Precipitation is a basic and vital component of the global water and energy cycles [1
]. A robust knowledge of precipitation processes at finer spatiotemporal resolutions has become increasingly important for hydrological modeling, flood monitoring, soil moisture estimation, and water resource management [2
]. Currently, there are generally three mainstream methods to obtain precipitation information: traditional in situ observations, estimations from remote sensing, and numerical weather prediction (NWP) [7
In situ precipitation observations are generally obtained from conventional ground rain gauge stations. This type of data is generally considered to be the most accurate measurements, and served as the grounds for true precipitation values [10
]. However, the application of these data in the hydrological field is severely limited by poor point-to-area representativeness, incomplete opening to the public, and several well-recognized issues of the station network, such as poor spatial distribution and wind-induced deviation [12
]. As remote sensing techniques developed, various satellite precipitation products based on visible, infrared, and microwave wavelengths have emerged during the last few decades, such as the Global Precipitation Climatology Project (GPCP) [14
], the Climate Prediction Center Morphing technique (CMORPH) [15
], the Tropical Rainfall Measuring Mission (TRMM) [16
], and its ongoing replacement Global Precipitation Measurement project (GPM) [17
]. These products not only cover a nearly global area, they also are available to the public free of charge. Nevertheless, they still have coarse spatiotemporal resolutions, which are incapable of representing consecutive precipitation process and have difficulty in detecting extreme events at high latitudes [18
]. Since the NWP model is built on precise physical governing equations, it can resolve the inherent dynamics of precipitation and nearly represent the entire spatial pattern of the precipitation process [19
]. However, when solving the equations and initialization errors, approximations due to incomplete observations often induce many uncertainties to the model outputs [21
]. In terms of simulated area ranges, the NWP model is usually divided into general circulation models (GCMs), which cover the global or continental scales, and regional climate models (RCMs), which cover the regional scale or mesoscale. Compared with GCMs, RCMs can better simulate the exact distribution of a climatic field since they have a finer spatial resolution; this enables them to resolve finer details of land surface characteristics, such as topography, land cover, and surface vegetation [23
]. Frequently used RCMs include the National Meteorological Center (NMC) forecast model [25
], the next-generation Weather Research and Forecasting (WRF) model [28
], the operational Japan Meteorological Agency (JMA) mesoscale model [29
], and the European Center for Medium-Range Weather Forecasts (ECMWF) model [30
Considering the merits and drawbacks of these three types of precipitation data, we attempted to integrate two of them to obtain a type of precipitation data. It is not only independent from in situ observations, it can also reflect the precipitation consecutiveness at higher spatiotemporal resolutions [31
]. Therefore, we used the data assimilation (DA) method to integrate freely downloaded remote sensing precipitation estimations with precipitation prediction from a more physically realistic NWP model. Among the various DA algorithms, such as the polynomial interpolation method [33
], optimum interpolation [34
], three-dimensional variational (3D-Var) assimilation [35
], four-dimensional variational (4D-Var) assimilation [36
], and the Kalman filter [37
], 4D-Var is particularly appropriate for assimilating synoptic satellite data due to its advantages regarding a definite theoretical basis, simple formulation, and no limitations on the type of assimilated data that is utilized [39
At present, there have been extensive studies regarding integrating various precipitation data with the NWP model via the 4D-Var data assimilation method. Zupanski and Mesinger [40
] first carried out a 4D-Var experiment with 24-hour accumulated precipitation data and the NMC forecast model in the United States of America (USA) and demonstrated its improvement in precipitation forecasting. Koizumi et al. [21
] used the JMA mesoscale 4D-Var system to assimilate one-hour radar-based precipitation data at a spatial resolution of 20 km over the islands of Japan and demonstrated improvements in precipitation forecasts for an 18-hour forecast time. Lopez [41
] assimilated the National Centers for Environmental Prediction (NCEP) stage IV gauge-corrected radar precipitation data into the ECMWF Global Integrated Forecasting System over the eastern USA, and found a substantial improvement in short-term (i.e., up to 12 h) precipitation forecasts. Lin et al. [42
] assimilated NCEP stage IV rainfall data into the WRF model with the 4D-Var method in the USA, and they successfully downscaled a six-hour precipitation product with a 20-km resolution to an hourly precipitation product with a resolution of less than 10 km. These studies were mainly carried out to resolve the problems such as the highly nonlinear and discontinuous in cumulus convection parameterization [7
], the sensitivity of the different global datasets for the initial and boundary conditions [21
], and the effectiveness of applying different observational and background error covariance matrices [44
]. We will not go into the many details of 4D-Var techniques, but rather will investigate its potential in hydrological applications.
From an application perspective, a majority of these existing studies were performed and evaluated at the mesoscale, whereas a limited number of studies focused on the basin scale evaluation, even though basin is the most commonly used unit in hydrological studies. Moreover, since the GPM was just released in 2014, there are very few studies on the feasibility and efficiency of the GPM application in 4D-Var data assimilation, and the discrepancies in assimilation effectiveness between assimilating GPM and assimilating TRMM are less investigated. Therefore, we assimilated the Integrated Multi-satellitE Retrievals for GPM (GPM IMERG) and the TRMM Multi-satellite Precipitation Analysis 3B42 (TRMM 3B42) with the 4D-Var method into the NWP model of WRF, and assessed their performances in simulating two heavy precipitation events that occurred over the Huaihe River basin (HRB) in eastern China. Before assimilation, we first drove the WRF model with two different forcing data to choose a more suitable forcing datum and determine the control experiment for the subsequent assimilation work. Then, DA experiments were carried out with different assimilation data and for different precipitation events. Finally, we evaluated the experimental precipitation results with the daily in situ observations and the hourly merged CMORPH estimations at different spatial and temporal scales in detail. The manuscript is organized as follows: Section 2
introduces the study area, study events, and data. Section 3
introduces the WRF configuration, 4D-Var methodology, experimental design, and evaluation metrics. Section 4
shows the evaluations of the simulated precipitation from the WRF and the WRF 4D-Var that is assimilated with TRMM 3B42 and GPM IMERG. Section 5
discusses the WRF sensitivity to different rainfall events, forcing data, and spatial resolutions; examines the effectiveness of WRF 4D-Var at different thresholds and time; and compares the 4D-Var performances assimilated with TRMM 3B42 and GPM IMERG. The conclusions are drawn in Section 6
To reduce data acquisition difficulty for precipitation in hydrological studies and obtain independent, consecutive, and high-resolution precipitation data, we used a 4D-Var data assimilation method to assimilate the remotely sensed precipitation products of the TRMM 3B42 and GPM IMERG into the atmospheric WRF model. By focusing on two heavy precipitation events that occurred during the flood and non-flood seasons over the HRB in 2015, CTL experiments were first carried out to choose the best forcing data for the WRF model and determine the control experiment for the subsequent DA experiments. Then, DA experiments were carried out to investigate the feasibility and efficiency of the GPM IMERG to be assimilated into the WRF model with the 4D-Var method, and the 4D-Var performances assimilating with the GPM IMERG and the TRMM 3B42 were compared as well. All of the simulated precipitation values from the CTL experiment and the DA experiment were evaluated with in situ CMA observations and hourly merged CMORPH data.
CTL experiments were performed based on the WRF model with different forcing data and for different events. The assessment of the simulated precipitation in the CTL experiments found that when predicting heavy rainfall events over the HRB, the WRF performance for event N, which represented non-convective precipitation, outperformed the performance for event A, which represented convective precipitation. Moreover, the simulated precipitation generated by forcing data ds083.3 and the output from nested domain D02, which had a higher spatial resolution (9 km), could generally yield better agreement with the in situ CMA data and the merged CMORPH data.
DA experiments were carried out with forcing data ds083.3. The 4D-Var performances that were assimilated with TRMM 3B42 and GPM IMERG based on the WRF model were evaluated in detail. The simulated precipitation results of the DA experiments were assessed at spatial scales of D01, D02, and the HRB, and at hourly, 12-hour, daily, and 48-hour timescales. The evaluation results showed that (1) the 4D-Var with both the TRMM 3B42 and GPM IMERG based on the WRF model could significantly improve the precipitation simulations. The improvements made by GPM IMERG generally outperformed those made by TRMM 3B42, as GPM IMERG was more sensitive to light rain (≤0.5 mm/hour), which accounted for significant portions of the precipitation occurrences at mid and high latitudes. (2) For event A, the enhancement of simulated precipitation was mainly attributed to the corrections of false alarms for non-occurrences. For event N, this improvement was primarily due to more accurate forecasting of these occurrences. The accuracy enhancement for event A was larger than that for event N. (3) The accuracy improvement in simulated precipitation over D01 (27 km) by 4D-Var could be effectively achieved over D02 (9 km); assimilation in D01 and downscaling to D02 with a nested domain based on the WRF model could provide an effective way to obtain finer-resolution precipitation forecasts. (4) Due to error accumulations in the WRF running, essential improvements made by the 4D-Var were maintained for approximately 12 h; it was also not recommended to use the cycling mode for error accumulations in the WRF model.
Further studies can be conducted to deepen the understanding of the 4D-Var algorithm from the following aspects: (1) investigating the performance of a longer duration precipitation simulation; (2) assimilating more remotely sensed precipitation products into other NWP models; and (3) analyzing the sensitivity of the WRF 4D-Var to background errors, which would be worthwhile for future studies.