scholarly journals Error Analysis of Satellite Precipitation Products in Mountainous Basins

2014 ◽  
Vol 15 (5) ◽  
pp. 1778-1793 ◽  
Author(s):  
Yiwen Mei ◽  
Emmanouil N. Anagnostou ◽  
Efthymios I. Nikolopoulos ◽  
Marco Borga
Keyword(s):  
Error Analysis ◽  
Warm Season ◽  
Tropical Rainfall Measuring Mission ◽  
Heavy Precipitation ◽  
Rain Gauge ◽  
Satellite Precipitation ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Regimes ◽  
Precipitation Estimation ◽  
High Precipitation

Abstract Accurate quantitative precipitation estimation over mountainous basins is of great importance because of their susceptibility to hazards such as flash floods, shallow landslides, and debris flows, triggered by heavy precipitation events (HPEs). In situ observations over mountainous areas are limited, but currently available satellite precipitation products can potentially provide the precipitation estimation needed for hydrological applications. In this study, four widely used satellite-based precipitation products [Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42, version 7 (3B42-V7), and in near–real time (3B42-RT); Climate Prediction Center (CPC) morphing technique (CMORPH); and Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks (PERSIANN)] are evaluated with respect to their performance in capturing the properties of HPEs over different basin scales. Evaluation is carried out over the upper Adige River basin (eastern Italian Alps) for an 8-yr period (2003–10). Basin-averaged rainfall derived from a dense rain gauge network in the region is used as a reference. Satellite precipitation error analysis is performed for warm (May–August) and cold (September–December) season months as well as for different quantile ranges of basin-averaged precipitation accumulations. Three error metrics and a score system are introduced to quantify the performances of the various satellite products. Overall, no single precipitation product can be considered ideal for detecting and quantifying HPE. Results show better consistency between gauges and the two 3B42 products, particularly during warm season months that are associated with high-intensity convective events. All satellite products are shown to have a magnitude-dependent error ranging from overestimation at low precipitation regimes to underestimation at high precipitation accumulations; this effect is more pronounced in the CMORPH and PERSIANN products.

Evaluation of High-Resolution Satellite Precipitation Products over Very Complex Terrain in Ethiopia

2010 ◽  
Vol 49 (5) ◽  
pp. 1044-1051 ◽  
Author(s):  
Feyera A. Hirpa ◽  
Mekonnen Gebremichael ◽  
Thomas Hopson
Keyword(s):  
Neural Networks ◽  
Complex Terrain ◽  
Tropical Rainfall Measuring Mission ◽  
High Elevation ◽  
Rain Gauge ◽  
Satellite Precipitation ◽  
Precipitation Estimation ◽  
Precipitation Analysis ◽  
Climate Prediction Center ◽  
Microwave Data

Abstract This study focuses on the evaluation of 3-hourly, 0.25° × 0.25°, satellite-based precipitation products: the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42RT, the NOAA/Climate Prediction Center morphing technique (CMORPH), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN). CMORPH is primarily microwave based, 3B42RT is primarily microwave based when microwave data are available and infrared based when microwave data are not available, and PERSIANN is primarily infrared based. The results show that 1) 3B42RT and CMORPH give similar rainfall fields (in terms of bias, spatial structure, elevation-dependent trend, and distribution function), which are different from PERSIANN rainfall fields; 2) PERSIANN does not show the elevation-dependent trend observed in rain gauge values, 3B42RT, and CMORPH; and 3) PERSIANN considerably underestimates rainfall in high-elevation areas.

scholarly journals Evaluation of Three Satellite Precipitation Products TRMM 3B42, CMORPH, and PERSIANN over a Subtropical Watershed in China

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Junzhi Liu ◽  
Zheng Duan ◽  
Jingchao Jiang ◽  
A-Xing Zhu
Keyword(s):  
Comprehensive Evaluation ◽  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Lake Basin ◽  
Satellite Precipitation ◽  
Precipitation Estimation ◽  
Daily Scale ◽  
Using Data ◽  
Hydrological Applications ◽  
Trmm 3B42

This study conducted a comprehensive evaluation of three satellite precipitation products (TRMM (Tropical Rainfall Measuring Mission) 3B42, CMORPH (the Climate Prediction Center (CPC) Morphing algorithm), and PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks)) using data from 52 rain gauge stations over the Meichuan watershed, which is a representative watershed of the Poyang Lake Basin in China. All the three products were compared and evaluated during a 9-year period at different spatial (grid and watershed) and temporal (daily, monthly, and annual) scales. The results showed that at daily scale, CMORPH had the best performance with coefficients of determination (R2) of 0.61 at grid scale and 0.74 at watershed scale. For precipitation intensities larger than or equal to 25 mm, RMSE% of CMORPH and TRMM 3B42 were less than 50%, indicating CMORPH and TRMM 3B42 might be useful for hydrological applications at daily scale. At monthly and annual temporal scales, TRMM 3B42 had the best performances, with highR2ranging from 0.93 to 0.99, and thus was deemed to be reliable and had good potential for hydrological applications at monthly and annual scales. PERSIANN had the worst performance among the three products at all cases.

scholarly journals Evaluation of high-resolution satellite precipitation products using rain gauge observations over the Tibetan Plateau

2012 ◽  
Vol 9 (8) ◽  
pp. 9503-9532 ◽  
Author(s):  
Y. C. Gao ◽  
M. F. Liu
Keyword(s):  
High Resolution ◽  
Tibetan Plateau ◽  
Correlation Coefficients ◽  
Tropical Rainfall Measuring Mission ◽  
Global Precipitation Climatology Project ◽  
Rain Gauge ◽  
The Tibetan Plateau ◽  
Satellite Precipitation ◽  
Precipitation Estimation ◽  
Mean Square Errors

Abstract. High-resolution satellite precipitation products are very attractive for studying the hydrologic processes in mountainous areas where rain gauges are generally sparse. Three high-resolution satellite precipitation products are evaluated using gauge measurements over different climate zones of the Tibetan Plateau (TP) within a 6 yr period from 2004 to 2009. The three satellite-based precipitation datasets are: Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA), Climate Prediction Center Morphing Technique (CMOPRH) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network (PERSIANN). TMPA and CMORPH, with higher correlation coefficients and lower root mean square errors (RMSEs), show overall better performance than PERSIANN. TMPA has the lowest biases among the three precipitation datasets, which is likely due to the correction process against monthly gauge observations from global precipitation climatology project (GPCP). The three products show better agreement with gauge measurements over humid regions than that over arid regions where correlation coefficients are less than 0.5. Moreover, the three precipitation products generally tend to overestimate light rainfall (0–10 mm) and underestimate moderate and heavy rainfall (>10 mm). PERSIANN produces obvious underestimation at low elevations and overestimation at high elevations. CMORPH and TMPA do not present strong bias-elevation relationships in most regions of TP.

scholarly journals Improving Satellite Quantitative Precipitation Estimation Using GOES-Retrieved Cloud Optical Depth

2016 ◽  
Vol 17 (2) ◽  
pp. 557-570 ◽  
Author(s):  
Ronald Stenz ◽  
Xiquan Dong ◽  
Baike Xi ◽  
Zhe Feng ◽  
Robert J. Kuligowski
Keyword(s):  
Optical Depth ◽  
Temporal Resolution ◽  
Heavy Precipitation ◽  
The United States ◽  
Rain Gauge ◽  
Geostationary Satellite ◽  
Quantitative Precipitation Estimation ◽  
Spatial Coverage ◽  
Skill Scores ◽  
Precipitation Estimation

Abstract To address gaps in ground-based radar coverage and rain gauge networks in the United States, geostationary satellite quantitative precipitation estimation (QPE) such as the Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR) can be used to fill in both spatial and temporal gaps of ground-based measurements. Additionally, with the launch of Geostationary Operational Environmental Satellite R series (GOES-R), the temporal resolution of satellite QPEs may be comparable to Weather Surveillance Radar-1988 Doppler (WSR-88D) volume scans as GOES images will be available every 5 min. However, while satellite QPEs have strengths in spatial coverage and temporal resolution, they face limitations, particularly during convective events. Deep convective systems (DCSs) have large cloud shields with similar brightness temperatures (BTs) over nearly the entire system, but widely varying precipitation rates beneath these clouds. Geostationary satellite QPEs relying on the indirect relationship between BTs and precipitation rates often suffer from large errors because anvil regions (little or no precipitation) cannot be distinguished from rain cores (heavy precipitation) using only BTs. However, a combination of BTs and optical depth τ has been found to reduce overestimates of precipitation in anvil regions. A new rain mask algorithm incorporating both τ and BTs has been developed, and its application to the existing SCaMPR algorithm was evaluated. The performance of the modified SCaMPR was evaluated using traditional skill scores and a more detailed analysis of performance in individual DCS components by utilizing the Feng et al. classification algorithm. SCaMPR estimates with the new rain mask benefited from significantly reduced overestimates of precipitation in anvil regions and overall improvements in skill scores.

scholarly journals A Prototype Quantitative Precipitation Estimation Algorithm for Operational S-Band Polarimetric Radar Utilizing Specific Attenuation and Specific Differential Phase. Part II: Performance Verification and Case Study Analysis

2019 ◽  
Vol 20 (5) ◽  
pp. 999-1014 ◽  
Author(s):  
Stephen B. Cocks ◽  
Lin Tang ◽  
Pengfei Zhang ◽  
Alexander Ryzhkov ◽  
Brian Kaney ◽  
...  
Keyword(s):  
Real Time ◽  
Heavy Precipitation ◽  
Estimation Algorithm ◽  
Differential Phase ◽  
Specific Attenuation ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Bias Ratio ◽  
Using Data ◽  
Precipitation Events

Abstract The quantitative precipitation estimate (QPE) algorithm developed and described in Part I was validated using data collected from 33 Weather Surveillance Radar 1988-Doppler (WSR-88D) radars on 37 calendar days east of the Rocky Mountains. A key physical parameter to the algorithm is the parameter alpha α, defined as the ratio of specific attenuation A to specific differential phase KDP. Examination of a significant sample of tropical and continental precipitation events indicated that α was sensitive to changes in drop size distribution and exhibited lower (higher) values when there were lower (higher) concentrations of larger (smaller) rain drops. As part of the performance assessment, the prototype algorithm generated QPEs utilizing a real-time estimated and a fixed α were created and evaluated. The results clearly indicated ~26% lower errors and a 26% better bias ratio with the QPE utilizing a real-time estimated α as opposed to using a fixed value as was done in previous studies. Comparisons between the QPE utilizing a real-time estimated α and the operational dual-polarization (dual-pol) QPE used on the WSR-88D radar network showed the former exhibited ~22% lower errors, 7% less bias, and 5% higher correlation coefficient when compared to quality controlled gauge totals. The new QPE also provided much better estimates for moderate to heavy precipitation events and performed better in regions of partial beam blockage than the operational dual-pol QPE.

scholarly journals Quantitative Precipitation Estimation by Combining Rain gauge and Meteorological Radar Network in Viet Nam

2020 ◽  
Vol 5 (5) ◽  
pp. 36-50
Author(s):  
Chiho Kimpara ◽  
Michihiko Tonouchi ◽  
Bui Thi Khanh Hoa ◽  
Nguyen Viet Hung ◽  
Nguyen Minh Cuong ◽  
...  
Keyword(s):  
Rain Gauge ◽  
Viet Nam ◽  
Quantitative Precipitation Estimation ◽  
Radar Network ◽  
Precipitation Estimation ◽  
Meteorological Radar

scholarly journals Impact of the Altitudinal Gradients of Precipitation on the Radar QPE Bias in the French Alps

Atmosphere ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 306 ◽  
Author(s):  
Dominique Faure ◽  
Guy Delrieu ◽  
Nicolas Gaussiat
Keyword(s):  
Ground Level ◽  
Radar Data ◽  
Rain Gauge ◽  
Altitudinal Gradients ◽  
French Alps ◽  
Rain Gauges ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Radar Data Processing

In the French Alps the quality of the radar Quantitative Precipitation Estimation (QPE) is limited by the topography and the vertical structure of precipitation. A previous study realized in all the French Alps, has shown a general bias between values of the national radar QPE composite and the rain gauge measurements: a radar QPE over-estimation at low altitude (+20% at 200 m a.s.l.), and an increasing underestimation at high altitudes (until −40% at 2100 m a.s.l.). This trend has been linked to altitudinal gradients of precipitation observed at ground level. This paper analyzes relative altitudinal gradients of precipitation estimated with rain gauges measurements in 2016 for three massifs around Grenoble, and for different temporal accumulations (yearly, seasonal, monthly, daily). Comparisons of radar and rain gauge accumulations confirm the bias previously observed. The parts of the current radar data processing affecting the bias value are pointed out. The analysis shows a coherency between the relative gradient values estimated at the different temporal accumulations. Vertical profiles of precipitation detected by a research radar installed at the bottom of the valley also show how the wide horizontal variability of precipitation inside the valley can affect the gradient estimation.

scholarly journals Evaluation of global fine-resolution precipitation products and their uncertainty quantification in ensemble discharge simulations

2016 ◽  
Vol 20 (2) ◽  
pp. 903-920 ◽  
Author(s):  
W. Qi ◽  
C. Zhang ◽  
G. Fu ◽  
C. Sweetapple ◽  
H. Zhou
Keyword(s):  
Hydrological Model ◽  
Tropical Rainfall Measuring Mission ◽  
Heavy Precipitation ◽  
Hydrological Models ◽  
Distributed Hydrological Model ◽  
Precipitation Product ◽  
Physically Based ◽  
Precipitation Estimation ◽  
Discharge Simulation ◽  
Fine Resolution

Abstract. The applicability of six fine-resolution precipitation products, including precipitation radar, infrared, microwave and gauge-based products, using different precipitation computation recipes, is evaluated using statistical and hydrological methods in northeastern China. In addition, a framework quantifying uncertainty contributions of precipitation products, hydrological models, and their interactions to uncertainties in ensemble discharges is proposed. The investigated precipitation products are Tropical Rainfall Measuring Mission (TRMM) products (TRMM3B42 and TRMM3B42RT), Global Land Data Assimilation System (GLDAS)/Noah, Asian Precipitation – Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and a Global Satellite Mapping of Precipitation (GSMAP-MVK+) product. Two hydrological models of different complexities, i.e. a water and energy budget-based distributed hydrological model and a physically based semi-distributed hydrological model, are employed to investigate the influence of hydrological models on simulated discharges. Results show APHRODITE has high accuracy at a monthly scale compared with other products, and GSMAP-MVK+ shows huge advantage and is better than TRMM3B42 in relative bias (RB), Nash–Sutcliffe coefficient of efficiency (NSE), root mean square error (RMSE), correlation coefficient (CC), false alarm ratio, and critical success index. These findings could be very useful for validation, refinement, and future development of satellite-based products (e.g. NASA Global Precipitation Measurement). Although large uncertainty exists in heavy precipitation, hydrological models contribute most of the uncertainty in extreme discharges. Interactions between precipitation products and hydrological models can have the similar magnitude of contribution to discharge uncertainty as the hydrological models. A better precipitation product does not guarantee a better discharge simulation because of interactions. It is also found that a good discharge simulation depends on a good coalition of a hydrological model and a precipitation product, suggesting that, although the satellite-based precipitation products are not as accurate as the gauge-based products, they could have better performance in discharge simulations when appropriately combined with hydrological models. This information is revealed for the first time and very beneficial for precipitation product applications.

scholarly journals An Integrated Method of Multiradar Quantitative Precipitation Estimation Based on Cloud Classification and Dynamic Error Analysis

2017 ◽  
Vol 2017 ◽  
pp. 1-6
Author(s):  
Yong Huang ◽  
Huijuan Liu ◽  
Yun Yao ◽  
Ting Ni ◽  
Yan Feng
Keyword(s):  
Kalman Filter ◽  
Error Analysis ◽  
Dynamic Error ◽  
Cloud Systems ◽  
Integrated Method ◽  
Optimum Interpolation ◽  
Quantitative Precipitation Estimation ◽  
Stratiform Clouds ◽  
Precipitation Estimation ◽  
The Huaihe River

Relationships between radar reflectivity factor and rainfall are different in various precipitation cloud systems. In this study, the cloud systems are firstly classified into five categories with radar and satellite data to improve radar quantitative precipitation estimation (QPE) algorithm. Secondly, the errors of multiradar QPE algorithms are assumed to be different in convective and stratiform clouds. The QPE data are then derived with methods of Z-R, Kalman filter (KF), optimum interpolation (OI), Kalman filter plus optimum interpolation (KFOI), and average calibration (AC) based on error analysis on the Huaihe River Basin. In the case of flood on the early of July 2007, the KFOI is applied to obtain the QPE product. Applications show that the KFOI can improve precision of estimating precipitation for multiple precipitation types.

Sign in / Sign up

Export Citation Format

Share Document