Accuracy of Quantitative Precipitation Estimation Using Operational Weather Radars: A Case Study of Heavy Rainfall on 9–10 September 2015 in the East Kanto Region, Japan

2016 ◽  
Vol 11 (5) ◽  
pp. 1003-1016 ◽  
Author(s):  
Shakti P. C. ◽  
◽  
Ryohei Misumi ◽  
Tsuyoshi Nakatani ◽  
Koyuru Iwanami ◽  
...  
Keyword(s):  
Heavy Rainfall ◽  
Temporal Variations ◽  
Rain Gauge ◽  
Rainfall Event ◽  
Rain Gauge Data ◽  
Frequency Distributions ◽  
Radar Network ◽  
Radar Networks ◽  
Gauge Data ◽  
Kanto Region

On 9–10 September 2015, the East Kanto region of Japan experienced a period of record-breaking heavy rainfall that caused a number of fatalities and serious property damage. The maximum 24-hr rainfall total (0600 UTC 9 September 2015 to 0600 UTC 10 September 2015), about 500 mm, was recorded over Tochigi Prefecture. Spatial and temporal variations in the meteorological and hydrological characteristics of this rainfall event were analyzed using data from the Japan Meteorological Agency’s (JMA) C-band radar network and data from the X-band polarimetric radar network (XRAIN). The rain gauge data available from the Kanto region has a temporal resolution of 10 min. The spatial and temporal resolutions of the JMA C-band radar data are 1000 m and 5 min, respectively, whereas the XRAIN radar has spatial and temporal resolutions of 250 m and 1 min, respectively. Data from the two radar networks were compared, both with each other and with data from various rain gauge networks to validate their accuracy. The 24-hr total rainfall data from both radar networks showed frequency distributions similar to those showed by the rain gauge data. However, the JMA and XRAIN data showed different distributions for the higher rainfall intensity thresholds. There was no relationship evident between rainfall and elevation in either of the radar datasets recorded during this event. The spatial distribution of rainfall over the study area derived from XRAIN showed clear variations, whereas the JMA radar did not. This is most probably related to the coarser spatial and temporal resolutions of the JMA observations. Based on a comparison of data from the rain gauge and radar networks, the XRAIN data more accurately reflected the rain gauge stations than did the JMA data. From a hydrological perspective, the Kinugawa watershed is unique in terms of its topography. The upper part of the watershed is wide and mountainous, whereas the rest is narrow and elongate north–south. The rain echo moved from south to north over the catchment, and the highest 24-hr accumulated rainfall totals were recorded mostly in the upper (northern) part of the Kinugawa watershed, whereas there was less rainfall in the lower (southern) part. This pattern suggests a high probability of serious flooding along the Kinugawa River in the days following such a rainfall event if the heaviest rainfall moves northwards over the watershed.

Analysis of the 1 November 2015 heavy rainfall episode in Algarve by using weather radar and rain gauge data

2017 ◽  
Vol 93 (S1) ◽  
pp. 61-76
Author(s):  
Sérgio Barbosa ◽  
Álvaro Silva ◽  
Paulo Narciso
Keyword(s):  
Heavy Rainfall ◽  
Weather Radar ◽  
Rain Gauge ◽  
Rain Gauge Data ◽  
Gauge Data

scholarly journals Vertical Variation of Z-R Relationship at Hallasan Mountain during Typhoon Nakri in 2014

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Chulsang Yoo ◽  
Jung Mo Ku
Keyword(s):  
Rain Rate ◽  
Rain Gauge ◽  
Rainfall Event ◽  
Jeju Island ◽  
Vertical Variation ◽  
Rain Gauge Data ◽  
Orographic Effect ◽  
Gauge Data ◽  
Reflectivity Data ◽  
Average Rain

Hallasan Mountain is located at the center of Jeju Island, Korea. Even though the height of the mountain is just 1,950 m, the orographic effect is strong enough to cause heavy rainfall. In this study, a rainfall event, due to Typhoon Nakri in 2014, observed in Jeju Island was analyzed fully using the radar and rain gauge data. First, the Z-R relationship Z=ARb was derived for every 250 m interval from the sea level to the mountain top. The resulting Z-R relationships showed that the exponent b could be assumed as constant but that the parameter A showed a significant decreasing trend up to an altitude around 1,000 m before it increased again. The orographic effect was found to be most significant at this altitude of 1,000 m. Second, the derived Z-R relationships were applied to the corresponding altitude radar reflectivity data to generate the rain rate field over Jeju Island. This rain rate field was then used to derive the areal-average rain rate data. These data were found to be very similar to the rain gauge estimates but were significantly different from those derived from the application of the Marshall-Palmer equation to the 1.5 km CAPPI data, which is the data type that is generally used by the Korea Meteorological Administration (KMA).

scholarly journals Analysis of heavy rainfall using radar rain gauge data in Hiroshima Prefecture

2002 ◽  
Vol 46 ◽  
pp. 43-48
Author(s):  
Akihide WATANABE ◽  
Shoji FUKUOKA ◽  
Yosihiko AOYAMA ◽  
Fumiharu ADACHI
Keyword(s):  
Heavy Rainfall ◽  
Rain Gauge ◽  
Rain Gauge Data ◽  
Gauge Data ◽  
Hiroshima Prefecture

scholarly journals Improving Multisensor Precipitation Estimation via Adaptive Conditional Bias–Penalized Merging of Rain Gauge Data and Remotely Sensed Quantitative Precipitation Estimates

2019 ◽  
Vol 20 (12) ◽  
pp. 2347-2365 ◽  
Author(s):  
Ali Jozaghi ◽  
Mohammad Nabatian ◽  
Seongjin Noh ◽  
Dong-Jun Seo ◽  
Lin Tang ◽  
...  
Keyword(s):  
Computational Cost ◽  
Mean Field ◽  
Remotely Sensed ◽  
Rain Gauge ◽  
Rain Gauge Data ◽  
Gauge Data ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Radar Satellite ◽  
Quantitative Precipitation Estimates

Abstract We describe and evaluate adaptive conditional bias–penalized cokriging (CBPCK) for improved multisensor precipitation estimation using rain gauge data and remotely sensed quantitative precipitation estimates (QPE). The remotely sensed QPEs used are radar-only and radar–satellite-fused estimates. For comparative evaluation, true validation is carried out over the continental United States (CONUS) for 13–30 September 2015 and 7–9 October 2016. The hourly gauge data, radar-only QPE, and satellite QPE used are from the Hydrometeorological Automated Data System, Multi-Radar Multi-Sensor System, and Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR), respectively. For radar–satellite fusion, conditional bias–penalized Fisher estimation is used. The reference merging technique compared is ordinary cokriging (OCK) used in the National Weather Service Multisensor Precipitation Estimator. It is shown that, beyond the reduction due to mean field bias (MFB) correction, both OCK and adaptive CBPCK additionally reduce the unconditional root-mean-square error (RMSE) of radar-only QPE by 9%–16% over the CONUS for the two periods, and that adaptive CBPCK is superior to OCK for estimation of hourly amounts exceeding 1 mm. When fused with the MFB-corrected radar QPE, the MFB-corrected SCaMPR QPE for September 2015 reduces the unconditional RMSE of the MFB-corrected radar by 4% and 6% over the entire and western half of the CONUS, respectively, but is inferior to the MFB-corrected radar for estimation of hourly amounts exceeding 7 mm. Adaptive CBPCK should hence be favored over OCK for estimation of significant amounts of precipitation despite larger computational cost, and the SCaMPR QPE should be used selectively in multisensor QPE.

scholarly journals Experiences in using the TMPA-3B42R satellite data to complement rain gauge measurements in the Ecuadorian coastal foothills

2013 ◽  
Vol 17 (7) ◽  
pp. 2905-2915 ◽  
Author(s):  
M. Arias-Hidalgo ◽  
B. Bhattacharya ◽  
A. E. Mynett ◽  
A. van Griensven
Keyword(s):  
Satellite Data ◽  
Bias Correction ◽  
Hydrological Modeling ◽  
New Technologies ◽  
Simple Procedure ◽  
Rain Gauge ◽  
Experimental Methodology ◽  
Rain Gauge Data ◽  
Gauge Data ◽  
Satellite Rainfall

Abstract. At present, new technologies are becoming available to extend the coverage of conventional meteorological datasets. An example is the TMPA-3B42R dataset (research – v6). The usefulness of this satellite rainfall product has been investigated in the hydrological modeling of the Vinces River catchment (Ecuadorian lowlands). The initial TMPA-3B42R information exhibited some features of the precipitation spatial pattern (e.g., decreasing southwards and westwards). It showed a remarkable bias compared to the ground-based rainfall values. Several time scales (annual, seasonal, monthly, etc.) were considered for bias correction. High correlations between the TMPA-3B42R and the rain gauge data were still found for the monthly resolution, and accordingly a bias correction at that level was performed. Bias correction factors were calculated, and, adopting a simple procedure, they were spatially distributed to enhance the satellite data. By means of rain gauge hyetographs, the bias-corrected monthly TMPA-3B42R data were disaggregated to daily resolution. These synthetic time series were inserted in a hydrological model to complement the available rain gauge data to assess the model performance. The results were quite comparable with those using only the rain gauge data. Although the model outcomes did not improve remarkably, the contribution of this experimental methodology was that, despite a high bias, the satellite rainfall data could still be corrected for use in rainfall-runoff modeling at catchment and daily level. In absence of rain gauge data, the approach may have the potential to provide useful data at scales larger than the present modeling resolution (e.g., monthly/basin).

scholarly journals On evaluation of ensemble precipitation forecasts with observation-based ensembles

2007 ◽  
Vol 10 ◽  
pp. 139-144 ◽  
Author(s):  
B. Ahrens ◽  
S. Jaun
Keyword(s):  
Rain Gauge ◽  
Performance Measure ◽  
Ensemble Prediction ◽  
Limited Area ◽  
Large Area ◽  
Ensemble Prediction System ◽  
Rain Gauge Data ◽  
Forecast Lead Time ◽  
Gauge Data ◽  
The One

Abstract. Spatial interpolation of precipitation data is uncertain. How important is this uncertainty and how can it be considered in evaluation of high-resolution probabilistic precipitation forecasts? These questions are discussed by experimental evaluation of the COSMO consortium's limited-area ensemble prediction system COSMO-LEPS. The applied performance measure is the often used Brier skill score (BSS). The observational references in the evaluation are (a) analyzed rain gauge data by ordinary Kriging and (b) ensembles of interpolated rain gauge data by stochastic simulation. This permits the consideration of either a deterministic reference (the event is observed or not with 100% certainty) or a probabilistic reference that makes allowance for uncertainties in spatial averaging. The evaluation experiments show that the evaluation uncertainties are substantial even for the large area (41 300 km2) of Switzerland with a mean rain gauge distance as good as 7 km: the one- to three-day precipitation forecasts have skill decreasing with forecast lead time but the one- and two-day forecast performances differ not significantly.

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