FIFTH INTERNATIONAL WORKSHOP on TROPICAL CYCLONES
PRESENT AND FUTURE USES OF SATELLITE OBVSERVATIONS
FOR TROPICAL CYCLONE FORECASTING AND RESEARCH
Chris Velden (USA) and John LeMarshall (Australia)
0.4a Imminent uses and data assimilation
Expected contributions of satellite observation data
to the operational numerical prediction of typhoons.
Presentor: Hideo Tada
Numerical Prediction Division, Forecast Department
Japan Meteorological Agency
1-3-4 Ote-machi, Chiyoda-ku, Tokyo 100-8122, Japan
In order to extract useful information from satellite observations, sophisticated methods are essential for data assimilation processing. Satellite data are very useful due to their wide geographical coverage. However, characteristics of satellite data are different from those of traditional observations. Hence, extensive development is required in the numerical weather prediction (NWP) systems.
The Japan Meteorological Agency (JMA) has operated a three-dimensional variational assimilation (3D-Var) method for the analysis of the Global Spectral Model (GSM) since September 2001, and a four-dimensional variational (4D-Var) method for the Meso-Scale Model (MSM) since March 2002. A direct assimilation of NOAA/ATOVS is planned to be introduced for the global 3D-Var analysis by the end of 2002. A positive impact on the typhoon track prediction was reported from preliminary tests. Meanwhile, experiments of MSM with 4D-Var have shown improvements of the predicted typhoon structures by assimilating the precipitation data derived from TRMM/TMI and DMSP/SSMI.
Important keys of data processing can be put forward as quality controls, statistical monitoring, management of huge data volumes, and so on. Considerable efforts are required to manage each item. Therefore, useful information on these treatments have to be exchanged among NWP centers. A long-term perspective should be established on satellite data utilization in the operational system.
JMA is planning to replace the operational NWP system in 2006. In the next system, the JMA aim is to utilize advanced sounder data, precipitation data from Global Precipitation Measurement (GPM), occultation data of navigation satellites such as Global Positioning System (GPS), and so on. JMA expects a great impact on the tropics by assimilating these data comprehensively. However, developing efficient assimilation schemes for various satellites is expected to be a hard work. It's important to create a framework to be able to introduce new schemes smoothly into the operational system by maintaining balance between independent and cooperative developments with other forecast centers.
Satellite data are indispensable to improve the accuracy of the initial state of NWP models. However, characteristic features of satellite data such as reliability or error conditions are different from those of traditional observations, which will force us to do extensive developments in operational NWP systems. Due to rapid developments in computing technology, NWP models have been significantly improved. On the other hand, many satellites with various kinds of new instruments are planned. Development of analysis schemes to assimilate these huge observation data sets into elaborate NWP models is important.
To improve prediction of tropical cyclones, techniques that utilize observations of water vapor, precipitation, and winds that related directly to the phenomena have to be developed. In addition, comprehensive information of satellite data is very important to analyze the environment around tropical cyclones over the oceans where the traditional observations are sparse.
NWP systems are complex with many subsystems such as observations, quality control, and data assimilation. Hence, well-balanced developments of these subsystems are important. In this paper, I will explain the JMA's tentative problems and future plans for satellite data utilization.
0.2.2 Developments of the JMA data assimilation schemes
The objective analyses of JMA had adopted an optimum interpolation (OI) method for a long time. This method is cheap and has a strong statistical interpolation framework. Objective analyses of the observations are used to correct the error in the first-guess fields produced by NWP models. The information for this correction is extracted as a departure of the observed value relative to the first-guess value. However, OI schemes only accept departures of the same variables as those of the prognostic variables (e.g., temperature, wind (u,v)components...). The physical constraints allowed in the OI are also limited to the simple ones such as a geostrophic constraint.
In the variational assimilation framework, various types of satellite data such as radiances or precipitation can be assimilated as long as they can be calculated in terms of the prognostic variables. JMA introduced a three-dimensional variational assimilation (3D-Var) method for the Global Spectral Model (GSM) in September 2001, and a four-dimensional (4D-Var) scheme for the Meso-Scale Model (MSM) in March 2002. These developments are the operational foundations to cope with an future increases of satellite data and the effective utilization of them.
0.2.3 Satellite data utilization in the JMA operational NWP system
The JMA's current satellite data utilization procedures have been developed under the framework of OI analyses. JMA are still using temperature and humidity retrieved from satellite radiances by other satellite centers even after the global analysis was updated to 3D-Var. This procedure was necessary due to the considerable delay for technology transition at JMA compared with other major NWP centers.
Several problems arise in using the data retrieved by other satellite centers. One major problem is that the retrieval algorithms tend to be a "black box" so the error characteristics are hard to determine quantitatively. An additional problem is that the background information (e.g., first-guess) used in the retrieval algorithm at the other centers would indirectly affect the accuracy of the assimilated results. Under ordinary circumstances, quality of observational data is independent of the performance of NWP models. So it's not desirable to have the data retrieved by other centers to contain some kind of information dependent on the other NWP models.
To overcome these problems, JMA is now developing direct assimilation schemes of raw satellite data via the variational framework. If extra retrievals are avoided, data assimilation systems become immune to additional conversion errors. Treatments of the observational error also become easier if raw information from the instruments is used. As the first step of the development, JMA plans to introduce the direct assimilation scheme of the ATOVS vertical sounding data from NOAA satellites in December 2002 for the global 3D-Var analysis.
The utilization of other satellite data is in preliminary experimental stage, such as ocean-surface wind vectors by the microwave scatterometer on the QuikSCAT satellite, precipitable water vapor derived by the microwave imagers on the DMSP and TRMM satellites, and so on.
0.2.4 Recent topics
a) Improvement of JMA forecast skill with the global 3D-Var scheme
Figure 1 shows the time sequence of the forecast error of 250hPa wind by the operational global model. A significant reduction of the JMA errors (thick lines) after the 3D-Var was introduced in September 2001 can be seen.
Figure 1: Sequence of monthly mean Root-Mean-Square Errors (RMSE) of 250hPa wind forecasts in the Northern Hemisphere at 24h(left) and 48h(right). The thick lines correspond the sequence of JMA global model while the other lines are for other NWP centers. The 3D-Var assimilation was introduced on September 2001.
b) An impact of NOAA/ATOVS direct assimilation on the typhoon track prediction
The JMA global model has some problems in the prediction of subtropical high pressure systems that have been related to a low temperature bias over the tropics. Improvements of cumulus convection scheme have been tried from various aspects. At the same time, JMA has kept using bogus data of relative humidity vertical profiles estimated from black body temperature (TBB) observations from geostationary satellites to cope with the humidity data shortage over tropical oceans. The introduction of NOAA/ATOVS direct assimilation was tested with an improved cumulus convection scheme and removal of the bogus humidity data.
Figure 2 shows the predicted tracks of Typhoon 0104 (UTOR). A significant improvement can be seen with the new scheme. It is probable that the impact was affected by the improvement of tropical ~ subtropical condition by the new cumulus convection scheme plus the improvement of the initial condition of atmosphere surrounding the typhoon by the NOAA/ATOVS direct radiance assimilation.
Figure 2: The predicted track of Typhoon 0104 (UTOR) from initial date of 12UTC of 1 July 2001. The line with typhoon-marks shows the "best-track" while the other lines correspond to various options of the prediction.
c) An improvement of the typhoon structure with the precipitation data from TRMM/TMI and DMSP/SSMI
This is one of the examples of the on-going developments with the JMA Meso-Scale Model. Figure 3 shows a result of the experiment of 4D-Var that assimilated the precipitation data retrieved from TRMM/TMI and DMSP/SSMI. An increase of the typhoon intensity can be seen by assimilating precipitation. This 4D-Var scheme is still in a preliminary experimental stage. JMA is planning to promote utilization of precipitation data through the subsequent implementations of the 4D-Var scheme into the Regional and Global Spectral Models.
d) A development of creating "observation-type" typhoon bogus data
The center position of typhoon is a very important factor for the track prediction. In terms of a provision of disaster information, it's highly critical that the center position be correct in the initial state. Therefore JMA has applied a "grid-type" typhoon bogus implanted in the first-guess field. To assimilate typhoon information more properly, an "observation-type" typhoon bogus has been developed and tested with the meso-scale 4D-Var scheme.
Figure 4 shows an example of new bogus data. An axial-symmetric wind component is derived based on the statistical research, while an asymmetric component is calculated from the typhoon structure in the first-guess field. Many options such as data distribution or variables have been tested to represent the proper typhoon structure in the initial state.
Figure 3: An example of the impact of assimilating precipitation data for the Typhoon 0115 (Danas). The left panel shows the observation, the middle panel shows the operational prediction (ft=6h) and the right panel is the corresponding 6h prediction after assimilating the precipitation retrieved from TRMM/TMI and DMSP/SSMI.
Figure 4: An example of the "observation-type" typhoon bogus data (star-marks).
0.2.5 Critical issues on satellite data utilization
a) Quality control procedures
Whenever new kinds of observations are to be used, the characteristic features of their reliability have to be documented through statistical research. Quality control procedures should be designed with sufficient knowledge of the data error characteristics. Just one strange datum may ruin the processing even for highly sophisticated schemes.
Especially, operational real-time processing is a fight against time. Huge volumes of data have to be processed in the structured schedule. Under these circumstances, processing schemes are often evaluated based on cost / performance basis. Operational processing schedules do not permit adoption of sophisticated schemes that may be used in academic research. Reducing the degrees of freedom in the processing while maintaining the total effects as high as possible is an important key. Therefore, sufficient knowledge of observation error and background error characteristics is necessary. Collaborations with satellite/data processing centers should be encouraged on a routine basis.
b) Statistical monitoring of observational data
The various types of errors in observational data include measurement error, bias (calibration) error, conversion error, transmission error, etc. In addition, an error may be caused by observational representativeness. Quantitative knowledge of various types of errors is achieved by statistical monitoring. Whereas developments of NWP model and data assimilation schemes tend to be given highest importance, unspectacular work such as quality control monitoring should be more emphasized that may also improve NWP performance.
Procedures for statistical monitoring or scheme regulation are usually hard to get published. These kinds of information should be exchanged more openly. However, it seems to be difficult to achieve the communication level given the absence of a strong sense of obligation to share such information.
d) Management of huge volumes of data
Rapid increases in the satellite data volume is also a severe problem in the operational processing. The more you process raw data, the larger the data volume tends to be. This data volume problem was not large when only conventional observations were used. Now, JMA is planning to actively promote utilization of raw satellite data. Thus, the data volume is expected to be ten times or more relative to the present level. Therefore, how to manage huge volumes of data with limited operational resources will be a great factor in the design of NWP systems. There are many stages and phases in data processing: quality monitoring and controls, data assimilation, validation of model outputs, and so on. You have to have a clear concept of satellite data utilization and storage management from the long-time point of view.
0.2.6 JMA's future plans of satellite data utilization
JMA is addressing major topics on the NWP developments in the next computer system starting in 2006. Some of the important factors on satellite data utilization that relate closely to the NWP performance are given here.
Based upon the above arguments, JMA will promote comprehensive developments for the effective utilization of satellite observations.