Scientific instruments, environmental sensors, and large-scale simulations are creating more scientific data than ever before. By using advanced, large-scale information processing facilities, scientists are now able to analyze massive volumes of data in ways that never would have been possible just a few years ago. While a few researchers have access to these large computer systems, most are limited by the processing capacity they can access conveniently and quickly. Cloud computing solutions utilizing Microsoft Azure allow environmental science researchers to access the compute and storage resources that they need, when they need them—without the up-front financial investment required—and helps reduce the time between progress and breakthroughs. Microsoft Azure brings on-demand computing and data access to environmental scientists and researchers everywhere.

4. Microsoft Research: Computational Ecology
and Environmental Science Group
http://research.microsoft.com/en-us/groups/ecology/

6. Manual Measurement
Automated Measurement
Sample Collection
Historical Photographs
Counting
Ubiquitous
Motes
Aircraft Surveys
Model Output
Typing

7. Monitoring
Collation
Quality assurance
Aggregation
Analysis
Reporting
Forecasting
Distribution
Done poorly,
but a few notable
counter-examples
Done poorly to moderately,
not easy to find
Sometimes done well,
generally discoverable and available,
but could be improved
Integration
(I. Zaslavsky & CSIRO, BOM, WMO)

8. Data-intensive Science
Data
Acquisition &
modelling
Collaboration
and
visualisation
Analysis &
data mining
Dissemination
& sharing
Archiving and
preserving
fourthparadigm.org

11. Complex shared detector Simple instrument (if any)
Complex and Heavy process by experts Ad hoc observations and models
KB
GB
TB
PB
Science happens when PBs, TBs, GBs, and KBs can be mashed up simply
Provenance and trust widely varies
Data acquisition, early processing, and reporting ranges
from a large government agency to individual scientists.
Smaller data often passed around in email; big data
downloads can take days (if at all)
Data sharing concerns and patterns vary
Open access followed by (non-repeatable and tedious)
pre-processing
True science ready data set but concerns about misuse,
misunderstanding particularly for hard won data.
Computational tools differ.
Not everyone can get an account at a supercomputer
center
Very large computations require engineering (error
handling)
Space and time aren’t always simple dimensions

13. Getting what you need,
when you need it
Cloud computing is good for…

16. http://github.com/windowsazure

21. Customer Data Center

23. http://fetchclimate2.cloudapp.net/

24. Data Marketplaces

25. Web search:
“open weather
data azure”

28. Weather Forecast
Computation as a Service
ttp://aka.ms/oljnt2

29. http://weatherservice.cloudapp.net

34. http://research.microsoft.com/en-us/projects/azure/technical-papers.aspx

35. http://aka.ms/dm0 http://research.microsoft.com/projects/msrceesdm/

37. Windows Azure for
Research Group
@azure4research
www.azure4research.com

41. MODIS Azure:
Computing Evapotranspiration (ET) in the Cloud
A pipeline for
download,
processing, and
reduction of
diverse NASA
MODIS satellite
imagery.
Catharine van Ingen (Microsoft Research), Jie Li, Marty Humphrey (UVA), Youngryel Ryu (UCB), Deb Agarwal (BWC/LBL), Keith Jackson
(BL), Jay Borenstein (Stanford) , Team SICT: Vlad Andrei, Klaus Ganser, Samir Selman, Nandita Prabhu (Stanford), Team Nimbus: David Li,
Sudarshan Rangarajan, Shantanu Kurhekar, Riddhi Mittal (Stanford)

42. MODIS Azure Service
Reduction #1 Queue
Scientific
Results
Downloa
d
Reduction #2 Queue
Source
Metadata
MODIS Azure
Service Web Role
Portal
Request
Queue
Analysis Reduction Stage
Data Collection Stage
Source Imagery Download Sites
. . .
Reprojection
Queue
Derivation Reduction StageReprojection Stage
Download
Queue
Scientists
Science
results
Catharine van Ingen (Microsoft Research), Jie Li, Marty Humphrey (UVA), Youngryel Ryu (UCB), Deb Agarwal (BWC/LBL), Keith Jackson
(BL), Jay Borenstein (Stanford) , Team SICT: Vlad Andrei, Klaus Ganser, Samir Selman, Nandita Prabhu (Stanford), Team Nimbus: David Li,
Sudarshan Rangarajan, Shantanu Kurhekar, Riddhi Mittal (Stanford)

46. Use laptops &
desktop computers
Overwhelmed by
data
Finding analysis
ever more difficult;
sharing even
harder

47. www.azure4research.com

48. Windows Azure for
Research Group
@azure4research
www.azure4research.com