Download presentation

Presentation is loading. Please wait.

Published byMohammed Molpus Modified over 2 years ago

1
ROMS/TOMS European Workshop Maison Jean Kuntzmann, Grenoble, France October 7, 2008 ROMS Framework and Algorithms Hernan G. Arango Institute of Marine and Coastal Sciences Rutgers University, New Brunswick, NJ, USA

2
Outline Algorithms and Documentation Status The Good, The Bad, and The Ugly … Advection Operator Detiding Algorithm Observation Sensitivity Balance Operator Grid Nesting

3
The Good…The Bad…The Ugly… AdjointNestingAdjoint Maintenance Data AssimilationOpen BoundariesROMS Code Divergence ESMF/MCT CouplingAdvection OperatorCompiler Bugs Released Version 3.1MonotonicityTreatment of Rivers WikiROMSDocumentation Forum ActivityWetting and Drying ROMS BlogParallel IO www.myroms.orgGrid Generation Frequent ReleasesTutorials Version ControlPost-processing Copyright/Open Source Algorithms and Documentation Status

4
Advection Operator: North Atlantic (DAMEE_4) Resolution0.75x0.75 degrees Grid128x128x20 DX40.8 - 95.8 km DY43.5 - 102.1 km DT(5400, 200) sec BathymetryETOPO-5 S-coordinates5.0 and 0.4 Initial ConditionsLevitus (1994), Feb OBCLevitus (1994) ForcingCOADS, monthly SSH, Year 10, Winter

5
HadvVadvHmix T,SC2C40 u,vU3C40 HadvVadvHmix T,SC2C4 50, H-G u,vU3C40 HadvVadvHmix T,SU3C40 u,vU3C40 Advection Operator: GOM ¾° resolution Initial (red) and 10-year (blue) T-S Diagram Curves

6
HadvVadvHmix T,SC4 50, H-G u,vU3C40 HadvVadvHmix T,SC4 2x10 12,B-G u,vC4 8x10 12,B-S HadvVadvHmix T,SA4 50, H-G u,vU3C40 Advection Operator: GOM ¾° resolution Initial (red) and 10-year (blue) T-S Diagram Curves

7
HadvVadvHmix T,S MPDATA 0 u,vU3C40 HadvVadvHmix T,SU3-S Yes, B-G u,v U3-S Yes, B-S HadvVadvHmix T,SU3-S Yes, B-G u,vU3C40 Advection Operator: GOM ¾° resolution Initial (red) and 10-year (blue) T-S Diagram Curves

8
Remarks There is excessive numerical, diapycnal mixing in the default third-order, upstream-bias (U3) scheme. The second- and fourth-order centered differences schemes are dispersive and overshoot. The MPDATA (Multidimensional Positive Definite Advection Transport Algorithm) is monotonic and maintains the extrema. However, there is some deep-water modification. The split schema (QUICK operator is split into advective and diffusive components) is the best, showing few spurious maxima and minima. However, the diffusion operator (along geopotentials) has some stability problems that we need to address.

9
ROMS Tides Least-Squares Fit A ROMS state variable,, can be represented in terms of its time mean,, plus a set of -tidal harmonics of frequency,. The unknowns,, and coefficients are evaluated by minimizing the least-squares error function defined by: Minimization subject to the additional constraints,, result in a linear set of equations:

10
Least-Squares: Linear Equations System

11
Tidal Forcing NetCDF File

12
K1 (23.93 h)P1 (24.07 h)O1 (25.82 h)Q1 (26.87 h) K2 (11.97 h)S2 (12.00 h)M2 (12.42 h)N2 (12.66 h) Philippine Archipelago Tides: SSH Amplitude and Phase B. Zhang

13
Remarks The detiding algorithm (AVERAGES_DETIDE) is working nicely. As the number of tidal constituents increases, the time needed to resolve the beat frequencies increases. That is, the beat period (sum of all frequencies) becomes longer. For example, if only M2 and S2 components are used, the beat period is around 28-days (spring-neap cycle). Therefore we need to run for a least 28 days to resolve the harmonic coefficients in matrix A. I recommend you have a single NetCDF for tidal forcing and save a copy before using the detiding option since this algorithm add new variables to it.

14
PhilEx Real-Time Predictions ONR-DRI in the Philippine Archipelago Real-time forecasts to support the PhilEx Exploratory Cruise. Coarse (~5 km) and fine (~2 km) grid resolution. Initial and lateral boundary conditions from 1/12 HyCOM with NCODA. Forcing from NOGAPS ½, 3-hours forecast Tides from global OTPS model Sequential 9-day forecast cycles without data assimilation http://www.myroms.org/philex

15
Philippine Archipelago Forecast Salinity at 10m J. Levin

16
PhilEx 4DVar Assimilation: Salinity J. Levin

17
PhilEx 4DVar Assimilation: Temperature J. Levin

18
Observation Sensitivity Driver

19
Intra-America Sea (IAS) Real-time forecasts onboard the RCCL vessel Explorer of the Seas.Explorer of the Seas Running continuously since January 17, 2007 to present. Fully automatic since end of February 2007. IS4DVAR, 14-day sequential data assimilation cycles. 50 ensembles members per week running on a 4-CPUs Linux box. Observations: Satellite SST Satellite SSH Shipborne ADCP http://www.myroms.org/ias Arango, Di Lorenzo, Milliff, Moore, Powell, Sheinbaum

20
IAS 4DVar Observation Sensitivity: SSH SSH observations SSH sensitivity 13-20 Apr 2007 Arango, Moore, Powell

21
IAS 4DVar Observation Sensitivity: SST 13-20 Apr 2007 SSH observations SSH sensitivity Arango, Moore, Powell

22
Remarks We are still developing and fine tuning this algorithm (OBS_SENSITIVITY). The mathematical formulation is similar to that of Zhu and Gelaro (2008). It is a powerful tool to quantify the sensitivity of the IS4DVAR system to the observations. It can help us to determine the type of measurements that need to be made, where to observe, and when: Adapting Sampling.

23
IS4DVAR Balanced Operator Covariances: EAC The cross-covariances are computed from a single sea surface height observation using multivariate physical balance relationships. Free-surface (m) 2 Temperature (Celsius) 2 Salinity (nondimensional) 2 U-velocity (m/s) 2 V-velocity (m/s) 2 Z = -300m Arango, Moore, Zavala

24
IS4DVAR Balanced Operator Covariances: EAC Free-surface (m) 2 Temperature (Celsius) 2 Salinity (nondimensional) 2 U-velocity (m/s) 2 V-velocity (m/s) 2 The cross-covariances are computed from a single temperature observation at the surface using multivariate physical balance relationships. Z = 0mZ = -300m Arango, Moore, Zavala

25
IS4DVAR Balanced Operator Covariances: EAC Free-surface (m) 2 Temperature (Celsius) 2 Salinity (nondimensional) 2 U-velocity (m/s) 2 V-velocity (m/s) 2 The cross-covariances are computed from a single U-velocity observation at the surface using multivariate physical balance relationships. Z = -300m Z = 0mZ = -300m Arango, Moore, Zavala

26
Remarks We are still developing and fine tuning this algorithm (BALANCE_OPERATOR). The approach is similar to that proposed by Weaver et al. (2006). This is a multivariate approach to constraint the background and model error covariances in the 4DVar system using linear balance relationships (T-S empirical relationships, linear equation of state, hydrostatic and geotrophic balances). It allows the unobserved variables information to be extracted from directly observed quantities. State vector is split between balanced and unbalanced components.

27
Nested Grids Types Refinement Mosaics Composite Arango, Warner

28
Grid (ng) Free-surface2D Momentum3D MomentumTracers WestEastNorthSouthWestEastNorthSouthWestEastNorthSouthWestEastNorthSouth 1 2 3 N:NestedC:Closed F:FlatherM:Clamped G:GradientP:Periodic R:RadiationD:Reduced H:Chapman Nested Grids: Lateral Boundary Conditions Arango, Warner

29
Grid (ng) P*WestEastNorthSouth 102000 210100 311111 * Who is your parent? To whom are you connected to on ______ boundary edge? 1 3 2 Nested Grid Connectivity Arango, Warner

Similar presentations

OK

ROMS/TOMS Tangent Linear and Adjoint Models Andrew Moore, CU Hernan Arango, Rutgers U Arthur Miller, Bruce Cornuelle, Emanuele Di Lorenzo, Doug Neilson.

ROMS/TOMS Tangent Linear and Adjoint Models Andrew Moore, CU Hernan Arango, Rutgers U Arthur Miller, Bruce Cornuelle, Emanuele Di Lorenzo, Doug Neilson.

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

How to make ppt on macbook pro Ppt on diode circuits tutorial Ppt on home security system project Download ppt on science and technology Muscular system anatomy and physiology ppt on cells Ppt on robert frost Ppt on climate change in india Ppt on beer lambert law examples Ppt on carbon and its compounds for 10 class Marketing ppt on product