NOAA Technical Memorandum OAR PSD-311

A GUIDE TO MAKING CLIMATE QUALITY METEOROLOGICAL AND 

FLUX MEASUREMENTS AT SEA 

F. Bradley 

C. Fairall 

Earth System Research Laboratory 

Physical Sciences Division 

Boulder, Colorado 

October 2006 

NATIONAL OCEANIC AND noaa ATMOSPHERIC ADMINISTRATION 

Office of Oceanic and / Atmospheric Research 

UNITED STATES

DEPARTMENT OF COMMERCE

Carlos M. Gutierrez

Secretary

NATIONAL OCEANIC AND

ATMOSPHERIC ADMINISTRATION

VADM Conrad C. Lautenbacher, Jr.

Under Secretary for Oceans

and Atmosphere/Administrator

Office of Oceanic and 

Atmospheric Research

Dr. Richard Spinrad

Assistant Administrator

NOAA Technical Memorandum OAR PSD-311

A GUIDE TO MAKING CLIMATE QUALITY METEOROLOGICAL AND

FLUX MEASUREMENTS AT SEA

Frank Bradley

CSIRO Land and Water, P.O. Box 1666, Canberra 2601 Australia

Christopher Fairall

NOAA Earth System Research Laboratory, Boulder, CO

Earth System Research Laboratory

Physical Sciences Division

Boulder, Colorado

October 2006
ii

NOTICE 

Mention of a commercial company or product does not constitute an endorsement by 

NOAA/Earth System Research Laboratory. Use of information from this publication concerning 

proprietary products or the test of such products for publicity or advertising purposes is not 

authorized. 

______________________________________________________ 

For sale by the National Technical Information Service, 5285 Port Royal Road 

Springfield, VA 22061
iii

Contents 

BACKGROUND ......................................................................................................................... vii

QUICK REFERENCE ........................................................................................................... QR-1 

QR1. Instruments and Calibration ..................................................................................... QR-1 

 QR2. Installation (Location and Exposure) ....................................................................... QR-3 

 QR3. Documentation and Event Logging .......................................................................... QR-5 

 QR4. Monitoring and Maintenance ................................................................................... QR-5 

 QR5. Recording and Security the Data .............................................................................. QR-6 

FLUX MEASUREMENTS FROM SHIPS AND BUOYS ........................................................... 1 

1. The Air-Sea Fluxes .................................................................................................................. 1 

1.1. Introduction ........................................................................................................................ 1 

 1.2. Turbulent fluxes ................................................................................................................. 1 

 1.3. Radiative fluxes .................................................................................................................. 1 

 1.4. Freshwater flux ................................................................................................................. 2 

 1.5. Net surface fluxes ............................................................................................................... 2 

2. Basic Variables Input to Bulk Flux Algorithms ................................................................... 2 

 2.1. Introduction ........................................................................................................................ 2 

 2.2. Air temperature .................................................................................................................. 3 

 2.3. Humidity ............................................................................................................................. 4

 2.4. Atmospheric pressure ......................................................................................................... 5 

 2.5. Wind speed and direction.................................................................................................... 5 

 2.6. Sea surface temperature ................................................................................................... 6 

 2.7. Radiation ............................................................................................................................ 7 

 2.8. Precipitation ...................................................................................................................... 8 

3. Bulk-Flux Meteorological Sensors ......................................................................................... 9

 3.1. Introduction ........................................................................................................................ 9 

 3.2. Temperature ..................................................................................................................... 10 

 3.3. Humidity ........................................................................................................................... 10 

 3.4. Atmospheric pressure ....................................................................................................... 15 

 3.5. Wind speed and direction ................................................................................................. 15 

 3.6. Sea temperature .............................................................................................................. 16 

 3.7. Radiation .......................................................................................................................... 17 

 3.8. Precipitation .................................................................................................................... 19 

4. Measurement Systems .......................................................................................................... 20 

5. Particular Problems on Ships and Buoys ............................................................................ 22 

 5.1. Introduction ...................................................................................................................... 22 

 5.2. Wind flow distortion ......................................................................................................... 22 

 5.3. Sea spray and salt contamination .................................................................................... 24 

 5.4. Ship and buoy motion ....................................................................................................... 25 
iv

 5.5. Exhaust contamination ..................................................................................................... 26 

 5.6. Electrical problems .......................................................................................................... 26 

6. Location of Instruments ....................................................................................................... 28 

 6.1. Introduction ...................................................................................................................... 28 

 6.2. Temperature ..................................................................................................................... 29 

 6.3. Humidity ........................................................................................................................... 29 

 6.4. Wind speed and direction ................................................................................................. 29 

 6.5. Sea temperature .............................................................................................................. 29 

 6.6. Radiation .......................................................................................................................... 29 

 6.7. Rainfall ............................................................................................................................. 30 

7. Instrument Calibration ........................................................................................................ 30 

8. Intercomparisons .................................................................................................................. 31 

 8.1. Portable standards ........................................................................................................... 31 

 8.2. Replication of sensors ...................................................................................................... 31 

 8.3. Field intercomparisons .................................................................................................... 33 

 8.4. Manual observations ........................................................................................................ 33 

9. Documentation (Metadata) .................................................................................................. 34 

 9.1. Introduction ...................................................................................................................... 34 

 9.2. The basics ......................................................................................................................... 35 

 9.3. Sensor calibration and history ......................................................................................... 35 

 9.4. Instrument location .......................................................................................................... 35 

 9.5. Digital photographs ......................................................................................................... 35 

10. Securing the Data ................................................................................................................. 37 

 10.1. Introduction .................................................................................................................... 37 

 10.2. Data storage ................................................................................................................... 37 

 10.3. Data archival .................................................................................................................. 37 

11. Bulk Flux Algorithms .......................................................................................................... 38 

Appendix A – Useful Formulae, Parameters, and Conversions .......................................... A-1 

 A1. Equations of State .......................................................................................................... A-1 

 A2. Ice-Related Expressions.................................................................................................. A-3 

 A3. Radiometry...................................................................................................................... A-3 

 A4. Barometer Correction ..................................................................................................... A-4 

 A5. Conversions .................................................................................................................... A-4 

 A6. Gravity ........................................................................................................................... A-4 

 A7. Relative Wind Conversions Aboard Ship ...................................................................... A-5 

 A8. Height Adjustment ......................................................................................................... A-6 
v

Appendix B – The TOGA-CORE Bulk Flux Algorithm ...................................................... B-1 

 B1. History and Features ....................................................................................................... B-1 

 B2. Examples of COARE 3.0 Performance........................................................................... B-2 

 B3. Estimate of Turbulent Flux Errors .................................................................................. B-4 

Appendix C – IR Radiative Flux Errors Caused by Objects in the Field of View ............ C-1 

Appendix D – Examples of Meteorological Observations and Fluxes ................................ D-1 

Appendix E – The Beaufort Wind Scale ................................................................................ E-1 

Appendix F – Useful Websites .................................................................................................F-1 

Appendix G – Metadata Documentation: SAMOS Example .............................................G-1 

 G1. Introduction .................................................................................................................... G-1 

 G2. Vessel Metadata ............................................................................................................. G-1 

 G3. Primary Instrument Metadata ......................................................................................... G-5 

 G4. SAMOS Data Format ..................................................................................................... G-8

References ..............................................................................................................................Ref-1 

Bibliography ......................................................................................................................Biblio-1 
vi
vii

BACKGROUND 

The importance of accurate fluxes of heat and momentum in the coupled oceanatmosphere system has been acknowledged since the mid-1980s. Arbitrary adjustment to the airsea fluxes when coupling ocean and atmospheric models was common practice as a means of 

keeping sea surface temperatures within realistic bounds. In response to this demonstrated 

sensitivity of coupled air-sea models to small changes in values of air-sea fluxes, the World 

Ocean Circulation Experiment (WOCE) observing program (WCRP 1989) and process studies 

such as the Tropical Ocean Global Atmosphere – Coupled Ocean-Atmosphere Response 

Experiment (TOGA-COARE) (Webster and Lukas 1992) set accuracy goals for the measurement 

of net heat exchange across the ocean-atmosphere interface of ±10 Wm-2 over short to medium 

time scales. However, the comparison of observations from several research ships during 

TOGA-COARE revealed that raw measurements fell short of this goal. In the subsequent 

analysis, the reasons for these disagreements were examined and identified, and in most cases 

corrections could be made. 

Problems were traced to interference of the measurement by the ship including: poor 

location of sensors; inadequate knowledge of how an instrument designed for use over land 

performed on an unstable platform and in the marine environment; and inappropriate calibration 

procedures. Overall, it became apparent that, if the requirements of climate research were to be 

met, more care must be taken to ensure the accuracy of measurement of basic meteorological 

variables used for the calculation of turbulent and radiative air-sea fluxes (Weller et al. 2004). 

Such careful observations may be referred to as of “climate-quality”. 

Following the publication of its report on the status of air-sea flux datasets and 

observational methods (WCRP 2000), the WCRP/SCOR Air-Sea Fluxes Working Group 

convened an international workshop to discuss its findings, and to consider the implications for 

future air-sea flux measurement for climate research generally, and for validation of satellite 

observations and initialization of models (WCRP 2001). The Workshop noted that “the 

techniques to obtain high-quality data for flux estimation at sea are very demanding” and 

recommended “the assembly of a Technical Manual on air-sea flux measurement methods”. 

In March 2003, Florida State University hosted the First High-Resolution Marine 

Meteorology (HRMM) workshop, under the auspices of NOAA/OGP Ocean Observing 

Initiative. The quality of basic measurements needed to ensure accurate air-sea fluxes was 

discussed, as was the fact that valuable data could be obtained when research ships operate in 

rarely visited regions. Often these ships have the necessary sensors onboard, and technicians 

capable of maintaining them, but no mechanism or protocol exists to ensure that flux-relevant 

data are collected even when meteorological conditions are not important for the objectives of 

that particular cruise. 

To improve this situation and ensure good data return from as many ships as possible, the 

first step is to make those who would be involved aware of the difficulties in collecting highquality meteorological data at sea. Recommendation 5 from the report of that meeting (COAPS 

2003) was to ‘Produce a reference manual of best procedures and practices for the observation 

and documentation of meteorological parameters, including radiative and turbulent fluxes, in the 

marine environment. The manual will be maintained online and will be a resource for marine 

weather system standards.’ 
viii

This manual is intended for a wide readership. Primarily, it is a guide for scientists and 

technicians who are responsible for installing and/or maintaining meteorological equipment 

onboard ships, whether research vessels specifically engaged in air-sea studies, ships able to 

provide relevant data of opportunity, or commercial vessels recruited as part of the Voluntary 

Observing Ship network (the same general principles apply to meteorological sensors installed 

on surface buoys). It is also intended to provide background for scientists on oceanographic 

research cruises who need air-sea flux information from the research vessel as auxiliary data for 

their study. A quick perusal of this document should allow the scientist to ask the right questions 

about the particular measurements for the cruise. Importantly, this manual should also serve as 

background material for students interested in ship-based meteorological and air-sea flux 

measurements. 

The second workshop of the HRMM in April 2004 (COAPS 2004) decided that 

electronic meteorological sensors existing or subsequently installed on ships and maintained 

according to these principles be identified as part of the Shipboard Automated Meteorological 

and Oceanographic System (SAMOS) Initiative. SAMOS will collect and distribute climatequality data via an assigned Data Assembly Centre (DAC) and ensure the data are archived at 

appropriate world data centers. This handbook will be a guide to SAMOS and similar projects. 

In prescribing costly equipment and calibration standards, and exacting installation procedures, 

we also presume that technical attention is available each day for the associated routine 

maintenance, monitoring and data archiving tasks. Reasonable time must also be committed to 

troubleshooting in event of instrument failure. 

The organisation of the manual is as follows. We first provide a Summary of the most 

critical information and procedures, intended as a “stand-alone” practical reference. The main 

body of the handbook describes the nature of the environmental variables that need to be 

measured, and why this is so much more exacting at sea than over land. It deals with the 

practical issues of coping with these difficulties on board a ship or mooring, to ensure the data 

are as reliable as possible. We also refer to procedures such as calibration before and after the 

deployment, and comparison with other instruments, which help ensure the quality of the data. 

Emphasis is also given to the critical importance of documentation, particularly of the location 

and state of the measuring instruments (now easily captured with digital photos), and notes of 

any occurrence, e.g., roosting birds, which may impair data quality. 

There are several specialized Appendices; physical formulae, constants and conversion 

factors used in the analysis of atmospheric data and the calculation of air-sea fluxes (which you 

can never find when you need them); a description of the TOGA-COARE bulk flux algorithm; 

an analysis of thermal radiative flux errors; examples of shipboard observations; the Beaufort 

wind scale; a list of links to relevant web sites; and details of the SAMOS DAC with 

specifications for standardization of data formats, and metadata requirements. 

Acknowledgments 

Many colleagues contributed to this handbook with careful reviews and writings. 

We are indebted to Liz Kent, Bob Weller, Roger Lukas, Ed Andreas, Peter Taylor, Shawn Smith, 

Ben Moat, Mark Bourassa, Mike Reynolds, Eric Schulz, Will Drennan, and Steven Hartz. 

This work was supported by the NOAA Office of Climate Observation. Frank Bradley 

thanks the CSIRO Division of Land and Water for continuing support by way of a postretirement fellowship.
QR- 1

QUICK REFERENCE

The body of this handbook describes in detail the factors to be considered in equipping a 

vessel to obtain climate-quality meteorological and flux data. It discusses the nature of the basic 

quantities to be measured, the relevant instruments, and special considerations because the 

measuring site is a ship at sea. This Summary is a practical reference for the benefit of the 

scientist or technician assigned the task of installing and maintaining a package of instruments on 

a ship, without needing too much detail or rationale. It follows roughly the order of the various 

procedures involved. 

QR1. Instruments and Calibration

The meteorological measurements required for determination of air-sea fluxes comprise: 

• Wind speed 

• Wind direction 

• Air temperature 

• Air humidity 

• Atmospheric pressure 

• Downward shortwave radiation 

• Downward longwave radiation 

• Rainfall 

• Sea surface temperature (not strictly meteorology, but a vital measurement) 

Table 1 lists the required accuracy for each of these quantities; the suite of instruments 

provided should have been assembled to meet these specifications. Whether or not the accuracy 

is achieved will depend on installation and maintenance. In general, there will be more than one 

sensor of each type available. If possible, two sets of instruments should be deployed to ensure 

good exposure for any ship-relative wind or sun direction. At least one spare instrument of each 

type should be set aside as replacement should its operational counterpart fail. Spare instruments 

may be stored on the vessel if the operator feels that replacements at sea are feasible. 

Each instrument comes with a calibration from a certified facility to which it should be 

returned for re-calibration as necessary, and at least once a year. It is important to record the 

calibration and deployment history of each sensor, so that the correct calibration can be applied 

should instruments be exchanged or replaced. These metadata (see section 9) are critical when 

the raw data are re-analysed during post-processing. 

The data record will also include input from the ship’s navigation system: 

a) Latitude and longitude from GPS. 

b) The ship’s true heading, and the ship’s course and speed over the ground, and speed through 

the water. These are required to convert relative wind speed and direction to true values. 

c) Although the instrument package to be installed may include a separate sea temperature 

measurement, if a built-in ship thermo-salinograph exists, its data should be recorded. 

d) If the ship’s bridge meteorological measurements are available on the vessel’s computer 

network, they should be logged, and the instrument locations included in the metadata. 

e) A copy of the bridge event log; this is particularly useful when investigating anomalous data, 

revealing if the ship was hove to (e.g., for a CTD cast) or maneuvering and creating flow 

distortion, stack exhaust problems, etc. 
QR- 2

Table 1: Accuracy, precision and random error targets for SAMOS. 

Parameter 

Accuracy of Mean 

(bias) 

Data 

Precision 

Random Error 

(uncertainty) 

Latitude and Longitude 0.001° 0.001° 

Heading 2° 0.1° 

Course over ground 2° 0.1° 

Speed over ground Larger of 2% or 0.2 m/s 0.1 m/s Greater of 10% or 0.5 m/s 

Speed over water Larger of 2% or 0.2 m/s 0.1 m/s Greater of 10% or 0.5 m/s 

Wind direction 3° 1° 

Wind speed Larger of 2% or 0.2 m/s 0.1 m/s Greater of 10% or 0.5 m/s 

Atmospheric Pressure 0.1 hPa (mb) 0.01 hPa (mb)

Air Temperature 0.2°C 0.05°C 

Dewpoint Temperature 0.2°C 0.1°C 

Wet-bulb Temperature 0.2°C 0.1°C 

Relative Humidity 2% 0.5 % 

Specific Humidity 0.3 g/kg 0.1 g/kg 

Precipitation ~0.4 mm/day 0.25 mm 

Radiation (SW in, LW in) 5 W/m2

 1 W/m2 

Near surface: 

 Sea Temperature 0.1°C 0.05°C 

 Salinity 0.1 psu 0.05 psu 

 Current 0.1 m/s 0.05 m/s 

Notes: The above accuracy estimates are based on the goal to determine Hnet in equation (1.1) 

to within ±10 Wm-2 on the monthly to seasonal time scales appropriate for climate studies. 

The reader should recognize that they are nominal values which apply to typical marine weather 

conditions from the tropics to mid-latitudes. They cannot be expected to apply in unusual or 

extreme conditions. In the Arctic, for example, if the air temperature is -40oC, it makes no sense 

to measure relative humidity to 2%. Calculated bulk turbulent heat fluxes can incur errors from 

uncertainties in the measurements of temperature and wind speed in extreme conditions. 

Consider the ±10 Wm-2 goal arbitrarily apportioned equally between radiative and turbulent 

fluxes. 5 Wm-2 accuracy in the turbulent fluxes is less likely to be met when wind speeds exceed 

15 ms-1 and highly unlikely above 20 ms-1. This level of accuracy is also difficult to achieve in 

conditions where the 10-m air-sea temperature difference exceeds ±3oC. What happens in a 

50-kt gale in the Labrador sea in January is anybody’s guess. However, very strong wind and/or 

extremely large sea-air temperature or humidity differences are sufficiently rare that long term 

averages of the fluxes should fall within, or close to, the desired target. This topic is discussed 

further in Appendix B3. 
QR- 3

QR2. Installation (Location and Exposure) 

On an otherwise uniform and relatively flat ocean, the ship is an obstacle that distorts the 

wind flow and air temperature, and shadows radiometers and rain gages. Thoughtful location of 

sensors on the ship can minimize errors due to ship influence. 

Figure QR1. Examples of ships with good foremast locations for instruments: R/V Ronald H. 

Brown (NOAA) and R/V Southern Surveyor (CSIRO). Locations A, B, etc., are described in 

the text. 

Ideally, sensors should be exposed to the air before it has blown across the decks and 

superstructure. In Figure QR1, position A on a foremast is usually the best place for 

meteorological instruments. However, a tall enough mast may not exist or be unsuitable for 

regular climbing; on smaller ships such a mast may be swamped by seas over the bow. If 

practical and acceptable to the ship (operators, officers, technicians and crew), a guyed lattice 

mast could be specially installed on the foredeck for the instruments at B (see Figures QR1 

and QR2). 

A final option may be a pole above the wheelhouse at C in Figure QR1. This position 

will suffer from flow distortion and some thermal contamination from the foredeck, both of 

which will vary with relative wind direction. On the other hand, it provides better accessibility 

to instruments for maintenance, should this be an important issue. Location of instruments often 
QR- 4

entails trade-offs and matters of judgment. If only one set of instruments is available, a forward 

facing support arm from A, B, or C will provide the best all-round exposure to relative wind. If 

two sets are provided, they should be installed on either side of the ship, at D or E for example, 

to improve exposure. 

Figure QR2. Guyed mast installed on foredeck for good exposure of meteorological instruments.

In principle, radiation instruments need to be mounted so that surrounding objects do not 

cast shadows on them. On the restricted domain of a ship, this requirement is virtually 

impossible to achieve. For the two ships in Figure QR1, location F would serve, but such 

elevated sites are usually unsuitable because of prohibited access in rough weather, and 

proximity to RF antennae. Radiation instruments need careful leveling and regular attention to 

clean the domes. Compromise sites would be at G in the figure. At low viewing angles errors in 

the measurement are less important. Long- and shortwave instruments would normally be 

mounted as a pair on a rigid plate at the top of a pole attached, for example, to the rail around the 

wheelhouse roof. A gimbal mount would benefit the shortwave radiometer, but would need to 

be very carefully designed. If two sets of instruments are available, they should be widely 

separated to avoid coincident shadows. 

For radiation instruments, but not those for wind speed or air temperature, a position well 

aft such as H may be used as a last resort, recognizing that the longwave signal may be 

significantly in error whenever the exhaust plume is above the pyrgeometer. More frequent 

washing of the domes may also be necessary to remove soot. 

Barometers can be located within the bridge, a science lab, or can be mounted on a mast 

with other instruments. Whether inside or outside, it is important to ensure that the port for the 

barometer is located so as to avoid dynamic pressure fluctuations due to the wind, or if inside, 

free from a space that may be pressurised by, for example, air conditioning. 

Rain gages are susceptible to wind effects that cause optical gauges to overestimate and 

funnel gauges to underestimate. The wind is deflected upwards when it encounters the ship, and 
QR- 5

carries raindrops away from the funnel instead of falling in. The loss can be corrected to some 

extent providing the relative wind speed at or near the sensor is known. Thus, a location on the 

same mast as the anemometer is best. 

If sea temperature is to be measured with a floating sensor, it should be trailed from the 

end of a light boom (or pole) as far forward and as far out as practicable, to avoid the bow wave. 

Nearly all meteorological sensors, and particularly those for radiation, are susceptible to 

interference from the many sources of RF transmission aboard a ship. This should be borne in 

mind when locating the instruments, as noise in the signals can often be attributed to 

RF interference. 

QR3. Documentation and Event Logging 

The importance of documenting the location and serial numbers of all instruments 

deployed, and the date and time of any changes, cannot be overstated. Ideally, this should be an 

electronic document accompanied by digital photographs of the installation. The most useful 

photos are taken at sufficient distance to show the sensor in its environment, and possible 

obstacles to wind flow around it. A photograph from the wharf can also be helpful. This is also 

an opportunity to record the height of all instruments above the water, and above some ship 

datum (e.g., the deck below). Knowledge of instrument height is crucial for calculating bulk 

fluxes. 

In addition, significant events that may affect the quality of the data should be recorded 

with the time in a daily log (e.g., cleaning radiometer domes, power failure, bird on 

anemometer). Information about the ship’s speed, heading, position, etc., can be extracted from 

the link to the ship’s network, but such eyewitness accounts are invaluable, particularly when 

trying to explain anomalous data. 

QR4. Monitoring and Maintenance 

The computer recording software should permit real-time display, in physical units, of the 

variables being logged. This may be as a list, a graphic display of time series, or both. This 

display should be monitored as part of a daily routine, and also from time to time as convenient. 

If paired sensors are installed, their values can be compared – if different by more than some 

amount (e.g., twice the specified instrumental accuracy), the reason should be sought. Whether a 

single or pair of sensors is installed, it is also useful to compare them daily with a handheld 

standard (e.g., an Assman psychrometer or portable barometer). 

It is worth checking that the ship’s navigation data are being recorded properly. 

A graphic display will also reveal anomalies in the measurements, such as spikes, noise, 

unreasonable values (e.g., air temperature (T) 75oC, relative humidity (RH) 150%!). Such 

information should be logged and, as time permits, investigated. The first approach is usually to 

replace the sensor with a spare. If that does not solve the problem, replace the original and 

troubleshoot in the usual way. 

The marine environment is hard on instruments mostly designed for use over land. 

Regular maintenance includes washing salt from radiometer domes, replacing the Gortex filter 

around humidity sensors, checking that the aspirator fan on the temperature/humidity instrument 

is working, that the rain gage funnel is not blocked (e.g., bird droppings). An expensive factory 

calibration intended to be valid for a year is useless if the sensor is crusted with guano. 
QR- 6

Upward facing cable ties or metal spikes have been used to discourage birds from roosting on 

sensors, but with limited success. 

QR5. Recording and Securing the Data 

The computer date and time will be set to GMT (UTC) and the event log should also be 

referenced to GMT. 

The recorded data will normally consist of the raw time series at the logger sampling 

speed, and a conversion to physical units via the instrument calibrations and transfer functions. 

This processing will often involve some computation involving several signals and sensors; 

for example, combining the three pyrgeometer signals for downward longwave radiation; or 

obtaining true wind from the measured relative wind and the ship’s speed, course, and heading. 

In many cases (SAMOS, for example), the meteorological data collected automatically 

by computer on the ship will be destined for use by scientists engaged in climate research 

elsewhere - modelers and analysts for example. The role of the shipboard operator is to maintain 

the quality of the data by monitoring the performance of the sensors, and making sure that all 

detail (e.g., time of radiometer dome cleaning, or a faulty instrument) is noted in the daily log. 

This individual should be provided with training to enable recovery of the system in the event of 

a computer crash; since extended time series are most valuable. 

The capacity of the computer hard disc will be sufficient to hold several weeks’ data, 

which should be backed up regularly according to normal computing practice. Every few days 

both raw and derived data should be written onto a CD or DVD, together with a copy of the 

metadata. If possible, an electronic copy should be made of the event log (e.g., in Word) and 

saved with the data and metadata. 

Each vessel operator should establish a protocol for long-term archival of the 

meteorological observations with a national or international archive center. Data residing on a 

disk or tape in someone’s desk drawer will not aid climate science, and the media will degrade 

with time. Archive centers are equipped, in most cases, to ensure the long-term viability of the 

data, event logs, and metadata on digital media. On a regular schedule, (at the end of each 

cruise, quarterly, etc.) all data and metadata should be forwarded to a national or international 

archive center.
1

FLUX MEASUREMENTS FROM SHIPS AND BUOYS 

1. The Air-Sea Fluxes

1.1. Introduction 

The dynamic coupling between the ocean and the atmosphere depends on the transfer 

across the interface of energy, momentum and freshwater. It is the fluxes of these quantities that 

we seek to determine experimentally from global networks of ships and moorings, to provide 

constraints on coupled models of the climate system, and for validation of similar observations 

from satellites. Producing these flux estimates will require measurements of traditional nearsurface meteorological variables (wind speed, air temperature, humidity, water temperature) with 

more than sufficient accuracy to make them useful for numerous other applications. 

The basic set of fluxes we consider are those of sensible and latent heat, of momentum 

(or wind stress), the shortwave and longwave radiative fluxes, and the freshwater flux. 

1.2. Turbulent fluxes 

Air-sea exchange of sensible heat (Hs), latent heat (Hl), and momentum (τ) occur 

predominantly by turbulent transport processes in the atmosphere. They are described by 

turbulence theory and may be obtained directly by measuring the fluctuating quantities and 

applying the covariance (or eddy-correlation) technique. This is a research tool, as yet unsuitable 

for routine use, which will not be discussed in this manual; rather, we will consider the bulk flux 

parameterization of the turbulent fluxes. When the situation changes, the manual will be updated 

accordingly. 

1.3. Radiative fluxes

Shortwave fluxes are in the wavelength band 0.3 to 3 μm. Downwelling shortwave 

radiation at the surface (Rs↓) has a component due to the direct solar beam, and a diffuse 

component scattered from atmospheric constituents and reflected from clouds. Upwelling 

shortwave radiation (Rs↑) comes from reflection at the surface and the re-emergence of radiation 

backscattered from the upper ocean. In clear water, shortwave radiation penetrates to a depth of 

several tens of meters, influencing the thermal structure of the ocean surface layer. The ratio of 

downwelling to upwelling shortwave is the surface albedo (α), which depends on solar elevation, 

cloudiness and wavelength. For use in bulk algorithms, a single value of 0.058 for broadband

albedo, based on the ratio of daily averaged upwelling and downwelling shortwave flux, has 

been found to be satisfactory. 

Longwave fluxes range from 3 to around 50 μm wavelength. Downwelling longwave 

radiation (Rl↓) originates from the emission by atmospheric gases (mainly water vapour, carbon 

dioxide and ozone), aerosols and cloud droplets. It is thus linked quite closely to the particular 

regional climate conditions. Upwelling longwave from the sea surface Rl↑ depends on the ocean 

skin temperature and surface emissivity (ε), with a small contribution due to reflection of the 

downwelling component. Emissivity is wavelength-dependant, and a spectrally integrated value 

of 0.97 is commonly used. Longwave absorption and emission take place in about the top 

0.5 mm of water.
2

1.4. Freshwater flux

The vertical density structure of the ocean surface layer determines its stability and 

mixing, which in turn has consequences for the transport of heat to and from the interface. 

Density is a function of both temperature and salinity, so that the freshwater exchange through 

evaporation (E) and precipitation (P) is an important component of the coupled system. 

1.5. Net surface fluxes 

The fluxes described above are illustrated in Figure 1.1. They are measured individually, 

and required separately to study various atmospheric processes. The net surface heat and 

freshwater fluxes are important quantities that prescribe the evolution of the coupled ocean/ 

atmosphere system for use in climate models. The net heat flux into the ocean surface is given 

by 

Hnet = -(Hs+Hl) + (Rs↓- Rs↑) + (Rl↓- Rl↑) - Hrain , (1.1) 

where the second and third terms on the right-hand side are the net shortwave and longwave 

radiative fluxes, and the fourth term is the small heat contribution from rainfall (see section 2.8). 

Hnet is the quantity for which the WOCE and TOGA accuracy goals of ±10 Wm-2 were 

proposed, on monthly to seasonal time scales. 

The net freshwater exchange (P-E) is usually expressed as mm of water in unit time. 

Note that E is Hl divided by the latent heat of vaporisation (see Appendix A). 

2. Basic Variables Input to Bulk Flux Algorithms

2.1. Introduction

Bulk air-sea flux algorithms are generally of the form Fx = Cxu(δs – δz), where Fx is the 

vertical flux of entity x (heat, moisture, momentum), u the wind speed, and δ the value of the 

corresponding meteorological variable (temperature, humidity, wind speed). Subscripts s and z

refer to the value at the sea surface and at height z, so the quantity in parentheses is a sea-air 

difference of the particular variable, which depends upon the height of measurement. It is 

therefore common practice to refer all measurements to a “standard height” (usually 10 m above 

the sea surface), using knowledge of the vertical profile of the particular variable. Cx is an 

empirical transfer coefficient for entity x, determined from direct measurement (e.g., by the 

covariance method) and specified at the standard height. More detailed information on this 

subject is given in section 11. 

Given a reliable value or functional form for Cx, the observational accuracy of Fx depends 

on the other quantities on the right hand side of the equation. In modern algorithms these will 

not necessarily be the values as measured; as discussed below, they may have been corrected for 

known error, reduced to standard height, or combined with other physical quantities. The 

required data set will consist of the state variables (temperature, humidity, and pressure), 

wind speed and direction, the radiative fluxes, and sea temperature at some specified depth. 

The target net heat flux accuracy of ±10 Wm-2 implies certain accuracies for the 

measured variables, as shown in Table 1, and discussed in Appendix B3. The TOGA-COARE 

process study demonstrated that, even for the research-quality instruments installed on survey 

vessels, these accuracies are only achievable with very careful attention to instrument location 

and performance, calibration and post-cruise scrutiny (details and references can be found in 

WCRP 2000). We consider some issues with the measurement of each variable: 
3

Figure 1.1. Schematic showing the net surface energy balance at the sea-air interface. Hs and Hl

are the turbulent sensible and latent heat flux, Rs and Rl the shortwave and longwave radiative 

fluxes (upward or downward according to the arrows), Hrain the sensible heat contributed by rain, 

P, precipitation and E, evaporation. The gray bar separates heat and freshwater components. 

2.2. Air temperature 

The most usual causes of error in air temperature measurement are sources of anomalous 

heating; the sun and the ship. The temperature sensor is often installed within an enclosure that 

shades it from the sun but which relies on natural ventilation, i.e., through slots in the sides of the 

enclosure, as shown in Figure 2.1 (left). These may be effective in overcast conditions or strong 

winds; but in light winds and strong sun, the temperature in such a simple housing has been 

shown to rise several degrees above the true air temperature. To achieve the accuracy cited in 

Table 1, the sensor element must be within a specially designed, shielded and ventilated 

enclosure such as the one illustrated in Figure 2.1 (right). Even such an arrangement is 

ineffective if the system is poorly located. The ship itself is a massive source of heat, and 
4

almost any location aft of the bow will measure air that has passed over some area of warm steel. 

Usually, the best location is high on a foremast (e.g., A in Figure QR1), if one exists. 

Experiments that rely on continuous and accurate measurement of air temperature (and other 

meteorological quantities) will often duplicate instrument packages on port and starboard, taking 

data from the sensors most favourably exposed to the wind. Even so, the wind will sometimes 

be directly over the stern of the ship and the data will have to be discarded. Thus, relative wind 

direction is a critical part of the data record. 

Figure 2.1. Temperature/humidity screens; left with natural ventilation; right with double 

screening and forced ventilation. 

2.3. Humidity 

Atmospheric humidity is variously specified by the partial pressure of water vapour 

(e, mbar or equivalently hPa), vapour density (ρv, gm-3), specific humidity (q, g/g of moist air), 

mixing ratio (rv, g/g) or relative humidity (RH=100 e/es) where es is the saturation vapour 

pressure at air temperature Ta. At a particular ambient humidity, reducing air temperature 

reaches the point where e equals es. This is known as the dew-point Td. Formulae to convert 
5

between these various definitions of humidity are given in Appendix A, as are empirical 

equations for es as a function of Ta. 

Humidity sensors in common use are described in section 3.3. Depending on the 

particular measuring principle the output may be any one of the above definitions of humidity. 

Some sensors are more suited to use at sea than others, and most need periodic maintenance to 

remove salt deposited on the sensor or the filter provided to protect some sensors. Some systems 

combine air temperature and humidity sensors in the same package, so they are subject to the 

same conditions of ventilation and screening from solar heating. Conversion between some 

forms of humidity, for example from RH, requires the temperature of the air surrounding the 

humidity sensor. Since water vapour is a conservative quantity, the corresponding error in the 

water vapour measurement is less severe than an error in temperature when the latter is obtained 

from the collocated sensor. 

2.4. Atmospheric pressure 

Pressure is one of the state variables which define the thermodynamic properties of the 

atmosphere. It varies with elevation above sea level and slowly with synoptic weather changes. 

The World Meteorological Organization (WMO) target accuracy for pressure measurement is 

±0.1 mb. In boundary layer and climate studies, pressure most commonly appears in the 

calculation of dry and moist air density (needed for air-sea flux calculation), and in humidity 

conversions; it also appears in the psychrometer equation (see Appendix A). Under “normal” 

synoptic conditions (i.e., no hurricanes or severe storms), pressure at sea level lies between about 

990 and 1030 mb, with a diurnal variation (the atmospheric tide) of around ±3 mb in the tropics, 

less at higher latitudes. Relative to “standard” sea level pressure of 1013.25 mb, the above range 

typically represents a ±2% difference in air density or specific humidity. Pressure near the 

surface varies with height by roughly 0.1 mb per metre, so overall it’s seldom the most severe 

source of error in flux calculation, providing the barometer is installed in such a way as to avoid 

the effects of dynamic pressure. With increasing demands for accuracy in climate applications, it 

is wise to include the measurement of pressure and to document the actual location of the 

barometer. 

2.5. Wind speed and direction 

Accurate wind data are important because, as shown above, the fluxes calculated using a 

bulk algorithm are directly proportional to the wind speed. Thus, any error in wind speed will 

carry through to the latent and sensible heat fluxes. For momentum flux (or wind stress), the 

difference term (δs – δz) represents the wind speed relative to the surface, so the flux is 

proportional to u

2

. In fact, it increases rather faster than the square of the wind speed, because 

the exchange coefficient for momentum also increases with wind speed. Wind stress is also an 

important factor in determining the atmospheric stability, again affecting both scalar and 

momentum fluxes. The need for care in determining true ambient wind cannot be emphasized 

too strongly. As indicated in section 5.2, the location of the wind/direction sensors is critical to 

minimise errors caused by wind flow distortion around the ship. 

Wind speed and direction are taken together, partly because they are often both obtained 

from a single instrument, but also because they are measured relative to the ship and must be 

combined with the ship’s heading, course, and speed to arrive at the true wind vector (the correct 

equations with which to combine these vectors are given in Appendix A). The demands on 
6

accuracy of the ship’s velocity are therefore equivalent to those of the anemometer measurement, 

a fact not always appreciated. It is thus necessary to record the ship’s navigational data stream 

together with the meteorological data, and to document whatever information is available on the 

accuracy of the various components. The appropriate wind speed to use in bulk flux algorithms 

is that relative to the ocean surface; i.e., taking account of the surface current. This introduces 

another source of uncertainty, because the water velocity at the interface itself is very seldom 

measured. There are two ways in which conversion from relative to true wind can take some 

account of the surface velocity: by combining the ship motion in Earth coordinates (e.g., from 

GPS) with currents from the ship’s ADCP, or by using the Döppler-log/gyro which measures the 

ship’s motion through the water. Data reports should indicate which method has been used; both 

incur additional sources of instrumental error, and furthermore the measured currents are at 

considerable depth (of order 10 m). Fortunately, in many cases current is a small fraction of the 

wind speed, so its contribution to the error is also small, but in light winds it can be significant. 

2.6. Sea surface temperature 

Historically, sea surface temperature was understood to be the temperature measured 

from a ship by whatever means available, and reported as SST irrespective of the depth of 

measurement. We now know that temperature in the ocean surface layer can vary with depth by 

an amount that is significant in the context of accurate bulk flux determination. It is the 

temperature of the sea-air interface itself that physically determines the magnitude of the 

turbulent heat fluxes and also the outward flux of longwave radiation. At the same time, these 

fluxes produce a cooling at the interface, the so-called “cool skin” of order 1 mm thick and 

typically a few tenths of °C. 

In moderate to strong winds the water below the skin will be well mixed, and its “bulk” 

temperature will vary little in the vertical. During the day, however, penetration and absorption 

of solar radiation can produce a diurnal warm layer below the cool skin. Under clear skies and 

with light winds, as found in tropical oceans, this layer may be a few °C higher than in the bulk 

below. “Sea surface temperature” may thus vary with depth, as shown in Figure 2.2, and for the 

purposes of flux calculation, the temperature value should always be accompanied by the depth 

at which it was measured (e.g., SST(d) = 18.3o(4.5 m)). As indicated in section 3.6, this depth can 

be ambiguous. The characteristics of the ocean surface mixed layer are discussed in Price et al. 

(1986), and the physics of the cool skin and diurnal warm layer is given in Fairall et al. (1996a). 

Traditional bulk transfer coefficients have usually been determined using a bulk sea 

temperature. However, newer algorithms use transfer coefficients determined with respect to the 

interface value. The true interface temperature cannot be measured with present technology, but 

the measurement of an infrared radiometer (at a few μm depth) comes close; and is sometimes 

available from shipboard or satellite sensors. Also, models of both the cool skin and diurnal 

warm layer, which enable skin temperature to be estimated from a bulk measurement at known 

depth, are becoming more reliable. 

The TOGA program specified an accuracy of ±0.3oC for SST over a 2 x 2 degree region 

as a target for validation of space-borne radiometers (WCRP 1985). An error of 0.3oC changes 

sensible and latent heat fluxes calculated with a bulk flux algorithm by 2 Wm-2 and 10 Wm-2

respectively, for typical climatic conditions in the tropics. The past decade has seen the 

development of several high-resolution infrared radiometers for shipboard deployment that 

achieve 0.1oC accuracy. 
7

4 Dec 1992

Local Time

0

1

2

3

4

5

6

7

8

9

10

29.4 29.8 30.2 30.6 31 31.4 31.8 32.2

Temperature °C

Depth m

1400

1431

1641

1711

1819

1950

Figure 2.2. Profiles of sea water temperature measured during the TOGA COARE program 

with a near-surface undulating towed sensor, known as Seasoar. The different symbols denote 

the (local) time of the profile. The strong temperature increase near the surface is caused by 

solar heating. Later in the afternoon, the surface mixing is eroding the warm layer. 

2.7. Radiation 

Besides direct application in equation (1.1), the net radiative fluxes (Rs↓- Rs↑) and 

(Rl↓- Rl↑) are also used in bulk algorithms for models of the oceanic mixed layer temperature 

profile and to estimate SST. For these reasons they are increasingly being measured routinely 

aboard ships and moorings. 

On a clear day at low and middle latitudes, Rs↓ is the dominant component of surface 

heating, peaking in the vicinity of 1000 Wm-2. Thus, any deterioration in performance of the 

measuring instrument can lead to significant error in determining the net flux, and the thermal 

and density structure of the ocean mixed layer. Studies of cloud-radiation interaction, currently 

in their infancy, will need to distinguish between the direct and diffuse components of Rs↓. 

Over tropical oceans Rl↓ is determined largely by very high humidity in the boundary 

layer, with little diurnal variability or effect from clouds (typically Rl↓ ~350-400 Wm-2); at higher 

latitudes and under clear skies, Rl↓ is significantly lower. The warm water of the tropics can emit 

450 Wm-2 of thermal energy, cooler waters of higher latitudes correspondingly less. (Rl↓- Rl↑) is 

therefore the difference of two fairly large quantities, and typically of order 50 Wm-2. 
8

Accurate measurement of both Rs↓ and Rl↓ requires an unobstructed hemispheric view of 

the sky, which is virtually impossible to achieve onboard ship while retaining access to the 

instruments for maintenance. In the case of Rs↓, shadowing by the highest parts of the ship, 

masts, funnel, antennae and the like, is the main difficulty. Instrumental problems have plagued 

the measurement of Rl↓ for some years, partly associated with the fact that sources of thermal 

radiant energy are ubiquitous. These issues are dealt with in detail in sections 3.7, 6.6, and 

Appendix C. 

2.8. Precipitation 

Rainfall, particularly during convective storms, is perhaps the “patchiest” of all 

meteorological variables. Single point measurements from ships and buoys are generally less 

relevant for climate models than area-averaged values or spatial characteristics. Nevertheless, 

accurate point measurements over the ocean are invaluable for validating satellites and radar 

which do obtain spatial rainfall patterns, but must be calibrated against ground truth. Currently 

such validation is mostly obtained from rain-gauges located on islands and atolls, which have 

been found to distort the rainfall field.

263 264 265 266 267 268 269

Year day 2001

0

10

20

30

40

50

60

70

Relative wind m/s

-40

0

40

80

120

160

200

240

Rainfall mm

ORG#2

Stbd2

Port3

Relative wind

ORG#2corr

Stbd.2corr

Port3corr

EPIC2001

Figure 2.3. Cumulative rainfall measured by optical and funnel rain gages on a ship, 

before and after wind correction. The ORGs overestimate slightly when the raindrops are blown 

through the optical path at an angle to the vertical (dark and light blue traces). Siphon gauges 

underestimate when strong winds are distorted up over the ship and deflect raindrops away from 

the funnel (dark and light pairs of red and green traces). The black curve is the relative wind 

speed. Rainfall events started around days 264.4, 265.7, 266.6, and 267.3. 
9

The main problem in measuring rainfall from ships (and to a lesser extent from buoys) 

using the traditional funnel gauge is error due to wind flow distortion that can lead to underestimation depending on the location of the gauge. The problem has been studied, using an array 

of gauges distributed around the ship, and correction schemes devised which can improve the 

accuracy of rain measurement, to within 10-15%, as shown in Figure 2.3. Operationally, it is 

important to ensure the rain gage is well exposed and near the location where relative wind speed 

and direction are recorded. A well-positioned gauge adjacent to a wind instrument is better than 

several gauges scattered around the ship. The range of rainrates observed, from drizzle 

registering less than 1 mmhr-1 to tropical storms producing 200 mmh-1 (often accompanied by 

strong winds) also presents challenges for rain-gauge design (see section 3.8). 

As shown in equation (1.1), the net air-sea heat flux includes a component of sensible 

heat from rainfall. Heat exchange with the ocean can be calculated from the rain rate and the 

temperature of raindrops, usually assumed to be close to the wet-bulb temperature at sea level. 

In the case of tropical deep convection it has been found that raindrops are about 0.2oC cooler 

than this temperature. Over extended periods, the contribution is small, but during heavy storms 

it can be several hundred Wm-2 and a significant component of a daily average net flux 

(Figure D2). Note that the momentum flux imparted to the ocean by raindrops may also be nonnegligible. 

3. Bulk-Flux Meteorological Sensors 

3.1. Introduction 

In this section we consider the types of sensors in common use at sea for measuring 

atmospheric temperature, humidity, wind speed, pressure, and sea temperature. The sensor is the 

part of a measuring instrument that is directly exposed to the entity being measured, and whose 

characteristics respond in a predictable way to changes in that entity (e.g., resistance of a 

platinum wire to temperature). Other important components of the measuring system are the 

sensor housing and any associated electronics or recording equipment. These sensors have been 

developed mainly for observations over land, and their use on ships and buoys has required some 

adaptation. At the very least they need protection from the highly corrosive environment of salt 

air and spray, which usually means that the housing has to be specially designed for marine 

applications. It may also be important to take account of platform motion, and systems on longterm moorings may need modification for low power consumption. Sensors evolve continuously 

in the research and commercial environment; for use in either testing new physical principles of 

measurement or to quantify some newly significant entity (e.g., a trace gas transferred across the 

air-sea interface), and with the advance of measurement technology.

There are often several choices of sensor for each variable, the most suitable for a 

particular application depending on several factors, including the required accuracy and 

resolution, frequency response, and overall convenience of operation. Atmospheric variables 

fluctuate on time scales from below 0.1 seconds to several hours. Rapid sampling, typically at 

20 Hz or more, is required to obtain the turbulent fluctuations of wind, temperature and humidity 

for eddy correlation or inertial dissipation determination of the fluxes. These methods are not 

considered in this handbook, in which we focus on the observations required to calculate bulk

fluxes. A sensor responds to a step change exponentially, the time taken to reach 1-1/e ( ≈ 0.632) 
10

of the final value being its time response. By virtue of their mass, most bulk sensors have a time 

response of many seconds, and to avoid aliasing are sampled at about once per second. The 

resulting data are then time-averaged over suitable periods from a few minutes to one hour to 

reduce unsteadiness. We note, however, that some fast-response instruments (e.g., sonic 

anemometers) have become sufficiently stable that, if deployed for other purposes, they can also 

provide reliable long time averages. 

3.2. Temperature 

Sensors commonly used to measure atmospheric temperature are thermocouples, 

platinum resistance thermometers (PRTs), thermistors, and mercury-in-glass thermometers. The 

latter are still used operationally in handheld instruments such as Assman psychrometers and the 

sling thermometers used by observers who file ships’ weather reports, as well as in some fixed 

thermometer screens. Accuracy depends on the quality of the thermometer and the care with 

which the observer reads it. High-quality Assman thermometers can be read to 0.1°C. Being 

free from instrumental errors, their value in the present context is to verify data from the 

electronic measuring systems installed on the ship by taking careful “spot” readings at a location 

free from ship influence (Figures 3.1 and 3.4). 

The other three types of sensors lend themselves to automatic, continuous data logging. 

Thermocouple systems have the disadvantage of low output voltage, and for absolute 

measurement require a reference “cold” junction. Good-quality PRTs are very stable, and with 

careful calibration, accuracy of about 0.01°C can be achieved, although their typical resistance of 

100 ohms requires a high-resolution resistance bridge. PRTs are the temperature sensors most 

commonly used in high-quality commercial instruments. Both thermocouples and PRTs can be 

easily configured for differential measurement, which can improve the measurement accuracy of 

the wet bulb depression when they are used in a psychrometer (see next section). 

Thermistors are semi-conductor devices with much higher resistance values (typically 

3000 ohms) than PRTs, making the measurement of resistance changes easier. Unlike the linear 

response of PRTs, the larger signal comes at the expense of nonlinearity. Formerly, they were 

prone to uncertainties of stability and calibration, but guaranteed interchangeability of ±0.1°C is 

now available from some manufacturers, and micro-processor technology enables their 

logarithmic response to be linearized. 

Radiometric air temperature sensors are just starting to be used (Minnett et al. 2005) and 

are likely to become more common in the future. Being non-invasive, they have some 

advantages over traditional methods but validation against high-quality in situ air temperature 

measurements have yet to take place. 

3.3. Humidity

The traditional instrument for atmospheric humidity measurement is the psychrometer, 

consisting of a pair of thermometers, one being covered with a moist wick. Air drawn over the 

thermometers evaporates the moisture, cooling the wick until the evaporation rate is in 

equilibrium with the atmospheric water vapour. This wet bulb depression is understood from 

thermodynamic theory, and described by the psychrometer equation given in Appendix A. 

Handheld sling or Assman psychrometers use mercury-in-glass thermometers, the former 

achieving ventilation by rapid movement through the air, while the Assman is equipped with a 

spring-wound or electrically driven fan which draws air over the thermometer bulbs. The basic 
11

accuracy of 0.1oC for both wet and dry bulb thermometers leads to an uncertainty of 0.20 g kg-1

in specific humidity or bout 1% in RH. To achieve this, the wick must be moistened (but not 

flooded) with distilled water, washed from time to time to remove salt, and changed after a 

period of use. 

Figure 3.1. Measuring wet and dry bulb temperatures with an Assman ventilated psychrometer. 

The use of the forward chock as a sampling location ensures good exposure and some shielding 

from the sun. 

For automatic data logging, psychrometers can be constructed using either PRTs or 

thermocouples as the sensing elements. Accurate measurement requires adequate airflow over 

the thermometers to ensure full wet bulb depression, and that they be well shielded from solar 

radiation. This is best achieved by using a double heat-reflecting shield, as illustrated in 

Figure 2.1 on the right, with the air drawn over it and through the space between the shields at a 

rate of at least 4 ms-1. With PRTs in a differential bridge, temperature resolution of ±0.01°C is 

possible, and with care, specific humidity accuracy of 0.05 g kg-1. 

Most ships are provided with a pair of wet and dry thermometers in a naturally ventilated 

screen, which are read by the bridge officers for the bridge log, and in the case of ships 

participating in the Voluntary Observing Ships (VOS) program, for the hourly weather 

observations transmitted to shore. The example shown in Figure 3.2 is in a well-exposed 

location, but the screen design is poor leading to inadequate ventilation of the wet and dry-bulb 
12

thermometers. In sunny conditions the temperature inside the box could be several degrees above 

ambient and the air-flow through the box is insufficient to achieve the full web-bulb depression 

and thus overestimate the humidity. Observations from this unit during mostly overcast 

Figure 3.2. Example of the installation of wet and dry bulb thermometers. 
13

Figure 3.3. A more usual type of screen to house the wet and dry bulb thermometers read hourly 

by the bridge officers. It is also well exposed, and all four sides are louvered to allow free 

airflow to the thermometers. 

conditions are given in Figure 3.4. The more usual type of screen, with louvres all round to 

improve air flow to the thermometers is shown in Figure 3.3. Nevertheless, under clear sky with 

light winds even this screen could become much warmer inside than the ambient. 

Thin-film polymers which absorb or desorb water as the relative humidity changes are 

the most common humidity sensors currently used on research vessels at sea. Early versions of 

these sensors often failed at very high humidity, but recent developments have largely overcome 

this problem and improved their accuracy and stability of calibration. The polymer usually 

forms the dielectric of a capacitance in a circuit that provides an electrical output proportional to 

relative humidity. Conversion to mixing ratio, specific or absolute humidity requires the 

temperature of the air surrounding the dielectric, often using a collocated PRT. The best quoted 

accuracy is ±2% RH (or ±0.3 g kg-1 at 20°C and 70% RH). For accurate measurement these 

temperature/RH sensors are ventilated and screened as for the psychrometer. There is also a 

Gortex filter around the sensing element that must be changed or washed to remove salt. 
14

Figure 3.4. Comparison between a high quality T/RH sensor used by ESRL (formerly ETL) 

and: (green crosses) an Assman psychrometer; (blue circles) the ship’s IMET system; 

(red diamonds) the wet and dry bulb thermometers read by the bridge officers hourly for their 

weather reports. 
15

The dew-point hygrometer incorporates a mirror that is maintained, by optical and 

electronic feedback, at the temperature, Td , where moisture or ice just condense on its surface. 

Using the relationships in Appendix A, this dew point can be converted to any of the other units. 

It is an absolute instrument, not well suited for operational use at sea, but often carried as a 

secondary standard to calibrate other sensors. Best-quoted accuracy for a dew-point instrument 

is ±0.2°C, which converts into an uncertainty in RH of ±1%. 

Humidiometers that measure the absorption of ultraviolet (Lyman-α) or infrared radiation 

by water vapour respond to rapid changes in humidity and are used for eddy-correlation flux 

measurement. Currently they are not sufficiently stable to be suitable for routine measurement 

of long time series. 

3.4. Atmospheric pressure

Ships monitor atmospheric pressure routinely to include in their daily weather reports, 

transmitted on the Global Telecommunication System (GTS) for use by national weather 

forecasting institutions. The barometer is normally located on the bridge. The proper 

installation and operation of mercury barometers at sea has proven very difficult, and they are 

now rarely used aboard ships. Modern aneroid barometers with a digital readout have a 

resolution of 0.1 mb and are relatively stable, but require checking against standard instruments 

from time to time. However, for applications requiring continuous time series of pressure to be 

recorded, solid state sensors with high resolution and long-term stability of 0.1 mb are now 

available. Whether they are located inside the wheelhouse or outside, it is important to ensure 

that the port for the barometer is located so as to avoid dynamic pressure fluctuations due to the 

wind, or if inside, free from a space that may be pressurised by, for example, the ship’s air 

conditioning. Special inlet ports designed to overcome dynamic pressure fluctuations from the 

wind are available, for connection to the barometer via a plastic tube. 

3.5. Wind speed and direction 

For average wind speed and/or direction over some time period, cup (or propeller) 

anemometers and wind vanes are usually the most convenient. Some operational designs will 

withstand continuous exposure to stormy conditions, but there are also more sensitive 

instruments intended for research work. Apart from mechanical strength, the difference is 

reflected in their starting speed and distance constant (response time converted to run of wind). 

A sensitive cup anemometer will start from rest in a breeze of 0.3 ms-1 and have a distance 

constant less than 1 metre. 

For best accuracy (typically 1%), cups must be calibrated individually, although 

calibration in the steady horizontal flow of a wind tunnel can be misleading when the instrument 

is exposed to the natural fluctuating wind. In such a situation, cup anemometers usually 

overestimate for two reasons; the rotor responds more quickly to an increasing wind than to the 

reverse; and in a wind gust with a vertical component, shielding by the upwind cup is reduced. 

Numerous studies have been made of these effects. Propellers have poor response to off-axis 

wind direction, but this is normally overcome by mounting them on the front of a wind vane. 

The one instrument thus measures both wind speed and direction. Otherwise, a cup 

anemometer/wind vane pair is often mounted at opposite ends of a horizontal bar. Ideally, the 

wind direction sensor should have a complete 0 to 360 ̊ response. However, some instruments 

use a potentiometer that has a finite deadband (≤10°), in which case care must be taken to ensure 
16

that readings in this deadband are infrequent and do not corrupt the average reading. The 

orientation of the 0/360 degree transition relative to the centreline of the ship is an 

important item of metadata, to ensure correct calculation of true or ocean-relative winds. 

As noted above, sonic anemometers, which are commonly used for fast-response 

applications in the research environment, have become sufficiently stable to enable observation 

of long time series. They have many advantages: they have no moving parts; less distortion to 

the wind flow than cups or propellers; they obtain the total wind vector; and some have an air 

temperature output. Sonic anemometers are likely to become more widely used at sea as the 

more robust, and less costly, models appearing on the market prove their suitability and gain 

acceptance. 

3.6. Sea temperature 

The so-called “bucket” sea temperature is aptly named. An open cylindrical container, 

usually insulated and equipped with a mercury-in-glass thermometer, is attached to a line and 

cast from the after deck to collect a sample of water. Allowing for some change during the time 

it takes to read the thermometer, this procedure produces spot values of a well-mixed sample of 

surface water every hour or so, probably to an accuracy of 0.5oC depending on atmospheric 

conditions. As described in section 2.6, this frequency and accuracy are no longer adequate for 

the calculation of research quality air-sea heat fluxes; furthermore, disturbance by the ship makes 

it uncertain what depth the sample represents. Some of the errors in bucket measurements of sea 

temperature are predictable and can be corrected, and routine bucket temperatures from VOS still 

form an important part of the climatic record.

On some research vessels, a thermosalinograph measures the temperature of engine 

cooling water near the intake port. The basic accuracy of the instrument is a few 0.001oC and the 

flow sufficiently large that spurious heating from inside the ship is not significant. The depth of 

the intake is known but it is usually well aft. It has been found that, because of the pattern of 

flow along the hull, the water entering the intake may have originated from some shallower 

depth ahead of the ship. With a well-mixed surface layer, at night for example, the difference 

may be small, but in daytime if there is a significant vertical temperature gradient near the 

surface due to light winds and solar heating, it can be several tenths of a degree. 

A better arrangement is when the thermosalinograph has its own intake port and pump 

near the bow of the ship. There is still some uncertainty about the effective depth of 

measurement, particularly with the ship pitching in heavy seas when there is also the danger of 

the intake breaking the surface. Often the thermosalinograph is turned off in port and in some 

coastal conditions to prevent fouling of the sensors by oil and other contaminants. 

Another class of sensors is attached inside the hull of the ship and these sensors measure 

some sort of average temperature over the surface layer, providing they are located well below 

the water line. 

Some research cruises measure sea temperature close to the surface by trailing a sensor 

(usually a thermistor) mounted at the end of a length of plastic hose, or a rope with an internal 

conductor. One type is known as a “Seasnake”. It is towed from a light boom near the bow of 

the ship and extends as far out as practicable, preferably outside the bow wave. Underway in 

slight seas, the hose will follow the surface at a depth of 5-10 cm, but in heavier seas will often 

become airborne. This can be overcome to some extent using streamlined weights. 
17

Comparisons with ships’ thermosalinographs at night, and when the surface layer is well mixed 

to a considerable depth, indicates that the Seasnake is capable of 0.1oC accuracy. During the day 

it captures nearly all the daytime surface warming, but is below the cool skin regime. In 

persistent stormy conditions it may have to be brought inboard and stowed to prevent its 

destruction. When the ship stops, it normally sinks, even if not weighted, but under these 

conditions the water temperature is contaminated by the ship in any case. 

During the past decade, a number of high-resolution infrared (IR) radiometers have been 

developed for use at sea. This instrument is normally mounted forward on a siderail of the ship, 

high enough to view the sea surface outside the bow wave. Its view is a narrow cone operating 

within spectral bands in the range 8-12 μm, similar to the channels of space-borne IR 

radiometers. The view angle to the undisturbed surface will depend on the geometry of the ship, 

and is usually between 30o and 60o to the vertical. SST is obtained from the measured radiance 

and surface emissivity, which is a function of view angle, and a correction made for reflected sky 

radiation using a second radiometer pointed skyward at the same angle (which is covered during 

rain). Depending on sky conditions and atmospheric water vapour content, this correction can 

vary from near zero to at least 1oC. Some instruments self-calibrate the radiometer sensor using 

internal blackbody targets at different temperatures. The most sophisticated examples of this 

type of instrument claim SST accuracy of 0.1oC. However, combined with estimates of the cool 

skin from recent models, a Seasnake is the more economical option.

3.7. Radiation 

Because of its dominant role in the Earth’s energy budget, much attention has been given 

to the study of solar and terrestrial radiation components, their intensity, spectral characteristics 

and distribution. In the course of this, accurate instruments and methodology have evolved, 

often requiring precise directional pointing, meaning that they can only be operated from a 

completely stable platform. This requirement precludes their routine deployment from ships and 

moorings. The following describes instruments currently suitable for marine studies. 

Downwelling shortwave and longwave radiation are measured with a pyranometer and a 

pyrgeometer, respectively. These instruments are physically similar, both accepting broadband, 

whole-sky radiation through a hemispherical dome with the relevant spectral transmission 

characteristics (Figure 3.5). Solar radiation passing through the glass dome of the pyranometer 

impinges on a flat thermopile with a blackened upper surface. The instrument is so constructed, 

using two concentric domes to overcome convection within the instrument, that the thermopile 

output has a linear response to the radiative intensity. Accuracy of the instrument is usually 

quoted as 2%. The pyrgeometer works by determining its own thermal balance, combining the 

contributions from dome and case temperatures with longwave radiation through the silicon 

dome which is detected with a thermopile. There are thus three output signals to be recorded and 

combined externally using the pyrgeometer formula (Appendix A). An alternative method 

provided by the manufacturer, using an internal compensating circuit to provide just a single 

output signal, is to be avoided since it severely degrades the potential accuracy of the instrument 

from about 3% to worse than 20%. Both radiation instruments are vulnerable to the many 

sources of electromagnetic interference aboard ships, since the domes leave the thermopile 

unscreened. Pyrgeometer domes also suffer from problems of shortwave leakage. 

Ideally both instruments should be in a location with an unobstructed horizon-to-horizon 

view in any direction, but shipboard it is virtually impossible to avoid shadowing of the 
18

instruments while still maintaining accessibility for maintenance. At sea, the domes become 

contaminated with salt and soot and need washing frequently. The shadowing problem means that 

the pyranometer location is usually a compromise. The instruments shown in Figure 3.5 are quite 

well exposed at position G (Figure 6.1) and duplicated for increased reliability. In less favourable 

exposure, the pairs could be separated far enough to avoid being covered simultaneously by the same 

shadow. With their relative locations carefully documented, shadows can usually be diagnosed and 

flagged from the data record. In the case of pyrgeometers, the effect of IR flux contamination by 

objects in the field of view is analysed in Appendix C.

Figure 3.5. Example of duplicated pyranometer and pyrgeometer sensors mounted on a ship at 

position G (see Figure 6.1). 

Platform motion is also a potential source of error when radiation instruments are used at 

sea. For correct measurement, the instruments must be horizontal, but both ships and buoys can 

roll through several degrees or take on a systematic lean caused by wind force or poor trim. The 

severity of the error depends on the inherent stability of the particular platform, but also on 

factors such as cloudiness, latitude, season and time of day. It is less severe for the pyrgeometer, 

since any sea in the field of view would be close to the near-surface air temperature. A possible 

solution is to set the instruments on gimbals but, unless very carefully designed, gimbals 

introduce other problems due to damping and phase variations. The better arrangement would be 

a dynamic system, such as a servo-controlled platform whose stability is achieved by feedback 

from a motion sensor, but so far such an arrangement is not available. 
19

Regular dome cleaning may not be sufficient to overcome erroneous measurement. A 

recent observation of condensation on the inside of a pyranometer dome, despite the provision of 

a desiccant within the instrument, was found to reduce the output by about 100 Wm-2. The 

probability that this phenomenon would be noticed is small because the instruments are usually 

mounted well above eye level. This example prompts the question of whether condensation 

inside the domes of pyrgeometers may be the cause of anomalous signals found with those 

instruments also. Because of the interference filter deposited inside pyrgeometer domes, 

condensation would not be seen but perhaps suspected if found in an adjacent pyranometer. 

Over land or on a fixed platform at sea, diffuse radiation is measured by fitting the 

pyranometer with a “shadowband”, set to shield the sensing element from the direct solar beam 

as it tracks across the sky. Unstable platforms preclude the use of a fixed shadowband, but 

measurements of the diffuse component have been made from shipboard using a rotating 

shadowband which, whatever the relative position of the sun, casts a shadow on a fast-response 

radiation sensor once per revolution. The diffuse signal produced by the shadowband is 

determined from the time series, and is unambiguous under clear skies but under broken cloud 

can be difficult to recognise. The fast-response sensor is usually of poor accuracy and stability, 

and so is continuously referenced to a regular pyranometer during the non-shadow periods. 

The essential steps that must be taken to ensure the required accuracy from radiation 

instruments are careful data acquisition, a well-exposed location, frequent washing of the domes, 

regular replacement of the desiccant, and calibration before and after each deployment. 

3.8. Precipitation 

Traditional rain gages measure the rain falling into a funnel of known area. For 

automatic recording, either a weighing system is used, or a tipping-bucket rain gage in which the 

funnel discharges to a pair of buckets in a “see-saw” arrangement which flips over at every 

0.1 mm of rainfall. Neither of these methods is feasible on the unsteady platform of a ship or 

buoy. The most common rain gage in this case is the siphon gauge in which the funnel 

discharges to a reservoir that fills to its capacity (about 50 mm of rain), when it siphons 

automatically and then starts filling again. An electronic sensor keeps track of the level of water 

in the reservoir. 

Rain gages used at sea must handle rain rates up to around 200 mm hr-1, which would be 

an extreme tropical storm. A heavy rainstorm in mid-latitudes might produce instantaneous rain 

rates of 50 to 100 mm hr-1, but more commonly, rain rates are between 1 and 20 mm hr-1. All 

funnel gauges lose catch in strong winds, when the gauge deflects airflow so raindrops are 

carried past the funnel. This phenomenon is exacerbated at sea by wind flow distortion over the 

entire bulk of the ship. The siphon gauge also misses rain while the instrument is siphoning. A 

rain gage intended to overcome both of these problems has been developed by the 

Oceanographic Institute at Kiel, but is not yet fully proven (Hasse et al. 1998). 

Optical rain gauges (ORGs) measure rain rate by detecting raindrops falling through an 

optical path. One system measures extinction of a light beam by the raindrops; another measures 

the intensity of scintillations caused by raindrops passing through the semi-coherent infrared 

beam from a light-emitting diode. Rainfall amount is obtained by integrating the rain rate. 

ORGs must be calibrated against a funnel gauge in natural or simulated rainfall. Their main 

drawback is that the light path has a particular (and arbitrary) direction relative to the rainfall, 
20

whose vertical component is thus uncertain. Some indication of errors due to this uncertainty 

may be obtained by mounting two ORGs orthogonally (Figure 3.6). 

Figure 3.6. Example deployment of optical rain gauges. A pair of sensors is mounted with their 

axes oriented at 90o to each other. This geometry helps with the wind correction procedure. 

Disdrometers are primarily intended for the measurement of drop size and drop 

distribution in rainfall. The most usual is an acoustic device that converts the sound of the 

impact of raindrops hitting the sensor surface into an electrical signal related to the size of the 

drop. Continuous recording of the size and number of drops provides a time series of rain rate 

and total rainfall by integration. Disdrometers are still regarded as a research tool and are 

seldom used operationally on ships. 

4. Measurement Systems

The sensors described above and their physical shortcomings and potential for poor 

exposure, represent only one aspect of the possible sources of error in the final dataset. Between 

the actual sensor and the raw data record there is a hierarchy of stages, each of which is capable 

of degrading the accuracy of the measurement. Figure 4.1 (left) illustrates a typical measurement 

system, comprising the sensor, which is presumed to have an analogue output signal, analogue 

amplifier/filter unit, multiplexer, analogue to digital converter, data logger, data processing and 

archiving system. 
21

Figure 4.1. Schematic of components of a measurement system. The left part of the diagram 

shows a conventional sensor-to-acquisition component cascade; the right part shows an example 

where a fully digital sensor is used. 

Whereas each component is a discrete unit with known characteristics (calibration, gain, 

etc.), it should in principle be possible to combine these to determine the overall transfer function 

of the system. In reality, individual errors, together with electronic noise and offsets produced 

by connecting the units together, often combine to produce an overall uncertainty that exceeds 

what is acceptable. The most critical part of the signal processing system is what takes place 

between the sensor and its conversion to digital form; i.e., the analogue stages and the 

digitization prior to recording. Providing that the bit resolution of the logger and subsequent 

computer hardware are adequate, the digital record will retain its accuracy through ensuing 

manipulation. 

It is therefore good practice to calibrate the system as a whole from sensor to digital 

record, and repeat this as often as practical to verify that offsets do not change with time. 
22

Ground loops are easily created when many instruments are distributed around a ship, and may 

change when the load on the line power changes. Most research vessels provide “clean” power 

which is isolated from the surges produced by starting large electric motors, and should be used 

when available. These problems are minimized in some instruments where integrated circuit 

technology enables the analogue signal processing, and conversion to digital mode, to be 

packaged together with the sensor (Figure 4.1; right). In this case the instrument output is 

already in digital format (e.g., RS232) and an overall system calibration is implicit. 

If it is not possible to log all information (meteorological sensors, navigation information, 

etc.) on the same system, particular attention must be paid to synchronising the time stamps. 

In any case, the time reading used by the logging should be checked periodically. 

5. Particular Problems on Ships and Buoys 

5.1. Introduction 

Land-based meteorologists seeking an observing site for research studies or operational 

use will try to select an area free of local anomalies such as buildings, trees or surface 

inhomogeneity. They aim to avoid features that may introduce local gradients of the quantities 

(temperature, humidity, wind speed and direction) that would make the recorded data 

unrepresentative of the surrounding region. They normally have a choice of candidate locations, 

and are able to distribute measuring instruments to avoid mutual interference. Furthermore, the 

land does not move around. 

A ship represents a challenging measurement platform. It is a local heat island and a 

bulky obstruction to the ambient wind flow. When the ship is steaming, it disturbs the ocean 

surface layer and when hove to transfers heat to the surrounding water, so that a water 

temperature measurement close to the hull is always uncertain. A ship is a forest of tall 

obstacles, masts, funnels, cranes, and communication antennae which severely restrict the 

exposure of instruments and cast shadows on radiometers. Moored buoys and meteorological 

stations on pilings can have similar problems

5.2. Wind flow distortion

Because of the height dependence of all meteorological variables in the surface layer, and 

the need to reduce all observations to “standard” height (usually 10 m), it is important to know 

the average height of each sensor above the sea surface. Except in very calm weather this is 

difficult to determine at sea, and a measurement made at the wharf must be accepted. However, 

in blowing over the ship the wind is deflected upward, so that the true height from which the air 

measured by the sensor originates is unknown. There are also consequences for the 

measurement of rainfall using siphon gauges as described in section 3.8. Particularly in strong 

winds, and when the ship speed augments the true wind, the upward flow carries raindrops away 

from the funnel instead of falling in. As for the wind measurement itself, flow distortion by the 

ship not only creates uncertainty in the height of origin upwind, but also changes the wind speed 

(and often direction) by accelerating the wind around the obstacle of the ship. Obviously, errors 

in both wind speed and direction measurement affect the true wind calculation, and hence the 

bulk fluxes (Figure 5.1). 
23

Figure 5.1. Illustrating the effects of flow distortion by the ship’s structure on the determination 

of true wind speed. On this day the ship steamed in a star pattern performing a series of CTDs, 

which required the ship to stop periodically (ship speed is the green line) and change course at 

the vertices of the star. Such a course change occurred after the stop at 130.13. The red trace 

shows true wind speed computed from a research anemometer mounted well forward on the 

foremast (at A in Figures QR1 and 6.1). This anemometer has a flow distortion correction 

applied. The blue trace shows true wind speed computed from the ship’s anemometer mounted 

to the port of the foremast, with no flow distortion correction. The course change alters the flow 

distortion pattern and thus the relative wind; the effect on the uncorrected anemometer is 

apparent in the blue trace. 

Correction for wind distortion is seldom attempted due to lack of knowledge at this stage. 

From simple aerodynamic considerations, the deflection will be less severe when wind blows 

directly over the bow than abeam, when the ship represents a larger obstacle. The effect of wind 

distortion can be quantified with wind tunnel, water flume or computational fluid dynamics 

(CFD) model experiments. Studies have already been made of the air flow distortion around a 

number of research vessels. Figure 5.2 is such a CFD simulation of particle trajectories for wind 

flow head on to NOAA’s R/V Ron Brown. The effect caused by the central superstructure is 

particularly dramatic; the upstream wind of about 12 ms-1 at around standard instrument height is 

lifted by two decks and decelerated to around 5 ms-1. 
24

It is necessary to establish the wind field around various generic ship designs and 

determine corrections as a function of wind incidence angle to the ship. Meanwhile, we 

emphasize the importance of documenting the location of anemometers (with photographs), and 

the height of all measuring instruments above some reference level on the ship (e.g., the 

foredeck). The height of the reference level relative to the sea surface is also needed. The 

planned flow distortion studies provide the opportunity for retrospective correction providing 

sensor locations are properly documented. 

Figure 5.2. CFD simulation of wind flow around R/V Ron Brown. Positions of two funnel rain 

gages are shown as black dots on particle trajectories that pass through them, showing the flow 

deflected sideways and upward at each site. Other trajectories originate 100 m upwind on the 

centreline of the ship, at heights of 5, 7, 9, and 15 m above the sea surface. The colours 

represent wind speed and scale from 5 to 15 ms-1. Courtesy Ben Moat. 

5.3. Sea spray and salt contamination 

Even without strong winds and flying spray, instruments at sea become coated with a 

layer of salt which needs cleaning periodically, especially from the domes of radiometers. 

Temperature and humidity sensors must be well shielded from salt contamination, which would 

directly affect the measurement by absorbing and desorbing water vapour. All electrical 

connections and electronic instruments outside must be enclosed in sealed boxes. Signal cables 
25

must be protected against damage, such as that due to chafing on sharp edges. There have been 

cases where a slight, barely noticeable nick in the jacket of a cable allowed salt to corrode the 

copper conductor and cause an intermittent fault which was almost impossible to locate. 

In rough weather, even the largest research vessels pitching into swell will send spray 

surprisingly high over the ship. This spray is sometimes detected in rain-gauge records. 

5.4. Ship and buoy motion 

The irregular motion, rolling and pitching, of ships and buoys produce various effects on 

measuring instruments. There is no such thing as “level” on a ship, unless it is produced by a 

stabilized platform as is done for some radar installations, or naval guns. Instruments for which 

orientation is important, such as radiometers and anemometers, must be set up with reference to 

the axes of the ship. 

Except in extreme conditions, a ship will typically pitch through ±3o, and roll through 

±10o (Class 2 without stabilizers) or ±5o (Class 1 with stabilizers). The effect of this motion on 

radiation measurements is less severe for the diffuse shortwave and downwelling longwave than 

for the direct solar beam. For the latter, providing that the motion is symmetrical about zenith, 

experiments have shown that for rolls of 10o amplitude at a zenith angle of about 50o, the largest 

error was 2%; thus for low latitudes and modest seas, the effect of platform roll may not be too 

serious for radiation measurement. Even at mid-latitudes, more severe seas causing greater roll 

tend to be associated with bad weather and cloudiness, reducing the absolute radiance which is 

also mostly diffuse. However, a persistent tilt of only 5o has been found to produce errors much 

greater than 5%. Such tilt angles may be caused by misalignment of the instrument relative to 

the ship, or by the ship listing due to poor trim or a strong wind abeam. 

The effect of pitching on the meteorological sensors is to move them up and down 

through a gradient of the quantity they are measuring. The vertical motion at the bow of the ship 

(the preferred location for many sensors) can be several metres in rough weather. The frequency 

of pitching motion is irregular, but typically has a period of a few seconds, of the same order as 

the time constant of the sensor. Thus, the measurement is some sort of average over the sensor 

path, assumed to be at an average height close to the one determined at the wharf. The height of 

measurement is thus subject to yet one more uncertainty. Fortunately, in more moderate 

conditions the vertical motion is less and at typical instrument height, the gradient of most 

boundary layer variables is relatively small. 

Wind sensors are affected directly since the ship motion creates a complicated apparent 

wind pattern relative to the instrument. In section 2.5 we discussed determining the mean true 

wind speed from the relative wind speed (i.e., accounting for the heading and vector motion of 

the ship). However, pitch and roll may produce second-order errors in true wind speed when 

non-ideal wind sensors are used. For example, with cup anemometers the motion acts like a 

continuous sequence of vertical wind gusts on the rotor’s imperfect cosine response. Because 

sonic anemometers measure all three instantaneous components of the wind vector, it is possible 

to continuously correct for pitch, roll, and other ship motions. This correction produces the most 

accurate true wind vector measurements but requires an additional system to provide all of the 

motion information. Such systems are now entering the research vessel fleet in an effort to 

improve the accuracy of ADCP measurements of current profiles, but have yet to see general 

application to wind measurements. 
26

5.5. Exhaust contamination 

On a ship it is unavoidable that periodically the stack exhaust plume will cross the 

location of the meteorological sensors. The impact of the exhaust is most severe on temperature, 

humidity, and radiation sensors downwind from the stack. One may also expect contamination 

problems with any optical sensor (e.g., optical rain gages, radiometers). The likelihood of 

exhaust contamination is lessened by locating sensors as far forward of the vessel stack as 

possible (e.g., in Figures QR1 or 6.1, positions A and B for temperature or G for radiation 

sensors). 

The exhaust plume will result in shadowing errors for shortwave radiation sensors. The 

impact on longwave radiation sensors is caused by the exhaust plume, being a heat anomaly, 

resulting in overestimated downward longwave readings. Also, the plume will deposit soot on 

the radiometer domes, so frequent cleaning of the domes is essential if the radiometers are aft of 

the stacks. 

Abrupt changes in temperature and humidity are caused by the exhaust plume. Figure 5.3 

shows the abrupt rise in air temperature, and consequent reduction in relative humidity, that 

occurs when the exhaust plume on the R/V Meteor passes these sensors. In this case, although 

the sensors are forward of the stack, the relative wind is from astern, causing sensor 

contamination. One would expect that similar problems would occur with dew-point or wet-bulb 

temperature sensors. 

For all measurements affected by the stack exhaust, the ship-relative wind can be used to 

eliminate suspect values. In the case of temperature and humidity sensors positioned forward of 

the stack, but above the wheelhouse, upward of 25% of these measurements will be in error 

when the relative wind is from the stern (typically when the ship is manoeuvring at low speed). 

Even sensors on a bow mast can be affected by the exhaust when the relative winds are from the 

stern, but the range of relative wind angles that will result in sensor contamination is reduced as 

sensors are placed farther forward of the stack. 

5.6. Electrical problems

Lightning and buildup of static electrical charge must be considered as a source of 

damage and interference. Use of plastic instrument cases that accumulate rather than bleed off 

static charge is not advised. The ship’s own power system may present challenges in the form of 

uncertain grounding, potential between neutral and ground, and surges and level fluctuations, 

especially during port call or when generators are shifted on- and off line for servicing. 
27

Figure 5.3. One-minute sampled relative wind direction, air and dewpoint temperature, and 

relative humidity from the scientific instrument system on the R/V Meteor. Data are all from the 

portside sensors collected on 11 February 1990. The increases in air and dewpoint temperature 

and the decrease in relative humidity occur when the relative winds are from the stern (~160-

200 ̊), indicating contamination of the sensors by the vessel exhaust. 
28

6. Location of Instruments

6.1. Introduction

Location of the sensors on the ship is the most critical aspect for accurate measurement of 

the basic meteorological variables, and therefore of the fluxes. The particular difficulties of 

making these measurements aboard an unstable, bulky obstacle were noted in sections 3 and 5. 

In general, meteorological instruments should be located forward on the ship, ahead of the 

engine and air-conditioner exhausts. The ideal position would be high on a forward mast, high 

enough to be above spray when the ship pitches in heavy seas. Because ships are various shapes, 

sizes, and have different appendages, such decisions must be made on a ship-by-ship basis. But 

there are principles, mostly common sense, which can help minimize defective observations. 

They are illustrated in relation to typical ships in Figure 6.1. 

Figure 6.1. Examples of ships with good foremast locations for instruments: R/V Ronald H. 

Brown (NOAA) and R/V Southern Surveyor (CSIRO). Locations A, B, etc., are described in the 

text of section QR2, where this figure is repeated. 
29

6.2. Temperature

The temperature sensor should be as far forward as possible to avoid heat contamination 

from the ship. Again, this is impossible when the wind is from astern, so duplicate sensors to 

port and starboard provide better data recovery. The temperature sensor should be shielded and 

ventilated, but care must be taken to ensure that there is no possibility of sea spray being drawn 

into the air inlet. Although the mainmast may have a well exposed site for wind instruments and 

be clear of sea spray, it is usually a poor location for temperature sensors that can then “see” 

large areas of the deck. 

6.3. Humidity

As described above, water vapour measurement is little affected by wind and thermal 

distortion caused by the ship. It is important that the temperature of air surrounding the sensor is 

recorded, and since the two measurements are commonly made in the same package the more 

stringent exposure requirements of the temperature sensor ensure that the humidity sensor is also 

well exposed. The location must, however, permit access to the humidity element for periodic 

maintenance. If a psychrometer is being used, it will also be necessary to top up the water 

reservoir with distilled water from time to time. 

6.4. Wind speed and direction

The most important requirement of the wind sensors is that they should have no 

obstruction upwind. A single speed/direction set can be mounted on a forward-facing arm from 

a foremast, or high on the mainmast. With only one set of instruments, there will always be a 

sector astern over which the relative wind will be in error. If two wind sets are available, it is 

good practice to mount one on each side of the ship, and give preference to whichever has the 

best exposure to the relative wind. 

Note that even an object behind the anemometer will cause some disturbance to the 

wind, the error scaling with the size of the object. Thus, mounting a sensor high on the top of a 

mast or pole is a good location. If mounted on an arm facing forward from the mast, it should be 

at least 5 mast diameters forward. However, a horizontal boom in front of the bow is not a good 

place because, with the bulk of the ship behind, it is not possible to go far enough forward to 

measure undisturbed flow. 

6.5. Sea temperature 

The location of a ship’s thermosalinograph and its inlet port are usually outside of the 

investigator’s control. Ideally, as noted above, the port should be in the bow at sufficient depth 

(e.g., about 5 m) so that it does not break the water surface. A hull sensor should similarly be 

mounted inside the bow. A Seasnake-type sensor should be towed from a point as far forward 

and as far out as practical, intending that the sensor will spend much of its time outside the ship’s 

bow wave. For the same reason, infrared radiometers for SST measurement, when available, are 

mounted as far forward and as high as possible (on the wheelhouse roof for example) so that 

their view is of an undisturbed ocean surface. 

6.6. Radiation 

Upward-facing radiometers need an all-round, horizon-to-horizon view with minimal 

obstruction by parts of the ship, which would cast shadows on the pyranometer and be a source 
30

of thermal radiation for the pyrgeometer. Possible locations are the top of the mainmast or 

foremast, providing they are accessible at sea under moderate weather conditions so that the 

domes can by cleaned periodically, and the desiccant replaced. In some cases installing water 

jets, controlled from a convenient tap on the deck, has been successful in cleaning domes without 

climbing an instrument mast at sea. The pair of instruments is normally mounted together on a 

single aluminium plate, and levelled. If the masts prove impractical, the plate can be mounted on 

the top of a rigid galvanized metal pipe (e.g., a scaffolding tube or thick water pipe), clamped in 

some way to a convenient rail, perhaps above the wheelhouse. 

Shadows can often be diagnosed by installing a second pyranometer, separated widely 

enough from the first that they are not covered by common shadows (see section 3.7). Both 

pyranometers and pyrgeometers are ‘cosine’ detectors, so objects near the horizon have a much 

smaller effect than objects overhead. Appendix C describes the error in a pyrgeometer 

measurement when it receives thermal radiation from parts of the ship unavoidably in its field of 

view. In a real-life example, pyrgeometers mounted in position G (Figure 6.1 top ship) “see” an 

area 6 m×1.5 m of the bulkhead 10 m aft on deck 3, and 16 m×2.5 m of the bridge 16 m aft. If 

the sky and bulkhead temperatures are as given in Figure C1 (lower panel), by interpolating the 

upper curve we can estimate that these two barriers would produce errors of 1 Wm-2 and 3 Wm-2, 

respectively. According to the middle curve on each diagram, the foremast, 16 m forward, has 

negligible effect. 

6.7. Rainfall

The difficulties of making accurate measurements of rainfall on ships, and the strong 

dependence on location of the instruments, have been described above. Funnel gauges should 

not be mounted in a location of strong upflow, such as on a rail just above the side of the ship or 

above the wheelhouse, where they will lose catch. Rain gages located on the aft part of the ship 

may overestimate by catching water that has accumulated on the superstructure. Once again, the 

best location is on a foremast. If that becomes too crowded, a position on the foredeck near the 

centre-line of the ship will help avoid updraughts. 

Because wind information is used to correct both funnel and optical rain gages, a location 

near the wind sensor is preferred. 

7. Instrument Calibration 

While the absolute accuracy of an atmospheric measurement is the result of the 

cumulation of errors in each step of the measurement/archival process, it is clear that the 

calibration of the sensor is the starting point. The operator of a ship observation system must 

establish (and document) a routine for regular replacement and recalibration of each sensor in 

use, at least once a year and preferably before and after each cruise. The routine will involve 

having a stock of precalibrated sensors onboard to replace those that are away for calibration, or 

any found to be faulty or performing poorly while in operation. 

The calibration facility used should be traceable to a national standard. The system 

operator may choose to rely on factory calibrations (i.e., regular maintenance/calibration by the 

manufacturer of the sensor), a secondary calibration laboratory, or maintain an in-house 

calibration facility. For institutions with one or two research vessels, an in-house calibration 

facility is unlikely to be cost-effective. Reputable manufacturers of meteorological equipment 
31

(e.g., Vaisala, Rotronic, ATI, Gill, R.M. Young, Eppley, Kipp and Zonen) have large, wellequipped facilities, calibrate thousands of instruments every year, and usually represent a solid 

standard. In some cases secondary calibration laboratories provide more comprehensive 

information that may be useful. For example, the NOAA Climate Monitoring and Diagnostics 

Laboratory (Boulder, Colorado) can provide cosine-response curves for pyranometers and domeheating correction coefficients for pyrgeometers. A pyranometer with a poor cosine response 

curve could be retired or relegated to the emergency backup shelf. 

Regardless of the approach, the process must include keeping a documentation record of 

the calibration and deployment history of each sensor. It is important to realise that seemingly 

identical sensors from a production line may differ sufficiently in their calibrations to be 

significant in the context of the accuracies of Table 1. So when sensors are switched, this history 

will ensure that the correct calibration is associated with the active sensor. In view of the many 

possible hazards to sensors deployed on ships which have been described in the above sections, 

and which may remain undetected particularly on long voyages, it is good practice to calibrate 

both before and after the deployment. Gradual deterioration of a sensor may thus be detected 

and corrected, perhaps by simple linear regression, to improve data accuracy. 

8. Intercomparisons 

8.1. Portable standards

While it is sometimes justified to equip laboratories that handle large numbers of 

instruments with standard calibration facilities, or in some cases to carry calibration equipment 

onboard ship, this is usually impractical. In any case, not every ship could be so equipped, 

which would lead to non-uniformity of data quality. With many ships involved in a cooperative 

project such as SAMOS, it is feasible to verify the operational instruments installed against a 

common set of portable secondary standards. These are instruments whose calibration is 

traceable to a recognised standards laboratory. They can be operated alongside the ship 

instruments in a realistic field situation, on part of a regular cruise for example, and recorded 

independently of the ship’s system. The portable standard can be rotated around several ships, 

and verify not only the performance of the ship sensors, but the measuring system as a whole, 

from instrument location to recorded data. 

8.2. Replication of sensors 

It has been noted above that there are benefits in having duplicate sensors on opposite 

sides of the ship, to deal with the range of relative wind direction or the shielding of radiometers. 

However, there are times when both sets of instruments might be reasonably well exposed, and 

expected to agree fairly closely. The ability to compare their measurements, and to analyze any 

differences between them, is a further advantage. While it is not always feasible to have two sets 

of instruments in operation, it is good practice to carry a complete set of spare, freshly calibrated 

sensors, ready to replace the operational ones in the event of failure. However, except for 

trouble-shooting purposes, replication of sensors should not normally be done at the expense of 

exposing a freshly calibrated spare unit. The intermittent loss of data through unfavourable wind 

or sun direction is usually less important than having a data time series cut short by instrument 

failure on a long cruise. 
32

Figure 8.1. Field intercomparison of ship true wind speed measurements from TOGA COARE. 

Three ships ran side-by-side for about one day in the vicinity of the WHOI IMET mooring; a fourth 

ship (Wecoma) transited the area on two occasions. The raw comparison (upper panel) showed mean 

differences between platforms. Each platform was then corrected by that difference with the lower 

panel showing the new comparison. The corrected wind speeds were used for flux calculations for 

the entire experiment. 
33

8.3. Field intercomparisons

Field intercomparisons between sets of instruments on different platforms should be 

made whenever the opportunity presents itself. During TOGA-COARE such comparison periods 

were scheduled as part of the operation plans of ships and aircraft, and there were also other 

occasions when platforms in the same vicinity could compare measurements. The quality of 

surface meteorology and flux datasets resulting from COARE is due in large measure to these 

field intercomparisons, and their careful analysis (Figure 8.1). This alerted participants to 

several potential sources of measurement error, and has influenced methodology in many 

subsequent air-sea measurement campaigns. For example, the ship used for deployment and 

retrieval of the Woods Hole Oceanographic Institution (WHOI) ocean reference moorings is 

equipped with a set of high-quality meteorological instruments, and stands off the mooring for a 

few days at either end of a deployment while the old and new buoy instruments are compared 

with those on the ship. 

Note that the scheduled intercomparison periods must be long enough, typically 24 hours 

or more, to give statistically meaningful results. 

8.4. Manual observations 

In the same way that ship’s officers preserve their skills in the use of a sextant to check 

the ship position in case GPS fails, it is prudent for meteorological observers to remain familiar 

with observation techniques before the days of SAMOS. The reason is not so much to fill in data 

should the automatic system fail, but as an aid to monitoring the health of the sensor array and 

signal processing system. There are at least four “traditional” observations that should be made 

regularly for this purpose. 

a) Temperature/humidity. Use of the Assman psychrometer is referred to in sections 3.2 and 3.3, 

and little needs to be added. The Assman is preferred over the sling psychrometer because it 

usually has superior thermometers, and they can be read while the instrument is held in situ. 

This has the advantage that short-term fluctuations in temperature (in light wind, convective 

conditions for example) may be averaged visually. If possible, the instrument should be stored 

outside air-conditioned space for quicker equilibration with ambient conditions. Along with the 

precautions described in section 3, it is critical to note the time of reading to the minute for 

comparison with the automatically logged value. 

b) Sea temperature. The traditional measurement is of the “bucket” temperature. The nature and 

probable accuracy of this method is referred to in section 3.6. A bucket measurement may be 

impossible from very large vessels, but most research ships possess such an insulated bucket. Its 

size is important; too small, and the water sample will change temperature before it can be read; 

too large and it will be heavy and awkward to handle. The technique is to throw it forward and 

out from the ship, and bring it in when level with the observer. Several casts are needed to 

ensure that the bucket is close to the water temperature. Obviously, the temperature should be 

read as soon as possible after bringing it onboard, but timing is not so critical because sea 

temperature does not fluctuate as rapidly as air. 

c) Wind speed. The traditional estimate of wind speed at sea is by observation of its effect on 

sea state. Unlike cup and acoustic anemometers, the Beaufort scale (Table E1) does not break, 

fuse or rust and is independent of ship speed and heading. In recent years the various sea state 

descriptions have been refined by comparison with careful instrumental wind measurements. 
34

As indicated in sections 2.5 and 5.2 there are several potential sources of error in the true wind 

measurement, from instruments measuring wind and ship speed, the calculation from relative 

wind, and flow distortion. It is almost impossible to estimate the true wind by “feel”, so the 

Beaufort scale enables the observer to judge, within a couple of ms-1, whether the logged wind 

speed is within reasonable limits. 

d) Downwelling radiation. Except under special conditions, short-term variability in solar and 

IR radiative fluxes, especially due to cloudiness, make these signals the most difficult to check 

with spot observations. There are various parameterizations for radiative fluxes, based on 

surface air temperature and humidity combined with visual observations of cloud fraction, but 

these are too uncertain to be of use in this context. However, reliable models of these fluxes for 

a cloud-free sky may be used to check the radiation observations for this particular condition. 

The IR flux [see equation (1.1)] may be written 4 Rl e0σTa = ε ↓ , where Ta is the air temperature 

(Kelvin) and εe0 an effective emissivity for clear skies for which Brunt (1932) proposed a 2-

parameter form e0 = A + B qa ε . From a database of several cruises, Hare et al. (2005) 

determined A and B as linear functions of latitude φ, such that 

4

60

0.03 0.082

60

0.13 Rl 0.52 φ φ qa ⎥σTa

⎦

⎤ ⎢

⎣

⎡ ⎟

⎠

⎞ ⎜

⎝

⎛ ⎟+ − ⎠⎞ ⎜

⎝

⎛ = + ↓ . (8.1) 

The pyrgeometer output, being a combination of three temperature signals, is vulnerable to stray 

thermal contamination, but equation (8.1) can provide a check to within ±10 Wm-2. 

Clear-sky parameterizations for solar flux are also available, but require atmospheric 

profiles of certain constituents, and involve strong dependencies on location and season, making 

them too complex for checking the pyranometer. However, knowledge that the solar flux falls 

identically to zero at night (note, a standard pyranometer will normally read a few Wm-2 negative 

at night), and takes a maximum clear-sky value around 1100 Wm-2 in the tropics, enables the 

observer to identify unreasonable values in most situations. 

9. Documentation (Metadata)

9.1. Introduction

Careful documentation of the sensor installation, calibration practices, and known data 

faults is an essential task of the person responsible for maintaining a shipboard meteorological 

system. These metadata are crucial to the future application of the observations; a detailed 

example of a metadata structure is given in Appendix G. The importance of documenting the 

calibration and deployment history of each instrument cannot be emphasized too strongly. In the 

chaos that sometimes accompanies replacement of a faulty sensor, it is easy to postpone and 

eventually forget to note the circumstances. This can subsequently lead to puzzling features in 

the air-sea flux time series that can never be resolved with certainty. Similarly, it is sometimes 

unavoidable that the location of the sensor is less than optimal. Providing that the location is 

carefully documented, ideally supported by digital photographs, seemingly anomalous data from 

that sensor can often be explained, and in some cases, corrected. Equally, data from a sensor 

known to be very badly exposed for a given relative wind direction can be flagged as erroneous 

without fearing that the information is being lost. The following indicates details and incidents 
35

that should be recorded (with date and time) in an event log during the cruise, and if possible 

transcribed into an electronic document. This, and the digital photos, will be part of the metadata 

to accompany the measurements. 

9.2. The basics 

• Time convention (preferably GMT [UTC]) 

• Recorded units of observations (preferably SI) 

• Ship name 

• Data sampling rates 

• Averaging or calculation methods (e.g., true wind vs. ocean-relative winds) 

9.3. Sensor calibration and history 

• For each instrument, the make, model and serial number. 

• The date and source of each calibration (indicates stability of sensor) 

• Sensor dates of deployment period 

• Incidents during deployment period (maintenance, repairs, mishaps--e.g., swamped by 

wave over bow) 

9.4. Instrument location

• Description of main support (e.g., foremast, forward rail above wheelhouse) 

• Position w.r.t. main support (e.g., 1.2 m to port or stbd., 0.8 m forward) 

• Position w.r.t. ship’s centreline (e.g., 2.5 m to port or stbd) 

• Distance from bow 

• Height above the water, and/or height above some ship reference (e.g., 15.3 m above 

foredeck) 

• Height above the deck immediately below the sensor 

• Any significant object that may affect the exposure of the instrument (e.g., Inmarsat 

dome on rail 2 m to port; after installation large instrument box mounted 1 m forward) 

9.5. Digital photographs

Rough sketches in the logbook of the locations of instruments, with heights and salient 

dimensions with respect to the ship, are extremely helpful. It would be even better to have 

digital schematics of the vessel (top and side view) showing instrument locations (similar to 

Figure 6.1). Together with digital photographs of the installations, these enable the analyst to 

assess the overall quality of the ensuing measurements, and provide valuable information on the 

likely cause of any suspect data. 

Close-up photographs of the instrument itself can sometimes be helpful in detecting 

instrument faults (e.g., damaged cables), but are most useful when taken at a distance sufficient 

to show the sensor’s environment and possible obstacles to airflow around the sensor; in the case 

of radiation sensors, objects or installations likely to cast a shadow. If possible, after installation, 

photographs should be taken from the wharf, as in Figure 9.1 of the NOAA research vessel 

Ronald H. Brown. If written documentation were lost or mislaid, having the plans of the ship 

(e.g., Figure 6.1), together with such photographs, would enable the heights of the instruments, 

and their relative positions, to be estimated reasonably well. 
36

Figure 9.1. Meteorological instruments 

on the foremast of the NOAA ship 

Ronald H. Brown; a) below the upper 

cross-arm is a covariance package. The 

sensor of the sonic anemometer is well 

exposed, although the instrument boxes 

below and behind it represent a greater 

obstacle to airflow than is desirable; 

b) close-up of the bulk flux instrument 

package. From left to right on crossarm: 

optical rain gage; T/RH sensor with 

forced ventilation; second T/RH sensor 

in naturally ventilated screen; siphon 

rain gage; propeller anemometer and 

wind vane unit. Below these instruments 

can be seen a laser wave-height sensor 

aimed forward of the bow and a second 

sonic anemometer. 
37

10. Securing the Data 

10.1. Introduction

All data and metadata should be stored in a manner that will preserve the information for 

current and future scientists. This requires each operator to establish a protocol for managing the 

output from their sensor system. A detailed data protocol should include plans to store the 

observations, metadata, and event log on digital media during each cruise and to ensure the longterm availability of the observations at a national or international archive center.

10.2. Data storage

The computer date and time should be set to GMT (UTC). The event log should also be 

written in GMT, although some relationships (cloud-radiation forcing, for example) make more 

sense when analysed in local solar time. It is often helpful if the difference between local time 

and GMT is noted (but note that ship time, i.e., the time to which the ship’s clocks are set, may 

differ from local solar time). 

The recorded data will normally consist of the raw time series at the logger sampling 

speed, and a conversion to physical units via the instrument calibrations and transfer functions. 

This will often involve some computation using several signals and sensors; for example, 

combining the three pyrgeometer signals for downward longwave radiation, or obtaining true 

wind from the measured relative wind and the ship’s speed/course. 

In many cases (SAMOS, for example) the meteorological data collected automatically by 

computer on the ship will be destined for use by scientists (e.g., modelers and analysts) engaged 

in climate research elsewhere. The role of the shipboard operator is to maintain the quality of the 

data by monitoring the performance of the sensors, and making sure that all detail (e.g., roosting 

birds, or a faulty instrument) is noted in the daily log. This individual should be provided with 

training to enable recovery of the system in the event of a computer crash, since extended time 

series are most valuable. 

The capacity of the computer hard disk will be sufficient to hold several weeks’ data, 

which should be backed up regularly according to normal computing practice. Every few days 

both raw and derived data should be written onto a CD or DVD, together with a copy of the 

metadata. If possible an electronic copy should be made of the event log (e.g., in Word) and 

saved with the data and metadata. 

10.3. Data Archival

“Archiving” is a term that is rather poorly utilized in the climate community. Simply 

storing the observations collected on a cruise on a DVD or other digital media and placing this 

on a shelf does not constitute archival of the data. 

As part of an ocean observing system, the mission of a national or international archive 

center is “to acquire, preserve, and provide access to data in perpetuity." High-priority 

objectives include integrity and completeness of the archives. Essential functions include 

constant monitoring of data streams, accounting for all files and records, and frequent checks of 

accuracy. Metadata are equally important since they ensure that the maximum information can 

be derived from the data. Archive centers must have maintenance strategies that protect the data 

as storage media and systems change. Data stewards must constantly guard against changes in 
38

formats and software that could make accessing the data more difficult, more costly, or even 

impossible. Since important collections are seldom static, a significant effort is required to 

integrate new metadata, add improvements and corrections to the data, and make additional 

related historical archives easier to access (Hankin et al. 2005). This effort goes beyond the 

capability and resources of most vessel operators.

Each vessel operator should establish a data pathway whereby meteorological 

observations are transferred from the vessel to a national or international archive center. The 

pathway may be direct, with copies of all data sent by the operator to the archive center on 

digital media to a pre-defined schedule (e.g., after each cruise, quarterly, annually). 

Alternatively, the data can flow through a specialized data center (e.g., the SAMOS data center), 

which will ensure the observations arrive at an appropriate archive center. Establishing an 

archive protocol will ensure that the investment in time and money that goes into collecting the 

observations will not only benefit current scientists, but those 10, 20, or even 50 years in the 

future. 

11. Bulk Flux Algorithms (and the atmospheric surface layer) 

Bulk flux algorithms enable the turbulent air-sea fluxes (sensible heat Hs, latent heat Hl, 

and momentum τ) to be calculated from the measured difference between the values of the 

corresponding bulk meteorological variable (temperature t, humidity q, wind speed u) at height z

and at the sea surface. The simple form of the bulk air-sea flux equation given in section 2.1 is 

repeated here for convenience 

Fx = Cxu(δs – δz), (11.1) 

where Fx is the vertical flux of entity x (heat, moisture, momentum), u the wind speed, and δ the 

value of the corresponding bulk meteorological variable (temperature, humidity, wind speed). 

This equation suggests that Cx can be determined by measuring the surface fluxes by 

whatever means possible, together with the mean physical variables. During the second half of 

the 1900s, many such determinations were made using profile, covariance and dissipation 

techniques for the fluxes. Many of these employed atmospheric boundary-layer relationships 

between the fluxes and the (stability-dependent) vertical profiles of each variable, which had 

been determined over uniform sites on land and were subsequently applied over the ocean. To 

compare observations taken in different situations, these relationships are used to reference all 

measured values to the “standard” height of 10 m, and to define the transfer coefficient as a 

“neutral” value. The neutral value would give the same flux had the measurement been made at 

10 m height under conditions of neutral atmospheric stability, and is normally represented by 

Cx10 n. 

From this early work the exchange coefficient for momentum (or drag coefficient) was 

found to increase at higher wind speeds. A relationship suggested by Smith (1980), 

CD10 n=(0.61+0.063U10 n)x10-3 , appears also to fit more modern data sets (Yelland et al. 1998, 

Drennan et al. 2005). The exchange coefficients for the “scalar” variables, sensible and latent 

heat, seemed to be fairly constant; for example, Large and Pond (1982) found for sensible heat 

CH10 n = 1.13x10-3 (unstable conditions), 0.66 (stable conditions) and for moisture CE10 n = 

1.15x10-3. Reviews by Smith (1988,1989) concluded that CH10 n=1.0x10-3 and CE10 n = 

(1.2±0.1)x10-3 for winds between 4 and 14 ms-1. At that stage there were few measurements 
39

below 4 ms-1 or above 14 ms-1, and only recently has a clear increase of CE10 n as winds increase 

been observed (Fairall et al. 2003). Values outside these limits were best regarded as 

extrapolations from the mid-range wind speeds where data were more plentiful. Further, as wind 

speed and atmospheric stability were usually the only variables considered, other variables such 

as sea state which might affect the air-sea exchange process were simply absorbed into the 

exchange coefficient. 

The uncertainty in behaviour of the transfer coefficients, and the consequent limits to 

accuracy with which the fluxes could be calculated, became unacceptable as the sensitivity of 

climate models to the fluxes was recognised, particularly from efforts to couple ocean and 

atmospheric models. New bulk formulae were developed, incorporating better and more 

complete physical descriptions of the air-sea exchange process. The following is a brief account 

of the direction that these algorithms followed. To aid readability, in the context of profiles we 

have ignored the distinction between temperature and potential temperature, and the possible 

complication of a displacement height. However, such details may be found in WCRP (2000) 

and standard texts (e.g., Garrett 1992; Kraus and Businger 1994). 

The way in which the bulk meteorological variables (t, q, u) change with height above the 

sea surface depends on atmospheric stability, and is well known. The dimensionless profiles of 

the variables are given by Monin-Obhukov similarity theory (e.g., Businger et al. 1971) 

m t q z

q

q

z

z

t

t

z

z

u

u

z φ κ φ κ φ κ = ∂

∂ = ∂∂ = ∂∂

* * *

; , (11.2) ;

where κ (= 0.4) is von Karman’s constant, and the scaling parameters (u*, t*, q*) are defined with 

reference to the surface fluxes 

ρ ρ ρλ

τ l

p

s H

u q C

H − u = − u*t* = − * * = 2

* , . (11.3) ,

ρ and Cp are air density and specific heat, respectively, and λ the latent heat of vaporisation. The 

dimensionless profiles φx are functions of the atmospheric stability ζ (= z/L), where 

[ ] ( ) * *

2

*

t 1 0.61q 0.61tq

u

g

T

L v

+ + − = κ

 (11.4) 

is the Monin-Obhukov stability length (the height at which contributions to atmospheric 

turbulence by shear stress and buoyancy flux are roughly equal). The buoyancy flux is the 

quantity in square brackets, Tv [= T(1+0.61q)] is the virtual temperature of the air, and g is the 

acceleration due to gravity. Over the ocean, particularly in the tropics, the contribution of 

moisture to buoyancy is significant. Experiments over land have established formulae for the 

dimensionless profiles of the form 

=(1− ) <0 − φ α ς ς β for m unstable boundary layer (11.5a) 

 0 φ m =(1+γ ς ) for ς > stable boundary layer , (11.5b) 

where α, β and γ are empirically determined constants (see, e.g., WCRP 2000). The observations 

also support the assumptions that 

0; 0 2 φt =φ q =φ m for ς < φt =φ q =φ m for ς > . (11.6) 
40

Both unstable and stable forms of the dimensionless profiles (11.2) can be integrated 

between the surface and measurement height z 

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ = + − ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ = + − ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ = + − q

oq

q

t z

t

t

m z

o

u

z z

q z

q q

z

t z

t t

z

u z

u u ψ

κ

ψ

κ

ψ

κ

ln ; ln ; ln *

0

0

*

0

*

0 , (11.7) 

where u0 , t0 , q0 are the surface values, and the stability functions ψχ are integrals of the 

dimensionless profiles φχ . In neutral conditions the ψχ are zero and the profiles take the familiar 

logarithmic form. The heights for the wind and scalar measurements can be different; the 

denominators are the surface roughness lengths for each variable. 

Certain aspects of these land-based equations differ when applied over the ocean. 

Over land u0 ≡ 0, whereas the water surface is mobile. Physically, the correct wind velocity to 

use when calculating sea-air transfer is relative to the water surface, so when uz is measured with 

respect to Earth coordinates (e.g., using GPS) the surface current, u0, should be subtracted 

vectorially (but see comments in section 2.5). Also, since the humidity at the air-sea interface, 

q0, is calculated as the saturation vapour pressure (svp) over pure water at the SST (ClausiusClapeyron relationship), it must be reduced by 2% to allow for the reduction of svp over saline 

water at 34 psu (Kraus and Businger 1994). 

Comparing equation (11.7) with the formal bulk flux equation (11.1) for the case of 

momentum, 

( )2

0

2 =u* =CD u −uz

−

ρ

τ , (11.8) 

we see that the drag coefficient can be expressed as 

2

0

2 ln

−

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ = − m

u

D

z

z C κ ψ , (11.9) 

whence the 10 m neutral value 

2

0

2

10

10 ln

−

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ = z

CD n κ . (11.10) 

This emphasizes the important point that the neutral drag coefficient is directly related to 

the wind roughness length z0 – the two are interchangeable. If the roughness of the sea surface 

can be specified, for example by a physically based wind/wave relationship or a well-founded 

empirical parameterization, the above equations can be solved to obtain the wind stress. 

From classical studies in fluid mechanics, surface roughness and wind stress are related 

through the dimensionless roughness Reynolds number Rr =u* z0 /ν , where ν is the kinematic 

viscosity of air. When Rr > 2.0 the surface wind regime is said to be aerodynamically “rough” 

and for Rr < 0.13 it is “smooth”, with Rr approaching a constant value of about 0.11 as wind 

speed decreases. Over the ocean, these limits on Rr correspond to 10 m wind speeds of about 

8 ms-1 and 2 ms-1,respectively; in between is a transition regime between rough and smooth 

flow. 

On the basis that the ocean roughness results mainly from gravity waves generated by 

wind stress, Charnock (1955, 1958) proposed that z c u /g

2

0 = α * , where g is the acceleration due 

to gravity and αc is Charnock’s “constant”. However, the wide range of values found for αc
41

signal that wind/wave characteristics are more complicated than this simple relation suggests. 

Smith (1988) proposed that the entire smooth to rough flow regime be written, 

*

2

*

0 0.11

g u

u

z

α c ν = + , (11.11) 

but this only fits the data if αc is allowed to vary. The transfer coefficients for heat and moisture 

depend on both the momentum roughness length, and those for temperature and humidity, which 

can be similarly parameterized as Rt =u* z0t /ν and Rq =u* z0q /ν . By considering physical 

transfer processes across the interface using surface renewal theory, in which small eddies 

transfer heat intermittently between the bulk and the ocean surface, Liu et al. (1979) determined 

empirical relationships between Rr, Rt, and Rq. This approach is the basis of some modern bulk 

algorithms, which solve equations for the fluxes, profiles and atmospheric stability iteratively. 

The bulk algorithm developed for community use in the TOGA-COARE experiment (Fairall 

et al. 1996b, 2003) is described in Appendix B. 

In recent years, the imperatives of climate research that set the 10 Wm-2 goal for net airsea flux measurement have also led to greatly improved measurements, extending the wind speed 

range over which the exchange coefficients are valid. There remain difficulties at high winds (> 

~20 ms-1) when spray droplets contribute to the transport of heat and water vapour, for which 

reliable parameterizations have yet to be developed. At very low winds, equation (11.1) predicts 

vanishing fluxes at zero mean wind. In reality, this limit is usually associated with variable, 

gusty winds which transport energy and stress even when the vector mean wind is zero (Bradley 

et al. 1991). Godfrey and Beljaars (1991) avoid this problem by introducing a “gustiness” 

velocity, ug, proportional to the convective scaling velocity for the atmospheric boundary layer 

W*, (Deardorff 1970) where 

i

l

p

s z

H

T

C

H

T

g W

⎥

⎥

⎦

⎤

⎢

⎢

⎣

⎡ = +

ρ ρλ

0.61 3

* , (11.12) 

so that ug = βW* , the constant β to be determined empirically. (11.13) 

The sea state (wave characteristics) and surface current also modify fluxes, the surface 

currents, by changing the wind shear. A current moving in the same direction as the wind will 

result in smaller surface fluxes, while currents running counter to the wind will increase them. 

A situation can be imagined in which changing tidal currents cause variation in the fluxes under 

a constant wind. The magnitude of the vector difference of the wind and the surface current 

should replace wind speed in flux calculations, with the stress in the direction of the vector 

difference. 

There have been many approaches to parameterize the influence of waves on surface 

stress. Most agree that the stress is larger when the wind waves (waves supported by the local 

winds) are relatively steep. This effect can be explained in terms of wave age (which has several 

different definitions), and usually parameterized through the drag coefficient or Charnock’s 

parameter [αc in equation (11.11)]. For example, short and sharp waves that initially form when 

there is sufficient wind over a smooth surface have a much larger drag coefficient than very tall, 

long and gently rolling waves. There is no convincing evidence that waves influence transfer 

coefficients for heat and moisture. 
42

Figure 11.1. Sensitivity of bulk flux calculations to algorithm and input data; time series of sum 

of sensible, latent, and net longwave fluxes. Upper panel: COARE 3.0 (C3 blue) and Large and 

Pond 1982 (LP green). Lower panel: LP - C3 (blue), C3(Ts-0.5) - C3(Ts) (red), C3(qa-0.5) - 

C3(qa) (green), and C3(U - 0.5) – C3 (mauve). 
43

 The influence of swell, waves produced by distant winds and not supported by the 

local winds, is more controversial. Wind waves tend to propagate in a direction parallel to the 

wind vector. Swell propagating in other directions has been observed to cause large variability 

in the traditional drag coefficient, which cannot be explained in terms of wave age. An 

alternative approach, which considers wave-related modifications in a similar manner to the 

current modifications discussed above rather than the drag coefficient, has shown promise for 

both wind waves and swell, and is an area of ongoing research. Figure 11.1 looks at the ocean 

heat loss terms in equation (1.1) to examine the effect of different algorithms or small biases in 

the data. The upper panel is computed using two of the best known bulk flux algorithms, Large 

and Pond (1982; LP) and COARE 3.0 (Fairall et al. 2003, C3). These are from different eras and 

contain different functions and assumptions, so this is not intended to evaluate them, but rather to 

provide natural differences in calculated fluxes. We consider the two consecutive but contrasting 

days of Figures D1 and D2 with wind speeds in the range 3-6 ms-1. 

The lower panel shows the difference between LP and C3 (blue trace). LP produces 

greater ocean cooling, by around 10-15 Wm-2 on the steady day, but more variable and as much 

as 25 Wm-2 on the convective day. The other three traces indicate the effect of a realistic error 

(an electronic signal offset or calibration error for example) in the input data to the COARE 3.0 

flux algorithm. The ±10 Wm-2 target accuracy is indicated by the green- and red-dashed lines. 

An error in specific humidity of -0.5 g kg-1 (drier air) will increase latent heat flux and hence 

produce too much ocean cooling, by around 10 Wm-2 in this example (green trace ); an error that 

reduces true wind speed by 0.5 ms-1 will reduce both latent and sensible heat loss totalling 

10 Wm-2 (mauve trace); a -0.5oC error in sea temperature affects sensible and latent heat, and 

also reduces the outgoing longwave radiation, the three effects together assigning around 

20 Wm-2 too much warming of the ocean (red trace). 

These figures assume that only one of the input variables is in error, while the other two 

are exact. In practice this is unlikely, hence the more stringent accuracy requirement given in 

Table 1. Note also that use of the routine ship weather observations, which Figure 3.4 showed 

can contain significant errors, will not produce air-sea fluxes of sufficient accuracy for climate 

studies without careful analysis and corrections to the observations.

A-1

Appendix A - Useful Formulae, Parameters, and Conversions

Studies of the atmospheric and oceanic surface layers, and the transport of energy and 

matter across the interface between them, involve the dynamic and thermodynamic properties of 

the respective fluids, air and seawater. The following equations and values are frequently needed 

in the analysis following measurements of the state variables, air temperature, pressure, density 

and moisture content. In some cases they are not strictly rigorous - simplifications are made by 

neglecting small quantities, insignificant in the present context. More complete derivations are 

given in Andreas (2005). Comprehensive discussions of atmospheric thermodynamics can be 

found in standard texts, e.g., Garratt (1992), Bohren and Albrecht (1998). 

A1. Equations of State 

Temperatures in degrees Kelvin are denoted by T and in Celsius by t, whereT = t + 273.16 K. 

Quantities relevant to the thermodynamic properties of moist air are: 

The universal gas constant R = 8.3145 (J mol-1 K-1) 

Molecular mass of dry air ma = 0.028965 (kg mol-1) 

Gas constant for dry air Ra = R/ma = 287.05 (J kg-1 K-1) 

Molecular mass for water vapour mv = 0.018015 (kg mol-1) 

Gas constant for water vapour Rv = R/mv = 461.5305 (J kg-1 K-1) . 

From the ideal gas law we obtain the 

Density of dry air ρ a =( p − e)/ RaT (kg m-3) , 

with temperature T in kelvins and (p - e) pressure in Pascals (100 Pa = 1 mb). Note, to compute 

the dry air density we must remove the pressure contribution of water vapour from the pressure 

gauge reading. At standard temperature (273.15 K = 0°C) and pressure (101325.0 Pa = 

1013.25 mb) ρa = 1.2922 kg m-3. 

The behaviour of water vapour in the atmospheric surface layer approximates that of an ideal gas 

so that the 

Water vapour density ρ v = 100e / RvT (kg m-3) , 

where, traditionally in meteorology, e is the partial pressure of water vapour in millibars (mb). 

The vapour density is also referred to as the absolute humidity. 

Other ways of specifying the water vapour content in a sample of moist air are the 

Mixing ratio ) r / 0.622e /( p e = ρ v ρ a = − , (kg kg-1 of dry air) , 

where the factor 0.622 = mv/ma , and the barometric pressure p and e are in the same units. 

Specific humidity ) q / 0.622e /( p 0.378e = ρ v ρ = − , (kg kg-1 of moist air) , 

where the factor 0.378 = (1 - mv/ma), and the density of moist air 

100 p /(R T (1 0.61q)) ρ = ρ a + ρ v = a + (kg m-3) . 
A-2

The factor 100 converts from mb to Pa, and 0.61 = (ma/mv-1). The quantity T (1+0.61q) is called 

the virtual temperature Tv , which is the temperature that air of given pressure and density would 

have if it were completely free of water vapour. 

Saturation vapor pressure 6.1121 (1.0007 3.46 10 ) exp(17.502 /(240.97 )) 6e x p t t s = + + − (mb),

where t (°C) is air temperature, and p (mb) is atmospheric pressure. This empirical expression is 

valid above pure water between -20° and 50°C (Buck, 1981). 

Some relationships below require a value of the saturation vapor pressure (svp). 

Above seawater the svp is depressed to ) esea = es (1− 0.000537S (mb), 

where S is salinity (psu). If S = 35 psu, esea = 0.981es or roughly 2% less than over pure water. 

Relative humidity RH =100(e / es ) as % ; or (e / es ) as a fraction . 

Dew point Td : for given e, Td may be obtained by inverting the svp equation; however, most 

often Td is measured (see section 3.3) and e calculated by putting t = Td (°C) in the svp equation. 

Specific heat of dry air at constant pressure is given by the polynomial, 

 2 C 1005.6 0.0172t 0.000392t p = + + (J kg-1 °C-1) 

valid in the range ±40°C and for barometric pressures around one atmosphere. The temperature 

dependence is weak, and a constant value of 1006 J kg-1 °C-1 is usually adequate. 

Latent heat of vaporization 6 λv = (2.501−0.00237t)×10 (J kg-1), 

with the constant value 6 2.50×10 J kg-1 being accurate enough for most purposes in the surface 

layer. 

Psychrometric equation t t p

C

m

m

e e t w

v

p

v

a

s w = ( ) − ( − ) λ (mb), 

where es(tw) is the svp at wet-bulb temperature tw , (t – tw) is the wet-bulb depression and p the 

barometric pressure. The factors before the parentheses are known as the psychrometric 

constant. Using the constant values quoted above, and combining with p = 1013.25 mb 

(the standard atmosphere at sea level), the practical psychrometer equation is usually written 

 ) ( ) 0.66 ( s w w e = e t − t − t (mb), 

where t and tw are referred to as dry-bulb and wet-bulb temperatures (°C), respectively.

Kinematic viscosity of dry air 

1.326 10 (1 6.542 10 8.301 10 4.840 10 ) 5 3 6 2 9 3 t t t a

− − − − ν = × + × + × − × (m2

 s-1) 

valid for the temperature range t = ±50°C. 

Dry adiabatic lapse rate 0.0098

.

⎟ = − = − Γ = − ⎠

⎞ ⎜

⎝

⎛

∂

∂

=

d

const Cp

g

z

t

θ

°C m-1

where θ is the potential temperature. 
A-3

Potential temperature, θ, is the temperature an air parcel with absolute temperature t (K) at 

height z would have if brought adiabatically to the sea surface. From thermodynamic 

considerations, we obtain: 

 z

C

g t

p

θ = + . 

Calculation of the sensible heat flux using the bulk equation (1.1) should therefore use the 

potential temperature difference 

 t t z Δθ = sea − air − 0.0098

with sea and air temperatures in °C and z the height of the air temperature measurement (m). 

A2. Ice-Related Expressions 

Latent heat of fusion of ice 6 = 0.334×10 λ f (J kg-1) 

Saturation vapor pressure over ice (or snow) 

6.1115 (1.0003 4.18 10 ) exp(22.452 /(272.55 )) 6

e x p t t s = + + − (mb)

valid for -50° ≤ t ≤ 0° (Buck, 1981) 

Specific heat of ice 114.19 8.1288 3.421 exp[ ( /125.1) ]

2

c pice = − + T + T − T (J kg-1 K-1). 

A3. Radiometry

The following values of albedo and emissivity of the ocean surface were obtained during the 

TOGA-COARE campaign, and are broadband and isotropic. 

Short-wave albedo of sea surface α = 0.055 

Long-wave emissivity of sea surface ε = 0.97

Stefan-Boltzmann constant σ = 5.670 x 10-8 (Wm-2K-4 ) 

Pyrgeometer equation ) / ( 4 4 4

l Tc B Tc Td R = V s + + − ↓ σ σ (Wm-2) 

with thermopile output V (μV) and calibration factor s in μV/(Wm-2), Tc and Td temperatures (K) 

of the instrument case and dome, respectively, and B a factor representing the IR transmission 

and absorption characteristics of the dome. For a given instrument, s will be given but B may 

not, in which case the value B = 3.5 is a fair average. 

Outgoing long-wave radiation ↑ ↓ = + − l s l R T (1 )R 4 εσ ε (Wm-2) , 

where Ts is the ocean surface temperature (K), ε (= 0.97) is its emissivity and σ (= 5.67x10-8) is 

the Stefan-Boltzmann constant. 
A-4

A4. Barometer Correction

Near the surface, atmospheric pressure falls at about 0.12 mb m-1 as height increases. Since we 

need the value near the surface and the barometer is usually on the bridge some tens of metres 

higher, we make a correction based on the hydrostatic equation for the atmosphere, 

g

z

p

a

= − ∂

∂

ρ

1 where z is height, p is pressure and g is the acceleration due to gravity. 

Introducing the ideal gas law, integrating this expression from observation height to the surface 

and simplifying, we have for the surface pressure 

 ) ps = p exp(gz/ RaT , 

where p is the observed pressure at height z, Ra is the gas constant for dry air, and T (K) is a 

reference temperature (e.g., at z = 10 m). For example, if the pressure at a barometer height of 

30 m was 1000.0 mb, with g = 9.81 ms-2, Ra = 287.05 Jkg-1K-1 and T = 290 K, ps = 1003.54 mb. 

A5. Conversions

1 nautical mile (nm) = 1.852 km by definition. 

1 knot = 1.852 kmhr-1= 0.51444 ms-1. 

1 standard atmosphere = 1013.25 mb. 

Standard gravity g = 9.80665 ms-2. 

An average latent heat flux of 100 Wm-2 evaporates 3.456 mm water per day. 

A6. Gravity 

Acceleration due to gravity, g (ms-2) is calculated as a function of latitude at sea level by the 

expression 

 )) 9.78033(1 (0.0053sin 5.8 10 sin 2 2 6 2 φ φ − g = + − × (m s-2) 

or by the polynomial 

 ) 9.7803267715 (1 1sin 2sin 3sin 4sin 2 4 6 8

g = × + c φ + c φ + c φ + c φ (m s-2) , 

where c1 = 0.0052790414 

 c2 = 0.0000232718 

 c3 = 0.0000001262 

 c4 = 0.0000000007. 

These two methods agree to four places of decimals. They give g = 9.7803 and 9.8322 ms-2

at the equator and the poles, respectively. The standard value 9.80665 ms-2 is given at 

latitude ~ 45.5°. 
A-5

A7. Relative Wind Conversions Aboard Ship 

Conversion of Relative to True wind speed and direction 

“True” wind and direction will be defined as being in Earth-relative coordinates. 

Directions follow the meteorological convention – “northerly” winds are from the north, 

“easterly” from the east. 

Then, cog = ship’s course over ground from GPS 

 sog = ship’s speed over ground from GPS 

 head = gyrocompass heading, north = 0° east = 90° 

 rels = relative wind speed 

 reld = relative wind direction, 0° over bow, 90° over starboard 

relsn = rels.cos(head+reld) northerly component of relative wind 

relse = rels.sin(head+reld) easterly component of relative wind 

sogn = sog.cos(cog) north component of ship speed 

soge = sog.sin(cog) east component of ship speed 

un = relsn-sogn northerly component of true wind (-ve sign because wind dir. “from”). 

ue = relse-soge easterly component of true wind (-ve sign because wind dir. “from”). 

dirt = mod(atan2(ue,un)+360,360) true wind direction 

ut = (un2

 + ue2)

1/2 true wind speed 

Notes. The above implies that angles are expressed in degrees. Some computer software 

requires that angles be expressed in radians, and entering degrees can generate bewildering 

results. In this case angles should be multiplied by the factor rdcon = pi/180 (pi = 3.14159). 

 The atan2 function is commonly available in software such as Matlab, Fortran, Excel, 

etc. It computes, in our case, the arc tangent of un/ue, returning an angle in the range –pi to +pi, 

expressed in radians, using the signs of un and ue to compute the quadrant of the result. If the 

calculation is done by hand, care must be taken to resolve the ambiguity of the arctan function. 

Conversion of relative to water-relative wind speed and direction 

Certain applications, in particular air-sea flux calculation, require the wind speed relative 

to the surface water [see section 2.5 and equation (11.7) et seq.]. We call this the “waterrelative” wind. When the near-surface current speed and direction (Earth-relative) are available 

(e.g., from a surface mooring) this current can be resolved into north and east components cn and 

ce. These would normally be in the oceanographic convention “to”. Then, having 

acknowledged this unusual convention, the true wind components can be converted to waterrelative, and the water-relative wind speed uw = [(un+cn)2

 + (ue+ce)2]

1/2 which is equivalent to 

(uz – u0) in equation (11.7). 

Alternatively, uw may be obtained by replacing cog and sog with course and speed from 

the ship’s 2-axis Döppler-log and gyro-compass, as follows: 

uf = forward speed from Döppler-log 

us = side-slip to starboard (positive) 
A-6

head = gyro-compass heading, north = 0° east = 90° 

rels = relative wind speed 

reld = relative wind direction, 0° over bow, 90° over starboard 

relsn = rels.cos(head+reld) northerly component of relative wind (as before) 

relse = rels.sin(head+reld) easterly component of relative wind (as before) 

sown = uf.cos(head)-us.sin(head) north component of ship speed 

sowe = uf.sin(head)+us.cos(head) east component of ship speed 

unw = relsn-sown northerly component of water-relative wind (-ve sign, wind dir. “from”) 

uew = relse-sowe easterly component of water-relative wind (-ve sign, wind dir. “from”) 

dirw = mod(atan2(uew,unw)+360,360) water-relative wind direction 

uw = (unw2

 + uew2)

1/2 water-relative wind speed , 

where again uw is equivalent to (uz - u0) in equation (11.7). 

The above comments regarding the mode of angles and use of atan2 apply. 

These issues and other aspects of the reporting of true wind are discussed in considerable 

detail by Smith et al. (1999). Program codes for calculating true (earth-relative) winds can be 

obtained from http://www.coaps.fsu.edu/woce/truewind/. 

A8. Height Adjustment 

The variation with height of wind speed, temperature, and water vapour content in the 

atmospheric surface layer (their profiles), depend on surface conditions and thermal stability, as 

described in section 11. There are two main reasons for needing to estimate the value of any of 

these variables at a height other than that at which it was measured; adjustment to standard 

reference height (10 m) to relate to other experiments (e.g., comparing CD10 n and CE10 n); and 

when inter-comparing instruments at different heights and on different platforms. The latter is of 

particular importance in the context of procedures for the SAMOS initiative and this Handbook 

(see section 8.3). 

Based on the principles of Monin-Obhukov similarity theory, equation (11.7) defines the 

three profiles, in the familiar “log-linear” form. The critical assumption is that the fluxes, or 

equivalently the M-O scaling parameters (u*, t*, q*), are constant with height within the surface 

layer. In neutral conditions, the logarithmic profiles are specified by their slope (u* etc.) and 

intercept on the height axis (z0 etc.). In diabatic conditions the Ψ (z/L) term [also dependent on 

the fluxes; see equation (11.4)] produces an increasing departure from logarithmic with 

increasing height and thermal instability (or stability). Thus, for a convenient averaging period 

(e.g., 30 min or 1 hour), profiles can be constructed when the fluxes and the roughness lengths 

are known. This can be achieved with modern advanced and validated bulk flux algorithms. 

The profiles shown in Figure A1 are derived from meteorological data obtained from the 

IMET instruments aboard R/V Revelle during a 27-hour inter-comparison with the WHOTS 

mooring 100 km north of Hawaii. The COARE Version 3.0 bulk algorithm was used to calculate 

the fluxes, roughness lengths and stability-related parameters needed in equation (11.7). 
8

6420

201816141210

0123456

Wind speed m/s

Height

ProfileMeasurementsSys-2 buoy

WHOTS-2 June 24 2006 Hr 6

86420

201816141210

24.4 24.6 24.8 25.0 25.2 25.4

Potential Temperature °C

ProfileMeasurementsSurfaceSys-2 buoyAssman

86420

201816141210

14 15 16 17 18 19 20

Humidity g/kg

ProfileMeasurementsSurfaceSys-2 buoyAssman

Figure A1. (Data courtesy Bob Weller). Example of need for height adjustment when comparing measured values at various levels 

above the sea surface. The ship’s anemometer was at 18 m on the foremast, and the temperature/humidity sensor at 17.4 m. Temperature and humidity were measured with an Assman psychrometer through a forward chock at 6.8 m height (see Figure 3.1). 

The ship was standing about 0.25 nm downwind of the WHOI buoy, which had two wind sensors at 3.22 m above the sea surface and two temperature/humidity sensors at 2.88 m. The ship and buoy data points are hourly averages; the Assman values are spot readings. 

The profiles were constructed from flux/gradient parameters calculated using Version 3.0 of the COARE bulk flux algorithm, and 

used in equation (11.7), as follows; 

Parameter u* t* q* zo zot , zoq ψm ψt , ψq z/L

Units ms-1 C g kg-1 m m 

Value 0.184 -0.026 -0.192 4.75e-5 7.74e-5 0.705 1.257 -0.427 

Surface values (u0, t0 and q0) were 0 ms-1, 25.22 °C, and 19.57 g kg-1 respectively; κ = 0.4. 
A-8 

Their values for this particular one-hour period are given beneath the figure, to acquaint 

the reader with the typical magnitude and sign of these symbols that feature in the theoretical 

development. A z/L of -0.4 is moderately unstable, but not in the realm of convective conditions. 

The algorithm input data consisted of the wind speed and temperature/humidity 

measurements near the top of the foremast, the thermo-salinograph sea temperature from 

5-m depth, and other relevant variables (barometric pressure, short- and long-wave radiation). 

The surface temperature and humidity values in the figure are extrapolated from 5 m to the 

surface using models of the cool skin and diurnal warming. No surface current data was 

available, so u0 was taken to be zero. A linear height scale is used to illustrate more clearly the 

characteristics of near-surface profiles over the ocean. Because the sea is very “smooth” 

compared with land surfaces (typically, z0 over grassland is 0.01 m), most of the sea-air 

difference occurs in the lowest 1-2 metres. Note that the profiles in Figure A1 have each been 

produced from just two measurements (at the top of the mast and the sea surface) and with the 

benefit of knowledge gained from many decades of boundary layer study over land sites. 

We can now compare measurements from the same local regime, but at different heights, 

in this case by a hand-held Assman psychrometer and from the buoy that has been in situ for a 

year. The buoy is equipped with two independent meteorological systems – for clarity we 

illustrate only one. Without allowing for the height difference the buoy wind speed would have 

seemed almost 1 ms-1 too low compared with the ship’s ultrasonic anemometer. The profile 

indicates that at buoy height (2.88 m) the potential temperature is 0.15oC higher, and the specific 

humidity 0.73 g kg-1 higher, than at the top of the foremast (17.4 m). After a year of unattended 

operation both the potential temperature and specific humidity measurements by the buoy during 

this hour agree remarkably well with the height adjusted ship instruments. The differences from 

the profile were 0.03oC and 0.12 g kg-1, well within the accuracy targets in Table 1. The role of 

the Assman psychrometer is to validate the ship’s temperature and humidity instruments (see 

section 8.4). For this hour agreement is within 0.05oC and 0.11 g kg-1, better than the resolution 

of the thermometers. 

For the purpose of illustration, from the 27 hours available for this intercomparison we 

have selected an hour with reasonable overall agreement. The horizontal bars indicate the 

variability of the measurements; the centre dot in each case being the average difference over the 

27 hours (the bias) with the bars indicating ±1 standard deviation. Variability is due mainly to 

the separation between the ship and the buoy, and also the different sampling strategies of the 

ship and Assman. Nevertheless, with the possible exception of the buoy humidity, the 

comparisons on this day were within the goals of Table 1, possibly aided by the fact that 

conditions were fairly steady. We make the point, however, that intercomparison periods should 

run for at least 24 hours to overcome the sampling problem. 

It was convenient for us to perform this height analysis using the COARE flux algorithm. 

However, we emphasize that any model (or combination of models) of air-sea exchange that 

produces values for the fluxes, the roughness parameters, and the atmospheric stability is capable 

of relating an observation at a particular height in the surface layer to any other height (e.g., 

standard 10 m) through the use of equation (11.7).
B-1 

Appendix B – The TOGA-COARE Bulk Flux Algorithm 

B1. History and Features 

In 1993, as part of the TOGA-COARE Air-Sea Interaction (Flux) Working Group 

activity, Chris Fairall, Frank Bradley and David Rogers began development of a bulk air-sea flux 

algorithm for use by the COARE community. The purpose was to ensure that the bulk flux 

results from every measuring platform were derived from identical assumptions, physical 

functions and computational methods. Faced with the challenging target of net heat flux 

accuracy of 10 Wm-2, any disagreements would be due to the basic observations, not differences 

in the bulk algorithm. In some respects, the same situation applies to the SAMOS initiative. 

The COARE algorithm had to take account of the light wind, strongly convective 

conditions found in the region of the tropical Pacific warm pool. It was based on the model of 

Liu, Katsaros and Businger (1979), hereafter referred to as LKB, which used the formalism of 

Monin-Obhukov similarity theory for the atmospheric surface layer, solving equations (11.2) to 

(11.7) iteratively for the surface fluxes. The velocity roughness was specified by the 

Charnock/Smith expression (11.11), and the scalar roughness lengths from relationships between 

the velocity and scalar roughness Reynolds numbers given by LKB. Independent estimates of 

αc (= 0.011) and the gustiness parameter, β (= 1.20) in equation (11.13) were made from 

covariance and dissipation flux measurements made during COARE. The unstable profile 

functions were a blend of the Kansas functions, ψk, near neutral (Businger et al. 1971) with a 

form, ψc, that obeys the theoretical scaling limit in highly convective conditions (Fairall et al. 

1996b). The stable forms were as determined for Kansas. 

At Version 2.5b the COARE bulk flux algorithm was made generally available (Fairall 

et al. 1996b). Its major shortcomings were that the exchange coefficients were based on less 

than 1000 hours of directly measured fluxes and solely on COARE data, and so was effectively 

“tuned” to tropical conditions with few wind speed observations greater than 10 ms-1. Clearly, 

the algorithm needed to be generalized for more global applications and tested against a much 

broader dataset. By 1999 Fairall’s group at NOAA/ESRL [Earth System Research Laboratory; 

formerly Environmental Technology Laboratory (ETL)] had undertaken cruises in all ocean 

basins (Fairall et al. 1997). From these a flux database of over 7200 h was assembled, including 

800 h with wind speeds in excess of 10 ms-1, and 2200 h at high latitudes. It was augmented 

with 94 h of high wind data from the HEXMAX experiment (De Cosmo et al. 1996). A subset 

of these data were used to refine the algorithm as Version COARE 3.0, and it was tested against 

the entire database (Fairall et al. 2003). 

Codes are available at ftp://ftp.etl.noaa.gov/user/cfairall/bulkalg/cor3_0/ and 

accompanied by a “readme” description. Inputs required are time series of the meteorological 

variables, together with the height (or depth) of measurement; i.e., date, time, wind speed, air 

temperature and humidity, sea temperature, downward short- and long-wave radiation, rainfall, 

latitude, longitude. The radiation data are required for the calculation of ocean surface warming, 

the ship’s position for gravity and the solar time. 

Features of Version 3.0 are as follows: 

1. Below 10 ms-1 the Charnock parameter in equation (11.11) retains the value (αc = 0.011), but 

above 10 ms-1 takes a simple wind speed dependence based on data from various sources (e.g., 

Hare et al. 1999). 
B-2 

2. The Liu et al. (1979) scalar roughness relationship has been replaced with a much simpler one 

that fits both the COARE and HEXMAX databases, 5 0.6

0 0 5.5 10− −

t = q = × Rr z z . 

3. A gustiness factor [equations (11.12) and (11.13)] is calculated in the flux loop, using β = 1.25 

determined from COARE measurements, and applied as ( ) 2 2

u +ug to avoid singularity as u→0. 

4. An empirical constant in the convective portion of the scalar profile function has been 

optimized to match direct profile observations (Grachev et al. 2000). 

5. The Kansas stable profile functions (Businger et al. 1971) have been replaced by those from 

Beljaars and Holtslag (1991), which compare well with new profile data taken over the Arctic ice 

cap (Persson et al. 2002) and appear to be a better fit at extreme stability. 

6. The stability iteration loop uses a bulk Richardson number parameterization as a first guess 

(Grachev and Fairall, 1997), thus reducing calculation time and making the algorithm more 

attractive for use in numerical models. 

7. SST (skin temperature) can be obtained from measurements at depth, using models of the 

diurnal warm layer and cool skin (see section 2.6, Fairall et al. 1996a and Wick et al. 2005). 

The models require downwelling long- and short-wave radiation data; values for broadband 

surface albedo and ocean emissivity are taken as 0.055 and 0.97, respectively (from COARE 

observations). These optional models are not used if SST is obtained using an infrared or 

microwave radiometer. 

8. An option has been included to allow the velocity roughness to be calculated from wave 

parameters. We have taken two models from the recent literature that are wave age and/or wave 

slope based. Oost et al. (2002) requires the wave period to be specified, and Taylor and Yelland 

(2001) need both wave period and significant wave height. However, these schemes have not 

been tested with reliable wave measurements. 

9. The so-called Webb correction (Webb et al. 1980) to latent heat flux arises from the 

requirement that the net dry mass flux be zero. The latent heat flux is therefore formulated in 

terms of mixing ratio, the fundamentally conserved quantity, instead of specific humidity. 

However, the model returns the mean Webb velocity, which can be used to compute Webb 

corrections for any trace gas or particle fluxes measured simultaneously. 

10. The momentum and sensible heat fluxes due to rainfall are calculated (Caldwell and Elliott, 

1971; Gosnell et al. 1995). 

B2. Examples of COARE 3.0 Performance 

The essence of the bulk flux scheme is the specification of roughness lengths or, 

equivalently, of the 10-m neutral transfer coefficients. Version 3.0 is based on averaging 

thousands of data points; in Figure B1 the actual data are shown with the model. An example of 

the model’s ability to yield the correct values, on average, for fluxes is shown in Figure B2, 

where both model-derived and measured latent heat fluxes have been composited in wind speed 

bins (the lines denote means and the symbols denote medians). The agreement is excellent from 

0 to 20 m/s. Another way to view the state of the transfer coefficients is to ratio with the Version 3.0 

specifications (Figure B3). Here the ESRL (ETL) data are shown as points with statistical 

uncertainties in the mean quantity. The dashed lines are the transfer coefficients used by the two 

major operational weather forecast centers (NCEP and ECMWF). NCEP recently replaced their 
B-3 

Figure B1. Wind speed dependence of the momentum (lower panel) and scalar transfer (upper 

panel) coefficients for COARE versions 3.0 (solid line) and measurements (thin line with 

circles). 

Figure B2. The average of covariance and ID latent heat fluxes computed in 10-m neutral wind 

speed bins. Mean values are shown by lines and medians by symbols: the solid line and circles 

are measured fluxes, and the broken line and crosses are calculated with COARE 3.0. 
B-4 

model (labeled ‘old’ in the figure) with a derivative of the COARE 2.5 model. The operational 

parameterizations are now within 10% of the ESRL (ETL) data and the COARE algorithm for 

wind speeds from 0 to 20 m/s. 

Figure B3. The average wind speed dependence of 10-m neutral transfer coefficients divided by 

the COARE 3.0 values (Upper panel, Ce10 n; lower panel Cd10 n). The dashed lines are NCEP and 

ECMWF formulae (as labeled); the solid line with symbols is the average ESRL (ETL) data. 

B3. Estimate of Turbulent Flux Errors 

If we want to determine the value of some variable, x, then we perform a ‘measurement’ 

with an instrument that provides an estimate of the value, xm. A simple method to illustrate the 

relationship between what we want and what we get is a linear form with a bias (offset) and a 

slope, 

m m m x = bias + slope * x = x + bias + (slope −1) * x . (B3.1) 

The bias represents a persistent offset in the device and the (slope-1) corresponds to a persistent 

percentage error in each measurement. In principal, the bias and slope can be determined by a 

laboratory calibration and subsequently removed as a source of error by correcting the device 

output. To actually use the device on a ship, we ship it from the calibration facility, mount it 

somewhere in an environment that may be quite different (variable and influenced by flow 

distortion, heat island, etc.), connect it to data-logging system and operate it for a time period on 

the order of a year. Thus, the correct bias and slope corrections to be applied to this one-year 

record now must be considered uncertain. In many cases, we may need to apply in situ
B-5 

calibration or intercomparison methods to constrain these uncertainties to meet our guidelines 

(see Table 1) as discussed in detail in the previous sections. 

A second aspect of measurement uncertainty must be considered when dealing with 

geophysical variables, which vary considerably with space and time. Typically, we are 

interested in statistical properties of the variables, such as the mean, standard deviation, or 

frequency spectrum. For climate purposes, the one-month average temperature at a particular 

location is of more significance than the instantaneous temperature at any specific time. If we 

now consider the variable to be a function of time, x(t), then we are interested in estimating the 

intrinsic mean of the variable, μx, or its standard deviation, σx. At a given place and time, we can 

take a sample of the time series of x and compute the average of x (denoted <x>) over some time 

interval, Δt. 

∫

+Δ

Δ Δ

< > =

t t

t

t x t dt

t

x ( ) 1 . (B3.2) 

However, this particular average is only an estimate of the intrinsic mean – there is uncertainty in 

the estimate. This is analogous to asking 100 people how they will vote (a sample) to try to 

guess the outcome of an election. You cannot expect the 100 people you happen to poll to give 

exactly the same result as the 1 million that vote in the election. We can compute how uncertain 

our estimate of μx is by using normal statistics theory 

 

x

x

x t

t

x

Δ Τ

=< >Δ ±

/

σ

μ , (B3.3)

where Τx is the decorrelation time scale for x. Suppose you make a measurement of x at a 

specific time, then the decorrelation time is how long you must wait before a subsequent 

measurement will be independent of the previous one. Another way of stating it is “how long do 

you have to wait before x changes by about σx?” Thus, the quantity inside the squareroot symbol 

in equation (B3.3) is essentially the number of independent samples contained in the time 

intervalΔt (analogous to the 100 people we sampled in our poll). For climate applications, the 

trick is to make the number of independent samples so large (or, Δt so long) that the sampling 

uncertainty term in equation (B3.3) become negligible. Of course, we are still left with the 

residual errors caused by uncertainty in the bias and slope errors of the instrument. 

To examine uncertainty in fluxes, we begin with an approximation to the net surface 

energy budget equation (1.1) as the sum of sensible and latent heat plus the net radiative flux 

components. 

 Hnet = −Hs − Hl + Rsn + Rln . (B3.4)

The uncertainty in the net flux,δHnet , is estimated as 

 2

ln

2 2 2 2 ( H ) ( H ) ( H ) ( R ) ( R ) δ net = δ s + δ l + δ sn + δ . (B3.5) 

If we want the net flux uncertainty on the order of 10 Wm-2 and, for example, we allow each 

term to contribute equally, then the individual terms would have to be accurate to 5 Wm-2

(i.e., 4*25 = 100). The uncertainty of each term is computed by expanding the fundamental 
B-6 

computation relationship as a series of partial derivatives of the fundamental measured variables 

(x, y, z...) that go into the estimate of the flux terms. The uncertainties of each of those variables 

(δx, δy, δz...) gives an expression for the total error 

( ) ( ) ( ) ( ) ... 2 2 2 2z

z

F

y

y

F

x

x

F δF δ δ δ

∂

∂

+

∂

∂

+

∂

∂ = (B3.6) 

For a turbulent heat flux, we use equation (11.3), * * F u x x = − and represent each scaling 

parameter in terms of measured variables: 

u = Cd ( )S − x = Cx ( )(X s − X z ) = Cx ( )ΔX 1/ 2 1/ 2

*

1/ 2

* ς ς ς . (B3.7) 

Using (B3.7) in (B3.6) we have done the derivatives and expanded each system of 

equations for stress, sensible heat, and latent heat. This involves combining all common terms 

resulting from the stability length derivative before squaring. We will spare you the 25 pages of 

fascinating algebra and show just a few examples of the results. To do this, we created an error 

computation program (bulk_err_3.m) where all of the algebra is coded without a single error. 

This program is accessed with a simple driver program (err_drive.m) in which basic conditions 

and the accuracy of the measurements are specified. The accuracies are expressed as an offset 

and slope uncertainty for a linear measurement relationship 

ue=[.2 .03];%'true' wind speed relative to sea surface

te=[.3 0];%sea-air temperature difference

qe=[.3 0];%sea-air humidity difference

In this example, the wind measurement is uncertain by a 0.2 ms-1 offset plus 3% of the 

reading. In this case it is the wind error relative to the surface so it includes errors in the 

anemometer, the surface current, and the true wind computation. Here we assume that the seaair temperature difference and the sea-air humidity difference suffer only from offset errors. 

We have run the program for tropical conditions. The program graphs the errors as a 

function of wind speed for different values of the sea-air temperature difference. In the TOGA 

COARE field program in the tropical western Pacific, the typical wind speed was 5 ms-1 and the 

sea-air temperature difference was 1.5 C. These figures yield an uncertainty in bulk sensible flux 

of 1.5 Wm-2 and latent heat flux of 7 Wm-2 for a total uncertainty of the combined turbulent 

fluxes of about half our allowed total. Errors in the radiometric components account for most of 

the remainder. 
B-7 

Figure B4. The uncertainty in the turbulent heat flux computed by expanding the basic bulk flux 

relationships with matlab program err_driv.m where the instrument uncertainties are specified as 

above. Different curves are for different values of the sea-air potential temperature difference, 

ΔΘ : upper panel, sensible heat flux error; lower panel, latent heat flux error. 

C-1 

Appendix C - IR Radiative Flux Errors Caused by Objects in the Field of View 

Calculations of errors in standard PIR radiative flux radiometers (pyrgeometer) begins 

with the relationship of the radiance (radiant flux from a particular location in the sky), I, to the 

irradiance (total flux normal to a horizontal plane), R (W/m2

) 

R = ∫∫I(θ,φ) cos(θ )dΩ , 

where φ is the azimuth angle, θ is the zenith angle, and dΩ=cos(θ)dθdφ is the incremental solid 

angle. 

A pyrgeometer measures the downwelling IR radiation integrated over the hemisphere of 

the sky. If we assume the downwelling IR is isotropic (independent of φ and θ), the integral for 

a PIR with an unobstructed view of the sky becomes 

4

0

2

/ 2

0

2

0 cos ( ) / 2 Tsky R I dφ θ dθ π I σ

π π

π

= = = ∫ ∫ −

, 

where σ is the Stefan-Boltzmann constant, and Tsky is an effective radiative temperature for the 

sky. 

In fact, the IR radiation is somewhat anisotropic. At low elevation angles, the radiation 

temperature is close to the air temperature and at zenith it is lower than Tsky. The approximation 

used here will overestimate the effect of objects near the horizon and underestimate for objects 

near zenith. 

We can also compute this integral for the energy received by the PIR from an object of 

intensity Ix in the field of view defined by some width w and some height h with its bottom some 

distance d away. In this case, the limits of the integral are from ±δφ and 0 to δθ where the angles 

depend on h: 

( / )

( / 2 / )

atn h d

atn w d

=

=

δθ

δφ

where δθ describes the elevation angle of the object above the horizon. This yields 

4

1

/ 2

/ 2

2

1 cos ( ) [ cos( )sin( )] Tx R I dφ θ dθ I δφ δθ δθ δθ fσ

π

π δθ

φ

φ

= = + = ∫ ∫

−

+

−

, 

where Tx is the temperature of the object in question and 

[ cos( )sin( )] 2

2 δφ δθ δθ δθ

π

f = + . 

It now follows that the flux error (i.e., additional flux sensed by the PIR) is simply 

( ) 4 4 Tx Tsky ΔR = fσ − . 

We show two figures of examples of estimated errors for a 10-m tall pole of width 10 cm, a 10-m 

tall mast of width 30 cm, and a nearby ship bulkhead that is 6 m wide and 3 m high. The PIR 

(i.e., it is in the field of view). The results are given as a function of the distance of the object 

from the PIR. 
C-2 

In the case of the pole, the correction might be useful for a PIR mounted on the side of the 

pole or mounted on a forward rail where there are antennas or GPS receiver poles nearby. The mast 

example might be useful when mounting a PIR on the side of a mast. The bulkhead example 

describes the situation where a PIR is mounted on a lower deck some distance from higher parts of 

the ship (e.g., the bridge deck). Finally, note that the effects described here are functions of the 

angular size of the object (distance does not enter into it except as it relates to the angle). Thus, a 

10-m-tall, 10-cm-wide pole 10 m away has the same effect as a 5-m-tall, 5-cm-wide pole 5 m away 

(because δφ and δθ are the same). Also, the temperature effects scale almost linearly. 

Figure C1. Longwave flux errors caused by shipboard objects in the field of view of a pyrgeometer; 

a tubular pole (blue), a mast (red), and a bulkhead (green). The assumed radiative sky temperature of 

0oC is typical of mid-latitude clear-sky conditions. The lower panel is for an object at roughly 

ambient temperature; the upper panel is for an object warmed considerably by strong sunlight.
D-1 

Appendix D – Examples of Meteorological Observations and Fluxes 

The magnitude and behaviour of the meteorological variables, and the resulting bulk 

air-sea fluxes are illustrated in the following figures. We give two contrasting cases in tropical 

waters, on consecutive days from EPIC2001, and one from a mid-latitude cruise (Stratus-5, 

2005). 

Figure D1 shows that conditions on day 258 were fairly steady with no sign of convective 

activity. There was no rain, although the solar trace Rs indicates broken cloud. The wind 

steadily increased from around 2 to 5 ms-1 during the course of the day. During the daylight 

hours, the wind was probably strong enough to prevent the formation of a warm layer, and the 

air-sea temperature difference was remarkably steady. Consequently, the sensible and latent heat 

fluxes increased modestly, without major fluctuations. The fairly high 420 Wm-2 of downward 

longwave radiation was mainly due to the high humidity typical of the atmospheric boundary 

layer over the tropical ocean; at a temperature of 30oC the ocean surface emits about 465 Wm-2

of thermal energy so that Rnl is a loss of 45 Wm-2. Thus, the net energy is a loss to the ocean at 

night, and a gain due to solar absorption during the day. 

In contrast (Figure D2), day 259 was convectively active, with a series of rainstorms 

throughout the night. These were accompanied by increasing wind speed and humidity, while 

the air cooled through several degrees from the associated downdrafts. Over this period the sea 

surface temperature decreased only slightly (~1oC) so the air-sea temperature difference varied 

considerably, reflected in variability of the turbulent heat fluxes. Note that the rain produced 

more ocean cooling than any other flux component at the time of the storm. The solar trace 

indicates considerable cloudiness, although the solar energy still peaked at over 1000 Wm-2. 

However, the cloud caused much greater variability and larger values in downward longwave 

radiation than on the previous day. 

The Stratus cruises study the climatology of the stratus cloud deck off the west coast of 

Ecuador, Peru and Chile. Day 287 of the 2005 cruise (Figure D3), contrasts markedly with the 

tropical examples; stronger winds (consistently around 9 ms-1) sea temperature lower by about 

10oC, lower humidity, and the persistent stratus cloud cover. The air-sea temperature difference 

varied somewhat but, as with the tropical examples, Hs was very small. The drier air had two 

consequences for fluxes, tending to increase Hl and decrease Rl . However, because of the lower 

sea temperature, outgoing longwave radiation was only about 400 Wm-2 so net longwave 

radiation was small until breaks in the cloud cover late in the day. Despite the persistent cloud, 

shortwave radiation is substantial and the net heat input to the ocean is as in the other two 

examples; a small loss throughout the night and a gain during the day. 
D-2 

Figure D1. Bulk meteorology and flux variables from a typical relatively clear day in the tropics. 

Reading down from the upper panel, the variables are: rain rate; water (green line) and air temperature 

(blue line); wind speed; relative humidity; downward solar flux; downward IR flux; heat flux components 

[-Hs (blue), -Hl,(green), Rnl (red), -Hrain (cyan)]; and net heat flux. 
D-3 

Figure D2. Bulk meteorology and flux variables for a convectively active day in the tropics. 

Reading down from the upper panel, the variables are: rain rate; water (green line) and air 

temperature (blue line); wind speed; relative humidity; downward solar flux; downward IR flux; 

heat flux components [-Hs (blue), -Hl (green), Rnl (red), -Hrain (cyan)]; and net heat flux. 
D-4 

Figure D3. As for Figures D1 and D2, but for a region of ocean with quite different climatology; higher 

wind speeds, much lower sea and air temperatures, and lower humidity. Reading down from the upper 

panel, the variables are: rain rate; water (green line) and air temperature (blue line); wind speed; relative 

humidity; downward solar flux; downward IR flux; heat flux components [-Hs (blue), -Hl (green), Rnl

(red), -Hrain (cyan]; and net heat flux. 
E-1 

Appendix E: The Beaufort Wind Scale 

 In the early nineteenth century, Rear-Admiral Sir Francis Beaufort developed a 

13-interval scale for wind force based on the behavior of a well-conditioned man-of-war sailing 

ship (Huler 2004). In 1838, the British Admiralty adopted this scale as a method for unifying the 

reporting of winds at sea. But not until the early twentieth century did that scale emerge as the 

descriptive list of wind effects on both land and seat that we now know as the Beaufort Scale 

(Huler 2004). 

Reports of winds and sea state made using the Beaufort Scale still form an important part 

of the climate record.. Over time the Beaufort Scale has evolved to include descriptions of 

Beaufort Force depending on: conditions on land, in coastal waters an on sailing vessels 

(Simpson 1906); the open ocean sea state (Peterson 1927); and the conditions aboard a Canadian 

Ocean Weather Ship (Millar and McPhail 1951). Verploegh (1967) showed that all of these 

methods give similar estimates of the wind strength. There have been may studies which have 

attempted to define mean wind speeds and wind speed ranges for the intervals of the Beaufort 

Scale, known as Beaufort Equivalent Scales; one of the first was by Curtis (1897). 

Recent research has refined the Beaufort Equivalents used in climate research and Kent 

and Taylor (1997) showed that the scale of Lindau (1995) produced the most consistent datasets 

of anemometer and Beaufort wind speeds. However the wind observations made at sea as part of 

the Voluntary Observing Ships (VOS) Program must be made using the Official International 

Codes for the Beaufort Scale (WMO 1995). Table E1 shows the Beaufort Scale and includes 

descriptions of conditions for a given Beaufort force over the sea. 

References for Appendix E: 

Curtis, R.H., 1897: An attempt to determine the velocity equivalents of wind forces estimated by 

Beaufort's scale. Q. J. R. Meteorol. Soc., 23, 24-61. 

Huler, S., 2004: Defining the wind. Three Rivers Press, 290 pp. 

Kent, E.C., and P.K. Taylor, 1997: Choice of a Beaufort Equivalent Scale. J. Atmos. Oceanic Tech., 14, 

228-242. 

Kinsman, B., 1965: Wind Waves. Prentice-Hall, 676 pp. 

Lindau, R., 1995: A new Beaufort equivalent scale. Proc., Internat. COADS Winds Workshop, Kiel, 

Germany. H. F. Diaz and H.-J. Isemer (eds), 232-252. 

Millar F.G., and H.W. McPhail, 1951: Beaufort Equivalents of the Apparent Wind on Shipboard. The 

Marine Observer, 21(152), 126-130. 

Petersen, P., 1927: Zur Bestimmung der Windstarke auf See. Annalen der Hydrographie 55, 69-72. 

Simpson, C.G., 1906: The Beaufort Scale of Wind-Force. Report of the Director of the Meteorological 

Office, Official Report No 180, London. 

Verploegh, G.,1967: Observation and Analysis of the Surface Wind over the Ocean, G, KNMI Report 

No. 89, 62 pp. 

WMO, 1995: Manual on Codes. Vol. 1, Part A: Alphanumeric codes. WMO Report No. 306, 

loose leaf. See section E for Beaufort Scale definition. [Available from: 

http://www.wmo.int/web/www/WMOCodes/ManualCodes/WMO306vol-I-1PartA.pdf] 
E-2 

Table E1: Beaufort Force Intervals, Equivalent Speeds and Probable Wave Heights 

(Adapted from WMO 1995, Manual on Codes, volume 1, part A: Alphanumeric Codes, 

WMO Report No. 306, section E). H1/3 is the significant wave height, the average height 

of the highest one-third of all waves occurring during a period (Kinsman 1965). 

 Velocity equivalent at 

10 m height 

Force Descriptive 

Term 

knots ms-1 m.p.h. Sea specification H1/3

(m) 

0 Calm < 1 0.0-0.2 <1 Sea like a mirror. - 

1 Light air 1-3 0.3-1.5 1-3 Ripples with the appearance of scales are formed, 

but without foam crests. 

0.1 (0.1) 

2 Light breeze 4-6 1.6-3.3 4-7 Small wavelets, still short but more pronounced. 

Crests have a glassy appearance and do not break. 

0.2 (0.3) 

3 Gentle 

breeze 

7-10 3.4-5.4 8-12 Large wavelets. Crests begin to break. Foam of 

glassy appearance. Perhaps scattered white horses. 

0.6 (1) 

4 Moderate 

breeze 

11-16 5.5-7.9 13-18 Small waves, becoming longer, fairly frequent 

white horses. 

1 (1.5) 

5 Fresh breeze 17-21 8.0-10.7 19-24 Moderate waves, taking a more pronounced long 

form; many white horses are formed. Chance of 

some spray 

2 (2.5) 

6 Strong 

breeze 

22-27 10.8-13.8 25-31 Large waves begin to form; the white foam crests 

are more extensive everywhere. Probably some 

spray. 

3 (4) 

7 Near gale 28-33 13.9-17.1 32-38 Sea heaps up and white foam from breaking waves 

begins to be blown in streaks along the direction of 

the wind. 

4 (5.5) 

8 Gale 34-40 17.2-20.7 39-46 Moderately high waves of greater length; edges of 

crests begin to break into spindrift. The foam is 

blown in well-marked streaks along the direction of 

the wind. 

5.5 (7.5) 

9 Strong gale 41-47 20.8-24.4 47-54 High waves. Dense streaks of foam along the 

direction of the wind. Crests of waves begin to 

topple, tumble and roll over. Spray may affect 

visibility. 

7 (10) 

10 Storm 48-55 24.5-28.4 55-63 Very high waves with long overhanging crests. 

The resulting foam, in great patches, is blown in 

dense white streaks along the direction of the wind. 

On the whole, the surface of the sea takes a white 

appearance. The 'tumbling' of the sea becomes 

heavy and shock-like. Visibility affected. 

9 (12.5) 

11 Violent 

Storm 

56-63 28.5-32.6 64-72 Exceptionally high waves (small- and mediumsized ships might be lost to view for a time behind 

the waves). The sea is completely covered with 

long white patches of foam lying along the 

direction of the wind. Everywhere the edges of the 

wave crests are blown into froth. Visibility 

affected. 

11.5 

(16) 

12 Hurricane 64 

and 

over 

32.7 and 

over 

73 and 

over 

The air is filled with foam and spray. Sea 

completely white with driving spray; visibility very 

seriously affected. 

14 (-) 

This table is only intended as a guide to show roughly what may be expected in the open sea, remote 

from land. It should never be used in the reverse way; i.e., for logging or reporting the state of the 

sea. In enclosed waters, or when near land, with an off-shore wind, wave heights will be smaller and 

the waves steeper. Figures in brackets indicate the probably maximum height of the waves.
F-1 

Appendix F – Useful Websites 

http://www.etl.noaa.gov/et6/wgsf/ 

http://www.gfdi.fsu.edu/SEAFLUX/ 

http://uop.whoi.edu/projects/projects.htm 

http://woodshole.er.usgs.gov/operations/sea-mat/air_sea-html/index.html 

http://oaflux.whoi.edu/ 

http://www.ncdc.noaa.gov/oa/climate/vosclim/vosclim.html 

http://woodshole.er.usgs.gov/operations/sea-mat/ 

http://www.pmel.noaa.gov/home/data.shtml 

http://www.ifremer.fr/ird/soopip/ 

http://www.meteo-technology.com/ 

http://seaboard.ndbc.noaa.gov/index.shtml 

http://daac.gsfc.nasa.gov/CAMPAIGN_DOCS/hydrology/TRMM_analysis.html 

http://www.researchvessels.org/ 

http://las.ngdc.noaa.gov/las/servlets/dataset?catitem=9 

http://www.uea.ac.uk/env/solas/ 

http://en.wikipedia.org/wiki/Main_Page 

http://www.noc.soton.ac.uk/JRD/MET/ 

http://amsglossary.allenpress.com/glossary 

http://samos.coaps.fsu.edu/html/ 

http://www.coaps.fsu.edu/RVSMDC/ 

http://www.coaps.fsu.edu/RVSMDC/FSU/Fluxes/ 

G-1 

Appendix G – Metadata Documentation: SAMOS Example 

G1. Introduction 

 The importance of comprehensive metadata alongside the observations was stressed in 

section 9. Section 10 deals with the necessity of establishing a routine for transmitting both data 

and metadata to a secure archive, where it will be available for climate research far into the 

future. There are clearly many ways in which these measures can be achieved, and fresh ones 

surface under the title of Data Management during the planning of each new field programme. 

 This Appendix describes the procedures that are being put in place to ensure uniform 

metadata documentation and data format among ships participating in the Shipboard Automated 

Meteorological and Oceanographic System (SAMOS) Initiative. SAMOS is referred to briefly 

in the Background section, and described fully in COAPS (2004). The SAMOS Data Assembly 

Center (DAC) will collate the data and associated metadata, perform initial quality assurance and 

ensure archival and accessibility arrangements. The DAC has designed metadata forms 

(Figures G1 and G2) to be filled out when a vessel is recruited to the SAMOS data exchange. 

They are an indication of the wide range of metadata needed for quality-assurance purposes, to 

ensure long-term availability of the observations for climate research. Some notes on these 

forms are as follows: 

G2. Vessel Metadata (Figure G1)

The vessel metadata are required to uniquely identify the vessel providing data to the SAMOS 

Initiative. They consist of the vessel identifiers, contact, layout and data file information. 

Vessel Name - The registered name of the vessel (e.g., Melville). 

Call Sign - Used to identify the vessel (e.g., WECB). 

IMO Number - Issued by the International Maritime Organization (e.g., 8717283) to uniquely 

identify the vessel. This number stays with the vessel even if the name and call sign are changed.

Recruiting Country (if participating in the VOS program) - The International Organization for 

Standardization (ISO) 2-character code for the country whose Meteorological Service recruited 

the vessel (e.g., AU). 

Vessel Type - A 2-letter code defining the type of vessel. 

Operating Country - The country operating the research vessel or responsible for installing and 

maintaining the SAMOS on a merchant vessel. 

Home Port (optional) - If no home port exists, then a commonly visited port. 

Date of Recruitment - When a vessel agrees to participate in SAMOS (yyyymmdd). 

Data Reporting Interval - Interval (seconds) between recorded values; ideally the same for each 

navigation, meteorological, and oceanographic parameter (SAMOS seeks a 60-sec interval). 

Participation in other data exchanges (optional) - SAMOS would like to know if the vessel 

participates in VOS, VOSClim, SOOP, ASAP, SEAS, GOSUD, etc.). 

The contact metadata are essential to maintain the open exchange of data, metadata, and dataquality information with the vessel and the vessel’s home institution. Two-way communication 

is needed to provide data-quality feedback while the vessel is at sea. 
G-2 

Vessel Information 

Vessel Name Call Sign IMO Number Recruiting Country Vessel Type 

 

Operating 

Country

Home Port Date of 

Recruitment

Data 

Reporting

Participation in other data 

exchanges 

 

Contact Information 

Home Institution Aboard Vessel 

Name Technician Name(s) 

Address 1 2 

Technician Email(s) 

 1 

Contact Person 2 

Name Alternate Contact email(s) 

Email 1 

Phone Fax 2 

Vessel Home Page 

Vessel Layout 

Dimensions Digital Imagery and Schematics Date Submitted

Length m Photo – Vessel side view 

Breadth 

m Photo(s) – Instrument Mast(s) / Site(s) 

Freeboard 

m Schematic – Top View 

Draught m Schematic – Side View 

Cargo height* m Schematic – Bow / Stern View(s) 

*If applicable Submit electronic imagery to: samos@coaps.fsu.edu 

Data File Specifications 

File Format Format Version File Compression 

(zip, gzip, etc.) Email Data Sent From 

 

Submit Files as Email Attachments to: samos_data@coaps.fsu.edu 

Figure G1. Vessel metadata form used in SAMOS initiative. 
G-3 

Home Institution - Name and postal address of the institution that operates the vessel or, for 

merchant ships, the institution that installs and maintains the SAMOS. 

Contact Person - Name, email, phone, and fax for the primary SAMOS data contact at the home 

institution. This person should have overall knowledge of the SAMOS installation and datamanagement procedures for the vessel. 

Vessel Home Page URL (if available) - A link from the SAMOS data center web page will be 

made to each participating vessel’s home page. 

Technician Name(s) and Email(s) - of marine technician(s) responsible for meteorological data 

collection and SAMOS service while at sea, or “no tech onboard” if appropriate. Ability for the 

SAMOS data center to reach a technician will enable data quality feedback to the vessel while it 

is underway. 

Alternate Contact(s) - for the purpose of real-time data quality feedback. Contact points should 

be decided by vessel operators and could include a generic email for the chief scientist or a 

contact at the vessel’s home institution when no onboard technician is available. 

The dimensions and design of the vessel are valuable information for data-quality evaluation. 

Knowing the position of the instruments relative to windward obstacles or to the vessel exhaust 

stack can help identify suspect data values. Dimensions should be expressed in meters to the 

nearest 0.1 m. These parameters are defined in WMO publication number 47, Annex V: 

Length - The length overall (LOA) of the vessel (e.g., 94.9 m), 

Breadth - The molded breadth (beam) of the vessel (e.g., 20.3 m), 

Freeboard - The average freeboard of the vessel as measured from the maximum summer 

loadline (e.g., 2.6 m), 

Draught - The average vertical distance between the vessel’s keel and the maximum summer 

loadline (e.g., 7.9 m), 

Cargo height - The average height of the cargo above the maximum summer load line on the 

particular route where observations are made (e.g., 6.5 m). If the cargo is below the main deck 

(e.g., a bulk tanker or in ballast), the height of the main deck itself. 

Digital Photography and Vessel Schematics - Digital photos (.jpg format) and scanned 

schematics (.pdf format) of vessels and/or sensor locations provide a wide range of information 

for data quality assurance and applications. 

Photos should include a side view of the entire vessel and the masts or sites that house the 

SAMOS instruments. The latter are most useful when taken at a distance sufficient to show the 

sensor’s environment and possible obstacles to airflow around the sensor. The naming 

convention for the digital file(s) is in the following format: 

xxxxxxxxxyyyymmddaaa...aaa.jpg, where 

 xxxxxxxxx IMO number (a nine-digit number, include leading zeros if applicable) 

 yyyymmdd year, month, day 

 aaa...aaa short description of the photo or schematic 

Example: 00085124520020214anemometer_port_side.jpg 
G-4 

Schematics desired include a top, side, and bow or stern view of the vessel. Marking the 

location of the meteorological and oceanographic sensors on the schematics would be helpful.

Example 00085124520020214aft_view_schematic.pdf 

Please send to samos@coaps.fsu.edu and provide the date submitted on the metadata form. 

Examples of requested files can be viewed on the SAMOS web page under the “digital imagery” 

button on the metadata portal (http://samos.coaps.fsu.edu/html/meta.php). 

SAMOS data exchange is designed around daily email attachments containing the observations 

collected over the previous day. The data file specification provides information needed by the 

DAC to uncompress and process each attached file. Although designed for SAMOS, the same 

principles would apply to any data exchange or archiving program. 

File Format - Name of the format used for emailed data file attachments. The format must be 

self-describing (what variables are where in the file), have a known delimiter between values, 

and a known missing value (e.g., SAMOS data exchange format, see following section). 

Format Version - Version number of the file format (e.g., 001 for SAMOS format). 

File Compression - If files are compressed, please indicate the algorithm used (e.g., zip, gzip) 

Email Data Sent From - The email address to verify that files originate from a known provider. 
G-5 

G3. Primary Instrument Metadata (Figure G2) 

This form lists information related to the individual parameters typically observed by a SAMOS 

and suitable for the calculation of turbulent air-sea fluxes by bulk methods. It is critical for 

initial data quality evaluation and for the future scientific application of the data. Gray areas in 

Figure G2 indicate metadata that are not applicable to the particular parameter. 

Logging System Name - Name or acronym to identify the combined instrument and data-logging 

system used on the vessel (e.g., NOAA SCS, WHOI IMET). 

System Version - Version number of data-logging software. 

Wind Direction Convention - Whether the direction given is toward or away from that from 

which the wind is blowing. 

Anemometer Zero-line Reference - The installed orientation of the zero reference on the 

anemometer compass in degrees measured clockwise from the bow. 

0 ̊ – reference pointed toward bow 

90 ̊ – reference pointed toward starboard 

180 ̊ – reference pointed toward stern 

270 ̊ – reference pointed toward port 

Pressure Adjusted to Sea Level – Yes or No. 

Designator for SAMOS - For SAMOS, a short alphanumeric tag is used to identify the type of 

data value within each record (see section G4). For other data transmission systems, it might be 

the column heading for a fixed format tabular file.

Instrument Make - Manufacturer of the instrument (e.g., R.M. Young). 

Instrument Model - Model or series number of instrument (e.g., 5103). 

Units - Original units for each parameter (e.g., Deg. + East, knots,  ̊C). SI units are preferred, 

but providing the original units are specified, the DAC can convert values to SI units. 

Instrument Location – This is specified by measurements (to the nearest 0.1m) that include: 

From Bow - Distance from the foremost point of the vessel above the mean waterline 

back to the instrument on a line parallel to the vessel centerline (positive value); 

From Center Line - Distance to port (P indicator or negative value) or starboard 

(S indicator or positive value) on a line perpendicular to the centerline; 

Height/Depth - Height above (depth below) the mean waterline (positive above the 

water, negative for a depth measurement) 

Measured vs. Calculated - Indicates whether the parameter was directly measured (M) or 

calculated (C) from other measured parameters (e.g., true winds must be derived from the 

vessel-relative winds, course, heading, and speed of the vessel). When possible, please 

provide (via email or an attached document) the formula used for each calculated value. 
G-6 

Data Averaging

Spot vs. Average Value – Indicates whether the parameter represents an instantaneous 

(spot) or a time-averaged value. 

Value Time Center – When the value is time-averaged, indicate whether the time stamp 

associated with the value represents the beginning, center, or end of the averaging period. 

Length – Of the averaging period (in seconds), if applicable. 

Sampling Rate - The sampling rate from each individual instrument (Hertz). 

Data Precision - The fractional value (decimal) to which the sensor can resolve changes in the 

measured parameter, as specified by the manufacturer. 

Date In or Last Calibration - SAMOS will record the installation date or the last date of 

calibration for each sensor (yymmdd format). 

Radiation Direction Convention - Indicates whether the sensor is measuring downwelling (dn) 

or upwelling (up) radiation. 
Figure G2. Instrument metadata form used in SAMOS initiative. 

Primary Instrument Metadata Vessel Name 

Logging System Name System Version 

Use this form to provide primary sensor information for each available parameter. 

Wind direction convention (to/from) Anemometer zero-line reference (deg) Pressure adjusted to sea level (yes/no)

Instrument Distance (nearest 0.1 m) Data Averaging 

Parameter

Make Model Units From bow 

P/S 

from 

center 

line 

Height 

/depth 

Measured/ 

Calculated 

Spot vs. 

Average 

Value 

Value 

Time 

Center 

Length 

(sec) 

Sampling rate 

(Hz) 

Data 

precision 

(decimal) 

Date in/last 

calibration 

(YYMMDD) 

Latitude Longitude Heading Course over ground Speed over ground Speed over water Vessel-relative wind direction Vessel-relative wind speed Earth-relative wind direction Earth-relative wind speed Atm. pressure Air temperature Wet-bulb temperature Dewpoint temperature Relative humidity Specific humidity Precipitation Shortwave radiation (up/dn) Longwave radiation (up/dn) Visibility Ceiling height Sea temperature Salinity Conductivity 
G-8 

G4. SAMOS Data Format 

 There are two ways of providing unique identifiers for each data element within a data 

storage format; as a header line at the top of a fixed format tabular file or imbedded within the 

individual data records. In the former, missing values must be replaced with a dummy, or the 

file may be impossible to interpret. Although the latter results in longer data lines, pairs 

(identifier:value) can drop out when the value is missing. This is the scheme adopted for 

SAMOS. 

 The SAMOS exchange format uses two separators, "," between tagged pairs and “:” 

between the designator and the data value ":". Each tagged pair consists of an alphanumeric 

designator and the associated data value. Therefore an example record at date (YMD) and time 

(HMS) will read: 

$SAMOS:001,CS:KAOU,YMD:20030907,HMS:000011,AT:17.40,BP:1010.27,..., 

WSP:5.6,WDP:354.4,TWP:5.4,TIP:278.3,WSS:6.7,WDS:350.5,TWS:6.6,TIS:274.4,..., 

LA:44.66956,LO:-130.35859,COG:149.5,SOG:0.9,GY:284.7,CS8:23, 

where air temperature (AT) is 17.40oC, relative wind speed (WSP) is 5.6 m/s, etc., and the units 

and other value identifiers, etc., are known by reference to the metadata form (Figure G2). 

1. Observation times must be in the Universal Time Coordinate (UTC). 

2. $SAMOS:001 represents the first version of the SAMOS data format, and is followed with the 

ship call sign pair (CS:call_sign). Beyond these first two tagged pairs, the order of the data is 

immaterial since the designators uniquely identify each tagged pair and their data values. 

3. Each institute can decide whether or not to include an 8-bit checksum for each line in the file 

(at the end of each line). If you do plan to have a CS8, please provide us with details on how the 

number is calculated so we can decode the value. 
Ref-1 

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Ref-3 

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