Observations and Forecasts of Urban Transportation Meteorology in China: A Review
Abstract
:1. Introduction
2. Urban Transportation Meteorological Observation
2.1. Urban Meteorological Observation Network
2.2. Urban Meteorological Outfield Observation Experiments
2.3. Urban Transportation Meteorological Monitoring System
3. Urban Transportation Meteorological Early Warning and Forecast
4. Commercial Services of Urban Transportation Meteorology
5. Discussion
- (1)
- With the rapid development of observation facilities and methodologies, equipment such as radar, satellite, microwave radiometers, unmanned aerial vehicles, and mobile observations would further enrich the existing urban transportation meteorological observation system. The in-depth and effective integration of multi-source observations is favorable to establish a more comprehensive and more reliable urban transportation meteorological observation big data system with higher spatial and temporal resolutions. This would help to further reveal the spatiotemporal distribution and variation characteristics of urban transportation meteorology-associated factors and to provide solid support with a database for more accurate and effective forecasts and early warnings.
- (2)
- Thus far, numerical weather prediction models have become the most important tool for meteorological forecasts around the world, which discretize the dynamical and physical equations of the atmosphere. Increasingly, operational business agencies have begun to develop a series of global numerical models with high spatial and temporal resolutions, generating more complete forecast systems. In this context, the quality of numerical forecast products has been continuously improved, with the product sources also being continuously expanded. However, their applications in the field of transportation meteorology, especially urban transportation meteorology, are still relatively lacking. The corresponding refinement and postprocessing of the model outputs are important scientific and technical issues that need to be investigated.
- (3)
- Along with the recent advancement of machine learning, plenty of complex but efficient deep learning models (a branch of machine learning and artificial intelligence) are nowadays emerging in an endless stream and they have been considered as core technologies in many fields. However, many of them have not yet been timely and effectively applied in the field of meteorology, especially urban transportation meteorology, which needs extensive and in-depth experiments and analyses. At the same time, facing specific application scenarios such as urban transportation meteorology, it is always necessary to construct targeted, high-resolution meteorological observation datasets based on multi-source observation systems and collaborative observation experiments, which could give full play to the advantages of artificial intelligence’s nonlinearity in data modeling and generate more reasonable and more accurate urban transportation meteorological forecast and early warning products.
- (4)
- With regard to the different meteorological conditions and elements, they certainly tend to result in different impacts on urban transportation due to their different mechanisms of onset, development, and retreat. Respective research and development towards their optimal observation schemes, forecasts, and early warning technologies are necessary to predict the impacts of various meteorological conditions and complex weather events on all aspects of urban transportation in advance and to ultimately provide stable and reliable safeguarding services.
Author Contributions
Funding
Conflicts of Interest
References
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Name | Country | Time | Content |
---|---|---|---|
URBAN 2000 [15] | USA | October 2000 | Tracer and meteorological field. |
Joint Urban [16] | USA | July 2003 | Tracer, dispersion, meteorological field, boundary layer structure, and urban energy balance. |
Pentagon Shield [17] | USA | 2004 | Boundary layer thermal structure, wind field, tracer, and dispersion. |
Madison Square Garden [18] | USA | 2004 | Wind field, tracer, and dispersion. |
HEAT [19] | USA | 2005 | Air pollution, meteorological field, convective and mesoscale process. |
ESCOMPTE [20] | France | July–September 2005 | Dispersion, air pollution, meteorological field, boundary layer structure, and urban energy balance. |
CAPITOUL [21] | France | June–July 2001 | Tracer, dispersion, air pollution, meteorological field, boundary layer structure, and urban energy balance. |
DAPPLE [22] | UK | February 2004 | Tracer, dispersion, air pollution, and meteorological field. |
REPARTEE [23] | UK | February 2005 | Tracer, dispersion, air pollution, meteorological field, and boundary layer structure. |
ClearfLo [24] | UK | May 2002 | Air pollution, meteorology field, boundary layer structure, urban energy balance, and mesoscale process. |
BUBBLE [25] | Switzerland | July 2006 | Tracer, dispersion, meteorological field, boundary layer structure, and urban energy balance. |
TOMACS [26] | Japan | October 2006 | Meteorological field, convective and mesoscale process. |
Name | Country | Time | Content |
---|---|---|---|
NYC mesonet [27] | USA | 2003–present | Meteorological field, convective and mesoscale process. |
DCNet [27] | USA | 2003–present | Tracer and dispersion. |
Helsinki Testbed [28] | Finland | 2005–present | Dispersion, meteorological field, boundary layer structure, convective and mesoscale process. |
METROS [29] | Japan | 2002–2005 | Boundary layer structure, convective and mesoscale process. |
SUIMON [30] | China | 2000–present | Air pollution, meteorological field, BL structure, urban energy balance, convective and mesoscale process. |
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Zhu, S.; Yang, H.; Liu, D.; Wang, H.; Zhou, L.; Zhu, C.; Zu, F.; Wu, H.; Lyu, Y.; Xia, Y.; et al. Observations and Forecasts of Urban Transportation Meteorology in China: A Review. Atmosphere 2022, 13, 1823. https://doi.org/10.3390/atmos13111823
Zhu S, Yang H, Liu D, Wang H, Zhou L, Zhu C, Zu F, Wu H, Lyu Y, Xia Y, et al. Observations and Forecasts of Urban Transportation Meteorology in China: A Review. Atmosphere. 2022; 13(11):1823. https://doi.org/10.3390/atmos13111823
Chicago/Turabian StyleZhu, Shoupeng, Huadong Yang, Duanyang Liu, Hongbin Wang, Linyi Zhou, Chengying Zhu, Fan Zu, Hong Wu, Yang Lyu, Yu Xia, and et al. 2022. "Observations and Forecasts of Urban Transportation Meteorology in China: A Review" Atmosphere 13, no. 11: 1823. https://doi.org/10.3390/atmos13111823
APA StyleZhu, S., Yang, H., Liu, D., Wang, H., Zhou, L., Zhu, C., Zu, F., Wu, H., Lyu, Y., Xia, Y., Zhu, Y., Fan, Y., Zhang, L., & Zhi, X. (2022). Observations and Forecasts of Urban Transportation Meteorology in China: A Review. Atmosphere, 13(11), 1823. https://doi.org/10.3390/atmos13111823