Validation Schemes for Tropical Cyclone Quantitative Precipitation Forecasts: Evaluation of Operational Models for U.S. Landfalling Cases

2007 ◽  
Vol 22 (4) ◽  
pp. 726-746 ◽  
Author(s):  
Timothy Marchok ◽  
Robert Rogers ◽  
Robert Tuleya
Keyword(s):  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
Forecast Error ◽  
Heavy Rain ◽  
Track Forecast ◽  
The Core ◽  
Quantitative Precipitation Forecasts ◽  
Operational Models ◽  
Landfalling Tropical Cyclones ◽  
The Impact

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.

scholarly journals The Impact of Convective Momentum Transport on Tropical Cyclone Track Forecasts Using the Emanuel Cumulus Parameterization

2007 ◽  
Vol 135 (4) ◽  
pp. 1195-1207 ◽  
Author(s):  
Timothy F. Hogan ◽  
Randal L. Pauley
Keyword(s):  
Tropical Cyclone ◽  
Data Assimilation ◽  
Tropical Cyclones ◽  
Momentum Transport ◽  
Cumulus Parameterization ◽  
Transport Parameter ◽  
Medium Range Forecast ◽  
Medium Range ◽  
Convective Momentum Transport ◽  
The Impact

Abstract The influence of convective momentum transport (CMT) on tropical cyclone (TC) track forecasts is examined in the Navy Operational Global Atmospheric Prediction System (NOGAPS) with the Emanuel cumulus parameterization. Data assimilation and medium-range forecast experiments show that for 35 tropical cyclones during August and September 2004 the inclusion of CMT in the cumulus parameterization significantly improves the TC track forecasts. The tests show that the track forecasts are very sensitive to the magnitude of the Emanuel parameterization’s convective momentum transport parameter, which controls the CMT tendency returned by the parameterization. While the overall effect of this formulation of CMT in NOGAPS data assimilation/medium-range forecasts results in the surface pressure of tropical cyclones being less intense (and more consistent with the analysis), the parameterization is not equivalent to a simple diffusion of winds in the presence of convection. This is demonstrated by two data assimilation/medium-range forecast tests in which a vertical diffusion algorithm replaces the CMT. Two additional data assimilation/medium-range forecast experiments were conducted to test whether the skill increase primarily comes from the CMT in the immediate vicinity of the tropical cyclones. The results show that the inclusion of the CMT calculation in the vicinity of the TC makes the largest contribution to the increase in forecast skill, but the general contribution of CMT away from the TC also plays an important role.

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

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.

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

2019 ◽  
Vol 147 (8) ◽  
pp. 2961-2977 ◽  
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.

scholarly journals The Impact of Dropwindsonde Data on Typhoon Track Forecasts in DOTSTAR

2007 ◽  
Vol 22 (6) ◽  
pp. 1157-1176 ◽  
Author(s):  
Chun-Chieh Wu ◽  
Kun-Hsuan Chou ◽  
Po-Hsiung Lin ◽  
Sim D. Aberson ◽  
Melinda S. Peng ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
Error Reduction ◽  
Track Forecast ◽  
Cyclone Track ◽  
Tropical Cyclone Track ◽  
Global Spectral Model ◽  
Track Error ◽  
Typhoon Track ◽  
The Mean ◽  
The Impact

Abstract Starting from 2003, a new typhoon surveillance program, Dropwindsonde Observations for Typhoon Surveillance near the Taiwan Region (DOTSTAR), was launched. During 2004, 10 missions for eight typhoons were conducted successfully with 155 dropwindsondes deployed. In this study, the impact of these dropwindsonde data on tropical cyclone track forecasts has been evaluated with five models (four operational and one research models). All models, except the Geophysical Fluid Dynamics Laboratory (GFDL) hurricane model, show the positive impact that the dropwindsonde data have on tropical cyclone track forecasts. During the first 72 h, the mean track error reductions in the National Centers for Environmental Prediction’s (NCEP) Global Forecast System (GFS), the Navy Operational Global Atmospheric Prediction System (NOGAPS) of the Fleet Numerical Meteorology and Oceanography Center (FNMOC), and the Japanese Meteorological Agency (JMA) Global Spectral Model (GSM) are 14%, 14%, and 19%, respectively. The track error reduction in the Weather Research and Forecasting (WRF) model, in which the initial conditions are directly interpolated from the operational GFS forecast, is 16%. However, the mean track improvement in the GFDL model is a statistically insignificant 3%. The 72-h-average track error reduction from the ensemble mean of the above three global models is 22%, which is consistent with the track forecast improvement in Atlantic tropical cyclones from surveillance missions. In all, despite the fact that the impact of the dropwindsonde data is not statistically significant due to the limited number of DOTSTAR cases in 2004, the overall added value of the dropwindsonde data in improving typhoon track forecasts over the western North Pacific is encouraging. Further progress in the targeted observations of the dropwindsonde surveillances and satellite data, and in the modeling and data assimilation system, is expected to lead to even greater improvement in tropical cyclone track forecasts.

scholarly journals Experimental Tropical Cyclone Forecasts Using a Variable-Resolution Global Model

2015 ◽  
Vol 143 (10) ◽  
pp. 4012-4037 ◽  
Author(s):  
Colin M. Zarzycki ◽  
Christiane Jablonowski
Keyword(s):  
High Resolution ◽  
Tropical Cyclone ◽  
Initial Conditions ◽  
Weather Prediction ◽  
Horizontal Resolution ◽  
Hurricane Sandy ◽  
Track Forecast ◽  
Time Step ◽  
Variable Resolution ◽  
The Impact

Abstract Tropical cyclone (TC) forecasts at 14-km horizontal resolution (0.125°) are completed using variable-resolution (V-R) grids within the Community Atmosphere Model (CAM). Forecasts are integrated twice daily from 1 August to 31 October for both 2012 and 2013, with a high-resolution nest centered over the North Atlantic and eastern Pacific Ocean basins. Using the CAM version 5 (CAM5) physical parameterization package, regional refinement is shown to significantly increase TC track forecast skill relative to unrefined grids (55 km, 0.5°). For typical TC forecast integration periods (approximately 1 week), V-R forecasts are able to nearly identically reproduce the flow field of a globally uniform high-resolution forecast. Simulated intensity is generally too strong for forecasts beyond 72 h. This intensity bias is robust regardless of whether the forecast is forced with observed or climatological sea surface temperatures and is not significantly mitigated in a suite of sensitivity simulations aimed at investigating the impact of model time step and CAM’s deep convection parameterization. Replacing components of the default physics with Cloud Layers Unified by Binormals (CLUBB) produces a statistically significant improvement in forecast intensity at longer lead times, although significant structural differences in forecasted TCs exist. CAM forecasts the recurvature of Hurricane Sandy into the northeastern United States 60 h earlier than the Global Forecast System (GFS) model using identical initial conditions, demonstrating the sensitivity of TC forecasts to model configuration. Computational costs associated with V-R simulations are dramatically decreased relative to globally uniform high-resolution simulations, demonstrating that variable-resolution techniques are a promising tool for future numerical weather prediction applications.

scholarly journals The Impact of Dropwindsonde Data from the THORPEX Pacific Area Regional Campaign and the NOAA Hurricane Field Program on Tropical Cyclone Forecasts in the Global Forecast System

2011 ◽  
Vol 139 (9) ◽  
pp. 2689-2703 ◽  
Author(s):  
Sim D. Aberson
Keyword(s):  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
Initial Conditions ◽  
Unexpected Result ◽  
Cyclone Track ◽  
East Pacific ◽  
Global Impacts ◽  
The Impact ◽  
Pacific Area ◽  
Taiwan Region

Four aircraft released dropwindsondes in and around tropical cyclones in the west Pacific during The Observing System Research and Predictability Experiment (THORPEX) Pacific Area Regional Campaign (T-PARC) in 2008 and the Dropwindsonde Observations for Typhoon Surveillance near the Taiwan Region (DOTSTAR); multiple aircraft concurrently participated in similar missions in the Atlantic. Previous studies have treated each region separately and have focused on the tropical cyclones whose environments were sampled. The large number of missions and tropical cyclones in both regions, and additional tropical cyclones in the east Pacific and Indian Oceans, allows for the global impact of these observations on tropical cyclone track forecasts to be studied. The study shows that there are unintended global consequences to local changes in initial conditions, in this case due to the assimilation of dropwindsonde data in tropical cyclone environments. These global impacts are mainly due to the spectral nature of the model system. These differences should be small and slightly positive, since improved local initial conditions should lead to small global forecast improvements. However, the impacts on tropical cyclones far removed from the data are shown to be as large and positive as those on the tropical cyclones specifically targeted for improved track forecasts. Causes of this unexpected result are hypothesized, potentially providing operational forecasters tools to identify when large remote impacts from surveillance missions might occur.

A Parameter for Forecasting Tornadoes Associated with Landfalling Tropical Cyclones

2014 ◽  
Vol 29 (5) ◽  
pp. 1238-1255 ◽  
Author(s):  
Matthew J. Onderlinde ◽  
Henry E. Fuelberg
Keyword(s):  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
Stepwise Regression ◽  
Atlantic Coast ◽  
Convective Available Potential Energy ◽  
Environmental Parameters ◽  
Time Of Day ◽  
Skill Scores ◽  
Landfalling Tropical Cyclones ◽  
Center Time

Abstract The authors develop a statistical guidance product, the tropical cyclone tornado parameter (TCTP), for forecasting the probability of one or more tornadoes during a 6-h period that are associated with landfalling tropical cyclones affecting the coastal Gulf of Mexico and the southern Atlantic coast. TCTP is designed to aid forecasters in a time-limited environment. TCTP provides a “quick look” at regions where forecasters can then conduct detailed analyses. The pool of potential predictors included tornado reports and tropical cyclone data between 2000 and 2008, as well as storm environmental parameters. The original pool of 28 potential predictors is reduced to six using stepwise regression and logistic regression. These six predictors are 0–3-km wind shear, 0–3-km storm relative helicity, azimuth angle of the tornado report from the tropical cyclone, distance from the cyclone’s center, time of day, and 950–1000-hPa convective available potential energy. Mean Brier scores and Brier skill scores are computed for the entire TCTP-dependent dataset and for corresponding forecasts produced by the Storm Prediction Center (SPC). TCTP then is applied to four individual cyclone cases to qualitatively and quantitatively assess the parameter and compare its performance with SPC forecasts. Results show that TCTP has skill at identifying regions of tornado potential. However, tornadoes in some tropical systems are overpredicted, but underpredicted in others. TCTP 6-h forecast periods provide slightly poorer statistical performance than the 1-day tornado probability forecasts from SPC, probably because the SPC product includes forecaster guidance and because their forecasts are valid for longer periods (24 h).

Characteristics of Landfalling Tropical Cyclones in the United States and Mexico: Climatology and Interannual Variability

2005 ◽  
Vol 18 (8) ◽  
pp. 1247-1262 ◽  
Author(s):  
Joshua Larson ◽  
Yaping Zhou ◽  
R. Wayne Higgins
Keyword(s):  
United States ◽  
Tropical Cyclone ◽  
Climate Variability ◽  
Interannual Variability ◽  
Atmospheric Circulation ◽  
Tropical Cyclones ◽  
El Niño ◽  
El Nino ◽  
The Arctic ◽  
Landfalling Tropical Cyclones

Abstract The climatology and interannual variability of landfalling tropical cyclones and their impacts on precipitation in the continental United States and Mexico are examined. The analysis is based on National Hurricane Center 6-hourly tropical cyclone track data for the Atlantic and eastern Pacific basins and gridded daily U.S. precipitation data for the period August–October 1950–98. Geographic maps of total tropical cyclone strike days, and the mean and maximum percentage of precipitation due to tropical cyclones, are examined by month. To make the procedures objective, it is assumed that precipitation is symmetric about the storm’s center. While this introduces some uncertainty in the analysis, sensitivity tests show that this assumption is reasonable for precipitation within 5° of the circulation center. The relationship between landfalling tropical cyclones and two leading patterns of interannual climate variability—El Niño–Southern Oscillation (ENSO) and the Arctic Oscillation (AO)—are then examined. Relationships between tropical cyclone frequency and intensity and composites of 200-hPa geopotential height and wind shear anomalies are also examined as a function of ENSO phase and AO phase using classifications devised at the Climate Prediction Center. The data show that tropical cyclone activity in the Atlantic basin is modulated on both seasonal and intraseasonal time scales by the AO and ENSO and that impact of the two modes of climate variability is greater together than apart. This suggests that, during La Niña conditions, atmospheric circulation is more conducive to activity in the main development region during AO-positive conditions than during AO-negative ones and that, during El Niño conditions, atmospheric circulation appears even less conducive to tropical cyclone development during the negative phase of the AO than during the positive phase.

Sign in / Sign up

Export Citation Format

Share Document