When it comes to weather, we often hear the term forecast, but sometimes we also come across with the word hindcast. They terms sound similar, however they have some differences. A forecast is a prediction of future conditions based on current data and simulations make with models, whereas a hindcast is a retrospective analysis that uses models and historical data to simulate past events. In this article, we will examine hindcasting in more detail and explore its significance in various fields.
What is hindcasting?
Hindcasting (also called backcasting or retrospective analysis) is the process of running atmospheric, marine, or other environmental models over a historical period, usually spanning decades (typically 20–30 years), to simulate events that have already occurred. Once the simulation is complete, the model’s output can be compared to actual recorded events to assess its accuracy.
Hindcasting allows scientists to determine how well a model performs under known conditions, serving as a powerful tool for testing, improving, and calibrating forecasting models. If a model can accurately replicate the past, it can likely provide better predictions for the future.
Applications of hindcasting
In which fields of industry are hindcasts considered useful?
1. Meteorology
In meteorology, hindcasting is used to simulate past events such as hurricanes and heat waves making it possible to research their development and behavior. It also allows us to simulate past climate trends or events such as El Niño and La Niña by simulating past ocean-atmosphere interactions. This leads to better seasonal weather predictions and long-term climate projections.
2. Oceanography
In oceanography, hindcasting is used to simulate the state of the ocean. For example, wave height, currents, and sea level. This helps us understand the behavior of the ocean over time and is a good tool in coastline planning and designing marine structures.
3. Hydrology
In hydrology, hindcasting allows us to simulate the behavior of rivers in the past to more effectively manage water resources that are associated with extreme river events.
4. Climate science
Speaking of climate change, the retrospective analysis helps in forecasting potential future climate scenarios and understanding the impact of human activities on the environment.
5. Statistics
Past simulations can be used to compute climate statistics for a specific region or location. This helps us not only to better understand the climate in the area of interest but also to conduct feasibility studies for future engineering projects.
6. Model evaluation and improvement
A hindcast allows us to evaluate the performance of a model by comparing it to past events. This helps us identify the model's strengths and weaknesses, enabling us to work on improving it. The data obtained can also be used to identify persistent model deviations, which can then be calibrated for greater accuracy.
A hindcast can be applied across a wide range of fields, making it a powerful tool in various disciplines. Weather patters, extreme events, sea level changes or flood events are often part of these studies. In summary, hindcasting is a versatile tool that allows experts across various fields to gain a deeper understanding of past events, refine predictive models, and ultimately make better-informed decisions for the future.
Why is hindcasting important?
First of all, retrospective analysis is an important tool in improving forecasting models. It provides a way to study past events and apply these lessons to forecasting future events. Also, if models are able to simulate past events with high accuracy, this allows for less uncertainty in future forecasting. This type of analysis also leads us to a more accurate understanding of certain natural processes, which in turn is an important tool in more accurate forecasting.
Thus, a hindcast is a reverse forecast that uses past data to test, compare and improve existing models and simulate past events, leading to better forecasting and decision making in the future.
How we use hindcasts at Infoplaza
Hindcasting plays a big role in our daily work. Thanks to hindcasting we can:
• Provide our clients with historical data all over the world and statistics that can help them plan their operations.
• Calibrate the grid's output against satellite data helping us to improve not only the hindcast but also the forecast conditions.
• Test and tweak the grids configurations that will be used for both forecast and hindcast.