scholarly journals Assesment of Satellite and Radar Quantitative Precipitation Estimates for Real Time Monitoring of  Meteorological Extremes Over the Southeast of Iberian Peninsula

2018 ◽  
Author(s):  
Fulgencio Cánovas-García ◽  
Sandra García-Galiano ◽  
Francisco Alonso-Sarría
Keyword(s):  
Iberian Peninsula ◽  
Real Time ◽  
Extreme Rainfall ◽  
Rain Gauge ◽  
Real Time Monitoring ◽  
Rain Gauges ◽  
Extreme Precipitation Events ◽  
Empirical Calibration ◽  
Precipitation Estimates ◽  
Quantitative Precipitation Estimates

QPEs (Quantitative Precipitation Estimates) obtained from remote sensing or ground-based radars could complement or even be an alternative to rain gauge readings. However, to be used in operational applications, a validation process has to be carried out, usually by comparing their estimates with those of a rain gauges network. In this paper, the accuracy of two QPEs are evaluated for three extreme precipitation events in the last decade in the southeast of the Iberian Peninsula. The first QPE is PERSIANN-CCS, a satellite-based QPE. The second is a meteorological radar with Doppler capabilities that works in the C band. Pixel-to-point comparisons are made between the values offered by the QPEs and those obtained by two networks of rain gauges. The results obtained indicate that both QPEs were well below the rain gauge values, especially in extreme rainfall time slots. There seems to be a weak linear association between the value of the discrepancies and the precipitation value of the QPEs. It does not seem that radar is more accurate than PERSIANN-CCS, despite its larger spatial resolution and its commonly higher effectiveness. The main conclusion is that neither PERSIANN-CCS nor radar, without empirical calibration, are acceptable QPEs for the real-time monitoring of meteorological extremes in the southeast of the Iberian Peninsula.

scholarly journals Automatic quality control of telemetric rain gauge data for operational applications at IMGW-PIB

2018 ◽  
Vol 23 ◽  
pp. 00028 ◽  
Author(s):  
Irena Otop ◽  
Jan Szturc ◽  
Katarzyna Ośródka ◽  
Piotr Djaków
Keyword(s):  
Quality Control ◽  
Real Time ◽  
Error Detection ◽  
Rain Gauge ◽  
Temporal Consistency ◽  
Consistency Check ◽  
Gross Error Detection ◽  
Individual Measurement ◽  
Precipitation Estimates ◽  
Quantitative Precipitation Estimates

The automatic procedure of real-time quality control (QC) of telemetric rain gauge measurements (G) has been developed to produce quantitative precipitation estimates mainly for the needs of operational hydrology. The developed QC procedure consists of several tests: gross error detection, a range check, a spatial consistency check, a temporal consistency check, and a radar and satellite conformity check. The output of the procedure applied in real-time is quality index QI(G) that quantitatively characterised quality of each individual measurement. The QC procedure has been implemented into operational work at the Institute of Meteorology and Water Management since 2016. However, some elements of the procedure are still under development and can be improved based on the results and experience collected after about two years of real-time work on network of telemetric rain gauges

scholarly journals Evaluation of precipitation estimates over CONUS derived from satellite, radar, and rain gauge datasets (2002–2012)

2014 ◽  
Vol 11 (10) ◽  
pp. 11489-11531 ◽  
Author(s):  
O. P. Prat ◽  
B. R. Nelson
Keyword(s):  
Real Time ◽  
Stage Iv ◽  
Rain Gauge ◽  
Contingency Analysis ◽  
Western Us ◽  
Precipitation Estimates ◽  
Satellite Radar ◽  
Quantitative Precipitation Estimates ◽  
Rain Rates ◽  
Surface Observations

Abstract. We use a suite of quantitative precipitation estimates (QPEs) derived from satellite, radar, and surface observations to derive precipitation characteristics over CONUS for the period 2002–2012. This comparison effort includes satellite multi-sensor datasets (bias-adjusted TMPA 3B42, near-real time 3B42RT), radar estimates (NCEP Stage IV), and rain gauge observations. Remotely sensed precipitation datasets are compared with surface observations from the Global Historical Climatology Network (GHCN-Daily) and from the PRISM (Parameter-elevation Regressions on Independent Slopes Model). The comparisons are performed at the annual, seasonal, and daily scales over the River Forecast Centers (RFCs) for CONUS. Annual average rain rates present a satisfying agreement with GHCN-D for all products over CONUS (± 6%). However, differences at the RFC are more important in particular for near-real time 3B42RT precipitation estimates (−33 to +49%). At annual and seasonal scales, the bias-adjusted 3B42 presented important improvement when compared to its near real time counterpart 3B42RT. However, large biases remained for 3B42 over the Western US for higher average accumulation (≥ 5 mm day-1) with respect to GHCN-D surface observations. At the daily scale, 3B42RT performed poorly in capturing extreme daily precipitation (> 4 in day-1) over the Northwest. Furthermore, the conditional analysis and the contingency analysis conducted illustrated the challenge of retrieving extreme precipitation from remote sensing estimates.

scholarly journals Evaluation of precipitation estimates over CONUS derived from satellite, radar, and rain gauge data sets at daily to annual scales (2002–2012)

2015 ◽  
Vol 19 (4) ◽  
pp. 2037-2056 ◽  
Author(s):  
O. P. Prat ◽  
B. R. Nelson
Keyword(s):  
Real Time ◽  
Pacific Northwest ◽  
Stage Iv ◽  
Rain Gauge ◽  
Sensor Data ◽  
Data Sets ◽  
Precipitation Estimates ◽  
Satellite Radar ◽  
Quantitative Precipitation Estimates ◽  
Surface Observations

Abstract. We use a suite of quantitative precipitation estimates (QPEs) derived from satellite, radar, and surface observations to derive precipitation characteristics over the contiguous United States (CONUS) for the period 2002–2012. This comparison effort includes satellite multi-sensor data sets (bias-adjusted TMPA 3B42, near-real-time 3B42RT), radar estimates (NCEP Stage IV), and rain gauge observations. Remotely sensed precipitation data sets are compared with surface observations from the Global Historical Climatology Network-Daily (GHCN-D) and from the PRISM (Parameter-elevation Regressions on Independent Slopes Model). The comparisons are performed at the annual, seasonal, and daily scales over the River Forecast Centers (RFCs) for CONUS. Annual average rain rates present a satisfying agreement with GHCN-D for all products over CONUS (±6%). However, differences at the RFC are more important in particular for near-real-time 3B42RT precipitation estimates (−33 to +49%). At annual and seasonal scales, the bias-adjusted 3B42 presented important improvement when compared to its near-real-time counterpart 3B42RT. However, large biases remained for 3B42 over the western USA for higher average accumulation (≥ 5 mm day−1) with respect to GHCN-D surface observations. At the daily scale, 3B42RT performed poorly in capturing extreme daily precipitation (> 4 in. day−1) over the Pacific Northwest. Furthermore, the conditional analysis and a contingency analysis conducted illustrated the challenge in retrieving extreme precipitation from remote sensing estimates.

scholarly journals Advanced Real-Time Monitoring of Rainfall Using Commercial Satellite Broadcasting Service: A Case Study

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 691
Author(s):  
Gian Luigi Gragnani ◽  
Matteo Colli ◽  
Emanuele Tavanti ◽  
Daniele D. Caviglia
Keyword(s):  
Real Time ◽  
Rain Gauge ◽  
Real Time Monitoring ◽  
The Sustainable Development ◽  
Rain Gauges ◽  
Weather Radars ◽  
Satellite Broadcasting ◽  
Fundamental Goal ◽  
Polarimetric Weather Radar ◽  
Broadcasting Service

Correct regulation of meteoric surface and subsurface flow waters is a fundamental goal for the sustainable development of the territories. A new system, aimed at real-time monitoring of the rainfall and of the cumulated rainfall, is introduced and discussed in the present paper. The system implements a Sensor Network based on the IoT paradigm and can cover safety-critical “hot spots” with a relatively small number of sensors, strategically placed, in areas not covered by traditional weather radars and rain gauges, and lowering the costs of deployment and maintenance with respects to these devices. A real application case, based on the implementation of the pilot plant at the Monte Scarpino landfill (Genoa, Italy), is presented and discussed. The system performances are assessed on the basis of comparisons with data provided by a polarimetric weather radar and by a traditional rain gauge.

scholarly journals Data Transformation and Uncertainty in Geostatistical Combination of Radar and Rain Gauges

2012 ◽  
Vol 13 (4) ◽  
pp. 1332-1346 ◽  
Author(s):  
Rebekka Erdin ◽  
Christoph Frei ◽  
Hans R. Künsch
Keyword(s):  
Precipitation Amount ◽  
Data Transformation ◽  
Rain Gauge ◽  
Point Estimate ◽  
Test Bed ◽  
Rain Gauges ◽  
Test Application ◽  
Precipitation Estimates ◽  
Quantitative Precipitation Estimates ◽  
The Impact

Abstract Geostatistics provides a popular framework for deriving high-resolution quantitative precipitation estimates (QPE) by combining radar and rain gauge data. However, the skewed and heteroscedastic nature of precipitation is in contradiction to assumptions of classical geostatistics. This study examines the potential of trans-Gaussian kriging to overcome this contradiction. Combination experiments are undertaken with kriging with external drift (KED) using several settings of the Box–Cox transformation. Hourly precipitation data in Switzerland for the year 2008 serve as test bed to compare KED with and without transformation. The impact of transformation is examined with regard to compliance with model assumptions, accuracy of the point estimate, and reliability of the probabilistic estimate. Data transformation improves the compliance with model assumptions, but some level of contradiction remains in situations with many dry gauges. Very similar point estimates are found for KED with untransformed and appropriately transformed data. However, care is needed to avoid excessive transformation (close to log) because this can introduce a positive bias. Strong benefits from transformation are found for the probabilistic estimate, which is rendered positively skewed, sensitive to precipitation amount, and quantitatively more reliable. Without transformation, 44% of all precipitation observations larger than 5 mm h−1 are considered as extremely unlikely by the probabilistic estimate in the test application. Transformation reduces this rate to 4%. Although transformation cannot fully remedy the complications for geostatistics in radar–gauge combination, it seems a useful procedure if realistic and reliable estimation uncertainties are desired, such as for the stochastic simulation of QPE ensembles.

scholarly journals Assessment of Snowfall Accumulation from Satellite and Reanalysis Products Using SNOTEL Observations in Alaska

2021 ◽  
Vol 13 (15) ◽  
pp. 2922
Author(s):  
Yang Song ◽  
Patrick D. Broxton ◽  
Mohammad Reza Ehsani ◽  
Ali Behrangi
Keyword(s):  
Snow Water Equivalent ◽  
Stage Iv ◽  
Microwave Remote Sensing ◽  
Rain Gauge ◽  
Snow Accumulation ◽  
Correction Factors ◽  
Rainfall Analysis ◽  
Rain Gauges ◽  
Snow Properties ◽  
Precipitation Estimates

The combination of snowfall, snow water equivalent (SWE), and precipitation rate measurements from 39 snow telemetry (SNOTEL) sites in Alaska were used to assess the performance of various precipitation products from satellites, reanalysis, and rain gauges. Observation of precipitation from two water years (2018–2019) of a high-resolution radar/rain gauge data (Stage IV) product was also utilized to give insights into the scaling differences between various products. The outcomes were used to assess two popular methods for rain gauge undercatch correction. It was found that SWE and precipitation measurements at SNOTELs, as well as precipitation estimates based on Stage IV data, are generally consistent and can provide a range within which other products can be assessed. The time-series of snowfall and SWE accumulation suggests that most of the products can capture snowfall events; however, differences exist in their accumulation. Reanalysis products tended to overestimate snow accumulation in the study area, while the current combined passive microwave remote sensing products (i.e., IMERG-HQ) underestimate snowfall accumulation. We found that correction factors applied to rain gauges are effective for improving their undercatch, especially for snowfall. However, no improvement in correlation is seen when correction factors are applied, and rainfall is still estimated better than snowfall. Even though IMERG-HQ has less skill for capturing snowfall than rainfall, analysis using Taylor plots showed that the combined microwave product does have skill for capturing the geographical distribution of snowfall and precipitation accumulation; therefore, bias adjustment might lead to reasonable precipitation estimates. This study demonstrates that other snow properties (e.g., SWE accumulation at the SNOTEL sites) can complement precipitation data to estimate snowfall. In the future, gridded SWE and snow depth data from GlobSnow and Sentinel-1 can be used to assess snowfall and its distribution over broader regions.

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.

Commercial microwave links instead of rain gauges: fiction or reality?

2014 ◽  
Vol 71 (1) ◽  
pp. 31-37 ◽  
Author(s):  
Martin Fencl ◽  
Jörg Rieckermann ◽  
Petr Sýkora ◽  
David Stránský ◽  
Vojtěch Bareš
Keyword(s):  
Rainfall Variability ◽  
A Priori ◽  
Rain Gauges ◽  
Precipitation Estimates ◽  
Spatio Temporal ◽  
Areal Rainfall ◽  
Quantitative Precipitation Estimates ◽  
Commercial Microwave Links ◽  
Unique Dataset

Commercial microwave links (MWLs) were suggested about a decade ago as a new source for quantitative precipitation estimates (QPEs). Meanwhile, the theory is well understood and rainfall monitoring with MWLs is on its way to being a mature technology, with several well-documented case studies, which investigate QPEs from multiple MWLs on the mesoscale. However, the potential of MWLs to observe microscale rainfall variability, which is important for urban hydrology, has not been investigated yet. In this paper, we assess the potential of MWLs to capture the spatio-temporal rainfall dynamics over small catchments of a few square kilometres. Specifically, we investigate the influence of different MWL topologies on areal rainfall estimation, which is important for experimental design or to a priori check the feasibility of using MWLs. In a dedicated case study in Prague, Czech Republic, we collected a unique dataset of 14 MWL signals with a temporal resolution of a few seconds and compared the QPEs from the MWLs to reference rainfall from multiple rain gauges. Our results show that, although QPEs from most MWLs are probably positively biased, they capture spatio-temporal rainfall variability on the microscale very well. Thus, they have great potential to improve runoff predictions. This is especially beneficial for heavy rainfall, which is usually decisive for urban drainage design.

scholarly journals Retrospective Dynamic Inundation Mapping of Hurricane Harvey Flooding in the Houston Metropolitan Area Using High-Resolution Modeling and High-Performance Computing

Water ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 597 ◽  
Author(s):  
Seong Noh ◽  
Jun-Hak Lee ◽  
Seungsoo Lee ◽  
Dong-Jun Seo
Keyword(s):  
High Resolution ◽  
Metropolitan Area ◽  
High Performance ◽  
Extreme Rainfall ◽  
Extreme Weather Events ◽  
Urban Flood ◽  
Precipitation Estimates ◽  
Inundation Extent ◽  
Quantitative Precipitation Estimates ◽  
Houston Metropolitan Area

Hurricane Harvey was one of the most extreme weather events to occur in Texas, USA; there was a huge amount of urban flooding in the city of Houston and the adjoining coastal areas. In this study, we reanalyze the spatiotemporal evolution of inundation during Hurricane Harvey using high-resolution two-dimensional urban flood modeling. This study’s domain includes the bayou basins in and around the Houston metropolitan area. The flood model uses the dynamic wave method and terrain data of 10-m resolution. It is forced by radar-based quantitative precipitation estimates. To evaluate the simulated inundation, on-site photos and water level observations were used. The inundation extent and severity are estimated by combining the retrieved water depths, images collected from the impacted area, and high-resolution terrain data. The simulated maximum inundation extent, which is frequently found outside of the designated flood zones, points out the importance of capturing multi-scale hydrodynamics in the built environment under extreme rainfall for effective flood risk and emergency management.

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