Weather forecasts have improved significantly in recent years. In fact, the information provided for five days ahead is now considered as reliable as the two-day forecasts that were available twenty years ago.
However, looking out the window to see what the weather is like is still a common routine for many people. Because if there’s one thing we more or less assume, it’s that the weather is extremely capricious and displays local peculiarities that are often not captured by meteorological forecasts.
But something is changing in terms of the availability of local-scale information: immediate weather forecasting or nowcasting is beginning to show its true potential.
Weather forecasting: Science and technology to determine the weather
Weather forecasting can generally be defined as the application of technologies and scientific methods that allow the expected values for various meteorological variables to be determined for a given location in the future. Forecasts are based on objective methodologies (such as numerical prediction models), and an interpretation or prediction is subsequently made, which is what usually reaches the general public.
However, depending on the time period they focus on, different types of forecasts can be distinguished (1):
- Immediate (or nowcasting), 0 to 2 hours in advance (some sources extend it to 6 hours).
- Very short-term, up to 12 hours.
- Short-term, between 12 and 72 hours.
- Medium-term, 72 to 240 hours.
- Extended, 10 to 30 days.
- Long-term, between 30 days and 2 years.
- Climate, over 2 years.
Among these, immediate weather forecasting, or nowcasting, is one of the most interesting. One reason is the number of extreme weather and meteorological events, which have increased by 35% since 1990 (2).
What is immediate weather forecasting or nowcasting?
The term “nowcasting,” derived from combining “now” and “forecasting,” was originally defined in the late 1970s by Keith Browning (3). It involves a detailed description of the current weather along with a prediction of the expected changes in the next few hours. It is particularly useful for phenomena such as (4):
- Storms, including associated manifestations such as lightning, hail, damaging winds, heavy precipitation, tornadoes, etc.
- Heavy precipitation that can trigger flash floods or sudden floods.
- Gales.
- Episodes of fog that may affect visibility.
- Phenomena related to the winter period (snow, sleet, freezing rain, ice, etc.).
The obtained information is also characterized by a very high spatial resolution, ranging from 0.5 to 1 km2 (5, 6).
Applications of immediate weather forecasting
These immediate forecasts have multiple applications. Wapler, de Coning & Buzzi (4) highlight the following:
- Aviation, where nowcasting can be used for the detection of localized storms or downdrafts, also known as wind shear.
- Winter road maintenance, allowing preventive operations to increase driving safety.
- Maritime forecasts that can jeopardize navigation.
- Hydrological predictions, especially for basins with short response times, such as valleys in mountainous areas.
- Early warnings directed at the general public.
- Operations related to civil protection and events with significant climatic impact.
- Renewable energy production, with an increasing demand for forecasts related to wind and solar irradiation.
In addition to these applications, other sources mention additional ones. For example, wind farms. Lightning strikes can cause damage to wind turbines, and immediate prediction can help reduce the consequences (7). Furthermore, some of these utilities are part of “integrated urban services” (8), which aim to reduce disaster risks.
As you can see, issuing early warnings for extreme weather is one of the main benefits. Having detailed weather information can undoubtedly improve responsiveness and decision-making.
In fact, considering the evolution that this type of forecasting is experiencing, from simple weather communication to impact-based prediction, it is likely that the implementation of applications based on immediate forecasting will increase in the coming years.
The science and technology behind nowcasting
What is necessary to obtain immediate weather forecasting? Data, millions of data points obtained from resources such as (9):
- Surface observations, obtained through various meteorological instruments or reported by physical observers.
- Upper air observations, obtained through weather balloons or aircraft.
- Orbital satellites.
- Lightning detection systems.
- Weather radars.
The most sophisticated and accurate methods of immediate weather prediction rely on the combination of multiple observation systems. For example, a system to predict the occurrence of storms could rely on:
- Satellite information, monitoring cloud lines (convergence lines) and their growth.
- Radars, identifying thunderstorms (intensity, movement, and their relationship with convergence lines).
- Lightning detection systems.
- Systems for measuring upper air temperature, humidity, and winds (obtaining vertical profiles).
- Surface weather stations to monitor changes in atmospheric stability.
Once the data is obtained, the next step is to process it. Methodologies vary from simple extrapolation of radar information to much more complex systems that employ algorithms and visualizations to generate numerical prediction models.
This field of information processing is not immune to the technological revolution of recent years. The introduction of advances such as artificial intelligence is showing new possibilities for development. The project “Machine Learning for Precipitation Nowcasting from Radar Images,” driven by Google and materialized in the MetNet model, is a clear example. Thanks to neural networks and automatic processing of Doppler radar images, a future rainfall prediction can be obtained within a few seconds and with a resolution of 1 km, improving upon the models used by NOAA, for example.
What role do automatic weather stations play?
Automatic weather stations, one of the equipment we offer at Arantec, play a relevant role in obtaining immediate forecasts. It is true that they have certain limitations in alerting about very specific phenomena, as can be seen in the attached table (click on it to enlarge), compiled from data from the WMO (9). However, a dense network of surface stations, with an installation every 10 km, for example, can provide localized and valuable information about meteorological conditions.
However, automatic weather stations acquire special value during the verification process. This process is necessary to assess the quality of the predictions, determine their strengths and weaknesses, and implement appropriate improvements (4). In fact, it is considered impossible to carry out this analysis without relevant observations from different sources.
Conclusion
1.7 billion. That’s the number of people affected by extreme weather events over the past decade, phenomena that, as we mentioned at the beginning of the article, are on the rise.
Early preparedness and early warning systems are key to addressing these events, and weather forecasts are essential to activate response protocols. But what is the essential element to set these mechanisms in motion and contribute to saving lives? Data, such as those provided by Arantec’s weather stations.
Sources consulted:
- (1) Organización Meteorológica Mundial (2020). Manual del Sistema Mundial de Proceso de Datos y de Predicción: Anexo IV al Reglamento Técnico de la OMM. Ginebra: OMM (2019). ISBN 978-92-63-30485-8
- (2) International Federation of Red Cross and Red Crescent Societies (2020). World Disasters Report 2020. Geneva: IFRC (2020). ISBN 978-2-9701289-5-3. Disponible en https://media.ifrc.org/ifrc/world-disaster-report-2020
- (3) Browning, K. A. (1980). Review Lecture: Local Weather Forecasting. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 371(1745), 179–211. doi:10.1098/rspa.1980.0076
- (4) Wapler, K., de Coning, E., & Buzzi, M. (2019). Nowcasting. Reference Module In Earth Systems And Environmental Sciences. doi: 10.1016/b978-0-12-409548-9.11777-4
- (5) Heuvelink, D., Berenguer, M., Brauer, C., & Uijlenhoet, R. (2020). Hydrological application of radar rainfall nowcasting in the Netherlands. Environment International, 136, 105431. doi: 10.1016/j.envint.2019.105431
- (6) Franch, G., Maggio, V., Coviello, L., Pendesini, M., Jurman, G., & Furlanello, C. (2020). TAASRAD19, a high-resolution weather radar reflectivity dataset for precipitation nowcasting. Scientific Data, 7(1). doi: 10.1038/s41597-020-0574-8
- (7) Mostajabi, A., Finney, D., Rubinstein, M., & Rachidi, F. (2019). Nowcasting lightning occurrence from commonly available meteorological parameters using machine learning techniques. Npj Climate And Atmospheric Science, 2(1). doi: 10.1038/s41612-019-0098-0
- (8) Baklanov, A., Cárdenas, B., Lee, T., Leroyer, S., Masson, V., & Molina, L. et al. (2020). Integrated urban services: Experience from four cities on different continents. Urban Climate, 32, 100610. doi: 10.1016/j.uclim.2020.10061
