G2.15    Lack of unified tools for automated MWR data quality control

Gap detailed description

Quality control (QC) procedures are fundamental for providing users with tools for judging and eventually screening MWR data and products. Most operational MWRs apply QC procedures that are developed by either the MWR manufacturer or by the operators based on their experience. There are different levels of QC procedures, going from sanity checks of the system electronics, to monitoring the presence of rain/dew on the instrument window, to Radio Frequency Interference detection, to monitoring calibration against independent reference measurements (usually by radiosondes). The nature of the QC procedures varies, as these may be applicable to all instruments or conversely be instrument and/or site specific. Therefore, there is currently a lack of harmonization and automation of MWR QC procedures. This impacts the quantity and quality of the data delivered, as poor QC may result in either delivering of faulty data, or screening out of good data. This gap shall be addressed at both manufacturer and network levels. An attempt is currently being carried out within the EU COST action TOPROF. Progress will be reported within GAIA-CLIM.

Activities within GAIA-CLIM related to this gap

The activities within GAIA-CLIM are to follow the developments at TOPROF and report to GAIA-CLIM as well as MWR users/manufactures.

Gap remedy(s)

Remedy #1

Specific remedy proposed

MWR QC procedures shall be harmonized and automated to the maximum extent possible. In the framework of the EU COST Action TOPROF, the Working Group on Microwave Radiometers (WG3) is actively addressing this issue by interacting with manufacturers and proposing ways for QC automation. The leader of GAIA-CLIM Task 2.1.2 is co-chairing TOPROF WG3. The results of these activities will be followed and reported within the GAIA-CLIM project as suggestions to users and manufacturers.

Measurable outcome of success

A successful outcome is the transmission of TOPROF findings to MWR manufacturers and users. An additional measure of success is the effective usage of the proposed QC procedures by MWR manufacturers and users.

Achievable outcomes

Technological / organizational viability: medium.  Activities are ongoing, both at manufacturer and research levels. The transfer to MWR network management highlights yet to be resolved organizational challenges.

Indicative cost estimate: low (<1 million).


The remedy will foster the application of improved QC procedures by MWR manufacturers and users. Better QC will reduce the effect of suspicious data and faulty calibration. This also helps in addressing G2.16, as better QC leads to more solid characterization of MWR temperature and humidity retrieval uncertainties.


2 years.

Gap risks to non-resolution

Identified future risk / impact

Probability of occurrence if gap not remedied

Downstream impacts on ability to deliver high quality services to science / industry / society

Inspection by eye is recommended to detect suspicious data and faulty calibration


Additional personnel costs, prone to human error


Work package: