Analysis of Meteorological Conditions on Riverbed Dust Aerosol in Taiwan

Extremely high concentrations of dust particles are occasionally generated from the riverbeds of Taiwan, affecting the visibility and traffic safety of the local and nearby areas. The condition is most severe during the winter monsoon when surface wind is strong. This study analyzes the concentration of particulate matter of 10 µm or less (PM10), wind direction, wind speed, temperature, and humidity of riverbed stations adjacent to the Daan, Dajia, Dadu, Zhuoshui, and Beinan Rivers in Taiwan for a period of two years. The weather conditions that cause the high concentration of PM10 are classified into typhoon and non-typhoon types, and the latter type is further classified into three stages: ahead of front, ahead of anticyclone, and behind anticyclone. The associated meteorological influences of these weather types on high-concentration events in the riverbed are explored. The monitoring data show that the hourly PM10 concentration of the four riverbed stations exceeded 125 µg m−3 for 35–465 h per year, and the maximum PM10 in the Daan (and Dajia), and Zhuoshui Rivers was more than 800 µg m−3. Weather analysis showed that the extreme PM10 concentration on the riverbed was caused by weather types: typhoon and ahead of anticyclone, in which the peak hourly concentration reached average values of more than 600 and 400 µg m−3, respectively. The high PM10 caused by the typhoon type mainly occurred in October, with an average wind speed of 6 m s−1, high temperature of 25 °C, and mostly northeasterly winds. The ahead of anticyclone type mainly occurred in December, with an average wind speed of 5 m s−1, and northeasterly and northwesterly winds. Both weather types of riverbed events were observed during the daytime, especially at noon time, when strong wind speed, high temperature, and low relative humidity is favorable for riverbed dust generation. On the other hand, the main months of the high PM10 concentrations of the ahead of front and behind anticyclone stages are February and April. The peak PM10 concentrations of these two types of riverbed events are both about 300 µg m−3, but sporadic riverbed dust in these weather stages is mixed with Asian dust or pollution transported to the rivers through weak northwesterly and northeasterly winds. The high concentrations of these two types of riverbed events can occur at any time; but for the Dadu River, the high concentrations are often observed in the morning, when land breezes from the southeast bring local pollutants to the river.

Synergistic Modulations of Large-Scale Synoptic Patterns and Local-Scale Urbanization Effects on Summer Rainfall in South China

Using the ERA5 (the fifth major global reanalysis produced by the European Centre for Medium-Range Weather Forecasts) data and the T-PCA (Principal Component Analysis in T-mode) objective classification method to classify the 850-hPa geopotential height, we summarize four conceptual models of large-scale synoptic weather types over East Asia. By combining this with the daily precipitation observation data of 36 meteorological stations in Guangdong, South China, during summer (June to August) of 2014–2018, we found that summer precipitation in Guangdong Province is closely related to the position of the northwestern Pacific subtropical high and the strong upward motion of the warm airflow over the Pearl River Delta. It is further revealed the regulation effect of different weather patterns on summer precipitation in Guangdong Province and their urban–rural differences. More specifically, both urban and rural areas have a decreasing proportion of light rainfall and an increasing proportion of heavy and torrential rainfall, which are mainly regulated by the trend of frequency changes of four different weather types: Type 1 (47.39%) and Type 2 (32.39%) days are decreasing year by year, modulating the trend of light rainfall, while Type 3 (13.26%) and Type 4 (6.96%) days are steadily increasing, dominating the trend of heavy rainfall. In addition, it was further found that the frequency of light rainfall is decreasing more significantly in cities compared to that in rural areas, while the proportion of heavy and stormy rainfall is increasing more significantly, which is closely related to the effects of rapid urbanization.

Using a Self-Organizing Map to Explore Local Weather Features for Smart Urban Agriculture in Northern Taiwan

Weather plays a critical role in outdoor agricultural production; therefore, climate information can help farmers to arrange planting and production schedules, especially for urban agriculture (UA), providing fresh vegetables to partially fulfill city residents’ dietary needs. General weather information in the form of timely forecasts is insufficient to anticipate potential occurrences of weather types and features during the designated time windows for precise cultivation planning. In this research, we intended to use a self-organizing map (SOM), which is a clustering technique with powerful feature extraction ability to reveal hidden patterns of datasets, to explore the represented spatiotemporal weather features of Taipei city based on the observed data of six key weather factors that were collected at five weather stations in northern Taiwan during 2014 and 2018. The weather types and features of duration and distribution for Taipei on a 10-day basis were specifically examined, indicating that weather types #2, #4, and #7 featured to manifest the dominant seasonal patterns in a year. The results can serve as practical references to anticipate upcoming weather types/features within designated time frames, arrange potential/further measures of cultivation tasks and/or adjustments in response, and use water/energy resources efficiently for the sustainable production of smart urban agriculture.

Investigation of future climate change over the British Isles using weather patterns

For those involved in planning for regional and local scale changes in future climate, there is a requirement for climate information to be available in a context more usually associated with meteorological timescales. Here we combine a tool used in numerical weather prediction, the 30 weather patterns produced by the Met Office, which are already applied operationally to numerical weather prediction models, to assess changes in the UK Climate Projections (UKCP) Global ensemble. Through assessing projected changes in the frequency of the weather patterns at the end of the 21st Century, we determine that future changes in large-scale circulation tend towards an increase in winter of weather patterns associated with cyclonic and westerly wind conditions at the expense of more anticyclonic, settled/blocked weather patterns. In summer, the results indicate a shift towards an increase in dry settled weather types with a corresponding reduction in the wet and windy weather types. Climatologically this suggests a shift towards warmer, wetter winters and warmer, drier summers; which is consistent with the headline findings from the UK Climate Projections 2018. This paper represents the first evaluation of weather patterns analysis within UKCP Global. It provides a detailed assessment of the changes in these weather patterns through the 21st Century and how uncertainty in emissions, structural and perturbed parameters affects these results. We show that the use of these weather patterns in tandem with the UKCP projections is useful for future work investigating changes in a range of weather-related climate features such as extreme precipitation.