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DA-1

Mechanisms of Typhoon Intensity Error Associated with SST Bias

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1. Introduction

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Typhoons over the Western North Pacific exhibit strong ocean–atmosphere interactions.

Sea surface temperature (SST) plays a crucial role in tropical cyclone intensity change.

2. Key Takeaway

A. SST Sensitivity to Tropical Cyclone Intensity (Sun et al., 2022)
→ How small SST biases significantly modify intensity change

B. Physical Mechanisms Behind Right-Rear (RR) Error Amplification
→ Ocean cooling, vertical mixing, and boundary-layer processes

C. Impact of SST Representation Error on Intensity Forecast Bias
→ From localized warm bias to systematic overestimation

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2.1 SST–Intensity Sensitivity

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SST Sensitivity to Intensity Change

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Recent research by Sun et al. (2022) has transitioned our understanding from qualitative observation to rigorous quantification. By developing an "Empirical Parameterization Formula," researchers have successfully modeled the physical link between sea surface temperature and the change in a typhoon’s Maximum Wind Speed (ΔMWS) over six-hour intervals.

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  • Empirical relationship between SST and 6-hour maximum wind change

  •  +0.1°C SST bias → significant intensity amplification

  •  −0.2°C SST bias → systematic weakening

  • SST representation error directly translates into intensity forecast bias
     

The High Sensitivity of the "SST-Intensity" formula is expressed as:

With the optimized coefficients:

  • a=5.597

  • b=0.03469

  • c=−3.084

  • n=0.6706

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This model reveals an extraordinary sensitivity. For instance, a "Cold Bias" in satellite data of just -0.26°C results in a daily intensity drop of -2.16 m/s (calculated as -0.54 m/s every six hours). When we consider that these seemingly negligible temperature deviations can fundamentally alter coastal warning protocols, the necessity of precise SST data becomes clear.

This model reveals an extraordinary sensitivity. For instance, a "Cold Bias" in satellite data of just -0.26°C results in a daily intensity drop of -2.16 m/s (calculated as -0.54 m/s every six hours). When we consider that these seemingly negligible temperature deviations can fundamentally alter coastal warning protocols, the necessity of precise SST data becomes clear.

The "Warm Bias" in Our Digital Atmospheres

 

-  Major atmospheric reanalysis models (like NCEP FNL and ERA5) suffer from a systematic "Warm Bias."

  =>  The ocean surface being +0.11°C warmer than it actually is.

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- This bias creates a cascading error.

 => It overestimates the Latent Heat Flux (LHF)—the energy transfer that facilitates storm development.

 

This specific +0.11°C error leads to an intensity overestimation of approximately +0.88 m/s per 6 hours.

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 The simulated storm appears significantly stronger than what the underlying ocean conditions can realistically sustain.

2.2 Quadrant-Dependent Error Pattern
 

A breakthrough study by Hailun He (2024) discovered that forecast errors are not distributed evenly across a storm's structure.

  • Largest RMSE observed in the Right-Rear (RR) quadrant

  • Right-side asymmetry stronger than left-side

  • Error magnitude varies with storm-relative coordinates

 

To analyze this, researchers divide the storm into four sectors:

  1. Right-Front (RF)

  2. Right-Rear (RR)

  3. Left-Front (LF)

  4. Left-Rear (LR)
     

In the Northern Hemisphere, the RR quadrant is a zone of extreme dynamics
where the storm’s forward motion aligns with its rotational winds. This area experiences:

  • The most intense vertical mixing and upwelling.

  • The largest wind inflow angle 
      -  increases further as one moves away from the storm center.

  • Complex asymmetric wind structures 
      -  difficult to match with satellite observations

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2.3 Physical Mechanism of SST Change


Oceanic Response under Typhoon Forcing
=> COLD AWAKE & Ocean negative feedback
 

  1. Enhanced latent heat flux over warm SST

  2. Strong vertical mixing induced by intense winds

  3. Upwelling of cold subsurface water

  4. Formation of cold wake behind the storm

  5. Ocean-driven negative feedback on intensity

If the model underestimates ocean cooling, the storm is assumed to continue receiving excessive surface energy.
This results in systematic overestimation of tropical cyclone intensity.

2.4 Why Right-Rear Quadrant?
 

Why Does Error Concentrate in the Right-Rear Quadrant?

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  • Maximum wind speed due to storm-motion asymmetry

  • Strongest vertical mixing and cold wake formation

  • Stable boundary layer (SBL) formation over cooled SST

  • Large inflow angle variability

  • Sparse in-situ observations for data assimilation

This demonstrates that SST variability does more than simply supply surface energy

— it fundamentally governs the structural evolution and dynamic efficiency of the coupled ocean–atmosphere system.

→ A clear example of representation error under extreme ocean–atmosphere coupling conditions.

Conclusion


SST representation errors are structurally concentrated in the Right-Rear quadrant under typhoon conditions.
Underestimated ocean cooling generates systematic warm bias and excessive latent heat flux.
This leads to persistent overestimation of tropical cyclone intensity in forecast systems.

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