Browsing by Author "Opio, Ronald"
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Item Evaluation of WRF‑chem simulations of NO2 and CO from biomass burning over East Africa and its surrounding regions(Atmospheric and Oceanic Sciences, 2022) Opio, Ronald; Mugume, Isaac; Nakatumba‑Nabende, Joyce; Nanteza, Jamiat; Nimusiima, Alex; Mbogga, Michael; Mugagga, FrankIn East Africa, biomass burning in the savanna region emits nitrogen dioxide ( NO2), carbon monoxide (CO), and aerosols among other species. These emissions are dangerous air pollutants which pose a health risk to the population. They also affect the radiation budget. Currently, limited academic research has been done to study their spatial and temporal distribution over this region by means of numerical modeling. This study therefore used the Weather Research and Forecasting model coupled with chemistry (WRF-chem) to simulate, for the first time, the distribution of NO2 during the year 2012 and CO during the period June 2015 to May 2016 over this region. These periods had the highest atmospheric abundances of these species. The model’s performance was evaluated against satellite observations from the Ozone Monitoring Instrument (OMI) and the Measurement of Pollution in the Troposphere (MOPITT). Three evaluation metrics were used, these were, the normalized mean bias (NMB), the root mean square error (RMSE) and Pearson’s correlation coefficient (R). Further, an attempt was made to reduce the bias shown by WRF-chem by applying a deep convolutional autoencoder (WRF-DCA) algorithm and linear scaling (WRF-LS). The results showed that WRF-chem simulated the seasonality of the gases but made below adequate estimates of the gas abundances. It overestimated NO2 and underestimated CO throughout all the seasons. Overall, for NO2, WRF-chem had an average NMB of 3.51, RMSE of 2 × 1015 molecules/cm2 and R of 0.44 while for CO, it had an average NMB of − 0.063, RMSE of 0.65 × 1018 molecules/cm2 and R of 0.13. Furthermore, even though both WRF-DCA and WRF-LS successfully reduced the bias in WRF-chem’s NO2 estimates, WRF-DCA had a superior performance compared to WRF-LS. It reduced the NMB by an average of 3.2 (90.2%). Finally, this study has shown that deep learning has a strong ability to improve the estimates of numerical models, and this can be a cue to incorporate this approach along other stages of the numerical modeling process.Item Modeling the atmospheric dispersion of SO2 from Mount Nyiragongo(Journal of African Earth Sciences, 2023) Opio, Ronald; Mugume, Isaac; Nakatumba-Nabende, Joyce; Mbogga, MichaelMount Nyiragongo, an active volcano, is the most dominant natural source of sulphur dioxide (SO2) in Africa. While a number of studies have employed atmospheric models to simulate the dispersion of SO2 from this mountain, prior to this study, no attempt has been made to use deep learning to bias correct the model’s estimates. Here, the Weather Research and Forecasting model coupled with chemistry (WRF-Chem) was used to simulate massive SO2 plumes degassed from this mountain between September 2014 and August 2015. Satellite observations by the Ozone Monitoring Instrument (OMI) showed that the SO2 spread to over 500 km from the volcano site. A deep convolutional autoencoder algorithm (WRF-DCA) was then applied to reduce the bias that WRF-Chem showed against the OMI observations. Finally, the correction performance of WRF-DCA was compared with a conventional bias correction method, linear scaling (WRF-LS). The performance of WRF-Chem, WRF-DCA, and WRF-LS was analyzed using three metrics, that is, the normalized mean bias (NMB), the root mean square error (RMSE), and Pearson’s correlation coefficient (R). The results showed that WRF-Chem overestimated SO2 at locations near the volcano site and underestimated SO2 at locations further away from the volcano site. It generated an overall average NMB of 0.61 against the OMI observations. Respectively, WRFDCA and WRF-LS reduced this bias by an average of 0.25 (40.9%) and 0.21 (34.4%). Furthermore, although both methods also reduced the RMSE and improved the correlation, WRF-DCA consistently performed better than WRF-LS. This study demonstrates the advantage that deep learning can provide in estimating volcanic SO2 emissions.Item Understanding the Trend of NO2, SO2 and CO over East Africa from 2005 to 2020(Atmosphere, 2021) Opio, Ronald; Mugume, Isaac; Nakatumba-Nabende, JoyceThe atmospheric chemistry constituents of nitrogen dioxide (NO2), sulphur dioxide (SO2) and carbon monoxide (CO) are associated with air pollution and climate change. In sub-Saharan Africa, a lack of sufficient ground-based and aircraft observations has, for a long time, limited the study of these species. This study thus utilized satellite observations as an alternative source of data to study the abundance of these species over the East African region. The instruments used included the Ozone Monitoring Instrument (OMI), the Atmospheric InfraRed Sounder (AIRS), and the TROPOspheric Monitoring Instrument (TROPOMI). An investigation of trends in the data series from 2005 to 2020 was carried out using the sequential Mann-Kendall test while the Pearson correlation coefficient was used to compare the data records of the instruments. The analysis revealed no trend in NO2 (p > 0.05), a decreasing trend in SO2 (p < 0.05), a decreasing trend (p < 0.05) in CO closer to the surface (850 hPa to 500 hPa) and an increasing trend (p < 0.05) in CO higher up in the atmosphere (400 hPa to 1 hPa). There is likely a vertical ascent of CO. The correlation between the instrument records was 0.54 and 0.77 for NO2 and CO, respectively. Furthermore, seasonal fires in the savanna woodlands were identified as the major source of NO2 and CO over the region, while cities such as Kampala, Nairobi, and Bujumbura and towns such as Dar es Salaam and Mombasa were identified as important NO2 hotspots. Similarly, the active volcano at Mt. Nyiragongo near Goma was identified as the most important SO2 hotspot.Item WRF Simulations of Extreme Rainfall over Uganda’s Lake Victoria Basin: Sensitivity to Parameterization, Model Resolution and Domain Size(Journal of Geoscience and Environment Protection, 2020) Opio, Ronald; Sabiiti, Geoffrey; Nimusiima, Alex; Mugume, Isaac; Sansa-Otim, JulianneRainfall extremes have strong connotations to socio-economic activities and human well-being in Uganda’s Lake Victoria Basin (LVB). Reliable prediction and dissemination of extreme rainfall events are therefore of paramount im-portance to the region’s development agenda. The main objective of this study was to contribute to the prediction of rainfall extremes over this region using a numerical modelling approach. The Weather Research and Forecasting (WRF) model was used to simulate a 20-day period of extremely heavy rainfall that was observed in the March to May season of 2008. The underlying interest was to investigate the performance of different combinations of cumulus and mi-crophysical parameterization along with the model grid resolution and do-main size. The model output was validated against rainfall observations from the Tropical Rainfall Measuring Mission (TRMM) using 5 metrics; the rain-fall distribution, root mean square error, mean error, probability of detection and false alarm ratio. The results showed that the model was able to simulate extreme rainfall and the most satisfactory skill was obtained with a model se-tup using the Grell 3D cumulus scheme combined with the SBU_YLin micro-physical scheme. This study concludes that the WRF model can be used for simulating extreme rainfall over western LVB. In the other 2 regions, central and eastern LVB, its performance is limited by failure to simulate nocturnal rainfall. Furthermore, increasing the model grid resolution showed good po-tential for improving the model simulation especially when a large domain is used.