scholarly journals The Quantile-Matching Approach to Improving Radar Quantitative Precipitation Estimation in South China

2021 ◽  
Vol 13 (23) ◽  
pp. 4956
Author(s):  
Linye Song ◽  
Shangfeng Chen ◽  
Yun Li ◽  
Duo Qi ◽  
Jiankun Wu ◽  
...  
Keyword(s):  
South China ◽  
Weather Radar ◽  
Absolute Error ◽  
Rain Gauge ◽  
Observation Data ◽  
Matching Method ◽  
Study Region ◽  
Surface Precipitation ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation

Weather radar provides regional rainfall information with a very high spatial and temporal resolution. Because the radar data suffer from errors from various sources, an accurate quantitative precipitation estimation (QPE) from a weather radar system is crucial for meteorological forecasts and hydrological applications. In the South China region, multiple weather radar networks are widely used, but the accuracy of radar QPE products remains to be analyzed and improved. Based on hourly radar QPE and rain gauge observation data, this study first analyzed the QPE error in South China and then applied the Quantile Matching (Q-matching) method to improve the radar QPE accuracy. The results show that the rainfall intensity of the radar QPE is generally larger than that determined from rain gauge observations but that it usually underestimates the intensity of the observed heavy rainfall. After the Q-matching method was applied to correct the QPE, the accuracy improved by a significant amount and was in good agreement with the rain gauge observations. Specifically, the Q-matching method was able to reduce the QPE error from 39–44%, demonstrating performance that is much better than that of the traditional climatological scaling method, which was shown to be able to reduce the QPE error from 3–15% in South China. Moreover, after the Q-matching correction, the QPE values were closer to the rainfall values that were observed from the automatic weather stations in terms of having a smaller mean absolute error and a higher correlation coefficient. Therefore, the Q-matching method can improve the QPE accuracy as well as estimate the surface precipitation better. This method provides a promising prospect for radar QPE in the study region.

scholarly journals An Enhancement to The Quantitative Precipitation Estimation Using Radar-Gauge Merging

2020 ◽  
Vol 17 (1) ◽  
pp. 65
Author(s):  
Abdullah Ali ◽  
Gumilang Deranadyan ◽  
Iddam Hairuly Umam
Keyword(s):  
Correction Factor ◽  
Mean Field ◽  
Real Data ◽  
Heavy Rain ◽  
Absolute Error ◽  
Rain Gauge ◽  
Light Rain ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
The Difference

Quantitative Precipitation Estimation (QPE) is quite important information for the hydrology fields and has many advantages for many purposes. Its dense spatial and temporal resolution can be combined with the surface observation to enhance the accuracy of the estimation. This paper presents an enhancement to the QPE product from BMKG weather radar network at Surabaya by adjusting the estimation value form radar to the real data observation from rain gauge. A total of 58 rain gauge is used. The Mean Field Bias (MFB) method used to determine the correction factor through the difference between radar estimation and rain gauge observation value. The correction factor obtained at each gauge points are interpolated to the entire radar grid in a multiplicative adjustment. Radar-gauge merging results a significant improvement revealed by the decreasing of mean absolute error (MAE) about 40% and false alarm ratio (FAR) as well an increasing of possibility of detection (POD) more than 50% at any rain categories (light rain, moderate rain, heavy rain, and very heavy rain). This performance improvement is very beneficial for operational used in BMKG and other hydrological needs.

scholarly journals Quantitative precipitation estimation with weather radar using a data- and information-based approach

2019 ◽  
Author(s):  
Malte Neuper ◽  
Uwe Ehret
Keyword(s):  
Information Content ◽  
Rain Rate ◽  
Empirical Relation ◽  
Weather Radar ◽  
Circulation Pattern ◽  
Rain Gauge ◽  
Information Theoretic ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Synoptic Circulation

Abstract. In this study we propose and demonstrate a data-driven approach in an information-theoretic framework to estimate precipitation quantitatively. In this context predictive relations are expressed by empirical discrete probability distributions directly derived from data instead of fitting and applying deterministic functions as it is standard operational practice. Applying a probabilistic relation has the benefit to provide joint statements about rain rate and the related estimation uncertainty. The information-theoretic framework furthermore allows integration of any kind of data considered useful and explicitly considers the uncertain nature of quantitative precipitation estimation (QPE). With this framework we investigate the information gains and losses associated with various data and practices typically applied in QPE. To this end we conduct six experiments using four years of data from six laser optical disdrometers, two micro rain radars MRR regular rain gauges, weather radar reflectivity and other operationally available meteorological data from existing stations. Each experiment addresses a typical question related to QPE: First, we measure the information about ground rainfall contained in various operationally available predictors. Here weather radar proves to be the single most important source of information, which can be further improved when distinguishing radar reflectivity – ground rainfall relationships (Z-R relations) by season and prevailing synoptic circulation pattern. Second, we investigate the effect of data sample size on QPE uncertainty using different data based predictive models. It shows that the combination of reflectivity and month of the year as a double predictor model is the best trade-off between robustness of the model and information gain. Third, we investigate the information content in spatial position by learning and applying site-specific Z-R relations. The related information gains are only moderate and especially lower than when distinguishing Z-R relations according to time of the year or synoptic circulation pattern. Fourth, we measure the information loss when fitting and using a deterministic Z-R relation, as it is standard practice in operational radar based QPE applying e.g. the standard Marshal-Palmer relation, instead of using the empirical relation derived directly from the data. It shows that while the deterministic function captures the overall shape of the empirical relation quite well, it introduces additional 60% of uncertainty when estimating rain rate. Fifth, we investigate how much information is gained along the radar observation path, starting with reflectivity measured by radar at height, continuing with the reflectivity measured by a MRR along a vertical profile in the atmosphere and ending with the reflectivity observed by a disdrometer directly at the ground. The results reveal that considerable additional information is gained by using observations from lower elevations by avoiding information losses caused by ongoing microphysical precipitation processes from cloud height to ground. This underlines both the importance of vertical corrections for accurate QPE and of the required MRR observations. In the sixth experiment we evaluate the information content of radar data only, rain gauge data only and a combination of both as a function of the distance between the target and predictor rain gauge. The results show that station-only QPE outperforms radar-only QPE up to a distance of 7 to 8 km and that radar-and-gauge QPE performs best, even compared to radar-based models applying season or circulation pattern.

scholarly journals Quantitative precipitation estimation with weather radar using a data- and information-based approach

2019 ◽  
Vol 23 (9) ◽  
pp. 3711-3733 ◽  
Author(s):  
Malte Neuper ◽  
Uwe Ehret
Keyword(s):  
Information Content ◽  
Rain Rate ◽  
Empirical Relation ◽  
Weather Radar ◽  
Circulation Pattern ◽  
Rain Gauge ◽  
Information Theoretic ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Synoptic Circulation

Abstract. In this study we propose and demonstrate a data-driven approach in an “information-theoretic” framework to quantitatively estimate precipitation. In this context, predictive relations are expressed by empirical discrete probability distributions directly derived from data instead of fitting and applying deterministic functions, as is standard operational practice. Applying a probabilistic relation has the benefit of providing joint statements about rain rate and the related estimation uncertainty. The information-theoretic framework furthermore allows for the integration of any kind of data considered useful and explicitly considers the uncertain nature of quantitative precipitation estimation (QPE). With this framework we investigate the information gains and losses associated with various data and practices typically applied in QPE. To this end, we conduct six experiments using 4 years of data from six laser optical disdrometers, two micro rain radars (MRRs), regular rain gauges, weather radar reflectivity and other operationally available meteorological data from existing stations. Each experiment addresses a typical question related to QPE. First, we measure the information about ground rainfall contained in various operationally available predictors. Here weather radar proves to be the single most important source of information, which can be further improved when distinguishing radar reflectivity–ground rainfall relationships (Z–R relations) by season and prevailing synoptic circulation pattern. Second, we investigate the effect of data sample size on QPE uncertainty using different data-based predictive models. This shows that the combination of reflectivity and month of the year as a two-predictor model is the best trade-off between robustness of the model and information gain. Third, we investigate the information content in spatial position by learning and applying site-specific Z–R relations. The related information gains are only moderate; specifically, they are lower than when distinguishing Z–R relations according to time of the year or synoptic circulation pattern. Fourth, we measure the information loss when fitting and using a deterministic Z–R relation, as is standard practice in operational radar-based QPE applying, e.g., the standard Marshall–Palmer relation, instead of using the empirical relation derived directly from the data. It shows that while the deterministic function captures the overall shape of the empirical relation quite well, it introduces an additional 60 % uncertainty when estimating rain rate. Fifth, we investigate how much information is gained along the radar observation path, starting with reflectivity measured by radar at height, continuing with the reflectivity measured by a MRR along a vertical profile in the atmosphere and ending with the reflectivity observed by a disdrometer directly at the ground. The results reveal that considerable additional information is gained by using observations from lower elevations due to the avoidance of information losses caused by ongoing microphysical precipitation processes from cloud height to ground. This emphasizes both the importance of vertical corrections for accurate QPE and of the required MRR observations. In the sixth experiment we evaluate the information content of radar data only, rain gauge data only and a combination of both as a function of the distance between the target and predictor rain gauge. The results show that station-only QPE outperforms radar-only QPE up to a distance of 7 to 8 km from the nearest station and that radar–gauge QPE performs best, even compared with radar-based models applying season or circulation pattern.

scholarly journals Generation and Verification of Rainfall Estimates from 10-Yr Volumetric Weather Radar Measurements

2016 ◽  
Vol 17 (4) ◽  
pp. 1223-1242 ◽  
Author(s):  
Edouard Goudenhoofdt ◽  
Laurent Delobbe
Keyword(s):  
Mean Field ◽  
Weather Radar ◽  
Rain Gauge ◽  
Absolute Difference ◽  
Precipitation Regime ◽  
Rain Gauges ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Field Bias ◽  
Rain Rates

Abstract Volumetric measurements from a C-band weather radar in Belgium are reprocessed over the years 2005–14 to improve the quantitative precipitation estimation (QPE). The data quality is controlled using static clutter and beam blockage maps and clutter identification based on vertical gradients, horizontal texture, and satellite observations. A new QPE is obtained using stratiform–convective classification, a 40-min averaged vertical profile of reflectivity (VPR), a brightband identification, and a specific transformation to rain rates for each precipitation regime. The rain rates are interpolated on a 500-m Cartesian grid, linearly accumulated, and combined with hourly rain gauge measurements using mean field bias or kriging with external drift (KED). The algorithms have been fine-tuned on 13 cases with various meteorological situations. A detailed validation against independent daily rain gauge measurements reveals the importance of VPR correction. A 10-yr verification shows a significant improvement of the new QPE, especially at short and long range, with roughly 50% increase in coverage. Adding the KED allows average improvements of 38%, 35%, and 80% for the mean absolute difference, the multiplicative error spread, and the fraction of good estimates, respectively. The benefit is higher in widespread situations and increases when considering higher rainfall amounts. The mitigation of radar artifacts is clearly visible on 10-yr statistics, including mean annual totals, probabilities to exceed 10 mm, and maxima for hourly and daily accumulation. The correlation of mean totals with rain gauges increases from 0.54 to 0.66 with the new QPE and to 0.8 adding KED.

scholarly journals Evaluation of the dual-polarization weather radar quantitative precipitation estimation using long-term datasets

2021 ◽  
Vol 25 (3) ◽  
pp. 1245-1258
Author(s):  
Tanel Voormansik ◽  
Roberto Cremonini ◽  
Piia Post ◽  
Dmitri Moisseev
Keyword(s):  
Weather Radar ◽  
Early Warning Systems ◽  
Rain Gauge ◽  
Convective Precipitation ◽  
Dual Polarization ◽  
Climate Conditions ◽  
Differential Phase ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation

Abstract. Accurate, timely, and reliable precipitation observations are mandatory for hydrological forecast and early warning systems. In the case of convective precipitation, traditional rain gauge networks often miss precipitation maxima, due to density limitations and the high spatial variability of the rainfall field. Despite several limitations like attenuation or partial beam blocking, the use of C-band weather radar has become operational in most European weather services. Traditionally, weather-radar-based quantitative precipitation estimation (QPE) is derived from horizontal reflectivity data. Nevertheless, dual-polarization weather radar can overcome several shortcomings of the conventional horizontal-reflectivity-based estimation. As weather radar archives are growing, they are becoming increasingly important for climatological purposes in addition to operational use. For the first time, the present study analyses one of the longest datasets from fully operational polarimetric C-band weather radars; these are located in Estonia and Italy, in very different climate conditions and environments. The length of the datasets used in the study is 5 years for both Estonia and Italy. The study focuses on long-term observations of summertime precipitation and their quantitative estimations by polarimetric observations. From such derived QPEs, accumulations for 1 h, 24 h, and 1-month durations are calculated and compared with reference rain gauges to quantify uncertainties and evaluate performances. Overall, the radar products showed similar results in Estonia and Italy when compared to each other. The product where radar reflectivity and specific differential phase were combined based on a threshold exhibited the best agreement with gauge values in all accumulation periods. In both countries reflectivity-based rainfall QPE underestimated and specific differential-phase-based product overestimated gauge measurements.

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 Application of Attenuation Correction to Quantitative Precipitation Estimation on C-Band Weather Radar in Bengkulu

2021 ◽  
Vol 893 (1) ◽  
pp. 012054
Author(s):  
M F Handoyo ◽  
M P Hadi ◽  
S Suprayogi
Keyword(s):  
Attenuation Correction ◽  
Estimation Error ◽  
Weather Radar ◽  
Radar Data ◽  
Signal Interference ◽  
Active System ◽  
Estimation Errors ◽  
Rain Gauges ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation

Abstract A weather radar is an active system remote sensing tool that observes precipitation indirectly. Weather radar has an advantage in estimating precipitation because it has a high spatial resolution (up to 0.5 km). Reflectivity generated by weather radar still has signal interference caused by attenuation factors. Attenuation causes the Quantitative Precipitation Estimation (QPE) by the C-band weather radar to underestimate. Therefore attenuation correction on C-band weather radar is needed to eliminate precipitation estimation errors. This study aims to apply attenuation correction to determine Quantitative Precipitation Estimation (QPE) on the c-band weather radar in Bengkulu in December 2018. Gate-by-gate method attenuation correction with Kraemer approach has applied to c-band weather radar data from the Indonesian Agency for Meteorology and Geophysics (BMKG) weather radar network Bengkulu. This method uses reflectivity as the only input. Quantitative Precipitation Estimation (QPE) has obtained by comparing weather radar-based rain estimates to 10 observation rain gauges over a month with the Z-R relation equation. Root Mean Square Error (RMSE) is used to calculate the estimation error. Weather radar data are processed using Python-based libraries Wradlib and ArcGIS 10.5. As a result, the calculation between the weather radar estimate precipitation and the observed rainfall obtained equation Z=2,65R1,3. The attenuation correction process with Kreamer's approach on the c-band weather radar has reduced error in the Qualitative Precipitation Estimation (QPE). Corrected precipitation has a smaller error value (r = 0.88; RMSE = 8.38) than the uncorrected precipitation (r = 0.83; RMSE = 11.70).

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 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.

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