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 Difficulties with Correcting Radar Rainfall Estimates Based on Rain Gauge Data: A Case Study of Severe Weather in Montana on 16–17 June 2007

2009 ◽  
Vol 24 (5) ◽  
pp. 1334-1344 ◽  
Author(s):  
Steven V. Vasiloff ◽  
Kenneth W. Howard ◽  
Jian Zhang
Keyword(s):  
Quality Control ◽  
Real Time ◽  
Flash Flood ◽  
Rain Gauge ◽  
Data Quality Control ◽  
Radar Rainfall ◽  
Rain Gauge Data ◽  
Gauge Data ◽  
Precipitation Estimates ◽  
Radar Calibration

Abstract The principal source of information for operational flash flood monitoring and warning issuance is weather radar–based quantitative estimates of precipitation. Rain gauges are considered truth for the purposes of validating and calibrating real-time radar-derived precipitation data, both in a real-time sense and climatologically. This paper examines various uncertainties and challenges involved with using radar and rain gauge data in a severe local storm environment. A series of severe thunderstorm systems that occurred across northeastern Montana illustrates various problems with comparing radar precipitation estimates and real-time gauge data, including extreme wind effects, hail, missing gauge data, and radar quality control. Ten radar–gauge time series pairs were analyzed with most found to be not useful for real-time radar calibration. These issues must be carefully considered within the context of ongoing efforts to develop robust real-time tools for evaluating radar–gauge uncertainties. Recommendations are made for radar and gauge data quality control efforts that would benefit the operational use of gauge data.

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 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.

A direct comparison of patient-based real-time quality control techniques: The importance of the analyte distribution

2020 ◽  
Vol 57 (3) ◽  
pp. 206-214 ◽  
Author(s):  
Joel D Smith ◽  
Tony Badrick ◽  
Francis Bowling
Keyword(s):  
Quality Control ◽  
Real Time ◽  
Error Detection ◽  
Alanine Aminotransferase ◽  
Clinical Chemistry ◽  
Moving Average ◽  
Moving Averages ◽  
Skewed Distributions ◽  
Real Patient ◽  
Box Cox Transformation

Background Patient-based real-time quality control (PBRTQC) techniques have been described in clinical chemistry for over 50 years. PBRTQC has a number of advantages over traditional quality control including commutability, cost and the opportunity for real-time monitoring. However, there are few systematic investigations assessing how different PBRTQC techniques perform head-to-head. Methods In this study, we compare moving averages with and without truncation and moving medians. For analytes with skewed distributions such as alanine aminotransferase and creatinine, we also investigate the effect of Box–Cox transformation of the data. We assess the ability of each technique to detect simulated analytical bias in real patient data for multiple analytes and to retrospectively detect a real analytical shift in a creatinine and urea assay. Results For analytes with symmetrical distributions, we show that error detection is similar for a moving average with and without four standard deviation truncation limits and for a moving median. In contrast to analytes with symmetrically distributed results, moving averages perform poorly for right skewed distributions such as alanine aminotransferase and creatinine and function only with a tight upper truncation limit. Box–Cox transformation of the data both improves the performance of moving averages and allows all data points to be used. This was also confirmed for retrospective detection of a real analytical shift in creatinine and urea. Conclusions Our study highlights the importance of careful assessment of the distribution of patient results for each analyte in a PBRTQC program with the optimal approaches dependent on whether the patient result distribution is symmetrical or skewed.

scholarly journals Global gridded precipitation over land: a description of the new GPCC First Guess Daily product

2014 ◽  
Vol 6 (1) ◽  
pp. 49-60 ◽  
Author(s):  
K. Schamm ◽  
M. Ziese ◽  
A. Becker ◽  
P. Finger ◽  
A. Meyer-Christoffer ◽  
...  
Keyword(s):  
Quality Control ◽  
Real Time ◽  
Daily Precipitation ◽  
Grid Cell ◽  
Rain Gauge ◽  
Kriging Interpolation ◽  
Telecommunication System ◽  
Full Data ◽  
Data Set ◽  
Global Telecommunication System

Abstract. This paper describes the new First Guess Daily product of the Global Precipitation Climatology Centre (GPCC). The new product gives an estimate of the global daily precipitation gridded at a spatial resolution of 1° latitude by 1° longitude. It is based on rain gauge data reported in near-real time via the Global Telecommunication System (GTS) and available about three to five days after the end of each observation month. In addition to the gridded daily precipitation totals in mm day−1, the standard deviation in mm day−1, the kriging interpolation error in % and the number of measurements per grid cell are also encoded into the monthly netCDF product file and provided for all months since January 2009. Prior to their interpolation, the measured precipitation values undergo a preliminary automatic quality control. For the calculation of the areal mean of the grid, anomalies are interpolated with ordinary block kriging. This approach allows for a near-real-time release. Therefore, the purely GTS-based data processing lacks an intensive quality control as well as a high data density and is denoted as First Guess. The daily data set is referenced under doi:10.5676/DWD_GPCC/FG_D_100. Two further products, the Full Data Daily and a merged satellite-gauge product, are currently under development at Deutscher Wetterdienst (DWD). These additional products will not be available in near-real time, but based on significantly more and strictly quality controlled observations. All GPCC products are provided free of charge via the GPCC webpage: ftp://ftp-anon.dwd.de/pub/data/gpcc/html/download_gate.html.

scholarly journals Evaluation of radar-gauge merging methods for quantitative precipitation estimates

2008 ◽  
Vol 5 (5) ◽  
pp. 2975-3003 ◽  
Author(s):  
E. Goudenhoofdt ◽  
L. Delobbe
Keyword(s):  
Bias Correction ◽  
Mean Field ◽  
Daily Rainfall ◽  
Absolute Error ◽  
Rain Gauge ◽  
Radar Observations ◽  
Geostatistical Methods ◽  
Precipitation Estimates ◽  
Field Bias ◽  
Quantitative Precipitation Estimates

Abstract. Accurate quantitative precipitation estimates are of crucial importance for hydrological studies and applications. When spatial precipitation fields are required, rain gauge measurements are often combined with weather radar observations. In this paper, we evaluate several radar-gauge merging methods with various degrees of complexity: from mean field bias correction to geostatical merging techniques. The study area is the Walloon region of Belgium, which is mostly located in the Meuse catchment. Observations from a C-band Doppler radar and a dense rain gauge network are used to retrieve daily rainfall accumulations over this area. The relative performance of the different merging methods are assessed through a comparison against daily measurements from an independent gauge network. A 3-year verification is performed using several statistical quality parameters. It appears that the geostatistical merging methods perform best with the mean absolute error decreasing by 40% with respect to the original data. A mean field bias correction still achieves a reduction of 25%. A seasonal analysis shows that the benefit of using radar observations is particularly significant during summer. The effect of the network density on the performance of the methods is also investigated. For this purpose, a simple approach to remove gauges from a network is proposed. The analysis reveals that the sensitivity is relatively high for the geostatistical methods but rather small for the simple methods. The geostatistical methods give the best results for all network densities except for a very low density of 1 gauge per 500 km2 where a range-dependent adjustment complemented with a static local bias correction performs best.

scholarly journals Precipitation Estimates from Commercial Microwave Links: Practical Approaches to Wet-antenna Correction

2021 ◽  
Author(s):  
Jaroslav Pastorek ◽  
Martin Fencl ◽  
Jörg Rieckermann ◽  
Vojtěch Bareš
Keyword(s):  
Rainfall Intensity ◽  
Rain Gauge ◽  
Model Parameters ◽  
Rainfall Events ◽  
Precipitation Estimates ◽  
Attenuation Models ◽  
Using Data ◽  
Quantitative Precipitation Estimates ◽  
Rain Gauge Network ◽  
Commercial Microwave Links

An inadequate correction for wet antenna attenuation (WAA) often causes a notable bias in quantitative precipitation estimates (QPEs) from commercial microwave links (CMLs) limiting the usability of these rainfall data in hydrological applications. This paper analyzes how WAA can be corrected without dedicated rainfall monitoring for a set of 16 CMLs. Using data collected over 53 rainfall events, the performance of six empirical WAA models was studied, both when calibrated to rainfall observations from a permanent municipal rain gauge network and when using model parameters from the literature. The transferability of WAA model parameters among CMLs of various characteristics has also been addressed. The results show that high-quality QPEs with a bias below 5% and RMSE of 1 mm/h in the median could be retrieved, even from sub-kilometer CMLs where WAA is relatively large compared to raindrop attenuation. Models in which WAA is proportional to rainfall intensity provide better WAA estimates than constant and time-dependent models. It is also shown that the parameters of models deriving WAA explicitly from rainfall intensity are independent of CML frequency and path length and, thus, transferable to other locations with CMLs of similar antenna properties.

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