Portfolio: Data analysis, visualization, & science

Data analysis, visualization, and science


Work and project pages AI application development Research: atmospheric science MS thesis (low-res, high-res, defense) Atmospheric and Earth science data NASA JPL ECOSTRESS CDC VAERS COVID-19 NASA CRYSTAL-FACE Meteorology, SJSU Cloud modeling, UCSC Cryptocurrency sentiment analysis Wind resource assessment		Atmospheric science essentials & derivations Thermodynamics Radiation Dynamic meteorology Stratospheric ozone Physical meteorology & cloud physics Numerical methods & modeling Thermal wind Vorticity Phase speed of a sound wave Helmholtz equation for Pⁿ⁺¹ (from compressible Boussinesq eqs.) Turbulent kinetic energy equation for turbulence

Images and links below open PDFs, interactive figures, and related conferences/publications. If a figure in an iframe does not load: right click on the frame and select 'Reload Frame', or try another browser such as Chrome or Firefox.

*I contributed in varying capacities to all projects except those marked with * (e.g. Atmospheric CO₂ concentration, NCA5, VAERS, ..), i.e. no direct connection with those latter groups, but I wrote programs to analyze and visualize their data for informational purposes.

Fortran, Interactive data language (IDL), Python, scikit-learn, SciPy, statsmodels, Matplotlib, R, ggplot2
American Geophysical Union, 2019
Note: This figure is one of hundreds generated in automated sweeps of various geospatial parameters & statistical tests.

Python, Plotly*
Open: 3D, 2D
References: IPCC, 2021; Bereiter et al., 2015; Jouzel et al., 2007; Keeling et al., 2005; Jones et al., 1999
Data: IPCC; Antarctic CO₂; Antarctic Δ T; Mauna Loa CO₂; NOAA Δ T

Python, scikit-learn, SciPy, statsmodels, Plotly
Open

ECOSTRESS land surface temperature, Silicon Valley, CA, USA – 2020.08.03

HDFView, Python, QGIS, GDAL, OpenLayers, JavaScript, Esri
GIS maps with NASA JPL ECOSTRESS land surface temperature data
Open

Santa Cruz County, Voronoi polygons from USGS landslide points; average annual precipitation, 1971–2000

QGIS, GDAL, Esri*
References: Landslides, Precipitation

ECOSTRESS_2020.08.14

HDFView, Python, QGIS, GDAL
GIS maps with NASA JPL ECOSTRESS land surface temperature data

Wildfire and flood risks – climate data and projections, Santa Cruz County, USA

ArcGIS, Esri*
Open: map, dashboard
References: IPCC, 2013; IPCC, 2021; Fifth National Climate Assessment (NCA5)

Esri, QGIS, Python, Bokeh, Holoviews, Holoviz*
References: Fifth National Climate Assessment

Climate projections (temperature and precipitation), global

ArcGIS, Esri*
Open
References: IPCC, 2013; IPCC, 2021; Fifth National Climate Assessment

IDL, Python, SciPy, statsmodels, Pingouin, Matplotlib
American Geophysical Union, 2019
Note: This figure is one of hundreds generated in automated sweeps of various statistical parameters & tests.

Python, Matplotlib



Python, SciPy, Plotly* Open: ρ comparison, u₀ = 0, v₀ = 1, w₀ = 1.05 Time animation, u₀ = 0.1, v₀ = 1.2, w₀ = 1.13 Deterministic nonperiodic flow (Lorenz, 1963)

Python, Matplotlib

Fortran, IDL, Adobe Illustrator
American Geophysical Union, 2015; Mars Atmosphere Workshop 4, 2011 (paper)

Python, Matplotlib

Python, Matplotlib

Fortran, IDL, Adobe Illustrator
American Geophysical Union, 2015, 2016

Fortran, IDL, Adobe Illustrator
American Geophysical Union, 2019; Eighth International Conference on Mars, 2014 (poster, paper)

Python, Matplotlib

Python, SciPy, statsmodels, Pingouin, Matplotlib
R, ggplot2 (Mirror kernel density estimates)
American Geophysical Union, 2019

Python, SciPy, Matplotlib, Seaborn, Holoviz*
Open full versions:
- Fig 1 (distance metric: Euclidean)
- Fig 2 (distance metrics: Canberra, correlation)

Python, pymdp, Matplotlib, BeeWare* (Heins et al. 2022)
AI application development (MacOS, iOS)

IDL, R, ggplot2
American Geophysical Union, 2019

Python, LangChain, BabyAGI, BeeWare
AI application development (MacOS, iOS)
Hello World app: Python, Reverse Geocoder, BeeWare

Python, Bokeh, Holoviews, Holoviz*
Open full version

IDL
Eighth International Conference on Mars, 2014 (poster, paper)
Note: This figure is a subset of a 5 × 36 matrix of figures generated in a lat/lon (5 × 5°) parameter sweep.

Python, Matplotlib*

Python, Matplotlib, NetworkX*

Python, Bokeh, Holoviews, Holoviz, Datashader, NetworkX*
Edge color = vaccination date. Clicking on nodes/edges dims unconnected nodes and edges.
Open: small, medium, large

Python, scikit-learn, Plotly, iGraph*
Open

Fortran, IDL
MS thesis (low-res, high-res)

Python, Matplotlib, spaCy*
All CDC VAERS figures

Python, Bokeh, Holoviews, Holoviz*
Open full version (NB. figure does not render fully in Safari)

IDL

IDL

Python, Bokeh, Holoviews, Holoviz*
Open: small, medium, large

Python, Matplotlib, Holoviz*
Open
Note: Venn diagram legend may not be visible in Safari

Analysis of Land vs. Ocean Radar-Derived South Florida Rainfall Data During NASA CRYSTAL-FACE

IDL

Python, Bokeh, Holoviews, Holoviz*
Edge color = vaccination date. Clicking on nodes/edges dims unconnected nodes and edges.
Open: small, large

IDL

Fortran, IDL
MS thesis (low-res, high-res)

Python, Bokeh, Holoviz*

Planewave form of the geostrophic streamfunction

• IDL

IDL

MATLAB
Arctic warming feedbacks and amplifications. 2009, Earth 254, UCSC
References: Understanding Climate Change Feedbacks. 2003, NRC

Fortran, OpenDX

Fortran, Explicit mixing parcel model, MATLAB

Fortran, Explicit mixing parcel model, MATLAB

IDL, Fortran (Numerical model of the equatorial stratosphere)

MICRODEM, WindPro, WindSim
Preliminary wind resource assessment

Fortran, MM5, GrADS
American Meteorological Society, 2005

Fortran, MM5, GrADS
American Meteorological Society, 2005

Python, Bokeh, Holoviews, Holoviz*
Open

Python, Vega, Altair, Holoviz*
Open

ECOSTRESS land surface temperature distribution, kernel density estimation, 2020.08.03



	Python, QGIS, GDAL, Plotly GIS maps with NASA JPL ECOSTRESS land surface temperature data North Fair Oaks land parcels (8 MB)

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