James Acker NASA Goddard Earth Sciences Data and Information Services Center (GES DISC)

Giovanni  Web-based application Developed by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC)  Easy to use No need to learn data formats, programming or download large amounts of data  Customized data analyses and visualizations with only a few mouse clicks…

Aerosol from MODIS and GOCART model Particulate Matter (PM 2.5) from AIRNow Ozone Hole from OMI Aerosol from GOCART model ppmv Carbon Monoxide from AIRS Water Vapor from AIRS MODIS vs SeaWiFS Chlorophyll Giovanni Interfaces CloudSat HIRDLS MLS OMI TRMM SeaWiFS AMSR-E HALOE TOMSModelsParasol CALIOP Data Inputs MODIS AIRS MISR and more… Analysis Tools: Giovanni Data

Getting Started with Giovanni Select Area of Interest Select Display (info, unit) Select Parameters Select Time Period Select Plot type Generate Visualization

Refine constraints and edit plot preferences Outputs: Refine/Modify

Download Data: files and images

Giovanni data download page HDF, NetCDF, ASCII and KMZs (for Google Earth)

Using Giovanni to observe the oceans

Available Science Data Sets Giovanni Oceans Tools and Datasets  Chlorophyll concentration  Diffuse attenuation coefficient at 490 nm (K490)  Normalized water-leaving radiance at 555 nm (SeaWiFS) or 551 nm (MODIS)  Absorption coefficient of dissolved and detrital matter at 443 nm  Particulate backscatter coefficient at 443 nm  Sea surface temperature (MODIS)  Assimilated chlorophyll and other output fields from the NASA Ocean Biogeochemical Model (NOBM) Giovanni’s ocean data is from either: the Sea-viewing Wide Field-of-view Sensor (SeaWiFS), or the Moderate Resolution Imaging Spectro-radiometer (MODIS)

Giovanni output types Area plot (lat-lon map) Animations display successive area plots Hövmoller plots – ideal for visualization of seasonal signals Time - series

Time-series analysis with Giovanni

Five good reasons to perform time- series analyses: Detecting changes (trends) over time Detecting changes (trends) over time Assigning causation to trends Assigning causation to trends Distinguishing between short-term Distinguishing between short-term variability and long-term trends variability and long-term trends Predicting changes in the future Predicting changes in the future Determining consistency of observations Determining consistency of observations with measurable (and significant) trends with measurable (and significant) trends

Significant chlorophyll trends in the global ocean

What is necessary for a useful time-series analysis? 1.A sufficiently long data set 2.A meaningful environmental parameter 3.Consistency of measurement methodology 4.Exclusion of erroneous or questionable data 5.“Unbiased” statistical tools A foolish consistency is the hobgoblin of little minds, adored by little statesmen and philosophers and divines. - Ralph Waldo Emerson

SeaWiFS chlorophyll data is sufficiently accurate for valid time-series analysis, due to rigorous and consistent QA/QC. Basic steps for time-series analysis with Giovanni: 1.Choose parameter, region, and time period 2.Generate the “raw” time-series 3.Identify outliers – consider exclusion criteria 4.Acquire ASCII numerical data 5.Export ASCII data to MS Excel (or other statistical package) 6. Calculate trend and significance

Choose Parameter, Region, and Time Period Parameter: chlorophyll a concentration (chl a) Region: Pacific Tehuano Wind Zone North: 12.0 South: 11.0 West: East: Time Period: October 1997 – December 2007

Region Selection

Generate “raw” time-series Identify outliers: consider exclusion criteria ?

Acquire ASCII numerical data ASCII data download icon Time series image

Export ASCII data to MS Excel (or other statistical analysis package)  Save the ASCII data to a text file  Open the file in Excel  Choose “Fixed Width”  Choose “MDY” for Column Data Format  Data will appear in two columns in spreadsheet MS Excel will require the Analysis Toolpak to be installed; don’t Browse, MANAGE!

Calculate trend and significance Significance F

River-influenced coastal zones; Sampling the watershed In Acker, McMahon, Shen, Hearty, and Casey (2009), we took chl a as proxy for river effects in general, because of the known problems with the data in coastal zones, particularly due to colored dissolved organic matter (CDOM). Trends in chl a thus indicated changes in the influence of the river, due to the effects of river discharge to the ocean on nutrient concentrations, CDOM export, and sediments. Changes in the influence of the river were primarily attributed to changes in the flow regime of the river.

River-influenced coastal zones; Sampling the watershed The primary two exceptions (we think) are the Mississippi River, with reduced nutrients – i.e., agricultural management is working [ironic in light of the oil spill] and The Pearl River in China, which clearly shows the effects of increasing fertilizer use (and pollution) in a region of heavy agricultural activity Our analyses also clearly showed the impact of extreme events (floods) on time-series analysis The most interesting case was the Eel River in California; the high flow period was essentially unchanged, but the low flow period had an increasing trend due to the marine environment, indicating a decreasing flow during the low flow period

Links and places of interest GES DISC: Mirador: Giovanni: Facebook Group: NASA Giovanni: Remote Sensing Data Analysis

Thank you! Any questions?