scholarly journals Quantitative Precipitation Estimation over Antarctica Using Different Ze-SR Relationships Based on Snowfall Classification Combining Ground Observations

2021 ◽  
Vol 14 (1) ◽  
pp. 82
Author(s):  
Alessandro Bracci ◽  
Luca Baldini ◽  
Nicoletta Roberto ◽  
Elisa Adirosi ◽  
Mario Montopoli ◽  
...  
Keyword(s):  
Hydrological Cycle ◽  
3D Structure ◽  
Power Spectra ◽  
Temporal Sampling ◽  
Quantitative Precipitation Estimation ◽  
Vertical Range ◽  
Spatial Coverage ◽  
Precipitation Estimation ◽  
High Vertical Resolution ◽  
K Band

Snow plays a crucial role in the hydrological cycle and energy budget of the Earth, and remote sensing instruments with the necessary spatial coverage, resolution, and temporal sampling are essential for snowfall monitoring. Among such instruments, ground-radars have scanning capability and a resolution that make it possible to obtain a 3D structure of precipitating systems or vertical profiles when used in profiling mode. Radars from space have a lower spatial resolution, but they provide a global view. However, radar-based quantitative estimates of solid precipitation are still a challenge due to the variability of the microphysical, geometrical, and electrical features of snow particles. Estimations of snowfall rate are usually accomplished using empirical, long-term relationships between the equivalent radar reflectivity factor (Ze) and the liquid-equivalent snowfall rate (SR). Nevertheless, very few relationships take advantage of the direct estimation of the microphysical characteristics of snowflakes. In this work, we used a K-band vertically pointing radar collocated with a laser disdrometer to develop Ze-SR relationships as a function of snow classification. The two instruments were located at the Italian Antarctic Station Mario Zucchelli. The K-band radar probes the low-level atmospheric layers, recording power spectra at 32 vertical range gates. It was set at a high vertical resolution (35 m), with the first trusted range gate at a height of only 100 m. The disdrometer was able to provide information on the particle size distribution just below the trusted radar gate. Snow particles were classified into six categories (aggregate, dendrite aggregate, plate aggregate, pristine, dendrite pristine, plate pristine). The method was applied to the snowfall events of the Antarctic summer seasons of 2018–2019 and 2019–2020, with a total of 23,566 min of precipitation, 15.3% of which was recognized as showing aggregate features, 33.3% dendrite aggregate, 7.3% plates aggregate, 12.5% pristine, 24% dendrite pristine, and 7.6% plate pristine. Applying the appropriate Ze-SR relationship in each snow category, we calculated a total of 87 mm water equivalent, differing from the total found by applying a unique Ze-SR. Our estimates were also benchmarked against a colocated Alter-shielded weighing gauge, resulting in a difference of 3% in the analyzed periods.

scholarly journals Radar Quantitative Precipitation Estimation improvement in Champagne vineyard.

2018 ◽  
Vol 50 ◽  
pp. 02013
Author(s):  
Basile PAUTHIER ◽  
Sébastien DEBUISSON ◽  
Arnaud DESCOTES ◽  
Julien PERGAUD ◽  
Sylvain MAILLARD
Keyword(s):  
Soil Erosion ◽  
Nitrogen Mineralization ◽  
Radar Data ◽  
Crucial Importance ◽  
Rainfall Events ◽  
Quantitative Precipitation Estimation ◽  
Spatial Coverage ◽  
Precipitation Estimation ◽  
Weather Stations ◽  
External Drift

Rainfall has a crucial importance in viticulture, especially in Champagne vineyards, where irrigation is prohibited. Rainfall directly influences the phytosanitary pressure, nitrogen mineralization, flowering conditions, parcel practicability, soil erosion etc… In these conditions, implementing a weather stations network is the solution that the Comité Champagne chose to monitor rainfall all over the Champagne appellation since the 1990's. This networks is actually composed of 42 weather stations implemented in order to have the best spatial coverage as possible. The Comité Champagne also obtain some weather stations data from Météo France, the French national weather service. Even with that network, capturing all rainfall events accurately is difficult, especially in convective cases. Therefore, the interest in radar data has increased, to capture rainfall everywhere. Some tests have been previously made with PANTHERE radar data from Météo France with a resolution of 1 km2, results were promising, but presented inaccuracies particularly in convective events. In this article, we use a radar merging technique similar to the ANTILOPE method from Météo France, with a higher resolution network. The tool employed is the Estimages toolbox merger, based on krigine with external drift (KED) which has been demonstrated to give good results in quantitative precipitation estimation (QPE) improvement.

scholarly journals Improving Satellite Quantitative Precipitation Estimation Using GOES-Retrieved Cloud Optical Depth

2016 ◽  
Vol 17 (2) ◽  
pp. 557-570 ◽  
Author(s):  
Ronald Stenz ◽  
Xiquan Dong ◽  
Baike Xi ◽  
Zhe Feng ◽  
Robert J. Kuligowski
Keyword(s):  
Optical Depth ◽  
Temporal Resolution ◽  
Heavy Precipitation ◽  
The United States ◽  
Rain Gauge ◽  
Geostationary Satellite ◽  
Quantitative Precipitation Estimation ◽  
Spatial Coverage ◽  
Skill Scores ◽  
Precipitation Estimation

Abstract To address gaps in ground-based radar coverage and rain gauge networks in the United States, geostationary satellite quantitative precipitation estimation (QPE) such as the Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR) can be used to fill in both spatial and temporal gaps of ground-based measurements. Additionally, with the launch of Geostationary Operational Environmental Satellite R series (GOES-R), the temporal resolution of satellite QPEs may be comparable to Weather Surveillance Radar-1988 Doppler (WSR-88D) volume scans as GOES images will be available every 5 min. However, while satellite QPEs have strengths in spatial coverage and temporal resolution, they face limitations, particularly during convective events. Deep convective systems (DCSs) have large cloud shields with similar brightness temperatures (BTs) over nearly the entire system, but widely varying precipitation rates beneath these clouds. Geostationary satellite QPEs relying on the indirect relationship between BTs and precipitation rates often suffer from large errors because anvil regions (little or no precipitation) cannot be distinguished from rain cores (heavy precipitation) using only BTs. However, a combination of BTs and optical depth τ has been found to reduce overestimates of precipitation in anvil regions. A new rain mask algorithm incorporating both τ and BTs has been developed, and its application to the existing SCaMPR algorithm was evaluated. The performance of the modified SCaMPR was evaluated using traditional skill scores and a more detailed analysis of performance in individual DCS components by utilizing the Feng et al. classification algorithm. SCaMPR estimates with the new rain mask benefited from significantly reduced overestimates of precipitation in anvil regions and overall improvements in skill scores.

scholarly journals A Prototype Quantitative Precipitation Estimation Algorithm for Operational S-Band Polarimetric Radar Utilizing Specific Attenuation and Specific Differential Phase. Part II: Performance Verification and Case Study Analysis

2019 ◽  
Vol 20 (5) ◽  
pp. 999-1014 ◽  
Author(s):  
Stephen B. Cocks ◽  
Lin Tang ◽  
Pengfei Zhang ◽  
Alexander Ryzhkov ◽  
Brian Kaney ◽  
...  
Keyword(s):  
Real Time ◽  
Heavy Precipitation ◽  
Estimation Algorithm ◽  
Differential Phase ◽  
Specific Attenuation ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Bias Ratio ◽  
Using Data ◽  
Precipitation Events

Abstract The quantitative precipitation estimate (QPE) algorithm developed and described in Part I was validated using data collected from 33 Weather Surveillance Radar 1988-Doppler (WSR-88D) radars on 37 calendar days east of the Rocky Mountains. A key physical parameter to the algorithm is the parameter alpha α, defined as the ratio of specific attenuation A to specific differential phase KDP. Examination of a significant sample of tropical and continental precipitation events indicated that α was sensitive to changes in drop size distribution and exhibited lower (higher) values when there were lower (higher) concentrations of larger (smaller) rain drops. As part of the performance assessment, the prototype algorithm generated QPEs utilizing a real-time estimated and a fixed α were created and evaluated. The results clearly indicated ~26% lower errors and a 26% better bias ratio with the QPE utilizing a real-time estimated α as opposed to using a fixed value as was done in previous studies. Comparisons between the QPE utilizing a real-time estimated α and the operational dual-polarization (dual-pol) QPE used on the WSR-88D radar network showed the former exhibited ~22% lower errors, 7% less bias, and 5% higher correlation coefficient when compared to quality controlled gauge totals. The new QPE also provided much better estimates for moderate to heavy precipitation events and performed better in regions of partial beam blockage than the operational dual-pol QPE.

scholarly journals Quantitative Precipitation Estimation of the Epic 2013 Colorado Flood Event: Polarization Radar-Based Variational Scheme

2016 ◽  
Vol 55 (7) ◽  
pp. 1477-1495 ◽  
Author(s):  
Wei-Yu Chang ◽  
Jothiram Vivekanandan ◽  
Kyoko Ikeda ◽  
Pay-Liam Lin
Keyword(s):  
Power Law ◽  
Rain Rate ◽  
Flood Event ◽  
Polarimetric Radar ◽  
Dual Polarization ◽  
Observational Error ◽  
Quantitative Precipitation Estimation ◽  
Precipitation Estimation ◽  
Radar Measurements ◽  
Event Based

AbstractThe accuracy of rain-rate estimation using polarimetric radar measurements has been improved as a result of better characterization of radar measurement quality and rain microphysics. In the literature, a variety of power-law relations between polarimetric radar measurements and rain rate are described because of the dynamic or varying nature of rain microphysics. A variational technique that concurrently takes into account radar observational error and dynamically varying rain microphysics is proposed in this study. Rain-rate estimation using the variational algorithm that uses event-based observational error and background rain climatological values is evaluated using observing system simulation experiments (OSSE), and its performance is demonstrated in the case of an epic Colorado flood event. The rain event occurred between 11 and 12 September 2013. The results from OSSE show that the variational algorithm with event-based observational error consistently estimates more accurate rain rate than does the “R(ZHH, ZDR)” power-law algorithm. On the contrary, the usage of ad hoc or improper observational error degrades the performance of the variational method. Furthermore, the variational algorithm is less sensitive to the observational error of differential reflectivity ZDR than is the R(ZHH, ZDR) algorithm. The variational quantitative precipitation estimation (QPE) retrieved more accurate rainfall estimation than did the power-law dual-polarization QPE in this particular event, despite the fact that both algorithms used the same dual-polarization radar measurements from the Next Generation Weather Radar (NEXRAD).

scholarly journals Observed Response of the Raindrop Size Distribution to Changes in the Melting Layer

Atmosphere ◽  
2018 ◽  
Vol 9 (8) ◽  
pp. 319 ◽  
Author(s):  
Patrick Gatlin ◽  
Walter Petersen ◽  
Kevin Knupp ◽  
Lawrence Carey
Keyword(s):  
Size Distribution ◽  
Basic Assumption ◽  
Weighted Mean ◽  
Radar Rainfall ◽  
Melting Layer ◽  
Quantitative Precipitation Estimation ◽  
Raindrop Size Distribution ◽  
Precipitation Estimation ◽  
Raindrop Size ◽  
Stratiform Rainfall

Vertical variability in the raindrop size distribution (RSD) can disrupt the basic assumption of a constant rain profile that is customarily parameterized in radar-based quantitative precipitation estimation (QPE) techniques. This study investigates the utility of melting layer (ML) characteristics to help prescribe the RSD, in particular the mass-weighted mean diameter (Dm), of stratiform rainfall. We utilize ground-based polarimetric radar to map the ML and compare it with Dm observations from the ground upwards to the bottom of the ML. The results show definitive proof that a thickening, and to a lesser extent a lowering, of the ML causes an increase in raindrop diameter below the ML that extends to the surface. The connection between rainfall at the ground and the overlying microphysics in the column provide a means for improving radar QPE at far distances from a ground-based radar or close to the ground where satellite-based radar rainfall retrievals can be ill-defined.

scholarly journals Quantitative Precipitation Estimation using Overlapped Area in Radar Network

2017 ◽  
Vol 19 (1) ◽  
pp. 112-121
Author(s):  
Jeongho Choi ◽  
Myoungsun Han ◽  
Chulsang Yoo ◽  
Jiho Lee
Keyword(s):  
Quantitative Precipitation Estimation ◽  
Radar Network ◽  
Precipitation Estimation

scholarly journals Improved Attenuation-Based Radar Precipitation Estimation Considering the Azimuthal Variabilities of Microphysical Properties

2020 ◽  
Vol 21 (7) ◽  
pp. 1605-1620
Author(s):  
Hao Huang ◽  
Kun Zhao ◽  
Haonan Chen ◽  
Dongming Hu ◽  
Peiling Fu ◽  
...  
Keyword(s):  
Variational Approach ◽  
Optimization Approach ◽  
Radar Rainfall ◽  
Microphysical Properties ◽  
Quantitative Precipitation Estimation ◽  
Hourly Rainfall ◽  
Precipitation Estimation ◽  
Raindrop Size ◽  
Differential Reflectivity ◽  
The Impact

AbstractThe attenuation-based rainfall estimator is less sensitive to the variability of raindrop size distributions (DSDs) than conventional radar rainfall estimators. For the attenuation-based quantitative precipitation estimation (QPE), the key is to accurately estimate the horizontal specific attenuation AH, which requires a good estimate of the ray-averaged ratio between AH and specific differential phase KDP, also known as the coefficient α. In this study, a variational approach is proposed to optimize the coefficient α for better estimates of AH and rainfall. The performance of the variational approach is illustrated using observations from an S-band operational weather radar with rigorous quality control in south China, by comparing against the α optimization approach using a slope of differential reflectivity ZDR dependence on horizontal reflectivity factor ZH. Similar to the ZDR-slope approach, the variational approach can obtain the optimized α consistent with the DSD properties of precipitation on a sweep-to-sweep basis. The attenuation-based hourly rainfall estimates using the sweep-averaged α values from these two approaches show comparable accuracy when verified against the gauge measurements. One advantage of the variational approach is its feasibility to optimize α for each radar ray, which mitigates the impact of the azimuthal DSD variabilities on rainfall estimation. It is found that, based on the optimized α for radar rays, the hourly rainfall amounts derived from the variational approach are consistent with gauge measurements, showing lower bias (1.0%), higher correlation coefficient (0.92), and lower root-mean-square error (2.35 mm) than the results based on the sweep-averaged α.

scholarly journals Quantitative Precipitation Estimation by Combining Rain gauge and Meteorological Radar Network in Viet Nam

2020 ◽  
Vol 5 (5) ◽  
pp. 36-50
Author(s):  
Chiho Kimpara ◽  
Michihiko Tonouchi ◽  
Bui Thi Khanh Hoa ◽  
Nguyen Viet Hung ◽  
Nguyen Minh Cuong ◽  
...  
Keyword(s):  
Rain Gauge ◽  
Viet Nam ◽  
Quantitative Precipitation Estimation ◽  
Radar Network ◽  
Precipitation Estimation ◽  
Meteorological Radar

scholarly journals Global trends in downward surface solar radiation from spatial interpolated ground observations during 1961-2019

2021 ◽  
pp. 1-56
Author(s):  
Menghan Yuan ◽  
Thomas Leirvik ◽  
Martin Wild
Keyword(s):  
Solar Radiation ◽  
Spatial Interpolation ◽  
Hydrological Cycle ◽  
Explanatory Power ◽  
Surface Solar Radiation ◽  
Global Trends ◽  
Cloud Coverage ◽  
Crucial Component ◽  
Spatial Coverage ◽  
Global Dimming

AbstractDownward surface solar radiation (SSR) is a crucial component of the Global Energy Balance, affecting temperature and the hydrological cycle profoundly, and it provides crucial information about climate change. Many studies have examined SSR trends, however they are often concentrated on specific regions due to limited spatial coverage of ground based observation stations. To overcome this spatial limitation, this study performs a spatial interpolation based on a machine learning method, Random Forest, to interpolate monthly SSR anomalies using a number of climatic variables (various temperature indices, cloud coverage, etc.), time point indicators (years and months of SSR observations), and geographical characteristics of locations (latitudes, longitudes, etc). The predictors that provide the largest explanatory power for interannual variability are diurnal temperature range and cloud coverage. The output of the spatial interpolation is a 0:5° ×0:5° monthly gridded dataset of SSR anomalies with complete land coverage over the period 1961-2019, which is used afterwards in a comprehensive trend analysis for i) each continent separately, and ii) the entire globe.The continental level analysis reveals the major contributors to the global dimming and brightening. In particular, the global dimming before the 1980s is primarily dominated by negative trends in Asia and North America, while Europe and Oceania have been the two largest contributors to the brightening after 1982 and up until 2019.

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