Interpretation of mean-field bias correction of radar rain rate using the concept of linear regression

2013 ◽  
Vol 28 (19) ◽  
pp. 5081-5092 ◽  
Author(s):  
Chulsang Yoo ◽  
Cheolsoon Park ◽  
Jungsoo Yoon ◽  
Jungho Kim
Keyword(s):  
Linear Regression ◽  
Bias Correction ◽  
Rain Rate ◽  
Mean Field ◽  
Field Bias ◽  
Radar Rain Rate

Mean Field Bias Correction of the Very-Short-Range-Forecast Rainfall using the Kalman Filter

2011 ◽  
Vol 11 (3) ◽  
pp. 17-28 ◽  
Author(s):  
Chul-Sang Yoo ◽  
Jung-Ho Kim ◽  
Jae-Hak Chung ◽  
Dong-Min Yang
Keyword(s):  
Kalman Filter ◽  
Bias Correction ◽  
Short Range ◽  
Mean Field ◽  
Field Bias

scholarly journals Evaluation of radar-gauge merging methods for quantitative precipitation estimates

2008 ◽  
Vol 5 (5) ◽  
pp. 2975-3003 ◽  
Author(s):  
E. Goudenhoofdt ◽  
L. Delobbe
Keyword(s):  
Bias Correction ◽  
Mean Field ◽  
Daily Rainfall ◽  
Absolute Error ◽  
Rain Gauge ◽  
Radar Observations ◽  
Geostatistical Methods ◽  
Precipitation Estimates ◽  
Field Bias ◽  
Quantitative Precipitation Estimates

Abstract. Accurate quantitative precipitation estimates are of crucial importance for hydrological studies and applications. When spatial precipitation fields are required, rain gauge measurements are often combined with weather radar observations. In this paper, we evaluate several radar-gauge merging methods with various degrees of complexity: from mean field bias correction to geostatical merging techniques. The study area is the Walloon region of Belgium, which is mostly located in the Meuse catchment. Observations from a C-band Doppler radar and a dense rain gauge network are used to retrieve daily rainfall accumulations over this area. The relative performance of the different merging methods are assessed through a comparison against daily measurements from an independent gauge network. A 3-year verification is performed using several statistical quality parameters. It appears that the geostatistical merging methods perform best with the mean absolute error decreasing by 40% with respect to the original data. A mean field bias correction still achieves a reduction of 25%. A seasonal analysis shows that the benefit of using radar observations is particularly significant during summer. The effect of the network density on the performance of the methods is also investigated. For this purpose, a simple approach to remove gauges from a network is proposed. The analysis reveals that the sensitivity is relatively high for the geostatistical methods but rather small for the simple methods. The geostatistical methods give the best results for all network densities except for a very low density of 1 gauge per 500 km2 where a range-dependent adjustment complemented with a static local bias correction performs best.

Mean-Field-Bias Correction of the Rainfall Forecasts Using Backward Storm Tracking

2017 ◽  
Vol 17 (1) ◽  
pp. 253-263
Author(s):  
Wooyoung Na ◽  
◽  
Eunsaem Cho ◽  
Jinwook Lee ◽  
Chulsang Yoo ◽  
...  
Keyword(s):  
Bias Correction ◽  
Mean Field ◽  
Storm Tracking ◽  
Field Bias

scholarly journals Evaluation of radar-gauge merging methods for quantitative precipitation estimates

2009 ◽  
Vol 13 (2) ◽  
pp. 195-203 ◽  
Author(s):  
E. Goudenhoofdt ◽  
L. Delobbe
Keyword(s):  
Bias Correction ◽  
Mean Field ◽  
Daily Rainfall ◽  
Absolute Error ◽  
Rain Gauge ◽  
Network Density ◽  
Radar Observations ◽  
Precipitation Estimates ◽  
Field Bias ◽  
Quantitative Precipitation Estimates

Abstract. Accurate quantitative precipitation estimates are of crucial importance for hydrological studies and applications. When spatial precipitation fields are required, rain gauge measurements are often combined with weather radar observations. In this paper, we evaluate several radar-gauge merging methods with various degrees of complexity: from mean field bias correction to geostatistical merging techniques. The study area is the Walloon region of Belgium, which is mostly located in the Meuse catchment. Observations from a C-band Doppler radar and a dense rain gauge network are used to estimate daily rainfall accumulations over this area. The relative performance of the different merging methods are assessed through a comparison against daily measurements from an independent gauge network. A 4-year verification is performed using several statistical quality parameters. It appears that the geostatistical merging methods perform best with the mean absolute error decreasing by 40% with respect to the original data. A mean field bias correction still achieves a reduction of 25%. A seasonal analysis shows that the benefit of using radar observations is particularly significant during summer. The effect of the network density on the performance of the methods is also investigated. For this purpose, a simple approach to remove gauges from a network is proposed. The analysis reveals that the sensitivity is relatively high for the geostatistical methods but rather small for the simple methods. The geostatistical merging methods give the best results for all tested network densities and their relative benefit increases with the network density.

scholarly journals Application of bias correction methods to improve the accuracy of quantitative radar rainfall in Korea

2015 ◽  
Vol 8 (4) ◽  
pp. 4011-4047 ◽  
Author(s):  
J.-K. Lee ◽  
J.-H. Kim ◽  
M.-K. Suk
Keyword(s):  
Bias Correction ◽  
Mean Field ◽  
Correction Method ◽  
Rainfall Estimation ◽  
Radar Rainfall ◽  
Reflectivity Measurement ◽  
Estimation Process ◽  
Post Process ◽  
Field Bias ◽  
Calculation System

Abstract. There are many potential sources of bias in the radar rainfall estimation process. This study classified the biases from the rainfall estimation process into the reflectivity measurement bias and QPE model bias and also conducted the bias correction methods to improve the accuracy of the Radar-AWS Rainrate (RAR) calculation system operated by the Korea Meteorological Administration (KMA). For the Z bias correction, this study utilized the bias correction algorithm for the reflectivity. The concept of this algorithm is that the reflectivity of target single-pol radars is corrected based on the reference dual-pol radar corrected in the hardware and software bias. This study, and then, dealt with two post-process methods, the Mean Field Bias Correction (MFBC) method and the Local Gauge Correction method (LGC), to correct rainfall-bias. The Z bias and rainfall-bias correction methods were applied to the RAR system. The accuracy of the RAR system improved after correcting Z bias. For rainfall types, although the accuracy of Changma front and local torrential cases was slightly improved without the Z bias correction, especially, the accuracy of typhoon cases got worse than existing results. As a result of the rainfall-bias correction, the accuracy of the RAR system performed Z bias_LGC was especially superior to the MFBC method because the different rainfall biases were applied to each grid rainfall amount in the LGC method. For rainfall types, Results of the Z bias_LGC showed that rainfall estimates for all types was more accurate than only the Z bias and, especially, outcomes in typhoon cases was vastly superior to the others.

scholarly journals Bias correction of radar and satellite rainfall estimates and increasing its accuracy using modified merging

MAUSAM ◽  
2021 ◽  
Vol 71 (3) ◽  
pp. 377-390
Author(s):  
GIARNO GIARNO ◽  
HADI MUHAMMAD PROMONO ◽  
SUPRAYOGI SLAMET ◽  
HERUMURTI SIGIT
Keyword(s):  
Remote Sensing ◽  
Bias Correction ◽  
Mean Field ◽  
Tropical Rainfall Measuring Mission ◽  
Absolute Error ◽  
Data Types ◽  
Local Bias ◽  
Field Bias ◽  
The Stability ◽  
Rainfall Estimates

Bias correction in the weather radar and the tropical rainfall measuring mission (TRMM) rainfall estimates are used to improve its accuracy. This correction is usually done separately for both radar and TRMM. Even though the corrections are done separately, the results of these corrections can be further improved using the merging. Among the methods of merging, modified local bias, mean field bias and conditional merging may be suitable methods used to correct rainfall estimates from remote sensing surrounding in the Makassar Strait. The aim of this research corrects radar and TRMM rainfall estimates, then combining them to obtain more accurate rainfall estimates. The performance will be validated using correlation, root mean square error (RMSE) and mean absolute error (MAE). The result shows that modified mean field bias (Mod_MFB) and local bias (LB) can increase accuracy, mainly RMSE and MAE but not in correlation. However, conditional merging (CM) and modified LB can improve accuracy by increasing correlation and decrease RMSE and MAE. The modification of CM, LB modification and original estimation of remote sensing successively are the order of the best methods. Moreover, merging three data types is not automatically better than merging the two types of data. However, combination 3 types of data offer the stability of accuracy.

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