北京时间2012年8月6日6点12分，在贵博的寝室醒来，开始洗漱。7点，坐上第一班经过联合苑开往大学站的小巴。搭MTR到沙田，坐上直达机场的通天巴士（air bus，中文翻译更霸气）A41。8点20分，到达香港机场 terminal 1。
Delta的值机柜台在D区，排了一条长长的队伍。称行李时发现超重了，27Kg，比规定多了4Kg，无奈只好交了580港币，后来一想其实可以把箱子里的小书包和衣物拿出来，因为看很多人其实拿了两三件小包登机。
9点在机场吃了大快活。可能最后一次吃大快活的早餐了，点的滑蛋炸鱼柳，竟然感觉味道不错。
9点20进入安检、出关，9点40到达31号闸口。不一会儿就登机了。
从香港直飞底特律(Detroit)，历经约15个小时。飞机上的电影大部分竟没有字幕，发现自己还真听不懂对白，在美国有的受了。
美国东部时间下午1点20左右，飞机在Detroit降落。本来想把沿途拍下来给亲爱的作参考，后来发现指示牌非常清楚，又有工作人员指导，肯定不会走错，惟一需要注意的是入关有两个通道：US citizens和foreign visitors，我们要属于后者。拿了行李，开始入关，将护照 、I-20和飞机上填的I-94给海关人员，他会在I-20上盖章，并把I-94的出境卡钉到护照上，这两个文件非常重要，要小心保管。
走两步是入关物品检查，会有只警犬闻你的行李，警犬竟不是那种大狼狗，而是那种特可爱的beable（听后面的美国小男孩说的）。海关会问你带了多少现金，要求不多于10000美金。
海关的人其实并不严肃，会和你聊天。当我说要去Penn State时，他长长地叹了一口气：Oh boy。原来是最近发生的橄榄球虐童事件，看来给学校的声誉造成了很大的影响。
整个转机手续用了约40分钟就完成了，因此之前担心的转机时间不够（2小时）根本不是问题。2点多就坐在B6闸口等去State College的飞机。可是却迟迟不来。据说是打印不出乘客的信息？Paperwork之类的，听不太明白。不过那个检票的大叔态度挺好，很详细地向我们解释晚点的原因。
终于在5点40登机了。飞机真的是非常小，从闸口到飞机没有以往的通道，只用简单的一个木板架住，我的登山包放不进飞机的行李箱，只好拿去托运。起飞没多久我就沉沉地睡着了，醒来时竟然已经着陆了，没能鸟瞰一下整个State College。
下飞机时享受了国家领导人的待遇，从机舱沿着台阶走下来的，四周非常空旷，虽然已经下午7点10分，但太阳仍然高照，天空蓝的出奇，我一下子被这种乡村的空旷的宁静的美丽震憾了。
拿了行李没多久，于博师兄就来了。路上得知他的老婆和妈妈都在，马上就有baby了，在美国有个温馨的house，让我马上憧憬起我和瓜瓜的未来。晚上在麦当劳随便吃的快餐，6刀多。然后晚上和他家里人一起去了Walmat，买了点生活用品。那个超市很大，还卖家具，24小时营业。神奇的是收银台没有人，是自助check out的。将物品的条形码一刷就好。有趣的是，你还必须在刷完条码后把这件东西放入旁边的购物袋内，否则是不能刷第二件物品的。
这几天暂住一个叫陈旭的师兄这，在copper beach W. Aaron Dr.。睡沙发，竟然睡不着，12点躺下竟然2点半就醒了。折腾到3点半，索性起来，记下来美国的第一天。
第二天，也就是今天，去学校办各种证件。看看这个我未来要呆五六年的地方长什么样。
8/07/2012
6/29/2012
GeoInformatics 2012
2012年6月15日至17日，GeoInformatics 2012在香港中文大学举办，由我们实验室承办。GeoInformatics是CPGIS这个华人地理信息协会每年都要举办的国际性会议。1992年，在美国纽约Buffalo大学，几个年轻的留美博士生自发组织成立了这样一个华人协会，领头的就是现在的林珲教授。20年后，当时的协会成员或成为工业界的领头人，或成为国内外顶尖大学的学者、教授，共同支撑着GIS学科的发展。我能想像这种从无到有的成就感，也很感叹这帮人敢想敢干的精神。
这届的会议非常成功，也邀请到了许多国际GIS大佬，如Goodchild, Michael Batty等。甚至还请来了GIS之父Tomlinson. 当2米多高的Tomlinson拄着拐杖颤颤巍巍地走上讲台，以一个父辈的身份，用颤抖的、接近哭腔的口吻对我们说“Now, I talk to you directly, the future of GIS”时，我仿佛接受了一次洗礼，似乎那个学科发展的巨担已传递到了我们肩头。
会上我还意外地碰到了John Radke，让我一下子想起了三年前的我。那会儿我利用寒假的时间去美国UC Davis交流，期间去了UC Berkeley. 因时间仓促，事先没联系好那边的老师，只在网上查了地理系的地址，就凭着一股年轻人的蛮不讲理去了。在系楼的过道上看到了John Radke的名字，只知道是和GIS相关的，就和秘书说我要找他。没想到他并没有怪我来得莽撞。很奇怪的，我们并没有讨论GIS的话题，而是就中国的问题争论起来。他说他小时候在中国长大，对中国很失望，中国的政府和学术很腐败；我便和他说现在已经不一样了，你大可以再去看一下。争论的最后我们都按捺不住脾气了，一小时的谈话就终止了。现在想来，当时的我真是好笑，而John的风度也的确让我印象深刻：一个大牌教授突然被一个不知哪来的毛头小孩打扰，还莫名其妙地和他讨论了一小时中国问题！记得讨论完的我异常兴奋，回去后还给他发了邮件，贴在下面，以供玩味。
这次会议是我认识GIS圈里人的大好机会，在这里把CPGIS的一些会员贴出来，供膜拜学习（不保证信息完整准确）。
1. 宫鹏。19岁即于南京大学地理系本科毕业，21岁于南京大学地理系硕士毕业，25岁于加拿大滑铁卢大学地理系获得博士学位，现任美国UC Berkeley大学教授，清华大学地球系统科学研究中心主任。
2. 丁跃民。美国Verizon Communication公司信息技术部资深经理。Verizon主要运营无限服务，美国最新的4G网络就是他们运营的。
3. 龚健雅。江西人。毕业于华东地质大学（今东华理工大学）。中国科学院院士，武汉大学教授，武汉大学测绘遥感信息工程国家重点实验室主任。吉奥公司总工。
4. 周成虎。福建人。南京大学陆地水文专业学士，中科院地理所博士。陈述彭学生。中科院地理所副所长，资源与环境信息系统国家重点实验室主任。
5. 李荣兴。上海同济大学surveying and mapping专业学士、硕士，Technical University of Berlin 大学 Photogrammetry and Remote Sensing博士。曾是NASA火星探索计划科学家。现任Director, Mapping & GIS Laboratory，Department of Civil and Environmental Engineering and Geodetic Science， The Ohio State University
6. 李斌。广东人。Ph.D., Syracuse University。现为Central Michigan University地理系教授。
7. 柳林。中山大学地理科学与规划学院院长。美国辛辛那提大学教授。
8. 林戈。美国内布拉斯加大学医学中心副教授。
9. 夏福祥。浙江衢州龙游人。浙江大学毕业。现为ESRI 资深构架师。
10. 周启鸣。香港浸会大学地理系教授，地学计算与分析研究中心主任。
11. 王野乔。美国罗德岛大学自然资源科学系教授。
12. 关蔚禾。哈佛大学地理分析中心研究服务部主任。
13. 涂汉明。Octagon Research公司临床信息技术部总监。
14. 陶阅。湖北人。武汉大学毕业，后赴加拿大卡尔加里大学用两年半时间获得博士学位。32岁获终身教授职称。开发了GlobalView等软件，现为PPLIVE的CEO.
这届的会议非常成功，也邀请到了许多国际GIS大佬，如Goodchild, Michael Batty等。甚至还请来了GIS之父Tomlinson. 当2米多高的Tomlinson拄着拐杖颤颤巍巍地走上讲台，以一个父辈的身份，用颤抖的、接近哭腔的口吻对我们说“Now, I talk to you directly, the future of GIS”时，我仿佛接受了一次洗礼，似乎那个学科发展的巨担已传递到了我们肩头。
会上我还意外地碰到了John Radke，让我一下子想起了三年前的我。那会儿我利用寒假的时间去美国UC Davis交流，期间去了UC Berkeley. 因时间仓促，事先没联系好那边的老师，只在网上查了地理系的地址，就凭着一股年轻人的蛮不讲理去了。在系楼的过道上看到了John Radke的名字，只知道是和GIS相关的，就和秘书说我要找他。没想到他并没有怪我来得莽撞。很奇怪的，我们并没有讨论GIS的话题，而是就中国的问题争论起来。他说他小时候在中国长大，对中国很失望，中国的政府和学术很腐败；我便和他说现在已经不一样了，你大可以再去看一下。争论的最后我们都按捺不住脾气了，一小时的谈话就终止了。现在想来，当时的我真是好笑，而John的风度也的确让我印象深刻：一个大牌教授突然被一个不知哪来的毛头小孩打扰，还莫名其妙地和他讨论了一小时中国问题！记得讨论完的我异常兴奋，回去后还给他发了邮件，贴在下面，以供玩味。
Prof. Radke,John竟也马上给我回了一封长信：
Hello, I'm the Chinese student who met you this afternoon. I'm from Zhejiang University with GIS as my major.I really admire you, not for your great achievements in GIS, but that you are truly thinking about what we are doing and what we are doing that for. You made me think deep into why I learn GIS. I have too much to say, so it might be a little bit long letter.Money is necessity for life, but I never worry too much about it. I have my dream which means more to me and I keep it all the way I grow up: I want to do something to change the world( big dream, err?) I keep on asking myself: but how? until I met GIS. GIS tools are powerful but they are not interface friendly and are complicated to use. Why not make it as popular as photoshop, thus pushing GIS research one step forward?But after the talk with you today, something new came into my mind. GIS is a tool, and only a tool which can be used in any way or not in any way at all if you are not determined to solve a problem! There exist many problems--both in developing and developed countries--environmental degradation, resource shortage, transportation problem, etc. Actually the government could solve them, but it costs. What we might do is, to make the cost smallest so that the governor is willing to take the risk, with the tool of GIS. And I might turn to politician if necessary.I don't know if it is another naive idea. It just hits my mind on my way back home.As for the "China" you mentioned today, I just feel sorry. But it's not apology. I just feel sad that you didn't see the other side of the country. That's not real China. There are many problems in China like government corruption and environmental degradation. But we have identified the problems and we have taken action. We just need time. Time is indeed impressing but it's never too late to take action. China is no longer the poor country before. It is a big responsible country, just look at 2008 Olympics and the important role China plays in the Copenhagen conference. I truly hope you pay a visit to China once more, and perhaps to Zhejiang University, Hangzhou.By the way, Beijing is the capital of P.R.China, while Capital Nanjing is history. They are two different periods of China of 5000 years old.
Dong Chen,这次再见，很可惜他已经不记得我了。希望有一天，在我学有所成之时，能再和他"talk around GIS problems"。
It was good to meet you. If I could only transfer my experience to you
directly you would see that in my mind I see the beauty of your home
country and in fact all the counties of the world. I also see the future
and the custodians of the future. This is my dilemma. It starts with
humans and their inability to see the bigger more important problems.
They are for the most part goal directed and this is their weakness and
downfall. No doubt China will fix its current problems but will they
learn? The US is young, has experienced many growing pains and I believe
rarely learns form those experiences. If we only had time to go into this
further.
To save a life is to save your own. Save mother earth and look what you
have done.
Governors (politicians) are usually caught up in themselves and by the
time they realize they could have done something good ... their time is up
and a new one enters the scene. Faith in their ability to see the
problems and articulate a solution is likely naive but this is not a
failure of yours but theirs.
Spatial reasoning and understanding brings us closer to recognition of
what is. This is why I have embraced GIS. I see you feel the same. I
salute you for it.
kind regards,
这次会议是我认识GIS圈里人的大好机会，在这里把CPGIS的一些会员贴出来，供膜拜学习（不保证信息完整准确）。
1. 宫鹏。19岁即于南京大学地理系本科毕业，21岁于南京大学地理系硕士毕业，25岁于加拿大滑铁卢大学地理系获得博士学位，现任美国UC Berkeley大学教授，清华大学地球系统科学研究中心主任。
2. 丁跃民。美国Verizon Communication公司信息技术部资深经理。Verizon主要运营无限服务，美国最新的4G网络就是他们运营的。
3. 龚健雅。江西人。毕业于华东地质大学（今东华理工大学）。中国科学院院士，武汉大学教授，武汉大学测绘遥感信息工程国家重点实验室主任。吉奥公司总工。
4. 周成虎。福建人。南京大学陆地水文专业学士，中科院地理所博士。陈述彭学生。中科院地理所副所长，资源与环境信息系统国家重点实验室主任。
5. 李荣兴。上海同济大学surveying and mapping专业学士、硕士，Technical University of Berlin 大学 Photogrammetry and Remote Sensing博士。曾是NASA火星探索计划科学家。现任Director, Mapping & GIS Laboratory，Department of Civil and Environmental Engineering and Geodetic Science， The Ohio State University
6. 李斌。广东人。Ph.D., Syracuse University。现为Central Michigan University地理系教授。
7. 柳林。中山大学地理科学与规划学院院长。美国辛辛那提大学教授。
8. 林戈。美国内布拉斯加大学医学中心副教授。
9. 夏福祥。浙江衢州龙游人。浙江大学毕业。现为ESRI 资深构架师。
10. 周启鸣。香港浸会大学地理系教授，地学计算与分析研究中心主任。
11. 王野乔。美国罗德岛大学自然资源科学系教授。
12. 关蔚禾。哈佛大学地理分析中心研究服务部主任。
13. 涂汉明。Octagon Research公司临床信息技术部总监。
14. 陶阅。湖北人。武汉大学毕业，后赴加拿大卡尔加里大学用两年半时间获得博士学位。32岁获终身教授职称。开发了GlobalView等软件，现为PPLIVE的CEO.
6/28/2012
A dip into NCL
NCL is an interpreted language designed for scientific data analysis and visualization. I use it mostly for visualization, but not seriously, because there is another developed platform for visualization. For NCL, I just use it to check the model results.
So I just got a dip of NCL for the last three days, in an attempt to plot a wind vertical profile with eta level. The wind vector goes along the terrain and the plot is blank where there is a hill, so that the impact of topography could be seen. Shame on myself, I still haven't gone through it. And in terms of time, I have to give up. Here I'd lie to summarize up what I've learned with NCL.
Coordinate variables are the key information to plot right. Variables from model results (in my case, WRF, CMAQ, and SMOKE) usually bear no coordinate, but columns and rows. It is fine if you just want to see the patterns. However, if you want a base map overlaid, the right coordinates have to be assigned. Coordinates are usually stored in another output variable, e.g. XLAT for latitude and XLONG for longitude in WRF. What you have to do is to assign lat/lon to the right dimension of, say, wind. Here is the code:
It comes out that there are two grids in WRF, the staggered grid and the mass grid. Value in mass grid is refers to value in the center of the grid while value in staggered grid means value in the boundary of the grid. So the dimension size of staggered grids is always greater than mass grids by 1. To solve the inconsistency, we average the value to mass grid points.
Finally gsn_csm_pres_hgt_vector (example is here and here) is used to plot the vector. However, the result (shown in figure below, at 24.5N along 113-114E) is quite different from the example, mainly for the three points:
1. I am not sure whether the eta level is terrain-following. Though there are "ups and downs", no specific "hills" are to be found. Of course, it may be a problem of scales.
2. The temperature contour goes along eta level, which is weird.
3. Wind always flows to the east. There seems no "up" wind.
Some other resources:
To plot vectors:
gsn_vector(), gsn_csm_vector(). And the latter is more advanced.
gsn_csm_vector_map() overlays the vector plot on a base map.
To draw vector and scalar simultaneously.
gsn_csm_vector_scalar_map()
So I just got a dip of NCL for the last three days, in an attempt to plot a wind vertical profile with eta level. The wind vector goes along the terrain and the plot is blank where there is a hill, so that the impact of topography could be seen. Shame on myself, I still haven't gone through it. And in terms of time, I have to give up. Here I'd lie to summarize up what I've learned with NCL.
Coordinate variables are the key information to plot right. Variables from model results (in my case, WRF, CMAQ, and SMOKE) usually bear no coordinate, but columns and rows. It is fine if you just want to see the patterns. However, if you want a base map overlaid, the right coordinates have to be assigned. Coordinates are usually stored in another output variable, e.g. XLAT for latitude and XLONG for longitude in WRF. What you have to do is to assign lat/lon to the right dimension of, say, wind. Here is the code:
f = addfile(filename, "r")But wait, there is a mistake here. NCL would prompt dimension inconsistent between U and W. Let's take a close look at it. By the funtion printVarSummary we find the dimension of both U and W is time*level*latitude*longitude. So, why inconsistent?
U = f->U ; wind in east-west direction, time*lev*lat*lon
W = f->W ; wind in bottom-up direction
lat = f->XLAT(0,:,0) ; time*lat*lon
lon = f->XLON(0,0,:)
znu = f->ZNU(0,:) ; time*lev
lat@units = "degrees_north"
lon@units = "degrees_east"
lat!0 = "lat"
lon!0 = "lon"
lat&lat = lat
lon&lon = lon
lev = znu*1000 ; [-105.1526..-82.84741]
lev@long_name = "eta*1000"
lev@units = "hPa"
lev!0 = "lev"
lev&lev = lev
U!0 = "lev"
U!1 = "lat"
U!2 = "lon"
U&lev = lev
U&lat = lat
U&lon = lon
; And the same for w, left out here
It comes out that there are two grids in WRF, the staggered grid and the mass grid. Value in mass grid is refers to value in the center of the grid while value in staggered grid means value in the boundary of the grid. So the dimension size of staggered grids is always greater than mass grids by 1. To solve the inconsistency, we average the value to mass grid points.
dimU = dimsizes(U)In this case, u is on the common grid. And the same is for w.
nlonU = dimU(3)
u = 0.5 * (U(:,:,:,0:nlonU-2) + U(:,:,:,1:nlonU-1))
Finally gsn_csm_pres_hgt_vector (example is here and here) is used to plot the vector. However, the result (shown in figure below, at 24.5N along 113-114E) is quite different from the example, mainly for the three points:
1. I am not sure whether the eta level is terrain-following. Though there are "ups and downs", no specific "hills" are to be found. Of course, it may be a problem of scales.
2. The temperature contour goes along eta level, which is weird.
3. Wind always flows to the east. There seems no "up" wind.
To plot vectors:
gsn_vector(), gsn_csm_vector(). And the latter is more advanced.
gsn_csm_vector_map() overlays the vector plot on a base map.
To draw vector and scalar simultaneously.
gsn_csm_vector_scalar_map()
6/25/2012
完成论文
查看了下上一篇日志，还是在上月初写的。一晃就是50天，时间过得真快。
没写日志的一个原因是这期间确实没取得实质性的进展。除了为WRF和CMAQ写了两个顶层的控制脚本，就为在其他机器上安装CMAQ进行尝试。这不是技术问题，而是沟通与制度的问题。结果实验室新买的电脑因为权限问题到现在还没配置好，而中大的集群也因升级而暂时无法使用。结果白白浪费了一些时间。不过在此期间，发现CMAQ竟然升级了。最新的CMAQ 为5.0版本，加入了 CMAQ-WRF two-way couple 的新特性。这个升级非常让人兴奋。众所周知，气象场对污染物的分布产生很大的影响，是污染物扩散的驱动力；反过来，污染物的浓度也会对局部地区的温度、相对湿度等产生影响。因此，将这种双向的反馈反应到模式中，会使模拟结果更加准确。
在此期间，还写了MSc的毕业论文。本打算非常认真、严谨地对待这篇文章，但是时间紧迫，平时积累不够（想起Robert说的，每天读半小时文献，写半小时文摘，这些积累使他最后写论文时简单的copy-paste就完成了。真是汗颜）。因此论文不是很让我满意，模拟的结果也没有时间再做改善。
在做毕业论文内容的过程中，我也发现了一些问题：
1. 我拿到的污染源数据中，香港的排放源不完整，甚至是空白的。具体的数据还没有分析，但从CMAQ模拟的结果来看，香港地区除了塔门这个监测点，其他监测点的污染浓度值都与模拟值相差较大。这可以用排放源解释：塔门附近没有大型的排放源，它监测的是背景浓度，而且它离深圳较近。相对而言，深圳的排放源更准确，模拟的污染物浓度也更符合实际。所以塔门模拟得较好。而其他站点，因为排放源的缺失，模拟的浓度值大大低于实际值。
2. 跑模式分析污染物是一件很荒诞的事。在我看来，很多这方面的论文就是为了发表而发表。很多人跑模式，是为了分析过去的某个空气污染事件。当模拟值与实际值不符时，就提高或减少排放源的排放量，直到结果较为满意。试问，这样有什么意义？当然，有些人依此得到较为可信的排放源数据，并希望将它用于未来的模拟。可问题是，怎么证明适用于那个事件的污染源同样适用于其他事件、其他时期？
很高兴我今后不再研究这个领域。
没写日志的一个原因是这期间确实没取得实质性的进展。除了为WRF和CMAQ写了两个顶层的控制脚本，就为在其他机器上安装CMAQ进行尝试。这不是技术问题，而是沟通与制度的问题。结果实验室新买的电脑因为权限问题到现在还没配置好，而中大的集群也因升级而暂时无法使用。结果白白浪费了一些时间。不过在此期间，发现CMAQ竟然升级了。最新的CMAQ 为5.0版本，加入了 CMAQ-WRF two-way couple 的新特性。这个升级非常让人兴奋。众所周知，气象场对污染物的分布产生很大的影响，是污染物扩散的驱动力；反过来，污染物的浓度也会对局部地区的温度、相对湿度等产生影响。因此，将这种双向的反馈反应到模式中，会使模拟结果更加准确。
在此期间，还写了MSc的毕业论文。本打算非常认真、严谨地对待这篇文章，但是时间紧迫，平时积累不够（想起Robert说的，每天读半小时文献，写半小时文摘，这些积累使他最后写论文时简单的copy-paste就完成了。真是汗颜）。因此论文不是很让我满意，模拟的结果也没有时间再做改善。
在做毕业论文内容的过程中，我也发现了一些问题：
1. 我拿到的污染源数据中，香港的排放源不完整，甚至是空白的。具体的数据还没有分析，但从CMAQ模拟的结果来看，香港地区除了塔门这个监测点，其他监测点的污染浓度值都与模拟值相差较大。这可以用排放源解释：塔门附近没有大型的排放源，它监测的是背景浓度，而且它离深圳较近。相对而言，深圳的排放源更准确，模拟的污染物浓度也更符合实际。所以塔门模拟得较好。而其他站点，因为排放源的缺失，模拟的浓度值大大低于实际值。
2. 跑模式分析污染物是一件很荒诞的事。在我看来，很多这方面的论文就是为了发表而发表。很多人跑模式，是为了分析过去的某个空气污染事件。当模拟值与实际值不符时，就提高或减少排放源的排放量，直到结果较为满意。试问，这样有什么意义？当然，有些人依此得到较为可信的排放源数据，并希望将它用于未来的模拟。可问题是，怎么证明适用于那个事件的污染源同样适用于其他事件、其他时期？
很高兴我今后不再研究这个领域。
5/06/2012
所谓认知
一直以为“认知”是心理学领域的东西，而且因为其抽象、玄乎，而本能的排斥它。后来才知道，“认知”早与“地理”扯上关系，在国外，地理是一门“社会科学”，研究地理是为了更好地研究人，将人与地理结合，或者说将人与环境结合，是地理学的崭新的研究方向，而地理、行为、认知则构成了这新方向的关键词。
可是，认知究竟是什么？在我看来，科学的意义就是用逻辑来解释某种现象，而这种现象必须是可重复发生、可用数据定量描述的。那么认知，符合这样的条件吗？
最近看了一篇心理学的论文，虽不足以解开我所有的疑惑，但让我有所启发；更让我印象深刻的是，之前一些模糊的、关于主客观的“思想”，原来可以用严谨的科学语言来表达，让我重新认识了心理学。
这是广州大学心理与脑科学研究中心的叶浩生老师写的《有关具身认知思潮的理论心理学思考》，对具身认知、离身认知等一些“人如何看待世界”的方式进行了综述，并认为具身认知才是正确的理解心理学的基础。
离身认知（disembodied cognition）认为思维和身体是分离的，身体只是意识的载体。所谓身心二元论，即心和身分属两个世界，主体和客体是一个“表征与被表征”的关系，主体若能镜像般地表征世界，则能真正认识世界。以计算机为比喻，身体就像硬件，负责数据的输入和信息的输出，而思维像程序一样负责信息的加工处理。从这个意义上讲，认知就是信息的表征和操控，“以抽象的符号表征着外在于我们的带我，然后通过操纵这些符号完成思维”。
反过来说，如今的计算机其实正在模拟人的认知过程，只不过如今的程序还非常稚嫩，如今的硬件也无法如人的身体那样感知到足够丰富的信息。另一方面，既然思维和身体是分离的，那就代表它们是可以替换的；那么如果有一天，物理系统足够复杂，就可以承担人类的智能。这与当时陈老师们在吃饭时谈笑的内容多么相似：随着计算机系统越来越复杂，计算机能自动完成的事件越来越多，可以预见，当计算机足够复杂时，它们能够自己debug，自己reflection，那么它们与人类的距离又有多大呢？这或许是所有计算机专家的梦想，也是许多像《黑客帝国》之类的科幻片的灵感来源。
我本人也非常信仰这种观点。可如今，一种新的看法，即具身认知（embodied cognition）被越来越多人认可，也似乎更“高级”，如同当时只研究环境，后来加入的“人”的因素一样，后者显得更合理。这种观点认为，认知在很大程度上与身体的物理属性相关，脑神经水平上的细节、身体的结构、运动系统等都对认知的形成有重要的影响。“人认识世界的方式是用我们的身体以合适的方式与世界中的其他物体互动，在互动的过程中获得对世界的认识”。它认为认知在本质上并非使用抽象符号的表征和加工，而是一种模拟（simulation），而所谓模拟，是“身体、世界和心智互动过程中产生的知觉、运动和内海状态的复演”。我对此并不十分理解，紧接着文章举了个例子，认为同情心的产生即是因为我们“通过大脑与身体的特殊通道模拟了他人的感受”，同样让我不太理解。
文章还举了一个更有说服力的例子。Yale大学的研究者在2010年做了一个实验，把41个大学生随机分成两组，A组学生双手捧着一杯热咖啡，B组学生捧着一杯冰咖啡。然后两组学生分别对同一个想象中的中性人物的人格特征进行评分。结果显示，A组学生比B组学生更有可能把这个人评估为热情、友好。从而认为，身体上感知到的温暖影响了学生认知上的判断。这个结果的确让人惊讶，但仔细一想却也不是那么奇怪。正如所有人都知道的那样，同样的风景给不同的人可能有不同的感受，有人喜有人悲，最简单的，如果人身体健康，那很可能就是乐观的心态，反之如果他正遭受疾病，他更趋向于悲观的心情，这似乎是理所当然的。
最后说说具身认知的方法论。具身认知告诉我们要把有机体放在它的环境中，视有机体、行为和环境紧密相连。这就回到文首的问题，有关“有机体”的变量是可重复出现的吗？我们知道环境是可以量化的，可人在那一刻是喜是悲却无法估计，也不可能重复发生，那如何用科学的方法来研究？另外，具身认知认为，神经科学和心理学是对同一问题的两种不同的解释，应该将这两种方法结合起来，可是具体如何结合呢？
这些都是具身认知让我不可接受的地方。尽管现代科学都越来越重视“人”的因素，都试图把“人”加入研究的范畴（是否和兴起的社交网络有关？），但我还是很难理解怎样用科学、用数字和规则来研究人的行为，甚至人的认知。
可是，认知究竟是什么？在我看来，科学的意义就是用逻辑来解释某种现象，而这种现象必须是可重复发生、可用数据定量描述的。那么认知，符合这样的条件吗？
最近看了一篇心理学的论文，虽不足以解开我所有的疑惑，但让我有所启发；更让我印象深刻的是，之前一些模糊的、关于主客观的“思想”，原来可以用严谨的科学语言来表达，让我重新认识了心理学。
这是广州大学心理与脑科学研究中心的叶浩生老师写的《有关具身认知思潮的理论心理学思考》，对具身认知、离身认知等一些“人如何看待世界”的方式进行了综述，并认为具身认知才是正确的理解心理学的基础。
离身认知（disembodied cognition）认为思维和身体是分离的，身体只是意识的载体。所谓身心二元论，即心和身分属两个世界，主体和客体是一个“表征与被表征”的关系，主体若能镜像般地表征世界，则能真正认识世界。以计算机为比喻，身体就像硬件，负责数据的输入和信息的输出，而思维像程序一样负责信息的加工处理。从这个意义上讲，认知就是信息的表征和操控，“以抽象的符号表征着外在于我们的带我，然后通过操纵这些符号完成思维”。
反过来说，如今的计算机其实正在模拟人的认知过程，只不过如今的程序还非常稚嫩，如今的硬件也无法如人的身体那样感知到足够丰富的信息。另一方面，既然思维和身体是分离的，那就代表它们是可以替换的；那么如果有一天，物理系统足够复杂，就可以承担人类的智能。这与当时陈老师们在吃饭时谈笑的内容多么相似：随着计算机系统越来越复杂，计算机能自动完成的事件越来越多，可以预见，当计算机足够复杂时，它们能够自己debug，自己reflection，那么它们与人类的距离又有多大呢？这或许是所有计算机专家的梦想，也是许多像《黑客帝国》之类的科幻片的灵感来源。
我本人也非常信仰这种观点。可如今，一种新的看法，即具身认知（embodied cognition）被越来越多人认可，也似乎更“高级”，如同当时只研究环境，后来加入的“人”的因素一样，后者显得更合理。这种观点认为，认知在很大程度上与身体的物理属性相关，脑神经水平上的细节、身体的结构、运动系统等都对认知的形成有重要的影响。“人认识世界的方式是用我们的身体以合适的方式与世界中的其他物体互动，在互动的过程中获得对世界的认识”。它认为认知在本质上并非使用抽象符号的表征和加工，而是一种模拟（simulation），而所谓模拟，是“身体、世界和心智互动过程中产生的知觉、运动和内海状态的复演”。我对此并不十分理解，紧接着文章举了个例子，认为同情心的产生即是因为我们“通过大脑与身体的特殊通道模拟了他人的感受”，同样让我不太理解。
文章还举了一个更有说服力的例子。Yale大学的研究者在2010年做了一个实验，把41个大学生随机分成两组，A组学生双手捧着一杯热咖啡，B组学生捧着一杯冰咖啡。然后两组学生分别对同一个想象中的中性人物的人格特征进行评分。结果显示，A组学生比B组学生更有可能把这个人评估为热情、友好。从而认为，身体上感知到的温暖影响了学生认知上的判断。这个结果的确让人惊讶，但仔细一想却也不是那么奇怪。正如所有人都知道的那样，同样的风景给不同的人可能有不同的感受，有人喜有人悲，最简单的，如果人身体健康，那很可能就是乐观的心态，反之如果他正遭受疾病，他更趋向于悲观的心情，这似乎是理所当然的。
最后说说具身认知的方法论。具身认知告诉我们要把有机体放在它的环境中，视有机体、行为和环境紧密相连。这就回到文首的问题，有关“有机体”的变量是可重复出现的吗？我们知道环境是可以量化的，可人在那一刻是喜是悲却无法估计，也不可能重复发生，那如何用科学的方法来研究？另外，具身认知认为，神经科学和心理学是对同一问题的两种不同的解释，应该将这两种方法结合起来，可是具体如何结合呢？
这些都是具身认知让我不可接受的地方。尽管现代科学都越来越重视“人”的因素，都试图把“人”加入研究的范畴（是否和兴起的社交网络有关？），但我还是很难理解怎样用科学、用数字和规则来研究人的行为，甚至人的认知。
3/31/2012
A first glimpse of CMAQ
In the last three weeks, I did three things:
1. Modify the python script written before to interpolate preciser DEM into WRF.
2. Learn NCL
3. Successfully run CMAQ model.
DEM Interpolation
As mentioned in the last post, I wrote a small python script to interpolate our 30-meter-resolution DEM data into WRF, and tried to make the result more accurate. After the script has been written, however, the validation is another challenging work. First we just used geogrid.exe to take in the output data, and compared the result with that using coarser resolution data, say 30 s(econd) and 2 m(inus), to see the rough trend. The result was OK, but not persuasive enough to claim the correctness, because the terrain is too complex and you can not reach every detail with only eyes!
Then we did more accurate validation. First, we made up some experimental data, say, a block of DEM where four quarters were different constant values. The simplified data was easy to check if the output was right or not. Then real data were transferred in WPS, and we did an overlay on the coarser data, and made a "minus" computation in the topography height. In this way, the correctness of the script can be fully validated.
The following work should be to check how much the more precise topography influences WRF. From the geogrid result, you can hardly tell the difference between 1s and 30s terrain. However, from reports by other researchers, the topography does make a difference. The same method is applied: overlay and minus.
NCL
NCL is an interpreted language (like python) designed specifically for scientific data analysis and visualization. As far as I see, the tool has following advantages:
1. Interpreted language. It's easy to use, with compiling and linking problems. Any change can be immediately applied. It is much like python, and can also be run in both interactive mode and batch mode.
2. Powerful I/O ability. It can read almost all scientific data, from ASCII to binary, and netcdf, grib1 & grib2, and shapefile, etc. That's why we abandon GrADS, which can only deal with specific format, and may not be used for WPS, CMAQ and SMOKE.
3. Programming freedom. The language is rather flexible, and give users enough freedom to do what they want. And it is easy to be extended to wrap C and Fortran.
4. Numerous built-in functions. Though I haven't used these mathematical functions, but it is good to know there are many available tools at hands, something like Matlab.
5. Easy map overlay. As it is especially designed for atmospheric and oceanic models, map overlay is very important to study a specific issue.
More features:
There are several different kinds of data in NCL. A variable can have attribute data, coordinate data and missing values. Attribute data explains additional info apart from the "real data", like variable descriptions. Coordinate data facilitates the mapping procedure. Missing value support is another feature that highlights. Many models have a special value that is assigned to those with no sufficient info (e.g. initial condition) to simulate. NCL can recognize these data and do some special tricks.
Also, unlike C, NCL can deal with the whole array. On the other hand, loop though every element in an array is inefficient and not recommended.
CMAQ
It finally comes to CMAQ. Thanks to Prof. Zheng Junyu, now we have SMOKE output, and can run CMAQ. CMAQ consists of many sub-programs. It is annoying to set up configurations one by one. So I write a top script to control these sub-scripts.
It's interesting to learn the shell programming. Linux shell is much more powerful than Windows Dos. It not only has basic commands, but also variables, statements, functions and procedures, and logical expressions. Some confusions I now have is Linux has various shells, like sh, bash, csh, and different shells has some different syntax. Besides, the concept "process" is important, and affects the pass of variables between functions and scripts.
It is really time-consuming to run CMAQ. For basic configurations, there are six output files, including pollution concentration and average concentration, and wet and dry deposition. One problem I recently solved is variables in CCTM output has no coordinates, but only rows and columns, making it difficult to overlay with the map. Actually there is such info, and it resides in MCIP output, in GRDCRO2D file. There are variables LON AND LAT. They are 4-dimension array, with time steps, layers, rows and columns. In fact for coordinates the array is duplicated. Only one dimension is enough, which is also required by NCL (in NCL, coordinate variables must be one dimensional. For longitude, pick columns and for latitude, pick rows).
1. Modify the python script written before to interpolate preciser DEM into WRF.
2. Learn NCL
3. Successfully run CMAQ model.
DEM Interpolation
As mentioned in the last post, I wrote a small python script to interpolate our 30-meter-resolution DEM data into WRF, and tried to make the result more accurate. After the script has been written, however, the validation is another challenging work. First we just used geogrid.exe to take in the output data, and compared the result with that using coarser resolution data, say 30 s(econd) and 2 m(inus), to see the rough trend. The result was OK, but not persuasive enough to claim the correctness, because the terrain is too complex and you can not reach every detail with only eyes!
Then we did more accurate validation. First, we made up some experimental data, say, a block of DEM where four quarters were different constant values. The simplified data was easy to check if the output was right or not. Then real data were transferred in WPS, and we did an overlay on the coarser data, and made a "minus" computation in the topography height. In this way, the correctness of the script can be fully validated.
The following work should be to check how much the more precise topography influences WRF. From the geogrid result, you can hardly tell the difference between 1s and 30s terrain. However, from reports by other researchers, the topography does make a difference. The same method is applied: overlay and minus.
NCL
NCL is an interpreted language (like python) designed specifically for scientific data analysis and visualization. As far as I see, the tool has following advantages:
1. Interpreted language. It's easy to use, with compiling and linking problems. Any change can be immediately applied. It is much like python, and can also be run in both interactive mode and batch mode.
2. Powerful I/O ability. It can read almost all scientific data, from ASCII to binary, and netcdf, grib1 & grib2, and shapefile, etc. That's why we abandon GrADS, which can only deal with specific format, and may not be used for WPS, CMAQ and SMOKE.
3. Programming freedom. The language is rather flexible, and give users enough freedom to do what they want. And it is easy to be extended to wrap C and Fortran.
4. Numerous built-in functions. Though I haven't used these mathematical functions, but it is good to know there are many available tools at hands, something like Matlab.
5. Easy map overlay. As it is especially designed for atmospheric and oceanic models, map overlay is very important to study a specific issue.
More features:
There are several different kinds of data in NCL. A variable can have attribute data, coordinate data and missing values. Attribute data explains additional info apart from the "real data", like variable descriptions. Coordinate data facilitates the mapping procedure. Missing value support is another feature that highlights. Many models have a special value that is assigned to those with no sufficient info (e.g. initial condition) to simulate. NCL can recognize these data and do some special tricks.
Also, unlike C, NCL can deal with the whole array. On the other hand, loop though every element in an array is inefficient and not recommended.
CMAQ
It finally comes to CMAQ. Thanks to Prof. Zheng Junyu, now we have SMOKE output, and can run CMAQ. CMAQ consists of many sub-programs. It is annoying to set up configurations one by one. So I write a top script to control these sub-scripts.
It's interesting to learn the shell programming. Linux shell is much more powerful than Windows Dos. It not only has basic commands, but also variables, statements, functions and procedures, and logical expressions. Some confusions I now have is Linux has various shells, like sh, bash, csh, and different shells has some different syntax. Besides, the concept "process" is important, and affects the pass of variables between functions and scripts.
It is really time-consuming to run CMAQ. For basic configurations, there are six output files, including pollution concentration and average concentration, and wet and dry deposition. One problem I recently solved is variables in CCTM output has no coordinates, but only rows and columns, making it difficult to overlay with the map. Actually there is such info, and it resides in MCIP output, in GRDCRO2D file. There are variables LON AND LAT. They are 4-dimension array, with time steps, layers, rows and columns. In fact for coordinates the array is duplicated. Only one dimension is enough, which is also required by NCL (in NCL, coordinate variables must be one dimensional. For longitude, pick columns and for latitude, pick rows).
3/16/2012
读《激荡三十年》
很早就听说这本书了，由赫赫有名的吴晓波老师写的，当初因为他与我们强化班的导师同名而让我印象深刻。自然，和其他经济学类书一样，这本书受到了强化班同学的追捧。很多人表示“赞”，或想读，或者读过了的出来发表一堆宏论。我自然不喜这些，因此也就耽搁了。
不过，对经济学类的书的好奇让我又翻看了这本书。似乎第一次看这么“务实”的书。书的内容让我很意外，写的很浅显，也很真实，让不谙世事的我第一次对这些“身边的最实实在在的事”有了一个认识。尤其是读到台州的李书福甚至玉环的那些事时，我深切地体会到书中讲的事情是多么的现实。
所以说经济学是很有意思的一门学科，它不像我平时做的学科如计算机等那么抽象、那么脱离现实，它是研究这个社会如何动作的学科，与古时读书人的清风傲骨隔隔不入。
或许以后也该多读点这类务实的书。有时想想，何必执着于那些所谓的技术呢？
不过，对经济学类的书的好奇让我又翻看了这本书。似乎第一次看这么“务实”的书。书的内容让我很意外，写的很浅显，也很真实，让不谙世事的我第一次对这些“身边的最实实在在的事”有了一个认识。尤其是读到台州的李书福甚至玉环的那些事时，我深切地体会到书中讲的事情是多么的现实。
所以说经济学是很有意思的一门学科，它不像我平时做的学科如计算机等那么抽象、那么脱离现实，它是研究这个社会如何动作的学科，与古时读书人的清风傲骨隔隔不入。
或许以后也该多读点这类务实的书。有时想想，何必执着于那些所谓的技术呢？
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