TOUGH: Targeting Optimal Use of GPS Humidity Measurements in Meteorology
The main purpose of this project is to develop and refine methods enabling the use of Global Positioning System (GPS) data from existing European GPS stations in numerical weather prediction models, and to assess the impact of such data upon the quality of weather forecasts. After the project, the resulting methodologies can be utilised by European weather forecast agencies at large, and the results help pave the road for a future co-ordinated, operational European observation system. The exploitation of this new source of Earth Observation data is expected to benefit in particular the prediction of precipitation.
Weather forecasting of today is strongly dependent on the application of numerical weather prediction (NWP) techniques. Starting from initial states representing the atmosphere at a certain time, numerical models are integrated forward in time to obtain the future state of the atmosphere. The initial atmospheric states, the quality of which are of crucial importance to the quality of the forecasts, are obtained from the time history of observations through a process that is generally referred to as atmospheric data assimilation. Thousands of observations are required for the determination of the state variables of the atmospheric models, the most important ones being vertical profiles of wind, temperature and moisture, in addition to the pressure at the surface of the earth.
Throughout the history of NWP, the observation and model initialisation of the moisture has been treated with less care than the other variables. The moisture initialisation has generally been carried out without coupling to the initialisation of temperature, surface pressure and wind. Only radiosonde observations of atmospheric moisture profiles have been available, and these observations are often not representative of the scales of motion described by the models and are also affected by observational errors. Remote sensing observations and modern data assimilation methods, based on e.g. variational techniques, have the potential of bringing the moisture field initialisation to a more advanced state.
The measurement of the atmospheric delay of radio signals from navigation system satellites, such as the GPS, offer an opportunity for the NWP community to get access to high quality atmospheric moisture information from already established networks of GPS ground stations. The atmospheric delay of GPS radio signals is due to the sensitivity of atmospheric refraction to atmospheric pressure, temperature and moisture. The total delay of the radio signals between a GPS satellite and a GPS ground station is essentially dependent on the total atmospheric mass, i.e. the pressure at the surface, and the columnar atmospheric moisture content. Provided the surface pressure can be determined from another source of information, e.g., an NWP model, the delay of the GPS signals provides a unique source of information related to the atmospheric moisture content. Normally the GPS data processing results in a single delay measure, reflecting the average properties of the atmosphere around the site. More advanced techniques, which determines the delay between the site and each GPS satellite on the sky are being introduced – thereby enhancing the information content by nearly a factor ten.
The utilisation of data from GPS ground stations for numerical weather prediction, and also for climate monitoring and research, is the subject of the COST Action 716 (Exploitation of Ground-based GPS for Climate and Numerical Weather Prediction Analysis). Several of the members of COST 716 Action have furthermore contributed to EC-funded MAGIC (Meteorological Applications of GPS Integrated Water Vapour Measurements in the Western Mediterranean) Project. Considerable progress has been achieved both within COST 716 and within the MAGIC project. The quality of the data has steadily been improved and the extraction techniques work in near real time and are approaching operational status in Europe. COST 716 data assimilation tests for the June 2000 period using Central and Northern European model integration areas have indicated significant bias (systematic observation error) problems between the GPS total zenith delay measurements and model predictions. Preliminary results from MAGIC assimilation show a neutral impact in the overall statistics over 2 weeks of data, but indicate positive impact for rapidly evolving localised storm systems or in situations where the humidity field is not dominated by large-scale dynamics. Thus, GPS delays are potentially very useful to meteorology, but further research is needed before the GPS data can be used in an optimal way to the benefit of numerical weather prediction. It is based on these promising results that 7 meteorological institutes now join forces in this project in order to optimise the methods by which GPS data can be utilised in NWP models. In total 15 institutes will partake in the project, seven of which will process the GPS data into zenith delays do research on improving such processing.
The GENERAL OBJECTIVES for the present project proposal are to improve the use of GPS data for numerical weather prediction and climate monitoring. This shall be done by innovation of new techniques and methodologies enabling proper correction of error sources identified in previous work, as well as by initiating use of the more detailed information available in the form of the individual delays between each receiver and the GPS satellites visible to it, rather than the single average type delay used by current methods.
Considering the experiences and the achievements from the COST 716 Action and from the MAGIC Project, these general objectives may be stated more precisely through the following verifiable sub-objectives:
- Carry out research to optimise the assimilation of ground-based GPS in numerical weather prediction models. This research will include, for example, a proper modelling of the GPS measurement errors and application of more advanced, 4-dimensional, assimilation techniques. Each step/component in the optimisation of the assimilation techniques will be verified by impact studies.
- Develop methods for use of GPS slant delays in numerical weather prediction.
- Running a research mode data collection, by co-ordinated pre-processing and distribution of ground-based GPS measurements from Europe through a few European processing centres in support of the proposed data assimilation research efforts. This work will be closely linked with the COST 716 Action. The data processing centres will provide pre-processed data from subsets of the total European network, and each subset of the data should have comparable error characteristics. These error characteristics will be documented through comparisons of data from stations included in several of the network subsets (network overlap).
- Investigate the benefit of using ground-based GPS-data in numerical weather prediction using the improved assimilation software through extended parallel data assimilation and forecast experiments, with and without ground-based GPS measurements, covering all four seasons. Special emphasis will be devoted to the verification of precipitation forecasts.
- Promote the idea of an operational utilisation of ground-based GPS measurements to the numerical weather prediction community in Europe.
The raw GNSS data consist of ranging measurements from visible navigation system satellites such as the Global Positioning System (GPS). If the positions of the satellites and receivers are precisely known, the ranging measurements can be used to detect delays due to the atmosphere. This is possible since the propagation speed of the radio signals is sensitive to the refractive index of the atmosphere, which is a function of pressure, temperature and humidity, and the ionospheric electron content. The ionospheric delay is dispersive and can be removed using observations on two frequencies. The remaining accumulated delay for a raypath is the integral of the refractivity along the trajectory of the ray through the atmosphere
The refractivity N is described as a function of temperature T, the partial pressure of dry air Pd, and the partial pressure of water vapour e and constants, k1, k2, and k3, which have been determined experimentally (Smith et al 1953, Thayer 1974, Bevis et al 1994). Small scale horizontal variations may be neglected, to first order, so that observations at all satellite elevation angles can be mapped to a single zenith delay value which can then be transformed to integrated water vapour with auxiliary information on the surface pressure field (Bevis et al 1992).
Since the concept was initially proposed, the quality of the data has steadily improved through several major efforts, for example the EC projects MAGIC (Haase et al 2001, Vedel et al 2001) and WAVEFRONT (Dodson et al 1999), and NEWBALTIC (Emardson et al 1998), and the U.S. ARM (Gou et al 2000), GPS/STORM (Rocken et al 1995), CORS (Fang et al 1998), and CLIMAP(Haas et al 2001),
Figure 1 Time dependent behaviour of the standard deviation of the GPS-radiosonde ZTD difference over a 1.5 year time period in the Mediterranean area.
MAGIC (Meteorological Applications of GPS Integrated Column Water Vapour Measurements in the Western Mediterranean) was a 3-year research project financed in part by the European Commission to develop the tools necessary for the meteorological users to integrate the GPS derived humidity products into their numerical weather prediction models, and test these models in severe storm situations. In the project, a prototype system for deriving and validating robust GPS integrated water vapour (IWV) and zenith tropospheric delay (ZTD) data sets was developed, both in post-processing and near-real-time mode. An extensive a database of 1.5 years of ZTD data is available for more than 50 sites in Spain, France, and Italy. The database has been validated through continuous comparisons with radiosondes. The comparison shows differences with a standard deviation on the order of 10 mm ZTD (see fig. 1) or the equivalent error in IWV of 1.6 kg/m2. The continuous comparison with independent data sets demonstrated that there are long-term differences that require further investigation, especially for climate applications. Continuous comparisons with HIRLAM NWP fields show a standard deviation of 17 mm ZTD or 2.7 kg/m2. A higher standard deviation for the HIRLAM fields than radiosondes indicates that there is significant information contained in the GPS observations that is unknown to the NWP model, and hence the potential to improve the model.
The European weather services have invested scientific development efforts over the past 5-10 years into a new generation of data assimilation based on variational techniques. The 3-dimensional versions of these assimilation schemes (3D-Var) have recently been introduced operationally (Lorenc et al 1999, Gustafsson et al 2001). One of the advantages of these variational data assimilation schemes is the possibility to utilise observed quantities with complicated, e.g. non-linear, relations to the forecast model variables. Thus it is, for example, possible to directly assimilate the atmospheric delay data as measured at the ground-based GPS stations. Early trials to assimilate simulated ground-based GPS measurements with simplified variational data assimilation schemes were carried out by the Mesoscale Meteorology group at the National Centre for Atmospheric Research (NCAR), Boulder, USA (Kou et al 1996, de Pondeca et al 2000). The main limitation of these early NCAR trials with variational data assimilation of GPS data was the lack of a background error, thus the forecast errors were not described properly and therefore the assimilation became sub-optimal. The more mature variational data assimilation schemes developed by European weather services for operational purposes included proper background error constraints. The meteorological services involved in the COST 716 Action and the MAGIC Project developed and tested 3D variational methods for the assimilation of ground-based GPS data. Assimilation tests were carried out for a 2 weeks period in June 2000. The overall large scale statistical impact on forecasts of temperature, wind, and humidity fields was neutral for the GPS ZTD data set, which was not unexpected given the number of GPS ZTD observations compared with conventional observations. However, rainfall forecasts for specific case studies were improved, especially in localised regions of high precipitation (see fig 2, next page). This was a very encouraging result, that was undetectable in the overall statistics, but has the potential to have a significant socio-economic impact, since these intense short duration high precipitation events are a principal cause of weather related damage in the Mediterranean region.
On the other hand, COST 716 data assimilation tests for the same June 2000 period and for Central and Northern European model integration areas have indicated significant bias (systematic observation error) problems associated with the GPS Total Zenith Delay measurements. These bias problems were temporarily avoided by introduction of Bias Reduction Algorithms, based on a comparison between GPS measurements and forecast model data. The origin of the problem is yet not clear, however. Simulation studies () and results from trials to model the spatial correlation of GPS observation errors () support the possibility of slowly varying and horizontally correlated observation errors associated with the GPS measurements.
Figure 2 (left panel) observed 12 hour accumulated precipitation for an event the 9 June 2000 which produced high rainfall in the Pyrenees and north-eastern Spain, (centre panel) forecast precipitation without GPS data, (right panel) forecast precipitation with GPS data.
European geodesists and meteorologists have joined forces in the COST 716 Action on “Exploitation of ground-based GPS for climate and numerical weather prediction application”, with participation from 17 European countries. A benchmark data collection, near-real time processing, data distribution and data assimilation test was successfully carried out for a two-week period in June 2000. A near-real time data collection, processing and distribution exercise is continuously ongoing from April 2001 until February 2002. A working group (WG4) on the design of an operational ground-based European GPS network for meteorological purposes has started its activities.
The innovative elements of the present project proposal include
- Optimisation of the 3 dimensional assimilation of ground-based GPS data by a proper modelling of observation error biases and spatial/temporal correlation
- Development of 4-dimensional assimilation to utilise the temporal resolution of the GPS data.
- Processing, validation and assimilation of GPS slant delays.
- Development of methods for assimilation of GPS slant delays in 3 dimensional data assimilation
- Investigation of the optimal use of the GPS data in meteorology by extended parallel data assimilation and forecast experiments distributed over all seasons, by objective and subjective verification.