| Regional reanalysis dataset is a kind of atmospheric optimal estimation data with high spatial-temporal resolution and long-term continuity,which combines meteorological observation,data assimilation and forecast model simultaneously.This kind of data can provide us with the information of atmospheric state with dynamical and physical constraints in the region which is covered by the data.Compared with the spatial-temporal heterogeneity of the observations,the regional reanalysis data not only possess the advantage of homogeneous spatial-temporal resolution,but also can estimate various kinds of atmospheric variables in the regions where the observational data are scarce by taking advantage of the observational data and using the assimilation-prediction system.In addition,because of the high spatial and temporal resolution of regional reanalysis data and the more accurate reproduction of small-scale and mesoscale weather systems,it can compensate the deficiencies which the global reanalysis dataset possesses,therefore regional reanalysis is also an important complement to the global reanalysis dataset.It can be seen that the regional reanalysis data,because of its unique performance and advantages,become an important member in the collection of comprehensive atmospheric data information.Moreover,the phenomena and mechanism of various atmospheric processes can be verified and explained resulted from the abundant information gain from regional reanalysis dataset.Therefore,regional reanalysis dataset plays an important role in enriching the understanding of the dynamics and physical processes of atmospheric activities and improving our cognition of the details about regional climate evolution.For the fact that regional reanalysis dataset plays an important role in deepening the understanding of the weather and climate in the region,however,up to now,there has not been any regional reanalysis with a temporal coverage of more than 10 years and spatial coverage of the entire mainland China and East Asia.The lack of regional reanalysis data may lead to the imperfection of atmospheric related data richness and the inadequacy of the research on weather and climate processes in the region.Consequently,it is particularly important to produce a set of regional reanalysis datasets that focus on China and cover most of East Asia.The disparity appears in the two listed aspects:1.Not enough evaluation and performance tests in generating regional reanalysis of China and its surrounding areas.2.How to take full advantage of all useful observational information retrieved from hyperspectral satellite radiances,especially after FY4-A GIIRS hyperspectral infrared observations comes to operational.Focusing Question 1,three widely used methods in regional reanalysis:nudging,dynamical downscaling,data assimilation are tested and evaluated with the case of entire year of 2013.Besides,a relatively new method which combines data assimilation and nudging together is also tested.By evaluating daily precipitation,2m Altitude Ground Level temperature and upper level atmospheric variables,viable approach of generating regional reanalysis dataset is selected.Focusing on Question 2,we use two Tornado cases to prove the feasibility of assimilating hyperspectral satellite sounding retrievals into numerical weather prediction(NWP)system and its potential contribution.In addition,9km Operational NWP system is set up to perform a long time series evaluation of hyperspectral satellite sounding retrievals application.In the meantime,a 3km operational NWP system focusing on State of Virginia is set up to verify these improvements on step further.Based on the operational results from June to July,it can be confirmed that the accept ratio of hyperspectral satellite sounding is around 36%and the root mean square error of initial condition is minimized by 40%compared to the one before data assimilation.In the meantime,two test cases(southeast China heavy precipitation in spring and tornado outbreak in northeast China during the summertime)for FY-4A application in China are performed and the results provide us with a promising conclusion that FY-4A GIIRS hyperspectral satellite sounding retrieval do have positive impact in improving precipitation and severe storm prediction accuracy. |
PDF Full Text Request