A Basic Standards Use Case for Atmospheric Data

A Basic Standards Use Case for Atmospheric Data Types • Compare model output and observation data near airport • Specify 3 D bounding box centered on airport • Specify time frame of interest (e. g. , periods of severe storms) • Request observed and forecast atmospheric parameter values • In GALEON 1, WCS worked well for gridded data from forecast model output and some satellite imagery

Airport Weather Use Case: Examples of Unidata “Common Data Model” Scientific Data Types and Climate Science Modelling Language Scientific Feature Types • point data from lightning strike observations • "station" observations from fixed weather stations • vertical profiles from balloon soundings and wind profilers • trajectory data obtained from instruments onboard aircraft which have taken off and landed recently • volumetric scans from ground-based radars • visible, infrared, and water-vapor (and possibly other wavelength) satellite imagery • gridded output from national or hemispheric weather forecasts (typically run at centers like NCEP and ECMWF) -- sometimes used as boundary conditions for a higher-resolution local forecast model.

Multiple Platforms Sampling the Atmosphere

Special Requirements • Real-time access • Elevation/altitude dimension is important • Elevation dimension often given in terms of pressure • Range value interpolation depends on physics (and data) as well as geometry • Automated processing components, e. g. , – Gridding/assimilation – Forecast models – Transformations between pressure and height

GALEON Lessons • Relatively simple WCS use case is valuable: Bounding box, time frame, coverage name (e. g. , surface temperature) subsetting is practical o CF-net. CDF payload works for many clients o • WCS limitations: o o o gridded data (regularly spaced in some projection WCS 1. 1 complicated (all things to all people) Proposed core and extensions approach value not clear yet

Status in FES Realm • OPe. NDAP delivers many dataset types, but it operates in index space rather than coordinate space • ADDE (Abstract Data Distribution Environment) has value at the CDM Scientific Data Type level, but is not widely adopted • THREDDS provides catalog data framework for its own community • THREDDS Data Server integrates services • CF conventions: available for gridded data, coordinate system specs are more explicit now o proposed for point, station, trajectory -- including means for specifying locations for non-gridded data collections. o

Climate Science Modelling Language Scientific Feature Types Profile. Feature Ragged. Section. Feature Scanning. Radar. Feature Grid. Feature Thanks to Andrew Woolf of BADC Profile. Series. Feature

CSML-CDM Mapping CSML Feature Type CDM Feature Type Point. Feature Point. Series. Feature Station. Feature Trajectory. Feature Point. Collection. Feature Point. Feature collection at fixed time Profile. Feature Profile. Series. Feature Station. Profile. Feature at one location and fixed vertical levels Ragged. Profile. Series. Feature Station. Profile. Feature at one location Section. Feature with fixed number of vertical levels Ragged. Section. Feature

Standards for Non-gridded or Irregularly-gridded Datasets? • Apply to Collections of: lightning strike point observations, weather station observations, vertical profiles, onboard aircraft observation trajectories, volumetric radar scans, satellite swath images • Fit with Sensor Web Enablement (SWE) Observations and Measurements (O&M)? • Relationship to ISO 19123 coverage specification? • Delivery via WCS, WFS, SOS? • Coordinate Reference System for collections • GML role: CSML, Nc. ML-GML, GML-JP 2 K? • CS-W cataloging (for completeness)?

WCS and SWE O&M • Observations and Measurements Documents (under revision) • Feature of Interest – bounding box and time frame in WCS • Sampling Feature (FES data sets are discrete samples of continuously varying properties of the feature of interest) • Collections of Sampling Features as “Sampling Coverages”?

ISO Coverage Definition: Background Information • A coverage is a feature that associates positions within a bounded space (its domain) to feature attribute values (its range). In other words, it is both a feature and a function. • Examples include a raster image, a polygon overlay or a digital elevation matrix. • A coverage may represent a single feature or a set of features • A coverage domain is a set of geometric objects described in terms of direct positions. • The direct positions are associated with a spatial or temporal coordinate reference system. • Commonly used domains include point sets, grids, collections of closed rectangles, and other collections of geometric objects.

Coverage Range Characteristics • The range of a coverage is a set of feature attribute values. • Coverages often model many associated functions sharing the same domain. • EXAMPLE A coverage might assign to each direct position in a county the temperature, pressure, humidity, and wind velocity at noon, today, at that point. The coverage maps every direct position in the county to a record of four fields.

ISO 19123 Coverages • Up for revision • In most cases, a continuous coverage is also associated with a discrete coverage that provides a set of control values to be used as a basis for evaluating the continuous coverage. • Evaluation of the continuous coverage at other direct positions is done by interpolating between the geometry value pairs of the control set (thiessen polygon, quadrilateral grid, hexagonal grid, TIN, segmented curve)* l • Discrete coverage types can represent sampling features of O&M • Collections of sampling features as sampling coverages* *Possible candidates for revision that’s underway

Scientific Data Types Mapping to ISO Coverages Unidata CDM Scientific Data Type ISO 19123 Coverage Type Unstructured Grid Discrete. Point. Coverage* Structured Grid Discrete. Grid. Point. Coverage Swath Discrete. Surface. Coverage Unconnected Points Discrete. Point. Coverage* Station observation/Timeseries Discrete. Point. Coverage General Trajectory Discrete. Point. Coverage* or Discrete. Curve. Coverage Vertical Profile Discrete. Point. Coverage* Radar Radial Discrete. Surface. Coverage or Discrete. Curve. Coverage *Generally, the domain is a set of irregularly distributed points

Data Access: WCS, WFS, SOS • WCS makes sense for grids and images • Coverages are a special type of feature • CSML defines Scientific Feature Types • WFS delivers coverages? • WCS for grids; WFS for non-gridded collections? • WCS / SOS relationship • • Efforts at Washington U in St. Louis Oceans I. E. 2 Topic? SOS feeds observations into WCS? SOS serves observation data from WCS?

Data Types and Service Protocols GIS Clients WCS Clients OGC Protocols Web Feature Service Web Coverage Service ON LE GA Sensor Observation Service FES Data Collections on Server(s) Point data Vertical Soundings Trajectories Radar Volume Scans WCS: Regularly Spaced Grids Satellite Images Forecast Model Output Grids

Data Types and Service Protocols GIS Clients SOS Clients WCS Clients OGC Protocols Web Feature Service Web Coverage Service Oce ans ON LE I. E. GA Sensor Observation Service FES Data Collections on Server(s) Point data Vertical Soundings Trajectories Radar Volume Scans WCS: Regularly Spaced Grids Satellite Images Forecast Model Output Grids

ISO 19111 CRS • OGC Document • Earth referenced coordinate reference system (CRS) • Engineering coordinate system (with point in Earth-referenced CRS as origin • Image coordinate system

Engineering Coordinate Systems • • Not directly Earth referenced Most remote sensing systems Examples: Wind profiler Surface radar scanning Satellite scanning algorithms Aircraft-borne radar

Data point locations • Explicit with each data point, e. g. , lightning • Tabular, e. g. , repeated observations at fixed* station locations (*Note that station locations may change, but not often compared to data value changes) • Fixed algorithmic grid, e. g. , output of forecast models • Moving platform - explicit locations, e. g. aircraftborne observations along flight paths (trajectories) • Moving platform – algorithmic location, e. g. , satellite position given by orbital mechanics

Earth Coordinate System Basics • Coordinates relative to mean sea level (MSL) ellipsoid or geoid (gravity irregularities) • 2 D position on surface o o geographic (latitude, longitude) or projected (onto x, y coordinates) • Elevation relative spatial elevation relative to MSL elevation relative to actual surface of Earth (digital elevation model relative to MSL) o data dependent proxy (e. g. , air pressure, data-dependent physics, e. g. , hydrostatic equation, relative to MSL) o o

Compound CRS (Ben’s simplified version to illustrate atmospheric data use cases) Lightning Station obs Aircraft obs* Model output Vertical Profiles Ground Radar Satellite* GOES Satellite Earth referenced Engineering Explicit N/A Tabular N/A Explicit trajectory N/A Fixed algorithm N/A Tabular Vertical “scan” Tabular Radar scan Algorithmic trajectory Instrument scan Explicit or algorithmic trajectory *Observing platform in motion

Compound CRS (Ben’s simplified version for atmospheric data use cases) Lightning Station obs Aircraft obs* Model output Vertical Profiles Ground Radar Satellite* GOES Satellite Earth referenced Engineering Explicit N/A Tabular N/A Explicit trajectory N/A Fixed algorithm N/A Tabular Vertical “scan” Tabular Radar scan Algorithmic trajectory Instrument scan Explicit or algorithmic trajectory Instrument scan *Observing platform in motion

GML • OGC Document • Core plus extensions approach • Related to GALEON o WCS manifest o CSML o Nc. ML-GML o GML-JP 2 K

Web Processing Services • • Interpolating gridded data to points Assimilating observed data samples to grid Converting from pressure to height and back Most transformations depend on physics (and data as well) • WCPS available as well as WPS • References?

CS-W Cataloging • • CS-W Specification Gi-GO Client ESRI Client GMU CS-W service for THREDDS Data Server

CS/W-THREDDS Gateway OGC Clients Search/Browse Data Access CS/W Interface CS/W Server CS/W Database Ingestor On-Demand Scheduled Pulling TDS WCS Interface THREDDS to CSW Metadata Mapping THREDDS Data Server TDS Catalog Interface

Action Plan Outline • Agree on high-level dataset categories • Clarify relationships among: – Unidata CDM Scientific Data Types – CSML Scientific Feature Types – Obs. & Meas. Sampling Features • Establish extensions to CF conventions for each dataset category • Map CF-net. CDF categories to ISO 19123 • Establish metadata forms: CSML, nc. ML-G • Experiment with CF-net. CDF encoded coverages as payload for WCS, WFS, SOS

Divide (Labor) and Conquer • Coordinate individual efforts toward a whole greater than the sum of the parts • Each group focuses on areas of expertise • Work on tasks group has funding for • Stay aware of other groups’ efforts • Coordinate efforts wherever possible • Results of lessons learned from implementation and experimentation feeds into standard definition process

Future Directions • CF conventions for non-gridded CDM data types -- including explicit Coordinate Reference System (CRS) information • Mappings o o o CDM data types to ISO 19123 coverage data model CDM data types to CSML scientific feature types CDM data types to SWE O&M sampling feature types • CF-net. CDF coverage encoding spec for all Unidata Common Data Model data types • Figure out delivery protocol later (WCS, WFS, SOS?

References • • • GALEON Wiki Unidata Net. CDF CF Conventions WCS Specification O&M Paper ISO 19123 Coverage Specification GML ISO 19111 CRS CS-W Interoperability Day Presentations – – – Andrew Woolf Stefano Nativi Wenli Yang Stefan Falke ESIN Paper • Proposed CF conventions for non-gridded datasets