A13G-01 INVITED
Trends in wind climates over the contiguous US: Implications for the wind energy resource
Understanding how evolution of the global climate system has been manifest as changes in near-surface wind regimes in the past and how near-surface flow regimes might alter in the future is of great relevance to; the insurance industry, the construction and maritime industries, surface energy balance estimation, the community charged with mitigating coastal erosion, the agricultural industry, forest and infrastructure protection communities, and the burgeoning wind energy industry. A comprehensive analysis of wind speed trends over the contiguous USA during the end of the twentieth century and early twenty-first century is presented using output from 2 observational data sets, 4 reanalysis products and 2 regional climate models. The analysis indicates substantial differences between trends derived from observational wind speed data, reanalysis products and RCMs, and indeed between wind speeds from different reanalysis data sets and RCMs. The two observational data sets both exhibit an overwhelming dominance of declining trends in both the 50th and 90th percentile wind speeds, which is also the case for simulations conducted using MM5 with NCEP-2 boundary conditions. However, converse trends are seen in NARR, other global reanalyses and the RSM. Trends in percentile of the wind speed distribution are regionally consistent within each data source but exhibit large discrepancies between data sources particularly over the Midwest. In order to address questions regarding whether changes in the annual mean wind speed are associated with increased inter- annual variability, output from the data sets were used to compute the annual mean at each station or grid cell and one metric of variability (the variance of seven year windows of annual mean wind speed). Each metric was then subject to trend analysis. Results indicate stations or grid cells that exhibit a statistically significant trend in mean wind speed also tend to exhibit a statistically significant change in the inter-annual variability over the time periods of record. However, there is no clear consensus between the data sets with regards to possible links between a change in the annual mean wind speed and inter-annual variability. By analyzing these trends we will identify causes of the discrepancy and the absence or presence of trends associated with global climate change, and underlying causes.
A13G-02
Wind Resource Estimation: From Trades to Turbines
Risø DTU has been involved in wind resource estimation for many years. Initially, we used observed winds and microscale modeling within the WAsP (the Wind Atlas Analysis and Application Program) software. This method has proven very accurate in places such as Denmark where the landscape is relatively flat, the wind climatology is mainly driven by synoptic-scale weather patterns, and where a dense observational network is available. For regions with a sparse observation network or with more complex terrain, large-scale geostrophic winds derived from global reanalysis are brought down to the mesoscale by using a statistical- dynamical downscaling method. The method uses a classification of the large-scale wind patterns which is then adjusted to the higher resolutions by the use of idealized mesoscale model simulations. Once mesoscale-corrected winds classes are calculated, they are linked to microscale models to take into account the true characteristics of the landscape. In both methods the common goal is to create generalized wind climates, from which location-specific wind climates can be calculated. The generalized (or regional) wind climate gives the frequency distributions of wind speed and direction for standardized and idealized terrain conditions. It serves to link mesoscale to microscale and vice-verse. Without this link, it is not possible to make a proper verification of wind climates derived from modeling against measured winds or apply the mesoscale results for wind farm studies. The results of the statistical-dynamical approach are not as accurate in regions where topographically forced and thermally induced effects dominate the boundary layer wind climatology. For this reason, we are currently developing enhancements to the system by exploring the use of stronger dynamical components to the statistical-dynamical method including comparison to a purely dynamical downscaling simulation for the area of interest. In this presentation we will discuss the range of methods and give examples of recent applications to wind atlas generation.
A13G-03
A WRF and MM5-based four-dimensional data assimilation weather analysis and forecasting system for wind energy applications
Accurate high-resolution weather analyses and forecasts are very important for wind energy production and management. A Real-Time Four Dimensional Data Assimilation (RTFDDA) and forecasting system has been developed at NCAR to address meteorological needs for estimating wind- energy generation through downscaling with nested grids. The RTFDDA system is built around the Penn State/NCAR Mesoscale Model version 5 (MM5) and the Weather Research and Forecasting (WRF) model. It is capable of continuously collecting and ingesting diverse synoptic and asynoptic weather observations from conventional and unconventional platforms, and provides continuous 4-D synthetic weather analyses, nowcasts and short-term forecasts for mesoscale regions. Operational RTFDDA systems have been implemented at seven US Army test ranges and also have supported tens of other applications in military, public and private sectors in the last seven years, providing rapidly updated, multi-scale weather analyses and forecasts with the fine-mesh domain having 0.5 - 3 km grid increments. The observational data ingested by the system includes WMO standard upper-air and surface reports, wind profilers, satellite cloud-drift winds, commercial aircraft reports, all available mesonet data, radar observations, and any special instruments that report temperature, winds and moistures. Recently, the system has been expanded to include several new modeling and data assimilation capabilities that are highly valuable for wind energy applications: a) Ensemble RTFDDA, which is a multi-model, mesoscale data analysis and forecasting system that samples uncertainties in the major components of RTFDDA and predicts the uncertainties in the weather forecasts by performing an ensemble of RTFDDA analyses and forecasts; b) LES (Large Eddy Simulation) modeling, which is nested down from the RTFDDA mesoscale data assimilation and forecasts to LES models with grid sizes of ~100 m for wind farm regions using GIS 30-m resolution terrain; and c) HRLDAS (High- Resolution Land-Surface Data Assimilation System), which assimilates high-resolution satellite vegetation and soil data to generate high-resolution, accurate soil moisture and temperature, which is critical for the evolution of the boundary layer structure and thus improves turbine-height wind energy and turbulence predictions. The capability of the modeling system is demonstrated with a case study for a wind farm located in southwest New Mexico, where nested domains were used with grid increments from 30 km down to 0.123 km.
A13G-04
Global assessment of high-altitude wind power
Wind speed generally increases with altitude to the tropopause; hence, the power available in high-altitude winds is enormous, especially near the jet streams. We assess for the first time the available wind power resource worldwide at altitudes between 500 and 12,000 m. The highest wind power densities are found near 10,000 m over Japan and eastern China, the eastern coast of the United States, southern Australia, and north-eastern Africa. Below 1000 m, the best locations are the southern tip of South America, the coasts along the northern Pacific and Atlantic oceans, the central-eastern coast of Africa, and the north-eastern coast of South America. Because jet streams vary locally and seasonally, however, the high-altitude wind power resource is less steady than needed for baseload power. However, dynamically reaching the height with the highest winds, increasing the area covered with high-altitude devices, and using batteries for storage can effectively reduce intermittency. When high-altitude wind power devices are distributed uniformly throughout the entire atmosphere, numerical simulations show negligible effects on the global climate for low densities, but surface cooling, decreased precipitation, and greater sea ice cover for high densities.
A13G-05 INVITED
The Impact of Wind Data as Indicated by Observing System Simulation Experiments
Observing System Simulation Experiments (OSSEs), when done correctly, provide an effective means to evaluate the potential impact of a proposed observing system, as well as to determine tradeoffs in their design, and to evaluate data assimilation methodology. Great care must be taken to ensure the realism of the OSSE's, and in the interpretation of OSSE results. All of the OSSEs that we have conducted to date have demonstrated tremendous potential for space-based wind profile data to improve atmospheric analyses, forecasts, and research. This has been true for differing data assimilation systems, analysis methodology, and model resolutions. OSSEs have shown the impact of wind profile data to be only very slightly dependent on the magnitude of the random (uncorrelated) errors of the observations. OSSEs also clearly show much greater potential for observations of the complete wind profile than for single-level wind data or observations of the boundary layer alone. A new methodology, referred to as QuickOSSE, also shows significant potential for lidar winds to improve hurricane prediction. OSSE's continue to be conducted as a joint project to determine the quantitative impact of space-based lidar wind observations in current and future operational and research data assimilation systems, the precise requirements for accuracy, coverage and resolution, the relative impact of proposed coherent and incoherent lidars, the redundancy of lidar with other observing systems, and the ability of adaptive targeting to maximize the impact of lidar (and other) data.
A13G-06 INVITED
Current and future wind observations from space and their impact on weather forecasting
Generation of atmospheric wind information from satellite data has been explored since the launch of the first meteorological satellites over 40 years ago. These space based observations are particularly important in areas that are not well covered by traditional ground based observation networks, like over oceans or the polar regions. The methods that formed an important part of the global observing system, with a positive impact on Numerical Weather Prediction were based on the tracking of cloud features observed over time with satellite imagery data. This data is now being complemented by new methods based on more advanced technologies like observing the sea surface roughness with scatterometry or space-based lidar observations. This paper will give an overview of the existing methodologies to derive wind information with satellite data, focussing on present and future operational methodologies and the current and future impact of this data on weather prediction.
A13G-07 INVITED
The Need for Wind Profile Measurements From Space
Measurements of atmospheric winds are inadequate in the present global observing system. Wind profiles have been stated as the most urgently needed observation type for climate studies as well as numerical weather prediction (WMO, 2004). Within the Earth Explorer programme ESA will launch a new satellite mission devoted to wind observations, the Atmospheric Dynamics Mission (ADM/Aeolus). It will provide line- of-sight wind profiles using a Doppler lidar measurement technique. An overview of the mission can be found in Stoffelen et al. (2005). The main purpose of the mission is to measure winds, in particular in tropical regions there is a serious lack of wind information. Tropical atmospheric variability is governed by the winds, with only temperature and pressure information present day weather prediction systems have difficulties in properly representing some aspects of tropical weather. One example of this is given by Kistler et al. (2001) when they compare zonally averaged wind fields from two separate re-analysis systems. It is clear that two separate re-analyses differ markedly in the tropical regions while they are quite similar in mid-latitude and polar regions. This is not due to a deficiency in any of the re-analysis models used, but can be argued to result from a lack of wind information in the tropics. Other important areas where wind observations are needed is the prediction of midlatitude storms and analysis of meridional heat transports. Vertically deep structures with a limited horizontal scale are most sensitive to wind information. The meridional heat transport in midlatitudes can be estimated from re-analysis products, but it has been found that such estimates must be bias corrected as the re-analysis fields have relatively large systematic errors in the zonally averaged meridional wind field (Graversen et al., 2007). Part of this bias can be due to a lack of sufficient wind information to define the ageostrophic, zonally averaged meridional wind. This has implications for our understanding of the processes that govern Arctic warming and the retreat of Arctic sea ice (Graversen et al., 2008). References: Graversen, R.G., Källén, E., M. Tjernström and H. Körnich, 2007: Atmospheric mass transport inconsistencies in the ERA-40 reanalysis. Q. J. R. Meteorol. Soc., 133, 673-680. Graversen, R.G., Mauritsen, T., Tjernström, M., E. Källén and G. Svensson, 2008: Vertical structure of recent Arctic warming. Nature, 451, 53-57. Kistler, R. et al., 2001: The NCEP-NCAR 50-year reanalysis: Monthly means CD-ROM and documentation. Bull. Amer. Meteor. Soc., 82, 247-267. Stoffelen et al., 2005: The atmospheric dynamics mission for global wind field measurement. Bull. Amer. Meteor. Soc., 86, 73-87. WMO, 2004: Third WMO Workshop on the Impact of Various Observing Systems on NWP. Alpbach, Austria, 9-12 March 2004. Proceedings published by WMO.
A13G-08 INVITED
Airborne Doppler Wind Lidars: Data Utility and NWP Impacts
Since the early 1980s there have been a long series of airborne Doppler Wind Lidar (DWL) studies targeting general data utility and, in some limited cases, specific impacts on Numerical Weather Prediction. Activities in both Europe and the USA have used the likelihood of a space-based DWL as a primary motivation for airborne DWL research over complex terrain and open water. These data sets have been used for validation of both simulated DWL observations used in Observing System Simulation Experiments and data assimilation demonstrations with operational NWP models. A recent international experiment (TPARC) hosted two airborne DWLs to explore the evolution of tropical cyclones. This presentation will cover the history of airborne DWLs and the potential data utility within NWP based, in part, upon the TPARC experience.