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Introduction Precipitation is a crucial component in the hydrological cycle of the Earth and has a profound influence on climate and hydrology at regional to global scales [ 1 ]. To sum up, the data assimilation has important influence on the precipitation forecast with significantly changes of the rainfall area and accumulation. In the case of high temporal and spatial resolution satellite data, the European Center for Medium-Range Weather Forecasts ECMWF pioneered the direct assimilation of microwave radiance data affected by precipitation, first in a Dvar assimilation approach [ 13 , 14 ] and later in an implementation of all-sky radiance assimilation in the operational 4Dvar system [ 15 , 16 ]. Three assimilation experiments are designed using different observation parameters from different data sources. The red triangles represent the observation sites Jiuquan, Tuole, Dingxin, and Jinta in the northwestern of the study area. The biggest change of moisture flux occurs in Figure 12 d for DA-BOTH experiment, which covers the largest area compared with other numerical experiments, and it could give greatest estimated precipitation in northeastern Tibetan Plateau. In conclusion, both for the spatial and temporal distribution of rainfall, the satellite radiances have greater effect than surface and upper-air meteorological observations in high-latitude regions of the northeastern edge, and the improvement for the assimilation of satellite radiances together with surface and upper-air meteorological observations is greater than that for assimilating either satellite radiances or meteorology observations.

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In the case of high temporal and spatial resolution satellite data, the European Center for Medium-Range Weather Forecasts ECMWF pioneered the direct assimilation of microwave radiance data affected by precipitation, first in a Dvar assimilation approach [ 1314 ] and later in an implementation of all-sky radiance assimilation in the operational 4Dvar system [ 1516 ].

Introduction Precipitation is a crucial component in the hydrological cycle of the Earth and has a profound influence on climate and hydrology at regional to global scales [ 1 ]. Two heavy rainfall events with different evenness in space and time are used to examine the improvement for rainfall forecast. The rain-gauge data were used to analyze the changed precipitation area and accumulation after data assimilation experiments.

Figure 7 compares the measured and modeled results in the rainfall Event A. For Event A, the data assimilation experiments provide larger rainfall areas and accumulation, while for Event B the data assimilation experiments provide smaller precipitation accumulation on the southeastern of the study area. Figure 3 illustrates the spatial distribution of the rainfall accumulation for the durations of the two events, while Figure 4 presents the time series bars and the cumulative curves of the observed precipitation for the two events at Laohugou 3rvar.

Figure 9 a shows the rainfall intensities and cumulative curves of the measured and modeled results for Event A at Laohugou station.

The AE of rainfall intensity between the values measured at the sites and the simulated results from the four numerical experiments: To sum up, the data assimilation has important influence on the precipitation forecast with significantly changes of the rainfall area and accumulation.

The rainfall intensities and cumulative curves of the values measured at the sites and the simulated results from the four numerical experiments: By comparing the evenness of the rainfall distribution in time and space in Figures 3 and 4it can be found that Event A has even rainfall distribution neither in space nor in time.

The performance of the data-assimilation system largely depends on the plausibility of the background error covariance BEthat is, the matrix in 1.

It should be noticed that the effects of the forecast depends on the different rainfall event. The 3cvar difference between Event A and Event B is the rainfall distribution in time.

Real-time observations have generally been used in the assimilation systems and shown to improve markedly the performance of NWP 3dvzr, in spite of the poor initial conditions provided by the global NWP models [ 12 ].

In Figure 6 cthe precipitation area in DA-OBS experiment is same as that in Figure 6 abut the precipitation accumulation is obviously decreased on the southeastern of the region. And for Event B, data assimilation experiments improve the accuracy on the southeastern of the study area by providing smaller precipitation accumulation.

It is found that better and more detailed information is added to the initial fields e.

The cumulative curves of CTRL experiment are lie below and far away from that of the measured values. Abstract The numerical weather prediction NWP is gaining more attention in providing high-resolution rainfall forecasts in the arid and semiarid region.

Figure 11 shows the geopotential height differences between data assimilation 3dvvar control experiments.

Location map showing the northeastern edge of the Tibetan Plateau and 44 observation stations. In order to evaluate quantitatively the impact of data assimilation experiments which have positive effects on the rainfall forecast, rain-gauge data from 43 sites were used.

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Method and Experimental Design 2. Differences between observation and numerical experiments. The results show a marked improvement in the model simulations after the assimilation.

In the model integration, the coordinates of the central point are However, the modeling accuracy is negatively affected 3dvarr errors in the initial conditions. As shown in CTRL experiment Figure 8 athe green triangles which represent observation sites Hezuo, Lintao, linxia, and Yuzhong on the southeastern of the study area are lie above and far away from the line. The CTRL experiment predicts only a small amount of rainfall on the northwestern of the study area, while in DA-SAT experiment precipitation area extends to the central and southern regions of 3dvsr study area and rainfall accumulation increases significantly Figure 5 b.

Figure 5 d shows the result of DA-BOTH experiment, in which the precipitation area is the largest and accumulation is the biggest compared with the other three experiments.

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Atmospheres —vol. This means that the four experiments produce underestimated rainfall accumulations for Event A, but overestimated wrr accumulations for Event B. In Figure 12 cthere is a clear flow of moisture to the northwestern part of Gansu Province which results in the heavy rain.