Using Generative Adversarial Networks (GANs) to downscale tropical cyclone precipitation.&#160;
Authors: Vosper Emily , Mitchell Dann , Watson Peter , Aitchison Laurence , Santos-Rodriguez Raul 
Published: March 2022

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&lt;p&gt;&lt;span&gt;Fluvial flood hazards from tropical cyclones (TCs) are frequently the leading cause of mortality and damages (Rezapour and Baldock, 2014).&amp;#160;Accurately modeling TC precipitation is vital for studying the current and future impacts of TCs.&amp;#160;However, general circulation models at typical resolution&amp;#160;struggle to accurately reproduce TC&amp;#160;rainfall,&amp;#160;especially for the most&amp;#160;extreme storms&amp;#160;(Murakami et al., 2015).&amp;#160;Increasing horizontal resolution can&amp;#160;improve&amp;#160;precipitation estimates&amp;#160;(Roberts et al., 2020;&amp;#160;Zhang et al., 2021), but as these methods are computationally expensive there is a trade-off between accuracy and&amp;#160;generating enough ensemble members&amp;#160;to generate sufficient&amp;#160;high impact, low probability events.&amp;#160;Often, downscaling models are used as a computationally cheaper alternative.&lt;/span&gt;&lt;span&gt;&amp;#160;&lt;/span&gt;&lt;/p&gt;
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&lt;p&gt;&lt;span&gt;Here, we downscale TC precipitation data from 100 km to 10 km resolution using a generative adversarial network (GAN). Generative approaches have the potential to well reproduce &lt;/span&gt;&lt;span&gt;the fine spatial detail&lt;/span&gt;&lt;span&gt;&amp;#160;and stochastic nature of precipitation (Ravuri et al., 2021).&amp;#160;Using&amp;#160;observational products for tracking&amp;#160;(IBTrACS) and rainfall&amp;#160;(MSWEP), we train our GAN over the historical period 1979 - 2020.&amp;#160;We are interested in&amp;#160;how well&amp;#160;our model reproduces precipitation intensity and structure&amp;#160;with a focus on the most extreme events, where models have traditionally struggled.&lt;/span&gt;&lt;span&gt;&amp;#160;&lt;/span&gt;&lt;/p&gt;
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&lt;p&gt;&lt;strong&gt;&lt;span&gt;Bibliography&lt;/span&gt;&lt;/strong&gt;&lt;span&gt;&amp;#160;&lt;/span&gt;&lt;/p&gt;
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&lt;p&gt;&lt;span&gt;Murakami, H., et al., 2015. Simulation and Prediction of Category 4 and 5 Hurricanes in the High-Resolution GFDL HiFLOR Coupled Climate Model*.&amp;#160;&lt;/span&gt;&lt;em&gt;&lt;span&gt;Journal of Climate&lt;/span&gt;&lt;/em&gt;&lt;span&gt;, 28(23), pp.9058-9079.&lt;/span&gt;&lt;span&gt;&amp;#160;&lt;/span&gt;&lt;/p&gt;
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&lt;p&gt;&lt;span&gt;Ravuri, S., et al., 2021. Skilful precipitation nowcasting using deep generative models of radar.&amp;#160;&lt;/span&gt;&lt;em&gt;&lt;span&gt;Nature&lt;/span&gt;&lt;/em&gt;&lt;span&gt;, 597(7878), pp.672-677.&lt;/span&gt;&lt;span&gt;&amp;#160;&lt;/span&gt;&lt;/p&gt;
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&lt;p&gt;&lt;span&gt;Rezapour, M. and Baldock, T., 2014. Classification of Hurricane Hazards: The Importance of Rainfall.&amp;#160;&lt;/span&gt;&lt;em&gt;&lt;span&gt;Weather and Forecasting&lt;/span&gt;&lt;/em&gt;&lt;span&gt;, 29(6), pp.1319-1331.&lt;/span&gt;&lt;span&gt;&amp;#160;&lt;/span&gt;&lt;/p&gt;
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&lt;p&gt;&lt;span&gt;Roberts, M., et al., 2020. Impact of Model Resolution on Tropical Cyclone Simulation Using the HighResMIP&amp;#8211;PRIMAVERA Multimodel Ensemble.&amp;#160;&lt;/span&gt;&lt;em&gt;&lt;span&gt;Journal of Climate,&lt;/span&gt;&lt;/em&gt;&lt;span&gt; 33(7), pp.2557-2583.&lt;/span&gt;&lt;span&gt;&amp;#160;&lt;/span&gt;&lt;/p&gt;
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&lt;p&gt;&lt;span&gt;Zhang, W., et al., 2021. Tropical cyclone precipitation in the HighResMIP atmosphere-only experiments of the PRIMAVERA Project.&amp;#160;&lt;/span&gt;&lt;em&gt;&lt;span&gt;Climate Dynamics&lt;/span&gt;&lt;/em&gt;&lt;span&gt;, 57(1-2), pp.253-273.&lt;/span&gt;&lt;span&gt;&amp;#160;&lt;/span&gt;&lt;/p&gt;
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