This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 640276.
G5.09 Need to propagate various fiducial reference quality geophysical measurements and uncertainties to TOA radiances and uncertainties to enable characterisation of satellite FCDRs
Submitted by Anna Mikalsen on 12 June, 2017 - 15:24
Gap abstract:
Presently, the evaluation of the quality of Fundamental Climate Data Records (FCDR) (observations at radiance level that serve as key inputs for model-based reanalyses and retrievals of GCOS ECVs) is based mainly on isolated activities by individual research groups. Given the importance of FCDRs for all downstream data records, there is an important and evolving requirement to improve the assessment of FCDRs by utilising non-satellite reference measurements and model fields, among other means, for validation. The utilisation of non-satellite reference measurements for this purpose requires the use of observation operators (often in the form of radiative transfer models) to transfer the reference measurements into the measurement space of the satellite instrument. There is currently no readily accessible, maintained, online tool (except for the GRUAN processor as part of GAIA-CLIM) that would enable the broader scientific community to contribute to the quality evaluation of FCDRs.
Part I Gap description
Primary gap type:
- Technical (missing tools, formats etc.)
Secondary gap type:
- Uncertainty in relation to comparator measures
ECVs impacted:
- Temperature
- Water vapour
User category/Application area impacted:
- Operational services and service development (meteorological services, environmental services, Copernicus Climate Change Service (C3S) and Atmospheric Monitoring Service (CAMS), operational data assimilation development, etc.)
- Climate research (research groups working on development, validation and improvement of ECV Climate Data Records)
Non-satellite instrument techniques involved:
- Radiosonde
Detailed description:
The GAIA-CLIM user survey highlighted the need for a readily accessible radiative-transfer capability available as part of the Virtual Observatory to allow the transfer of reference measurements into the measurement space of satellite instruments. Such a tool would enable a more direct characterisation of the satellite measurements. The validation of satellite measurements in terms of the measured radiance is more straightforward than a validation of retrieved (or analysed) quantities. This is because the forward calculation from the geophysical profile is unique, whereas solutions to the inverse problem are non-unique in that several distinct geophysical profiles can be consistent with a given radiance measurement. As part of this, the uncertainty information in reference measurements needs to be appropriately transformed in the mapping (e.g. from reference measurements to top-of-atmosphere (TOA) brightness temperatures). In turn, this requires knowledge of the vertical and / or horizontal correlation structures present in the reference measurement.
The GAIA-CLIM project realised the development and demonstration of a GRUAN-processor, which is able to monitor Numerical Weather Prediction (NWP) model temperature and humidity fields relative to GRUAN radiosonde observations, and to monitor the differences in computed TOA radiances for a wide range of meteorological satellite sensors from both measured (GRUAN) and modelled (NWP) state estimates. The GRUAN-processor is built around several core capabilities that are likely to be supported longer-term by EUMETSAT (the fast RT modelling capability [RTTOV] and the flexible interface to NWP model fields [the Radiance Simulator]), nevertheless there is a foreseen governance gap beyond the term of GAIA-CLIM regarding the ongoing development priorities and support for the GRUAN-processor.
The key stakeholders include: satellite agencies (engaged in assessing the quality of long term satellite datasets and implementing Cal/Val plans for forthcoming missions); NWP centres (with an interest in determining traceable uncertainties in model fields); GRUAN governance groups and site operators (with an interest in assessing the value of NWP for cross–checking GRUAN-data quality); and the wider climate-research community (also with an interest in assessing the quality of long term satellite datasets). The future governance of the processor would ideally take account of the priorities of this group of stakeholders.
Associated with this top-level requirement for a flexible observation operator is a specific requirement, related to the need for comprehensive information on the error characteristics of reference measurements. In the context of reference radiosonde measurements, this includes estimates of the error correlations between measurements. Other ground-based data sources such as microwave radiometers and Lidar systems could be developed into reference measurements, including the full assessment of uncertainty.
GRUAN was established with the goal of creating a network of sites around the world where reference measurements of atmospheric vertical profiles are performed (Seidel et al., 2009). Data processing for GRUAN sondes attempts to account for all known sources of systematic and random error affecting the temperature and humidity sensors (Dirksen et al., 2014). However, although vertically resolved best-estimate uncertainties are available, the error correlation structure (i.e. between vertical levels) in the sonde measurements is not presently available, constituting a current gap.
Many applications of reference radiosonde measurements require an estimate of error correlations. For example, as part of the comparison of reference-sonde measurements and NWP fields in terms of TOA brightness temperatures, it is necessary to have realistic estimates of these error covariances. Only then is it possible to estimate realistically, using a radiative-transfer model, the uncertainty in TOA brightness temperature that propagates from sonde profile uncertainty.
Calbet et al. (2017) performed a study into the calibration-traceability chain for forward modelling of the Infrared Atmospheric Sounding Interferometer (IASI), using collocated GRUAN sondes and the LBLRTM radiative transfer model. They found the propagation of uncertainties from sonde profiles was hampered by the lack of covariance information between levels. They resorted to analysing two extreme cases: where the level-by-level sonde profile uncertainties are perfectly correlated or perfectly uncorrelated. The uncertainty in modelled TOA radiances was assumed to lie between the two extremes.
The vertical error correlation structure in GRUAN-sonde profiles is the subject of current research. Such uncertainties are envisaged to be reported in the version 3 GRUAN product (correlated, partially correlated and random terms) being developed by the GRUAN Lead Centre.
A tractable means of representing vertical error covariances is by parametrisation. If the measurement variance at each vertical level is known, the correlated errors between levels can be represented by Gaussian statistics assuming a characteristic correlation length (see e.g. Haefele and Kämpfer, 2010). The correlations should be based on physical constraints where these are known.
Operational space missions or space instruments impacted:
- Meteosat Second Generation (MSG)
- Meteosat Third Generation (MTG)
- Meteosat First Generation (MFG)
- MetOp
- MetOp-SG
Other agencies comparable missions in polar and geostationary orbit
Validation aspects addressed:
- Radiance (Level 1 product)
- Spectroscopy
Gap status after GAIA-CLIM:
- GAIA-CLIM has partly closed this gap
The GAIA-CLIM Virtual Observatory has partly closed this gap at the conceptual demonstrator level by addressing the ECVs upper-air temperature and humidity for the HIRS satellite instruments measuring in the infrared spectral ranges. The Virtual Observatory contains results obtained by an offline forward modelling capability to transfer GRUAN radiosonde measurements into the measurement space of the satellite instruments using a radiative transfer model that is sustained in operational mode within the EUMETSAT Numerical Weather Prediction Satellite Application Facility.
The gap is only partly closed, because more GCOS ECVs and associated satellite instruments need to be considered in the future and because the capability is not available online and operationally, which would require additional funding. In addition, more sophisticated radiative transfer models could be coupled with the Virtual Observatory to address eventual shortcomings of the operational fast model and more reference measurement techniques could be added.
With respect to the requirement for comprehensive knowledge of the error characteristics of reference data (specifically, error correlations for GRUAN data), initial estimates have been generated and tested within the timeframe of GAIA-CLIM, but it is expected that this activity will need to continue beyond the end of the GAIA-CLIM project in part because further information is expected from GRUAN ,but not yet available on the specific correlation structures apparent in the radiosonde profiles.
Part II Benefits to resolution and risks to non-resolution
| Identified benefit | User category/Application area benefitted | Probability of benefit being realised | Impacts |
|---|---|---|---|
Integration of a forward radiative transfer capability into the GAIA-CLIM Virtual Observatory enables direct comparison of satellite radiances to non-satellite reference measurements. |
|
| The realisation will lead to the use of the GAIA-CLIM Virtual Observatory for the validation of Fundamental Climate Data Records forming the basis for GCOS ECV climate data records via the use of FCDRs in NWP-model based reanalysis and retrieval schemes. |
The forward radiative transfer capability in the Virtual Observatory provides the potential for a further development of the Virtual Observatory into a general satellite Cal/Val facility. |
|
| The quality of satellite data is monitored in real time using various, often mission specific, tools. Non-satellite reference data play only a marginal role. The Level-1 capability of the GAIA-CLIM Virtual Observatory makes it viable to be considered to become part of a real time monitoring system. |
| Identified risk | User category/Application area at risk | Probability of risk being realised | Impacts |
|---|---|---|---|
Limited uptake of Virtual Observatory as comparisons not possible at level-1b radiance space. |
|
| Value of reference-quality measurements for satellite-data characterisation not realised with the consequence that the Virtual Observatory has no potential for satellite Cal/Val activities. On the long term, justification for non-satellite reference measurements may fade. |
Lack of penetration and acceptance of proposed methodology (NWP, coupled to GRUAN, for the validation of meteorological EO data) into wider user community. |
|
| Sub-optimal (slower !) evolution of the community’s understanding of the quality of key measured datasets. |
Part III Gap remedies
Gap remedies:
Remedy 1: Implement means to provide the community with a forward radiative transfer capability or results of computations
Primary gap remedy type:
Technical
Secondary gap remedy type:
Deployment
Proposed remedy description:
GAIA-CLIM has developed the GRUAN processor that is able to simulate measurements for many satellite instruments operating in the infrared and microwave spectral ranges consistent with GRUAN-profile measures and their uncertainties. Here, it is proposed to integrate the GRUAN processor into the Virtual Observatory and make it accessible online to create simulated measurements for any satellite instrument for which co-locations with the GRUAN-reference measurements exist in the Virtual Observatory database. This could then provide a working model that would enable development of similar operators for measurements arising from other non-satellite reference quality measurements. In particular, many of the modules in the GRUAN processor could be extended to enable the use of additional measurements in future.
Alternatively, potentially at lower cost, a service could provide online results of radiative transfer calculations for ground-based reference measurements that can form an element of match-up data bases and GUI such as the Virtual Observatory.
Relevance:
Implementing the proposed remedy would help to satisfy a clear user need expressed by the GAIA-CLIM user survey. The remedy presents an important step forward towards the validation of Fundamental Climate Data Records that can be evaluated for many instruments using non-satellite reference measurements available within the GAIA-CLIM VO.
Measurable outcome of success:
The measurable outcome of success for the specific remedy proposed is the accessible online radiative transfer capability, available as part of the Virtual Observatory, and provision for the long-term maintenance and development of the capability, in accordance with the evolving requirements of stakeholders.
Expected viability for the outcome of success:
- High
Scale of work:
- Programmatic multi-year, multi-institution activity
Time bound to remedy:
- Less than 5 years
Indicative cost estimate (investment):
- Medium cost (< 5 million)
Indicative cost estimate (exploitation):
- Yes
In case a service is established that provides results from forward calculations or co-located data.
Potential actors:
- EU H2020 funding
- Copernicus funding
- ESA, EUMETSAT or other space agency
Remedy 2: Improved characterisation of error covariances in GRUAN measurements.
Primary gap remedy type:
Technical
Proposed remedy description:
Uncertainty-covariance information needs to be made available and used appropriately within applications that convert from geophysical-profile data to TOA radiances. Firstly, the profile information needs to contain the uncertainty and the correlation structure in a usable format. Within GAIA-CLIM, simple parametrised versions of the vertical error covariances have been developed and tested as part of the significance testing in the GRUAN processor. Further work could refine approaches to more robustly utilising the uncertainty covariance information available.
Alternative approaches based on methods (Desroziers et al, 2005) routinely used to characterise errors in data assimilation systems should also be tested. This method requires that observations are actively assimilated. Initial estimates could be obtained from sub-selecting from the larger set of GUAN data currently assimilated in operational NWP systems, where the selection is based on those GUAN stations exhibiting gross-error characteristics similar to those of GRUAN measurements.
Relevance:
The solution proposed here is fully aligned with the requirement (to establish traceable uncertainties for NWP fields and radiances calculated from them).
Measurable outcome of success:
Parametrised error covariances, developed and tested in consultation with experts from the GRUAN community.
Expected viability for the outcome of success:
- High
Scale of work:
- Single institution
- Consortium
Time bound to remedy:
- Less than 3 years
Indicative cost estimate (investment):
- Low cost (< 1 million)
Indicative cost estimate (exploitation):
- Yes
Potential actors:
- EU H2020 funding
- National funding agencies
- National Meteorological Services
References:
- Calbet, X., Peinado-Galan, N., Rípodas, P., Trent, T., Dirksen, R., and Sommer, M.: Consistency between GRUAN sondes, LBLRTM and IASI, Atmos. Meas. Tech., 10, 2323-2335, doi: 10.5194/amt-10-2323-2017, 2017.
- Desroziers, G., Berre, L., Chapnik, B., and Poli. P., Diagnosis of observation, background and analysis - error statistics in observation space. Q. J. R. Meteorol. Soc., 131:3385 –3396, 2005.
- Dirksen, R. J., Sommer, M., Immler, F. J., Hurst, D. F., Kivi, R., and Vömel, H.: Reference quality upper-air measurements: GRUAN data processing for the Vaisala RS92 radiosonde, Atmos. Meas. Tech., 7, 4463-4490, https://doi.org/10.5194/amt-7-4463-2014, 2014.
- Seidel, D. J.; Berger, F. H.; Diamond, H. J.; Dykema, J.; Goodrich, D.; Immler, F.; Murray, W.; Peterson, T.; Sisterson, D.; Sommer, M.; Thorne, P.; Vömel, H. & Wang, J., Reference Upper-Air Observations for Climate: Rationale, Progress, and Plans. Bulletin of the American Meteorological Society, 2009, 90, 361–369, doi:10.1175/2008BAMS2540.1