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Remote sensing brings unprecedented opportunities to estimate precipitation at regional, continental, and even global scales. Nevertheless, the spatial resolutions of current mainstream satellite precipitation estimates are still too coarse to be directly used in the hydrological and meteorological applications over the medium and small scales. The spatio-temporal distribution of precipitation is often affected by various land surface factors, such as geographical location, vegetational cover, topographic characteristics, and ground temperature. Based on the relationship between precipitation and multi-geospatial factors, this study proposed a new spatial downscaling approach named as gradient boosting decision tree (GBDT) to downscale the annual satellite precipitation estimates of Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) from 0.1° to 0.01° gridded resolution over Mainland China from 2015 to 2018. As for the preprocessing of downscaling algorithm, the entire study area of Mainland China was separated into four different climate regions due to the spatial discrepancy of the relationship between precipitation and geospatial factors. To validate the effectiveness of GBDT approach, we compared it with other two types of downscaling methods based on random forests (RF) and support vector machine (SVM), respectively. Our evaluation results show that the geographical location (including both latitude and longitude) seems to be relatively more important and stable for GBDT modelling than other land surface factors across the four climate regions in Mainland China. In terms of large spatial scales, the GBDT and RF algorithms generally are superior to SVM for downscaling the IMERG precipitation, as SVM reduced the considerable accuracy of the downscaled results. Last but not least, GBDT exhibits more robust features in downscaling the satellite precipitation retrievals than RF over complex terrains, where the amount of precipitation has strong spatial heterogeneity.
Zhehui Shen; Bin Yong. Downscaling the GPM-based satellite precipitation retrievals using gradient boosting decision tree approach over Mainland China. Journal of Hydrology 2021, 602, 126803 .
AMA StyleZhehui Shen, Bin Yong. Downscaling the GPM-based satellite precipitation retrievals using gradient boosting decision tree approach over Mainland China. Journal of Hydrology. 2021; 602 ():126803.
Chicago/Turabian StyleZhehui Shen; Bin Yong. 2021. "Downscaling the GPM-based satellite precipitation retrievals using gradient boosting decision tree approach over Mainland China." Journal of Hydrology 602, no. : 126803.
Accounting for freshwater resources and monitoring floods are vital functions for societies throughout the world. Remote-sensing methods offer great prospects to expand stream monitoring in developing countries and to smaller, headwater streams that are largely ungauged worldwide. This study evaluates the potential to estimate discharge using eight radar units that have been installed over streams in diverse hydrologic and hydraulic settings across the United States. The research highlights error characteristics associated with the measurements of stage using pulsed wave radars, mean channel velocity from continuous wave Doppler radars, and their combined use to estimate discharge at sites that were collocated with conventional streamgauges. Potential stage biases caused by the thermal expansion and contraction of supporting structures due to diurnal temperature changes were examined. A dry concrete, flume showed the temperature-dependent stage variations were no more than 2 cm. Surface velocity retrievals needed to be adjusted to represent the mean channel velocity when estimating discharge. Different approaches were evaluated and application of two different, depth-dependent adjustment factors was found to yield the most accurate estimates. This study found that it is possible to get accurate discharge estimates from noncontact radar measurements, providing cost-effective solutions for remote sensing of ungauged streams. Lastly, radar measurements of the raw variables (i.e., stage and surface velocity) can be used in an early alerting context to detect flash floods in ungauged streams.
Mushfiqur Rahman Khan; Jonathan J. Gourley; Jorge A. Duarte; Humberto Vergara; Daniel Wasielewski; Pierre-Alain Ayral; John W. Fulton. Uncertainty in remote sensing of streams using noncontact radars. Journal of Hydrology 2021, 603, 126809 .
AMA StyleMushfiqur Rahman Khan, Jonathan J. Gourley, Jorge A. Duarte, Humberto Vergara, Daniel Wasielewski, Pierre-Alain Ayral, John W. Fulton. Uncertainty in remote sensing of streams using noncontact radars. Journal of Hydrology. 2021; 603 ():126809.
Chicago/Turabian StyleMushfiqur Rahman Khan; Jonathan J. Gourley; Jorge A. Duarte; Humberto Vergara; Daniel Wasielewski; Pierre-Alain Ayral; John W. Fulton. 2021. "Uncertainty in remote sensing of streams using noncontact radars." Journal of Hydrology 603, no. : 126809.
The spatial-temporal error characteristics of four mainstream satellite precipitation products developed by China and United States, respectively, including the Fengyun-based (FY-2F and FY-2G) and the GPM-based (IMERG-Late and IMERG-Final) over Chinese mainland were comprehensively analyzed from January 2018 to December 2019. In general, both IMERG-Final and FY-2G perform better at the hourly and daily scales. While the FY-2F product has the relatively worst performance with lowest correlation coefficient (CC) and highest root mean square error (RMSE) values. In particular, FY-2F has considerable total bias and missed precipitation errors in summer when compared to other three precipitation products. As for winter, the IMERG product suites exhibit significantly overestimation in north-central region of China, while an opposite underestimation occurred in the Fengyun products. Among the four precipitation estimates, IMERG-Final and FY-2G have the lowest normalized-RMSE (NRMSE) at the elevation range of 100–300 m and 300–500 m at daily scale, respectively. The performance at the hourly scale was found to be similar for IMERG-Final and IMERG-Late, but both of which are slightly superior to FY-2G across the elevation ranges. In terms of the detectability for different rain intensities, the IMERG products performed best at higher rain rates, while the Fengyun-based precipitation estimates are superior to IMERG at relatively lower rain ones. The assessment results reported here will provide some valuable feedbacks for algorithm developers of Fengyun products, and enable data users to further understand the error characteristics and potential deficiencies of Fengyun precipitation estimates.
Hao Wu; Bin Yong; Zhehui Shen; Weiqing Qi. Comprehensive error analysis of satellite precipitation estimates based on Fengyun-2 and GPM over Chinese mainland. Atmospheric Research 2021, 263, 105805 .
AMA StyleHao Wu, Bin Yong, Zhehui Shen, Weiqing Qi. Comprehensive error analysis of satellite precipitation estimates based on Fengyun-2 and GPM over Chinese mainland. Atmospheric Research. 2021; 263 ():105805.
Chicago/Turabian StyleHao Wu; Bin Yong; Zhehui Shen; Weiqing Qi. 2021. "Comprehensive error analysis of satellite precipitation estimates based on Fengyun-2 and GPM over Chinese mainland." Atmospheric Research 263, no. : 105805.
Revealing the error components of satellite-only precipitation products (SPPs) can help algorithm developers and end-users understand their error features and improve retrieval algorithms. Here, two error decomposition schemes are employed to explore the error components of the IMERG-Late, GSMaP-MVK, and PERSIANN-CCS SPPs over different seasons, rainfall intensities, and topography classes. Global maps of the total bias (total mean squared error) and its three (two) independent components are depicted for the first time. The evaluation results for similar regions are discussed, and it is found that the evaluation results for one region cannot be extended to another similar region. Hit and/or false biases are the major components of the total bias in most overland regions globally. The systematic error contributes less than 20 % of the total error in most areas. Large systematic errors are primarily due to missed precipitation. It is found that the SPPs show different topographic patterns in terms of systematic and random errors. Notably, among the SPPs, GSMaP-MVK shows the strongest topographic dependency of the four bias scores. A novel metric, namely the normalized error component (NEC), is proposed as a means to isolate the impact of topography on the systematic and random errors. Potential methods of improving satellite precipitation retrievals and error adjustment models are discussed.
Hanqing Chen; Bin Yong; Pierre-Emmanuel Kirstetter; Leyang Wang; Yang Hong. Global component analysis of errors in three satellite-only global precipitation estimates. Hydrology and Earth System Sciences 2021, 25, 3087 -3104.
AMA StyleHanqing Chen, Bin Yong, Pierre-Emmanuel Kirstetter, Leyang Wang, Yang Hong. Global component analysis of errors in three satellite-only global precipitation estimates. Hydrology and Earth System Sciences. 2021; 25 (6):3087-3104.
Chicago/Turabian StyleHanqing Chen; Bin Yong; Pierre-Emmanuel Kirstetter; Leyang Wang; Yang Hong. 2021. "Global component analysis of errors in three satellite-only global precipitation estimates." Hydrology and Earth System Sciences 25, no. 6: 3087-3104.
Total contributing area (TCA) has important hydrological, geological and geomorphological implications in digital terrain analysis. Currently, the validity of estimated TCA is challenged by the approximation error of digital elevation models (DEMs) to real‐world terrains, the choice of flow direction algorithms and the difficulty in a quantitative evaluation. To solve these problems, this work employs a range of synthetic surfaces for free from approximation errors. Theoretical TCA for any cell on synthetic surfaces is analytically solved by using subsection integral and L'Hospital's rule based on the topographical definition of TCA. The impacts of grid discretization on the spatial patterns of theoretical TCA are explained mathematically and geometrically. In case studies, the analytically solved theoretical TCA is treated as a benchmark to quantitatively evaluate the TCAs estimated by three SFD algorithms (i.e. D8, Rho8 and D8‐LTD) and three MFD algorithms (i.e. FDFM, MFD‐md and D∞). Results show that (1) SFD algorithms obtain unsmooth and discontinuous spatial patterns of estimated TCA, due to the existence of source cells caused by the basic hypothesis of allowing only one receiving cell; (2) Cross compensation induced by artificial dispersion in MFD algorithms leads to a less error in the quantity of estimated TCA but a larger error in the physical position of estimated TCA; (3) The addition of contour length errors does not promise larger specific contributing area (SCA) errors than TCA errors. To summarize, this work offers a theoretical and quantitative evaluation on the precision of the TCAs estimated by flow direction algorithms.
Zhenya Li; Tao Yang; Chao Wang; Pengfei Shi; Bin Yong; Ying Song. Assessing the Precision of Total Contributing Area (TCA) Estimated by Flow Direction Algorithms Based on the Analytical Solution of Theoretical TCA on Synthetic Surfaces. Water Resources Research 2021, 57, 1 .
AMA StyleZhenya Li, Tao Yang, Chao Wang, Pengfei Shi, Bin Yong, Ying Song. Assessing the Precision of Total Contributing Area (TCA) Estimated by Flow Direction Algorithms Based on the Analytical Solution of Theoretical TCA on Synthetic Surfaces. Water Resources Research. 2021; 57 (4):1.
Chicago/Turabian StyleZhenya Li; Tao Yang; Chao Wang; Pengfei Shi; Bin Yong; Ying Song. 2021. "Assessing the Precision of Total Contributing Area (TCA) Estimated by Flow Direction Algorithms Based on the Analytical Solution of Theoretical TCA on Synthetic Surfaces." Water Resources Research 57, no. 4: 1.
New operational tools for monitoring flash flooding based on radar quantitative precipitation estimates (QPEs) have become available to U.S. National Weather Service forecasters. Herman and Schumacher examined QPE exceedance thresholds for several tools and compared them to each other, to flash flood reports (FFRs), and to flash flood warnings. The Next Generation Radar network has been updated with dual-polarization capabilities since the publication of Herman and Schumacher, which has changed the characteristics of the derived QPEs. Updated thresholds on Multi-Radar Multi-Sensor version 12 products that are associated to FFRs are provided and thus can be used as guidance by the operational forecasting community and other end-users of the products.
Jonathan J. Gourley; Humberto Vergara. Comments on “Flash Flood Verification: Pondering Precipitation Proxies”. Journal of Hydrometeorology 2021, 22, 739 -747.
AMA StyleJonathan J. Gourley, Humberto Vergara. Comments on “Flash Flood Verification: Pondering Precipitation Proxies”. Journal of Hydrometeorology. 2021; 22 (3):739-747.
Chicago/Turabian StyleJonathan J. Gourley; Humberto Vergara. 2021. "Comments on “Flash Flood Verification: Pondering Precipitation Proxies”." Journal of Hydrometeorology 22, no. 3: 739-747.
An improved cumulative distribution function (CDF)-based approach to reduce the systematic biases of multi-satellite precipitation estimates in real time is proposed and verified over Mainland China. Efforts are primarily focused on establishing the bias-adjusting model by adopting the CDF based on a Self-adaptive Moving Window (CSMW), which systematically integrates the China Gauge-based Daily Precipitation Analysis (CGDPA) into the real-time TRMM Multisatellite Precipitation Analysis (TMPA-RT). In our modelling experiments, the first 9-yr (2008–2016) precipitation data pairs were used to calibrate the CSMW model and establish a satellite-gauge relationship, which was then applied to the last 3 years of 2017–2019 as validation. Assessment results during the independent validation period show that the CSMW approach can significantly reduce the systematic positive bias of original TMPA-RT precipitation estimates in that the relative bias (RB) during the validation period decreases from 16.01% before adjustments to −0.29%, and the root-mean-square error (RMSE) also has a dramatic drop of 13%. The error component analysis indicates that the substantial improvement is mainly manifested in the hit events (observed rain was correctly detected by satellite) but it failed to reduce the miss bias (observed rain was not detected by satellite). This arises because a majority of missed precipitation is drizzle and falls below the rain/no-rain discriminant threshold, which is normally excluded from the CSMW algorithm. Additionally, the CSMW approach seems to have significantly improved the TMPA-RT estimates at the medium-high rain rates (>8 mm/day), but it also has a limitation in enhancing the correlation coefficient between satellite retrievals and ground observations. The major advantage of this approach is its applicability when real-time gauge data are not available, which could further facilitate the expansion of satellite-based precipitation estimates for real-time natural hazards forecasting.
Zhehui Shen; Bin Yong; Jonathan J. Gourley; Weiqing Qi. Real-time bias adjustment for satellite-based precipitation estimates over Mainland China. Journal of Hydrology 2021, 596, 126133 .
AMA StyleZhehui Shen, Bin Yong, Jonathan J. Gourley, Weiqing Qi. Real-time bias adjustment for satellite-based precipitation estimates over Mainland China. Journal of Hydrology. 2021; 596 ():126133.
Chicago/Turabian StyleZhehui Shen; Bin Yong; Jonathan J. Gourley; Weiqing Qi. 2021. "Real-time bias adjustment for satellite-based precipitation estimates over Mainland China." Journal of Hydrology 596, no. : 126133.
Precipitation is an essential climate and forcing variable for modeling the global water cycle. Particularly, the Integrated Multi-satellitE Retrievals for GPM (IMERG) product retrospectively provides unprecedented two-decades of high-resolution satellite precipitation estimates (0.1-deg, 30-min) globally. The primary goal of this study is to examine the similarities and differences between the two latest and also arguably most popular GPM IMERG Early and Final Run (ER and FR) products systematically over the globe. The results reveal that: (1) ER systematically estimates 13.0% higher annual rainfall than FR, particularly over land (13.8%); (2) ER and FR show less difference with instantaneous rates (Root Mean Squared Difference: RMSD=2.38 mm/h and normalized RMSD: RMSD_norm=1.10), especially in Europe (RMSD=2.16 mm/h) and cold areas (RMSD_norm=0.87); and (3) with similar detectability of extreme events and timely data delivery, ER is favored for use in hydrometeorological applications, especially in early warning of flooding. Throughout this study, large discrepancies between ER and FR are found in inland water bodies, (semi) arid regions, and complex terrains, possibly owing to morphing differences and gauge corrections while magnified by surface emissivity and precipitation dynamics. The exploration of their similarities and differences provides a first-order global assessment of various hydrological utilities: FR is designed to be more suitable for retrospective hydroclimatology and water resource management, while the earliest available ER product, though not bias-corrected by ground gauges, shows suitable applicability in operational modeling setting for early rainfall-triggered hazard alerts.
Zhi LiiD; Guoqiang Tang; Zhen Hong; Mengye CheniD; Shang GaoiD; Pierre-Emmanuel Kirstetter; Jonathan Gourley; Teshome Yami; Yang Hong. Two-decades of GPM IMERG Early and Final Run Products Intercomparison: Similarity and Difference in Climatology, Rates, Extremes and Hydrologic Utilities. 2020, 1 .
AMA StyleZhi LiiD, Guoqiang Tang, Zhen Hong, Mengye CheniD, Shang GaoiD, Pierre-Emmanuel Kirstetter, Jonathan Gourley, Teshome Yami, Yang Hong. Two-decades of GPM IMERG Early and Final Run Products Intercomparison: Similarity and Difference in Climatology, Rates, Extremes and Hydrologic Utilities. . 2020; ():1.
Chicago/Turabian StyleZhi LiiD; Guoqiang Tang; Zhen Hong; Mengye CheniD; Shang GaoiD; Pierre-Emmanuel Kirstetter; Jonathan Gourley; Teshome Yami; Yang Hong. 2020. "Two-decades of GPM IMERG Early and Final Run Products Intercomparison: Similarity and Difference in Climatology, Rates, Extremes and Hydrologic Utilities." , no. : 1.
The evaluation uncertainty caused by a standard reference itself is harmful to both algorithm developers and data users in substantially understanding the error features and the performance of satellite precipitation products (SPPs). In this study, the Climate Precipitation Center Unified (CPCU) data and the Merged Precipitation Analysis (MPA) data are used as the benchmark to investigate the evaluation uncertainties of satellite precipitation estimates generated by the reference itself. Two SPPs, IMERG-Late and GSMaP-MVK, are employed here. The results show that the approach using two different ground-based precipitation products as the references can effectively reveal the potential evaluation uncertainties. Interestingly, it is found that the evaluation results are prone to resulting in larger uncertainties over semihumid areas. Furthermore, evaluation uncertainty of statistical metrics is closely related to rainfall intensity in that it has a gradually decreasing tendency with increasing rainfall intensities. Additionally, we also found that the dependency of the false alarm ratio (FAR) and root-mean-square error (RMSE) scores on the spatial density of rain gauges is relatively low. Both relative bias (RBIAS) and normalized root-mean-square error (NRMSE) scores for light precipitation (1–5 mm day−1) increase with the spatial density of the rain gauges, suggesting that the evaluation of light precipitation can easily cause uncertainties relative to medium-to-high rain rates. Finally, the minimum gauge density required for different scores and different rainfall intensities is discussed. This study is expected to provide criteria to investigate the reliability of evaluation results for the satellite quantitative precipitation estimation community.
Hanqing Chen; Bin Yong; Weiqing Qi; Hao Wu; Liliang Ren; Yang Hong. Investigating the Evaluation Uncertainty for Satellite Precipitation Estimates Based on Two Different Ground Precipitation Observation Products. Journal of Hydrometeorology 2020, 21, 2595 -2606.
AMA StyleHanqing Chen, Bin Yong, Weiqing Qi, Hao Wu, Liliang Ren, Yang Hong. Investigating the Evaluation Uncertainty for Satellite Precipitation Estimates Based on Two Different Ground Precipitation Observation Products. Journal of Hydrometeorology. 2020; 21 (11):2595-2606.
Chicago/Turabian StyleHanqing Chen; Bin Yong; Weiqing Qi; Hao Wu; Liliang Ren; Yang Hong. 2020. "Investigating the Evaluation Uncertainty for Satellite Precipitation Estimates Based on Two Different Ground Precipitation Observation Products." Journal of Hydrometeorology 21, no. 11: 2595-2606.
The Ensemble Framework For Flash Flood Forecasting (EF5) was developed specifically for improving hydrologic predictions to aid in the issuance of flash flood warnings by the US National Weather Service. EF5 features multiple water balance models and two routing schemes which can be used to generate ensemble forecasts of streamflow, streamflow normalized by upstream basin area (i.e., unit streamflow), and soil saturation. EF5 is designed to utilize high-resolution precipitation forcing datasets now available in real time. A study on flash-flood-scale basins was conducted over the conterminous United States using gauged basins with catchment areas less than 1000 km2. The results of the study show that the three uncalibrated water balance models linked to kinematic wave routing are skillful in simulating streamflow.
Zachary L. Flamig; Humberto Vergara; Jonathan J. Gourley. The Ensemble Framework For Flash Flood Forecasting (EF5) v1.2: description and case study. Geoscientific Model Development 2020, 13, 4943 -4958.
AMA StyleZachary L. Flamig, Humberto Vergara, Jonathan J. Gourley. The Ensemble Framework For Flash Flood Forecasting (EF5) v1.2: description and case study. Geoscientific Model Development. 2020; 13 (10):4943-4958.
Chicago/Turabian StyleZachary L. Flamig; Humberto Vergara; Jonathan J. Gourley. 2020. "The Ensemble Framework For Flash Flood Forecasting (EF5) v1.2: description and case study." Geoscientific Model Development 13, no. 10: 4943-4958.
In the so-called point-mass modeling, surface densities are represented by point masses, providing only an approximated solution of the surface integral for the gravitational potential. Here, we propose a refinement for the point-mass modeling based on Taylor series expansion in which the zeroth-order approximation is equivalent to the point-mass solution. Simulations show that adding higher-order terms neglected in the point-mass modeling reduces the error of inverted mass changes of up to 90% on global and Antarctica scales. The method provides an alternative to the processing of the Level-2 data from the Gravity Recovery and Climate Experiment (GRACE) mission. While the evaluation of the surface densities based on improved point-mass modeling using ITSG-Grace2018 Level-2 data as observations reveals noise level of approximately 5.77 mm, this figure is 5.02, 6.05, and 5.81 mm for Center for Space Research (CSR), Goddard Space Flight Center (GSFC), and Jet Propulsion Laboratory (JPL) mascon solutions, respectively. Statistical tests demonstrate that the four solutions are not significant different (95% confidence) over Antarctica Ice Sheet (AIS), despite the slight differences seen in the noises. Therefore, the estimated noise level for the four solutions indicates the quality of GRACE mass changes over AIS. Overall, AIS shows a mass loss of −7.58 mm/year during 2003–2015 based on the improved point-mass solution, which agrees with the values derived from mascon solutions.
Vagner Ferreira; Bin Yong; Kurt Seitz; Bernhard Heck; Thomas Grombein. Introducing an Improved GRACE Global Point-Mass Solution—A Case Study in Antarctica. Remote Sensing 2020, 12, 3197 .
AMA StyleVagner Ferreira, Bin Yong, Kurt Seitz, Bernhard Heck, Thomas Grombein. Introducing an Improved GRACE Global Point-Mass Solution—A Case Study in Antarctica. Remote Sensing. 2020; 12 (19):3197.
Chicago/Turabian StyleVagner Ferreira; Bin Yong; Kurt Seitz; Bernhard Heck; Thomas Grombein. 2020. "Introducing an Improved GRACE Global Point-Mass Solution—A Case Study in Antarctica." Remote Sensing 12, no. 19: 3197.
Revealing the error components for satellite-only precipitation products (SPPs) can help algorithm developers and end-users substantially understand their error features and meanwhile is fundamental to customize retrieval algorithms and error adjustment models. Two error decomposition schemes were employed to explore the error components for five SPPs (i.e., MERG-Late, IMERG-Early, GSMaP-MVK, GSMaP-NRT, and PERSIANN-CCS) over different seasons, rainfall intensities, and topography classes. Firstly, this study depicted global maps of the total bias (total mean squared error) and its three (two) independent components for these five SPPs over four seasons for the first time. We found that the evaluation results between similar regions could not be extended to one another. Hit and/or false biases are major components of the total bias in most regions of the global land areas. In addition, the proportions of the systematic error are less than 20 % of total errors in most areas. One should note that each SPP has larger systematic errors in several regions (i.e., Russia, China, and Conterminous United States) for all four seasons, these larger systematic errors from retrieval algorithms are primarily due to the missed precipitation. Furthermore, IMERG suite and GSMaP-NRT display less systematic error in the rain rates with intensity less than 40 mm/day, while the systematic errors of GSMaP-MVK and PERSIANN-CCS increase with increasing rainfall intensity. Given that mean elevation cannot reflect the complex degree of terrain, we introduced the standard deviation of elevation (SDE) to replace mean elevation to better describe topographic complexity. Compared with other SPPs, GSMaP suite shows a stronger topographic dependency in the four bias scores. A novel metric namely normalized error component (NEC) was proposed to fairly evaluate the impact of the solely topographic factor on systematic (random) error. It is found that these products show different topographic dependency patterns in systematic (random) error. Meanwhile, the pattern of the impact of the solely topographic factor on systematic (random) error is similar to the relationship between systematic (random) error and topography because the average precipitations of all topography categories are very close. Finally, the potential directions of the improvement in satellite precipitation retrieval algorithms and error adjustment models were identified in this study.
Hanqing Chen; Bin Yong; Leyang Wang; Liliang Ren; Yang Hong. Global component analysis of errors in five satellite-only global precipitation estimates. 2020, 2020, 1 -50.
AMA StyleHanqing Chen, Bin Yong, Leyang Wang, Liliang Ren, Yang Hong. Global component analysis of errors in five satellite-only global precipitation estimates. . 2020; 2020 ():1-50.
Chicago/Turabian StyleHanqing Chen; Bin Yong; Leyang Wang; Liliang Ren; Yang Hong. 2020. "Global component analysis of errors in five satellite-only global precipitation estimates." 2020, no. : 1-50.
Hanqing Chen; Bin Yong; Leyang Wang; Liliang Ren; Yang Hong. Supplementary material to "Global component analysis of errors in five satellite-only global precipitation estimates". 2020, 1 .
AMA StyleHanqing Chen, Bin Yong, Leyang Wang, Liliang Ren, Yang Hong. Supplementary material to "Global component analysis of errors in five satellite-only global precipitation estimates". . 2020; ():1.
Chicago/Turabian StyleHanqing Chen; Bin Yong; Leyang Wang; Liliang Ren; Yang Hong. 2020. "Supplementary material to "Global component analysis of errors in five satellite-only global precipitation estimates"." , no. : 1.
The Climate Hazards group Infrared Precipitation (CHIRP) and with Stations (CHIRPS) datasets are two new quasi-global (50oS-50oN), high-resolution (0.05°×0.05°), long-term (1981-present) precipitation estimates based on infrared Cold Cloud Duration (CCD) observations. This study investigates, for the first time, the global performance of CHIRP and CHIRPS against the gauge-based GPCC (Global Precipitation Climatology Centre) data at monthly scale using 36 complete years of data record (1981-2016). Global assessment results indicate that both CHIRP and CHIRPS have negative biases (-5.93% for CHIRP and -2.01% for CHIRPS) before 2000, while this systematic underestimation was effectively removed after 2000. Global analyses also show that the gauge-adjusted CHIRPS estimates generally represent a substantial improvement over CHIRP due to gauge-based bias correction. With respect to regional statistics, temporal analysis and intensity distribution, the gauge-adjusted CHIRPS estimates agree well with GPCC and outperforms CHIRP over most regions, such as the United States, Europe, Africa, Australia and South America. However, southeast China is an exception. Over this region, CHIRPS has a systematic overestimation of 5.55% against GPCC during 2000-2016, especially for spring and summer months, while such positive biases were not found for the pure satellite-derived CHIRP. Possible causes for the discrepancy between these two satellite products over the global and regional scales were further discussed and analyzed. The results reported here will both provide the algorithm developers of CHIRP and CHIRPS with some valuable information and offer the hydrometeorological users a better understanding of their error characteristics and potential limits for various hydrological applications from the global perspective.
Zhehui Shen; Bin Yong; Jonathan J. Gourley; Weiqing Qi; Dekai Lu; Jiufu Liu; Liliang Ren; Yang Hong; Jianyun Zhang. Recent global performance of the Climate Hazards group Infrared Precipitation (CHIRP) with Stations (CHIRPS). Journal of Hydrology 2020, 591, 125284 .
AMA StyleZhehui Shen, Bin Yong, Jonathan J. Gourley, Weiqing Qi, Dekai Lu, Jiufu Liu, Liliang Ren, Yang Hong, Jianyun Zhang. Recent global performance of the Climate Hazards group Infrared Precipitation (CHIRP) with Stations (CHIRPS). Journal of Hydrology. 2020; 591 ():125284.
Chicago/Turabian StyleZhehui Shen; Bin Yong; Jonathan J. Gourley; Weiqing Qi; Dekai Lu; Jiufu Liu; Liliang Ren; Yang Hong; Jianyun Zhang. 2020. "Recent global performance of the Climate Hazards group Infrared Precipitation (CHIRP) with Stations (CHIRPS)." Journal of Hydrology 591, no. : 125284.
Spectral similarity measures can be regarded as potential metrics for kernel functions, and can be used to generate spectral-similarity-based kernels. However, spectral-similarity-based kernels have not received significant attention from researchers. In this paper, we propose two novel spectral-similarity-based kernels based on spectral angle mapper (SAM) and spectral information divergence (SID) combined with the radial basis function (RBF) kernel: Power spectral angle mapper RBF (Power-SAM-RBF) and normalized spectral information divergence-based RBF (Normalized-SID-RBF) kernels. First, we prove these spectral-similarity-based kernels to be Mercer’s kernels. Second, we analyze their efficiency in terms of local and global kernels. Finally, we consider three hyperspectral datasets to analyze the effectiveness of the proposed spectral-similarity-based kernels. Experimental results demonstrate that the Power-SAM-RBF and SAM-RBF kernels can obtain an impressive performance, particularly the Power-SAM-RBF kernel. For example, when the ratio of the training set is 20 % , the kappa coefficient of Power-SAM-RBF kernel (0.8561) is 1.61 % , 1.32 % , and 1.23 % higher than that of the RBF kernel on the Indian Pines, University of Pavia, and Salinas Valley datasets, respectively. We present three conclusions. First, the superiority of the Power-SAM-RBF kernel compared to other kernels is evident. Second, the Power-SAM-RBF kernel can provide an outstanding performance when the similarity between spectral signatures in the same hyperspectral dataset is either extremely high or extremely low. Third, the Power-SAM-RBF kernel provides even greater benefits compared to other commonly used kernels when the sizes of the training sets increase. In future work, multiple kernels combining with the spectral-similarity-based kernel are expected to be provide better hyperspectral classification.
Ke Wang; Ligang Cheng; Bin Yong. Spectral-Similarity-Based Kernel of SVM for Hyperspectral Image Classification. Remote Sensing 2020, 12, 2154 .
AMA StyleKe Wang, Ligang Cheng, Bin Yong. Spectral-Similarity-Based Kernel of SVM for Hyperspectral Image Classification. Remote Sensing. 2020; 12 (13):2154.
Chicago/Turabian StyleKe Wang; Ligang Cheng; Bin Yong. 2020. "Spectral-Similarity-Based Kernel of SVM for Hyperspectral Image Classification." Remote Sensing 12, no. 13: 2154.
Jonathan J. Gourley; Humberto Vergara; Ami Arthur; Robert A. Clark; Dennis Staley; John Fulton; Laura Hempel; David C. Goodrich; Katherine Rowden; Peter R. Robichaud. Predicting the Floods that Follow the Flames. Bulletin of the American Meteorological Society 2020, 101, E1101 -E1106.
AMA StyleJonathan J. Gourley, Humberto Vergara, Ami Arthur, Robert A. Clark, Dennis Staley, John Fulton, Laura Hempel, David C. Goodrich, Katherine Rowden, Peter R. Robichaud. Predicting the Floods that Follow the Flames. Bulletin of the American Meteorological Society. 2020; 101 (7):E1101-E1106.
Chicago/Turabian StyleJonathan J. Gourley; Humberto Vergara; Ami Arthur; Robert A. Clark; Dennis Staley; John Fulton; Laura Hempel; David C. Goodrich; Katherine Rowden; Peter R. Robichaud. 2020. "Predicting the Floods that Follow the Flames." Bulletin of the American Meteorological Society 101, no. 7: E1101-E1106.
The Qiangtang Basin is a large endorheic basin as the inner part of the Tibetan Plateau and has been thought to be a dry region in contrast with its wet surrounding outer region that feeds all the major Asian rivers. Combing surface hydrological data with modelling and satellite data between 2002 and 2017, our study reveals that an enormous amount of water of 54.52±15.36 km is unaccounted for annually in the Qiangtang Basin. The amount of this missing water is comparable to the total annual discharge of the Yellow River. Data from the Gravity Recovery and Climate Experiment (GRACE) show no increase of the local terrestrial water storage. Thus the missing water must have flowed out of the basin through underground passages. Interpreting this result with recent seismic and geological studies of Tibet, we suggest that a significant amount of meteoric water in the Qiangtang Basin have leaked out by way of groundwater flow through deep normal faults and tensional fractures along the nearly N-S rift valleys that are oriented sub-normal to and cross the surficial hydrological divide on the southern margin of the basin. Cross-basin groundwater outflow of such a magnitude defies the traditional view of basin-scale water cycle and leads to a very different picture from the previous hydrological view of the Qiangtang Basin. The finding calls for major rethinking of the water balance in Tibet and the nearby regions.
Bin Yong; Chi-Yuen Wang; Jiansheng Chen; Jiaqi Chen; Tao Wang; Ling Li; D. Andrew Barry. Missing water from the Qiantang Basin on the Tibetan Plateau. 2020, 1 .
AMA StyleBin Yong, Chi-Yuen Wang, Jiansheng Chen, Jiaqi Chen, Tao Wang, Ling Li, D. Andrew Barry. Missing water from the Qiantang Basin on the Tibetan Plateau. . 2020; ():1.
Chicago/Turabian StyleBin Yong; Chi-Yuen Wang; Jiansheng Chen; Jiaqi Chen; Tao Wang; Ling Li; D. Andrew Barry. 2020. "Missing water from the Qiantang Basin on the Tibetan Plateau." , no. : 1.
Extreme precipitation is one of the most devastating forms of atmospheric phenomenon, causing severe damages worldwide and is likely to intensify in strength and occurrence in a warming climate. This contribution gives an overview of the potential and challenges associated with using weather radar data to investigate extreme precipitation. We illustrate this by presenting radar data sets for Germany, the U.S. and the UK that resolve small-scale heavy rainfall events of just a few km² with return periods of 5 years or more. Current challenges such as relatively short radar records and radar-based QPE uncertainty are discussed. An example from a precipitation climatology derived from the German weather radar network with spatial resolution of 1 km reveals the necessity of radars for observing short-term (1-6 hours) extreme precipitation. Only 17.3% of hourly heavy precipitation events that occurred in Germany from 2001 to 2018 were captured by the rain gauge station network, while 81.8% of daily events were observed. This is underlined by a similar study using data from the UK radar network for 2014. Only 36.6% (52%) of heavy hourly (daily) rain events detected by the radar network were also captured by precipitation gauging stations. Implications for the monitoring of hydrologic extremes are demonstrated over the U.S. with a continental-scale radar-based reanalysis. Hydrologic extremes are documented over ~1000 times more locations than stream gauges, including in the majority of ungauged basins. This underlines the importance of high-resolution weather radar observations for resolving small-scale rainfall events, and the necessity of radar-based climatological data sets for understanding the small-scale and high-temporal resolution characteristics of extreme precipitation.
Katharina Lengfeld; Pierre-Emmanuel Kirstetter; Hayley J Fowler; Jingjing Yu; Andreas Becker; Zachary Flamig; Jonathan J. Gourley. Use of radar data for characterizing extreme precipitation at fine scales and short durations. Environmental Research Letters 2020, 15, 085003 .
AMA StyleKatharina Lengfeld, Pierre-Emmanuel Kirstetter, Hayley J Fowler, Jingjing Yu, Andreas Becker, Zachary Flamig, Jonathan J. Gourley. Use of radar data for characterizing extreme precipitation at fine scales and short durations. Environmental Research Letters. 2020; 15 (8):085003.
Chicago/Turabian StyleKatharina Lengfeld; Pierre-Emmanuel Kirstetter; Hayley J Fowler; Jingjing Yu; Andreas Becker; Zachary Flamig; Jonathan J. Gourley. 2020. "Use of radar data for characterizing extreme precipitation at fine scales and short durations." Environmental Research Letters 15, no. 8: 085003.
Near-field remote sensing of surface velocity and river discharge (discharge) were measured using coherent, continuous wave Doppler and pulsed radars. Traditional streamgaging requires sensors be deployed in the water column; however, near-field remote sensing has the potential to transform streamgaging operations through non-contact methods in the U.S. Geological Survey (USGS) and other agencies around the world. To differentiate from satellite or high-altitude platforms, near-field remote sensing is conducted from fixed platforms such as bridges and cable stays. Radar gages were collocated with 10 USGS streamgages in river reaches of varying hydrologic and hydraulic characteristics, where basin size ranged from 381 to 66,200 square kilometers. Radar-derived mean-channel (mean) velocity and discharge were computed using the probability concept and were compared to conventional instantaneous measurements and time series. To test the efficacy of near-field methods, radars were deployed for extended periods of time to capture a range of hydraulic conditions and environmental factors. During the operational phase, continuous time series of surface velocity, radar-derived discharge, and stage-discharge were recorded, computed, and transmitted contemporaneously and continuously in real time every 5 to 15 min. Minimum and maximum surface velocities ranged from 0.30 to 3.84 m per second (m/s); minimum and maximum radar-derived discharges ranged from 0.17 to 4890 cubic meters per second (m3/s); and minimum and maximum stage-discharge ranged from 0.12 to 4950 m3/s. Comparisons between radar and stage-discharge time series were evaluated using goodness-of-fit statistics, which provided a measure of the utility of the probability concept to compute discharge from a singular surface velocity and cross-sectional area relative to conventional methods. Mean velocity and discharge data indicate that velocity radars are highly correlated with conventional methods and are a viable near-field remote sensing technology that can be operationalized to deliver real-time surface velocity, mean velocity, and discharge.
John W. Fulton; Christopher A. Mason; John R. Eggleston; Matthew J. Nicotra; Chao-Lin Chiu; Mark F. Henneberg; Heather R. Best; Jay R. Cederberg; Stephen R. Holnbeck; R. Russell Lotspeich; Christopher D. Laveau; Tommaso Moramarco; Mark E. Jones; Jonathan J. Gourley; Daniel Wasielewski. Near-Field Remote Sensing of Surface Velocity and River Discharge Using Radars and the Probability Concept at 10 U.S. Geological Survey Streamgages. Remote Sensing 2020, 12, 1296 .
AMA StyleJohn W. Fulton, Christopher A. Mason, John R. Eggleston, Matthew J. Nicotra, Chao-Lin Chiu, Mark F. Henneberg, Heather R. Best, Jay R. Cederberg, Stephen R. Holnbeck, R. Russell Lotspeich, Christopher D. Laveau, Tommaso Moramarco, Mark E. Jones, Jonathan J. Gourley, Daniel Wasielewski. Near-Field Remote Sensing of Surface Velocity and River Discharge Using Radars and the Probability Concept at 10 U.S. Geological Survey Streamgages. Remote Sensing. 2020; 12 (8):1296.
Chicago/Turabian StyleJohn W. Fulton; Christopher A. Mason; John R. Eggleston; Matthew J. Nicotra; Chao-Lin Chiu; Mark F. Henneberg; Heather R. Best; Jay R. Cederberg; Stephen R. Holnbeck; R. Russell Lotspeich; Christopher D. Laveau; Tommaso Moramarco; Mark E. Jones; Jonathan J. Gourley; Daniel Wasielewski. 2020. "Near-Field Remote Sensing of Surface Velocity and River Discharge Using Radars and the Probability Concept at 10 U.S. Geological Survey Streamgages." Remote Sensing 12, no. 8: 1296.
Quantifying uncertainties of precipitation estimation, especially in extreme events, could benefit early warning of water-related hazards like flash floods and landslides. Rain gauges, weather radars, and satellites are three mainstream data sources used in measuring precipitation but have their own inherent advantages and deficiencies. With a focus on extremes, the overarching goal of this study is to cross-examine the similarities and differences of three state-of-the-art independent products (Muti-Radar Muti-Sensor Quantitative Precipitation Estimates, MRMS; National Center for Environmental Prediction gridded gauge-only hourly precipitation product, NCEP; Integrated Multi-satellitE Retrievals for GPM, IMERG), with both traditional metrics and the Multiplicative Triple Collection (MTC) method during Hurricane Harvey and multiple Tropical Cyclones. The results reveal that: (a) the consistency of cross-examination results against traditional metrics approves the applicability of MTC in extreme events; (b) the consistency of cross-events of MTC evaluation results also suggests its robustness across individual storms; (c) all products demonstrate their capacity of capturing the spatial and temporal variability of the storm structures while also magnifying respective inherent deficiencies; (d) NCEP and IMERG likely underestimate while MRMS overestimates the storm total accumulation, especially for the 500-year return Hurricane Harvey; (e) both NCEP and IMERG underestimate extreme rainrates (>= 90 mm/h) likely due to device insensitivity or saturation while MRMS maintains robust across the rainrate range; (g) all three show inherent deficiencies in capturing the storm core of Harvey possibly due to device malfunctions with the NCEP gauges, relative low spatiotemporal resolution of IMERG, and the unusual “hot” MRMS radar signals. Given the unknown ground reference assumption of MTC, this study suggests that MRMS has the best overall performance. The similarities, differences, advantages, and deficiencies revealed in this study could guide the users for emergency response and motivate the community not only to improve the respective sensor/algorithm but also innovate multidata merging methods for one best possible product, specifically suitable for extreme storm events.
Zhi Li; Mengye Chen; Shang Gao; Zhen Hong; Guoqiang Tang; Yixin Wen; Jonathan J. Gourley; Yang Hong. Cross-Examination of Similarity, Difference and Deficiency of Gauge, Radar and Satellite Precipitation Measuring Uncertainties for Extreme Events Using Conventional Metrics and Multiplicative Triple Collocation. Remote Sensing 2020, 12, 1258 .
AMA StyleZhi Li, Mengye Chen, Shang Gao, Zhen Hong, Guoqiang Tang, Yixin Wen, Jonathan J. Gourley, Yang Hong. Cross-Examination of Similarity, Difference and Deficiency of Gauge, Radar and Satellite Precipitation Measuring Uncertainties for Extreme Events Using Conventional Metrics and Multiplicative Triple Collocation. Remote Sensing. 2020; 12 (8):1258.
Chicago/Turabian StyleZhi Li; Mengye Chen; Shang Gao; Zhen Hong; Guoqiang Tang; Yixin Wen; Jonathan J. Gourley; Yang Hong. 2020. "Cross-Examination of Similarity, Difference and Deficiency of Gauge, Radar and Satellite Precipitation Measuring Uncertainties for Extreme Events Using Conventional Metrics and Multiplicative Triple Collocation." Remote Sensing 12, no. 8: 1258.