This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 640276.
G2.36 Lack of traceable uncertainties in MWR measurements and retrievals
Submitted by Anna Mikalsen on 12 June, 2017 - 11:01
Gap abstract:
Ground-based microwave radiometers (MWR) provide continuous and unattended retrievals of atmospheric temperature and humidity profiles, as well as of vertically-integrated total column water vapour (TCWV) and cloud liquid water (TCLW). Despite the significant scientific advancements allowed by MWR observations over the last forty years, current operational MWR retrievals are still lacking a traceable uncertainty estimate. The characterization of the total uncertainty budget for MWR retrievals requires quantification of the contributions from the instrument hardware (including absolute calibration) and the retrieval method (including the radiative transfer model). These contributions have been quantified in open literature, but they often refer to one particular instrument and/or environmental condition, and thus are not able to be generalized. A systematic approach that dynamically evaluates the total uncertainty budget of MWR (i.e. as function of instrument/environment conditions) at the network level is lacking. Initiatives for mitigating this gap are being undertaken in Europe as well as in the United States.
Part I Gap description
Primary gap type:
- Knowledge of uncertainty budget and calibration
Secondary gap type:
- Uncertainty in relation to comparator measures
- Technical (missing tools, formats etc.)
- Governance (missing documentation, cooperation etc.)
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.)
- International (collaborative) frameworks and bodies (space agencies, EU institutions, WMO programmes/frameworks etc.)
Non-satellite instrument techniques involved:
- Microwave Radiometer
Argument: The remedy of G2.13, i.e. the development of MW standards maintained at national/international measurement institutes and the availability of transfer standards, will set the basis for SI-traceability of MWR observations and retrievals. However, tools for evaluating the MWR total uncertainty budget can be developed independently of the solution of G2.13.
Detailed description:
The characterization of the total uncertainty budget for MWR retrievals requires quantification of contributions from the instrument hardware and the retrieval method. These contributions have been quantified in the open literature (e.g. Han and Westwater 2000; Hewison, 2006; Maschwitz et al., 2013; Stähli et al., 2013), but they often refer to one particular instrument and/or set of environmental conditions, and thus should not be generalized.
A proper uncertainty quantification for MWR retrievals shall result from the propagation of the uncertainty in calibration (transfer from raw voltages to the primary observable, the brightness temperature Tb) and the uncertainty in the retrieval method (transfer from Tb to atmospheric variables). As the uncertainty depends on the instrument and environmental conditions, the quantification shall be made dynamically, such that each measurement will be associated with one, generally different, uncertainty. The estimated uncertainty is thus time- and, for profiles, height-dependent. For a MWR network, the estimated uncertainty is also space-dependent, as it will depend on the instrument types deployed at various sites.
A systematic approach that dynamically evaluates the total uncertainty budget of MWR at the network level is lacking. In the following, the contributions to the total uncertainty are divided into four aspects: calibration and instrument characterization, retrieval method, radiative transfer and absorption model uncertainty, quality control.
Calibration and instrument characterization
Calibration and instrument characterization of MWR are to be performed regularly as they are time-dependent. Common procedures are applied by the operators to perform MWR calibration and instrument characterization. Currently, these procedures are usually provided by the manufacturers, and thus they are instrument-specific, or are based on user experience, and thus may be site-specific. Therefore, there is currently a lack of standardization in calibration procedures and uncertainty characterization. This in turn impacts negatively on the uniformity of products provided by a heterogeneous MWR network. This gap shall need to be addressed at both manufacturer and network levels.
Retrieval method
Different methods are currently applied for the retrieval of atmospheric variables from MWR observations. Different retrieval methods are adopted by different MWR manufacturers, operators, and users. Common retrieval methods include, but are not limited to, multivariate regression, neural networks and optimal estimation. This situation holds true for heterogeneous networks, such as the one currently establishing in Europe. The uncertainty of MWR retrievals depends partially on the used retrieval method. Documentation, versioning, and settings are usually not accessible nor maintained. Information on retrieval uncertainty is often completely missing. The traceability of software documentation and versioning is also not guaranteed. This lack of coordination impacts negatively on the harmonization and spatio-temporal consistency of products from a heterogeneous MWR network. This gap shall need to be addressed at the network level.
Radiative transfer and absorption model uncertainty
Most common MWR retrieval methods are based on radiative transfer simulations through the atmospheric medium. Thus, uncertainties in modelling the absorption/emission of microwave (MW) radiation by atmospheric gases and hydrometeors affect all the retrieval methods based on simulated MW radiances. Only retrieval methods based on historical datasets of MWR observations and simultaneous atmospheric soundings are not affected by absorption model uncertainties. Currently, the information on MW absorption model uncertainties are dispersed and not easily accessible. Most operational MWR operate in the 20-60 GHz range, where relevant absorption comes from water vapour, oxygen, and liquid water. A variety of models are available which combine the absorption of water vapour, oxygen, and liquid water, as well as other minor contributions. Absorption model uncertainties are currently estimated from the output difference of different models, while a more rigorous estimate is lacking. An attempt to mitigate this gap is currently being carried out within GAIA-CLIM.
Quality control
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 on the quantity and quality of the data delivered, as poor QC may result in either delivery of faulty data, or screening out of good data. This gap shall need to be addressed at both manufacturer and network levels.
Operational space missions or space instruments impacted:
- Meteosat Third Generation (MTG)
- MetOp-SG
- Polar orbiters
- Geostationary satellites
- Microwave nadir
- Passive sensors
- GNSS-RO
Temperature and humidity sounders in general
Validation aspects addressed:
- Radiance (Level 1 product)
- Geophysical product (Level 2 product)
- Gridded product (Level 3)
- Assimilated product (Level 4)
- Time series and trends
- Calibration (relative, absolute)
- Spectroscopy
Gap status after GAIA-CLIM:
- GAIA-CLIM has partly closed this gap
Attempts to mitigate this gap are currently being carried out within and outside of GAIA-CLIM. Within GAIA-CLIM, a review of state-of-the-art MW absorption models and associated uncertainty has started (Cimini et al., 2017a). The absorption model uncertainties need to be propagated through radiative transfer and inverse operator to estimate the total uncertainties affecting the simulated brightness temperatures and the retrieval methods. A review paper shall collect the outcome of this analysis.
Outside of GAIA-CLIM, attempts to mitigate this gap are currently being carried out in the framework of the EU COST Action TOPROF, specifically by the Microwave Radiometer Working Group (WG3). WG3 is actively tackling the above challenges by interacting with manufacturers and users. WG3 produced a report on calibration best practices. New developments on calibration target design have been stimulated through the interactions with manufacturers. Network-suitable retrieval methods are currently under development within TOPROF WG3 (De Angelis et al. 2016; 2017). The role of GAIA-CLIM is to follow the developments at TOPROF and report to GAIA-CLIM as well as MWR users/manufacturers.
The present overarching MWR gap will be considered closed when procedures for MWR calibration and instrument characterization and a unified retrieval method will be performed uniformly across the network.
Part II Benefits to resolution and risks to non-resolution
| Identified benefit | User category/Application area benefitted | Probability of benefit being realised | Impacts |
|---|---|---|---|
Availability of best practices for MWR calibration and instrument characterization |
|
| Best practices procedures will help operators in producing quality MWR observations and related uncertainty |
Availability of a homogeneous and unified MWR retrieval method |
|
| A network-wide common retrieval method will make documentation, versioning, and maintenance easier. It will guarantee spatiotemporal consistency of retrieval across the network |
Full characterization of the uncertainty related to microwave absorption model |
|
| The contribution to uncertainty due to microwave absorption model can be fully accounted in the uncertainty budget of MWR retrieved products and the associated time series and trends. |
Availability of unified tools for automated MWR data quality control |
|
| Trustable and unified tools for automated MWR data quality control will make MWR observations less user-dependent and thus more uniform across the network |
Increased confidence in MWR retrieved products |
|
| The following will yield increased confidence and utilization of MWR observations in reanalyses and climate research: - Instrument- and site-independent procedures for MWR calibration and characterization - Understanding absorption model uncertainties - Network-wide consistent retrieval method, with sustained versioning and documentation - Trustable MWR data quality control |
| Identified risk | User category/Application area at risk | Probability of risk being realised | Impacts |
|---|---|---|---|
Continued non-uniform practices for MWR calibration and error characterization |
|
| Higher probability of human error in MWR calibration and error characterization. Lack of network-harmonised MWR products which reduces their utility to applications requiring cross-network harmonised values such as satellite cal/val. |
Lack of rigorous estimate for MW forward model uncertainty |
|
| Uncertainty of ground-based MWR retrievals lacks the contribution of the absorption model, which potentially affects time series and trend recognition |
Quality of MWR products varying throughout a network |
|
| Lack of network-harmonised MWR products leading to challenges for applications that require a harmonised network of measurements such as satellite cal/val |
Continued lack of unified tools for automated MWR data quality control |
|
| MWR observations will continue to depend substantially on user experience. This can potentially introduce fake time/location differences. Quality uniform network products would be hampered. |
Inspection by eye is recommended to detect suspicious data and faulty calibration |
|
| Additional personnel costs, prone to human error |
Decreasing trust in MWR data quality |
|
| MWR users at operational services may not necessarily be able to develop their own QC procedures. Features caused by quality uncontrolled data may impact the trustiness and use of MWR systems. Lack of harmonization across the MWR network may negatively impact the trustiness of MWR systems. |
Non-traceable MWR-based validation for satellite ECVs |
|
| No traceable validation for satellite boundary layer thermodynamical profiles |
Part III Gap remedies
Gap remedies:
Remedy 1: Adoption of an international approach to implement recommendations for addressing existing gaps in MWR operational products for climate monitoring utilization
Primary gap remedy type:
Technical
TRL 5-7
Secondary gap remedy type:
Deployment
Research
Education/Training
Governance
Proposed remedy description:
In order to close this overarching MWR gap, specific work plans should be developed to all the four aspects mentioned above: calibration and instrument characterization, retrieval method, radiative transfer and absorption model uncertainty, quality control. This may be best achieved via a collective set of actions which would be best achieved as a single project but could also be achieved via smaller distinct units of work as follows:
Calibration and instrument characterization
The currently available practices for MWR calibration and instrument characterization shall be reviewed. From these, the best practices should be defined and reported, and the documentation shall be made available to operators and users. Close collaboration with MWR manufacturers is desirable. The starting point is the outcome of the Microwave Radiometers Working Group (WG3) of the EU COST Action TOPROF, ended in October 2017. TOPROF WG3 produced a report on recommendations for operation and calibration of MWR within a network (Pospichal et al., 2016).
Retrieval method
The different types and flavours of retrieval methods currently exploited shall be reviewed and reported. A common retrieval method is recommended for MWR belonging to a network. The recommended retrieval method must produce explicitly and transparently the time-dependent estimated uncertainty of each atmospheric retrievals. A software package for a common retrieval method shall be developed and maintained. The starting point is the outcome of the TOPROF WG3 (Cimini et al. 2017b).
Radiative transfer and absorption model uncertainty
Modifications of absorption models are continuously proposed within the open literature based on laboratory data and MWR field observations. To estimate the total uncertainties affecting the MWR retrievals, the following activities are needed: (i) a review of the state-of-the-art and the associated uncertainty of MW absorption models; (ii) propagation of absorption model uncertainties through radiative transfer and inverse operator. Activities in this direction have started within GAIA-CLIM and shall eventually lead to a review paper (Cimini et al. 2017a).
Quality control
MWR quality control (QC) procedures shall be harmonized and automated to the maximum extent possible. A common network-wide data processing would be recommendable for the network products. Activities in this direction have started within TOPROF WG3, actively interacting with manufacturers for proposing ways for QC automation. Results of these activities shall be transferred as recommendations to users and manufacturers.
Activities contributing to the solution of the above issues have started within the COST action TOPROF and GAIA-CLIM. These two projects are ending in October 2017 and February 2018, respectively. Currently no plan is set for following up on these activities with research-oriented projects. The members of the TOPROF core group have submitted a proposal to the Policy and Finance Advisory Committee of EIG EUMETNET (grouping 31 European Meteorological Services) for including MWR into the next phase of their E-PROFILE project. If accepted, part of the above tasks may be accomplished in that framework, specially those concerning calibration and instrument characterization, and quality control. The next phase of E-PROFILE is scheduled for 2019-2023.
Relevance:
Once the above issues are addressed, traceable MWR observations and retrievals will be available together with the estimate of the time-dependent uncertainty uniformly across the network. The remedies above will foster:
- The application of standardized calibration and uncertainty characterization procedures by MWR manufacturers and users;
- The use of a common network-suitable retrieval method. This will harmonise the MWR network products. Product harmonization leads also to more solid characterization of uncertainties;
- The consideration of MW forward model uncertainties in MWR retrievals, as quantifying the MW absorption model uncertainties will provide a common reference for MWR retrieval methods;
- The application of improved QC procedures by MWR manufacturers and users. Better QC leads to more solid characterization of MWR retrieval uncertainties, as it reduces the impact of suspicious data and faulty calibration.
Measurable outcome of success:
The measurable outcome of success for the above specific remedies are the following:
- The number of MWR sites, users, and manufacturers adopting the proposed calibration and uncertainty characterization procedures;
- The number of MWR users and manufacturers considering the rigorous estimates of MW forward model uncertainties in their MWR retrievals;
- The number of MWR sites (i.e. network nodes) providing retrievals and associated uncertainty produced with the recommended uniform retrieval method;
- The number of MWR sites, users, and manufacturers adopting the proposed QC procedures.
Expected viability for the outcome of success:
- Medium
Scale of work:
- Consortium
Time bound to remedy:
- Less than 5 years
Indicative cost estimate (investment):
- Medium cost (< 5 million)
Indicative cost estimate (exploitation):
- Yes
Potential actors:
- EU H2020 funding
- Copernicus funding
- National Meteorological Services
- Academia, individual research institutes
- SMEs/industry
References:
- Cimini D., P. Rosenkranz, M. Tratyakov, and F. Romano, Sensitivity of microwave downwelling brightness temperatures to spectroscopic parameter uncertainty, ITSC21, Darmstadt, Nov. 2017a.
- Cimini D., P. Martinet, F. De Angelis, Network 1DVAR temperature and humidity profile retrievals from ground-based microwave radiometers in Europe, European Meteorological Society Annual Meeting, Dublin, 4-8 September, 2017b.
- De Angelis, F., Cimini, D., Löhnert, U., Caumont, O., Haefele, A., Pospichal, B., Martinet, P., Navas-Guzmán, F., Klein-Baltink, H., Dupont, J.-C., and Hocking, J.: Long-term observations minus background monitoring of ground-based brightness temperatures from a microwave radiometer network, Atmos. Meas. Tech., 10, 3947-3961, https://doi.org/10.5194/amt-10-3947-2017, 2017.
- De Angelis, F., Cimini, D., Hocking, J., Martinet, P., and Kneifel, S.: RTTOV-gb – adapting the fast radiative transfer model RTTOV for the assimilation of ground-based microwave radiometer observations, Geosci. Model Dev., 9, 2721-2739, https://doi.org/10.5194/gmd-9-2721-2016, 2016.
- Han Y. and E. R. Westwater: Analysis and Improvement of Tipping Calibration for Ground-based Microwave Radiometers. IEEE Trans. Geosci. Remote Sens., 38(3), 1260–127, 2000.
- Hewison T., Profiling Temperature and Humidity by Ground-based Microwave Radiometers, PhD Thesis, Department of Meteorology, University of Reading, 2006.
- Maschwitz G., U. Löhnert, S. Crewell, T. Rose, and D.D. Turner, 2013: Investigation of Ground-Based Microwave Radiometer Calibration Techniques at 530 hPa, Atmos. Meas. Tech., 6, 2641–2658, doi:10.5194/amt-6-2641-2013
- Pospichal B., N. Küchler, U. Löhnert, J. Güldner, Recommendations for operation and calibration of Microwave Radiometers (MWR) within a network, Online: http://tinyurl.com/TOPROF-MWR-recommend-2016 , 2016.
- Stähli, O., Murk, A., Kämpfer, N., Mätzler, C., and Eriksson, P.: Microwave radiometer to retrieve temperature profiles from the surface to the stratopause, Atmos. Meas. Tech., 6, 2477-2494, doi:10.5194/amt-6-2477-2013, 2013.