This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 640276.
G2.34 Limit in traceability of GNSS-IPW ZTD estimates owing to dependency on 3rd party software
Submitted by Anna Mikalsen on 12 June, 2017 - 10:58
Gap abstract:
The Zenith Total Delay (ZTD) uncertainty is a dominant component in the total Global Navigation Satellite Systems Integrated Precipitable Water (GNSS-IPW) uncertainty budget. If not handled properly, it may drastically affect the GNSS-IPW uncertainty estimate. It is essential to understand possible software-dependent peculiarities and to find recommendations while using uncertainty estimates obtained by different data processing software packages for undertaking GRUAN-type uncertainty analysis. The goal is to investigate at least two geodetic software packages using the same GNSS-data processing method, comparing the uncertainty definition and uncertainty handling, leading to potentially large differences in the uncertainty estimates.
Part I Gap description
Primary gap type:
- Knowledge of uncertainty budget and calibration
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:
- Radiosonde
- GNSS-PW
Detailed description:
The Zenith Total Delay uncertainty is a key component of the total uncertainty in GNSS-IPW measurements (Ning et al., 2016). If it is not handled in a proper way, it may drastically affect the GNSS-IPW uncertainty estimate. Fixing it equal to 4mm is just a compromise, excluding GAIA-CLIM outliers from longer time series.
When discussing GRUAN GNSS-IPW uncertainties, we only discuss data analysis using Precise Point Positioning (PPP) in the EPOS software package. While suggesting GRUAN GNSS-IPW uncertainties should be implemented by other data analysis centres, we talk about implementing the GNSS-IPW uncertainty analysis method as described by T. Ning et al. (AMT, 2016) in different software (i.e. not EPOS, solely used by GFZ and GRUAN data analysis). This task is not trivial; for example, the orbital error components described by J. Dousa (GPS Solutions, 2010) and used by T. Ning et al in AMT 2015 are not delivered for end users like ZTDs from IGS (or simply obtainable from standard software for GNSS-data analysis).
Preliminary analysis has been made (and is still in progress) on documentation and related articles published by the developers of Bernese and GAMIT/GLOBK software. ZTD uncertainty is known to be substantial if not the main contributor to the GNSS-IPW uncertainty budget. Therefore, it is essential to understand and to find recommendations when using uncertainty estimates obtained by different data processing software packages for undertaking GRUAN-type uncertainty analysis. The goal is to investigate at least two geodetic software packages using the same GNSS-data processing method, comparing the uncertainty definition and uncertainty handling, leading to (often remarkably) different numeric values of uncertainty estimates.
Operational space missions or space instruments impacted:
- Microwave nadir
- Infrared nadir
- Other, please specify:
In fact, all instruments/methods using channels with frequencies affected by signal absorption in atmospheric water vapour should benefit from knowing trustable estimates of Total Column Water (particularly obtained from GNSS-observations)
Validation aspects addressed:
- Time series and trends
- Representativity (spatial, temporal)
- Calibration (relative, absolute)
Task 2.1.6 within GAIA-CLIM aims to close this gap
Part II Benefits to resolution and risks to non-resolution
| Identified benefit | User category/Application area benefitted | Probability of benefit being realised | Impacts |
|---|---|---|---|
GNSS-IPW data can be used in GAIA-CLIM for the calibration and validation of satellite products. |
|
| Handling uncertainty budget in a consistent way for all instruments (and also for non-GRUAN GNSS products) will improve geographic coverage of reference grade GNSS-data products for calibration and validation of satellite products. |
All researchers or institutions using GNSS-IPW in calibration/validation of different instruments or models can benefit from more “trustful” time-series. |
|
| GNSS-IPW can be used for calibration of any ground-based instruments capable to retrieve atmospheric Total Column Water values |
GNSS-IPW trend analysis in climate research can benefit from having reference-quality (or “close to reference quality”) data from geographically more densely distributed sites (i.e. not only GRUAN sites), but data processed by different software. |
|
| Remarkable improvement in better global coverage of reference-quality (or close to reference-quality) GNSS-IPW data. |
| Identified risk | User category/Application area at risk | Probability of risk being realised | Impacts |
|---|---|---|---|
GNSS-IPW data cannot be used in GAIA-CLIM for the calibration and validation of satellite products. |
|
| Calibration/validation of satellite products would miss a global set of reference sites offering timely continuous and all-weather conditions data for tropospheric Total Column Water. This will affect all remote sensing instruments using channels for dete |
Climate research and operative services will miss a lot of valuable high-quality data for calibration/validation of methods/instruments only because of not having the uncertainty budget calculated in a consistent way. |
|
| GNSS-data from many high-quality, but non-GRUAN networks cannot be used for evaluating trends in GNSS-IPW. GNSS-IPW cannot be used for calibration of other in-situ measurements with tropospheric Total Column Water measuring capability. |
Part III Gap remedies
Gap remedies:
Remedy 1: Comparison of at least two geodetic software packages
Primary gap remedy type:
Research
Relevance:
The proposed remedy will help to better define GRUAN GNSS-IPW uncertainties, starting from the level reached thus far, with data processing and uncertainty estimation.
Measurable outcome of success:
The first outcome will be making GRUAN GNSS-PW with transparent uncertainty analysis usable for the ‘Virtual Observatory’. It should also help to make decisions for selecting and extending the ‘Virtual Observatory’ database with verified and usable non-GRUAN GNSS-data available worldwide (potentially processed with different geodetic software sand data processing strategies compared to GFZ). In the future there could be a relatively dense global dataset for GNSS-PW data usable for the ‘Virtual Observatory’.
Expected viability for the outcome of success:
- High
Scale of work:
- Consortium
- Programmatic multi-year, multi-institution activity
Time bound to remedy:
- Less than 3 years
Indicative cost estimate (investment):
- Low cost (< 1 million)
Indicative cost estimate (exploitation):
- No
Potential actors:
- EU H2020 funding
- Copernicus funding