scholarly journals Evaluation and Improvement of FY-4A AGRI Quantitative Precipitation Estimation for Summer Precipitation over Complex Topography of Western China

2021 ◽  
Vol 13 (21) ◽  
pp. 4366
Author(s):  
Jing Ren ◽  
Guirong Xu ◽  
Wengang Zhang ◽  
Liang Leng ◽  
Yanjiao Xiao ◽  
...  
Keyword(s):  
Correlation Coefficient ◽  
Mean Squared Error ◽  
Time Integration ◽  
Summer Precipitation ◽  
Western China ◽  
Complex Topography ◽  
Brightness Temperatures ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Dynamic Time

Satellite quantitative precipitation estimation (QPE) can make up for the insufficiency of ground observations for monitoring precipitation. Using an Advanced Geosynchronous Radiation Imager (AGRI) on the FengYun-4A (FY-4A) satellite and rain gauges (RGs) for observations in the summer of 2020. The existing QPE of the FY-4A was evaluated and found to present poor accuracy over the complex topography of Western China. Therefore, to improve the existing QPE, first, cloud classification thresholds for the FY-4A were established with the dynamic clustering method to identify convective clouds. These thresholds consist of the brightness temperatures (TBs) of FY-4A water vapor and infrared channels, and their TB difference. Then, quantitative cloud growth rate correction factors were introduced to improve the QPE of the convective-stratiform technique. This was achieved using TB hourly variation rates of long-wave infrared channel 12, which is able to characterize the evolution of clouds. Finally, the dynamic time integration method was designed to solve the inconsistent time matching between the FY-4A and RGs. Consequently, the QPE accuracy of the FY-4A was improved. Compared with the existing QPE of the FY-4A, the correlation coefficient between the improved QPE of the FY-4A and the RG hourly precipitation increased from 0.208 to 0.492, with the mean relative error and root mean squared error decreasing from −47.4% and 13.78 mm to 8.3% and 10.04 mm, respectively. However, the correlation coefficient is not sufficiently high; thus, the algorithm needs to be further studied and improved.

scholarly journals Leveraging machine learning for quantitative precipitation estimation from Fengyun-4 geostationary observations and ground meteorological measurements

2021 ◽  
Author(s):  
Xinyan Li ◽  
Yuanjian Yang ◽  
Jiaqin Mi ◽  
Xueyan Bi ◽  
You Zhao ◽  
...  
Keyword(s):  
Rural Areas ◽  
Urban Areas ◽  
Large Scale ◽  
Predictive Ability ◽  
Summer Precipitation ◽  
Reanalysis Data ◽  
Probability Of Detection ◽  
Model Framework ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation

Abstract. Deriving large-scale and high-quality precipitation products from satellite remote sensing spectral data is always challenging in quantitative precipitation estimation (QPE), and limited studies have been conducted even using the China’s latest Fengyun-4A (FY-4A) geostationary satellite. Taking three rainstorm events over South China as examples, a Random Forest (RF) model framework for FY-4A QPE during daytime/nighttime is established by using FY-4A multi-band spectral information, cloud parameters, high-density precipitation observations, and physical quantities from reanalysis data. During daytime (nighttime), the probability of detection of the RF model for precipitation is 0.99 (0.99), while the correlation coefficient and root-mean-square error between the retrieved and observed precipitation are 0.77 (0.82) and 1.84 (2.32) mm/h, respectively, indicating that the RF model of FY-4A QPE has high precipitation retrieval accuracy. In particular, the RF model exhibits good spatiotemporal predictive ability for precipitation intensities within the range of 0.5–10 mm/h. For the retrieved accumulated precipitation, the precipitation intensity exhibits a greater impact on the predictive ability of the QPE algorithm than the precipitation duration. Due to the higher density of automatic stations in urban areas, the accuracy of FY-4A QPE over such areas is higher compared with rural areas. Both the accumulated precipitation and the distribution density of automatic stations are more important factors for the predictive ability of the RF model of FY-4A QPE. In general, our proposed FY-4A QPE algorithm has advantages for near-real-time monitoring of summer precipitation over East Asia.

scholarly journals Leveraging machine learning for quantitative precipitation estimation from Fengyun-4 geostationary observations and ground meteorological measurements

2021 ◽  
Vol 14 (11) ◽  
pp. 7007-7023
Author(s):  
Xinyan Li ◽  
Yuanjian Yang ◽  
Jiaqin Mi ◽  
Xueyan Bi ◽  
You Zhao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Rural Areas ◽  
Land Surface ◽  
Large Scale ◽  
Quantitative Estimation ◽  
Summer Precipitation ◽  
Bias Score ◽  
Surface Precipitation ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation

Abstract. Deriving large-scale and high-quality precipitation products from satellite remote-sensing spectral data is always challenging in quantitative precipitation estimation (QPE), and limited studies have been conducted even using China's latest Fengyun-4A (FY-4A) geostationary satellite. Taking three rainstorm events over South China as examples, a machine-learning-based regression model was established using the random forest (RF) method to derive QPE from FY-4A observations, in conjunction with cloud parameters and physical quantities. The cross-validation results indicate that both daytime (DQPE) and nighttime (NQPE) RF algorithms performed well in estimating QPE, with the bias score, correlation coefficient and root-mean-square error of DQPE (NQPE) of 2.17 (2.42), 0.79 (0.83) and 1.77 mm h−1 (2.31 mm h−1), respectively. Overall, the algorithm has a high accuracy in estimating precipitation under the heavy-rain level or below. Nevertheless, the positive bias still implies an overestimation of precipitation by the QPE algorithm, in addition to certain misjudgements from non-precipitation pixels to precipitation events. Also, the QPE algorithm tends to underestimate the precipitation at the rainstorm or even above levels. Compared to single-sensor algorithms, the developed QPE algorithm can better capture the spatial distribution of land-surface precipitation, especially the centre of strong precipitation. Marginal difference between the data accuracy over sites in urban and rural areas indicate that the model performs well over space and has no evident dependence on landscape. In general, our proposed FY-4A QPE algorithm has advantages for quantitative estimation of summer precipitation over East Asia.

scholarly journals A Dynamical Z-R Relationship for Precipitation Estimation Based on Radar Echo-Top Height Classification

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Wenxin Wu ◽  
Haibo Zou ◽  
Jiusheng Shan ◽  
Shanshan Wu
Keyword(s):  
Mean Squared Error ◽  
The Other ◽  
Intense Rainfall ◽  
Rainfall Events ◽  
Quantitative Precipitation Estimation ◽  
Squared Error ◽  
Hourly Rainfall ◽  
Precipitation Estimation ◽  
Radar Echo ◽  
Short Time

Using echo-top height and hourly rainfall datasets, a new reflectivity-rainfall (Z-R) relationship is established in the present study for the radar-based quantitative precipitation estimation (RQPE), taking into account both the temporal evolution (dynamical) and the types of echoes (i.e., based on echo-top height classification). The new Z-R relationship is then applied to derive the RQPE over the middle and lower reaches of Yangtze River for two short-time intense rainfall cases in summer (2200 UTC 1 June 2016 and 2200 UTC 18 June 2016) and one stratiform rainfall case in winter (0000 UTC 15 December 2017), and then the comparative analyses between the RQPE and the RQPEs derived by the other two methods (the fixed Z-R relationship and the dynamical Z-R relationship based on radar reflectivity classification) are accomplished. The results show that the RQPE from the new Z-R relationship is much closer to the observation than those from the other two methods because the new method simultaneously considers the echo intensity (reflecting the size and concentration of hydrometers to a certain extent) and the echo-top height (reflecting the updraft to a certain extent). Two statistics of 720 rainfall events in summer (April to June 2017) and 50 rainfall events in winter (December 2017) over the same region show that the correlation coefficient (root-mean-squared error and relative error) between RQPE derived by the new Z-R relationship and observation is significantly increased (decreased) compared to the other two Z-R relationships. Besides, the new Z-R relationship is also good at estimating rainfall with different intensities as compared to the other two methods, especially for the intense rainfall.

Comparison of Quantitative Precipitation Estimation Using Spatial Interpolation methods in Bukhan River Basin

2020 ◽  
Author(s):  
Sanghoo Yoon ◽  
Junseok Kim ◽  
Taeyong Kwon
Keyword(s):  
Spatial Interpolation ◽  
Mean Squared Error ◽  
Additive Model ◽  
Absolute Error ◽  
Interpolation Methods ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Weight Method ◽  
Inverse Distance ◽  
Spatial Interpolation Methods

<p>Quantitative precipitation estimation is needed to reduce damages from weather disasters such as torrential rain. This study is dealt with estimates of the quantitative precipitation using multiple spatial interpolation methods and compares the results. Inverse distance weight method and k-nearest neighborhood algorithm were considered as a deterministic approach and the general additive model and kriging methods were used as a stochastic approach. To evaluate the prediction performance, leave-one-out cross-validation was performed with the root mean squared error (RMSE), mean absolute error (MAE), bias, and correlation coefficient. The research data were rain gauged and radar data in the Bukhan river, which were collected from May 2018 to August 2019. The results showed that the inverse distance weight method reflected the spatial rainfall characteristics well. However, caution is needed because the best models vary depending on the pattern of rainfall in the sense of RMSE.</p><p>*This work was supported by KOREA HYDRO & NUCLEAR POWER CO., LTD(No. 2018-Tech-20)</p>

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 of the Epic 2013 Colorado Flood Event: Polarization Radar-Based Variational Scheme

2016 ◽  
Vol 55 (7) ◽  
pp. 1477-1495 ◽  
Author(s):  
Wei-Yu Chang ◽  
Jothiram Vivekanandan ◽  
Kyoko Ikeda ◽  
Pay-Liam Lin
Keyword(s):  
Power Law ◽  
Rain Rate ◽  
Flood Event ◽  
Polarimetric Radar ◽  
Dual Polarization ◽  
Observational Error ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Radar Measurements ◽  
Event Based

AbstractThe accuracy of rain-rate estimation using polarimetric radar measurements has been improved as a result of better characterization of radar measurement quality and rain microphysics. In the literature, a variety of power-law relations between polarimetric radar measurements and rain rate are described because of the dynamic or varying nature of rain microphysics. A variational technique that concurrently takes into account radar observational error and dynamically varying rain microphysics is proposed in this study. Rain-rate estimation using the variational algorithm that uses event-based observational error and background rain climatological values is evaluated using observing system simulation experiments (OSSE), and its performance is demonstrated in the case of an epic Colorado flood event. The rain event occurred between 11 and 12 September 2013. The results from OSSE show that the variational algorithm with event-based observational error consistently estimates more accurate rain rate than does the “R(ZHH, ZDR)” power-law algorithm. On the contrary, the usage of ad hoc or improper observational error degrades the performance of the variational method. Furthermore, the variational algorithm is less sensitive to the observational error of differential reflectivity ZDR than is the R(ZHH, ZDR) algorithm. The variational quantitative precipitation estimation (QPE) retrieved more accurate rainfall estimation than did the power-law dual-polarization QPE in this particular event, despite the fact that both algorithms used the same dual-polarization radar measurements from the Next Generation Weather Radar (NEXRAD).

scholarly journals Observed Response of the Raindrop Size Distribution to Changes in the Melting Layer

Atmosphere ◽  
2018 ◽  
Vol 9 (8) ◽  
pp. 319 ◽  
Author(s):  
Patrick Gatlin ◽  
Walter Petersen ◽  
Kevin Knupp ◽  
Lawrence Carey
Keyword(s):  
Size Distribution ◽  
Basic Assumption ◽  
Weighted Mean ◽  
Radar Rainfall ◽  
Melting Layer ◽  
Quantitative Precipitation Estimation ◽  
Raindrop Size Distribution ◽  
Precipitation Estimation ◽  
Raindrop Size ◽  
Stratiform Rainfall

Vertical variability in the raindrop size distribution (RSD) can disrupt the basic assumption of a constant rain profile that is customarily parameterized in radar-based quantitative precipitation estimation (QPE) techniques. This study investigates the utility of melting layer (ML) characteristics to help prescribe the RSD, in particular the mass-weighted mean diameter (Dm), of stratiform rainfall. We utilize ground-based polarimetric radar to map the ML and compare it with Dm observations from the ground upwards to the bottom of the ML. The results show definitive proof that a thickening, and to a lesser extent a lowering, of the ML causes an increase in raindrop diameter below the ML that extends to the surface. The connection between rainfall at the ground and the overlying microphysics in the column provide a means for improving radar QPE at far distances from a ground-based radar or close to the ground where satellite-based radar rainfall retrievals can be ill-defined.

scholarly journals Quantitative Precipitation Estimation using Overlapped Area in Radar Network

2017 ◽  
Vol 19 (1) ◽  
pp. 112-121
Author(s):  
Jeongho Choi ◽  
Myoungsun Han ◽  
Chulsang Yoo ◽  
Jiho Lee
Keyword(s):  
Quantitative Precipitation Estimation ◽  
Radar Network ◽  
Precipitation Estimation

scholarly journals Improved Attenuation-Based Radar Precipitation Estimation Considering the Azimuthal Variabilities of Microphysical Properties

2020 ◽  
Vol 21 (7) ◽  
pp. 1605-1620
Author(s):  
Hao Huang ◽  
Kun Zhao ◽  
Haonan Chen ◽  
Dongming Hu ◽  
Peiling Fu ◽  
...  
Keyword(s):  
Variational Approach ◽  
Optimization Approach ◽  
Radar Rainfall ◽  
Microphysical Properties ◽  
Quantitative Precipitation Estimation ◽  
Hourly Rainfall ◽  
Precipitation Estimation ◽  
Raindrop Size ◽  
Differential Reflectivity ◽  
The Impact

AbstractThe attenuation-based rainfall estimator is less sensitive to the variability of raindrop size distributions (DSDs) than conventional radar rainfall estimators. For the attenuation-based quantitative precipitation estimation (QPE), the key is to accurately estimate the horizontal specific attenuation AH, which requires a good estimate of the ray-averaged ratio between AH and specific differential phase KDP, also known as the coefficient α. In this study, a variational approach is proposed to optimize the coefficient α for better estimates of AH and rainfall. The performance of the variational approach is illustrated using observations from an S-band operational weather radar with rigorous quality control in south China, by comparing against the α optimization approach using a slope of differential reflectivity ZDR dependence on horizontal reflectivity factor ZH. Similar to the ZDR-slope approach, the variational approach can obtain the optimized α consistent with the DSD properties of precipitation on a sweep-to-sweep basis. The attenuation-based hourly rainfall estimates using the sweep-averaged α values from these two approaches show comparable accuracy when verified against the gauge measurements. One advantage of the variational approach is its feasibility to optimize α for each radar ray, which mitigates the impact of the azimuthal DSD variabilities on rainfall estimation. It is found that, based on the optimized α for radar rays, the hourly rainfall amounts derived from the variational approach are consistent with gauge measurements, showing lower bias (1.0%), higher correlation coefficient (0.92), and lower root-mean-square error (2.35 mm) than the results based on the sweep-averaged α.

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