基于TIGGE多模式集合的24小时气温BMA 概率预报

利用TIGGE(THORPEX Interactive Grand Global Ensemble)单中心集合预报系统(ECMWF、United Kingdom Meteorological Office、China Meteorological Administration和NCEP)以及由此所构成的多中心模式超级集合预报系统24小时地面日均气温预报,结合淮河流域地面观测率定贝叶斯模型平均(Bayesian model averaging,BMA)参数,从而建立地面日均气温BMA概率预报模型.由此针对淮河流域进行地面日均气温BMA概率预报及其检验与评估,结果表明BMA模型比原始集合预报效果好;单中心的BMA概率预报都有较好的预报效果,其中ECMWF最好.多中心模式超级集合比单中心BMA概率预报效果更好,采用可替换原则比普通的多中心模式超级集合BMA模型计算量小,且在上述BMA集合预报系统中效果最好.它与原始集合预报相比其平均绝对误差减少近7%,其连续等级概率评分提高近10%.基于采用可替换原则的多中心模式超级集合BMA概率预报,针对研究区域提出了极端高温预警方案,这对防范高温天气有着重要意义.     

The 2020/21 Extremely Cold Winter in China Influenced by the Synergistic Effect of La Niña and Warm Arctic

In the first half of winter 2020/21,China has experienced an extremely cold period across both northern and southern regions,with record-breaking low temperatures set in many stations of China.Meanwhile,a moderate La Ni?a event which exceeded both oceanic and atmospheric thresholds began in August 2020 and in a few months developed into its mature phase,just prior to the 2020/21 winter.In this report,the mid?high-latitude large-scale atmospheric circulation anomalies in the Northern Hemisphere,which were forced by the negative phase of Arctic Oscillation,a strengthened Siberian High,an intensified Ural High and a deepened East Asian Trough,are considered to be the direct reason for the frequent cold surges in winter 2020/21.At the same time,the synergistic effect of the warm Arctic and the cold tropical Pacific(La Ni?a)provided an indispensable background,at a hemispheric scale,to intensify the atmospheric circulation anomalies in middle-to-high latitudes.In the end,a most recent La Ni?a prediction is provided and the on-coming evolution of climate is discussed for the remaining part of the 2020/21 winter for the purpose of future decision-making and early warning.     

Experiments with the Improved Dynamical-Statistical-Analog Ensemble Forecast Model for Landfalling Typhoon Precipitation over South China

In recent work, three physical factors of the Dynamical-Statistical-Analog Ensemble Forecast Model for Landfalling Typhoon Precipitation (DSAEF_LTP model) have been introduced, namely, tropical cyclone (TC) track, TC landfall season, and TC intensity. In the present study, we set out to test the forecasting performance of the improved model with new similarity regions and ensemble forecast schemes added. Four experiments associated with the prediction of accumulated precipitation were conducted based on 47 landfalling TCs that occurred over South China during 2004-2018. The first experiment was designed as the DSAEF_LTP model with TC track, TC landfall season, and intensity (DSAEF_LTP-1). The other three experiments were based on the first experiment, but with new ensemble forecast schemes added (DSAEF_LTP-2), new similarity regions added (DSAEF_LTP-3), and both added (DSAEF_LTP- 4), respectively. Results showed that, after new similarity regions added into the model (DSAEF_LTP-3), the forecasting performance of the DSAEF_LTP model for heavy rainfall (accumulated precipitation ≥250 mm and ≥100 mm) improved, and the sum of the threat score (TS250 + TS100) increased by 4.44%. Although the forecasting performance of DSAEF_LTP-2 was the same as that of DSAEF_LTP-1, the forecasting performance was significantly improved and better than that of DSAEF_LTP-3 when the new ensemble schemes and similarity regions were added simultaneously (DSAEF_LTP-4), with the TS increasing by 25.36%. Moreover, the forecasting performance of the four experiments was compared with four operational numerical weather prediction models, and the comparison indicated that the DSAEF_LTP model showed advantages in predicting heavy rainfall. Finally, some issues associated with the experimental results and future improvements of the DSAEF_LTP model were discussed.     

Experiments of DSAEF_LTP Model with Two Improved Parameters for Accumulated Precipitation of Landfalling Tropical Cyclones over Southeast China

The Dynamical-Statistical-Analog Ensemble Forecast model for landfalling tropical cyclones (TCs) precipitation (DSAEF_LTP) utilises an operational numerical weather prediction (NWP) model for the forecast track, while the precipitation forecast is obtained by finding analog cyclones, and making a precipitation forecast from an ensemble of the analogs. This study addresses TCs that occurred from 2004 to 2019 in Southeast China with 47 TCs as training samples and 18 TCs for independent forecast experiments. Experiments use four model versions. The control experiment DSAEF_LTP_1 includes three factors including TC track, landfall season, and TC intensity to determine analogs. Versions DSAEF_LTP_2, DSAEF_LTP_3, and DSAEF_LTP_4 respectively integrate improved similarity region, improved ensemble method, and improvements in both parameters. Results show that the DSAEF_LTP model with new values of similarity region and ensemble method (DSAEF_LTP_4) performs best in the simulation experiment, while the DSAEF_LTP model with new values only of ensemble method (DSAEF_LTP_3) performs best in the forecast experiment. The reason for the difference between simulation (training sample) and forecast (independent sample) may be that the proportion of TC with typical tracks (southeast to northwest movement or landfall over Southeast China) has changed significantly between samples. Forecast performance is compared with that of three global dynamical models (ECMWF, GRAPES, and GFS) and a regional dynamical model (SMS-WARMS). The DSAEF_LTP model performs better than the dynamical models and tends to produce more false alarms in accumulated forecast precipitation above 250 mm and 100 mm. Compared with TCs without heavy precipitation or typical tracks, TCs with these characteristics are better forecasted by the DSAEF_LTP model.     

黑河实验区地表植被指数的区域分布及季节变化

利用具有较高空间分辨率的Ｌａｎｄｓａｔ ＴＭ卫星资料估算了黑河实验区夏季和近冬季地表标准化差值植被指数ＮＤＶＩ,分析了ＮＤＶＩ的区域分布特征和季节变化。结果表明,由于该实验区下垫面的复杂性,ＮＤＶＩ表现出明显的空间和季节变化,ＮＤＶＩ的图像能够很好地反映出地表植被的分布状况。     

Assessing Disagreement and Tolerance of Misclassification of Satellite-derived Land Cover Products Used in WRF Model Applications

As more satellite-derived land cover products used in the study of global change,especially climate modeling,assessing their quality has become vitally important.In this study,we developed a distance metric based on the parameters used in weather research and forecasting(WRF) to characterize the degree of disagreement among land cover products and to identify the tolerance for misclassification within the International Geosphere Biosphere Programme(IGBP) classification scheme.We determined the spatial degree of disagreement and then created maps of misclassification of Moderate Resolution Imaging Spectoradiometer(MODIS) products,and we calculated overall and class-specific accuracy and fuzzy agreement in a WRF model.Our results show a high level of agreement and high tolerance of misclassification in the WRF model between large-scale homogeneous landscapes,while a low level of agreement and tolerance of misclassification appeared in heterogeneous landscapes.The degree of disagreement varied significantly among seven regions of China.The class-specific accuracy and fuzzy agreement in MODIS Collection 4 and 5 products varied significantly.High accuracy and fuzzy agreement occurred in the following classes:water,grassland,cropland,and barren or sparsely vegetated.Misclassification mainly occurred among specific classes with similar plant functional types and low discriminative spectro-temporal signals.Some classes need to be improved further;the quality of MODIS land cover products across China still does not meet the common requirements of climate modeling.Our findings may have important implications for improving land surface parameterization for simulating climate and for better understanding the influence of the land cover change on climate.     

Elucidating Dominant Factors Affecting Land Surface Hydrological Simulations of the Community Land Model over China

In order to compare the impacts of the choice of land surface model(LSM) parameterization schemes, meteorological forcing, and land surface parameters on land surface hydrological simulations, and explore to what extent the quality can be improved, a series of experiments with different LSMs, forcing datasets, and parameter datasets concerning soil texture and land cover were conducted. Six simulations are run for the Chinese mainland on 0.1° × 0.1° grids from 1979 to 2008, and the simulated mon...     

珠穆朗玛峰北坡山谷太阳辐射和大气的特征与分析

2006年5月27日～6月30日HEST2006大气科学实验对珠峰北坡山谷的辐射(总辐射、净辐射)和温、湿度、风等进行了综合观测.沿珠峰北坡山谷布设了3个观测站,3个测站的辐射、温度、风都表现出明显的日变化规律,它们在08:00或09:00(地方时,下同)达到极大值.3个测站总辐射和净辐射的日变化都七匕较一致.从日变化最大值出现的时间来看,各站的辐射通量早于气温,气温早于风速.3个测站中任意2站之间辐射(总辐射、净辐射)最大值之比与温度和风速最大值之比均比较接近.因辐射状况、地形结构、大气温度等不同,远离珠峰区域的风一天之内多次改变风向,靠近珠峰区域则24h都为南风.珠峰北坡山谷不同区域风向风速变化存在明显时差,南风强于北风,且持续时间长.研究表明,辐射能量对于珠峰北坡大气运动具有重要的驱动作用,是控制和改变其大气运动方式最基本、最重要的因子.净辐射在不同区域风向转变或风速变化过程中起着决定性的作用.     

Changes in Water Use Efficiency Caused by Climate Change,CO2 Fertilization,and Land Use Changes on the Tibetan Plateau

Terrestrial ecosystem water use efficiency(WUE) is an important indicator for coupling plant photosynthesis and transpiration, and is also a key factor linking the carbon and water cycles between the land and atmosphere. However,under the combination of climate change and human intervention, the change in WUE is still unclear, especially on the Tibetan Plateau(TP). Therefore, satellite remote sensing data and process-based terrestrial biosphere models(TBMs) are used in this study to investigate ...     

中国区域秋季旱涝特征预测

利用国家气候中心综合气象干旱指数(CI),以干旱日数为预报对象,以前期500 hPa高度场为预报因子,结合最优子集回归(OSR)方法建立回归预报模型,对1971-2007年期间中国区域秋季干旱形势进行交叉检验预报。针对秋季华西秋雨与华南干旱特征,分别建立以华西洪涝站数与华南干旱站数为预报对象的预报模型,研究OSR方法的预报技巧。研究表明:OSR预报方法对秋季中国区域的干旱日数具有较高的预报技巧,距平相关系数(ACC)在全国大部地区均通过α=0.05的显著性水平检验,中国东部地区整体预报水平高于西部地区,ACC在中国区域平均达到0.45。对于秋季华西洪涝站数与华南干旱站数,OSR方法也体现出较好的预报能力,ACC分别达到0.71和0.55。此外,对华南区域干旱站数的独立预报检验表明,模型能较好地预报出2003-2007年秋季华南地区偏旱的趋势,但对干旱范围的预报有所低估。