Buoy wind inhomogeneities related to averaging method and anemometer type: application to long time series

Transcription

1 INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 31: (2011) Published online 19 April 2011 in Wiley Online Library (wileyonlinelibrary.com) DOI: /joc.2339 Buoy wind inhomogeneities related to averaging method and anemometer type: application to long time series B. R. Thomas a *andv.r.swail b a Environment Canada, Science and Technology Branch, Climate Research Division, Dartmouth, NS, Canada b Environment Canada, Science and Technology Branch, Climate Research Division, Toronto, ON, Canada ABSTRACT: Moored buoy observations began two to three decades ago. These datasets have value for calibration of remotely sensed data, validation of weather and ocean wave models, input to reanalysis models, and studies of climate trend and variability. Changes in buoy wind observation methods have the potential to introduce inhomogeneities into the time series. These include changes in anemometer height (typically between 5 and 10 m) with changes in platform type; the transition in the 1980s and 1990s from use of a vector-mean to a scalar-mean averaging method; and, in recent years, the introduction of ultrasonic anemometers in the buoy networks, as well as the continued use of mechanical propellervane type of anemometers. This study examines differences in buoy wind measurements from RM Young propeller-vane and Vaisala ultrasonic anemometers installed on the same buoy, and differences in vector-averaged and scalar-averaged wind speeds from the same RM Young anemometer. This study also considers the effect of waves on these differences. The comparisons are based on large multi-year datasets from 6-m buoys with boat-shaped hulls (6N) and 3-m buoys with round hulls (3D), deployed off the coasts of Canada in the northeast Pacific and northwest Atlantic Oceans. Results of the anemometer type comparison suggest that the Vaisala ultrasonic winds are 0.16 ms % of the RM Young winds. Results of the vector scalar comparison of Canadian buoy data show that scalar mean winds were 2.1% and 2.7% greater than RM Young vector mean winds from 6N and 3D buoys, respectively. Vector-scalar differences increased with increasing wave height, more quickly with 3D than with 6N buoys. Results are used to adjust the winds from a long-term buoy in the northeast Pacific. The height adjustment is shown to be more important than the adjustment for vector or scalar averaging of the sustained wind speed, in terms of the impact of monthly mean wind speeds. A homogeneity testing program finds other unexplained significant shifts in the time series of monthly mean wind speeds. Copyright 2011 Royal Meteorological Society and Crown in the right of Canada. KEY WORDS buoy; wind; anemometer; averaging method; inhomogeneities Received 25 June 2009; Revised 7 September 2010; Accepted 4 March Introduction Long time series of marine data are available from moored weather buoy networks operated by the US NOAA/National Data Buoy Center (NDBC) since the early 1970s and by Environment Canada (EC)/Meteorological Service of Canada (MSC) since Global marine wind climate trend analyses have used ship reports from the International Comprehensive Ocean- Atmosphere DataSet (ICOADS), satellite wind retrievals, and surface winds from reanalysis/forecast models. Buoy records are now two to three decades in length, and there is interest in assessing regional trend and variability from hourly moored buoy measurements (e.g. Gower 2002) for regional applications, and for validation of global results on a regional scale. Cardone et al. (1990) and Thomas et al. (2008) showed the importance of adjusting ship winds for observation method and for measurement height. In this study, we investigate two changes in buoy * Correspondence to: B. R. Thomas, Environment Canada, 16 th Floor, 45 Alderney Drive, Dartmouth, NS, Canada B2Y 2N6. bridget.thomas@ec.gc.ca wind measurement methods which have the potential to introduce inhomogeneities in the long-term record: the change in sustained wind speed averaging method, from vector to scalar (Gilhousen, 2007), and the introduction of ultrasonic anemometers in addition to propeller-vane anemometers. The sustained wind speed changed from a vector to a scalar average in the 1980s in the NDBC network (Gilhousen, 1987) and in the 1990s in the MSC network. Gilhousen (1987) reported that vector averages were about 7% lower than scalar averages for winds above 8ms 1. Short-term MSC field studies showed smaller differences, with vector means on average about 3% lower than scalar means (Skey et al., 1998; Thomas and Swail 1999). A large MSC buoy dataset, with both vector and scalar means from the same anemometer, allows us to study the differences over a wide range of conditions spanning several years, for two different hull types. The mechanical propeller-vane anemometer has been the standard since the beginning of moored weather buoy programs, but in recent years, ultrasonic anemometers have been introduced widely on MSC buoys, usually in Copyright 2011 Royal Meteorological Society and Crown in the right of Canada.

2 BUOY WIND INHOMOGENEITIES 1041 the backup location. In this study, we compare ultrasonic and RM Young (RMY) winds from several ocean buoys over a wide range of conditions. The longest operating moored weather buoy station in Canadian waters is the Middle Nomad Buoy , in the northeast Pacific off the coast of British Columbia. We adjust for anemometer height changes and apply the vector-scalar averaging results to this station, for an analysis of the long-term monthly mean wind speeds. We also assess the adjusted time series for additional remaining inhomogeneities. Section 2 describes the buoy networks, metadata, and archived datasets. In Section 3, we describe the methods of quality control, adjustment of winds to a common reference height, and homogeneity assessment for the time series analysis. Sections 4 and 5 give results of the comparison of vector and scalar means and the comparison of ultrasonic and RMY wind speeds, respectively. In Section 6, we describe the application of results to the long time series of winds from the Middle Nomad Buoy Discussion follows in Section 7 with a summary and conclusions in Section Data and metadata 2.1. MSC moored buoy data and metadata Table I gives information valid for the present, for the MSC Pacific and Atlantic moored buoys used in this study. Some of these metadata at individual stations have changed over the years; relevant changes are described in the text where appropriate. The two primary hull types are the 6-m NOMAD (Navy Oceanographic and Meteorological Automated Device) (6N or NOMAD) with a boat-shaped hull, used at the 3 offshore Pacific buoy stations and most Atlantic stations, and the 3-m Discus (3D), used at the other Pacific stations and one Atlantic station. The buoys transmit hourly reports of sustained wind speed and direction, gust wind speed, air and sea temperature, and wave data including significant wave height and peak wave period. Technical details of the MSC buoy program are available in two reports by Axys (1996, 2000). Each buoy has two anemometers, on different arms of the same mast, at slightly different heights (Table II), with a nominal height of 5 m above the water level. The MSC buoys use the RMY helicoid propeller-vane Table II. Metadata for RM Young and Vaisala Ultrasonic anemometers and for the thermometer installed on MSC 6-m NOMAD and 3-m Discus buoys: height (m) of anemometers, in the first and second positions, and height of the thermometer; Values in parenthesis for the 3D apply to an older (pre-1996) mast configuration. 6 m NOMAD 3 m Discus Anemometer Position: RM Young (RMY) (3.73) Vaisala Ultrasonic (sonic) Thermometer Height (m) (3.69) Table I. Information presently valid for moored Atlantic and Pacific MSC buoys used in the study. ID Name Hull Type Date 1st Deployed Lat ( N) Long ( W) Depth (m) North Nomad 6N Middle Nomad 6N South Nomad 6N South Brooks 3D Central Dixon Entrance 3D South Moresby 3D North Hecate Strait 3D South Hecate Strait 3D West Sea Otter 3D West Dixon Entrance 3D La Perouse Bank 3D East Dellwood 3D West Moresby 3D Gannet Rock a 3D East Scotian Slope 6N SW Grand Banks 6N Banquereau Bank 6N Tail of the Banks 6N Laurentian Fan 6N Lahave Bank b 6N NE Burgeo Bank 6N Halifax Harbour Approaches 3D a discontinued in 1997; b the WMO ID was until January 2006.

3 1042 B. R. THOMAS AND V. R. SWAIL anemometer, model numbers or for marine applications (here referred to as RMY), and the Vaisala (formerly Handar) WS425 Ultrasonic (here referred to as the sonic). After initial testing on a limited number of 3D Pacific buoys, the sonic was installed in the secondary position on 3D Pacific buoys, beginning around 2005, and on Atlantic 6N buoys, beginning in Both types of sensors are two-dimensional, measuring wind horizontal to the buoy deck. Until the early 2000s, there was an informal practice of using a new RMY in the first position and a refurbished RMY in the second position (personal communication, V. Williams). Since then, with increased use of a sonic in the second position on the Pacific 3D buoys, either a new or refurbished anemometer could be used in the first position. We used the raw data from the MSC buoys as archived by the Fisheries and Oceans Canada/Integrated Science Data Management (ISDM) division, and available online from the ISDM web site. The archived meteorological data are not quality controlled but do include values from all duplicate sensors. For metadata such as hull, processor, and anemometer type, we used archived buoy network status reports, as well as information from the buoy specialists, and recently, detailed inspection reports, available since 2007 for the Atlantic and 2008 for the Pacific buoys. We compiled metadata separately for each buoy. MSC buoys have used two different on-board processor (also referred to as payload) types over the years, both developed by Axys. The first, known as the Zeno, originally calculated a 10-min vector-averaged wind speed and an 8-s gust speed (the highest 8-s moving scalar average of the wind speed within the 10-min averaging period). The sample frequency for both speed and direction is 1 Hz. The u and v components were determined for each sample, then averaged over 10 min. The vector average speed and direction were determined from the averaged u and v components. The vector mean is less than a scalar mean, depending on the variability of the wind during the averaging period. Beginning around 1994, the scalar mean speed was calculated as an additional field. The vector mean wind speed, rather than the scalar, continued to be included in the FM13 ship format reports until 1997, but ISDM archived both values. The second processor, known as the Watchman (WM) and introduced in the mid- to late 1990s, calculates a 10-min scalar mean wind speed and a 5-s gust. Table IV gives the start and end dates, and number of observations with both vector and scalar means from the same anemometer, for each buoy in this study. This dataset spans 2 6 years at individual stations, mostly from the late 1990s, and allows us to study vector scalar differences from the same anemometer over a wide range of conditions NDBC buoy data and metadata We used quality controlled NDBC buoy data for the Middle Nomad Buoy 46004, , available online from NDBC and from the US National Oceanographic Data Center (NODC). The NODC archived data, in F291 format, includes additional fields such as the anemometer height. The NDBC buoy network has included different hull and payloads over time, as detailed for individual stations in NDBC s online Data Inventory and Data Availability Summary. The payloads included the GSBP (General Service Buoy Payload), introduced in 1978, which computed a vector mean wind speed averaged over 8.5 min, and the DACT (Data Acquisition, Control, and Telemetry), introduced in 1983, which computed a scalar mean wind speed (Gilhousen 1987), averaged over 8 min. Both reported a 5-s gust. Earlier NDBC payload types in use at buoy 46004, with large 10-m and 12-m Discus buoys, included the PEB (Prototype Environmental Buoy), the PEB UDACS, and the UHF Data Acquisition and Control System (UDACS) (A) Hamilton (1980). The Bendix Aerovane propeller anemometer was the most common type by 1981 but the RMY 5103 gradually became standard during 1980s (Gilhousen, 1987). Baynton (1976) noted that the Bendix Aerovane ran 7% slower than specifications. 3. Method 3.1. Quality control Assessment of data by deployment phase (the period between service visits) was helpful in the diagnosis and flagging of suspect data. We also used historical buoy status reports to determine service dates, and used various types of plots of the hourly raw data to detect (and exclude) periods of erroneous wind data. We also excluded periods when the buoy was adrift but still reporting, or reporting before it was fully deployed. The very simplest quality control (QC) step, choosing the sensor with the stronger readings, was usually sufficient for the scalar-vector analysis. The scalar mean values (when produced by the Zeno as an extra parameter) came after the standard reported values in the raw messages, so were more likely to be corrupted on the rare occasions when a buoy report was truncated due to transmission problems. These were eliminated by rejecting scalar means greater than the gust speed or less than the vector mean. As would be expected, vector means could be reduced by direction errors from a faulty compass or potentiometer, which did not affect the scalar means. On occasion, both sensor types, but particularly the RMY installed on 3D buoys, failed in severe storms, returning near-zero wind speeds thereafter. On other occasions, the RMY deteriorated gradually, and started to read low compared to the other anemometer. If the other anemometer has already failed, it is harder to screen out periods of reduced wind speeds from the remaining anemometer. Without use of another reference, this is the situation with potential for poor quality data to adversely affect the time series. A more detailed procedure was needed for the sonic-rmy comparison, to ensure that degraded RMY buoy winds were not being compared to

4 BUOY WIND INHOMOGENEITIES 1043 Table III. Height Adjustment Factors for anemometers installed on MSC 6-m NOMAD (6N) and 3-m Discus (3D) buoys, to adjust from anemometer height to 10 m, or to the height of an RMY anemometer in position 1, using KNMI and SW models (the median value, over a range of conditions and locations, is given for the SW model). Position Sensor type From (m) To (m) Ht diff (m) KNMI factor Median SW factor 6N, to 10 m nominal RMY Sonic RMY Sonic N, to Ht. of W1-RMY 2 RMY Sonic D, to 10 m 1 RMY Sonic RMY Sonic (pre-1996) RMY D, to Ht. of W1-RMY 2 RMY Sonic (pre-1996) RMY sonic winds. The sonic-rmy comparison period for each deployment phase typically ended when the buoy or its transmitter failed completely, when the buoy was taken in for annual servicing, or when the RMY failed. The sonic has reported zero or near-zero wind speeds for short periods of time when the sensors were observed to be covered with ice from freezing spray (personal communication, R Sheppard, 2009). On rare occasions, high spikes have also been observed from sonic anemometers. These rare occurrences were not excluded from the figures in this paper, but were excluded in the regression analyses. The NDBC buoy data is quality controlled prior to archiving so little additional work was needed (NDBC 2009) Height adjustment Winds increase approximately logarithmically with height in the surface layer of the marine atmospheric boundary layer. The rate depends on the atmospheric stability in the surface layer, and the surface roughness. Winds measured at different heights need to be adjusted to a common reference height for comparison. We used two wind profile models to adjust the winds for height, the Smith-Walmsley model (Smith, 1988; Walmsley, 1988) and the KNMI model (Benschop, 1996), as described by Thomas et al. (2005) for use in adjusting buoy and ship winds to a common reference height. The Smith- Walmsley (SW) model accounts for atmospheric stability using the air sea temperature difference and varies surface roughness as a function of wind speed. We used this model in comparison of ultrasonic and RMY anemometer wind speeds, to adjust the ultrasonic winds to the same measurement height as the RMY winds. The KNMI model assumes neutral stability and a constant surface roughness. We used this simpler method in the long time series analysis, to adjust buoys winds from measurement height (near 5 m for NOMAD buoys) to 10 m (the typical anemometer height on the large 10D and 12D buoys). Table III gives the KNMI model adjustment factors for various buoy configurations, and the median SW adjustment factor for some of the datasets. No height adjustment is needed for the scalar to vector comparison, as it uses winds from the same anemometer Adjustments and homogeneity assessment for time series analysis For the time series analysis, we analysed wind speeds before and after adjustment for height, as described in the previous section. We also analysed winds before and after correction of vector mean wind speeds. We used results of the vector scalar comparison to adjust individual buoy winds that were known to be vector means, when scalar means were not available. We used a constant factor rather than the wave-dependent factor to adjust the vector means, since the choice of factor makes little difference for monthly means. For the times series analysis, we calculated monthly mean wind speeds and used only months with at least 65% of the possible number of observations. We calculated monthly means for three datasets: the original wind speeds, the winds adjusted to 10 m, and the winds adjusted to 10 m then corrected for averaging method, denoted WS, WS10, and WS10C, respectively. Since it is possible that there were other program changes that could also cause changes in the time series, we also used a statistical package, RHTestV3, in combination with a reference time series (Wang 2008; Wang

5 1044 B. R. THOMAS AND V. R. SWAIL and Feng 2009), to detect mean shifts (step changes) in the time series data, adjust for those shifts as appropriate, and calculate trends. Use of the reference series increases the sensitivity of the test and the statistical significance of the results. It reduces the likelihood that step changes are due to climatic variations. RHTest output using a reference series includes plots of monthly means and step changes for the difference series, for the de-seasonalized (monthly anomaly) series, and for the original (base) series (with the timing of the step changes determined from the difference-estimated steps), as well as for the series adjusted with the difference-estimated steps. The step changes may be explained by metadata, or related to undocumented program changes or uncorrected data quality problems. The RHTest user can accept or reject each detected step, through assessment of its statistical significance or the existence of metadata, then rerun the software, in an iterative process. Since the process of accepting or rejecting the step changes and making changes in the timing based on metadata is somewhat subjective, the final results vary, depending on choices made. This level of detail is beyond the scope of this study, and results presented here are from the first run of RHTest in each case, without any user modifications. 4. Comparison of scalar- and vector-averaged wind speeds from the same anemometer We analysed a large dataset of hourly vector and scalar mean sustained winds from each RMY anemometer, for many of the MSC buoys, over the period The data from each buoy were analysed separately, as well as being grouped by platform type. Table IV includes intercepts and slopes of the best fit regression lines, as well as the peak significant wave height at each buoy and the corresponding ratio of scalar to vector mean wind speed. Results for the two platform types differ. Figure 1 shows frequency scatter plots of the scalar means against the vector means, for Pacific and Atlantic (a) 6N and (b) 3D buoys. There is less scatter with the NOMAD buoys. The best-fit line for the NOMAD buoys has a slope of and a near-zero intercept, i.e. scalar means were 2.1% higher than the vector means, overall. One point shows as an outlier but examination of the data shows no reason to exclude it. The best-fit line for the 3D buoys has a slope of Figure 2 shows the data for the individual open ocean 3D buoys that were combined in Figure 1b. Only the South Moresby buoy shows the need for additional quality control. For about 3 months, after one Table IV. Vector (Uv) to scalar (Us) mean wind speed (from the same anemometer) comparison for MSC Pacific and Atlantic buoys: start and end dates of period when both vector and scalar means were reported, number of observations, regression coefficients a and b for the regression equation (U s U v,u s = a + bu v, the peak significant wave height (Hs) at each buoy during the specified period, and the scalar to vector ratio of the corresponding wind speeds. ID Hull Type-Region Start End N a b Pk Hs RSV at pk Hs N-Atl N-Atl N-Atl N-Atl N-Atl N-Atl N-Pac N-Pac N-Pac All 6N Atl All 6N Pac All 6N D-Atl / D-Pac D-Pac D-Pac a 3D-Pac D-Pac D-Pac D-Pac D-Pac D-Pac D-Pac D-Pac All 3D All 3D a a Excluding period of degraded data.

6 BUOY WIND INHOMOGENEITIES 1045 Figure 1a. Frequency scatterplot of scalar mean against vector mean wind speed from NOMAD buoys, in the MSC Pacific and Atlantic networks, with best-fit lines. This figure is available in colour online at wileyonlinelibrary.com/journal/joc Figure 1b. As in Fig. 1a, for open ocean 3D buoys. This figure is available in colour online at wileyonlinelibrary.com/journal/joc Figure 2. Scalar mean wind speed against vector mean wind speed for each Pacific and Atlantic MSC 3D buoy (as in Table IV), using the anemometer (1 or 2) with the stronger vector mean wind speed. The period of obviously reduced vector mean speeds at Buoy are excluded from Figure 1b. This figure is available in colour online at wileyonlinelibrary.com/journal/joc

7 1046 B. R. THOMAS AND V. R. SWAIL Figure 3a. As in Fig. 3b, for open ocean 3D buoys. This figure is available in colour online at wileyonlinelibrary.com/journal/joc Figure 3b. Box plot of ratio of scalar-to-vector mean binned on significant wave height, Hs in 1 m bins, for vector mean winds over 5 ms 1 for the 3D buoys, with best-fit lines to the median. This figure is available in colour online at wileyonlinelibrary.com/journal/joc anemometer failed in a storm and the other was damaged, the remaining sensor reported degraded winds: vector means were intermittently reduced in comparison to the scalar. This period was excluded from Figure 1b. The scalar vector difference increased with increasing winds and waves. This is consistent with results by Skey et al. (1998) who showed that the variability of wind direction over individual waves increased with increasing sea state. The box plots in Figure 3(a) 6N and (b) 3D buoys, for the same datasets as Figure 1, show that the median ratio of scalar to vector means (binned on significant wave height) increases with wave height. The increase is approximately linear at the 6N buoys. The increase is faster at the 3D buoys up to about 7 m, then the trend starts to level off. The 3D buoy values also show more variability for all wave heights. We use the median scalar to vector ratio as an indication of the typical percent difference between the scalar and vector mean wind speeds. This reaches 3 4% for NOMAD buoys, 4 5% for 3D buoys, for Hs 7 to 13 m. The best-fit lines express the median scalarto-vector ratio as a function of wave height. For storm case studies, or climate analyses where extremes are of interest, these wave-height-dependent relationships could be used to adjust the vector mean wind speeds to make them equivalent to scalar means. We noted and examined several instances where one of the anemometers reported erroneous wind directions. However, in most of those cases the other anemometer was still working properly, so the erroneous data are not included in the scalar vector analysis, which used the sensor with the stronger of the two wind speeds. In some

8 BUOY WIND INHOMOGENEITIES 1047 cases with storm-damaged anemometers, there were large reductions in vector means (compared to scalar), which were associated with direction errors. RMY anemometers on the smaller Discus buoys generally showed a greater rate of degradation of the vector mean wind speed data over time, and a higher rate of complete sudden failure, compared to those on NOMADs. 5. Comparison of winds from anemometers on the same buoy: duplicate RMY anemometers, and RMY and Vaisala ultrasonic anemometers 5.1. Height differences between anemometer positions 1and2 For the comparison of sonic and RMY anemometers, we examine a large dataset of winds from open ocean 3D Pacific buoys and 6N Atlantic buoys. In both cases the sonic is in the second, slightly lower position, although the difference is not as great on the 3D buoy (in the current configuration) as on the NOMAD (Table II). According to the theory of wind variation with height in the surface layer, these height differences would result in measured wind speed differences. Ideally, to compare winds from different heights we need to adjust the winds from one anemometer to the height of the other anemometer, using the model with the better physics, the SW model. This height adjustment (increasing the lower height sonic winds by about 1.2%, for 6N buoys and about 0.2% for 3D buoys, as in Table III) will affect the sonic RMY comparison results Duplicate RMY anemometers In order to assess the validity of this fine-scale level of height adjustment, we compare the predicted difference in wind speeds with the observed differences, using a buoy with duplicate RMY sensors at slightly different heights. We used data from the 6N East Scotian Slope Buoy 44137, from 2003 to 2008 when new RMY were used. The scatter plot of W1 against W2 before height adjustment of W2 (Figure 4) includes the equations for best-fit lines of W1 against W2 before and after the SW height adjustment. In both cases, the intercept is nearzero. The slope is about before adjustment and reduces to near-unity after adjustment (median adjustment factor 1.016, Table III). These results seem to confirm the validity of adjusting winds from the height of the lower anemometer to the height of the higher anemometer, prior to comparison. Relatively small anemometer height differences of 0.8 m do appear to make a small but detectable difference in wind speeds, at heights of 4 5 m. We also noted a difference between W1 and W2 in data from the early 3D buoys, with a greater height difference between the two anemometers than with current 3D buoys, which was consistent with model predictions (not shown). Results of comparing duplicate RMY wind speeds from the buoy in the approaches to Halifax Harbour, buoy 44258, both when it was a 6N ( ) Figure 4. Comparison of W1-RMY against W2-RMY at 6N Buoy 44137, service years , with the best-fit line. Equations are given for the best-fit lines for W2 not adjusted, and, in brackets, adjusted to the height of W1. This figure is available in colour online at wileyonlinelibrary.com/journal/joc and when it was a 3D with anemometers only 0.3 m apart ( , ), do not show the modelpredicted difference in wind speeds. The slope of the regression line before adjustment of W2 was near-unity: about (3D) and (6N) (not shown). For consistency, for the comparison of RMY winds in one position with sonic winds at a slightly lower height, we do adjust the sonic winds for both 6N and 3D buoys RMY and Vaisala ultrasonic anemometers Scatter plots for 6N Atlantic buoys (Figure 5) and 3D Pacific buoys (Figure 6) of (a) sustained winds, W2 sonic (adjusted) against W1 RMY, and (b) gusts, G2 sonic against G1 RMY, show sonic winds to be slightly stronger than RMY winds, overall. These are for recent data (as given in Table V, but excluding Buoy 44137) with new anemometers. The few outliers are plotted but were excluded from calculation of the best-fit lines. Regression results for these datasets, of W2-sonic against W1-RMY, both before and after the SW adjustment of W2, separately for each buoy and deployment phase are presented in Table V. The table also gives the median SW adjustment factor for each grouping. We found that individual 6N buoys and deployment phases gave similar results, except at Buoy where sonic-rmy differences were slightly greater, with a slope of 1.04, unadjusted winds. For that reason, we give regression results for the grouped buoys with and without Buoy data. Overall, prior to height adjustment and excluding Buoy 44137, the regression line intercept is about 0.16 ms 1 and the slope is After the SW adjustment, the slope increases to Results for the 3D buoys were similar to those for the 6N buoys, both

9 1048 B. R. THOMAS AND V. R. SWAIL Figure 5a. For 6N Atlantic buoys 44138, 44140, 44141, 44150, and 44255, frequency scatter plots of W2 sonic adjusted to height of W1, against W1 RMY and best-fit lines (excluding outliers: WS differences >2 ms 1. This figure is available in colour online at wileyonlinelibrary.com/journal/joc Figure 5b. For 6N Atlantic buoys 44138, 44140, 44141, 44150, and 44255, frequency scatter plots of G2 sonic against G1 RMY, and best-fit lines (excluding outliers: gust speed differences >3 ms 1 ). This figure is available in colour online at wileyonlinelibrary.com/journal/joc in the mean and the gust wind speeds, although the gust sonic-rmy difference appears to be slightly greater for 3D than 6N buoys. Results are consistent even in extreme conditions. The strongest sustained winds in the comparisons represented by Table V were measured at the 6N Lahave Bank Buoy in Post-Tropical Storm Noel on 4 November This buoy had slightly better overall agreement between the sonic and RMY sensors, than the other buoys. In the storm, the two sensors types agreed very well: the RMY measured winds of 26.4 ms 1 with gusts to 34.5 ms 1 ; the sonic measured winds of 26.1 ms 1 (26.5 ms 1 after adjustment to the RMY height) with gusts to 33 ms 1. We also analysed sonic-rmy differences for individual buoys and individual deployment phases for all open ocean 3D Pacific buoys, between 2005 and 2007, with Table V. Sonic RMY comparison for MSC Atlantic 6N buoys, by buoy and deployment period, all 6N buoys combined, and all 6N buoys excluding (6N), and for MSC Pacific 3D buoys, by buoy, and all 3D buoys combined: WMO ID; start and end of deployment period; number of cases; linear regression coefficients a and b for W2-sonic regressed against W1-RMY, i.e. W 2 sonic = a + bw 1 RMY, for original W2, and for W2 adjusted to height 1 using SW model; median SW model adjustment factor. WMO ID Start End N W 2 W 1 W 2 adj SW W 1 Med SW adj factor a B a b /06/ /01/ /08/ /08/ /08/ /10/ /08/ /04/ /06/ /01/ /05/ /10/ /05/ /08/ /08/ /01/ All 6N All6N a /05/ /04/ /05/ /03/ /11/ /04/ /05/ /04/ /05/ /01/ All 3D a All 6N buoys excluding (6N).

10 BUOY WIND INHOMOGENEITIES 1049 Figure 6a. Frequency scatter plots of mean wind speed, as in Figure 5a for 3D Pacific buoys 46132, 46145, 46185, 46206, and 46208, deployed in 2009 with new RMY; best-fit line excluding outliers. This figure is available in colour online at wileyonlinelibrary.com/journal/joc extensions to 2010 at some stations. During this period, both new and in-house or Campbell-Scientific refurbished RMY anemometers were used (personal communication, V. Williams). The particular status of each RMY over this extended period, especially before 2008, is not readily available. However, we did get similar results (not shown) at most buoys and deployment phases during the longer period as in the recent period with new RMY. In some deployment periods the sonic and RMY differences were slightly greater, with slopes for W2-sonic unadjusted regressed against W1-RMY up to about We think that these larger differences are not representative of new or of most refurbished RMY anemometers (whether the refurbishing was done inhouse or by Campbell-Scientific). The difference for most buoys/deployment phases remains fairly constant for increasing wave heights or wind speeds. The percentage difference (sonic minus RMY, relative to RMY), actually decreases slightly for increasing wave heights or winds speeds, contrary to the scalar to vector mean results. A characteristic of the RMY to sonic wind speed ratios (as opposed to the Figure 6b. Frequency scatter plots of gust wind speed, as in Figure 5b for 3D Pacific buoys 46132, 46145, 46185, 46206, and 46208, deployed in 2009 with new RMY; best-fit line excluding outliers. This figure is available in colour online at wileyonlinelibrary.com/journal/joc ratios from duplicate RMY wind speeds) was a more frequent occurrence of very small values (ratios between 0.2 and 0.8), for light wind speeds. This is likely a result of the higher starting speedof the helicoid-propeller anemometer compared to the ultrasonic. As a result of the offset, it would be misleading to use a single percentage difference or ratio to describe the sonic-rmy difference over all cases. This could suggest larger differences at high wind speeds than actually observed. 6. Application to long time series For the Middle Nomad Buoy 46004, October 1976 to December 2009, we adjust wind speeds to a standard reference height of 10 m and apply vector-scalar adjustments as needed, then calculate monthly mean wind speeds. Sonic anemometers have not been installed on this buoy, or on the other two offshore Pacific NOMAD buoys. Table VI details the dates of data availability, changes in hull type, payload (processor), and anemometer heights over time, and the adjustments applied to each period. Gaps in the time series are due to sensor Table VI. Metadata and adjustment factors for Middle Nomad Buoy 46004: dates of operation, hull and payload type with wind averaging method if known, anemometer height(s), Z (m) (positions 1/2 if different), and KNMI height adjustment (to 10 m) and vector to scalar (VS) adjustment factors. Date Range Hull/Payload Z(m) Ht Adj Factor to10m VS adjustment Factor 1976/10/ /10/25 10D/PEB /09/ /02/23 12D/PEB UDACS /02/ /06/26 12D/UDACS (A) /06/ /06/29 6N/GSBP-vector (Uv>8) 1988/08/ /07/30 6N/Zeno-vector 5.25/ / (Uv) 1995/07/ /05/09 6N/Zeno-scalar 5.25/ / /05/ /12/31 6N/WM-scalar 5.25/ /

11 1050 B. R. THOMAS AND V. R. SWAIL Figure 7. Monthly means of original wind speed, WS (the base series) and RHTestV3 detected step changes for (Middle Nomad) Oct 1976-Dec 2009, using GROW2000 grid point 42312, Jan 1970-Dec 2009, as a reference time series. (a) difference (base minus reference) series, and difference-estimated step changes, (b) de-seasonalized (base with mean annual cycle removed) series and de-seasonalized-estimated step changes, (c) base series and multi-phase regression model fit with de-seasonalized-estimated (solid) and difference-estimated (dashed) step changes, and (d) base series adjusted using the de-seasonalized-estimated steps (solid) and difference-estimated steps (dashed). This figure is available in colour online at wileyonlinelibrary.com/journal/joc or payload failure caused by operation in a severe wave environment. For the reference time series at the Middle Nomad buoy we used the nearest gridded wind speed from the GROW2000 (Global Reanalysis of Ocean Waves) dataset (Oceanweather Inc., 2000), which are highly correlated with the measured winds. These winds are based on the US National Center for Environmental Prediction Re-analysis (NRA) 10-m surface wind fields, and are objectively adjusted on a basin-wide scale, to improve calibration with satellite wind speeds, and modified to include information on tropical systems (Cox and Cardone 2000; Cox and Swail 2001). Figures 7, 8, and 9 present results for the time series without adjustments, the series after height adjustment, and after adjustments for height and averaging method. Trends in the resulting time series are shown in Table VII. These were produced by the statistical software package RHTestV3, run with a reference series, and without any user modification of the detected step changes. There is considerable seasonal variation with original monthly means varying from summer to winter from about 6 to about 12 ms 1. Monthly maxima (not shown) vary from about 13 ms 1 insummerto24to30ms 1 in winter.

12 BUOY WIND INHOMOGENEITIES 1051 Figure 8. As in Figure 7 for 46004, where the base series is from monthly means of the wind speed adjusted to 10 m, WS10. This figure is available in colour online at wileyonlinelibrary.com/journal/joc Several shifts in the original WS time series (Figure 7) can be related to documented hull and processor changes, although the specific reason for a difference is not always known. There is a positive shift after a gap in 1978, coinciding with the change from 10D/PEB before the gap to 12D/UDACS after the gap. A large negative shift in March 1981 coincides with the change from 12D/PEB UDACS to the 12D/UDACS (A). The most prominent negative shift occurs in July 1983, which coincides with the change from12d/udacs (A) to the 6N/GSBP (with vector mean winds). This is consistent with the decrease in anemometer height. The step is still evident in Figure 8 after height adjustment but it is less pronounced. In the late 1980s, there is a negative step followed by a positive step a few months later, which coincides with the change from the 6N/GSBP to the 6N/Zeno (both reporting vector means). The apparently reduced values between the two steps may have been due to degraded wind speeds from a damaged anemometer; they are not affected by the adjustments and are still evident in Figure 9. There were other small shifts in the later years of the time series, which are not clearly linked to known programming changes. Detection of the small positive shift from MSC vector to scalar means in July 1995 is probably hampered by a long gap in the data just before. It is no longer present in Figure 9 after adjustment of vector means. A small positive step in May 1999, which persists in the adjusted data, seems to coincide with the change from

13 1052 B. R. THOMAS AND V. R. SWAIL Figure 9. As in Figure 7 for Buoy 46004, where the base series is from monthly means of the wind speed adjusted to 10 m and corrected to scalar means, WS10C, as described in the text. This figure is available in colour online at wileyonlinelibrary.com/journal/joc the 6N/Zeno (scalar) to the 6N/WM (scalar), although reasons for that are not known and may warrant further investigation. The trend in the original series, prior to any adjustments, is negative. Adjusting for anemometer height switches the trend to positive. The vector adjustment applied to the height-adjusted winds makes a slight change (decrease) in the trend but it is still positive. Further adjustments, made with the RHTest-detectedstep changes, give different trends depending on whether the difference-estimated steps or the de-seasonalizedestimated steps are used. The time series adjusted with the de-seasonalized-estimated steps shows the largest trends, but all the trends are fairly small, not statistically significant. 7. Discussion The height adjustment factor to adjust from 5 m to the standard level of 10 m has a larger impact on trend analysis of long term wind speed than the averaging method or RMY-sonic differences. Vector means need to be adjusted to be equivalent to scalar means, up to about 1994 for MSC buoys. For monthly statistical parameters an adjustment (increase) of 2 3% for quality-controlled vector means would be sufficient. For case studies, or

14 BUOY WIND INHOMOGENEITIES 1053 Table VII. Trend in monthly mean wind speed (ms 1 yr 1 )at for WS, WS10, and WS10C, 1976/10 to 2009/12, before and after adjusting for difference or anomaly estimated step changes, using RHTest output (without any user modifications). Wind Trend, ignoring any step changes (ms 1 yr 1 ) Trend, adjusting for difference estimated steps (ms 1 yr 1 ) Trend, adjusting for anomaly estimated steps (ms 1 yr 1 ) WS WS WS10C when extremes are of interest, it would be important to use a wave-dependent correction factor for the vector means. For further work with long term time series of winds from NDBC buoys, it would be helpful to confirm whether the earliest winds that are available in the archives are based on vector or scalar averaging, as this is not always clearly documented. Also, changes in anemometer type in the earlier NDBC data may also contribute to inhomogeneities. Scalar-vector differences found in this study for properly working anemometers on MSC 3D and 6N buoys were typically smaller (at winds above 8 m s 1 ) than indicated in the earlier study by Gilhousen (1987), which was based on more limited data from a different and earlier observing program. Slight height differences between the primary and secondary anemometers on the buoys introduce small wind speed differences. Comparison of different wind sensors would be less complicated if the sensors were installed at the same height. Additional adjustments related to factors not investigated in this paper, such as buoy tilt and other buoy motions, or wave sheltering (Pond, 1968; Skey et al., 1998, 1999; Taylor et al., 2002; Howden et al., 2008), may be needed. These adjustments may differ depending on hull type or size. Undiagnosed quality problems with individual anemometers can depress the monthly means, particularly over the course of a winter season, and these would introduce unexplained steps in the climate record. Quality control of past and ongoing MSC buoy wind data would be enhanced by use of modelled winds as a reference (as is done in near-real time at NDBC (Gilhousen 2007)). The frequent data gaps that are inherent in wind data from open ocean buoys reduce the suitability of the dataset for time series analysis. We have not explored the possible effect on a monthly or annual time series analysis, of fewer gaps in the winter months resulting from use of more robust anemometers or larger buoys. RHTest can be used to test and adjust for observationrelatedstepchangesin the long time series. We have done this here for one station using well correlated reanalysis/modelled winds as a reference series, and this analysis could be applied to other long-term buoy stations. Ideally, some uncertainties would be resolved before making conclusions about trends in the marine wind climate at these locations, or confirming trends based on other sources. Additional metadata for early years of the NDBC buoy program would be helpful. Use of difference-estimated steps to adjust the dataset gave slightly different (weaker) trends than using step changes detected from seasonal anomalies, in this study. A more indepth analysis should confirm the homogeneity of the reference time series and the similarity of the trends in the reference and observation series. This study shows the value in overlapping observations from two different methods or instrument types for a period of time, to assess differences that may arise in the time series as a result of an observing change. It is important to track metadata on instruments and processing methods so that appropriate adjustments for systematic observing changes can be made to the climate record. Similar periods should be compared: the buoy data are from the mid 1970s or mid-1980s onward, while most other trend analyses span a longer time period. Both Wan et al. (2009) (using RHTest) and Tuller (2004) found slight decreasing trends in nearby coastal station winds of British Columbia, over the past five decades. Yu (2007) used a composite wind field product ( ) derived from satellite wind speed retrievals, reanalysis models, and ship reports, and related increases in oceanic evaporation to increases in surface marine winds over the western and southeast North Pacific, from the 1970s to 1990s. That paper does not indicate the relative importance of ship reports compared to reanalyis model winds in the objective analysis, or whether the ship wind reports used in the analysis were adjusted for a spurious increasing trend due to increasesin measurement height as shown by Thomas et al. (2008). Yu s analysis of wind speed change from the 1970s to the 1990s showed no change near the location of the Middle Nomad Buoy (51N 136 W), a slight decrease to the north, and increases to the south. 8. Summary and conclusions This study examines two possible contributions to inhomogeneities in long-term wind datasets: use of different anemometer types, in particular the RMY and the Vaisala Ultrasonic, and the change from vector- to scalaraveraging method for the sustained wind speed. Both these changes appear to make real, but relatively small, differences, on the order of a few percent. Changes in buoy anemometer height between 5 and 10 m, resulting from changes in hull type, have more impact on the long time series. The contribution of the change from MSC vector to scalar mean wind speeds was very small, in comparison to other mean shifts in the data. Other unexplained changes or undetected quality problems in the early years of the buoy program make more of a difference to the time series. Overall, the scalar mean wind speed was 2.1% higher than the vector mean wind speed for MSC 6N buoys, and

15 1054 B. R. THOMAS AND V. R. SWAIL 2.7% higher for MSC 3D buoys, for good quality RMY anemometer data. The difference increased with wave height, at a greater rate for the 3D than for the NOMAD, reaching 4 5% (3D) and 3 4% (6N) for significant wave heights of 7 13 m. These values are smaller than indicated by Gilhousen (1987) for NDBC buoys. With damaged anemometers or faulty compasses, the differences were larger, and increased more rapidly with wave height. RMY anemometers installed on open ocean 3D buoys deteriorated, or failed completely, more often than those installed on 6N buoys. Separate correction factors are needed for MSC and NDBC vector mean wind speeds. While nominally 5 m above the water level, the two anemometers on the MSC buoys are at slightly different heights. The difference is nearly 1 m on the NOMAD buoys and early 3D buoys, but less on the recent 3D buoys. These height differences introduce small consistent differences in mean wind speed: about 1% for NOMAD buoys, but less than 1% for recent 3D buoys. Especially for the NOMAD buoys and for the early 3D buoys, height adjustment is necessary to compare winds from the two anemometers. Vaisala Ultrasonic height-adjusted winds were very slightly stronger than RMY winds, by 0.16 ms 1 plus 1.6% of the RMY mean wind speed, over a wide range of wind speeds and wave heights. Ultrasonic gust speeds were also slightly stronger than RMY gust speeds. In the northeast North Pacific, at the Middle Nomad buoy 46004, first deployed in 1976 with a large Discus hull, the decrease in anemometer height with the change to the NOMAD buoy introduced a negative step change in the monthly mean wind speeds. Adjustment for measurement height switches the long term trend from negative to positive over the period , but the trends are not statistically significant. The transition from reporting of a vector mean to a scalar mean in the 1990s introduced a very small positive step change in the record; a 2% adjustment for averaging method makes a small negative correction to the long-term trend. There are unexplained apparent inhomogeneities in the wind record in the 1970s and 1980s, which may relate to NDBC observing program changes. Statistical adjustments based on these estimated step changes result in very small positive trends in monthly mean wind speeds, of about ms 1 yr 1, but the trends are not statistically significant. Acknowledgements Fisheries and Oceans Integrated Science Data Management (ISDM) maintained the complete archive of raw data from the Canadian moored buoy reports. MSC Buoy specialists R. Sheppard, V. Williams, M. McNeil (now retired), and R. McLaren (now retired) operated the Atlantic and Pacific buoy networks and answered many questions on buoy program changes over the years. L. 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