Presentation on theme: "ROMS User Workshop, October 2, 2007, Los Angeles"— Presentation transcript:
1 ROMS User Workshop, October 2, 2007, Los Angeles A Three-Dimensional Variational Data Assimilation in Support of Coastal Ocean Observing SystemsZhijin Li and Yi ChaoJet Propulsion LaboratoryJim McWilliams and Kayo IdeUCLAAs I was preparing my talk today during the past few days, it is clearly very difficult to decide how broad my talk should cover and how details I should penetrate on individual topics. In the end, I decide to give a relatively broad talk covering the variety of topics that I am working on. If you are interested in hearing more about a particular topic, I will be more than happy to talk to you after the seminar. Here is my and phone number, so feel free to call me to follow up. JPL is only 3 miles from here, so you are certainly welcome to visit us up the hill.Before I go into my talk, I want to emphasize that this is very important and exciting time for oceanography, although many of my colleagues expressed great frustrations during this transition and uncertain time.Oceanography before 1960s involves many energetic individuals who charted ships into the restless sea for weeks and sometimes months trying to observe the ocean. They came back from these long cruises, very different from the vacation cruises you and I are familiar with, very tired and spend the next few years processing the data, and eventually publish them in journal papers. Once the ship data are documented and papers are published, they wrote the next proposals to pick another piece of the world ocean to explore.The next three decades from 1960s-1990s, oceanographers are gradually getting organized into field programs or expeditions. Easy to get funded, simultaneous measurements are required and have to be funded at the same time. Many oceanographers are working together, involving a fleet of large ships, centralized data center, and the associated modeling program. During the same time, there are major breakthroughs in satellite oceanography, measuring a number of ocean surface parameters from space.Now in the 21st century, the field of oceanography is facing another major challenge. To answer many of the important questions, there is a need for a much closer coordination between various disciplines in oceanography, ranging from physical, chemical to biological oceanography. Global sea level rise, carbon cycle. Ocean scientists need also to work with experts in the information technology ranging from data management, scientific computing, and visualization. Advanced sensors are also needed to measure those parameters that have never been measured before, so scientists need to work with engineers and technologists.There is an emerging need for shared infrastructure, in the same way as the astronomers sharing the telescope, and physicists sharing the accelerator. So the next few decades, oceanographers will learn how to develop this community-based observatory. What I am going to talk about today is the companion piece of the ocean observatory, also known as the virtual observatory, and the new term within the National Science Foundation is called Cyberinfrastructure. My talk has certain bias toward the coastal ocean simply because of its strong link to people and societal applications.ROMS User Workshop, October 2, 2007, Los Angeles
2 Feedback & Adaptive Sampling Integrated Ocean Observing System (IOOS): Modeling, data assimilation, forecasting and adaptive samplingTheoreticalUnderstanding &NumericalModelsUsers:ManagersEducation &OutreachObservations(satellite, in situ)DataAssimilationProductsInformationObservingSystem DesignFeedback & Adaptive Sampling
3 Outline Real-time Regional Ocean Modeling System (ROMS) Three-dimensional variational data assimilationAssimilated observationsEvaluation of analyses and forecastsObserving system experiments (OSE)Relocatability
4 Coupled with Tides Sea Surface M2 Tidal Currents RMS Error of SSHs Tide GaugeSea Surface M2Tidal CurrentsROMS SimulationHF Radar ObsRMS Error of SSHs
5 Regional Ocean Modeling System (ROMS): From Global to Regional/Coastal Modeling Approach15-km5-km1.5-kmRegional Ocean Modeling System (ROMS): From Global to Regional/Coastal12-kmMulti-scale (or “nested”) ROMS modeling approach is developed in order to simulate the 3D ocean at the spatial scale (e.g., 1.5-km) measured by in situ and remote sensors
6 Data AssimilationData AssimilationModelWhen the numerical model is so good as its predictionis superior to the climatological (almanac) forecast
7 ROMS Analysis and Forecast Cycle: Incremental 3DVARy: observationx: model3-dayforecastxf6-hourforecast6-hourassimilationcyclexaInitialconditionTimeAug.100ZAug.106ZAug.112ZAug.118ZAug.200Z
8 Why a There-Dimensional Variational Data Assimilation Real-time capabilityImplementation with sophisticated and high resolution model configurationsFlexibility to assimilate various observation simultaneouslyDevelopment for more advanced scheme
9 3DVAR: Weak Geostrophic Constraint and Hydrostatic Balance Geostrophic balanceVertical integral of the hydrostatic equationageostrophic streamfunction and velocity potentialThe basic variables for an oceanic model are sea surface height, velocity u/v, temperature and salinity. You can compare them to atmospheric variables, surface presure, u/v, temperature and moisture. We consider two weak constraints: geostrophic and hydrostatic balance. With the geostrophic balance, we have the variables from u/v to ageostrophic streamfunction and velocity potential. With hydrostatic balance, we have the variable of non-steric sea surface height. Non-steric SSH variation is purely due to the mass convergence.
10 Inhomogeneous and anisotropic 3D Global Error Covariance Cross-shore and verticalsection salinity correlationSSH correlationsKronecker Product
11 Assimilated observations: Satellite infrared SSTs Infrared, High resolutionCloud contaminationMicrowave, Low resolution (25km)No cloud contaminationNOAAGOESNASA AquaAMSR-ENASA TRIMMTMINOAAAVHRRCross validation. Another important aspect is the difference btewwen the bulk SST and skin/subskin SST. Currently, We use winds. When wind peed is weaker that 3.5m/s, no satellite SSTsAre assimiulated.
12 Assimilated observations: Satellite SSHs along track JASON-1Resolution: 120km cross track, 6km along track
13 Integrated Ocean Observing Systems AOSN-IIT/S profiles from glidersShip CTD profilesAircraft SSTsAUV sections
14 Assimilated Current Observations ShipboardBottomBuoyAcoustic Doppler Current Profiler (ADCP)High Frequency RadarMapped 2D surface current
15 3DVAR with First Guess at Appropriate Time (FGAT) IfFDAT 3DVar is equivalent to 4DVar
16 ROMS Performance Against Assimilated Data August 2006 MeanAll GlidersMean DiffRMS DiffTemperature (C)-0.30.30.00.75Salinity (PSU)-0.10.10.00.20
17 Comparison of Glider-Derived Currents (vertically integrated current) AOSN-II, August 2003Black: SIO glider; Red: ROMSOff shore, T/S can constrait u/v much better. MB06 is near shore. This is AOSN From Russ Davis.
18 Predictability during AOSN-II Forecast CorrelationRMSEWe calc correlation. It is about forcast going wown. Again, anaysis to 24h forcast are marginally useful.Note: because gliders are moving, one cannot estimate the persistence
19 Observing System Experiment (OSE) – Typically aimed at assessing the impact of a given existing data type on a system– Using existing observational data and operational analyses, the candidate data are either added to withheld from the forecast system– Assessing the impact
20 Observing System Experiment (OSE): Glider Data Denial Experiment TemperatureRMS ErrorSalinityw/o CalPoly gliderwith CalPoly gliderCalPolySIOWHOI1st week2nd week
21 ROMS without HF radar data assimilation ROMS with Impact ofHF RadarHF radarROMS without HF radar data assimilationROMS withHF radar data assimilation
22 Southern California Coastal Ocean Observing System (SCCOOS) Southern California BightUS WEST COAST
23 Real-Time SCCOOS Data Assimilation and Forecasting System