Search Results

You are looking at 1 - 10 of 33 items for

  • Author or Editor: Yuejian Zhu x
  • Refine by Access: All Content x
Clear All Modify Search
Hong Guan
and
Yuejian Zhu

Abstract

In 2006, the statistical postprocessing of the National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System (GEFS) and North American Ensemble Forecast System (NAEFS) was implemented to enhance probabilistic guidance. Anomaly forecasting (ANF) is one of the NAEFS products, generated from bias-corrected ensemble forecasts and reanalysis climatology. The extreme forecast index (EFI), based on a raw ensemble forecast and model-based climatology, is another way to build an extreme weather forecast. In this work, the ANF and EFI algorithms are applied to extreme cold temperature and extreme precipitation forecasts during the winter of 2013/14. A highly correlated relationship between the ANF and EFI allows the determination of two sets of thresholds to identify extreme cold and extreme precipitation events for the two algorithms. An EFI of −0.78 (0.687) is approximately equivalent to a −2σ (0.95) ANF for the extreme cold event (extreme precipitation) forecast. The performances of the two algorithms in forecasting extreme cold events are verified against analysis for different model versions, reference climatology, and forecasts. The verification results during the winter of 2013/14 indicate that ANF forecasts more extreme cold events with a slightly higher skill than EFI. The bias-corrected forecast performs much better than the raw forecast. The current upgrade of the GEFS has a beneficial effect on the extreme cold weather forecast. Using the NCEP Climate Forecast System Reanalysis and Reforecast (CFSRR) as a climate reference gives a slightly better score than the 40-yr reanalysis. The verification methodology is also extended to an extreme precipitation case, showing a broad potential use in the future.

Full access
Yuejian Zhu
and
Yan Luo

Abstract

A postprocessing technique is employed to correct model bias for precipitation fields in real time based on a comparison of the frequency distributions of observed and forecast precipitation amounts. Essentially, a calibration is made by defining an adjustment to the forecast value in such a way that the adjusted cumulative forecast distribution over a moving time window dynamically matches the corresponding observed distribution accumulated over a domain of interest, for example, the entire conterminous United States (CONUS), or different River Forecast Center (RFC) regions in the cases examined herein. In particular, the Kalman filter method is used to catch the flow dependence and bias information. Calibration is done on a pointwise basis for a specified domain. Using this unique technique, the calibration of precipitation forecasts for the National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System (GEFS) was implemented in May 2004. To further satisfy various users, a recent upgrade to the May 2004 implementation has been made for higher resolution with better analyses. From this study, it was found that this method has a positive impact on the intensity-dominated errors but has some common limitations with extreme events and dry bias elimination like other precipitation calibration methods. Overall, the frequency-matching algorithm substantially improves NCEP Global Forecast System (GFS) and GEFS systematic precipitation forecast errors (or biases) over a wide range of forecast amounts and produces more realistic precipitation patterns. Moreover, this approach improves the deterministic forecast skills measured by most verification scores through applying this method to GFS and GEFS ensemble means.

Full access
Ferdinand Baer
and
Yuejian Zhu

Abstract

The National Center for Atmospheric Research Community Climate Model 1 was used as an experimental prediction model to assess the value of reassigning model levels in the vertical based on an optimizing hypothesis. The model was considered for T31 horizontal truncation and 12 vertical levels. The levels were relocated in a model called test, and the model with the conventional levels was denoted standard. Both models were integrated for 5 days with six independent initial states, and the results were composited. Analyses of the composites for both models were compared to actual observations. The results of the experiments indicate that the barotropic component of the flow was predicted with equal quality by both models but that the baroclinic component was predicted better by the test model. This observation may be explained by the increased fidelity of the vertical structure in the test model, since it has more resolution in the stratosphere.

Additional analyses were performed using a hypothesized three-dimensional scale index that relates the vertical to the horizontal truncation. The results of those analyses were sufficiently suggestive to encourage further studies to find optimum truncation in all three dimensions simultaneously.

Full access
Hong Guan
,
Bo Cui
, and
Yuejian Zhu

Abstract

The National Oceanic and Atmospheric Administration/Earth System Research Laboratory (NOAA/ESRL) generated a multidecadal (from 1985 to present) ensemble reforecast database for the 2012 version of the Global Ensemble Forecast System (GEFS). This dataset includes 11-member reforecasts initialized once per day at 0000 UTC. This GEFS version has a strong cold bias for winter and warm bias for summer in the Northern Hemisphere. Although the operational decaying average bias-correction approach performs well in winter and summer, it sometimes fails during the spring and fall transition seasons at long lead times (>~5 days). In this paper, 24- (1985–2008) and 25-yr (1985–2009) reforecast biases are used to calibrate 2-m temperature forecasts in 2009 and 2010, respectively. The reforecast-calibrated forecasts for both years are more accurate than those adjusted by the decaying average method during transition seasons. A long training period (>5 yr) is necessary to help avoid a large impact on bias correction from an extreme year case and keep a broader diversity of weather scenarios. The improvement from using the full 25-yr, 31-day window, weekly training dataset is almost equivalent to that from using daily training samples. This provides an option to reduce computational expenses while maintaining a desired accuracy. To provide the potential to improve forecast accuracy for transition seasons, reforecast information is added into the current operational bias-correction method. The relative contribution of the two methods is determined by the correlation between the ensemble mean and analysis. This method improves the forecast accuracy for most of the year with a maximum benefit during April–June.

Full access
Zoltan Toth
,
Yuejian Zhu
, and
Timothy Marchok

Abstract

In the past decade ensemble forecasting has developed into an integral part of numerical weather prediction. Flow-dependent forecast probability distributions can be readily generated from an ensemble, allowing for the identification of forecast cases with high and low uncertainty. The ability of the NCEP ensemble to distinguish between high and low uncertainty forecast cases is studied here quantitatively. Ensemble mode forecasts, along with traditional higher-resolution control forecasts, are verified in terms of predicting the probability of the true state being in 1 of 10 climatologically equally likely 500-hPa height intervals. A stratification of the forecast cases by the degree of overall agreement among the ensemble members reveals great differences in forecast performance between the cases identified by the ensemble as the least and most uncertain. A new ensemble-based forecast product, the “relative measure of predictability,” is introduced to identify forecasts with below and above average uncertainty. This measure is standardized according to geographical location, the phase of the annual cycle, lead time, and also the position of the forecast value in terms of the climatological frequency distribution. The potential benefits of using this and other ensemble-based measures of predictability is demonstrated through synoptic examples.

Full access
Xiaqiong Zhou
,
Yuejian Zhu
,
Dingchen Hou
, and
Daryl Kleist

Abstract

Two perturbation generation schemes, the ensemble transformation with rescaling (ETR) and the ensemble Kalman filter (EnKF), are compared for the NCEP operational environment for the Global Ensemble Forecast System (GEFS). Experiments that utilize each of the two schemes are carried out and evaluated for two boreal summer seasons. It is found that these two schemes generally have comparable performance. Experiments utilizing both perturbation methods fail to generate sufficient spread at medium-range lead times beyond day 8. In general, the EnKF-based experiment outperforms the ETR in terms of the continuous ranked probability skill score (CRPSS) in the Northern Hemisphere (NH) for the first week. In the SH, the ensemble mean forecast is more skillful from the ETR perturbations. Additional experiments are performed with the stochastic total tendency perturbation (STTP) scheme, in which the total tendencies of all model variables are perturbed to represent the uncertainty in the forecast model. An improved spread–error relationship is found for the ETR-based experiments, but the STTP increases the ensemble spread for the EnKF-based experiment that is already overdispersive at early lead times, especially in the SH. With STTP employed, an increase in the EnKF-based CRPSS in the NH is reduced with a larger degradation in both the probability and ensemble-mean forecast skills in the SH. The results indicate that a rescaling of the EnKF initial perturbations and/or tuning of the STTP scheme is required when STTP is applied using the EnKF-based perturbations. This study provided guidance for the replacement of ETR with EnKF perturbations as part of the 2015 GEFS implementation.

Full access
M. M. Nageswararao
,
Yuejian Zhu
, and
Vijay Tallapragada

Abstract

Indian summer monsoon rainfall (ISMR) from June to September (JJAS) contributes 80% of the total annual rainfall in India and controls the agricultural productivity and economy of the country. Extreme rainfall (ER) events are responsible for floods that cause widespread destruction of infrastructure, economic damage, and loss of life. A forecast of the ISMR and associated ER events on an extended range (beyond the conventional one-week lead time) is vital for the agronomic economy of the country. In September 2020, NOAA/NCEP implemented Global Ensemble Forecast System, version 12 (GEFSv12) for various risk management applications. It has generated consistent reanalysis and reforecast data for the period 2000–19. In the present study, the Raw-GEFSv12 with day-1–16 lead-time rainfall forecasts are calibrated using the quantile (QQ) mapping technique against Indian Monsoon Data Assimilation and Analysis (IMDAA) for further improvement. The present study evaluated the prediction skill of Raw and QQ-GEFSv12 for ISMR and ER events over India by using standard skill metrics. The results suggest that the ISMR patterns from Raw and QQ-GEFSv12 with (lead) day 1–16 are similar to IMDAA. However, Raw-GEFSv12 has a dry bias in most parts of prominent rainfall regions. The low- to medium-intensity rainfall events from Raw-GEFSv12 is remarkably higher than the IMDAA, while high- to very-high-intensity rainfall events from Raw-GEFSv12 are lower than IMDAA. The prediction skill of Raw-GEFSv12 in depicting ISMR and associated ER events decreased with lead time, while the prediction skill is almost equal for all lead times with marginal improvement after calibration.

Full access
Bo Zhang
,
Ge Liu
,
Yuejian Zhu
, and
Ning Shi

Abstract

Based on a recently developed approach that can recognize both persistent blocking and ridge events effectively, the contributions of the frequency of these persistent events (FOPE) over different regions in Eurasia to precipitation over eastern China were investigated. The results reveal that, the FOPE over the longitudinal range of 110°–130°E, near the Stanovoy Mountains and the Okhotsk Sea, is significantly correlated with precipitation over the middle and lower reaches of the Yangtze River (MLRYR) during summer, particularly in August. The preceding full July (or 1–20 July) mean Balkhash Lake–Caucasus geopotential height index, which measures the combined effect of the Balkhash Lake and Caucasus geopotential height anomalies, is closely related to the August geopotential height anomaly around the Stanovoy Mountains and the Okhotsk Sea, and can therefore reflect the August 110°–130°E FOPE. The predictability based on this preceding atmospheric signal seems to be attributable to slow-varying atmospheric processes on a subseasonal (20-day mean) time scale. On this time scale, the Balkhash Lake and Caucasus geopotential height anomalies occur prior to, and seem to modulate, the geopotential height anomaly around the Stanovoy Mountains and the associated 110°–130°E FOPE through an eastward extension and through exciting a positive–negative–positive pattern in 500-hPa geopotential heights, respectively. As a result of the slow-varying atmospheric processes, this preceding atmospheric signal performs well in predicting the August 110°–130°E FOPE, which also facilitates the prediction of the MLRYR precipitation.

Full access
Andrew D. Snyder
,
Zhaoxia Pu
, and
Yuejian Zhu

Abstract

This study evaluates the performance of the NCEP global ensemble forecast system in predicting the genesis and evolution of five named tropical cyclones and two unnamed nondeveloping tropical systems during the NASA African Monsoon Multidisciplinary Analyses (NAMMA) between August and September 2006. The overall probabilities of the ensemble forecasts of tropical cyclone genesis are verified relative to a genesis time defined to be the first designation of the tropical depression from the National Hurricane Center (NHC). Additional comparisons are also made with high-resolution deterministic forecasts from the NCEP Global Forecast System (GFS). It is found that the ensemble forecasts have high probabilities of genesis for the three strong storms that formed from African easterly waves, but failed to accurately predict the pregenesis phase of two weaker storms that formed farther west in the Atlantic Ocean. The overall accuracy for the genesis forecasts is above 50% for the ensemble forecasts initialized in the pregenesis phase. The forecast uncertainty decreases with the reduction of the forecast lead time. The probability of tropical cyclone genesis reaches nearly 90% and 100% for the ensemble forecasts initialized near and in the postgenesis phase, respectively. Significant improvements in the track forecasts are found in the ensemble forecasts initialized in the postgenesis phase, possibly because of the implementation of the NCEP storm relocation scheme, which provides an accurate initial storm position for all ensemble members. Even with coarser resolution (T126L28 for the ensemble versus T384L64 for the GFS), the overall performance of the ensemble in predicting tropical cyclone genesis is compatible with the high-resolution deterministic GFS. In addition, false alarm rates for nondeveloping waves were low in both the GFS and ensemble forecasts.

Full access
Bo Cui
,
Zoltan Toth
,
Yuejian Zhu
, and
Dingchen Hou

Abstract

The main task of this study is to introduce a statistical postprocessing algorithm to reduce the bias in the National Centers for Environmental Prediction (NCEP) and Meteorological Service of Canada (MSC) ensemble forecasts before they are merged to form a joint ensemble within the North American Ensemble Forecast System (NAEFS). This statistical postprocessing method applies a Kalman filter type algorithm to accumulate the decaying averaging bias and produces bias-corrected ensembles for 35 variables. NCEP implemented this bias-correction technique in 2006. NAEFS is a joint operational multimodel ensemble forecast system that combines NCEP and MSC ensemble forecasts after bias correction. According to operational statistical verification, both the NCEP and MSC bias-corrected ensemble forecast products are enhanced significantly. In addition to the operational calibration technique, three other experiments were designed to assess and mitigate ensemble biases on the model grid: a decaying averaging bias calibration method with short samples, a climate mean bias calibration method, and a bias calibration method using dependent data. Preliminary results show that the decaying averaging method works well for the first few days. After removing the decaying averaging bias, the calibrated NCEP operational ensemble has improved probabilistic performance for all measures until day 5. The reforecast ensembles from the Earth System Research Laboratory’s Physical Sciences Division with and without the climate mean bias correction were also examined. A comparison between the operational and the bias-corrected reforecast ensembles shows that the climate mean bias correction can add value, especially for week-2 probability forecasts.

Full access