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Targeting Optimal Use of GPS Humidity Measurements in Meteorology

 

TOUGH

 

 

 

 

 

 

 

 

 

 

 

 

A RESEARCH and TECHNOLOGICAL DEVELOPMENT PROJECT

Submitted to Energy/Environment and Sustainable Development

 

Theme 7.2.1 Generic Earth Observation Technologies:

Introduce scientific results into new or existing applications

 

Theme 7.2.2 Generic Earth Observation Technologies:

Improve the exploitation of earth observation data

 

Prepared 2002-07-19


 

Content list

Title Page.............................................................................................................................................................................. 1

Content list........................................................................................................................................................................ 2

1. Project Summary........................................................................................................................................................ 4

2. Scientific/Technical Objectives and Innovation................................................................................ 5

State of the art in GNSS observations of water vapour................................................................. 7

State of the art in meteorological data assimilation.................................................................. 8

Innovation by the present project proposal............................................................................................ 9

3. Project Workplan.................................................................................................................................................. 10

a) Introduction.......................................................................................................................................................... 10

Modelling of observation error characteristics for data assimilation (WP 3000)......................................... 11

Development and testing of 4-dimensional data assimilation techniques (WP 4000)................................... 12

Optimisation of GPS and surface humidity assimilation (WP 5000)................................................................. 13

Development of methods for assimilation of slant GPS delays (WP  6000)...................................................... 13

Impact studies and extreme case studies (WP 7000)............................................................................................. 14

GPS ZTD data provision and monitoring (WP 8000)........................................................................................... 15

GPS ZTD system research (WP 9000)...................................................................................................................... 15

b) Project planning and time table............................................................................................................... 18

c) Graphical presentation of the project's components................................................................ 19

d. Work package descriptions.......................................................................................................................... 21

d_2. List of Deliverables........................................................................................................................................ 25

d_3. Workpackage Descriptions....................................................................................................................... 27

WP 1000 - Management............................................................................................................................................. 27

WP 1100 - Overall Management................................................................................................................................. 28

WP 1200 – Scientific Co-ordination........................................................................................................................... 29

WP 1300 – Data supply co-ordination....................................................................................................................... 29

WP 1400 - Meeting preparation and participation..................................................................................................... 30

WP 2000 – User Requirements................................................................................................................................... 31

WP 3000 – Error modelling for variational assimilation............................................................................................. 32

WP 3100 – Bias reduction schemes............................................................................................................................ 33

WP 3200 – Modelling of spatial error correlation...................................................................................................... 34

WP 3300 – Modelling of temporal error correlation................................................................................................... 35

WP 4000 – Variational data assimilation development and tests............................................................................... 35

WP 4100 – Develop and optimise  4DVAR assimilation........................................................................................... 36

WP 4200 – Mesoscale data assimilation development and tests................................................................................ 37

WP 5000 – Optimisation of GPS and surface humidity assimilation......................................................................... 38

WP 5100 – Refining methods for surface humidity assimilation................................................................................ 38

WP 5200 – Testing combined GPS and surface humidity assimilation...................................................................... 39

WP 6000 – Development of methods for use of slant delays.................................................................................... 40

WP 6100 – Slant delay retrievals................................................................................................................................ 41

WP 6200 – Slant delay validation and observation error studies................................................................................ 42

WP 6300 – Observation operator development......................................................................................................... 43

WP 6400 – Assimilation tests.................................................................................................................................... 44

WP 7000 – Assimilation impact statistics and extreme case studies.......................................................................... 45

WP 7100 – Co-ordination of case studies and compiling results................................................................................ 46

WP 7200 – Extensive assimilation tests..................................................................................................................... 47

WP 7300 – EUCOS scenario impact studies.............................................................................................................. 48

WP 8000 – GPS ZTD data provision and monitoring................................................................................................ 49

WP 8100 – Product quality monitoring and reporting................................................................................................ 50

WP 8200 – Maintain facilities for data exchange for NWP users............................................................................... 50

WP 8300 – Regional GPS data production and validation.......................................................................................... 51

WP 8400 – Furnishing continuous radiosonde and NWP output............................................................................... 53

WP 8500 – Validation database development and maintenance................................................................................. 54

WP 8600 – User Validation and Feedback.................................................................................................................. 54

WP 9000 – GPS ZTD system research...................................................................................................................... 55

WP 9100 – Robust quality indicators......................................................................................................................... 56

WP 9200 – Long term bias elimination....................................................................................................................... 57

WP 9300 Co-ordinate system biases.......................................................................................................................... 58

WP 9400 Biases correlated with seasonal signals....................................................................................................... 59

WP 9500 Optimal combination of regional solutions................................................................................................. 60

WP 10000 – Exploitation and dissemination.............................................................................................................. 61

4. Contribution to Objectives of Programme/Call............................................................................. 63

5. Community added value and contribution to EU policies........................................................... 63

European (and global) dimension of the problem............................................................................... 63

European added value for the consortium............................................................................................ 64

Contribution to European Union policies................................................................................................. 64

6. Contribution to Community social objectives................................................................................. 65

Improving the quality of life and health and safety..................................................................... 65

Improving employment prospects and development of skills in Europe.............................. 65

Preserving and/or enhancing the environment.................................................................................... 66

7. Economic development and scientific and technological prospects............................. 66

Economic benefits..................................................................................................................................................... 66

Strategic impact....................................................................................................................................................... 66

Exploitation plans.................................................................................................................................................. 67

Dissemination strategies.................................................................................................................................... 68

Graphical description of exploitation plan.......................................................................................... 69

8. The consortium.......................................................................................................................................................... 70

Co-operation between Research Institutes and End Users........................................................... 71

9. Project management............................................................................................................................................. 72

Management structure....................................................................................................................................... 72

Communication and Quality Plan................................................................................................................ 74

Risk assessment, alternatives......................................................................................................................... 76

Milestones and Project Schedule Monitoring....................................................................................... 76

References......................................................................................................................................................................... 77

 


 

1. Project Summary

 

Knowledge of the atmospheric distribution of water vapour is of key importance in weather prediction and climate research. It is tightly coupled to processes like energy transfer, precipitation, and is an important greenhouse gas. However, currently there is lack of knowledge about the actual humidity field, both due to a shortage of observations and a sub optimal handling of humidity in the data assimilation systems, which are used to make estimates of the actual atmospheric field. Such fields are used to start numerical weather prediction models and for climate monitoring. Global Positioning System (GPS) signals are particularly sensitive to water vapour. The main purpose of this project is to develop and refine methods enabling the use of GPS data from existing European GPS stations in numerical weather prediction models, and to assess the impact of such data upon the skill of weather forecasts. 

 

The GENERAL OBJECTIVES for the project 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. In the project we will:

-         Carry out research to optimise the assimilation of ground-based GPS in numerical weather prediction models. This research will include a proper modelling of the GPS measurement errors and application of more advanced 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. Use of slants will enhance the amount of information available from each ground station.

-         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. 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.

 

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 GPS moisture observation system. The exploitation of this new source of Earth Observation data is expected to benefit in particular the prediction of precipitation. In the longer run it will benefit also climate monitoring. When the Galileo satellites are launched the amount of observations of this type will increase and some of the error sources can be more easily controlled.

2. Scientific/Technical Objectives and Innovation

 

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.

 

State of the art in GNSS observations of water vapour

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.

 

State of the art in meteorological data assimilation

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 ([1]) and results from trials to model the spatial correlation of GPS observation errors ([2]) 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.

 

Innovation by the present project proposal

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.

 

3. Project Workplan

a) Introduction

The main objectives for the present project proposal are to improve the use of GPS data for numerical weather prediction and climate monitoring. This will 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. In order to make the required progress to meet the objectives, research efforts and technical developments over a wide range of problem areas need to be carried out. This research and development require active participation from the geodetic and the meteorological communities. To get an initial overview of the required efforts, we here mention a few scientific and technical key problems that will be solved:

 

·        The pre-processing of the raw GPS measurements will be handled by a number of Processing Centres. In order to meet the future operational timeliness requirements from the numerical weather prediction community, algorithms for near-real-time pre-processing will be introduced. Furthermore, this pre-processing will be carefully co-ordinated and monitored in order to guarantee the meteorological community a homogeneous data set, with stable and known (documented) error characteristics.

 

·        Early trials to assimilate ground-based GPS data have indicated that these data may be affected by systematic observation errors (error biases) as well as spatially and temporally correlated observation errors. Significant efforts will be devoted in the present project to (a) increase our understanding of the origin of these observation errors; (b) eliminate these errors to the extent possible and (c) model the characteristics of the observation errors. Realistic statistical models of the observation errors are needed for an optimal assimilation of the data.

 

·        It is foreseen that the most significant impact of ground-based GPS measurement will be possible only through application of 4-dimensional assimilation techniques. First of all, GPS data have a high temporal resolution. More important may be that GPS data provides information mainly on the atmospheric moisture. In order to derive atmospheric pressure, temperature and wind fields that are consistent with the moisture field as seen by the GPS data, the forecast model must be utilised in the assimilation process. This is exactly what is done in 4-dimensional data assimilation. Two forms of 4-dimensional data assimilation, namely 4 dimensional variational data assimilation (4D-Var) and nudging, will be applied in the present project in order to maximise the impact of GPS data.

 

·        The ground-based GPS data provide information only about the vertical integrated atmospheric moisture content. In order improve the vertical distribution of the observed water vapour during the assimilation process, the GPS data assimilation will be supplemented in the present project with assimilation of moisture measurements from surface stations.

 

·        Ground-based GPS information has so far been utilised in the form of Zenith Total Delay (ZTD) data. Each ZTD data value is obtained through a mapping from a number of slant delay measurements. It is expected that the meteorological data assimilation would benefit from a direct assimilation of slant delays. The explicit mapping to zenith delays, which may introduce unnecessary errors, is avoided and information on horizontal gradients may also become possible to extract.

 

·        To assess the impact of the ground-based GPS data, and the best way in which to process and use such data, three types of studies will be carried out. First, cases of significant weather events will be selected. Data assimilation experiments will be conducted to assess the impact of the data on these cases. Special attention will be given to short range precipitation forecasts, as we expect that most additional information from the data should be in humidity.  Secondly, data assimilation experiments using different data assimilation systems will be conducted for long periods (e.g. a month) and for all seasons, in order to draw general conclusions on the operational use of the data. Forecasters and other end-users will evaluate the quality of the resulting weather forecasts, produced with and without the GPS data.

 

The scientific and technical work of the proposal has been divided into 7 basic work-packages WP 3000 – WP 9000, the content of which is briefly described below. These basic work-packages have been further sub-divided into sub-work-packages, described with details later in this section. Three additional work-packages involve Project Management (WP 1000), User requirements (WP 2000) and Exploitation and dissemination (WP 10000).

 

Modelling of observation error characteristics for data assimilation (WP 3000)

Data assimilation for NWP (Numerical Weather Prediction) optimally estimates the atmospheric state using observation information. The observed values always contain observation errors. In case these errors are un-correlated between different observations, more plentiful observations lead to a more accurate state estimate. Observation error correlation generally implies reduced information content of the observations. Use of more observations does not in this case improve, but degrade the quality of the state estimate, unless the error correlation is properly accounted for.

 

In static data assimilation schemes, such as 3D-Var (3-dimensional variational assimilation), observations are used from one instant close to the analysis time. Serially correlated observations errors from one station, i.e. temporal observation error correlations, do not play any role in this case. Horizontal error correlations, i.e. observation error correlations between stations at one instant, need to be accounted for by error modelling in order to obtain an optimal state estimate. In temporally extended data assimilation schemes, such as 4D-VAR, observations are used at appropriate time over a data-window. In this case also temporal correlations of observation errors need to be accounted for.

 

Mean observation errors, i.e. error biases, need a specific treatment of bias reduction. Generally it is very difficult, however, to distinguish the slowly varying horizontal observation error correlation from the mean observation errors, or from the systematic errors of the NWP model. Comparison of ground-based GPS measurements with forecast model data and with radiosonde data have revealed that the GPS measurements may be affected by error biases. Early data assimilation experiments have indicated that it is necessary to apply bias reduction algorithms in order to avoid detrimental effects of these error biases on, for example, precipitation forecasts. Ideally, these error bias problems should be avoided by applying remedy actions as close as possible to the source of the information, e.g. at the GPS station or by improving the pre-processing algorithms. It is foreseen, however, that the need for bias reduction schemes will remain. Statistical comparison between GPS observations and model data will be applied to design the bias reduction algorithms.

 

The design of the ground-based GPS measurements and pre-processing systems implies theoretically the measurements to be affected by spatially correlated errors. Simulation studies by Jarlemark et al. (2001) and studies of empirical spatial correlations by Stoew et al. (2001) support this theory. These studies suggest that the length scale of the GPS observation error correlation may be significantly larger than the length scale of the forecast error. This separation of length scales can possibly be utilised for a determination of the spatial (horizontal) correlation of GPS observations errors from innovation vectors, i.e. the differences between GPS observations and the model data. Other observations of the atmospheric moisture could in principle serve as references for the estimation of the GPS observation errors, but the limited spatial resolution and relatively poor quality of radiosonde moisture measurements do not make this approach meaningful. It will furthermore be investigated whether the observation error and forecast error contributions to the spatial correlation of the GPS data innovation vectors can be separated through a modelling of the forecast error correlation by simulation techniques, based on ensemble assimilation experiments. 

 

With the introduction of 4-dimensional variational data assimilation (4D-Var), several observations from the assimilation window, for example a 6 hour period, and from the same station may be utilised. Experiences from the 4D-Var assimilation of surface observations have shown that the sensitivity of the assimilation to systematic observation errors may become critical and that models for the temporal correlation of observation error need to be specified (Järvinen et. al., 1999). Models for the temporal correlation will alternatively be developed from innovation vectors, i.e. differences between GPS observations and model data, or from differences between GPS observations and high quality radiosonde observations.

 

The efficiency of the developed bias reduction schemes and the developed models for spatial and temporal observation error correlation will be tested through data assimilation and forecast experiments.

 

Development and testing of 4-dimensional data assimilation techniques (WP 4000)

It is foreseen that ground based GPS observations due to their high time resolution, and due to the use of the forecast model in the assimilation will have the highest impact when assimilated using 4D-Var assimilation systems. 3D-Var assimilation systems are currently in operational use by DMI, MetO, and SMHI, while 4D-Var assimilation systems are under development.

 

The 4D-Var assimilation schemes will be developed to handle GPS observations in an optimal manner. Since GPS observations mainly are related to the moisture variables of the forecast model, it is important to include condensation and precipitation processes in the 4D-Var schemes. This requires mathematical formulations, called parameterization, of these processes and their computer codes in nonlinear, tangent linear and adjoint forms. In most of the state of the art NWP models, these schemes are highly nonlinear and non-differentiable. Therefore, they often need to be simplified or regularized in mathematical formulations before the development of the tangent linear and adjoint schemes needed by 4D-Var.. The application of 4D-Var to GPS data will be tested and validated through case studies and through data impact studies, covering at least ten days.

 

DMI and MetO expect to apply complete 4-dimensional variational data assimilation (4D-Var) schemes for their operational NWP forecast models, while LAQ will apply a simplified 4-dimensional assimilation based on nudging to a mesoscale forecast model (MM5) and compare this with 3D-Var assimilation.

Optimisation of GPS and surface humidity assimilation (WP 5000)

The ground-based GPS measurements of Zenith Total Delay (ZTD) in principle only provide information on the vertically integrated water vapour in the atmosphere above the GPS stations. In case no other water vapour information is available, 3-dimensional variational data assimilation (3D-Var), for example, will use statistical knowledge only to distribute the observed information in the vertical. It was shown by Kuo et al. (1996) in an observing system simulation study that more information on the vertical distribution of water vapour could be retrieved by adding humidity observations from surface stations. This possibility to improve the utilisation of ground-based GPS measurements will be investigated by running a 3D-Var data assimilation and forecast experiment over one month with and without 2 meter relative humidity observations.

 

This shall be done by INM and SMHI. The variational data assimilation system to be applied by these two project partners already includes preliminary observation operators based non-linear, tangent-linear and adjoint versions of the post-processing for 2 meter relative humidity. These observation operators will be upgraded to be consistent with the latest version of the forecast model and complemented with models for observation error statistics. 

 

Development of methods for assimilation of slant GPS delays (WP  6000)

 Instead of deriving zenith quantities, GPS signal delay and integrated water vapour can also be measured along slant paths from ground-based receivers to GPS satellites. By using not only the zenith delay of a receiver but also the slant delays the number of observations will increase by roughly a factor ten. By applying variational algorithms a three-dimensional water vapour field can be retrieved from slant observations, at least from a dense network of receivers. Furthermore, the horizontal resolution of the retrieved water vapour field will also profit from this larger amount of observations.

 

The derivation of zenith and slant GPS delays from GPS observations involves several assumptions about the atmospheric structure. In particular, assumptions about atmospheric homogeneity and receiver multipath when observing satellites are at low elevation angles (close to the horizon) influence the results. The multipath must be carefully modelled as a function of receiver environment while the atmospheric model used for the mapping must be carefully chosen in cases of atmospheric inhomogeneities. Even when estimating only slant delays, mapping functions are still needed in order to separate receiver clock errors from atmospheric delays. Traditionally, mapping functions are empirical functions derived from multi-year averages of radiosonde data. A new approach is to derive the mapping function directly from NWP model output. This could result in a significant improvement of IWV measurements for low elevations. Pre-processing of raw slant delays before assimilation will be investigated, using additional input from NWP analysis. This will help to discriminate site dependent effects (multipath, antenna phase center variations) and receiver clock errors from atmospheric delays. It can also be used to derive intermediate quantities such as ZTD, horizontal gradients, scale height and or timing information, which could be used as an alternative to assimilating slant delays. Currently used software will be modified, if necessary, and additional modules to estimate slant delays and model multipath will be developed. Furthermore, mapping procedures based on forecast model input will be developed and tested by one weather service, KNMI,  and  a geodetic institute, TUD.

 

In order to obtain realistic results the error biases and correlations of the GPS slant measurements must be modelled. Observations for a network of ground-based receivers will be simulated from a 3-D water vapour field and used for assimilation trials. The goal of these simulations is to test our software and to estimate the capability of a network of GPS receivers to reconstruct refractivity field inhomogeneities at different scales. In addition we need to determine an optimal discretisation and interpolation scheme of the refractivity field to be used for the processing of observational data. The retrieved fields will be validated against water vapour radiometer measurements during the CLIWANET campaign.

 

The natural first step towards using slant-delay measurements in NWP assimilation is to properly evaluate them against the model counterparts. For this task an appropriate observation operator[1] is needed. The zenith delay observation operator is simple to develop, as the observation geometry is relatively straightforward and similar to the NWP model geometry. The slant-delay observation operator, in contrast, requires a model profile along a slanted path with unknown intersections with the model levels. Once the problem of interpolating the model variables on a slanted path is solved, the associated delay calculation problem can be fairly easily solved.

 

A demonstration version of a GPS slant delay observation operator will be developed by FMI and KNMI in co-operation, and this observation operator will be adapted to the HIRLAM three-dimensional variational data assimilation system. The operational NWP model of KNMI will be used for impact studies with a resolution of at least 10 km x 10 km. The performance of the assimilation of these slant delays will be investigated by conducting observation system simulation experiments (OSSE). Impact studies will be performed with the analysed water vapour fields, obtained from the GPS data of a dense GPS network (Observation System Experiment, OSE). DMI will perform assimilation tests using the software developed by KNMI and FMI.

 

Impact studies and extreme case studies (WP 7000)

DMI will monitor the operational forecasts and information about the actual weather in order to identify periods and areas in which the forecasts are particularly poor, or in which “special” weather occurred in areas with good coverage of GPS stations partaking in the project. For the selected cases, each participating institute will carry out extensive, full-scale data assimilation experiment. Month long assimilation experiments will  be carried out for each of the four seaasons. Standard statistical methods will be used for objective verification. Analyses and forecasts with and without the ground-based GPS data will be verified against observations and analyses. Special attention will be given to short range forecasts of moisture, clouds and precipitation. Forecasters will participate with subjective verification of the forecasts.

 

One of the objectives of the EUCOS program of EUMETNET is to increase the cost-efficiency of the European observing system while staying at the same overall cost. It is proposed to replace some radiosonde stations by AMDAR aircraft soundings. Comparing with radiosondes, one of the drawbacks of the current AMDAR is the lack of humidity information. The ground-based GPS ZTD data could provide useful complementary humidity information that allows this cost-redistribution with less negative effect on numerical weather predictions. A well documented EUCOS observation period will be selected, see e.g. Amstrup (2000), and the impact of replacing radiosonde data with combined AMDAR/GPS data will be studied.

 

GPS ZTD data provision and monitoring (WP 8000)

Currently GPS data is available from regional geodetic networks under pre-existing agreements with regional processing centres. In past research methodology has been developed to process the data to retrieve atmospheric properties. This methodology will be used in demonstration mode in this project, to allow the users to gain experience using the EO products in their NWP application. The GPS data will be retrieved from the sites and quality checked. The refractive delays in the GPS signals will be calculated and then geometrically mapped to the zenith delay (ZTD. For a period of at least one year this will be done in near real time (NRT), as necessary for operational NWP. These products will be used by NWP groups, which are developing ZTD assimilation algorithms. The data will also be further processed to remove the hydrostatic component of the delay based on surface pressure measured at the site. This non-hydrostatic, or "wet" delay will then be transformed to integrated water vapour. These products will be used by NWP users, which are developing nudging assimilation systems.

 

Each regional data processing centre will be responsible for retrieving the GPS data, processing the data, and transferring the data to the project ftp site in NRT. In processing the data, the centres will include stations from a common reference network in their solutions to provide a means for cross-checking the quality of the data and to ensure that the reference frames used are consistent. Similar products that are available from organisations outside the consortium that cover other regions will also be made available to the meteorological users.

 

The first 3 months are to be used to improve the raw data flow as necessary, to verify the robustness of the processing system and to make any adjustments to the processing concerning the station distribution, following the recommendations of the work-package leader and a processing committee. During these 3 months and the following 21 months, the products will be provided continuously to the users as a demonstration prototype system. 6 months into the project quality control standards will be implemented.

 

Radiosonde observations can be used as an important independent data set for validating GPS ZTD data both on a daily basis and on long term statistics. The quality of the radiosondes is high, but the temporal and spatial resolutions sometimes lead to problems. NWP analyses and forecasts, on the other hand, can be used as another source of data with a uniform resolution in 4 dimensions. The database will contain radiosonde data, NWP data and precipitation data that is collected for validating the short term precipitation forecasts.

 

GPS ZTD system research (WP 9000)

In previous work developing the methodology and its validation, it was established that the GPS ZTD and IWV products are of a quality comparable or superior to existing data sources available to the NWP user community. In particular, the products were shown to be in overall good agreement with radiosondes (less than 10mm of delay). However, the products occasionally had epochs of unexplained poor data quality. In addition, long spatial and temporal signals in the residuals from radiosonde and NWP comparisons have been detected. This work-package will investigate the source of these errors and contribute new techniques to the methodology implemented in the demonstration processing.

 

Most of the GPS software packages provide the standard deviation of the estimated Zenith Total Delay (ZTD) parameter as an estimate of the quality of the solution. The standard deviation is a formal measure of quality computed from the inverse of the normal matrix. As a measure of quality it is seriously flawed because it does not take into account the actual quality of the observations, it is unaware of important errors such as multipath, and it assumes the orbits (and sometimes satellites clocks) are perfect. The standard deviation is always too optimistic and cannot be used to model the errors during the assimilation into NWP. A new quality indicator for the ZTD will be developed and tested. The new indicator will be computed from the estimated least squares residuals by using variance component estimation techniques, taking into account the degree of freedom over the domain of the ZTD parameter.

 

The strength of ground-based GPS is certainly not its absolute accuracy. Because of its sensitivity to signal multipath effects, varying the elevation angle cut-off limits - or using different schemes for down-weighting low elevation angle observations - will typically have a significant impact on the estimated ZTD value. A constant bias over decades is in principle not a problem but if there are variations at the time scales of years it will influence both NWP models and long term climate monitoring. We will use long time series (> 5 years) of independent radiosonde and microwave radiometer data to study these effects and believe that a correct assessment can be made at the 5-10 mm level in ZTD. Very-Long-Baseline Interferometry (VLBI) is another method, which will be used. Several European VLBI sites, e.g., Wettzeell, Matera, and Onsala, are co-located with important GPS sites in the IGS network, where data are publicly available. The VLBI estimates of ZTD are obtained from the same type of estimation technique as in GPS but due to the large directional antennas used the multipath effect is in practise eliminated. VLBI observations are, however, not continuous, but 24-hour observing sessions bi-weekly or monthly for more than five years provide a sufficient data base.

 

GPS tropospheric zenith delay is correlated with the site co-ordinates, especially with the vertical one. For meteorological applications there is no need to estimate them when processing GPS data, but, in order to derive the ‘best’ possible ZTD estimates, there is a need to know site co-ordinates with a certain level of accuracy. Generally they are obtained averaging daily station estimates over a longer period of time. So even for pure meteorological applications there is the need of station co-ordinates monitoring. Of course, they are related to the terrestrial reference frame (TRF) in which they have been computed. The changing of TRF could introduce biases into the GPS ZTD and IWV products. Furthermore constrains to the reference frame are also induced by fixing the GPS orbits (IGS orbits are given in a TRF) during the data reduction, what is commonly done when regional network are considered. Therefore it is an interesting question to understand how to deal with the biases related to the reference frame, even for climate investigations. Furthermore, the geodetic reference frame is always being improved. There are occasionally slight changes which can lead to offsets in the long term trend of GPS ZTD. The influence different reference frames have on GPS ZTD estimates will be evaluated and a methodology for dealing with updates to the reference frame will be established. It will be verified that differences between processing centres estimates for the reference IGS stations are not due to orbit errors,  co-ordinate errors or reference frame errors. Guidelines for verifying the quality of GPS ZTD and IWV data will be established by examining repeatability of co-ordinates and these guidelines will be implemented in the GPS ZTD and IWV processing.

 

Results from the EC MAGIC project showed that the difference between GPS ZTD and radiosondes increased in magnitude in high humidity regimes, producing a seasonal signal in these differences.  These signals limit the ability to separate a climatic signal from the noise in the of GPS ZTD products. Biases correlated with seasonal signals due to systematic differences in actual and modelled vertical structure will be investigated as well as noise sources in the radiosonde and GPS ZTD data that could have a seasonal variation.

 

The International GPS Service (IGS) has developed a method for combining ZTD solutions from different processing centres by removing a bias between processing centres and averaging the results. The same method is applied for the 12 analysis centres of the EUREF Permanent GPS Network (EPN). Typical for IGS and EUREF is, that almost all stations are processed by at least three processing centres. In our distributed network, only a subset of stations will be common among processing centres, but these can be used to verify that there are no offsets. The batch type of processing used by IGS and EUREF will be converted into a Kalman filter approach that can be used in near real-time applications. The differential biases between the analysis centres will be modelled for the stations in common. Special techniques for the detection, identification and adaptation of outliers and biases will be used. Algorithms will be developed and tested and possible refinements will be investigated. For example, the NRT combination could be further combined with bias reduction algorithms (using output from NWP analysis) to model absolute biases. TUD  will also develop automated methodology for a regional combination of solutions following the EUREF model, in order to provide the best integrated product from the regional products. They will aid in the implementation of this methodology at the processing centres.

 

 

 

 

 

 


 

b) Project planning and time table

 

WP#

Wpname

Start

End

Year 1

Year 2

Year 3

1000

Management

00

36

 

 

 

 

 

 

1100

Overall Management

00

36

 

 

 

 

 

 

1200

Scientific Coordination

00

36

 

 

 

 

 

 

1300

Data supply co-ordination

00

36

 

 

 

 

 

 

1400

Meeting preparation and participation

00

36

 

 

 

 

 

 

2000

User Requirements

00

03

 

 

 

 

 

 

3000

Error Modelling for variational assimilation

00

24

 

 

 

 

 

 

3100

Bias reduction schemes

00

24

 

 

 

 

 

 

3200

Modelling of spatial error correlation

00

24

 

 

 

 

 

 

3300

Modelling of temporal error correlation

00

24

 

 

 

 

 

 

4000

Variational assimilation development and tests

00

36

 

 

 

 

 

 

4100

Develop and optimise 4Dvar assimilation

00

33

 

 

 

 

 

 

4200

Mesoscale data assimilation development and tests

00

36

 

 

 

 

 

 

5000

Optimisation of GPS/surface humidity assimilation

00

36

 

 

 

 

 

 

5100

Refining methods of surface humidity assimilation

00

24

 

 

 

 

 

 

5200

Testing combined GPS / surface humidity assimilation

24

36

 

 

 

 

 

 

6000

Development of methods for use of slants delays

00

36

 

 

 

 

 

 

6100

Slant delay retrievals

00

30

 

 

 

 

 

 

6200

Slant delay validation and observation error studies

06

24

 

 

 

 

 

 

6300

Observation operator development

00

18

 

 

 

 

 

 

6400

Assimilation tests

18

36

 

 

 

 

 

 

7000

Assimilation impact statistics / extreme case studies

00

36

 

 

 

 

 

 

7100

Co-ordination of case studies and compiling results

00

36

 

 

 

 

 

 

7200

Case studies and extensive impact studies

06

30

 

 

 

 

 

 

7300

EUCOS scenario impact studies

00

24

 

 

 

 

 

 

8000

GPS ZTD data provision and monitoring

00

36

 

 

 

 

 

 

8100

Product quality monitoring and reporting

00

36

 

 

 

 

 

 

8200

NWP User GPS ZTD/IWV data server maintenance

00

36

 

 

 

 

 

 

8300

Regional GPS ZTD data production

00

36

 

 

 

 

 

 

8400

Furnishing continuous radiosonde and NWP output

00

36

 

 

 

 

 

 

8500

Validation database development and maintenance

00

36

 

 

 

 

 

 

8600

User validation and feedback

03

33

 

 

 

 

 

 

9000

GPS ZTD System Research

00

30

 

 

 

 

 

 

9100

Robust quality indicators

00

09

 

 

 

 

 

 

9200

Long term bias elimination

00

30

 

 

 

 

 

 

9300

Co-ordinate system biases

00

24

 

 

 

 

 

 

9400

Biases correlated with seasonal signals

00

24

 

 

 

 

 

 

9500

Optimal combination of regional solutions

00

09

 

 

 

 

 

 

10000

Exploitation and dissemination

00

36

 

 

 

 

 

 

 

Table 1 Project planning and timetable.


c) Graphical presentation of the project's components

 

 


 

 

 

 

 

 

 

 

 

 

 

 

WP1000 two-way interacts with all WP´s in the large box. All these provide input to WP10000.

 

 


 

d. Work package descriptions

d_1. Work package list

WPNO

Wpname

PM

Leader

Start

End

1000

Management

37

DMI

00

36

1100

Overall Management

12

DMI

00

36

1200

Scientific Co-ordination

2

DMI

00

36

1300

Data supply co-ordination

8

ACRI-ST

00

36

1400

Meeting preparation and participation

15

DMI

00

36

2000

User Requirements

1

MetOffice

00

03

3000

Error Modelling for variational assimilation

38

FMI

00

24

3100

Bias reduction schemes

4

SMHI

00

24

3200

Modelling of spatial error correlation

25

FMI

00

24

3300

Modelling of temporal error correlation

9

DMI

00

24

4000

Variational assimilation development and tests

24

MetOffice

00

36

4100

Develop and optimise 4Dvar assimilation

12

MetOffice

00

33

4200

Mesoscale data assimilation development and tests

12

LAQ

00

36

5000

Optimisation of GPS and surface humidity assimilation

13

SMHI

00

36

5100

Refining methods of surface humidity assimilation

3

SMHI

00

24

5200

Testing combined GPS and  surface humidity assimilation

10

INM

24

36

6000

Development of methods for use of slants delays

42

KNMI

00

36

6100

Slant delay retrievals

10

TUD

00

30

6200

Slant delay validation and observation error studies

8

KNMI

06

24

6300

Observation operator development

15

KNMI

00

18

6400

Assimilation tests

9

KNMI

18

36

7000

Assimilation impact statistics and extreme case studies

73

DMI

00

36

7100

Co-ordination of case studies and compiling results

1

DMI

00

36

7200

Case studies and extensive impact studies, including validation by forecasters.

68

DMI

06

30

7300

EUCOS scenario impact studies

4

DMI

00

24

8000

GPS ZTD data provision and monitoring

97

ACRI-ST

00

36

8100

Product quality monitoring and reporting

3

ACRI-ST

00

36

8200

NWP User GPS ZTD/IWV data server maintenance

2

MetOffice

00

36

8300

Regional GPS ZTD data production

84

ACRI-ST

00

36

8400

Furnishing continuous radiosonde and NWP output

3

DMI

00

36

8500

Validation database development and maintenance

3

ACRI-ST

00

36

8600

User validation and feedback

2

MetOffice

03

33

9000

GPS ZTD System Research

21

Chalmers

00

30

9100

Robust quality indicators

3

TUD

00

09

9200

Long term bias elimination

7

Chalmers

00

30

9300

Co-ordinate system biases

3

ASI

00

24

9400

Biases correlated with seasonal signals

5

ACRI-ST

00

24

9500

Optimal combination of regional solutions

3

TUD

00

09

10000

Exploitation and dissemination

2

DMI

00

36

 

Table 2 Work package list and personnel resources. Note that only the work package leader is listed, though the person-months resources are include all participating partners.

 



Workpackage / partner personnel resource matrix

The following table gives the number of person-months allocated to each work package for each  partner.

 

 

Workpackage

DMI

SMHI

Met Office

INM

LAQ

KNMI

FMI

ACRI-ST

Chalmers

NMA

ASI

IEEC

LPT

GOP

TUD

Total

1000

Management

11.5

2

0.5

0.5

0.5

0.5

0.5

7.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

27

2000

User requirements

0

0

0.5

0

0

0

0

0

0

0

0

0

0

0

0

0.5

3000

Error modelling for variational assimilation

2

11

0

0

0

0

7

0

12

0

0

0

0

0

0

32

4000

4-dimensional assimilation development and tests

5

0

5.5

0

10

0

0

0

0

0

0

0

0

0

0

20.5

5000

Optimisation of GPS and surface humidity assimilation

0

6

0

9

0

0

0

0

0

0

0

0

0

0

0

15

6000

Development of methods for use of slants delays

3

0

0

0

0

17

9

0

0

0

0

0

0

0

6

34

7000

Assimilation impact statistics and extreme case studies

12

0

14

18

14

0

0

0

0

0

0

0

0

0

0

58

8000

GPS ZTD data provision and monitoring

2

0

2

0

0

0

0

12

4

7.5

6.5

11

8

9

0

62

9000

GPS ZTD system research

0

0

0

0

0

0

0

0

9

0

3

0

0

0

5

17

10000

Exploitation and dissemination

1..5

1

0.5

0.5

0.5

1.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

10

Total by Partner

37

20

23

28

25

19

17

20

26

8.5

10.5

12

9

10

11

276

 

Table 34 Work package / partner personnel resource matrix

 


 

d_2. List of Deliverables

 

Due date is the first day of the month corresponding to T0 + the month number.

 

Type refers to the nature of the deliverable using one of the following codes:

Re = Report; Da = Data set; Eq = Equipment; Pr = Prototype; Si = Simulation;

Th = Theory; De = Demonstrator; Me = Methodology; Co = Code; O = other

 

Dissemination level uses one of the following codes:

PU = Public

RE = Restricted to a group specified by the consortium (including the Commission Services).

CO = Confidential, only for members of the consortium (including Commission Services).

 

PPC refers to all Associated contractors, which are processing centres, PACRI through GOP

PNWP refers to all contractors who are NWP meteorological agency users, DMI through FMI

 

D  1

kickoff meeting minutes

DMI 00 Re CO

 

D  2

kickoff meeting minutes

DMI 03 Re PU

 

D  3

1st project meeting minutes

DMI 06 Re CO

 

D  4

semi-annual progress report - report 1

DMI 06 Re CO

 

D  5

2st project meeting minutes

DMI 12 Re CO

 

D  6

annual progress report - report 2

DMI 12 Re PU

 

D  7

3st project meeting minutes

DMI 18 Re CO

 

D  8

Semi-annual progress report - report 3

DMI 18 Re CO

 

D  9

4st project meeting minutes

DMI 24 Re CO

 

D  10

annual progress report - report 4

DMI 24 Re PU

 

D  11

5st project meeting minutes

DMI 30 Re CO

 

D  12

Semi-annual progress report - report 5

DMI 30 Re CO

 

D  13

final project meeting minutes

DMI 36 Re CO

 

D  14

final report - report 6

DMI 36 Re PU

 

D  15

user requirements document

MetO 03 Re PU

 

D  16

Bias reduction scheme

SMHI 12 Co+Re PU

 

D  17

Impact of Bias reduction scheme on assimilation

SMHI 24 Co+Re PU

 

D  18

Development of spatial error correlation model

FMI 18 Re PU

 

D  19

Report on spatial error correlations

Chalmers 24 Re PU

 

D  20

Impact of spatial error correlation model

SMHI 30 Re PU

 

D  21

Development of temporal error correlation model

SMHI 18 Re PU

 

D  22

Report on temporal correlations

Chalmers 18 Re PU

 

D  23

Impact of temporal error correlation model in 4D-Var

DMI 30 Re PU

 

D  24

HIRLAM 4DVAR results

DMI 33 Re PU

 

D  25

MetO model 4DVAR results

MetO 30 Re PU

 

D  26

4DVAR software (GPS-specific forward operator)

MetO 33 Re PU

 

D  27

Report on MM5 GPSPW nudging

PLAQ 12 Re PU

 

D  28

Report on MM5 GPSPW 3DVAR

PLAQ 24 Re PU

 

D  29

Report on MM5 GPS-ZTD 3DVAR

PLAQ 36 Re PU

 

D  30

Surface moisture observation operator

SMHI 12 Co+Re PU

 

D  31

Surface moisture impact study

SMHI 24 Co+Re PU

 

D  32

Impact of surface humidity obs. on GPS data assim.

INM 36 Re PU

 

D  33

Software for slant delay retrieval/multipath mapping.

TUD 18 Co+Re PU

 

D  34

3 month test dataset.

TUD 12 Data PU

 

D  35

2 month data set from period of interest.

TUD 24 Re PU

 

D  36

Software for direct mapping function approach.

TUD 30 Co+Re PU

 

D  37

Slant delay validation and observation error report

KNMI 24 Co+Re PU

 

D  38

3Dvar Slant delay observation operator implementation

FMI 12 Co PU

 

D  39

Initial evaluation of the observation operator

FMI 18 Re PU

 

D  40

Impact assessment of moisture on HIRLAM forecasts

KNMI 36 Re PU

 

D  41

Impact study of assimilation of slant delays.

DMI 36 Re PU

 

D  42

Selected cases for first year

DMI 13 Re PU

 

D  43

Selected cases for second year

DMI 25 Re PU

 

D  44

Comparison of case studies

DMI 32 Re PU

 

D  45

DMI assimilation results

DMI 30 Re PU

 

D  46

INM assimilation results

INM 30 Re PU

 

D  47

PLAQ assimilation results

PLAQ 30 Re PU

 

D  48

MetO assimilation results

MetO 30 Re PU

 

D  49

comparison of different assimilation methods

DMI 30 Re PU

 

D  50

selected EUCOS IOP assimilation impact results

DMI 24 Re PU

 

D  51

start of monthly GPS ZTD IWV quality reports

ACRI-ST 06 Re PU

 

D  52

Project database web site

ACRI-ST 06 web PU

 

D  53

data exchange formats

MetO 03 Re PU

 

D  54

support software

MetO 06 Co PU

 

D  55

Initial delivery of GPS ZTD IWV products

PPC 04 Da PU

 

D  56

GPS ZTD IWV valid. reports

PPC 24 Re PU

 

D  57

Final GPS ZTD IWV system evaluation

PPC 30 Re PU

 

D  58

Radiosonde data specification document

DMI 03 Re PU

 

D  59

HIRLAM output specification document

DMI 03 Re PU

 

D  60

Start of delivery European radiosonde data

DMI 03 Da PU

 

D  61

Start of delivery HIRLAM analyses/forecast

DMI 03 Da PU

 

D  62

Validation data sets with web site access

ACRI-ST 12 Da PU

 

D  63

start delivery of monthly monitoring/validation report

MetO 06 Re PU

 

D  64

monitoring and validation performance summary

MetO 33 Re PU

 

D  65

quality indicator algorithm

TUD 09 Me PU

 

D  66

Biases in ZTD

Chalmers 30 Re PU

 

D  67

GPS ZTD and reference frame correlations

ASI 24 Re PU

 

D  68

GPS ZTD IWV seasonal bias report - northern climate

Chalmers 33 Re PU

 

D  69

Regional Combination methodology and report

TUD 09 Me+Re PU

 

D  70

Project web site

DMI 03 Other PU

 

D  71

Project Publicity brochure

DMI 04Other PU

 

D  72

User workshop proceedings

KNMI 24 Re PU

 

D  73

GPS Data Recommendations for European NWP

PNWP 36 Re PU

 

 

D  74

Final project publisity brochure

DMI 36 Other PU

 

D  75

TIP

ALL 36 Re

 

 

Table 45 List of project deliverables

 

The annual reports will follow the FP5 guidelines at http://www.cordis.lu.eesd.manage.htm, and eventual further guidelines provided by the EC Scientific Officer.

d_3. Workpackage Descriptions

 

WP 1000 - Management

Start date: 0

End date: 36

WP leader: DMI

Total person months per participant (including sub-workpackages): DMI 12, PSHMI 2, ACRI-ST 7.5, and all other partners 0.5

 

·        Overall project management will be carried out by DMI.

·        Scientific co-ordination will be carried out by SMHI and will assure the progress of the scientific workpackages concerning development of new techniques and methods for NWP assimilation.

·        Data supply co-ordination will be carried out by ACRI-ST who is responsible for assuring the delivery and quality of all the GPS ZTD products that are used as inputs to the scientific and assimilation workpackages.

·        Meeting preparation and participation will be carried out by all partners to assure timely reporting of results.

 

The detailed descriptions are provided in the sub-workpackages below.

 


WP 1100 - Overall Management

Start date: 0

End date: 36

WP leader: DMI

Person months per participant: DMI 10

 

WP objectives:

The overall project management and co-ordination will be carried out by DMI, which will be the single contact point of the project for EC and external communication.

 

Methodology/Work Description:

·        Interface with scientific co-ordinator, data-supply co-ordinator and work-package managers

·        Maintain communication tools (email, personnel directory, internal web site)

·        Maintain the external project web site

·        Ensure high level communication link with users

·        High level quality assurance and verification of deliverables

·        Define high level standards, distribution and access for deliverables

·        Monitor high level action items and schedule.

·        Overall meeting co-ordination and recording of minutes and action item list

·        Compilation of annual reports

·        Ensure communication with EC and delivery of reports and minutes

·        Financial co-ordination

·        Make formal requests to outside organisations for required additional data on behalf of the consortium.

 

Deliverables:

Deliverable title

Resp. Partner

DelivDate

Type

DissemLevel

D  1     kickoff meeting minutes                                                       DMI            00   Re          CO

D  2     kickoff meeting minutes                                                       DMI            03   Re          PU

D  3     1st project meeting minutes                                                  DMI            06   Re          CO

D  4     semi-annual progress report - report 1                                DMI            06   Re          CO

D  5     2st project meeting minutes                                                  DMI            12   Re          CO

D  6     annual progress report - report 2                                        DMI            12   Re          Pu

D  7     3st project meeting minutes                                                  DMI            18   Re          CO

D  8     Semi-annual progress report - report 3                               DMI            18   Re          CO

D  9     4st project meeting minutes                                                  DMI            24   Re          CO

D  10   annual progress report - report 4                                        DMI            24   Re          Pu

D  11   5st project meeting minutes                                                  DMI            30   Re          CO

D  12   Semi-annual progress report - report 5                               DMI            30   Re          CO

D  13   final project meeting minutes                                               DMI            36   Re          CO

D  14   final report - report 6                                                          DMI            36   Re          Pu


WP 1200 – Scientific Co-ordination

Start date: 0

End date: 36

WP leader: DMI

Person months per participant: SMHI 1.5, DMI 1.5

 

WP objectives:

Oversee the research tasks of the project, establish priorities for case studies.

 

Methodology/Work Description:

·        Interface work package managers.

·        Review schedule monitoring and advise on work plan adjustments where necessary.

·        Contribute to progress meeting agendas.

·        Aid in compilation of meeting minutes and annual reports.

·        Call additional group working meetings when necessary.

 

 

 

WP 1300 – Data supply co-ordination

Start date: 0

End date: 36

WP leader: ACRI-ST

Person months per participant:  ACRI-ST 7

 

WP objectives:

Oversee the data exchange and maintenance.

 

Methodology/Work Description:

·        Correspond with partner representatives to establish detailed data requirements for each workpackage and for establishing a Project Dataset Description

·        Co-ordinate smooth exchange of data

·        Correspond with leaders of data provision sub-workpackages and assure the delivery of the data required for the efficient execution of the project

·        Manage additional requests for data as they evolve following the progress of the project.

·        Co-ordinate the GPS ZTD processing committee.

·        Act as the single contact point between the user meteorological agencies, and the GPS ZTD processing centres as a unit.

 


WP 1400 - Meeting preparation and participation

Start date: 0

End date: 36

WP leader: ALL

Person months per participant: All participants- 0.5 PM

 

WP objectives:

Meeting participation.

 

Methodology/Work Description:

·        Provide individual participant progress reports 2 weeks before meeting to co-ordinator.

·        Provide hard copy of transparencies presented at the meeting.

·        Participate in meetings.

·        At end of project provide recommendations for European use/processing of GPS delay data (PM resources are shared with workpackage 10000 dissemination and exploitation).

·        Each NWP participant will contribute to the recommendations in the 1 month prior to the final meeting in a 2 page report format with the following indicative headings:

·        Background description of operational NWP system at their agency

·        Description of methodological approach for using GPS ZTD or delay data developed and tested in the project

·        Short summary of tests and extreme cases

·        One illustrative figure

·        Conclusions on perspectives at the national level and European level

·        Each processing centre and non-NWP partner will contribute to the recommendations in the 1 month prior to the final meeting in a 2 page report format with the following indicative headings:

·        Summary description of their implementation of the GPS ZTD IWV system including improvements brought about by the WCHAL000 research activities and recommendations for future processing systems.

·        Summarised evaluation of validation activities with mention of any remaining problem areas.

 

Deliverables:

Deliverables are the progress reports that are provided in the semi-annual and annual reports in WASI00, and the final recommendations report in WPNMA000 and are not listed again here.


WP 2000 – User Requirements

Start date: 00

End date: 03

WP leader: MetO

 Total person months per participant: MetO 0.5 PM

WP objectives:

Define User Requirements for NWP and specify project Q/A requirements.

 

Methodology/Work Description:

·        Establish User Requirements for near-real time GPS data for operational NWP purposes (User Workshop, Questionnaire, WMO UR documents)

·        Define quality assurance and quality control procedures for project network data deliverables (in consultation with processing centres)

 

Deliverables:

Deliverable title

Resp. Partner

DelivDate

Type

DissemLevel

D  15   user requirements document                                                MetO          03   Re          Pu


WP 3000 – Error modelling for variational assimilation 

Start date: 0

End date: 24

WP leader: FMI

Person months per participant: DMI 2, SMHI 11, FMI 7, Chalmers 12

WP objectives:

Data assimilation for NWP (Numerical Weather Prediction) optimally estimates the atmospheric state using observational information.