Comparison between precipitation estimates of ground-based weather radar composites and GPM's DPR rainfall product over Germany

Pejcic, Velibor; Garfias, Pablo Saavedra; Mühlbauer, Kai; Trömel, Silke; Simmer, Clemens

doi:10.1127/metz/2020/1039

Cited by 16 publications

(11 citation statements)

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“…When using GPM DPR products, the uncertainty in precipitation estimation and microphysical retrieval of heavy rainfalls is reported in Pejcic et al (2020) and Huang et al (2021), which might be affected by the measurement errors of Z e in Ka-band and Z e in Ku-band as well as the assumptions of DSD model, mixing model and melting process (Seto and Iguchi, 2011;Seto et al, 2013). In theory, the attenuation difference in Kaband and Ku-band increases monotonically with range and the increasing rate suddenly changes at the height where the phase state changes (such as the melting layer).…”

Section: Discussionmentioning

confidence: 99%

“…The DPR operates at Ku band (KuPR,13.6 GHz) and Ka band (KaPR,35.5 GHz), which can provide information on drop size distributions (DSDs) in rain and snow as well as estimations of rainfall and snowfall rates (Beauchamp et al, 2015;Hamada and Takayabu, 2016;Jackson et al, 2016). These GPM parameters are evaluated and widely adopted to study deep convective precipitation and its vertical structure (Chen et al, 2020b;Huang and Chen, 2019;Libertino et al, 2016;Liu and Liu, 2016;Pejcic et al, 2020;Zhang and Fu, 2018a;. Compared to the observation of a global rain gauge network, Libertino et al (2016) found that GPM might improve the ability of detecting extreme rainfall than TRMM.…”

Section: Introductionmentioning

confidence: 99%

“…Using observations from TRMM PR, Zhong et al (2017) investigated vertical structures of rainfalls over southwest China, they found differences in vertical structures for different rain types and rain intensities. Some studies mentioned that terrain effect was evident for low-level VPRs and rainfall rates (Cao et al, 2013;Zhong et al, 2017;Pejcic et al, 2020). Therefore, based on ground rain gauges and GPM satellite data, this paper studies vertical structures and microphysical processes of AHREs over southwest China with complex topography, such as basins, mountains, hills and plains, aiming to enhance the understanding of AHREs over different terrains, and this would benefit the mechanism research and simulation of AHREs as well as the QPE by groundbased weather radar.…”

Section: Introductionmentioning

confidence: 99%

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Vertical structures of abrupt heavy rainfall events over southwest China with complex topography detected by dual‐frequency precipitation radar of global precipitation measurement satellite

Zhou

Xiao

et al. 2022

Intl Journal of Climatology

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Southwest China is with complex topography including basins, mountains, hills and plains, where abrupt heavy rainfall events (AHREs) occur frequently and are difficult to quantitatively estimate due to limited ground‐based observations. Using the data of ground rain gauges and GPM dual‐frequency precipitation radar during April to September in 2014–2020, this study investigates vertical structures of AHREs over southwest China. Both mass‐weighted mean diameter (Dm) and reflectivity (Ze) of AHREs increase rapidly in ice‐phase process but slowly in liquid‐phase process. For convective rainfall of AHREs, abundant water vapour and strong atmospheric convective motion cause higher Dm and Ze but lower generalized intercept parameter (dBNw) than those for stratiform rainfall. Moreover, ice‐phase process is active while liquid‐phase process is weak in the mountains, but the situation is opposite in the plains, which results in large‐size and low‐concentration raindrops in the mountains while small‐size and high‐concentration raindrops in the plains. Furthermore, statistical models of vertical profile of reflectivity (VPR) indicate that VPR patterns are affected by surface rain intensity and present different trends around the 0°C level between stratiform and convective rainfalls. In addition, higher rain top height in the mountains is conducive to ice‐phase process while lower terrain height in the plains is favourable for liquid‐phase process. Therefore, the VPR pattern depends on rain type, terrain and rainfall intensity, and its fine model is beneficial for understanding microphysical process of AHREs and improving quantitative precipitation estimation by ground‐based radars.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Vertical structures of abrupt heavy rainfall events over southwest China with complex topography detected by dual‐frequency precipitation radar of global precipitation measurement satellite

Zhou

Xiao

et al. 2022

Intl Journal of Climatology

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“…Afterward, the obtained rain rates are mapped from the original polar grid onto the above-mentioned 1 km polar stereo-graphic grid. A more detailed description of the composite steps and the performance of the RY product is given by [49].…”

Section: A Data Eventsmentioning

confidence: 99%

Nationwide Radar-Based Precipitation Nowcasting—A Localization Filtering Approach and its Application for Germany

Reinoso-Rondinel

Rempel

Schultze

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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One approach to improve classic advection methods is Short-Term Ensemble Prediction System (STEPS). STEPS decomposes the precipitation field into different spatial scales and filters those having a short lifetime. The latter is achieved by using an auto-regressive (AR) model that considers a sequence of recent observations. However, such a model tends to smooth the nowcasting fields especially in small but convective precipitation areas and at longer lead-times. With focus on the deterministic configuration of STEPS, i.e., the spectral prognosis model (SPROG), this work 1) extends the STEPS approach by estimating spatially localized parameters of the AR process, 2) conducts a sensitivity analysis of the SPROG model to the order of the AR process, the spatial decomposition levels, and post-processing, and 3) analyzes the forecast skill of the extended STEPS. For such purpose, the performance of the localized AR model was demonstrated and evaluated at several precipitation thresholds and window sizes using a varied set of precipitation events collected by the radar network of the German Weather Service. The statistical results exhibited an improved performance of the localized AR model over SPROG when both are evaluated at precipitation thresholds and window sizes larger than 0.1 mm h −1 and 1 km, respectively, and for lead-times up to 2 h. Moreover, the analysis suggested a first-order AR process, six cascades levels, and a mean adjustment post-processing procedure. Our results show a key role of the localization aspect when generating nationwide forecasts in scenarios that include large precipitation areas which are non-uniformly distributed having isolated convective features.

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“…Since the launch of the Global Precipitation Measurement mission (GPM) on 28 Febuary 2014, an extended and high quality set of spaceborne radar precipitation measurements have become available [47][48][49]. The Dual-frequency Precipitation Radar (DPR) at Ku-band (13.6 GHz) and Ka-band (35.5 GHz) [50] on-board the NASA/JAXA GPM Core Observatory (GPM-CO) provides precipitation measurements, covering the globe from 67 • N to 67 • S. The high quality of DPR-based products is supported by several validation studies [51][52][53][54] and field campaigns [55,56].…”

Section: Introductionmentioning

confidence: 99%

The Passive Microwave Neural Network Precipitation Retrieval Algorithm for Climate Applications (PNPR-CLIM): Design and Verification

et al. 2021

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This paper describes the Passive microwave Neural network Precipitation Retrieval algorithm for climate applications (PNPR-CLIM), developed with funding from the Copernicus Climate Change Service (C3S), implemented by ECMWF on behalf of the European Union. The algorithm has been designed and developed to exploit the two cross-track scanning microwave radiometers, AMSU-B and MHS, towards the creation of a long-term (2000–2017) global precipitation climate data record (CDR) for the ECMWF Climate Data Store (CDS). The algorithm has been trained on an observational dataset built from one year of MHS and GPM-CO Dual-frequency Precipitation Radar (DPR) coincident observations. The dataset includes the Fundamental Climate Data Record (FCDR) of AMSU-B and MHS brightness temperatures, provided by the Fidelity and Uncertainty in Climate data records from Earth Observation (FIDUCEO) project, and the DPR-based surface precipitation rate estimates used as reference. The combined use of high quality, calibrated and harmonized long-term input data (provided by the FIDUCEO microwave brightness temperature Fundamental Climate Data Record) with the exploitation of the potential of neural networks (ability to learn and generalize) has made it possible to limit the use of ancillary model-derived environmental variables, thus reducing the model uncertainties’ influence on the PNPR-CLIM, which could compromise the accuracy of the estimates. The PNPR-CLIM estimated precipitation distribution is in good agreement with independent DPR-based estimates. A multiscale assessment of the algorithm’s performance is presented against high quality regional ground-based radar products and global precipitation datasets. The regional and global three-year (2015–2017) verification analysis shows that, despite the simplicity of the algorithm in terms of input variables and processing performance, the quality of PNPR-CLIM outperforms NASA GPROF in terms of rainfall detection, while in terms of rainfall quantification they are comparable. The global analysis evidences weaknesses at higher latitudes and in the winter at mid latitudes, mainly linked to the poorer quality of the precipitation retrieval in cold/dry conditions.

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Comparison between precipitation estimates of ground-based weather radar composites and GPM's DPR rainfall product over Germany

Cited by 16 publications

References 0 publications

Vertical structures of abrupt heavy rainfall events over southwest China with complex topography detected by dual‐frequency precipitation radar of global precipitation measurement satellite

Vertical structures of abrupt heavy rainfall events over southwest China with complex topography detected by dual‐frequency precipitation radar of global precipitation measurement satellite

Nationwide Radar-Based Precipitation Nowcasting—A Localization Filtering Approach and its Application for Germany

The Passive Microwave Neural Network Precipitation Retrieval Algorithm for Climate Applications (PNPR-CLIM): Design and Verification

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