scholarly journals STUDY ON AREAL RAINFALL USING RAIN-GAUGE DATA AND HIGH-RESOLUTION RADAR DATA IN MEDIUM AND SMALL RIVER BASINS

2016 ◽  
Vol 72 (4) ◽  
pp. I_1231-I_1236
Author(s):  
Yoshiaki HAYASHI ◽  
Taichi TEBAKARI ◽  
Koreyoshi YAMASAKI
Keyword(s):  
High Resolution ◽  
River Basins ◽  
Radar Data ◽  
Rain Gauge ◽  
Small River ◽  
Rain Gauge Data ◽  
Gauge Data ◽  
High Resolution Radar ◽  
Areal Rainfall

scholarly journals Automatic Prediction of High-Resolution Daily Rainfall Fields for Multiple Extents: The Potential of Operational Radar

2007 ◽  
Vol 8 (6) ◽  
pp. 1204-1224 ◽  
Author(s):  
J. M. Schuurmans ◽  
M. F. P. Bierkens ◽  
E. J. Pebesma ◽  
R. Uijlenhoet
Keyword(s):  
High Resolution ◽  
Hydrological Modeling ◽  
Daily Rainfall ◽  
Rain Gauge ◽  
Added Value ◽  
Rain Gauge Data ◽  
Geostatistical Methods ◽  
Gauge Data ◽  
Event Based ◽  
Better Than

Abstract This study investigates the added value of operational radar with respect to rain gauges in obtaining high-resolution daily rainfall fields as required in distributed hydrological modeling. To this end data from the Netherlands operational national rain gauge network (330 gauges nationwide) is combined with an experimental network (30 gauges within 225 km2). Based on 74 selected rainfall events (March–October 2004) the spatial variability of daily rainfall is investigated at three spatial extents: small (225 km2), medium (10 000 km2), and large (82 875 km2). From this analysis it is shown that semivariograms show no clear dependence on season. Predictions of point rainfall are performed for all three extents using three different geostatistical methods: (i) ordinary kriging (OK; rain gauge data only), (ii) kriging with external drift (KED), and (iii) ordinary collocated cokriging (OCCK), with the latter two using both rain gauge data and range-corrected daily radar composites—a standard operational radar product from the Royal Netherlands Meteorological Institute (KNMI). The focus here is on automatic prediction. For the small extent, rain gauge data alone perform better than radar, while for larger extents with lower gauge densities, radar performs overall better than rain gauge data alone (OK). Methods using both radar and rain gauge data (KED and OCCK) prove to be more accurate than using either rain gauge data alone (OK) or radar, in particular, for larger extents. The added value of radar is positively related to the correlation between radar and rain gauge data. Using a pooled semivariogram is almost as good as using event-based semivariograms, which is convenient if the prediction is to be automated. An interesting result is that the pooled semivariograms perform better in terms of estimating the prediction error (kriging variance) especially for the small and medium extent, where the number of data points to estimate semivariograms is small and event-based semivariograms are rather unstable.

scholarly journals Multisensor Precipitation Reanalysis

2010 ◽  
Vol 11 (3) ◽  
pp. 666-682 ◽  
Author(s):  
Brian R. Nelson ◽  
D-J. Seo ◽  
Dongsoo Kim
Keyword(s):  
Quality Control ◽  
South Carolina ◽  
Stage Iv ◽  
Radar Data ◽  
Rain Gauge ◽  
Data Quality Control ◽  
Rain Gauge Data ◽  
Wide Range ◽  
Gauge Data ◽  
Validation Experiments

Abstract Temporally consistent high-quality, high-resolution multisensor precipitation reanalysis (MPR) products are needed for a wide range of quantitative climatological and hydroclimatological applications. Therefore, the authors have reengineered the multisensor precipitation estimator (MPE) algorithms of the NWS into the MPR package. Owing to the retrospective nature of the analysis, MPR allows for the utilization of additional rain gauge data, more rigorous automatic quality control, and post factum correction of radar quantitative precipitation estimation (QPE) and optimization of key parameters in multisensor estimation. To evaluate and demonstrate the value of MPR, the authors designed and carried out a set of cross-validation experiments in the pilot domain of North Carolina and South Carolina. The rain gauge data are from the reprocessed Hydrometeorological Automated Data System (HADS) and the daily Cooperative Observer Program (COOP). The radar QPE data are the operationally produced Weather Surveillance Radar-1988 Doppler digital precipitation array (DPA) products. To screen out bad rain gauge data, quality control steps were taken that use rain gauge and radar data. The resulting MPR products are compared with the stage IV product on a daily scale at the withheld COOP gauge locations. This paper describes the data, the MPR procedure, and the validation experiments, and it summarizes the findings.

The climate of daily precipitation in the Alps: development and analysis of a high-resolution grid dataset from pan-Alpine rain-gauge data

2013 ◽  
Vol 34 (5) ◽  
pp. 1657-1675 ◽  
Author(s):  
Francesco A. Isotta ◽  
Christoph Frei ◽  
Viktor Weilguni ◽  
Melita Perčec Tadić ◽  
Pierre Lassègues ◽  
...  
Keyword(s):  
High Resolution ◽  
Daily Precipitation ◽  
Rain Gauge ◽  
Rain Gauge Data ◽  
Gauge Data ◽  
The Alps

scholarly journals Point-Based Rainfall Intensity Information System in Mt. Merapi Area by X-Band Radar

2019 ◽  
Vol 14 (1) ◽  
pp. 80-89 ◽  
Author(s):  
Santosa Sandy Putra ◽  
Banata Wachid Ridwan ◽  
Kazuki Yamanoi ◽  
Makoto Shimomura ◽  
Sulistiyani ◽  
...  
Keyword(s):  
Satellite Data ◽  
Early Warning Systems ◽  
Radar Data ◽  
Rain Gauge ◽  
Data Grid ◽  
Rainfall Data ◽  
Rainfall Events ◽  
Rain Gauge Data ◽  
X Band ◽  
Gauge Data

An X-band radar was installed in 2014 at Merapi Museum, Yogyakarta, Indonesia, to monitor pyroclastic and rainfall events around Mt. Merapi. This research aims to perform a reliability analysis of the point extracted rainfall data from the aforementioned newly installed radar to improve the performance of the warning system in the future. The radar data was compared with the monitored rain gauge data from Balai Sabo and the IMERG satellite data from NASA and JAXA (The Integrated Multi-satellitE Retrievals for GPM), which had not been done before. All of the rainfall data was compared on an hourly interval. The comparisons were conducted based on 11 locations that correspond to the ground rainfall measurement stations. The locations of the rain gauges are spread around Mt. Merapi area. The point rainfall information was extracted from the radar data grid and the satellite data grid, which were compared with the rain gauge data. The data were then calibrated and adjusted up to the optimum state. Based on January 2017–March 2018 data, it was obtained that the optimum state has a NSF value of 0.41 and R2value of 0.56. As a result, it was determined that the radar can capture around 79% of the hourly rainfall occurrence around Mt. Merapi area during the chosen calibration period, in comparison with the rain gauge data. The radar was also able to capture nearby 40–50% of the heavy rainfall events that pose risks of lahar. In contrast, the radar data performance in detecting drizzling and light rain types were quite precise (55% of cases), although the satellite data could detect slightly better (60% of cases). These results indicate that the radar sensitivity in detecting the extreme rainfall events must receive higher priority in future developments, especially for applications to the existing Mt. Merapi lahar early warning systems.

scholarly journals High-Resolution Spatiotemporal Trend Analysis of Precipitation Using Satellite-Based Products over the United Arab Emirates

Water ◽  
2021 ◽  
Vol 13 (17) ◽  
pp. 2376
Author(s):  
Khalid A. Hussein ◽  
Tareefa S. Alsumaiti ◽  
Dawit T. Ghebreyesus ◽  
Hatim O. Sharif ◽  
Waleed Abdalati
Keyword(s):  
High Resolution ◽  
Water Resources ◽  
United Arab Emirates ◽  
21St Century ◽  
Rain Gauge ◽  
Accurate Information ◽  
Late 20Th Century ◽  
Rain Gauge Data ◽  
Gauge Data

Current water demands are adequately satisfied in the United Arab Emirates (UAE) with the available water resources. However, the changing climate and growing water demand pose a great challenge for water resources managers in the country. Hence, there is a great need for management strategies and policies to use the most accurate information regarding water availability. Understanding the frequency and the short- and long-term trends of the precipitation by employing high-resolution data in both the spatial and temporal domains can provide invaluable information. This study examines the long-term precipitation trends over the UAE using 17 years of data from three of the most highly cited satellite-based precipitation products and rain gauge data observed at 18 stations. The UAE received, on average, 42, 51, and 120 wet hours in a year in the 21st century as recorded by CMORPH, PERSIANN, and IMERG, respectively. The results show that the areal average annual precipitation of the UAE is significantly lower in the early 21st century than that of the late 20th century, even though it shows an increasing trend by all the products. The Mann–Kendall trend test showed positive trends in six rain gauge stations and negative trends in two stations out of 18 stations, all of which are located in the wetter eastern part of the UAE. Results indicate that satellite products have great potential for improving the spatial aspects of rainfall frequency analysis and can complement rain gauge data to develop rainfall intensity–duration–frequency curves in a very dry region, where the installation of dense rain gauge networks is not feasible.

scholarly journals A Rainfall Data Intercomparison Dataset of RADKLIM, RADOLAN, and Rain Gauge Data for Germany

Data ◽  
2019 ◽  
Vol 4 (3) ◽  
pp. 118 ◽  
Author(s):  
Kreklow ◽  
Tetzlaff ◽  
Kuhnt ◽  
Burkhard
Keyword(s):  
Radar Data ◽  
Rain Gauge ◽  
Rainfall Data ◽  
Rain Gauge Data ◽  
Additional Information ◽  
Weather Radars ◽  
Gauge Data ◽  
Quality Checks ◽  
Quantitative Precipitation Estimates ◽  
Rainfall Statistics

Quantitative precipitation estimates (QPE) derived from weather radars provide spatially and temporally highly resolved rainfall data. However, they are also subject to systematic and random bias and various potential uncertainties and therefore require thorough quality checks before usage. The dataset described in this paper is a collection of precipitation statistics calculated from the hourly nationwide German RADKLIM and RADOLAN QPEs provided by the German Weather Service (Deutscher Wetterdienst (DWD)), which were combined with rainfall statistics derived from rain gauge data for intercomparison. Moreover, additional information on parameters that can potentially influence radar data quality, such as the height above sea level, information on wind energy plants and the distance to the next radar station, were included in the dataset. The resulting two point shapefiles are readable with all common GIS and constitutes a spatially highly resolved rainfall statistics geodataset for the period 2006 to 2017, which can be used for statistical rainfall analyses or for the derivation of model inputs. Furthermore, the publication of this data collection has the potential to benefit other users who intend to use precipitation data for any purpose in Germany and to identify the rainfall dataset that is best suited for their application by a straightforward comparison of three rainfall datasets without any tedious data processing and georeferencing.

scholarly journals Coastal and orographic effects on extreme precipitation revealed by weather radar observations

2021 ◽  
Author(s):  
Francesco Marra ◽  
Moshe Armon ◽  
Efrat Morin
Keyword(s):  
Extreme Precipitation ◽  
Weather Radar ◽  
Radar Data ◽  
Rain Gauge ◽  
Exceedance Probability ◽  
Precipitation Frequency ◽  
Rain Gauge Data ◽  
Orographic Effects ◽  
Gauge Data ◽  
Convective Cells

Abstract. The yearly exceedance probability of extreme precipitation of multiple durations is crucial for infrastructure design, risk management and policymaking. Local extremes emerge from the interaction of weather systems with local terrain features such as coastlines and orography, however multi-duration extremes do not follow exactly the patterns of cumulative precipitation and are still not well understood. High-resolution information from weather radars could help us better quantifying their patterns, but traditional extreme-value analyses based on radar records were found too inaccurate for quantifying the extreme intensities for impact studies. Here, we propose a novel methodology for extreme precipitation frequency analysis based on relatively short weather radar records, and we use it to investigate coastal and orographic effects on extreme precipitation of durations between 10 minutes and 24 hours. Combining 11 years of radar data with 10-minute rain gauge data in the southeastern Mediterranean, we obtain estimates of the 1 in 100 years intensities with ~22 % standard error, which is lower than those obtained using traditional approaches on rain gauge data. We identify three distinct regimes, which respond differently to coastal and orographic forcing: short durations (~10 minutes), related to peak convective rain rates; hourly durations (~1 hours), related to the yield of individual convective cells; and long durations (~6–24 hours), related to the accumulation of multiple convective cells and to stratiform processes. At short and hourly durations, extreme return levels peak at the coastline, while at longer durations they peak corresponding to the orographic barriers. The distributions tail heaviness is rather uniform above the sea and rapidly changes in presence of orography, with opposing directions at short (decreasing tail heaviness, with a peak at hourly durations) and long (increasing) durations. These distinct effects suggest that short-scale hazards such as urban pluvial floods could be more of concern for the coastal regions, while longer-scale hazards such as flash floods could be more relevant in mountainous areas.

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