Weather Forecasting Methods

Article by Sankha Wishwanath, UCSC 2nd Year

Curiosity was the key to human success. Every invention throughout human history was driven by curiosity. Within this journey, we used uncountable methods to fill gaps in our minds. Throughout history, humans were curious about something special. Something special is called the future. They wanted to know what happens in the next moment. They wanted to know will it be raining or a sunny day. So, to achieve that they invented something. One of the fine craftsmanship of curiosity. Meteorology. Meteorology is the science of dealing with the atmosphere and its phenomena, including both weather and climate. When we are discussing meteorology, we cannot forget the father of meteorology Luke Howard.

Figure 1. Luke Howard

History

Meteorology takes place in human history for millenniums.  In ancient times meteorology was considered something divine. They used prophecies and so many other religious habits to forecast the weather. Egyptians had rain-making rituals and that was nearly five millennials before. Ancient Indian Upanishads contain mention clouds and seasons. The Samaveda mentions sacrifices to be performed when certain phenomena were noticed. Varahamihira’s Brihatsamhita provides evidence of weather observation. In ancient Babylon, tablets included the association between thunder and rain.

Ancient Greeks were the first to build theories on meteorology.  Well, known philosopher in history Aristotle himself wrote a treatise on meteorology called Meteorologica around 340 B.C.  So, the theories, and methods were developed until it comes to the modern era.

Figure 2. Meteorologica

Previously Used Forecasting Strategies

Old weather forecasting methods were based primarily on observing and tracking patterns in the natural environment, such as cloud formations, wind directions, and barometric pressure. Meteorologists would use this information, along with knowledge of seasonal patterns, to make predictions about future weather conditions.

One of the oldest methods of weather forecasting was the use of weather proverbs, which are based on folk wisdom and the observation of local weather patterns. For example, “red sky at night, sailor’s delight” and “red sky in morning, sailor’s warning” is based on the idea that a red sky in the morning or evening indicates a change in weather conditions.

Another traditional method of weather forecasting is the use of barometers, which measure changes in air pressure to predict changes in weather. Meteorologists would also observe cloud formations and wind patterns, as these can indicate changes in the weather. For example, a strong wind from the east typically indicates that a warm and dry spell is on the way, while a wind from the west is usually a sign of rainy weather.

Ground-based weather stations were also an important component of old weather forecasting methods. These stations would measure various atmospheric conditions, such as temperature, pressure, wind speed and direction, and precipitation, and the data would be used to produce local weather forecasts.

In addition, ships and sailors have been key to weather forecasting for centuries, as they were able to observe and track weather patterns over long distances, especially across the open ocean. This information would then be passed on to shore-based meteorologists who could use it to make predictions about weather conditions in other parts of the world.

So, until the dawn of the modern digital automated era, it was like that. But the new age changed it in every aspect.

Figure 3. Ground based weather station

Modern-Day Forecasting Methods

As we previously discussed in old days meteorologists used so many methods to predict nature. But with the dawn of the digital era now things have changed. With the advancement of computation and machine learning, things have been handed over to the hands of more advanced technology like numerical weather prediction models.

Numerical weather prediction models are one of the most used methods for modern weather forecasting. These models use complex algorithms and mathematical equations to simulate the physical processes that occur in the atmosphere, including the transfer of heat, moisture, and air. The models are based on observations from weather satellites, ground-based weather stations, and weather balloons, which provide the data necessary to initialize the model.

One of the most widely used numerical weather prediction models is the Global Forecast System (GFS), which is maintained by the National Oceanic and Atmospheric Administration (NOAA) in the United States. The GFS model is a global model that predicts weather conditions up to 16 days in advance and is updated every six hours. It uses data from more than 20,000 weather stations and multiple satellite systems to produce high-resolution forecasts that cover the entire globe.

Another popular numerical weather prediction model is the European Centre for Medium-Range Weather Forecasts (ECMWF) model, which is maintained by the European Union. The ECMWF model is considered one of the most accurate weather models in the world, and it provides forecasts up to 10 days in advance. Unlike the GFS model, the ECMWF model uses a different mathematical algorithm, which allows it to produce more accurate forecasts.

In addition to these global models, some regional models are used for more specific weather forecasting purposes. For example, the High-Resolution Rapid Refresh (HRRR) model is a regional model that focuses on the United States and provides forecasts up to 18 hours in advance. The HRRR model is particularly useful for weather forecasting in complex terrains, such as mountainous regions, where the local weather patterns can be influenced by the topography.

Another modern method of weather forecasting is the use of machine learning and artificial intelligence techniques. These methods use computer algorithms to analyze vast amounts of data from weather satellites, ground-based weather stations, and other sources to produce highly accurate forecasts. For example, Google has developed a machine learning model called DeepMind, which is capable of predicting the likelihood of rain up to six hours in advance with an accuracy of around 85%.

Satellites also play a crucial role in modern weather forecasting. Meteorological satellites, such as the Geostationary Operational Environmental Satellite (GOES) series, are equipped with instruments that measure various atmospheric conditions, including temperature, pressure, wind speed and direction, and precipitation. The data from these satellites is used to create high-resolution images of the atmosphere, which are then used to produce weather forecasts.

Another important component of modern weather forecasting is the use of ground-based observations, such as weather balloons and surface weather stations. Weather balloons are released into the atmosphere several times a day to measure temperature, pressure, wind speed, and direction at various levels of the atmosphere. This data is then used to initialize the numerical weather prediction models and validate their forecasts.

Surface weather stations also play a crucial role in weather forecasting by providing data on the local weather conditions. These stations measure temperature, pressure, wind speed, direction, and precipitation, and the data is then used to produce local weather forecasts.

Figure 4. GOES Satellite Series

Weather Forecasting in Sri Lanka

When talking about weather forecasting in Sri Lanka meteorology department is playing a crucial role. They were established in 1875 and have since become evolved to become one of the leading meteorological services in south Asia.

The department is responsible for collecting and analyzing data from various sources, including weather balloons, surface weather stations, radar systems, and satellites, to produce accurate and reliable weather forecasts. In addition to providing forecasts, the department also provides warnings and alerts to the public and relevant authorities in the event of severe weather conditions, such as hurricanes, cyclones, and heavy rainfall.

The Meteorology Department of Sri Lanka uses a combination of traditional and modern methods to forecast weather. One of the traditional methods used by the department is the use of barometers, which measure changes in air pressure to predict changes in weather conditions. Meteorologists also observe cloud formations, wind patterns, and other meteorological indicators to make weather predictions.

In recent years, the department has embraced modern technology to enhance its weather forecasting capabilities. The department now uses computer-based numerical weather prediction models, such as the Global Forecast System (GFS), to produce high-resolution weather forecasts up to 16 days in advance. The department also uses satellite imagery and radar systems to track the movement of storms and other weather systems and to provide early warnings of severe weather conditions.

In addition to providing weather forecasts and warnings, the Meteorology Department of Sri Lanka also provides various other services to the public and other stakeholders, including climate monitoring and analysis, agricultural weather services, and hydrological forecasts. The department also works closely with other government agencies, such as the Department of Disaster Management, to provide emergency response services in the event of severe weather conditions.

One of the major challenges facing the Meteorology Department of Sri Lanka is the lack of modern infrastructure and equipment. Despite this, the department has been able to maintain its reputation as one of the leading meteorological services in South Asia through its commitment to innovation and the use of modern technology. The department is also continuously working to enhance its services by improving the accuracy of its forecasts and expanding its outreach to the public and other stakeholders.

References & Image Courtesy

  1. Featured Image: https://bit.ly/40OJfvO
  2. Figure 1: https://bit.ly/3Rd5OHt
  3. Figure 2: https://bit.ly/3PcBmLl
  4. Figure 3: https://bit.ly/3PaLp3m
  5. Figure 4: https://bit.ly/44Lf0WQ

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