A Quality Assurance Procedure and Evaluation of Rainfall Estimates for C-Band Polarimetric Radar

A mobile C-band dual polarimetric weather radar J type （PCDJ）, which adopts simultaneous transmission and simultaneous reception （STSR） of horizontally and vertically polarized signals, was first developed in China in 2008. It was deployed in the radar observation plan in the South China Heavy Rainfall Experiment （SCHeREX） in the summer of 2008 and 2009, as well as in Tropical Western Pacific Ocean Observation Experiments and Research on the Predictability of High Impact Weather Events from 2008 to 2010 in China （TWPOR）. Using the observation data collected in these experiments, the radar systematic error and its sources were analyzed in depth. Meanwhile an algorithm that can smooth differential propagation phase （~Dp） for estimating the high-resolution specific differential phase （KDP） was developed. After attenuation correction of reflectivity in horizontal polarization （ZH） and differential reflectivity （ZDR） of PCDJ radar by means of KDP, the data quality was improved significantly. Using quality-controlled radar data, quantitative rainfall estimation was performed, and the resutls were compared with rain-gauge measurements. A synthetic ZH /KDp-based method was analyzed. The results the traditional ZH-based method when the rain suggest that the synthetic method has the advantage over rate is 〉5 mm h^-1. The more intensive the rain rates, the higher accuracy of the estimation.

Evaluation of CHIRPS Satellite Gridded Dataset as an Alternative Rainfall Estimate for Localized Modelling over Uganda

The Ugandan economy is largely dependent on rural-based and rain-fed agriculture. This creates a critical need to understand the rainfall dynamics at the local scale. However, the country has a sternly sparse and unreliable rain gauge network. This research, therefore, sets out to evaluate the use of the CHIRPS satellite gridded dataset as an alternative rainfall estimate for local modelling of rainfall in Uganda. Complete, continuous and reliable in situ station observations for the period between 2012 and 2020 were used for the comparison with CHIRPS satellite data models in the same epoch. Rainfall values within the minimum 5 km and maximum 20 km radii from the in situ stations were extracted at a 5 km interval from the interpolated in situ station surface and the CHIRPS satellite data model for comparison. Results of the 5 km radius were adopted for the evaluation as it’s closer to the optimal rain gauge coverage of 25 km2. They show the R2 = 0.91, NSE = 0.88, PBias = -0.24 and RSR = 0.35. This attests that the CHIRPS satellite gridded datasets provide a good approximation and simulation of in situ station data with high collinearity and minimum deviation. This tallies with related studies in other regions that have found CHIRPS datasets superior to interpolation surfaces and sparse rain gauge data in the comprehensive estimation of rainfall. With a 0.05° * 0.05° (Latitude, longitude) spatial resolution, CHIRPS satellite gridded rainfall estimates are therefore able to provide a comprehensive rainfall estimation at a local scale. Essentially these results reward research science in regions like Uganda that have sparse rain gauges networks characterized by incomplete, inconsistent and unreliable data with an empirically researched alternative source of rainfall estimation data. It further provides a platform to scientifically interrogate the rainfall dynamics at a local scale in order to infuse local policy with evidence-based formulation and application.

Determination of the critical rainfall of runoff-initiated debris flows by the perspective of physical mechanics and Shields stress

The critical rainfall of runoff-initiated debris flows is utmost importance for local early hazard forecasting.This paper presents research on the critical rainfall of runoff-initiated debris flows through comparisons between slope gradients and three key factors,including topographic contributing area,dimensionless discharge,and Shields stress.The rainfall amount was estimated by utilizing in-situ rainfall records and a slope-dependent Shields stress model was created.The created model can predict critical Shields stress more accurately than the other two models.Furthermore,a new dimensionless discharge equation was proposed based on the corresponding discharge-gradient datasets.The new equation,along with factors such as contributing area above bed failure sites,channel width,and mean diameter of debris flow deposits,predicts a smaller rainfall amount than the in-situ measured records.Although the slope-dependent Shields stress model performs well and the estimated rainfall amount is lower than the in-situ records,the sediment initiation in the experiments falls within sheet flow regime due to a large Shields stress.Therefore,further sediment initiation experiments at a steeper slope range are expected in the future to ensure that the sediment transport belongs to mass failure regime characterized by a low level of Shields stress.Finally,a more accurate hazard forecast on the runoff-initiated debris flow holds promise when the corresponding critical slope-dependent dimensionless discharge of no motion,fluvial sediment transport,mass flow regime,and sheet flow regime are considered.

Accuracy and spatio-temporal variation of high resolution satellite rainfall estimate over the Ganjiang River Basin

Based on spatial interpolation rainfall of the ground gauge measurement,we proposed a method to comprehensively evaluate and compare the accuracy of satellite rainfall estimates (SREs) at three spatial scales:0.25°×0.25° grid scale,sub-catchment scale and the whole basin scale.Using this method,we evaluated the accuracy of six high-resolution monthly SREs (TRMM 3B42 V6,3B42RT V6,CMORPH,GSMaP MWR+,GSMaP MVK+ and PERSIANN) and revealed the spatio-temporal variation of the SRE accuracy based on spatial interpolated rainfall from a dense network of 325 gauges during 2003-2009 over the Ganjiang River Basin in the Southeast China.The results showed that ground gauge-calibrated 3B42 had the highest accuracy with slight overestimation,whereas the other five uncalibrated SREs had severe underestimation.The accuracy of the six SREs in wet seasons was remarkably higher than that in the dry seasons.When the time scale was expanded,the accuracy of SRE,particularly 3B42,increased.Furthermore,the accuracy of SREs was relatively low in the western mountains and northern piedmont areas,while it was relatively high in the central and southeastern hills and basins of the Ganjiang River Basin.When the space scale was expanded,the accuracy of the six SREs gradually increased.This study provided an example for of SRE accuracy validation in other regions,and a direct basis for further study of SRE-based hydrological process.

Validation of the Satellite-Derived Rainfall Estimates over the Tibet

Measuring rainfall from space appears to be the only cost effective and viable means in estimating regional precipitation over the Tibet, and the satellite rainfall products are essential to hydrological and agricultural modeling. A long-standing problem in the meteorological and hydrological studies is that there is only a sparse raingauge network representing the spatial distribution of precipitation and its quantity on small scales over the Tibet. Therefore, satellite derived quantitative precipitation estimates are extremely usefill for obtaining rainfall patterns that can be used by hydrological models to produce forecasts of river discharge and to delineate the flood hazard area. In this paper, validation of the US National Oceanic and Atmospheric Administration （NOAA） Climate Prediction Center （CPC） RFE （rainfall estimate） 2.0 data was made by using daily rainfall observations at 11 weather stations over different climate zones from southeast to northwest of the Tibet during the rainy season from 1 June to 30 September 2005 and 2006. Analysis on the time series of daily rainfall of RFE-CPC and observed data in different climate zones reveals that the mean correlation coefficients between satellite estimated and observed rainfall is 0.74. Only at Pali and Nielamu stations located in the southern brink of the Tibet along the Himalayan Mountains, are the correlation coefficients less than 0.62. In addition, continuous validations show that the RFE performed well in different climate zones, with considerably low mean error （ME） and root mean square error （RMSE） scores except at Nielamu station along the Himalayan range. Likewise, for the dichotomous validation, at most stations over the Tibet, the probability of detection （POD） values is above 73% while the false alarm rate （FAR） is between 1% and 12%. Overall, NOAA CPC RFE 2.0 products performed well in the estimation and monitoring of rainfall over the Tibet and can be used to analyze the precipitation pattern, produce discharge forecast, and delineate the flood hazard area.

Satellite estimates and subpixel variability of rainfall in a semi-arid grassland

Uncertainties in satellite rainfall estimation may derive from both the local rainfall characteristics and its subpixel variability.To study this issue,Micro Rain Radars and a rain gauge network were deployed within a 9-km satel-lite pixel in the semi-arid Xilingol grassland of China in summer 2009.The authors characterized the subpixel variability with the coefficient of variation(CV)and evaluated the satellite rainfall estimation for this semi-arid area.The results showed that rainfall events with a high CV were mostly convective with a small amount of rain-fall.Spatially inhomogeneous rainfall was most likely to occur at the edges of small clouds producing rain.The performance of the TRMM(Tropical Rainfall Measuring Mission)3B42V7 product for daily rainfall was better than that of the CMORPH(Climate Prediction Center morphing technique)and PERSIANN(Precipitation Estima-tion from Remotely Sensed Information Using Artificial Neural Networks)products,although the TRMM product tended to overestimate rainfall in a lake area of the semi-arid grassland.

Convective and Stratiform Cloud Rainfall Estimation from Geostationary Satellite Data

The Bayes Decision (BD) method was used to distinguish the convective and stratiform components of cloud systems from GMS-4 satellite data. A technique originally developed by Adler and Negri (1988, hereafter abbreviated AN) was improved for estimating the convective and stratiform cloud precipitation areas and rates of cloud systems from GMS satellite imagery. It has been applied to a tropical cyclonic cloud cluster observed over east coast area of China on September 23, 1992, which brought about flood disaster in that region. Overlaid 6-hour surface rainfall observations show that the rainfall areas and amounts match with results from improved AN technique. The successful application of the Adler and Negri's technique to convective and stratiform clouds provides encouragement for the use of this method over large region of mid-latitude China where radar data are not fully covered.

Rainfall Estimation using Image Processing and Regression Model on DWR Rainfall Product for Delhi-NCR Region

Observed rainfall is a very essential parameter for the analysis of rainfall,day to day weather forecast and its validation.The observed rainfall data is only available from five observatories of IMD;while no rainfall data is available at various important locations in and around Delhi-NCR.However,the 24-hour rainfall data observed by Doppler Weather Radar(DWR)for entire Delhi and surrounding region(up to 150 km)is readily available in a pictorial form.In this paper,efforts have been made to derive/estimate the rainfall at desired locations using DWR hydrological products.Firstly,the rainfall at desired locations has been estimated from the precipitation accumulation product(PAC)of the DWR using image processing in Python language.After this,a linear regression model using the least square method has been developed in R language.Estimated and observed rainfall data of year 2018(July,August and September)was used to train the model.After this,the model was tested on rainfall data of year 2019(July,August and September)and validated.With the use of linear regression model,the error in mean rainfall estimation reduced by 46.58% and the error in max rainfall estimation reduced by 84.53% for the year 2019.The error in mean rainfall estimation reduced by 81.36% and the error in max rainfall estimation reduced by 33.81%for the year 2018.Thus,the rainfall can be estimated with a fair degree of accuracy at desired locations within the range of the Doppler Weather Radar using the radar rainfall products and the developed linear regression model.

Application of a Cloud-Texture Analysis Scheme to the Cloud Cluster Structure Recognition and Rainfall Estimation in a Mesoscale Rainstorm Process

It is thought that satellite infrared （IR） images can aid the recognition of the structure of the cloud and aid the rainfall estimation. In this article, the authors explore the application of a classification method relevant to four texture features, viz. energy, entropy, inertial-quadrature and local calm, to the study of the structure of a cloud cluster displaying a typical meso-scaie structure on infrared satellite images. The classification using the IR satellite images taken during 4-5 July 2003, a time when a meso-scale torrential rainstorm was occurring over the Yangtze River basin, illustrates that the detailed structure of the cloud cluster can be obviously seen by means of the neural network classification method relevant to textural features, and the relationship between the textural energy and rainfall indicates that the structural variation of a cloud cluster can be viewed as an exhibition of the convection intensity evolvement. These facts suggest that the scheme of following a classification method relevant to textural features applied to cloud structure studies is helpful for weather analysis and forecasting.

Research on Rainfall Estimation Based on Improved Kalman Filter Algorithm

In order to solve the rainfall estimation error caused by various noise factors such as clutter,super refraction,and raindrops during the detection process of Doppler weather radar.This paper proposes to improve the rainfall estimation model of radar combined with rain gauge which calibrated by common Kalman filter.After data preprocessing,the radar data should be classified according to the precipitation intensity.And then,they are respectively substituted into the improved filter for calibration.The state noise variance Q(k)and the measurement noise variance R(k)can be adaptively calculated and updated according to the input observation data during this process.Then the optimal parameter value of each type of precipitation intensity can be obtained.The state noise variance Q(k)and the measurement noise variance R(k)could be assigned optimal values when filtering the remaining data.This rainfall estimation based on semiadaptive Kalman filter calibration not only improves the accuracy of rainfall estimation,but also greatly reduces the amount of calculation.It avoids errors caused by repeated calculations,and improves the efficiency of the rainfall estimation at the same time.

Improving Radar Rainfall Estimation by Accounting for Microphysical Processes Using a Micro Rain Radar in West Africa

This study evaluates the improvement of the radar Quantitative Precipitation Estimation (QPE) by involving microphysical processes in the determination of Z-R algorithms. Within the framework of the AMMA campaign, measurements of an X-band radar (Xport), a vertical pointing Micro Rain Radar (MRR) to investigate microphysical processes and a dense network of rain gauges deployed in Northern Benin (West Africa) in 2006 and 2007 were used as support to establish such estimators and evaluate their performance compared to other estimators in the literature. By carefully considering and correcting MRR attenuation and calibration issues, the Z-R estimator developed with the contribution of microphysical processes and non-linear least-squares adjustment proves to be more efficient for quantitative rainfall estimation and produces the best statistic scores than other optimal Z-R algorithms in the literature. We also find that it gives results comparable to some polarimetric algorithms including microphysical information through DSD integrated parameter retrievals.

Test Study on Flood Forecast by Merging Multi Precipitation Data

Shuibuya control basin in upper reaches of Qingjiang River,Hubei Province was taken as the case. By combining grouping Z-I relation with ground meteorological rainfall station,rainfall estimation by radar was calibrated,and actual average surface rainfall in the basin was calculated.By combining genetic algorithm with neural network,the corrected AREM rainfall forecast model was established,to improve rainfall forecast accuracy by AREM. Finally,AREM rainfall forecast models before and after correction were input in Xin＇an River hydrologic model for flood forecast test. The results showed that the corrected AREM rainfall forecast model could significantly improve forecast accuracy of accumulative rainfall,and decrease range of average relative error was more than 60%. Hourly rainfall forecast accuracy was improved somewhat,but there was certain difference from actual situation. Average deterministic coefficient of AREM flood forest test before and after correction was improved from -32. 60% to 64. 38%,and relative error of flood peak decreased from 39. 00% to 25. 04%. The improved effect of deterministic coefficient was better than relative error of flood peak,and whole flood forecast accuracy was improved somewhat.

A FUSING TECHNIQUE WITH SATELLITE PRECIPITATION ESTIMATE AND RAINGAUGE DATA

Satellite rainfall estimate can provide rainfall information over large areas,and raingauge can provide point-based ground observations with high accuracy.With the combination of satellite and raingauge data together,the estimated rainfall fields are greatly improved.This combination method,called 'fusing technique',is discussed in this paper,and the validation for this technique is accomplished with HUBEX IOP data.

A Comparison of De-noising Methods for Differential Phase Shift and Associated Rainfall Estimation

Measured differential phase shift ΦDP is known to be a noisy unstable polarimetric radar variable, such that the quality of ΦDP data has direct impact on specific differential phase shift KDP estimation, and subsequently, the KDP-based rainfall estimation. Over the past decades, many ΦDP de-noising methods have been developed; however, the de-noising effects in these methods and their impact on KDP-based rainfall estimation lack comprehensive comparative analysis. In this study, simulated noisy ΦDP data were generated and de-noised by using several methods such as finite-impulse response（FIR）, Kalman, wavelet,traditional mean, and median filters. The biases were compared between KDP from simulated and observedΦDP radial profiles after de-noising by these methods. The results suggest that the complicated FIR, Kalman,and wavelet methods have a better de-noising effect than the traditional methods. After ΦDP was de-noised,the accuracy of the KDP-based rainfall estimation increased significantly based on the analysis of three actual rainfall events. The improvement in estimation was more obvious when KDP was estimated with ΦDP de-noised by Kalman, FIR, and wavelet methods when the average rainfall was heavier than 5 mm h-1.However, the improved estimation was not significant when the precipitation intensity further increased to a rainfall rate beyond 10 mm h-1. The performance of wavelet analysis was found to be the most stable of these filters.

Study of Different Attenuation Correction Methods in Association with Rainfall Estimation for X-Band Polarimetric Radars

Different attenuation correction methods for the X-band dual linear polarimetric radar are analyzed in this paper.The specific differential phase shift KDP is always considered as an effective factor in radar signal attenuation correction.However,the values of KDP for light rains are too small,which results in unstable quality and large errors of rainfall estimation.Therefore,radar horizontal reflectivity ZH and specific differential phase shift are combined together in the ZH-KDP method to correct the attenuation error.Based on the similar consideration,a ZH-KDP-R combined technique is also proposed to estimate rainfall（R）.During the development and set-up of the synchronous transmitting and receiving dual polaximetric Doppler weather radar with 3.2 cm wave length,a set of observational data were obtained in the field experiment in Pingliang,Gansu Province in August 2005.Some continuous measurements with 5-12-minute intervals were gained in the time period from 1508 to 2205（Beijing Time）11 August 2005.Using the data,the performance of the combined attenuation correction and rainfall estimation methods is examined.The results indicate that the ZH-KDP combined method is effective and the correction speed meets the requirement of real-time operations.The analysis of the precipitation process shows that the ZH-KDP-R combined technique is more suitable for rainfall estimation than single factor methods such as the KDP-R or Z-R relation,and the estimated results are in good agreement with automatic rain gauge records.As to the Z-R relation,the deviation between the precipitation estimation and the available gauge measurement decreases obviously when the corrected ZH is used,indicating that the radar data quality has been obviously improved after the attenuation correction.