了解天气地图的颜色编码-地球科学堆栈交换江南电子竞技平台江南体育网页版 最近30从www.hoelymoley.com 2023 - 07 - 09 - t20:16:04z //www.hoelymoley.com/feeds/question/19854 https://creativecommons.org/licenses/by-sa/4.0/rdf //www.hoelymoley.com/q/19854 3 了解天气地图的颜色编码 伽利略 //www.hoelymoley.com/users/20633 2020 - 06 - 21 - t09:33:39z 2020 - 06 - 22 - t23:46:24z < p >我遇到这样一个红外天气图< a href = " https://i.stack.imgur.com/oAS7I.jpg " rel = " nofollow noreferrer " > < img src = " https://i.stack.imgur.com/oAS7I.jpg " alt = "在这里输入图像描述" / > < / > < / p > < p >我找不到任何传奇colorbar公约的颜色。最初,我以为可能是颜色反射的光的波长,但没有办法找到的。这张图片是来自< a href = " https://mausam.imd.gov.in/imd_latest/contents/satellite.php " rel = " nofollow noreferrer " > https://mausam.imd.gov.in/imd_latest/contents/satellite.php < / >。< / p > //www.hoelymoley.com/questions/19854/-/19855 # 19855 0 答案由user20217理解天气地图的颜色编码 user20217 //www.hoelymoley.com/users/0 2020 - 06 - 21 - t10:03:19z 2020 - 06 - 22 - t23:46:24z < p > Op:你有发布一个红外图像,但是你的问题和与它的传说是关于可见光图像。虽然类似的规则,结果是完全不同的,例如黑暗大海和光明大陆上可见,红外在天亦然。< / p > < p >链接:可见光和红外图像。虽然接近近红外,它不显示温度相同,但从传说一个规模可以是(很可能)基于数量的< a href = " https://en.wikipedia.org/wiki/Albedo " rel = " nofollow noreferrer " >扩散反射光< / >。< / p > < p >在图像发布一个问题:这是一个红外图像。都显示出不同的东西。我建议你告诉他们分开,并决定如果你想红外或可见光信息。< / p > < p >有关维基文章说明了不同的表面的反射率(可见光),积云非常明亮,层云,卷云聚集在图的中间,而水面反射更少,所以显得更黑。< / p > //www.hoelymoley.com/questions/19854/-/19860 # 19860 2 答案由Deditos理解天气地图的颜色编码 Deditos //www.hoelymoley.com/users/106 2020 - 06 - 22 - t17:26:23z 2020 - 06 - 22 - t17:26:23z < p >不幸的是常见的卫星数据服务来显示这些类型的图像定性,省略单位甚至colorbar。< / p > < p >这是一个红外图像从10.8微米(TIR1) INSAT-3D频道。你网站链接有点误导,因为(至少对我来说)后,链接文本描述了0.65微米可见信道下的形象。If you click "Infrared" in the sidebar of that page the text is replaced with the correct text about the 10.8 micron channel.

Given that TIR1 records are stored as 10-bit values (i.e., in the range 0 to 1023) and the color scale maxes out at 939, I suspect that they are just plotting the raw count data. This is normally transformed into radiances by applying a linear scale and offset to put the data in physical units.

I had a quick look at the equivalent 10.8 micron image from Meteosat over the Indian Ocean (see below) and I get a similar count range and a similar image when I apply their color scale. I also suspect that they've inverted their data, e.g., plotted count_inv = 1024 - count, to fit with the viewers' expectation that cold clouds appear white.

enter image description here

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