Toward the Use of Integral Radar Volume Descriptors for Quantitative Areal Precipitation Estimation: Results from Pseudoradar Observations

Abstract The central objective of this analysis is to significantly enhance the quality of radar-derived precipitation estimates by as fully as possible exploiting the information contained in the spatial and temporal variability of 3D radar volume data. The results presented are based on pseudoradar data and rain rates of a regional weather forecasting model and 12 true radiosoundings as well. Two approaches are pursued: the first approach estimates total rainfall from an individual storm over its lifetime, whereas the second approach assesses the areawide instantaneous rainfall from a multiplicity of such storms by the use of measurements of the areal coverage of the storms exceeding a threshold radar reflectivity. The concept is extended by adding more predictors to significantly enhance the rainfall estimates. The horizontal expected value and the horizontal standard deviation of enclosed reflectivities at the ground, the mean brightband fraction and its trend, the fractional area with reflectivities exceeding a threshold τ, and an orographic rainfall amplifier provide relative errors smaller than 10% in approximately 75% of the considered rain events in the first approach. In the second approach, a relative error is achieved that is below 10% in approximately 63% elements of the test set.

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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

Remote Sensing ◽

10.3390/rs12081258 ◽

2020 ◽

Vol 12 (8) ◽

pp. 1258 ◽

Cited By ~ 1

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.

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Evaluation of Satellite-Retrieved Extreme Precipitation over Europe using Gauge Observations

Journal of Climate ◽

10.1175/jcli-d-13-00194.1 ◽

2014 ◽

Vol 27 (2) ◽

pp. 607-623 ◽

Cited By ~ 24

Author(s):

M. Lockhoff ◽

O. Zolina ◽

C. Simmer ◽

J. Schulz

Keyword(s):

Variable Parameter ◽

Global Precipitation Climatology Project ◽

Precipitation Variability ◽

Spatial And Temporal Variability ◽

Evaluation Approach ◽

Fuzzy Methods ◽

Verification Methods ◽

Precipitation Estimates ◽

Rain Rates ◽

Global Precipitation

Abstract Climate change is expected to change precipitation characteristics and particularly the frequency and magnitude of precipitation extremes. Satellite observations form an important part of the observing system necessary to monitor both temporal and spatial patterns of precipitation variability and extremes. As satellite-based precipitation estimates are generally only indirect, however, their reliability has to be verified. This study evaluates the ability of the satellite-based Global Precipitation Climatology Project One-Degree Daily (GPCP1DD) dataset to reliably reproduce precipitation variability and extremes over Europe compared to the European Daily High-resolution Observational Gridded Dataset (E-OBS). The results show that the two datasets agree reasonably well not only when looking at climatological statistics such as climatological mean, number of wet days (rain rates 1 mm), and mean intensity (i.e., mean over all wet days) but also with respect to their distributions. The results also reveal a pronounced seasonal cycle in the performance of GPCP1DD that is worse in winter and spring. Both deterministic and fuzzy verification methods are used to assess the ability of the GPCP1DD dataset to capture extremes. Fuzzy methods prove to be the better suited evaluation approach for such a highly variable parameter as precipitation because it compensates for slight spatial and temporal displacements. Whereas the deterministic diagnostics confirm previous findings on the deficiencies of satellite products, the “fuzzy” results show that at larger spatiotemporal scales (e.g., 3°/5 days) GPCP1DD has useful skill and is able to reliably represent the spatial and temporal variability of extremes.

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Quantitative Precipitation Estimation from a C-Band Dual-Polarized Radar for the 8 July 2013 Flood in Toronto, Canada

Journal of Hydrometeorology ◽

10.1175/jhm-d-15-0003.1 ◽

2015 ◽

Vol 16 (5) ◽

pp. 2027-2044 ◽

Cited By ~ 23

Author(s):

Sudesh Boodoo ◽

David Hudak ◽

Alexander Ryzhkov ◽

Pengfei Zhang ◽

Norman Donaldson ◽

...

Keyword(s):

New York ◽

Rain Gauge ◽

Heavy Rainfall Event ◽

High Temporal Resolution ◽

Horizontal Polarization ◽

Precipitation Estimation ◽

Precipitation Estimates ◽

Dual Polarized ◽

Quantitative Precipitation Estimates ◽

Rain Rates

Abstract A heavy rainfall event over a 2-h period on 8 July 2013 caused significant flash flooding in the city of Toronto and produced 126 mm of rain accumulation at a gauge located near the Toronto Pearson International Airport. This paper evaluates the quantitative precipitation estimates from the nearby King City C-band dual-polarized radar (WKR). Horizontal reflectivity Z and differential reflectivity ZDR were corrected for attenuation using a modified ZPHI rain profiling algorithm, and rain rates R were calculated from R(Z) and R(Z, ZDR) algorithms. Specific differential phase KDP was used to compute rain rates from three R(KDP) algorithms, one modified to use positive and negative KDP, and an R(KDP, ZDR) algorithm. Additionally, specific attenuation at horizontal polarization A was used to calculate rates from the R(A) algorithm. High-temporal-resolution rain gauge data at 44 locations measured the surface rainfall every 5 min and produced total rainfall accumulations over the affected area. The nearby NEXRAD S-band dual-polarized radar at Buffalo, New York, provided rain-rate and storm accumulation estimates from R(Z) and S-band dual-polarimetric algorithm. These two datasets were used as references to evaluate the C-band estimates. Significant radome attenuation at WKR overshadowed the attenuation correction techniques and resulted in poor rainfall estimates from the R(Z) and R(Z, ZDR) algorithms. Rainfall estimation from the Brandes et al. R(KDP) and R(A) algorithms were superior to the other methods, and the derived storm total accumulation gave biases of 2.1 and −6.1 mm, respectively, with correlations of 0.94. The C-band estimates from the Brandes et al. R(KDP) and R(A) algorithms were comparable to the NEXRAD S-band estimates.

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A Comprehensive Evaluation of Latest GPM IMERG V06 Early, Late and Final Precipitation Products across China

Remote Sensing ◽

10.3390/rs13061208 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1208

Author(s):

Linfei Yu ◽

Guoyong Leng ◽

Andre Python ◽

Jian Peng

Keyword(s):

Comprehensive Evaluation ◽

Probability Of Detection ◽

Light Rain ◽

Spatial And Temporal Scales ◽

Rain Gauges ◽

Precipitation Estimation ◽

Major River ◽

Low Levels ◽

Rain Events ◽

Early Late

This study evaluated the performance of the early, late and final runs of IMERG version 06 precipitation products at various spatial and temporal scales in China from 2008 to 2017, against observations from 696 rain gauges. The results suggest that the three IMERG products can well reproduce the spatial patterns of precipitation, but exhibit a gradual decrease in the accuracy from the southeast to the northwest of China. Overall, the three runs show better performances in the eastern humid basins than the western arid basins. Compared to the early and late runs, the final run shows an improvement in the performance of precipitation estimation in terms of correlation coefficient, Kling–Gupta Efficiency and root mean square error at both daily and monthly scales. The three runs show similar daily precipitation detection capability over China. The biases of the three runs show a significantly positive (p < 0.01) correlation with elevation, with higher accuracy observed with an increase in elevation. However, the categorical metrics exhibit low levels of dependency on elevation, except for the probability of detection. Over China and major river basins, the three products underestimate the frequency of no/tiny rain events (P < 0.1 mm/day) but overestimate the frequency of light rain events (0.1 ≤ P < 10 mm/day). The three products converge with ground-based observation with regard to the frequency of rainstorm (P ≥ 50 mm/day) in the southern part of China. The revealed uncertainties associated with the IMERG products suggests that sustaining efforts are needed to improve their retrieval algorithms in the future.

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Performance Evaluation of IMERG GPM Products during Tropical Storm Imelda

Atmosphere ◽

10.3390/atmos12060687 ◽

2021 ◽

Vol 12 (6) ◽

pp. 687

Author(s):

Salman Sakib ◽

Dawit Ghebreyesus ◽

Hatim O. Sharif

Keyword(s):

Stage Iv ◽

Tropical Storm ◽

Average Correlation ◽

Acceptable Range ◽

Precipitation Estimates ◽

Overall Performance ◽

Early Late ◽

Statistical Metrics ◽

Better Than

Tropical Storm Imelda struck the southeast coastal regions of Texas from 17–19 September, 2019, and delivered precipitation above 500 mm over about 6000 km2. The performance of the three IMERG (Early-, Late-, and Final-run) GPM satellite-based precipitation products was evaluated against Stage-IV radar precipitation estimates. Basic and probabilistic statistical metrics, such as CC, RSME, RBIAS, POD, FAR, CSI, and PSS were employed to assess the performance of the IMERG products. The products captured the event adequately, with a fairly high POD value of 0.9. The best product (Early-run) showed an average correlation coefficient of 0.60. The algorithm used to produce the Final-run improved the quality of the data by removing systematic errors that occurred in the near-real-time products. Less than 5 mm RMSE error was experienced in over three-quarters (ranging from 73% to 76%) of the area by all three IMERG products in estimating the Tropical Storm Imelda. The Early-run product showed a much better RBIAS relatively to the Final-run product. The overall performance was poor, as areas with an acceptable range of RBIAS (i.e., between −10% and 10%) in all the three IMERG products were only 16% to 17% of the total area. Overall, the Early-run product was found to be better than Late- and Final-run.

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

Scientific Data ◽

10.1038/s41597-021-00940-9 ◽

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.

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Validation of CHIRPS Precipitation Estimates over Taiwan at Multiple Timescales

Remote Sensing ◽

10.3390/rs13020254 ◽

2021 ◽

Vol 13 (2) ◽

pp. 254 ◽

Cited By ~ 1

Author(s):

Jie Hsu ◽

Wan-Ru Huang ◽

Pin-Yi Liu ◽

Xiuzhen Li

Keyword(s):

Annual Cycle ◽

Interannual Variation ◽

Winter Precipitation ◽

Monthly Precipitation ◽

Precipitation Estimation ◽

Precipitation Estimates ◽

Precipitation Variation ◽

Global Precipitation ◽

Multiple Timescale ◽

Better Than

The Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), which incorporates satellite imagery and in situ station information, is a new high-resolution long-term precipitation dataset available since 1981. This study aims to understand the performance of the latest version of CHIRPS in depicting the multiple timescale precipitation variation over Taiwan. The analysis is focused on examining whether CHIRPS is better than another satellite precipitation product—the Integrated Multi-satellitE Retrievals for Global Precipitation Mission (GPM) final run (hereafter IMERG)—which is known to effectively capture the precipitation variation over Taiwan. We carried out the evaluations made for annual cycle, seasonal cycle, interannual variation, and daily variation during 2001–2019. Our results show that IMERG is slightly better than CHIRPS considering most of the features examined; however, CHIRPS performs better than that of IMERG in representing the (1) magnitude of the annual cycle of monthly precipitation climatology, (2) spatial distribution of the seasonal mean precipitation for all four seasons, (3) quantitative precipitation estimation of the interannual variation of area-averaged winter precipitation in Taiwan, and (4) occurrence frequency of the non-rainy grids in winter. Notably, despite the fact that CHIRPS is not better than IMERG for many examined features, CHIRPS can depict the temporal variation in precipitation over Taiwan on annual, seasonal, and interannual timescales with 95% significance. This highlights the potential use of CHIRPS in studying the multiple timescale variation in precipitation over Taiwan during the years 1981–2000, for which there are no data available in the IMERG database.

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Improving Multisensor Precipitation Estimation via Adaptive Conditional Bias–Penalized Merging of Rain Gauge Data and Remotely Sensed Quantitative Precipitation Estimates

Journal of Hydrometeorology ◽

10.1175/jhm-d-19-0129.1 ◽

2019 ◽

Vol 20 (12) ◽

pp. 2347-2365 ◽

Cited By ~ 1

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.

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Novel Assistive Device for Tomographic Ultrasound Neck Imaging vs. Freehand

Current Directions in Biomedical Engineering ◽

10.1515/cdbme-2020-3008 ◽

2020 ◽

Vol 6 (3) ◽

pp. 28-31

Author(s):

Marcel Köhler ◽

Elmer Jeto Gomes Ataide ◽

Jens Ziegle ◽

Axel Boese ◽

Michael Friebe

Keyword(s):

Computed Tomography ◽

Image Quality ◽

Diagnostic Procedures ◽

Assistive Device ◽

Volume Data ◽

Data Set ◽

Scanning Method ◽

Neck Area ◽

2D Ultrasound

AbstractFor assessing clinically relevant structures in the neck area, especially the thyroid, it has been shown that 3D or tomographic ultrasound (3D US or tUS) is able to outperform standard 2D ultrasound [1] and computed tomography [2] for certain diagnostic procedures. However, when using a freehand and unassisted scanning method to acquire a 3D US volume data set in this area overlapping image slices, a variation of the probe angulation or differences in training might lead to unusable scanning results. Based on previous works [3] [4] we propose the design - with subsequent testing - of an assistive device that is able to aid physicians during the tUS scanning process on the neck. To validate the feasibility and efficacy we compared the image quality of both freehand and assisted scanning.

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Separation Standards—II

Journal of Navigation ◽

10.1017/s0373463300047196 ◽

1966 ◽

Vol 19 (2) ◽

pp. 169-186 ◽

Cited By ~ 26

Author(s):

P. G. Reich

Keyword(s):

One Dimension ◽

Collision Risk ◽

Practical Applications ◽

Upper Limit ◽

Intensive Observation ◽

Relative Errors ◽

Separation Standards

In the first part of this series of papers an outline was given of the approach made at the Royal Aircraft Establishment to the problems of estimating collision risk and of specifying the quality of navigation needed to make separation standards safe. It was stressed that estimates should be based on intensive observation of flying errors, rather than on speculative theories, and that it is more feasible to develop ‘upper limit’ estimating techniques than those which purport to give the exact risk. In summary, a list of seven ‘requirements’ was given, as a reminder of the essential principles which can so easily be overlooked in the piecemeal task of relating separation standards to collision risk.The purpose of this paper is to show some of the theoretical techniques which have been developed at R.A.E. to satisfy five of these requirements. (The remaining two do not call for special techniques and will be dealt with when practical applications are described in Part III.) The paper contains three Appendixes, dealing with the frequency of losing separation in one dimension, the computation of P's from the assumed tail shapes, and the treatment of relative errors. These are not included here but will appear in the off-printed version which may be obtained from the Royal Aircraft Establishment.Both this paper and the paper that follows by Mr. Attwooll are crown copyright and are reproduced with the permission of H.M. Stationery Office.

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