SlideShare ist ein Scribd-Unternehmen logo
1 von 35
ecosystem Modelling And Scaling
infrasTructure (eMAST)
- Where models and data become one
Presentation by Brad Evans based on contributions by Colin
Prentice, Michael Hutchinson, Gab Abramowitz, Ben Evans,
Rhys Whitley, Julie Pauwels
eMAST : Data assimilation
eMAST’s objectives 2013-2015
DELIVER research data infrastructure to
integrate TERN (and other) data streams on the
National Computing Infrastructure

ENABLE data assimilation, model evaluation and
accreditation and ecosystem model optimization
DRIVE advances in ecosystem science, impact
assessment and land management
Driving science questions
CARBON: How much CO2 is exchanged? How
much carbon can be stored and where?
WATER: What drives water use by ecosystems,
and runoff in rivers?
CLIMATE CHANGE: How does it change the
rules?
LAND MANAGEMENT: What will work, in a
changing climate?
More driving science questions
FIRE: What are the risks? How can they be
mitigated?
CLIMATE FEEDBACKS: How will ecosystem
changes influence the exchanges of carbon,
water and energy with the atmosphere?
BIODIVERSITY: What species are threatened?
Where are likely refugia? Is there a tipping
point?
What eMAST is delivering
High-resolution data products: climate, canopy
conductance, water use, primary production
Tools for interpolation, downscaling, upscaling,
hindcasting, forecasting
A state-of-the-art data assimilation system for
ecosystem model optimization
Software for model evaluation (based on PALS)
Top-level ecosystem drivers and targets for
models
http://www.tern.org.au/e-MAST-Data-Products-pg26355.html
ANUClimate
A NEW approach to interpolating our national network
0.01 degree climate surfaces

Who? Professor Mike Hutchinson (ANU)
Climate data sets (1 km)
Tmin

Tmax

vp

P

daily
✔
1970-2011

✔

✔

✔

monthly
✔
1970-2011

✔

✔

✔

✔

mean
monthly

pan
evap.

wet
days

✔

✔

✔

✔

✔

✔

solar
rad.

wind
speed

✔

✔
ANUClimate

When? Delivery timeline…
Complete set of Climate and
Bioclimatic data available on
RDSI
RDSI opendap netCDF CF
& Metadata store complete
= public release
Data starts propagating
to RDSI*
ADVANCED USER ACCESS
DOI’s NOT YET AVAILABLE = NO PUBLISH

30 Nov 2013

24 Dec 2013

31 Jan 2013

*Currently experiencing delays in RDSI allocation – delays in the Raijin cloud roll out etc…
ANUClimate

What is different?

• Improved ‘background-anomaly-interpolation’
approach
•

•
•

Temperature and both positive and zero rainfall
can be effectively interpolated by the thin plate
splines method - with adaptive capacity !
Monthly means, topographically corrected yield
influence of atmospheric processes and terrain
Significant improvement over both direct (nonanomaly) and current anomaly approach

• Coastal proximity: A new ‘proximity to coast’
modifier captures marine perturbation of
climate
ANUClimate

What can we expect?
• Temperature estimates improved by around
25% compared to Jones et al. 2009 (RMSE
cross validation)
• Precipitation estimates a modest, but
significant, improvement (7-15% RMSE cross
validation)
The model makes no further improvement on
accuracy beyond the 1km mark !
ANUClimate

How is it done?
AMOS 2014
Bioclimate data sets (1 km T, P and R)
ecosystem Production in Space and
Time: ePiSaT
eMAST: How does gross primary
productivity (GPP) vary in space and time
across Australia?
Colin: How can we ‘simply’ estimate
GPP across Australia?
What data does TERN provide that
might be useful for addressing this
research question?
User workflow: ePiSaT GPP
Choose the ePiSaT
model from the TERN
portal

Produce continental
scale estimates of GPP
and evaluate them

Obtain OzFlux data via
the TERN/ OzFlux
portals

Obtain climate (eMAST)
and satellite data
(AusCover) to scale the
ePiSaT parameters

Run the ePiSaT model –
generate estimates of
ecosystem parameters,
evaluate them
http://episat-software.blogspot.com.au/
OzFlux
ePiSaT : Flux tower scaling
OzFlux: Flux partitioning
1
Respiration
R=

1
Data filtering:
Removal of outliers
etc.. Gap filling of
PAR (PPFD) for GPP

Amax = - 2

Quantum

3

Rectangular
Hyperbole
3 parameter

1

Assimilation

2

2

Efficiency
2

Φ=
3

3

FC = R -

Amax * Φ I
Amax +Φ I
ePiSaT v 1.0 : Tower GPP

GPP =

Amax * I
Amax + C

Where: Amax is the maximum rate of carboxylation, I is PAR (PPFD) and C = parameter
3 from the rectangular hyperbola described in the previous slide
ePiSaT v 1.0 : Map GPP
GPP = fAPAR *I* LUE
Where: fAPAR is the fraction of absorbed photosynthetic active radiation,
I is PAR (PPFD) and LUE is light use efficiency derived from the relationship of
Tower GPP (previous slide) and fAPAR and I.

ePiSaT v 2.0 : Map GPP
GPP = fAPAR *I* LUE*WUE*Trange
Where: fAPAR is the fraction of absorbed photosynthetic active radiation,
I is PAR (PPFD) and LUE is light use efficiency derived from the relationship of
Tower GPP (previous slide) and fAPAR and I. WUE and Trange are derived similarly.
ePiSaT : Partitioning evaluation
ePiSaT : Partitioning evaluation
Model data evaluation

from Gab Abramowitz (UNSW)
Plant trait surfaces
•
•
•
•
•
•
•
•

Leaf nitrogen
Leaf phosphorus
Specific leaf area
Leaf area
Maximum plant height
Photosynthesis per leaf
area
Photosynthesis per leaf
dry mass
Leaf stomatal
conductance

Dr. Rhys Whitley
Plant trait surfaces
NEON & TERN
TERN Data Discovery Portal
Summary: Data-model fusion tools
Data assimilation collaboration with NEON and NCAR, CSIRO,
Macquarie University and the Australian National University
- ACEAS workshop on data assimilation early 2014
eMAST : An R-Package ‘emast’ for the computation and visualization of
bioclimatic indices

ePiSaT : Collaboration with OzFlux and AusCover to model Gross
Primary Production across the landscape, another R-Package ‘ePiSaT’
-ACEAS worskshop on SPEDDEXES

Protocol for the Analysis of Land Surface Models (PALS) for evaluation
of data and models
The future of eMAST
Continue delivery of our key datasets through
the RDSI, Data Discovery, Visualization &
Exploitation… consolidation of our tools and
porting them to Raijin.

Weitere ähnliche Inhalte

Was ist angesagt?

Akiyo yatagai
Akiyo yatagaiAkiyo yatagai
Akiyo yatagai
ClimDev15
 
Forecasting long term global solar radiation with an ann algorithm
Forecasting long term global solar radiation with an ann algorithmForecasting long term global solar radiation with an ann algorithm
Forecasting long term global solar radiation with an ann algorithm
mehmet şahin
 
kellndorfer_WE3.T05.4.pptx
kellndorfer_WE3.T05.4.pptxkellndorfer_WE3.T05.4.pptx
kellndorfer_WE3.T05.4.pptx
grssieee
 
1_Buck - Wavemil Steps IGARSS-11.ppt
1_Buck - Wavemil Steps IGARSS-11.ppt1_Buck - Wavemil Steps IGARSS-11.ppt
1_Buck - Wavemil Steps IGARSS-11.ppt
grssieee
 
GENERATING FINE RESOLUTION LEAF AREA INDEX MAPS FOR BOREAL FORESTS OF FINLAND...
GENERATING FINE RESOLUTION LEAF AREA INDEX MAPS FOR BOREAL FORESTS OF FINLAND...GENERATING FINE RESOLUTION LEAF AREA INDEX MAPS FOR BOREAL FORESTS OF FINLAND...
GENERATING FINE RESOLUTION LEAF AREA INDEX MAPS FOR BOREAL FORESTS OF FINLAND...
grssieee
 
Time integration of evapotranspiration using a two source surface energy bala...
Time integration of evapotranspiration using a two source surface energy bala...Time integration of evapotranspiration using a two source surface energy bala...
Time integration of evapotranspiration using a two source surface energy bala...
Ramesh Dhungel
 
Larson_REU_2015_Poster
Larson_REU_2015_PosterLarson_REU_2015_Poster
Larson_REU_2015_Poster
Talin Larson
 
5 IGARSS_Riishojgaard July 25 2011_rev2.ppt
5 IGARSS_Riishojgaard July 25 2011_rev2.ppt5 IGARSS_Riishojgaard July 25 2011_rev2.ppt
5 IGARSS_Riishojgaard July 25 2011_rev2.ppt
grssieee
 

Was ist angesagt? (19)

Climate Models
Climate ModelsClimate Models
Climate Models
 
IUKWC Workshop Nov16: Developing Hydro-climatic Services for Water Security –...
IUKWC Workshop Nov16: Developing Hydro-climatic Services for Water Security –...IUKWC Workshop Nov16: Developing Hydro-climatic Services for Water Security –...
IUKWC Workshop Nov16: Developing Hydro-climatic Services for Water Security –...
 
Akiyo yatagai
Akiyo yatagaiAkiyo yatagai
Akiyo yatagai
 
Climate Modelling for Ireland -Dr Ray McGrath, Met Eireann
Climate Modelling for Ireland -Dr Ray McGrath, Met EireannClimate Modelling for Ireland -Dr Ray McGrath, Met Eireann
Climate Modelling for Ireland -Dr Ray McGrath, Met Eireann
 
Wandera_et_al_HESS_2017
Wandera_et_al_HESS_2017Wandera_et_al_HESS_2017
Wandera_et_al_HESS_2017
 
Forecasting long term global solar radiation with an ann algorithm
Forecasting long term global solar radiation with an ann algorithmForecasting long term global solar radiation with an ann algorithm
Forecasting long term global solar radiation with an ann algorithm
 
CLIM: Transition Workshop - Optimization Methods in Remote Sensing - Jessica...
CLIM: Transition Workshop - Optimization Methods in Remote Sensing  - Jessica...CLIM: Transition Workshop - Optimization Methods in Remote Sensing  - Jessica...
CLIM: Transition Workshop - Optimization Methods in Remote Sensing - Jessica...
 
EC-Earth Climate Modelling Activities - Ray McGrath, Met Eireann
EC-Earth Climate Modelling Activities - Ray McGrath, Met EireannEC-Earth Climate Modelling Activities - Ray McGrath, Met Eireann
EC-Earth Climate Modelling Activities - Ray McGrath, Met Eireann
 
kellndorfer_WE3.T05.4.pptx
kellndorfer_WE3.T05.4.pptxkellndorfer_WE3.T05.4.pptx
kellndorfer_WE3.T05.4.pptx
 
1_Buck - Wavemil Steps IGARSS-11.ppt
1_Buck - Wavemil Steps IGARSS-11.ppt1_Buck - Wavemil Steps IGARSS-11.ppt
1_Buck - Wavemil Steps IGARSS-11.ppt
 
IUKWC Workshop Nov16: Developing Hydro-climatic Services for Water Security –...
IUKWC Workshop Nov16: Developing Hydro-climatic Services for Water Security –...IUKWC Workshop Nov16: Developing Hydro-climatic Services for Water Security –...
IUKWC Workshop Nov16: Developing Hydro-climatic Services for Water Security –...
 
GENERATING FINE RESOLUTION LEAF AREA INDEX MAPS FOR BOREAL FORESTS OF FINLAND...
GENERATING FINE RESOLUTION LEAF AREA INDEX MAPS FOR BOREAL FORESTS OF FINLAND...GENERATING FINE RESOLUTION LEAF AREA INDEX MAPS FOR BOREAL FORESTS OF FINLAND...
GENERATING FINE RESOLUTION LEAF AREA INDEX MAPS FOR BOREAL FORESTS OF FINLAND...
 
Program on Mathematical and Statistical Methods for Climate and the Earth Sys...
Program on Mathematical and Statistical Methods for Climate and the Earth Sys...Program on Mathematical and Statistical Methods for Climate and the Earth Sys...
Program on Mathematical and Statistical Methods for Climate and the Earth Sys...
 
Leaf Area Index (LAI) in the quantification of vegetation disturbance in Iris...
Leaf Area Index (LAI) in the quantification of vegetation disturbance in Iris...Leaf Area Index (LAI) in the quantification of vegetation disturbance in Iris...
Leaf Area Index (LAI) in the quantification of vegetation disturbance in Iris...
 
Measuring Solar Spectral Energy
Measuring Solar Spectral EnergyMeasuring Solar Spectral Energy
Measuring Solar Spectral Energy
 
Time integration of evapotranspiration using a two source surface energy bala...
Time integration of evapotranspiration using a two source surface energy bala...Time integration of evapotranspiration using a two source surface energy bala...
Time integration of evapotranspiration using a two source surface energy bala...
 
Larson_REU_2015_Poster
Larson_REU_2015_PosterLarson_REU_2015_Poster
Larson_REU_2015_Poster
 
5 IGARSS_Riishojgaard July 25 2011_rev2.ppt
5 IGARSS_Riishojgaard July 25 2011_rev2.ppt5 IGARSS_Riishojgaard July 25 2011_rev2.ppt
5 IGARSS_Riishojgaard July 25 2011_rev2.ppt
 
INTRODUCING NREL’S BEST PRACTICES HANDBOOK FOR COLLECTION AND USE OF SOLAR RE...
INTRODUCING NREL’S BEST PRACTICES HANDBOOK FOR COLLECTION AND USE OF SOLAR RE...INTRODUCING NREL’S BEST PRACTICES HANDBOOK FOR COLLECTION AND USE OF SOLAR RE...
INTRODUCING NREL’S BEST PRACTICES HANDBOOK FOR COLLECTION AND USE OF SOLAR RE...
 

Ähnlich wie EcoTas13 BradEvans e-Mast UNSW

Ajayi_M_Senior Thesis Poster
Ajayi_M_Senior Thesis PosterAjayi_M_Senior Thesis Poster
Ajayi_M_Senior Thesis Poster
Moyo Ajayi
 
3178_IGARSS11.ppt
3178_IGARSS11.ppt3178_IGARSS11.ppt
3178_IGARSS11.ppt
grssieee
 
Reanalysis Datasets for Solar Resource Assessment - 2014 SPI
Reanalysis Datasets for Solar Resource Assessment - 2014 SPI Reanalysis Datasets for Solar Resource Assessment - 2014 SPI
Reanalysis Datasets for Solar Resource Assessment - 2014 SPI
Gwendalyn Bender
 
The Physics Of Earth Atmospheric Co2 Concentration Essay
The Physics Of Earth Atmospheric Co2 Concentration EssayThe Physics Of Earth Atmospheric Co2 Concentration Essay
The Physics Of Earth Atmospheric Co2 Concentration Essay
Rochelle Schear
 

Ähnlich wie EcoTas13 BradEvans e-Mast UNSW (20)

EcoTas13 BradEvans e-MAST
EcoTas13 BradEvans e-MASTEcoTas13 BradEvans e-MAST
EcoTas13 BradEvans e-MAST
 
EcoTas13 BradEvans e-MAST framework
EcoTas13 BradEvans e-MAST frameworkEcoTas13 BradEvans e-MAST framework
EcoTas13 BradEvans e-MAST framework
 
Colin Prentice SPEDDEXES 2014
Colin Prentice SPEDDEXES 2014Colin Prentice SPEDDEXES 2014
Colin Prentice SPEDDEXES 2014
 
Atmospheric Correction of Remote Sensing Data_RamaRao.pptx
Atmospheric Correction of Remote Sensing Data_RamaRao.pptxAtmospheric Correction of Remote Sensing Data_RamaRao.pptx
Atmospheric Correction of Remote Sensing Data_RamaRao.pptx
 
The Role of Semantics in Harmonizing YOPP Observation and Model Data
The Role of Semantics in Harmonizing YOPP Observation and Model DataThe Role of Semantics in Harmonizing YOPP Observation and Model Data
The Role of Semantics in Harmonizing YOPP Observation and Model Data
 
Trial and error in determining carbon budgets at policy relevant scales
Trial and error in determining carbon budgets at policy relevant scalesTrial and error in determining carbon budgets at policy relevant scales
Trial and error in determining carbon budgets at policy relevant scales
 
Wu, Mousong: Using SMOS soil moisture data combining CO2 flask samples to con...
Wu, Mousong: Using SMOS soil moisture data combining CO2 flask samples to con...Wu, Mousong: Using SMOS soil moisture data combining CO2 flask samples to con...
Wu, Mousong: Using SMOS soil moisture data combining CO2 flask samples to con...
 
Scheel et al_2011_trmm_andes
Scheel et al_2011_trmm_andesScheel et al_2011_trmm_andes
Scheel et al_2011_trmm_andes
 
Integration of flux tower data and remotely sensed data into the SCOPE simula...
Integration of flux tower data and remotely sensed data into the SCOPE simula...Integration of flux tower data and remotely sensed data into the SCOPE simula...
Integration of flux tower data and remotely sensed data into the SCOPE simula...
 
Ajayi_M_Senior Thesis Poster
Ajayi_M_Senior Thesis PosterAjayi_M_Senior Thesis Poster
Ajayi_M_Senior Thesis Poster
 
''Copernicus for sustainable land management'' by Markus Erhard, European Env...
''Copernicus for sustainable land management'' by Markus Erhard, European Env...''Copernicus for sustainable land management'' by Markus Erhard, European Env...
''Copernicus for sustainable land management'' by Markus Erhard, European Env...
 
Remote Sensing Methods for operational ET determinations in the NENA region, ...
Remote Sensing Methods for operational ET determinations in the NENA region, ...Remote Sensing Methods for operational ET determinations in the NENA region, ...
Remote Sensing Methods for operational ET determinations in the NENA region, ...
 
My presentation at ICEM 2017: From data mining to information extraction: usi...
My presentation at ICEM 2017: From data mining to information extraction: usi...My presentation at ICEM 2017: From data mining to information extraction: usi...
My presentation at ICEM 2017: From data mining to information extraction: usi...
 
Advanced weather forecasting for RES applications: Smart4RES developments tow...
Advanced weather forecasting for RES applications: Smart4RES developments tow...Advanced weather forecasting for RES applications: Smart4RES developments tow...
Advanced weather forecasting for RES applications: Smart4RES developments tow...
 
Application of the extreme learning machine algorithm for the
Application of the extreme learning machine algorithm for theApplication of the extreme learning machine algorithm for the
Application of the extreme learning machine algorithm for the
 
3178_IGARSS11.ppt
3178_IGARSS11.ppt3178_IGARSS11.ppt
3178_IGARSS11.ppt
 
E-MAST
E-MASTE-MAST
E-MAST
 
Reanalysis Datasets for Solar Resource Assessment - 2014 SPI
Reanalysis Datasets for Solar Resource Assessment - 2014 SPI Reanalysis Datasets for Solar Resource Assessment - 2014 SPI
Reanalysis Datasets for Solar Resource Assessment - 2014 SPI
 
The Physics Of Earth Atmospheric Co2 Concentration Essay
The Physics Of Earth Atmospheric Co2 Concentration EssayThe Physics Of Earth Atmospheric Co2 Concentration Essay
The Physics Of Earth Atmospheric Co2 Concentration Essay
 
Climate Modeling for the Asia-Pacific
Climate Modeling for the Asia-PacificClimate Modeling for the Asia-Pacific
Climate Modeling for the Asia-Pacific
 

Mehr von TERN Australia

Mehr von TERN Australia (20)

Careers Grounded in Soils
Careers Grounded in SoilsCareers Grounded in Soils
Careers Grounded in Soils
 
TERN Australia Soil & Herbarium Collection Brochure
TERN Australia Soil & Herbarium Collection BrochureTERN Australia Soil & Herbarium Collection Brochure
TERN Australia Soil & Herbarium Collection Brochure
 
Summary of TERN monitoring plots in the Pilbara WA, Apr2015 - Jun2021
Summary of TERN monitoring plots in the Pilbara WA, Apr2015 - Jun2021Summary of TERN monitoring plots in the Pilbara WA, Apr2015 - Jun2021
Summary of TERN monitoring plots in the Pilbara WA, Apr2015 - Jun2021
 
Summary of TERN plots on Kangaroo Island, SA, Oct 2018 - Oct 2021
Summary of TERN plots on Kangaroo Island, SA, Oct 2018 - Oct 2021Summary of TERN plots on Kangaroo Island, SA, Oct 2018 - Oct 2021
Summary of TERN plots on Kangaroo Island, SA, Oct 2018 - Oct 2021
 
MER Pilot Network flyer 2020
MER Pilot Network flyer 2020MER Pilot Network flyer 2020
MER Pilot Network flyer 2020
 
Australia's Environmental Predictive Capability
Australia's Environmental Predictive CapabilityAustralia's Environmental Predictive Capability
Australia's Environmental Predictive Capability
 
Biodiversity Management in Tasmania's Temperate Native Forests
Biodiversity Management in Tasmania's Temperate Native ForestsBiodiversity Management in Tasmania's Temperate Native Forests
Biodiversity Management in Tasmania's Temperate Native Forests
 
Observing Environmental Change in Australia: Conversations for Sustainability
Observing Environmental Change in Australia: Conversations for SustainabilityObserving Environmental Change in Australia: Conversations for Sustainability
Observing Environmental Change in Australia: Conversations for Sustainability
 
Observing Environmental Change in Australia: Conversations for Sustainability
Observing Environmental Change in Australia: Conversations for SustainabilityObserving Environmental Change in Australia: Conversations for Sustainability
Observing Environmental Change in Australia: Conversations for Sustainability
 
Dr Michael Mirtl (ILTER Chair) presenting at the AusLTER Forum 2018
Dr Michael Mirtl  (ILTER Chair) presenting at the AusLTER Forum 2018Dr Michael Mirtl  (ILTER Chair) presenting at the AusLTER Forum 2018
Dr Michael Mirtl (ILTER Chair) presenting at the AusLTER Forum 2018
 
Prof Bob Scholes (Wits University, South Africa) presenting at the AusLTER Fo...
Prof Bob Scholes (Wits University, South Africa) presenting at the AusLTER Fo...Prof Bob Scholes (Wits University, South Africa) presenting at the AusLTER Fo...
Prof Bob Scholes (Wits University, South Africa) presenting at the AusLTER Fo...
 
Prof Phil Robertson (Michigan State University, USA) presenting at the AusLTE...
Prof Phil Robertson (Michigan State University, USA) presenting at the AusLTE...Prof Phil Robertson (Michigan State University, USA) presenting at the AusLTE...
Prof Phil Robertson (Michigan State University, USA) presenting at the AusLTE...
 
Dr Manuel Maass (National Autonomous University of Mexico) presenting at the ...
Dr Manuel Maass (National Autonomous University of Mexico) presenting at the ...Dr Manuel Maass (National Autonomous University of Mexico) presenting at the ...
Dr Manuel Maass (National Autonomous University of Mexico) presenting at the ...
 
Yuxia Liu Phenology 2018 poster on tracking grass phenology
Yuxia Liu Phenology 2018 poster on tracking grass phenologyYuxia Liu Phenology 2018 poster on tracking grass phenology
Yuxia Liu Phenology 2018 poster on tracking grass phenology
 
Qiaoyun Xie Phenology 2018 presentation on agricultural phenology
Qiaoyun Xie Phenology 2018 presentation on agricultural phenologyQiaoyun Xie Phenology 2018 presentation on agricultural phenology
Qiaoyun Xie Phenology 2018 presentation on agricultural phenology
 
Ha Nguyen Phenology 2018 presentation on Melbourne pollen trends
Ha Nguyen Phenology 2018 presentation on Melbourne pollen trendsHa Nguyen Phenology 2018 presentation on Melbourne pollen trends
Ha Nguyen Phenology 2018 presentation on Melbourne pollen trends
 
Paul Beggs Phenology 2018 presentation on AusPollen
Paul Beggs Phenology 2018 presentation on AusPollenPaul Beggs Phenology 2018 presentation on AusPollen
Paul Beggs Phenology 2018 presentation on AusPollen
 
GEOSS Ecosystem Mapping for Australia
GEOSS Ecosystem Mapping for AustraliaGEOSS Ecosystem Mapping for Australia
GEOSS Ecosystem Mapping for Australia
 
TERN Ecosystem Surveillance Plots Roy Hill Station
TERN Ecosystem Surveillance Plots Roy Hill StationTERN Ecosystem Surveillance Plots Roy Hill Station
TERN Ecosystem Surveillance Plots Roy Hill Station
 
TERN Ecosystem Surveillance Plots Kakadu National Park
TERN Ecosystem Surveillance Plots Kakadu National ParkTERN Ecosystem Surveillance Plots Kakadu National Park
TERN Ecosystem Surveillance Plots Kakadu National Park
 

Kürzlich hochgeladen

Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Victor Rentea
 
Architecting Cloud Native Applications
Architecting Cloud Native ApplicationsArchitecting Cloud Native Applications
Architecting Cloud Native Applications
WSO2
 
Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businessWhy Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire business
panagenda
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Safe Software
 

Kürzlich hochgeladen (20)

Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...
Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...
Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...
 
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost SavingRepurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
 
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
 
Architecting Cloud Native Applications
Architecting Cloud Native ApplicationsArchitecting Cloud Native Applications
Architecting Cloud Native Applications
 
Exploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with MilvusExploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with Milvus
 
Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businessWhy Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire business
 
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, AdobeApidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
 
Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...
 
Corporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptxCorporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptx
 
Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)Introduction to Multilingual Retrieval Augmented Generation (RAG)
Introduction to Multilingual Retrieval Augmented Generation (RAG)
 
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot TakeoffStrategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
 
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data DiscoveryTrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
 
Artificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : UncertaintyArtificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : Uncertainty
 
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
 
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfBoost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdf
 
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ..."I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
 
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
 
WSO2's API Vision: Unifying Control, Empowering Developers
WSO2's API Vision: Unifying Control, Empowering DevelopersWSO2's API Vision: Unifying Control, Empowering Developers
WSO2's API Vision: Unifying Control, Empowering Developers
 
presentation ICT roal in 21st century education
presentation ICT roal in 21st century educationpresentation ICT roal in 21st century education
presentation ICT roal in 21st century education
 

EcoTas13 BradEvans e-Mast UNSW

  • 1. ecosystem Modelling And Scaling infrasTructure (eMAST) - Where models and data become one Presentation by Brad Evans based on contributions by Colin Prentice, Michael Hutchinson, Gab Abramowitz, Ben Evans, Rhys Whitley, Julie Pauwels
  • 2.
  • 3.
  • 4. eMAST : Data assimilation
  • 5. eMAST’s objectives 2013-2015 DELIVER research data infrastructure to integrate TERN (and other) data streams on the National Computing Infrastructure ENABLE data assimilation, model evaluation and accreditation and ecosystem model optimization DRIVE advances in ecosystem science, impact assessment and land management
  • 6. Driving science questions CARBON: How much CO2 is exchanged? How much carbon can be stored and where? WATER: What drives water use by ecosystems, and runoff in rivers? CLIMATE CHANGE: How does it change the rules? LAND MANAGEMENT: What will work, in a changing climate?
  • 7. More driving science questions FIRE: What are the risks? How can they be mitigated? CLIMATE FEEDBACKS: How will ecosystem changes influence the exchanges of carbon, water and energy with the atmosphere? BIODIVERSITY: What species are threatened? Where are likely refugia? Is there a tipping point?
  • 8. What eMAST is delivering High-resolution data products: climate, canopy conductance, water use, primary production Tools for interpolation, downscaling, upscaling, hindcasting, forecasting A state-of-the-art data assimilation system for ecosystem model optimization Software for model evaluation (based on PALS) Top-level ecosystem drivers and targets for models
  • 10.
  • 11. ANUClimate A NEW approach to interpolating our national network 0.01 degree climate surfaces Who? Professor Mike Hutchinson (ANU)
  • 12. Climate data sets (1 km) Tmin Tmax vp P daily ✔ 1970-2011 ✔ ✔ ✔ monthly ✔ 1970-2011 ✔ ✔ ✔ ✔ mean monthly pan evap. wet days ✔ ✔ ✔ ✔ ✔ ✔ solar rad. wind speed ✔ ✔
  • 13. ANUClimate When? Delivery timeline… Complete set of Climate and Bioclimatic data available on RDSI RDSI opendap netCDF CF & Metadata store complete = public release Data starts propagating to RDSI* ADVANCED USER ACCESS DOI’s NOT YET AVAILABLE = NO PUBLISH 30 Nov 2013 24 Dec 2013 31 Jan 2013 *Currently experiencing delays in RDSI allocation – delays in the Raijin cloud roll out etc…
  • 14. ANUClimate What is different? • Improved ‘background-anomaly-interpolation’ approach • • • Temperature and both positive and zero rainfall can be effectively interpolated by the thin plate splines method - with adaptive capacity ! Monthly means, topographically corrected yield influence of atmospheric processes and terrain Significant improvement over both direct (nonanomaly) and current anomaly approach • Coastal proximity: A new ‘proximity to coast’ modifier captures marine perturbation of climate
  • 15. ANUClimate What can we expect? • Temperature estimates improved by around 25% compared to Jones et al. 2009 (RMSE cross validation) • Precipitation estimates a modest, but significant, improvement (7-15% RMSE cross validation) The model makes no further improvement on accuracy beyond the 1km mark !
  • 18. Bioclimate data sets (1 km T, P and R)
  • 19. ecosystem Production in Space and Time: ePiSaT eMAST: How does gross primary productivity (GPP) vary in space and time across Australia? Colin: How can we ‘simply’ estimate GPP across Australia? What data does TERN provide that might be useful for addressing this research question?
  • 20. User workflow: ePiSaT GPP Choose the ePiSaT model from the TERN portal Produce continental scale estimates of GPP and evaluate them Obtain OzFlux data via the TERN/ OzFlux portals Obtain climate (eMAST) and satellite data (AusCover) to scale the ePiSaT parameters Run the ePiSaT model – generate estimates of ecosystem parameters, evaluate them
  • 23. ePiSaT : Flux tower scaling
  • 24. OzFlux: Flux partitioning 1 Respiration R= 1 Data filtering: Removal of outliers etc.. Gap filling of PAR (PPFD) for GPP Amax = - 2 Quantum 3 Rectangular Hyperbole 3 parameter 1 Assimilation 2 2 Efficiency 2 Φ= 3 3 FC = R - Amax * Φ I Amax +Φ I
  • 25. ePiSaT v 1.0 : Tower GPP GPP = Amax * I Amax + C Where: Amax is the maximum rate of carboxylation, I is PAR (PPFD) and C = parameter 3 from the rectangular hyperbola described in the previous slide
  • 26. ePiSaT v 1.0 : Map GPP GPP = fAPAR *I* LUE Where: fAPAR is the fraction of absorbed photosynthetic active radiation, I is PAR (PPFD) and LUE is light use efficiency derived from the relationship of Tower GPP (previous slide) and fAPAR and I. ePiSaT v 2.0 : Map GPP GPP = fAPAR *I* LUE*WUE*Trange Where: fAPAR is the fraction of absorbed photosynthetic active radiation, I is PAR (PPFD) and LUE is light use efficiency derived from the relationship of Tower GPP (previous slide) and fAPAR and I. WUE and Trange are derived similarly.
  • 27. ePiSaT : Partitioning evaluation
  • 28. ePiSaT : Partitioning evaluation
  • 29. Model data evaluation from Gab Abramowitz (UNSW)
  • 30. Plant trait surfaces • • • • • • • • Leaf nitrogen Leaf phosphorus Specific leaf area Leaf area Maximum plant height Photosynthesis per leaf area Photosynthesis per leaf dry mass Leaf stomatal conductance Dr. Rhys Whitley
  • 34. Summary: Data-model fusion tools Data assimilation collaboration with NEON and NCAR, CSIRO, Macquarie University and the Australian National University - ACEAS workshop on data assimilation early 2014 eMAST : An R-Package ‘emast’ for the computation and visualization of bioclimatic indices ePiSaT : Collaboration with OzFlux and AusCover to model Gross Primary Production across the landscape, another R-Package ‘ePiSaT’ -ACEAS worskshop on SPEDDEXES Protocol for the Analysis of Land Surface Models (PALS) for evaluation of data and models
  • 35. The future of eMAST Continue delivery of our key datasets through the RDSI, Data Discovery, Visualization & Exploitation… consolidation of our tools and porting them to Raijin.