Synoptic Verification of Precipitation Forecast of Three NWP Models from May to August of 2008 in Liaoning Province

In order to evaluate the precipitation forecast performance of mesoscale numerical model in Northeast China,mesoscale model in Liaoning Province and T213 model,and improve the ability to use their forecast products for forecasters,the synoptic verifications of their 12 h accumulated precipitation forecasts of 3 numerical modes from May to August in 2008 were made on the basis of different systems impacting weather in Liaoning Province.The time limitations were 24,36,48 and 60 h.The verified contents included 6 aspects such as intensity and position of precipitation center,intensity,location,scope and moving velocity of precipitation main body.The results showed that the three models had good forecasting capability for precipitation in Liaoning Province,but the cupacity of each model was obviously different.

Test and Evaluation of ECMWF Model on Precipitation Forecast in Shaoyang Area

Using actual precipitation in Shaoyang of 2018-2020,precipitation forecast of ECMWF model was tested.The results showed that winter accuracy rate was the highest,followed by autumn,and summer accuracy rate was the lowest.24-h TS scoring results showed that the shorter the cumulative time,the lower the TS.Forecasters had a strong ability to predict summer rainstorms.

Application of the Physical Quantity Field Evolution under Numerical Model in Precipitation Forecast of Yantai

[Objective] The research aimed to understand role of the forecast data about physical quantity field in precipitation forecast.[Method] By contrasting forecast and actual situation of the precipitation in Yantai during 2-3 July and 12-15 September,2011,advantages and disadvantages of the different numerical forecast models (Japan fax chart,European center,MM5,Grapes and T639) were analyzed.[Result] MICAPS system could provide live situation of the physical quantity field,but couldn't provide the future evolution situation.Japan fax chart,European center,MM5,Grapes and T639 could provide future evolution situation of the physical quantity field.[Conclusion] The contrasts and analyses on forecast situations of the physical quantity fields in many precipitation processes showed that evolutions of the vertical velocity,temperature dew point difference,relative humidity and wind field at the different heights could improve forecast accuracy of the precipitation in Yantai.

New Thought of Meteorological Forecasting and Warning Models of Geological Disasters in Loess Plateau of North Shaanxi

The study established daily comprehensive precipitation equations and calculated respective critical daily comprehensive precipitation value of loess-collapse disasters and landslide disasters by dint of the geological disasters and corresponding precipitation data in 47 years.Considering geological disaster risk divisions,precipitation influence coefficient and daily comprehensive precipitation,hourly rolling daily-forecasting and hourly warning fine and no-gap models on the base of high temporal and spatial resolution rainfall data of automatic meteorological station were developed.Through the verifying of combination of dynamical forecasting model and warning model,the results showed that it can improve efficiency of forecast and have good response at the same time.

Seasonal Predictions of Summer Precipitation in the Middle-lower Reaches of the Yangtze River with Global and Regional Models Based on NUIST-CFS1.0

Accurate prediction of the summer precipitation over the middle and lower reaches of the Yangtze River(MLYR)is of urgent demand for the local economic and societal development.This study assesses the seasonal forecast skill in predicting summer precipitation over the MLYR region based on the global Climate Forecast System of Nanjing University of Information Science and Technology(NUIST-CFS1.0,previously SINTEX-F).The results show that the model can provide moderate skill in predicting the interannual variations of the MLYR rainbands,initialized from 1 March.In addition,the nine-member ensemble mean can realistically reproduce the links between the MLYR precipitation and tropical sea surface temperature(SST)anomalies,but the individual members show great discrepancies,indicating large uncertainty in the forecasts.Furthermore,the NUIST-CFS1.0 can predict five of the seven extreme summer precipitation anomalies over the MLYR during 1982-2020,albeit with underestimated magnitudes.The Weather Forecast and Research(WRF)downscaling hindcast experiments with a finer resolution of 30 km,which are forced by the large-scale information of the NUIST-CFS1.0 predictions with a spectral nudging method,display improved predictions of the extreme summer precipitation anomalies to some extent.However,the performance of the downscaling predictions is highly dependent on the global model forecast skill,suggesting that further improvements on both the global and regional climate models are needed.

NUMERICAL EXPERIMENTS FOR THE EFFECTS OF TWO MODEL INITIALIZATION SCHEMES ON RAINFALL FORECAST IN THE 2008 FLOODING SEASON

In this paper, based on heavy rain numerical forecast model AREM(Advanced Regional Eta Model), two different initialization schemes, LAPS and GRAPES-3DVAR, are used to run assimilation experiments of AREM-LAPS and AREM-3DVAR with the same data source(NCEP forecast field, surface data and radio-soundings) during the period from 21 May to 30 July 2008 to investigate the effect of the two initialization schemes on the rainfall simulation. The result suggests that:(1) the forecast TS score by the AREM-LAPS is higher than that by the AREM-3DVAR for rainfall in different areas, at different valid time and with different intensity, especially for the heavy rain, rainstorm and extremely heavy rain;(2) the AREM-3DVAR can generally simulate the average rainfall distribution, but the forecast area is smaller and rainfall intensity is weaker than the observation, while the AREM-LAPS significantly improves the forecast;(3) the AREM-LAPS gives a better forecast for the south-north shift of rainfall bands and the rainfall intensity variation than the AREM-3DVAR;(4) the AREM-LAPS can give a better reproduction for the daily change in the mean-rainfall-rate of the main rain band, and rainfall intensity changes in the eastern part of Southwest China, the coastal area in South China, the middle-lower valleys of Yangtze river, the Valleys of Huaihe river, and Shandong peninsula, with the rainfall intensity roughly close to the observation, while the rainfall intensity simulated by the AREM-3DVAR is clearly weaker than the observation, especially in the eastern part of Southwest China; and(5) the comparison verification between the AREM-LAPS and AREM-3DVAR for more than 10 typical rainfall processes in the summer of 2008 indicates that the AREM-LAPS gives a much better forecast than AREM-3DVAR in rain-band area, rainfall location and intensity, and in particular, the rainfall intensity forecast is improved obviously.

Statistical Downscaling for Multi-Model Ensemble Prediction of Summer Monsoon Rainfall in the Asia-Pacific Region Using Geopotential Height Field

The 21-yr ensemble predictions of model precipitation and circulation in the East Asian and western North Pacific （Asia-Pacific） summer monsoon region （0°-50°N, 100° 150°E） were evaluated in nine different AGCM, used in the Asia-Pacific Economic Cooperation Climate Center （APCC） multi-model ensemble seasonal prediction system. The analysis indicates that the precipitation anomaly patterns of model ensemble predictions are substantially different from the observed counterparts in this region, but the summer monsoon circulations are reasonably predicted. For example, all models can well produce the interannual variability of the western North Pacific monsoon index （WNPMI） defined by 850 hPa winds, but they failed to predict the relationship between WNPMI and precipitation anomalies. The interannual variability of the 500 hPa geopotential height （GPH） can be well predicted by the models in contrast to precipitation anomalies. On the basis of such model performances and the relationship between the interannual variations of 500 hPa GPH and precipitation anomalies, we developed a statistical scheme used to downscale the summer monsoon precipitation anomaly on the basis of EOF and singular value decomposition （SVD）. In this scheme, the three leading EOF modes of 500 hPa GPH anomaly fields predicted by the models are firstly corrected by the linear regression between the principal components in each model and observation, respectively. Then, the corrected model GPH is chosen as the predictor to downscale the precipitation anomaly field, which is assembled by the forecasted expansion coefficients of model 500 hPa GPH and the three leading SVD modes of observed precipitation anomaly corresponding to the prediction of model 500 hPa GPH during a 19-year training period. The cross-validated forecasts suggest that this downscaling scheme may have a potential to improve the forecast skill of the precipitation anomaly in the South China Sea, western North Pacific and the East Asia Pacific regions, where the anomaly correlation coefficient （ACC） has been improved by 0.14, corresponding to the reduced RMSE of 10.4% in the conventional multi-model ensemble （MME） forecast.

Probabilistic Precipitation Forecasting Based on Ensemble Output Using Generalized Additive Models and Bayesian Model Averaging

A probabilistic precipitation forecasting model using generalized additive models （GAMs） and Bayesian model averaging （BMA） was proposed in this paper. GAMs were used to fit the spatial-temporal precipitation models to individual ensemble member forecasts. The distributions of the precipitation occurrence and the cumulative precipitation amount were represented simultaneously by a single Tweedie distribution. BMA was then used as a post-processing method to combine the individual models to form a more skillful probabilistic forecasting model. The mixing weights were estimated using the expectation-maximization algorithm. The residual diagnostics was used to examine if the fitted BMA forecasting model had fully captured the spatial and temporal variations of precipitation. The proposed method was applied to daily observations at the Yishusi River basin for July 2007 using the National Centers for Environmental Prediction ensemble forecasts. By applying scoring rules, the BMA forecasts were verified and showed better performances compared with the empirical probabilistic ensemble forecasts, particularly for extreme precipitation. Finally, possible improvements and a^plication of this method to the downscaling of climate change scenarios were discussed.

An application of the LTP＿DSEF model to heavy precipitation forecasts of landfalling tropical cyclones over China in 2018

Recently, a track-similarity-based Dynamical-Statistical Ensemble Forecast(LTP_DSEF) model has been developed in an attempt to predict heavy rainfall from Landfalling Tropical cyclones(LTCs). In this study, the LTP_DSEF model is applied to predicting heavy precipitation associated with 10 LTCs occurring over China in 2018. The best forecast scheme of the model with optimized parameters is obtained after testing 3452 different schemes for the 10 LTCs. Then, its performance is compared to that of three operational dynamical models. Results show that the LTP_DSEF model has advantages over the three dynamical models in predicting heavy precipitation accumulated after landfall, especially for rainfall amounts greater than 250 mm. The model also provides superior or slightly inferior heavy rainfall forecast performance for individual LTCs compared to the three dynamical models. In particular, the LTP_DSEF model can predict heavy rainfall with valuable threat scores associated with certain LTCs, which is not possible with the three dynamical models. Moreover, the model can reasonably capture the distribution of heavier accumulated rainfall, albeit with widespread coverage compared to observations. The preliminary results suggest that the LTP_DSEF model can provide useful forecast guidance for heavy accumulated rainfall of LTCs despite its limited variables included in the model.

Seasonal Forecasts of Precipitation during the First Rainy Season in South China Based on NUIST-CFS1.0

Current dynamical models experience great difficulties providing reliable seasonal forecasts of regional/local rainfall in South China.This study evaluates seasonal forecast skill for precipitation in the first rainy season(FRS,i.e.,April–June)over South China from 1982 to 2020 based on the global real-time Climate Forecast System of Nanjing University of Information Science and Technology(NUIST-CFS1.0,previously known as SINTEX-F).The potential predictability and the practical forecast skill of NUIST-CFS1.0 for FRS precipitation remain low in general.But NUIST-CFS1.0 still performs better than the average of nine international models in terms of correlation coefficient skill in predicting the interannual precipitation anomaly and its related circulation index.NUIST-CFS1.0 captures the anomalous Philippines anticyclone,which transports moisture and heat northward to South China,favoring more precipitation in South China during the FRS.By examining the correlations between sea surface temperature(SST)and FRS precipitation and the Philippines anticyclone,we find that the model reasonably captures SST-associated precipitation and circulation anomalies,which partly explains the predictability of FRS precipitation.A dynamical downscaling model with 30-km resolution forced by the large-scale circulations of the NUIST-CFS1.0 predictions could improve forecasts of the climatological states and extreme precipitation events.Our results also reveal interesting interdecadal changes in the predictive skill for FRS precipitation in South China based on the NUIST-CFS1.0 hindcasts.These results help improve the understanding and forecasts for FRS precipitation in South China.

An Objective Approach to Generating Multi-Physics Ensemble Precipitation Forecasts Based on the WRF Model

Selecting proper parameterization scheme combinations for a particular application is of great interest to the Weather Research and Forecasting(WRF)model users.This study aims to develop an objective method for identifying a set of scheme combinations to form a multi-physics ensemble suitable for short-range precipitation forecasting in the Greater Beijing area.The ensemble is created by using statistical techniques and some heuristics.An initial sample of 90 scheme combinations was first generated by using Latin hypercube sampling(LHS).Then,after several rounds of screening,a final ensemble of 40 combinations were chosen.The ensemble forecasts generated for both the training and verification cases using these combinations were evaluated based on several verification metrics,including threat score(TS),Brier score(BS),relative operating characteristics(ROC),and ranked probability score(RPS).The results show that TS of the final ensemble improved by 9%-33%over that of the initial ensemble.The reliability was improved for rain≤10 mm day^-1,but decreased slightly for rain>10 mm day^-1 due to insufficient samples.The resolution remained about the same.The final ensemble forecasts were better than that generated from randomly sampled scheme combinations.These results suggest that the proposed approach is an effective way to select a multi-physics ensemble for generating accurate and reliable forecasts.

ON THE SENSITIVITY OF PRECIPITATION FORECASTS TO THE MOIST PHYSICS AND THE HORIZONTAL RESOLUTION OF NUMERICAL MODEL

The impacts of the enhanced model's moist physics and horizontal resolution upon the QPFs (quantitative precipitation forecasts)are investigated by applying the HIRLAM(high resolution limited area model)to the summer heavy-rain cases in China.The performance of the control run, for which a 0.5°×0.5°grid spacing and a traditional“grid-box supersaturation removal+Kuo type convective paramerization”are used as the moist physics,is compared with that of the sensitivity runs with an enhanced model's moist physics(Sundqvist scheme)and an increased horizontal resolution(0.25°×0.25°),respectively.The results show: (1)The enhanced moist physics scheme(Sundqvist scheme),by introducing the cloud water content as an additional prognostic variable and taking into account briefly of the microphysics involved in the cloud-rain conversion,does bring improvements in the model's QPFs.Although the deteriorated QPFs also occur occasionally,the improvements are found in the majority of the cases,indicating the great potential for the improvement of QPFs by enhancing the model's moist physics. (2)By increasing the model's horizontal resolution from 0.5°×0.5°,which is already quite high compared with that of the conventional atmospheric soundings,to 0.25°×0.25°without the simultaneous enhancement in model physics and objective analysis,the improvements in QPFs are very limited.With higher resolution,although slight amelioration in locating the rainfall centers and in resolving some finer structures of precipitation pattern are made,the number of the mis- predicted fine structures in rainfall field increases with the enhanced model resolution as well.

A Short-Range Quantitative Precipitation Forecast Algorithm Using Back-Propagation Neural Network Approach

A back-propagation neural network （BPNN） was used to establish relationships between the shortrange （0-3-h） rainfall and the predictors ranging from extrapolative forecasts of radar reflectivity, satelliteestimated cloud-top temperature, lightning strike rates, and Nested Grid Model （NGM） outputs. Quan- titative precipitation forecasts （QPF） and the probabilities of categorical precipitation were obtained. Results of the BPNN algorithm were compared to the results obtained from the multiple linear regression algorithm for an independent dataset from the 1999 warm season over the continental United States. A sample forecast was made over the southeastern United States. Results showed that the BPNN categorical rainfall forecasts agreed well with Stage Ⅲ observations in terms of the size and shape of the area of rainfall. The BPNN tended to over-forecast the spatial extent of heavier rainfall amounts, but the positioning of the areas with rainfall ≥25.4 mm was still generally accurate. It appeared that the BPNN and linear regression approaches produce forecasts of very similar quality, although in some respects BPNN slightly outperformed the regression.

Analysis of a Heavy Rainfall Event over Beijing During 21-22 July2012 Based on High Resolution Model Analyses and Forecasts

The heaviest rainfall over 61 yr hit Beijing during 21-22 July 2012.Characterized by great rainfall amount and intensity,wide range,and high impact,this record-breaking heavy rainfall caused dozens of deaths and extensive damage.Despite favorable synoptic conditions,operational forecasts underestimated the precipitation amount and were late at predicting the rainfall start time.To gain a better understanding of the performance of mesoscale models,verification of high-resolution forecasts and analyses from the WRFbased BJ-RUCv2.0 model with a horizontal grid spacing of 3 km is carried out.The results show that water vapor is very rich and a quasi-linear precipitation system produces a rather concentrated rain area.Moreover,model forecasts are first verified statistically using equitable threat score and BIAS score.The BJ-RUCv2.0forecasts under-predict the rainfall with southwestward displacement error and time delay of the extreme precipitation.Further quantitative analysis based on the contiguous rain area method indicates that major errors for total precipitation（〉 5 mm h^（-1）） are due to inaccurate precipitation location and pattern,while forecast errors for heavy rainfall（〉 20 mm h^（-1）） mainly come from precipitation intensity.Finally,the possible causes for the poor model performance are discussed through diagnosing large-scale circulation and physical parameters（water vapor flux and instability conditions） of the BJ-RUCv2.0 model output.

Short-Term Precipitation Forecasting Rolling Update Correction Technology Based on Optimal Fusion Correction

In order to improve the availability of regional model precipitation forecast, this project intends to use the measured heavy rainfall data of dense automatic stations to carry out historical precipitation in the high resolution: the Severe Weather Automatic Nowcast System (SWAN) quantitative precipitation forecast and the High-Resolution Rapid Refresh (HRRR) regional numerical model precipitation forecast in short-term nowcasting aging. Based on the error analysis, the grid fusion technology is used to establish the measured rainfall, HRRR regional model precipitation forecast, and optical flow radar quantitative precipitation forecast (QPF) three-source fusion correction scheme, comprehensively integrate the revised forecasting effect, adjust the fusion correction parameters, establish an optimal correction plan, generate a frozen rolling update revised product based on measured dense data and short-term forecast, and put it into business operation, and perform real-time effect rolling test evaluation on the forecast product.

多源融合降水和智能网格预报在渭河一次洪水预报中的应用

选取2018年6月28日至7月15日渭河干流魏家堡水文站洪水过程为研究对象,开展多源融合降水和智能网格预报在洪水模拟中的应用试验。结果表明,以多源融合和站点实况降水为输入均能较好地模拟洪水过程,因此在无降水观测资料的流域,采用多源融合降水产品代替站点实况降水进行洪水模拟分析和计算是可行的,但多源融合降水模拟的径流深和洪峰流量较小。在魏家堡水文站两次洪水过程洪峰出现前48 h的面雨量相差不大的情况下,产流量相差显著,说明前期土壤湿度对渭河流域的产流影响很大,防汛时特别要关注持续性降水的影响。以落地雨进行洪水预报的预见期为12 h,以智能网格预报产品作为降水输入,能够提前84 h预报出超保证流量的洪峰流量,将洪水预见期由12 h延长至84 h。但以智能网格降水预报计算的面雨量与实况面雨量相比有一定的高估,造成了模拟的洪峰流量和径流深相对误差较大。建议针对不同的天气背景开展面雨量订正方法研究,以提高面雨量预报准确率,在延长洪水预见期的同时,进一步提高洪水预报精度。

海河“23·7”流域性特大洪水复盘模拟

为支撑海河流域特大洪水的决策及预报预警工作,基于实测降水与流量资料,采用新安江-海河模型对海河流域2023年7月发生特大洪水的大清河2个典型小流域——紫荆关流域和漫水河流域进行了洪水模拟,并选取紫荆关流域1996—2020年5场洪水、漫水河流域1953—2016年8场洪水进行了参数率定,选取海河“23·7”流域性特大洪水进行了验证。结果表明:新安江-海河模型对“23·7”流域性特大洪水的模拟精度较高,可反映实际洪水过程,两个小流域洪峰、洪量模拟的相对误差均在20%以内,峰现时间误差均为0 h;相较于受人类活动影响小的紫荆关流域,在受人类活动影响更大的漫水河流域,新安江-海河模型的模拟效果较新安江模型的模拟效果提升更为明显。

基于华东区域模式云南短时强降水客观预报技术研究

为提高云南短时强降水的预报效率和预报准确率,使用华东区域模式基础物理量数据,计算云南短时强降水发生前1 h 700 hPa相对湿度、700 hPa比湿、K指数和6 km垂直风切变,对短时强降水的历史个例进行物理量阈值训练和单物理量的敏感性实验,确定物理量阈值。使用阈值判定法,对华东区域模式实时预报数据进行后处理,得到基于小时雨量和物理量的云南本地化短时强降水客观预报产品。研究结果表明:使用阈值判定法研发的基于华东区域模式云南短时强降水客观预报产品能较好地预报云南短时强降水的落区和走向,但是基于小时雨量的短时强降水客观预报产品漏报明显,基于物理量阈值的客观预报产品能有效降低云南短时强降水的漏报率。

GRIST模式全球0.125度基线配置的中期降水预报性能分析

利用国内自主研发的全球—区域一体化预测系统(GRIST)0.125度天气预报模式配置开展了中期天气预报试验,通过与ERA5再分析数据、卫星观测数据(Global Precipitation Measurement,GPM)和成熟的业务全球数值天气预报模式结果进行比较评估了该系统基线配置下的降水预报性能,并探索了模式对不同动力配置的敏感性。结果表明,冷启动下的GRIST模式能较好地模拟全球500 hPa环流场,其对500 hPa位势高度异常相关系数(ACC500)的预报性能和美国国家环境预测中心(NCEP)的全球预报系统(GFS)较为接近。降水方面,GRIST能够再现和观测一致的全球平均降水的空间分布。随着积分时间增加,模式在赤道辐合带和青藏高原南坡附近相比于NCEP-GFS出现了稍高的系统性降水湿偏差。基于降水强度和频次的分析表明,这种湿偏差很可能源于GRIST模式对这些区域降水频次的高估。针对全球6个降水关键区,考察了模式的强度—频次谱结构和日变化特征。GRIST对“较强降水”强度—频次结构的模拟能力优于NCEP-GFS,且对降水日循环的总体模拟较好,但部分地区存在对降水峰值的略微高估和提前。GRIST静力和非静力动力内核在0.125度分辨率的降水预报统计特征具有较高一致性。垂直60层较30层在环流和降水的模拟上均有一定增益效果。

全球气象模型GFDL-SPEAR月降水预报订正与检验

由于全球气象模型产生的原始预报通常包含复杂的误差,为评估新一代的GFDL-SPEAR模型对我国各二级水资源区的适用性,构建伯努利-伽马-高斯模型开展统计订正的对比实验,从逐月与累计降水2个方面评估预报的相关性、系统偏差、可靠性以及预报精度,从而辨析原始预报的误差并分析预报订正的作用。结果表明:GFDL-SPEAR原始预报与观测呈现良好的相关关系,但包含-20%到50%的系统偏差,导致预报可靠性与预报精度较低;伯努利-伽马-高斯模型能够有效订正系统偏差,生成可靠的预报时间序列,使逐月与累计降水的预报精度分别提高约25%和45%;相比总量订正,逐月订正能够进一步提高预报精度。整体上,订正后的GFDL-SPEAR降水预报可为流域水资源调控与防洪抗旱提供6个月乃至1年预见期的重要信息。