ROMS User Workshop, October 2, 2007, Los Angeles A Three-Dimensional Variational Data Assimilation in Support of Coastal Ocean Observing Systems Zhijin Li and Yi Chao Jet Propulsion Laboratory Jim McWilliams and Kayo Ide UCLA As 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 email 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

Feedback & Adaptive Sampling Integrated Ocean Observing System (IOOS): Modeling, data assimilation, forecasting and adaptive sampling Theoretical Understanding & Numerical Models Users: Managers Education & Outreach Observations (satellite, in situ) Data Assimilation Products Information Observing System Design Feedback & Adaptive Sampling

Outline Real-time Regional Ocean Modeling System (ROMS) Three-dimensional variational data assimilation Assimilated observations Evaluation of analyses and forecasts Observing system experiments (OSE) Relocatability

Coupled with Tides Sea Surface M2 Tidal Currents RMS Error of SSHs Tide Gauge Sea Surface M2 Tidal Currents ROMS Simulation HF Radar Obs RMS Error of SSHs

Regional Ocean Modeling System (ROMS): From Global to Regional/Coastal Modeling Approach 15-km 5-km 1.5-km Regional Ocean Modeling System (ROMS): From Global to Regional/Coastal 12-km Multi-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

Data Assimilation Data Assimilation Model When the numerical model is so good as its prediction is superior to the climatological (almanac) forecast

ROMS Analysis and Forecast Cycle: Incremental 3DVAR y: observation x: model 3-day forecast xf 6-hour forecast 6-hour assimilation cycle xa Initial condition Time Aug.1 00Z Aug.1 06Z Aug.1 12Z Aug.1 18Z Aug.2 00Z

Why a There-Dimensional Variational Data Assimilation Real-time capability Implementation with sophisticated and high resolution model configurations Flexibility to assimilate various observation simultaneously Development for more advanced scheme

3DVAR: Weak Geostrophic Constraint and Hydrostatic Balance Geostrophic balance Vertical integral of the hydrostatic equation ageostrophic streamfunction and velocity potential The 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.

Inhomogeneous and anisotropic 3D Global Error Covariance Cross-shore and vertical section salinity correlation SSH correlations Kronecker Product

Assimilated observations: Satellite infrared SSTs Infrared, High resolution Cloud contamination Microwave, Low resolution (25km) No cloud contamination NOAA GOES NASA Aqua AMSR-E NASA TRIMM TMI NOAA AVHRR Cross 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 SSTs Are assimiulated.

Assimilated observations: Satellite SSHs along track JASON-1 Resolution: 120km cross track, 6km along track

Integrated Ocean Observing Systems AOSN-II T/S profiles from gliders Ship CTD profiles Aircraft SSTs AUV sections

Assimilated Current Observations Shipboard Bottom Buoy Acoustic Doppler Current Profiler (ADCP) High Frequency Radar Mapped 2D surface current

3DVAR with First Guess at Appropriate Time (FGAT) If FDAT 3DVar is equivalent to 4DVar

ROMS Performance Against Assimilated Data August 2006 Mean All Gliders Mean Diff RMS Diff Temperature (C) -0.3 0.3 0.0 0.75 Salinity (PSU) -0.1 0.1 0.0 0.20

Comparison of Glider-Derived Currents (vertically integrated current) AOSN-II, August 2003 Black: SIO glider; Red: ROMS Off shore, T/S can constrait u/v much better. MB06 is near shore. This is AOSN 2003. From Russ Davis.

Predictability during AOSN-II Forecast Correlation RMSE We calc correlation. It is about 0.6-0.7 forcast going wown. Again, anaysis to 24h forcast are marginally useful. Note: because gliders are moving, one cannot estimate the persistence

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

Observing System Experiment (OSE): Glider Data Denial Experiment Temperature RMS Error Salinity w/o CalPoly glider with CalPoly glider CalPoly SIO WHOI 1st week 2nd week

ROMS without HF radar data assimilation ROMS with Impact of HF Radar HF radar ROMS without HF radar data assimilation ROMS with HF radar data assimilation

Southern California Coastal Ocean Observing System (SCCOOS) Southern California Bight US WEST COAST http://ourocean.jpl.nasa.gov/SCB

Real-Time SCCOOS Data Assimilation and Forecasting System http://ourocean.jpl.nasa.gov/SCB

Evaluation with HF radar velocities

Toward a Relocatable ROMS Forecasting System: Demonstration for Prince William Sound, Alaska 9-km 3-km 1-km