不当回事 不仔细思考为什么学不下去

1. 思维逻辑能力，写文章，说话，都要一步一步推导。不要东一榔头西一棒槌
Blank is the bed for creation.

2. 坚持，耐心。
Interruption is the killer of productivity.

3. 专注+精神注意力+能量管理+作息。多位一体。
读paper，用什么法子能够让自己真的读paper，建立起papers的档案，和实际纸打的数量，监督自己读书。读的还是太少了！！！

期末小结&感恩

感谢的客套话我也说不动了，简单回忆一下：
给我最大指导和帮助的，除了老板之外，就是韩国大哥了，多少无私的时间都贡献给小白我！从1月份开始generally讨论科研，到2月份正式合作，中间交流不多，后面4、5月份每次交流我都收获太多！老板给我的更多是直接了当的指点，而韩国大哥是启发式教育啊！！
铭叔麻烦他不能再多了，也是难为他肯为我付出那么多，同时还有Islam，估计这家伙恨我恨的牙痒痒的！我只能说我最恨water5了！
其他组里的人都超级nice，比如Amanda，Julio，至少态度上都很nice很认真！Nate是一个让我觉得能融入组里生活的一个重大因素，他总是让我莫名的放松和安心！意大利人实在是好心肠，我也是最近才发现的，以后要多交流多接触！
Keita这个人定位很模糊，我把他当做精神支柱来着，但他估计没有怎么瞧得起我吧那是，不过没关系，我已经习惯了向高手大牛们索取精神食粮，等着他们需要我回馈的一天吧！Emily总是那个给我温暖的人，很喜欢她的自然光环！
老线、波哥是我系里最铁的朋友了吧，感谢学期初波哥对流体力学入门的开导，解除了我的很多疑虑。德国回来就四年级了，韩国大哥还有一年就毕业，航哥也转眼就走了，要好好珍惜这些师兄师姐！涛涛雷少是在普林这边贴心的学术生活伙伴啊，让我对未来的phd生活抱有期待和憧憬。黄总猫哥能来支持我的报告，很感动也是！
我其实最感激的一个遥远的人是Scott Young，不仅仅是技术上的支持，而是精神上的全线帮助！Miki也是，两次听我给talk都给了很多精神鼓励和支持！
女神、潘大神、翔哥在我期中考试之前都给了很大帮助！我这学期感恩认识了Elie这个老师，是我最幸福的事，他让我意识到，只要好好学，任何东西都是有办法的！
总之，这是充满正能量的一个学期！what a semester!

[GrADS]maskout()

Today I will discuss the usage of maskout function, which is simple but powerful.
syntax:
maskout(expression, mask) 
It basically means using a mask from 'mask' file/variable to remove the corresponding grids in 'expression' data/variable.

(1) If you already have some mask files:
ga->d maskout(data,mask(t=1))
this command will suffice.
Remember to put (t=1) behind the mask, because most of well-made mask files don't have time step.
if the mask is 0 and 1, then you either change the undef value in the mask control file into 0 (the actual missing value in the binary file); or just use maskout(data,data-0.5), then all the value below 0 will be maskout.


(2) If you need to make mask files by yourself:
for example,
if you want to mask out the value lower than 100, in other words, you want to leave the value greater than 100,
ga->define mask = const(maskout(data,data-100),1)


if you want to mask out the slope that insignificant with p>0.05,
ga->define mask = const(maskout(p,0.05-p),1)

Notice the difference here, for conditioning data > value, then the mask = data-value; for conditioning data < value, then the mask = value-data.

Besides using with const() function to produce mask, maskout can also be combined with aave and tloop to plot time series.
ga->d tloop(aave(maskout(data,mask(t=1)),g))

The idea here is to upscale by spatial averaging the masked region.

代码-画图流程

我发现写代码可以分为以下几个过程：
（1）big idea: 严格意义上不算coding的过程，但其实是最重要的，如果在coding之前不想清楚，那就是为了coding而coding，做出来的东西一点价值都没有。
（2）detailed idea: 具体想实现什么。
先把已有数据列出示意图来，是map?是table?多少time step，spatial resolution多少，都罗列出来。
思考可以进行什么样的分析？
Map：
     A. magnitude: mean, median, mode, maximum, minimum, range
     B. variability (time series or inter datasets or inter models): var/std, cov, mad

     C. trend stat (slope, P) (kind of regression against ts)
     D. correlation/regression
Scatterplot:
     time series correlation VAR1 vs VAR2 at one point (regional averaged)
Timeseries plot:
     plot rawdata and linear trend

Histogram plot:

     A. how total effect attributed to different components

          e.g. physical controls; seasonal attribution; regional attribution.

     B. seasonal comparison

（3）搜索实现该分析画图的工具和想法
可以与其他过程独立出来，通过平时积累，网上搜索。

（4）写代码、调试
这才是真正的写代码的过程。

（5）完善调整修饰
这是最坑爹最考验人耐心的画图过程。

Boundary Layer

对于这一门课，我不能褒奖更多！
除了对流体力学有所涉猎，我居然通过雷诺平均对统计有了更深刻的认识！

The relationship between Penman Equation, Bulk transfer model, and profile method

There are tons of ways to measure evaporation, and most of these approaches are some sort of interconnected. And it is always confusing to term evaporation in different contexts.

Before you know more about Penman, you need to understand what is turbulent flux <q'w'>(angle quotation means average, same as bar) and how people parameterize them.

First, regard turbulence as the deviation of mean in temporal statistical sense, so as the transfer of momentum, heat, and moisture. Second, we are using a zero-order closure method similarity theory to parameterize the mean qualities needed.(see turbulence closure problem) Similarity theory can be interpreted as parameterizing the mean qualities, but in fact it doesn't mean using any simulation or calculation to retrieve those values. It is based on the similarity between the connected dimensionless groups, which is profound and powerful. Here, one thing that confusing me before was the K theory. K theory is an one-order closure method, by that it means the second order flux term is parameterized by one order variables, such as the mean profiles.

By applying Taylor Hypothesis, one can replace the temporal fluctuations in flux by the spatial changes of mean qualities. (mean: averaging over time)
Bulk transfer model is based on this simplified idea so that the product of the flux <q'w'>  can be rewritten as the product of bulk difference in wind speed and humidity.

(to be continued)