The end users perspective – who are we doing this for? (e.g., climate science and data assimilation efforts) Peter 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 2

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 3

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 4

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: 5

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 Model-obs 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” 8

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 2011 Mar 2011 Apr 2011 May 2011 Jun 2011 Jul 2011 XBT TAO Jason-2 All altims SST Argo

Provision of Observation Impact Statements (OISs) 10

Provision of Observation Impact Statements (OISs)

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

QC inter-comparison: Recall 13 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. 14

In Situ Observations 15 Contributed by Y. Xue, NOAA/NCEP – from Saha et al. (2010)

Contributed by Y. Xue, NOAA/NCEP Inter-comparison of CLIVAR systems: HC300 in Equatorial Pacific (2 o S-2 o N)

17 Inter-comparison of CLIVAR systems: HC300 in Equatorial Atlantic (2 o S-2 o N) 2005 Contributed by Y. Xue, NOAA/NCEP

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 18

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? 19

Thank you! 20

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 - Keith Haines - GSOP Co-Chair Detlef Stammer - Yosuke Fujii - ocean/seasonal forecasting Matt Martin - Ocean Forecasting Jim Cummings - ocean forecasting Gary Brassington - ocean forecasting Laurent Bertino - (Norwegian ocean forecasting) Tony Lee - 21

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)

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 Contributed by G. Halliwell, NOAA/AOML/PhOD No assimilation ~ 4-5 degrees No assimilation < 0.5 No assimilation +- 1 degree

ExperimentBias (°C)RMS Diff. (°C) Skill Score P-3 Profiles Assimilated P-3 Profiles Denied No Data Assimilation Error Analysis, Nancy Foster T Profiles, 9 July ExperimentBias (m)RMS Diff. (m) Skill Score P-3 Profiles Assimilated P-3 Profiles Denied < 0 No Data Assimilation < 0 20°C isotherm depth Temperature, 30 – 360 m Contributed by G. Halliwell, NOAA/AOML/PhOD 8-10 July

Inter-comparisons of intermediate-resolution reanalyses Xue et al. 27

In Situ Observations 28 Contributed by Y. Xue, NOAA/NCEP – from Saha et al. (2010)

Contributed by Y. Xue, NOAA/NCEP Inter-comparison of CLIVAR systems: HC300 in Equatorial Pacific (2 o S-2 o N)

30 Inter-comparison of CLIVAR systems: HC300 in Equatorial Atlantic (2 o S-2 o N) 2005 Contributed by Y. Xue, NOAA/NCEP

Evaluating options for altimeter constellations Larnicol et al. 31

Altimeter constellations 3 x Nadir1x SWOT 11 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. 33

34 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

35 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