This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 640276.
G3.01 Incomplete knowledge of spatiotemporal atmospheric variability at the scale of the measurements and of their co-location
Submitted by Anna Mikalsen on 12 June, 2017 - 12:05
Gap abstract:
The atmospheric concentration of nearly all ECVs varies in space and time at the scale of the individual measurements, and at the scale of their co-location in the context of data comparisons (e.g., for the purpose of satellite validation, data merging, and data assimilation). However, the amplitude and patterns of these variations are often unknown on such small scales. Consequently, it is impossible to quantify the uncertainties that result from sampling and smoothing properties of the measurements of the variable, structured atmospheric field. This gap thus concerns the need for a better quantification of atmospheric spatiotemporal variability at the small scales of individual measurements and co-locations.
Part I Gap description
Primary gap type:
- Uncertainty in relation to comparator
Secondary gap type:
- Knowledge of uncertainty budget and calibration
- Parameter (missing auxiliary data etc.)
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:
- Independent of instrument technique
G3.04. To be addressed after G3.01
Argument: To estimate the additional uncertainties on a measurement that result from spatiotemporal atmospheric variability at the measurement sampling and smoothing scales, a quantification of that spatiotemporal variability is a prerequisite.
G3.06. To be addressed after G3.01
Argument: Understanding the uncertainty budget of a comparison (in a validation context) requires a quantification of the impact of co-location mismatch. This cannot be done without an estimate of the spatiotemporal variability of the ECV under study.
Detailed description:
Spatiotemporal variability of the atmosphere at the scale of the airmass being measured or - in the case of a measurement intercomparison - at the scale of the co-location, leads to additional uncertainties, not accounted for by the uncertainty budget reported with an individual measurement (Lambert et al., 2012). To quantify these additional uncertainties (cf. gaps G3.04 and G3.06), or to ensure that they remain negligible through the use of appropriate co-location criteria (cf. G3.02), a prerequisite is a proper understanding of atmospheric variability of the targeted ECV on those scales.
While scales above approx. 100km and 1h are relatively well captured for several GAIA-CLIM target ECVs in model or satellite gridded data (e.g., Verhoelst et al., 2015, for total ozone), information on smaller scales is most often restricted to results from dedicated campaigns or specific case studies, e.g., Sparling et al. (2006) for ozone profiles, Hewison (2013) for meteorological variables, and Pappalardo et al. (2010) for aerosols. Due to the exploratory nature of these studies, neither global nor complete vertical coverage is achieved. For instance, information on small-scale variability in the ozone field is limited to altitudes and regions probed with dedicated aircraft campaigns. The validation of satellite data records with pseudo global networks of ground-based reference instruments on the other hand requires an appropriate quantification of atmospheric variability in very diverse conditions, covering all latitudes, altitudes, dynamical conditions, degrees of pollution etc.
This gap therefore concerns the need for a better, more comprehensive, quantification of the spatiotemporal variability of the ECVs targeted by GAIA-CLIM.
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
Within GAIA-CLIM, a work package (WP3) was dedicated to research on co-location mismatch in an inhomogeneous and variable atmosphere. In the context of this work package, some studies were performed that quantified spatiotemporal variability for a few ECVs at a limited scale domain (e.g. temperature and water vapour temporal variability at 6-hour scale from radiosonde inter-comparisons, and aerosol optical depth variability at the scale of a satellite-ground co-location in the North-East US). Although this work was limited to a few ECVs, scales, geographical coverage etc. owing to the limited resources and data availability, GAIA-CLIM has demonstrated use cases / case studies which may permit a more exhaustive approach in future. Fully addressing this gap requires significant observational and modelling work, far beyond the scope of GAIA-CLIM, as described in detail in the remedies.
Part II Benefits to resolution and risks to non-resolution
| Identified benefit | User category/Application area benefitted | Probability of benefit being realised | Impacts |
|---|---|---|---|
Improved understanding of single measurement uncertainty, including the impact of the instrument smoothing and sampling properties |
|
| More reliable uncertainty estimates allow for more confidence in the data and optimized use in e.g. assimilation and other applications. |
Improved definition of appropriate co-location criteria for validation work, minimizing errors due to co-location mismatch |
|
| Lower uncertainty due to co-location mismatch will result in tighter constraints on the products from validation work, supporting further instrument and algorithm development. |
Improved interpretation of comparison results because co-location mismatch errors can be quantified. |
|
| Improved quantification of the uncertainty due to co-location mismatch will allow more stringent tests of the reported measurement uncertainties, supporting further instrument and algorithm development. |
| Identified risk | User category/Application area at risk | Probability of risk being realised | Impacts |
|---|---|---|---|
Incomplete uncertainty budget for single measurements and derived products |
|
| Poor confidence in data and services; potential over-interpretation; difficult/unreliable generation of higher level data products (through data assimilation and/or merging). |
Incomplete uncertainty budget for data comparisons |
|
| Sub-optimal feedback from data comparisons, in particular in the context of satellite validation. Potential of both EO and ground segments not fully realized. |
Part III Gap remedies
Gap remedies:
Remedy 1: Improved high-resolution modelling to quantify mismatch effects
Primary gap remedy type:
Research
Secondary gap remedy type:
Technical
Proposed remedy description:
A first remedy to gain better insight in the small-scale spatiotemporal variability of atmospheric ECVs is by high-resolution modelling studies at the global scale, resulting in comprehensive data sets of atmospheric fields, at high horizontal, vertical, and temporal resolution, based not solely on higher-resolution grids but also including the relevant physics and (photo) chemistry at those scales.
Improved spatiotemporal resolution in atmosphere models is a much broader scientific goal, with great computational and theoretical (e.g. convection and turbulence treatment) challenges. As such, this remedy probably requires a level of effort and resources beyond what can be justified solely by the need for satellite data validation. The technological/ organizational viability is therefore considered medium and the cost estimate high.
Relevance:
If successful, this remedy would largely close the gap, and it would facilitate remedies for most other gaps related to comparator uncertainties through the use of OSSEs (Observing System Simulation Experiments) based on these modelled fields.
Measurable outcome of success:
The quality of the model output at its finest resolution can be estimated by comparison with high-resolution measurement data sets, preferably those with limited horizontal, vertical, and temporal smoothing effects, e.g. from balloon-borne sondes. Ideally, an agreement is found within the combined model and measurement uncertainties.
Expected viability for the outcome of success:
- Medium
Scale of work:
- Single institution
- Consortium
Time bound to remedy:
- Less than 10 years
Indicative cost estimate (investment):
- High cost (> 5 million)
Indicative cost estimate (exploitation):
- No
Potential actors:
- EU H2020 funding
- Copernicus funding
- National funding agencies
- National Meteorological Services
Remedy 2: Use of statistical analysis techniques based upon available and targeted additional observations
Primary gap remedy type:
Research
Secondary gap remedy type:
Technical
Proposed remedy description:
This remedy concerns the statistical analysis of existing and future satellite and non-satellite high-resolution data sets, which allows us to separate the contribution of atmospheric variability from the total uncertainty budget of a data comparison, e.g. using so-called ‘structure functions’ or heteroskedastic functional regression. Within the geographical and temporal coverage of the data set, these methods produce an estimate of the variability (or auto-correlation) of the field. Note that, as for Remedy G3.01(1), the scientific interest for higher resolution in the data sets is much broader than only the validation needs, e.g. for the identification of emission sources in an urban environment.
The technological and organizational effort required to make step changes in the spatiotemporal resolution of the observational data sets is in general very large, and comes with a large financial cost (more than 5M euro), in particular if global coverage is aimed for. Hence, such developments need a much larger user base and the use proposed here should be considered secondary to the scientific objectives of such new missions. Nevertheless, smaller dedicated campaigns with for instance aircraft or Unmanned Aerial Vehicles (UAVs) can offer great insight at particularly interesting sites (e.g. at ground stations with a multitude of instruments observing a particular ECV), and this at medium cost (between 1M and 5M euro).
Relevance:
This remedy directly addresses the gap, as already illustrated for instance with aircraft data for ozone by Sparling et al. (2006).
Measurable outcome of success:
The primary outcome would be publications describing for the different ECVs and various atmospheric regimes, locations and altitude ranges the atmospheric variability at scales ranging from those of in-situ measurements (e.g. 10s of meters for balloon sonde measurements) to that of a satellite pixel (several 10s to 100s of kilometres). These can be based either on existing data sets, or represent an exploitation of newly designed campaigns and missions.
Expected viability for the outcome of success:
- High
Scale of work:
- Single institution
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
References:
- Butterfield et al.:” Determining the temporal variability in atmospheric temperature profiles measured using radiosondes and assessment of correction factors for different launch schedules”, AMT, v8, 2015
- 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
- Pappalardo et al., “EARLINET correlative measurements for CALIPSO: First intercomparison results”, J.G.R.: Atmospheres v115, 2010
- Sparling et al., “Estimating the impact of small-scale variability in satellite measurement validation”, J.G.R.: Atmospheres v111, 2006
- Verhoelst et al., “Metrology of ground-based satellite validation: Co-location mismatch and smoothing issues of total ozone comparisons”, AMT v8, 2015