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Yiwen Mei, Efthymios I. Nikolopoulos, Emmanouil N. Anagnostou, and Marco Borga

; Cunha et al. 2012 ), and the choice of hydrologic model or modeling complexity ( Carpenter and Georgakakos 2006 ; Zhu et al. 2013 ). This study builds upon the above works, providing a comprehensive evaluation of three different satellite products and their gauge-adjusted counterparts and comparing them against a reference precipitation dataset derived from a dense gauge network over the upper Adige River basin of the eastern Italian Alps. Given the strong mountainous relief (200–3900 m MSL) of the

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Hamed Ashouri, Phu Nguyen, Andrea Thorstensen, Kuo-lin Hsu, Soroosh Sorooshian, and Dan Braithwaite

, we seek to evaluate the performance of PERSIANN-CDR in a distributed rainfall–runoff modeling application and compare its performance with other high-resolution precipitation products. For this purpose, the National Weather Service (NWS) HL-RDHM is used. This study is conducted in two phases. In the first phase, a period of time when PERSIANN-CDR, TMPA, and stage IV gauge-adjusted radar data products are available is chosen and the hydrological modeling is performed for this period. The study

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Yumeng Tao, Xiaogang Gao, Kuolin Hsu, Soroosh Sorooshian, and Alexander Ihler

from other possible combinations. Other potential hyperparameters, such as learning rate and training iterations, were decided manually to optimize the result within the training process. Table 5. Hyperparameters considered for SDAE in the study. 5. Results and discussion The results presented here show the performances of the SDAE model in the validation periods (summer of 2013 and winter of 2013/14) in comparison with the original PERSIANN-CCS data. The evaluation includes both detection of rain

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Chris Kidd, Toshihisa Matsui, Jiundar Chern, Karen Mohr, Chris Kummerow, and Dave Randel

, manuscript submitted to J. Adv. Model. Earth Syst. ). Another advantage of using the MMF is that the spatial scale of MMF-simulated cloud–precipitation parameters is close to those of satellite observations. While a traditional climate model often assumes complex subgrid distributions or overlapping patterns for evaluation against satellite data ( Webb et al. 2001 ), the MMF explicitly simulates the physical structure of cloud and convective processes through the 2D GCE; thus, the satellite instrumental

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E. Cattani, A. Merino, and V. Levizzani

drought changes under climate change. Regional climate studies on extreme event occurrences (droughts and floods) and their connections with other climatic features require long, homogenous, and possibly uninterrupted precipitation datasets. Not all satellite datasets can fulfill such requirements on temporal coverage, but they can satisfy the other requirements at the global scale and can be used to construct short-term climatologies for comparisons with model simulations or to evaluate precipitation

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Clément Guilloteau, Rémy Roca, and Marielle Gosset

Mapping of Precipitation (GSMaP), and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Cloud Classification System (PERSIANN-CCS). The last three products are also evaluated as rain masks. A multiscale approach based on discrete wavelet decomposition is used to investigate the scale dependence of the masks’ performance. Satellite-derived rain masks are compared with ground radar–derived rain masks over Burkina Faso and with TRMM Precipitation Radar (PR

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Abebe Sine Gebregiorgis, Pierre-Emmanuel Kirstetter, Yang E. Hong, Nicholas J. Carr, Jonathan J. Gourley, Walt Petersen, and Yaoyao Zheng

sounding unit . IEEE Trans. Geosci. Remote Sens. , 43 , 1036 – 1049 , doi: 10.1109/TGRS.2004.843249 . 10.1109/TGRS.2004.843249 Gebregiorgis , A. S. , and F. Hossain , 2011 : How much can a priori hydrologic model predictability help in optimal merging of satellite precipitation products? J. Hydrometeor. , 12 , 1287 – 1298 , doi: 10.1175/JHM-D-10-05023.1 . 10.1175/JHM-D-10-05023.1 Gebregiorgis , A. S. , and F. Hossain , 2013 : Performance evaluation of merged satellite rainfall products

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Toshi Matsui, Jiun-Dar Chern, Wei-Kuo Tao, Stephen Lang, Masaki Satoh, Tempei Hashino, and Takuji Kubota

current global storm-resolving models can reproduce signals of land–ocean contrast in relation to satellite observations ( section 3 ). Finally, the capabilities, limitations, and physical processes associated with the land–ocean contrast in convective systems are contrasted between the TRMM observations and the two global numerical cloud models ( section 4 ). 2. Observed TRMM climatology a. T3EF database This study utilizes the TRMM Triple-Sensor Three-Step Evaluation Framework (T3EF; Matsui et al

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F. Joseph Turk, R. Sikhakolli, P. Kirstetter, and S. L. Durden

quantify the precipitation rate. With GPM, the design of the PMW precipitation retrieval is a Bayesian-based estimation for all surfaces, whose veracity and accuracy relies upon the capability to physically model the multichannel TB of all scenes, and under all atmospheric conditions ( Kummerow et al. 2011 ). A probabilistic Bayesian type of estimate is appropriate for inversion of profiling radar and/or radiometric observations of precipitation, since the space–time variability of the factors

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Ali Behrangi, Bin Guan, Paul J. Neiman, Mathias Schreier, and Bjorn Lambrigtsen

over land, however, relies mainly on scattering properties of ice particles, partly because quantification of emissivity over land is difficult and is an ongoing challenge ( Ferraro et al. 2013 ). This has limited the performance of MW-based precipitation retrievals in capturing warm rainfall observed in many regions ( Liu and Zipser 2009 ) including the North American west coast (e.g., Neiman et al. 2005 ; Martner et al. 2008 ). Furthermore, the presence of ice and snow on the surface adds more

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