Precipitation Prediction Skill Research Articles

Understanding Skill of Seasonal Mean Precipitation Prediction over California during Boreal Winter and Role of Predictability Limits

AbstractUsing extensive hindcasts from seasonal prediction systems participating in the North American Multi-Model Ensemble (NMME), possible causes for low skill in predicting seasonal mean precipitation over California during December–February (DJF) are investigated. The analysis focuses on investigating two possibilities for low prediction skill: role model biases or inherent predictability limits. The motivation for the analysis was the seasonal prediction during DJF 2015/16 that called for enhanced probability for above normal precipitation over southern California (which was consistent with expected conditions during an extreme El Niño) while the observed precipitation was below normal. Based on various analysis approaches and using hindcast datasets from multiple seasonal prediction systems, we build up the evidence that low skill in predicting seasonal mean precipitation over California is likely to be due to inherent predictability associated with a low signal-to-noise (SNR) regime. For the same set of seasonal prediction systems, the precipitation variability over California is contrasted with that over the southeast United States where prediction skill, as well as the SNR, is higher. The discussion also notes that building a knowledge base that goes beyond the well-known response to ENSO (based on the linear regression or composite techniques) has proven to be difficult and a systematic approach to reaching resolution to some of the overarching questions is required, and toward that end, a pathway is suggested.

Multi-Model Ensemble Sub-Seasonal Forecasting of Precipitation over the Maritime Continent in Boreal Summer

The Maritime Continent (MC) is a critical region with unique geographical conditions and significant monsoon activities that plays a vital role in global climate variation. In this study, the weekly prediction of precipitation over the MC during boreal summer (from May to September) was analyzed using the 12-year reforecasts data from five Sub-seasonal to Seasonal (S2S) models, including the China Meteorological Administration (CMA), the European Centre for Medium-Range Weather Forecasts (ECMWF), Environment and Climate Change Canada (ECCC), the National Centers for Environmental Prediction (NCEP), and the Met Office (UKMO). The result shows that, compared with the individual models, our newly derived median multi-model ensemble (MME) can significantly improve the prediction skill of sub-seasonal precipitation in the MC. Both the Temporal Correlation Coefficient (TCC) skill and the Pattern Correlation Coefficient (PCC) skill reached 0.6 in lead week 1, dropped the following week, did not exceed 0.2 in lead week 3, and then lost their significance. The results show higher prediction skill near the Equator than in the north at 10° N. It is difficult to make effective predictions with the models beyond three weeks. The prediction ability of the median MME improves significantly as the total number of model members increases. The prediction performance of the median MME depends not only on the diversity of models but also on the number of model members. Moreover, the prediction skill is particularly sensitive to the intensity and phase of Boreal Summer Intraseasonal Oscillation 1 (BSISO1) with the highest skills appearing at initial phases 1 and 5.

Prediction Skill of NCEP CFSv2 for Seasonal Precipitation and Surface Air Temperature Forecast over Southeast Asia

Climate Forecast System version 2 (CFSv2) is the ocean-atmosphere-land coupled model and the latest version of seasonal climate forecast from National Centers for Environmental Prediction (NCEP). This study presents the prediction skill of seasonal precipitation and surface air temperature forecasting from CFSv2 over Southeast Asia. The objective of the study was to verify the prediction accuracy of CFSv2 by quantifying the deterministic quantities in term of correlation coefficients with respect to different lead times and target seasons based on a the 28-year ensemble means (1983/84 - 2010/11) for each variables. Additionally, the prediction skill of 20 sub-regions over Southeast Asia are verified with observation for regional assessment of the accuracy of seasonal precipitation and surface air temperature forecasted by CFSv2. In general, the result showed that the prediction skill of CFSv2 for seasonal precipitation and surface air temperature forecasting is reasonable, especially prediction skill after lead month-0 for all target seasons compared to other lead months. The lowest prediction skill is after lead month-6. Overall, the prediction skill of seasonal surface air temperature forecasting is better than precipitation. Moreover, the result obtained in this study highlights the advantages of using an ensemble technique for seasonal forecasting in Southeast Asia.

On the interpretation of seasonal Southern Africa precipitation prediction skill estimates during Austral summer

Differences between two types of prediction skill estimates over Southern Africa are illustrated to better inform the users of seasonal precipitation forecasts over the region who desire assessments of forecast accuracy. Both seasonal precipitation prediction skill estimates for the African continent south of 15°S during the December–March rainy season are derived from the perfect-model method. The perfect-model method is based on a 40-member ensemble of Community Atmosphere Model version 5 simulations forced by observed time-evolving boundary conditions during 1920–2016. The first skill estimate is based on the verification of an ensemble mean forecast spanning many seasons and therefore unconditional on a single boundary forcing. The second skill estimate is based on the verification of an ensemble mean forecast for a single season and is therefore conditional on that year’s boundary forcing. Unconditional prediction skill calculated in 30-year increments for each of the 40 possible forecasts reveals: (1) large spread in skill among the individual forecasts for any given year and (2) temporal variations in skill for each forecast. The magnitude of conditional prediction skill varies greatly from 1 year to the next, revealing that the boundary conditions offer little prediction skill during some years and comparably large skill during others. The simultaneous behaviors of the El Nino–Southern Oscillation and the subtropical Indian Ocean Dipole are related to the largest conditional precipitation prediction skill years. Unconditional skill estimates may therefore mislead users of forecasts who desire assessments of forecast accuracy. Unconditional skill may be temporally unstable, and unlike conditional skill, is not representative of the skill for a given season.

Evaluation of 2-m temperature and precipitation products of the Climate Forecast System version 2 over Iran

Using a continuous multi-decadal simulations over the period 1981–2010, subseasonal to seasonal simulations of the Climate Forecast System version 2 (CFSv2) over Iran against the Climatic Research Unit (CRU) dataset are evaluated. CFSv2 shows cold biases over northern hillsides of the Alborz Mountains with the Mediterranean climate and warm biases over northern regions of the Persian Gulf and the Oman Sea with a dry climate. Magnitude of the model bias for 2-m temperature over different regions of Iran varies by season, with the least bias in temperate seasons of spring and autumn, and the largest bias in summer. The model bias decreases as temporal averaging period increases from seasonal to annual. The forecast generally produces dry and wet biases over dry and wet regions of Iran, respectively. In general, 2-m temperature over Iran is better captured than precipitation, but the prediction skill of precipitation is generally high over western Iran. Averaged over Iran, observations indicated that 2-m temperature has been gradually increasing during the studied period, with a rate of approximately 0.5 °C per decade, and the upward trend is well simulated by CFSv2. Averaged over Iran, both observations and simulation results indicated that precipitation has been decreasing in spring, with averaged decreasing trends of 0.8 mm (observed) and 1.7 mm (simulated) per season each year during the period 1981–2010. Observations indicated that the maximum increasing trend of 2-m temperature has occurred over western Iran (nearly 0.7 °C per decade), while the maximum decreasing trend of annual precipitation has occurred over western and parts of southern Iran (nearly 45 to 50 mm per decade).

Combined impact of tropical central‐eastern Pacific and North Atlantic sea surface temperature on precipitation variation in monsoon transitional zone over China during August–September

AbstractPrevious studies suggested that sea surface temperature (SST) anomalies in the tropical central‐eastern Pacific (TCEP) and tropical Northern Atlantic (TNA) both have significant impacts on the inter‐annual variation of precipitation over the monsoon transitional zone (MTZ) in China during August–September. This study further reveals that the relationship between TCEP (TNA) SST and MTZ precipitation during August–September is strongly modulated by the sign of the TNA (TCEP) SST. When TCEP and TNA SST anomalies have the same sign, connections of the TCEP and TNA SST with the MTZ precipitation are unclear. In contrast, TCEP and TNA SST changes both have significant relation with the MTZ precipitation when they have the opposite sign. During the same‐sign years, the anticyclonic (cyclonic) anomaly over the western North Pacific generated by the TCEP SST cooling (warming) is weakened by the cyclonic (anticyclonic) anomaly triggered by the TNA SST cooling (warming). Thus, connections of the MTZ precipitation with the TCEP and TNA SST are weak. However, during the opposite‐sign years, the anticyclonic anomalies over the western North Pacific generated by the TCEP SST anomalies have a constructive superposition on those induced by the TNA SST changes. As such, connections of the TCEP and TNA SST with the MTZ precipitation variation are significant. Further analysis shows that the prediction skill of precipitation over the MTZ is enhanced if taking both the TCEP and TNA SST signals into account.

How does ENSO diversity limit the skill of tropical Pacific precipitation forecasts in dynamical seasonal predictions?

The prediction skill of tropical Pacific sea surface temperature (SST) and precipitation has been investigated, based on retrospective forecasts from 1983 to 2005 of the APEC Climate Center multimodel ensemble (MME) 6-month climate prediction, with a focus on El Nino-Southern Oscillation (ENSO) and its diversity. It was found that the MME prediction skill is related to both strength and flavor of ENSO. To better analyze the relationship between ENSO diversity and tropical climate predictions on a year-to-year basis, anomalous SST data were stratified into those due to typical ENSO and their residue. The former is defined as the reconstruction of data based on the leading empirical orthogonal function of the Pacific SST itself. It was found that typical ENSO and its impacts on tropical rainfall are well captured by dynamical seasonal prediction systems. Singular vector decomposition (SVD) analyses were further carried out, in order to identify climate modes related to the co-variability between residual SST signals and precipitation. Observational results show that the first SVD mode gives a SST singular vector that greatly resembles the east–west shift from typical El Nino features to El Nino diverse patterns. Dynamical models, on the other hand, have difficulties in reproducing the strength of this mode on the interannual scale. Their skill in predicting the temporal variation of this mode is also much lower than that for typical ENSO. Finally, the role of ENSO diversity on SST and precipitation potential predictability was also examined. Typical ENSO is the major predictability source of tropical Pacific SST. On the other hand, it was demonstrated that residual ENSO variability actually acts as a limit to tropical rainfall predictability. In other words, residual ENSO plays a rather crucial role in seasonal forecasting of tropical Pacific rainfall.

Precipitation Prediction Skill for the West Coast United States: From Short to Extended Range

AbstractPrecipitation variability significantly influences the heavily populated West Coast of the United States, raising the need for reliable predictions. We investigate the region’s short- to extended-range precipitation prediction skill using the hindcast database of the Subseasonal-to-Seasonal Prediction Project (S2S). The prediction skill–lead time relationship is evaluated, using both deterministic and probabilistic skill scores. Results show that the S2S models display advantageous deterministic skill at week 1. For week 2, prediction is useful for the best-performing model, with a Pearson correlation coefficient larger than 0.6. Beyond week 2, predictions generally provide little useful deterministic skill. Sources of extended-range predictability are investigated, focusing on El Niño–Southern Oscillation (ENSO) and the Madden–Julian oscillation (MJO). We found that periods of heavy precipitation associated with ENSO are more predictable at the extended range period. During El Niño years, Southern California tends to receive more precipitation in late winter, and most models show better extended-range prediction skill. On the contrary, during La Niña years Oregon tends to receive more precipitation in winter, with most models showing better extended-range skill. We believe the excessive precipitation and improved extended-range prediction skill are caused by the meridional shift of baroclinic systems as modulated by ENSO. Through examining precipitation anomalies conditioned on the MJO, we verified that active MJO events systematically modulate the area’s precipitation distribution. Our results show that most models do not represent the MJO or its associated teleconnections, especially at phases 3–4. However, some models exhibit enhanced extended-range prediction skills under active MJO conditions.

Deterministic and probabilistic evaluation of raw and post processed sub-seasonal to seasonal precipitation forecasts in different precipitation regimes

Precipitation is an important and difficult climate variable to predict. Skillful sub-seasonal precipitation forecast can provide useful information for agriculture and water resources management communities. Nevertheless, sub-seasonal forecasts have been given less attention compared with forecasts of shorter/longer time horizons. Recently, the S2S database has made sub-seasonal to seasonal forecasts/reforecasts from 11 operational centers available to researchers. In this work, reforecasts of the European Centre for Medium-Range Weather Forecasts (ECMWF) spanning over a 20-year period were evaluated. Raw and post-processed precipitation forecasts were put against observed precipitation series of a number of synoptic stations with different precipitation regimes for all months of the year. By comparison, precipitation forecasts were more skillful in wet months. Raw forecasts in December and February were better than that in other months, while the weakest results were detected in August. There was no significant relationship between precipitation regime and prediction skill. Furthermore, quantile mapping (QM), Bayesian model averaging (BMA), and QM_BMA combination were adopted for post-processing. BMA outperformed QM and QM_BMA through improving the correlation coefficient between observations and average of ensemble forecasts; however, BMA performed weaker in other evaluation criteria, in particular in humid regions. It is concluded that the application of post-processing techniques greatly improved the results of ensemble precipitation forecasts. However, in a number of stations/months, the forecast results were not acceptable even after post-processing.

Improved Teleconnection‐Based Dynamical Seasonal Predictions of Boreal Winter

AbstractClimate and weather variability in the North Atlantic region is determined largely by the North Atlantic Oscillation (NAO). The potential for skillful seasonal forecasts of the winter NAO using an ensemble‐based dynamical prediction system has only recently been demonstrated. Here we show that the winter predictability can be significantly improved by refining a dynamical ensemble through subsampling. We enhance prediction skill of surface temperature, precipitation, and sea level pressure over essential parts of the Northern Hemisphere by retaining only the ensemble members whose NAO state is close to a “first guess” NAO prediction based on a statistical analysis of the initial autumn state of the ocean, sea ice, land, and stratosphere. The correlation coefficient between the reforecasted and observation‐based winter NAO is significantly increased from 0.49 to 0.83 over a reforecast period from 1982 to 2016, and from 0.42 to 0.86 for a forecast period from 2001 to 2017. Our novel approach represents a successful and robust alternative to further increasing the ensemble size, and potentially can be used in operational seasonal prediction systems.

Multi-pentad prediction of precipitation variability over Southeast Asia during boreal summer using BCC_CSM1.2

Precipitation is highly variable in space and discontinuous in time, which makes it challenging for models to predict on subseasonal scales (10–30 days). We analyze multi-pentad predictions from the Beijing Climate Center Climate System Model version 1.2 (BCC_CSM1.2), which are based on hindcasts from 1997 to 2014. The analysis focus on the skill of the model to predict precipitation variability over Southeast Asia from May to September, as well as its connections with intraseasonal oscillation (ISO). The effective precipitation prediction length is about two pentads (10 days), during which the skill measured by anomaly correlation is greater than 0.1. In order to further evaluate the performance of the precipitation prediction, the diagnosis results of the skills of two related circulation fields show that the prediction skills for the circulation fields exceed that of precipitation. Moreover, the prediction skills tend to be higher when the amplitude of ISO is large, especially for a boreal summer intraseasonal oscillation. The skills associated with phases 2 and 5 are higher, but that of phase 3 is relatively lower. Even so, different initial phases reflect the same spatial characteristics, which shows higher skill of precipitation prediction in the northwest Pacific Ocean. Finally, filter analysis is used on the prediction skills of total and subseasonal anomalies. The results of the two anomaly sets are comparable during the first two lead pentads, but thereafter the skill of the total anomalies is significantly higher than that of the subseasonal anomalies. This paper should help advance research in subseasonal precipitation prediction.

Efficacy of tendency and linear inverse models to predict southern Peru's rainy season precipitation

ABSTRACTSouthern Peru receives over 60% of its annual climatological precipitation during the short period of January–March. This rainy season precipitation exhibits strong inter‐annual and decadal variability, including severe drought events that incur devastating societal impacts and cause agricultural communities and mining facilities to compete for limited water resources. Improving existing seasonal prediction models of summertime precipitation could aid in water resource planning and allocation across this water‐limited region. While various underlying mechanisms modulating inter‐annual variability have been proposed by past studies, operational forecasts continue to be largely based on rudimentary El Niño‐Southern Oscillation (ENSO)‐based indices, such as Niño3.4, justifying further exploration of predictive skill. To bridge the gap between understanding precipitation mechanisms and operational forecasts, we perform systematic studies on the predictability and prediction skill of southern Peru's rainy season precipitation by constructing statistical forecast models using best available weather station and reanalysis data sets. We construct a simple regression model, based on the principal component (PC) tendency of tropical Pacific sea surface temperatures (SST), and a more advanced linear inverse model (LIM), based on the empirical orthogonal functions of tropical Pacific SST and large‐scale atmospheric variables from reanalysis. Our results indicate that both the PC tendency and LIM models consistently outperform the ENSO‐only based regression models in predicting precipitation at both the regional scale and for individual station, with improvements for individual stations ranging from 10 to over 200%. These encouraging results are likely to foster further development of operational precipitation forecasts for southern Peru.

Impact of Land Surface Initialization and Land-Atmosphere Coupling on the Prediction of the Indian Summer Monsoon with the CFSv2

The impact of initial land-surface states on monthly to seasonal prediction skill of the Indian summer monsoon (June- September) is investigated using a suite of hindcasts made with the Climate Forecast System version 2 (CFSv2) operational forecast model. The modern paradigm of land-atmosphere coupling is applied to quantify biases in different components of the land-atmosphere coupled system and their effect on systematic errors. Three sets of hindcasts are performed for the period spanning 1982-2009 initialized at the start of April, May and June. For a particular initial date of a given year, one member (Control run) has the analyzed land initial state consistent with the atmosphere, sea ice and ocean states for that year; the other 27 members have land states taken from each of the remaining 27 years. There is significant improvement in the deterministic prediction skill of near surface temperature and soil moisture on monthly and seasonal time scales due to realistic land initial conditions. The improvement occurs in those areas where the land-atmosphere coupling is strongest. Improvements in the prediction skill of precipitation are confined to relatively small areas. The pattern of skill differences resembles patterns of land-atmosphere coupling strength, while biases in the representation of land-atmosphere coupling affect the skill of temperature and rainfall. The re-emergence of skill in temperature and precipitation towards the end of the season over northwest India within April and June IC hindcasts may be attributed to better simulation of the withdrawal phase of the monsoon as well as increased land-atmosphere coupling. For May IC hindcasts, increased skill in air temperature on the sub-seasonal time scales could be due to other large-scale factors. Errors in the parameterization of radiation, convection, boundary layer processes, surface moisture fluxes and the representation of vegetation contribute to decay in potential predictability and skill attributable to land initial conditions. Furthermore, incorrect representation of daily and sub-daily precipitation statistics over land also likely lead to errors in land-atmosphere coupling. Above all, the importance of accurate land surface initialization and land-atmosphere coupling in improving the Indian summer monsoon prediction on sub-seasonal to seasonal time scales is emphasized.

Multi-scheme corrected dynamic–analogue prediction of summer precipitation in northeastern China based on BCC_CSM

Based on summer precipitation hindcasts for 1991–2013 produced by the Beijing Climate Center Climate System Model (BCC_CSM), the relationship between precipitation prediction error in northeastern China (NEC) and global sea surface temperature is analyzed, and dynamic–analogue prediction is carried out to improve the summer precipitation prediction skill of BCC_CSM, through taking care of model historical analogue prediction error in the real-time output. Seven correction schemes such as the systematic bias correction, pure statistical correction, dynamic–analogue correction, and so on, are designed and compared. Independent hindcast results show that the 5-yr average anomaly correlation coefficient (ACC) of summer precipitation is respectively improved from –0.13/0.15 to 0.16/0.24 for 2009–13/1991–95 when using the equally weighted dynamic–analogue correction in the BCC_CSM prediction, which takes the arithmetical mean of the correction based on regional average error and that on grid point error. In addition, probabilistic prediction using the results from the multiple correction schemes is also performed and it leads to further improved 5-yr average prediction accuracy.

The analogue method for precipitation prediction: finding better analogue situations at a sub-daily time step

Abstract. Analogue methods (AMs) predict local weather variables (predictands) such as precipitation by means of a statistical relationship with predictors at a synoptic scale. The analogy is generally assessed on gradients of geopotential heights first to sample days with a similar atmospheric circulation. Other predictors such as moisture variables can also be added in a successive level of analogy. The search for candidate situations similar to a given target day is usually undertaken by comparing the state of the atmosphere at fixed hours of the day for both the target day and the candidate analogues. This is a consequence of using standard daily precipitation time series, which are available over longer periods than sub-daily data. However, it is unlikely for the best analogy to occur at the exact same hour for the target and candidate situations. A better analogue situation may be found with a time shift of several hours since a better fit can occur at different times of the day. In order to assess the potential for finding better analogues at a different hour, a moving time window (MTW) has been introduced. The MTW resulted in a better analogy in terms of the atmospheric circulation and showed improved values of the analogy criterion on the entire distribution of the extracted analogue dates. The improvement was found to increase with the analogue rank due to an accumulation of better analogues in the selection. A seasonal effect has also been identified, with larger improvements shown in winter than in summer. This may be attributed to stronger diurnal cycles in summer that favour predictors taken at the same hour for the target and analogue days. The impact of the MTW on the precipitation prediction skill has been assessed by means of a sub-daily precipitation series transformed into moving 24 h totals at 12, 6, and 3 h time steps. The prediction skill was improved by the MTW, as was the reliability of the prediction. Moreover, the improvements were greater for days with heavy precipitation, which are generally related to more dynamic atmospheric situations in which the timing is more specific and for which fewer records are available in the meteorological archive. The improvements of the analogy criterion and the performance scores on precipitation were both found to be higher for MTWs with a smaller time step of 3 h. A 3 h MTW provides 8 times more candidate situations even though they are not fully independent. Since the MTW provides additional situations to the pool of possible analogues, it can be considered as an inflation of the meteorological archive. Because this technique is simple and easily applicable, it should be considered for several applications in different contexts, such as operational forecasting or climate-related studies.

Skillful seasonal predictions of winter precipitation over southern China

Southern China experiences large year-to-year variability in the amount of winter precipitation, which can result in severe social and economic impacts. In this study, we demonstrate prediction skill of southern China winter precipitation by three operational seasonal prediction models: the operational Global seasonal forecasting system version 5 (GloSea5), the NCEP Climate Forecast System (CFSv2) and the Beijing Climate Center Climate System Model (BCC-CSM1.1m). The correlation scores reach 0.76 and 0.67 in GloSea5 and CFSv2, respectively; and the amplitude of the ensemble mean forecast signal is comparable to the observed variations. The skilful predictions in GloSea5 and CFSv2 mainly benefit from the successful representation of the observed ENSO teleconnection. El Niño weakens the Walker circulation and leads to the strengthening of the subtropical high over the northwestern Pacific. The anti-cyclone then induces anomalous northward flow over the South China Sea and brings water vapor to southern China, resulting in more precipitation. This teleconnection pattern is too weak in BCC-CSM1.1m, which explains its low skill (0.13). Whereas the most skilful forecast system is also able to simulate the influence of the Indian Ocean on southern China precipitation via changes in southwesterly winds over the Bay of Bengal. Finally, we examine the real-time forecast for 2015/16 winter when a strong El Niño event led to the highest rainfall over southern China in recent decades. We find that the GloSea5 system gave good advice as it produced the third wettest southern China in the hindcast, but underestimated the observed amplitude. This is likely due to the underestimation of the Siberian High strength in 2015/2016 winter, which has driven strong convergence over southern China. We conclude that some current seasonal forecast systems can give useful warning of impending extremes. However, there is still need for further model improvement to fully represent the complex dynamics of the region.

North American Multi Model Ensemble (NMME) Performance of Monthly Precipitation Forecast over South Sulawesi, Indonesia

The North American Multi-Model Ensemble (NMME) as one of the multi-model seasonal forecasting system, regularly generate monthly precipitation forecast for all globe with 0.5 – 11.5 months lead time. This useful information can be used as general input to regional and local precipitation forecast. This study quantifies monthly precipitation hindcast data performance in South Sulawesi provided by seven coupled models from the NMME during 29 years (1982 – 2010) period. Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) dataset and the Standardized Verification System (SVS) for long-range forecasts (LRF) analysis applied to asses monthly precipitation prediction. Almost all individual model shows relatively high skill when used to make June – November monthly precipitation prediction and low skill when used to forecast monthly precipitation in December – May periods. Multi-Model Ensemble (MME) performed using Simple Composite Method (SCM) increased performance of monthly precipitation prediction. Nevertheless, this improvement only applies at short lead time (< 3.3 months). This result also shows that NMME is more promising when it used to make precipitation forecast application during dry period (i.e. drought prediction) rather than wet period over South Sulawesi region.

Evaluation of precipitation forecasts for Dabie Mountain area and application countermeasures for the reservoirs regulation

对降水预报进行性能评估及应用对策研究可以更好地发挥降水预报在水库调度中的决策支持作用.基于大别山库区近10 a汛期（2007-2016年5月1日-9月30日）24~168 h共7个预见期降水预报和地面降水观测资料，采用正确率、TS评分、概率统计、ROC曲线以及CTS等方法评估大别山库区降水预报性能，并以响洪甸水库为重点研究区域分析降水预报在水库调度中的应用对策.结果表明：1）大别山库区各量级的降水预报都有正预报技巧；24~72 h预见期降水预报的TS评分较高且空报率、漏报率也较低，具有较高的预报性能；但96 h及以上预见期降水预报性能明显下降，中雨以上量级空报率、漏报率较大，特别是对大暴雨及其以上量级的降水预报性能显著下降.2）大别山库区预报降水量级与实况降水量级基本符合，预报降水量级大于等于实况降水量级的概率超过75%；虽然降水预报量级上呈现出过度预报的现象，但降水过程预报对水库调度仍有较好的应用价值，应用时要考虑到降水预报量级可能存在偏差.3）转折性天气预报96 h及以上预见期CTS评分较低，但72 h以内预见期的性能明显改进，尤其是24 h预见期CTS评分也提高到了38.2%；水库调度可从长预见期的降水预报获取降水过程及其可能发生转折的信息，根据短预见期的降水预报进行调度方案调整.;The evaluation and application research of precipitation forecasts for reservoirs area can help to optimize the reservoir regulation. Based on the precipitation forecasts with 7 lead time(24-168 h) and the observation precipitation data in the flood season during 2007-2016, evaluation methods (e.g. Percentage Correct, Threat Score, Probability Statistics, ROC curve, CTS) were introduced to estimate the precipitation prediction skill, and the application countermeasures for the reservoir regulation in Xianghongdian Reservoir of Dabie Mountain area were analyzed. The results indicate that:1) The precipitation forecasting skills for all precipitation magnitudes are all positive in the Dabie Mountains reservoir area. The precipitation forecast performance of 24-72 h lead time in the Dabie Mountains reservoir area is the best, and the TS score is relatively high and false alarm rate and missing forecast rate are also relatively low. However, the forecasting performance of 96 h lead time and above is obviously decreased, the false alarm rate and missing forecast rate are getting higher for moderate rain, especially for heavy rain. 2) Overall, even though the rainfall scale predictions are generally consistent with the observations, the probability of precipitation forecast greater than or equal to the observation is more than 75%. Although it is inclined to the over forecast the precipitation scale, the precipitation forecast products still havea good application value. However, precipitation scale forecast deviation must be considered during application. 3) Compared to the forecasts with long lead time which have great uncertainty, forecasts with short lead time are more reliable and have better performance in heavy precipitation processes and transition weather. The CTS scores of 96 h lead time and above are relatively low,but the forecasting performance is improved significantly within 72 h lead time. Especially, the CTS scores of 24 h lead time increases to 38.2%. In practical application, the combination of precipitation forecasts with different forecast periods, including the latest forecasts, are strongly suggested to get the information of transition weather. Such a forecast combination can be utilized to adjust the reservoirs regulation timely when the transition weather is approaching. Our statistic results have justified the usability of precipitation forecast, and the research results could be a valuable reference for the reservoir regulation decision making.

Seamless precipitation prediction skill comparison between two global models

The prediction of precipitation by two ocean–atmosphere ensemble systems is compared with observations and each other over a broad range of time‐scales. The systems are the 2015 version of the Australian Bureau of Meteorology's Predictive Ocean–Atmosphere Model for Australia (POAMA) with a T47 atmosphere, and the 2011 version of the European Centre for Medium‐range Weather Forecasts' (ECMWF) monthly system with a variable atmospheric resolution of T639 to T319. To facilitate the comparison across a seamless range of time‐scales, verification against observations is performed using data averaged over time windows equal in length to the forecast lead time, from 1 day to 4 weeks. In addition to this ‘actual’ skill, potential skill is computed by taking one ensemble member as truth and computing how well the other members forecast that member.Overall, ECMWF shows higher actual skill than POAMA across all time‐scales and in both the Tropics and extratropics, as expected given its greater sophistication. ECMWF is particularly more skilful than POAMA in the Tropics for the shorter leads. Consistent between the two systems, however, is that as lead time and averaging window are simultaneously increased the near‐equatorial skill remains approximately constant, whereas it drops in all other latitude bands. As a result, both systems show much higher skill in the Tropics than extratropics for the 1 week time‐scale and beyond, with that skill concentrated over the equatorial Pacific.Although potential skill in both systems is almost everywhere higher than their actual skill, there remains a strong similarity in the spatial patterns of potential and actual skill for the longer time‐scales. Within‐model comparisons of potential and actual skill show largest differences for POAMA in the Tropics at short lead times, and largest differences for ECMWF in the Southern Hemisphere high latitudes (50–70°S). The implications of these findings are discussed.

Methods for Improving the Prediction Skill of Summer Precipitation over East Asia–West Pacific

AbstractThe performance of summer precipitation prediction skill of the Beijing Climate Center Climate System Model (BCC_CSM1.1; hereafter CSM) over the East Asia–west Pacific (EA–WP) region indicates the need for further development in order to improve model prediction skill. Two methods, namely the statistical alone prediction and the statistical and model combined prediction, are proposed to improve prediction skill. For the former, more than 2000 combinations of precursory predictors are considered in the predictive model; both most similar and dissimilar information are considered in the prediction process. For the latter, both the statistical analog information and the CSM’s forecast are considered in the predictive model; this new prediction method is based on merging information from both a dynamical model and historical analogs. Cross validation of summer precipitation over EA–WP for the years 1991–2010 shows that the average spatial anomaly correlation coefficient (ACC) of the statistical alone prediction is 0.16 and the average spatial root-mean-standard error (RMSE) is 22.7, which are much improved compared to the systematic error correction with ACC and RMSE being 0.09 and 25.0 respectively. The ACC and RMSE results from the statistical and CSM combined prediction are 0.22 and 21.8, respectively, which also indicates an improvement compare to the statistical alone prediction. Independent sample validation for the years 2011–13 indicates that the average ACCs of the statistical alone prediction and the statistical and CSM combined prediction (0.25 and 0.24) are enhanced compared to the systematic error correction (−0.02). The average RMSE is also improved from 25.0 to 23.6 and 22.7. Therefore, the two new prediction methods proposed in this paper both demonstrate the possibilities for the operational prediction of summer precipitation over EA–WP.