Resolution effects on regional climate model simulations of seasonal precipitation over Europe

We analyze a set of nine regional climate model simulations for the period 1961–2000 performed at 25 and 50 km horizontal grid spacing over a European domain in order to determine the effects of horizontal resolution on the simulation of precipitation. All of the models represent the seasonal mean spatial patterns and amount of precipitation fairly well. Most models exhibit a tendency to over-predict precipitation, resulting in a domain-average total bias for the ensemble mean of about 20% in winter (DJF) and less than 10% in summer (JJA) at both resolutions, although this bias could be artificially enhanced by the lack of a gauge correction in the observations. A majority of the models show increased precipitation at 25 km relative to 50 km over the oceans and inland seas in DJF, JJA, and ANN (annual average), although the response is strongest during JJA. The ratio of convective precipitation to total precipitation decreases over land for most models at 25 km. In addition, there is an increase in interannual variability in many of the models at 25 km grid spacing. Comparison with gridded observations indicates that a majority of models show improved skill in simulating both the spatial pattern and temporal evolution of precipitation at 25 km compared to 50 km during the summer months, but not in winter or on an annual mean basis. Model skill at higher resolution in simulating the spatial and temporal character of seasonal precipitation is found especially for Great Britain. This geographic dependence of the increased skill suggests that observed data of sufficient density are necessary to capture fine-scale climate signals. As climate models increase their horizontal resolution, it is thus a key priority to produce high quality fine scale observations for model evaluation.     

Bayesian regression model for seasonal forecast of precipitation over Korea

In this paper, we apply three different Bayesian methods to the seasonal forecasting of the precipitation in a region around Korea (32.5°N?C42.5°N, 122.5°E-132.5°E). We focus on the precipitation of summer season (June?CJuly?CAugust; JJA) for the period of 1979?C2007 using the precipitation produced by the Global Data Assimilation and Prediction System (GDAPS) as predictors. Through cross-validation, we demonstrate improvement for seasonal forecast of precipitation in terms of root mean squared error (RMSE) and linear error in probability space score (LEPS). The proposed methods yield RMSE of 1.09 and LEPS of 0.31 between the predicted and observed precipitations, while the prediction using GDAPS output only produces RMSE of 1.20 and LEPS of 0.33 for CPC Merged Analyzed Precipitation (CMAP) data. For station-measured precipitation data, the RMSE and LEPS of the proposed Bayesian methods are 0.53 and 0.29, while GDAPS output is 0.66 and 0.33, respectively. The methods seem to capture the spatial pattern of the observed precipitation. The Bayesian paradigm incorporates the model uncertainty as an integral part of modeling in a natural way. We provide a probabilistic forecast integrating model uncertainty.     

Compound summer temperature and precipitation extremes over central Europe

Theoretical and Applied Climatology - Reliable knowledge of the near-future climate change signal of extremes is important for adaptation and mitigation strategies. Especially compound extremes,...     

ECMWF seasonal forecast system 3 and its prediction of sea surface temperature

The latest operational version of the ECMWF seasonal forecasting system is described. It shows noticeably improved skill for sea surface temperature (SST) prediction compared with previous versions, particularly with respect to El Nino related variability. Substantial skill is shown for lead times up to 1?year, although at this range the spread in the ensemble forecast implies a loss of predictability large enough to account for most of the forecast error variance, suggesting only moderate scope for improving long range El Nino forecasts. At shorter ranges, particularly 3?C6?months, skill is still substantially below the model-estimated predictability limit. SST forecast skill is higher for more recent periods than earlier ones. Analysis shows that although various factors can affect scores in particular periods, the improvement from 1994 onwards seems to be robust, and is most plausibly due to improvements in the observing system made at that time. The improvement in forecast skill is most evident for 3-month forecasts starting in February, where predictions of NINO3.4 SST from 1994 to present have been almost without fault. It is argued that in situations where the impact of model error is small, the value of improved observational data can be seen most clearly. Significant skill is also shown in the equatorial Indian Ocean, although predictive skill in parts of the tropical Atlantic are relatively poor. SST forecast errors can be especially high in the Southern Ocean.     

基于降水极端预报指数的福建台风极端降水预报研究

应用1961—2017年中国气象局热带气旋最佳路径数据集、国家地面气象观测站日降水观测资料和2015年8月—2017年12月欧洲中期天气预报中心(ECMWF)集合预报系统降水极端预报指数(EFI)数据,根据百分位法定义台风影响期间福建省各站点的台风极端降水阈值,采用最小阈值法剔除台风极端降水时EFI箱线图中的异常值,保...     

Using soil moisture forecasts for sub-seasonal summer temperature predictions in Europe

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.     

DOGRAFS逐小时气温和降水预报的释用

采用2016—2018年DOGRAFS(沙漠绿洲戈壁区域分析预报系统)的气温、降水逐小时预报资料,在检验的基础上开展释用方法研究,并用2019年资料进行试验测试,结果表明:DOGRAFS气温预报,08时起报的准确率要高于20时起报的;北疆好于南疆,准确率为50%,平均绝对误差为2.5℃,采用最高、最低气温建模方案,预报准确率提高到64%,平均绝对误差为1.9℃,预报的离散度降低。DOGRAFS降水预报,08时起报的T_S评分略高,20时起报的晴雨准确率略高,南疆好于北疆;晴雨预报准确率可达95%,但降水T_S评分仅有20%,空报率超过50%;采用消空订正方案,晴雨预报准确率提高1%,T_S评分提高2%,空报率大大降低,但漏报率较大。释用方案对模式气温预报有较好的提升效果,降水预报仍有较大的改进空间。     

Changes in Seasonal Patterns of Temperature and Precipitation in China During 1971-2000

Many studies have shown evidence for significant changes in surface climate in different regions of the world and during different seasons over the past 100 years. Based on daily temperature and precipitation data from 720 climate stations in China, cluster analysis was used to identify regions in China that have experienced similar changes in the seasonal cycle of temperature and precipitation during the 1971-2000 climate normal period. Differences in 11-day averages of daily mean temperature and total precipitation between the first （1971-1985） and second （1986-2000） halves of the record were analyzed using the Mann- Whitney U test and the global κ-means clustering algorithm. Results show that most parts of China experienced significant increases in temperature between the two periods, especially in winter, although some of this warming may be attributable to the urban heat island effect in large cities. Most of western China experienced more precipitation in 1986-2000, while precipitation decreased in the Yellow River valley. Changes in the summer monsoon were also evident, with decreases in precipitation during the onset and decay phases, and increases during the wettest period.     

Historical effects of temperature and precipitation on California crop yields

For the 1980–2003 period, we analyzed the relationship between crop yield and three climatic variables (minimum temperature, maximum temperature, and precipitation) for 12 major Californian crops: wine grapes, lettuce, almonds, strawberries, table grapes, hay, oranges, cotton, tomatoes, walnuts, avocados, and pistachios. The months and climatic variables of greatest importance to each crop were used to develop regressions relating yield to climatic conditions. For most crops, fairly simple equations using only 2–3 variables explained more than two-thirds of observed yield variance. The types of variables and months identified suggest that relatively poorly understood processes such as crop infection, pollination, and dormancy may be important mechanisms by which climate influences crop yield. Recent climatic trends have had mixed effects on crop yields, with orange and walnut yields aided, avocado yields hurt, and most crops little affected by recent climatic trends. Yield-climate relationships can provide a foundation for forecasting crop production within a year and for projecting the impact of future climate changes.     

Soil moisture influence on summertime surface air temperature over East Asia

Soil moisture influence on surface air temperature in summer is statistically quantified across East Asia using the Global Land Data Assimilation System soil moisture and observational temperature. The analysis uses a soil moisture feedback parameter computed based on lagged covariance ratios. It is found that significant negative soil moisture feedbacks on temperature mainly appear over the transition zones between dry and wet climates of northern China and Mongolia. Over these areas, the feedbacks account for typically 5–20% of the total temperature variance, with the feedback parameter of ?0.2°C to ?0.5°C (standardized soil moisture)^?1. Meanwhile, positive feedbacks may exist over some areas of Northeast Asia but are much less significant. These findings emphasize the importance of soil moisture-temperature feedbacks in influencing summer climate variability and have implications for seasonal temperature forecasting.     

基于频率匹配法和最优TS评分法的降水预报订正效果对比

利用2020年1月1日—2023年1月2日ECMWF、NCEP模式降水预报资料和眉山地区降水观测资料,采用频率匹配法（Frequency Matching Method,FM）、最优TS评分法（Optimal Threat Score,OTS）对ECMWF和NCEP的模式降水预报进行订正,对比检验评分和预报个例,并验证两种方法的适用性。结果表明：FM、OTS订正明显改善了小雨空报和暴雨漏报,提高了晴雨预报准确率、小雨ETS评分、暴雨TS评分,且OTS优于FM;FM可能出现参考数据与实况数据有较大偏差的情况,从而影响订正效果,OTS则不受参考数据的影响;数值模式降水的预报偏度明显大于1或小于1时,FM、OTS订正效果越好;FM、OTS难以订正小雨漏报、暴雨空报,也难以对降水差值较大的空漏报或降水落区偏差进行订正。     

营口地区数值预报降水产品定量检验和预报指标研究

选取2010年8月至2011年7月天气在线、日本传真图、美国全球预报系统（以下简称GFS）、以及T639等数值预报降水预报产品,采用统计学方法和天气学方法,按照预报时效、预报降水量级、影响系统等不同方面对营口地区的降水预报产品进行检验分析,以便更好的利用数值产品做好降水预报,提高营口地区的降水预报准确率。结果表明：从整体角度看各种数值预报产品预报准确率随时间变化逐渐降低,但天气在线和GFS预报效果相对较好且稳定,日本传真图次之,T639稳定性最差;各种数值预报产品均存在预报偏小的情况,特别是对暴雨的预报效果均不太理想,稳定性差、量级偏小;小雨量级降水空报和漏报明显;各种数值预报产品对高空槽和冷涡漏报情况较明显,主要为小雨量级。     

黑龙江省降水概率分县指导预报

根据１９９５￣１９９７年的Ｔ１０６数值预报产品资料和晴雨资料,利用逐步回归方法建立黑龙江省７５个市、县短期降水概率预报方程,并利用１９９８年１￣１０月的资料进行了试报,检验结果表明该方法有一定的应用价值。     

Evaluation of summer temperature and precipitation predictions from NCEP CFSv2 retrospective forecast over China

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.     

江西及其临近区域同化系统中不同季节背景误差协方差特征及其对降水预报影响

在不同季节背景下,对比分析多元变量相关的背景误差协方差特征,了解其在天气过程中的作用,可以改进同化系统性能,提高降水数值预报水平。对比分析汛期和非汛期江西及其临近区域多元变量相关的背景误差协方差特征,分别选取2019年汛期和非汛期的2次降水过程,设计2组循环同化试验,探讨多元变量相关的背景误差协方差对江西降水预报效果的影响。结果表明,相较于非汛期,汛期分析变量对风场、温度场和水汽场的贡献值整体上要更大,且汛期各个控制变量的背景场误差更为显著。多元变量试验的降水预报评估效果整体上更好,其汛期预报效果改善更显著。汛期与非汛期的多元变量试验降水预报结果更接近实况,且相对非汛期降水而言,汛期多元变量试验模拟效果更好,模拟的降水分布及强度与实况更为接近。     

定量降水预报技术进展

现有的降水预报后处理方法多倾向于对整个区域开展日降水量统一订正,忽视了降水偏差的区域差异,且无法满足精细化预报业务需求。在综合评估中国气象局区域中尺度天气数值预报系统CMA-MESO 3 km (China Meteorological Administration Mesoscale Model at 3 km resolution)在2020年山西汛期降水预报偏差的基础上,基于频率匹配方法,对小时降水预报开展分时段、分区域订正试验,并对订正结果进行了多角度检验分析。结果表明：(1)模式对山西日降水预报空报突出,其中盆地降水预报量级明显偏大。(2)不同区域、不同时段小时降水预报偏差不同,其中弱降水在白天低海拔地区预报偏多,夜间高海拔地区预报偏少。(3)分区域、分时段频率匹配订正后,日累计降水平均误差较订正前降低76.8%,强降水过度预报问题显著改善。(4)通过降低过度预报的降水频率和强度,降水量峰值提前、上午峰值虚报等问题得以解决,可更好再现降水日变化特征。(5)订正后有效改善了模式降水预报的地形分布特征,主要体现在强度预报改进上,但对模式降水预报频率的地形分布偏差调整有限。

     

定量降水预报技术进展

四川盆地地形地貌复杂,强降水预报难度大,对模式降水预报产品进行订正,是提升强降水预报质量的重要手段。本文选取2018—2019年发生在四川盆地的35次强降水过程,对ECMWF、CMA_MESO和SWC_WARMS三种模式的24 h强降水预报采用常规评分和空间平移两个方法进行检验,并利用最优评分、多模式集成和位移订正三种方法进行订正试验。结果表明: 最优评分订正方法可以有效改善各模式降水预报的强度,而多模式集成订正法可以改进降水落区和极值预报,在此基础上计算位移偏差,根据最优的位移偏差值对降水预报进行位移订正,可以进一步改进强降水落区预报效果。然后利用2020—2021年强降水过程进行订正效果验证,结果显示：经订正后的降水极值预报更接近实况,各量级降水预报评分明显优于单一模式,暴雨和大暴雨预报的TS评分提高率较最优单模式分别可达24.3%和42.8%,订正后空报率基本维持,漏报率显著下降,订正效果良好。

     

定量降水预报技术进展

为提升现有业务系统的降水临近预报（0—2 h）能力,以业务应用为目标,基于中国气象局强对流天气短时临近预报系统—SWAN （Severe Weather Automatic Nowcasting）开展降水临近预报算法改进和应用评估研究。首先使用分钟雨量数据,采用分雨团的雷达-雨量站降水订正技术,提高降水的实况订正频率,提升定量降水估计格点场的准确率;再通过基于光流法的回波运动矢量反演技术优化改进回波运动矢量;最终将改进后的定量降水估计格点场和外推矢量相结合进行降水临近预报。通过对2021年7月全国1 km分辨率回波和降水临近预报检验评估和个例分析发现：（1）改进的基于光流法的回波运动矢量反演技术可以捕捉不同天气尺度下的回波运动规律,反演结果具有较好的一致性和平滑性,可以提高外推的准确率。（2）新的降水临近预报子系统和现有SWAN系统相比,在0—1 h预报中,TS评分相对提高50%以上,Bias更趋近于1。在1—2 h预报中,除了20 mm/h的强降水以外,TS评分相对提升约20%,Bias下降1—3。新的降水临近预报子系统基于SWAN算法标准开发接口实现,运行效率满足业务需求,预报性能得到明显提升,具备业务应用的潜力。