題目：Possible Causes of Model Biases in the Simulation of Two Tropical-Arctic Teleconnections 

報告摘要🏋️‍♀️：

The central role of tropical sea surface temperature (SST) variability in modulating Northern Hemisphere (NH) extratropical climate has long been known. However, the prevailing pathways of teleconnections in observations and the ability of climate models to replicate these observed linkages remain elusive. Here, we reveal two co-existing tropical-extratropical teleconnections albeit with distinctive spatiotemporal characteristics. The first mode, resembling the Pacific-North American (PNA) pattern, favors a Tropical-Arctic in-phase teleconnection (warm-Pacific-warm-Arctic) in boreal spring and winter. However, the second mode, with a seasonal preference of summer, is manifested as an elongated Rossby-wave train emanating from the tropical eastern Pacific that features an out-of-phase relationship (cold-Pacific-warm-Arctic) between tropical Pacific SSTs and temperature variability over the Arctic (referred to as the PARC mode). While climate models participating in CMIP6 appear to successfully simulate the PNA mode, the majority of models’ skill in reproducing the PARC mode is obstructed to some extent by biases in simulating low-frequency SST and rainfall variability over the tropical eastern Pacific and the climatological mean flow over the North Pacific during boreal summer. Considering the contribution of the PARC mode in shaping low frequency climate variations from the tropics to the Arctic, improving models’ capability to capture the PARC mode is essential to reduce uncertainties associated with decadal prediction and climate change projection over the NH.

個人簡介⛹🏻‍♀️：馮小芳👌🏻，沐鸣2博士後。2021年博士畢業於南京信息工程大學🐕，2018-2021年美國加州大學聖巴巴拉大學聯合培養博士研究生👳🏼。研究興趣：臺風氣候🧑‍🦼🙅🏽、熱帶-中高緯相互作用、古氣候等🦹🏽🤸🏻‍♂️。

題目：基於深度學習的 Himawari-8 夜間相態產品反演算法

報告摘要：

雲相態反演在衛星遙感和下遊應用中具有至關重要的作用。然而，目前缺乏高效的夜間相態數據產品。本研究提出了一種融合殘差神經網絡和 Unet 神經網絡的 Res-Unet34 算法，用於基於熱紅外波段反演 Himawari-8 的夜間相態產品🙅🏿。與 CALIPSO 和 MODIS 數據對比發現，通過 Res-Unet34 算法反演得到的雲相態數據精度與Himawari-8 官方提供的日間雲相態數據精度相當。此外，與廣泛使用的隨機森林相比👨🏻‍🎤，ResNet-34 反演速度更快且更準確🧑🏿‍🎓。

個人簡介🙇🏿：童宣，沐鸣2平台博士後🤷🏻‍♂️，2021年博士畢業於中國科學院大氣物理研究所，隨後就職於上海期智研究院工作一年。2022年10月進入沐鸣2平台大氣科學系從事博士後研究工作✭。研究方向為利用機器學習和深度學習進行衛星數據反演以及華北夏季降水診斷和預報😥。