Marine X-band weather radar data calibration
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Quantitative precipitation estimation
Application of weather radar data in urban hydrology is evolving and radar data is now applied for both modelling, analysis and real time control purposes. In these contexts, it is all-important that the radar data well calibrated and adjusted in order to obtain valid quantitative precipitation estimates. This paper compares two calibration procedures for a small marine X-band radar by comparing radar data with rain gauge data. Validation shows a very good consensus with regards to precipitation volumes, but more diverse results on peak rain intensities.
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This chapter summarizes principles of radar quantitative precipitation estimates (QPE), as used by the National Weather Service, and considerations in applying radar and gauge QPE to hydrologic modeling tasks. It describes the processing sequence for Weather Service Radar 1988 Doppler (WSR-88D) and gauge-radar precipitation estimates and offers basic principles of radar data acquisition and processing. The chapter provides radar QPE errors’ statistical characteristics and briefly describes basic approaches to statistical blending of radar and rain gauge estimates. The chapter considers the relative value of radar and gauge QPE in situations in which gauge data are readily available, or where radar coverage is compromised. It also summarizes possibilities for using daily rain gauge input to adjust subdaily estimates and lists current sources for WSR-88D, gauge-radar multisensor, and rain gauge data.
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Weather radar networks provide data with good spatial coverage and temporal resolution. Hence they are able to describe the variability of precipitation. Typical radar stations determine the rain rate for every square kilometre and make a full volume scan within about 5 minutes. A weakness however, is their often poor metering precision limiting the applicability of the radar for hydrological purposes. In contrast to rain gauges, which measure precipitation directly on the ground, the radar determines the reflectivity aloft and remote. Due to this principle, several sources of possible errors occur. Therefore improving the radar estimates of rainfall is still a vital topic in radar meteorology and hydrology.
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A series of experiments were undertaken to determine if any operational benefit might be realized from an upgrade of WSR-88D hourly precipitation products to the highest spatial resolution now possible, namely 0.5° x 250 m. The precipitation products are currently disseminated in arrays of 1.0° x 1000 m spacing. Reflectivity from the NCAR S-Pol S-band radar (a radar with similar characteristics to the WSR-88D) in east-central Florida during the summer of 1998 were converted into precipitation estimates and collated with 1-hour gauge accumulations collected from several high-density rain gauge networks that were deployed simultaneously in the region. Radar-gauge correlations were calculated for several degrees of spatial aggregation, ranging from 1.0° x 150 m to 1.0° x 900 m. Correlations were estimated from over 8000 radar-gauge pairs, for both single points and areal averages. It appears that the degree of spatial aggregation of the radar estimates has little effect on radar-gauge correlations.
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Assessment of radar-based precipitation estimates using rain gauge observations is a critical exercise in evaluating pre-and postcorrected (gauge-adjusted) radar-based precipitation data. A comprehensive assessment framework combining several visual, quantitative, and statistical measures, indexes, and skill scores is proposed and developed for evaluation of radar-based precipitation estimates in space and time. Contingency measures, skill scores, and a few new metrics are proposed and are evaluated along with several indexes. Visual measures provide a quick check of agreement between radar and rain gauge data sets. Quantitative measures provide information about errors, and skill scores assess the quality of radar data for dichotomous (rain and no-rain) events. Summary statistics and hypothesis tests in statistical categories provide insights into distributional aspects of the rain gauge and radar data sets. The framework is used for evaluation of 15-min radar-based precipitation data obtained from the South Florida Water Management District (SFWMD). Four years of radar and rain gauge data available at 189 sites are used for analysis. Results suggest that radar data in the SFWMD region have progressively improved during the period of analysis. All indexes and skill scores used in the current study suggest that radar data are of good quality at different temporal resolutions and in agreement with rain gauge data. However, spatial bias evaluation suggests that radar data underestimate precipitation amounts in two areas of the SFWMD region.
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One of the main concerns with quantitative precipitation estimates (QPE) based on weather radar observations is the extent to which nowcasters should believe them as they rush to issue warnings for dangerous weather phenomena that might endanger human lives and goods. This paper aims to improve QPE by adjusting the mean field bias using rain gauge measurements. Radar data used in this research were supplied from a single polarization S-band Doppler radar, WSR-98D (Weather Surveillance Radar – 98 Doppler), located almost in the centre of Romania, at Bobohalma, and a network consisting of 27 rain gauges within weather stations belonging to the Romanian National Meteorological Administration. The procedure consisted of two main steps: in step one, the reflectivity data were converted into rain rate using the Z–R relationship; in step two, differences between radar data and gauge data were investigated using four objective functions, the ratio between radar data and gauge data, the root-mean-square factor, and Pearson and Spearman correlations. The findings are consistent with previous studies, emphasizing that both the differences and correlations between radar data and rain gauge amounts have local significance rather than general relevance over the studied area.
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정량적인 강수량 추정은 기상학 수문학적 연구와 활용에 가장 중요한 요소 중 하나이다. 본 논문에서는 정량적 강수량 추정을 위하여 레이더 강우의 지리통계적 오차 구조 함수를 공동크리깅에 적용하여 레이더 강우강도를 조정하였다. 레이더 강우강도의 오차는 공동크리깅의 주변수로서 지상 우량계와 레이더의 강우강도의 차이로 산출되었다. 지상 우량계 강우장은 공동크리깅의 이차변수로서 정규크리깅에 의해 산출되었다. 레이더 강우강도의 오차 분포는 실험적 베리오그램으로 결정된 이론적 베리오그램을 공동크리깅에 적용하여 생성되었고, 레이더 강우강도 조정을 위하여 레이더 강우강도의 오차 분포를 레이더 강우장에 적용하였다. 본 연구의 검증을 위하여 국지적으로 호우가 발생하였던 2009년 7월 6일에서 7일까지의 강우 사례를 선정하였다. 오차가 조정된 1시간 레이더 누적강우량과 지상 우량계 누적강우량의 상관성은 조정 전에 비하여 0.55에서 0.84로 향상되었고, 평균제곱근오차는 7.45에서 3.93 mm로 조정되었다. Quantitative precipitation estimation (QPE) is one of the most important elements in meteorological and hydrological applications. In this study, we adjusted the QPE from an S-band weather radar based on co-kriging method using the geostatistical structure function of error distribution of radar rainrate. In order to estimate the accurate quantitative precipitation, the error of radar rainrate which is a primary variable of co-kriging was determined by the difference of rain rates from rain gauge and radar. Also, the gauge rainfield, a secondary variable of co-kriging is derived from the ordinary kriging based on raingauge network. The error distribution of radar rain rate was produced by co-kriging with the derived theoretical variogram determined by experimental variogram. The error of radar rain rate was then applied to the radar estimated precipitation field. Locally heavy rainfall case during 6-7 July 2009 is chosen to verify this study. Correlation between adjusted one-hour radar rainfall accumulation and rain gauge rainfall accumulation improved from 0.55 to 0.84 when compared to prior adjustment of radar error with the adjustment of root mean square error from 7.45 to 3.93 mm.
Quantitative precipitation estimation
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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.
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Quantitative precipitation forecast
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Gridded quantitative precipitation estimation (QPE) of high spatial and temporal resolution is now being increasingly used for instance for hydrological modelling. The combination between observations from radar data and in-situ rain-gauge measurement is a popular mean to produce QPEs. Since the QPE generated from radar data only is considered to be not satisfactory, some corrections should be introduced in order to achieve better accuracy of the instant precipitation estimation. The correction method that were used were Mean Field Bias Correction (MFB) and static adjustment. MFB was applied by the assumption that the radar estimation was affected by uniform multiplicative error. The static adjustment was applied to correct radar rainfall data through plotting between radar rainfall and rain gauge so that the regression line between radar and rain gauge was approaching one. The purpose of radar QPE corrections was for hydrological modelling applications.
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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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