Advancements and Characteristics of Gauge Ingest and Quality Control within the Multi-Radar Multi-Sensor System

2021 ◽  
Author(s):  
Steven M. Martinaitis ◽  
Stephen B. Cocks ◽  
Micheal J. Simpson ◽  
Andrew P. Osborne ◽  
Sebastian S. Harkema ◽  
...  
Keyword(s):  
Quality Control ◽  
Grid Cell ◽  
Sensor System ◽  
Multiple Pass ◽  
Extensive Evaluation ◽  
Latency Analysis ◽  
Gauge Data ◽  
Data Latency ◽  
Station Site

AbstractThis study describes recent advancements in the Multi-Radar Multi-Sensor (MRMS) automated gauge ingest and quality control (QC) processes. A data latency analysis for the combined multiple gauge collection platforms provided guidance for a multiple-pass generation and delivery of gauge-based precipitation products. Various advancements to the gauge QC logic were evaluated over a 21-month period, resulting in an average of 86% of hourly gauge observations per hour being classified as useful. The fully-automated QC logic was compared to manual human QC for a limited domain, which showed a > 95% agreement in their QC reasoning categories. This study also includes an extensive evaluation of various characteristics related to the gauge observations ingested into the MRMS system. Duplicate observations between gauge collection platforms highlighted differences in site coordinates; moreover, errors in Automated Surface Observing System (ASOS) station site coordinates resulted in > 79% of sites being located in a different MRMS 1-km grid cell. The ASOS coordinate analysis combined with examinations of other limitations regarding gauge observations highlight the need for robust and accurate metadata to further enhance the quality control of gauge data.

scholarly journals Validation of Satellite and Merged Rainfall Data over Ghana, West Africa

Atmosphere ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 859 ◽  
Author(s):  
Winifred Ayinpogbilla Atiah ◽  
Leonard Kofitse Amekudzi ◽  
Jeffrey Nii Armah Aryee ◽  
Kwasi Preko ◽  
Sylvester Kojo Danuor
Keyword(s):  
Grid Cell ◽  
Rain Gauge ◽  
Climate Impact ◽  
Rain Gauge Data ◽  
Ecological Zones ◽  
Transition Zones ◽  
Impact Studies ◽  
Gauge Data ◽  
Satellite Rainfall ◽  
Agro Ecological Zones

In regions of sparse gauge networks, satellite rainfall products are mostly used as surrogate measurements for various rainfall impact studies. Their potential to complement rain gauge measurements is influenced by the uncertainties associated with them. This study evaluates the performance of satellites and merged rainfall products over Ghana in order to provide information on the consistency and reliability of such products. Satellite products were validated with gridded rain gauge data from the Ghana Meteorological Agency (GMet) on various time scales. It was observed that the performance of the products in the country are mostly scale and location dependent. In addition, most of the products showed relatively good skills on the seasonal scale (r > 0.90) rather than the annual, and, after removal of seasonality from the datasets, except ARC2 that had larger biases in most cases. Again, all products captured the onsets, cessations, and spells countrywide and in the four agro-ecological zones. However, CHIRPS particularly revealed a better skill on both seasonal and annual scales countrywide. The products were not affected by the number of gauge stations within a grid cell in the Forest and Transition zones. This study, therefore, recommends all products except ARC2 for climate impact studies over the region.

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.

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 A Real-Time Automated Quality Control of Hourly Rain Gauge Data Based on Multiple Sensors in MRMS System

2016 ◽  
Vol 17 (6) ◽  
pp. 1675-1691 ◽  
Author(s):  
Youcun Qi ◽  
Steven Martinaitis ◽  
Jian Zhang ◽  
Stephen Cocks
Keyword(s):  
Quality Control ◽  
Rain Gauge ◽  
Computationally Efficient ◽  
Meteorological Model ◽  
Freezing Level ◽  
Multiple Sensors ◽  
Precipitation Regimes ◽  
Gauge Data ◽  
Precipitation Estimation ◽  
Automated Quality Control

Abstract Automated rain gauge networks provide direct measurements of precipitation and have been used for numerous applications, such as generating regional and national precipitation maps, calibrating remote sensing quantitative precipitation estimation (QPE), and validating hydrological and meteorological model predictions. However, automated gauge observations are prone to be affected by a variety of error sources and require a careful quality-control (QC) procedure. Many previous gauge QC techniques were based on spatiotemporal checks within the gauge network itself, and their effectiveness can be dependent on gauge densities and precipitation regimes. The current study takes advantage of the multisensor data sources in the Multi-Radar Multi-Sensor (MRMS) system and develops an automated and computationally efficient gauge QC scheme based on the consistency of hourly gauge and radar QPE observations. Radar and gauge error characteristics related to radar sampling geometry, precipitation regimes, and freezing-level height is utilized within this scheme. This QC scheme is evaluated by testing its capability to identify suspect gauges and comparing the ability to quality-controlled gauges through statistical and spatial comparisons of gauge-influenced gridded QPE products. Spatial analysis of the gridded QPE products in MRMS resulted in a more physical spatial QPE distribution using quality-controlled gauges versus the same product created with non-quality-controlled gauge data.

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.

A Method for Bias Correction of Remotely Sensed Precipitation across Western Ghats Region of India

2020 ◽  
Author(s):  
Aiswarya Kunnath-Poovakka ◽  
Eldho T Iype
Keyword(s):  
Bias Correction ◽  
Western Ghats ◽  
Grid Cell ◽  
Remotely Sensed ◽  
Rain Gauge ◽  
Error Distribution ◽  
Rain Gauge Data ◽  
Multiplicative Error ◽  
Gauge Data ◽  
The Western Ghats

<p>The systematic and random errors in different remotely sensed (RS) precipitation products varies spatially and seasonally.  Error characterisation of the satellite precipitation products is vital for improved hydrologic and climatic modelling as precipitation is the key component of surface and subsurface hydrologic system. In this study, a new approach is developed for the bias correction of different satellite and processed rainfall products across Western Ghats region of India. The Western Ghats are mountainous ranges of about 1600 Kms length parallel to west coast of peninsular India, which consists the largest tropical rainforest in India.  Many studies have reported that most of the RS rainfall products are underestimated in Western Ghats region. In the present study, a multiplicative error distribution model for the entire Western Ghats for each of the RS precipitation products used is developed. Quality controlled interpolated gridded rain gauge data from Indian Meteorological Department (IMD) is used as the base. The IMD rainfall data is cross validated with available rain gauge data in the Western Ghats region. The bias correction of four multisatellite high-resolution precipitation products namely, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) precipitation products, 3B42 version 7 and TMPA-3B42RT (Real Time) version 7 and Precipitation data from NASA (National Aeronautics and Space Administration) Modern-Era Retrospective Analysis for Research and Applications (MERRA) is performed in this study. The multiplicative monthly bias factor for each grid cell of Western Ghats is generated with the IMD rainfall as reference and it is found that the monthly multiplicative error for Western Ghats fluctuates around a common mean for each of the grid cell. Based on this a rainfall multiplicative error distribution is generated for each month for the Western Ghats regions. Systematic errors in rainfall were corrected using this distribution and the efficacy of error-corrected rainfall is evaluated with the help of conceptual rainfall-runoff models. The results depict that the proposed method helps to reduce the bias in different rainfall products and provide improved runoff estimations at Western Ghats.</p>

scholarly journals Effect of bias adjustment and rain gauge data quality control on radar rainfall estimation

1999 ◽  
Vol 35 (8) ◽  
pp. 2487-2503 ◽  
Author(s):  
Matthias Steiner ◽  
James A. Smith ◽  
Stephen J. Burges ◽  
Carlos V. Alonso ◽  
Robert W. Darden
Keyword(s):  
Quality Control ◽  
Data Quality ◽  
Rain Gauge ◽  
Data Quality Control ◽  
Rainfall Estimation ◽  
Radar Rainfall ◽  
Rain Gauge Data ◽  
Bias Adjustment ◽  
Gauge Data
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