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Miranti Indri Hastuti<\/strong> graduated with a master’s degree in Atmospheric Science from Kyungpook National University (KNU), South Korea. She <\/span>has<\/span>\u00a0worked as a <\/span>weather forecaster<\/span> for almost seven<\/span>\u00a0years in the Agency for Meteorology, Climatology, and Geophysics of the Republic of Indonesia (BMKG).<\/span><\/p>\nIn Indonesia, <\/span><\/span>w<\/span>eather forecast accuracy <\/span>remains<\/span> a major challenge in tropical regions due to the dominance of convection events<\/span><\/span>.\u00a0<\/span><\/span><\/span>These <\/span><\/span><\/span>events<\/span><\/span><\/span> are strongly influenced by topography and seasons<\/span><\/span><\/span>. According to National Agency for Disaster Countermeasure (Badan Nasional <\/span>Penanggulangan<\/span> Bencana<\/span>, BNPB) data, in 2023,<\/span> hydrometeorological disasters (flood and extreme weather) contributed to more than 50% of natural hazards that happened in Indonesia, causing significant<\/span> casualties and environment damage<\/span>. Thus,<\/span>\u00a0Miranti\u00a0plans to<\/span>\u00a0research<\/span>\u00a0conve<\/span><\/span>ction<\/span><\/span><\/span>–<\/span>permitting<\/span> <\/span>numerical weather prediction\u00a0<\/span><\/span><\/span>(NWP)<\/span><\/span><\/span> for <\/span><\/span>an <\/span><\/span><\/span>Indonesia<\/span><\/span>n<\/span><\/span><\/span> domain to enhance <\/span><\/span>the forecast of high-impact weather events<\/span><\/span><\/span>.\u00a0<\/span><\/span>Specifically, she will develop a realistic and correctly spread high-resolution ensemble for Indonesia using perturbed physics and ensemble data assimilation techniques. She will also work on the assimilation of infrared all-sky satellite observations.<\/p>\n <\/p>\n Kate Huxtable<\/strong> has recently joined DARC as a PhD student, looking at using explainable AI techniques to improve convective-scale ensemble data assimilation. The aim is to develop a means of identifying and categorising different weather features, such as fronts and boundary layer inversions, and the errors associated with them in the UKV domain. In the long term it is hoped an approach such as this could be used to develop an adaptive weather-dependent localisation scheme. She\u2019ll be undertaking this project with Alison Fowler and Ross Bannister from DARC, as well as Todd Jones (Department of Computer Science) and Marek Wlasak (Met Office).<\/p>\n Kate will be studying for her PhD part-time and mostly remotely, as she also works in the Atmosphere and Land Data Assimilation team at the Met Office. She\u2019s worked at the Met Office for two years, and her work has been focused on developing Met Office’s next-generation global ensemble data assimilation system in JEDI (Joint Effort for Data assimilation Integration). Prior to this she studied MSc Applied Meteorology at the University of Reading. Her dissertation was titled \u2018The flow-dependency of forecast errors in a simple convective model\u2019 and was supervised by Ross Bannister, Guannan Hu and Sarah Dance. She studied BSc Mathematics at the University of Birmingham.<\/p>\n <\/p>\n After that, Ravi completed a Master of Technology in Remote Sensing & GIS with a specialization in Marine and Atmospheric Sciences at the Indian Institute of Remote Sensing, Indian Space Research Organisation (IIRS, ISRO). There, he honed his skills in satellite data assimilation, particularly utilizing hyperspectral data for regional weather forecasting and improving tropical cyclone track and intensity prediction.<\/p>\n Ravi’s PhD project is machine Learning driven balance relationships for next-generation data assimilation systems. His supervisors are\u00a0Dr. Ross N Bannister (Research Fellow, Dept of Meteorology \u2013 UoR and NCEO), Prof. Amos Lawless (Professor, Dept of Mathematics & Statistics \u2013 UoR and NCEO), Dr. Christopher Thomas (Senior Scientist, MetOffice@Reading) and Dr. Hong Wei (Associate Professor, Dept of Computer Science, UoR).\u00a0His PhD research is intended to investigate the forecast error statistics of high-resolution numerical weather prediction systems, focussing on data assimilation and how machine learning can be used to model the background error covariance matrix.<\/p>\n","protected":false},"excerpt":{"rendered":"![\"\"](\"https:\/\/research.reading.ac.uk\/met-darc\/wp-content\/uploads\/sites\/48\/2024\/10\/Kate_round-300x300.png\")
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Ravi Shankar Nemani\u00a0<\/strong>is\u00a0<\/span>an AFESP-DTP PhD research student based at the Department of Meteorology, University of Reading. He holds a Bachelor\u2019s degree in Computational Physics and has advanced his expertise with a Master of Science in Meteorology from the Department of Meteorology & Oceanography, Andhra University. During his MSc, he focused on applying deep learning models to identify various cloud types using data from geostationary satellites. This allowed him to explore the intersection of artificial intelligence and atmospheric sciences, developing robust tools for satellite data analysis.<\/span><\/p>\n