Estimation of radar-rainfall error spatial correlation
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Keywords:
Spatial correlation
Spatial Dependence
Weather radar
Rainfall information plays a crucial part in predicting the occurrences of debris flows. Due to the limitation of its spatial resolution, the traditional rain gauge network is increasingly challenged by the weather radar in rainfall measurements. However, despite its popularity among academic water engineers, the weather radar has not been recognised by many applied water engineers as a viable alternative rainfall device to rain gauges. The authors believe that the uncertainty nature of the weather radar is one of the major inhibitors in its wider applications. This paper describes a study on the uncertainty features of weather radar rainfall based on its error characteristics (bias, variance and temporal autocorrelation). Adense rain gauge network over Brue catchment of SWEngland (49 gauges over 136 km2) and two nearby C-band radars are used to derive these three important features required for the development of a realistic error model of radar rainfall data. It has been found that error bias and variance are variable depending on the rainfall magnitude. The error time series shows a significant autocorrelation among its data points. It has been found that no existing error models are able to address these features, hence there is an urgent need for more research work in this area.
Weather radar
Hydrological modelling
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Weather Radar is an equipment used for detecting the position and direction of atmospheric movement. It was calculated the time travel of electromagnetic wave which sent and received to the objects. The radar reflectance could be estimated amount of water vapor in the cloud, but it was not exact value of surface rain water. Therefore, to examine the relationship and trend between rainfall from weather radar and surface rain gauge station, we use Z-R relationship equation to calculate rainfall from weather radar and compared with rain gauge station. The study area is Chiang Mai province and the chosen period were as March 4th, June 10th and August 10th in year 2013. The data acquisition of rainfall values measured by surface rain gauge station from Northern Meteorological Center and the weather radar map from Lamphun radar station covered 6 upper-north provinces (83 rain gauge stations) which included of the weather radar map from Omkoi radar station covered 63 rain gauge station. The results shown that consistency coefficients of determination (R-Squared) of the rainfall between from rain gauge station and calculated from weather radar map. The interpolation and hot spot analysis were shown the similar relationship and trend of the rainfall from both places in term of spatial analysis.
Weather radar
Weather station
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Weather radar
Quantitative precipitation estimation
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Abstract Inversion of regional rainfall by the attenuation of microwave links has been confirmed a promising method for monitoring large areas of rainfall. To further explore precipitation information from microwave links data, a reconstruct method of rainfall field using microwave links, weather radar and rain gauges is presented in this letter. We utilize mean correction factor method to adjust the rainfall field from radar by microwave links and rain gauges; and validate it by a field experiment using two microwave links, a S-band radar, and 15 rain gauges. The results show that the rainfall field can be improved significantly by joint reconstruction of microwave links, weather radar and rain gauges, and the joint reconstruction in evenly distributed area is better than that in unevenly distributed area. This method supplements an effective approach to reduce the uncertainty of single instrument and improve the rainfall field by using multiple instruments.
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Abstract. The first operational weather radar with dual polarization capabilities was recently installed in Austria. The use of polarimetric radar variables rises several expectations: an increased accuracy of the rain rate estimation compared to standard Z-R relationships, a reliable use of attenuation correction methods, and finally hydrometeor classification. In this study the polarimetric variables of precipitation events are investigated and the operational quality of the parameters is discussed. For the new weather radar also several polarimetric rain rate estimators, which are based on the horizontal polarization radar reflectivity, ZH, the differential reflectivity, ZDR, and the specific differential propagation phase shift, KDP, have been tested. The rain rate estimators are further combined with an attenuation correction scheme. A comparison between radar and rain gauge indicates that ZDR based rain rate algorithms show an improvement over the traditional Z-R estimate. KDP based estimates do not provide reliable results, mainly due to the fact, that the observed KDP parameters are quite noisy. Furthermore the observed rain rates are moderate, where KDP is less significant than in heavy rain.
Weather radar
Differential phase
Disdrometer
C band
Early-warning radar
Rain rate
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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.
Quantitative precipitation estimation
Weather radar
Quantitative precipitation forecast
Nowcasting
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This paper presents a comparison between rain gauge network and weather radar data in Angra dos Reis city, located in the State of Rio de Janeiro (RJ), Brazil. The city has a high incidence of natural disasters, especially associated with heavy rains in densely populated areas. In this work, weather radar data with a spatial resolution of 1 km were obtained from dual-polarimetric S-band radar operated by the Environmental State Institute of Rio de Janeiro (INEA), located in the Guaratiba neighborhood in Rio de Janeiro city, Brazil; the rain gauge measurements were provided by the National Center for Monitoring and Warning of Natural Disasters (CEMADEN), which is composed of a network with 30 rain gauges covering the studied area. The comparison between the two datasets enables the analysis of which radar products better fit the rain gauge network’s accumulated rainfall by quantifying the uncertainties in precipitation estimates at radar pixels where rain gauges are located. The results indicated that radar products generated with the help of regression techniques obtained from the relation between radar reflectivities and rain gauge measurements were a better fit, constituting essential information while dealing with efficient regulation for rainfall monitoring and forecasting to minimize the risks associated with extreme events.
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Isohyetal maps of daily accumulated precipitation were made using data collected in January, 2015, by a rain gauge network and an S-band Doppler weather radar. The maps were compared to determine the quality of the precipitation estimated using the radar data. The results indicate that within the study area, the S-band Doppler radar and the rain gauge network produced similar results, suggesting that within certain conditions the weather radar can perform as well as a rain gauge network.
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