Invest 98L is a tropical disturbance that has been monitored by the National Hurricane Center (NHC) since September 20, 2022. The system is located in the eastern Caribbean Sea and is expected to move westward across the basin over the next several days. As of September 23, 2022, the NHC gives Invest 98L a 60% chance of developing into a tropical depression or storm within the next five days. If it does, it will be named Emily, the fifth named storm of the 2022 Atlantic hurricane season.
What are spaghetti models?
Spaghetti models are computer-generated simulations that show the possible trajectories of a tropical cyclone based on various input data and assumptions. The name comes from the fact that when multiple models are plotted on a map, they look like strands of spaghetti. Each strand represents a different model output, which can vary depending on the initial conditions, parameters, and equations used by the model.
Spaghetti models are useful for providing an early indication of where a tropical cyclone may go and how much uncertainty there is in the forecast. However, they are not definitive or accurate predictions, as they do not account for all the factors that can influence the movement and intensity of a tropical cyclone, such as wind shear, sea surface temperature, land interaction, and steering currents. Spaghetti models also do not show the size, shape, or strength of a tropical cyclone, which can affect its impact and damage potential.
How are spaghetti models created?
Spaghetti models are created by running various numerical weather prediction (NWP) models that use mathematical equations to simulate the behavior of the atmosphere and ocean. NWP models require input data from various sources, such as satellites, radars, buoys, aircrafts, and weather stations. The input data is then processed and analyzed by supercomputers to generate output data that shows how the weather variables, such as pressure, temperature, humidity, wind, and precipitation, will change over time and space.
There are many different NWP models that are used to create spaghetti models for tropical cyclones. Some of the most common ones are:
- Global Forecast System (GFS): A global model that runs four times a day and produces forecasts up to 16 days in advance.
- European Centre for Medium-Range Weather Forecasts (ECMWF): A global model that runs twice a day and produces forecasts up to 10 days in advance.
- Hurricane Weather Research and Forecasting (HWRF): A regional model that runs every six hours and produces forecasts up to five days in advance for individual tropical cyclones.
- Geophysical Fluid Dynamics Laboratory (GFDL): A regional model that runs every six hours and produces forecasts up to five days in advance for individual tropical cyclones.
Each NWP model has its own strengths and weaknesses, depending on its design, resolution, data assimilation, physics, and initialization. Therefore, it is important to compare and contrast multiple models to get a better sense of the range and consensus of possible outcomes.
How to interpret spaghetti models?
Spaghetti models can be accessed online from various websites, such as [Tropical Tidbits], [Weather Underground], or [Stormpulse]. These websites provide maps that show the spaghetti plots for different NWP models or ensembles (groups of models with slightly different initial conditions). The maps also show other information, such as the current location and intensity of the tropical cyclone, its official forecast track and cone of uncertainty from the NHC, and its historical track.
To interpret spaghetti models, here are some tips to keep in mind:
- Look for clusters or patterns: Spaghetti models that are close together or form a clear pattern indicate a higher confidence in the forecast track. Spaghetti models that are spread apart or diverge indicate a lower confidence in the forecast track.
- Look for outliers or extremes: Spaghetti models that are far away from the rest or show an unusual direction indicate a lower probability or reliability of the forecast track. These models may be influenced by errors or biases in their input data or assumptions.
- Look for trends or changes: Spaghetti models that show consistent or similar results over time indicate a more stable or robust forecast track. Spaghetti models that show different or varying results over time indicate a more uncertain or dynamic forecast track.
- Look for agreement or disagreement: Spaghetti models that agree with each other or with the official forecast from the NHC indicate a higher likelihood or accuracy of the forecast track. Spaghetti models that disagree with each other or with the official forecast from the NHC indicate a lower likelihood or accuracy of the forecast track.
Spaghetti models are computer-generated simulations that show the possible trajectories of a tropical cyclone based on various input data and assumptions. They are useful for providing an early indication of where a tropical cyclone may go and how much uncertainty there is in the forecast. However, they are not definitive or accurate predictions, as they do not account for all the factors that can influence the movement and intensity of a tropical cyclone.