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Earth System Grid Federation Future Architecture, Copernicus, Cloud and ESA

ClimateData.ca Meeting, 23 November 2021

Philip Kershaw, Technical Manager

Centre for Environmental Data Analysis

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Earth System Grid Federation: a globally distributed data archive for climate data

ESGF Dashboard: http://esgf-ui.cmcc.it

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ESGF – Application and Evolution

ESGF Frontend

ESA Climate Change Initiative Open Data Portal

CMIP/CORDEX for Copernicus Climate Data Store

CMIP5 >> Earth System Grid Federation >> CMIP6

Public Cloud Public Dataset Programme(s)

ESGF Future Architecture >> ESGF 2.0

Institutional-based hosting

Data lakes – Government-sponsored

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ESA Climate Change Initiative Open Data Portal

2 Phases:

  1. Leveraged ESGF
    1. Quick win with search and download
    2. Bespoke search API incompatible with other community standards - OGC CSW
    3. THREDDS Data Server couldn’t scale to our needs

  • Redesigned to address issues
    • OpenSearch API replaced ESG Search
    • Scalable data service with Kubernetes
    • Zarr format cache of netCDF data on object store for performance

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C3S 34[a-f] Projects for the CDS

  • Architected a system for delivering resilient CMIP5 and CORDEX data access for the CDS by creative application of federated architecture for Earth System Grid Federation

Node

Node

Node

Node

Node

Node

ESGF: an international federation of nodes providing a network of access points to model data

Single point of Access

[DNS Load Balancing]

Node

Node

Node

C3S 34a/b system: a single resilient point of access to data delivered through replication and redundancy

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C3S Resilient CMIP and CORDEX Data Access

Single point of Access

[DNS-based load balancing]

Data Node

[DKRZ]

Master Data Node

[CEDA]

Data Node

[IPSL]

CDS

Replicate netCDF model data

Replicate netCDF model data

download data

Access control complex to maintain

OPeNDAP for data sub-setting is inefficient

  • Data replication:
    • Complex
    • Time consuming
    • Difficult to maintain consistency

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Sub-setting Services for C3S 34e Project

Analyse datasets and make an inventory of fixes

Applies fixes to datasets before applying subsetting/regridding operations

C3S 34e Project

Credit: Ag Stephens, CEDA

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ESA Earth Observation Exploitation Platform Common Architecture (EOEPCA)

  • Architectural blueprint for the federation of platforms
  • Interlinked with the work on recent OGC testbeds
  • CEDA involved with consultancy role
  • Processing and chaining of particular interest
    • ADES and EMS
    • Ability to push customised shrink-wrapped processes to 3rd party WPS instances
  • Innovations with ID management: UMA

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ESGF – Application and Evolution

ESGF Frontend

ESA Climate Change Initiative Open Data Portal

CMIP/CORDEX for Copernicus Climate Data Store

CMIP5 >> Earth System Grid Federation >> CMIP6

Public Cloud Public Dataset Programme(s)

ESGF Future Architecture >> ESGF 2.0

Institutional-based hosting

Data lakes – Government-sponsored

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ESGF Future Architecture

Platforms and systems administration

Modular, scalable architecture: Containers, Kubernetes

Embrace infrastructure-as-code approach

Search services

Modernise, centralise and simplify

Use community standards: STAC

ID Management and Access Entitlement

Modernise, centralise and simplify

Use industry standards: OpenID Connect / OAuth 2.0

  • Container and Container+Kubernetes installs available
  • Deployed on AWS (GFDL) and at CEDA
  • Major community engagement on use of STAC for ESM data
  • Prototype developed by CEDA
  • Integration tests
  • CoG and MetaGrid futures??
  • OpenID Connect / OAuth 2.0 done
  • New Authorisation system with Open Policy Agent
  • Authentication integrated with C4I in test

Progress and Achievements

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ESGF Future Architecture

New modes for Data Access + Storage

Augment trad. file serving with object store

New models for aggregation and subsetting, retire OPeNDAP

Compute Services

Important but no consensus for ESGF-wide standard offering yet

Metrics Collection

Leverage advances in industry with standard tooling to exploit - Prometheus and InfluxDb, Grafana

  • Factored out TDS
  • Test CMIP6 data caches on object store at CEDA and DKRZ

  • Ported C3S WPS Data Reduction Services for use in ENES CDI
  • Used with Climate4Impact
  • Reboot of Compute Working Team
  • New Metrics system integrated with CMCC

Progress and Achievements

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Future Architecture Node – Phase 1

Kubernetes Cluster

Horizontal Pod Auto-scaler

Auto-Scaling

Elastic

POSIX Storage

Access Control

Identity Provider

TDS

Nginx File Serving

ESG Search

V5 esg-publisher

Ingress

Solr

Client App – Search + Data Access

Metrics

✂️

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Future Architecture Node – Phase 2

Site Deployment(s)

Centralised Deployment(s)

Kubernetes Cluster

Kubernetes Cluster

Horizontal Pod Auto-scaler

Auto-Scaling

Elastic

POSIX Storage

Access Control

Identity Provider

TDS

Nginx File Serving

STAC Search

Elastic Search

Client App - Data Access

Ingress

Metrics

Ingress

Client App – Data Search

Client App – Publishing

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STAC API for ESGF

  • Implementation of STAC API
  • ElasticSearch backend
  • Filter extensions to support faceted search
  • Fully featured STAC equivalent API to ESG Search
  • Simple frontend created to demonstrate its features

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IS-ENES3 - Data Analytics using Notebooks/icclim

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DestinE and Blueprint Architecture

Destination Earth (DestinE) - major EU initiative:

    • “to develop a very high precision digital model of the Earth (a ‘digital twin’) to monitor and predict environmental change and human impact to support sustainable development”

��

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JASMIN

Cloud Infrastructure

Data Sources

Data Analytics Platform

High Performance Computing

Data production / processing

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ESA Digital Twin Earth (DestinE) Precursor - land surface modelling and climate

  • Using JULES (Joint UK Land Environment Simulator)
    • the land surface component in the Met Office Unified Model

  • Improvements with Data Assimilation
    • LaVEnDAR (The Land Variational Ensemble Data Assimilation fRamework)
    • Feed in satellite observations – SIF and SMAP data

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What could be the future impact of climate change on the soil moisture?

  • JULES driven with climate projections from ISIMIP data (Inter-Sectoral Impact Model Intercomparison Project)

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Make a surrogate AI model to JULES

  • Experimented with Machine Learning (ML) techniques
  • Goal: a general-purpose algorithm -

time series of daily weather data 🡺 time series of soil moisture data

  • Successfully applied XGBoost (eXtreme Gradient Boosting) algorithm.
  • trained on up to 1000 grid cells, representative of the various biomes in continental Africa
  • Demonstrated to accurately emulate JULES output at other locations
  • The credibility of the model is enhanced by its transparency and explainability

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Digital Twin Precursor on JASMIN: HPC for data production, cloud for analysis

External JASMIN Infrastructure

external cloud tenancy

Cluster-as-a-Service

Managed (Internal) JASMIN

Group Workspace (GWS)

SOF

[POSIX]/

JULES / LAVENDAR Data Assimilation

Batch compute (Lotus)

netCDF

Soil Moisture model outputs netCDF files to regular file system

Cluster-as-a-Service deploys ready-made Jupyter service

Move data into object store so that it can be accessed by Jupyter Service on JASMIN cloud

Data accessed using Jupyter Notebook service

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Arrangement of data and efficient access

Time axis

  • Data output from models as netCDF format
  • Data in files arranged in spatial dimensions one per time step
  • But predominate access pattern for analysis of climate data in the project is time series query (grey blocks)

Latitude

Longitude

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Object Store: Different storage strategies showed radically different performance

  • We experimented with different storage chunking arrangements
  • 20-year dataset of soil moisture

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Using Object Store for re-arrangement of data to suite our access patterns

  • Using zarr and xarray Python libraries to store and access the data

  • Chunked data into a series of strips along the time axis

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Rechunking of data made possible interactive maps with long time series

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Take home message: object store for analysis-ready cache specific to project needs

  • Object store can be efficient for access on cloud
  • It is essential to orient data storage to suit predominant access patterns
  • Good news – re-writing data into different orientations was fast

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Futures

Public Cloud Public Dataset Programme(s)

ESGF Future Architecture >> ESGF 2.0

Institutional-based hosting

Data lakes – Government-sponsored

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Acknowledgements + Further Info

@ISENES_RI

@cedanews

@PhilipJKershaw

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N°824084

IS-ENES3 website

https://is.enes.org/

Contact us at

is-enes@ipsl.fr

Subscribe to the IS-ENES3 H2020 Youtube channel !

ESGF Future Architecture Report: https://doi.org/10.5281/zenodo.3928222