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1.
Daily output from the hindcasts by the National Centers for Environmental Prediction (NCEP) Climate Forecast System version 2 (CFSv2) is analyzed to understand the skill of forecasting atmospheric variability on quasi-biweekly (QBW) time scale. Eight dominant quasi-biweekly oscillation (QBWO) modes identified by the extended empirical orthogonal function analysis are focused. In the CFSv2, QBW variability exhibits a significant weakening tendency with lead time for all seasons. For most QBWO modes, the variance drops to only 50 % of the initial value at lead time of 11–15 days. QBW variability has better prediction skill in the winter hemisphere than in the summer hemisphere. Skillful forecast can reach about 10–15 days for most modes but those in the winter hemisphere have better forecast skills. Among the eight QBWO modes, the North Pacific mode and the South Pacific (SP) mode have the highest forecast skills while the Asia–Pacific mode and the Central American mode have the lowest skills. For the Asia–Pacific and Central American modes, the forecasted QBWO phase shows an obvious eastward shift with increase in lead time compared to observations, indicating a smaller propagating speed. However, the predicted feature for the SP mode is more realistic. Air–sea coupling on the QBW time scale is perhaps responsible for the different prediction skills for different QBWO modes. In addition, most QBWO modes have better forecasting skills in El Niño years than in La Niña years. Different dynamical mechanisms for various QBWO modes may be partially responsible for the differences in prediction skill among different QBWO modes.  相似文献   

2.
An assessment of six coupled Atmosphere-Ocean General Circulation Models (AOGCMs) is undertaken in order to evaluate their ability in simulating winter atmospheric blocking highs in the northern hemisphere. The poor representation of atmospheric blocking in climate models is a long-standing problem (e.g. D’Andrea et?al. in Clim Dyn 4:385–407, 1998), and despite considerable effort in model development, there is only a moderate improvement in blocking simulation. A modified version of the Tibaldi and Molteni (in Tellus A 42:343–365, 1990) blocking index is applied to daily averaged 500?hPa geopotential fields, from the ERA-40 reanalysis and as simulated by the climate models, during the winter periods from 1957 to 1999. The two preferred regions of blocking development, in the Euro-Atlantic and North Pacific, are relatively well captured by most of the models. However, the prominent error in blocking simulations consists of an underestimation of the total frequency of blocking episodes over both regions. A more detailed analysis revealed that this error was due to an insufficient number of medium spells and long-lasting episodes, and a shift in blocking lifetime distributions towards shorter blocks in the Euro-Atlantic sector. In the Pacific, results are more diverse; the models are equally likely to overestimate or underestimate the frequency at different spell lengths. Blocking spatial signatures are relatively well simulated in the Euro-Atlantic sector, while errors in the intensity and geographical location of the blocks emerge in the Pacific. The impact of models’ systematic errors on blocking simulation has also been analysed. The time-mean atmospheric circulation biases affect the frequency of blocking episodes, and the maximum event duration in the Euro-Atlantic region, while they sometimes cause geographical mislocations in the Pacific sector. The analysis of the systematic error in time-variability has revealed a negative relationship between the high-frequency variability of the transient eddies in the areas affected by blocking and blocking frequency. The blocking responses to errors in the low-frequency variability are different according to the region considered; the amplitude of the low-frequency variability is positively related to the blocking frequency and persistence in the Euro-Atlantic sector, while no such consistency is observed in the Pacific.  相似文献   

3.
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   

4.
The real-time forecasting of monsoon activity over India on extended range time scale (about 3 weeks) is analyzed for the monsoon season of 2012 during June to September (JJAS) by using the outputs from latest (CFSv2 [Climate Forecast System version 2]) and previous version (CFSv1 [Climate Forecast System version 1]) of NCEP coupled modeling system. The skill of monsoon rainfall forecast is found to be much better in CFSv2 than CFSv1. For the country as a whole the correlation coefficient (CC) between weekly observed and forecast rainfall departure was found to be statistically significant (99 % level) at least for 2 weeks (up to 18 days) and also having positive CC during week 3 (days 19–25) in CFSv2. The other skill scores like the mean absolute error (MAE) and the root mean square error (RMSE) also had better performance in CFSv2 compared to that of CFSv1. Over the four homogeneous regions of India the forecast skill is found to be better in CFSv2 with almost all four regions with CC significant at 95 % level up to 2 weeks, whereas the CFSv1 forecast had significant CC only over northwest India during week 1 (days 5–11) forecast. The improvement in CFSv2 was very prominent over central India and northwest India compared to other two regions. On the meteorological subdivision level (India is divided into 36 meteorological subdivisions) the percentage of correct category forecast was found to be much higher than the climatology normal forecast in CFSv2 as well as in CFSv1, with CFSv2 being 8–10 % higher in the category of correct to partially correct (one category out) forecast compared to that in CFSv1. Thus, it is concluded that the latest version of CFS coupled model has higher skill in predicting Indian monsoon rainfall on extended range time scale up to about 25 days.  相似文献   

5.
MJO prediction in the NCEP Climate Forecast System version 2   总被引：3，自引：0，他引：3  
The Madden–Julian Oscillation (MJO) is the primary mode of tropical intraseasonal climate variability and has significant modulation of global climate variations and attendant societal impacts. Advancing prediction of the MJO using state of the art observational data and modeling systems is thus a necessary goal for improving global intraseasonal climate prediction. MJO prediction is assessed in the NOAA Climate Forecast System version 2 (CFSv2) based on its hindcasts initialized daily for 1999–2010. The analysis focuses on MJO indices taken as the principal components of the two leading EOFs of combined 15°S–15°N average of 200-hPa zonal wind, 850-hPa zonal wind and outgoing longwave radiation at the top of the atmosphere. The CFSv2 has useful MJO prediction skill out to 20 days at which the bivariate anomaly correlation coefficient (ACC) drops to 0.5 and root-mean-square error (RMSE) increases to the level of the prediction with climatology. The prediction skill also shows a seasonal variation with the lowest ACC during the boreal summer and highest ACC during boreal winter. The prediction skills are evaluated according to the target as well as initial phases. Within the lead time of 10 days the ACC is generally greater than 0.8 and RMSE is less than 1 for all initial and target phases. At longer lead time, the model shows lower skills for predicting enhanced convection over the Maritime Continent and from the eastern Pacific to western Indian Ocean. The prediction skills are relatively higher for target phases when enhanced convection is in the central Indian Ocean and the central Pacific. While the MJO prediction skills are improved in CFSv2 compared to its previous version, systematic errors still exist in the CFSv2 in the maintenance and propagation of the MJO including (1) the MJO amplitude in the CFSv2 drops dramatically at the beginning of the prediction and remains weaker than the observed during the target period and (2) the propagation in the CFSv2 is too slow. Reducing these errors will be necessary for further improvement of the MJO prediction.  相似文献   

6.
Diagnostic evaluations of the relative performances of CFSv1 and CFSv2 in prediction of monthly anomalies of the ENSO-related Nino3.4 SST index are conducted using the common hindcast period of 1982–2009 for lead times of up to 9 months. CFSv2 outperforms CFSv1 in temporal correlation skill for predictions at moderate to long lead times that traverse the northern spring ENSO predictability barrier (e.g., a forecast for July made in February). However, for predictions during less challenging times of the year (e.g., a forecast for January made in August), CFSv1 has higher correlations than CFSv2. This seeming retrogression is caused by a cold bias in CFSv2 predictions for Nino3.4 SST during 1982–1998, and a warm bias during 1999–2009. Work by others has related this time-conditional bias to changes in the observing system in late 1998 that affected the ocean reanalysis serving as initial conditions for CFSv2. A posteriori correction of these differing biases, and of a similar (but lesser) situation affecting CFSv1, allows for a more realistic evaluation of the relative performances of the two CFS versions. After the dual bias corrections, CFSv2 has slightly better correlation skill than CFSv1 for most months and lead times, with approximately equal skills for forecasts not traversing the ENSO predictability barrier and better skills for most (particularly long-lead) predictions traversing the barrier. The overall difference in correlation skill is not statistically field significant. However, CFSv2 has statistically significantly improved amplitude bias, and visibly better probabilistic reliability, and lacks target month slippage as compared with CFSv1. Together, all of the above improvements result in a highly significantly reduced overall RMSE—the metric most indicative of final accuracy.  相似文献   

7.
Skill as a function of time scale in ensembles of seasonal hindcasts   总被引：1，自引：0，他引：1  
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.
Summary Southeastern Pacific blocking episodes are studied using 17 years of reanalyzed daily data from the National Centers for Environmental Prediction (NCEP). The anomalous sea level pressure (SLP) within the area bounded by the longitudes of 130° W and 100° W and the latitudes of 50° S and 70° S is used as the base variable to determine periods with 7 or more sequential days with positive anomalies in this domain. Using these periods, composites are calculated for the SLP and its anomalies, 500-hPa geopotential height anomalies and the 250-hPa and 925-hPa wind vectors in the western southern hemisphere (SH). Composites for austral winter and summer exhibit atmospheric circulation features quite similar to those associated with the blocking episodes in the southeastern Pacific. The corresponding composite patterns of the precipitable water (Pw) and 925-hPa temperature anomalies for the South American sector are also discussed. For both seasons blocking episodes in the southeastern Pacific change the distributions of these thermodynamic variables over South America, in particular in its southern and southeastern regions by reducing (increasing) the Pw and low-level temperature in the southern South America (the central part of the continent). Therefore, monitoring the southeastern Pacific circulation patterns may lead to improved weather forecast for the South American sector.With 9 Figures  相似文献   

9.
This study investigates the variation and prediction of the west China autumn rainfall (WCAR) and their associated atmospheric circulation features, focusing on the transitional stages of onset and demise of the WCAR. Output from the 45-day hindcast by the National Centers for Environmental Prediction (NCEP) Climate Forecast System version 2 (CFSv2) and several observational data sets are used. The onset of WCAR generally occurs at pentad 46 and decays at pentad 56, with heavy rainfall over the northwestern China and moderate rainfall over the south. Before that, southerly wind changes into southeasterly wind, accompanied by a westward expansion and intensification of the western Pacific subtropical high (WPSH), favoring rainfall over west China. On the other hand, during the decay of WCAR, a continental cold high develops and the WPSH weakens and shifts eastward, accompanied by a demise of southwest monsoon flow, leading to decay of rainfall over west China. The CFSv2 generally well captures the variation of WCAR owing to the high skill in capturing the associated atmospheric circulation, despite an overestimation of rainfall. This overestimation occurs at all time leads due to the overestimated low-level southerly wind. The CFSv2 can pinpoint the dates of onset and demise of WCAR at the leads up to 5 days and 40 days, respectively. The lower prediction skill for WCAR onset is due to the unrealistically predicted northerly wind anomaly over the lower branch of the Yangtze River and the underestimated movement of WPSH after lead time of 5 days.  相似文献   

10.
从梅雨预测的业务需求出发,系统开展了CFSv2模式对2018年浙江梅雨期降水预报能力的多时间尺度评估。结果发现3月1日—5月31日的起报结果整体上未能较准确地预测6月浙江大部降水偏少的趋势、仅5月31日的预测结果与实况相符;在延伸期尺度上,CFSv2预测的梅雨期总降水量较实况偏少30%左右;基于相关系数、均方根误差和新定义的综合预报技巧指数等指标分析模式的延伸期预报性能,发现对梅雨期总降水量、逐日区域平均降水量和逐日全省各站降水量的预报技巧有限,对浙江梅雨区的预报水平总体高于浙江全省。评估结果表明CFSv2预报产品表现出显著的系统性干偏差;在延伸期尺度上,随着预报时效的缩短,预报效果并非逐步提升、而是客观存在一个最佳预报时效,各起报日也分别对应着不同的最优预报时段,整体而言梅雨降水的延伸期预测可能对初值并不敏感。  相似文献   

11.
The present study assesses the forecast skill of the Madden–Julian Oscillation (MJO) observed during the period of DYNAMO (Dynamics of the MJO)/CINDY (Cooperative Indian Ocean Experiment on Intraseasonal Variability in Year 2011) field campaign in the GFS (NCEP Global Forecast System), CFSv2 (NCEP Climate Forecast System version 2) and UH (University of Hawaii) models, and revealed their strength and weakness in forecasting initiation and propagation of the MJO. Overall, the models forecast better the successive MJO which follows the preceding event than that with no preceding event (primary MJO). The common modeling problems include too slow eastward propagation, the Maritime Continent barrier and weak intensity. The forecasting skills of MJO major modes reach 13, 25 and 28 days, respectively, in the GFS atmosphere-only model, the CFSv2 and UH coupled models. An equal-weighted multi-model ensemble with the CFSv2 and UH models reaches 36 days. Air–sea coupling plays an important role for initiation and propagation of the MJO and largely accounts for the skill difference between the GFS and CFSv2. A series of forecasting experiments by forcing UH model with persistent, forecasted and observed daily SST further demonstrate that: (1) air–sea coupling extends MJO skill by about 1 week; (2) atmosphere-only forecasts driven by forecasted daily SST have a similar skill as the coupled forecasts, which suggests that if the high-resolution GFS is forced with CFSv2 forecasted daily SST, its forecast skill can be much higher than its current level as forced with persistent SST; (3) atmosphere-only forecasts driven by observed daily SST reaches beyond 40 days. It is also found that the MJO–TC (Tropical Cyclone) interactions have been much better represented in the UH and CFSv2 models than that in the GFS model. Both the CFSv2 and UH coupled models reasonably well capture the development of westerly wind bursts associated with November 2011 MJO and the cyclogenesis of TC05A in the Indian Ocean with a lead time of 2 weeks. However, the high-resolution GFS atmosphere-only model fails to reproduce the November MJO and the genesis of TC05A at 2 weeks’ lead. This result highlights the necessity to get MJO right in order to ensure skillful extended-range TC forecasting.  相似文献   

12.
Twenty-one-year hindcasts of sea surface temperature (SST) anomalies in the tropical Pacific were performed to validate the influence of ocean subsurface entrainment on SST prediction.A new hybrid coupled model was used that considered the entrainment of subsurface temperature anomalies into the sea surface.The results showed that predictions were improved significantly in the new coupled model.The predictive correlation skill increased by about 0.2 at a lead time of 9 months,and the root-mean-square (RMS) errors were decreased by nearly 0.2°C in general.A detailed analysis of the 1997-98 El Nio hindcast showed that the new model was able to predict the onset,peak (both time and amplitude),and decay of the 1997-98 strong El Nio event up to a lead time of one year,factors that are not represented well by many other forecast systems.This implies,in terms of prediction,that subsurface anomalies and their impact on the SST are one of the controlling factors in ENSO cycles.Improving the presentation of such effects in models would increase the forecast skill.  相似文献   

13.
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14.
We assess the depiction and prediction of blocking at 140°E and its impact on Australian intra-seasonal climate variability in the Bureau of Meteorology’s dynamical intra-seasonal/seasonal forecast model Predictive Ocean Atmosphere Model for Australia version 2 (POAMA-2). The model simulates well the strong seasonality of blocking but underestimates its strength and frequency increasingly with lead time, particularly after the first fortnight of the hindcast, in connection with the model’s drifting basic state. POAMA-2 reproduces well the large-scale structure of weekly-mean blocking anomalies and associated rainfall anomalies over Australia; the depiction of total blocking in POAMA-2 may be improved with the reduction of biases in the distribution of Indian Ocean rainfall via a tropical-extratropical wave teleconnection linking blocking activity at 140°E with tropical variability near Indonesia. POAMA-2 demonstrates the ability to skilfully predict the daily blocking index out to 16 days lead time for the ensemble mean hindcast, surpassing the average predictive skill of the individual hindcast members (5 days), the skill obtained from persistence of observed (2 days), and the decorrelation timescale of blocking (3 days). This skilful prediction of the blocking index, together with effective simulation of blocking rainfall anomalies, translates into higher skill in forecasting rainfall in weeks 2 and 3 over much of Australia when blocking is high at the initial time of the hindcast, compared to when the blocking index is small. POAMA-2 is thus capable of providing forecast skill for blocking rainfall on the intra-seasonal timescale to meet the needs of Australian farming communities, whose management practises often rely upon decisions being made a few weeks ahead.  相似文献   

15.
This study evaluates the prediction skill of stratospheric temperature anomalies by the Climate Forecast System version 2 (CFSv2) reforecasts for the 12-year period from January 1, 1999 to December 2010. The goal is to explore if the CFSv2 forecasts for the stratosphere would remain skillful beyond the inherent tropospheric predictability time scale of at most 2 weeks. The anomaly correlation between observations and forecasts for temperature field at 50 hPa (T50) in winter seasons remains above 0.3 over the polar stratosphere out to a lead time of 28 days whereas its counterpart in the troposphere at 500 hPa drops more quickly and falls below the 0.3 level after 12 days. We further show that the CFSv2 has a high prediction skill in the stratosphere both in an absolute sense and in terms of gain over persistence except in the equatorial region where the skill would mainly come from persistence of the quasi-biennial oscillation signal. We present evidence showing that the CFSv2 forecasts can capture both timing and amplitude of wave activities in the extratropical stratosphere at a lead time longer than 30 days. Based on the mass circulation theory, we conjecture that as long as the westward tilting of planetary waves in the stratosphere and their overall amplitude can be captured, the CFSv2 forecasts is still very skillful in predicting zonal mean anomalies even though it cannot predict the exact locations of planetary waves and their spatial scales. This explains why the CFSv2 has a high skill for the first EOF mode of T50, the intraseasonal variability of the annular mode while its skill degrades rapidly for higher EOF modes associated with stationary waves. This also explains why the CFSv2’s skill closely follows the seasonality and its interannual variability of the meridional mass circulation and stratosphere polar vortex. In particular, the CFSv2 is capable of predicting mid-winter polar stratosphere warming events in the Northern Hemisphere and the timing of the final polar stratosphere warming in spring in both hemispheres 3–4 weeks in advance.  相似文献   

16.
Summary An earlier developed multidecadal database of Northern Hemisphere cut-off low systems (COLs), covering a 41 years period (from 1958 to 1998) is used to study COLs interannual variability in the European sector (25°–47.5° N, 50° W–40° E) and the major factors controlling it. The study focus on the influence on COLs interannual variability, of larger scale phenomena such as blocking events and other main circulation modes defined over the Euro-Atlantic region. It is shown that there is a very large interannual variability in the COLs occurrence at the annual and seasonal scales, although without significant trends. The influence of larger scale phenomena is seasonal dependent, with the positive phase of the NAO favoring autumn COL development, while winter COL occurrence is mostly related to blocking events. During summer, the season when more COLs occur, no significant influences were found.  相似文献   

17.
This work evaluates the skill of retrospective predictions of the second version of the NCEP Climate Forecast System (CFSv2) for the North Atlantic sea surface temperature (SST) and investigates the influence of El Niño-Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO) on the prediction skill over this region. It is shown that the CFSv2 prediction skill with 0–8 month lead displays a “tripole”-like pattern with areas of higher skills in the high latitude and tropical North Atlantic, surrounding the area of lower skills in the mid-latitude western North Atlantic. This “tripole”-like prediction skill pattern is mainly due to the persistency of SST anomalies (SSTAs), which is related to the influence of ENSO and NAO over the North Atlantic. The influences of ENSO and NAO, and their seasonality, result in the prediction skill in the tropical North Atlantic the highest in spring and the lowest in summer. In CFSv2, the ENSO influence over the North Atlantic is overestimated but the impact of NAO over the North Atlantic is not well simulated. However, compared with CFSv1, the overall skills of CFSv2 are slightly higher over the whole North Atlantic, particularly in the high latitudes and the northwest North Atlantic. The model prediction skill beyond the persistency initially presents in the mid-latitudes of the North Atlantic and extends to the low latitudes with time. That might suggest that the model captures the associated air-sea interaction in the North Atlantic. The CFSv2 prediction is less skillful than that of SSTA persistency in the high latitudes, implying that over this region the persistency is even better than CFSv2 predictions. Also, both persistent and CFSv2 predictions have relatively low skills along the Gulf Stream.  相似文献   

18.
The seasonal prediction skill for the Northern Hemisphere winter is assessed using retrospective predictions (1982–2010) from the ECMWF System 4 (Sys4) and National Center for Environmental Prediction (NCEP) CFS version 2 (CFSv2) coupled atmosphere–ocean seasonal climate prediction systems. Sys4 shows a cold bias in the equatorial Pacific but a warm bias is found in the North Pacific and part of the North Atlantic. The CFSv2 has strong warm bias from the cold tongue region of the eastern Pacific to the equatorial central Pacific and cold bias in broad areas over the North Pacific and the North Atlantic. A cold bias in the Southern Hemisphere is common in both reforecasts. In addition, excessive precipitation is found in the equatorial Pacific, the equatorial Indian Ocean and the western Pacific in Sys4, and in the South Pacific, the southern Indian Ocean and the western Pacific in CFSv2. A dry bias is found for both modeling systems over South America and northern Australia. The mean prediction skill of 2 meter temperature (2mT) and precipitation anomalies are greater over the tropics than the extra-tropics and also greater over ocean than land. The prediction skill of tropical 2mT and precipitation is greater in strong El Nino Southern Oscillation (ENSO) winters than in weak ENSO winters. Both models predict the year-to-year ENSO variation quite accurately, although sea surface temperature trend bias in CFSv2 over the tropical Pacific results in lower prediction skill for the CFSv2 relative to the Sys4. Both models capture the main ENSO teleconnection pattern of strong anomalies over the tropics, the North Pacific and the North America. However, both models have difficulty in forecasting the year-to-year winter temperature variability over the US and northern Europe.  相似文献   

19.
利用NCEP的气候预报系统第二版(CFSv2)提供的逐日降水模式资料,采用集合预报方法开展区域性夏季降水预报,使用出入梅日期均方根误差(RMSE)、准确率(ACCU),梅雨期长度均方根误差(RMSE)及梅雨雨强距平符号一致率(Pc)等3种方法评估模式资料对湖北省梅雨特征量的预报能力。结果表明:入梅预报提前13 d的ACCU可达0.5以上、RMSE小于3 d,出梅预报提前14 d的ACCU可达0.5以上、RMSE小于3 d,梅雨期长度预报提前14天的RMSE小于5 d,梅雨雨强预报提前14 d的Pc可达0.5以上。梅雨特征量总体预报时效为14 d左右,CFSv2模式资料对区域性夏季降水在梅雨延伸期时段表现出一定的预报技巧。  相似文献   

20.
Summary ?We evaluate United Kingdom Meteorological Office (UKMO) one-month ensemble forecasts of mean sea-level pressure (MSLP) in the southern hemisphere (SH) to 60° S, with a special focus on their utility near New Zealand (NZ). There are 105 9-member ensembles, at approximately two-week intervals, between 1995 and 1999. Each forecast is averaged over two successive 15-day periods and verified against the NCEP/NCAR reanalysis data set. Compared to climatology, the skill of the ensemble mean is slightly positive in days 1–15, and slightly negative in days 16–30. Skill near NZ is slightly lower than the SH averages. For SH-scale circulation patterns (as seen in the first few principal components), skill is greater than for most individual grid points, but is still negligible or negative in days 16–30. Moderate skill-spread correlations (ρ ≈−0.5) were found for some skill scores. The way that skill varies with season and the Southern Oscillation Index is consistent with other research but not statistically significant for this small data set. Probabilistic forecasts of low and high pressures have skill similar to that of the ensemble mean. The ensemble spread is generally too small, in that the analysis lies within the ensemble less often than the theoretically optimum value of 80% of the time. Measured as a fraction of the natural variability, the spread increases substantially with time and latitude: it is less than 0.5 near the equator in days 1–15, and takes values near 1 only at higher latitudes during days 16–30. The initial sequential structure of the ensembles (a consequence of the use of time lags in their genesis) is still apparent in days 1–15 but has disappeared by days 16–30. Three potential alternatives to the ensemble mean were all found to have less skill than it. Received June 17, 2001; revised July 4, 2002; accepted November 22, 2002 Published online March 17, 2003  相似文献   

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