G4.09 Imperfect knowledge of estimates of uncertainties in land surface microwave radiative transfer

Gap abstract: 

There is a lack of traceable uncertainties associated with the contribution of land surface microwave radiative transfer to Top of the Atmosphere (TOA) brightness temperatures for microwave imaging and sounding instruments. The land surface emission exhibits significant spatial and temporal variability, particularly in snow- and ice-covered regions. There are a number of sources of uncertainty in the approaches currently used to estimate the land-surface contribution, including the emissivity and skin temperature prior, ineffective cloud and precipitation screening and errors introduced by the simplification of the radiative-transfer equation for practical computations. The accuracy of simulated radiances using Numerical Weather Prediction (NWP) models is limited, for some applications, by the uncertainty in modelled surface emission. Solving this gap will require a combination of different approaches, including the use of experimental campaigns which are useful to validate the overall contribution of the land surface.

Part I Gap description

Primary gap type: 
  • Knowledge of uncertainty budget and calibration
Secondary gap type: 
  • Parameter (missing auxiliary data 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.)
  • 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:
  • G4.01 should be addressed with G4.09

    Argument: Gap 4.01 is concerned with the use of NWP fields for the validation of observations relating to temperature and humidity. This gap (G4.09) identifies one component of the challenge described in G4.01, and affects temperature sounding measurements as well as humidity sounding (and imaging) measurement in the boundary layer and lower troposphere over land.

    G4.08 and G4.10 can be addressed independently

Detailed description: 

Passive microwave observations from satellite radiometers operating in the spectral range from 1-200 GHz are widely used to make remote-sensing measurements of the Earths atmosphere and surface characteristics. Observations in this frequency range are sensitive to atmospheric humidity and temperature, as well as to emission and reflection from the surface. Microwave instruments, which are primarily used to estimate atmospheric temperature and humidity profiles (e.g. AMSU-A, MHS, AMSR-2), can also have a significant contribution from the surface, depending on atmospheric conditions. Currently the calibration/validation (cal/val) of these instruments tends to be carried out only over ocean due to more trustworthy estimates of the surface contribution, but in the future, this should be extended to the land so that cal/val can be performed over the full dynamic range of the instruments. To do this, it is necessary to validate the estimated land-surface contribution to the TOA radiances, and to calculate the associated uncertainties in radiance space.

The calculation of the land-surface contribution to the TOA radiances relies on simplified radiative-transfer equations, and estimates of the surface skin temperature and emissivity. It is assumed that the land surface represents a homogeneous body with an emission equal to the skin temperature multiplied by an emissivity. An additional contribution to the TOA brightness temperature is calculated as the atmospheric emission reflected off the surface, which can be assumed to be either a specular or Lambertian reflection (Lambertian for snow-covered surfaces, specular for many other surfaces). In reality, the surface emission is more complex, due to multi-layers with heterogeneous dielectric properties (varying both vertically and horizontally), particularly for snow cover, and the reflection is likely to be not entirely specular or Lambertian. Furthermore, microwave emissions come from layers deeper than the surface (depending on frequency and dielectric properties) and so the use of a skin temperature estimate may not be appropriate for some conditions, particularly over deserts where the penetration depth is higher (see e.g. Norouzi et al; 2012).

As with the ocean surface, physically-based models have been developed to allow the estimation of land surface emissivity (e.g. Wang and Choudhury, 1981; Njoku and Li, 1999; Weng et al., 2001) for different surface types and different frequencies. Methods to estimate the surface type from satellite observations have also been developed (e.g. Grody, 1988). However, in order to accurately calculate the emissivity using physically-based models, a large number of input parameters are required that are difficult to estimate accurately over the spatial scales needed for satellite measurements. Progress in this area is still ongoing, but as a result, it has become necessary to rely on retrievals from satellite observations, following the methods developed by Karbou et al (2006; 2010). At the Met Office, for example, the microwave skin temperature and emissivity values are retrieved simultaneously in a 1D-Var system from the window channels of temperature and humidity sounders. At ECMWF and Meteo-France, the emissivity is also calculated from window channel observations, but with the skin temperature taken from the NWP model values.

Uncertainties in the land-surface contribution to the TOA radiances are a combination of uncertainties in: the emissivity values used, skin-temperature estimates, and the simplified radiative-transfer equations. The individual uncertainties of each of these contributions should be accurately estimated. As well as validating the individual components, the overall contribution can also be validated using experimental campaigns with ground-truth data, as well as comparisons to the TOA brightness temperatures from satellite instruments. It is likely that a combination of approaches will be needed to close the gap on uncertainty.

Estimates of uncertainties in retrieved land-surface emissivity have been calculated by Prigent et al (1997, 2000) and Karbou et al. (2005a), from the standard deviations of values retrieved from the satellite observations of different instruments. The authors provided gridded maps of uncertainties, which were shown to be around 2% on average. However, these uncertainties are indicative rather than robust, and are likely to be underestimates since they do not account for uncertainties due to: the calibration of the satellite instruments used in the retrievals, the temperature-humidity profiles used to calculate the channel transmittances, cloud screening, and surface temperature data. Ruston et al (2004) also carried out emissivity retrievals from SSM/I satellite observations over the USA and estimated the uncertainties in retrieved emissivity by randomly perturbing the input parameters. The authors concluded that errors were around 2% for frequencies less than 85 GHz. Their methods did not include possible errors in the atmospheric component due to the water-vapour continuum, however.

A number of experimental campaigns have been carried out to evaluate land surface emissivities over different surface types. For example, Harlow (2011) demonstrated how airborne microwave measurements can be used to validate the emissivity of snow-covered ice, relating to snow depth and snow pack characteristics, and quasi-Lambertian reflectance behaviour. Comparisons of different emissivity models have also been carried out. Ferraro et al. (2013) attempted an inter-comparison of several EO land emissivity data sets over the USA. The authors found differences of around 10 K in radiance space (emissivity x skin temperature) for frequencies up to 37 GHz and greater differences up to around 20 K for higher frequencies. These differences appeared to be generally systematic rather than random, with similar seasonal trends captured by the different datasets. Tian et al (2014) estimated uncertainties in retrieved emissivity values by comparing retrievals from different satellite sensors. They estimated similar uncertainties to Ferraro et al (2013), with systematic differences around 3 12 K over desert and 3 20 K over rainforest (with largest differences at the higher frequencies above 80 GHz).  Random errors were estimated to be around 2 6 K.

As well as estimating uncertainties in the emissivity values retrieved, it is important to also consider uncertainties due to assumptions made in the simplified radiative transfer equations. For example, Karbou and Prigent (2005b) estimated the uncertainties in emissivity due to the specular assumption by performing emissivity retrievals from brightness temperatures simulated, using both the specular and Lambertian assumptions. They concluded that the errors in retrieved emissivities due to the specular assumption were less than 1% for most surfaces.

While to date there have been considerable efforts to validate the calculation of the surface contribution to TOA microwave radiances at frequencies between 1 200 GHz, none of the uncertainty estimates have been traceable or complete. This is in part due to the complexity of the problem and it is likely to take a combination of a number of approaches before the gap can be fully closed. However, in part 3 below we suggest two areas of development which could contribute to the estimation of uncertainties in the land surface radiative transfer to reference standards. 

Operational space missions or space instruments impacted: 
  • MetOp
  • MetOp-SG
  • Polar orbiters
  • Microwave nadir
  • Passive sensors
  • Other, please specify:

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 
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
Resolution of this gap will enable greater use of surface-sensitive satellite observations over land in NWP data assimilation systems (either by permitting the use of extra channels, or giving greater weight to existing observations).
  • 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
Potential improvements in ERA near-surface analyses; improved confidence in projected impacts.
Greater confidence in ECV parameters derived from passive microwave sensors, such as soil moisture.
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
Less confidence in findings based on observational data of unknown quality over land.
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.)
  • 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 land.
This will have knock-on effects on attempts to predict regionally resolved impacts of climate change.

Part III Gap remedies

Gap remedies: 

Remedy 1: The use of traceably calibrated radiometers in land surface measurement campaigns (both airborne and ground-based).

Primary gap remedy type: 
Deployment
Secondary gap remedy type: 
Research
Proposed remedy description: 

This remedy concerns the use of traceably calibrated radiometers in land surface measurement campaigns (both airborne and ground-based). Such campaigns can be used to validate both the (combined) emissivity and skin temperature estimates calculated from window-channels observations for temperature and humidity sounders, and emissivity models. Such campaigns would need to be undertaken across a sufficiently diverse set of land-surface types and meteorological seasons to provide representative results that enabled broad applicability. There is also a need for robust ground-truth activities in such campaigns to minimise the uncertainty. 

Relevance: 

It is proposed to use traceably calibrated radiometers in land surface measurements campaigns (both airborne and ground-based. Such campaigns can be used to validate both the (combined) emissivity and skin temperature estimates calculated from window-channels observations for temperature and humidity sounders, and emissivity models. 

Measurable outcome of success: 

Documented, quantitative evaluation of land surface radiative transfer contributions with respect to measurements obtained during airborne campaigns for a globally representative range of land surfaces. 

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 2: Use of models which require physical inputs either from Land Surface Models (LSMs) or remotely-sensed variables

Primary gap remedy type: 
Research
Proposed remedy description: 

While retrievals of emissivity and surface-skin temperature are currently used for microwave atmospheric sounding and imaging instruments over land, as a long-term goal, it would be beneficial to move towards the use of models which require physical inputs either from Land Surface Models (LSMs) or remotely-sensed variables. Uncertainties should also be estimated. We therefore suggest, as long-term goals:

  • The development of emissivity models over a wide range of frequencies (1-200 GHz) that rely on remotely-sensed parameters and/or atlases of land-surface characteristics; and are validated with ground-based or airborne radiometer measurements for different surface types.

  •      Inter-comparisons of available emissivity models, in particular physically based (e.g. multilayer) and simplified models. 

Relevance: 

There is a need to establish traceable uncertainties for NWP fields and radiances calculated from them. 

Measurable outcome of success: 

Documented, quantitative, evaluation of land surface emissivity values estimated from models with respect to measurements of land-surface emissivity obtained during experimental campaigns, for a globally representative range of surfaces. 

Expected viability for the outcome of success: 
  • Medium
Scale of work: 
  • Single institution
  • 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
References: 
  • Ferraro, R., Peters-Lidard, C., Hernandez, C. et al. (2013). An evaluation of microwave land surface emissivities over the continental United States to benefit GPM-era precipitation algorithms. IEEE Transactions on Geoscience and Remote Sensing, 51(1), 378-398.

  • Grody, N. C. (1988). Surface identification using satellite microwave radiometers. IEEE Trans. Geosci. Remote Sensing, 26, 850–859.

  • Harlow, R. C. (2011). Sea Ice Emissivities and Effective Temperatures at MHS Frequencies: An Analysis of Airborne Microwave Data Measured During Two Arctic Campaigns. IEEE Transactions on Geoscience and Remote Sensing, 49(4), 1223 – 1237.

  • Karbou, F., Prigent, C., Eymard, L. and Pardo, J. R. (2005a). Microwave land emissivity calculations using AMSU measurements. IEEE Transactions on Geoscience and Remote Sensing, 43, 948-959.

  • Karbou, F., and Prigent, C. (2005b). "Calculation of microwave land surface emissivity from satellite observations: Validity of the specular approximation over snow-free surfaces?" IEEE Geoscience and Remote Sensing Letters 2.3, 311-314.

  • Karbou, F., Gérard, É. and Rabier, F. (2006). Microwave land emissivity and skin temperature for AMSU-A and -B assimilation over land. Q. J. R. Meteorol. Soc., 132, 2333–2355.

  • Karbou, F., Gérard, É. and Rabier, F. (2010). Global 4DVAR Assimilation and Forecast Experiments Using AMSU Observations over Land. Part I: Impacts of Various Land Surface Emissivity Parameterizations. Weather and Forecasting, 25, 5-19.

  • Njoku, E. G., and Li, L.  (1999). “Retrieval of land surface parameters using passive microwave measurements at 6 to 18 GHz.” IEEE Trans. Geosci. Remote Sens., 37, 79 – 93.

  • Norouzi, H., Rossow, W., Temimi, M., et al. (2012). "Using microwave brightness temperature diurnal cycle to improve emissivity retrievals over land.” Remote Sensing of Environment, 123, 470 – 482

  • Prigent, C., Rossow, W. and Matthews, E. (1997). "Microwave land surface emissivities estimated from SSM/I observations." Journal of Geophysical Research: Atmospheres, 102.D18, 21867-21890.

  • Ruston, B. C. and Vonder Haar, T. H. (2004). "Characterization of summertime microwave emissivities from the Special Sensor Microwave Imager over the conterminous United States." Journal of Geophysical Research: Atmospheres, 109, D19103.

  •         Tian, Y., Peters-Lidard, C. D., Harrison, K.W., et al. (2014). "Quantifying uncertainties in land-surface microwave emissivity retrievals." IEEE Transactions on Geoscience and Remote Sensing, 52.2, 829-840.

  •      Wang, J.R. and Choudhury, B.J. (1981).Remote sensing of soil moisture content, over bare field at 1.4 GHz frequency. Journal of Geophysical Research, 86.C6, 52775282.

  •      Wang, J. R., and B. J. Choudhury (1981), Remote sensing of soil moisture content, over bare field at 1.4 GHz frequency, J. Geophys. Res., 86(C6), 52775282, doi:10.1029/JC086iC06p05277

  • Weng, F., Yan, B. and Grody, N. (2001). A microwave land emissivity model. J. Geophys. Res., 106, D17, 20115–20123.