scholarly journals Short- and Medium-Range Prediction of Tropical and Transitioning Cyclone Tracks within the NCEP Global Ensemble Forecasting System

2010 ◽  
Vol 25 (6) ◽  
pp. 1736-1754 ◽  
Author(s):  
Christian Buckingham ◽  
Timothy Marchok ◽  
Isaac Ginis ◽  
Lewis Rothstein ◽  
Dail Rowe
Keyword(s):  
North Pacific ◽  
Western North Pacific ◽  
Ensemble Forecasting ◽  
Lead Times ◽  
Ensemble Mean ◽  
Medium Range ◽  
Forecasting System ◽  
Left And Right ◽  
Cross Track ◽  
Larger Sample

Abstract The NCEP Global Ensemble Forecasting System (GEFS) is examined in its ability to predict tropical cyclone and extratropical transition (ET) positions. Forecast and observed tracks are compared in Atlantic and western North Pacific basins for 2006–08, and the accuracy and consistency of the ensemble are examined out to 8 days. Accuracy is quantified by the average absolute and along- and cross-track errors of the ensemble mean. Consistency is evaluated through the use of dispersion diagrams, missing rate error, and probability within spread. Homogeneous comparisons are made with the NCEP Global Forecasting System (GFS). The average absolute track error of the GEFS mean increases linearly at a rate of 50 n mi day−1 [where 1 nautical mile (n mi) = 1.852 km] at early lead times in the Atlantic, increasing to 150 n mi day−1 at 144 h (100 n mi day−1 when excluding ET tracks). This trend is 60 n mi day−1 at early lead times in the western North Pacific, increasing to 150 n mi day−1 at longer lead times (130 n mi day−1 when excluding ET tracks). At long lead times, forecasts illustrate left- and right-of-track biases in Atlantic and western North Pacific basins, respectively; bias is reduced (increased) in the Atlantic (western North Pacific) when excluding ET tracks. All forecasts were found to lag behind observed cyclones, on average. The GEFS has good dispersion characteristics in the Atlantic and is underdispersive in the western North Pacific. Homogeneous comparisons suggest that the ensemble mean has value relative to the GFS beyond 96 h in the Atlantic and less value in the western North Pacific; a larger sample size is needed before conclusions can be made.

scholarly journals Using eXtreme Gradient BOOSTing to Predict Changes in Tropical Cyclone Intensity over the Western North Pacific

Atmosphere ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 341 ◽  
Author(s):  
Qingwen Jin ◽  
Xiangtao Fan ◽  
Jian Liu ◽  
Zhuxin Xue ◽  
Hongdeng Jian
Keyword(s):  
North Pacific ◽  
Western North Pacific ◽  
Prediction Models ◽  
Weather Prediction ◽  
Back Propagation ◽  
Absolute Error ◽  
Gradient Boosting ◽  
Lead Times ◽  
Intensity Prediction ◽  
Extreme Gradient Boosting

Coastal cities in China are frequently hit by tropical cyclones (TCs), which result in tremendous loss of life and property. Even though the capability of numerical weather prediction models to forecast and track TCs has considerably improved in recent years, forecasting the intensity of a TC is still very difficult; thus, it is necessary to improve the accuracy of TC intensity prediction. To this end, we established a series of predictors using the Best Track TC dataset to predict the intensity of TCs in the Western North Pacific with an eXtreme Gradient BOOSTing (XGBOOST) model. The climatology and persistence factors, environmental factors, brainstorm features, intensity categories, and TC months are considered inputs for the models while the output is the TC intensity. The performance of the XGBOOST model was tested for very strong TCs such as Hato (2017), Rammasum (2014), Mujiage (2015), and Hagupit (2014). The results obtained show that the combination of inputs chosen were the optimal predictors for TC intensification with lead times of 6, 12, 18, and 24 h. Furthermore, the mean absolute error (MAE) of the XGBOOST model was much smaller than the MAEs of a back propagation neural network (BPNN) used to predict TC intensity. The MAEs of the forecasts with 6, 12, 18, and 24 h lead times for the test samples used were 1.61, 2.44, 3.10, and 3.70 m/s, respectively, for the XGBOOST model. The results indicate that the XGBOOST model developed in this study can be used to improve TC intensity forecast accuracy and can be considered a better alternative to conventional operational forecast models for TC intensity prediction.

scholarly journals Improving Operational Short- to Medium-Range (SR2MR) Streamflow Forecasts in the Upper Zambezi Basin and Its Sub-Basins Using Variational Ensemble Forecasting

Hydrology ◽  
2021 ◽  
Vol 8 (4) ◽  
pp. 188
Author(s):  
Rodrigo Valdés-Pineda ◽  
Juan B. Valdés ◽  
Sungwook Wi ◽  
Aleix Serrat-Capdevila ◽  
Tirthankar Roy
Keyword(s):  
Real Time ◽  
Optimal Parameter ◽  
Ensemble Forecasting ◽  
Hydrological Models ◽  
Streamflow Forecasting ◽  
Satellite Precipitation ◽  
Daily Streamflow ◽  
Medium Range ◽  
Streamflow Forecasts ◽  
Forecasting System

The combination of Hydrological Models and high-resolution Satellite Precipitation Products (SPPs) or regional Climatological Models (RCMs), has provided the means to establish baselines for the quantification, propagation, and reduction in hydrological uncertainty when generating streamflow forecasts. This study aimed to improve operational real-time streamflow forecasts for the Upper Zambezi River Basin (UZRB), in Africa, utilizing the novel Variational Ensemble Forecasting (VEF) approach. In this regard, we describe and discuss the main steps required to implement, calibrate, and validate an operational hydrologic forecasting system (HFS) using VEF and Hydrologic Processing Strategies (HPS). The operational HFS was constructed to monitor daily streamflow and forecast them up to eight days in the future. The forecasting process called short- to medium-range (SR2MR) streamflow forecasting was implemented using real-time rainfall data from three Satellite Precipitation Products or SPPs (The real-time TRMM Multisatellite Precipitation Analysis TMPA-RT, the NOAA CPC Morphing Technique CMORPH, and the Precipitation Estimation from Remotely Sensed data using Artificial Neural Networks, PERSIANN) and rainfall forecasts from the Global Forecasting System (GFS). The hydrologic preprocessing (HPR) strategy considered using all raw and bias corrected rainfall estimates to calibrate three distributed hydrological models (HYMOD_DS, HBV_DS, and VIC 4.2.b). The hydrologic processing (HP) strategy considered using all optimal parameter sets estimated during the calibration process to increase the number of ensembles available for operational forecasting. Finally, inference-based approaches were evaluated during the application of a hydrological postprocessing (HPP) strategy. The final evaluation and reduction in uncertainty from multiple sources, i.e., multiple precipitation products, hydrologic models, and optimal parameter sets, was significantly achieved through a fully operational implementation of VEF combined with several HPS. Finally, the main challenges and opportunities associated with operational SR2MR streamflow forecasting using VEF are evaluated and discussed.

scholarly journals Operational Performance of a New Barotropic Model (WBAR) in the Western North Pacific Basin

2006 ◽  
Vol 21 (4) ◽  
pp. 656-662 ◽  
Author(s):  
Charles R. Sampson ◽  
James S. Goerss ◽  
Harry C. Weber
Keyword(s):  
North Pacific ◽  
Western North Pacific ◽  
Prediction Models ◽  
Weather Prediction ◽  
Track Forecast ◽  
Forecast Performance ◽  
Barotropic Model ◽  
The North ◽  
Forecasting System ◽  
The Impact

Abstract The Weber barotropic model (WBAR) was originally developed using predefined 850–200-hPa analyses and forecasts from the NCEP Global Forecasting System. The WBAR tropical cyclone (TC) track forecast performance was found to be competitive with that of more complex numerical weather prediction models in the North Atlantic. As a result, WBAR was revised to incorporate the Navy Operational Global Atmospheric Prediction System (NOGAPS) analyses and forecasts for use at the Joint Typhoon Warning Center (JTWC). The model was also modified to analyze its own storm-dependent deep-layer mean fields from standard NOGAPS pressure levels. Since its operational installation at the JTWC in May 2003, WBAR TC track forecast performance has been competitive with the performance of other more complex NWP models in the western North Pacific. Its TC track forecast performance combined with its high availability rate (93%–95%) has warranted its inclusion in the JTWC operational consensus. The impact of WBAR on consensus TC track forecast performance has been positive and WBAR has added to the consensus forecast availability (i.e., having at least two models to provide a consensus forecast).

scholarly journals Reliable Probabilistic Quantitative Precipitation Forecasts from a Short-Range Ensemble Forecasting System

2007 ◽  
Vol 22 (1) ◽  
pp. 3-17 ◽  
Author(s):  
David J. Stensrud ◽  
Nusrat Yussouf
Keyword(s):  
Short Range ◽  
Ensemble Forecasting ◽  
Operating Characteristics ◽  
Rainfall Events ◽  
Skill Scores ◽  
Probabilistic Forecasts ◽  
Ensemble Mean ◽  
Quantitative Precipitation Forecasts ◽  
Forecasting System ◽  
Environmental Prediction

Abstract A simple binning technique is developed to produce reliable 3-h probabilistic quantitative precipitation forecasts (PQPFs) from the National Centers for Environmental Prediction (NCEP) multimodel short-range ensemble forecasting system obtained during the summer of 2004. The past 12 days’ worth of forecast 3-h accumulated precipitation amounts and observed 3-h accumulated precipitation amounts from the NCEP stage-II multisensor analyses are used to adjust today’s 3-h precipitation forecasts. These adjustments are done individually to each of ensemble members for the 95 days studied. Performance of the adjusted ensemble precipitation forecasts is compared with the raw (original) ensemble predictions. Results show that the simple binning technique provides significantly more skillful and reliable PQPFs of rainfall events than the raw forecast probabilities. This is true for the base 3-h accumulation period as well as for accumulation periods up to 48 h. Brier skill scores and the area under the relative operating characteristics curve also indicate that this technique yields skillful probabilistic forecasts. The performance of the adjusted forecasts also progressively improves with the increased accumulation period. In addition, the adjusted ensemble mean QPFs are very similar to the raw ensemble mean QPFs, suggesting that the method does not significantly alter the ensemble mean forecast. Therefore, this simple postprocessing scheme is very promising as a method to provide reliable PQPFs for rainfall events without degrading the ensemble mean forecast.

Development of a track-pattern-based medium-range tropical cyclone forecasting system for the western North Pacific

2021 ◽  
Author(s):  
Hung Ming Cheung ◽  
Chang-Hoi Ho ◽  
Minhee Chang ◽  
Dasol Kim ◽  
Jinwon Kim ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
North Pacific ◽  
Western North Pacific ◽  
Correlation Coefficients ◽  
Machine Learning Techniques ◽  
Track Forecast ◽  
Dynamical Models ◽  
Pattern Correlation ◽  
Medium Range ◽  
Track Pattern

AbstractDespite tremendous advancements in dynamical models for weather forecasting, statistical models continue to offer various possibilities for tropical cyclone (TC) track forecasting. Herein, a track-pattern-based approach was developed to predict a TC track for a lead time of 6–8 days over the western North Pacific (WNP), utilizing historical tracks in conjunction with dynamical forecasts. It is composed of four main steps: (1) clustering historical tracks similar to that of an operational five-day forecast in their early phase into track patterns, and calculating the daily mean environmental fields (500-hPa geopotential height and steering flow) associated with each track; (2) deriving the two environmental variables forecasted by dynamical models; (3) evaluating pattern correlation coefficients between the two environmental fields from step (1) and those from dynamical model for a lead times of 6–8 days; and (4) producing the final track forecast based on relative frequency maps obtained from the historical tracks in step (1) and the pattern correlation coefficients obtained from step (3). TCs that formed in the WNP and lasted for at least seven days, during the 9-year period 2011–2019 were selected to verify the resulting track-pattern-based forecasts. In addition to the performance comparable to dynamical models under certain conditions, the track-pattern-based model is inexpensive, and can consistently produce forecasts over large latitudinal or longitudinal ranges. Machine learning techniques can be implemented to incorporate non-linearity in the present model for improving medium-range track forecasts.

scholarly journals Internal Variability of the Dynamically Downscaled Tropical Cyclone Activity over the Western North Pacific by the IPRC Regional Atmospheric Model

2012 ◽  
Vol 25 (6) ◽  
pp. 2104-2122 ◽  
Author(s):  
Chun-Chieh Wu ◽  
Ruifen Zhan ◽  
Yi Lu ◽  
Yuqing Wang
Keyword(s):  
North Pacific ◽  
Western North Pacific ◽  
Climate Model ◽  
Initial Conditions ◽  
North Pacific Ocean ◽  
Internal Variability ◽  
Atmospheric Model ◽  
Regional Atmospheric Model ◽  
Highly Nonlinear ◽  
Ensemble Mean

Abstract As synoptic storms, tropical cyclones (TCs) are highly nonlinear systems resulting from multiscale interactions. In particular, the genesis of TCs involves complex nonlinear processes, exhibiting strong internal variability in climate model simulations. This study attempts to examine such internal variability of dynamically downscaled TCs over the western North Pacific Ocean based on four simulations of 20 typhoon seasons (1982−2001) initialized on 4 successive days using the International Pacific Research Center (IPRC) Regional Atmospheric Model (iRAM). The results show that on both seasonal and interannual time scales, the initial conditions significantly affect the downscaled TC activity, with the largest internal variability occurring in August on the seasonal time scale. The spreads between any of the individual simulations and the ensemble mean are comparable to and in some circumstances greater than the interannual variation of the observed TC frequency. The internal variability of the downscaled TC activity is found to be insensitive to the amplitude and the pattern of the initial perturbations. However, day-to-day model solutions are strongly affected by the internal variability. As a result, the development of nonlinear atmospheric instabilities significantly modulates the genesis and development of the TC-like vortices, leading to the large internal variability of the downscaled TC activity. In addition to the traditional initial value problem, criteria (in particular, threshold values) used in the TC detection contribute equally to the internal variability of the downscaled TCs in the simulations. Consistent with earlier studies, the results from this study also show that the ensemble mean provides the better downscaled information on seasonal and interannual frequencies of TC genesis and occurrence.

scholarly journals Impact of Storm Size on Prediction of Storm Track and Intensity Using the 2016 Operational GFDL Hurricane Model

2017 ◽  
Vol 32 (4) ◽  
pp. 1491-1508 ◽  
Author(s):  
Morris A. Bender ◽  
Timothy P. Marchok ◽  
Charles R. Sampson ◽  
John A. Knaff ◽  
Matthew J. Morin
Keyword(s):  
Tropical Cyclones ◽  
North Pacific ◽  
Western North Pacific ◽  
Numerical Models ◽  
Storm Track ◽  
Eastern Pacific ◽  
Lead Times ◽  
Rapid Intensification ◽  
Forecast Lead Time ◽  
The Impact

Abstract The impact of storm size on the forecast of tropical cyclone storm track and intensity is investigated using the 2016 version of the operational GFDL hurricane model. Evaluation was made for 1529 forecasts in the Atlantic, eastern Pacific, and western North Pacific basins, during the 2014 and 2015 seasons. The track and intensity errors were computed from forecasts in which the 34-kt (where 1 kt = 0.514 m s−1) wind radii obtained from the operational TC vitals that are used to initialize TCs in the GFDL model were replaced with wind radii estimates derived using an equally weighted average of six objective estimates. It was found that modifying the radius of 34-kt winds had a significant positive impact on the intensity forecasts in the 1–2 day lead times. For example, at 48 h, the intensity error was reduced 10%, 5%, and 4% in the Atlantic, eastern Pacific, and western North Pacific, respectively. The largest improvements in intensity forecasts were for those tropical cyclones undergoing rapid intensification, with a maximum error reduction in the 1–2 day forecast lead time of 14% and 17% in the eastern and western North Pacific, respectively. The large negative intensity biases in the eastern and western North Pacific were also reduced 25% and 75% in the 12–72-h forecast lead times. Although the overall impact on the average track error was neutral, forecasts of recurving storms were improved and tracks of nonrecurving storms degraded. Results also suggest that objective specification of storm size may impact intensity forecasts in other high-resolution numerical models, particularly for tropical cyclones entering a rapid intensification phase.

scholarly journals Comparative Assessment of Two Objective Forecast Models for Cases of Persistent Extreme Precipitation Events in the Yangtze–Huai River Valley in Summer 2016

2018 ◽  
Vol 33 (1) ◽  
pp. 221-238 ◽  
Author(s):  
Baiquan Zhou ◽  
Panmao Zhai ◽  
Ruoyun Niu
Keyword(s):  
Statistical Downscaling ◽  
Extreme Precipitation ◽  
Heavy Precipitation ◽  
Lead Times ◽  
Extreme Precipitation Events ◽  
Huai River ◽  
Ensemble Mean ◽  
Medium Range ◽  
Forecast Models ◽  
Precipitation Events

Abstract Two persistent extreme precipitation events (PEPEs) that caused severe flooding in the Yangtze–Huai River valley in summer 2016 presented a significant challenge to operational forecasters. To provide forecasters with useful references, the capacity of two objective forecast models in predicting these two PEPEs is investigated. The objective models include a numerical weather prediction (NWP) model from the European Centre for Medium-Range Weather Forecasts (ECMWF), and a statistical downscaling model, the Key Influential Systems Based Analog Model (KISAM). Results show that the ECMWF ensemble provides a skillful spectrum of solutions for determining the location of the daily heavy precipitation (≥25 mm day−1) during the PEPEs, despite its general underestimation of heavy precipitation. For lead times longer than 3 days, KISAM outperforms the ensemble mean and nearly one-half or more of all the ensemble members of ECMWF. Moreover, at longer lead times, KISAM generally performs better in reproducing the meridional location of accumulated rainfall over the two PEPEs compared to the ECMWF ensemble mean and the control run. Further verification of the vertical velocity that affects the production of heavy rainfall in ECMWF and KISAM implies the quality of the depiction of ascending motion during the PEPEs has a dominating influence on the models’ performance in predicting the meridional location of the PEPEs at all lead times. The superiority of KISAM indicates that statistical downscaling techniques are effective in alleviating the deficiency of global NWP models for PEPE forecasts in the medium range of 4–10 days.

scholarly journals Comparison of Probabilistic Quantitative Precipitation Forecasts from Two Postprocessing Mechanisms

2017 ◽  
Vol 18 (11) ◽  
pp. 2873-2891 ◽  
Author(s):  
Yu Zhang ◽  
Limin Wu ◽  
Michael Scheuerer ◽  
John Schaake ◽  
Cezar Kongoli
Keyword(s):  
The United States ◽  
Storm Event ◽  
Lead Times ◽  
Ensemble Spread ◽  
Ensemble Forecasts ◽  
Ensemble Mean ◽  
Medium Range ◽  
Quantitative Precipitation Forecasts ◽  
The Mean ◽  
Probability Of Precipitation

Abstract This article compares the skill of medium-range probabilistic quantitative precipitation forecasts (PQPFs) generated via two postprocessing mechanisms: 1) the mixed-type meta-Gaussian distribution (MMGD) model and 2) the censored shifted Gamma distribution (CSGD) model. MMGD derives the PQPF by conditioning on the mean of raw ensemble forecasts. CSGD, on the other hand, is a regression-based mechanism that estimates PQPF from a prescribed distribution by adjusting the climatological distribution according to the mean, spread, and probability of precipitation (POP) of raw ensemble forecasts. Each mechanism is applied to the reforecast of the Global Ensemble Forecast System (GEFS) to yield a postprocessed PQPF over lead times between 24 and 72 h. The outcome of an evaluation experiment over the mid-Atlantic region of the United States indicates that the CSGD approach broadly outperforms the MMGD in terms of both the ensemble mean and the reliability of distribution, although the performance gap tends to be narrow, and at times mixed, at higher precipitation thresholds (>5 mm). Analysis of a rare storm event demonstrates the superior reliability and sharpness of the CSGD PQPF and underscores the issue of overforecasting by the MMGD PQPF. This work suggests that the CSGD’s incorporation of ensemble spread and POP does help enhance its skill, particularly for light forecast amounts, but CSGD’s model structure and its use of optimization in parameter estimation likely play a more determining role in its outperformance.

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