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 www.coaps.fsu.edu/RVSMDC

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

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

Experience Our archive contains many high-time resolution (<15 min.) meteorology data sets 1990-1998 archive includes over 100 cruises with sampling < 15 min. intervals 30% of data are poleward of 40S and 50N.

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

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

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.

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 http://nautilus.baruch.sc.edu/twiki/bin/view

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.).

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

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

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

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

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

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

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

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)

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)

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

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)