A Tall Tower Study of the Impact of the Low-Level Jet on Wind Speed and Shear at Turbine Heights

Similar documents
Tall Tower Studies of Missouri Wind: Interim Data Report

Wind Ramp Events at Turbine Height Spatial Consistency and Causes at two Iowa Wind Farms

Boundary-layer Decoupling Affects on Tornadoes

The Use of Tall Tower Field Data for Estimating Wind Turbine Power Performance

ASSESMENT OF THE SEVERE WEATHER ENVIROMENT IN NORTH AMERICA SIMULATED BY A GLOBAL CLIMATE MODEL

DETERMINATION OF THE POWER LAW EXPONENT FOR SOUTHERN HIGHLANDS OF TANZANIA

Diagnosing the Climatology and Interannual Variability of North American Summer Climate with the Regional Atmospheric Modeling System (RAMS)

Climatology of Surface Wind Speeds Using a Regional Climate Model

California 120 Day Precipitation Outlook Issued Tom Dunklee Global Climate Center

Daniel J. Cecil 1 Mariana O. Felix 1 Clay B. Blankenship 2. University of Alabama - Huntsville. University Space Research Alliance

AN INDEX FOR ANTICIPATING EXCESSIVE PRECIPITATION WITH ELEVATED THUNDERSTORMS

The Impacts of Atmospheric Stability on the Accuracy of Wind Speed Extrapolation Methods

Seasonal and Spatial Patterns of Rainfall Trends on the Canadian Prairie

PRMS WHITE PAPER 2014 NORTH ATLANTIC HURRICANE SEASON OUTLOOK. June RMS Event Response

Inflow and Outflow through the Sea-to-Sky Corridor in February 2010: Lessons Learned from SNOW-V10 *

ENSO Cycle: Recent Evolution, Current Status and Predictions. Update prepared by Climate Prediction Center / NCEP 11 November 2013

ENSO Cycle: Recent Evolution, Current Status and Predictions. Update prepared by Climate Prediction Center / NCEP 5 August 2013

TAPM Modelling for Wagerup: Phase 1 CSIRO 2004 Page 41

P0.98 Composite Analysis of Heavy-Rain-Producing Elevated Thunderstorms in the MO-KS-OK region of the United States

Improving Surface Flux Parameterizations in the NRL Coupled Ocean/Atmosphere Mesoscale Prediction System

Warm weather s a comin!

Boundary layer Decoupling Affects on Tornadoes. Chris Karstens Meteorology 507 May 6, 2008

but 2012 was dry Most farmers pulled in a crop

REGIONAL VARIABILITY OF CAPE AND DEEP SHEAR FROM THE NCEP/NCAR REANALYSIS ABSTRACT

401 EXTRAPOLATION OF WIND SPEED DATA FOR WIND ENERGY APPLICATIONS

Performance of Radar Wind Profilers, Radiosondes, and Surface Flux Stations at the Southern Great Plains (SGP) Cloud and Radiation Testbed (CART) Site

INTEGRATED TURBULENCE FORECASTING ALGORITHM 2001 METEOROLOGICAL EVALUATION

Wind Resource Data Summary Cotal Area, Guam Data Summary and Transmittal for December 2011

2013 ATLANTIC HURRICANE SEASON OUTLOOK. June RMS Cat Response

WIND DATA REPORT FOR THE YAKUTAT JULY 2004 APRIL 2005

Allison Monarski, University of Maryland Masters Scholarly Paper, December 6, Department of Atmospheric and Oceanic Science

WIND AND MOUNTAIN CLIMATOLOGY IN SEVERE ENVIRONMENTS UNIVERSITY OF MASSACHUSETTS CLIMATOLOGICAL RESEARCH IN THE WHITE MOUNTAINS, NH SELECTED

P2.14 CLOUD TO GROUND FLASHES IN MEXICO AND ADJACENT OCEANIC AREAS: A PRELIMINARY STUDY USING DATA FROM THE WWLL NETWORK

ONE-YEAR EXPERIMENT IN NUMERICAL PREDICTION OF MONTHLY MEAN TEMPERATURE IN THE ATMOSPHERE-OCEAN-CONTINENT SYSTEM

ZANER WHEN DOES WEATHER MATTER? For more information, call: Or visit: Zaner is proud to present

Using Enstrophy Advection as a Diagnostic to Identify Blocking Regime Transition

Northwest Outlook September 2017

AnuMS 2018 Atlantic Hurricane Season Forecast

A Preliminary Climatology of Extratropical Transitions in the Southwest Indian Ocean

Will a warmer world change Queensland s rainfall?

An Analysis of Wind Resource Uncertainty in Energy Production Estimates

Climate Change Impact on Air Temperature, Daily Temperature Range, Growing Degree Days, and Spring and Fall Frost Dates In Nebraska

Meteorology. Circle the letter that corresponds to the correct answer

The Boundary Layer and Related Phenomena

M. Mielke et al. C5816

November 2018 Weather Summary West Central Research and Outreach Center Morris, MN

ENSO: Recent Evolution, Current Status and Predictions. Update prepared by: Climate Prediction Center / NCEP 30 October 2017

INVISIBLE WATER COSTS

Great Lakes Update. Volume 188: 2012 Annual Summary

Who is polluting the Columbia River Gorge?

ENSO Cycle: Recent Evolution, Current Status and Predictions. Update prepared by Climate Prediction Center / NCEP 23 April 2012

ENSO Cycle: Recent Evolution, Current Status and Predictions. Update prepared by Climate Prediction Center / NCEP 24 September 2012

A summary of the weather year based on data from the Zumwalt weather station

Boundary Layer Science Challenges in the Context of Wind Energy

Early May Cut-off low and Mid-Atlantic rains

ENSO Cycle: Recent Evolution, Current Status and Predictions. Update prepared by Climate Prediction Center / NCEP 25 February 2013

Why the Atlantic was surprisingly quiet in 2013

Analysis of meteorological measurements made over three rainy seasons in Sinazongwe District, Zambia.

Comparison of Radiosonde and Profiler Data with ACARS Data for Describing the Great Plains Low-Level Jet

AnuMS 2018 Atlantic Hurricane Season Forecast

Urban heat island effects over Torino

The benefits and developments in ensemble wind forecasting

ENSO Cycle: Recent Evolution, Current Status and Predictions. Update prepared by Climate Prediction Center / NCEP July 26, 2004

Monthly Long Range Weather Commentary Issued: APRIL 1, 2015 Steven A. Root, CCM, President/CEO

Life Science Archives (LSA)

An Analysis of 500 hpa Height Fields and Zonal Wind: Examination of the Rossby Wave Theory

The Pennsylvania Observer

NAM-WRF Verification of Subtropical Jet and Turbulence

Probabilistic Decision-Making and Weather Assessment

Impacts of the April 2013 Mean trough over central North America

Weather and Climate Summary and Forecast October 2018 Report

High initial time sensitivity of medium range forecasting observed for a stratospheric sudden warming

Application and verification of the ECMWF products Report 2007

A Note on the Estimation of Eddy Diffusivity and Dissipation Length in Low Winds over a Tropical Urban Terrain

Sensitivity of cold air pool evolution in hilly terrain regions

Warm Episode over Indian Sub-continent March 2010 By Richard Grumm National Weather Service State College Contributions by Lance Bosart SUNY Albany

THE LOW-LEVEL JET FOR BUCHAREST S AIRPORTS - A STUDY OF ITS CHARACTERISTICS IN WINTER SEASON BETWEEN 1959 AND 1982

South & South East Asian Region:

Monthly Long Range Weather Commentary Issued: February 15, 2015 Steven A. Root, CCM, President/CEO

1. Introduction. 2. Verification of the 2010 forecasts. Research Brief 2011/ February 2011

ENSO: Recent Evolution, Current Status and Predictions. Update prepared by: Climate Prediction Center / NCEP 9 November 2015

Annex I to Target Area Assessments

Assessment of wind energy for Nevada using towers and mesoscale modeling

Severe Weather with a strong cold front: 2-3 April 2006 By Richard H. Grumm National Weather Service Office State College, PA 16803

Assessing WRF PBL Schemes for Wind Energy Applications

National Weather Service-Pennsylvania State University Weather Events

A Preliminary Severe Winter Storms Climatology for Missouri from

Wind Assessment & Forecasting

Keywords: lightning climatology; lightning flashes; Macedonia Greece.

Upgrade of JMA s Typhoon Ensemble Prediction System

Boundary-Layer Study at FINO1

April Forecast Update for Atlantic Hurricane Activity in 2018

The Pennsylvania Observer

Eurasian Snow Cover Variability and Links with Stratosphere-Troposphere Coupling and Their Potential Use in Seasonal to Decadal Climate Predictions

Fort Lewis, Washington (47 05'N, 'W)

1.4 USEFULNESS OF RECENT NOAA/CPC SEASONAL TEMPERATURE FORECASTS

Climate Forecast Applications Network (CFAN)

Verification of precipitation forecasts by the DWD limited area model LME over Cyprus

ENSO Cycle: Recent Evolution, Current Status and Predictions. Update prepared by Climate Prediction Center / NCEP 15 July 2013

Dependence of evaporation on meteorological variables at di erent time-scales and intercomparison of estimation methods

Transcription:

JP2.11 A Tall Tower Study of the Impact of the Low-Level Jet on Wind Speed and Shear at Turbine Heights Ali Koleiny Keith E. Cooley Neil I. Fox University of Missouri-Columbia, Columbia, Missouri 1. INTRODUCTION In 2006 ten tall communication towers were outfitted with anemometers and wind vanes at heights up to 150 m across the state of Missouri (Figure 1). The majority of the towers are located in northwest MO where the best wind resource is thought to be. One tower is located in Raytown in the Kansas City suburbs, and two are located in the southwest. The primary goal of this network was to improve the wind climatology at heights corresponding to those at which modern utility scale wind turbines operate. In this work a complete year of observations is used to further investigate the wind resource in Missouri. Redburn (2007) concluded that the 100 m wind map produced by AWS Truewind Ltd. overestimated the observed tall-tower wind speeds. Figure 2. Average diurnal wind speed variations at Blanchard tall tower during February 2007 (time in UTC). A comparison was made between the wind conditions during periods when the low-level jet is active compared to times when it is not at heights relevant to wind power applications. In particular, it is found that wind speed and shear at these heights are greater when the jet is present. The magnitude of these increases is important when assessing the likely wind power potential in an area, but also has application to wind farm operations when energy output is being forecast. Most notably, wind farm operators are concerned about rapid changes in wind speed that increase or decrease power generation over a short period. Such changes are frequently the result of low-level jet formation in the Midwest. Figure 1. The green stars represent the location of each tall tower location overlaid on the AWS Truewind map of projected wind speeds at 100 m. Initially it was seen that a strong diurnal variation in wind speed occurs at all towers with the maximum mean wind observed at night (Figure 2). This was seen as preliminary evidence of the importance of the low-level jet. However, very little has been done previously to determine the contribution of the nocturnal LLJ to the available wind resource in this part of the Great Plains. Knowledge of this contribution by the LLJ at such heights can allow wind farm developers to more efficiently harness wind power. * Corresponding author address: Ali Koleiny, 302 ABNR Building, Univ. of Missouri-Columbia, Dept of Soil, Environmental, and Atmos. Science, Columbia, MO, 65211; email: akgv7@mail.missouri.edu 2. A SHEAR BACKGROUND Wind shear can be assessed in a couple of ways. In previous wind energy application studies, use of the shear exponent alpha, α (dimensionless), has been calculated to infer the turbulent flow in the vicinity of these operational turbine heights. However, α tends to raise more questions about its true effectiveness in assessing the existing turbulence, which will be discussed further. Therefore, calculations of friction velocity, u * (m s -1 ), will also be calculated and compared to those of α during the same time period. 2.1 Alpha The power law equation gives rise to the shear exponent α, as seen in equation (1). Typically it is used to estimate higher level wind speeds, given knowledge of some reference level wind speed and corresponding

height. It is often assumed that the shear exponent has an assumed value of 0.143 under neutral conditions. u u 2 1 z z However, more recent wind energy studies have used equation (1) to obtain α as a measure of the existing shear in the hub-height vicinity. The drawback to the power law is that it is empirical in nature and has no real underlying physical basis. Given this equation, it can be inferred that it does not take into account either the effects of surface roughness or the stability of the atmosphere. 2.2 Friction Velocity The logarithmic wind profile employs similarity theory and gives rise to an expression for the friction velocity, which provides a measure of the vertical flux of horizontal momentum. Values of friction velocity can be determined from performing field experiments, and give this shear measure a much stronger physical basis. Larger values indicate a higher amount of mechanical turbulence in the atmosphere. Equation 2 shows a common form of the logarithmic law equation. 2 1 u * z ( z) ln k z0 (1) u (2) Unlike the calculation for the shear exponent, this measure of shear accounts for stability and roughness of the surface. The major drawback of measuring shear in this manner is that the log wind profile assumes constant stability over the entire layer. 3. DATA AND METHODOLOGY Regional reanalyses acquired from the National Centers for Environmental Prediction (NCEP) were used to identify potential nocturnal LLJ events affecting talltower locations between the times of 00 Z and 12 Z using a modified version of criteria used by Walters and Winkler (2004). As a result, Table 1 outlines each identified jet event throughout the 12-month period (September 2006 through August 2007). The same number of possible nocturnal jet events identified during each month were then compared with an even number of days when the LLJ is not thought to be present. For example, by looking at the month of September, there were 9 days where a jet max appeared at or below 850 mb and 14 days that month where no jet max appeared to be occurring. To compare an even number of jet days to non-jet days for that month, 9 of those 14 nonjet days were chosen for purpose of analysis. Observational Period LLJ Events (at or below 850 mb) Non-Jet Days Only September 2006 9 14 (9) October 2006 14 (11) 11 November 2006 18 (8) 8 December 2006 20 (2) 2 January 2007 20 (8) 8 February 2007 18 (6) 6 March 2007 18 (7) 7 April 2007 8 15 (8) May 2007 15 16 (15) June 2007 4 26 (4) July 2007 3 28 (3) August 2007 11 19 (11) Table 1. Summary of jet events during each month. To determine wind speed differences in jet and non-jet periods, observational data from the towers were analyzed for corresponding days in which a possible jet event is either occurring or is not. Comparisons of alpha and friction velocity also accompany calculations of average wind speeds for the same jet and non-jet event days each month. Three of the tall towers are not included in the study, as they either did not begin collecting data until the latter part of 2007, or did not record reliable wind speed data for the period of interest. 4. RESULTS AND DISCUSSION Calculations of wind speeds, alpha, and friction velocity are presented in this section. To give an effective picture of the observed wind data at turbine heights across the state, the Blanchard, Raytown, Chillicothe, and Monett data will be used herein. The first three of these towers each have instruments at similar heights (~65 m, ~95 m, and ~140 m), but are situated in differing environments. The Monett tower has instruments at lower heights (50m, 60m and 70m), but is representative of southwest Missouri. As such, it is easier to determine the differences in the wind in different parts of the state. 4.1 Mid-Level (90-100m) Wind Speeds Generally, each tower followed the same trend at the mid-level anemometer heights for both the jet and nonjet time periods. The Blanchard tower in the far northwestern corner of the state experienced stronger wind speeds at the mid-levels compared to the other towers discussed in this section (Figure 3). The Raytown tower experiences relatively weak wind speeds at similar heights throughout the jet and non-jet periods, probably due to its location with respect to the KC metropolitan area (Figure 4). An interesting artifact of the Monett data reveals that Monett has a quite impressive resource of wind. The mid-level anemometer heights at Monett are approximately 60 m above the base of the tower, 30-40 m lower than the mid-level winds at the Blanchard, Chillicothe, and Raytown towers. The Monett jet and non-jet curves in Figure 6 are similar to the Raytown curves, and even the Chillicothe curves in Figure 5 as well. The difference between the jet and non-jet periods tends to be a bit

larger in the late fall towards the end of 2006. As spring arrives in 2007, the difference in the spread between the jet and non-jet curves becomes less. This appeared to be a common artifact of the data for all the towers, possibly as a result of seasonality changes. Figure 6. Monett mid-level wind speeds during jet and non-jet periods. 4.2 Alpha Calculation Figure 3. Blanchard mid-level wind speeds during jet and non-jet periods. Figure 4. Raytown mid-level wind speeds during jet and non-jet periods. When investigating differences in α between towers, the Blanchard, Raytown, and Chillicothe towers were grouped and plotted against each other due to the similarity in their anemometer heights. Trends in α calculated for the Blanchard, Raytown, and Chillicothe towers during the jet and non-jet periods are displayed in Figures 7 and 8 respectively. Since the Monett tower has significantly different anemometer heights, the jet and non-jet curves are plotted against each other and displayed in Figure 9. Figures 7 and 8 show very similar trends in α during the jet and non-jet periods for the three towers. Raytown appears to have the greatest variability in α throughout the observational period for both the jet and non-jet curves. The Blanchard and Chillicothe trends appear to mirror each other a bit more as compared to the Raytown curves. When α was averaged over the whole observational period, the Blanchard tower had just a slightly higher mean value during the jet period opposed to the non-jet period. The Raytown tower had a significantly higher value in the non-jet period curve compared to the jet curve, while the Chillicothe tower had only a slightly higher mean value for the non-jet period curve. Similarly, comparing the jet and non-jet period curves at Monett (Figure 9) reveals that the jet period had a slightly higher mean α value during the jet period as well. Since these entire mean values were only slightly higher in the jet periods for these two towers, the result does not appear to be all that significant or conclusive. Figure 5. Chillicothe mid-level wind speeds during jet and non-jet periods.

same data set. This seems to illustrate that α is not only a function of the wind shear, but also of the mean wind speed, with lower wind speeds giving higher values of α. Similarly, comparing the jet and non-jet period curves in Figure 12 reveals that Monett also has a higher mean friction velocity during the jet period. The mean friction velocities appear reasonable for all four towers focused on in this section. As the friction velocity does not have a dependence on mean wind speed it is apparent that there is more shear during jet events than when the jet is not present. Figure 7. Trends in alpha during the jet periods at the Blanchard, Raytown, and Chillicothe towers. Figure 10. Trends in friction velocity during jet periods at the Blanchard, Raytown, and Chillicothe towers. Figure 8. Trends in alpha during the non-jet periods at the Blanchard, Raytown, and Chillicothe towers. Figure 9. Trends in alpha during both the jet and non-jet periods at the Monett tower. Figure 11. Trends in friction velocity during non-jet periods at the Blanchard, Raytown, and Chillicothe towers. 4.3 Friction Velocity Calculation The friction velocity trends in Figures 10 and 11 appear similar to the alpha trends in Figures 7 and 8. The major difference is that when the friction velocity is averaged over the whole observational period, every tower had a higher mean value during the jet event periods over the non-jet event periods. Somewhat noteworthy, the Blanchard and Chillicothe friction velocity trend curves are almost identical to one another during the jet periods whereas Chillicothe had a consistently higher α compared to Blanchbard for the

Figure 12. Trends in friction velocity during both the jet and non-jet periods at the Monett tower. 5. CONCLUSIONS As wind power density is a function of wind speed cubed, these results show that there is substantially more wind power available at turbine heights when the low-level jet is active than when it is not. The calculations of friction velocity show that wind shear is also increased during low-level jet periods. However, this signal is not clear when using α as a measure of wind shear. This is because this parameter increases with lower wind speeds and so the increase in wind shear during low-level jet episodes is offset by the increase in wind speed. 6. REFERENCES Redburn, R., 2007: A Tall Tower Wind Investigation of Northwest Missouri. M.S. Thesis, Dept. of Soil, Environmental, and Atmospheric Sciences, The University of Missouri-Columbia, 109 pp. Walters, C. K., and J. A. Winkler, 2001: Airflow configurations of warm season southerly low-level wind maxima in the Great Plains. Part I: Spatial and temporal characteristics and relationship to convection. Wea. Forecasting, 16, 513 530. ACKNOWLEDGEMENTS This research was funded the Missouri Department of Natural Resources, Ameren UE, Aquila, Kansas City Power and Light, and Empire District Electric Co.