Climate Change Observations Using Ground Based Optical Remote Sensing Instruments vs. Cameras
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1 NYCCT Spring 15 Research Methods for the Social and Behavioral Sciences Climate Change Observations Using Ground Based Optical Remote Sensing Instruments vs. Cameras Rudy Escobar 12/8/2015 Rudy Escobar LSAMP Meeting 1
2 Why is Climate Change Important? With investigating climate change we could help determine weather patterns We would also be able to predict natural disasters including how to help prevent it Through predicting climate change we could determine the changes in temperature all over the world, showing the areas that will have high and low moisture for growing certain plant establishments (grains, vineyards,gardens,etc.) 12/8/2015 Rudy Escobar LSAMP Meeting 2
3 Parameters to Climate Change Cloud Fraction the percentage of each pixel in satellite imagery that is covered with clouds (1= 100% 0=0% coverage) Relative Humidity the amount of water vapor present in air as a percentage compared towhat the air can hold at that temperature Cloud Depth/density How thick the cloud actually is Cloud Height how high the cloud actually is from the location Radiation Backscatter the radiation levels above the sky reflecting upon the sky Liquid Water The percentage of actual water in the air/clouds Temperature sky temperature 12/8/2015 Rudy Escobar LSAMP Meeting 3
4 Proposal Hypothesis Can we use digital cameras to conduct advanced research on climate change? Thesis My claim is to agree with the hypothesis that cameras CAN be used to conduct proper advanced research on climate change through proper programming and observations. 12/8/2015 Rudy Escobar LSAMP Meeting 4
5 Proposal We will conduct a qualitative experiment where we pick a specific location on CCNY campus and for a year we will take daily pictures at the same times (12:00 PM-12:10OM, 2:50PM-3:00PM) During those times we will activate the optical ground based sensors located in CCNY Grove School of engineering rooftop and take observations of the climate. The goal of the year long experiment is to create a correlation with pixelated number Figure 1: extacting RGB and pixalation information from cloud photography (Aguire) 12/8/2015 Rudy Escobar LSAMP Meeting 5
6 Independent variable In our experiment out independent variables will be the Fujifilm Finepix S1 parameters and the outputs produced by it Focal Length= 215 mm F## (focal ratio)= 5.6 Effective Aperature Diameter= 38.4 mm Output: 3456x 4608 pixels 16-bit RGB (color depth, high color graphics) 64 levels of color intensity 12/8/2015 Rudy Escobar LSAMP Meeting 6
7 Dependent variables In the experiment we will be comparing the information we receive from the outputs of the camera with the observations recorded from the following instruments located at CCNY Grove School of Engineering rooftop: LIDAR CELIOMETER MFRSR SHADOW BAND MICROWAVE RADIOMETER CIMEL PHOTOMETER 12/8/2015 Rudy Escobar LSAMP Meeting 7
8 MFRSR Shadow band Microwave Radiometer LIDAR 12/8/2015 Celiometer Rudy Escobar LSAMP Meeting CIMEL Sun Photometer 8
9 Proposal Layout Figure 4: A diagram of the ground based optical remote sensing devices on the roof of the Grove school of Engineering at CCNY 12/8/2015 Rudy Escobar LSAMP Meeting 9
10 Independent Results Date: 3/2/2015 Time: 12:48 PM Location: CCNY Date: 3/2/2015 Time: 12:41 PM Location: CCNY 12/8/2015 Rudy Escobar LSAMP Meeting 10
11 Observational Results LIDAR Backscatter Microwave Radiometer Water Vapor 12/8/2015 Rudy Escobar LSAMP Meeting 11
12 Observational Results Microwave Radiometer Liquid Water Microwave Radiometer relative Humidity 12/8/2015 Rudy Escobar LSAMP Meeting 12
13 Observational results MFRSR 12/8/2015 Rudy Escobar LSAMP Meeting 13
14 Results So Far 12/8/2015 Rudy Escobar LSAMP Meeting 14
15 You need to be more clear Maybe you should copy the images of the clouds on this slide again 12/8/2015 Rudy Escobar LSAMP Meeting 15
16 The spatial autocorrelation function contains very important information about the spatial arrangement and morphology of clouds and is thus an indicator of the dependence of the radiation on the 3 D structure. Considering the camera pixel footprint at 1 km of ~30 mm, the cloud dimension in the x direction is calculated to be ~ 12 m (30 mm/pixel *400 pixels). 12/8/2015 Rudy Escobar LSAMP Meeting 16
17 References "Cloud Fraction : Global Maps." Cloud Fraction : Global Maps. Web. 16 Apr < "Weather Questions & Answers." Weather Questions. Web. 16 Apr < Web. 16 Apr < Dunbar, Brian. NASA. NASA. Web. 16 Apr < 12/8/2015 Rudy Escobar LSAMP Meeting 17
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