Www.cmar.csiro.au/staff/oke/ The end users perspective – who are we doing this for? (e.g., climate science and data assimilation efforts) Peter Oke et

www.cmar.csiro.au/staff/oke/

The end users perspective – who are we doing this for? (e.g., climate science and data assimilation efforts)

Peter Oke et alPeter Oke et al

CAWCR, CSIRO Marine and Atmospheric Research

June 2013


Talk outline

What does the data assimilation community need from the observational community?

What can the data assimilation community do for the observational community?

GODAE OceanView

RTQC

Example


What does the data assimilation community need from the observational community?

Observations delivered in NRT:

QC information would be used

Error estimates:

Known instrument errors the standard deviation of errors

Unbiased observations no systematic errors


What can the data assimilation community do for the observational community?

Demonstrate impact of data:

Operational forecasts short-range (7-d), seasonal, …

Quantify the benefits to marine industry/applications:

Search & rescue; oil spill, fisheries, shipping, military support, …

Help identify quality control issues:

Feedback on what data are being excluded, and what data are developing systematic errors (bias)

Help identify emerging gaps in the GOOS

Help plan for future observation strategies


GODAE OceanView (2009- )

Mission: Develop capabilities in operational ocean forecasting

Five Task Teams

Coastal Ocean and Shelf Seas

Inter-comparison and Validation

Marine ecosystem and prediction

Observing System Evaluation (OSEval)

Short- to medium-range coupled prediction

GODAE OceanView website: https://www.godae-oceanview.org/


GOV OSEVal-TT organisation

Co-Chairs:

Peter Oke (CSIRO)

Gilles Larnicol (CLS)

Core Members:

Magdalena Balmaseda (ECMWF)

Laurent Bertino (NERSC)

Gary Brassington (BoM)

Jim Cummings (NRL)

Yosuke Fujii (JMA/MRI)

Pat Hogan (NRL)

Villy Kourafalou (Univ. Miami)

Daniel Lea (UKMet)

Matthew Martin (UKMet)

Avichal Mehra (NOAA)

Pavel Sakov (NERSC)

Anthony Weaver (CERFACS)

Associate members:

Mike Bell (UKMet)

Eric Dombrowsky (Mercator)

Fabrice Hernandez (Mercator)

Eric Lindstrom (NASA)

Andreas Schiller (CSIRO)


Responses to “observing system events”

Continuation of Jason-1 data processing in inter-leaved orbit (June 2009)

UKMet and BoM provided a demonstration of the impact of Jason-1 data in inter-leaved orbit during recent outages

Contributed by G. Brassington, BoM

Mo

de

l-o

bs

mis

-fit

Many GODAE contributions to observing system evaluation

have been ad hoc


Operational community needs a coordinated plan to respond to “observing system events”


NRT OSEs

Routinely run parallel forecast at operational centers and with-hold a different data each month:

Quantify the impact of each data type on forecasts

Multi-system approach

Feb 2011Mar 2011Apr 2011May 2011Jun 2011Jul 2011

XBTTAOJason-2All altimsSSTArgo


Provision of Observation Impact Statements (OISs)

10


Provision of Observation Impact Statements (OISs)


Inter-comparison participants: BoM, Coriolis, MyOcean, FNMOC, UKmet


QC inter-comparison: Recall

Temperature

Salinity

Pressure

Recall:

A measure of success

Recall = 1: is perfect

RTQC could be useful to the obs community for identifying bad floats


Inter-comparisons of intermediate-resolution reanalyses

Xue et al.


In Situ Observations

Contributed by Y. Xue, NOAA/NCEP – from Saha et al. (2010)


1993

Contributed by Y. Xue, NOAA/NCEP

Inter-comparison of CLIVAR systems:HC300 in Equatorial Pacific (2oS-2oN)


Inter-comparison of CLIVAR systems:HC300 in Equatorial Atlantic (2oS-2oN)

2005

2005



Conclusion

Active operational ocean forecasting community that it dependent on the obs community

Data availability in NRT is important

Each forecast center undertakes RTQC that could be useful for DMQC

GODAE OceanView is motivated to “support” the obs community by demonstrating impact

Historically been ad-hoc;

Plans to make it more organised (NRT OSEs and OIS).

Many DM OSE studies have been published that demonstrate impact


Future opportunities

Help GODAE OceanView figure out how to disseminate/condense technical metrics into something that is meaningful

OSE study using DMQC-ed and RTQC-ed data – what is the benefit to a data assimilating model?


Thank you!

[email protected]


• how we can help them (e.g., what is important for their data assimilation efforts) and in what they can help us?

• to cover the user needs from short term & longer term forecasts/predictions & climate hindcasts, and in what they also might be able to help us.

• need for gridded data to initialise models (e.g., decadal predictions)? which fields? what are the requirements?

• how observational fields/observations might be used to evaluate data assimilation efforts? what would be required?

• how poor data quality has impacted /might impact on data assimilation efforts? or lack of uncertainties?

• can assimilation efforts somehow contribute back to the QC system? (e.g., pointing to erroneous data).

Send to:

Jim Carton - [email protected]

Keith Haines - [email protected] GSOP Co-Chair

Detlef Stammer - [email protected]

Yosuke Fujii - [email protected] ocean/seasonal forecasting

Matt Martin - [email protected] Ocean Forecasting

Jim Cummings - [email protected] ocean forecasting

Gary Brassington - [email protected] ocean forecasting

Laurent Bertino - [email protected] (Norwegian ocean forecasting)

Tony Lee - [email protected]


Example of an OSE (with-holding XBT)

OSEs using HYCOM after the DWH Oil spill to assess the impact of XBT data

Halliwell et al. (NRL & NOAA)

www.cmar.csiro.au/staff/oke/ Contributed by G. Halliwell, NOAA/AOML/PhOD


Impact of P-3 Observations on Ocean Analyses

Collaboration between AOML and NRL-Stennis

NRL ran two experiments with the 1/25° regional HYCOM:

1. Assimilate all observations

2. Deny only the P3 observations

Critical issues affecting this evaluation:

Results depend on choices of model and DA scheme

Impact of update cycle

Impact of relative weighting of synthetic T,S profiles derived from altimetry vs. in-situ T,S profiles

Contributed by G. Halliwell, NOAA/AOML/PhOD


RED: With P3 assimilation

BLACK: No P3 assimilation


No assimilation ~ 4-5 degrees

No assimilation < 0.5

No assimilation +- 1 degree


Experiment Bias (°C) RMS Diff. (°C)

Skill Score

P-3 Profiles Assimilated

-1.11 1.41 0.88

P-3 Profiles Denied

-1.18 1.79 0.84

No Data Assimilation

-0.40 4.5 0.31

Error Analysis, Nancy Foster T Profiles, 9 July

Experiment Bias (m) RMS Diff. (m)

Skill Score

P-3 Profiles Assimilated

-21.1 35.8 0.09

P-3 Profiles Denied

-24.3 44.3 < 0

No Data Assimilation

19.3 89.5 < 0

20°C isotherm depth

Temperature, 30 – 360 m


8-10 July


Inter-comparisons of intermediate-resolution reanalyses

Xue et al.


In Situ Observations

Contributed by Y. Xue, NOAA/NCEP – from Saha et al. (2010)


1993


Inter-comparison of CLIVAR systems:HC300 in Equatorial Pacific (2oS-2oN)


Inter-comparison of CLIVAR systems:HC300 in Equatorial Atlantic (2oS-2oN)

2005

2005



Evaluating options for altimeter constellationsLarnicol et al.


Altimeter constellations

3 x Nadir 1x SWOT11 x Nadir

(Iridium 6 + Jason-CS +GFO2+ HYC+ S3A + S3B)

1 x SWOT + 11 x Nadir

2 x SWOT

Contributed by G. Larnicol, CLS

Reconstruction error (% of reality signal variance) for

geostrophic U and V


OSEs using JMA/MRI seasonal prediction system

Fujii et al.


Observing System Experiments (OSEs) using JMA/MRI system

Impact of TAO data decreases, and Argo data increases, as the number of Argo floats increases

Difference when TAO/TRITON data are with-

held

Difference when Argo data are with-held

Contributed by Y. Fujjii, JMA/MRI


Impact of Argo and TAO data on JMA forecast skill

With-holding Argo data degrades the skill of forecasts over 8-13 months by almost 25% in the Pacific Ocean

With-holding TAO data degrades the skill of forecasts over 1-7 months by almost 15% in the Indian Ocean.

Contributed by Y. Fujjii, JMA/MRI


Oke, P., and P. Sakov: Design and Assessment of the Australian Integrated Marine Observing System

Simple method to assess the potential impact of data from moorings


Footprint of individual moorings

Cabbage Patch Mooring

Deep Slope Mooring

Documents

Www.cmar.csiro.au/staff/oke/ The end users perspective – who are we doing this for? (e.g., climate science and data assimilation efforts) Peter Oke et