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

  • Yichen Wu
    ,
  • Jiaxin Yang
    ,
  • Zhihua Zhang
    ,
  • Lipon Chandra Das
    ,
  • James Crabbe
  • Shandong University
    ,
  • University of Chittagong
    ,
  • University of Oxford
Research Output:
Contribution to journal
Article
 Peer-review

Open access

Sustainable Development Goals

  • SDG 13 - Climate Action
    SDG 13 Climate Action

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.

Publication Information

Output type

Research Output:
Contribution to journal
Article
 Peer-review

Original language

English

Article number

385

Journal (Volume, Issue Number)

Atmosphere (Volume 15, Issue 3)

Publication milestones

  • Accepted/In press - 15/03/2024
  • Published - 21/03/2024

Publication status

Published - 21/03/2024

ISSN

1598-3560

Publication IDs

  • handle.net: 10547/626208
  • Scopus: 85188820495

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