G3.04 Limited characterization of the multi-dimensional (spatiotemporal) smoothing and sampling properties of atmospheric remote sensing systems, and of the resulting uncertainties

Submitted by Anna Mikalsen on 12 June, 2017 - 12:09
Gap abstract: 

This gap concerns the need for a more detailed characterisation of the actual spatiotemporal smoothing and sampling properties of both satellite-based EO measurements and ground-based in-situ or remote-sensing measurements. Indeed, EO measurements are most often associated with single locations, or at best pixel footprints, while in fact the actual measurement sensitivity covers a larger spatiotemporal extent, due for instance to the radiative transfer determining the measured quantities, or the actual measurement geometry (choice of line-of-sight, trajectory of a weather balloon, etc.). In an inhomogeneous and variable atmosphere, this leads to additional errors and uncertainties that are not part of the reported measurement uncertainties, but still need to be quantified, in particular when performing comparisons with other types of measurements, with different smoothing and sampling characteristics. For several ECVs and measurement techniques, significant work is needed to (1) determine/model the actual spatiotemporal smoothing and sampling properties, and (2) quantify the resulting uncertainties on the measurements of the variable atmosphere.

Part I Gap description

Primary gap type: 
  • Knowledge of uncertainty budget and calibration
Secondary gap type: 
  • Uncertainty in relation to comparator measures
ECVs impacted: 
  • Temperature,Water vapour, Ozone, Aerosols, Carbon Dioxide, Methane
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.)
  • Climate research (research groups working on development, validation and improvement of ECV Climate Data Records)
Non-satellite instrument techniques involved: 
  • Radiosonde
  • Ozonesonde
  • Lidar
  • FPH/CFH
  • Microwave Radiometer
  • FTIR
  • Brewer/Dobson
  • UV/VIS zenith DOAS
  • UV/VIS MAXDOAS
Related gaps: 
G3.01 Incomplete knowledge of spatiotemporal atmospheric variability at the scale of the measurements and of their co-location
G3.06 Missing comparison (validation) uncertainty budget decomposition including uncertainty due to sampling and smoothing differences
G6.03 Lack of sustained dedicated periodic observations to coincide with satellite overpasses to minimise co-location effects

  • G3.01. To be addressed before G3.04

    Argument: A quantification of the uncertainties that result from the specific sampling and smoothing properties of an instrument requires information on the spatiotemporal variability of the atmospheric field.

    G3.06. To be addressed after G3.04

    Argument: Error/uncertainty budget decomposition of a comparison requires a proper understanding of the smoothing and sampling properties of the instruments involved, i.e. requires G3.04 to be remedied.

    G6.03. To be addressed after G3.02

    Argument: Deciding on the best time and location for targeted reference observations should be informed by information on the actual sampling and smoothing properties of the measurement systems.

Detailed description: 

Remotely sensed data are often considered as column-like or point-like samples of an atmospheric variable, associated for instance with the location of a ground-based instrument. This is also the general assumption for satellite data, which are assumed to represent the column or profile at the vertical of the satellite field-of-view footprint in case of nadir sounders, and atmospheric concentrations along a vertical set of successive tangent points in the case of limb and occultation sounders. In practice, the quantities retrieved from a remote sensing measurement integrate atmospheric information over a tri-dimensional airmass and also over time. E.g., ground-based zenith-sky measurements of the scattered light at twilight integrate stratospheric UV-visible absorptions (by ozone, NO2, BrO etc.) over several hundreds of kilometres in the direction of the rising or setting Sun (Lambert et al., 1997). A satellite limb measurement will actually be sensitive to the atmosphere along the entire line-of-sight towards the photon source, depending on the specific emission, absorption, and scattering processes at play (e.g. von Clarmann et al., 2009). Similarly, in-situ measurements of atmospheric profiles cannot be associated with a single geo-location and time stamp, due for instance to balloon drift for ozone- and radiosondes. In a variable and inhomogeneous atmosphere, this leads to additional uncertainties not covered in the 1-dimensional uncertainties reported with the data (e.g. Lambert et al., 2011).  A prerequisite for quantifying these additional uncertainties of multi-dimensional nature is not only a quantification of the atmospheric variability at the scale of the measurement (cfr. G3.01), but also a detailed understanding of the smoothing and sampling properties of the remote sensing system and associated retrieval scheme.  Pioneering work on multi-dimensional characterization of smoothing and sampling properties of remote sensing systems and associated uncertainties was initiated during the last decade (e.g. in the EC FP6 GEOmon project and in the current EC H2020 GAIA-CLIM project), but in the context of integrated systems like Copernicus and GCOS, appropriate knowledge of smoothing and sampling uncertainties, still missing for several ECVs and remote sensing measurement types, has to be further developed and harmonized.

Operational space missions or space instruments impacted: 
  • Independent of specific space mission or space instruments
Validation aspects addressed: 
  • Radiance (Level 1 product)
  • Geophysical product (Level 2 product)
  • Gridded product (Level 3)
  • Assimilated product (Level 4)
  • Time series and trends
  • Representativity (spatial, temporal)
  • Calibration (relative, absolute)
  • Spectroscopy
  • Auxiliary parameters (clouds, lightpath, surface albedo, emissivity)
Gap status after GAIA-CLIM: 
  • GAIA-CLIM explored and demonstrated potential solutions to close this gap in the future

Addressing this gap was a major objective of GAIA-CLIM, within which specific tasks were dedicated to the characterisation of smoothing and sampling properties of selected instruments and for selected ECVs.   Results have been obtained for total ozone columns, for ozone, temperature, and humidity profiles, and for aerosol columns and profiles from a diverse set of ground-based instruments. Regarding satellite data, only a selection of current missions were explored. Results were made available in technical notes, namely  D3.4 (“Report on measurement mismatch studies and their impact on data comparisons”) and D3.6 (“Library of (1) smoothing/sampling error estimates for key atmospheric composition measurement systems and (2) smoothing/sampling error estimates for key data comparisons”), and through the ‘Virtual Observatory’. In the long term, this gap will require continued efforts to fully characterize the spatiotemporal smoothing and sampling properties of both new ground-based instruments and upcoming satellite sensors. Hence the gap requires constant re-evaluation as technology and observing programs evolve. 

Part II Benefits to resolution and risks to non-resolution

Identified benefitUser category/Application area benefittedProbability of benefit being realisedImpacts
More complete assessment of the impact of natural variability on the measurements;
  • All users and application areas will benefit from it
  • High
Better uncertainty characterization. This in turn increases confidence in the data for the end user and allows more meaningful use in a variety of applications.
Improved definition of appropriate co-location criteria for validation work, taking into account the actual sampling and smoothing properties, and ultimately minimizing errors due to co-location mismatch.
  • All users and application areas will benefit from it
  • High
Lower uncertainty due to co-location mismatch will result in tighter constraints on the products from validation work, supporting further instrument and algorithm development.
Identified riskUser category/Application area at riskProbability of risk being realisedImpacts
Incomplete total uncertainty budget for a single measurements.
  • All users and application areas will suffer from it.
  • High
Incomplete data characterization and potentially limited or flawed interpretation, whatever the use type.
Incomplete uncertainty budget for measurement comparisons, e.g. for validation.
  • All users and application areas will suffer from it.
  • High
Flawed validation results: missing uncertainty components lead to failed consistency checks, and a less performant validation system

Part III Gap remedies

Gap remedies: 

Remedy 1: Comprehensive modelling studies of measurement process.

Primary gap remedy type: 
Research
Proposed remedy description: 

Detailed modelling of the measurement process, including multi-dimensional radiative transfer if applicable, to quantify the 4-D measurement sensitivity. An example are multi-D averaging kernels for retrieval-type measurements. This work requires a significant effort from the instrument teams, for which dedicated, though still relatively low (per instrument), resources are required, in particular for code modifications and additions. If appropriate, the results from these detailed calculations can be parametrized for easy and efficient use when calculating the resulting errors and uncertainties for large amounts of data. This uncertainty calculation is done by combining the quantification of the measurement sensitivity with knowledge on the spatiotemporal variability of the atmospheric field (cf. G3.01). When these detailed modelling studies are out of reach, a similar estimate of the multi-D measurement sensitivity can be made in a more pragmatic way based on the measurement principle and physical considerations (e.g. Lambert et al. 2011), or it can in some cases be estimated with empirical methods by comparing data sets with differing resolution. Note that an essential prerequisite is the availability of all required metadata with the measurements, such as viewing angles or GPS trajectories. 

Relevance: 

This remedy will provide a description for every instrument and measurement type of the full 4-D measurement sensitivity, and the errors and uncertainties resulting from the assumption that a measurement can be associated with a nominal geo-location and time. 

Measurable outcome of success: 

Publications and technical notes describing for every instrument and measurement type the full 4-D measurement sensitivity, and the errors and uncertainties resulting from the assumption that a measurement can be associated with a nominal geo-location and time.  

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: 
  • EU H2020 funding
  • Copernicus funding
  • National funding agencies
  • National Meteorological Services
  • ESA, EUMETSAT or other space agency
  • Academia, individual research institutes

Remedy 2: Empirical determination of true resolution by comparison with high-resolution data.

Primary gap remedy type: 
Research
Proposed remedy description: 

If temporally coinciding data with higher spatial resolution are available, the true horizontal resolution of a measurement system can be determined empirically by comparing the measurements of the two instruments as obtained on the same scene. This approach was for instance demonstrated by Sihler et al. (2017) for satellite and ground-based DOAS-type measurements. It is empirical in the sense that it does not require extensive modelling of the measurement process. Rather, it requires some basic assumptions on the actual footprint and the sensitivity therein of each measurement, which is then further optimized by comparison with the high-resolution data set, if necessary over a large set of diverse scenes. This approach was also explored within GAIA-CLIM, where it was used to estimate the true vertical resolution and weighting function of temperature and humidity soundings, as described in D3.4.

Relevance: 

This remedy addresses the gap partially (since it only deals with the resolution aspects) and it requires an independent, high-resolution data set of sufficient quality.  As such, it is not universally applicable, but it does provide a valuable resolution estimate, independent of any classical metrological modelling 

Measurable outcome of success: 

Publications and technical notes describing for every instrument and measurement type the full 4-D measurement sensitivity, and the errors and uncertainties resulting from the assumption that a measurement can be associated with a nominal geo-location and time. 

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: 
  • EU H2020 funding
  • Copernicus funding
  • National funding agencies
  • National Meteorological Services
  • ESA, EUMETSAT or other space agency
  • Academia, individual research institutes
References: 
  •         Lambert, J.-C., et al., Comparison of the GOME ozone and NO2 total amounts at mid-latitude with ground-based zenith-sky measurements, in Atmospheric Ozone - 18th Quad. Ozone Symp., L’Aquila, Italy, 1996, R. Bojkov and G. Visconti (Eds.), Vol. I, pp. 301-304, 1997.

  • Lambert et al., “Multi-dimensional characterisation of remotely sensed data”, EC FP6 GEOmon Technical Notes, 2011  

    Lambert, J.-C., et al., “Comparing and merging water vapour observations: A multi-dimensional perspective on smoothing and sampling issues”, in “Monitoring Atmospheric Water Vapour: Ground-Based Remote Sensing and In-situ Methods”, N. Kämpfer (Ed.), ISSI Scientific Report Series, Vol. 10, Edition 1, 326 pp., ISBN: 978-1-4614-3908-0, DOI 10.1007/978-1-4614-3909-7_2, © Springer New York 2012

  • Von Clarmann et al., “The horizontal resolution of MIPAS”, AMT v2, 2009

  • Seidel et al., “Global radiosonde balloon drift statistics”, J.G.R. v116, 2011

  •      Sihler, H., Lübcke, P., Lang, R., Beirle, S., de Graaf, M., Hörmann, C., Lampel, J., Penning de Vries, M., Remmers, J., Trollope, E., Wang, Y., and Wagner, T.: In-operation field-of-view retrieval (IFR) for satellite and ground-based DOAS-type instruments applying coincident high-resolution imager data, Atmos. Meas. Tech., 10, 881-903, https://doi.org/10.5194/amt-10-881-2017, 2017.