High resolution temperature evolution maps of Bangladesh via data-driven learning

Abstract

As a developing country with an agricultural economy as a pillar, Bangladesh is highly vulnerable to adverse effects of climate change, so the generation of high-resolution temperature maps is of great value for Bangladesh to achieve agricultural sustainable development. However, Bangladesh’s weak economy and sparse meteorological stations make it difficult to obtain such maps. In this study, by mining internal features and links inside observed data, we developed an efficient data-driven downscaling technique to generate high spatial-resolution temperature distribution maps of Bangladesh directly from observed temperature data at 34 meteorological stations with irregular distribution. Based on these high-resolution historical temperature maps, we further explored a data-driven forecast technique to generate high-resolution temperature maps of Bangladesh for the period 2025–2035. Since the proposed techniques are very low-cost and fully mine internal links inside irregular-distributed observations, they can support relevant departments of Bangladesh to formulate policies to mitigate and adapt to climate change in a timely manner.