Tropical cyclone forecast from NCMRWF global ensemble forecast system, verification and bias correction

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.

Download Full-text

Evaluation of NCEP next Global Ensemble Forecast System (GEFS v12)

10.5194/egusphere-egu2020-6379 ◽

2020 ◽

Author(s):

Bing Fu ◽

Yuejian Zhu ◽

Xiaqiong Zhou ◽

Dingchen Hou

Keyword(s):

Tropical Cyclone ◽

Ensemble Forecast ◽

Time Range ◽

Track Forecast ◽

Cyclone Track ◽

Tropical Cyclone Track ◽

Forecast System ◽

Microphysics Scheme ◽

Dynamical Core ◽

Probabilistic Forecasts

<p>With the successful upgrade of its deterministic model GFS (v15) on June 12, 2019, NCEP has scheduled the implementation of its next Global Ensemble Forecast System (GEFS v12) in the summer of 2020. These two model upgrades on deterministic and ensemble forecast systems are substantially different from previous upgrades. A new dynamical core (FV3) is adopted for the first time in the NCEP operational models, replacing the previous spectral dynamical core. The previous 3-category Zhao-Carr microphysics scheme is also being replaced by a more advanced 6-category GFDL microphysics scheme. From an ensemble model perspective, the previous GEFS has already demonstrated great success in past decades for weather and week-2 prediction by providing reliable probabilistic forecasts. Recently, there has been a large demand for subseasonal prediction, and GEFS v12 forecasts will be extended to 35 days to cover this time range. To better represent large uncertainties associated with this time scale, SPPT (stochastic physics perturbed tendency) and SKEB (stochastic kinetic energy backscatter) stochastic schemes are taking the place of the original STTP (stochastic total tendency perturbation), and a prescribed SST generated from combination of NSST and bias corrected CFS forecasts is also applied to simulate the sub-seasonal variation of SST forcing.&#160;&#160;&#160;&#160;</p><p>As a major system upgrade,&#160; a 2.5-year retrospective run of GEFS v12 is carried out to evaluate the model performance. A 30-year reforecast will be provided to stakeholders and the public to calibrate the forecast. The improvement of predictability and prediction skill will be studied through various measurements across tropical to extratropical areas in terms of deterministic (ensemble mean) and probabilistic (ensemble distribution) forecasts. The characteristics of model systematic error will be identified from comparing the major changes of the two state-of-art ensemble systems. As GEFS serves the most crucial model guidance for 5-7 day hurricane forecasts in support of the NHC (National Hurricane Center) and other customers, model capability in predicting tropical cyclone track and intensity is also examined from the retrospective runs. The results show there are significant improvements for tropical cyclone track forecast in North Atlantic and the western North Pacific, in particular, the intensity forecast is improved remarkably in all the basins.</p>

Download Full-text

Understanding the Processes Causing the Early Intensification of Hurricane Dorian through an Ensemble of the Hurricane Analysis and Forecast System (HAFS)

Atmosphere ◽

10.3390/atmos12010093 ◽

2021 ◽

Vol 12 (1) ◽

pp. 93

Author(s):

Andrew Hazelton ◽

Ghassan J. Alaka ◽

Levi Cowan ◽

Michael Fischer ◽

Sundararaman Gopalakrishnan

Keyword(s):

Tropical Cyclone ◽

Large Scale ◽

Early Stage ◽

Early Period ◽

Storm Track ◽

Key Factors ◽

Complex Interplay ◽

Forecast System ◽

Model Initialization

The early stages of a tropical cyclone can be a challenge to forecast, as a storm consolidates and begins to grow based on the local and environmental conditions. A high-resolution ensemble of the Hurricane Analysis and Forecast System (HAFS) is used to study the early intensification of Hurricane Dorian, a catastrophic 2019 storm in which the early period proved challenging for forecasters. There was a clear connection in the ensemble between early storm track and intensity: stronger members moved more northeast initially, although this result did not have much impact on the long-term track. The ensemble results show several key factors determining the early evolution of Dorian. Large-scale divergence northeast of the tropical cyclone (TC) appeared to favor intensification, and this structure was present at model initialization. There was also greater moisture northeast of the TC for stronger members at initialization, favoring more intensification and downshear development of the circulation as these members evolved. This study highlights the complex interplay between synoptic and storm scale processes in the development and intensification of early-stage tropical cyclones.

Download Full-text

A self-organizing map-based ensemble forecast system for extended range prediction of active/break cycles of Indian summer monsoon

Journal of Geophysical Research Atmospheres ◽

10.1002/jgrd.50688 ◽

2013 ◽

Vol 118 (16) ◽

pp. 9022-9034 ◽

Cited By ~ 19

Author(s):

N. Borah ◽

A. K. Sahai ◽

R. Chattopadhyay ◽

S. Joseph ◽

S. Abhilash ◽

...

Keyword(s):

Summer Monsoon ◽

Indian Summer Monsoon ◽

Ensemble Forecast ◽

Self Organizing Map ◽

Forecast System ◽

Extended Range ◽

Self Organizing

Download Full-text

Sample Optimization of Ensemble Forecast to Simulate a Tropical Cyclone Using the Observed Track

ATMOSPHERE-OCEAN ◽

10.1080/07055900.2018.1500881 ◽

2018 ◽

Vol 56 (3) ◽

pp. 162-177 ◽

Cited By ~ 2

Author(s):

Jihang Li ◽

Yudong Gao ◽

Qilin Wan

Keyword(s):

Tropical Cyclone ◽

Ensemble Forecast

Download Full-text

Impacts of Assimilating High-Resolution Atmospheric Motion Vectors Derived from Himawari-8 on Tropical Cyclone Forecast in HWRF

Monthly Weather Review ◽

10.1175/mwr-d-18-0261.1 ◽

2019 ◽

Vol 147 (10) ◽

pp. 3721-3740 ◽

Cited By ~ 2

Author(s):

Masahiro Sawada ◽

Zaizhong Ma ◽

Avichal Mehra ◽

Vijay Tallapragada ◽

Ryo Oyama ◽

...

Keyword(s):

High Resolution ◽

Tropical Cyclone ◽

Ensemble Forecast ◽

Lead Times ◽

Track Forecast ◽

Background Error ◽

Steering Flow ◽

Error Covariance ◽

Atmospheric Motion ◽

Atmospheric Motion Vectors

Abstract The impact of the assimilation of high spatial and temporal resolution atmospheric motion vectors (AMVs) on tropical cyclone (TC) forecasts has been investigated. The high-resolution AMVs are derived from the full disk scan of the new generation geostationary satellite Himawari-8. Forecast experiments for three TCs in 2016 in a western North Pacific basin are performed using the National Centers for Environmental Prediction (NCEP) operational Hurricane Weather Research and Forecasting Model (HWRF). Two different ensemble–variational hybrid data assimilation configurations (using background error covariance created by global ensemble forecast and HWRF ensemble forecast), based on the Gridpoint Statistical Interpolation (GSI), are used for the sensitivity experiments. The results show that the inclusion of high-resolution Himawari-8 AMVs (H8AMV) can benefit the track forecast skill, especially for long-range lead times. The diagnosis of optimal steering flow indicates that the improved track forecast seems to be attributed to the improvement of initial steering flow surrounding the TC. However, the assimilation of H8AMV increases the negative intensity bias and error, especially for short-range forecast lead times. The investigation of the structural change from the assimilation of H8AMV revealed that the following two factors are likely related to this degradation: 1) an increase of inertial stability outside the radius of maximum wind (RMW), which weakens the boundary layer inflow; and 2) a drying around and outside the RMW. Assimilating H8AMV using background error covariance created from HWRF ensemble forecast contributes to a significant reduction in negative intensity bias and error, and there is a significant benefit to TC size forecast.

Download Full-text

Medium-Range Convection-Allowing Ensemble Forecasts with a Variable-Resolution Global Model

Monthly Weather Review ◽

10.1175/mwr-d-18-0452.1 ◽

2019 ◽

Vol 147 (8) ◽

pp. 2997-3023 ◽

Cited By ~ 6

Author(s):

Craig S. Schwartz

Keyword(s):

High Resolution ◽

Ensemble Forecast ◽

The Other ◽

Forecast System ◽

Ensemble Forecasts ◽

Variable Resolution ◽

Cell Spacing ◽

Medium Range ◽

Potential Benefits ◽

Convective Mode

Abstract Two sets of global, 132-h (5.5-day), 10-member ensemble forecasts were produced with the Model for Prediction Across Scales (MPAS) for 35 cases in April and May 2017. One MPAS ensemble had a quasi-uniform 15-km mesh while the other employed a variable-resolution mesh with 3-km cell spacing over the conterminous United States (CONUS) that smoothly relaxed to 15 km over the rest of the globe. Precipitation forecasts from both MPAS ensembles were objectively verified over the central and eastern CONUS to assess the potential benefits of configuring MPAS with a 3-km mesh refinement region for medium-range forecasts. In addition, forecasts from NCEP’s operational Global Ensemble Forecast System were evaluated and served as a baseline against which to compare the experimental MPAS ensembles. The 3-km MPAS ensemble most faithfully reproduced the observed diurnal cycle of precipitation throughout the 132-h forecasts and had superior precipitation skill and reliability over the first 48 h. However, after 48 h, the three ensembles had more similar spread, reliability, and skill, and differences between probabilistic precipitation forecasts derived from the 3- and 15-km MPAS ensembles were typically statistically insignificant. Nonetheless, despite fewer benefits of increased resolution for spatial placement after 48 h, 3-km ensemble members explicitly provided potentially valuable guidance regarding convective mode throughout the 132-h forecasts while the other ensembles did not. Collectively, these results suggest both strengths and limitations of medium-range high-resolution ensemble forecasts and reveal pathways for future investigations to improve understanding of high-resolution global ensembles with variable-resolution meshes.

Download Full-text

Development of Verification Methodology for Extreme Weather Forecasts

Weather and Forecasting ◽

10.1175/waf-d-16-0123.1 ◽

2017 ◽

Vol 32 (2) ◽

pp. 479-491 ◽

Cited By ~ 6

Author(s):

Hong Guan ◽

Yuejian Zhu

Keyword(s):

Extreme Precipitation ◽

Weather Forecast ◽

Extreme Weather ◽

Ensemble Forecast ◽

Precipitation Forecast ◽

Forecast System ◽

Extreme Precipitation Events ◽

Cold Events ◽

Extreme Cold ◽

Verification Methodology

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.

Download Full-text

Seasonal Forecasting of Tropical Cyclone Landfall Using ECMWF’s System 4

Weather and Forecasting ◽

10.1175/waf-d-18-0032.1 ◽

2019 ◽

Vol 34 (5) ◽

pp. 1239-1255 ◽

Cited By ~ 1

Author(s):

Dan L. Bergman ◽

Linus Magnusson ◽

Johan Nilsson ◽

Frederic Vitart

Keyword(s):

Tropical Cyclone ◽

North American ◽

Seasonal Forecast ◽

Seasonal Forecasting ◽

Forecast System ◽

Forecast Period ◽

The North ◽

Hurricane Season ◽

Tropical Cyclone Landfall

Abstract A method has been developed to forecast seasonal landfall risk using ensembles of cyclone tracks generated by ECMWF’s seasonal forecast system 4. The method has been applied to analyze and retrospectively forecast the landfall risk along the North American coast. The main result is that the method can be used to forecast landfall for some parts of the coast, but the skill is lower than for basinwide forecasts of activity. The rank correlations between forecasts issued on 1 May and observations are 0.6 for basinwide tropical cyclone number and 0.5 for landfall anywhere along the coast. When the forecast period is limited to the peak of the hurricane season, the landfall correlation increases to 0.6. Moreover, when the forecast issue date is pushed forward to 1 August, basinwide tropical cyclone and hurricane correlations increase to 0.7 and 0.8, respectively, whereas landfall correlations improve less. The quality of the forecasts is in line with that obtained by others.

Download Full-text