Impact of track forecast error on tropical cyclone quantitative precipitation forecasts over the coastal region of China

Abstract A scheme for validating quantitative precipitation forecasts (QPFs) for landfalling tropical cyclones is developed and presented here. This scheme takes advantage of the unique characteristics of tropical cyclone rainfall by evaluating the skill of rainfall forecasts in three attributes: the ability to match observed rainfall patterns, the ability to match the mean value and volume of observed rainfall, and the ability to produce the extreme amounts often observed in tropical cyclones. For some of these characteristics, track-relative analyses are employed that help to reduce the impact of model track forecast error on QPF skill. These characteristics are evaluated for storm-total rainfall forecasts of all U.S. landfalling tropical cyclones from 1998 to 2004 by the NCEP operational models, that is, the Global Forecast System (GFS), the Geophysical Fluid Dynamics Laboratory (GFDL) hurricane model, and the North American Mesoscale (NAM) model, as well as the benchmark Rainfall Climatology and Persistence (R-CLIPER) model. Compared to R-CLIPER, all of the numerical models showed comparable or greater skill for all of the attributes. The GFS performed the best of all of the models for each of the categories. The GFDL had a bias of predicting too much heavy rain, especially in the core of the tropical cyclones, while the NAM predicted too little of the heavy rain. The R-CLIPER performed well near the track of the core, but it predicted much too little rain at large distances from the track. Whereas a primary determinant of tropical cyclone QPF errors is track forecast error, possible physical causes of track-relative differences lie with the physical parameterizations and initialization schemes for each of the models. This validation scheme can be used to identify model limitations and biases and guide future efforts toward model development and improvement.

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Prediction of Consensus Tropical Cyclone Track Forecast Error

Monthly Weather Review ◽

10.1175/mwr3390.1 ◽

2007 ◽

Vol 135 (5) ◽

pp. 1985-1993 ◽

Cited By ~ 39

Author(s):

James S. Goerss

Keyword(s):

Tropical Cyclone ◽

Regression Models ◽

Average Distance ◽

Forecast Error ◽

Dependent Data ◽

Track Forecast ◽

Consensus Model ◽

Cyclone Track ◽

Tropical Cyclone Track ◽

National Hurricane Center

Abstract The extent to which the tropical cyclone (TC) track forecast error of a consensus model (CONU) routinely used by the forecasters at the National Hurricane Center can be predicted is determined. A number of predictors of consensus forecast error, which must be quantities that are available prior to the official forecast deadline, were examined for the Atlantic basin in 2001–03. Leading predictors were found to be consensus model spread, defined to be the average distance of the member forecasts from the consensus forecast, and initial and forecast TC intensity. Using stepwise linear regression and the full pool of predictors, regression models were found for each forecast length to predict the CONU TC track forecast error. The percent variance of CONU TC track forecast error that could be explained by these regression models ranged from just over 15% at 48 h to nearly 50% at 120 h. Using the regression models, predicted radii were determined and were used to draw circular areas around the CONU forecasts that contained the verifying TC position 73%–76% of the time. Based on the size of these circular areas, a forecaster can determine the confidence that can be placed upon the CONU forecasts. Independent data testing yielded results only slightly degraded from those of dependent data testing, highlighting the capability of these methods in practical forecasting applications.

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Impact of Refractivity Profiles from a Proposed GNSS-RO Constellation on Tropical Cyclone Forecasts in a Global Modeling System

Monthly Weather Review ◽

10.1175/mwr-d-19-0360.1 ◽

2020 ◽

Vol 148 (7) ◽

pp. 3037-3057

Author(s):

Michael J. Mueller ◽

Andrew C. Kren ◽

Lidia Cucurull ◽

Sean P. F. Casey ◽

Ross N. Hoffman ◽

...

Keyword(s):

Tropical Cyclone ◽

North Atlantic ◽

Forecast Error ◽

Good Control ◽

Track Forecast ◽

Forecast Errors ◽

Random Errors ◽

Error Statistics ◽

The North ◽

The North Atlantic

Abstract A global observing system simulation experiment (OSSE) was used to assess the potential impact of a proposed Global Navigation Satellite System (GNSS) radio occultation (RO) constellation on tropical cyclone (TC) track, maximum 10-m wind speed (Vmax), and integrated kinetic energy (IKE) forecasts. The OSSE system was based on the 7-km NASA nature run and simulated RO refractivity determined by the spatial distribution of observations from the original planned (i.e., including both equatorial and polar orbits) Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (COSMIC-2). Data were assimilated using the NOAA operational weather analysis and forecasting system. Three experiments generated global TC track, Vmax, and IKE forecasts over 6 weeks of the North Atlantic hurricane season in the North Atlantic, east Pacific, and west Pacific basins. Confidence in our results was bolstered because track forecast errors were similar to those of official National Hurricane Center forecasts, and Vmax errors and IKE errors showed similar results. GNSS-RO assimilation did not significantly impact global track forecasts, but did slightly degrade Vmax and IKE forecasts in the first 30–60 h of lead time. Global forecast error statistics show adding or excluding explicit random errors to RO profiles made little difference to forecasts. There was large forecast-to-forecast variability in RO impact. For two cases studied in depth, track and Vmax improvements and degradations were traced backward through the previous 24 h of assimilation cycles. The largest Vmax degradation was traced to particularly good control analyses rather than poor analyses caused by GNSS-RO.

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Improvement of Tropical Cyclone Track Forecast over the Western North Pacific Using a Machine Learning Method

10.5194/egusphere-egu2020-13648 ◽

2020 ◽

Author(s):

Kyoungmin Kim ◽

Dong-Hyun Cha ◽

Jungho Im

Keyword(s):

Machine Learning ◽

Tropical Cyclone ◽

North Pacific ◽

Western North Pacific ◽

Numerical Models ◽

Forecast Error ◽

Selection Method ◽

Tropical Storm ◽

Track Forecast ◽

Forecast Lead Time

<p>&#160;The accurate tropical cyclone (TC) track forecast is necessary to mitigate and prepare significant damage. TC has been predicted by the numerical models, statistical models, and machine learning methods in previous researches. However, those models are separately used for TC track forecast, and historical data with satellite images were used as input variables for machine learning without forecast data from numerical models. In this study, we corrected the TC track forecast of a numerical model by artificial neural network (ANN). TCs that occurred from 2006 to 2015 over the western North Pacific were hindcasted by the Weather Research and Forecasting (WRF) model, and all categories of TCs except for tropical depression (i.e., tropical storm, severe tropical storm, and typhoon) from June to November were included in this study. We evaluated the performance of TC track forecast in terms of duration, translation speed, and direction compared with the best track data. The simulated positions of TCs at 24-hour, 48-hour, and 72-hour forecast lead time were used as variables for training and testing ANN. To optimize the number of neurons in ANN, simulated TCs were divided into two parts; TCs in 2006-2014 for ANN optimization and those in 2015 for a blind test. Also, the output selection method based on the forecast error of the WRF was applied to exclude the outlier of ANN results. By applying the output selection, the forecast error of ANN was further reduced than that of the WRF. As a result, ANN with the output selection method could improve TC track forecast by about 15% compared to the WRF. Also, the effect of ANN tended to increase when the forecast error of the WRF was large. The output selection method was particularly effective by excluding outliers of ANN results when the forecast error of the WRF was small.</p><p>&#8251; This research was supported by Next-Generation Information Computing Development Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT (NRF-2016M3C4A7952637).</p>

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Diagnosis of Track Forecast Errors for Tropical Cyclone Rita (2005) Using GEFS Reforecasts

Weather and Forecasting ◽

10.1175/waf-d-15-0036.1 ◽

2015 ◽

Vol 30 (5) ◽

pp. 1334-1354 ◽

Cited By ~ 4

Author(s):

Thomas J. Galarneau ◽

Thomas M. Hamill

Keyword(s):

Tropical Cyclone ◽

Weather Prediction ◽

Weather Research And Forecasting ◽

Track Forecast ◽

Forecast Errors ◽

Subtropical Anticyclone ◽

Cutoff Low ◽

Layer Flow ◽

Upper Level ◽

Mass Evacuation

Abstract Analysis and diagnosis of the track forecasts for Tropical Cyclone (TC) Rita (2005) from the Global Ensemble Forecast System (GEFS) reforecast dataset is presented. The operational numerical weather prediction guidance and GEFS reforecasts initialized at 0000 UTC 20–22 September 2005, 2–4 days prior to landfall, were all characterized by a persistent left-of-track error. The numerical guidance indicated a significant threat of landfall for the Houston, Texas, region on 24 September. The largest mass evacuation in U.S. history was ordered, with the evacuation resulting in more fatalities than TC Rita itself. TC Rita made landfall along the Texas–Louisiana coastal zone on 24 September. This study utilizes forecasts from the GEFS reforecast and a high-resolution regional reforecast. The regional reforecast was generated using the Advanced Hurricane Weather Research and Forecasting Model (AHW) with the GEFS reforecasts providing the initial and boundary conditions. The results show that TC Rita’s track was sensitive to errors in both the synoptic-scale flow and TC intensity. Within the GEFS reforecast ensemble, the nonrecurving members were characterized by a midlevel subtropical anticyclone that extended too far south and west over the southern United States, and an upper-level cutoff low west and anticyclone east of TC Rita that were too weak. The AHW regional reforecast ensemble further highlighted the role of intensity and steering-layer depth in TC Rita’s track. While the AHW forecast was initialized with a TC that was too weak, the ensemble members that were able to intensify TC Rita more rapidly produced a better track forecast because the TCs followed a deeper steering-layer flow.

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A Scale-Selective Data Assimilation Approach to Improving Tropical Cyclone Track and Intensity Forecasts in a Limited-Area Model: A Case Study of Hurricane Felix (2007)

Weather and Forecasting ◽

10.1175/waf-d-10-05033.1 ◽

2012 ◽

Vol 27 (1) ◽

pp. 124-140 ◽

Cited By ~ 11

Author(s):

Bin Liu ◽

Lian Xie

Keyword(s):

Data Assimilation ◽

Large Scale ◽

Dynamical Downscaling ◽

Weather Forecasting ◽

Forecast Error ◽

Regional Model ◽

Small Scale ◽

Limited Area ◽

Track Forecast ◽

Global Models

Abstract Accurately forecasting a tropical cyclone’s (TC) track and intensity remains one of the top priorities in weather forecasting. A dynamical downscaling approach based on the scale-selective data assimilation (SSDA) method is applied to demonstrate its effectiveness in TC track and intensity forecasting. The SSDA approach retains the merits of global models in representing large-scale environmental flows and regional models in describing small-scale characteristics. The regional model is driven from the model domain interior by assimilating large-scale flows from global models, as well as from the model lateral boundaries by the conventional sponge zone relaxation. By using Hurricane Felix (2007) as a demonstration case, it is shown that, by assimilating large-scale flows from the Global Forecast System (GFS) forecasts into the regional model, the SSDA experiments perform better than both the original GFS forecasts and the control experiments, in which the regional model is only driven by lateral boundary conditions. The overall mean track forecast error for the SSDA experiments is reduced by over 40% relative to the control experiments, and by about 30% relative to the GFS forecasts, respectively. In terms of TC intensity, benefiting from higher grid resolution that better represents regional and small-scale processes, both the control and SSDA runs outperform the GFS forecasts. The SSDA runs show approximately 14% less overall mean intensity forecast error than do the control runs. It should be noted that, for the Felix case, the advantage of SSDA becomes more evident for forecasts with a lead time longer than 48 h.

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Five-day track forecast skills of WRF model for the western North Pacific tropical cyclones

Weather and Forecasting ◽

10.1175/waf-d-20-0092.1 ◽

2021 ◽

Author(s):

Jihong Moon ◽

Jinyoung Park ◽

Dong-Hyun Cha ◽

Yumin Moon

Keyword(s):

Tropical Cyclones ◽

North Pacific ◽

Western North Pacific ◽

Forecast Error ◽

Wrf Model ◽

Analysis Data ◽

Track Forecast ◽

Steering Vector ◽

Group 2 ◽

Group 1

AbstractIn this study, the characteristics of simulated tropical cyclones (TCs) over the western North Pacific by a regional model (the WRF model) are verified. We utilize 12 km horizontal grid spacing, and simulations are integrated for 5 days from model initialization. One hundred and twenty-five forecasts are divided into five clusters through the k-means clustering method. The TCs in the cluster 1 and 2 (group 1), which includes many TCs moves northward in subtropical region, generally have larger track errors than for TCs in cluster 3 and 4 (group 2). The optimal steering vector is used to examine the difference in the track forecast skill between these two groups. The bias in the steering vector between the model and analysis data is found to be more substantial for group 1 TCs than group 2 TCs. The larger steering vector difference for group 1 TCs indicates that environmental fields tend to be poorly simulated in group 1 TC cases. Furthermore, the residual terms, including the storm-scale process, asymmetric convection distribution, or beta-related effect, are also larger for group 1 TCs than group 2 TCs. Therefore, it is probable that the large track forecast error for group 1 TCs is a result of unreasonable simulations of environmental wind fields and residual processes in the midlatitudes.

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Impact of Gulfstream-IV Dropsondes on Tropical Cyclone Prediction in a Regional OSSE System

Monthly Weather Review ◽

10.1175/mwr-d-18-0157.1 ◽

2019 ◽

Vol 147 (8) ◽

pp. 2961-2977 ◽

Cited By ~ 1

Author(s):

Kelly Ryan ◽

Lisa Bucci ◽

Javier Delgado ◽

Robert Atlas ◽

Shirley Murillo

Keyword(s):

Tropical Cyclone ◽

Real Time ◽

Environmental Conditions ◽

The United States ◽

Track Forecast ◽

Deterministic Models ◽

Research Division ◽

Sensitivity Studies ◽

Potential Impact ◽

The Impact

Abstract Aircraft reconnaissance missions remain the primary means of collecting direct measurements of marine atmospheric conditions affecting tropical cyclone formation and evolution. The National Hurricane Center tasks the NOAA G-IV aircraft to sample environmental conditions that may impact the development of a tropical cyclone threatening to make landfall in the United States or its territories. These aircraft data are assimilated into deterministic models and used to produce real-time analyses and forecasts for a given tropical cyclone. Existing targeting techniques aim to optimize the use of reconnaissance observations and partially rely on regions of highest uncertainty in the Global Ensemble Forecast System. Evaluating the potential impact of various trade-offs in the targeting process is valuable for determining the ideal aircraft flight track for a prospective mission. AOML’s Hurricane Research Division has developed a system for performing regional observing system simulation experiments (OSSEs) to assess the potential impact of proposed observing systems on hurricane track and intensity forecasting. This study focuses on improving existing targeting methods by investigating the impact of proposed aircraft observing system designs through various sensitivity studies. G-IV dropsonde retrievals were simulated from a regional nature run, covering the life cycle of a rapidly intensifying Atlantic hurricane. Results from sensitivity studies provide insight into improvements for real-time operational synoptic surveillance targeting for hurricanes and tropical storms, where dropsondes released closer to the vortex–environment interface provide the largest impact on the track forecast. All dropsonde configurations provide a positive 2-day impact on intensity forecasts by improving the environmental conditions known to impact tropical cyclone intensity.

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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.

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Tropical Cyclone Gale Wind Radii Estimates, Forecasts, and Error Forecasts for the Western North Pacific

Weather and Forecasting ◽

10.1175/waf-d-17-0153.1 ◽

2018 ◽

Vol 33 (4) ◽

pp. 1081-1092 ◽

Cited By ~ 1

Author(s):

Charles R. Sampson ◽

James S. Goerss ◽

John A. Knaff ◽

Brian R. Strahl ◽

Edward M. Fukada ◽

...

Keyword(s):

Tropical Cyclone ◽

Real Time ◽

Error Estimates ◽

Forecast Error ◽

Operational Reliability ◽

Cyclone Track ◽

Tropical Cyclone Track ◽

Initial Size ◽

Time Estimates ◽

Joint Typhoon Warning Center

Abstract In 2016, the Joint Typhoon Warning Center extended forecasts of gale-force and other wind radii to 5 days. That effort and a thrust to perform postseason analysis of gale-force wind radii for the “best tracks” (the quality controlled and documented tropical cyclone track and intensity estimates released after the season) have prompted requirements for new guidance to address the challenges of both. At the same time, operational tools to estimate and predict wind radii continue to evolve, now forming a quality suite of gale-force wind radii analysis and forecasting tools. This work provides an update to real-time estimates of gale-force wind radii (a mean/consensus of gale-force individual wind radii estimates) that includes objective scatterometer-derived estimates. The work also addresses operational gale-force wind radii forecasting in that it provides an update to a gale-force wind radii forecast consensus, which now includes gale-force wind radii forecast error estimates to accompany the gale-force wind radii forecasts. The gale-force wind radii forecast error estimates are computed using predictors readily available in real time (e.g., consensus spread, initial size, and forecast intensity) so that operational reliability and timeliness can be ensured. These updates were all implemented in operations at the Joint Typhoon Warning Center by January 2018, and more updates should be expected in the coming years as new and improved guidance becomes available.

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