1 / 11

Coastal Ocean Dynamics Data Assimilation for Analysis & Forecasting

This project focuses on the assimilative modeling of coastal ocean dynamics for maritime and ecosystem forecasting. It includes analysis of wave-current-sediment and air-sea interactions, as well as ecosystem physics feedbacks.

marthalewis
Download Presentation

Coastal Ocean Dynamics Data Assimilation for Analysis & Forecasting

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Ocean Modeling Group:Coastal ocean physics and ecosystem prediction Data assimilative modeling for analysis and forecasting of coastal ocean dynamics for: - maritime and ecosystem forecasting - observing system design and operation - wave-current-sediment & air-sea interaction - ecosystem-physics feedbacks John L. WilkinHernan Arango, Bronwyn Cahill, Naomi Fleming,Julia Levin, Javier Zavala-Garay ESPreSSO Experimental System for Predicting Shelf and Slope Optics Research developing bio-optics, CODAR and coastal altimetry assimilation methodologies jwilkin@rutgers.edu http://myroms.org/applications/espresso http://marine.rutgers.edu/~wilkin Ocean Modeling GroupInstitute of Marine and Coastal Sciences

  2. 4DVAR* Assimilation in ROMS for ESPreSSO/MARCOOS domain: Cape Hatteras to Cape Cod MARCOOS operational analysis and prediction system 72-hour forecast with forcing: • NCEP NAM-WRF meteorology • tides (TPXO) • daily river transport (USGS) • open boundary conditions HyCOM+NCODA Assimilates: • altimeter along-track SLA • satellite IR SST • CODAR surface currents • climatology • glider T,S • GTS: XBT/CTD, Argo, NDBC buoys ESPreSSO Experimental System for Predicting Shelf and Slope Optics (research) and MARCOOS (operational) *4-Dimensional Variational data assimilation

  3. MARCOOS operational system

  4. Work flow for operational MARCOOS 4DVAR Analysis interval is 00:00 – 24:00 UTC Input data preparation commences 01:00 EST (06:00 UT) • 72-hour forecast (NAM-WRF meteorology 0Z cycle at 10 pm EST) • RU CODAR is hourly - but with 4-hour delay • RU glider T,S where available (approx 1 hour delay) • USGS daily average flow available 11:00 EST • persist in forecast • AVHRR IR passes 6-8 per day (approx 2 hour delay) • HyCOM NCODA 7-day forecast updated daily • Jason-2 along-track SLA via RADS (4 to 16 hour delay for OGDR) • Also ENVISAT and Jason-1 NRT data (OGDR and IGDR) • SOOP XBT/CTD, Argo floats, NDBC buoys via GTS from AOML • T,S climatology (MOCHA*) *Mid-Atlantic Ocean Climatology Hydrographic Analysis

  5. Work flow for operational MARCOOS 4DVAR • Input preprocessing completes approximately 05:00 EST • 4DVAR analysis completes approx 08:00 EST • analysis is followed by 72-hour forecast using NCEP NAM 0Z cycle available from NOMADS OPeNDAP at 02:30 UT (10:30 pm EST) • Forecast complete and transferred to OPeNDAP by 09:00 EST OPeNDAP http://tashtego.marine.rutgers.edu:8080/thredds/catalog.htmlncWMS http://tashtego.marine.rutgers.edu:8081/ncWMS/godiva2.html • Effective forecast is ~ 60 hours SSH and velocity forecast during Nov 2009 glider OSSE Temp (5m depth) and velocity during Nov 2009 glider OSSE Temperature on cross-section 4 during Nov 2009 glider OSSE

  6. Work flow for operational MARCOOS 4DVAR Analysis interval is 00:00 – 24:00 UTC Input data preparation commences 01:00 EST (06:00 UT) • 72-hour forecast (NAM-WRF meteorology 0Z cycle at 10 pm EST) • RU CODAR is hourly - but with 4-hour delay • RU glider T,S where available (approx 1 hour delay) • USGS daily average flow available 11:00 EST • persist in forecast • AVHRR IR passes 6-8 per day (approx 2 hour delay) • HyCOM NCODA 7-day forecast updated daily • Jason-2 along-track SLA via RADS (4 to 16 hour delay for OGDR) • Also ENVISAT and Jason-1 NRT data (OGDR and IGDR) • SOOP XBT/CTD, Argo floats, NDBC buoys via GTS from AOML • T,S climatology (MOCHA*) *Mid-Atlantic Ocean Climatology Hydrographic Analysis

  7. Chlorophyll Pigments DOC, DON & DOP Aphy(λ,z) Grazing CDM Fecal Detritus DIC • Ecosystem models (7 ecosystem models in ROMS) • EcoSim – plankton, nutrients, pigments, light Losses NH4 NO3 SiO PO4 FeO Uptake / Heterotrophs Ed(0,λ) Uptake / Autotrophs Remineralization Carbon Fixation Phytoplankton 4 Groups 1 2 3 4 1% Ed(0, λ) Bacteria IOPs aCDM(λ,z) State variables (about 60):NO3, NH4, P, C, Fe, Si, Bac(4), DOM(4), CDM(4), Det(2x5), Phyt(4x4), Pigments(~15)

  8. EcoSim – phytoplankton mortality, POC export and oxygen depletion are affected by river plume dynamics and optics Freshwater anomaly Phytoplankton C1 (mmol C m-3) POC (mmol C m-3) POC (mmol C m-3) Rapid primary production within the re-circulating freshwater bulge Phytoplankton mortality generates particulate organic carbon (POC) that is exported to bottom waters. Site of this benthic oxygen demand depends no circulation Freshwater anomaly Phytoplankton C1 (mmol C m-3) POC (mmol C m-3) POC (mmol C m-3)

  9. Nitrification Water column Mineralization NH4 NO3 Uptake Phytoplankton Grazing Chlorophyll Zooplankton Mortality Large detritus Susp. particles Nitrification N2 NH4 NO3 Denitrification Aerobic mineralization Organic matter Sediment • Ecosystem models: • (2) BioFennel – plankton, nitrogen, oxygen, carbon, ΔpCO2 • Assimilation experiments with sequential update of chlorophyll • datacycle, ΔpCO2

  10. Initial Conditions Model Forcing + Boundaries Validation Skill Assessment Run Period January to July 2006 NCEP-NARR TIDES MABGOM Forward model no assimilation ROMS Forward + Biomass-Based (Fennel) Model Model Bias RMSE Model Skill Taylor diagrams forward Assimilation physics only T/S MLD Chl PP Espresso Reanalysis ROMS Forward + Biomass-Based (Fennel) Model + Continuous Update Physics 3 day update Chl 10 day update Assimilation physics and chlorophyll • ESPreSSO Re-analysis • Bias corrected ocean estimate by sequential assimilation of climatology, SST and SSH. Dynamically balanced T / S fields.

  11. Taylor Diagram for Chlorophyll: July 2006 test Assimilation improves chlorophyll solution Correlation coefficient, R Forward model no assimilation Centered pattern RMS error, E’ Assimilation physics only Assimilation physics and chlorophyll Data Taylor, K. E. (2001), Summarizing multiple aspects of model performance in a single diagram, JGR, 106, 7183-7192

More Related