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Satellite measurements (retrievals) of surface soil moisture are subject to errors and cannot provide complete space‐time coverage. Data assimilation systems merge available retrievals with information from land surface models and antecedent meteorological data, information that is spatio‐temporally complete but likewise uncertain. For the design of new satellite missions it is critical to understand just how uncertain retrievals can be and still be useful. Here, we present a synthetic data assimilation experiment that determines the contribution of retrievals to the skill of land assimilation products (soil moisture and evapotranspiration) as a function of retrieval and land model skill. As expected, the skill of the assimilation products increases with the skill of the model and that of the retrievals. The skill of the soil moisture assimilation products always exceeds that of the model acting alone; even retrievals of low quality contribute information to the assimilation product, particularly if model skill is modest.

Quality of precipitation products from the Integrated Multi‐satellitE Retrievals for Global Precipitation Measurement mission (IMERG) was evaluated over the Lower Colorado River Basin of Texas. Observations of several rainfall events of a wide range of magnitudes during May 2015 by a very dense network of 241 rain gauges over the basin were used as a reference. The impact of temporal and spatial downscaling of different satellite products (near/post‐real‐time) on their accuracy was studied. Generally, all IMERG products perform better when the temporal and spatial resolutions are downscaled. The Final product shows relatively better performance compared to the near‐real‐time products in terms of basic performance measures; however, regarding rainfall detection, all products show nearly similar performance. When considering rainfall detection, IMERG adequately captures the precipitation events; however, in terms of spatial patterns and accuracy, more improvements are needed. IMERG products analysis results may help developers gain insight into the regional performance of the product, improve the product algorithms, and provide information to end users on the products’ suitability for potential hydrometeorological applications. Overall, the IMERG products, even the uncalibrated product at its finest resolution, showed reasonable performance indicating their great potential for applications such as water resources management, prevention of natural disasters, and flood forecasting.

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[1] The primary advantage of radar observations of precipitation compared with traditional rain gauge measurements is their high spatial and temporal resolution and large areal coverage. Unfortunately, radar data require vigorous quality control before being converted into precipitation products that can be used as input to hydrologic models. In this study we coupled a physically based atmospheric model of convective rainfall with an active microwave radiative transfer model to simulate radar observation of thunderstorms. We used the atmospheric model to simulate a well-documented tornadic supercell storm that occurred near Del City, Oklahoma, on 20 May 1977. We then generated radar observations of that storm and used them to evaluate the propagation of radar rainfall errors through distributed hydrologic simulations. This physically based methodology allows us to directly examine the impact of radar rainfall estimation errors on land-surface hydrologic predictions and to avoid the limitations imposed by the use of rain gauge data. Results indicate that the geometry of the radar beam and coordinate transformations, due to radar-watershed-storm orientation, have an effect on radar rainfall estimation and runoff prediction errors. In addition to uncertainty in the radar reflectivity versus rainfall intensity relationship, there are significant range-dependent and orientation-related radar rainfall estimation errors that should be quantified in terms of their impact on runoff predictions. Our methodology provides a tool for performing experiments that address some operational issues related to the process of radar rainfall estimation and its uses in hydrologic prediction.

Hurricanes and other severe coastal storms have become more frequent and destructive during recent years. Hurricane Harvey, one of the most extreme events in recent history, advanced as a category IV storm and brought devastating rainfall to the Houston, TX, region during 25–29 August 2017. It inflicted damage of more than $125 billion to the state of Texas infrastructure and caused multiple fatalities in a very short period of time. Rainfall totals from Harvey during the 5-day period were among the highest ever recorded in the United States. Study of this historical devastating event can lead to better preparation and effective reduction of far-reaching consequences of similar events. Precipitation products based on satellites observations can provide valuable information needed to understand the evolution of such devastating storms. In this study, the ability of recent Integrated Multi-satellitE Retrievals for Global Precipitation Mission (GPM-IMERG) final-run product to capture the magnitudes and spatial (0.1° × 0.1°)/temporal (hourly) patterns of rainfall resulting from hurricane Harvey was evaluated. Hourly gridded rainfall estimates by ground radar (4 × 4 km) were used as a reference dataset. Basic and probabilistic statistical indices of the satellite rainfall products were examined. The results indicated that the performance of IMERG product was satisfactory in detecting the spatial variability of the storm. It reconstructed precipitation with nearly 62% accuracy, although it systematically under-represented rainfall in coastal areas and over-represented rainfall over the high-intensity regions. Moreover, while the correlation between IMERG and radar products was generally high, it decreased significantly at and around the storm core.