SlideShare ist ein Scribd-Unternehmen logo
1 von 58
Downloaden Sie, um offline zu lesen
Working with HDF and netCDF Data in ArcGIS
Tools and Case Studies

Nawajish Noman
Jeff Donze

HDF and HDF-EOS Workshop XIV, September 28-30, 2010, Champaign, IL
HDF28-
Outline

• HDF and netCDF in ArcGIS
• Time in ArcGIS
• Performing Analysis
• Use Cases and Applications
• Script Tools
•F t
Future Directions
Di
ti
HDF and netCDF in ArcGIS
Scientific Data and ESRI

• Direct support - NetCDF and HDF
• THREDDS – a data server technology for multidimensional array
data, integrated use by our customers
• Examples using ESRI technology
• National Climate Data Center
• National Weather Service
• National Center for
Atmospheric Research
• Air Force Weather
• Australian Navy
• Australian Bur.of Met.
• UK Met Office
HDF4 and HDF5 Support in ArcGIS
Displaying MODIS LST Data
HDFView

ArcGIS
HDF4 and HDF5 Support in ArcGIS

http://help.arcgis.com/en/arcgisdesktop/10.0/help/
What is NetCDF?
NetCDF?

• NetCDF (network Common Data Form)
network
A platform independent format for representing multi-dimensional
multiarrayarray-orientated scientific data.
• Self Describing: a netCDF file includes information about the data it
contains.
• Di
Direct A
t Access: a small subset of a large dataset may be accessed
ll b t f l
d t
t
b
d
efficiently, without first reading through all the preceding data.
• Sh
Sharable: one writer and multiple readers may simultaneously
bl
it
d
lti l
d
i lt
l
access the same netCDF file.
NetCDF is relatively new to the GIS community but widely used by scientific
communities for many years.
Storing Data in a netCDF File

Time = 1 to 3
Y = 1 to 4
X = 1 to 4
NetCDF Support in ArcGIS

• ArcGIS reads/writes netCDF since version 9.2
• An array based data structure for storing
multidimensional data.

T

• N-dimensional coordinates systems
• X, Y, Z, time, and other dimensions

• Variables – support for multiple variables
• Temperature, humidity, pressure, salinity, etc
Temperature,

• Geometry – implicit or explicit
• Regular grid (implicit)
(implicit)
• Irregular grid
• Points

Y

Z
X
Gridded Data

Regular Grid

Irregular Grid
Ingesting netCDF data in ArcGIS

• NetCDF data is accessed as
Raster
• Feature
• Table
•

• Di
Direct read
d
• Exports GIS data to netCDF
CF Convention
Climate and Forecast (CF) Convention
(CF)
http://cf-pcmdi.llnl.gov/
htt // f
di ll l
/

Initially developed for
• Climate and forecast data
• Atmosphere, surface and ocean model-generated data
model• Also for observational datasets
• The CF conventions generalize and extend the COARDS (Cooperative
cean/A
Ocean/Atmosphere Research Data Service) convention.
• CF i now th most widely used conventions f geospatial netCDF
is
the
t id l
d
ti
for
ti l tCDF
data. It has the best coordinate system handling.
NetCDF and Coordinate Systems

• Geographic Coordinate Systems (GCS)
X dimension units: degrees_east
• Y dimension units: degrees_north
•

• Projected Coordinate Systems (PCS)
X dimension standard_name: projection_x_coordinate
standard_name:
• Y dimension standard_name: projection_y_coordinate
standard_name:
• V i bl has a grid_mapping attribute.
Variable h
id
i
tt ib t
• CF 1.4 conventions currently supports twelve predefined coordinate
systems (Appendix F: Grid Mappings)
Mappings)
•

• Undefined
• If not GCS or PCS
• ArcGIS writes (and recognizes) PE String as a variable attribute.
NetCDF Tools

Toolbox: Multidimension Tools
Make NetCDF Raster Layer
• Make NetCDF Feature Layer
• Make NetCDF Table View
•

Raster to NetCDF
• Feature to NetCDF
• Table to NetCDF
•

•

Select by Dimension
y
NetCDF Layer/Table Properties

Raster
Feature
Feat re

Table
Changing Time Slice

Time = 1
Using NetCDF Data
Behaves the same as any layer or table
• Display
• Same display tools for raster and feature layers will work on netCDF
raster and netCDF feature layers.

• Graphing
• Driven by the table just like any other chart.

• Animation
• Multidimensional data can be animated through a dimension (e.g.
time, pressure, elevation)

• Analysis Tools
• A netCDF layer or table will work just like any other raster layer,
feature layer or table (e g create buffers around netCDF points
layer,
table. (e.g.
points,
reproject rasters, query tables, etc.)
rasters,
Help on netCDF
Time in ArcGIS
Why visualize data through time?
Temporal GIS Patterns
Dynamic

Discrete

Stationary

Change

something that moves

something that
“just happens”
“j t h
”

stands still but
records changes

change or growth

• Planes
• Vehicles
• Animals
• Satellites
• Storms

• Crimes

• Weather Stations

• Population

• Lightning
• Accidents

• Traffic Sensors

• Distribution
• Fire Perimeter
GIS Integration of Time
New Ways to Manage Visualize & Analyze Geography
Manage,

Multi
Dimensional
Data (netCDF)
(netCDF)

Visualize Change
Vi
li Ch

T

Real Time
Sensor Network

y

x

Files
DBMS

Mobile
Stationary

T1

Simulation
Modeling
Time is now built-in to ArcGIS
built-

•

Simple Temporal Mapping

•

Unified experience for Time
•
•

•

Configure time properties on the layer
Use Time Slider to visualize temporal data
is ali e

Share temporal visualization
TimeTime-enabled Map Services
Export videos or images
Generate temporal map books
using ArcPy scripting
• L
Layer and map packages
d
k
•
•
•
Visualizing temporal data using graphs

• Create a graph using a layer or table
• Create an animation in the usual way, attaching the
layer or table to a time layer track
• When the animation is played,
p y ,
the graph will animate
Animation examples

1979
Performing Analysis
Spatial and Temporal Analysis

• Several hundreds analytical tools available for raster, features,
and table
• Temporal Modeling
p
g
• Looping and iteration in ModelBuilder and Python
Generate Rainfall Statistics

• Calculates specified statistics for all time steps
• Outputs a raster catalog
• Optionally outputs a netCDF file
Generate Rainfall Statistics Table

• Calculates statistics for all time steps
• Outputs a table
• Optionally creates a graph
Demo
Use Cases and Applications
•
•
•
•
•

Hydrography and METOC Branch, Royal Australian Navy
Applied Science Associated, Inc.
Inc.
The Nature Conservancy
The University of Washington
The University of Southern Mississippi
Observations and Model Forecasts

• Near real-time observations
real•
•
•
•
•

Weather satellite imagery
Sea Surface Temperature
Significant Wave Height (altimeter)
Ocean Winds
Moisture and Precipitation

• Numerical model forecasts
• Fixed domain Global, Regional, Tropical and Local atmospheric models
(ACCESS)
• Fixed domain Global, Regional, and Local wave models (WaveWatch 3)
(WaveWatch
• Global Ocean Model (BLUElink)
(BLUElink)
highg
p
• Relocatable high-resolution atmospheric and ocean models

Source: Hydrography and METOC Branch, Royal Australian Navy.
Climatology – CCMP Winds

Source: Hydrography and METOC Branch, Royal Australian Navy.
Climatology – CCMP Winds

Source: Hydrography and METOC Branch, Royal Australian Navy.
Ocean Model Forecast – Custom Display

Source: Hydrography and METOC Branch, Royal Australian Navy.
Web Services - GHRSST

Source: Hydrography and METOC Branch, Royal Australian Navy.
Environmental Data Connector (EDC)

XML

DAP
THREDDS

Features

NetCDF

Java App,
Unidata

Rasters
ArcGIS
Extension

Source: Applied Science Associated, Inc.
EDC Screen and Time Slider Toolbar
Synchronize and animate time-varying data in

http://www.pfeg.noaa.gov/products/EDC/
http://www.asascience.com/TimeSlider/index.htm
Source: Applied Science Associated, Inc.
Time Series Graphs

Source: Applied Science Associated, Inc.
Climate Wizard

http://www.climatewizard.org/

Developers: The Nature Conservancy, The University of Washington, and The University of Southern Mississippi
Climate Change

http://tncclimate.esri.com/TNC/ClimateChange.html

Developers: The Nature Conservancy, The University of Washington, and ESRI
Script Tools
Community Developed Tools

• Geoprocessing Resource Center
http://resources.arcgis.com/geoprocessing/

• Marine Geospatial
Ecology Tools
• Developed at Duke Univ.
• Over 180 tools for import
management, and
analysis of marine data

• Australian Navy tools
(not publicly available)
Script Tools of interest

• Python is used to build custom tools for specific tasks or
datasets
NEXRAD Geoprocessing Tools

•
•
•
•
•
•

Currently 6 geoprocessing script tools
Designed to work with NEXRAD netCDF file
Can be easily modified for other datasets
Customized tools for various workflows
Simplify repetitive work
Automate GIS processes
Download NEXRAD NetCDF File

• Downloads netCDF file from the THREDDS server
Extract NEXRAD Rainfall To Points

• Extracts the cell values for all time steps
• Outputs a feature class
NEXRAD NetCDF To Raster Catalog

• Creates a raster catalog
• Each catalog item corresponds to a time slice in the netCDF file
Future Directions
Things to Consider…

• Embrace the Common Data Model (netCDF, HDF etc.)
(netCDF,
• Use Data and metadata standards (OGC, CF etc)
• Make your data “spatial” (by specifying geographic or a
p j
projected coordinate system)
y
)
• Clearly define workflow and requirements
• Create sample tools where possible
Tools Under Consideration

• Temporal Statistics
• Get Variable Statistics

Analysis

•
•
•
•

Clip
p
Extract By Variable
Extract By Dimension
Append By Dimension

Data Management
Future directions

• Multidimension data management
• Temporal analysis tools
• Additional support for HDF5 using netCDF 4.x library

• What do you need?
Questions?
Nawajish Noman
Lead Product Engineer
nnoman@esri.com
@ i

Jeff Donze
ESRI Federal Business Development
F d lB i
D
l
t
jdonze@esri.com
Sharing Temporal Maps & Data with ArcGIS 10

•

Publish time-aware maps
time-

•

Export videos or images, layer and map packages

•

Vi
li data
Visualize d t
Access via REST API
Web API
• FLEX
• JavaScript
• Silverlight
• Time Slider web control
•
•

1979
How to store temporal data?

• DATE is a special field type specific to time
• GeoDatabase provides DATE
• If at all possible – use DATE type
• DATE field should be indexed for faster query performance

• Numeric and String fields
•
•
•
•

YYYY
YYYYMM
YYYYMMDD
YYYYMMDDhhmmss

YYYY/MM/DD
YYYY/MM/DD hh:mm:ss

• Time dimension in a netCDF variable

YYYY-MM-DD
YYYY-MMYYYY-MM-DD hh:mm:ss
YYYY-MM-
Animation examples
Data source: Cavalieri, D., C. Parkinson, P. Gloerson,
and H.J. Zwally. 1996, updated 2005. Sea ice
concentrations from Nimbus-7 SMMR and DMSP SSM/I
Nimbuspassive microwave data, June to September 2001.
Boulder, CO, USA: National Snow and Ice Data Center.

Sea ice concentration

Solar Insolation
Data provided courtesy of Declan Butler - http://declanbutler.info/blog/

Stream flow analysis

Avian Influenza
Atmospheric Model Forecast – Surface Wind

Source: Hydrography and METOC Branch, Royal Australian Navy.

Weitere ähnliche Inhalte

Andere mochten auch

SATAID_manual_201611
SATAID_manual_201611SATAID_manual_201611
SATAID_manual_201611JMA_447
 
Jma hr gsm_data_gr_ads_20130529
Jma hr gsm_data_gr_ads_20130529Jma hr gsm_data_gr_ads_20130529
Jma hr gsm_data_gr_ads_20130529JMA_447
 
Annex 2 -- examples of observation data
Annex 2 -- examples of observation dataAnnex 2 -- examples of observation data
Annex 2 -- examples of observation dataJMA_447
 
Guide for visualizing JMA's GSM outputs using GrADS
Guide for visualizing JMA's GSM outputs using GrADSGuide for visualizing JMA's GSM outputs using GrADS
Guide for visualizing JMA's GSM outputs using GrADSJMA_447
 
Collectivités : quels enjeux de stratégie éditoriale
Collectivités : quels enjeux de stratégie éditorialeCollectivités : quels enjeux de stratégie éditoriale
Collectivités : quels enjeux de stratégie éditorialeUltramedia
 
Tutorial pengolahan data salinitas menggunakan aplikasi SeaDas, Excel dan ODV...
Tutorial pengolahan data salinitas menggunakan aplikasi SeaDas, Excel dan ODV...Tutorial pengolahan data salinitas menggunakan aplikasi SeaDas, Excel dan ODV...
Tutorial pengolahan data salinitas menggunakan aplikasi SeaDas, Excel dan ODV...dwi fitria ningsih
 
Interoperability with netCDF-4 - Experience with NPP and HDF-EOS5 products
Interoperability with netCDF-4 - Experience with NPP and HDF-EOS5 productsInteroperability with netCDF-4 - Experience with NPP and HDF-EOS5 products
Interoperability with netCDF-4 - Experience with NPP and HDF-EOS5 productsThe HDF-EOS Tools and Information Center
 
Scientific Visualization
Scientific VisualizationScientific Visualization
Scientific VisualizationJosef Heinen
 
Esri Course Catalog 2014
Esri Course Catalog 2014Esri Course Catalog 2014
Esri Course Catalog 2014Esri
 
Spirometry Interpretation
Spirometry Interpretation Spirometry Interpretation
Spirometry Interpretation Ashraf ElAdawy
 
Comparing and Contrasting photos
Comparing and Contrasting photosComparing and Contrasting photos
Comparing and Contrasting photosDavid Mainwood
 

Andere mochten auch (16)

Visualizing and Analyzing HDF-EOS5 and HDF5 data with NCL
Visualizing and Analyzing HDF-EOS5 and HDF5 data with NCLVisualizing and Analyzing HDF-EOS5 and HDF5 data with NCL
Visualizing and Analyzing HDF-EOS5 and HDF5 data with NCL
 
Matlab, Big Data, and HDF Server
Matlab, Big Data, and HDF ServerMatlab, Big Data, and HDF Server
Matlab, Big Data, and HDF Server
 
MATLAB, netCDF, and OPeNDAP
MATLAB, netCDF, and OPeNDAPMATLAB, netCDF, and OPeNDAP
MATLAB, netCDF, and OPeNDAP
 
Supporting HDF5 in GrADS
Supporting HDF5 in GrADSSupporting HDF5 in GrADS
Supporting HDF5 in GrADS
 
SATAID_manual_201611
SATAID_manual_201611SATAID_manual_201611
SATAID_manual_201611
 
Jma hr gsm_data_gr_ads_20130529
Jma hr gsm_data_gr_ads_20130529Jma hr gsm_data_gr_ads_20130529
Jma hr gsm_data_gr_ads_20130529
 
Annex 2 -- examples of observation data
Annex 2 -- examples of observation dataAnnex 2 -- examples of observation data
Annex 2 -- examples of observation data
 
Meteo penerbangan
Meteo penerbanganMeteo penerbangan
Meteo penerbangan
 
Guide for visualizing JMA's GSM outputs using GrADS
Guide for visualizing JMA's GSM outputs using GrADSGuide for visualizing JMA's GSM outputs using GrADS
Guide for visualizing JMA's GSM outputs using GrADS
 
Collectivités : quels enjeux de stratégie éditoriale
Collectivités : quels enjeux de stratégie éditorialeCollectivités : quels enjeux de stratégie éditoriale
Collectivités : quels enjeux de stratégie éditoriale
 
Tutorial pengolahan data salinitas menggunakan aplikasi SeaDas, Excel dan ODV...
Tutorial pengolahan data salinitas menggunakan aplikasi SeaDas, Excel dan ODV...Tutorial pengolahan data salinitas menggunakan aplikasi SeaDas, Excel dan ODV...
Tutorial pengolahan data salinitas menggunakan aplikasi SeaDas, Excel dan ODV...
 
Interoperability with netCDF-4 - Experience with NPP and HDF-EOS5 products
Interoperability with netCDF-4 - Experience with NPP and HDF-EOS5 productsInteroperability with netCDF-4 - Experience with NPP and HDF-EOS5 products
Interoperability with netCDF-4 - Experience with NPP and HDF-EOS5 products
 
Scientific Visualization
Scientific VisualizationScientific Visualization
Scientific Visualization
 
Esri Course Catalog 2014
Esri Course Catalog 2014Esri Course Catalog 2014
Esri Course Catalog 2014
 
Spirometry Interpretation
Spirometry Interpretation Spirometry Interpretation
Spirometry Interpretation
 
Comparing and Contrasting photos
Comparing and Contrasting photosComparing and Contrasting photos
Comparing and Contrasting photos
 

Ähnlich wie Working with HDF and netCDF Data in ArcGIS

Geo Analytics Canada Overview - May 2020
Geo Analytics Canada Overview - May 2020Geo Analytics Canada Overview - May 2020
Geo Analytics Canada Overview - May 2020GEO Analytics Canada
 
PEARC17: Visual exploration and analysis of time series earthquake data
PEARC17: Visual exploration and analysis of time series earthquake dataPEARC17: Visual exploration and analysis of time series earthquake data
PEARC17: Visual exploration and analysis of time series earthquake dataAmit Chourasia
 
DataStax and Esri: Geotemporal IoT Search and Analytics
DataStax and Esri: Geotemporal IoT Search and AnalyticsDataStax and Esri: Geotemporal IoT Search and Analytics
DataStax and Esri: Geotemporal IoT Search and AnalyticsDataStax Academy
 
Global mapper tutorial Jimma University Ethiopia
Global mapper tutorial Jimma University EthiopiaGlobal mapper tutorial Jimma University Ethiopia
Global mapper tutorial Jimma University Ethiopiachala hailu
 
iXblue - DELPH geophysical software
iXblue - DELPH geophysical softwareiXblue - DELPH geophysical software
iXblue - DELPH geophysical softwarePhilippe ALAIN
 
USING E-INFRASTRUCTURES FOR BIODIVERSITY CONSERVATION - Module 2
USING E-INFRASTRUCTURES FOR BIODIVERSITY CONSERVATION - Module 2USING E-INFRASTRUCTURES FOR BIODIVERSITY CONSERVATION - Module 2
USING E-INFRASTRUCTURES FOR BIODIVERSITY CONSERVATION - Module 2Gianpaolo Coro
 
The Homogenization and Reporting of Groundbased Atmospheric Datasets for the ...
The Homogenization and Reporting of Groundbased Atmospheric Datasets for the ...The Homogenization and Reporting of Groundbased Atmospheric Datasets for the ...
The Homogenization and Reporting of Groundbased Atmospheric Datasets for the ...The HDF-EOS Tools and Information Center
 
Implementing a VO archive for datacubes of galaxies
Implementing a VO archive for datacubes of galaxiesImplementing a VO archive for datacubes of galaxies
Implementing a VO archive for datacubes of galaxiesJose Enrique Ruiz
 
NASA Earth Exchange (NEX) Overview
NASA Earth Exchange (NEX) OverviewNASA Earth Exchange (NEX) Overview
NASA Earth Exchange (NEX) OverviewPlanet OS
 
0603 Esip Fed Wash Dc Tech Pres 060103 Esip Aq Tech Track
0603 Esip Fed Wash Dc Tech Pres 060103 Esip Aq Tech Track0603 Esip Fed Wash Dc Tech Pres 060103 Esip Aq Tech Track
0603 Esip Fed Wash Dc Tech Pres 060103 Esip Aq Tech TrackRudolf Husar
 
2006-01-11 Data Flow & Interoperability in DataFed Service-based AQ Analysis ...
2006-01-11 Data Flow & Interoperability in DataFed Service-based AQ Analysis ...2006-01-11 Data Flow & Interoperability in DataFed Service-based AQ Analysis ...
2006-01-11 Data Flow & Interoperability in DataFed Service-based AQ Analysis ...Rudolf Husar
 

Ähnlich wie Working with HDF and netCDF Data in ArcGIS (20)

Multidimensional Scientific Data in ArcGIS
Multidimensional Scientific Data in ArcGISMultidimensional Scientific Data in ArcGIS
Multidimensional Scientific Data in ArcGIS
 
Geo Analytics Canada Overview - May 2020
Geo Analytics Canada Overview - May 2020Geo Analytics Canada Overview - May 2020
Geo Analytics Canada Overview - May 2020
 
PEARC17: Visual exploration and analysis of time series earthquake data
PEARC17: Visual exploration and analysis of time series earthquake dataPEARC17: Visual exploration and analysis of time series earthquake data
PEARC17: Visual exploration and analysis of time series earthquake data
 
HDF & HDF-EOS Data & Support at NSIDC
HDF & HDF-EOS Data & Support at NSIDCHDF & HDF-EOS Data & Support at NSIDC
HDF & HDF-EOS Data & Support at NSIDC
 
view_hdf
view_hdfview_hdf
view_hdf
 
Reading HDF family of formats via NetCDF-Java / CDM
Reading HDF family of formats via NetCDF-Java / CDMReading HDF family of formats via NetCDF-Java / CDM
Reading HDF family of formats via NetCDF-Java / CDM
 
ArcGIS and Multi-D: Tools & Roadmap
ArcGIS and Multi-D: Tools & RoadmapArcGIS and Multi-D: Tools & Roadmap
ArcGIS and Multi-D: Tools & Roadmap
 
Improved Methods for Accessing Scientific Data for the Masses
Improved Methods for Accessing Scientific Data for the MassesImproved Methods for Accessing Scientific Data for the Masses
Improved Methods for Accessing Scientific Data for the Masses
 
DataStax and Esri: Geotemporal IoT Search and Analytics
DataStax and Esri: Geotemporal IoT Search and AnalyticsDataStax and Esri: Geotemporal IoT Search and Analytics
DataStax and Esri: Geotemporal IoT Search and Analytics
 
Global mapper tutorial Jimma University Ethiopia
Global mapper tutorial Jimma University EthiopiaGlobal mapper tutorial Jimma University Ethiopia
Global mapper tutorial Jimma University Ethiopia
 
CLIM Program: Remote Sensing Workshop, Distributed Access and Analysis: NASA ...
CLIM Program: Remote Sensing Workshop, Distributed Access and Analysis: NASA ...CLIM Program: Remote Sensing Workshop, Distributed Access and Analysis: NASA ...
CLIM Program: Remote Sensing Workshop, Distributed Access and Analysis: NASA ...
 
iXblue - DELPH geophysical software
iXblue - DELPH geophysical softwareiXblue - DELPH geophysical software
iXblue - DELPH geophysical software
 
Advancing Scientific Data Support in ArcGIS
Advancing Scientific Data Support in ArcGISAdvancing Scientific Data Support in ArcGIS
Advancing Scientific Data Support in ArcGIS
 
USING E-INFRASTRUCTURES FOR BIODIVERSITY CONSERVATION - Module 2
USING E-INFRASTRUCTURES FOR BIODIVERSITY CONSERVATION - Module 2USING E-INFRASTRUCTURES FOR BIODIVERSITY CONSERVATION - Module 2
USING E-INFRASTRUCTURES FOR BIODIVERSITY CONSERVATION - Module 2
 
The Homogenization and Reporting of Groundbased Atmospheric Datasets for the ...
The Homogenization and Reporting of Groundbased Atmospheric Datasets for the ...The Homogenization and Reporting of Groundbased Atmospheric Datasets for the ...
The Homogenization and Reporting of Groundbased Atmospheric Datasets for the ...
 
Implementing a VO archive for datacubes of galaxies
Implementing a VO archive for datacubes of galaxiesImplementing a VO archive for datacubes of galaxies
Implementing a VO archive for datacubes of galaxies
 
NASA Earth Exchange (NEX) Overview
NASA Earth Exchange (NEX) OverviewNASA Earth Exchange (NEX) Overview
NASA Earth Exchange (NEX) Overview
 
Geoscience Data Analysis and Visualization Tools from NCAR
Geoscience Data Analysis and Visualization Tools from NCARGeoscience Data Analysis and Visualization Tools from NCAR
Geoscience Data Analysis and Visualization Tools from NCAR
 
0603 Esip Fed Wash Dc Tech Pres 060103 Esip Aq Tech Track
0603 Esip Fed Wash Dc Tech Pres 060103 Esip Aq Tech Track0603 Esip Fed Wash Dc Tech Pres 060103 Esip Aq Tech Track
0603 Esip Fed Wash Dc Tech Pres 060103 Esip Aq Tech Track
 
2006-01-11 Data Flow & Interoperability in DataFed Service-based AQ Analysis ...
2006-01-11 Data Flow & Interoperability in DataFed Service-based AQ Analysis ...2006-01-11 Data Flow & Interoperability in DataFed Service-based AQ Analysis ...
2006-01-11 Data Flow & Interoperability in DataFed Service-based AQ Analysis ...
 

Mehr von The HDF-EOS Tools and Information Center

STARE-PODS: A Versatile Data Store Leveraging the HDF Virtual Object Layer fo...
STARE-PODS: A Versatile Data Store Leveraging the HDF Virtual Object Layer fo...STARE-PODS: A Versatile Data Store Leveraging the HDF Virtual Object Layer fo...
STARE-PODS: A Versatile Data Store Leveraging the HDF Virtual Object Layer fo...The HDF-EOS Tools and Information Center
 

Mehr von The HDF-EOS Tools and Information Center (20)

Cloud-Optimized HDF5 Files
Cloud-Optimized HDF5 FilesCloud-Optimized HDF5 Files
Cloud-Optimized HDF5 Files
 
Accessing HDF5 data in the cloud with HSDS
Accessing HDF5 data in the cloud with HSDSAccessing HDF5 data in the cloud with HSDS
Accessing HDF5 data in the cloud with HSDS
 
The State of HDF
The State of HDFThe State of HDF
The State of HDF
 
Highly Scalable Data Service (HSDS) Performance Features
Highly Scalable Data Service (HSDS) Performance FeaturesHighly Scalable Data Service (HSDS) Performance Features
Highly Scalable Data Service (HSDS) Performance Features
 
Creating Cloud-Optimized HDF5 Files
Creating Cloud-Optimized HDF5 FilesCreating Cloud-Optimized HDF5 Files
Creating Cloud-Optimized HDF5 Files
 
HDF5 OPeNDAP Handler Updates, and Performance Discussion
HDF5 OPeNDAP Handler Updates, and Performance DiscussionHDF5 OPeNDAP Handler Updates, and Performance Discussion
HDF5 OPeNDAP Handler Updates, and Performance Discussion
 
Hyrax: Serving Data from S3
Hyrax: Serving Data from S3Hyrax: Serving Data from S3
Hyrax: Serving Data from S3
 
Accessing Cloud Data and Services Using EDL, Pydap, MATLAB
Accessing Cloud Data and Services Using EDL, Pydap, MATLABAccessing Cloud Data and Services Using EDL, Pydap, MATLAB
Accessing Cloud Data and Services Using EDL, Pydap, MATLAB
 
HDF - Current status and Future Directions
HDF - Current status and Future DirectionsHDF - Current status and Future Directions
HDF - Current status and Future Directions
 
HDFEOS.org User Analsys, Updates, and Future
HDFEOS.org User Analsys, Updates, and FutureHDFEOS.org User Analsys, Updates, and Future
HDFEOS.org User Analsys, Updates, and Future
 
HDF - Current status and Future Directions
HDF - Current status and Future Directions HDF - Current status and Future Directions
HDF - Current status and Future Directions
 
H5Coro: The Cloud-Optimized Read-Only Library
H5Coro: The Cloud-Optimized Read-Only LibraryH5Coro: The Cloud-Optimized Read-Only Library
H5Coro: The Cloud-Optimized Read-Only Library
 
MATLAB Modernization on HDF5 1.10
MATLAB Modernization on HDF5 1.10MATLAB Modernization on HDF5 1.10
MATLAB Modernization on HDF5 1.10
 
HDF for the Cloud - Serverless HDF
HDF for the Cloud - Serverless HDFHDF for the Cloud - Serverless HDF
HDF for the Cloud - Serverless HDF
 
HDF5 <-> Zarr
HDF5 <-> ZarrHDF5 <-> Zarr
HDF5 <-> Zarr
 
HDF for the Cloud - New HDF Server Features
HDF for the Cloud - New HDF Server FeaturesHDF for the Cloud - New HDF Server Features
HDF for the Cloud - New HDF Server Features
 
Apache Drill and Unidata THREDDS Data Server for NASA HDF-EOS on S3
Apache Drill and Unidata THREDDS Data Server for NASA HDF-EOS on S3Apache Drill and Unidata THREDDS Data Server for NASA HDF-EOS on S3
Apache Drill and Unidata THREDDS Data Server for NASA HDF-EOS on S3
 
STARE-PODS: A Versatile Data Store Leveraging the HDF Virtual Object Layer fo...
STARE-PODS: A Versatile Data Store Leveraging the HDF Virtual Object Layer fo...STARE-PODS: A Versatile Data Store Leveraging the HDF Virtual Object Layer fo...
STARE-PODS: A Versatile Data Store Leveraging the HDF Virtual Object Layer fo...
 
HDF5 and Ecosystem: What Is New?
HDF5 and Ecosystem: What Is New?HDF5 and Ecosystem: What Is New?
HDF5 and Ecosystem: What Is New?
 
HDF5 Roadmap 2019-2020
HDF5 Roadmap 2019-2020HDF5 Roadmap 2019-2020
HDF5 Roadmap 2019-2020
 

Kürzlich hochgeladen

How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerThousandEyes
 
My Hashitalk Indonesia April 2024 Presentation
My Hashitalk Indonesia April 2024 PresentationMy Hashitalk Indonesia April 2024 Presentation
My Hashitalk Indonesia April 2024 PresentationRidwan Fadjar
 
CNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of ServiceCNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of Servicegiselly40
 
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024Rafal Los
 
Injustice - Developers Among Us (SciFiDevCon 2024)
Injustice - Developers Among Us (SciFiDevCon 2024)Injustice - Developers Among Us (SciFiDevCon 2024)
Injustice - Developers Among Us (SciFiDevCon 2024)Allon Mureinik
 
Scaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organizationScaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organizationRadu Cotescu
 
Swan(sea) Song – personal research during my six years at Swansea ... and bey...
Swan(sea) Song – personal research during my six years at Swansea ... and bey...Swan(sea) Song – personal research during my six years at Swansea ... and bey...
Swan(sea) Song – personal research during my six years at Swansea ... and bey...Alan Dix
 
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking MenDelhi Call girls
 
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Miguel Araújo
 
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...shyamraj55
 
SQL Database Design For Developers at php[tek] 2024
SQL Database Design For Developers at php[tek] 2024SQL Database Design For Developers at php[tek] 2024
SQL Database Design For Developers at php[tek] 2024Scott Keck-Warren
 
Maximizing Board Effectiveness 2024 Webinar.pptx
Maximizing Board Effectiveness 2024 Webinar.pptxMaximizing Board Effectiveness 2024 Webinar.pptx
Maximizing Board Effectiveness 2024 Webinar.pptxOnBoard
 
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptxHampshireHUG
 
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 3652toLead Limited
 
Google AI Hackathon: LLM based Evaluator for RAG
Google AI Hackathon: LLM based Evaluator for RAGGoogle AI Hackathon: LLM based Evaluator for RAG
Google AI Hackathon: LLM based Evaluator for RAGSujit Pal
 
Unblocking The Main Thread Solving ANRs and Frozen Frames
Unblocking The Main Thread Solving ANRs and Frozen FramesUnblocking The Main Thread Solving ANRs and Frozen Frames
Unblocking The Main Thread Solving ANRs and Frozen FramesSinan KOZAK
 
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...Drew Madelung
 
🐬 The future of MySQL is Postgres 🐘
🐬  The future of MySQL is Postgres   🐘🐬  The future of MySQL is Postgres   🐘
🐬 The future of MySQL is Postgres 🐘RTylerCroy
 
Salesforce Community Group Quito, Salesforce 101
Salesforce Community Group Quito, Salesforce 101Salesforce Community Group Quito, Salesforce 101
Salesforce Community Group Quito, Salesforce 101Paola De la Torre
 
Handwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed textsHandwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed textsMaria Levchenko
 

Kürzlich hochgeladen (20)

How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
 
My Hashitalk Indonesia April 2024 Presentation
My Hashitalk Indonesia April 2024 PresentationMy Hashitalk Indonesia April 2024 Presentation
My Hashitalk Indonesia April 2024 Presentation
 
CNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of ServiceCNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of Service
 
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024
 
Injustice - Developers Among Us (SciFiDevCon 2024)
Injustice - Developers Among Us (SciFiDevCon 2024)Injustice - Developers Among Us (SciFiDevCon 2024)
Injustice - Developers Among Us (SciFiDevCon 2024)
 
Scaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organizationScaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organization
 
Swan(sea) Song – personal research during my six years at Swansea ... and bey...
Swan(sea) Song – personal research during my six years at Swansea ... and bey...Swan(sea) Song – personal research during my six years at Swansea ... and bey...
Swan(sea) Song – personal research during my six years at Swansea ... and bey...
 
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
 
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
 
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
 
SQL Database Design For Developers at php[tek] 2024
SQL Database Design For Developers at php[tek] 2024SQL Database Design For Developers at php[tek] 2024
SQL Database Design For Developers at php[tek] 2024
 
Maximizing Board Effectiveness 2024 Webinar.pptx
Maximizing Board Effectiveness 2024 Webinar.pptxMaximizing Board Effectiveness 2024 Webinar.pptx
Maximizing Board Effectiveness 2024 Webinar.pptx
 
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
 
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
 
Google AI Hackathon: LLM based Evaluator for RAG
Google AI Hackathon: LLM based Evaluator for RAGGoogle AI Hackathon: LLM based Evaluator for RAG
Google AI Hackathon: LLM based Evaluator for RAG
 
Unblocking The Main Thread Solving ANRs and Frozen Frames
Unblocking The Main Thread Solving ANRs and Frozen FramesUnblocking The Main Thread Solving ANRs and Frozen Frames
Unblocking The Main Thread Solving ANRs and Frozen Frames
 
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
 
🐬 The future of MySQL is Postgres 🐘
🐬  The future of MySQL is Postgres   🐘🐬  The future of MySQL is Postgres   🐘
🐬 The future of MySQL is Postgres 🐘
 
Salesforce Community Group Quito, Salesforce 101
Salesforce Community Group Quito, Salesforce 101Salesforce Community Group Quito, Salesforce 101
Salesforce Community Group Quito, Salesforce 101
 
Handwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed textsHandwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed texts
 

Working with HDF and netCDF Data in ArcGIS

  • 1. Working with HDF and netCDF Data in ArcGIS Tools and Case Studies Nawajish Noman Jeff Donze HDF and HDF-EOS Workshop XIV, September 28-30, 2010, Champaign, IL HDF28-
  • 2. Outline • HDF and netCDF in ArcGIS • Time in ArcGIS • Performing Analysis • Use Cases and Applications • Script Tools •F t Future Directions Di ti
  • 3. HDF and netCDF in ArcGIS
  • 4. Scientific Data and ESRI • Direct support - NetCDF and HDF • THREDDS – a data server technology for multidimensional array data, integrated use by our customers • Examples using ESRI technology • National Climate Data Center • National Weather Service • National Center for Atmospheric Research • Air Force Weather • Australian Navy • Australian Bur.of Met. • UK Met Office
  • 5. HDF4 and HDF5 Support in ArcGIS
  • 6. Displaying MODIS LST Data HDFView ArcGIS
  • 7. HDF4 and HDF5 Support in ArcGIS http://help.arcgis.com/en/arcgisdesktop/10.0/help/
  • 8. What is NetCDF? NetCDF? • NetCDF (network Common Data Form) network A platform independent format for representing multi-dimensional multiarrayarray-orientated scientific data. • Self Describing: a netCDF file includes information about the data it contains. • Di Direct A t Access: a small subset of a large dataset may be accessed ll b t f l d t t b d efficiently, without first reading through all the preceding data. • Sh Sharable: one writer and multiple readers may simultaneously bl it d lti l d i lt l access the same netCDF file. NetCDF is relatively new to the GIS community but widely used by scientific communities for many years.
  • 9. Storing Data in a netCDF File Time = 1 to 3 Y = 1 to 4 X = 1 to 4
  • 10. NetCDF Support in ArcGIS • ArcGIS reads/writes netCDF since version 9.2 • An array based data structure for storing multidimensional data. T • N-dimensional coordinates systems • X, Y, Z, time, and other dimensions • Variables – support for multiple variables • Temperature, humidity, pressure, salinity, etc Temperature, • Geometry – implicit or explicit • Regular grid (implicit) (implicit) • Irregular grid • Points Y Z X
  • 12. Ingesting netCDF data in ArcGIS • NetCDF data is accessed as Raster • Feature • Table • • Di Direct read d • Exports GIS data to netCDF
  • 13. CF Convention Climate and Forecast (CF) Convention (CF) http://cf-pcmdi.llnl.gov/ htt // f di ll l / Initially developed for • Climate and forecast data • Atmosphere, surface and ocean model-generated data model• Also for observational datasets • The CF conventions generalize and extend the COARDS (Cooperative cean/A Ocean/Atmosphere Research Data Service) convention. • CF i now th most widely used conventions f geospatial netCDF is the t id l d ti for ti l tCDF data. It has the best coordinate system handling.
  • 14. NetCDF and Coordinate Systems • Geographic Coordinate Systems (GCS) X dimension units: degrees_east • Y dimension units: degrees_north • • Projected Coordinate Systems (PCS) X dimension standard_name: projection_x_coordinate standard_name: • Y dimension standard_name: projection_y_coordinate standard_name: • V i bl has a grid_mapping attribute. Variable h id i tt ib t • CF 1.4 conventions currently supports twelve predefined coordinate systems (Appendix F: Grid Mappings) Mappings) • • Undefined • If not GCS or PCS • ArcGIS writes (and recognizes) PE String as a variable attribute.
  • 15. NetCDF Tools Toolbox: Multidimension Tools Make NetCDF Raster Layer • Make NetCDF Feature Layer • Make NetCDF Table View • Raster to NetCDF • Feature to NetCDF • Table to NetCDF • • Select by Dimension y
  • 18. Using NetCDF Data Behaves the same as any layer or table • Display • Same display tools for raster and feature layers will work on netCDF raster and netCDF feature layers. • Graphing • Driven by the table just like any other chart. • Animation • Multidimensional data can be animated through a dimension (e.g. time, pressure, elevation) • Analysis Tools • A netCDF layer or table will work just like any other raster layer, feature layer or table (e g create buffers around netCDF points layer, table. (e.g. points, reproject rasters, query tables, etc.) rasters,
  • 21. Why visualize data through time?
  • 22. Temporal GIS Patterns Dynamic Discrete Stationary Change something that moves something that “just happens” “j t h ” stands still but records changes change or growth • Planes • Vehicles • Animals • Satellites • Storms • Crimes • Weather Stations • Population • Lightning • Accidents • Traffic Sensors • Distribution • Fire Perimeter
  • 23. GIS Integration of Time New Ways to Manage Visualize & Analyze Geography Manage, Multi Dimensional Data (netCDF) (netCDF) Visualize Change Vi li Ch T Real Time Sensor Network y x Files DBMS Mobile Stationary T1 Simulation Modeling
  • 24. Time is now built-in to ArcGIS built- • Simple Temporal Mapping • Unified experience for Time • • • Configure time properties on the layer Use Time Slider to visualize temporal data is ali e Share temporal visualization TimeTime-enabled Map Services Export videos or images Generate temporal map books using ArcPy scripting • L Layer and map packages d k • • •
  • 25. Visualizing temporal data using graphs • Create a graph using a layer or table • Create an animation in the usual way, attaching the layer or table to a time layer track • When the animation is played, p y , the graph will animate
  • 28. Spatial and Temporal Analysis • Several hundreds analytical tools available for raster, features, and table • Temporal Modeling p g • Looping and iteration in ModelBuilder and Python
  • 29. Generate Rainfall Statistics • Calculates specified statistics for all time steps • Outputs a raster catalog • Optionally outputs a netCDF file
  • 30. Generate Rainfall Statistics Table • Calculates statistics for all time steps • Outputs a table • Optionally creates a graph
  • 31. Demo
  • 32. Use Cases and Applications • • • • • Hydrography and METOC Branch, Royal Australian Navy Applied Science Associated, Inc. Inc. The Nature Conservancy The University of Washington The University of Southern Mississippi
  • 33. Observations and Model Forecasts • Near real-time observations real• • • • • Weather satellite imagery Sea Surface Temperature Significant Wave Height (altimeter) Ocean Winds Moisture and Precipitation • Numerical model forecasts • Fixed domain Global, Regional, Tropical and Local atmospheric models (ACCESS) • Fixed domain Global, Regional, and Local wave models (WaveWatch 3) (WaveWatch • Global Ocean Model (BLUElink) (BLUElink) highg p • Relocatable high-resolution atmospheric and ocean models Source: Hydrography and METOC Branch, Royal Australian Navy.
  • 34. Climatology – CCMP Winds Source: Hydrography and METOC Branch, Royal Australian Navy.
  • 35. Climatology – CCMP Winds Source: Hydrography and METOC Branch, Royal Australian Navy.
  • 36. Ocean Model Forecast – Custom Display Source: Hydrography and METOC Branch, Royal Australian Navy.
  • 37. Web Services - GHRSST Source: Hydrography and METOC Branch, Royal Australian Navy.
  • 38. Environmental Data Connector (EDC) XML DAP THREDDS Features NetCDF Java App, Unidata Rasters ArcGIS Extension Source: Applied Science Associated, Inc.
  • 39. EDC Screen and Time Slider Toolbar Synchronize and animate time-varying data in http://www.pfeg.noaa.gov/products/EDC/ http://www.asascience.com/TimeSlider/index.htm Source: Applied Science Associated, Inc.
  • 40. Time Series Graphs Source: Applied Science Associated, Inc.
  • 41. Climate Wizard http://www.climatewizard.org/ Developers: The Nature Conservancy, The University of Washington, and The University of Southern Mississippi
  • 42. Climate Change http://tncclimate.esri.com/TNC/ClimateChange.html Developers: The Nature Conservancy, The University of Washington, and ESRI
  • 44. Community Developed Tools • Geoprocessing Resource Center http://resources.arcgis.com/geoprocessing/ • Marine Geospatial Ecology Tools • Developed at Duke Univ. • Over 180 tools for import management, and analysis of marine data • Australian Navy tools (not publicly available)
  • 45. Script Tools of interest • Python is used to build custom tools for specific tasks or datasets
  • 46. NEXRAD Geoprocessing Tools • • • • • • Currently 6 geoprocessing script tools Designed to work with NEXRAD netCDF file Can be easily modified for other datasets Customized tools for various workflows Simplify repetitive work Automate GIS processes
  • 47. Download NEXRAD NetCDF File • Downloads netCDF file from the THREDDS server
  • 48. Extract NEXRAD Rainfall To Points • Extracts the cell values for all time steps • Outputs a feature class
  • 49. NEXRAD NetCDF To Raster Catalog • Creates a raster catalog • Each catalog item corresponds to a time slice in the netCDF file
  • 51. Things to Consider… • Embrace the Common Data Model (netCDF, HDF etc.) (netCDF, • Use Data and metadata standards (OGC, CF etc) • Make your data “spatial” (by specifying geographic or a p j projected coordinate system) y ) • Clearly define workflow and requirements • Create sample tools where possible
  • 52. Tools Under Consideration • Temporal Statistics • Get Variable Statistics Analysis • • • • Clip p Extract By Variable Extract By Dimension Append By Dimension Data Management
  • 53. Future directions • Multidimension data management • Temporal analysis tools • Additional support for HDF5 using netCDF 4.x library • What do you need?
  • 54. Questions? Nawajish Noman Lead Product Engineer nnoman@esri.com @ i Jeff Donze ESRI Federal Business Development F d lB i D l t jdonze@esri.com
  • 55. Sharing Temporal Maps & Data with ArcGIS 10 • Publish time-aware maps time- • Export videos or images, layer and map packages • Vi li data Visualize d t Access via REST API Web API • FLEX • JavaScript • Silverlight • Time Slider web control • • 1979
  • 56. How to store temporal data? • DATE is a special field type specific to time • GeoDatabase provides DATE • If at all possible – use DATE type • DATE field should be indexed for faster query performance • Numeric and String fields • • • • YYYY YYYYMM YYYYMMDD YYYYMMDDhhmmss YYYY/MM/DD YYYY/MM/DD hh:mm:ss • Time dimension in a netCDF variable YYYY-MM-DD YYYY-MMYYYY-MM-DD hh:mm:ss YYYY-MM-
  • 57. Animation examples Data source: Cavalieri, D., C. Parkinson, P. Gloerson, and H.J. Zwally. 1996, updated 2005. Sea ice concentrations from Nimbus-7 SMMR and DMSP SSM/I Nimbuspassive microwave data, June to September 2001. Boulder, CO, USA: National Snow and Ice Data Center. Sea ice concentration Solar Insolation Data provided courtesy of Declan Butler - http://declanbutler.info/blog/ Stream flow analysis Avian Influenza
  • 58. Atmospheric Model Forecast – Surface Wind Source: Hydrography and METOC Branch, Royal Australian Navy.