动力延伸预报产品释用方法的研究

目前以数值形势预报为基础的动力产品释用方法主要有模式输出统计法（ＭＯＳ）和完全预报方法（ＰＰＭ）。由这两种方法建立的预报方程缺乏明确的物理意义,所以文中提出了一种动力与统计相结合的方法。该方法从大尺度大气动力学方程组出发,推导出月降水距平百分率与月环流场的关系,从而建立了月降水距平百分率预报方程,随后利用月动力延伸预报的５００ｈＰａ高度场和实际降水场资料反演出月降水距平百分率预报方程的系数。该方程经过独立样本检验,表明这种方法对利用动力延伸集合预报的环流形势作月降水距平预报具有一定的能力。     

共轭方程理论在月平均温度距平季节预报中的应用

说明了共轭方程方法的可行性，使用历史资料代入共轭函数公式中计算了东北地区12月、6月月平均温度距平，并与实况进行了比较。为了建立东北地区12月、6月平均气温距平的长期预报方法，使用预报时间半年前的海温作参数化月平均热流量距平，代入简化的共轭函数公式中得到了预报值。     

资料

资料１９９４年１１月５００ｈＰａ环流指数、环流特征量资料中央气象台长期预报科１９９４年１１月亚洲地区逐日５００ｈＰａ西风环流指数中央气象台中期预报科１９９４年１１月５００ｈＰａ平均高度、涡度距平的球函数展开系数、振幅、位相国家气象中心资料室资料...     

结合历史资料的数值天气预报误差订正

运用基于历史资料的模式距平积分订正(ANO)方法,结合欧洲中心的ERA-interim再分析资料和0.1度分辨率的中国地面自动站与CMORPH卫星反演降水资料融合逐时降水产品,对高分辨非静力WRF模式的数值预报结果进行订正试验,检验了ANO方法对灾害性天气、尤其持续性强降水预报的订正改进效果。对1983-2013年7月中旬四川地区数值预报结果订正前后与观测和再分析数据的比较表明,ANO方法不仅在环流场的预报订正试验中有较为显著的效果,对模式降水预报结果也有改进,能够有效提高模式对强降水的预报精度和评分、减小预报偏差,其中对2013年7月8—13日高分辨预报结果的ANO订正试验发现,订正环流场各变量都有改进,其中位势高度距平相关系数ACC平均提高了7.8%,均方根误差RMSE平均降低了55.7%,降水(特别是暴雨以上量级)的ETS评分和TS评分也有不同程度的提高,并得到多年独立样本的高分辨数值预报订正结果的支持。     

资料

资料１９９４年５月５００ｈＰａ环流指数、环流特征量资料中央气象台长期预报科１９９４年５月亚洲地区逐日５００ｈＰａ西风环流指数中央气复会中期预报科１９９４年５月５００ｈＰａ平均高度、涡度距平的球函数展开系数、振幅、位相国家气象中。Ｃ资料室资料...     

资料

1996年4月500hPa环流指数、环流特征量资料国家气候中心气候预测室1996年4月亚洲地区逐日500hPa西风环流指数中央气象台中期预报科1996年4月500hPa平均高度、涡度距平的球函数展开系数、振幅、位相国家气候中心资料     

资料

资料１９９４年７月５００ｈＰａ环流指数、环流特征量资料１９９４年７月亚洲地区逐日５００ｈＰａ西风环流指数中央气象台中期预报科１９９４年７月５００ｈＰａ平均高度、涡度距平的球函数展开系数、振幅、位相国家气象中心资料室资料$国家气象中心资料室...     

资料

资料１９９５年９月５００ｈＰａ环流指数、环流特征量资料国家气候中心气候预测室１９９５年９月亚洲地区逐日５００ｈＰａ西风环流指数中央气象台中期预报科１９９５年９月５００ｈＰａ平均高度、涡度距平的球函数展开系数、振幅、位相国家气候中心资料...     

资料

资料１９９５年１１月５００ｈＰａ环流指数、环流特征量资料一国家气候中心气候预测室１９９５年１１月亚洲地区逐日５００ｈＰａ西风环流指数中央气象台中期预报科１９９５年１１月５００ｈＰａ平均高度、涡度距平的球函数展开系数、振幅、位相国家气候中心资料...     

资料

资料１９９６年１月５００ｈＰａ环流指数、环流特征量资料国家气候中心气候预测室１９９６年１月亚洲地区逐日５００ｋＰａ西风环流指数中央气象台中期预报科１９９６年１月５００ｈＰａ平均高度、涡度距平的球函数展开系数、振幅、位相国家气候中心资料...     

长期预报基本观测资料自动化处理

将微型气象无人驾驶飞机与无线数字影像采集、传输设备相结合,构成了一套微型无人机空中火情监测系统,并开发了相应的地面监测软件.经过地面测试和空中飞行试验证实,该系统运行良好,对于地面火点有较好的探测能力.目前试验系统已具备单架次完成半径30 km以内,面积80～100 km2林区巡护任务的能力,而且这一系统成本低、使用灵活方便,具有良好的应用前景.同时就系统开发中遇到的相关问题和今后的改进方向进行了讨论.     

Physical initialization in the NMC Global Data Assimilation System

Summary The impact of assimilating Goes Precipitation Index (GPI) rain rates in the National Meteorological Center Global Data Assimilation System (NMC GDAS) via physical initialization is explored. Reverse algorithms of the simplified Arakawa-Schubert cumulus parameterization and the parameterized surface fluxes are developed in order to assimilate the GPI data over the tropics during the six hour forecasts linking successive GDAS analyses. Results from two experiments show improvements to both analyses and forecasts when GPI rain rates are assimilated.With 16 Figures     

The impact of inhomogenous background errors on a global wave data assimilation system

One of the main limitations in current wave data assimilation systems is the lack of an accurate representation of the structure of the background errors. In this work, models for the observational error variance, background error variance and background error correlations are developed based on the results of previous studies. These are tested in a global wave data assimilation system and the resulting wave forecasts are verified against independent observations from buoys. Forecasts of significant wave height show substantial improvement over the Australian Bureau of Meteorology's current operational wave forecasting system. However, forecasts of peak period are not similarly improved. The regional impacts of the new assimilation scheme are found to vary on a seasonal basis. Overall, it is shown that the inclusion of a latitudinally dependent background error, and improved specification of the background and observational error variances can reduce the root-mean-square error of 24-hour forecast Significant Wave Height by almost 10%.     

Probabilistic estimates of recent changes in temperature: a multi-scale attribution analysis

The role of anthropogenic forcings in temperature changes during recent decades is investigated over a range of spatial scales. Changes in the annual mean surface temperature and also in the warmest night of the year, which has implications for human health, are considered. Distributions of regional trends with and without the effect of human activity are produced, using constraints from a global optimal detection analysis. Anthropogenic forcings are estimated to have more than doubled the likelihood of mean warming in all regions considered except central North America, where results are more model dependent. The likelihood of warming of the warmest night has also increased, but the estimated change is more uncertain. Inferences on sub-continental scales are indicative rather than definitive because of the absence of locally important forcings and processes in model simulations, as well as model biases. As model inconsistencies may impact regional analyses, a multi-model approach is essential.     

西太平洋热带地区环流低频变化的主振荡型预测试验

用1994年逐日西太平洋热带地区200 hPa纬向风场资料建立主振荡预测模型,对低频纬向风场（30～50天）的传播进行独立预报试验。试验结果表明,在夏半年109次预测中提前15天预报的相关性预报技巧在0.50以上。它是揭示低频时空演变的有效工具。同时也讨论了有关模型参数的选取方法。     

Can the uncertainties of Madden-Jullian Oscillation cause a significant “Spring Predictability Barrier” for ENSO events?

With the Zebiak-Cane model and a parameterized stochastic representation of intraseasonal forcing, the impact of the uncertainties of Madden-Jullian Oscillation (MJO) on the ??Spring Predictability Barrier (SPB)?? for El Ni?o-Southern Oscillation (ENSO) prediction is studied. The parameterized form of MJO forcing is added physically to the Zebiak-Cane model to obtain the so-called Zebiak-Cane-MJO model and then the effects of initial error, stochastic model error, and their joint error mode on the SPB associated with El Ni?o prediction are estimated. The results show that the model errors caused by stochastic MJO forcing could hardly lead to a significant SPB while initial errors can do; furthermore, the joint error mode of initial error and model error associated with the stochastic MJO forcing can also lead to a significant SPB. These demonstrate that the initial error is probably the main error source of the SPB, which may provide a theoretical foundation of data assimilation for ENSO forecasts.     

Past millennial solar forcing magnitude

A set of global climate model simulations for the last thousand years developed by the Max Planck Institute is compared with paleoclimate proxy data and instrumental data, focusing on surface temperatures for land areas between 30° and 75°N. The proxy data are obtained from six previously published Northern Hemispheric-scale temperature reconstructions, here re-calibrated for consistency, which are compared with the simulations utilizing a newly developed statistical framework for ranking several competing simulations by means of their statistical distance against past climate variations. The climate model simulations are driven by either “low” or “high” solar forcing amplitudes (0.1 and 0.25 % smaller total solar irradiance in the Maunder Minimum period compared to the present) in addition to several other known climate forcings of importance. Our results indicate that the high solar forcing amplitude results in a poorer match with the hemispheric-scale temperature reconstructions and lends stronger statistical support for the low-amplitude solar forcing. However, results are likely conditional upon the sensitivity of the climate model used and strongly dependent on the choice of temperature reconstruction, hence a greater consensus is needed regarding the reconstruction of past temperatures as this currently provides a great source of uncertainty.     

辐散风和瞬变的涡度通量对定常波的强迫

本文从理论分析、资料诊断和正压模式数值试验三个方面研究了瞬变的涡度通量的辐合与辐散风产生的涡度源之间的关系及它们在对流层上层定常波维持中的作用。结果表明， 瞬变涡度通量的辐合对辐散风产生的涡度源有抵消或耗散的趋势；定常波是在两种强迫力的共同作用下维持的。同时还研究了不同地区散度场对定常波的作用， 结果表明，中纬度东亚地区的辐合场对维持东亚大槽是重要的，并且这一辐合场与位于印度尼西亚地区的辐散场是维持西太平洋上副热带反气旋系统的主要强迫源。     

A Deep Learning Method for Bias Correction of ECMWF 24–240 h Forecasts

Correcting the forecast bias of numerical weather prediction models is important for severe weather warnings. The refined grid forecast requires direct correction on gridded forecast products, as opposed to correcting forecast data only at individual weather stations. In this study, a deep learning method called CU-net is proposed to correct the gridded forecasts of four weather variables from the European Centre for Medium-Range Weather Forecast Integrated Forecasting System global model(ECMWF-IFS): 2-m temperature, 2-m relative humidity, 10-m wind speed, and 10-m wind direction, with a forecast lead time of 24 h to 240 h in North China. First, the forecast correction problem is transformed into an image-toimage translation problem in deep learning under the CU-net architecture, which is based on convolutional neural networks.Second, the ECMWF-IFS forecasts and ECMWF reanalysis data(ERA5) from 2005 to 2018 are used as training,validation, and testing datasets. The predictors and labels(ground truth) of the model are created using the ECMWF-IFS and ERA5, respectively. Finally, the correction performance of CU-net is compared with a conventional method, anomaly numerical correction with observations(ANO). Results show that forecasts from CU-net have lower root mean square error, bias, mean absolute error, and higher correlation coefficient than those from ANO for all forecast lead times from 24 h to 240 h. CU-net improves upon the ECMWF-IFS forecast for all four weather variables in terms of the above evaluation metrics, whereas ANO improves upon ECMWF-IFS performance only for 2-m temperature and relative humidity. For the correction of the 10-m wind direction forecast, which is often difficult to achieve, CU-net also improves the correction performance.     

Assessment of Data Assimilation Approaches for the Simulation of a Monsoon Depression Over the Indian Monsoon Region

A low pressure system that formed on 21 September 2006 over eastern India/Bay of Bengal intensified into a monsoon depression resulting in copious rainfall over north-eastern and central parts of India. Four numerical experiments are performed to examine the performance of assimilation schemes in simulating this monsoon depression using the Fifth Generation Mesoscale Model (MM5). Forecasts from a base simulation (with no data assimilation), a four-dimensional data assimilation (FDDA) system, a simple surface data assimilation (SDA) system coupled with FDDA, and a flux-adjusting SDA system (FASDAS) coupled with FDDA are compared with each other and with observations. The model is initialized with Global Forecast System (GFS) forecast fields starting from 19 September 2006, with assimilation being done for the first 24 hours using conventional observations, sounding and surface data of temperature and moisture from Advanced TIROS Operational Vertical Sounder satellite and surface wind data over the ocean from QuikSCAT. Forecasts are then made from these assimilated states. In general, results indicate that the FASDAS forecast provides more realistic prognostic fields as compared to the other three forecasts. When compared with other forecasts, results indicate that the FASDAS forecast yielded lower root-mean-square (r.m.s.) errors for the pressure field and improved simulations of surface/near-surface temperature, moisture, sensible and latent heat fluxes, and potential vorticity. Heat and moisture budget analyses to assess the simulation of convection revealed that the two forecasts with the surface data assimilation (SDA and FASDAS) are superior to the base and FDDA forecasts. An important conclusion is that, even though monsoon depressions are large synoptic systems, mesoscale features including rainfall are affected by surface processes. Enhanced representation of land-surface processes provides a significant improvement in the model performance even under active monsoon conditions where the synoptic forcings are expected to be dominant.