By using GMS-5 IR1 TBB of landfalling typhoon(LT) and hourly rainfall from ground automatic weather stations,a preliminary method of quantitative precipitation estimation(QPE) suitable for LT was found. Based on the results from the QPE method,the short-term quantitative precipitation forecast of 0 h to 3 h(QPF) for LT can be realized with the extrapolation method preliminarily.
Then the estimation of precipitation is further modified by ground weather radar and Kalman filter. The result on test subcatchment in Shihuaihe river basin shows that the error of satellite precipitation estimation decreases from 31% down to 13% after the radar adjustment.
The main application of satellite data to study tropical cyclone associated with landfall precipitation is reviewed substantially and systematically,and the advantages and shortcomings of precipitation estimation and forecast techniques are pointed out along with a prospective study in this field at last.
This paper described the principle of rain estimation by radar. Based on the rain principle of radar estimation and the aspects of outside physical conditions, the error source of rain estimation in Lanzhou and the nearby areas is also analyzed.
Based on the study above, the rainfall estimate equations between six-hour rainfall and GMS-5 data are obtained by using double-judging & double-MOS regression method, which can be used to estimate six-hour rainfall.
Due to the scarcity of sufficient spatial ground-based rainfall data in hill areas, quantitative precipitation estimation using remote-sensing techniques such as radar and satellite is needed for debris flow pre-warning.
Automatic calibration and verification of radar accuracy for precipitation estimation.
In general, the scattering of the observational products may represent the uncertainty in global precipitation estimation.
Over the past three decades there have been numerous attempts to use satellite measurements for precipitation estimation.
Precipitation estimation from remotely sensed information using artificial neural networks.
The methods use radar precipitation (the radar-derived precipitation estimate based on column maximum reflectivity) and data from 81 on-line rain gauges routinely provided by the Czech Hydrometeorological Institute.
Averaged seasonal characteristics of vertical profiles in Slovenia are used as the climatological basis for the construction of an idealised profile for correcting the precipitation estimate.
In addition, each input parameter may be individually analyzed for its impact on the precipitation estimate.
This global precipitation estimate by ordinary thunderstorms is listed in Table 1.
The larger underbias reflects the radar's underbias in its precipitation estimate despite the recalibration of the BAR model.