Precipitation is perhaps the most important variable in controlling energy and mass fluxes that dominate climate and particularly the terrestrial hydrological and ecological systems. Precipitation estimates, together with hydrologic models, provide the foundation for understanding the global energy and water cycles (Sorooshian, 2004; Ebert et al., 2007). However, obtaining accurate measurements of precipitation at regional to global scales has always been challenging due to its small-scale, space–time variability, and the sparse networks in many regions. Such limitations impede precise modeling of the hydrologic responses to precipitation. There is a clear need for improved, spatially distributed precipitation estimates to support hydrological modeling applications.

In recent years, remotely sensed satellite precipitation has become a critical data source for a variety of hydrological applications, especially in poorly monitored regions such as sub-Saharan Africa due to its large spatial coverage. To date, a number of fine-scale, satellite-based precipitation estimates are now in operational production. One of the most frequently used is the Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA) product (Huffman et al., 2007). Over the 17-year lifetime since the launch of the Tropical Rainfall Measuring Mission (TRMM) in 1997, a series of high-resolution (0.25∘ and 3-hourly), quasi-global (50∘ S–50∘ N), near-real-time, TRMM-based precipitation estimates have been developed and made available to the research and applications communities (Huffman et al., 2007, 2010). Flood forecasting and monitoring is one major application for real-time satellite rainfall products (Wu et al., 2014). However, the applicability of satellite precipitation products for near-real-time hydrological applications that include drought and flood monitoring has been hampered by their need for gauge-based adjustment.

While it is possible to create such estimates solely from one type of sensor, researchers have increasingly moved to using combinations of sensors in an attempt to improve accuracy, coverage and resolution. A promising avenue for rainfall correction is through the assimilation of satellite-based surface soil moisture into a water balance model (Pan and Wood, 2006). Over land, the physical relationship between variations in soil water storage and rainfall accumulation contain complementary information that can be exploited for the mutual benefit of both types of products (Massari et al., 2014; Crow et al., 2009). Unlike instantaneous rain rate, satellite surface soil moisture retrievals utilize low-frequency microwave signals and possess some memory reflecting antecedent rainfall amounts.

Studies have demonstrated that in situ (Brocca et al., 2009, 2013; Matgen et al., 2012) and satellite (Francois et al., 2003; Pellarin et al., 2008, 2013; Brocca et al., 2014) estimates of surface soil moisture could contribute to precipitation estimates by providing useful information concerning the sign and magnitude of antecedent rainfall accumulation errors. In particular, Brocca et al. (2014) estimated daily rainfall on a global scale based on satellite SM products by inverting the soil water balance equation. Crow et al. (2003, 2009, 2011) corrected space-borne rainfall retrievals by assimilating remotely sensed surface soil moisture retrievals into an Antecedent Precipitation Index (API) based soil water balance model using a Kalman filter (Kalman, 1960). However, these studies focused on multi-day aggregation periods and a space aggregated correction at 1∘ resolution for the corrected precipitation totals. This limits their applicability in applications such as near real-time flood forecasting. Wanders et al. (2015) tried to overcome this limitation by the correction of 3-hourly satellite precipitation totals with a set of satellite soil moisture and land surface temperature observations. One important conclusion by Wanders et al. (2015) is that their results showed the limited potential for satellite soil moisture observations for correcting precipitation at high resolution if “all-weather” – i.e., microwave-based – land surface temperatures are not available coincidently, as was the case with AMSR-E.

But this is not always the case, and it is also noted that current low-frequency microwave soil moisture missions (specifically SMAP and SMOS) do not have radiometers at frequencies useful for estimating land surface temperatures, even though a 37 GHz sensor is part of the AMSR2 system. In fact, SMAP and SMOS use LST from weather model analysis fields in their algorithms. Unfortunately, the lowest microwave frequency of AMSR2 precludes retrieving soil moisture from many areas with heavy vegetation, and AMSR2 has a significant dry bias with less availability than AMSR-E, but is no longer operable. So, improvements to satellite precipitation from the Global Precipitation Mission products must rely solely on satellite soil moisture products, and the improvements to the assimilation algorithms are the goal of this study.

Thus, we focus exclusively on the usefulness of assimilating soil moisture products to improve satellite rainfall. We propose as part of the work how to improve the generation of rain particles and the bias correction of the satellite soil moisture observations, as well as to enhance the assimilation algorithm to maximize the information that can be gained from using soil moisture alone to adjust precipitation. Due to the very strong and complicated spatial structure of precipitation, that is non-Gaussian and non-stationary in both time and space (Wanders et al., 2015), a more advanced method is applied to generate possible precipitation fields than were used in earlier studies or in Wanders et al. (2015) (see Sect. 2.2.2). Furthermore, a more advanced bias-correction method is also applied to account for the reported problems in the second-order statistics of the soil moisture retrievals. We used a soil moisture remote sensing product to improve the real-time remote sensing precipitation product, TMPA 3B42RT, through a particle filter (PF), and therefore offer an improved basis for quantitatively monitoring and predicting flood events, especially in those parts of the world where in situ networks are too sparse to support more traditional methods of hydrologic monitoring and prediction. The precipitation enhancement experiments are carried out over the continental US (CONUS) and the precipitation skill is validated against the NLDAS gauge-radar precipitation product. Section 5 presents a comparison of the results from this study to the earlier studies related to improving satellite precipitation.

Before applying the particle filter assimilation algorithm to 3B42RT precipitation estimates, we conducted an idealized experiment where we treat the NLDAS precipitation as the “truth” and the NLDAS precipitation forced VIC simulations as “satellite observed” soil moisture. As an idealized experiment, we adjust TMPA real-time precipitation estimates based on these “satellite observations”. Phase 2 of the North American Land Data Assimilation System (NLDAS-2) rainfall forcing combines hourly WSR-88D radar analyses from the National Weather Service (NWS) and daily gauge reports (∼ 13 000/day) from the Climate Prediction Center (CPC) (Ek et al., 2011). The data set, with a spatial resolution of 0.125∘ and hourly observations, was pre-processed into 0.25∘ daily precipitation to be consistent with that of 3B42RT and AMSR-E/LSMEM SM data sets. Hourly NLDAS and 3-hourly 3B42RT precipitation is aggregated into daily precipitation defined by a period shifted ∼ 7.5 h into the future (21:00–21:00 LT), allowing for a necessary delay for soil moisture to respond to incoming rainfall. The idealized experiment is designed to test whether the algorithm is able to retrieve rainfall forcing with soil moisture change, assuming that the soil moisture observations are 100 % accurate.

Results show that, with the knowledge of first-layer soil moisture change (via the “satellite observations”), the adjustment is able to recover intensity and spatial pattern of forcing precipitation (Fig. 5g). Average mean absolute error (MAE) of daily rainfall amount is reduced by 52.9 % (2.91 to 1.37 mm day-1) over the region. Figure 5a to e shows an example of the recovered rainfall field from the idealized experiment for 27 October 2003. The spatial pattern matches the original NLDAS precipitation well.

The adjustment of real TMPA 3B42RT retrievals based on AMSR-E/LSMEM ΔSM is carried out using the methods described in Sect. 2.2.3, and results from the idealized experiment (Sect. 3) with regard to the circumstances where an adjustment is applied.

An example of TMPA 3B42RT adjustment is provided in Fig. 8, where a snapshot of the rainfall field is shown (Fig. 8b) and compared with NLDAS on 26 May 2006 and the adjusted rainfall pattern based on AMSR-E/LSMEM ΔSM. The 3B42RTADJ rainfall field (Fig. 8c) is similar in terms of its spatial distribution compared to NLDAS precipitation estimates (Fig. 8d).

On average, TMPA 3B42RT and AMSR-E/LSMEM ΔSM have a spatial Pearson correlation coefficient of 0.37 (shown in Fig. 9, left panels), compared to 0.52 for the correlation between NLDAS and ΔSM. After the adjustment procedure, the Pearson correlation coefficient between 3B42RTADJ and AMSR-E/LSMEM ΔSM increases to 0.53 (shown in Fig. 9), indicating that the correction method is successful. A below-average increase in correlation is found over the western mountainous region, the Great Lakes region and the eastern high vegetated and populated region. Additionally, the satellite soil moisture suffers from snow/ice/standing water contamination, which affects the potential for improved results after correction. The 3B42RTADJ has significant improvement over 3B42RT in terms of long-term precipitation bias. The bias in 3B42RT annual mean precipitation is reduced by 20.6 %, from -9.32 mm month-1 spatial average in 3B42RT to -7.40 mm month-1 in 3B42RTADJ (shown in Fig. 9, right panels). Frequency of rain days generally increases significantly everywhere (Fig. 10). The NLDAS data (Fig. 10, right panels) suggest an almost constant drizzling rainfall over parts of the western mountainous area (Montana, Idaho, Wyoming and Colorado), while assimilating AMSR-E/LSMEM ΔSM data sets do not have a signal of higher rainfall frequency (Fig. 10, middle panels). This is possibly due to deficiencies in satellite retrievals over the mountainous areas and frequent presence of snow and ice (3B42RT is not updated under such circumstances).

Figure 11 shows the assimilation results for the grids and days with soil moisture observations, using the NLDAS precipitation as a reference. Overall, the method is successful in correcting daily rainfall amount when 3B42RT overestimates precipitation (3B42RT - NLDAS > 0). Mean standard deviation (SD) of 3B42RTADJ - NLDAS is between 1 and 3 mm day-1 (statistics provided in Table 3). When 3B42RT underestimates rainfall (3B42RT - NLDAS < 0), the assimilation has limited improvement on 3B42RT. This is due to the effect of surface saturation. In terms of adding rainfall, effectiveness of the assimilation is limited under the following two circumstances.

The presence of wet conditions or (near) saturation. There is higher probability bringing the surface to saturation (wetter condition) when the assimilation adds rainfall into 3B42RT. However, soil moisture increments are less sensitive to incoming precipitation on wetter soil. Therefore, an error in ΔSM often translates into 3B42RTADJ in a magnified manner.

The applied method is ineffective for light rainfall < mm, where the adjustment tends to over-correct precipitation by adding excessive rainfall – mostly the result of the high-frequency AMSR-E noise. The MAE of light rainfall (< 2 mm day-1) increased from 0.65 mm day-1 in 3B42RT to 0.99 mm day-1 in 3B42RTADJ. On the other hand, satellite soil moisture assimilation is very effective in correcting satellite precipitation higher than 2 mm day-1: the MAE of medium to large rainfall (≥ 2 mm day-1) decreased from 7.07 mm day-1 in 3B42RT to 6.55 mm day-1 in 3B42RTADJ. The effect of the assimilation is different over the western mountainous region, the north-to-south central US band and the eastern US.

The western mountainous region has a dry climatology with more frequent rainfall in small amounts. The white noise in ΔSM, negatively impacting 3B42RTADJ, is comparable to the positive improvement brought by actual light rainfall signals in ΔSM. Therefore, the assimilation of ΔSM has no significant impact in these regions.

The north-to-south band over the central US experiences more medium to high (≥ 2 mm day-1) rainfall. In addition, the region is lightly vegetated (annual mean LAI < 1) with low elevation (< 1500 m), where soil moisture retrievals are of higher accuracy. Soil moisture climatology is wetter in the west, causing larger variations in 3B42RTADJ error from the white noise ΔSM (as discussed in Sect. 3.2). Despite that, satellite soil moisture is most effective correcting medium to large rainfall under normal surface conditions.

The decreased skill in 3B42RTADJ over the eastern US is primarily attributed to both precipitation and soil moisture climatology, a wet climate with more medium to high rainfall, neither of which is suitable for soil moisture assimilation.

In summary, the high-frequency noise in soil moisture product causes a major limitation. The noise impacts adjusted precipitation by introducing false alarm rain days. It is difficult to distinguish the noise and retrieve the true rainfall signals. A remedy to prevent the excessive rain days is applying a cutoff ΔSM threshold when rain days are added, at the expense of neglecting a part of the true rainfall signals. Figure 13 shows the probability of added rainy days being consistent with NLDAS (NLDAS > 0 mm day-1) with respect to ΔSM. When a new rainy day is added (3B42RT = 0 mm day-1, 3B42RTADJ > 0 mm day-1) based on AMSR-E/LSMEM ΔSM of 2 mm day-1, there's approximately 78 % chance that the added rain day is a true event (NLDAS > 0 mm day-1); That is, ∼ 22 % chance that it is a false alarm (NLDAS = 0 mm day-1). When AMSR-E/LSMEM ΔSM is larger than 2 mm day-1, the probability of added rainy days being true event is even higher, up to 90 % chance. Here we applied a threshold of 2 mm day-1 to AMSR-E/LSMEM ΔSM. That is, when new rainy days are introduced (3B42RT > 0, 3B42RTADJ > 0), we discard the update and keep the no-rain day if AMSR-E/LSMEM soil moisture increment is below 2 mm. Note that, the probability of the false alarms depends on soil moisture climatology: the wetter soil moisture climatology, the larger uncertainty in the signal. Therefore, this threshold should vary in accordance with local soil moisture climatology, i.e., a larger threshold over the wetter eastern US and a smaller threshold over the drier western US. Nevertheless, after the 2 mm day-1 ΔSM threshold is applied, expectedly, the statistics are largely improved: FAR is decreased significantly from 0.519 (wo. ΔSM threshold) to 0.066 (w. ΔSM threshold). MAE of light rainfall (< 2 mm day-1) in 3B42RTADJ decreased from 0.99 to 0.64 mm day-1, compared to 0.65 mm day-1 in 3B42RT. For medium to large 3B42RT rainfall (≥ 2 mm day-1), it effectively increased POD (0.362 in 3B42RT vs. 0.386 in 3B42RTADJ w. ΔSM threshold) and decreased FAR (0.037 in 3B42RT vs. 0.030 in 3B42RTADJ w. ΔSM threshold). Further work is needed to characterize, distinguish and decrease the high-frequency noise in SM retrievals. Figure 13 gives an example of evaluating the impact of SM uncertainties in assimilation as curves derived over different topography can be quantitatively compared.

Many other studies have utilized satellite microwave brightness temperatures or soil moisture retrievals to constrain satellite precipitation estimates (Pellarin et al., 2008), estimate precipitation (e.g., Brocca et al., 2013) or improve precipitation estimates through assimilation (Crow et al., 2009, 2011). Here, we review their approaches and findings in light of the results of this study, and compare our results with some of these studies to gain insight into their robustness and consistency.

Pellarin et al. (2008) used the temporal variations of the AMSR-E 6.7 GHz brightness temperature (TB) normalized polarization difference, PR = (TBV - TBH)/(TBV + TBH), to screen out anomalous precipitation events from a 4-day cumulative satellite-estimated precipitation (EPSAT-SG: Chopin et al., 2005) from 22 to 26 June 2004 over a 100 × 125 km box centered over Niger in West Africa. This was extended in Pellarin et al. (2013), where an API-based water balance model was used to correct three different satellite precipitation products (CMORPH, TRMM-3B42 and PERSIANN) over a 4-year period in West Africa at three 0.25∘ grids in Niger, Benin and Mali). The new algorithm was evaluated by comparing the corrected precipitation to estimates over the 0.25∘ grids from ground-based precipitation measurements. A sequential assimilation approach was applied where AMSR-E C-band TB measurements were used to estimate a simple multiplicative factor to the precipitation estimates in order to minimize the difference between observed (AMSR-E) and simulated TBs in terms of root mean square error (RMSE). The results show improvements over those found in Pellarin et al. (2009). Specifically, the Pellarin et al. (2013) study shows that the proposed methodology produces an improvement of the RMSE at daily, decadal and monthly timescales and at the three locations. For instance, the RMS mean error decreases from 7.7 to 3.5 mm day-1 at the daily timescale in Niger and from 18.3 to 7.7 mm day-1 at the decadal timescale in Mali.

Crow et al. (2003, 2009, 2011) demonstrated the effectiveness of the assimilation of remotely sensed microwave brightness temperatures or retrieved soil moisture in estimating precipitation based on airborne measurements over the Southern Great Plains (USA) region (Crow et al., 2003); 2- to 10-day accumulated precipitation within a simple API water budget model and assimilation scheme over CONUS (Crow et al., 2009); and 3-day, 1∘ precipitation accumulation over three African Monsoon Multidisciplinary Analysis (AMMA) sites in West Africa with an enhanced assimilation scheme and an API moisture model (Crow et al., 2011). Crow et al. (2009) recommend against estimating precipitation at a larger scale than 3 days based on assimilating AMSR-E/LSMEM soil moisture.

Brocca et al. (2013) estimated precipitation by inverting the water budget equation such that precipitation could be estimated from changes in soil moisture. The inverted equation was calibrated using in situ, 4-day averaged observations at two sites in Spain and Italy. In Brocca et al. (2014), the same approach was used globally to estimate daily precipitation at 1∘ spatially. Five-day cumulated rainfall estimates are derived from three satellite-derived soil moisture data sets (AMSR-E LPRM, ASCAT and SMOS), and linearly interpolated to daily values, for their precipitation estimation algorithm. No formal data assimilation was carried out. The newly created precipitation data set was compared to two satellite precipitation products (TRMM-3B42RT, GPCC) and two gauge-based precipitation products (GPCP, ERA-Interim). Five-day accumulated rainfall data, aggregated to a 1∘ spatial resolution, are considered in their assessment analyses with promising results. But, they do note that their approach has “poor scores in reproducing daily rainfall data”. Ciabatta et al. (2015) derived daily rainfall product using ASCAT over Italy and integrated it with TMPA 3B42RT precipitation. The merged product also shows promising results.

In the study reported here, four advances have been made over these earlier studies: (i) we adopted a state-of-the-art dynamic land surface model that has demonstrated high skill in simulating soil moisture when driven by high-quality precipitation data (Schaake et al., 2004); (ii) we applied a state-of-the-art data assimilation procedure based on particle filtering so as to extract (and hopefully maximize) the information content from the satellite most effectively; (iii) we increased the resolution of the precipitation estimation window down to 1 day, exceeding the conclusions in these earlier studies that the finest temporal resolution is 3 to 5 days. Additionally we increased (or matched) the spatial resolution to 0.25∘, limited primarily by the satellite soil moisture product resolution; and (iv) previous studies are based on the assumption that the SM retrievals are 100 % accurate and contain no errors. We evaluated this assumption by analyzing the impact of uncertainties associated with the soil moisture retrievals. These advances offer important benefits when satellite precipitation products are used for applications such as flood forecasting. Admittedly, by aggregating in space and time, the improvement is more robust since some errors are averaged out.

Wanders et al. (2015) performed a comprehensive inter-comparison study using multiple satellite soil moisture and land surface temperature (LST) data at fine temporal scale (3-hourly). Compared to their study, ours focuses on using soil moisture exclusively from one satellite and retrieval algorithm, and in improvements to the assimilation algorithm, specifically, (i) the longer temporal period (2010–2011 in Wanders et al. (2015) vs. 2002–2007 in this study), (ii) the temporal resolution (3-hourly vs. daily), and (iii) the particle generation and bias-correction method. We present in the paper improvements in the generation of rain particles and the bias-correction of the satellite soil moisture observations, as well as enhancements to the assimilation algorithm to maximize the information that can be gained from using soil moisture alone in adjusting precipitation. Due to the very strong and complicated spatial structure of precipitation, that is non-Gaussian and non-stationary in both time and space, a more advanced method is applied to generate possible precipitation fields than used or presented in earlier studies or in Wanders et al. (2015). Furthermore, a more advanced bias-correction method is also applied to account for the reported problems (Wanders et al., 2015) in the second-order statistics of the soil moisture retrievals, and (iv) SM retrieval products (and overpasses) used in assimilation. Our improved results are based on soil moisture retrievals from ascending overpasses only (vs. both descending and ascending overpasses from multiple data sets, i.e., AMSR-E/LSMEM, ASCAT and SMOS). Our exclusive focus on the usefulness of soil moisture product promises more applicability especially for improving satellite precipitation from the Global Precipitation Mission products. The descending overpasses have generally better performance than the ascending, suggesting the potentials of further improvements.

A quantitative comparison of Wanders et al. (2015) and our results is provided below. Despite the different time periods between Wanders et al. (2015, 2010–2011) and in our study (2002–2007), Wanders et al. (2015) show decreasing POD (-15.0 to -46.4 %, depending on the different products used) and FAR (-47.2 to -89.1 %, depending on the different products used) for all rainfall after assimilation using either (single or multiple) SM products alone or SM + LST data combined (see Table 4 of Wanders et al., 2015). While in our study, after applying ΔSM threshold, medium to large 3B42RTADJ rainfall (≥ 2 mm day-1) has an increase in POD (+6.6 %) and decrease in FAR (-18.9 %). Furthermore, the significant dry bias in adjusted precipitation (see Fig. 6 of Wanders et al., 2015) is not present in our results (Fig. 9). This is due to improvements in our precipitation ensemble generation and bias correction scheme. Wanders et al. (2015) applied an additional step generating precipitation particles sampling from a 3 × 3 window that over-eliminates most of the excessive rainfall along with some real signal. We suggest loosening this constraint to a larger window size or to sample from adjusted precipitation instead of original 3B42RT precipitation. However, sampling from adjusted precipitation at each time step would significantly increase the computational demand, limiting the potential for a global application at high temporal/spatial resolution.

Furthermore, the outcome is quite different for the distribution of soil moisture retrievals after pre-processing (Fig. 9 of Wanders et al. (2015) vs. Fig. 4 in our study) due to the different methods used. After pre-processing, distributions of soil moisture retrievals are more similar to that of NLDAS precipitation forced, VIC modeled first-layer soil moisture. CDF-matching used by Wanders et al. (2015) is based on the assumption that satellite soil moisture and modeled soil moisture respond to heavy rainfall in the same way – essentially having a rank correlation of 1. However, that is not observed because of the shallower detection depth of the satellite soil moisture. On the other hand, using the pre-processing method presented in this study, the signal of near-saturation in AMSR-E/LSMEM ΔSM tends to be overestimated after pre-processing, which indicates a heavy rain event that is often accompanied with surface saturation and thus does not provide effective information for the assimilation. The other benefit of the second-order polynomial regression lies in its non-linearity. An error in the soil moisture product impacts the precipitation adjustment in a predictable way, allowing for a more systematic post-processing treatment. Based on the known error characteristics, we demonstrate a potential remedy to deal with the error by applying a 2 mm day-1 cutoff ΔSM threshold. Meanwhile, it is also highlighted that the cutoff threshold should be variable and positively correlated with local soil moisture climatology. We acknowledge that the soil moisture product used in Wanders et al. (2015) is a blended product of multiple satellite soil moisture data sets. It is not clear how its error characteristics impact the adjusted precipitation.

Based on the retrieved soil moisture from AMSR-E using the LSMEM retrieval algorithm, we propose an assimilation procedure to integrate soil moisture information into the VIC land surface model so as to improve real-time, satellite precipitation estimates. The ability to estimate rainfall amount is now enhanced with the above improvements, especially for correcting medium rainfall amounts. However, constrained by the noise in AMSR-E TBs and thus soil moisture retrievals, the assimilation is not effective in detecting missed rainfall events. The improved precipitation estimates, referred to as 3B42RTADJ estimates, are overall consistent in reproducing the spatial pattern and time series of daily rainfall from NLDAS precipitation. The results illustrate the potential benefits of using data assimilation to merge satellite retrievals of surface soil moisture into a land surface model forced with real-time precipitation. Potentially the method can be applied globally for areas meeting vegetation cover and surface condition constraints that allows for soil moisture retrievals. Under these conditions, the approach can provide a supplementary source of information for enhancing the quality of satellite rainfall estimation, especially over poorly gauged areas like Africa.

Nonetheless, some caution is required. The results of this study show that the adjusted real-time precipitation tends to add additional rain (frequency) resulting in more time steps with rain but lower regional average in the western US and slightly higher regional average in the eastern US. It is also noticed that the precipitation adjustments are insensitive under saturated soil moisture conditions. A wetter surface magnifies any error associated with satellite observation by incorrectly adjusting precipitation. These errors, mixed with the “real” signal, generally add approximately ∼ 2 mm of precipitation (or higher), depending on the soil moisture climatology. It is important to consider these circumstances when observations are used so as to avoid introducing additional error. With these identified limitations, continued research is needed to assess the biases in the real-time precipitation retrievals on a local to regional basis so the assimilation system can be modified accordingly.

The assimilation scheme used here assumed that the errors were attributed to the real-time precipitation retrievals, but the precipitation estimates after adjustment includes errors from additional sources. The two primary sources are errors in soil moisture retrievals and errors in the land surface model that include model parameterizations (poorly or insufficiently represented processes as well as scale issues) and parameter errors (insufficient calibration). There are also errors in other model forcing fields besides precipitation. Further studies are needed to assess the attribution of these error sources to the total error. Such research will further improve the use of real-time satellite-based precipitation for global flood monitoring.

Besides the clear, heavy dependency of the assimilation effectiveness on the accuracy of satellite soil moisture product, it is also important to acquire adequate knowledge on the error characteristics of satellite soil moisture retrievals. Knowledge of the soil moisture errors could be important and the assimilation methods (including precipitation ensemble generation and pre-/post-processing method) should be chosen accordingly. On the other hand, the presence of data gaps between overpasses could be a large source of uncertainty with data assimilation. Further effort towards reliable spatial-temporal continuous (gap-filled) satellite soil moisture data sets is needed.

While it has been illustrated in this study that the enhancement of real-time satellite precipitation estimates can be realized through an assimilation approach using satellite soil moisture data products and a particle filter, additional satellite-based observations (e.g., multi-sensor soil moisture products) or variables (e.g., land surface temperatures as shown in Wanders et al. (2015), inundated areas) could be added/replaced in the assimilation process with different levels of complexity, e.g., by applying constraints on the particle generation. This opens up a great number of opportunities in using space-borne observations for supplementing direct retrievals of precipitation.