Windographer Software

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Windographer quickly imports virtually every data format, allows rapid quality control and statistical analyses including MCP, and exports to all wind flow models common to the wind power industry.

Data Management

Data Visualization

Quality Control

Quality Analyses

Measure – Correlate – Predict (MCP)

Create Professional Output

The industry-leading software for importing, visualizing and analyzing wind resource data measured by met tower, SODAR, or LIDAR. It quickly imports virtually every data format, allows rapid quality control and statistical analyses including MCP, and exports to all wind flow models common to the wind power industry.

Data Management

  • Import virtually every data format including: RWD, RLD, Nomad, Ammonit, Kintech, Campbell Scientific, Triton, AQSystem, ZephIR, Windcube and Pentalum
  • Combine multiple data files into one data set for easy analysis
  • Reads/writes to SQL databases
  • Calculate rotor-equivalent wind speed or solar azimuth
  • Combine co-located anemometers, extrapolate vertically, fill gaps, and apply time shifts
  • Reads calibration constants from raw data files, shows full calibration history of your data, and lets you adjust slopes and offsets easily
  • Download MERRA-2 data for free

Data Visualization

  • Transform your data into numerous interactive graphs for rapid analysis
  • Export images easily to documents

Quality Control

  • Flagging capability for highlighting, classifying and filtering data without harming the data
  • Define any number of your own flags
  • Apply flags to any number of data segments (manually or automatically)
  • Use flags as criteria to include or exclude data from calculations, graphs, and export files
  • Share flags and rules within a team easily

Detailed Analyses

  • Investigate shear, turbulence, tower distortion, vertical temperature profile, long-term trends, and other aspects of the wind regime
  • Estimate wind turbine energy production
  • Predict extreme wind speeds
  • Create a representative year
  • Compare and correlate with other data sets

Measure - Correlate – Predict

  • Choose from several MCP algorithms including linear least squares, orthogonal regression, variance ratio, and the matrix method
  • Compare algorithms and settings in terms of their performance and their uncertainty
  • Lengthen or scale the target data set to the expected long-term average wind speed

Create Professional Output

  • Export reports to PDF
  • Export graphs to PNG or spreadsheet formats
  • Use data from Windographer in Openwind, WAsP, WindSim and Meteodyn WT

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