Data Assimilation Systems for Operational Ocean Forecasting at

NCEPShastri Paturi¹, Zulema Garraffo¹, Jim Cummings¹, Ilya Rivin¹, Yan Hao¹,

Guillaume Vernieres², Avichal Mehra³, Arun Chawla³1IMSG at NOAA/NWS/NCEP/EMC; JCSDA/UCAR/NOAA, 3NOAA/NWS/NCEP/EMC

• I. Motivation & Overview

• II. RTOFS-DA QC & 3DVAR Assimilation

System

• III. Results – 3D VAR

• IV. Unified DA - JEDI/SOCA

• V. Summary & Implementation plan

I. Motivation & Overview• NWS mission

– Best possible guidance to emergency managers, forecasters, aviation community

• Next Generation Global Prediction System (NGGPS) aims to build a state-of-the-art operational modelling system in a unified coupled framework– Atmosphere, ocean, sea-ice, aerosols and waves

• NCEP needs a global eddy-resolving ocean model – Part of NOAA’s ocean modeling backbone capability (SAB 2004, NOAA response 2005)

» Partnering with NOS and IOOS-RA’s » Part of larger National Backbone capability in strong partnership with NAVY

– Internal needs for NCEP:» EMC/OPC/TPC/WFO’s need for real time eddy-resolving ocean products for customers.» NWS and NOS need for real-time eddy-resolving boundary data for areas of

interest:

❑ Coupled regional hurricane modeling

➢ Atlantic, East Pacific, …

❑ Centerpiece of integrated ocean modeling system ( e.g. plume modeling for radionuclide dispersion near Japan)

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Overview: Global RTOFSConfiguration:

• HYCOM coupled with LANL

CICE4 through ESMF4.0

• 1/12° global horizontal grid,

Arctic bipolar patch,

4500x3298 points

• 41 hybrid layers, 1m top

layer thickness

• KPP parameterization for

mixing.

• GDAS/GFS forcing

• 2 day analysis + 8 day

forecasts starting from

restarts (2 days before the

present), generated through

NCODA at NAVY.

• No tides

Users include: EMC, NHC, NOS, U.S. Coast Guard, AOML/HRD 3

I. Motivation & Overview

Present Global 0.08° HYCOM at EMC (RTOFS v1.1.4)

RTOFS-DA References: • Cummings, J. A. 2011: Ocean Data Quality Control, in Operational Oceanography in the 21st Century, A. Shiller and GB Brassington (eds), Springer, Chapter 4, 91-121.

• Cummings, J. A. and O. M. Smedstad. 2013: Variational Data Assimilation for the Global Ocean. Data Assimilation for Atmospheric, in Oceanic and Hydrologic

Applications vol II, S.Park and L.Xu (eds), Springer, Chapter 13, 303-343.

Quality Control:

•outcome is likelihood

observation is erroneous

•flags are appended that show

failed individual QC tests

•QC outcomes and flags are

used to select observations

for the analysis

•observations are sorted by

time into global databases

•supports efficient space/time

queries for real time analyses

II. RTOFS-DA Ocean Data Flow & Analysis

• Externally produced data: 1 year simulation Feb, 2017 through Jan, 2018:

– MODAS synthetic profiles for downward projection of altimeter SSH anomalies (SSHA)

– externally produced QC’d data from Navy and GODAE: SSH, SST, profiles, sea ice

– modified Cooper Haines method for limited 3-month run: Oct-Dec 2017

• assimilated NESDIS ADT altimeter SSH data

• RTOFS-DA observation processing and assimilation options:

– IAU method was based on assimilating increments 3 hrs prior to analysis time.

– SST, SSH, and Sea Ice data averaged to form super-observations:

• uses local correlation length scales, removes data redundancies

– 3DVAR runs on global grid using hybrid coordinates

–background error variances computed from forecast differences:

• 15-day sliding time window, 48-h forecasts

• represents model variability and model error

– flow dependent error correlations:

• innovations are spread along rather than

across forecast SSH gradients

1/12° Global RTOFS-Data Assimilation Simulations

HYCOM Flow

Dependence

II. Experiments : RTOFS-Data Assimilation

HYCOM SST Forecast Errors

Direct Assimilation of Absolute Dynamic

Topography (ADT) SSH

• ADT observations from Radar Altimeter Data System (RADS), good agreement between model SSH and ADT

data

• HYCOM SSH bias corrected by along-track difference between ADT and model equivalent (~50 cm)

• Corrects forecast density profile to be consistent with SSH innovation, conserves model TS relationships

• Constrained by SST, SSS, and MLD; assimilated by lowering/lifting/modifying HYCOM layers & thicknesses

Jason-2 track of ADT, HYCOM SSH, and ADT-HYCOM innovations Forecast, corrected TS profiles: -23.2 cm innovation

III. Results: Global RTOFS-Data Assimilation

HYCOM Layer Pressure Interface Corrections: Innovations and Increments

ADT SSH Innovations

• HYCOM layer pressure

corrections are greatest at

seasonal and permanent

thermocline depths

• Corrections are relatively

small at depths between 200

and 800 M (< 10 m)

• General tendency is to move

forecast density layers down

in water column

• Analysis increments greatest

in western boundary and

Antarctic circumpolar

currents

Analysis Increments: HYCOM Layer 12

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III. Results: Global RTOFS-Data Assimilation

DIRECT Method Simulation: ADT SSH 2 Oct – 30 Dec 2017

OmA

OmF

• Overall HYCOM temperature forecast errors now resemble Argo forecast errors

• Initial condition forecast errors decrease with time as the simulation progresses

• Maximum forecast bias errors occur at ~150-200 m depths with magnitudes of ~0.8°C

• OmA residuals are essentially zero: 3DVAR effectively analyzes the observations

III. Results: Global RTOFS-Data Assimilation

RTOFS-DA Observation Data Impact System

Application Innovations Cost Function Purpose

Observation Impact Real observations

(all known)

Forecast error

(known)

Evaluate impacts of

observations on forecast

error

Observing System

Design

Real and simulated

observations

(known, unknown)

Forecast error or

model variable

(known)

Develop more optimal

configurations of observing

systems

Targeted Observing Simulated observations

(all unknown)

Proxy for forecast

error (unknown)

Impact of adding

observations at some

future time

Multiple Uses of Data Impact System

Argo Data ImpactsTemp Forecast Error

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IV. Unified DA Effort-JEDI

• The Joint Effort for Data assimilation Integration (JEDI) is a collaborative development spearheaded by the JCSDA:

– Next generation unified data assimilation system• For research and operations (including R2O/O2R)• For various components of the earth system, including coupled• Mutualize as much as possible without imposing single approach

– Open-development software –model: in addition to supported releases, community, developers can obtain and collaborate on latest development branches

– Collaborative teams – NOAA, NASA, US NAVY• The joint ocean DA system for NOAA/EMC is through SOCA (Sea-ice Ocean

Coupled Assimilation)– SOCA core team : Guillaume Vernieres, Hamideh Ebrahimi, Rahul Mahajan and

Travis Sluka.

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sea surface salinity SMAP

AltimetryJason-2, Jason-3, Sentinel-3a,

Cryosat-2, SARAL

sea surface temperature (IR)

AVHRR (metopa, noaa19)VIIRS (suomi-npp)

sea surface temperature (MW)

GMI, AMSR2, WindSatInsitu T/S

1 day of observations( 2018-04-15 )

IV. Unified DA Effort-JEDI

Example: 30 days cycling

Figures: Travis Sluka, JCSDA

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Examples: 30 days Cycling (Travis)

IV. Unified DA Effort-JEDI

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Example: 30 days cycling

Figures: Travis Sluka, JCSDA

V. Summary

• ADT SSH observations are more accurate than SSHA observations – the data incorporate geoid information instead of a model-based SSH mean

field.

• A RT setup based on ADT simulation is planned for operational implementation – end of 2019.

• RTOFS-DA 3DVAR and RTOFS-DA QC development and job scripting complete:– some limited NRT cycling completed– evaluate skill of HYCOM forecasts in QC of new data: model bias, lack of

variability key issues– test 3DVAR scalability using different numbers of processors on WCOSS– reduced grid post-multiplication (4X speed-up)

• Work on JEDI is in Progress

• Basic experimental testing of 3DVAR SOCA is under progress.

• Testing of 3DVAR SOCA with the present ¼ deg MOM6-CICE5 coupled system is planned for future S2S forecasts.

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Questions?

Thank you!

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