general, results indicate that the
FASDAS forecast provides more realistic prognostic fields as compared to the other three forecasts. When compared with other
forecasts, results indicate that the FASDAS forecast yielded lower root-mean-square (r.m.s.) errors for the pressure field
and improved simulations of surface/near-surface temperature, moisture, sensible and latent heat fluxes, and potential vorticity.
Heat and moisture budget analyses to assess the simulation of convection revealed that the two forecasts with the surface
data assimilation (SDA and FASDAS) are superior to the base and FDDA forecasts. An important conclusion is that, even though
monsoon depressions are large synoptic systems, mesoscale features including rainfall are affected by surface processes. Enhanced
representation of land-surface processes provides a significant improvement in the model performance even under active monsoon
conditions where the synoptic forcings are expected to be dominant. 相似文献
3.
The performance of the Canadian Land Surface Scheme (CLASS) when coupled to the CCCma third generation general circulation model is evaluated in an AMIP II simulation. Our primary aim is to understand how CLASS processes moisture and to compare model estimates of moisture budget components with observations. The modelled mean annual precipitation and runoff, and their latitudinal structures, compare well with observations although some discrepancies remain in the simulation of regional values of these quantities. The amplitude and phase of the first harmonic of the precipitation annual cycle also compares well with observations although less well over regions of sparse precipitation and/or high topography. In the model, the canopy plays a major role in processing moisture at the land surface indicating the importance of vegetation in climate. The canopy intercepts a large fraction of the precipitation and provides the medium for returning much moisture back to the atmosphere as evapotranspiration. Though important locally, the snow moisture reservoir plays a relatively minor role in the global moisture budget. It acts primarily as a storage and delay mechanism with winter precipitation released to the ground reservoir on melting. The ground moisture reservoir also plays a major role and processes a similar amount of moisture as the canopy, although in a different manner. The globally averaged model runoff compares well with observation-based estimates, although the model partitioning into surface runoff and drainage does not agree particularly well with the single available observation-based estimate. How moisture is processed at the land surface serves as a basis for model intercomparison and for understanding the modelled moisture budget and its variation and changes with climate change. Only the most basic quantities (precipitation, runoff, and partitioning of runoff into surface runoff and drainage) may be compared with observation-based estimates, however, and the establishment of more complete moisture budget remains an important need. 相似文献
4.
Paul A. Dirmeyer 《Climate Dynamics》2013,41(3-4):1083-1097
The behavior of the water cycle in the Coupled Forecast System version 2 reforecasts and reanalysis is examined. Attention is focused on the evolution of forecast biases as the lead-time changes, and how the lead-time dependent model climatology differs from the reanalysis. Precipitation biases are evident in both reanalysis and reforecasts, while biases in soil moisture grow throughout the duration of the forecasts. Locally, the soil moisture biases may shrink or reverse sign. These biases are reflected in evaporation and runoff. The Noah land surface scheme shows the necessary relationships between evaporation and soil moisture for land-driven climate predictability. There is evidence that the atmospheric model cannot maintain the link between precipitation and antecedent soil moisture as strongly as in the real atmosphere, potentially hampering prediction skill, although there is better precipitation forecast skill over most locations when initial soil moisture anomalies are large. Bias change with lead-time, measured as the variance across ten monthly forecast leads, is often comparable to or larger than the interannual variance. Skill scores when forecast anomalies are calculated relative to reanalysis are seriously reduced over most locations when compared to validation against anomalies based on the forecast model climate at the corresponding lead-time. When all anomalies are calculated relative to the 0-month forecast, some skill is recovered over some regions, but the complex manner in which biases evolve indicates that a complete suite of reforecasts would be necessary whenever a new version of a climate model is implemented. The utility of reforecast programs is evident for operational forecast systems. 相似文献
5.
本文分析了2015年3月17—18日上海地区连续两天发生最高气温预报失误的天气背景,并使用当日实况观测和业务预报使用的数值模式资料,剖析预报失败的原因,分析表明:对天空状况的误判是导致17日预报失败的主要原因,且东南风预报偏强更进一步增大了预报误差;冷空气影响时间的判断失误是导致18日预报失败的主要原因。从模式预报的实时检验、预报的跳跃性和不确定性角度分析了预报中存在的问题:预报员应重视本地和上游实况,从传统对单一确定性模式预报的依赖向多模式多起报时次及能提供概率预报和不确定性信息的业务集合预报的分析思路转型。此外,还需加强对集合预报的系统性检验、评估及数值预报释用产品的开发,增加包含不确定性信息的公众天气预报发布形式。 相似文献
6.
bbGPS/PWV资料三维变分同化改进MM5降水预报连续试验的评估 总被引：5，自引：0，他引：5
利用区域地基GPS网反演的高时空密度的大气垂直方向水汽总量，也称为可降水量（PWV），可大大弥补常规探空探测水汽资料的不足。为了全面评估区域GPS网PWV资料同化对业务数值天气预报改进程度的目的，在个例研究分析的基础上，进行了连续38天的GPS/PWV资料三维同化（3D-Var）改进数值业务预报的试验。研究方法是根据长江三角洲地区GPS气象网在2002年梅雨和盛夏季节观测的刖资料，通过三维变分同化建立中尺度数值预报模式MM5的初始场，逐日作出长江三角洲地区24小时的降水量预报。以6小时累积雨量为对象，与未同化GPS／刖资料的MM5的相应预报比较，通过多种评分方法，评估了GPS／PWV资料改进MM5降水预报的效果。结果表明GPS／PWV资料同化后的MM5降水预报能力在大部分时间和大部分地区都有所提高，主要是伪击率有较明显的下降，对小范围降水预报的改进更为明显。预报明显改进的区域恰好位于GPS站填补常规探空站间距较大的地区。 相似文献
7.
GRAPES-GFS模式暴雨预报天气学检验特征 总被引：5，自引：4，他引：1
本文采用天气学检验方法,对2016年度国家气象中心GRAPES全球数值预报系统(GRAPES-GFS)业务预报暴雨过程及2013-2015年部分回算个例进行了检验,并结合对比欧洲中期天气预报中心确定性预报模式(EC模式)和国家气象中心全球谱模式T639L60(T639模式)降水预报,梳理总结业务GRAPES-GFS模式预报性能优势和系统性偏差特征。被检验暴雨过程共38次,其中南方暴雨过程20次,北方暴雨过程6次,热带扰动或台风降水过程12次。依靠预报员主观天气学检验分析,从降水预报效果检验出发,结合主要影响天气系统和示踪物理量检验,梳理总结模式预报系统性偏差,以期全面发掘该业务预报模式性能。结果表明对短期时效内的降水预报,GRAPES-GFS模式预报稳定性较好,整体明显优于T639模式。但还存在诸如对对流性降水预报较实况偏北或对主雨带南侧暖区降水预报不足的偏差特征;另对弱高空波动背景下的对流性降水预报偏弱;而在降水预报强度大致正确的情况下,对降水系统南侧偏南气流控制区域预报湿度偏大,对副热带地区的低涡系统预报偏强。 相似文献
8.
正1School of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610025, China2International Center for Climate and Environment Sciences, Institute of Atmospheric Physics,Chinese Academy of Sciences, Beijing 100029, China3University of Chinese Academy of Sciences, Beijing 100049, China 相似文献
9.
A conceptual coupled ocean-atmosphere model was used to study coupled ensemble data assimilation schemes with a focus on the role of ocean-atmosphere interaction in the assimilation. The optimal scheme was the fully coupled data assimilation scheme that employs the coupled covariance matrix and assimilates observations in both the atmosphere and ocean. The assimilation of synoptic atmospheric variability that captures the temporal fluctuation of the weather noise was found to be critical for the estimation of not only the atmospheric, but also oceanic states. The synoptic atmosphere observation was especially important in the mid-latitude system, where oceanic variability is driven by weather noise. The assimilation of synoptic atmospheric variability in the coupled model improved the atmospheric variability in the analysis and the subsequent forecasts, reducing error in the surface forcing and, in turn, in the ocean state. Atmospheric observation was able to further improve the oceanic state estimation directly through the coupled covariance between the atmosphere and ocean states. Relative to the mid-latitude system, the tropical system was influenced more by ocean-atmosphere interaction and, thus, the assimilation of oceanic observation becomes more important for the estimation of the ocean and atmosphere. 相似文献
10.
Summary The Regional Atmospheric Modeling System (RAMS) has been widely used to simulate relatively short-term atmospheric processes.
To perform full-year to multi-year model integrations, a climate version of RAMS (ClimRAMS) has been developed, and is used
to simulate diurnal, seasonal, and annual cycles of atmospheric and hydrologic variables and interactions within the central
United States during 1989. The model simulation uses a 200-km grid covering the conterminous United States, and a nested,
50-km grid covering the Great Plains and Rocky Mountain states of Kansas, Nebraska, South Dakota, Wyoming, and Colorado. The
model’s lateral boundary conditions are forced by six-hourly NCEP reanalysis products. ClimRAMS includes simplified precipitation
and radiation sub-models, and representations that describe the seasonal evolution of vegetation-related parameters. In addition,
ClimRAMS can use all of the general RAMS capabilities, like its more complex radiation sub-models, and explicit cloud and
precipitation microphysics schemes. Thus, together with its nonhydrostatic and fully-interactive telescoping-grid capabilities,
ClimRAMS can be applied to a wide variety of problems. Because of non-linear interactions between the land surface and atmosphere,
simulating the observed climate requires simulating the observed diurnal, synoptic, and seasonal cycles. While previous regional
climate modeling studies have demonstrated their ability to simulate the seasonal cycles through comparison with observed
monthly-mean temperature and precipitation data sets, this study demonstrates that a regional climate model can also capture
observed diurnal and synoptic variability. Observed values of daily precipitation and maximum and minimum screen-height air
temperature are used to demonstrate this ability.
Received September 27, 1999 Revised December 11, 1999 相似文献
11.
A Climate Version of the Regional Atmospheric Modeling System 总被引：1，自引：1，他引：0
Summary The Regional Atmospheric Modeling System (RAMS) has been widely used to simulate relatively short-term atmospheric processes.
To perform full-year to multi-year model integrations, a climate version of RAMS (ClimRAMS) has been developed, and is used
to simulate diurnal, seasonal, and annual cycles of atmospheric and hydrologic variables and interactions within the central
United States during 1989. The model simulation uses a 200-km grid covering the conterminous United States, and a nested,
50-km grid covering the Great Plains and Rocky Mountain states of Kansas, Nebraska, South Dakota, Wyoming, and Colorado. The
model’s lateral boundary conditions are forced by six-hourly NCEP reanalysis products. ClimRAMS includes simplified precipitation
and radiation sub-models, and representations that describe the seasonal evolution of vegetation-related parameters. In addition,
ClimRAMS can use all of the general RAMS capabilities, like its more complex radiation sub-models, and explicit cloud and
precipitation microphysics schemes. Thus, together with its nonhydrostatic and fully-interactive telescoping-grid capabilities,
ClimRAMS can be applied to a wide variety of problems. Because of non-linear interactions between the land surface and atmosphere,
simulating the observed climate requires simulating the observed diurnal, synoptic, and seasonal cycles. While previous regional
climate modeling studies have demonstrated their ability to simulate the seasonal cycles through comparison with observed
monthly-mean temperature and precipitation data sets, this study demonstrates that a regional climate model can also capture
observed diurnal and synoptic variability. Observed values of daily precipitation and maximum and minimum screen-height air
temperature are used to demonstrate this ability.
Received September 27, 1999 Revised December 11, 1999 相似文献
12.
Short-range prediction of a heavy precipitation event by assimilating Chinese CINRAD-SA radar reflectivity data using complex cloud analysis 总被引：5，自引：0，他引：5
Summary With the ARPS (Advanced Regional Prediction System) Data Analysis System (ADAS) and its complex cloud analysis scheme, the
reflectivity data from a Chinese CINRAD-SA Doppler radar are used to analyze 3D cloud and hydrometeor fields and in-cloud
temperature and moisture. Forecast experiments starting from such initial conditions are performed for a northern China heavy
rainfall event to examine the impact of the reflectivity data and other conventional observations on short-range precipitation
forecast.
The full 3D cloud analysis mitigates the commonly known spin-up problem with precipitation forecast, resulting a significant
improvement in precipitation forecast in the first 4 to 5 hours. In such a case, the position, timing and amount of precipitation
are all accurately predicted. When the cloud analysis is used without in-cloud temperature adjustment, only the forecast of
light precipitation within the first hour is improved.
Additional analysis of surface and upper-air observations on the native ARPS grid, using the 1 degree real-time NCEP AVN analysis
as the background, helps improve the location and intensity of rainfall forecasting slightly. Hourly accumulated rainfall
estimated from radar reflectivity data is found to be less accurate than the model predicted precipitation when full cloud
analysis is used. 相似文献
13.
To investigate the impact of various types of data on medium-range forecasts, observing system experiments are performed using an assimilation algorithm based on the National Centers for Environmental Prediction (NCEP)/Department of Energy (DOE) reanalysis system. Data-denial experiments for radiosonde, satellite, aircraft, and sea surface observations, and selected data experiments for radiosonde and surface data, are conducted for the boreal summer of 1997 and the boreal winter of 1997/1998. The data assimilation system used in this study is remarkably dependent on radiosonde data, which provides information about the three-dimensional structure of the atmosphere. As expected, the impact of radiosonde observations on medium-range forecasts is strongly positive over the Northern Hemisphere and tropics, whereas the satellite system is most beneficial over the Southern Hemisphere. These results are also found in experiments simulating historical changes in observation systems. Over the tropics, assimilation without radiosonde observations generates unbalanced analyses resulting in unrealistic forecasts that must be corrected by the forecast model. Forecasts based on analysis from the observation data before the era of radiosonde observation are found to be less meaningful. In addition, the impacts on forecasts are closely related to the geographical distribution of observation data. The memory of observation data embedded in the analysis tends to persist throughout forecasts. However, cases exist where the effect of forecast error growth is more dominant than that of analysis error, e.g., over East Asia in summer, and where the deficiency in observations is supplemented or the imbalance in analysis is adjusted by the forecast model during the period of forecasts. Forecast error growth may be related to the synoptic correction performed by the data assimilation system. Over data-rich areas, analysis fields are corrected to a greater extent by the data assimilation system than are those over data-poor areas, which can cause the forecast model to produce more forecast errors in medium-range forecasts. It is found that even one month per season is sufficient for forecast skill verification in data impact experiments. Additionally, the use of upper-air observations is found to benefit areas that are downstream of observation data-rich areas. 相似文献
14.
利用华南精细数值天气预报模式,设计了无同化资料(CTRL)、同化雷达反演水汽(EXP1)以及同化雷达反演水汽、地面和探空资料(EXP2)三个试验,对2017年登陆广东沿海的四个台风降水预报与路径预报进行模拟,以评估资料同化对登陆台风短期降水预报、路径预报的影响。分析结果如下:雷达反演水汽同化后对未来24小时降水预报技巧均有正的改善,对台风路径预报影响不大;在此基础上同化地面、探空资料后对台风路径预报有改进,对降水预报改进不明显(与EXP1比)。通过诊断分析台风“玛娃”,发现模式初值场水汽的增量配合对流上升区有利于短时间内成云致雨,从而提高短时降水预报;地面及探空资料同化有利于登陆台风的短时路径预报。 相似文献
15.
V. Guidard C. Fischer M. Nuret A. Džiedžic 《Meteorology and Atmospheric Physics》2006,92(3-4):161-173
Summary The aim of this work is to evaluate several 3D-VAR assimilation cycles in the limited area model ALADIN, in comparison with
the dynamical adaptation of the global model (ARPEGE) analysis, and with a focus on the precipitation forecasts. We perform
a detailed evaluation for a specific, well documented, test case: Intensive observing period 14 of the Mesoscale Alpine Programme
(MAP) field campaign, which is well described through various MAP data. The meteorological situation was of high interest,
with triggering of convection both over the Alps and over the sea, therefore it has been chosen as the framework of our case
study. There are clear benefits in favour of the 3D-VAR assimilation cycles over the first hours of forecast thanks to the
observations and to the preservation of small-scale features. These improvements are further enhanced with a large-scale update
step added to the 3D-VAR analysis. Innovative data are used, such as relative humidity pseudo-profiles which are processed
data generated from cloud classification. They bring an important information which can redesign the frontal areas. When they
are used jointly with the conventional observations in the 3D-VAR, they also lead to an improvement of the precipitation scores. 相似文献
16.
V. P. Meleshko V. M. Kattsov V. A. Govorkova P. V. Sporyshev I. M. Shkol’nik B. E. Shneerov 《Russian Meteorology and Hydrology》2008,33(9):541-552
Probable climate changes in Russia in the 21st century are considered based on the results of global climate simulations with an ensemble of coupled atmosphere-ocean CMIP3 models. The future changes in the surface air temperature, atmospheric pressure, cloud amount, atmospheric precipitation, snow cover, soil water content, and annual runoff in Russia and some of its regions in the early, middle, and late 21st century are analyzed using the A2 scenario of the greenhouse gas and aerosol emission. Future changes in the yearly highest and lowest surface air temperatures and in summer precipitation of high intensity are estimated for Russia. Possible oscillations of the Caspian Sea level associated with the expected global climate warming are estimated. In addition to the estimates of the ensemble mean changes in climatic characteristics, the information about standard deviations and statistical significance of the corresponding climate changes is given. 相似文献
17.
GRAPES-MESO模式浅对流参数化的改进与试验 总被引：5，自引：2，他引：3
参考Berg 等2005、2013年提出的扰动对流触发函数方法,对GRAPES-Meso模式积云对流参数化方案(KF eta)中的浅对流激发进行改进设计和试验,将单一的温、湿度触发改为对近地层进行一组温度、湿度扰动后的触发,并且用与该组扰动相关的边界层温、湿度分布确定的联合概率密度函数(JPDF)来表征浅对流云的特征参量及计算浅对流的强度。 着重分析了改进方案的浅对流激发、浅对流对环境场的反馈、模式地面降水和2 m气温的相关响应等,并与原方案和相关观测比较,验证了改进方案的合理性。 结果显示,改进方案比原方案能较早地激发出浅对流,且浅对流的激发频次高,浅对流激发的增加致使在模式低层距地数百米至2—3 km的垂直层内对环境温、湿度场和云雨水反馈增大,对GRAPES-Meso浅对流激发偏弱有改进作用,并对格点尺度与次网格尺度降水分配比不协调有改进。 对连续两个月批量试验的检验表明,浅对流激发的改进,可对GRAPES-Meso的24 h降水预报技巧的提高和2 m气温偏差的减小等产生不同程度的正影响。 相似文献
18.
四维数据同化在一次梅雨锋暴雨过程中的应用研究 总被引：1，自引：0，他引：1
通过2008年梅雨季内的一次暴雨过程来着重研究WRF模式中四维测站同化技术的应用情况以及不同的同化策略对模拟结果的影响,使用的同化资料包括常规高空和地面探测资料。结果表明:高空探测资料的同化能够较好地修正中α尺度系统的移动发展速度,从而有效地改进强降雨带的落区位置;湿度观测资料的同化反而降低了强降雨带的降水强度,这可能因为同化湿度观测量可能减小近饱和区域内的水汽混合比,从而导致这些区域内的潜热加热减小,对流强度变弱,降水减少;地面观测资料的同化能够对模式行星边界层进行修正,这种同化影响通常在模式最低层上表现得较为明显,向上逐渐减小,而边界层模拟质量对降水至关重要,因此地面观测资料的加入使得模拟降水更加接近于实况。区域嵌套模拟试验表明对粗网格区域进行四维分析同化处理可以有效抑制中α尺度模式误差的增长,对细网格区域进行四维测站同化处理则可以进一步抑制中β尺度模式误差增长。 相似文献
19.
基于GRAPES-MESO 10 km系统,提高模式动力框架计算精度和稳定性,选择调试适合高分辨率模式的物理过程参数化方案组合,建立面向数值天气预报的全国雷达质量控制拼图系统,通过云分析系统融合全国三维组网反射率因子拼图,建立面向中小尺度系统的对流可分辨同化系统和陆面资料同化系统,实现雷达径向风、风廓线雷达、FY-4A成像仪辐射率、卫星云导风、卫星GNSSRO、地面降水观测以及近地面资料等非常规局地稠密资料的同化应用,发展快速循环技术,建立全国3 km间隔3 h的快速循环同化预报系统——CMA-MESO（GRAPES-MESO 3 km）并实现业务化运行。2020年6—9月汛期业务检验结果表明:CMA-MESO预报的近地面要素（降水、2 m温度、10 m风场）检验评分全面超越GRAPES-MESO 10 km结果;CMA-MESO的24 h累积降水TS评分略低于欧洲中期天气预报中心（ECMWF）的结果,但逐3 h累积降水预报TS评分尤其是对于较大降水阈值评分明显优于ECMWF结果;同时,对于能够表征模式对降水时空精细化特征预报能力的降水频次和降水强度等检验,CMA-MESO对我国汛期的预报准确率超过了ECMWF细网格模式结果。 相似文献
20.
To improve the accuracy of short-term(0–12 h) forecasts of severe weather in southern China, a real-time storm-scale forecasting system, the Hourly Assimilation and Prediction System(HAPS), has been implemented in Shenzhen, China. The forecasting system is characterized by combining the Advanced Research Weather Research and Forecasting(WRF-ARW)model and the Advanced Regional Prediction System(ARPS) three-dimensional variational data assimilation(3DVAR) package. It is capable of assimilating radar reflectivity and radial velocity data from multiple Doppler radars as well as surface automatic weather station(AWS) data. Experiments are designed to evaluate the impacts of data assimilation on quantitative precipitation forecasting(QPF) by studying a heavy rainfall event in southern China. The forecasts from these experiments are verified against radar, surface, and precipitation observations. Comparison of echo structure and accumulated precipitation suggests that radar data assimilation is useful in improving the short-term forecast by capturing the location and orientation of the band of accumulated rainfall. The assimilation of radar data improves the short-term precipitation forecast skill by up to9 hours by producing more convection. The slight but generally positive impact that surface AWS data has on the forecast of near-surface variables can last up to 6–9 hours. The assimilation of AWS observations alone has some benefit for improving the Fractions Skill Score(FSS) and bias scores; when radar data are assimilated, the additional AWS data may increase the degree of rainfall overprediction. 相似文献