1. Marine Meteorology Quality Control at the Florida State University
Shawn R. Smith
Research Vessel Surface Meteorology Data Center
Center for Ocean-Atmospheric Prediction Studies
Florida State University

2. Who We Are
Data center specializing in the quality control (QC) of marine observations collected by automated instrumentation on research vessels (R/Vs)
We employ quality control procedures developed in-house to create value added data products
We freely distribute all products to science community and apply them to current scientific problems

3. History of RVSMDC
David M. Legler and James J. O’Brien formed the Data Assembly Center (DAC) for WOCE in 1993
Final WOCE archive contains meteorology data from over 439 hydrographic cruises (82% of completed cruises)
Expanded early on to include all surface meteorology data from TOGA/COARE
Late1990s, added data from select international, UNOLS, and NOAA R/Vs
With expansion beyond WOCE, renamed archive R/V Surface Meteorology Data Center (RVSMDC)
In 2004 we initiated the Shipboard Automated Meteorological and Oceanographic System (SAMOS) initiative

4. Experience
Our archive contains many high-time resolution (<15 min.) meteorology data sets
archive includes over 100 cruises with sampling < 15 min. intervals
30% of data are poleward of 40S and 50N.

5. Current Archive Over 11, 000 ship days have been quality controlled
Variables evaluated: vessel navigation, air and sea temperatures, pressure, moisture parameters, ship-relative and true winds, radiation, and precipitation.
Current focus 1-minute observations from U.S. RVs participating in SAMOS

6. Importance of metadata
Accurate metadata are essential for scientific application of marine observations
RVSMDC files contain detailed metadata that include instrument height and sensor type, units, time averaging period, ship ID, cruise ID (when available), and the facility that provided the data
Communication with data providers essential to collection of accurate metadata

7. Quality-Control: overview
The goal of our quality control (QC) is to provide well- documented, reliable, and consistent research vessel data to the scientific community.
Flags are applied values at the parametric level.
This means that each individual observation (for which QC is applied) will have a single quality control flag.
Alternative is to treat all values collected at a single time as one record and flag whole record
Our philosophy is to flag suspect values, not remove them from the data files.
Accepting or excluding flagged values may vary depending on the user's scientific goals; our system retains all values for that purpose.

8. Quality-Control: overview
Two primary types of QC: Real-time and Scientific
Real-time
Primarily used to meet needs of operational forecasting and modeling
Includes simplified, aggregate flag structure
0 - good
1 - suspect
2 - erroneous
3 - not evaluated
4 - parameter missing
Not ideal for climate research or future scientific applications
Example: From QARTOD

9. Quality-Control: overview
Scientific
Type of QC used at the RVSMDC and for SAMOS
Each flag corresponds to a specific quality test
For example, in our system we verify the relationship that the T≥Tw≥Td. If this test fails a D flag is applied to the appropriate T, Tw, or Td values
This method provides user with greater detail as to why a value was flagged.
The method also allows for flags that do not indicate problems, but interesting features (e.g., frontal passages, pressure minima, etc.).

10. Quality-Control: overview
Our system uses both automated and visual data inspection
Automated flagging
Pre-process for realistic ranges, time sequence, etc.
New statistical spike/step flagging tool (SASSI)
VIDAT (VIsual Data Assessment Tool (software developed in-house)
Visualize multiple data streams
Map positions/climatologies
Check automated flagging
Analyst adds additional flags
Provide feedback to vessel operators
Two way communication with data providers is essential to understand problems and have them corrected

11. Quality-Control: data flow
Original data/documentation combined into single file (netCDF)
Output from each QC process (flags) combined into data quality report
Report and value-added data (with flags) released to public
Pre-Screen
SASSI

12. Quality-Control: visual inspection
Identifies systematic errors (e.g., severe flow distortion, sensor heating, and acceleration errors)
Finds problems and features that are unique to new system deployments

13. Quality-Control: enhancement
New automated procedures developed to flag systematic errors
Based on experience from VIDAT
Greatly increases QC efficiency (less analyst hours per vessel)
Example: Stack exhaust impacts
With certain ship-relative winds, exhaust influences temperature and humidity

14. Quality-Control: spike/step
Increases in air temperature visually identified when ship-relative winds near 180 deg. (from stern)
Early QC: Analyst manually flagged suspect temperatures
QC today: Takes advantage of automated identification of suspect regions

15. Quality-Control: spike/step
Spikes, steps, suspect values identified (flagged)
Examines difference in near-neighbor values
Flags based on threshold derived from observations
Graphical Representation
Identifies flow conditions w/ severe problems
Flags plotted as function of ship-relative wind
% flagged in each wind bin on outer ring
Analyst determines range of data to autoflag

16. Quality-Control: spike/step
Analyst manual visual flags (top)
Flags applied by statistical auto-flagger (center)
Flags assigned to suspect ship-relative wind directions (bottom)
Final result similar to analyst flags, but w/ substantial time savings

17. Quality Control: true winds
Another common problem with automated marine instruments is incorrect estimation of the true (earth-relative) winds
Quality true winds (green) show no signal of ship motion (black)
180 deg. Error in reported ship-relative winds (blue) results in incorrect true winds (red)

18. Quality Control: true winds
Common causes for true wind errors
Incorrect anemometer installation
Failure to document wind direction convention (meteorological, oceanographic, merchant marine)
Incorrect code to compute true wind (must remove motion induced by ship from ship-relative wind data)
Confusing definitions for navigation parameters (course, heading, and speed)

19. Quality Control: true winds
Several vessels and agencies take advantage of our true winds analysis
R/V Polarstern (Germany) and R/V Wecoma (U.S.) modified data collection and reporting systems based upon our recommendations
Hankuk University of Foreign Studies (S. Korea) using our recommendations to improve winds on ferries
Some yacht clubs and small marine companies are using our advice to improve instrumentation on recreational vessels

20. Final Thoughts
Although we still conduct delayed-mode QC (3-6 month lag from collection to distribution), we now focus on near real-time QC through SAMOS Initiative.
Quality procedures undergo constant revision and updates. Future enhancements may include:
Automated ship heating algorithms
Improved radiation QC (only range checks and visual inspection now)
Procedures developed by the RVSMDC have been successfully applied to surface marine observations on multiple time scales (for both ships and buoys)