Evaluation of the Princeton Ocean Model Using South China Sea Monsoon Experiment (SCSMEX) Data

Abstract

The Princeton Ocean Model (POM) has been implemented in the South China Sea for hindcast of circulation and thermohaline structure. A two-step technique is used to initialize POM with temperature, salinity, and velocity for 1 April 1998 and integrate it from 1 April 1998 with synoptic surface forcing for 3 months with and without data assimilation. Hydrographic and current data acquired from the South China Sea Monsoon Experiment (SCSMEX) from April through June 1998 are used to verify, and to assimilate into, POM. The mean SCSMEX data (Apr-Jun 1998) are about 0.5ЊC warmer than the mean climatological data above the 50-m depth, and slightly cooler than the mean climatological data below the 50-m depth, and are fresher than the climatological data at all depths and with the maximum bias (0.2-0.25 ppt) at 75-m depth.POM without data assimilation has the capability to predict the circulation pattern and the temperature field reasonably well, but has no capability to predict the salinity field. The model errors have Gaussian-type distribution for temperature hindcast, and non-Gaussian distribution for salinity hindcast with six to eight times more frequencies of occurrence on the negative side than on the positive side. Data assimilation enhances the model capability for ocean hindcast, if even only conductivity-temperature-depth (CTD) data are assimilated. When the model is reinitialized using the assimilated data at the end of a month (30 Apr; 31 May 1998) and the model is run for a month without data assimilation (hindcast capability test), the model errors for both temperature and salinity hindcast are greatly reduced, and they have Gaussian-type distributions for both temperature and salinity hindcast. Hence, POM gains capability in salinity hindcast when CTD data are assimilated.

Ocean Nowcast/Forecast Systems for Improvement of Naval Undersea Capabilities

“…During SCSMEX, the hydrographic data set included over 1700 CTD ( Figure 2) and mooring stations (Chu et al, 2001(Chu et al, , 2004b. The hydrographic data collected during the SCSMEX IOP went through quality control procedures such as min-max check (e.g., disregarding any temperature data less than -2 o C and greater than 40 o C), error anomaly check (e.g., rejecting temperature data deviating more than 7 o C from climatology), ship-tracking algorithm (screening out data with obvious ship position errors), max-number limit (limiting a maximum number of observations within a specified and rarely exceeded space-time window), and buddy check (rejecting contradicting data).…”

“…After the quality control, the SCSMEX oceanographic data set contains 1742 conductivity-temperature-depth (CTD) and mooring stations. The majority of the CTDs were nominally capable of reaching a maximum depth of 2000 m. SCSMEX provided a unique opportunity to evaluate ocean nowcast system (Chu et al, 2004b) and forecast system (Chu et al 2001). …”

“…Representation of the Navy's nowcast (MODAS) and forecast (POM) systems for ocean environment (SSP through T, S profiles) was verified using the CTD data collected from the South China Sea Monsoon Experiment (SCSMEX) in April -June 1998 (Chu et al, 2001(Chu et al, , 2004b. The errors have a Gaussian-type distribution with mean temperature nearly zero and mean salinity of -0.2 ppt.…”

Evaluation of the U.S. Navy’s Modular Ocean Data Assimilation System (MODAS) Using South China Sea Monsoon Experiment (SCSMEX) Data

“…A recent development is to use the MODAS temperature and salinity fields to initialize an ocean prediction model such as the Princeton Ocean Model (Chu et al, 2001). There is thus an urgent need to evaluate the MODAS salinity field as well as the thermocline/halocline structures.…”

“…MODAS skill, on the other hand, is defined as the model accuracy relative to the accuracy of a nowcast produced by some reference procedure, such as climatology or persistence. To measure the model skill, we may compute the reduction of MSE over the climatological nowcast (Murphy, 1988;Chu et al, 2001),…”