scholarly journals SHIPS-MI Forecast Analysis of Hurricanes Claudette (2003), Isabel (2003), and Dora (1999)

2007 ◽  
Vol 22 (4) ◽  
pp. 689-707 ◽  
Author(s):  
Thomas A. Jones ◽  
Daniel J. Cecil
Keyword(s):  
Inner Core ◽  
Forecast Accuracy ◽  
Precipitation Forecast ◽  
Intensity Change ◽  
Forecast Errors ◽  
Positive Contribution ◽  
Hurricane Intensity ◽  
Intensity Prediction ◽  
Prediction Scheme ◽  
Dependent Sample

Abstract Three hurricanes, Claudette (2003), Isabel (2003), and Dora (1999), were selected to examine the Statistical Hurricane Intensity Prediction Scheme with Microwave Imagery (SHIPS-MI) forecast accuracy for three particular storm types. This research was conducted using model analyses and tropical cyclone best-track data, with forecasts generated from a dependent sample. The model analyses and best-track data are assumed to be a “perfect” representation of the actual event (e.g., perfect prog assumption). Analysis of intensity change forecasts indicated that SHIPS-MI performed best, compared to operational SHIPS output, for tropical cyclones that were intensifying from tropical storm to hurricane intensity. Passive microwave imagery, which is sensitive to the intensity and coverage of precipitation, improved intensity forecasts during these periods with a positive intensity change contribution resulting from above normal inner-core precipitation. Forecast improvement was greatest for 12–36-h forecasts, where the microwave contribution to SHIPS-MI was greatest. Once a storm reached an intensity close to its maximum potential intensity, as in the case of Isabel and Dora, both SHIPS and SHIPS-MI incorrectly forecast substantial weakening despite the positive contribution from microwave data. At least in Dora’s case, SHIPS-MI forecasts were slightly stronger than those of SHIPS. Other important contributions to SHIPS-MI forecasts were examined to determine their importance relative to the microwave inputs. Inputs related to sea surface temperature (SST) and persistence–climatology proved to be very important to intensity change forecasts, as expected. These predictors were the primary factor leading to the persistent weakening forecasts made by both models for Isabel and Dora. For Atlantic storms (Claudette and Isabel), the contribution from shear also proved important at characterizing the conduciveness of the environment toward intensification. However, the shear contribution was often small as a result of multiple offsetting shear-related predictors. Finally, it was observed that atmospheric parameters not included in SHIPS, such as eddy momentum flux, could substantially affect the intensity, leading to large forecast errors. This was especially true for the Claudette intensity change forecasts throughout its life cycle.

scholarly journals Reducing Operational Hurricane Intensity Forecast Errors during Eyewall Replacement Cycles

2016 ◽  
Vol 31 (2) ◽  
pp. 601-608 ◽  
Author(s):  
James P. Kossin ◽  
Mark DeMaria
Keyword(s):  
Forecast Error ◽  
Intensity Change ◽  
Forecast Errors ◽  
Hurricane Intensity ◽  
Intensity Forecast ◽  
Prediction Scheme ◽  
Hurricane Prediction ◽  
Eyewall Replacement Cycles ◽  
National Hurricane Center ◽  
Intensity Evolution

Abstract Eyewall replacement cycles (ERCs) are fairly common events in tropical cyclones (TCs) of hurricane intensity or greater and typically cause large and sometimes rapid changes in the intensity evolution of the TC. Although the details of the intensity evolution associated with ERCs appear to have some dependence on the ambient environmental conditions that the TCs move through, these dependencies can also be quite different than those of TCs that are not undergoing an ERC. For example, the Statistical Hurricane Prediction Scheme (SHIPS), which is used in National Hurricane Center operations and provides intensity forecast skill that is, on average, equal to or greater than deterministic numerical model skill, typically identifies an environment that is not indicative of weakening during the onset and subsequent evolution of an ERC. Contrarily, a period of substantial weakening does typically begin near the onset of an ERC, and this disparity can cause large SHIPS intensity forecast errors. Here, a simple model based on a climatology of ERC intensity change is introduced and tested against SHIPS. It is found that the application of the model can reduce intensity forecast error substantially when applied at, or shortly after, the onset of ERC weakening.

scholarly journals Understanding Error Distributions of Hurricane Intensity Forecasts during Rapid Intensity Changes

2020 ◽  
Vol 35 (6) ◽  
pp. 2219-2234
Author(s):  
Benjamin C. Trabing ◽  
Michael M. Bell
Keyword(s):  
Inner Core ◽  
Forecast Error ◽  
Vertical Wind Shear ◽  
Probability Of Detection ◽  
Intensity Change ◽  
Forecast Errors ◽  
Hurricane Intensity ◽  
East Pacific ◽  
Intensity Forecast ◽  
Error Distributions

AbstractThe characteristics of official National Hurricane Center (NHC) intensity forecast errors are examined for the North Atlantic and east Pacific basins from 1989 to 2018. It is shown how rapid intensification (RI) and rapid weakening (RW) influence yearly NHC forecast errors for forecasts between 12 and 48 h in length. In addition to being the tail of the intensity change distribution, RI and RW are at the tails of the forecast error distribution. Yearly mean absolute forecast errors are positively correlated with the yearly number of RI/RW occurrences and explain roughly 20% of the variance in the Atlantic and 30% in the east Pacific. The higher occurrence of RI events in the east Pacific contributes to larger intensity forecast errors overall but also a better probability of detection and success ratio. Statistically significant improvements to 24-h RI forecast biases have been made in the east Pacific and to 24-h RW biases in the Atlantic. Over-ocean 24-h RW events cause larger mean errors in the east Pacific that have not improved with time. Environmental predictors from the Statistical Hurricane Intensity Prediction Scheme (SHIPS) are used to diagnose what conditions lead to the largest RI and RW forecast errors on average. The forecast error distributions widen for both RI and RW when tropical systems experience low vertical wind shear, warm sea surface temperature, and moderate low-level relative humidity. Consistent with existing literature, the forecast error distributions suggest that improvements to our observational capabilities, understanding, and prediction of inner-core processes is paramount to both RI and RW prediction.

Further Improvements to the Statistical Hurricane Intensity Prediction Scheme Using Tropical Cyclone Rainfall and Structural Features

2018 ◽  
Vol 33 (6) ◽  
pp. 1587-1603 ◽  
Author(s):  
Udai Shimada ◽  
Hiromi Owada ◽  
Munehiko Yamaguchi ◽  
Takeshi Iriguchi ◽  
Masahiro Sawada ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
Real Time ◽  
Heat Content ◽  
Absolute Error ◽  
Structural Features ◽  
Japan Meteorological Agency ◽  
Intensity Change ◽  
Hurricane Intensity ◽  
Intensity Prediction ◽  
Prediction Scheme

Abstract The Statistical Hurricane Intensity Prediction Scheme (SHIPS) is a multiple regression model for forecasting tropical cyclone (TC) intensity [both central pressure (Pmin) and maximum wind speed (Vmax)]. To further improve the accuracy of the Japan Meteorological Agency version of SHIPS, five new predictors associated with TC rainfall and structural features were incorporated into the scheme. Four of the five predictors were primarily derived from the hourly Global Satellite Mapping of Precipitation (GSMaP) reanalysis product, which is a microwave satellite-derived rainfall dataset. The predictors include the axisymmetry of rainfall distribution around a TC multiplied by ocean heat content (OHC), rainfall areal coverage, the radius of maximum azimuthal mean rainfall, and total volumetric rain multiplied by OHC. The fifth predictor is the Rossby number. Among these predictors, the axisymmetry multiplied by OHC had the greatest impact on intensity change, particularly, at forecast times up to 42 h. The forecast results up to 5 days showed that the mean absolute error (MAE) of the Pmin forecast in SHIPS with the new predictors was improved by over 6% in the first half of the forecast period. The MAE of the Vmax forecast was also improved by nearly 4%. Regarding the Pmin forecast, the improvement was greatest (up to 13%) for steady-state TCs, including those initialized as tropical depressions, with slight improvement (2%–5%) for intensifying TCs. Finally, a real-time forecast experiment utilizing the hourly near-real-time GSMaP product demonstrated the improvement of the SHIPS forecasts, confirming feasibility for operational use.

scholarly journals Further Improvements to the Statistical Hurricane Intensity Prediction Scheme (SHIPS)

2005 ◽  
Vol 20 (4) ◽  
pp. 531-543 ◽  
Author(s):  
Mark DeMaria ◽  
Michelle Mainelli ◽  
Lynn K. Shay ◽  
John A. Knaff ◽  
John Kaplan
Keyword(s):  
Brightness Temperature ◽  
Satellite Altimetry ◽  
Heat Content ◽  
Satellite Observations ◽  
Global Model ◽  
Hurricane Intensity ◽  
East Pacific ◽  
Intensity Prediction ◽  
Time Period ◽  
Prediction Scheme

Abstract Modifications to the Atlantic and east Pacific versions of the operational Statistical Hurricane Intensity Prediction Scheme (SHIPS) for each year from 1997 to 2003 are described. Major changes include the addition of a method to account for the storm decay over land in 2000, the extension of the forecasts from 3 to 5 days in 2001, and the use of an operational global model for the evaluation of the atmospheric predictors instead of a simple dry-adiabatic model beginning in 2001. A verification of the SHIPS operational intensity forecasts is presented. Results show that the 1997–2003 SHIPS forecasts had statistically significant skill (relative to climatology and persistence) out to 72 h in the Atlantic, and at 48 and 72 h in the east Pacific. The inclusion of the land effects reduced the intensity errors by up to 15% in the Atlantic, and up to 3% in the east Pacific, primarily for the shorter-range forecasts. The inclusion of land effects did not significantly degrade the forecasts at any time period. Results also showed that the 4–5-day forecasts that began in 2001 did not have skill in the Atlantic, but had some skill in the east Pacific. An experimental version of SHIPS that included satellite observations was tested during the 2002 and 2003 seasons. New predictors included brightness temperature information from Geostationary Operational Environmental Satellite (GOES) channel 4 (10.7 μm) imagery, and oceanic heat content (OHC) estimates inferred from satellite altimetry observations. The OHC estimates were only available for the Atlantic basin. The GOES data significantly improved the east Pacific forecasts by up to 7% at 12–72 h. The combination of GOES and satellite altimetry improved the Atlantic forecasts by up to 3.5% through 72 h for those storms west of 50°W.

Implications of Ensemble Quantitative Precipitation Forecast Errors on Distributed Streamflow Forecasting

2010 ◽  
Vol 11 (1) ◽  
pp. 69-86 ◽  
Author(s):  
Giuseppe Mascaro ◽  
Enrique R. Vivoni ◽  
Roberto Deidda
Keyword(s):  
Hydrological Model ◽  
Initial Conditions ◽  
Forecast Accuracy ◽  
Precipitation Forecast ◽  
Forecast Errors ◽  
Distributed Hydrological Model ◽  
Streamflow Forecasting ◽  
Ensemble Forecasts ◽  
Wide Range ◽  
Operational Flow

Abstract Evaluating the propagation of errors associated with ensemble quantitative precipitation forecasts (QPFs) into the ensemble streamflow response is important to reduce uncertainty in operational flow forecasting. In this paper, a multifractal rainfall downscaling model is coupled with a fully distributed hydrological model to create, under controlled conditions, an extensive set of synthetic hydrometeorological events, assumed as observations. Subsequently, for each event, flood hindcasts are simulated by the hydrological model using three ensembles of QPFs—one reliable and the other two affected by different kinds of precipitation forecast errors—generated by the downscaling model. Two verification tools based on the verification rank histogram and the continuous ranked probability score are then used to evaluate the characteristics of the correspondent three sets of ensemble streamflow forecasts. Analyses indicate that the best forecast accuracy of the ensemble streamflows is obtained when the reliable ensemble QPFs are used. In addition, results underline (i) the importance of hindcasting to create an adequate set of data that span a wide range of hydrometeorological conditions and (ii) the sensitivity of the ensemble streamflow verification to the effects of basin initial conditions and the properties of the ensemble precipitation distributions. This study provides a contribution to the field of operational flow forecasting by highlighting a series of requirements and challenges that should be considered when hydrologic ensemble forecasts are evaluated.

scholarly journals Reassessing the Use of Inner-Core Hot Towers to Predict Tropical Cyclone Rapid Intensification*

2015 ◽  
Vol 30 (5) ◽  
pp. 1265-1279 ◽  
Author(s):  
Xiao-Yong Zhuge ◽  
Jie Ming ◽  
Yuan Wang
Keyword(s):  
Tropical Cyclone ◽  
Inner Core ◽  
Prediction Method ◽  
Vertical Shear ◽  
Intensity Change ◽  
False Alarms ◽  
Rapid Intensification ◽  
Central Pacific ◽  
Prediction Scheme ◽  
Skill Scores

Abstract The hot tower (HT) in the inner core plays an important role in tropical cyclone (TC) rapid intensification (RI). With the help of Tropical Rainfall Measurement Mission (TRMM) data and the Statistical Hurricane Intensity Prediction Scheme dataset, the potential of HTs in operational RI prediction is reassessed in this study. The stand-alone HT-based RI prediction scheme showed little skill in the northern Atlantic (NA) and eastern and central Pacific (ECP), but yielded skill scores of >0.3 in the southern Indian Ocean (SI) and western North Pacific (WNP) basins. The inaccurate predictions are due to four scenarios: 1) RI events may have already begun prior to the TRMM overpass. 2) RI events are driven by non-HT factors. 3) The HT has already dissipated or has not occurred at the TRMM overpass time. 4) Large false alarms result from the unfavorable environment. When the HT was used in conjunction with the TC’s previous 12-h intensity change, the potential intensity, the percentage area from 50 to 200 km of cloud-top brightness temperatures lower than −10°C, and the 850–200-hPa vertical shear magnitude with the vortex removed, the predictive skill score in the SI was 0.56. This score was comparable to that of the RI index scheme, which is considered the most advanced RI prediction method. When the HT information was combined with the aforementioned four environmental factors in the NA, ECP, South Pacific, and WNP, the skill scores were 0.23, 0.32, 0.42, and 0.42, respectively.

Predicting Hurricane Intensity and Associated Hazards: A Five-Year Real-Time Forecast Experiment with Assimilation of Airborne Doppler Radar Observations

2015 ◽  
Vol 96 (1) ◽  
pp. 25-33 ◽  
Author(s):  
Fuqing Zhang ◽  
Yonghui Weng
Keyword(s):  
Inner Core ◽  
Doppler Radar ◽  
Surface Wind ◽  
Forecast Errors ◽  
Hurricane Intensity ◽  
Radar Observations ◽  
Intensity Forecast ◽  
Probabilistic Forecasts ◽  
Atlantic Hurricanes ◽  
Airborne Doppler Radar

Abstract Performance in the prediction of hurricane intensity and associated hazards has been evaluated for a newly developed convection-permitting forecast system that uses ensemble data assimilation techniques to ingest high-resolution airborne radar observations from the inner core. This system performed well for three of the ten costliest Atlantic hurricanes: Ike (2008), Irene (2011), and Sandy (2012). Four to five days before these storms made landfall, the system produced good deterministic and probabilistic forecasts of not only track and intensity, but also of the spatial distributions of surface wind and rainfall. Averaged over all 102 applicable cases that have inner-core airborne Doppler radar observations during 2008–2012, the system reduced the day-2-to-day-4 intensity forecast errors by 25%–28% compared to the corresponding National Hurricane Center’s official forecasts (which have seen little or no decrease in intensity forecast errors over the past two decades). Empowered by sufficient computing resources, advances in both deterministic and probabilistic hurricane prediction will enable emergency management officials, the private sector, and the general public to make more informed decisions that minimize the losses of life and property.

scholarly journals On the sensitivity of hurricane intensity and structure to horizontal tracer advection schemes in FV3

2021 ◽  
Author(s):  
Kun Gao ◽  
Lucas Harris ◽  
Linjiong Zhou ◽  
Morris Bender ◽  
Matthew Morin
Keyword(s):  
Inner Core ◽  
Positive Definite ◽  
Moisture Distribution ◽  
Geophysical Fluid Dynamics Laboratory ◽  
Hurricane Intensity ◽  
Dynamical Core ◽  
Intensity Prediction ◽  
Advection Schemes ◽  
Structural Differences ◽  
Cubed Sphere

AbstractWe investigate the sensitivity of hurricane intensity and structure to the horizontal tracer advection in the Geophysical Fluid Dynamics Laboratory (GFDL) Finite-Volume Cubed-Sphere Dynamical Core (FV3). We compare two schemes, a monotonic scheme and a less diffusive positive-definite scheme. The positive-definite scheme leads to significant improvement in the intensity prediction relative to the monotonic scheme in a suite of five-day forecasts that mostly consist of rapidly intensifying hurricanes. Notable storm structural differences are present: the radius of maximum wind (RMW) is smaller and eyewall convection occurs farther inside the RMW when the positive-definite scheme is used. Moreover, we find that the horizontal tracer advection scheme affects the eyewall convection location by affecting the moisture distribution in the inner-core region. This study highlights the importance of dynamical core algorithms in hurricane intensity prediction.

scholarly journals The Environmental and Inner-Core Conditions Governing the Intensity of Hurricane Erin (2001)

2007 ◽  
Vol 22 (4) ◽  
pp. 708-725 ◽  
Author(s):  
Thomas A. Jones ◽  
Daniel J. Cecil ◽  
Jason Dunion
Keyword(s):  
Inner Core ◽  
Deep Convection ◽  
Intensity Change ◽  
Atmospheric Conditions ◽  
Negative Effects ◽  
Prediction Scheme ◽  
Thermodynamic Conditions ◽  
Core Conditions ◽  
Upper Level ◽  
Core Characteristics

Abstract The evolution of Hurricane Erin (2001) is presented from the perspective of its environmental and inner-core conditions, particularly as they are characterized in the Statistical Hurricane Intensity Prediction Scheme with Microwave Imagery (SHIPS-MI). Erin can be described as having two very distinct periods. The first, which occurred between 1 and 6 September 2001, was characterized by a struggling tropical storm failing to intensify as the result of unfavorable environmental and inner-core conditions. The surrounding environment during this period was dominated by moderate shear and mid- to upper-level dry air, both caused in some part by the presence of a Saharan air layer (SAL). Further intensification was inhibited by the lack of sustained deep convection and latent heating near the low-level center. The authors attribute this in part to negative effects from the SAL. The thermodynamic conditions associated with the SAL were not well sampled by the SHIPS parameters, resulting in substantial overforecasting by both SHIPS and SHIPS-MI. Instead, the hostile conditions surrounding Erin caused its dissipation on 6 September. The second period began on 7 September when Erin re-formed north of the original center. Erin began to pull away from the SAL and moved over 29°C sea surface temperatures, beginning a rapid intensification phase and reaching 105 kt by 1800 UTC 9 September. SHIPS-MI forecasts called for substantial intensification as in the previous period, but this time the model underestimated the rate of intensification. The addition of inner-core characteristics from passive microwave data improved the skill somewhat compared to SHIPS, but still left much room for improvement. For this period, it appears that the increasingly favorable atmospheric conditions caused by Erin moving away from the SAL were not well sampled by SHIPS or SHIPS-MI. As a result, the intensity change forecasts were not able to take into account the more favorable environment.

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