Over the last few decades, the frequency and intensity of extreme climate events have been increasing, especially due to global warming caused by human activities. According to a special report by the Intergovernmental Panel on Climate Change 2012, changes in the frequency, intensity, spatial extent, duration, and timing of weather and climate extremes can increase people’s vulnerability to natural disasters and become a major threat to global economies.
Weather forecasts, ranging from timescales of few hours to entire seasons, can reduce susceptibility to weather variations and climate-related disasters, improve food security and health outcomes, and enhance water resource management. However, a major challenge is predicting future weather patterns in a rapidly changing climate. The future is intrinsically uncertain. Further, dynamic physical processes of atmosphere and its interactions with surrounding systems (e.g. land, ocean, and ice surfaces) make forecasting even more difficult.
Forecasting future weather patterns
Weather forecasts are prepared in a systematic way, which involves observation, process understanding, prediction, and dissemination. Each of these components has benefitted, and will continue to benefit from advances in science and technology. Over the past few decades, substantial leaps in science have resulted in improved and more efficient methods of making timely observations, using a wide variety of sources including radars and satellites.
The use of such observation tools has dramatically increased the quality, value, and reliability of weather forecasts, assisting decision-making processes around the world. The impact of improved observational capability, increased scientific understanding of atmospheric processes, more sophisticated computer resources, skillful numerical models, and other forecasting tools have gradually changed the public’s perception of the accuracy of weather forecasts. Two areas that achieved significant improvements in this regard are numerical weather forecasting and seasonal predictions.
Numerical Weather Prediction (NWP)
Numerical Weather Prediction (NWP) is a forecasting method that uses complex mathematical equations, based on well-established physical laws, to predict atmospheric behaviour. This is done by undertaking complex mathematical operations that model the current state of atmosphere, obtained through observations. High speed, high-performing computers are required to carry out the enormous number of calculations involved in this process. To get the current status of the atmosphere, routine and accurate measurements at ground and upper air stations, as well as remote sensing systems, are required.
Seasonal predictions
Seasonal predictions are made in probabilistic terms, such as probability of receiving average, above average, or below average level of rainfall or temperature over a season. Currently, seasonal predictions are made using both statistical schemes and dynamical models. The statistical approach seeks to find recurring patterns in climate, associated with a ‘predictor field’ such as sea surface temperature. Such models have demonstrated the ability to forecast El Niño and some of its global climate impacts. The basic tools for dynamic prediction include both atmospheric and oceanic models.
Challenges in weather forecasting
Whatever the successes, some level of uncertainty remains and there are challenges that act as barriers against further progress in weather forecasting.
Nonlinearity of atmospheric systems: Successful weather forecasts are possible if the processes are understood properly, and if the current state of the atmosphere is well known. Even though the scientific understanding of atmospheric and oceanic systems has made considerable progress through a variety of research activities, including field experiments, theoretical work, and numerical simulations, atmospheric processes are inherently non-linear. Furthermore, all physical processes cannot be understood or represented in NWP models. Continued research efforts, using computer technology and physical measurements, will improve these approximations. Even then, it will not be possible to represent all atmospheric motions and processes in NWPs, especially over tropical regions.
Limitations in observations: Despite recent advances, there are limitations in making observations, especially in desert areas and oceans. As a result, there is a need for improved observation systems and methods to assimilate these data into NWP models.
Sub-seasonal prediction: Substantial progress has been made in recent years in the development and application of short-range to medium-range weather forecasts and seasonal climate predictions. However, forecasting on the time scale beyond 14 days to season (sub-seasonal time scale) that lies between daily weather and seasonal climate has received much less attention because it is considered a ‘predictability desert’. Sub-seasonal to seasonal prediction is a crucial planning window for the agricultural sector, water resource management, and other stakeholders, such as transport planners. There is a growing interest in the scientific community to develop forecasts that would fill the gap between medium-range weather forecasts and long-range or seasonal ones. This interest in sub-seasonal prediction was triggered not only by a growing demand from potential users, but also from the progress in areas such as medium-range forecasting, as well as the predictive sources like Madden Julian Oscillation (MJO), over the past decades
Weather forecasting in Sri Lanka
Sri Lanka has major challenges in forecasting weather due to its geographical location. Sri Lanka is close to the equator and is surrounded by the Indian Ocean, where observation density is sparse. Some atmospheric processes in the tropical region are not fully understood and impossible to resolve due to technology constraints. Data sparseness in the surrounding ocean, lack of scientific understanding of some weather systems inherent to the tropical region, and frequent, vigorous changes in the equatorial tropical atmosphere hinder the accuracy of weather forecasting in Sri Lanka.
It is important to understand that, even with advancements in science, some meteorological phenomena associated with extreme weather events will remain inherently unpredictable.
However, the advances in computational technology, together with improvements in physical representation of the main atmospheric processes, have increased the accuracy of forecasts. It has prompted the use of short-range NWP forecasts and seasonal forecasts in many activities dependent on weather and climate.
The Department of Meteorology (DoM) runs the Weather Research and Forecasting (WRF) model for operational purposes, as a tool in short range and medium-range forecasting. The present system has 15x15 km2 horizontal resolutions for the outer domain, and 5x5 km2 for the inner domain covering Sri Lanka. The DoM’s goal is to provide detailed, accurate, and reliable information for weather-dependent customers and stakeholders.
Therefore, the use of a very high-resolution limited area NWP model with horizontal grid size of nearly 1 km, is well justified for countries like Sri Lanka which have highly varying terrain from the central highlands (containing many complex topographical features such as ridges, peaks, plateaus, basins, and valleys) to low-lying plains. To achieve this goal, institutional strengthening, capacity building, strengthening and enhancement of the observation network and enhancement of research activities is important. In addition, improving computer resources, such as high-end computer servers, is needed.
The DoM currently provides seasonal as well as monthly probabilistic rainfall and temperature forecasts, at district levels, using statistical downscaling of global models. Monthly and seasonal climate predictions are valuable in agricultural decision-making, water resource management, and other climate sensitive activities. It is important to provide guidance on how to use probabilistic predictions to support these decisions. Some scientific research has identified that, provided with sufficient information about level of uncertainty, people would take better risk-based decisions. Understanding the limitations of weather and climate forecasts will result in the improved, rational use of forecasts and other weather information by decision-makers.
(This article was published recently in the Institute of Policy Studies’ CLIMATEnet Blog)
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