scholarly journals Cross-Examination of Similarity, Difference and Deficiency of Gauge, Radar and Satellite Precipitation Measuring Uncertainties for Extreme Events Using Conventional Metrics and Multiplicative Triple Collocation

2020 ◽  
Vol 12 (8) ◽  
pp. 1258 ◽  
Author(s):  
Zhi Li ◽  
Mengye Chen ◽  
Shang Gao ◽  
Zhen Hong ◽  
Guoqiang Tang ◽  
...  
Keyword(s):  
Extreme Events ◽  
Spatial And Temporal Variability ◽  
Storm Events ◽  
Spatiotemporal Resolution ◽  
Rain Gauges ◽  
Weather Radars ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Environmental Prediction ◽  
Quantitative Precipitation Estimates

Quantifying uncertainties of precipitation estimation, especially in extreme events, could benefit early warning of water-related hazards like flash floods and landslides. Rain gauges, weather radars, and satellites are three mainstream data sources used in measuring precipitation but have their own inherent advantages and deficiencies. With a focus on extremes, the overarching goal of this study is to cross-examine the similarities and differences of three state-of-the-art independent products (Muti-Radar Muti-Sensor Quantitative Precipitation Estimates, MRMS; National Center for Environmental Prediction gridded gauge-only hourly precipitation product, NCEP; Integrated Multi-satellitE Retrievals for GPM, IMERG), with both traditional metrics and the Multiplicative Triple Collection (MTC) method during Hurricane Harvey and multiple Tropical Cyclones. The results reveal that: (a) the consistency of cross-examination results against traditional metrics approves the applicability of MTC in extreme events; (b) the consistency of cross-events of MTC evaluation results also suggests its robustness across individual storms; (c) all products demonstrate their capacity of capturing the spatial and temporal variability of the storm structures while also magnifying respective inherent deficiencies; (d) NCEP and IMERG likely underestimate while MRMS overestimates the storm total accumulation, especially for the 500-year return Hurricane Harvey; (e) both NCEP and IMERG underestimate extreme rainrates (>= 90 mm/h) likely due to device insensitivity or saturation while MRMS maintains robust across the rainrate range; (g) all three show inherent deficiencies in capturing the storm core of Harvey possibly due to device malfunctions with the NCEP gauges, relative low spatiotemporal resolution of IMERG, and the unusual “hot” MRMS radar signals. Given the unknown ground reference assumption of MTC, this study suggests that MRMS has the best overall performance. The similarities, differences, advantages, and deficiencies revealed in this study could guide the users for emergency response and motivate the community not only to improve the respective sensor/algorithm but also innovate multidata merging methods for one best possible product, specifically suitable for extreme storm events.

scholarly journals PERSIANN-CCS-CDR, a 3-hourly 0.04° global precipitation climate data record for heavy precipitation studies

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Mojtaba Sadeghi ◽  
Phu Nguyen ◽  
Matin Rahnamay Naeini ◽  
Kuolin Hsu ◽  
Dan Braithwaite ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Heavy Precipitation ◽  
Climate Data ◽  
Spatiotemporal Resolution ◽  
Data Record ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Global Precipitation ◽  
Climate Data Record

AbstractAccurate long-term global precipitation estimates, especially for heavy precipitation rates, at fine spatial and temporal resolutions is vital for a wide variety of climatological studies. Most of the available operational precipitation estimation datasets provide either high spatial resolution with short-term duration estimates or lower spatial resolution with long-term duration estimates. Furthermore, previous research has stressed that most of the available satellite-based precipitation products show poor performance for capturing extreme events at high temporal resolution. Therefore, there is a need for a precipitation product that reliably detects heavy precipitation rates with fine spatiotemporal resolution and a longer period of record. Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR) is designed to address these limitations. This dataset provides precipitation estimates at 0.04° spatial and 3-hourly temporal resolutions from 1983 to present over the global domain of 60°S to 60°N. Evaluations of PERSIANN-CCS-CDR and PERSIANN-CDR against gauge and radar observations show the better performance of PERSIANN-CCS-CDR in representing the spatiotemporal resolution, magnitude, and spatial distribution patterns of precipitation, especially for extreme events.

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.

scholarly journals Detecting Beam Blockage in Radar-Based Precipitation Estimates

2017 ◽  
Vol 34 (7) ◽  
pp. 1407-1422 ◽  
Author(s):  
D. Brent McRoberts ◽  
John W. Nielsen-Gammon
Keyword(s):  
Hydrological Modeling ◽  
Tall Buildings ◽  
Eastern United States ◽  
Spatiotemporal Resolution ◽  
Transmitted Beam ◽  
Geographical Regions ◽  
Precipitation Estimates ◽  
Low Pass ◽  
Prerequisite Knowledge ◽  
Quantitative Precipitation Estimates

AbstractGridded radar-based quantitative precipitation estimates (QPEs) are potentially ideal inputs for hydrological modeling and monitoring because of their high spatiotemporal resolution. Beam blockage is a common type of bias in radar QPEs related to the blockage of the radar beam by an obstruction, such as topography or tall buildings. This leads to a diminishment in the power of the transmitted beam beyond the range of obstruction and a systematic underestimation of reflectivity return to the radar site. A new spatial analysis technique for objectively identifying regions in which precipitation estimates are contaminated by beam blockage was developed. The methodology requires only a long-term precipitation climatology with no prerequisite knowledge of topography or known obstructions needed. For each radar domain, the QPEs are normalized by climatology and a low-pass Fourier series fit captures the expected precipitation as a function of azimuth angle. Beam blockage signatures are identified as radially coherent regions with normalized values that are systematically lower than the Fourier fit. Precipitation estimates sufficiently affected by beam blockage can be replaced by values estimated using neighboring unblocked estimates. The methodology is applied to the correction of the National Weather Service radar-based QPE dataset, whose estimates originate from the NEXRAD network in the central and eastern United States. The methodology is flexible enough to be useful for most radar installations and geographical regions with at least a few years of data.

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

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

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

scholarly journals Ground Clutter Mitigation for Weather Radars Using the Autocorrelation Spectral Density

2014 ◽  
Vol 31 (10) ◽  
pp. 2049-2066 ◽  
Author(s):  
Sebastián M. Torres ◽  
David A. Warde
Keyword(s):  
Spectral Density ◽  
Meteorological Data ◽  
Meteorological Variable ◽  
Clutter Suppression ◽  
Environment Analysis ◽  
Weather Radars ◽  
Ground Clutter ◽  
Precipitation Estimates ◽  
Quantitative Precipitation Estimates

Abstract Radar returns from the ground, known as ground clutter, can contaminate weather signals, often resulting in severely biased meteorological estimates. If not removed, these contaminants may artificially inflate quantitative precipitation estimates and obscure polarimetric and Doppler signatures of weather. A ground-clutter filter is typically employed to mitigate this contamination and provide less biased meteorological-variable estimates. This paper introduces a novel adaptive filter based on the autocorrelation spectral density, which is capable of mitigating the adverse effects of ground clutter without unnecessarily degrading the quality of the meteorological data. The so-called Clutter Environment Analysis using Adaptive Processing (CLEAN-AP) filter adjusts its suppression characteristics in real time to match dynamic atmospheric environments and meets Next Generation Weather Radar (NEXRAD) clutter-suppression requirements.

scholarly journals Precipitation Analysis over the French Alps Using a Variational Approach and Study of Potential Added Value of Ground-Based Radar Observations

2017 ◽  
Vol 18 (5) ◽  
pp. 1425-1451 ◽  
Author(s):  
Camille Birman ◽  
Fatima Karbou ◽  
Jean-François Mahfouf ◽  
Matthieu Lafaysse ◽  
Yves Durand ◽  
...  
Keyword(s):  
Snow Depth ◽  
Positive Impact ◽  
Weather Prediction ◽  
Rain Gauge ◽  
Added Value ◽  
Background Information ◽  
Time Step ◽  
Rain Gauges ◽  
Weather Radars ◽  
Precipitation Estimates

Abstract A one-dimensional variational data assimilation (1DVar) method to retrieve profiles of precipitation in mountainous terrain is described. The method combines observations from the French Alpine region rain gauges and precipitation estimates from weather radars with background information from short-range numerical weather prediction forecasts in an optimal way. The performance of this technique is evaluated using measurements of precipitation and of snow depth during two years (2012/13 and 2013/14). It is shown that the 1DVar model allows an effective assimilation of measurements of different types, including rain gauge and radar-derived precipitation. The use of radar-derived precipitation rates over mountains to force the numerical snowpack model Crocus significantly reduces the bias and standard deviation with respect to independent snow depth observations. The improvement is particularly significant for large rainfall or snowfall events, which are decisive for avalanche hazard forecasting. The use of radar-derived precipitation rates at an hourly time step improves the time series of precipitation analyses and has a positive impact on simulated snow depths.

scholarly journals Gauge-adjusted rainfall estimates from commercial microwave links

2017 ◽  
Vol 21 (1) ◽  
pp. 617-634 ◽  
Author(s):  
Martin Fencl ◽  
Michal Dohnal ◽  
Jörg Rieckermann ◽  
Vojtěch Bareš
Keyword(s):  
Urban Areas ◽  
Ground Truth ◽  
Telecommunication Networks ◽  
Monitoring Networks ◽  
Mobile Telecommunication ◽  
Rain Gauges ◽  
Precipitation Estimates ◽  
Set Up ◽  
Quantitative Precipitation Estimates ◽  
Commercial Microwave Links

Abstract. Increasing urbanization makes it more and more important to have accurate stormwater runoff predictions, especially with potentially severe weather and climatic changes on the horizon. Such stormwater predictions in turn require reliable rainfall information. Especially for urban centres, the problem is that the spatial and temporal resolution of rainfall observations should be substantially higher than commonly provided by weather services with their standard rainfall monitoring networks. Commercial microwave links (CMLs) are non-traditional sensors, which have been proposed about a decade ago as a promising solution. CMLs are line-of-sight radio connections widely used by operators of mobile telecommunication networks. They are typically very dense in urban areas and can provide path-integrated rainfall observations at sub-minute resolution. Unfortunately, quantitative precipitation estimates (QPEs) from CMLs are often highly biased due to several epistemic uncertainties, which significantly limit their usability. In this manuscript we therefore suggest a novel method to reduce this bias by adjusting QPEs to existing rain gauges. The method has been specifically designed to produce reliable results even with comparably distant rain gauges or cumulative observations. This eliminates the need to install reference gauges and makes it possible to work with existing information. First, the method is tested on data from a dedicated experiment, where a CML has been specifically set up for rainfall monitoring experiments, as well as operational CMLs from an existing cellular network. Second, we assess the performance for several experimental layouts of ground truth from rain gauges (RGs) with different spatial and temporal resolutions. The results suggest that CMLs adjusted by RGs with a temporal aggregation of up to 1 h (i) provide precise high-resolution QPEs (relative error  < 7 %, Nash–Sutcliffe efficiency coefficient  >  0.75) and (ii) that the combination of both sensor types clearly outperforms each individual monitoring system. Unfortunately, adjusting CML observations to RGs with longer aggregation intervals of up to 24 h has drawbacks. Although it substantially reduces bias, it unfavourably smoothes out rainfall peaks of high intensities, which is undesirable for stormwater management. A similar, but less severe, effect occurs due to spatial averaging when CMLs are adjusted to remote RGs. Nevertheless, even here, adjusted CMLs perform better than RGs alone. Furthermore, we provide first evidence that the joint use of multiple CMLs together with RGs also reduces bias in their QPEs. In summary, we believe that our adjustment method has great potential to improve the space–time resolution of current urban rainfall monitoring networks. Nevertheless, future work should aim to better understand the reason for the observed systematic error in QPEs from CMLs.

Dynamic rainfall mapping using multi-radar multi-gauge observations in changing synoptic environments

2021 ◽  
Author(s):  
Yabin Gou ◽  
Haonan Chen
Keyword(s):  
Real Time ◽  
Physical Model ◽  
Feedback Mechanism ◽  
Radar Rainfall ◽  
Weather Radars ◽  
Raindrop Size Distribution ◽  
Precipitation Estimates ◽  
Storm Cells ◽  
Raindrop Size ◽  
Quantitative Precipitation Estimates

&lt;p&gt;It is well known that the performance of radar-derived quantitative precipitation estimates greatly relies on the physical model of the raindrop size distribution (DSD) and the relation between the physical model and radar parameters. However, incorporating changing precipitation microphysics to dynamically adjust the radar reflectivity (Z) and rain rate (R) relations can be challenging for real-time applications. In this study, two adaptive radar rainfall approaches are developed based on the radar-gauge feedback mechanism using 16 S-band Doppler weather radars and 4579 surface rain gauges deployed over the Eastern JiangHuai River Basin (EJRB) in China. Although the Z&amp;#8211;R relations in both approaches are dynamically adjusted within a single precipitation system, one is using a single global optimal (SGO) Z&amp;#8211;R relation, whereas the other is using different Z&amp;#8211;R relations for different storm cells identified by a storm cell identification and tracking (SCIT) algorithm. Four precipitation events featured by different rainfall microphysical characteristics are investigated to demonstrate the performances of these two rainfall mapping methodologies. In addition, the short-term vertical profile of reflectivity (VPR) clusters are extensively analyzed to resolve the storm-scale characteristics of different storm cells. The verification results based on independent gauge observations show that both rainfall estimation approaches with dynamic Z&amp;#8211;R relations perform much better than fixed Z&amp;#8211;R relations. The adaptive approach incorporating the SCIT algorithm and real-time gauge measurements performs best since it can better capture the spatial variability and temporal evolution of precipitation.&lt;/p&gt;

A new insight to daily precipitation estimation using different interpolation methods

2021 ◽  
Author(s):  
Ehsan Modiri ◽  
András Bárdossy
Keyword(s):  
Daily Precipitation ◽  
Interpolation Methods ◽  
Rain Gauges ◽  
Large Size ◽  
Daily Precipitation Data ◽  
Distance Weighting ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Fine Resolution ◽  
Nearest Neighborhood

&lt;p&gt;The gridded interpolated daily precipitation data has a vast application in hydrometeorology. The fine resolution gridded precipitation gained terrestrial measurements is a tool to evaluate satellite, reanalysis, and radar-based gridded products. In this study, the daily time series of 1561 rain gauges over Iran for the period of 2003-20010 is used to compute 1 km * 1 km interpolated maps. The nearest neighborhood, Inverse Distance Weighting (IDW), Ordinary Kriging (OK), External Drift Kriging (EDK), and Quantile Kriging (QK) interpolation methods are applied to compare their performance. Due to the large size of the interpolated region and different climates, six clusters for estimating the variogram function are determined. The distinct interpolation methods lead to different daily precipitation estimates, however in the same spatial resolution, OK is showed slightly better results with the mean RMSE and correlation equal to 2.355 and 0.766, respectively. Also, the spatially aggregated gridded maps illustrated that the interpolation methods only play a significant role in the fine resolutions than the coarser ones.&lt;/p&gt;

scholarly journals Radar-Based Precipitation Climatology in Germany—Developments, Uncertainties and Potentials

Atmosphere ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 217 ◽  
Author(s):  
Jennifer Kreklow ◽  
Björn Tetzlaff ◽  
Benjamin Burkhard ◽  
Gerald Kuhnt
Keyword(s):  
Ground Truth ◽  
Winter Precipitation ◽  
Rain Gauge ◽  
Rain Gauge Data ◽  
Weather Radars ◽  
Hourly Rainfall ◽  
Gauge Data ◽  
Precipitation Estimates ◽  
Quantitative Precipitation Estimates

Precipitation is a crucial driver for many environmental processes and weather radars are capable of providing precipitation information with high spatial and temporal resolution. However, radar-based quantitative precipitation estimates (QPE) are also subject to various potential uncertainties. This study explored the development, uncertainties and potentials of the hourly operational German radar-based and gauge-adjusted QPE called RADOLAN and its reanalyzed radar climatology dataset named RADKLIM in comparison to ground-truth rain gauge data. The precipitation datasets were statistically analyzed across various time scales ranging from annual and seasonal aggregations to hourly rainfall intensities in regard to their capability to map long-term precipitation distribution, to detect low intensity rainfall and to capture heavy rainfall. Moreover, the impacts of season, orography and distance from the radar on long-term precipitation sums were examined in order to evaluate dataset performance and to describe inherent biases. Results revealed that both radar products tend to underestimate total precipitation sums and particularly high intensity rainfall. However, our analyses also showed significant improvements throughout the RADOLAN time series as well as major advances through the climatologic reanalysis regarding the correction of typical radar artefacts, orographic and winter precipitation as well as range-dependent attenuation.

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