Utilization of regression models for rainfall estimates using radar-derived rainfall data and rain gauge data

2003 ◽  
Vol 278 (1-4) ◽  
pp. 144-152 ◽  
Author(s):  
Zbynĕk Sokol
Keyword(s):  
Regression Models ◽  
Rain Gauge ◽  
Rainfall Data ◽  
Rain Gauge Data ◽  
Gauge Data ◽  
Rainfall Estimates

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 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 Evaluation of TRMM rainfall estimates over a large Indian river basin (Mahanadi)

2014 ◽  
Vol 18 (7) ◽  
pp. 2493-2502 ◽  
Author(s):  
D. Kneis ◽  
C. Chatterjee ◽  
R. Singh
Keyword(s):  
Real Time ◽  
Stream Flow ◽  
Rain Gauge ◽  
Rainfall Runoff ◽  
Runoff Simulation ◽  
Rain Gauge Data ◽  
Gauge Data ◽  
Precipitation Estimates ◽  
Rainfall Estimates

Abstract. The paper examines the quality of satellite-based precipitation estimates for the lower Mahanadi River basin (eastern India). The considered data sets known as 3B42 and 3B42-RT (version 7/7A) are routinely produced by the tropical rainfall measuring mission (TRMM) from passive microwave and infrared recordings. While the 3B42-RT data are disseminated in real time, the gauge-adjusted 3B42 data set is published with a delay of some months. The quality of the two products was assessed in a two-step procedure. First, the correspondence between the remotely sensed precipitation rates and rain gauge data was evaluated at the sub-basin scale. Second, the quality of the rainfall estimates was assessed by analysing their performance in the context of rainfall–runoff simulation. At sub-basin level (4000 to 16 000 km2) the satellite-based areal precipitation estimates were found to be moderately correlated with the gauge-based counterparts (R2 of 0.64–0.74 for 3B42 and 0.59–0.72 for 3B42-RT). Significant discrepancies between TRMM data and ground observations were identified at high-intensity levels. The rainfall depth derived from rain gauge data is often not reflected by the TRMM estimates (hit rate < 0.6 for ground-based intensities > 80 mm day-1). At the same time, the remotely sensed rainfall rates frequently exceed the gauge-based equivalents (false alarm ratios of 0.2–0.6). In addition, the real-time product 3B42-RT was found to suffer from a spatially consistent negative bias. Since the regionalisation of rain gauge data is potentially associated with a number of errors, the above results are subject to uncertainty. Hence, a validation against independent information, such as stream flow, was essential. In this case study, the outcome of rainfall–runoff simulation experiments was consistent with the above-mentioned findings. The best fit between observed and simulated stream flow was obtained if rain gauge data were used as model input (Nash–Sutcliffe index of 0.76–0.88 at gauges not affected by reservoir operation). This compares to the values of 0.71–0.78 for the gauge-adjusted TRMM 3B42 data and 0.65–0.77 for the 3B42-RT real-time data. Whether the 3B42-RT data are useful in the context of operational runoff prediction in spite of the identified problems remains a question for further research.

scholarly journals Assessment of SWAT Model Performance in Simulating Daily Streamflow under Rainfall Data Scarcity in Pacific Island Watersheds

Water ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 1533 ◽  
Author(s):  
Olkeba Leta ◽  
Aly El-Kadi ◽  
Henrietta Dulai ◽  
Kariem Ghazal
Keyword(s):  
Spatial Variability ◽  
Swat Model ◽  
Model Performance ◽  
Rain Gauge ◽  
Rainfall Data ◽  
Small Scale ◽  
Pacific Island ◽  
Rain Gauge Data ◽  
Gauge Data ◽  
Low Performance

Evaluating the performance of watershed models is essential for a reliable assessment of water resources, particularly in Pacific island watersheds, where modeling efforts are challenging due to their unique features. Such watersheds are characterized by low water residence time, highly permeable volcanic rock outcrops, high topographic and rainfall spatial variability, and lack of hydrological data. The Soil and Water Assessment Tool (SWAT) model was used for hydrological modeling of the Nuuanu area watershed (NAW) and Heeia watershed on the Island of Oahu (Hawaii). The NAW, which had well-distributed rainfall gauging stations within the watershed, was used for comparison with the Heeia watershed that lacked recoded rainfall data within the watershed. For the latter watershed, daily rain gauge data from the neighboring watersheds and spatially interpolated 250 m resolution rainfall data were used. The objectives were to critically evaluate the performance of SWAT under rain gauge data scarce conditions for small-scale watersheds that experience high rainfall spatial variability over short distances and to determine if spatially interpolated gridded rainfall data can be used as a remedy in such conditions. The model performance was evaluated by using the Nash–Sutcliffe efficiency (NSE), the percent bias (PBIAS), and the coefficient of determination (R2), including model prediction uncertainty at 95% confidence interval (95PCI). Overall, the daily observed streamflow hydrographs were well-represented by SWAT when well-distributed rain gauge data were used for NAW, yielding NSE and R2 values of > 0.5 and bracketing > 70% of observed streamflows at 95PCI. However, the model showed an overall low performance (NSE and R2 ≤ 0.5) for the Heeia watershed compared to the NAW’s results. Although the model showed low performance for Heeia, the gridded rainfall data generally outperformed the rain gauge data that were used from outside of the watershed. Thus, it was concluded that finer resolution gridded rainfall data can be used as a surrogate for watersheds that lack recorded rainfall data in small-scale Pacific island watersheds.

Combined rainfall estimates from personal weather station and commercial microwave link data in Germany

2021 ◽  
Author(s):  
Maximilian Graf ◽  
Abbas El Hachem ◽  
Micha Eisele ◽  
Jochen Seidel ◽  
Christian Chwala ◽  
...  
Keyword(s):  
Water Resource Management ◽  
Hydrological Cycle ◽  
Spatial Scales ◽  
Rainfall Variability ◽  
Rain Gauge ◽  
Data Sets ◽  
Rain Gauge Data ◽  
Rain Gauges ◽  
Gauge Data ◽  
Rainfall Estimates

&lt;p&gt;Rain gauges and weather radars are the default sources of rainfall information. Rainfall estimates from these sensors improve our understanding of the hydrological cycle and are vital for water-resource management, agriculture, urban planning, as well as for weather, climate, and hydrological modelling. Still, due to the high spatio-temporal variability of rainfall and the specific drawbacks of the individual rainfall sensors, the rainfall variability cannot be captured completely. In the last decade, the number and availability of opportunistic rainfall sensors increased rapidly. These sensors are initially not meant to measure rainfall for scientific or operational purposes, but, if processed carefully, can be used for these cases . Here we present an analysis of two years of data from two opportunistic rainfall sensors, namely personal weather stations (PWS) and commercial microwave links (CMLs). We evaluate the performance of rainfall maps derived from these sensors on different spatial and temporal scales in Germany.&lt;/p&gt;&lt;p&gt;The data from around 15000 PWS tipping bucket-style rain gauges from the Netatmo network were accessed via Netatmos API. The data from around 4000 CMLs, which can be used to derive rainfall estimates from the rain-induced attenuation of the CMLs&amp;#8217; signal, were obtained from Ericsson. As both, PWS and CML data, can suffer from various error sources e.g. from unfavourable positioning and poor maintenance of PWS and from non-rain induced attenuation of the CMLs signal, we used a strict filtering routine. A total of seven gridded rainfall products were derived from different combinations of PWS, CML, and rain gauge data from the German Weather Service (DWD) with a geostatistical interpolation approach. This approach incorporates the uncertainty of the opportunistic sensors and the path-averaging characteristic of the CML observations.&lt;/p&gt;&lt;p&gt;To evaluate the resulting rainfall maps, we used three rain gauge data sets with different temporal and spatial scales covering the whole of Germany, the state of Rhineland-Palatinate and the city of Reutlingen, respectively. For all three reference data sets, rainfall maps from opportunistic sensors provided good agreement, with best results being derived from the combinations with PWS. Rainfall maps including CML data had the lowest bias. In a comparison with gauge adjusted radar products from the DWD, the radar products yielded better results than the rainfall maps from opportunistic sensors for the country-wide comparison of daily rainfall sums, which was carried out using the DWD&amp;#8217;s independent network of manual rain gauges. But for the hourly references covering Rhineland-Palatinate and Reutlingen, the rainfall maps derived from opportunistic sensors outperformed the radar products. These results highlight the capabilities of opportunistic rainfall sensors which could be used in many hydrometeorological applications.&lt;/p&gt;

scholarly journals Mapping the Daily Rainfall over an Ungauged Tropical Micro-Watershed: A Downscaling Algorithm Using GPM Data

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1661 ◽  
Author(s):  
Mohd. Rizaludin Mahmud ◽  
Aina Afifah Mohd Yusof ◽  
Mohd Nadzri Mohd Reba ◽  
Mazlan Hashim
Keyword(s):  
High Resolution ◽  
Daily Rainfall ◽  
Rain Gauge ◽  
Rainfall Data ◽  
Precipitation Data ◽  
Rain Gauge Data ◽  
Satellite Precipitation ◽  
Daily Rainfall Data ◽  
Gauge Data

In this study, half-hourly Global Precipitation Mission (GPM) satellite precipitation data were downscaled to produce high-resolution daily rainfall data for tropical coastal micro-watersheds (100–1000 ha) without gauges or with rainfall data conflicts. Currently, daily-scale satellite rainfall downscaling techniques rely on rain gauge data as corrective and controlling factors, making them impractical for ungauged watersheds or watersheds with rainfall data conflicts. Therefore, we used high-resolution local orographic and vertical velocity data as proxies to downscale half-hourly GPM precipitation data (0.1°) to high-resolution daily rainfall data (0.02°). The overall quality of the downscaled product was similar to or better than the quality of the raw GPM data. The downscaled rainfall dataset improved the accuracy of rainfall estimates on the ground, with lower error relative to measured rain gauge data. The average error was reduced from 41 to 27 mm/d and from 16 to 12 mm/d during the wet and dry seasons, respectively. Estimates of localized rainfall patterns were improved from 38% to 73%. The results of this study will be useful for production of high-resolution satellite precipitation data in ungauged tropical micro-watersheds.

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.

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