The Variability and Predictability of Axisymmetric Hurricanes in Statistical Equilibrium

2013 ◽  
Vol 70 (4) ◽  
pp. 993-1005 ◽  
Author(s):  
Gregory J. Hakim
Keyword(s):  
Environmental Variability ◽  
Initial Conditions ◽  
Forecast Error ◽  
Statistical Equilibrium ◽  
Intensity Change ◽  
Order Estimate ◽  
Forecast Errors ◽  
Initial Structure ◽  
Axisymmetric Solution ◽  
Maximum Wind

Abstract The variability and predictability of axisymmetric hurricanes are determined from a 500-day numerical simulation of a tropical cyclone in statistical equilibrium. By design, the solution is independent of the initial conditions and environmental variability, which isolates the “intrinsic” axisymmetric hurricane variability. Variability near the radius of maximum wind is dominated by two patterns: one associated primarily with radial shifts of the maximum wind, and one primarily with intensity change at the time-mean radius of maximum wind. These patterns are linked to convective bands that originate more than 100 km from the storm center and propagate inward. Bands approaching the storm produce eyewall replacement cycles, with an increase in storm intensity as the secondary eyewall contracts radially inward. A dominant time period of 4–8 days is found for the convective bands, which is hypothesized to be determined by the time scale over which subsidence from previous bands suppresses convection; a leading-order estimate based on the ratio of the Rossby radius to band speed fits the hypothesis. Predictability limits for the idealized axisymmetric solution are estimated from linear inverse modeling and analog forecasts, which yield consistent results showing a limit for the azimuthal wind of approximately 3 days. The optimal initial structure that excites the leading pattern of 24-h forecast-error variance has largest azimuthal wind in the midtroposphere outside the storm and a secondary maximum just outside the radius of maximum wind. Forecast errors grow by a factor of 24 near the radius of maximum wind.

scholarly journals ROAD TEMPERATURE MODELLING WITHOUT IN-SITU SENSORS

2017 ◽  
Vol 12 (4) ◽  
pp. 241-247 ◽  
Author(s):  
Karol Opara ◽  
Jan Zieliński
Keyword(s):  
Energy Balance ◽  
Urban Areas ◽  
Initial Conditions ◽  
Forecast Error ◽  
Weather Data ◽  
Forecast Errors ◽  
The Road ◽  
In Situ Sensors ◽  
Pavement Temperature

Modelling of the pavement temperature facilitates winter road maintenance. It is used for predicting the glaze formation and for scheduling the spraying of the de-icing brine. The road weather is commonly forecasted by solving the energy balance equations. It requires setting the initial vertical profile of the pavement temperature, which is often obtained from the Road Weather Information Stations. The paper proposes the use of average air temperature from seven preceding days as a pseudo-observation of the subsurface temperature. Next, the road weather model is run with a few days offset. It first uses the recent, historical weather data and then the available forecasts. This approach exploits the fact that the energy balance models tend to “forget” their initial conditions and converge to the baseline solution. The experimental verification was conducted using the Model of the Environment and Temperature of Roads and the data from a road weather station in Warsaw over a period of two years. The additional forecast error introduced by the proposed pseudo-observational initialization averages 1.2 °C in the first prediction hour and then decreases in time. The paper also discusses the use of Digital Surface Models to take into account the shading effects, which are an essential source of forecast errors in urban areas. Limiting the use of in-situ sensors opens a perspective for an economical, largescale implementation of road meteorological models.

scholarly journals Comparing Adjoint- and Ensemble-Sensitivity Analysis with Applications to Observation Targeting

2007 ◽  
Vol 135 (12) ◽  
pp. 4117-4134 ◽  
Author(s):  
Brian Ancell ◽  
Gregory J. Hakim
Keyword(s):  
Variance Reduction ◽  
Initial Conditions ◽  
Sampling Error ◽  
Forecast Error ◽  
Single Point ◽  
Error Variance ◽  
Initial Time ◽  
Forecast Errors ◽  
Adjoint Sensitivity ◽  
Time Optimal

Abstract The sensitivity of numerical weather forecasts to small changes in initial conditions is estimated using ensemble samples of analysis and forecast errors. Ensemble sensitivity is defined here by linear regression of analysis errors onto a given forecast metric. It is shown that ensemble sensitivity is proportional to the projection of the analysis-error covariance onto the adjoint-sensitivity field. Furthermore, the ensemble-sensitivity approach proposed here involves a small calculation that is easy to implement. Ensemble- and adjoint-based sensitivity fields are compared for a representative wintertime flow pattern near the west coast of North America for a 90-member ensemble of independent initial conditions derived from an ensemble Kalman filter. The forecast metric is taken for simplicity to be the 24-h forecast of sea level pressure at a single point in western Washington State. Results show that adjoint and ensemble sensitivities are very different in terms of location, scale, and magnitude. Adjoint-sensitivity fields reveal mesoscale lower-tropospheric structures that tilt strongly upshear, whereas ensemble-sensitivity fields emphasize synoptic-scale features that tilt modestly throughout the troposphere and are associated with significant weather features at the initial time. Optimal locations for targeting can easily be determined from ensemble sensitivity, and results indicate that the primary targeting locations are located away from regions of greatest adjoint and ensemble sensitivity. It is shown that this method of targeting is similar to previous ensemble-based methods that estimate forecast-error variance reduction, but easily allows for the application of statistical confidence measures to deal with sampling error.

Initial Condition Sensitivity of Western Pacific Extratropical Transitions Determined Using Ensemble-Based Sensitivity Analysis

2009 ◽  
Vol 137 (10) ◽  
pp. 3388-3406 ◽  
Author(s):  
Ryan D. Torn ◽  
Gregory J. Hakim
Keyword(s):  
Sensitivity Analysis ◽  
Tropical Cyclone ◽  
Initial Conditions ◽  
Forecast Error ◽  
Wrf Model ◽  
Weather Research And Forecasting ◽  
Forecast Errors ◽  
Initial Condition ◽  
Ensemble Forecasts ◽  
The Relationship

Abstract An ensemble Kalman filter based on the Weather Research and Forecasting (WRF) model is used to generate ensemble analyses and forecasts for the extratropical transition (ET) events associated with Typhoons Tokage (2004) and Nabi (2005). Ensemble sensitivity analysis is then used to evaluate the relationship between forecast errors and initial condition errors at the onset of transition, and to objectively determine the observations having the largest impact on forecasts of these storms. Observations from rawinsondes, surface stations, aircraft, cloud winds, and cyclone best-track position are assimilated every 6 h for a period before, during, and after transition. Ensemble forecasts initialized at the onset of transition exhibit skill similar to the operational Global Forecast System (GFS) forecast and to a WRF forecast initialized from the GFS analysis. WRF ensemble forecasts of Tokage (Nabi) are characterized by relatively large (small) ensemble variance and greater (smaller) sensitivity to the initial conditions. In both cases, the 48-h forecast of cyclone minimum SLP and the RMS forecast error in SLP are most sensitive to the tropical cyclone position and to midlatitude troughs that interact with the tropical cyclone during ET. Diagnostic perturbations added to the initial conditions based on ensemble sensitivity reduce the error in the storm minimum SLP forecast by 50%. Observation impact calculations indicate that assimilating approximately 40 observations in regions of greatest initial condition sensitivity produces a large, statistically significant impact on the 48-h cyclone minimum SLP forecast. For the Tokage forecast, assimilating the single highest impact observation, an upper-tropospheric zonal wind observation from a Mongolian rawinsonde, yields 48-h forecast perturbations in excess of 10 hPa and 60 m in SLP and 500-hPa height, respectively.

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 Potential Predictability during a Madden–Julian Oscillation Event

2017 ◽  
Vol 30 (14) ◽  
pp. 5345-5360 ◽  
Author(s):  
Charles Jones ◽  
Jimy Dudhia
Keyword(s):  
Initial Conditions ◽  
Spatial Scales ◽  
Forecast Error ◽  
Winter Season ◽  
Wrf Model ◽  
Forecast Errors ◽  
Madden Julian Oscillation ◽  
Potential Predictability ◽  
The Tropics ◽  
Mean Square Errors

The Madden–Julian oscillation (MJO) is an important source of predictability. The boreal 2004/05 winter is used as a case study to conduct predictability experiments with the Weather Research and Forecasting (WRF) Model. That winter season was characterized by an MJO event, weak El Niño, strong North Atlantic Oscillation, and extremely wet conditions over the contiguous United States (CONUS). The issues investigated are as follows: 1) growth of forecast errors in the tropics relative to the extratropics, 2) propagation of forecast errors from the tropics to the extratropics, 3) forecast error growth on spatial scales associated with MJO and non-MJO variability, and 4) the relative importance of MJO and non-MJO tropical variability on predictability of precipitation over CONUS. Root-mean-square errors in forecasts of normalized eddy kinetic energy (NEKE) (200 hPa) show that errors in initial conditions in the tropics grow faster than in the extratropics. Potential predictability extends out to about 4 days in the tropics and 9 days in the extratropics. Forecast errors in the tropics quickly propagate to the extratropics, as demonstrated by experiments in which initial conditions are only perturbed in the tropics. Forecast errors in NEKE (200 hPa) on scales related to the MJO grow slower than in non-MJO variability over localized areas in the tropics and short lead times. Potential predictability of precipitation extends to 1–5 days over most of CONUS but to longer leads (7–12 days) over regions with orographic precipitation in California. Errors in initial conditions on small scales relative to the MJO quickly grow, propagate to the extratropics, and degrade forecast skill of precipitation.

scholarly journals Growth of Forecast Errors from Covariances Modeled by 4DVAR and ETKF Methods

2011 ◽  
Vol 139 (5) ◽  
pp. 1505-1518 ◽  
Author(s):  
Chiara Piccolo
Keyword(s):  
Weather Forecasting ◽  
Initial Conditions ◽  
Forecast Error ◽  
Forecast Errors ◽  
Model Errors ◽  
Background Error ◽  
Background Error Covariance ◽  
Error Covariance ◽  
Numerical Weather Forecasting ◽  
Ensemble Transform Kalman Filter

Numerical weather forecasting errors grow with time. Error growth results from the amplification of small perturbations due to atmospheric instability or from model deficiencies during model integration. In current NWP systems, the dimension of the forecast error covariance matrices is far too large for these matrices to be represented explicitly. They must be approximated. This paper focuses on comparing the growth of forecast error from covariances modeled by the Met Office operational four-dimensional variational data assimilation (4DVAR) and ensemble transform Kalman filter (ETKF) methods over a period of 24 h. The growth of forecast errors implied by 4DVAR is estimated by drawing a random sample of initial conditions from a Gaussian distribution with the standard deviations given by the background error covariance matrix and then evolving the sample forward in time using linearized dynamics. The growth of the forecast error modeled by the ETKF is estimated by propagating the full nonlinear model in time starting from initial conditions generated by an ETKF. This method includes model errors in two ways: by using an inflation factor and by adding model perturbations through a stochastic physics scheme. Finally, these results are compared with a benchmark of the climatological error. The forecast error predicted by the implicit evolution of 4DVAR does not grow, regardless of the dataset used to generate the static background error covariance statistics. The forecast error predicted by the ETKF grows more rapidly because the ETKF selects balanced initial perturbations, which project onto rapidly growing modes. Finally, in both cases it is not possible to disentangle the contribution of the initial condition error from the model error.

Variability and Predictability of a Three-Dimensional Hurricane in Statistical Equilibrium

2013 ◽  
Vol 70 (6) ◽  
pp. 1806-1820 ◽  
Author(s):  
Bonnie R. Brown ◽  
Gregory J. Hakim
Keyword(s):  
Inverse Modeling ◽  
Environmental Variability ◽  
Initial Conditions ◽  
Three Dimensional ◽  
Tangential Velocity ◽  
Internal Variability ◽  
Maximum Wind ◽  
Tangential Wind ◽  
Cloud Resolving Model ◽  
Anomalous Wind

Abstract The internal variability and predictability of idealized three-dimensional hurricanes is investigated using 100-day-long, statistically steady simulations in a compressible, nonhydrostatic, cloud-resolving model. The equilibrium solution is free of the confounding effects of initial conditions and environmental variability in order to isolate the “intrinsic” characteristics of the hurricane. The variance of the axisymmetric tangential velocity is dominated by two patterns: one characterized by a radial shift of the maximum wind, and the other by intensity modulation at the radius of maximum wind. These patterns are associated with convectively coupled bands of anomalous wind speed that propagate inward from large radii with a period of roughly 5 days, the strongest of which is associated with an eyewall replacement cycle. The asymmetric tangential wind is strongest radially inward of the radius of maximum wind. On average, asymmetries decelerate the azimuthal-mean tangential wind at the radius of maximum wind and accelerate it along the inner edge of eyewall. Predictability of axisymmetric storm structure is measured through the autocorrelation e-folding time and linear inverse modeling. Results from both methods reveal an intrinsic predictability time scale of about 2 days. The predictability and variability of the axisymmetric storm structure are consistent with recently obtained results from idealized axisymmetric hurricane modeling.

scholarly journals Understanding Biases in Tropical Cyclone Intensity Forecast Error

2018 ◽  
Vol 33 (1) ◽  
pp. 129-138 ◽  
Author(s):  
Wei Na ◽  
John L. McBride ◽  
Xing-Hai Zhang ◽  
Yi-Hong Duan
Keyword(s):  
Tropical Cyclone ◽  
Forecast Error ◽  
Correlation Coefficients ◽  
Rate Of Change ◽  
Intensity Change ◽  
Forecast Errors ◽  
Second Derivative ◽  
Rapid Intensification ◽  
Time Curve ◽  
Large Intensity

Abstract The characteristics of 24-h official forecast errors (OFEs) of tropical cyclone (TC) intensity are analyzed over the North Atlantic, east Pacific, and western North Pacific. The OFE is demonstrated to be strongly anticorrelated with TC intensity change with correlation coefficients of −0.77, −0.77, and −0.68 for the three basins, respectively. The 24-h intensity change in the official forecast closely follows a Gaussian distribution with a standard deviation only ⅔ of that in nature, suggesting the current official forecasts estimate fewer cases of large intensity change. The intensifying systems tend to produce negative errors (underforecast), while weakening systems have consistent positive errors (overforecast). This asymmetrical bias is larger for extreme intensity change, including rapid intensification (RI) and rapid weakening (RW). To understand this behavior, the errors are analyzed in a simple objective model, the trend-persistence model (TPM). The TPM exhibits the same error-intensity change correlation. In the TPM, the error can be understood as it is exactly inversely proportional to the finite difference form of the concavity or second derivative of the intensity–time curve. The occurrence of large negative (positive) errors indicates the intensity–time curve is concave upward (downward) in nature during the TC’s rapid intensification (weakening) process. Thus, the fundamental feature of the OFE distribution is related to the shape of the intensity–time curve, governed by TC dynamics. All forecast systems have difficulty forecasting an accelerating rate of change, or a large second derivative of the intensity–time curve. TPM may also be useful as a baseline in evaluating the skill of official forecasts. According to this baseline, official forecasts are more skillful in RW than in RI.

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.

scholarly journals Tropical cyclones prediction by numerical models in India Meteorological Department

MAUSAM ◽  
2021 ◽  
Vol 57 (1) ◽  
pp. 47-60
Author(s):  
Y. V. RAMA RAO ◽  
H. R. HATWAR ◽  
GEETA AGNIHOTRI
Keyword(s):  
Initial Conditions ◽  
Numerical Models ◽  
Forecast Error ◽  
India Meteorological Department ◽  
Lagrangian Model ◽  
Limited Area ◽  
Track Forecast ◽  
Forecast Errors ◽  
Error Statistics ◽  
The Impact

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