A Study of Wind Speed Variability Using Global Reanalysis ......Serial Correlation with Observations Table 1 compares the average coefficient of determination (r 2 ) calculated using

A STUDY OF WIND SPEED VARIABILITY USING GLOBAL REANALYSIS DATA

May 2013

Michael C. Brower, Chief Technical Officer

Michael S. Barton, Research Scientist

Llorenç Lledó, Team LeadForecasting Operations and Research

Jason Dubois, Senior Meteorologist

Page | 1 A Study of Wind Speed Variability Using Global Reanalysis Data

May 2013

BACKGROUND

One of the main factors determining the uncertainty in the predicted energy production of a wind project is the variability of the wind resource. This is often represented by the inter‐annual variability (IAV), which is defined as the standard deviation of the annual mean speed over a representative number of years. It is usually expressed as a percentage of the mean speed.

In the past, estimates of the IAV have been based mainly on historical wind speed measurements from meteorological stations. The typical range of values derived in this manner is 4% to 10%. This approach has the virtue of being simple and direct, but it has several disadvantages:

Wind project sites are often not located near meteorological stations. Meteorological stations are often in sheltered settings, whereas wind projects tend to

be on ridge tops and other exposed terrain features. At many meteorological stations, instrument designs and measurement protocols, as

well as surroundings and tower heights and placement, have changed over the years, resulting sometimes in significant discontinuities and trends in the historical data.

For these reasons, the IAV calculated from meteorological station records may not be representative of that experienced by wind projects in many cases. Any differences are likely to be site dependent and cannot easily be determined without new methods and sources of data.

Reanalysis data, a relatively new source of meteorological information, offer a potential solution to this problem. Reanalyses are produced by a numerical weather prediction (NWP) model – the same type of model used in weather forecasting – driven by historical weather observations from satellites, aircraft, balloons, and surface stations. The model and data‐assimilation system are “frozen” in time to provide as consistent a record as possible; only the observational data change. With this approach it is possible to generate a time series of gridded atmospheric variables, including temperature, pressure, wind, humidity, precipitation, and others, extending back several decades. Such synthesized weather records have proven to be of great use in climate studies.

The first generation of reanalysis data sets, released in the 1990s, is exemplified by the National Center for Atmospheric Research (NCAR)/National Centers for Environmental Prediction (NCEP) Global Reanalysis (NNGR; Kalnay et al. 1996). This data set provides weather data from 1948 to the present every 6 hours on a three‐dimensional grid with a horizontal spacing of approximately 200 km. However, the wind data were shown to have significant consistency problems in some locations, caused most likely by the challenges of assimilating data from a changing observational system (Brower, 2006). A subsequent release, Reanalysis 2, fixed some errors and updated parameterizations of physical processes but retained the same grid (Kanamitsu et al, 2002).


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In the past several years, a number of third‐generation reanalysis data sets have become available.1 The three leading global data sets are described below:

CFSR: the Climate Forecast System Reanalysis. Based on the Climate Forecast System, the NCEP global forecast model. Horizontal resolution ~38 km. Spans 1979 to the present.2 Most parameters available every 6 hours, selected variables every hour. (http://rda.ucar.edu/pub/cfsr.html) (Saha et al. 2010)

MERRA: the Modern‐Era Retrospective Analysis for Research and Applications. Based on the National Aeronautics and Space Administration (NASA) global data assimilation system (GEOS‐5). Horizontal resolution ~55 km (0.5° latitude, 0.66° longitude). Spans 1979 to the present. Most parameters available every 6 hours, selected ones every hour. (http://gmao.gsfc.nasa.gov/merra/) (Rienecker et al. 2011)

ERA: the European Center for Medium‐Range Weather Forecasts (ECMWF) reanalysis series (including ERA‐15, ERA‐40, and ERA‐Interim). ERA‐Interim is the most current. Based on the Integrated Forecast System (IFS), the main ECMWF global forecasting model. Horizontal resolution ~80 km (0.75°). Spans 1979 to the present. Most variables provided every 3 hours. (http://www.ecmwf.int/research/era/do/get/era‐interim) (Dee et al. 2011)

There is reason to hope that with their greater spatial resolution, as well as improvements in data assimilation methods, the new data sets will perform better for wind energy applications than NNGR. To test this hypothesis, AWS Truepower set out to (a) assess the suitability of the data sets for estimating IAV, and (b) using the best data set, create a global map of IAV for use in wind energy studies.

EVALUATION OF REANALYSIS DATA QUALITY

The first step was to assess the overall quality of the four global data sets. This was done in two stages. First, we tested the serial correlation of wind speeds between each data set and high‐quality observations from tall towers. Second, we examined the consistency of the data sets over time.

Serial Correlation with Observations

Table 1 compares the average coefficient of determination (r2) calculated using daily wind speed averages for 37 tall towers from wind project sites in the United States, Europe, and India. (See Figure 1 for a map of the tower locations.) The towers were selected for their geographic representation and good data quality. Each tower had at least one year of valid data. The

1 A complete list of reanalysis data sets can be found here: http://reanalyses.org/atmosphere/comparison‐table. 2 From January 2011 onwards, CFSR uses the operational CFS rather than the “frozen” CFSR model. However, for the time being, the operational CFS is reported to be identical to the CFSR.


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reanalysis daily averages were computed from samples every six hours extrapolated to 60 m height. This approach ensured a consistent comparison between the four data sets.

Table 1. Coefficient of determination (r2) of daily averages of four reanalysis data sets with respect to 37 tall towers in three regions

Region NNGR CFSR ERA‐I MERRA

Europe 0.64 0.74 0.73 0.66

India 0.55 0.69 0.71 0.67

USA 0.63 0.75 0.73 0.67

Average 0.61 0.73 0.73 0.67

As expected, NNGR performs the worst of the three, with a mean r2 across all regions of 0.61. Among the three newer data sets, ERA‐Interim and CFSR perform the best, with a mean r2 of 0.73. MERRA falls in between.

It should be noted that the results are averages, and that the performance of each data set varies considerably among the towers.

Figure 1. Locations of 37 tall towers (blue dots) and 23 additional points (red dots) employed for the reanalysis quality tests.


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Consistency Tests

Next, we assessed the consistency over time of the four data sets. Ideally, this is done by comparing the data against high‐quality measurements of long duration. Unfortunately, there are few wind monitoring stations where it can be said with confidence that the monitoring equipment, measurement protocols, and station surroundings have remained substantially the same for a period longer than a few years.

Therefore, we decided to rely on internal consistency tests instead. Our approach starts with the assumption that the wind climate over the past several decades has been relatively stable. Evidence on this point is admittedly mixed; however, the most reliable data – including the newer global reanalyses as well as studies upper‐air wind speeds from rawinsonde measurements – tend to support it (Brower et al. 2012). If this assumption holds true, then the “best” data set is the one that exhibits the fewest inhomogeneities, which we define as a statistically significant trend or discontinuity in the monthly average wind speed, as determined by standard statistical tests.

For these tests we employed a sample of 60 points, which included the 37 tall towers mentioned previously and an additional 23 land points selected to provide coverage in other parts of the world. No observational data were used. Each time series of reanalysis wind speeds was averaged on a monthly basis, and the monthly averages were converted to monthly anomalies by subtracting the long‐term averages for each calendar month. This approach removes the effect of seasonality in the time series.

Two tests were performed. The first, the Standard Normal Homogeneity Test (SNHT; Alexandersson, 1986), tests for statistically significant changes in the mean of a time series between an earlier period and a later period. By moving the dividing point between the two periods, the time of greatest inconsistency can be found, and the probability of this change occurring by chance can be determined. This type of test is especially well suited for identifying the effects of significant changes in observational systems, e.g., the launching of a new earth‐observing satellite or the moving of a rawinsonde station. The second, Mann‐Kendall (MK; Kendall, 1975), tests for the presence of statistically significant linear trends such as might occur as a result of gradual changes in land cover or of climate change due to global warming. The two tests are well documented in the literature. We tested for failures of the null hypothesis (no statistically significant trend or discontinuity) at the 1% confidence level (p


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with between 13 and 19 points failing to meet the 1% threshold. Once again, MERRA performs the worst. Comparing the results of the two tests, there are more significant trends than significant discontinuities in the data. The trends may reflect the influence of long‐period climate oscillations or climate change; gradual changes in the observational systems are also a possible explanation.

The number of points failing each test tends to increase with longer periods of record. This not surprising considering the large changes that have occurred in the global

Table 2. Number of points (out of 60) failing each of two tests (p


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points can be expected to fail a 1% threshold test purely by chance.3 Taking this fact into account, ERA‐I appears to do very well under the SNHT test for 1983‐2012 and shorter periods, while NNGR and CFSR do well for 1993‐2012 and 1998‐2012. However, all four models fail the trend tests for all periods.

In summary, we conclude the following:

Overall, ERA‐I is the best of the reanalysis data sets when judged by correlation with high‐quality wind speed measurements from tall towers and by long‐term homogeneity.

The 25‐year period 1988‐2012 appears to provide a suitable baseline for variability and anomaly studies.

INTER‐ANNUAL VARIABILITY

The next step was to calculate the IAV using the ERA‐I data set. First, for every grid point, we calculated the mean speed at 80 m height above ground for each year in the record. Next, we calculated the standard deviation of the annual means over the period 1988‐2012, and divided the result by the overall average speed over the same period. A global map of the results is shown in Figure 2. Maps for four regions – North America, South America, Europe, and India ‐ are presented in Figure 3.

It is apparent that the IAV varies considerably with location. In most of the central and eastern United States, for example, it is less than 4%, and in some areas it is less than 3%. This indicates a high degree of consistency in the average wind speed from year to year. In the western United States, the IAV is generally around 5%‐6%.

On a global scale, the inter‐tropical convergence zone along the equator is associated with a high degree of wind variability, with IAV values approaching or exceeding 10%. Outside of this zone, the IAV appears to fall in a range similar to that in the United States, i.e., 2%‐6%.

3 A 1% confidence threshold implies that there is a 1% probability that any given point fails due to chance variations alone. In a sample of 60 points, assuming they are independent, the probability that 5 or more points fail due to chance alone is 2.9%.


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Figure 2. Global map of IAV for the period 1988‐2012 based on the ERA‐Interim data set for a height of 80 m above ground. Values are given as a fraction of the mean speed.


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Figure 3. Same as Figure 2 for the United States, South America, Europe, and India

The final step of the analysis was to compare the predicted and observed IAV at 19 tall towers in different parts of the United States with periods of record ranging from 4 to 25 years. The results are shown in Figure 4. The chart shows that the ERA Interim data set explains the great majority of variations in observed IAV by location. This is somewhat surprising, considering that the towers have much shorter periods of record (an average of 7.7 years for all towers) than the reanalysis data. The implication of this finding is that patterns of IAV are relatively persistent over time; a corollary is that differences in variability between sites are not mainly the result of


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short‐term climate fluctuations.

A further observation is that the average observed IAV is about 85% of the average predicted IAV. This discrepancy may arise because there is a tendency for the IAV to increase with the number of years due possibly to long‐period climate oscillations such as ENSO, the Arctic Oscillation, and Pacific Decadal Oscillation. The ERA‐Interim data show a gradual increase in the IAV of about 0.02% per additional year of data (Figure 5). The difference in IAV between 5‐10 years of data and 25 years of data is fairly consistent with the 15% discrepancy observed between the reanalysis IAV and observed IAV.

Figure 4. Comparison of predicted and observed IAV at tall towers in the United States with records of at least 4 years.

Figure 5. Average IAV from the ERA‐I reanalysis data set as a function of the number of years included in the calculation.

y=2.00E‐04x+2.94E‐02

2.0%2.5%3.0%3.5%4.0%4.5%5.0%

0 5 10 15 20 25 30 35

IAV (%

)

Number of Years in IAV Calculation

IAV vs. Number of Years

y=0.85xR²=0.7935

0.0%1.0%2.0%3.0%4.0%5.0%6.0%7.0%

0.0% 2.0% 4.0% 6.0% 8.0% 10.0%

Observed IAV

ERA‐Interim IAV

Predicted vs. Observed IAV


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CONCLUSIONS

We offer the following conclusions:

Of the four reanalysis data sets studied and for the sample points chosen, ERA‐Interim offers the greatest serial correlation with high‐quality observations and greatest internal consistency over time.

The 25‐year period from 1988 to 2012 provides a suitable long‐term baseline for studying wind anomalies and variability. Before this period, inhomogeneities in all data sets become more common, possibly reflecting changes in the observational systems driving the models.

The IAV values calculated from ERA‐Interim data for 1988‐2012 compare well (r2 ~ 0.80) with those observed at 19 tall towers in the United States with periods of record of at least 4 years. The slight overestimation of observed IAV by ERA‐I is consistent with its longer period of record.

The IAV varies widely by location, both globally and within regions, from less than 3% to more than 10% as a fraction of the mean. This variation implies that it is not appropriate to assume a single value for all wind projects.

REFERENCES

Alexandersson, H. (1986). A homogeneity test applied to precipitation data. Journal of Climatology 6: 661–675.

Brower, M.C. (2006). The Use of Reanalysis Data for Climate Adjustments. AWS Truepower, LLC. Albany, New York.

Brower, M.C., et al. (2012). Wind Resource Assessment: A Practical Guide to Developing a Wind Project. Wiley, New York.

Dee, D.P., et al. (2011). The ERA‐Interim reanalysis: configuration and performance of the data assimilation system. Q.J.R. Meteor. Soc. 137: 553–597

Kendall M.G. (1975). Multivariate Analysis. Charles Griffin & Company, London.

Kalnay, E., et al. (1996). The NCEP‐NCAR 40‐year reanalysis project. Bulletin of the American Meteorological Society 77: 437–471.

Rienecker, M.M., et al. (2011). MERRA: NASA’s Modern‐Era Retrospective Analysis for Research and Applications. J. Climate 24: 3624–3648.

Saha, S., et al. (2010). The NCEP Climate Forecast System Reanalysis. Bull. Amer. Meteor. Soc. 91: 1015–1057.

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A Study of Wind Speed Variability Using Global Reanalysis ......Serial Correlation with Observations Table 1 compares the average coefficient of determination (r 2 ) calculated using