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Nonlinear Evolution Characteristics of the NCEP Ensemble Forecast Products  [PDF]
Yong Li, Xiakun Zhang
Atmospheric and Climate Sciences (ACS) , 2018, DOI: 10.4236/acs.2018.83022
Abstract: By using NCEP/NCAR reanalysis products to forecast the nonlinear evolution of the spatial and temporal characteristics, the results shows that on the Spatial dimensions, NCEP ensemble forecast that the products of nonlinear evolution have obvious zonal features. The overall distribution situation is the nonlinear evolution of the southern hemisphere, which is larger than that of the northern hemisphere. In the same hemisphere, low value area is near the equator, and high value area for middle and high latitude area. On the time dimension, the nonlinear evolution of NCEP ensemble prediction products will increase with the extension of the forecast period. In addition, the nonlinear evolution of NCEP ensemble forecast products in North America is greater than the Asian region.
Application and Verification of Multi-Model Products in Medium Range Forecast  [PDF]
Rong Yao, Zhiming Kang, Yong Li, Xiangning Cai
Journal of Geoscience and Environment Protection (GEP) , 2018, DOI: 10.4236/gep.2018.67012
Abstract: The verification analysis is applied to medium-range forecast products of T639, ECMWF, Japan model, NCEP ensemble forecast and NMC multi-model integration in late October 2012. The results show that ECMWF model has obvious advantage over other models in terms of height field and precipitation forecast; the westerly-wind index, geostrophic U wind and 850 hPa temperature prediction products can reflect the adjustment of atmospheric circulation and the activity of cold air, which have a good reference for the medium-range temperature forecast in the eastern China; the prediction of ECMWF height field and wind field can well grasp the main weather processes within 192 h, but beyond 192 h the model forecast ability decreases significantly; different models have large deviations in the medium-range forecast of typhoon track and the intensity and range of typhoon precipitation.
A New Way to Predict Forecast Skill
TAN Jiqing,XIE Zhenghui,JI Liren,
TAN Jiqing
,XIE Zhenghui,JI Liren

大气科学进展 , 2003,
Abstract: Forecast skill (Anomaly Correlated Coefficient, ACC) is a quantity to show the forecast quality ofthe products of numerical weather forecasting models. Predicting forecast skill, which is the foundationof ensemble forecasting, means submitting products to predict their forecast quality before they are used.Checking the reason is to understand the predictability for the real cases. This kind of forecasting servicehas been put into operational use by statistical methods previously at the National Meteorological Center(NMC), USA (now called the National Center for Environmental Prediction (NCEP)) and European Centerfor Medium-range Weather Forecast (ECMWF). However, this kind of service is far from satisfactorybecause only a single variable is used with the statistical method. In this paper, a new way based onthe Grey Control Theory with multiple predictors to predict forecast skill of forecast products of theT42L9 of the NMC, China Meteorological Administration (CMA) is introduced. The results show: (1)The correlation coefficients between "forecasted" and real forecast skill range from 0.56 to 0.7 at differentseasons during the two-year period. (2) The grey forecasting model GM(1,8) forecasts successfully thehigh peaks, the increasing or decreasing tendency, and the turning points of the change of forecast skill ofcases from 5 January 1990 to 29 February 1992.
An Effective Configuration of Ensemble Size and Horizontal Resolution for the NCEP GEFS

MA Juhui,Yuejian ZHU,Richard WOBUS,Panxing WANG,

大气科学进展 , 2012,
Abstract: Two important questions are addressed in this paper using the Global Ensemble Forecast System (GEFS) from the National Centers for Environmental Prediction (NCEP): (1) How many ensemble members are needed to better represent forecast uncertainties with limited computational resources? (2) What is the relative impact on forecast skill of increasing model resolution and ensemble size? Two-month experiments at T126L28 resolution were used to test the impact of varying the ensemble size from 5 to 80 members at the 500-hPa geopotential height. Results indicate that increasing the ensemble size leads to significant improvements in the performance for all forecast ranges when measured by probabilistic metrics, but these improvements are not significant beyond 20 members for long forecast ranges when measured by deterministic metrics. An ensemble of 20 to 30 members is the most effective configuration of ensemble sizes by quantifying the tradeoff between ensemble performance and the cost of computational resources. Two representative configurations of the GEFS---the T126L28 model with 70 members and the T190L28 model with 20 members, which have equivalent computing costs---were compared. Results confirm that, for the NCEP GEFS, increasing the model resolution is more (less) beneficial than increasing the ensemble size for a short (long) forecast range.
Monthly Forecast of Indian Southwest Monsoon Rainfall Based on NCEP’s Coupled Forecast System  [PDF]
Dushmanta R. Pattanaik, Biswajit Mukhopadhyay, Arun Kumar
Atmospheric and Climate Sciences (ACS) , 2012, DOI: 10.4236/acs.2012.24042
Abstract: The monthly forecast of Indian monsoon rainfall during June to September is investigated by using the hindcast data sets of the National Centre for Environmental Prediction (NCEP)’s operational coupled model (known as the Climate Forecast System) for 25 years from 1981 to 2005 with 15 ensemble members each. The ensemble mean monthly rainfall over land region of India from CFS with one month lead forecast is underestimated during June to September. With respect to the inter-annual variability of monthly rainfall it is seen that the only significant correlation coefficients (CCs) are found to be for June forecast with May initial condition and September rainfall with August initial conditions. The CFS has got lowest skill for the month of August followed by that of July. Considering the lower skill of monthly forecast based on the ensemble mean, all 15 ensemble members are used separately for the preparation of probability forecast and different probability scores like Brier Score (BS), Brier Skill Score (BSS), Accuracy, Probability of Detection (POD), False Alarm Ratio (FAR), Threat Score (TS) and Heidke Skill Score (HSS) for all the three categories of forecasts (above normal, below normal and normal) have been calculated. In terms of the BS and BSS the skill of the monthly probability forecast in all the three categories are better than the climatology forecasts with positive BSS values except in case of normal forecast of June and July. The “TS”, “HSS” and other scores also provide useful probability forecast in case of CFS except the normal category of July forecast. Thus, it is seen that the monthly probability forecast based on NCEP CFS coupled model during the southwest monsoon season is very encouraging and is found to be very useful.
Dynamic downscaling of the NCEP EPS forecast using the PROMES limited area model  [PDF]
E. Hagel,V. E. Gil,C. Tejeda,M. de Castro
Tethys : Journal of Mediterranean Meteorology & Climatology , 2012,
Abstract: When making numerical weather predictions, it is important to forecast not only the future state of the atmosphere, but also to predict the uncertainty related to this forecast. Keeping this in mind, research has started at iMetCam in order to develop a limited area ensemble prediction system. As a start, the simple dynamical downscaling approach was tried. Initial conditions and lateral boundary conditions were provided by the global ensemble system of NCEP and the PROMES limited area model was used for the downscaling. In this paper the rst results and the future plans of this experiment are presented. Results show that both systems (global and limited area) are lacking spread, at least for the veri cation area in question, which indicates that additional perturbations are desirable, which will be the direction of our future work.
Coupling ensemble weather predictions based on TIGGE database with Grid-Xinanjiang model for flood forecast
H.-J. Bao, L.-N. Zhao, Y. He, Z.-J. Li, F. Wetterhall, H. L. Cloke, F. Pappenberger,D. Manful
Advances in Geosciences (ADGEO) , 2011,
Abstract: The incorporation of numerical weather predictions (NWP) into a flood forecasting system can increase forecast lead times from a few hours to a few days. A single NWP forecast from a single forecast centre, however, is insufficient as it involves considerable non-predictable uncertainties and lead to a high number of false alarms. The availability of global ensemble numerical weather prediction systems through the THORPEX Interactive Grand Global Ensemble' (TIGGE) offers a new opportunity for flood forecast. The Grid-Xinanjiang distributed hydrological model, which is based on the Xinanjiang model theory and the topographical information of each grid cell extracted from the Digital Elevation Model (DEM), is coupled with ensemble weather predictions based on the TIGGE database (CMC, CMA, ECWMF, UKMO, NCEP) for flood forecast. This paper presents a case study using the coupled flood forecasting model on the Xixian catchment (a drainage area of 8826 km2) located in Henan province, China. A probabilistic discharge is provided as the end product of flood forecast. Results show that the association of the Grid-Xinanjiang model and the TIGGE database gives a promising tool for an early warning of flood events several days ahead.
Ensemble Forecast: A New Approach to Uncertainty and Predictability
Ensemble Forecast: A New Approach to Uncertainty and Predictability

Yuejian ZHU,

大气科学进展 , 2005,
Abstract: Ensemble techniques have been used to generate daily numerical weather forecasts since the 1990s in numerical centers around the world due to the increase in computation ability. One of the main purposes of numerical ensemble forecasts is to try to assimilate the initial uncertainty (initial error) and the forecast uncertainty (forecast error) by applying either the initial perturbation method or the multi-model/multiphysics method. In fact, the mean of an ensemble forecast offers a better forecast than a deterministic (or control) forecast after a short lead time (3 5 days) for global modelling applications. There is about a 1-2-day improvement in the forecast skill when using an ensemble mean instead of a single forecast for longer lead-time. The skillful forecast (65% and above of an anomaly correlation) could be extended to 8 days (or longer) by present-day ensemble forecast systems. Furthermore, ensemble forecasts can deliver a probabilistic forecast to the users, which is based on the probability density function (PDF)instead of a single-value forecast from a traditional deterministic system. It has long been recognized that the ensemble forecast not only improves our weather forecast predictability but also offers a remarkable forecast for the future uncertainty, such as the relative measure of predictability (RMOP) and probabilistic quantitative precipitation forecast (PQPF). Not surprisingly, the success of the ensemble forecast and its wide application greatly increase the confidence of model developers and research communities.
Ensemble data assimilation with an adjusted forecast spread  [cached]
Sabrina Rainwater,Brian R. Hunt
Tellus A , 2013, DOI: 10.3402/tellusa.v65i0.19929
Abstract: Ensemble data assimilation typically evolves an ensemble of model states whose spread is intended to represent the algorithm's uncertainty about the state of the physical system that produces the data. The analysis phase treats the forecast ensemble as a random sample from a background distribution, and it transforms the ensemble according to the background and observation error statistics to provide an appropriate sample for the next forecast phase. We find that in the presence of model nonlinearity and model error, it can be fruitful to rescale the ensemble spread prior to the forecast and then reverse this rescaling after the forecast. We call this approach forecast spread adjustment, which we discuss and test in this article using an ensemble Kalman filter and a 2005 model due to Lorenz. We argue that forecast spread adjustment provides a tunable parameter, that is, complementary to covariance inflation, which cumulatively increases ensemble spread to compensate for underestimation of uncertainty. We also show that as the adjustment parameter approaches zero, the filter approaches the extended Kalman filter if the ensemble size is sufficiently large. We find that varying the adjustment parameter can significantly reduce analysis and forecast errors in some cases. We evaluate how the improvement provided by forecast spread adjustment depends on ensemble size, observation error and model error. Our results indicate that the technique is most effective for small ensembles, small observation error and large model error, though the effectiveness depends significantly on the nature of the model error.
A Regional Ensemble Forecast System for Stratiform Precipitation Events in Northern China. Part I: A Case Study

ZHU Jiangshan,Fanyou KONG,LEI Hengchi,

大气科学进展 , 2012,
Abstract: A single-model, short-range, ensemble forecasting system (Institute of Atmospheric Physics, Regional Ensemble Forecast System, IAP REFS) with 15-km grid spacing, configured with multiple initial conditions, multiple lateral boundary conditions, and multiple physics parameterizations with 11 ensemble members, was developed using the Weather and Research Forecasting Model Advanced Research modeling system for prediction of stratiform precipitation events in northern China. This is the first part of a broader research project to develop a novel cloud-seeding operational system in a probabilistic framework. The ensemble perturbations were extracted from selected members of the National Center for Environmental Prediction Global Ensemble Forecasting System (NCEP GEFS) forecasts, and an inflation factor of two was applied to compensate for the lack of spread in the GEFS forecasts over the research region. Experiments on an actual stratiform precipitation case that occurred on 5–7 June 2009 in northern China were conducted to validate the ensemble system. The IAP REFS system had reasonably good performance in predicting the observed stratiform precipitation system. The perturbation inflation enlarged the ensemble spread and alleviated the underdispersion caused by parent forecasts. Centering the extracted perturbations on higher-resolution NCEP Global Forecast System forecasts resulted in less ensemble mean root-mean-square error and better accuracy in probabilistic quantitative precipitation forecasts (PQPF). However, the perturbation inflation and recentering had less effect on near-surface-level variables compared to the mid-level variables, and its influence on PQPF resolution was limited as well.
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