Precipitation is one of the most important components in the water and energy cycles. Radars are considered the best available technology for observing the spatial distribution of precipitation either from the ground since the 1980s or from space since 1998. This study, for the first time ever, compares and evaluates the only three existing spaceborne precipitation radars, i.e., the Ku‐band precipitation radar (PR), the W‐band Cloud Profiling Radar (CPR), and the Ku/Ka‐band Dual‐frequency Precipitation Radar (DPR). The three radars are matched up globally and intercompared in the only period which they coexist: 2014–2015. In addition, for the first time ever, TRMM PR and GPM DPR are evaluated against hourly rain gauge data in Mainland China. Results show that DPR and PR agree with each other and correlate very well with gauges in Mainland China. However, both show limited performance in the Tibetan Plateau (TP) known as the Earth's third pole. DPR improves light precipitation detectability, when compared with PR, whereas CPR performs best for light precipitation and snowfall. DPR snowfall has the advantage of higher sampling rates than CPR; however, its accuracy needs to be improved further. The future development of spaceborne radars is also discussed in two complementary categories: (1) multifrequency radar instruments on a single platform and (2) constellations of many small cube radar satellites, for improving global precipitation estimation. This comprehensive intercomparison of PR, CPR, and DPR sheds light on spaceborne radar precipitation retrieval and future radar design.
Spaceborne precipitation radars are powerful tools used to acquire adequate and highquality precipitation estimates with high spatial resolution for a variety of applications in hydrological research. The Global Precipitation Measurement (GPM) mission, which deployed the first spaceborne Ka-and Ku-dual frequency radar (DPR), was launched in February 2014 as the upgraded successor of the Tropical Rainfall Measuring Mission (TRMM). This study matches the swath data of TRMM PR and GPM DPR Level 2 products during their overlapping periods at the global scale to investigate their similarities and DPR's improvements concerning precipitation amount estimation and type classification of GPM DPR over TRMM PR. Results show that PR and DPR agree very well with each other in the global distribution of precipitation, while DPR improves the detectability of precipitation events significantly, particularly for light precipitation. The occurrences of total precipitation and the light precipitation (rain rates < 1 mm/h) detected by GPM DPR are~1.7 and~2.53 times more than that of PR. With regard to type classification, the dual-frequency (Ka/Ku) and single frequency (Ku) methods performed similarly. In both inner (the central 25 beams) and outer swaths (1-12 beams and 38-49 beams) of DPR, the results are consistent. GPM DPR improves precipitation type classification remarkably, reducing the misclassification of clouds and noise signals as precipitation type "other" from 10.14% of TRMM PR to 0.5%. Generally, GPM DPR exhibits the same type division for around 82.89% (71.02%) of stratiform (convective) precipitation events recognized by TRMM PR. With regard to the freezing level height and bright band (BB) height, both radars correspond with each other very well, contributing to the consistency in stratiform precipitation classification. Both heights show clear latitudinal dependence. Results in this study shall contribute to future development of spaceborne radar precipitation retrievals and benefit hydrological and meteorological research.
The Earth's water cycle involves energy exchange and mass movement in the hydrosphere and thus sustains the dynamic balance of global hydrologic cycle. All water cycle variables on the Earth are closely interconnected with each other through the process of energy and water circulation. Observing, understanding and predicting the storage, movement, and quality of water remains a grand challenge for contemporary water science and technology, especially for researches across different spatio-temporal scales. The remote sensing observing platform has a unique advantage in acquiring complex water information and has already greatly improved observing, understanding, and predicting ability of the water cycle. Methods of obtaining comprehensive water cycle data are also expanded by new remote sensing techniques, and the vast amount of data has become increasingly available and thus accelerated a new Era: the Remote Sensing Big Data Study of Global Water Cycle. The element inversion, time and space reconstruction, and scale conversion are three key scientific issues for remote sensing water cycle in such Era. Moreover, it also presents a huge opportunity of capitalizing the combinations of Remote Sensing and Big Data to advance and improve the global hydrology and water security research and development, and uncork the new bottlenecks.
Ant colony optimization algorithm for continuous domains is a major research direction for ant colony optimization algorithm. In this paper, we propose a distribution model of ant colony foraging, through analysis of the relationship between the position distribution and food source in the process of ant colony foraging. We design a continuous domain optimization algorithm based on the model and give the form of solution for the algorithm, the distribution model of pheromone, the update rules of ant colony position, and the processing method of constraint condition. Algorithm performance against a set of test trials was unconstrained optimization test functions and a set of optimization test functions, and test results of other algorithms are compared and analyzed to verify the correctness and effectiveness of the proposed algorithm.
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