This study evaluates the performance of four satellite-based precipitation (SBP) products over the western Black Sea region of Turkey, a region characterized by complex topography that exerts strong controls on the precipitation regime. The four SBP products include the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis version 7 experimental near-real-time product (TMPA-7RT) and post-real-time research-quality product (TMPA-7A), the Climate Prediction Center morphing technique (CMORPH), and the Multisensor Precipitation Estimate (MPE) of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). Evaluation is performed at various spatial (point and grid) and temporal (daily, monthly, seasonal, and annual) scales over the period 2007–11. For the grid-scale evaluation, a rain gauge–based gridded precipitation dataset was constructed using a knowledge-based system in which “physiographic descriptors” are incorporated in the precipitation estimation through an optimization framework. The results indicated that evaluated SBP products generally had difficulty in representing the precipitation gradient normal to the orography. TMPA-7RT, TMPA-7A, and MPE products underestimated precipitation along the windward region and overestimated the precipitation on the leeward region, more significantly during the cold season. The CMORPH product underestimated the precipitation on both windward and leeward regions regardless of the season. Further investigation of the datasets used in the development of these SBP products revealed that, although both infrared (IR) and microwave (MW) datasets contain potential problems, the inability of MW sensors to detect precipitation especially in the cold season was the main challenge over this region with complex topography.

An extensive evaluation of nine global-scale high-resolution satellite-based rainfall (SBR) products is performed using a minimum of 6 years (within the period of 2000–13) of reference rainfall data derived from rain gauge networks in nine mountainous regions across the globe. The SBR products are compared to a recently released global reanalysis dataset from the European Centre for Medium-Range Weather Forecasts (ECMWF). The study areas include the eastern Italian Alps, the Swiss Alps, the western Black Sea of Turkey, the French Cévennes, the Peruvian Andes, the Colombian Andes, the Himalayas over Nepal, the Blue Nile in East Africa, Taiwan, and the U.S. Rocky Mountains. Evaluation is performed at annual, monthly, and daily time scales and 0.25° spatial resolution. The SBR datasets are based on the following retrieval algorithms: Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis (TMPA), the NOAA/Climate Prediction Center morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN), and Global Satellite Mapping of Precipitation (GSMaP). SBR products are categorized into those that include gauge adjustment versus unadjusted. Results show that performance of SBR is highly dependent on the rainfall variability. Many SBR products usually underestimate wet season and overestimate dry season precipitation. The performance of gauge adjustment to the SBR products varies by region and depends greatly on the representativeness of the rain gauge network.

The great success of the Tropical Rainfall Measuring Mission (TRMM) and its successor Global Precipitation Measurement (GPM) has accelerated the development of global high-resolution satellite-based precipitation products (SPP). However, the quantitative accuracy of SPPs has to be evaluated before using these datasets in water resource applications. This study evaluates the following GPM-era and TRMM-era SPPs based on two years (2014–2015) of reference daily precipitation data from rain gauge networks in ten mountainous regions: Integrated Multi-SatellitE Retrievals for GPM (IMERG, version 05B and version 06B), National Oceanic and Atmospheric Administration (NOAA)/Climate Prediction Center Morphing Method (CMORPH), Global Satellite Mapping of Precipitation (GSMaP), and Multi-Source Weighted-Ensemble Precipitation (MSWEP), which represents a global precipitation data-blending product. The evaluation is performed at daily and annual temporal scales, and at 0.1 deg grid resolution. It is shown that GSMaPV07 surpass the performance of IMERGV06B Final for almost all regions in terms of systematic and random error metrics. The new orographic rainfall classification in the GSMaPV07 algorithm is able to improve the detection of orographic rainfall, the rainfall amounts, and error metrics. Moreover, IMERGV05B showed significantly better performance, capturing the lighter and heavier precipitation values compared to IMERGV06B for almost all regions due to changes conducted to the morphing, where motion vectors are derived using total column water vapor for IMERGV06B.

The representation of precipitation variability over mountainous regions by ground-based sensors is an open problem in hydrometeorological applications that necessitates the use of satellite-based precipitation products (SPPs). An extended network of ground-based X-band radar (GR) deployments over complex terrain areas, including the northeastern Italian Alps, North Carolina, Olympic Mountain, and the southern tip of Vancouver Island, is used in this study as a benchmark rainfall data set for error characterization and modeling of Level 2 PMW retrievals (Goddard profiling (GPROF) V05 algorithm) for the different sensors: the Microwave Humidity Sounder (MHS), the Special Sensor Microwave Imager/Sounder (SSMIS), the Global Precipitation Measurement Microwave Imager (GMI), and the Advanced Microwave Scanning Radiometer 2 (AMSR2). Matchups of Level 2 PMW/GR rainfall are extracted based on a matching methodology that identifies GR volume scans with PMW overpasses, and scales GR parameters to the satellite products’ nominal spatial resolution. The error model is the nonparametric machine learning tree-based quantile regression forest (QRF), which we developed using matchups of PMW/GR rainfall data from the different study areas. Validation of the error model is conducted using three cross-validation techniques: the k-fold, leave-one region out, and enforced. All validations showed that the error model-based corrections can significantly reduce both the mean relative error and the random component of PMW products. Moreover, the error reduction demonstrated with the leave-one region out cross-validation technique indicated that the error model is transferable among complex terrain regions. Algorithm developers may find this error model useful to integrate in the Level 3 products.

Accurate rainfall estimation over complex terrain is critical for science and applications concerning life and economy, but it is challenging due to the multifactorial relationship between topography, environmental parameters, and rainfall intensity. In this work, a graph convolutional neural network-based approach named multi-graph convolutional neural network (M-GCN) is used to interpolate precipitation at a 30-min temporal scale. Furthermore, to enable the model to adapt to the variabilities of spatial correlation, we cluster the ground radar nodes based on their geographical information and expand the network with the multi-graph mechanism. Thus, we can avoid overfitting caused by varying conditions over a wide area, and the estimation accuracy can be improved. The method was tested on ground radar-gauge precipitation data over three months on the West Coast of the United States, in 2015. The experimental result confirms that our proposed method outperforms the state-of-the-art interpolation methods. Besides interpolation capacity, the M-GCN also has the advantage in computational efficiency. The distributed graphs in the M-GCN architecture make it possible to train the networks on edge servers and the cloud.