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LROSE: The Lidar Radar Open Software Environment


Image: T.Kiya from Japan, CC BY-SA 2.0, via Wikimedia Commons

The current LROSE release is called “Colette” (a versatile climbing rose) and encompasses six key toolsets that define a core lidar/radar workflow: Convert, Display, QC, Grid, Echo, and Winds. Colette focuses on high-quality, well-tested, well-maintained and well-documented key applications as ‘building blocks’, allowing users to assemble trusted, reproducible workflows to accomplish more complex scientific tasks.

Some highlights for Colette:

  • The cmake build option now supports qt6.
  • Packages are available for Centos, Ubuntu, Fedora 37, 38, 39, Alma Linux, Suse.
  • Bug fixes and updates to Radx applications.
  • HawkEdit is now a beta version, and has undergone considerable testing from users.

Colette can be compiled in C++ for native apps on Linux or Mac. Preliminary support is available for some tools on Windows.

We encourage users to register in order to receive critical software updates, and sign up for the mailing list to help build the LROSE community.

Help can be obtained by posting issues directly to the lrose-core GitHub repository, via our help mailing list, or Discourse user forum.

LROSE is a co-operative project between:

LROSE is funded by the National Science Foundation.

Citations for LROSE tools

Please cite the version of LROSE tools you use for publication. If you are unsure of the version, please cite the latest stable release.

Installation Instructions

  • Homebrew Installation
    • Mac Homebrew installation - For Native applications on the Mac, the recommended method is to use Homebrew. The formula contains all the necessary dependencies and builds instructions.
  • Source Installation - Intended for users who wish to do a manual build or build in a non-standard location. Source compilation is best performed using a supplied Python script.
    • Build system - For LINUX and MAC OS cmake/autoconf/manual builds and code development
  • CIDD Binary Installation
    • CIDD Binary Release - CIDD depends on a 32-bit build, which complicates the build and install for the core. The CIDD display application is not included in the standard lrose-core packages (above).


  • echo tutorials
    • basic elle echo tutorial - Go through the basic steps necessary to convert a raw radar file to CfRadial, calculate Kdp and three-dimensional rain rate, and estimate the surface rainfall. The purpose of this tutorial is to confirm that the install process was successful and that some programs are working.
    • basic+ elle echo tutorial - Similar to the basic elle tutorial with the added tasks of downloading GFS analysis from which to estimate a sounding near the radar and running the RadxBeamBlock application.
    • full elle echo tutorial - This tutorial assumes the user has radar data downloaded in an acceptable radar format and walks through the most important parameters that need to be edited to run the Quantitative Precipitation Estimation (QPE) workflow.
  • wind tutorial
    • VORTRAC tutorial - run VORTRAC to retrieve the winds using the GBVTD/GVTD algorithm from a single Doppler radar data.
  • CSU Radartools tutorial
    • CSU-Radartools tutorial - Step through the processing of a raw radar file through editing, QC and gridding using LROSE and CSU-Radartools
  • Airborne radar navigation correction tutorial


In the current release, the following tools are available:


  • RadxPrint - Query files to determine properties and support by the Radx engine
  • RadxConvert - Convert 24 different lidar and radar formats to CfRadial NetCDF format
  • RadxBufr - Convert Bufr format to CfRadial NetCDF format


  • HawkEye - Real-time and archive display suitable for both scanning and vertically pointing radars.

Quality Control


  • Radx2Grid - Gridding and interpolation of ground-based radar data


  • RadxKdp - KDP and Attenuation calculations
  • RadxPid - KDP, Attenuation, and Particle Identification
  • RadxRate - KDP, Attenuation, PID, and Rain Rate
  • RadxQpe - Accumulated Quantitative Precipitation Estimation
  • RadxHca - NEXRAD Hydrometeor Classification Algorithm
  • RateAccum - Accumulated Precipitation (recommended)
  • PrecipAccum - Accumulated Precipitation (not recommended)
  • RadxBeamBlock - Beam Blockage Estimation
  • ConvStrat - Identify convective and stratiform regions in Cartesian radar volume
  • RadxMesoCyclone - Identify mesocyclones in radar data
  • QpeVerify - Compare radar-derived and observed precipitation accumulation
  • RefractCompute - Compute refractivity
  • RefractCalib - Create calibration file used by RefractCompute
  • CalcMoisture - Calculate moisture fields from refractivity
  • Titan - Thunderstorm Identification, Tracking, Analysis, and Nowcasting application
  • Tracks2Ascii - Print out storm and track data in ASCII format
  • Tstorms2Xml - Convert storms data to XML or Spdb
  • StormInitLocation - Write out the initiation location of significant storms
  • ScaleSep - Separate a radar image into different spatial scales
  • Colide - Detect and extrapolate boundaries
  • ctrec - Track echo motion
  • Rview - Visualize Titan data (spatially)
  • TimeHist - Visualize Titan data (through time)


  • RadxEvad - Extended Velocity Azimuth Display single-Doppler retrieval
  • FRACTL - Fast Reorder and CEDRIC Technique in LROSE multi-Doppler retrieval
  • SAMURAI - Variational multi-Doppler retrieval and analysis package
  • VORTRAC - Vortex Objective Radar Tracking and Circulation single-Doppler retrieval
  • OpticalFlow - Estimate 2-D velocity of a radar field

Practical Radar Meteorology

The material contained here is designed to supplement radar textbooks and course materials with scientific background on common procedures used in radar meteorology. When combined with the above tutorials and documentation, these practical guides will help apply LROSE tools for scientific applications.

  1. Cartesian Gridding of Polar Radar Data
  2. Convective/Stratiform Partitioning
  3. Kdp Calculation
  4. Rain Rate Calculations
  5. Attenuation Correction
  6. Particle Identification using Fuzzy Logic
  7. Quantitative Precipitation Estimation
  8. Velocity Dealiasing

LROSE Workshops

LROSE AMS 2020 Mini Workshop

LROSE Fall 2021 Virtual Workshop

LROSE AMS 2023 Workshop