Articles | Volume 15, issue 19
https://doi.org/10.5194/amt-15-5581-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-15-5581-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
04 Oct 2022
Research article |
| 04 Oct 2022
| 04 Oct 2022
Automatic quality control of telemetric rain gauge data providing quantitative quality information (RainGaugeQC)
Katarzyna Ośródka
Centre of Meteorological Modelling, Institute of Meteorology and Water Management – National Research Institute, Podleśna 61, 01-673 Warsaw, Poland
Irena Otop
Research and Development Centre, Institute of Meteorology and Water
Management – National Research Institute, Podleśna 61, 01-673 Warsaw, Poland
Centre of Meteorological Modelling, Institute of Meteorology and Water Management – National Research Institute, Podleśna 61, 01-673 Warsaw, Poland
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Anna Jurczyk, Katarzyna Ośródka, Jan Szturc, Magdalena Pasierb, and Agnieszka Kurcz
Atmos. Meas. Tech., 16, 4067–4079, https://doi.org/10.5194/amt-16-4067-2023, https://doi.org/10.5194/amt-16-4067-2023, 2023
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A data-processing algorithm, RainGRS Clim, has been developed to work on precipitation accumulations such as daily or monthly totals. The algorithm makes the most of additional opportunities: access to high-quality data that are not operationally available and greater efficiency of the algorithms for data quality control and merging for longer accumulations. Monthly accumulations estimated by RainGRS Clim were found to be significantly more reliable than accumulations generated operationally.
Katarzyna Ośródka and Jan Szturc
Atmos. Meas. Tech., 15, 261–277, https://doi.org/10.5194/amt-15-261-2022, https://doi.org/10.5194/amt-15-261-2022, 2022
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Weather radar data are used in weather monitoring and forecasting, but they are affected by numerous errors and require advanced corrections. Different systems are designed and implemented to suit specific local conditions, like the RADVOL-QC system. The radar errors are divided into several groups: disturbance by non-meteorological echoes (from the mountains, RLAN signals, wind turbines, etc.), beam blockage, attenuation, etc. Each of them has different properties and is corrected differently.
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Weather radar data are processed to obtain various 2D products. In this research, an algorithm of interpolation of polar reflectivity data with respect to quality index (QI) data is applied to find the Cartesian reflectivity as PPI products and generate a corresponding QI field. On this basis, quality-based algorithms for the generation of the standard products have been developed: ETOP, MAX, and VIL. Moreover a detection of convection has been defined as a specific combination of the products.
Anna Jurczyk, Katarzyna Ośródka, Jan Szturc, Magdalena Pasierb, and Agnieszka Kurcz
Atmos. Meas. Tech., 16, 4067–4079, https://doi.org/10.5194/amt-16-4067-2023, https://doi.org/10.5194/amt-16-4067-2023, 2023
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A data-processing algorithm, RainGRS Clim, has been developed to work on precipitation accumulations such as daily or monthly totals. The algorithm makes the most of additional opportunities: access to high-quality data that are not operationally available and greater efficiency of the algorithms for data quality control and merging for longer accumulations. Monthly accumulations estimated by RainGRS Clim were found to be significantly more reliable than accumulations generated operationally.
Katarzyna Ośródka and Jan Szturc
Atmos. Meas. Tech., 15, 261–277, https://doi.org/10.5194/amt-15-261-2022, https://doi.org/10.5194/amt-15-261-2022, 2022
Short summary
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Weather radar data are used in weather monitoring and forecasting, but they are affected by numerous errors and require advanced corrections. Different systems are designed and implemented to suit specific local conditions, like the RADVOL-QC system. The radar errors are divided into several groups: disturbance by non-meteorological echoes (from the mountains, RLAN signals, wind turbines, etc.), beam blockage, attenuation, etc. Each of them has different properties and is corrected differently.
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Atmos. Meas. Tech., 8, 2173–2181, https://doi.org/10.5194/amt-8-2173-2015, https://doi.org/10.5194/amt-8-2173-2015, 2015
Short summary
Short summary
Weather radar data are processed to obtain various 2D products. In this research, an algorithm of interpolation of polar reflectivity data with respect to quality index (QI) data is applied to find the Cartesian reflectivity as PPI products and generate a corresponding QI field. On this basis, quality-based algorithms for the generation of the standard products have been developed: ETOP, MAX, and VIL. Moreover a detection of convection has been defined as a specific combination of the products.
Related subject area
Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: In Situ Measurement | Topic: Data Processing and Information Retrieval
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Atmos. Meas. Tech., 17, 4789–4802, https://doi.org/10.5194/amt-17-4789-2024, https://doi.org/10.5194/amt-17-4789-2024, 2024
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Research is conducted to identify special rainfall patterns in the Netherlands using multiple types of rainfall sensors. A total of eight potentially unique events are analyzed, considering both the number and size of raindrops. However, no clear evidence supporting the existence of a special rainfall regime could be found. The results highlight the challenges in experimentally confirming well-established theoretical ideas in the field of precipitation sciences.
Laura Warwick, Jonathan E. Murray, and Helen Brindley
Atmos. Meas. Tech., 17, 4777–4787, https://doi.org/10.5194/amt-17-4777-2024, https://doi.org/10.5194/amt-17-4777-2024, 2024
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We describe a method for measuring the emissivity of natural surfaces using data from the new Far-INfrarEd Spectrometer for Surface Emissivity (FINESSE) instrument. We demonstrate our method by making measurements of the emissivity of water. We then compare our results to the emissivity predicted using a model and find good agreement. The observations from FINESSE are novel because they allow us to determine surface emissivity at longer wavelengths than have been routinely measured before.
Jairo M. Valdivia, José Luis Flores-Rojas, Josep J. Prado, David Guizado, Elver Villalobos-Puma, Stephany Callañaupa, and Yamina Silva-Vidal
Atmos. Meas. Tech., 17, 2295–2316, https://doi.org/10.5194/amt-17-2295-2024, https://doi.org/10.5194/amt-17-2295-2024, 2024
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In this study, we explored hailstorms in the Central Andes of Peru. We used historical records and radar measurements to understand the frequency, timing, and characteristics of these hail events. Our research found a trend of decreasing hail frequency, probably due to anthropogenic climate change. Understanding these weather patterns is critical for local communities, as it can help improve weather forecasts and manage risks related to these potentially destructive events.
Nishant Ajnoti, Hemant Gehlot, and Sachchida Nand Tripathi
Atmos. Meas. Tech., 17, 1651–1664, https://doi.org/10.5194/amt-17-1651-2024, https://doi.org/10.5194/amt-17-1651-2024, 2024
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This research focuses on the optimal placement of hybrid instruments (sensors and monitors) to maximize satisfaction function considering population, PM2.5 concentration, budget, and other factors. Two algorithms are developed in this study: a genetic algorithm and a greedy algorithm. We tested these algorithms on various regions. The insights of this work aid in quantitative placement of air quality monitoring instruments in large cities, moving away from ad hoc approaches.
Arun Rao Karimindla, Shweta Kumari, Saipriya SR, Syam Chintala, and BVN Phanindra Kambhammettu
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2023-253, https://doi.org/10.5194/amt-2023-253, 2024
Revised manuscript accepted for AMT
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This study is aimed at investigating the role of the averaging period of Eddy Covariance (EC) fluxes on EBR and further propagation into WUE dynamics. Application was demonstrated on a Maize field considering EC flux data. We obtained that the time averages of EC fluxes that yield the most effective EBR are at 45 min and 60 min. The 30-min averaging period proves insufficient for capturing low-frequency fluxes. Time-averaging of EC fluxes needs to be performed based on crop growth stage.
Luke R. Allen, Sandra E. Yuter, Matthew A. Miller, and Laura M. Tomkins
Atmos. Meas. Tech., 17, 113–134, https://doi.org/10.5194/amt-17-113-2024, https://doi.org/10.5194/amt-17-113-2024, 2024
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We present a data set of high-precision surface air pressure observations and a method for detecting wave signals from the time series of pressure. A wavelet-based method is used to find wave signals at specific times and wave periods. From networks of pressure sensors spaced tens of kilometers apart, the wave phase speed and direction are estimated. Examples of wave events and their meteorological context are shown using radar data, weather balloon data, and other surface weather observations.
Arianna Cauteruccio, Mattia Stagnaro, Luca G. Lanza, and Pak-Wai Chan
Atmos. Meas. Tech., 16, 4155–4163, https://doi.org/10.5194/amt-16-4155-2023, https://doi.org/10.5194/amt-16-4155-2023, 2023
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Adjustments for the wind-induced bias of traditional rainfall gauges are applied to data from the Hong Kong Observatory using numerical simulation results. An optical disdrometer allows us to infer the collection efficiency of the rainfall gauge. Due to the local climatology, adjustments are limited but result in a significant amount of available freshwater resources that would be missing from the calculated hydrological budget of the region should the adjustments be neglected.
Ulrike Egerer, John J. Cassano, Matthew D. Shupe, Gijs de Boer, Dale Lawrence, Abhiram Doddi, Holger Siebert, Gina Jozef, Radiance Calmer, Jonathan Hamilton, Christian Pilz, and Michael Lonardi
Atmos. Meas. Tech., 16, 2297–2317, https://doi.org/10.5194/amt-16-2297-2023, https://doi.org/10.5194/amt-16-2297-2023, 2023
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This paper describes how measurements from a small uncrewed aircraft system can be used to estimate the vertical turbulent heat energy exchange between different layers in the atmosphere. This is particularly important for the atmosphere in the Arctic, as turbulent exchange in this region is often suppressed but is still important to understand how the atmosphere interacts with sea ice. We present three case studies from the MOSAiC field campaign in Arctic sea ice in 2020.
Jianbin Zhang, Zexia Duan, Shaohui Zhou, Yubin Li, and Zhiqiu Gao
Atmos. Meas. Tech., 16, 2197–2207, https://doi.org/10.5194/amt-16-2197-2023, https://doi.org/10.5194/amt-16-2197-2023, 2023
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In this paper, we used a random forest model to fill the observation gaps of the fluxes measured during 2015–2019. We found that the net radiation was the most important input variable. And we justified the reliability of the model. Further, it was revealed that the model performed better after relative humidity was removed from the input. Lastly, we compared the results of the model with those of three other machine learning models, and we found that the model outperformed all of them.
Jinwook Lee, Jongyun Byun, Jongjin Baik, Changhyun Jun, and Hyeon-Joon Kim
Atmos. Meas. Tech., 16, 707–725, https://doi.org/10.5194/amt-16-707-2023, https://doi.org/10.5194/amt-16-707-2023, 2023
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Our study addresses raindrop size distribution and rain rate by extracting rain streaks using a k-nearest-neighbor-based algorithm, estimating rainfall intensity using raindrop size distribution based on physical optics analysis, and verifying the estimated raindrop size distribution using a disdrometer. Experimentation demonstrated the possibility of estimating an image-based raindrop size distribution and rain rate obtained based on such low-cost equipment in dark conditions.
Zolal Ayazpour, Shiqi Tao, Dan Li, Amy Jo Scarino, Ralph E. Kuehn, and Kang Sun
Atmos. Meas. Tech., 16, 563–580, https://doi.org/10.5194/amt-16-563-2023, https://doi.org/10.5194/amt-16-563-2023, 2023
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Accurate knowledge of the planetary boundary layer height (PBLH) is essential to study air pollution. However, PBLH observations are sparse in space and time, and PBLHs used in atmospheric models are often inaccurate. Using PBLH observations from the Aircraft Meteorological DAta Relay (AMDAR), we present a machine learning framework to produce a spatially complete PBLH product over the contiguous US that shows a better agreement with reference PBLH observations than commonly used PBLH products.
Richard Wilson, Clara Pitois, Aurélien Podglajen, Albert Hertzog, Milena Corcos, and Riwal Plougonven
Atmos. Meas. Tech., 16, 311–330, https://doi.org/10.5194/amt-16-311-2023, https://doi.org/10.5194/amt-16-311-2023, 2023
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Strateole-2 is an French–US initiative designed to study atmospheric events in the tropical upper troposphere–lower stratosphere. In this work, data from several superpressure balloons, capable of staying aloft at an altitude of 18–20 km for over 3 months, were used. The present article describes methods to detect the occurrence of atmospheric turbulence – one efficient process impacting the properties of the atmosphere composition via stirring and mixing.
Norbert Pirk, Kristoffer Aalstad, Sebastian Westermann, Astrid Vatne, Alouette van Hove, Lena Merete Tallaksen, Massimo Cassiani, and Gabriel Katul
Atmos. Meas. Tech., 15, 7293–7314, https://doi.org/10.5194/amt-15-7293-2022, https://doi.org/10.5194/amt-15-7293-2022, 2022
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In this study, we show how sparse and noisy drone measurements can be combined with an ensemble of turbulence-resolving wind simulations to estimate uncertainty-aware surface energy exchange. We demonstrate the feasibility of this drone data assimilation framework in a series of synthetic and real-world experiments. This new framework can, in future, be applied to estimate energy and gas exchange in heterogeneous landscapes more representatively than conventional methods.
Basivi Radhakrishna
Atmos. Meas. Tech., 15, 6705–6722, https://doi.org/10.5194/amt-15-6705-2022, https://doi.org/10.5194/amt-15-6705-2022, 2022
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Raindrop size distributions (DSDs) measured by various types of disdrometers are different in the same environmental conditions. The mass-weighted mean diameter (Dm) measured from JWD is larger, and ZDR is smaller than LPM and PARSIVEL due to the resonance effect at X-band frequency. The effect of wind on DSD measured by various disdrometers is not uniform in different regions of a tropical cyclone.
Gina Jozef, John Cassano, Sandro Dahlke, and Gijs de Boer
Atmos. Meas. Tech., 15, 4001–4022, https://doi.org/10.5194/amt-15-4001-2022, https://doi.org/10.5194/amt-15-4001-2022, 2022
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During the MOSAiC expedition, meteorological conditions over the lowest 1 km of the atmosphere were sampled with the DataHawk2 uncrewed aircraft system. These data were used to identify the best method for atmospheric boundary layer height detection by comparing visually identified subjective boundary layer height to that identified by several objective automated detection methods. The results show a bulk Richardson number-based approach gives the best estimate of boundary layer height.
Antonio R. Segales, Phillip B. Chilson, and Jorge L. Salazar-Cerreño
Atmos. Meas. Tech., 15, 2607–2621, https://doi.org/10.5194/amt-15-2607-2022, https://doi.org/10.5194/amt-15-2607-2022, 2022
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The mitigation of undesired contamination, sensor characterization, and signal conditioning and restoration is crucial to improve the reliability of the weather unmanned aerial system (UAS) deliverables. This study presents an overview of the general considerations and procedures to compensate for slow sensor response and other sources of error for temperature and humidity measurements collected using a UAS.
Soo-Hyun Kim, Jeonghoe Kim, Jung-Hoon Kim, and Hye-Yeong Chun
Atmos. Meas. Tech., 15, 2277–2298, https://doi.org/10.5194/amt-15-2277-2022, https://doi.org/10.5194/amt-15-2277-2022, 2022
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The cube root of the energy dissipation rate (EDR), as a standard reporting metric of atmospheric turbulence, is estimated using 1 Hz commercial quick access recorder data from Korean-based national air carriers with two different types of aircraft. Various EDRs are estimated using zonal, meridional, and derived vertical wind components and the derived equivalent vertical gust. Characteristics of the observed EDR estimates using 1 Hz flight data are examined to observe strong turbulence cases.
Francesca M. Lappin, Tyler M. Bell, Elizabeth A. Pillar-Little, and Phillip B. Chilson
Atmos. Meas. Tech., 15, 1185–1200, https://doi.org/10.5194/amt-15-1185-2022, https://doi.org/10.5194/amt-15-1185-2022, 2022
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This study evaluates how a classically defined variable, air parcel buoyancy, can be used to interpret transitions in the atmospheric boundary layer (ABL). To capture the high-resolution variations, remotely piloted aircraft systems are used to collect data in two field campaigns. This paper finds that buoyancy has distinct evolutions prior to low-level jet and convective initiation cases. Additionally, buoyancy mixes well to act as an ABL height indicator comparable to other methods.
Shaohui Zhou, Yuanjian Yang, Zhiqiu Gao, Xingya Xi, Zexia Duan, and Yubin Li
Atmos. Meas. Tech., 15, 757–773, https://doi.org/10.5194/amt-15-757-2022, https://doi.org/10.5194/amt-15-757-2022, 2022
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Our research has determined the possible relationship between Weibull natural wind mesoscale parameter c and shape factor k with height under the conditions of a desert steppe terrain in northern China, which has great potential in wind power generation. We have gained an enhanced understanding of the seasonal changes in the surface roughness of the desert grassland and the changes in the incoming wind direction.
Xinhua Zhou, Tian Gao, Eugene S. Takle, Xiaojie Zhen, Andrew E. Suyker, Tala Awada, Jane Okalebo, and Jiaojun Zhu
Atmos. Meas. Tech., 15, 95–115, https://doi.org/10.5194/amt-15-95-2022, https://doi.org/10.5194/amt-15-95-2022, 2022
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Air temperature from sonic temperature and air moisture has been used without an exact equation. We present an exact equation of such air temperature for closed-path eddy-covariance flux measurements. Air temperature from this equation is equivalent to sonic temperature in its accuracy and frequency response. It is a choice for advanced flux topics because, with it, thermodynamic variables in the flux measurements can be temporally synchronized and spatially matched at measurement scales.
Haoyu Jiang
Atmos. Meas. Tech., 15, 1–9, https://doi.org/10.5194/amt-15-1-2022, https://doi.org/10.5194/amt-15-1-2022, 2022
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Sea surface wind and waves are important ocean parameters that can be continuously observed by meteorological buoys. Meteorological buoys are sparse in the ocean due to their high cost of deployment and maintenance. In contrast, low-cost compact wave buoys are suited for deployment in large numbers. Although wave buoys are not designed for wind measurement, we found that deep learning can estimate wind from wave measurements accurately, making wave buoys a good-quality data source for sea wind.
Matthias Mauder, Andreas Ibrom, Luise Wanner, Frederik De Roo, Peter Brugger, Ralf Kiese, and Kim Pilegaard
Atmos. Meas. Tech., 14, 7835–7850, https://doi.org/10.5194/amt-14-7835-2021, https://doi.org/10.5194/amt-14-7835-2021, 2021
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Turbulent flux measurements suffer from a general systematic underestimation. One reason for this bias is non-local transport by large-scale circulations. A recently developed model for this additional transport of sensible and latent energy is evaluated for three different test sites. Different options on how to apply this correction are presented, and the results are evaluated against independent measurements.
Maryam Ilyas, Douglas Nychka, Chris Brierley, and Serge Guillas
Atmos. Meas. Tech., 14, 7103–7121, https://doi.org/10.5194/amt-14-7103-2021, https://doi.org/10.5194/amt-14-7103-2021, 2021
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Instrumental temperature records are fundamental to climate science. There are spatial gaps in the distribution of these measurements across the globe. This lack of spatial coverage introduces coverage error. In this research, a methodology is developed and used to quantify the coverage errors. It results in a data product that, for the first time, provides a full description of both the spatial coverage uncertainties along with the uncertainties in the modeling of these spatial gaps.
Jussi Leinonen, Jacopo Grazioli, and Alexis Berne
Atmos. Meas. Tech., 14, 6851–6866, https://doi.org/10.5194/amt-14-6851-2021, https://doi.org/10.5194/amt-14-6851-2021, 2021
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Measuring the shape, size and mass of a large number of snowflakes is a challenging task; it is hard to achieve in an automatic and instrumented manner. We present a method to retrieve these properties of individual snowflakes using as input a triplet of images/pictures automatically collected by a multi-angle snowflake camera (MASC) instrument. Our method, based on machine learning, is trained on artificially generated snowflakes and evaluated on 3D-printed snowflake replicas.
Longjiang Li, Suqin Wu, Kefei Zhang, Xiaoming Wang, Wang Li, Zhen Shen, Dantong Zhu, Qimin He, and Moufeng Wan
Atmos. Meas. Tech., 14, 6379–6394, https://doi.org/10.5194/amt-14-6379-2021, https://doi.org/10.5194/amt-14-6379-2021, 2021
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The zenith hydrostatic delay (ZHD) derived from blind models are of low accuracy, especially in mid- and high-latitude regions. To address this issue, the ratio of the ZHD to zenith total delay (ZTD) is firstly investigated; then, based on the relationship between the ZHD and ZTD, a new ZHD model was developed using the back propagation artificial neural network (BP-ANN) method which took the ZTD as an input variable. The model outperforms blind models.
Grant W. Petty
Atmos. Meas. Tech., 14, 1959–1976, https://doi.org/10.5194/amt-14-1959-2021, https://doi.org/10.5194/amt-14-1959-2021, 2021
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Aircraft measurements of turbulent fluxes of matter and energy are important in field investigations of the interaction of the Earth's surface and the atmosphere. Because these measurements are of randomly fluctuating quantities, averages must be taken over longer flight tracks to reduce uncertainty. This paper investigates the relationship between track length and measurement error using a computer model simulation of a marine environment and compares the results with published theory.
Yadong Wang, Lin Tang, Pao-Liang Chang, and Yu-Shuang Tang
Atmos. Meas. Tech., 14, 185–197, https://doi.org/10.5194/amt-14-185-2021, https://doi.org/10.5194/amt-14-185-2021, 2021
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The motivation of this work is to develop a precipitation separation approach that can be implemented on those radars with fast scanning schemes. In these schemes, the higher tilt radar data are not available, which poses a challenge for the traditional approaches. This approach uses artificial intelligence, which integrates polarimetric radar variables. The quantitative precipitation estimation will benefit from the output of this algorithm.
Loiy Al-Ghussain and Sean C. C. Bailey
Atmos. Meas. Tech., 14, 173–184, https://doi.org/10.5194/amt-14-173-2021, https://doi.org/10.5194/amt-14-173-2021, 2021
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Unmanned aerial vehicles equipped with multi-hole probes are an effective approach to measure the wind vector with high spatial and temporal resolution. However, the aircraft motion must be removed from the measured signal first, a process often introducing bias due to small errors in the relative orientation of coordinates. We present an approach that has successfully been applied in post-processing, which was found to minimize the influence of aircraft motion on wind measurements.
Alessandro Fassò, Michael Sommer, and Christoph von Rohden
Atmos. Meas. Tech., 13, 6445–6458, https://doi.org/10.5194/amt-13-6445-2020, https://doi.org/10.5194/amt-13-6445-2020, 2020
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Modern radiosonde balloons fly from ground level up to the lower stratosphere and take temperature measurements. What is the uncertainty of interpolated values in the resulting atmospheric temperature profiles? To answer this question, we introduce a general statistical–mathematical model for the computation of interpolation uncertainty. Analysing more than 51 million measurements, we provide some understanding of the consequences of filling missing data with interpolated ones.
Jussi Leinonen and Alexis Berne
Atmos. Meas. Tech., 13, 2949–2964, https://doi.org/10.5194/amt-13-2949-2020, https://doi.org/10.5194/amt-13-2949-2020, 2020
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The appearance of snowflakes provides a signature of the atmospheric processes that created them. To get this information from large numbers of snowflake images, automated analysis using computer image recognition is needed. In this work, we use a neural network that learns the structure of the snowflake images to divide a snowflake dataset into classes corresponding to different sizes and structures. Unlike with most comparable methods, only minimal input from a human expert is needed.
Amber Ross, Craig D. Smith, and Alan Barr
Atmos. Meas. Tech., 13, 2979–2994, https://doi.org/10.5194/amt-13-2979-2020, https://doi.org/10.5194/amt-13-2979-2020, 2020
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The raw data derived from most automated accumulating precipitation gauges often suffer from non-precipitation-related fluctuations in the measurement of the gauge bucket weights from which the precipitation amount is determined. This noise can be caused by electrical interference, mechanical noise, and evaporation. This paper presents an automated filtering technique that builds on the principle of iteratively balancing noise to produce a clean precipitation time series.
Soo-Hyun Kim, Hye-Yeong Chun, Jung-Hoon Kim, Robert D. Sharman, and Matt Strahan
Atmos. Meas. Tech., 13, 1373–1385, https://doi.org/10.5194/amt-13-1373-2020, https://doi.org/10.5194/amt-13-1373-2020, 2020
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We retrieve the eddy dissipation rate (EDR) from the derived equivalent vertical gust included in the Aircraft Meteorological Data Relay data for more reliable and consistent observations of aviation turbulence globally with the single preferred EDR metric. We convert the DEVG to the EDR using two methods (lognormal mapping scheme and best-fit curve between EDR and DEVG), and the DEVG-derived EDRs are evaluated against in situ EDR data reported by US-operated carriers.
Nicholas Hamilton
Atmos. Meas. Tech., 13, 1019–1032, https://doi.org/10.5194/amt-13-1019-2020, https://doi.org/10.5194/amt-13-1019-2020, 2020
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The identification of atmospheric conditions within a multivariable atmospheric data set is an important step in validating emerging and existing models used to simulate wind plant flows and operational strategies. The total variation approach developed here offers a method founded in tested mathematical metrics and can be used to identify and characterize periods corresponding to quiescent conditions or specific events of interest for study or wind energy development.
Dafina Kikaj, Janja Vaupotič, and Scott D. Chambers
Atmos. Meas. Tech., 12, 4455–4477, https://doi.org/10.5194/amt-12-4455-2019, https://doi.org/10.5194/amt-12-4455-2019, 2019
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A new method was developed to identify persistent temperature inversion events in a subalpine basin using a radon-based method (RBM). By comparing with an existing pseudo-vertical temperature gradient method, the RBM was shown to be more reliable and seasonally independent. The RBM has the potential to increase the understanding of meteorological controls on air pollution episodes in complex terrain beyond the capability of contemporary atmospheric stability classification tools.
Jens Faber, Michael Gerding, Andreas Schneider, Andreas Dörnbrack, Henrike Wilms, Johannes Wagner, and Franz-Josef Lübken
Atmos. Meas. Tech., 12, 4191–4210, https://doi.org/10.5194/amt-12-4191-2019, https://doi.org/10.5194/amt-12-4191-2019, 2019
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Atmospheric measurements on rising balloons can be compromised by the balloon's wake. The aim of this study is to provide a tool for assessing the likelihood of encountering the balloon's wake at the position of the gondola. This includes an uncertainty analysis of the calculation and a retrieval of vertical winds. We find an average wake encounter probability of 28 % for a standard radiosonde. Additionally, we evaluate the influence of wake from smaller objects on turbulence measurements.
Maosi Chen, Zhibin Sun, John M. Davis, Yan-An Liu, Chelsea A. Corr, and Wei Gao
Atmos. Meas. Tech., 12, 935–953, https://doi.org/10.5194/amt-12-935-2019, https://doi.org/10.5194/amt-12-935-2019, 2019
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Combining a new dynamic uncertainty estimation method with Gaussian process regression (GP), we provide a generic and robust solution to estimate the underlying mean and uncertainty functions of time series with variable mean, noise, sampling density, and length of gaps. The GP solution was applied and validated on three UV-MFRSR Vo time series at three ground sites with improved accuracy of the smoothed time series in terms of aerosol optical depth compared with two other smoothing methods.
Christoph Schlager, Gottfried Kirchengast, and Juergen Fuchsberger
Atmos. Meas. Tech., 11, 5607–5627, https://doi.org/10.5194/amt-11-5607-2018, https://doi.org/10.5194/amt-11-5607-2018, 2018
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In this work we further developed and evaluated an operational weather diagnostic application, the WegenerNet Wind Product Generator (WPG), and applied it to the WegenerNet Johnsbachtal (JBT), a dense meteorological station network located in a mountainous Alpine region. The WPG automatically generates gridded high-resolution wind fields in near-real time with a temporal resolution of 30 min and a spatial resolution of 100 m x 100 m.
Kaisa Lakkala, Antti Arola, Julian Gröbner, Sergio Fabian León-Luis, Alberto Redondas, Stelios Kazadzis, Tomi Karppinen, Juha Matti Karhu, Luca Egli, Anu Heikkilä, Tapani Koskela, Antonio Serrano, and José Manuel Vilaplana
Atmos. Meas. Tech., 11, 5167–5180, https://doi.org/10.5194/amt-11-5167-2018, https://doi.org/10.5194/amt-11-5167-2018, 2018
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The performance of the cosine error correction method for correcting spectral UV measurements of the Brewer spectroradiometer was studied. The correction depends on the sky radiation distribution, which can change during one spectral scan. The results showed that the correction varied between 4 and 14 %, and that the relative differences between the reference and the Brewer diminished by 10 %. The method is applicable to other instruments as long as the required input parameters are available.
Jason Kelley and Chad Higgins
Atmos. Meas. Tech., 11, 2151–2158, https://doi.org/10.5194/amt-11-2151-2018, https://doi.org/10.5194/amt-11-2151-2018, 2018
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Measuring fluxes of energy and trace gases using the surface renewal (SR) method can be economical and robust, but it requires computationally intensive calculations. Several new algorithms were written to perform the required calculations more efficiently and rapidly, and were tested with field data and computationally rigorous SR methods. These efficient algorithms facilitate expanded use of SR in atmospheric experiments, for applied monitoring, and in novel field implementations.
Viswanathan Bringi, Merhala Thurai, and Darrel Baumgardner
Atmos. Meas. Tech., 11, 1377–1384, https://doi.org/10.5194/amt-11-1377-2018, https://doi.org/10.5194/amt-11-1377-2018, 2018
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Raindrop fall velocities are important for rain rate estimation, soil erosion studies and in numerical modelling of rain formation in clouds. The assumption that the fall velocity is uniquely related to drop size is made inherently based on laboratory measurements under still air conditions from nearly 68 years ago. There have been very few measurements of drop fall speeds in natural rain under both still and turbulent wind conditions. We report on fall speed measurements in natural rain shafts.
Marta Wacławczyk, Yong-Feng Ma, Jacek M. Kopeć, and Szymon P. Malinowski
Atmos. Meas. Tech., 10, 4573–4585, https://doi.org/10.5194/amt-10-4573-2017, https://doi.org/10.5194/amt-10-4573-2017, 2017
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We propose two novel methods to estimate turbulent kinetic energy dissipation rate applicable to airborne measurements. In this way we increase robustness of the dissipation rate retrieval and extend its applicability to a wider range of data sets. The new approaches relate the predicted form of the dissipation spectrum to the mean of zero crossings of the measured velocity fluctuations. The methods are easy to implement numerically, and estimates remain unaffected by certain measurement errors.
Sabine Wüst, Verena Wendt, Ricarda Linz, and Michael Bittner
Atmos. Meas. Tech., 10, 3453–3462, https://doi.org/10.5194/amt-10-3453-2017, https://doi.org/10.5194/amt-10-3453-2017, 2017
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Cubic splines with equidistant spline sampling points are a common method in atmospheric science for the approximation of background conditions by means of filtering superimposed fluctuations from a data series. However, splines can generate considerable artificial oscillations in the background and the residuals. We introduce a repeating spline approach which is able to significantly reduce this phenomenon and to apply it to TIMED-SABER vertical temperature profiles from 2010 to 2014.
Mohamed Djallel Dilmi, Cécile Mallet, Laurent Barthes, and Aymeric Chazottes
Atmos. Meas. Tech., 10, 1557–1574, https://doi.org/10.5194/amt-10-1557-2017, https://doi.org/10.5194/amt-10-1557-2017, 2017
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The concept of a rain event is used to obtain a parsimonious characterisation of rain events using a minimal subset of variables at macrophysical scale. A classification in five classes is obtained in a unsupervised way from this subset. Relationships between these classes of microphysical parameters of precipitation are highlighted. There are several implications especially for remote sensing in the context of weather radar applications and quantitative precipitation estimation.
Christophe Praz, Yves-Alain Roulet, and Alexis Berne
Atmos. Meas. Tech., 10, 1335–1357, https://doi.org/10.5194/amt-10-1335-2017, https://doi.org/10.5194/amt-10-1335-2017, 2017
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The Multi-Angle Snowflake Camera (MASC) provides high-resolution pictures of individual falling snowflakes and ice crystals. A method is proposed to automatically classify these pictures into six classes of snowflakes as well to estimate the degree of riming and to detect whether or not the particles are melting. Multinomial logistic regression is used with a manually classified
reference set. The evaluation demonstrates the good and reliable performance of the proposed technique.
Zhaopeng Luan, Yongxiang Han, Tianliang Zhao, Feng Liu, Chong Liu, Mark J. Rood, Xinghua Yang, Qing He, and Huichao Lu
Atmos. Meas. Tech., 10, 273–279, https://doi.org/10.5194/amt-10-273-2017, https://doi.org/10.5194/amt-10-273-2017, 2017
Zofia Baldysz, Grzegorz Nykiel, Andrzej Araszkiewicz, Mariusz Figurski, and Karolina Szafranek
Atmos. Meas. Tech., 9, 4861–4877, https://doi.org/10.5194/amt-9-4861-2016, https://doi.org/10.5194/amt-9-4861-2016, 2016
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In this paper two official processing strategies of GPS observations were analysed. The main purpose was to assess differences in long-term (linear trends) and short-term (oscillations) changes between these two sets of data. Investigation was based on 18-year and 16-year time series and showed that, despite the general consistency, for selected stations a change of processing strategy may have caused significant differences (compared to the uncertainties) in estimated linear trend values.
Jussi Tiira, Dmitri N. Moisseev, Annakaisa von Lerber, Davide Ori, Ali Tokay, Larry F. Bliven, and Walter Petersen
Atmos. Meas. Tech., 9, 4825–4841, https://doi.org/10.5194/amt-9-4825-2016, https://doi.org/10.5194/amt-9-4825-2016, 2016
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In this study winter measurements collected in Southern Finland are used to document microphysical properties of falling snow. It is shown that a new video imager can be used for such studies. Snow properties do vary between winters.
Gutemberg Borges França, Manoel Valdonel de Almeida, and Alessana C. Rosette
Atmos. Meas. Tech., 9, 2335–2344, https://doi.org/10.5194/amt-9-2335-2016, https://doi.org/10.5194/amt-9-2335-2016, 2016
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This paper presents a novel model, based on neural network techniques, to produce short-term and locally specific forecasts of significant instability for flights in the terminal area of Rio de Janeiro's airport, Brazil. Twelve years of data were used for neural network training/validation and test. The test showed that the proposed model can grab the physical content inside the data set, and its performance is encouraging for the first and second hours to nowcast significant instability events.
Jacek M. Kopeć, Kamil Kwiatkowski, Siebren de Haan, and Szymon P. Malinowski
Atmos. Meas. Tech., 9, 2253–2265, https://doi.org/10.5194/amt-9-2253-2016, https://doi.org/10.5194/amt-9-2253-2016, 2016
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This paper is presenting a feasibility study focused on methods of estimating the turbulence intensity based on a class of navigational messages routinely broadcast by the commercial aircraft (known as ADS-B and Mode-S). Using this kind of information could have potentially significant impact on aviation safety. Three methods have been investigated.
Jörg Burdanowitz, Christian Klepp, and Stephan Bakan
Atmos. Meas. Tech., 9, 1637–1652, https://doi.org/10.5194/amt-9-1637-2016, https://doi.org/10.5194/amt-9-1637-2016, 2016
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We develop a new automatic algorithm to distinguish oceanic precipitation into rain, snow and mixed phase using optical disdrometers deployed on board research vessels. In combination, air temperature, relative humidity and the maximum precipitation particle diameter outperform human observer data and yield highest skill to predict the precipitation phase. This knowledge allows deriving accurate rain and snowfall rates with dense global ocean sampling, which enables satellite sensor validation.
Cited articles
Bárdossy, A., Seidel, J., and El Hachem, A.: The use of personal weather station observations to improve precipitation estimation and interpolation, Hydrol. Earth Syst. Sci., 25, 583–601, https://doi.org/10.5194/hess-25-583-2021, 2021.
Baserud, L., Lussana, C., Nipen, T. N., Seierstad, I. A., Oram, L., and
Aspelien, T.: TITAN automatic spatial quality control of meteorological
in-situ observations, Adv. Sci. Res., 17, 153–163,
https://doi.org/10.5194/asr-17-153-2020, 2020.
Blenkinsop, S., Lewis, E., Chan, S. C., and Fowler, H. J.: An hourly
precipitation dataset and climatology of extremes for the UK, Int. J. Climatol., 37, 722–740, https://doi.org/10.1002/joc.4735, 2017.
Buisán, S. T., Earle, M. E., Collado, J. L., Kochendorfer, J., Alastrué, J., Wolff, M., Smith, C. D., and López-Moreno, J. I.: Assessment of snowfall accumulation underestimation by tipping bucket gauges in the Spanish operational network, Atmos. Meas. Tech., 10, 1079–1091, https://doi.org/10.5194/amt-10-1079-2017, 2017.
Burszta-Adamiak, E., Licznar, P., and Zaleski, J.: Criteria for identifying
maximum rainfall determined by the peaks-over-threshold (POT) method under
the Polish Atlas of Rainfall Intensities (PANDa) project, Meteorol.
Hydrol. Water Manage., 7, 3–13,
https://doi.org/10.26491/mhwm/93595, 2019.
Colli, M., Lanza, L. G., and La Barbera, P.: Performance of a weighing rain
gauge under laboratory simulated time-varying reference rainfall rates,
Atmos. Res., 131, 3–12,
https://doi.org/10.1016/j.atmosres.2013.04.006, 2013.
de Vos, L. W., Leijnse, H., Overeem, A., and Uijlenhoet, R.: Quality control
for crowdsourced personal weather stations to enable operational rainfall
monitoring, Geophys. Res. Lett., 46, 8820–8829,
https://doi.org/10.1029/2019GL083731, 2019.
Einfalt, T., Szturc, J., and Ośródka, K.: The quality index for
radar precipitation data – a tower of Babel?, Atmos. Sci. Lett., 11,
139–144, https://doi.org/10.1002/asl.271, 2010.
Fiebrich, C. A., Morgan, C. R., and McCombs, A. G.: Quality assurance
procedures for mesoscale meteorological data, J. Atmos. Ocean. Tech., 27, 1565–1582,
https://doi.org/10.1175/2010JTECHA1433.1, 2010.
Førland, E. J., Allerup, P., Dahlstrom, B., Elomaa, E., Jonsson, T.,
Madsen, H., Perala, H., Rissanen, P., Vedin, H., and Vejen, F.: Manual for
operational correction of Nordic precipitation data, Report No. 24/96, DNMI,
Norway, 66 pp., https://www.met.no/publikasjoner/met-report/met-report-1996/ (last access: 27 September 2022), 1996.
Golz, C., Einfalt, T., Gabella, M., and Germann, U.: Quality control
algorithms for rainfall measurements, Atmos. Res., 77, 247–255,
https://doi.org/10.1016/j.atmosres.2004.10.027, 2005.
Goodison, B. E., Louie, P. Y. T., and Yang, D.: WMO solid precipitation
measurement intercomparison: Final report, World Meteorological Organization, Geneva, Switzerland, Instrum. Obs. Methods Rep. 67,
211 pp., https://library.wmo.int/doc_num.php?explnum_id=9694 (last access: 27 September 2022), 1998.
Grossi, G., Lendvai, A., Giovanni Peretti, G., and Ranzi, R.: Snow
precipitation measured by gauges: systematic error estimation and data
series correction in the Central Italian Alps, Water, 9, 461,
https://doi.org/10.3390/w9070461, 2017.
Habib, E., Krajewski, W., and Kruger, A.: Sampling errors of tipping-bucket
rain gauge measurements, J. Hydrol. Eng., 6, 159–166,
https://doi.org/10.1061/(ASCE)1084-0699(2001)6:2(159), 2001.
Jurczyk, A., Szturc, J., Otop, I., Ośródka, K., and Struzik, P.:
Quality-based combination of multi-source precipitation data, Remote
Sens., 12, 1709, https://doi.org/10.3390/rs12111709, 2020.
Kochendorfer, J., Earle, M. E., Hodyss, D., Reverdin, A., Roulet, Y.-A.,
Nitu, R., Rasmussen, R., Landolt, S., Buisan, S., and Laine, T.: Undercatch
adjustments for tipping-bucket gauge measurements of solid precipitation,
J. Hydrometeorol., 21, 1193–1205,
https://doi.org/10.1175/JHM-D-19-0256.1, 2020.
Kondragunta, C. R. and Shrestha, K.: Automated real-time operational rain
gauge quality-control tools in NWS Hydrologic Operations, 86th AMS
Annual Meeting, Atlanta, GA, 28 January–3 March 2006, P24, https://ams.confex.com/ams/pdfpapers/102834.pdf (last access: 27 September 2022), 2006.
Lewis, E., Quinn, N., Blenkinsop, S., Fowler, H. J., Freer, J., Tanguy, M.,
Hitt, O., Coxon, G., Bates, P., and Woods, R.: A rule based quality control
method for hourly rainfall data and a 1 km resolution gridded hourly
rainfall dataset for Great Britain: CEH-GEAR1hr, J. Hydrol., 564,
930–943, https://doi.org/10.1016/j.jhydrol.2018.07.034, 2018.
Lewis, E., Pritchard, D., Villalobos-Herrera, R., Blenkinsop, S., McClean,
F., Guerreiro, S., Schneider, U., Becker, A., Finger, P., Meyer-Christoffer,
A., Rustemeier, E., and Fowler, H. J.: Quality control of a global hourly
rainfall dataset, Environ. Modell. Softw., 144, 105169,
https://doi.org/10.1016/j.envsoft.2021.105169, 2021.
Martinaitis, S. M., Cocks, S. B., Qi, B., Kaney, Y., Zhang, J., and Howard,
K.: Understanding winter precipitation impacts on automated gauges within a
real-time system, J. Hydrometeorol., 16, 2345–2363,
https://doi.org/10.1175/JHM-D-15-0020.1, 2015.
Michelson, D.: Systematic correction of precipitation gauge observations
using analyzed meteorological variables, J. Hydrol., 290,
161–177, https://doi.org/10.1016/j.jhydrol.2003.10.005, 2004.
Moslemi, M. and Joksimovic, D.: Real-time quality control and infilling of
precipitation data using neural networks, EPiC Series in Engineering, in: HIC
2018, 13th International Conference on Hydroinformatics, edited by: La Loggia, G., Freni, G., Puleo, V., and De Marchis, M., 3, 1457–1464,
https://doi.org/10.29007/t5k7, 2018.
Neuper, M. and Ehret, U.: Quantitative precipitation estimation with weather radar using a data- and information-based approach, Hydrol. Earth Syst. Sci., 23, 3711–3733, https://doi.org/10.5194/hess-23-3711-2019, 2019.
Niu, G., Yang, P., Zheng, Y., Cai, X., and Qin, H.: Automatic quality
control of crowdsourced rainfall data with multiple noises: A machine
learning approach, Water Resour. Res., 57, e2020WR029121,
https://doi.org/10.1029/2020WR029121, 2021.
Ośródka, K. and Szturc, J.: Improvement in algorithms for quality control of weather radar data (RADVOL-QC system), Atmos. Meas. Tech., 15, 261–277, https://doi.org/10.5194/amt-15-261-2022, 2022.
Ośródka, K., Szturc, J., and Jurczyk A.: Chain of data quality
algorithms for 3-D single-polarization radar reflectivity (RADVOL-QC
system), Meteorol. Appl., 21, 256–270,
https://doi.org/10.1002/met.1323, 2014.
Otop, I., Szturc, J., Ośródka, K., and Djaków, P.: Automatic
quality control of telemetric rain gauge data for operational applications
at IMGW-PIB, ITM Web Conf., 23, 00028,
https://doi.org/10.1051/itmconf/20182300028, 2018.
Qi, Y., Martinaitis, S., Zhang, J., and Cocks, S.: A real-time automated
quality control of hourly rain gauge data based on multiple sensors in MRMS
System, J. Hydrometeorol., 17, 1675–1691,
https://doi.org/10.1175/JHM-D-15-0188.1, 2016.
Rasmussen, R., Baker, B., Kochendorfer, J., Meyers, T., Landolt, S.,
Fischer, A. P., Black, J., Theriault, J. M., Kucera, P., Gochis, D., Smith,
C., Nitu, R., Hall, M., Ikeda, K., and Gutmann, E.: How well are we
measuring snow? The NOAA/FAA/NCAR winter precipitation Test Bed, B. Meteorol. Soc., 93, 811–829,
https://doi.org/10.1175/BAMS-D-11-00052.1, 2012.
Savina, M., Schappi, B., Molnar, P., Burlando, P., and Sevruk, B.:
Comparison of a tipping-bucket and electronic weighing precipitation gauge
for snowfall, Atmos. Res., 103, 54–51,
https://doi.org/10.1016/j.atmosres.2011.06.010, 2012.
Scherrer, S. C., Frei, C., Croci-Maspoli, M., van Geijtenbeek, D., Hotz, C.,
and Appenzeller C.: Operational quality control of daily precipitation using
spatio-climatological plausibility testing, Meteorol. Z., 20,
397–407, https://doi.org/10.1127/0941-2948/2011/0236, 2011.
Sevruk, B.: Adjustment of tipping-bucket precipitation gauge measurements,
Atmos. Res., 42, 237–246,
https://doi.org/10.1016/0169-8095(95)00066-6, 1996.
Sevruk, B. and Nevenic, M.: The geography and topography effects on the
areal pattern of precipitation in a small prealpine basin, Water Sci. Technol., 37, 163–170,1998.
Sevruk, B., Ondras M., and Chvila B.: The WMO precipitation
intercomparisons, Atmos. Res., 92, 376–380,
https://doi.org/10.1016/j.atmosres.2009.01.016, 2009.
Shedekar, V. S., King, K. W., Fausey, N. R., Soboyejo, A. B. O., Harmel, R.
D., and Brown, L. C.: Assessment of measurement errors and dynamic
calibration methods for three different tipping bucket rain gauges,
Atmos. Res., 178, 445–458,
https://doi.org/10.1016/j.atmosres.2016.04.016, 2016.
Sieck, L. C., Burges, S. J., and Steiner, M.: Challenges in obtaining reliable
measurements of point rainfall, Water Resour. Res., 43, W01420,
https://doi.org/10.1029/2005WR004519, 2007.
Steinacker, R., Mayer, D., and Steiner, A.: Data quality control based on
self-consistency, Mon. Weather Rev., 139, 3974–3991,
https://doi.org/10.1175/MWR-D-10-05024.1, 2011.
Szturc, J., Jurczyk, A., Ośródka, K., Wyszogrodzki, A., and
Giszterowicz, M.: Precipitation estimation and nowcasting at IMGW (SEiNO
system), Meteorol. Hydrol. Water Manage., 6, 3–12,
https://doi.org/10.26491/mhwm/76120, 2018.
Szturc, J., Ośródka, K., Jurczyk, A., Otop, I., Linkowska, J.,
Bochenek, B., and Pasierb, M.: Quality control and verification of
precipitation observations, estimates, and forecasts, in: Precipitation
Science. Measurement, Remote Sensing, Microphysics and Modeling, 1st edn.,
edited by: Michaelides, S., Elsevier, 91–133,
https://doi.org/10.1016/B978-0-12-822973-6.00002-0, 2022.
Taylor, J. R. and Loescher, H. L.: Automated quality control methods for sensor data: a novel observatory approach, Biogeosciences, 10, 4957–4971, https://doi.org/10.5194/bg-10-4957-2013, 2013.
Upton, G. and Rahimi A.: On-line detection of errors in tipping-bucket
raingauges, J. Hydrol., 278, 197–212,
https://doi.org/10.1016/S0022-1694(03)00142-2, 2003.
Urban, G. and Strug, K.: Evaluation of precipitation measurements obtained
from different types of rain gauges, Meteorol. Z., 30,
445–463, https://doi.org/10.1127/metz/2021/1084, 2021.
Villalobos Herrera, R., Blenkinsop, S., Guerreiro, S. B., O'Hara, T., and
Fowler, H. J.: Sub-hourly resolution quality control of rain gauge data
significantly improves regional sub-daily return level estimates, Q. J. Roy. Meteor. Soc., 1–20,
https://doi.org/10.1002/qj.4357, early view, 2022.
WMO-No. 8: Guide to Instruments and Methods of Observation, vol. I:
Measurement of Meteorological Variables, 2018 edn., World Meteorological
Organization, Geneva, 548 pp., https://library.wmo.int/index.php?id=12407&lvl=notice_display#.YzKXu0zP2Uk (last access: 27 September 2022), 2018.
WMO-No. 305: Guide on the Global Data-processing System, 1993 edn., World
Meteorological Organization, Geneva, 199 pp., https://library.wmo.int/index.php?lvl=notice_display&id=6832#.YzKZNUzP2Uk (last access: 27 September 2022), 1993.
WMO-No. 488: Guide to the Global Observing System, 2010 edn., World Meteorological Organization, Geneva, 215 pp., https://library.wmo.int/index.php?lvl=notice_display&id=12516#.YzKZ1UzP2Uk (last access: 27 September 2022), 2017.
Yeung, H. Y., Man, C., Chan S. T., and Seed, A.: Development of an
operational rainfall data quality-control scheme based on radar-raingauge
co-kriging analysis, Hydrolog. Sci. J., 59, 1293–1307,
https://doi.org/10.1080/02626667.2013.839873, 2014.
You, J., Hubbard K. G., Nadarajah S., and Kunkel K. E.: Performance of
quality assurance procedures on daily precipitation, J. Atmos. Ocean. Tech., 24, 821–834, https://doi.org/10.1175/JTECH2002.1,
2007.
Short summary
The quality control of sub-hourly rain gauge data is a challenging task due to the high variability and low spatial consistency of the data. We developed an innovative approach to the quality control of telemetric rain gauge data focused on assessing the reliability of individual observations. Our scheme employs weather radar data to detect erroneous rain gauge measurements and to assess the data reliability. The scheme is used operationally by the Polish meteorological and hydrological service.
The quality control of sub-hourly rain gauge data is a challenging task due to the high...
