| dc.description.abstract | Abstract
In this article we discuss the reason and method of using satellite data to improve numerical weather forecast. And the satellite data have been applied to the case study of typhoon forecast. In the pass decade the development of variational data assimilation method the positive impact of using satellite data on northern hemisphere forecasts have been established. To study the potential improvement of using satellite data in weather forecast a variational data assimilation system and related technique have developed. Using this system we achieve some experiments about using satellite data in the variational data assimilation system. Firstly, the important of satellite data pre-processing when using variational data assimilation system to assimilate satellite observation have been discussed. Secondly, the bias correction and error estimation of satellite data have been achieved. Finally experiments are case studies about typhoon forecast with satellite observation. This experiment uses MM5 model and its three-dimensional variational (3DVAV) data assimilation system. And the satellite observations are using the Advanced Microwave Sounding Unit (AMSU) data form NOAA satellite.
The impact of satellite data on numerical weather forecast depend on how much weather information (ex: information about temperature profile/water vapor profile) could be extracted from satellite observation. The variational data retrieval/assimilation systems include not only weather parameters (ex: temperature profiles) but also others parameters (ex: cloud parameters or surface emissivity) in retrieval/assimilation process. In this situation the accurate estimation of others parameters is important for obtains accurate temperature/water vapor information. In our simulation study confirms this point. Using AVHRR data obtained accurate cloud parameters in HIRS data can provide same conclusion in real data retrieval experiments. The cirrus cloud effect has been discussed, as its presence generally degrades the quality of retrieval. Another key point for variational data retrieval/assimilation scheme is how to estimated observation errors. In practice, observation errors, both systematic and random, are often estimated from the difference between satellite observation and simulated satellite observation obtained from a radiative transfer operator with a 12-hr forecast as its input. Observation errors estimated by this approach may be contaminated by errors in the 12-hr forecast. This work describes a practical way to eliminate the 12-hr forecast error and improve the estimate of the observation error in the AMSU data. Following the philosophy of the National Meteorological Center (NMC) method (that derives the statistics of forecast error from the differences between pairs of forecasts at disparate ranges valid at the same time), in this study the pairs of forecasts at different ranges in the NMC method are first converted to brightness temperatures in the AMSU channels by a radiative transfer operator. The 12-hr forecast errors are then determined from the representations of these forecasts in radiance space spanned by the AMSU channels. Since most AMSU channels have beam position-dependent systematic observation errors, our procedure further takes into account this dependence by performing the statistics separately for sub-groups of data in each AMSU channel with different beam positions. In a case study, after eliminating the 12-hr forecast error obtained by this procedure from the total estimated observation error, the remaining random error of the satellite observation is shown to be smaller than the background error(provided by 12-hr forecasts of a numerical weather prediction model) in most of the AMSU temperature sounding channels. Using the error-corrected AMSU data in these channels, a retrieval experiment using a one-dimensional variational scheme shows an improvement of 0.2-0.4 K over the background error in the retrieved temperature profiles above 780 hPa. The observation errors estimated procedure has been test in 1DAVR retrieval experiment. But can be applied to the 3/4DVAR system. And it is useful for quality check for satellite data in an operational environment.
The AMSU data is useful for retrieving the structures of typhoons due to the high resolution and the cloud-penetrating ability of the AMSU measurements. In this study, a procedure modified form Zhu et al. (2002) is established to first retrieve the three-dimensional temperature field from the AMSU data. A vertical integration of the retrieved temperature leads to the height field, which is then used to obtain the rotational wind component by solving a nonlinear balance equation. The vertical integration is performed downward from a uniform 50 hPa height at the top of a typhoon as estimated from the environmental mean value. This gives no restriction on the shape of the typhoon and allows the structure of the retrieved typhoon vortex to depart from axial symmetry. With this technique, the basic structures of several typhoons in the Eastern Pacific are successfully retrieved from the AMSU data. The retrieved temperature and velocity associated with the typhoon are further incorporated into the initial condition of a meso-scale numerical weather prediction model by using a three-dimensional variational data assimilation system. Forecast experiments show that the inclusion of the AMSU-based typhoon structures in the initial state has an overall positive impact on the forecast of the track of the typhoon. | en_US |