G1.12 Propagate uncertainty from well-characterized locations and parameters to other locations and parameters

Gap detailed description

Reanalysis is a systematic approach to produce data sets for climate monitoring and research. Key limitations to re-analysis are:

1.       Observational constraints, and therefore reanalysis reliability, can considerably vary depending on the location, time period, and variable considered;

2.       The changing mix of observations, and biases in observations and models, can introduce spurious variability and trends into reanalysis output.

It is clear that to fully exploit the value of ground-based remote sensing observations, they must provide traceable uncertainty estimates.  On the other hand, the spatial coverage of ground-based measurements at the current state of the global observing system is often not sufficient for the satellite Cal/Val and climate monitoring and geographical gaps does not allow to have a sufficient representativeness in the observation available, to assess the NWP and reanalysis fields and the equivalent TOA radiances. In addition, there is a limited knowledge about how to propagate uncertainty from well-characterized locations and parameters to other locations and parameters.

Activities within GAIA-CLIM related to this gap

GAIA-CLIM work carried out by ECMWF in the frame of task 1.5

Gap remedy(s)


Specific remedy proposed

This is a relevant gap that requires several modelling studies focused on the characterization of uncertainty propagation in models and assimilation systems.

In the frame of GAIA-CLIM task 1.5, ECMWF will study the airmass-dependent biases for key satellite instruments (AMSU-A and MHS microwave temperature and humidity sounding instruments, Chinese FY-3C microwave temperature and humidity sounding instruments, MWTS-2 and MWHS-2) using reference in-situ radiosonde measurements for temperature and humidity with calculated uncertainty values, such as the GRUAN network. The objective is to assess whether the GRUAN network is ‘geographically capable’ of diagnosing such biases. This study will allow to learn more about how propagate uncertainty from well-characterized measurement sites of Reference networks, such as GRUAN sites, to other measurement sites of Baseline networks, such GUAN sites, for both temperature and humidity profiles.


Measurable outcome of success

Outcome of success is strongly related to the outcome of the proposed modelling studies. 

Achievable outcomes

Technological / organizational viability: medium

Indicative cost estimate: low (<1 million)


The proposed modelling studies may effectively allow to resolve this gap. Remedies to this gap can also provide essential contribution to make progress on the gap G4.01.


Timeline for the delivery of results for such studies is uncertain.

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

Lacking of appropriate techniques to propagate uncertainty from well-characterized locations limits the value of the re-analysis for the study of climate at the global scale.


Impact depends upon the outcome of the proposed modelling studies for specific ECVs.


Work package: