Using NASA’s Giovanni System to Detect and Monitor Saharan Dust Outbreaks James G. Acker NASA Goddard Earth Sciences Data and Information Services Center.

Slides:



Advertisements
Similar presentations
Ethiopia Ethiopia Bordering Countries Ethiopia Bordering Countries.
Advertisements

Deep Blue Algorithm: Retrieval of Aerosol Optical Depth using MODIS data obtained over bright surfaces 1.Example from the Saharan Desert. 2.Deep Blue Algorithm.
JERAL ESTUPINAN National Weather Service, Miami, Florida DAN GREGORIA National Weather Service, Miami, Florida ROBERTO ARIAS University of Puerto Rico.
ATS 351 Lecture 8 Satellites
Respiratory Health Applications Using New Satellite Air Quality Sensors Prof. Stanley Morain Earth Data Analysis Center University of New Mexico, USA Commission.
Satellite Imagery Meteorology 101 Lab 9 December 1, 2009.
What is the Saharan Air Layer? The Saharan Air Layer (SAL) is a layer of warm, dry, dusty air which normally overlays the cooler more humid surface air.
NASA Trace Gas Products for Air Quality Applications NASA Remote Sensing Training September 2014 ARSET Applied Remote SEnsing Training A project of NASA.
Evaluating Remote Sensing Data Or How to Avoid Making Great Discoveries by Misinterpreting Data Richard Kleidman ARSET-AQ Applied Remote Sensing Education.
Chapter 2: Satellite Tools for Air Quality Analysis 10:30 – 11:15.
Measurement of the Aerosol Optical Depth in Moscow city, Russia during the wildfire in summer 2010 DAMBAR AIR.
Infusing satellite Data into Environmental Applications (IDEA): PM2.5 forecasting tool hosted at NOAA NESDIS using NASA MODIS (Moderate Resolution Imaging.
Infusing satellite Data into Environmental Applications (IDEA): R. Bradley Pierce NOAA/NESDIS/STAR PM2.5 forecasting tool hosted at NOAA NESDIS using NASA.
Giovanni Facilitates Investigations of Coastal Environmental Processes with NASA Remote-Sensing Data James G. Acker NASA Goddard Earth Sciences Data and.
Satellite Imagery ARSET Applied Remote SEnsing Training A project of NASA Applied Sciences Introduction to Remote Sensing and Air Quality Applications.
Visible Satellite Imagery Spring 2015 ARSET - AQ Applied Remote Sensing Education and Training – Air Quality A project of NASA Applied Sciences Week –
An Overview of Satellite Imagery ARSET - AQ Applied Remote SEnsing Training – Air Quality A project of NASA Applied Sciences Originally presented as part.
Dust Detection in MODIS Image Spectral Thresholds based on Zhao et al., 2010 Pawan Gupta NASA Goddard Space Flight Center GEST/University of Maryland Baltimore.
Obtaining MISR Data and Information Jeff Walter Atmospheric Science Data Center April 17, 2009.
Using My NASA Data to Explore Earth Systems Lynne H. Hehr John G. Hehr University of Arkansas Department of Geosciences And Center for Math and Science.
Using NASA’s Giovanni System to Detect and Monitor Saharan Dust Outbreaks James G. Acker NASA Goddard Earth Sciences Data and Information Services Center.
S James Acker 2, Greg Leptoukh 1, Steve Kempler 1, Watson Gregg 3, Steve Berrick 1, Tong Zhu 2, Zhong Liu 2, Hualan Rui 2, Suhung Shen 4 1 NASA Goddard.
Visualization, Exploration, and Model Comparison of NASA Air Quality Remote Sensing data via Giovanni Ana I. Prados, Gregory Leptoukh, Arun Gopalan, and.
Chapter 4: How Satellite Data Complement Ground-Based Monitor Data 3:15 – 3:45.
James Acker 1, Greg Leptoukh 1, Steve Kempler 1, Watson Gregg 2, Steve Berrick 1, Tong Zhu 1, Zhong Liu 1, Hualan Rui 1, Suhung Shen 1 1 NASA Goddard Earth.
Chapter 7: Using Giovanni for Analysis of Air Quality Events in Central America 11:00 – 12:00.
Giovanni for AQ Gregory Leptoukh NASA Goddard Space Flight Center Goddard Earth Sciences Data and Information Services Center (GES DISC)
James Acker 1 Watson Gregg 2 Gregory Leptoukh 1 Steven Kempler 1 Nancy Casey 2 Gene Feldman 3 Charles McClain 3 Wayne Esaias 2 Suhung Shen 1 1 NASA Goddard.
THIS IS With Host... Your Winds and Air Masses Uneven Heating of Earth Weather Maps Scientific Tools VocabularyUp for Grabs!
James Acker NASA Goddard Earth Sciences Data and Information Services Center (GES DISC)
Miss Nelson SCIENCE ~ CHAPTER 8 WEATHER. Air Masses and Fronts SECTION 3.
September 4, 2003MODIS Ocean Data Products Workshop, Oregon State University1 Goddard Earth Sciences (GES) Distributed Active Archive Center (DAAC) MODIS.
ESIP Federation 2004 : L.B.Pham S. Berrick, L. Pham, G. Leptoukh, Z. Liu, H. Rui, S. Shen, W. Teng, T. Zhu NASA Goddard Earth Sciences (GES) Data & Information.
NCAR MDSS Functional Prototype Display System Preview – April 2002 Bill Mahoney National Center for Atmospheric Research Images shown are valid as of 15.
MODIS OCEAN QA Browse Imagery (MQABI Browse Tool) NASA Goddard Space Flight Center Sept 4, 2003
Satellite Imagery and Remote Sensing DeeDee Whitaker SW Guilford High EES & Chemistry
Aristeidis K. Georgoulias Konstantinos Kourtidis Konstantinos Konstantinidis AMFIC Web Data Base AMFIC Annual Meeting - Beijing October 2008 Democritus.
Aerosol Optical Depth during the Northern CA Fires of 2008 In situ aerosol light scattering and absorption measurements in Reno Nevada, 2008, indicated.
35 PC-HYSPLIT WORKSHOP Example Simulations Presented on the following slides are several basic trajectory and dispersion simulations and meteorological.
NASA and Earth Science Applied Sciences Program
NASA Snow and Ice Products NASA Remote Sensing Training Geo Latin America and Caribbean Water Cycle capacity Building Workshop Colombia, November 28-December.
Aristeidis K. Georgoulias Contribution of Democritus University of Thrace-DUTH in AMFIC-Project Democritus University of Thrace Laboratory of Atmospheric.
Contents of the Site On the MY NASA DATA homepage you can find: Data Access Lesson Plans Computer Tools Science Focus E-Notes.
NASA Earth Observing System Visualization Tools ARSET - AQ Applied Remote SEnsing Training – Air Quality A project of NASA Applied Sciences Introduction.
Satellite Imagery ARSET - AQ Applied Remote SEnsing Training – Air Quality A project of NASA Applied Sciences NASA ARSET- AQ – EPA Training September 29,
Suhung Shen 1 J. Acker 3, G. Leptoukh 2, H. Rui 3, S. Berrick 2, S. Kempler 2 1 George Mason University.
Preparing for GOES-R: old tools with new perspectives Bernadette Connell, CIRA CSU, Fort Collins, Colorado, USA ABSTRACT Creating.
CO Hands–on Activity: Hands-on activity II: Downloading L2 AIRS data & Visualizing L2 AIRS data (a time-consuming alternative to using IDL or Matlab to.
Studying impacts of the Saharan Air Layer on hurricane development using WRF-Chem/EnKF Jianyu(Richard) Liang Yongsheng Chen 6th EnKF Workshop York University.
The Giovanni-NEO Oceanographic Education Cookbook Instructional James G. Acker David Herring Gregory Leptoukh Suhung Shen Steven Kempler NASA Goddard Earth.
171 PC-HYSPLIT WORKSHOP Workshop Agenda Model Overview Model history and features Computational method Trajectories versus concentration Code installation.
Earth’s climate and how it changes
David Herring Kevin Ward M a y 2 2, n a s a e a r t h o b s e r v a t.
Giovanni and LOCUS: Innovative Ways for Teachers and Students to Conduct Online Learning and Research with Oceanographic Remote Sensing Data James G. Acker.
Goddard Earth Sciences Data and Information Services Center, NASA/GSFC, Code 902, Greenbelt, Maryland 20771, USA INTRODUCTION  NASA Goddard Earth Sciences.
Assimilation of Satellite Derived Aerosol Optical Depth Udaysankar Nair 1, Sundar A. Christopher 1,2 1 Earth System Science Center, University of Alabama.
Monitoring Global Droughts from Space Zhong Liu 1,4, W.L. Teng 2,4, S. Kempler 4, H. Rui 3,4, G. Leptoukh 4, and E. Ocampo 3,4 1 George Mason University,
Air pollutants, such as aerosols and various trace gases, are transported on a hemispheric or global scale. The Task Force on Hemispheric Transport of.
Satellite Imagery and Remote Sensing DeeDee Whitaker SW Guilford High EES & Chemistry
PM 2.5 Transport From Wildfires Case Study: Bugaboo Fire – Georgia/Florida, May 2007 Sean Ryan.
Zhong Liu George Mason University and NASA GES DISC
NASA’s Ocean Color Online Visualization and Analysis System
MERRA Data Access and Services
Jianyu Liang (York U.) Yongsheng Chen (York U.) Zhiquan Liu (NCAR)
NASA’s Ocean Color Online Visualization and Analysis System
NASA’s Ocean Color Online Visualization and Analysis System
Evaluating Remote Sensing Data
NASA’s Ocean Color Online Visualization and Analysis System
Satellite data that we’ve acquired
How Citizens Can Put the Data They Collect
Presentation transcript:

Using NASA’s Giovanni System to Detect and Monitor Saharan Dust Outbreaks James G. Acker NASA Goddard Earth Sciences Data and Information Services Center (GES DISC)

Part 1: Introduction to Giovanni First, let’s clear up some misconceptions. Giovanni is not: a)an Italian astronomer b)a boy band (like Menudo) c)a restaurant in Baltimore’s Little Italy, or d)an unfinished Mozart opera. So, then, what IS Giovanni?

Giovanni Giovanni used to stand for the G oddard Earth Sciences Data and Information Services Center (GES DISC) I nteractive O nline V isualization AN d a N alysis I nfrastructure. But we just call it “Giovanni” now.  It’s a Web-based application developed by the NASA GES DISC  It’s easy to use There’s no need to learn data formats, programming, or to download large amounts of data  You get customized data analyses and visualizations with only a few mouse clicks.

Main Elements of Giovanni Interactive map for region-of-interest selection Compendium of available data products for analysis Calendrical selection of time period of interest Menu of visualization options

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 24 different color palette options!

Giovanni data download page HDF, NetCDF, ASCII Visualization image is here Data download choices are here

Part 2: Finding Saharan Dust Outbreaks In this section, the use of the Giovanni system to find occurrences of Saharan dust outbreaks will be demonstrated. You will learn how to: Choose a region-of-interest Choose a time-period of interest Select a data product for visualization Select a visualization option View and interpret the generated visualization Save the visualization

Choosing a Giovanni Data Portal Go to the Giovanni home page, Choose eithe the MODIS Daily data portal (Atmospheric Portals) or the DICCE-Daily Portal (Applications and Education Portals). Both portals have MODIS Daily data: MODIS Terra and Aqua Daily Level-3 Data Atmosphere Daily Global 1X1 Degree Products

Choosing a region-of-interest Click-and- drag on the map MOVE ZOOM DRAW Blue Marble Borders

Choosing a data product & time period Time period selection Data product selection DICCE-G Daily Interface!

Choosing the visualization option In this case, the “Time Series” option is selected from a drop-down menu. In these steps, we have selected:  The coast of northwestern Africa as the region-of-interest;  The data parameter - Aerosol Optical Depth at 550 nanometers from MODIS  The time period January-August 2004  The time-series visualization option So what happens when “Generate Visualization” is clicked?

Giovanni produces this: This March 5 peak in AOD indicates a large dust storm The other peaks indicate smaller dust storms To save any image, right-click and “Save Image As” or “Save Picture As”, or the equivalent

Part 3. Visualizing (and Interpreting) Images of Saharan Dust Outbreaks Now that Giovanni has helped find a large Saharan dust outbreak in early March 2004, the next step is to use Giovanni to see what it looked like, according to the data.

But first… what did it look like from space? MODIS pseudo true color image of Saharan dust outbreak, March 2004

Back to the Giovanni interface… Adjust the region-of-interest slightly: Select the “Lat-Lon map, Time-averaged” option (very popular):

MODIS Aerosol Optical Depth at 550 nm, March 5, 2004

Now change the Plot Preferences: New color palette New parameter maximum value

which produces this:

Other color palette choices

New data parameter: MODIS “Deep Blue” AOD The MODIS “Deep Blue” aerosol optical depth data parameter allows retrieval of AOD values over bright land areas, where the standard AOD algorithm fails. Using “Deep Blue” AOD, the source areas of Saharan dust outbreaks which migrate over the Atlantic Ocean can be observed.

Deep Blue AOD, March 1-5, 1994 Approximate location of the Bodélé Depression

Deep Blue AOD animation frames, March 1-4, 2004 March 1March 2 March 3 March 4

Deep Blue AOD animation frame March 5, 2004 MODIS AOD, March 5, 2004

Tracking Saharan Dust Outbreaks Using Aerosol Optical Depth and adjusting its “sensitivity”, the impact of a Saharan dust outbreak over the tropical Atlantic Ocean can be tracked. MODIS AOD for the period March 5-15, 2004, using 1.5 as the upper bound value for the color palette. Leading edge

Tracking Saharan Dust Outbreaks Upper bound value for AOD palette is now set to 0.5. It now appears that elevated AOD from the dust is affecting the West Indies.

Tracking Saharan Dust Outbreaks Same color palette range is used here; now for the period March 15-20, Higher values of AOD over the West Indies (and even Puerto Rico), and notably on the northeast coast of South America. Fire ?

Where is the Saharan dust in the atmosphere? Employing the Atmospheric Infrared Sounder (AIRS) Daily data portal, we can examine the atmospheric environment of the Saharan dust outbreak.

Where is the Saharan dust in the atmosphere? Choose Vertical Profile Layers Choose Vertical Profile option

Where is the Saharan dust in the atmosphere? The relative humidity profile shows the dry air layer primarily between hPa, which is meters, or 13,000 – 18,000 feet. The temperature profile doesn’t provide as much information. Dry air layer

Where is the Saharan dust in the atmosphere? Mapping relative humidity in the 500 hPa layer shows the horizontal extent of the dry air layer.

Advanced: Latitude vs. Time Hovmöller plot Time Latitude As a guide, 36° N is the latitude of the Straits of Gibraltar, and 6° N is about the latitude where the West African coast turns westward. The Hovmöller plot shows occurrences of dry air off the “Saharan” coast. The dust storm we have been examining impacted this region between March 1 st and March 23 rd.

Impacts on the Caribbean Sea? February 2004 Orinoco River outflow region Sea surface temperature and phytoplankton chlorophyll might show an influence of dust, but there are other factors to be considered.

Impacts on the Caribbean Sea? March 2004

Impacts on the Caribbean Sea? April 2004 Influence of Amazon River waters Phytoplankton growth here might be augmented by iron from dust

Using Giovanni with Google Earth If you generate an image, one of the file download options is a KMZ file, which will open in Google Earth. GIF image KMZ file

Using Giovanni with Google Earth To examine the question of whether the Saharan dust outbreak in March 2004 affected Photosynthetically Available Radiation (PAR), three images for February, March, and April 2004 were generated. February April March Perhaps some influence here; needs better temporal resolution

Using Giovanni with Google Earth With practice and experience with Google Earth, multiple data images can be displayed with geographical context

The all-important final slide: Any questions?