Angel Vara Vela

and 5 more

The Weather Research and Forecasting with Chemistry (WRF-Chem) community model have been widely used for the study of pollutants transport, formation of secondary pollutants, as well as for the assessment of air quality policies implementation. A key factor to improve the WRF-Chem air quality simulations over urban areas is the representation of anthropogenic emission sources. There are several tools that are available to assist users in creating their own emissions based on global emissions information (e.g. anthro_emiss, prep_chem_src); however, there is no single tool that will construct local emissions input datasets for any particular domain at this time. Because the official emissions pre-processor (emiss_v03) is designed to work with domains located over North America, this work presents the Another Assimilation System for WRF-Chem (AAS4WRF), a ncl based mass-conserving emissions pre-processor designed to create WRF-Chem ready emissions files from local inventories on a lat/lon projection. AAS4WRF is appropriate to scale emission rates from both surface and elevated sources, providing the users an alternative way to assimilate their emissions to WRF-Chem. Since it was successfully tested for the first time for the city of Lima, Peru in 2014 (managed by SENAMHI, the National Weather Service of the country), several studies on air quality modelling have applied this utility to convert their emissions to those required for WRF-Chem. Two case studies performed in the metropolitan areas of Sao Paulo and Manizales in Brazil and Colombia, respectively, are here presented in order to analyse the influence of using local or global emission inventories in the representation of regulated air pollutants such as O3 and PM2.5. Although AAS4WRF works with local emissions information at the moment, further work is being conducted to make it compatible with global/regional emissions data file format. The tool is freely available upon request to the corresponding author.

Laurel Anne DiSera

and 7 more

The 2018 outbreak of dengue in the French overseas department of Réunion was unprecedented in size and mobility across the island. This research focuses on the cause of the outbreak, asserting that climate played a large role in both the proliferation of the mosquitoes, which transmitted the disease, and the vulnerability of the island’s occupants. Additionally, this study analyses if this outbreak could have been forecast in the sub-seasonal time scale. A stage-structured model was run using observed temperature and rainfall data to simulate the lifecycle and abundance of the mosquito. Further, the model was forced with uncalibrated sub-seasonal forecasts to determine if the event could have been forecast up to four weeks in advance. With unseasonably warm temperatures remaining above 25 degrees C, along with large tropical-cyclone-related rainfall events accumulating 10-15 mm per event, the modeled mosquito abundance did not decrease during the second half of 2017, contrary to the normal behavior, likely contributing to the large dengue outbreak in early 2018. Although sub-seasonal forecasts of rainfall for the Dec-Jan period in Réunion are skillful up to four weeks in advance, the outbreak could only have been forecast two weeks in advance, which along with seasonal forecast information could have provided enough time to enhance preparedness measures. Our research demonstrates the potential of using state-of-the-art sub-seasonal climate forecasts to produce actionable sub-seasonal dengue predictions. To the best of the authors’ knowledge, this is the first time sub-seasonal forecasts have been used this way.