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1.
Soil moisture exhibits outstanding memory characteristics and plays a key role within the climate system. Especially through its impacts on the evapotranspiration of soils and plants, it may influence the land energy balance and therefore surface temperature. These attributes make soil moisture an important variable in the context of weather and climate forecasting. In this study we investigate the value of (initial) soil moisture information for sub-seasonal temperature forecasts. For this purpose we employ a simple water balance model to infer soil moisture from streamflow observations in 400 catchments across Europe. Running this model with forecasted atmospheric forcing, we derive soil moisture forecasts, which we then translate into temperature forecasts using simple linear relationships. The resulting temperature forecasts show skill beyond climatology up to 2 weeks in most of the considered catchments. Even if forecasting skills are rather small at longer lead times with significant skill only in some catchments at lead times of 3 and 4 weeks, this soil moisture-based approach shows local improvements compared to the monthly European Centre for Medium Range Weather Forecasting (ECMWF) temperature forecasts at these lead times. For both products (soil moisture-only forecast and ECMWF forecast), we find comparable or better forecast performance in the case of extreme events, especially at long lead times. Even though a product based on soil moisture information alone is not of practical relevance, our results indicate that soil moisture (memory) is a potentially valuable contributor to temperature forecast skill. Investigating the underlying soil moisture of the ECMWF forecasts we find good agreement with the simple model forecasts, especially at longer lead times. Analyzing the drivers of the temperature forecast skills we find that they are mainly controlled by the strengths of (1) the soil moisture-temperature coupling and (2) the soil moisture memory. We find a negative relationship between these controls that weakens the forecast skills, nevertheless there is a middle ground between both controls in several catchments, as shown by our results. 相似文献
2.
土壤湿度影响中国夏季气候的数值试验 总被引：10，自引：0，他引：10
利用"全球土壤湿度计划第2阶段"提供的土壤湿度资料强迫区域气候模式RegCM3,通过数值试验讨论了土壤湿度对东亚夏季气候模拟效果的影响。结果表明,合理考虑土壤湿度的作用,能够提高区域气候模式对中国夏季降水和2 m气温的空间分布型及逐日变化的模拟效果;模拟结果与观测的相关分析显示,降水和2 m气温的年际变化都得到了有效改进,这种改进在气温上尤为明显。不过上述改进具有区域依赖性。数值试验结果表明,气温对土壤湿度的敏感性强于降水,这也从一个侧面说明提高降水模拟效果的难度。总体而言,合理的土壤湿度能够提高区域气候模式对中国夏季气候的模拟能力。因此,合理描述土壤湿度的变化,是提高中国夏季气候预报技巧的潜在途径之一。 相似文献
3.
Impact of snow initialization on sub-seasonal forecasts 总被引：2，自引：1，他引：1
Y. J. Orsolini R. Senan G. Balsamo F. J. Doblas-Reyes F. Vitart A. Weisheimer A. Carrasco R. E. Benestad 《Climate Dynamics》2013,41(7-8):1969-1982
The influence of the snowpack on wintertime atmospheric teleconnections has received renewed attention in recent years, partially for its potential impact on seasonal predictability. Many observational and model studies have indicated that the autumn Eurasian snow cover in particular, influences circulation patterns over the North Pacific and North Atlantic. We have performed a suite of coupled atmosphere-ocean simulations with the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble forecast system to investigate the impact of accurate snow initialisation. Pairs of 2-month ensemble forecasts were started every 15 days from the 15th of October through the 1st of December in the years 2004–2009, with either realistic initialization of snow variables based on re-analyses, or else with “scrambled” snow initial conditions from an alternate autumn date and year. Initially, in the first 15 days, the presence of a thicker snowpack cools surface temperature over the continental land masses of Eurasia and North America. At a longer lead of 30-day, it causes a warming over the Arctic and the high latitudes of Eurasia due to an intensification and westward expansion of the Siberian High. It also causes a cooling over the mid-latitudes of Eurasia, and lowers sea level pressures over the Arctic. This “warm Arctic—cold continent” difference means that the forecasts of near-surface temperature with the more realistic snow initialization are in closer agreement with re-analyses, reducing a cold model bias over the Arctic and a warm model bias over mid-latitudes. The impact of realistic snow initialization upon the forecast skill in snow depth and near-surface temperature is estimated for various lead times. Following a modest skill improvement in the first 15 days over snow-covered land, we also find a forecast skill improvement up to the 30-day lead time over parts of the Arctic and the Northern Pacific, which can be attributed to the realistic snow initialization over the land masses. 相似文献
4.
Evaluation of summer temperature and precipitation predictions from NCEP CFSv2 retrospective forecast over China 总被引：1，自引：0，他引：1
National Centers for Environmental Prediction recently upgraded its operational seasonal forecast system to the fully coupled climate modeling system referred to as CFSv2. CFSv2 has been used to make seasonal climate forecast retrospectively between 1982 and 2009 before it became operational. In this study, we evaluate the model’s ability to predict the summer temperature and precipitation over China using the 120 9-month reforecast runs initialized between January 1 and May 26 during each year of the reforecast period. These 120 reforecast runs are evaluated as an ensemble forecast using both deterministic and probabilistic metrics. The overall forecast skill for summer temperature is high while that for summer precipitation is much lower. The ensemble mean reforecasts have reduced spatial variability of the climatology. For temperature, the reforecast bias is lead time-dependent, i.e., reforecast JJA temperature become warmer when lead time is shorter. The lead time dependent bias suggests that the initial condition of temperature is somehow biased towards a warmer condition. CFSv2 is able to predict the summer temperature anomaly in China, although there is an obvious upward trend in both the observation and the reforecast. Forecasts of summer precipitation with dynamical models like CFSv2 at the seasonal time scale and a catchment scale still remain challenge, so it is necessary to improve the model physics and parameterizations for better prediction of Asian monsoon rainfall. The probabilistic skills of temperature and precipitation are quite limited. Only the spatially averaged quantities such as averaged summer temperature over the Northeast China of CFSv2 show higher forecast skill, of which is able to discriminate between event and non-event for three categorical forecasts. The potential forecast skill shows that the above and below normal events can be better forecasted than normal events. Although the shorter the forecast lead time is, the higher deterministic prediction skill appears, the probabilistic prediction skill does not increase with decreased lead time. The ensemble size does not play a significant role in affecting the overall probabilistic forecast skill although adding more members improves the probabilistic forecast skill slightly. 相似文献
5.
利用TIGGE资料集下欧洲中期天气预报中心(ECMWF)、日本气象厅(JMA)、美国国家环境预报中心(NCEP)、中国气象局(CMA)和英国气象局(UKMO)5个模式预报的结果,对基于卡尔曼滤波的气温和降水的多模式集成预报进行研究。结果表明,卡尔曼滤波方法的预报效果优于消除偏差集合平均(BREM)和单模式的预报,但是对于地面气温和降水,其预报效果也存在一定的差异。在中国区域2 m气温的预报中,卡尔曼滤波的预报结果最优。而对于24 h累积降水预报,尽管卡尔曼滤波在所有量级下的TS评分均优于BREM,但随着预报时效增加,其在大雨及以上量级的TS评分跟最佳单模式UKMO预报相当,改进效果不明显。卡尔曼滤波在地面气温和24 h累积降水每个预报时效下的均方根误差均最优,预报效果更佳且稳定。 相似文献
6.
The impact of realistic atmospheric initialisation on the seasonal prediction of tropical Pacific sea surface temperatures is explored with the Predictive Ocean–Atmosphere Model for Australia (POAMA) dynamical seasonal forecast system. Previous versions of POAMA used data from an Atmospheric Model Intercomparison Project (AMIP)-style simulation to initialise the atmosphere for the hindcast simulations. The initial conditions for the hindcasts did not, therefore, capture the true intra-seasonal atmospheric state. The most recent version of POAMA has a new Atmosphere and Land Initialisation scheme (ALI), which captures the observed intra-seasonal atmospheric state. We present the ALI scheme and then compare the forecast skill of two hindcast datasets, one with AMIP-type initialisation and one with realistic initial conditions from ALI, focussing on the prediction of El Niño. For eastern Pacific (Niño3) sea surface temperature anomalies (SSTAs), both experiments beat persistence and have useful SSTA prediction skill (anomaly correlations above 0.6) at all lead times (forecasts are 9 months duration). However, the experiment with realistic atmospheric initial conditions from ALI is an improvement over the AMIP-type initialisation experiment out to about 6 months lead time. The improvements in skill are related to improved initial atmospheric anomalies rather than an improved initial mean state (the forecast drift is worse in the ALI hindcast dataset). Since we are dealing with a coupled system, initial atmospheric errors (or differences between experiments) are amplified though coupled processes which can then lead to long lasting errors (or differences). 相似文献
7.
Forecast skill as a function of time lead and time averaging is examined in two 6-member ensembles of seasonal hindcasts.
One ensemble is produced with the second generation general circulation model of the Canadian Centre for Climate Modelling
and Analysis (GCM2) and the other with a reduced resolution version of the numerical weather prediction model of the Canadian
Meteorological Centre (SEF). The integrations are initiated from the NCEP/NCAR reanalyzed data. Monthly sea surface temperature
anomalies observed prior to the forecast period are maintained throughout the forecast season. A statistical forecast improvement
technique, based on the singular value decomposition of forecast and reanalyzed fields, is discussed and evaluated. A simple
analogue of the hindcast integrations is used to examine the behavior of two common skill scores, the correlation skill score
and the explained variance skill score. The maximal skill score and the corresponding optimal forecast in this analogue are
identified. The total skill of the optimal forecast is a sum of two terms, one associated with the initial conditions and
the other with the lower boundary forcing. The two sources of skill operate on different time scales, with initial conditions
being more important in the first one-two weeks and the atmospheric response to the boundary forcing becoming more dominant
for longer time leads and time averages. This suggests that these sources of skill should be considered separately in forecast
optimization. The statistical technique is moderately successful in improving the skill of monthly to seasonal forecasts of
500 hPa height (Z
500) and 700 hPa temperature (T
700) in the Northern Hemisphere and in the North Pacific/North America sector. The improvement is better when the forecasts for
the first week and for the rest of the season are optimized separately. The SEF model produces better Z
500 and T
700 forecasts than GCM2 in the first one-two weeks whereas GCM2 performs slightly better at longer time leads. The skill of zero
time lead forecast decays rapidly with averaging interval for time averages up to about 30–45 days and stabilizes, or even
rises, for longer time averages. Excluding the first week from seasonal forecasts results in substantial degradation of predictive
skill.
Received: 1 November 1999 / Accepted: 24 May 2000 相似文献
8.
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
9.
Based on the reforecast data (1999–2010) of three operational models [the China Meteorological Administration (CMA), the National Centers for Environmental Prediction of the U.S. (NCEP) and the European Centre for Medium-Range Weather Forecasts (ECMWF)] that participated in the Subseasonal to Seasonal Prediction (S2S) project, we identified the major sources of subseasonal prediction skill for heatwaves over the Yangtze River basin (YRB). The three models show limited prediction skills in terms of the fraction of correct predictions for heatwave days in summer; the Heidke Skill Score drops quickly after a 5-day forecast lead and falls down close to zero beyond the lead time of 15 days. The superior skill of the ECMWF model in predicting the intensity and duration of the YRB heatwave is attributable to its fidelity in capturing the phase evolution and amplitude of high-pressure anomalies associated with the intraseasonal oscillation and the dryness of soil moisture induced by less precipitation via the land–atmosphere coupling. The effects of 10–30-day and 30–90-day circulation prediction skills on heatwave predictions are comparable at shorter forecast leads (10 days), while the biases in 30–90-day circulation amplitude prediction show close connection with the degradation of heatwave prediction skill at longer forecast leads (> 15–20 days). The biases of intraseasonal circulation anomalies further affect precipitation anomalies and thus land conditions, causing difficulty in capturing extremely hot days and their persistence in the S2S models. 相似文献
10.
土壤湿度是影响天气和气候非常重要的因子之一,但目前针对土壤湿度可预报性的研究报道相对较少。该文在对BCC_CSM模式进行了适合的陆面初始化的条件下,设计了两组在中国东部地区采用不同土壤湿度初值的回报试验研究该地区土壤湿度的可预报性及初值对其可预报性影响问题。试验结果表明:BCC_CSM模式在真实的外场强迫下可以模拟出相对合理的土壤湿度;土壤湿度的可预报性在表层约为3候,随着深度的增加,土壤湿度的可预报性持续时间增加,在中层预报性甚至能达到月尺度以上;初值对于土壤湿度的预报存在影响,在表层影响时间约为2~3候,影响时间随着深度增加;浅层土壤湿度受降水的影响较大,浅层土壤湿度变化滞后降水变化约1~2 d,中层土壤湿度变化与降水变化存在5 d左右的滞后关系。 相似文献
11.
基于土壤湿度和年际增量方法的我国夏季降水预测试验 总被引：1，自引：0，他引：1
选取欧亚大陆9个关键区的土壤湿度年际增量作为预测因子,采用变形的典型相关分析(BP-CCA)结合集合典型相关分析(ECC)方法建立集合预测模型,对我国东部夏季降水的年际增量进行预测,进而预测夏季降水。其中,1980~2004年的资料用于历史预测试验,而2005~2014年的资料用于独立样本预测试验。首先利用BP-CCA方法对9个因子分别建立单因子预测模型,然后采用ECC方法对9个预测因子按照不同的组合方式建立集合预测模型,并且对独立样本检验的效果进行了评估。结果表明,不同预测因子的组合对我国夏季降水均表现出一定的预测能力:东欧平原、贝加尔湖以北、我国河套地区及长江以南地区的土壤湿度对华北夏季降水预测效果较好;而巴尔喀什湖以北地区、我国西北地区、河套地区以及长江以南地区的土壤湿度对江淮夏季降水有较好预测效果;东欧平原、巴尔喀什湖以北地区以及我国河套地区的土壤湿度对华南降水预测技巧较高。这三组模型预测出的降水变化趋势与相应区域的观测结果较为一致,且预测评分(PS)均超过70分,距平相关系数(ACC)均为正值。研究表明土壤湿度因子中包含了对我国夏季降水有用的预测信号,可以考虑将土壤湿度应用于夏季降水的预测业务中。 相似文献
12.
In this study,we evaluate the forecast skill of the subseasonal-to-seasonal(S2S)prediction model of the Beijing Climate Center(BCC)for the boreal summer intraseasonal oscillation(BSISO).We also discuss the key factors that inhibit the BSISO forecast skill in this model.Based on the bivariate anomaly correlation coefficient(ACC)of the BSISO index,defined by the first two EOF modes of outgoing longwave radiation and 850-hPa zonal wind anomalies over the Asian monsoon region,we found that the hindcast skill degraded as the lead time increased.The ACC dropped to below 0.5for lead times of 11 days and longer when the predicted BSISO showed weakened strength and insignificant northward propagation.To identify what causes the weakened forecast skill of BSISO at the forecast lead time of 11 days,we diagnosed the main mechanisms responsible for the BSISO northward propagation.The same analysis was also carried out using the observations and the outputs of the four-day forecast lead that successfully predicted the observed northward-propagating BSISO.We found that the lack of northward propagation at the 11-day forecast lead was due to insufficient increases in low-level cyclonic vorticity,moistening and warm temperature anomalies to the north of the convection,which were induced by the interaction between background mean flows and BSISO-related anomalous fields.The BCC S2S model can predict the background monsoon circulations,such as the low-level southerly and the northerly and easterly vertical shears,but has limited capability in forecasting the distributions of circulation and moisture anomalies. 相似文献
13.
Bin Wang June-Yi Lee In-Sik Kang J. Shukla C.-K. Park A. Kumar J. Schemm S. Cocke J.-S. Kug J.-J. Luo T. Zhou B. Wang X. Fu W.-T. Yun O. Alves E. K. Jin J. Kinter B. Kirtman T. Krishnamurti N. C. Lau W. Lau P. Liu P. Pegion T. Rosati S. Schubert W. Stern M. Suarez T. Yamagata 《Climate Dynamics》2009,33(1):93-117
We assessed current status of multi-model ensemble (MME) deterministic and probabilistic seasonal prediction based on 25-year (1980–2004) retrospective forecasts performed by 14 climate model systems (7 one-tier and 7 two-tier systems) that participate in the Climate Prediction and its Application to Society (CliPAS) project sponsored by the Asian-Pacific Economic Cooperation Climate Center (APCC). We also evaluated seven DEMETER models’ MME for the period of 1981–2001 for comparison. Based on the assessment, future direction for improvement of seasonal prediction is discussed. We found that two measures of probabilistic forecast skill, the Brier Skill Score (BSS) and Area under the Relative Operating Characteristic curve (AROC), display similar spatial patterns as those represented by temporal correlation coefficient (TCC) score of deterministic MME forecast. A TCC score of 0.6 corresponds approximately to a BSS of 0.1 and an AROC of 0.7 and beyond these critical threshold values, they are almost linearly correlated. The MME method is demonstrated to be a valuable approach for reducing errors and quantifying forecast uncertainty due to model formulation. The MME prediction skill is substantially better than the averaged skill of all individual models. For instance, the TCC score of CliPAS one-tier MME forecast of Niño 3.4 index at a 6-month lead initiated from 1 May is 0.77, which is significantly higher than the corresponding averaged skill of seven individual coupled models (0.63). The MME made by using 14 coupled models from both DEMETER and CliPAS shows an even higher TCC score of 0.87. Effectiveness of MME depends on the averaged skill of individual models and their mutual independency. For probabilistic forecast the CliPAS MME gains considerable skill from increased forecast reliability as the number of model being used increases; the forecast resolution also increases for 2 m temperature but slightly decreases for precipitation. Equatorial Sea Surface Temperature (SST) anomalies are primary sources of atmospheric climate variability worldwide. The MME 1-month lead hindcast can predict, with high fidelity, the spatial–temporal structures of the first two leading empirical orthogonal modes of the equatorial SST anomalies for both boreal summer (JJA) and winter (DJF), which account for about 80–90% of the total variance. The major bias is a westward shift of SST anomaly between the dateline and 120°E, which may potentially degrade global teleconnection associated with it. The TCC score for SST predictions over the equatorial eastern Indian Ocean reaches about 0.68 with a 6-month lead forecast. However, the TCC score for Indian Ocean Dipole (IOD) index drops below 0.40 at a 3-month lead for both the May and November initial conditions due to the prediction barriers across July, and January, respectively. The MME prediction skills are well correlated with the amplitude of Niño 3.4 SST variation. The forecasts for 2 m air temperature are better in El Niño years than in La Niña years. The precipitation and circulation are predicted better in ENSO-decaying JJA than in ENSO-developing JJA. There is virtually no skill in ENSO-neutral years. Continuing improvement of the one-tier climate model’s slow coupled dynamics in reproducing realistic amplitude, spatial patterns, and temporal evolution of ENSO cycle is a key for long-lead seasonal forecast. Forecast of monsoon precipitation remains a major challenge. The seasonal rainfall predictions over land and during local summer have little skill, especially over tropical Africa. The differences in forecast skills over land areas between the CliPAS and DEMETER MMEs indicate potentials for further improvement of prediction over land. There is an urgent need to assess impacts of land surface initialization on the skill of seasonal and monthly forecast using a multi-model framework. 相似文献
14.
基于欧洲中期天气预报中心（European Centre for Medium-Range Weather Forecasts,ECMWF）集合预报系统的降水相态产品（precipitation type,PTYPE）,分别以HSS评分最优、TS评分最优和频率偏差最优为标准,运用最优概率阈值法,生成雨、雨夹雪、雪和冻雨4类降水相态的确定性预报产品,并与ECMWF集合预报系统控制成员及细网格模式确定性预报进行对比。最优概率阈值显示:3种最优标准下,不同相态降水最优概率阈值不同,但冻雨和降雪最优概率阈值均最大,为40%~80%,雨夹雪最优概率阈值最小,约为10%,三者最优概率阈值均随预报时效延长而减小;降雨最优概率阈值为7%~25%,随预报时效延长而增大。对比检验结果显示:最优概率阈值法明显提高了降水相态预报能力,且以HSS评分最优时预报效果最佳;最优概率阈值法有效减小冻雨空报,同时显著改善降雨和降雪预报的频率偏差和TS评分,对雨夹雪预报改进效果有限。 相似文献
15.
M. Fulakeza L. M. Druyan T. N. Krishnamurti 《Meteorology and Atmospheric Physics》2002,79(1-2):105-126
Summary
A regional climate model (RCM) is described which incorporates an improved scheme for soil moisture availability (SMA) compared
to an earlier version. The improvement introduces a sensitivity of SMA to soil type, vegetation cover and ground albedo, making
the model more adaptable to divers regions. In addition, the interactive SMA depends on past precipitation, ground temperature
and terrain relief. Six RCM simulations of the monthly mean climate over southern Africa are performed at 0.5° grid spacing.
Improvements in the RCM climate simulations compared to control runs are attributed to the newer SMA scheme. Only a slight
improvement in skill results from driving the RCM with observational analyses as opposed to GCM “predicted” lateral boundary
conditions. The high spatial resolution of the RCM provides a distinct advantage in the simulated spatial distribution of
precipitation compared with a global model run at an effective grid spacing of 2.8°. The mesoscale precipitation signal in
the RCM simulations is more dominant during the rather dry December 1982 than during December 1988. The improved SMA scheme
contributed to a realistic partition between latent and sensible heat fluxes at the ground-atmosphere boundary and consequently
a realistic diurnal cycle of ground temperature. Simulated differences in the spatial distribution of rainfall between December
1982 and December 1988 are more realistic with the improved scheme.
Received June 28, 2001 Revised August 27, 2001 相似文献
16.
Tao Gao Xuebin Zhang Yiping Li Haimei Wang Sujun Xiao Wulan Qiaobin Teng 《Theoretical and Applied Climatology》2009,97(3-4):255-263
This study identifies potential predictors for seasonal forecast of dust storm frequency for the Inner Mongolia region of China. Regionally averaged antecedent annual precipitation anomaly has a significant influence on spring dust storm frequency. It is strongly linked to the frequency of severe dust storms that produce strong impact on the downstream Beijing-Tianjin area. A hindcast that uses precipitation anomaly to predict dust storm frequency in the following spring significantly outperforms climatologic forecast. Limited soil moisture observations indicate that an abundant annual precipitation increases the soil moisture of subsurface layer in the subsequent spring, which in turn improves vegetation growth that could lead to a reduction in the frequency of dust storms. 相似文献
17.
初始条件记忆和下垫面条件是延伸期可预报性的主要来源,它们在不同时段、不同区域的相对作用存在明显不同。利用中国国家气候中心最新一代大气环流模式BCC_AGCM,设计了由4组不同大气初值与海温边值构成的组合试验,研究了（大气）初值与（海温）边值对全球不同区域延伸期预报的相对作用。结果显示,模式预测技巧在3周以内强烈依赖于初值。在相同的边值条件强迫下,不同初值在月内尺度上的预测技巧差异非常明显,且在更长的时间尺度上,初值仍然能够提供一定的预测信息。从全球来看,边值强迫对预测技巧的影响从一周左右开始。在低层的850 hPa高度场上,边值的作用在热带地区于第4-5候与初值相当,其他区域的边值影响达到与初值相当的时间滞后于热带地区;在北半球500 hPa高度场上,边值的作用在热带地区第5候前后达到与初值作用相当,其他区域这个时间则推迟至第6候前后,比对低层的影响时间滞后1-2候;对于东亚地区而言,边值的贡献在第2候就已显现,对预测技巧产生了明显的改进。在延伸期尺度上,边值强迫主要作用的区域为低纬度区域,且对500 hPa高度场的影响更为稳定,在该区域第5候以后有较为明显的改进。初值与边值对延伸期预报都具有相当重要的作用,认识初、边值条件的相对作用能够为延伸期预报的改进奠定基础。 相似文献
18.
采用云变量自由变化的方案，在国家气象中心全球业务模式T213L31的初始场中增加有关云变量的信息，通过2005年6—8月和2015年12月—2006年2月各3个月的连续滚动对比试验的统计分析和个例预报分析，研究探讨了全球模式初值中增加云变量对模式预报性能的影响。初步研究结果表明:采用自由变化的方案在初值场中增加云的信息，使模式能够较为合理地描述出模式预报初期与云相关变量分布和变化特征，降低了spin-up现象对模式前期降水预报能力的影响，同时对500 hPa形势场预报也有一定程度提高。 相似文献
19.
Sun-Seon Lee June-Yi Lee Kyung-Ja Ha Bin Wang Jae Kyung E. Schemm 《Climate Dynamics》2011,36(5-6):1173-1188
Long-lead prediction of waxing and waning of the Western North Pacific (WNP)-East Asian (EA) summer monsoon (WNP-EASM) precipitation is a major challenge in seasonal time-scale climate prediction. In this study, deficiencies and potential for predicting the WNP-EASM precipitation and circulation one or two seasons ahead were examined using retrospective forecast data for the 26-year period of 1981–2006 from two operational couple models which are the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) and the Bureau of Meteorology Research Center (BMRC) Predictive Ocean–Atmosphere Model for Australia (POAMA). While both coupled models have difficulty in predicting summer mean precipitation anomalies over the region of interest, even for a 0-month lead forecast, they are capable of predicting zonal wind anomalies at 850 hPa several months ahead and, consequently, satisfactorily predict summer monsoon circulation indices for the EA region (EASMI) and for the WNP region (WNPSMI). It should be noted that the two models’ multi-model ensemble (MME) reaches 0.40 of the correlation skill for the EASMI with a January initial condition and 0.75 for the WNPSMI with a February initial condition. Further analysis indicates that prediction reliability of the EASMI is related not only to the preceding El Niño and Southern Oscillation (ENSO) but also to simultaneous local SST variability. On other hand, better prediction of the WNPSMI is accompanied by a more realistic simulation of lead–lag relationship between the index and ENSO. It should also be noted that current coupled models have difficulty in capturing the interannual variability component of the WNP-EASM system which is not correlated with typical ENSO variability. To improve the long-lead seasonal prediction of the WNP-EASM precipitation, a statistical postprocessing was developed based on the multiple linear regression method. The method utilizes the MME prediction of the EASMI and WNPSMI as predictors. It is shown that the statistical postprocessing is able to improve forecast skill for the summer mean precipitation over most of the WNP-EASM region at all forecast leads. It is noteworthy that the MME prediction, after applying statistical postprocessing, shows the best anomaly pattern correlation skill for the EASM precipitation at a 4-month lead (February initial condition) and for the WNPSM precipitation at a 5-month lead (January initial condition), indicating its potential for improving long-lead prediction of the monsoon precipitation. 相似文献
20.
土壤湿度初值对边界层物理量预报影响的分析 总被引：4，自引：2，他引：2
使用全球中期数值预报模式T213L31,对不同的土壤湿度初值对边界层物理量预报的影响进行敏感试验分析。试验表明土壤湿度初值的准确性非常重要,其变化导致模式计算的感热通量、潜热通量、地表温度和2m温度发生较大变化。而当使用与观测值相近的土壤湿度初值时得到的边界层物理量(如2m温度)更加准确。通过使用固定值与6h预报的背景场加权平均作为土壤湿度的初值,改进原有采用固定值的方法,经过检验模式系统,2m温度的预报误差明显减小。 相似文献