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Why Upgrade to Version 4?

From Version 3

Better Data Set Management

The new Calibration window shows the whole calibration history of your data set and makes it easy to adjust slopes and offsets. When you import RLD, RWD or NDF files, Windographer automatically reads the calibration constants so you can quickly see the calibration status and make any necessary corrections.

New calculated column options include rotor-equivalent wind speed, polynomial and piecewise linear functions, moving averages, solar azimuth, and clear-sky radiation. Plus, now you can tailor the settings of the shear exponent, wind veer, or air density columns.

Import much larger data files, hide data columns that you don’t want to see in graphs (like min/max wind direction!) and see a complete list of configuration changes in the Document History window.

Better Workflow

With the Windographer Data Downloader you can multiple sets of MERRA and MERRA-2 data for free from AWS Truepower's Windnavigator website. It takes just a couple minutes to download 35 years of hourly data via Windnavigator..


The new Combine Anemometers window lets you create a calculated column to combine each pair of co-located anemometers, configure the new calculated columns, and hide the original columns in a single step.

Sharing flags and flag rules within a team is now easy. Each flag or rule appears as an XML file in a folder, and team members can pull those files from a shared folder on a network drive or a cloud service such as Dropbox or OneDrive.

Better MCP Module

The measure-correlate-predict module now lets you choose between lengthening the target data set and simply scaling it to the expected long-term average wind speed. The new scaling option has advantages such as preserving the shear, turbulence, and temperature data that models like Openwind can use.

We’ve expanded the MCP performance test. Now you can compare not just different algorithms, but different settings – for all those times you wonder what the optimal number of direction sectors would be!

You can also run multiple iterations of the performance test for a more robust comparison and an indication of the uncertainty of the MCP process. Each iteration uses a different random 50/50 division of the concurrent data into training and testing halves. The standard deviation of the resulting errors reflects the uncertainty of the MCP process.

Better Data Synthesis

The Vertical Extrapolation window now extrapolates not just speed data, but also turbulence, direction, and temperature data to any height above ground. You can easily export the resulting data to Openwind in the form of an MM2 file.

Windographer can fill gaps both by extrapolating valid measurements made at another height, or by generating an entirely artificial data segment in a time interval containing no valid measurements. The Fill Gaps window now lets you choose the former option but not the latter, in cases where you want to avoid adding artificial data.

Other Additions

The new Representative Year module creates a synthetic one-year time series consisting of 8,760 hourly time steps from January 1 to December 31 with no gaps, which matches the measured data's distribution, diurnal and seasonal patterns, and autocorrelation.

If you have a forecast time series of wind speed or turbine output, you can compare against the true measured values in new Forecast Error Analysis window. It analyzes the errors in the forecast time series and displays the patterns in the errors.

The Turbulence Analysis window now shows turbulence versus height, as well as data for all measurement heights at once.


From Version 2

MCP and Related Features

Still using v2? Then our latest version will also let you enjoy all the improvements we made in version 3. The big news in v3 was the additional of an MCP module for correlating with reference data. It implemented multiple MCP algorithms and provided a performance test facility to choose among them. We've improved this module further in version 4.

Version 3 also introduced a Compare Data Sets window, which does a graphical comparison of any number of data sets, even if they all have different time steps and even if you imported some from data files, and some from a SQL database. This window can compare remote sensing data to met tower data, or help you choose reference data for use in the MCP module.

Improved Import, Flagging, and Filtering

The Enterprise Edition made its debut in v3. It can integrate with SQL databases by calling stored procedures, which means it does not need to know anything about the underlying data structure. It reads numerical data plus flag data and data set history information. It also lets you import a raw data file and then add that data to the database, either to append to an data set that already exists in the database, or to add it as a new data set.

Version 3 represented a big step forward in the ability to read and interpret data files, flag and filter data, and even just visualize data. It featured new modules for wind turbine modeling, extreme wind analysis, and temperature profile analysis.

From Version 1

If you are still using version 1 you have even more reasons to upgrade, including the powerful new quality control system that we introduced with version 2. It lets you highlight, classify, and filter data by applying flags to data segments. You can define your own flags, apply them either manually or automatically, and then use them as filter criteria for calculations, graphs, and export files. The flagging system is vastly more powerful, flexible, traceable, and reversible than the old system of quality control in v1.