In Jena: Week 29
· โ˜• 2 min read · โœ๏ธ Hoontaek Lee
์ด๋ฒˆ ์ฃผ๋Š”… ์ˆ˜์—… - biogeochemical cycles ๋ถ€์—Œ ๊ฐœ์ˆ˜๋Œ€+๋ƒ‰์žฅ๊ณ  ๊ต์ฒด 2021. 3. 15~19. ์›”~๊ธˆ์š”์ผ ์ˆ˜์—…์ด ์žˆ์—ˆ์ง€๋งŒ.. ๋”ฑํžˆ ์ง‘์ค‘ํ•˜์ง€๋Š” ์•Š์•˜๋‹ค. ๋‹ค๋ฅธ ์ผ์ด ๋” ๋ฐ”๋นด๊ธฐ์—&hell

In Jena: Week 28
· โ˜• 1 min read · โœ๏ธ Hoontaek Lee
์ด๋ฒˆ ์ฃผ๋Š”… ํ•˜์ดํ‚น 2021. 3. 13. ํ† ์š”์ผ ์ดˆ์† 8m์˜ ๋ฐ”๋žŒ์ด ๋ถˆ๊ณ  ์˜คํ›„์— ๋น„๊ฐ€ ๋‚ด๋ฆด ์˜ˆ์ •์ด์—ˆ๋˜ ๋‚ ์ด๋‹ค. ์ €์ €๋ฒˆ ์ฃผ์™€ ๋น„์Šทํ•œ ์ฝ”์Šค๋กœ ํ•˜์ดํ‚น์„ ๊ฐ”๋‹ค. Ladislav, Ulisse, Yunpeng, Chunhui. ๋น„

Paper Review: Ahlstrom (2015). Semi-Arid_to_NBP_IAV. Science.
· โ˜• 3 min read · โœ๏ธ Hoontaek Lee
05 March, 2021 (Ahlstrom, science, NBP IAV dominator) Ahlstrom A, Raupach MR, Schurgers G, Smith B, Arneth A, Jung M, et al. The dominant role of semi-arid ecosystems in the trend and variability of the land CO2 sink. Science. 2015 May 22;348(6237):895โ€“9. https://doi.org/10.1126/science.aaa1668 Message: Semi-arid regions dominate the land-carbon-sink variability The take-home message was simple and strong. Semi-arid regions dominated the global variability of land carbon sink variability (both long-term trend and interannual variability).

In Jena: Week 27
· โ˜• 1 min read · โœ๏ธ Hoontaek Lee
์ด๋ฒˆ ์ฃผ๋Š”… ์—ฐ๊ตฌ์—ฐ๊ตฌ ์‚ฌ์šฉ ์ค‘์ธ GRACE ๋ฐ์ดํ„ฐ์— ์ด์ƒ์น˜๊ฐ€ ์ข€ ์žˆ์–ด์„œ ์–ด๋–ป๊ฒŒ ๋‹ค๋ฃฐ์ง€ ๊ณ ๋ฏผํ–ˆ๋‹ค.์ •์ƒ์ ์ธ ์ด์ƒ์น˜์ธ ๋“ฏ..? SINDBAD ๋ฐ์ดํ„ฐ๋ฅผ ๋‹ค๋ฃจ๋Š” ์ฒซ ๋‹จ๊ณ„์— ์‹ค์ˆ˜

In Jena: Week 26
· โ˜• 2 min read · โœ๏ธ Hoontaek Lee
์ด๋ฒˆ ์ฃผ๋Š”… IMPRS-retreat call for TWS-NEE discussion Good bye Qian hiking 2021. 2. 22~23. ์›”~ํ™”์š”์ผ IMPRS-retreat๋‹ค. BGI-retreat์ฒ˜๋Ÿผ ์„œ๋กœ ์ž๊ธฐ ์ฃผ์ œ๋ฅผ ์กฐ๊ธˆ์”ฉ ๋ฐœํ‘œํ–ˆ

Ubuntu์—์„œ ๋ธ”๋กœ๊ทธ - git ๊ด€๋ จ
· โ˜• 1 min read · โœ๏ธ Hoontaek Lee
Ubuntu์—์„œ ๋ธ”๋กœ๊ทธ - git ๊ด€๋ จ ์งฑ์งฑํ•œ ์—ฐ๊ตฌ์†Œ ๋…ธํŠธ๋ถ์œผ๋กœ ๊ธ€ ์“ฐ๊ธฐ ์œ„ํ•ด ์ด๋ฏธ์ง€ ์ฒ˜๋ฆฌ ์™ธ์— ํ•ด๊ฒฐํ•  ๊ฒƒ์€ git ๊ด€๋ จ ์—…๋ฌด. dos2unix ์œˆ๋„์šฐ์—์„œ ์“ฐ๋˜ deploy.sh๋Š” ์œˆ๋„์šฐ

Ubuntu์—์„œ ๋ธ”๋กœ๊ทธ - ์ด๋ฏธ์ง€ ์ฒ˜๋ฆฌ
· โ˜• 1 min read · โœ๏ธ Hoontaek Lee
Ubuntu์—์„œ ๋ธ”๋กœ๊ทธ - ์ด๋ฏธ์ง€ ์ฒ˜๋ฆฌ ๋ธ”๋กœ๊ทธ์— ๊ธ€์„ ์˜ฌ๋ฆฌ๋ ค๋ฉด ์ด๋ฏธ์ง€ ๊ด€๋ จ ๋ช‡๊ฐ€์ง€ ์ž‘์—…์„ ํ•ด์•ผ ํ•œ๋‹ค. ๋‹ค์šด ๋ฐ›๊ธฐ, ์ด๋ฆ„ ๋ฐ”๊พธ๊ธฐ, ํฌ๊ธฐ ์ค„์ด๊ธฐ, ๋ธ”๋กœ๊ทธ ํด๋”์— ์˜ฎ๊ธฐ๊ธฐ&

In Jena: Week 25
· โ˜• 1 min read · โœ๏ธ Hoontaek Lee
์ด๋ฒˆ ์ฃผ๋Š”… ๋ด„ ๋ฏธํŒ…(Sujan, Martin, Nuno) ์Šฌ์Šฌ ๋ด„์ด ์˜ค๋ ค๋‚˜๋ณด๋‹ค. 2020. 2. 19. ๊ธˆ์š”์ผ ์—ฐ๊ตฌ ๊ด€๋ จ ๋ฏธํŒ…์„ ๊ฐ€์กŒ๋‹ค. Martin, Sujan์ด๋ž‘. Nuno๋Š” ์ž ๊น ์ฐธ์—ฌ ํ›„

In Jena: Week 24 (์„ค๋‚ )
· โ˜• 2 min read · โœ๏ธ Hoontaek Lee
์ด๋ฒˆ ์ฃผ๋Š”… ๋ˆˆ๋ˆˆ๋ˆˆ ์„ค๋‚  … ๋ˆˆ์ด ๊ฒ๋‚˜ ๋งŽ์ด ์˜จ ์ผ์ฃผ์ผ. ์„ค์€ ์ค‘๊ตญ์• ๋“ค์ด๋ž‘ ์ค‘๊ตญ์‹์œผ๋กœ ๋ณด๋ƒˆ๋‹ค(=๋งŒ๋‘+์Œ์ฃผ). ์—ฐ๊ตฌ๋Š” Martin์ด PAC ๋ฏธํŒ… ๋•Œ ์ œ์•ˆ

In Jena: Week 23
· โ˜• 2 min read · โœ๏ธ Hoontaek Lee
์ด๋ฒˆ ์ฃผ๋Š”… VS code ์„ค์น˜ ์‹คํŒจ ํŒŒ์šด๋ฐ์ด์…˜ ์Šค๋…ธ์šฐ VS code์—์„œ jupyter notebook์„ ์‹คํ–‰ํ•ด๋ดค๋‹ค. ๋ฆฌ๋ชจํŠธ ์„œ๋ฒ„ ์ ‘๊ทผ์€ ์‰ฌ์šด๋ฐ, ์„œ๋ฒ„ ๋‚ด์—์„œ ์ฃผํ”ผํ„ฐ ๋…ธ

In Jena: Week 22 (์ƒˆ ๋…ธํŠธ๋ถ)
· โ˜• 2 min read · โœ๏ธ Hoontaek Lee
์ด๋ฒˆ ์ฃผ๋Š”… German course (A1.2) ๋‹ค์‹œ ๋…์ผ์–ด๋ฅผ ์‹œ์ž‘ํ–ˆ๋‹ค. 2020. 1. 27. ์ˆ˜์š”์ผ ์ƒˆ ๋…ธํŠธ๋ถ์ด ๋„์ฐฉํ–ˆ๋‹ค. ์ด๋ฆ„์€ OSSLA. ๋…์ผ ์žํŒ์ธ ๊ฒŒ ์•„์‰ฝ์ง€๋งŒ, ๊ทธ๋ž˜๋„ ๊ดœ์ฐฎ๋‹ค. ํ‚ค๋ณด๋“œ๋Š” ๋”ฐ๋กœ

Paper Review: Pineiro (2008). OP vs. PO. ecolmodel.
· โ˜• 2 min read · โœ๏ธ Hoontaek Lee
27 December, 2020 (Jung, bg, FLUXCOM carbon) Gervasio Piรฑeiro, Susana Perelman, Juan P. Guerschman, Josรฉ M. Paruelo, How to evaluate models: Observed vs. predicted or predicted vs. observed?, Ecological Modelling, Volume 216, Issues 3โ€“4, 2008, Pages 316-322, ISSN 0304-3800, https://doi.org/10.1016/j.ecolmodel.2008.05.006. Prediction (x-axis) vs. Observation (y-axis) Martin added a comment to my proposal. He said that I should put prediction on the x-axis, referring to a paper: I have just look over the conclusion of the paper.

Robust linear regression
· โ˜• 1 min read · โœ๏ธ Hoontaek Lee
Robust linear regression Sujan recommended me to use the robust regression models (RLM) instead of the standard OLS (ordinary least squares). What are the differences? Robust: less affected by outliers A drawback of OLS is that the resulted regression line can be significantly altered by some outliers. As the OLS try to find a best line of the minimum SS, the optimum would likely to have more focus on outliers which have large SS.