Consistent Tropical Cyclone Wind and Wave Forecasts for the U.S. Navy

Abstract A new algorithm to generate wave heights consistent with tropical cyclone official forecasts from the Joint Typhoon Warning Center (JTWC) has been developed. The process involves generating synthetic observations from the forecast track and the 34-, 50-, and 64-kt wind radii. The JTWC estimate of the radius of maximum winds is used in the algorithm to generate observations for the forecast intensity (wind), and the JTWC-estimated radius of the outermost closed isobar is used to assign observations at the outermost extent of the tropical cyclone circulation. These observations are then interpolated to a high-resolution latitude–longitude grid covering the entire extent of the circulation. Finally, numerical weather prediction (NWP) model fields are obtained for each forecast time, the NWP model forecast tropical cyclone is removed from these fields, and the new JTWC vortex is inserted without blending zones between the vortex and the background. These modified fields are then used as input into a wave model to generate waves consistent with the JTWC forecasts. The algorithm is applied to Typhoon Yagi (2006), in anticipation of which U.S. Navy ships were moved from Tokyo Bay to an area off the southeastern coast of Kyushu. The decision to move (sortie) the ships was based on NWP model-driven long-range wave forecasts that indicated high seas impacting the coast in the vicinity of Tokyo Bay. The sortie decision was made approximately 84 h in advance of the high seas in order to give ships time to steam the approximately 500 n mi to safety. Results from the new algorithm indicate that the high seas would not affect the coast near Tokyo Bay within 84 h. This specific forecast verifies, but altimeter observations show that it does not outperform, the NWP model-driven wave analysis and forecasts for this particular case. Overall, the performance of the new algorithm is dependent on the JTWC tropical cyclone forecast performance, which has generally outperformed those of the NWP model over the last several years.

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A New Process for Producing First Guess TAFs

10.5194/egusphere-egu2020-5154 ◽

2020 ◽

Author(s):

Andre Lanyon ◽

Jessica Standen ◽

Piers Buchanan

Keyword(s):

Weather Prediction ◽

Future Research ◽

Forecast Performance ◽

New Process ◽

Low Cloud ◽

Production Research ◽

Nwp Model ◽

The Uk ◽

Consultation Services ◽

Further Development

<p>Terminal Aerodrome Forecasts (TAFs) are a widely accepted international form of aviation forecast used for flight planning procedures at all major airports. TAF production in the UK is currently a time-consuming, manual process carried out by Operational Meteorologists. It has long been speculated that providing a numerical weather prediction (NWP) model-derived first guess solution could bring large improvements in the efficiency of TAF production. Research into first guess TAFs has a long history but making progress has been challenging. However, significant progress has been made at the Met Office in recent months. A practical approach has been adopted that draws on experience of manually producing TAFs. Although NWP model data is utilised as much as possible, steps have been taken to ensure the first guess TAFs are kept as simple and readable as possible whilst retaining information important to the customer. By taking this approach, it is hoped that the first guess TAFs will require minimal intervention from Operational Meteorologists in the majority of weather situations. Development of first guess TAFs is still in the preliminary stages and not all weather parameters are currently included. However, they are produced in such a way that they can be verified using standard Met Office methods, allowing objective comparison with operationally issued TAFs. Verification scores analysed over a 3 year period are encouraging and suggest that forecast performance of first guess TAFs is generally similar to that of operationally issued TAFs. Occasionally, some large differences become apparent when comparing forecasts of rare events such as mist, fog and very low cloud bases, and this is likely to be an area of future research. With further development, it is speculated that the use of first guess TAFs could significantly reduce TAF production time, allowing Operational Meteorologists to make better use of their expertise, perhaps by adding value to model output or by providing valuable consultation services to aviation customers.</p>

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Day-Ahead Wind Power Forecast Through High-Resolution Mesoscale Model: Local Computational Fluid Dynamics Versus Artificial Neural Network Downscaling

Journal of Solar Energy Engineering ◽

10.1115/1.4045740 ◽

2020 ◽

Vol 142 (3) ◽

Author(s):

Matteo Mana ◽

Davide Astolfi ◽

Francesco Castellani ◽

Cathérine Meißner

Keyword(s):

High Resolution ◽

Wind Farm ◽

Mesoscale Model ◽

Weather Prediction ◽

Ensemble Prediction ◽

Cfd Model ◽

Forecast Performance ◽

Ensemble Prediction System ◽

Nwp Model ◽

The Impact

Abstract The importance of accurately forecasting the power production of wind farms is boosting the development of meteorological models and their processing. This work is a discussion of different forecast configurations for predicting the day ahead production of a wind farm sited in a moderately complex terrain. The numerical weather prediction (NWP) model MetCoOp Ensemble Prediction System with 2.5 km resolution focusing on the wind farm area is dynamically downscaled by the computational fluid model (CFD) model WindSim. The transfer of the NWP model to the CFD model can be done using NWP results from various heights above ground and using all or parts of the nodes of the NWP model within the wind farm area. In this work, many different forecasting configurations are validated and the impact on the forecast performance is discussed. The NWP-CFD downscaling results are compared to a day ahead forecast obtained through ANN methods and to the observed production. The main result of this work is that a deterministic downscaling method like CFD simulations can perform as good or better than statistical approaches when using high-resolution NWP models and more NWP model data.

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Wave Probabilities Consistent with Official Tropical Cyclone Forecasts

Weather and Forecasting ◽

10.1175/waf-d-15-0093.1 ◽

2016 ◽

Vol 31 (6) ◽

pp. 2035-2045 ◽

Cited By ~ 7

Author(s):

Charles R. Sampson ◽

James A. Hansen ◽

Paul A. Wittmann ◽

John A. Knaff ◽

Andrea Schumacher

Keyword(s):

Surface Wave ◽

Wind Speed ◽

Tropical Cyclone ◽

Wave Height ◽

Significant Wave Height ◽

Wave Model ◽

Significant Wave ◽

Wave Heights ◽

Significant Wave Heights ◽

Ocean Surface Wave

Abstract Development of a 12-ft-seas significant wave height ensemble consistent with the official tropical cyclone intensity, track, and wind structure forecasts and their errors from the operational U.S. tropical cyclone forecast centers is described. To generate the significant wave height ensemble, a Monte Carlo wind speed probability algorithm that produces forecast ensemble members is used. These forecast ensemble members, each created from the official forecast and randomly sampled errors from historical official forecast errors, are then created immediately after the official forecast is completed. Of 1000 forecast ensemble members produced by the wind speed algorithm, 128 of them are selected and processed to produce wind input for an ocean surface wave model. The wave model is then run once per realization to produce 128 possible forecasts of significant wave height. Probabilities of significant wave height at critical thresholds can then be computed from the ocean surface wave model–generated significant wave heights. Evaluations of the ensemble are provided in terms of maximum significant wave height and radius of 12-ft significant wave height—two parameters of interest to both U.S. Navy meteorologists and U.S. Navy operators. Ensemble mean errors and biases of maximum significant wave height and radius of 12-ft significant wave height are found to be similar to those of a deterministic version of the same algorithm. Ensemble spreads capture most verifying maximum and radii of 12-ft significant wave heights.

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Tropical Cyclone Impacts on Headland Protected Bay

Geosciences ◽

10.3390/geosciences10050190 ◽

2020 ◽

Vol 10 (5) ◽

pp. 190

Author(s):

Daniel Wishaw ◽

Javier X. Leon ◽

Matthew Barnes ◽

Helen Fairweather

Keyword(s):

Tropical Cyclone ◽

Wave Model ◽

Storm Events ◽

Shoreline Development ◽

Sea Levels ◽

Significant Wave ◽

Strong Force ◽

Sediment Volume ◽

Wave Heights ◽

Coastal Features

The response of headland protected beaches to storm events is complex and strongly site dependent. In this study, we investigated the response of several headland protected beaches in Noosa, Australia to a tropical cyclone event. Pre and post topographical surveys of all beaches were completed using both pole-mounted RTK-GNSS and structure-from-motion (SfM)-derived elevation models from survey-grade drone imagery to assess sediment volume differentials. Coastal imaging was used to assess shoreline development and identify coastal features while a nearshore wave model (SWAN) was used to project waves into the study site from a regional wave buoy. Obliquely orientated swells drive currents along the headland with sediment being eroded from exposed sites and deposited at a protected site. Elevated sea-levels were shown to be a strong force-multiplier for relatively small significant wave heights, with 10,000 m3 of sediment eroded from a 700 m long beach in 36 h. The SWAN model was adequately calibrated for significant wave height, but refraction of swell around the headland was under-represented by an average of 16.48 degrees. This research has coastal management implications for beaches where development restricts natural shoreline retreat and elevated sea states are likely to become more common.

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Prediction of sea state under tropical cyclones in the UK Met Office operational global wave model

MAUSAM ◽

10.54302/mausam.v48i4.4351 ◽

2021 ◽

Vol 48 (4) ◽

pp. 621-628

Author(s):

M.W. HOLT ◽

J.C.R. HUNT

Keyword(s):

Tropical Cyclone ◽

Prediction Model ◽

Tropical Cyclones ◽

Numerical Weather Prediction ◽

Weather Prediction ◽

Wave Model ◽

Numerical Weather Prediction Model ◽

Model Surface ◽

Sea State ◽

Numerical Weather

The United Kingdom Meteorological Office (UKMO) routinely runs a global operational numerical weather prediction model. Surface winds from this model are used by a spectral wave model to forecast sea state. A brief description is given of the formulation of the wave model, and two cases of Tropical Cyclones in the Bay of Bengal are examined using the archived data generated in real time by the operational wave model. These are Tropical Cyclone 3B, 14-15 June 1996 and Tropical Cyclone 07B, 4-6 November 1996. At a resolution of 1.25° in longitude by 0.833° in latitude the numerical weather prediction model does not represent the dynamics of a tropical cyclone and the surface wind speeds are underestimated. Consequently, the extreme sea state generated by a Tropical Cyclone is not modelled. However, the wave model was able to generate a long period swell of over 3m height, which propagated away from the area of generation. Finally, work in progress to blend the operational numerical model surface winds with synthetically generated tropical cyclone surf winds, for use in the operational wave model, is outlined.

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An Example of the Value of Strong Climatological Signals in Tropical Cyclone Track Forecasting: Hurricane Ivan (2004)

Monthly Weather Review ◽

10.1175/mwr3135.1 ◽

2006 ◽

Vol 134 (5) ◽

pp. 1568-1577 ◽

Cited By ~ 3

Author(s):

Bradford S. Barrett ◽

Lance M. Leslie ◽

Brian H. Fiedler

Keyword(s):

Tropical Cyclone ◽

Weather Prediction ◽

Computational Cost ◽

Historical Record ◽

Track Forecast ◽

Hurricane Ivan ◽

Simple Strategy ◽

Motion Characteristics ◽

Actual Damage ◽

Nwp Model

Abstract Since 1970, tropical cyclone (TC) track forecasts have improved steadily in the Atlantic basin. This improvement has been linked primarily to advances in numerical weather prediction (NWP) models. Concurrently, with few exceptions, the development and operational use of statistical track prediction schemes have experienced a relative decline. Statistical schemes provided the most accurate TC track forecasts until approximately the late 1980s. In this note, it is shown that increased reliance on the global NWP models does not always guarantee the best forecast. Here, Hurricane Ivan is used from the 2004 Atlantic TC season as a classical example, and reminder, of how strong climatological signals still can add substantial value to TC track forecasts, in the form of improved accuracy and increased timeliness at minimal computational cost. In an 8-day period in early September 2004, Hurricane Ivan was repeatedly, and incorrectly, forecast by 12 operational NWP models to move with a significant northward (poleward) component. It was found that the mean 24-h trajectory forecasts of a consensus of five commonly used NWP track prediction aids had a statistically significant right-of-track bias. Furthermore, the official track forecasts, which relied heavily on erroneous numerical guidance over this period, were also found to have significant poleward trajectory errors. At the same time, a climatology-based prediction technique, drawn entirely from the historical record of motion characteristics of TCs in geographical locations similar to Ivan, correctly and consistently indicated a more westward motion component, had a small directional spread, and was supported by a large number of archived cases. This climatological signal was in conflict with the deterministic NWP model output, and it is suggested that the large errors in the official track forecast for TC Ivan could have been reduced considerably by taking into greater account such a strong climatological signal. The potential impact of such an error reduction is a saving of lives and billions of dollars in both actual damage and unnecessary evacuations costs, for just this one hurricane. We also suggest that this simple strategy of examining the strength of the climatological signal be considered for all TCs to identify cases where the NWP and official forecasts differ significantly from strong, persistent climatological signals.

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A Technique for Verification of Convection-Permitting NWP Model Deterministic Forecasts of Lightning Activity

Weather and Forecasting ◽

10.1175/waf-d-16-0106.1 ◽

2017 ◽

Vol 32 (1) ◽

pp. 97-115 ◽

Cited By ~ 12

Author(s):

Jonathan M. Wilkinson

Keyword(s):

Large Scale ◽

Prediction Models ◽

Single Case ◽

Weather Prediction ◽

Lightning Activity ◽

Forecast Performance ◽

Verification Methods ◽

Skill Scores ◽

Resolution Model ◽

Nwp Model

Abstract This manuscript introduces a new technique for evaluating lightning forecasts from convection-permitting models. In recent years, numerical weather prediction models at the convection-permitting scales (horizontal grid resolutions of 1–5 km) have been able to produce realistic-looking forecasts of lightning activity when compared with observations. However, it is challenging to assess what value these forecasts add above standard large-scale indices. Examining this problem, it is found that existing skill scores and neighborhood verification methods are unable to cope with both the double-penalty effect and the model’s variable frequency bias. A displacement distance and a quasi-symmetric distance score are introduced based on the distance between the model and the observations, the latter showing any improvement the forecast has over a completely “hedged” forecast. This can be combined with a domain-improved contingency table and comparisons between modeled and observed lightning flashes to evaluate the forecast performance in three important dimensions: coverage, distance, and intensity. The verification metric is illustrated with a single case, which shows that the convective-scale U.K. variable resolution model (UKV) delivers improved forecasts compared with the large-scale indices in both coverage and distance. Additionally, a month-long analysis is performed, which reveals that the coverage of lightning is in good agreement with the observations; lightning is displaced by the model by a distance on the order of 50–75 km, but the model overpredicts the lightning intensity by at least a factor of 6 after observational detection efficiencies have been considered.

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WIND SPECIFICATION FOR SPECTRAL OCEAN-WAVE MODELS

Coastal Engineering Proceedings ◽

10.9753/icce.v20.28 ◽

1986 ◽

Vol 1 (20) ◽

pp. 28

Author(s):

M.L. Khandekar ◽

B.M. Eid

Keyword(s):

Weather Prediction ◽

Wave Model ◽

Numerical Weather Prediction Model ◽

Ocean Wave ◽

Operational Mode ◽

Spectral Models ◽

Ocean Wave Model ◽

Wave Heights ◽

Significant Wave Heights ◽

Wave Models

This paper investigates the utility of winds obtainable from a numerical weather prediction model for driving a spectral ocean-wave model in an operational mode. Wind inputs for two operational spectral wave models were analyzed with respect to observed winds at three locations in the Canadian east coast offshore. Also, significant wave heights obtainable from the two spectral models were evaluated against measured wave data at these locations. Based on this analysis, the importance of appropriate wind specification for operational wave analysis and forecasting is demonstrated.

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Evaluation of Global Wave Probabilities Consistent with Official Forecasts

Weather and Forecasting ◽

10.1175/waf-d-21-0037.1 ◽

2021 ◽

Author(s):

Charles R. Sampson ◽

Efren A. Serra ◽

John A. Knaff ◽

Joshua H. Cossuth

Keyword(s):

Tropical Cyclone ◽

Weather Prediction ◽

Forecast Accuracy ◽

Numerical Weather Prediction Model ◽

Lead Times ◽

Significant Wave ◽

Wave Heights ◽

High Bias ◽

Significant Wave Heights ◽

The U.S

AbstractThe U.S. Navy is keenly interested in analyses and predictions of waves at sea due to their effects on important tasks such as shipping, base preparedness and disaster relief. U.S. Tropical Cyclone (TC) Forecast Centers routinely disseminate wind probabilities consistent with official TC forecasts worldwide, but do not do the same for wave forecasts. These probabilities are especially important at longer leads where TC forecast accuracy diminishes. This work describes global wave probabilities consistent with both the official TC forecasts and their wind probabilities. Real-time runs for 84 TCs between May 2018 and March 2019, with probabilities generated for 12-ft and 18-ft significant wave heights are used to calculate verification statistics. This results in 347, 319, 261, 214, 155, and 112 verification cases at lead times of 1, 2, 3, 4, and 5 days where each verification case consists of a 20x20 degree latitude longitude grid around the verifying TC position. When compared with wave probabilities generated solely by a global numerical weather prediction model, the wind probability-based algorithm demonstrates improved consistency with official forecasts and provides additional benefits. Those benefits include an improved capability to discriminate between 12-ft and 18-ft significant wave events and non-events. The verification statistics also shows that the wind probability-based algorithm has a consistent high bias. How these biases can be reduced in future efforts is also discussed.

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Experimental assimilation of synthetic bogus tropical cyclone pressure observations into a high-resolution rapid-update NWP model

Journal of Southern Hemisphere Earth System Science ◽

10.1071/es19036 ◽

2020 ◽

Author(s):

Susan Rennie ◽

Jim Fraser

Keyword(s):

High Resolution ◽

Tropical Cyclone ◽

Weather Prediction ◽

Central Pressure ◽

Convective Scale ◽

Potential Impact ◽

Cycling System ◽

Nwp Model ◽

Large Improvement

The effect of synthetic ‘bogus’ tropical cyclone (TC) central pressure observations on TC Owen was tested in a convective-scale numerical weather prediction (NWP) system with hourly 4D-Var assimilation. TC Owen traversed the Gulf of Carpentaria over 10–14 December 2018, entering from the east and briefly making landfall on the western edge before reversing course and retracing its path east to cross the northern tip of Queensland. The Australian Bureau of Meteorology runs a high-resolution NWP model centred over Darwin, which covers much of the Gulf of Carpentaria. The next-generation developmental version of this model includes data assimilation. Therefore, when TC Owen presented the opportunity to investigate the simulation of a TC within the domain, the developmental system was run as a case study. The modelled cyclone initially failed to intensify. The case study was then repeated including assimilation of bogus central pressure observations. This new run showed a large improvement in the intensity throughout the simulation; however, the TC track was not substantially improved. This demonstration of the potential impact of using synthetic observations may guide whether the development of a bogus observation source with sufficiently low latency for use in an hourly-cycling system should be prioritised.

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