Observational data sets have been utilized in various capacities including surface wind stress fields as forcing to ocean circulation models, sea surface temperature fields as boundary conditions to atmospheric circulation models, and high quality in-situ data as reference time series for validating or calibrating either models or/and other observational data sets including satellite data.

Although it has been widely-recognized that accurate, global high-resolution observational data sets with uncertainty estimates for each model grid point or cell are crucial in improving numerical weather and climate predictions, it is a challenge providing this uncertainty information systematically due to the nature of the complexity, high dimensionality, and huge volume of data that one has to deal with in characterizing satellite data and products. In addition, the sources for the uncertainty of any final product may be accumulative but the impacts of all sources/processes are not equal. Therefore, identifying possible sources of uncertainties during each stage of creating satellite data and associated products will provide a useful framework for systematically estimating overall uncertainties and exploring their impact on climate predictions and decision-making.

An end-to-end framework for probabilistically characterizing uncertainty sources associated with climate satellite data and products is proposed with uncertainty types identified from the level 0 data to level 4 products. Ocean surface winds are used to demonstrate the utility of this end-to-end system.

N O A A ’s N a t i o n a l C l i m a t i c D a t a C e n t e r, A s h e v i l l e , N o r t h C a r o l i n a AMS 94th Annual Meeting Paper Number - 332

10th Annual Symposium on New Generation Operational Environmental Satellite Systems

We thank scientists from or affiliated with the Product Branch of NOAA/NCDC/RSAD for beneficial discussions and input, especially Richard Reynolds, Hilawe Semunegus, Lei Shi, Ken Knapp, and Huai-min Zhang.

CONTACT US

Ge.Peng@noaa.gov DeWayne.Cecil@noaa.gov Bryant.Gramer@noaa.gov

MEASUREMENTS PRODUCTS APPLICATIONS

Uncertainty Types • Satellite deterioration (orbital drift

and decay) • Instrument accuracy/precision • Instrument degradation • Instrumental calibration (pre-, on-

board, and post-operation) • Mission ground system • Geo- and time-referencing process

(RDRs, SDRs)

Uncertainty Types • Retrieval algorithm (ECVs,

EDRs, FCDRs, TCDRs, …) • Sampling (space/time) • Remapping (space/time)

Uncertainty Types • Product algorithm (CDRs,

CIRs, … ) • Remapping (space/time) • Inter-calibration • Source (in situ or model)

systematic error • Model uncertainty

Ground-based measurements are often used in calibration/validation/evaluation

and creation of satellite-based products: valuable but also induce error and uncertainties

Uncertainty Types • Downscaling • Uncertainty propagation • Model uncertainty • Social uncertainty

Platforms • DMSP • TRMM • EOS • QuikSCAT

Sensors • SSM/I • TMI • AMSR-E • QuikSCAT

Input Data • Satellite-based wind

speed • Model wind direction

Use Cases • Energy (wind turbine) • Hydrological cycle

(evaporation) • Marine monitoring &

transport (coral-reef, ship routing, …)

• Weather & climate prediction

Input Data • Brightness temperature • Sigma-0

Remote-Sensed Ocean Surface Wind Products

Level 0 unprocessed telemetry data as received from the observing platform excluding communications artifacts by the ground system

Level 1a telemetry data that have been extracted but not decommutated from Level 0 and formatted into time-sequenced datasets for easier processing. The Level 1a formats are NOAA’s internal formats and only exist for the purpose of creating the Level 1b datasets

Level 1b discrete, instrument-specific datasets from level 1a containing unprocessed data at full resolution, time-referenced, and annotated with ancillary information including data quality indicators, calibration coefficients and georeferencing parameters

Level 2 derived geophysical variables at the same resolution and locations as the level 1 source data

Level 3 variables mapped on uniform space-time grid scales, usually with some completeness and consistency

Level 4 variables derived from multiple measurements (e.g., model outputs or results from analyses of lower level data)

NOAA Product Levels

An End-to-End Framework for Probabilistic Uncertainty Characterization of Climate Satellite Data and Products

Ge Peng1, L. DeWayne Cecil2, and Bryant Cramer2

1 Cooperative Institute for Climate and Satellite, North Caroline State University (CICS-NC) and NOAA’s National Climatic Data Center (NCDC)/Remote Sensing Application Division (RSAD), Asheville, North Carolina, USA

2 Global Science & Technology, Inc. and NCDC’s Climate Data Record Program, Asheville, North Carolina, USA

BACKGROUND