Sonic Anemometer - BAO Tower, All levels - Raw Data
This dataset is used to monitor different atmospheric variables over many levels of the 300-m Boulder Atmospheric Observatory (BAO) tower.
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This dataset is used to monitor different atmospheric variables over many levels of the 300-m Boulder Atmospheric Observatory (BAO) tower.
This dataset contains raw data from the AWAKEN Site A2 WindCube v1 Profiling Lidar from CU Boulder.
This dataset contains reviewed data from the AWAKEN Site C1a WindCube v1 Profiling Lidar from CU Boulder.
This dataset is from WFIP3 BLOC site WindCube v1 Profiling Lidar / Raw Data from CU Boulder. Please note that the .sta files are in human-readable text, while the .rtd files require some processing software that is not easily automated. We plan to process the .rtd data following the campaign, but if you wish to try it sooner, we can make it available upon request.
This dataset is from WFIP3 BLOC site WindCube v1 Profiling Lidar / Raw Data from CU Boulder. Please note that the .sta files are in human-readable text, while the .rtd files require some processing software that is not easily automated. We plan to process the .rtd data following the campaign, but if you wish to try it sooner, we can make it available upon request.
This dataset contains reviewed data from the WFIP3 Site BLOC WindCube v1 Profiling Lidar from CU Boulder.
This dataset contains derived data from the CU Boulder WFIP3 BLOC site WindCube v1 Profiling Lidar.
This dataset contains raw data from the WFIP3 Site MVCO WindCube v1 Profiling Lidar from CU Boulder.
This dataset contains derived data from WFIP3 Site MVCO WindCube v1 Profiling Lidar from CU Boulder.
This dataset contains derived data from the CU Boulder WFIP3 BLOC site WindCube v1 Profiling Lidar.
This dataset is from WFIP3 BLOC site WindCube v1 Profiling Lidar / Raw Data from CU Boulder. Please note that the .sta files are in human-readable text, while the .rtd files require some processing software that is not easily automated. We plan to process the .rtd data following the campaign, but if you wish to try it sooner, we can make it available upon request.
This dataset contains reviewed data from the WFIP3 Site BLOC WindCube v1 Profiling Lidar from CU Boulder.
This dataset contains derived data from the CU Boulder WFIP3 BLOC site WindCube v1 Profiling Lidar.
This dataset contains reviewed data from the WFIP3 Site BLOC WindCube v1 Profiling Lidar from CU Boulder.
This dataset contains raw data from the WFIP3 Site MVCO WindCube v1 Profiling Lidar from CU Boulder.
This dataset contains derived data from WFIP3 Site MVCO WindCube v1 Profiling Lidar from CU Boulder.
The ARPA-E Grid Optimization (GO) Competition Challenge 2, from 2020 to 2021, expanded upon the problem posed in Challenge 1 by adding adjustable transformer tap ratios, phase shifting transformers, switchable shunts, price-responsive demand, ramp rate constrained generators and loads, and fast-start unit commitment. Furthermore, Challenge 2 was a maximization problem while Challenge 1 was a minimization problem. Specifically, the economic surplus, defined as the benefit of serving load minus the cost of generation, is being maximized. It was expected that the objective value of a given solution should be positive, representing economic gain, but negative objectives from poor solutions were possible. The two code submission feature of Challenge 1 was maintained. Additionally, Divisions 3 and 4 within the competition permitted on/off switching of transmission lines (Divisions 1 and 2 did not). After the initial release of the Problem Formulation on 7/20/2020, ARPA-E Director Lane Genatowski announced Challenge 2 on 9/12/2020. The final May 31, 2021, version of the Problem Formulation was 97 pages long with 299 equations. The Challenge proceeded with 2 non-prize Events and 2 prize Events. Teams receiving Challenge 1 FOA awards and prize money were required to use the prize money to fund their Challenge 2 efforts (Georgia Institute of Technology, Global Optimal Technology, Inc., Lawrence Livermore National Laboratory, Lehigh University, Northwestern University, Artelys, Columbia, Pearl Street Technologies, Pennsylvania State University, and University of Colorado Boulder). For more information on the competition and challenge 2 see the "GO Competition Challenge 2 Information" resource below. Challenge 1 and Challenge 3 information can be found in the resources linked below.
An expanded and updated compilation of long-term station precipitation data, together with a new set of gridded monthly mean fields for global land areas, are described. The present data set contains 5328 station records of monthly total precipitation, covering the period from the mid-1800s to the late 1980s. The station data were individually tested and visually inspected for the presence of spurious trends, jumps, and other measurement biases. The quality control procedure which was used to check the station records for nonclimatic discontinuities and other biases is detailed. We also discuss some of the problems which typically contribute to potential inhomogeneities in precipitation records. The station data were interpolated onto a 4° latitude by 5° longitude uniform grid. Comparisons of these data with two other global-scale precipitation climatologies are presented. We find good agreement among the three global-scale climatologies over the common areas in each set. Three different indices of long-term precipitation variations over the global land areas all indicate a general increase of annual precipitation since the 1940s, although a decline is evident over the last decade. There is some indication that the last few decades of the 19th century may have been as wet as the recent ones. An interesting feature of this study is the presence of relatively large differences in seasonal trends, with March-May and September-November becoming wetter in the last few decades. The December-February and June-August seasons exhibit smaller overall trends, although the northern winter season does exhibit large decadal-scale fluctuations.For access to the data files, click this link to the CDIAC data transition website: http://cdiac.ess-dive.lbl.gov/ndps/tr051.htmlThis dataset was transferred from the CDIAC Archive and published on ESS-DIVE in 2018 under the project title "Carbon Dioxide Information Analysis Center (CDIAC), Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (USA); Univ. of Colorado, Boulder, CO (United States). Cooperative Inst. for Research in Environmental Sciences (CIRES)". In 2023, the project title was updated to "Carbon Dioxide Information Analysis Center (CDIAC); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)" to enable consistent management of all datasets previously hosted by the CDIAC Archive that are now published on ESS-DIVE.