hdg geo discussion

Discussion on an HDF-GEO concept

HDF Workshop X30 November 2006

Abstract At the past several HDF & HDF-EOS Workshops, there has been some

informal discussion of building on the success of HDF-EOS to design a new profile, tentatively called HDF-GEO.

This profile would incorporate lessons learned from Earth science, Earth applications, and Earth model data systems.

It would encompass all types of data, data descriptions, and other metadata. It might support 1-, 2-, and 3-D spatial data as well as time series; and it would include raw, calibrated, and analyzed data sets.

It would support data exchange by building its needed complexity on top of minimal specialized features; and by providing clear mechanisms and requirements for all types of appropriate metadata.

The organizers propose to host a discussion among the workshop participants on the need, scope, and direction for HDF-GEO.

Which buzzwords would fit?

profile

bestpractices

datamodel

metadatacontent

atomic &compound

types

self docu-mentation

platformsupport

markup& schema

toolstest

suites

…

…ilities

HDF-GEO

namingrules

datalevels

georef

Questions for discussion by Earth science practitioners - Bottom-up analysis:

What are the successful features of existing community data formats and conventions? (HDF5, HDF-EOS, netCDF, CDF, GRIB BUFR, COARDS, CF-1, NITF, FITS, FGDC RS extensions, ISO, geoTIFF, ...)

Progress being made -- John Caron's Common Data Model

Specific needs for geo- and time-referenced data conventions

Specific needs to support observed (raw), calibrated, and analyzed data sets

Questions for discussion by Earth science practitioners - Top-down analysis: (1 of 2)

What is a profile? Consider specifics of how a standard or group of standards are implemented for a related set of uses and applications.

How does a profile relate to a format or other elements of a standard?

What constitutes overkill? How much profile would be beneficial, and how much would be difficult to implement and of limited utility?

Questions for discussion by Earth science practitioners - Top-down analysis: (2 of 2)

Why is it useful? Establishes specific meanings for complicated terms

or relationships Establishes common preferred terms for attributes

which can be described multiple ways. Establishes practices which are consistent with

portability across operating systems, hardware, or archives

Establishes common expectations and obligations for data stewardship

Clarifies community (and sponsor) long-term expectations, beyond short-term necessity

other ...

Discussion

Wrap-up

Send your list of provisional HDF-GEO requirements goals to be achieved How HDF-GEO would help

to me at [email protected]

Backup

Motivation: In many instances, application-specific 'profiles' or

'conventions' or best practices have shown their utility for users. In particular, profiles have encouraged data exchange within communities of interest. HDF provides minimal guidance for applications. HDF-EOS was a mission-specific profile; resulted in successes and lessons learned. HDF5 for NPOESS is another approach. Is it time for another attempt, benefitng from all the lessons, and targeted at a broader audience?

© 2005 The MITRE Corporation. All rights reserved

NOTICE

This technical data was produced for the U.S. Government under Contract No. 50-SPNA-9-00010, and is subject to the Rights in Technical Data - General clause at FAR 52.227-14 (JUN 1987)

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HDF Lessons from NPOESS & Future Opportunities (excerpt)

Alan M. Goldberg <[email protected]>

HDF Workshop IX, December 2005


Requirements for data products

Deal with complexity

– Large data granules Order of Gb

– Complex intrinsic data complexity Advanced sensors produce new challenges

– Multi-platform, multi-sensor, long duration data production

– Many data processing levels and product types

Satisfy operational, archival, and field terminal users

– Multiple users with heritage traditions


NPOESS products delivered at multiple levels

A/D Conversion

Detection

FluxManipulation

Packetization

Compression

FiltrationAux.SensorData

CCSDS (mux, code, frame) & Encrypt

CommXmitter

SE

NS

OR

S

OT

HE

RS

UB

SY

ST

EM

S

Cal.Source

DataStore

ENVIRONMENTALSOURCE

COMPONENTS

SP

AC

E S

EG

ME

NT

RDRProduction

EDRProduction

SDRProduction

EDR Level

SDR Level

RDR Level

IDP

S

CommReceiver

CommProcessing

Delivered Raw

C3S

TDR Level


Sensor product types

Swath-oriented multispectral imagery

– VIIRS – cross-track whiskbroom

– CMIS – conical scan

– Imagery EDRs – resampled on uniform grid

Slit spectra– OMPS SDRs – cross-track

spectra, limb spectra

Image-array fourier spectra– CrIS SDR

Directional spectra– SESS energetic particle sensor

SDR

Point lists– Active fires

3-d swath-oriented grid– Vertical profile EDRs

2-d map grid– Seasonal land products

Abstract byte structures– RDRs

Abstract bit structures– Encapsulated ancillary data

Bit planes– Quality flage

Associated arrays (w/ stride?)– geolocation


NPOESS product design development

Requirements- Multi-platform, multi-sensor, long duration data production- Many data processing levels and product types- Satisfy operational, archival, and field terminal users

Constraints- Processing architecture and optimization- Heritage designs- Contractor style and practices- Budget and schedule

Intentions- Use simple, robust standards- Use best practices and experience from previous operational and EOS missions- Provide robust metadata - Maximize commonality among products- Forward-looking, not backward-looking standardization

Resources- HDF5- FGDC- C&F conventions- Expectation of tools by others

Result Design Process - Experience- Trades& Analyses


Lessons & Way Forward


Observations from development to date

Avoid the temptation to use heritage approaches without reconsideration, but …

Novel concepts need to be tested Data concepts, profiles, templates, or best practices should be

defined before coding begins Use broad, basic standards to the greatest possible extent

– FGDC has flexible definitions, if carefully thought through Define terms in context; clarity and precision as appropriate Attempt to predefine data organizations in the past (e.g., HDF-EOS

‘swath’ or HDF4 ‘palette’) have offered limited flexibility. Keep to simple standards which can be built upon and described well. Lesson: be humble

It is a great service to future programs if we capture lessons and evolve the standards

How do we get true estimates of the life-cycle savings for good design?


Thoughts on future features for Earth remote sensing products Need to more fully integrate product components with HDF

features

Formalize the organization of metadata items which establish the data structure

– Need mechanism to associate arrays by their independent variables

Formalize the organization of metadata items which establish the data meaning

– XML is a potential mechanism – can it be well integrated?

– Work needed to understand the advantages and disadvantages

– Climate and Forecast (CF) sets a benchmark

Need a mechanism to encapsulate files in native format

– Case in which HDF is only used to provide consistent access

Need more investment in testing before committing to a design

Primary and Associated Arrays

Index Attribute

n-Dimensional Dependant Variable (Entity) Array

Primary Arraye.g., Flux, Brightness, Counts, NDVI

Associated Array(s)e.g., QC, Error barsdimension n

1-Dimensional Attribute Variables

Index Attribute

Primarye.g., UTC time or angle

Additionale.g., IET time, angle,

or presssure height

Associated Independent Variable(s)

Multi-Dimensional Attribute Variables

2-Dimensional Independent Variable Array(s)e.g., lat/lon, XYZ, sun alt/az,

sat alt/az, or land mask

Key concept: Index Attributes organize the primary dependant variables, or entities. The same Index Attributes maybe used to organize associated independent variables. Associated independent variables may be used singly (almost always), in pairs (frequently), or in larger combinations.


Issues going forward - style

Issues with assuring access understanding– How will applications know which metadata is present?

– Need to define a core set with a default approach Issues with users

– How to make providers and users comfortable with this or any standard

– How to communicate the value of: best practices; careful & flexible design; consistency; beauty of simplicity

– Ease of use as well as ease of creation Issues with policy

– Helping to meet the letter and intent of the Information Quality Act

Capturing data product design best practices– Flexibility vs. consistency vs. ease-of-use for a purpose


Issues going forward - features

Issues with tools

– Tools are needed to create, validate, and exploit the data sets. Understand structure and semantics

Issues with collections

– How to implement file and collection metadata, with appropriate pointers forward and backward

– How to implement quasi-static collection metadata

Issues with HDF

– Processing efficiency (I/O) of compression, of compaction

– Repeated (fixed, not predetermined) metadata items with the same <tag> not handled

– Archival format


Possible routes: Should there be an HDF-GEO? Specify a profile for the use of HDF in Earth science applications: Generalized point (list), swath (sensor coordinates), grid

(georeferenced), abstract (raw), and encapsulated (native) profiles. Generalized approach to associating georeferencing information

with observed information. Generalized approach to incorporating associated variables with

the mission data Generalized approach to ‘stride’ Preferred core metadata to assure human and machine readability Identification metadata in UserBlock Map appropriate metadata items from HDF native features (e.g.,

array rank and axis sizes) Preferred approach to data object associations: arrays-of-structs

or structs-of-arrays? Design guidelines or strict standardization?

hdg geo discussion

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