Make Non-Uniform Sparse Data Maps In Python¶

Lately, I have been working with gridded data sets. Climate scientists and oceanographers have been fervently creating gridded data sets using satellite and in situ data that are released. For example, see the Argo gridded product page for ocean grids, Here for precipitation. There is also vector grids available for wind CCMP Wind Vector Analysis Product

MLE Space-Time Numerical Optimization II: Hessian¶

In my last post, we saw the speedup boosts gained when providing Scipy's minimize solver a gradient. We will take this a step further and see what happens when we provide the Hessian. The Hessian matrix is a second partial derivatives of a function $f(x)$. In our case, the log-likelihood of a Gaussian process with an anisotropic covariance function.

Numerical Optimization Improvements for Maximum Likelihood Estimation of interpolated Argo Ocean Floats.¶

Ocean properties such as temperature and salinity are measured globally by the Argo float array, collecting more than 2 million profiles over a decade. It is enough data to create a climatological product. It is an active area of research and many such products exist

Python API to new modules at https://argovis.colorado.edu¶

The Argo program has provided temperature, salinity and pressure data (T/S/P) on a global scale.

The web app Argovis provides data access and visualization.

The following code is an interface to some of the new features at Argovis. Recently we have been including other data sets and data derived gridded products. API access is available for these datasets so that those inclined can retrieve Argovis data in a python environment.

R API to www.argovis.com¶

Mentioned in this blog post, Argovis has an API that can send JSON data of Argo profiles, platforms and selections, and their metadata. This post will again retrieve Argodata, but this time in an R environment.

This script will guide an R user to:

1. Query a specific profile its id, designated by its platform (WMO) number with its cycle number, connected by an underscore. For example '3900737_9'

Python API to www.argovis.com¶

This post is the first part of a three-part series on argovis. Supplementary tutorial videos are found here.

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.

Langly Extrapolation¶

My Summer on the volcano¶

I'm going to write about my 2018 Summer. This post is all about my time at NOAA's Mauna Loa Station. I began measuring ozone for the world's standard Dobson Spectrophotometer (083) in June 2018 and stayed until October 31, 2018. I measured through the Kilauea volcanic eruption and was there when we were hit by Hurricane Lane. It was quite an honor performing this calibration and was an experience working at 11000 feet above sea level. Here are some pictures of where I worked

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.