Argovis Data Visualization and Extraction Tutorials¶

Tutorials describe several website use cases step by step and offers the user a break from reading. The first tutorial narrates a typical use case on how a user can make a custom selection around the Labrador sea at 500-1000 meters during Summer 2017, and download the data and its metadata table locally. The second tutorial

Ocean Modeling with the Navier-Stokes Equations: or as I think of it, 'my missed oppurtunity.'¶

By Tyler Tucker

note to reader: I wrote this project for Peter Blomgrens MATH 793A course in numerical partial differential equations. This is the presentation.¶

Project outline¶

I wrote this project for Peter Blomgrens MATH 793A course in numerical partial differential equations. This is the presentation.

Bayesian Predictive Posterior for a Normal Distribution using SIR Prior¶

Hello all,

My Bayesian statistics class had me compute the predictive posterior distribution of a normal distribution. You can do this by hand if you use the standard improper reference (SIR) prior. This was painfull to do on my own, and I thought it would be helpful to include this on my blog, in case anyone wants to compute this, and wants to see how I did it.

Recursive Taylor Series Using Python's Sympy Library¶

Greetings everyone, I ran into some homework problems involving taking a multivariate Taylor expansion and noticed that Sympy's doesn't have this feature. They do have a series expansion at one point. However, things go sour for additional terms. I managed to write a multivariate Taylor series expanded about the origin up to the second order and provided the code below for those who are interested. If you see something that can be improved, let me know at ttucker@sdsu.edu. Thanks!

Python API to www.argovis.com¶

This post is the first part of a three-part series into argovis.

1. Argovis api introduces my website www.argovis.com, and how users can access data using a python API.
2. Linear time series analysis in R shows how time series models can be used to fit ocean temperatures from Argo data.

Nonlinear Least squares fitting time series in Python¶

This post is the third part of a three-part series into argovis.

1. Argovis api introduces my website www.argovis.com, and how users can access data using a python API.
2. Linear time series analysis in R shows how time series models can be used to fit ocean temperatures from Argo data.

Nonlinear least squares fitting of time series¶

This post is the 2nd part of a three-part series into argovis.

1. Argovis api introduces my website www.argovis.com, and how users can access data using a python API.
2. Linear time series analysis in R shows how time series models can be used to fit ocean temperatures from Argo data.

Introduction¶

Snow and Ice cover has been collected in .asc files here by the National Snow and Ice Data Center (NSIDC). One product of interest is the Ice Mapping System (IMS) which takes satellite and field measurements and puts them on a grid. Currently, they have resolutions of about 24x24km 4x4km and 1x1 km. Their website is located here

An explanation of IMS's map projection:¶

I feel the need to clarify how the National Ice Center's Interactive Multisensor Snow and Ice Mapping System (IMS) generates their maps.

But first a little background. The IMS project has been uploading zipped .asc files daily since 1997. From the start, they were using a 24x24 km grid. Since 2004, they have improved their measurement methods and process and implemented a 4x4 km resolution grid. Lastly, 2014 saw further improvements with a 1x1 km resolution map. Below is a picture was taken from their website.