Performance and Evaluation of Multisensor Precipitation Estimation Algorithm Using a High Density Rain Gauge Network and Hydrologic Simulation

2010 ◽  
Author(s):  
Alejandra M Rojas González ◽  
Eric W Harmsen ◽  
Sandra Cruz Pol
Keyword(s):  
Estimation Algorithm ◽  
Rain Gauge ◽  
High Density ◽  
Hydrologic Simulation ◽  
Precipitation Estimation ◽  
Performance And Evaluation ◽  
Rain Gauge Network

Validation of PERSIANN Precipitation Product Using TAWN Rain Gauge Network Over Different Agro-climatic Zones in Tamil Nadu

2021 ◽  
Vol 108 (september) ◽  
pp. 1-6
Author(s):  
Venkadesh Samykannu ◽  
◽  
Pazhanivelan S ◽  
Keyword(s):  
High Altitude ◽  
Tamil Nadu ◽  
Rain Gauge ◽  
Northeast Monsoon ◽  
Data Set ◽  
Climatic Zones ◽  
Rain Gauges ◽  
Precipitation Estimation ◽  
Cauvery Delta ◽  
Rain Gauge Network

Currently, several satellite-precipitation products were developed using multiple algorithms to estimate rainfall. This study carried out using Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) product over seven agro-climatic zones of Tamil Nadu during the northeast monsoon (NEM) season of October to December for 2015-2017 (three years) against 118 rain-gauges data of Tamil Nadu Agricultural Weather Network (TAWN). The performance compares aggregated seasonal scale of rainfall using continuous (CC, RMSE, and NRMSE) statistical approaches. It was observed that PERSIANN is accurate in the high-altitude hilly zone and the Cauvery delta zone. For 2015, 2016, and 2017, the correlation values were 0.77, 0.52, and 0.71, respectively. The highest RMSE value was measured for northeast zone (NEZ) during 2015 (222.17 mm), and the lowest was determined for 22.63 in the High-altitude hilly zone (HAHZ) during 2016 and NRMSE had less errors during all three seasons. The study concluded that the PERSIANN data set could be useful substitute for rain-gauge precipitation data.

scholarly journals Intercomparison of Rain Gauge, Radar, and Satellite-Based Precipitation Estimates with Emphasis on Hydrologic Forecasting

2005 ◽  
Vol 6 (4) ◽  
pp. 497-517 ◽  
Author(s):  
Koray K. Yilmaz ◽  
Terri S. Hogue ◽  
Kuo-lin Hsu ◽  
Soroosh Sorooshian ◽  
Hoshin V. Gupta ◽  
...  
Keyword(s):  
Model Performance ◽  
Rain Gauge ◽  
Hydrologic Model ◽  
Soil Moisture Accounting ◽  
Time Period ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
System Maps ◽  
Accounting Model ◽  
Rain Gauge Network

Abstract This study compares mean areal precipitation (MAP) estimates derived from three sources: an operational rain gauge network (MAPG), a radar/gauge multisensor product (MAPX), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) satellite-based system (MAPS) for the time period from March 2000 to November 2003. The study area includes seven operational basins of varying size and location in the southeastern United States. The analysis indicates that agreements between the datasets vary considerably from basin to basin and also temporally within the basins. The analysis also includes evaluation of MAPS in comparison with MAPG for use in flow forecasting with a lumped hydrologic model [Sacramento Soil Moisture Accounting Model (SAC-SMA)]. The latter evaluation investigates two different parameter sets, the first obtained using manual calibration on historical MAPG, and the second obtained using automatic calibration on both MAPS and MAPG, but over a shorter time period (23 months). Results indicate that the overall performance of the model simulations using MAPS depends on both the bias in the precipitation estimates and the size of the basins, with poorer performance in basins of smaller size (large bias between MAPG and MAPS) and better performance in larger basins (less bias between MAPG and MAPS). When using MAPS, calibration of the parameters significantly improved the model performance.

scholarly journals Evaluation of the Specific Attenuation Method for Radar-Based Quantitative Precipitation Estimation: Improvements and Practical Challenges

2020 ◽  
Vol 21 (6) ◽  
pp. 1333-1347 ◽  
Author(s):  
Bong-Chul Seo ◽  
Witold F. Krajewski ◽  
Alexander Ryzhkov
Keyword(s):  
Estimation Algorithm ◽  
Rain Gauge ◽  
Light Rain ◽  
Differential Phase ◽  
Specific Attenuation ◽  
Precipitation Estimation ◽  
Dynamic Estimation ◽  
Lower Variability ◽  
Drop Size Distributions ◽  
Differential Reflectivity

AbstractThis study demonstrates an implementation of the prototype quantitative precipitation R estimation algorithm using specific attenuation A for S-band polarimetric radar. The performance of R(A) algorithm is assessed, compared to the conventional algorithm using radar reflectivity Z, at multiple temporal scales. Because the factor α, defined as the net ratio of A to specific differential phase, is a key parameter of the algorithm characterized by drop size distributions (e.g., differential reflectivity Zdr dependence on Z), the estimation equations of α and a proper number of Zdr–Z samples required for a reliable α estimation are examined. Based on the dynamic estimation of α, the event-based evaluation using hourly rain gauge observations reveals that the performance of R(A) is superior to that of R(Z), with better agreement and lower variability. Despite its superiority, the study finds that R(A) leads to quite consistent overestimations of about 10%–30%. It is demonstrated that the application of uniform α over the entire radar domain yields the observed uncertainty because of the heterogeneity of precipitation in the domain. A climatological range-dependent feature of R(A) and R(Z) is inspected in the multiyear evaluation at yearly scale using rain totals for April–October. While R(Z) exposes a systematic shift and overestimation, each of which arise from the radar miscalibration and bright band effects, R(A) combining with multiple R(Z) values for solid/mixed precipitation shows relatively robust performance without those effects. The immunity of R(A) to partial beam blockage (PBB) based on both qualitative and quantitative analyses is also verified. However, the capability of R(A) regarding PBB is limited by the presence of the melting layer and its application requirement for the total span of differential phase (e.g., 3°), which is another challenge for light rain.

scholarly journals Incorporating Satellite Precipitation Estimates into a Radar-Gage Multi-Sensor Precipitation Estimation Algorithm

2017 ◽  
Author(s):  
Yuxiang He ◽  
Yu Zhang ◽  
Robert Kuligowski ◽  
Robert Cifelli ◽  
David Kitzmiller
Keyword(s):  
Estimation Algorithm ◽  
Rain Gauge ◽  
Systematic Bias ◽  
Effective Coverage ◽  
Region I ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Satellite Radar ◽  
Radar Satellite ◽  
Quantitative Precipitation Estimates

This paper presents a new and enhanced fusion module for the Multi-Sensor Precipitation Estimator (MPE) that would objectively blend real-time satellite quantitative precipitation estimates (SQPE) with radar and gauge estimates. This module consists of a preprocessor that mitigates systematic bias in SQPE, and a two-way blending routine that statistically fuses adjusted SQPE with radar estimates. The preprocessor not only corrects systematic bias in SQPE, but also improves the spatial distribution of precipitation based on SQPE and makes it closely resemble that of radar-based observations. It uses a more sophisticated radar-satellite merging technique to blend preprocessed datasets, and provides a better overall QPE product. The performance of the new satellite-radar-gauge blending module is assessed using independent rain gauge data over a 5-year period between 2003-2007, and the assessment evaluates the accuracy of newly developed satellite-radar-gauge (SRG) blended products versus that of radar-gauge products (which represents MPE algorithm currently used in the NWS operations) over two regions: I) inside radar effective coverage and II) immediately outside radar coverage. The outcomes of the evaluation indicate a) ingest of SQPE over areas within effective radar coverage improve the quality of QPE by mitigating the errors in radar estimates in region I; and b) blending of radar, gauge, and satellite estimates over region II leads to reduction of errors relative to bias-corrected SQPE. In addition, the new module alleviates the discontinuities along the boundaries of radar effective coverage otherwise seen when SQPE is used directly to fill the areas outside of effective radar coverage.

Quantitative Precipitation Estimation Integrated by Poisson’s Equation Using Radar Mosaic, Satellite, and Rain Gauge Network

2017 ◽  
Vol 22 (5) ◽  
Author(s):  
Leonardo Calvetti ◽  
Cesar Beneti ◽  
Réverton Luís Antunes Neundorf ◽  
Rafael Toshio Inouye ◽  
Tiago Noronha dos Santos ◽  
...  
Keyword(s):  
Rain Gauge ◽  
Poisson’S Equation ◽  
Poisson's Equation ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Rain Gauge Network

scholarly journals Evaluation of the TMPA-3B42 precipitation product using a high-density rain gauge network over complex terrain in northeastern Iberia

2015 ◽  
Vol 133 ◽  
pp. 188-200 ◽  
Author(s):  
Ahmed M. El Kenawy ◽  
Juan I. Lopez-Moreno ◽  
Matthew F. McCabe ◽  
Sergio M. Vicente-Serrano
Keyword(s):  
Complex Terrain ◽  
Rain Gauge ◽  
High Density ◽  
Precipitation Product ◽  
Rain Gauge Network
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