Giovanni Update:

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Giovanni Update

• Earth Science Data and Analysis Tools at your
  finger tips

Gregory Leptoukh Giovanni Team Code 610.2
Goddard Earth Sciences Data and Information
Services Center (GES DISC)
2
Outline

• Giovanni vision and concepts
• Giovanni instances, geophysical parameters, data
  sources
• Multi-sensor data environment
• A-Train Giovanni, the first real multi-sensor
  instance
• Aerosol Giovanni intercomparison and fusion
• Examples of Giovanni usage
• Supporting applications Air Quality,.
• Science quality and data lineage (provenance)
• Giovanni-related projects
• How to stay in touch with Giovanni
• Future

May 6, 2009
Giovanni update, Leptoukh
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New NRC Data Paradigm
To meet important needs, there must be a greater
ability to extract information coherently from
multiple observations and sensors and to address
the already-well-known challenges of data
management. Observations without analysis,
interpretation, and application are sterile, and
it is thus crucial to ensure the vitality of
research, analysis, and modeling programs.
--2007 NRC Decadal Survey, p. 69
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Goddard Interactive Online Visualization ANd
aNalysis Infrastructure (Giovanni)

• With Giovanni and a few mouse clicks, easily
  obtain information on the atmosphere around the
  world.
• No need to learn data formats to retrieve and
  process data.
• Try various combinations of parameters measured
  by different instruments.
• All the statistical analysis is done via a
  regular web browser.
• http//giovanni.gsfc.nasa.gov/
• Caution Giovanni is a rapidly evolving data
  exploration tool!

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Giovanni Allows Scientists to Concentrate on
the Science
The Old Way
The Giovanni Way
Web-based Services
Jan
Pre-Science
Find data
Minutes
Retrieve high volume data
Feb
Days for exploration
Read Data
Learn formats and develop readers
Miradr
Extract Parameter
Extract parameters
Mar
Use the best data for the final analysis
Subset Spatially
DO SCIENCE
Perform spatial and other subsetting
Derive conclusions
Filter Quality
Apr
Identify quality and other flags and constraints
Write the paper
Reformat
Giovanni
Submit the paper
Perform filtering/masking
Reproject
May
Visualize
Develop analysis and visualization
GES DISC tools allow scientists to compress the
time needed for pre-science preliminary tasks
data discovery, access, manipulation,
visualization, and basic statistical analysis.
Explore
Accept/discard/get more data (sat, model,
ground-based)
Jun
Analyze
Jul
DO SCIENCE
Aug
Sep
Scientists have more time to do science.
Oct
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Giovanni now

• Almost 30 customized Giovanni instances
• More than 1000 of geophysical parameters
• Data from
• 20 space-based instruments
• 50 models
• EPA and Aeronet stations
• Multiple visualization and statistical analysis
  functionalities including data intercomparison
• Data lineage
• Subsetted data downloads in multiple formats

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Science Analysis
Area Plot
Time Series
Model Output
Giovanni provides a suite of statistical analysis
and visualization tools for the comparison of
regional and global datasets.
C
B
A
B
A
C
E
C
F
D
D
E
F
May 6, 2009
Giovanni update, Leptoukh
Profile Cross-Section
Correlations
Column Densities
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Giovanni Instances
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Vertical Profiles
HIRDLS
MLS
HIRDLS and MLS ozone (top) and temperature
(bottom) profiles acquired March 12, 2007, over
France during the passage of a weather front.
MLS vertical resolution is 3 km, HIRDLS
vertical resolution is 1 km.
Ozone
Ozone
Temperature
Temperature
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Giovanni Provides Perspective on Sudden
Stratospheric Warming with AIRS Data

• The warming started at the stratopause level, and
  around the end of January 2009, penetrated down
  to the lower stratosphere. AIRS data are plotted
  for the northern polar region in a latitude band
  between 79.5 N and 81.5 N. The warming event
  (reds and yellows indicating a temperature range
  between 250 280 K) is observed commencing about
  January 18 -19.

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Studying correlations between Chlorophyll-a and
SST in the northern East China Sea using
MODIS-Aqua
Chl-a
SST
Temporal correlation map
Time-series
Case-1 waters with nutrient-rich cold water due
to upwelling are well identified by strong
negative correlation between chlorophyll and sea
surface temperature.In Case 2 coastal waters
nutrients are carried in by warm water from river
and runoff therefore resulting in positive
correlation between chl and SST. Shen,  S.,
Leptoukh, G. G., Acker, J. G., Yu, Z., Kempler,
S. J. (2008). Seasonal variations of chlorophyll
a concentration in the northern south China Sea.
IEEE Geosci. Remote Sens. Lett, 5, 315-319.
Giovanni update, Leptoukh
12
Between Level 2 and 3 L2G
Working closely with the OMI Team

• L2G provides unified data organization
• L2 disadvantages
• Swath projection, many files, complicated formats
• L3 disadvantages
• Fixed aggregation algorithm and QA flags
• Exact geometry and measurement time is lost
• Instead, L2G format (implemented for OMI)
• Bin each pixel or data point into a 0.25 x 0.25
  grid box
• 3rd dimension handles multiple pixels in each
  grid box
• Original pixel information (lat/lon, time, solar
  zenith angle, viewing zenith angle, quality
  flags, geophysical parameters, etc.) are all
  preserved no statistics applied (!)
• Users can tailor their own Level 3 product from
  the L2G using Giovanni

NO2 with Default or Recommended Values
without snow/ice pixels
with Solar Zenith Angle between 0º and 40º
May 6, 2009
Giovanni update, Leptoukh
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Data and analysis tools at your finger
tipsPerform research with satellite, model and
ground and airborne campaigns data as if they are
on your computer, i.e., independently of the data
location
Towards an Ideal Comprehensive Data System for
Earth Sciences
May 6, 2009
Giovanni update, Leptoukh
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Comprehensive Data System
Giovanni
Export to Applications
Science Team
DataFed
Statistical Analysis
Intelligent Comprehensive Services
User-Defined Services
Web Services Interface
CEOS
Data Download
Quality Assurance
Export to Models
Web Services Interface
Intercomparison and Data Fusion Services
Field Campaigns
Models
Ground- Based
Satellite Data
May 6, 2009
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Giovanni update, Leptoukh
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Multi-Sensor Data Systems

• Several Giovanni instances represent our effort
  to move into comprehensive data systems
• A-Train Data Depot
• Aerosol Giovanni
• Air Pollution Giovanni
• TOVAS (TRMM, GPCP)
• NEESPI
• Aerosol Data Fusion
• Hurricane Portal (prototype)

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Giovanni A-Train Data Depothttp//gdata1.gsfc.nas
a.gov/daac-bin/G3/gui.cgi?instance_idatrain

• CloudSat-collocated previews of data from 8
  instruments and ECMWF
• MODIS/Aqua
• AIRS
• AMSR-E
• CloudSat
• CALIPSO
• POLDER/PARASOL
• MLS
• OMI
• On-line archive of pre-processed collocated
  subsets available
• http//mirador.gsfc.nasa.gov/cgi-bin/mirador/colle
  ctionlist.pl?keywordatrain
• ftp//atrain.gsfc.nasa.gov/data/s4pa/

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Multi-sensor view of Ileana
Different A-Train sensors are sensitive to
different parts of a cloud thus providing
different cloud height assessment
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Importing Giovanni Data into Google Earth
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Aerosols
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Comprehensive Multi-Sensor Data Environment for
Aerosol Studies
Missions
US EPA PM2.5 (DataFed)
GOCART Model
AERONET
Instruments
Terra
MISR
(future)
Aqua
Aerosol Giovanni
MODIS
Aerosol Giovanni is being expanded to become a
Comprehensive Data System by including more
models and coincident ground-based datasets.
Aura
OMI
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Aerosol Dataset
Status
MODIS
Terra

MODIS
Aqua

OMI
Aura

Giovanni is already a powerful tool for the
analysis of satellite aerosol datasets. The next
obvious step in its evolution is the inclusion of
additional ground-based, validation campaign and
model aerosol data into AeroCAFÉ.
TOMS
N7/EP

CALIOP
Calipso

SeaWIFS
Orbview2

MISR
Terra

POLDER
Parasol


MERIS
Envisat


AVHRR
NOAA

APS
Glory

VIIRS
NPP

HIRDLS
Aura

MODIS-Deep Blue

MODIS-MAIAC

MODIS-NAAPS

AERONET


already included in Giovanni 3.08 in
preparation, prototype or testing to be included
MAPSS

GACP

EPA AirNOW PM2.5


Validation Campaigns

ISCCP

AEROCOM


GOCART

HTAP

GlobAEROSOL (ESA)

May 6, 2009
Giovanni update, Leptoukh
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MODIS Terra AOD Animation
Data from a single sensor dont provide
sufficient spatial coverage
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Merged AOD data from 5 retrieval algorithms (4
sensors MODIS-Terra, MODIS-Aqua, MISR, and OMI)
provide almost complete coverage.Caveat this is
just the simplest merging prototype
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MODIS Terra AOT and Cloud Optical Thickness (May)
2003
2004
2005
2006
2007
2008
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Seasonal displacement of Clouds and Aerosols in
ITCZ
2003
2004
2005
2006
2007
2008
2003
2004
2005
2006
2007
2008
Cloud Optical Thickness
Aerosol Optical Thickness
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Interdisciplinary
May 6, 2009
Giovanni update, Leptoukh
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Multi-sensor view of dry land in mid-Asia,
northwestern China, and Mongolia
GPCP Precipitation
AMSR-E Soil Moisture
MODIS Land Cover (bare land)
MODIS NDVI
May 6, 2009
Giovanni update, Leptoukh
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Fires, Temperature, Snow, AOT Maps
Fires
Warm enough?
?
Snow prevents spread of fires
Aerosol Optical Thickness
May 6, 2009
Giovanni update, Leptoukh
30
Environment for aerosol data and analysis tools
Environment for Aerosol Data and Analysis Tools
Fusion
Evaluation

• Flexibility is the key to providing a useful
  platform for comparison, analysis and evaluation
  of aerosol data.
• The user defines
• horizontal spatial resolution, temporal
  resolution
• vertical spatial resolution (when relevant)
• Web browser (Explorer, Mozilla Firefox, Safari)
• statistical tools
• quick-look options, color palettes
• Taylor diagrams for multivariate comparisons
• spatial re-gridding options
• data fusion/merger options
• empirical corrections
• data output options (NetCDF, HDF, ASCII, KMZ
  (Google Earth), XML, binary, IDL, MATLAB)
• coming soon!

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Putting It All Together
Web Portal
Data Processing
Data Management
Data Archive
Mirador Search
Co-Location

Aerosol Data
Data Fusion
Science Analysis
Giovanni Science
Export Data to
Evaluation
And netCDF, HDF, ASCII
May 6, 2009
Giovanni update, Leptoukh
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Applications
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Multi-Sensor Air Quality Giovanni

• Visualizations of Level-3 gridded satellite data
• The first Giovanni instance with point source
  data (PM2.5 ground-based aerosol observations
  from EPA) co-located with a variety of satellite
  datasets
• Multi-sensor observations of aerosols facilitate
  analysis of air pollution events
• Temporal and geographical selection of data
  products - Lat/lon maps with overlay option, time
  series, and Hovmöller plots for customized
  visualization of air quality events
• Combined surface monitor and satellite data -
  AOD/ PM2.5 scatter plots, correlation maps, time
  series and difference plots for analysis and
  source attribution of pollution events
• Temporal animation plots of air quality data
  products for examining long range transport of
  pollutants

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Data Sources Multi-sensor Giovanni End Users
Multi-Sensor Air Quality Giovanni
Scientists
Satellite Data
Air Quality Forecasters
Surface Monitor Data
Policy Makers and Regulators
Other Professionals
Model Data
Students
Public
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Giovanni PM2.5 Gridded Data

• PM2.5 data from EPA AirNow provided in GIOVANNI
  as 1 deg gridded product, which makes easy to
  compare with other satellite ground observation

Gridded GIOVANNI PM2.5 (µgm-3)
PM2.5 Air Quality Index
5/6/2009
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July 31st, 2007
In Canada and the north-central US, MODIS and OMI
show thick aerosols plumes. CALIOP overpass has a
plume above the boundary layer
5/6/2009
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Calipso Curtain PlotsJuly 31, 2007Transported
Canadian Smoke
Analysis and Source Attribution of Pollution
Events
Yellow Calipso aerosol flag Blue cloud
flag MODIS Aqua AOD in background
5/6/2009
38
Long-Range Transport of Smoke and Dust Aerosols
OMI Absorption Aerosol Optical Depth July 2007
Aerosol Index Time-Longitude Map Transport of
African Dust West to East
Dust
Smoke
The global map of July 2007 OMI Absorption
Optical Depth (on the left) shows North African
dust traveling westward over the Atlantic Ocean
and reaching Mexico and North America. It also
shows smoke plumes from the biomass burning from
central Africa. The Aerosol Index map on the
right shows westward transport of North African
dust plumes with time. The dust plume which
lifted above ground on June 6 at 20 deg E reached
Gulf of Mexico in two weeks. Aerosol index is a
very good indicator of absorbing aerosols such as
smoke and dust.
On the left, the global map of OMI Absorption
Optical Depth (July 2007), shows North African
dust traveling westward over the Atlantic Ocean
and reaching Mexico and North America. It also
shows smoke plumes from the biomass burning from
central Africa. The Aerosol Index map on the
right shows westward transport of North African
dust plumes with time.
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OMI can see UV absorbing Aerosols (dust smoke)
over clouds
Asian dust plumes usually start in March and
mainly build up over Taklamakan and Gobi deserts
and move eastwards, travel over the Pacific and
reach the west coast of America. On the right,
the map of OMI Aerosol Index of April 7, 2005
shows dust plume over East China, Korea and Japan.
Dust Plumes Over Cloud
ON the left is shown Plumes of smoke from a
Canadian Boreal Forest Fire for August 16,
2005 These Aerosol index maps have been overlaid
on OMI retrieved Cloud Cover to show that OMI can
detect aerosols even over clouds.
Smokes Over Cloud
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Applications Projects
5/6/2009
Intro instances data aerosols A-Train examples
applications quality future
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Giovanni Applications Projects
5/6/2009
Intro instances data aerosols A-Train examples
applications quality future
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Aeronet Synergy Tool using Giovanni
5/6/2009
43
Science quality of Giovanni results
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Science Quality and Data provenance
Data Provenance the source of data, including
the execution history of the processes that
produced them
Same parameter
Same space time
MODIS
MERIS
Different provenance
Different results why?
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Data Provenance and Science Quality

• We can save time by providing convenient services
  to scientists but
• Science quality of our results is imperative for
  scientists to be able actually trust and use them
• Documenting all the steps leading to the final
  product is paramount
• Also, providing assessment of sensitivity of the
  results to variations in processing
  algorithms/steps published in peer-reviewed
  papers and presented to users in convenient,
  easy-to-find-and-read fashion
• Only working closely with scientists can
  guarantee science quality

46
Science Quality of Giovanni Results

• Giovanni operates mostly on the standard data
  products
• Giovanni results are the same as produced using
  the standard data out-side of Giovanni
• Data can be misused in Giovanni as well as (and
  may be more so than) without Giovanni
• We implement Science Team recommendations
• We provide warnings and caveats
• We perform sensitivity studies together with
  scientists in the corresponding fields

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MLS Science Team recommendations
Convergence thresh (max)
Quality thresh (min)
Max. Altitude (hPa)
Min. Altitude (hPa)
Parameter
1.5
0.8
1.0
100
ClO
1.8
0.2 lt 100 hPa 1.2 100 hPa
0.0046
215
CO
1.2
0.6
0.001
316
Geopotential Height
N/A
0.9
0.002
316
H2O
1.5
1.0
0.15
100
HCl
2.0
0.2
0.1
10
HCN
1.8
0.4
3.2
215
HNO3
1.55
0.5
1.0
100
N2O
1.8
0.4 lt 100 hPa 1.2 100 hPa
0.022
215
O3
1.1
N/A
0.0032
32
OH
N/A
0.9
0.002
316
Relative Humidity with respect to Ice
1.2
0.6
0.001
316
Temperature

• All parameters are screened where Status Flag
  bit 0 is not set (i.e. odd value).
• Data values where the precision is negative are
  excluded.
• Quality flag where larger values indicate
  good radiance fits.
• Status various bit encoded flags, e.g. bit 0
  indicates error, bit 1 questionable data, etc.
• Convergence ratio of the fit from the retrieval
  algorithm to the estimated fit

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Product lineage in Giovanni
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Provenance for Intercomparison

• Automated or semi-automated intercomparison of
  two apparently comparable parameters exposes a
  challenge in the proper consideration of the data
  provenance.
• Dealing with two or more provenance chains is
  much more difficult.
• Provenance should be described with enough
  semantic richness for users to assess and
  eventually assure the scientific validity of an
  intercomparison operation.
• Complicating this task is the dispersion of data
  and services to multiple sources, to be accessed
  via heterogeneous workflows.
• Persisting and transmitting the rich provenance
  requires provenance interoperability in addition
  to data interoperability.

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Multi-Sensor Data Synergy Advisor (MDSA)
Expand Giovanni to include semantic web ontology
system that captures scientist knowledge data
quality characteristics, and to encode this
knowledge so the Advisor can assist user in
multi-sensor data analysis.  Identify and
present the caveats for comparisons. Funding
ESTO
Same Parameter
Same Location and Time
Different Provenance
Different Results
Importance of capturing and using provenance
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Statistical aspects of spatio-temporal
aggregation of MODIS aerosol data
MODIS Terra only AOD difference between diff.
aggregations
AOD difference between sensors
Mishchenko et al., 2007
Levy, Leptoukh, et al., 2009

• Q. How sensitive are AOT time-series to
  different aggregations to monthly products?
• A Very sensitive. For MODIS-Terra alone, AOD
  difference can be up to 40
• Pixel count weighting (correctly) applied to L3
  data represents L2 sampling, and leads to spatial
  and temporal (mostly clear sky) bias in the
  result.
• Applying Confidence weighting to L3 leads to a
  different Confidence-biased L2 sampling result
• Grid number weighting (correctly) applied to L3
  data during spatial or temporal aggregation
  represents L3 sampling and a lesser spatial and
  temporal (mostly clear sky) bias.
• To compare data from different sensors, it is
  important to use the same statistical aggregation

May 6, 2009
Giovanni update, Leptoukh
52
Statistical Aspects of Data Fusion MODIS Terra
Aqua AOD
Viktor Zubko, Gregory Leptoukh and Arun Gopalan,
GES DISC Code 610.2
Realistic Gaps
Continuous Original Scene

• Data
• MODIS Terra AOT (T)
• MODIS Aqua AOT (A)
• Combining Options
• Merging (M)
• Interpolation (I)
• Simultaneous M and I (MI)
• Used Combinations
• im(t,a) M(T, A)?I
• mi(t),i(a) I(T), I(A)?M
• mi(t,a) MI(T,A)
• mmi(t),mi(a) MI(T), MI(A)?M
• i(t) I(T)
• i(a) I(A)
• mi(t) MI(T)
• mi(a) MI(A)

Initial AOT
Merged AOT
Terra Aqua AOT
Interpolated AOT

• Data Fusion (DF) is a method of combining
  near-coincident (in time and space) satellite
  data to produce complete global or regional maps.
  This is important because satellite data contain
  gaps arising from Sun glint, clouds, interorbital
  areas, or retrieval algorithm failures.
• DF usually includes data merging and
  interpolation steps.
• The goal of the work is to find a fast and
  accurate DF method for implementation into
  Giovanni.
• We investigated various methods and limitations
  of data merging with and without interpolation
  using MODIS Terra and Aqua Daily Level 3 Aerosol
  Optical Thickness (AOT) data as a prototype.
• To assess the merger accuracy, we introduced a
  merging confidence function, which is the
  percentage of the merged AOT pixels as a function
  of the relative deviation of the merged AOT from
  the initial Terra and Aqua AOTs.

May 6, 2009
Giovanni update, Leptoukh
53
NASA ROSES Giovanni-related projects
Project Name
NASA Data Integration into Global Agricultural
Decision Support Systems Project (Steve Kempler,
REASoN CAN 02-OES_01).
Enhancing NOAA AWIPS DSS by Infusing NASA
Research Results for Drought and Other Disaster
Management
A-Train Data Depot, Steve Kempler,
NNH05ZDA001N-ACCESS
NASA NEESPI Data Center, Gregory Leptoukh,
NNH05ZDA001N-ACCESS
Monsoon Asia Integrated Regional Study in Eastern
Asia (MAIRS), Leptoukh, LCLCUC
Aerosol Integrated Inter-comparison and
Validation Project, Charles Ichoku, ACCESS
3-D VIS for A-Train, Steve Kempler, ACCESS
Long-Term Aerosol Data Records, Christina Hsu,
NNH06ZDA001N-MEASURES
Ocean Color Time-series Project, Watson Gregg,
REASoN CAN-02-OES-01
Multi-sensor Data Science Advisor, Leptoukh, AIST
(ESTO)
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Unsolicited Giovanni-related Projects
Project Name
HSB-GLDAS and HSB-NLDAS (Rodell)
GOCART Data into Giovanni (Mian Chin)
Ocean Color model data (Watson Gregg)
MERRA data into Giovanni (Mike Bosilovich)
HTAP (Terry Keating from EPA and Lawrence Friedl)
Langley NRT(Calipso and GE)
Langley CERES into Giovanni
Langley TES into Giovanni
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Giovanni publications

• Chen, Aijun, Gregory Leptoukh and Steven Kempler,
  2009. Using KML and Virtual Globes to Access and
  Visualize Heterogeneous Datasets and Explore
  their Relationships along the A-Train Tracks,
  Journal of Selected Topics in Applied Earth
  Observations and Remote Sensing, submitted
• Prados, Ana I., G. Leptoukh, J. Johnson, H. Rui,
  C. Lynnes, A. Chen, and R. B. Husar, 2009.
  Access, Visualization, and Interoperability of
  Air Quality Remote Sensing Data Sets via the
  Giovanni Online Tool, Journal of Selected Topics
  in Applied Earth Observations and Remote Sensing,
  submitted
• Liu, Zhong, H. Rui, W. Teng, L. Chiu, G. Leptoukh
  and S. Kempler, 2009. Developing an Online
  Information System Prototype for Global Satellite
  Precipitation Algorithm Validation and
  Inter-comparison, Journal of Applied Meteorology
  and Climatology, submitted
• Berrick, S.W., G. Leptoukh, J. Farley, H. Rui,
  2009. Giovanni A Web Services Workflow-Based
  Data Visualization and Analysis System, IEEE
  Trans. on Geoscience and Remote Sensing,, 46,
  2788 2795
• Chen, A., Leptoukh, G., Kempler, S., Nadeau, D.
  and Zhang, X. 2008. Augmenting the Research Value
  of Geospatial Data using Google Earth. Journal of
  the Virtual Explorer, Electronic Edition, 29,
  paper 100.
• Acker, J. Leptoukh, G. (2007). Online analysis
  enhances use of NASA Earth science data. EOS,
  Trans. Amer. Geophysical Union, 88, 14.
• Leptoukh, G., Csiszar, I., Romanov, P., Shen S.,
  Loboda T., Gerasimov, I. (2007). NASA NEESPI
  data center for satellite remote sensing data and
  services. Global and Planetary Change, Environ,
  Res. Lett., 2. 045009, doi10.1088/1748-9326/2/4/0
  45009.
• Liu, Z., Rui, H., Teng, W. L., Chiu, L. S.,
  Leptoukh, G. G., Vicente, G. A. (2007). Online
  visualization and analysis A new avenue to use
  satellite data for weather, climate and
  interdisciplinary research and applications.
  Measuring Precipitation from Space - EURAINSAT
  and the future, Advances in Global Change
  Research, 28, 549-558.

May 6, 2009
Giovanni update, Leptoukh
56
How to stay in touch with Giovanni?

• News on Giovanni pages http//giovanni.gsfc.nasa.
  gov
• Mailing list giovanni-disc_at_listserv.gsfc.nasa.gov
  
• Quarterly Newsletters
• Workshops (e.g., day-long at AMS09)
• Tutorials, e.g., at ASRS web site
  http//aerocenter.gsfc.nasa.gov/asrs/materials/ind
  ex.php?idgiovanni
• Feedback is VERY important

May 6, 2009
Giovanni update, Leptoukh
57
Future

• Multi-sensor and model data approach
• Utilize all EOS data, and start working with ESA,
  models, ground-based, campaign data
• NPOESS and Decadal Survey missions
• Enrich analysis tools suite
• Ensure science quality
• Provide and utilize data provenance
• Improve performance
• Add true-color imagery
• Instrument measurement simulation - taking care
  of spatio-temporal sampling

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Comprehensive Data Systems
Current Satellites (NASA, NOAA, ESA, JAXA)
Comprehensive Data Systems provide an environment
for working with all sources of relevant data
(satellite, ground-based and models) across the
full range of temporal and spatial scales.
CALIPSO
Aqua
POLDER
Terra
Aura
SORCE
TRMM
CloudSAT
Legacy Satellites
Precipitation Giovanni
UARS
Air Quality Giovanni
Earth Probe
Nimbus-7
Ground-Based Observations
Ozone Giovanni
Global Warming Giovanni
Sondes
Aircraft
AERONET, Dobson, Brewer, etc.
Giovanni
Lidar, Radar
near future
Computer Models
Earth System Models
GCMs
Cloud Cover Giovanni
Giovanni for Students
Regional Models
Process Models
My Giovanni