G4.08 Estimates of uncertainties in ocean surface microwave radiative transfer

Submitted by Anna Mikalsen on 12 June, 2017 - 12:17
Gap abstract: 

Several passive microwave missions (operating in the 1-200 GHz range) make measurements in spectral regions where the atmosphere is sufficiently transmissive so that the surface contributes significantly to measured radiances. The calibration/validation of microwave satellite data to reference standards is hampered, for some instruments and channels, by a lack of traceable estimates of the uncertainties in the modelled ocean surface contribution. This is particularly important for microwave imagers, sensitive to total column water vapour, which are routinely assessed within numerical weather prediction (NWP) frameworks. It also affects the lowest peaking channels of microwave-temperature sounders such as channel 5 of AMSU-A. The accuracy of retrievals of atmospheric temperature and humidity over the ocean is also dependent on the accuracy of ocean surface microwave radiative transfer. The dominant source of uncertainty for ocean surface microwave radiative transfer is expected to be ocean emissivity estimates.

Part I Gap description

Primary gap type: 
  • Knowledge of uncertainty budget and calibration
Secondary gap type: 
  • Parameter (missing auxiliary data etc.)
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: 
  • Other non-GAIA-CLIM targeted instrument techniques, please specify:

Radiosonde (through use of the GRUAN processor)

Related gaps: 
G4.01 Lack of traceable uncertainty estimates for NWP and reanalysis fields & equivalent TOA radiances – relating to temperature and humidity
G4.09 Imperfect knowledge of estimates of uncertainties in land surface microwave radiative transfer
G4.10 Incomplete estimates of uncertainties in land surface infrared emissivity atlases

  • Gap 4.01 is concerned with the use of NWP fields for the validation of observations relating to temperature and humidity. This gap (G4.08) identifies one component of the challenge described in G4.01, and affects temperature sounding measurements in the boundary layer and lower troposphere. It also covers humidity sounding (and imaging) in the boundary layer and lower troposphere

    G4.08 is related to, but can be addressed independently of, G4.09 and G4.10 

Detailed description: 

Passive microwave observations from satellite radiometers are widely used to make remote-sensing measurements of the Earth’s atmosphere and surface characteristics. Current missions operate in the spectral range of 1 – 200 GHz but this will be extended in the future to 229 GHz for the EPS-SG MWS instrument and to frequencies over 600 GHz for the ICI mission. Total column water vapour, cloud liquid water path, ocean surface wind speed and direction, sea-surface temperature and salinity, and profiles of humidity and temperature, are all derived from microwave observations. The top-of-atmosphere (TOA) spectral signals in this spectral range can, depending on the state of the atmosphere, comprise a significant component due to emission and reflection from the ocean surface. This is particularly true of microwave imagers (where data quality assessment and operational use at NWP centres rely on radiative transfer modelling including surface terms) and the surface-sensitive channels of microwave temperature and humidity sounders (e.g. AMSU-A channel 5 and window channels).  It is therefore critical that uncertainties in the ocean surface microwave radiative transfer are accurately calculated. This requirement spans applications ranging from the assimilation of Level-1 products (for example) in reanalysis efforts, to the generation of Level-2 (and higher) products at all levels of maturity, ranging from near-real-time operational products to climate data records.

Several emissivity models have been developed over the last two decades to support the assimilation of microwave-imager data at operational NWP centres and to support applications based on retrievals of the ECVs listed above from satellite-based microwave imager observations. These models account for several processes influencing the emissivity of the ocean surface, including: polarised reflection of the ocean’s (dielectric) surface derived from the Fresnel equations, large scale roughness due to wind-driven waves, small scale roughness due to capillary waves, and the radiative effect of foam at progressively higher wind speeds. An ocean surface emissivity model, which is widely used in the remote sensing and operational NWP community, is the Fast Ocean Emissivity Model (FASTEM), which forms part of the RTTOV fast radiative transfer model. Following the initial formulation by English and Hewison (1998), FASTEM has been developed over the last 20 years, with many recent developments guided and informed by an analysis of biases observed between satellite observations and simulations based on NWP models (Bormann et al (2011); Bormann et al (2012); Meunier at al (2014); and Kazumori et al (2015)). The current version of FASTEM (version 6) includes the dielectric constant model and wind speed terms developed by Liu et al (2011), the foam parameterisations of Stogryn (1972) and O’Monahan and Muircheartaigh (1986), and the wind-direction dependence terms developed by Kazumori et al (2015).

A number of studies have been carried out to estimate the uncertainties of ocean emissivity models (e.g. Guillou et al 1996; Guillou et al; 1998, Greenwald et al; 1999). However, most studies which estimated uncertainties were carried out before the latest versions of FASTEM, which include considerable updates made by Liu et al (2011) and Kazumori et al (2015), and also tended to focus on one aspect of the model or one frequency. Therefore, despite a number of studies being carried out to validate the FASTEM model, it still lacks traceable estimates of the uncertainties associated with the computed emissivities in the 1-200 GHz range.  This gap has been identified as an important deficiency in using NWP-based simulations for the validation of new satellite missions.

FASTEM is an approximate (fast) parameterisation of an underlying reference model (English et al., 2017). Such a reference model has three main components: (i) the dielectric model predicting the polarised reflection and refraction for a flat water surface (Lawrence et al. 2017); (ii) the roughness model which represents the ocean roughness due to large scale swell and wind-induced waves; and (iii) the foam model which commonly parameterises the ocean foam coverage as a function of wind speed and assigns a representative emissivity to the foam fraction. For a true reference model, each of these components should be associated with traceable uncertainties.

Operational space missions or space instruments impacted: 
  • Copernicus Sentinel 3
  • MetOp
  • MetOp-SG

Copernicus Sentinel 3: Microwave Radiometer (MWR) instruments .MetOp (2006-2025): Advanced Microwave Sounding Unit (AMSU); Microwave Humidity Sounder (MHS).MetOp-SG:  Microwave Imager (MWI); Microwave Sounder (MWS); Ice Cloud Imager (ICI)

Other:

  • S-NPP / JPSS (2012-2030):  Advanced Technology Microwave Sounder (ATMS)
  • Feng-Yun 3 (2008-2030):  Microwave Radiation Imager (MWRI); Microwave Temperature Sounder (-1 and -2);  Microwave Humidity Sounder (-1 and -2).
  • Global Change Observation Mission (GCOM-W1, 2012-2020):  Advanced Microwave Scanning Radiometer-2 (AMSR-2)
  • Special Sensor Microwave Imager / Sounder (SSMI/S, F-16 - F-19: 2003-2020)
  • Meteor-M (2009-2030):   MTVZA
  • GPM (2014-): Microwave Imager (GMI)
  • Megha-Tropiques (2011-): Microwave humidity sounder (SAPHIR)
  • Coriolis (2003-): microwave radiometer Windsat
  • Jason (2001-2021): microwave radiometers JMR and AMR
Validation aspects addressed: 
  • Radiance (Level 1 product)
  • Geophysical product (Level 2 product)
  • Gridded product (Level 3)
  • Auxiliary parameters (clouds, lightpath, surface albedo, emissivity)
Gap status after GAIA-CLIM: 
  • After GAIA-CLIM this gap remains unaddressed

Part II Benefits to resolution and risks to non-resolution

Identified benefitUser category/Application area benefittedProbability of benefit being realisedImpacts
Lower cost, effective and timely validation of new microwave missions, of which there are >10 planned over the next 2 decades.
  • International (collaboration) frameworks (SDGs, space agency, EU institutions, WMO programmes/frameworks etc.)
  • High
More timely integration of new, validated, satellite datasets into reanalyses.
Broader C3S user base
  • Operational services and service development (meteorological services, environmental services, Copernicus services C3S & CAMS, operational data assimilation development, etc.)
  • High
Improved ERA humidity analyses, improved consistency in-time and geographically and in different phases of the satellite era, through improved homogenisation of datasets. Improved regionally resolved analyses and improved confidence in projected impacts
Identified riskUser category/Application area at riskProbability of risk being realisedImpacts
Sub-optimal validation of new EO data
  • International (collaboration) frameworks (SDGs, space agency, EU institutions, WMO programmes/frameworks etc.)
  • Climate research (research groups working on development, validation and improvement of ECV Climate Data Records)
  • High
Continued uncertainty on the value of NWP for the validation of imager data drives a requirement for more costly Cal/Val campaigns for each new system based on airborne measurements or equivalent.
This will be a large and recurring cost for each new mission
Less confidence in findings based on observational data of unknown quality. Sub-optimal (slower) evolution of the community’s understanding of the quality of key measured datasets
High uncertainties associated with surface emissivity modelling
  • Operational services and service development (meteorological services, environmental services, Copernicus services C3S & CAMS, operational data assimilation development, etc.)
  • Climate research (research groups working on development, validation and improvement of ECV Climate Data Records)
  • Medium
The error component associated with surface emission modeling remains large and dominates the error budget for these observations, thereby limiting the weight given to these observations in climate reanalyses.
Consequently limiting the accuracy of NWP and reanalysis based analyses of lower tropospheric humidity over ocean.
This will have knock-on effects on attempts to predict regionally resolved impacts of climate change.

Part III Gap remedies

Gap remedies: 

Remedy 1: Intercomparison of existing surface emissivity models

Primary gap remedy type: 
Technical
TRL 4
Proposed remedy description: 

Undertake an in-depth intercomparison of available microwave ocean surface emissivity model outputs, for a carefully defined set of inputs (ocean state, atmospheric state). An intercomparison of emissivity models, in itself, will not achieve a validation of emissivity models, but the differences identified and quantified can shed light on the sources of bias in any given emissivity model.  Such an intercomparison exercise is, therefore, a useful step towards a full validation of emissivity models. In many cases, however, such an intercomparison yields valuable insights into the mechanisms, processes, and parameterisations that give rise to biases. This approach thus constitutes a useful first step in the validation of (in this case) ocean surface emissivity estimates.  The measurable output of success therefore, for this activity, will be a documented quantitative comparison of FASTEM (various versions) with another, independent, emissivity model, for a realistic sample of global ocean surface conditions. The probability of a successful outcome is high if the exercise can be coordinated through the appropriate international working groups (e.g. International TOVS Working Group, International Precipitation Working Group, GSICS, X-Cal), and is supported by national and/or international agencies. 

Relevance: 

An intercomparison exercise is a useful step towards a full validation of emissivity models. In many cases, such an intercomparison yields valuable insights into the mechanisms, processes and parameterisations that give rise to biases. 

Measurable outcome of success: 

Documented quantitative model inter-comparison: intercomparisons of non-traceable estimates, in this case outputs from independent ocean surface emissivity models, in themselves will not constitute a validation of any individual estimate. For example, independent estimates can be biased in the same sense. This motivates the need for the additional remedies associated with this gap. 

Expected viability for the outcome of success: 
  • High
Scale of work: 
  • Single institution
  • Consortium
Time bound to remedy: 
  • Less than 5 years
Indicative cost estimate (investment): 
  • Low cost (< 1 million)
Indicative cost estimate (exploitation): 
  • No
Potential actors: 
  • National funding agencies
  • National Meteorological Services
  • ESA, EUMETSAT or other space agency
  • Academia, individual research institutes

Remedy 2: The use of traceably calibrated radiometers in experimental campaigns to validate ocean emissivity models in the region 1 – 200 GHz

Primary gap remedy type: 
Deployment
Proposed remedy description: 

Typically, validation of ocean emissivity models has been carried out using airborne campaigns. However, to date these campaigns have not used traceably calibrated radiometers, since there have been no primary reference standards available. However, primary reference standards are beginning to be developed and there are now some capabilities in China, Russia, and the USA. We propose using these traceably calibrated radiometers for field campaigns as well as airborne campaigns. It would be useful to exploid this type of radiometers in laboratory experiments using wave tanks and field campaigns with radiometers mounted on oil rigs. A combination of different techniques should lead to more robust estimates of the uncertainties in the emissivity models. Note that the determination of emissivity will be reliant on sufficiently accurate co-located estimates (from models) or in-situ measurements, of ocean surface skin temperature, salinity, and ocean surface wind speed. 

Relevance: 

A combination of different techniques should lead to more robust estimates of the uncertainties in the emissivity models. 

Measurable outcome of success: 

Documented, quantitative, evaluation of ocean surface emissivity models with respect to measurements of ocean surface emissivity obtained during experimental campaigns with traceably calibrated radiometers, for a globally representative range of ocean surface wind speeds, temperatures, and salinity. 

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): 
  • No
Potential actors: 
  • National funding agencies
  • National Meteorological Services
  • ESA, EUMETSAT or other space agency
  • Academia, individual research institutes

Remedy 3: Establish an ocean emissivity reference model in the spectral region 1 – 200 GHz

Primary gap remedy type: 
Technical
TRL4
Proposed remedy description: 

Undertake the necessary research and modelling to establish a reference emissivity model where the constituent parts have associated robust traceable uncertainties. This should include a re-calibration of the dielectric constant model to new reference laboratory measurements of the dielectric constant of seawater (see Remedy 4). A roughness model which, incorporates information from a wave model (large scale ocean swell) and surface wind speed (influencing small scale ripples and waves) is also needed to predict scattering characteristics. Similarly, the contribution of foam can be derived in principle from a wave model and full radiative transfer (rather than assuming a nominal emissivity value for the foam fraction). These activities will require coordination. Traceable uncertainty estimation must be assured at each step, the documented code should be freely available, and the final reference model should be maintained and supported.

Relevance: 

Current fast emissivity models lack traceable uncertainty estimates which is a key source of uncertainty in the radiative transfer modelling of surface-sensitive microwave satellite observations over ocean in the 1-200 GHz range.

Measurable outcome of success: 

Documented and freely available software for the prediction of microwave ocean emissivity. The reference model constituent parts should have rigorous uncertainty estimates attached. The underlying basis of the model should be peer reviewed. The expertise for undertaking the necessary laboratory and modelling activities exists, but in disparate institutions that will require coordination. Establishing a fully characterised reference model would close this gap.

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): 
  • No
Potential actors: 
  • National funding agencies
  • National Meteorological Services
  • Academia, individual research institutes

Remedy 4: Reference-quality dielectric constant measurements of pure and saline water for the frequency range 1 – 200 GHz

Primary gap remedy type: 
Research
Proposed remedy description: 

Ocean emissivity models rely on accurate measurements of the dielectric constant of water and seawater for a range of temperatures and frequencies. However, there are inconsistencies between measurements available in the literature (Lawrence et al 2017) and none have SI-traceable uncertainties. Measurements should be taken that are reference quality, i.e. SI-traceable and with validated uncertainty estimates. The uncertainties should include a calculation of the correlation between measurements of the real and imaginary components of the dielectric constant, so that the uncertainties can be properly transformed into radiance space. As well as ocean emissivity, this would also support dielectric constant models for cloud radiative transfer (e.g. the dielectric constant of super-cooled liquid water).

Relevance: 

This will support a reference ocean emissivity model, allowing for cal/val of microwave imagers and surface sensitive channels of microwave sounders to reference standards.

Measurable outcome of success: 

Documented and freely available measurements of the dielectric constant of seawater and pure water for a range of frequencies (1 – 200 GHz) and temperatures (-5 to +35 °C) with traceable uncertainty estimates

Expected viability for the outcome of success: 
  • Medium
Scale of work: 
  • Other, please specify:
PhD or post-doctoral student
Time bound to remedy: 
  • Less than 5 years
Indicative cost estimate (investment): 
  • Medium cost (< 5 million)
Indicative cost estimate (exploitation): 
  • No
Potential actors: 
  • National funding agencies
  • Academia, individual research institutes
References: 

 

  •     

    Bormann, N., Geer, A. and Wilhelmsson, T. (2011). “Operational Implementation of RTTOV-10 in the IFS”. European Centre for Medium-Range Weather Forecasts Tech Memo, 650.

  •          Bormann, N., Geer, A., and English, S. J. (2012).“Evaluation of the Microwave Ocean Surface Emmisivity Model FASTEM-5 in the IFS”. European Centre for Medium-Range Weather Forecasts Tech. Memo., 667.

  •     English, S. J., and Hewison, T. J. (1998).“A fast generic millimetre-wave emissivity model.” Microwave Remote Sensing of the Atmosphere and Environment, T. Hayasaka et al., Eds., International Society for Optical Engineering (SPIE Proceedings, Vol. 3503), 288–300. 

  •          English, S., Geer, A., Lawrence, H., Meunier, L.-F., Prigent, C., Kilic, L., Johnson, B., Chen, M., Bell, W. and Newman, S., A reference model for ocean surface emissivity and backscatter from the microwave to the infrared, International TOVS Study Conference XXI, Darmstadt, 2017.

  •          Greenwald, T. J., and Jones, A. S. (1999). "Evaluation of seawater permittivity models at 150 GHz using satellite observations." IEEE transactions on geoscience and remote sensing, 37.5, 2159-2164.

  • Guillou, C., English, S. J., Prigent, C., et al. (1996). "Passive microwave airborne measurements of the sea surface response at 89 and 157 GHz." Journal of Geophysical Research: Oceans, 101.C2, 3775-3788.
  • Kazumori, M., and English, S. J. (2015). "Use of the ocean surface wind direction signal in microwave radiance assimilation." Quarterly Journal of the Royal Meteorological Society, 141.689, 1354-1375.
  • Lawrence, H., Bormann, N., Geer, A., and English, S., Uncertainties in the dielectric constant model for seawater used in FASTEM and implications for cal/val of new microwave instruments, International TOVS Study Conference XXI, Darmstadt, 2017.
  • Meunier, L.-F., English, S., and Janssen, P. (2014). “Improved ocean emissivity modelling for assimilation of microwave imagers using foam coverage derived from a wave model.” NWP-SAF visiting scientist report. Available online: https://nwpsaf.eu/publications/vs_reports/nwpsaf-ec-vs-024.pdf 

  • Monahan, E., and O’Muircheartaigh, I., (1986). ‘Whitecap and the passive remote sensing of the ocean surface’. Int. J. Remote Sensing, 7, 627 – 642.

  • Stogryn, A. (1972). ‘The emissivity of sea foam at microwave frequencies’. J. Geophys. Res., 77, 1658 – 1666.