Operational Radar Measurements of Rainfall: The Accuracy of Point Estimates of Rainfall Rate

1992 ◽  
pp. 55-59 ◽  
Author(s):  
Marjan Divjak
Keyword(s):  
Rainfall Rate ◽  
Point Estimates ◽  
Radar Measurements

scholarly journals Realistic Rain Model for the Estimation of the Rainfall Rate From Radar Measurements

2019 ◽  
Vol 67 (9) ◽  
pp. 6104-6114 ◽  
Author(s):  
Keyla M. Mora Navarro ◽  
Emanoel Costa ◽  
Carlos A. Morales Rodriguez ◽  
Sandra Cruz-Pol ◽  
Leyda V. Leon Colon
Keyword(s):  
Rainfall Rate ◽  
Radar Measurements

scholarly journals Detection of potentially hazardous convective clouds with a dual-polarized C-band radar

2013 ◽  
Vol 6 (2) ◽  
pp. 3675-3722
Author(s):  
A. Adachi ◽  
T. Kobayashi ◽  
H. Yamauchi ◽  
S. Onogi
Keyword(s):  
Heavy Rainfall ◽  
Cumulus Cell ◽  
Elevation Angle ◽  
Onset Time ◽  
Convective Cloud ◽  
Estimation Algorithm ◽  
Maximum Intensity ◽  
Rainfall Rate ◽  
Polarimetric Radar ◽  
Radar Measurements

Abstract. A method for forecasting very short-term rainfall to detect potentially hazardous convective cloud that produces heavy local rainfall was developed using actual volumetric C-band polarimetric radar data. Because the rainfall estimation algorithm used in this method removed the effect of ice particles based on polarimetric measurements, it was immune to the high reflectivity associated with hail. The reliability of the algorithm was confirmed by comparing the rainfall rate estimated from the polarimetric radar measurements at the lowest elevation angle with that obtained from an optical disdrometer on the ground. The rainfall rate estimated from polarimetric data agreed well with the results obtained from the disdrometer, and was much more reliable than results derived from reflectivity alone. Two small cumulus cells were analyzed, one of which developed and later produced heavy rainfall, whereas the other did not. Observations made by polarimetric radar with a volumetric scan revealed that a high vertical maximum intensity of rainfall rate and a vertical area of enhanced differential reflectivity extending above the freezing level, often termed a high ZDR column, were clearly formed about 10 min prior to the onset of heavy rainfall on the ground. The onset time of the heavy rainfall could be estimated in advance from the polarimetric data, which agreed fairly well with observations. These polarimetric characteristics were not observed for the cumulus cell that did not produce heavy rainfall. The results suggest that both the vertical maximum intensity of the rainfall rate and a high ZDR column, estimated from polarimetric measurements, can be used to identify potentially hazardous clouds. Furthermore, this study shows that polarimetric radar measurements with high spatial and temporal resolutions are invaluable for disaster reduction.

scholarly journals Radar Observations of Rainfall Variability Using Non-Rayleigh Signal Fluctuations

2008 ◽  
Vol 47 (2) ◽  
pp. 607-619 ◽  
Author(s):  
A. R. Jameson
Keyword(s):  
Rainfall Variability ◽  
Rainfall Rate ◽  
Relative Dispersion ◽  
Statistical Independence ◽  
Average Value ◽  
Quantitative Measurements ◽  
Statistical Heterogeneity ◽  
Average Rainfall ◽  
Radar Measurements ◽  
Meteorological Radar

Abstract The spatial variability and temporal variability of precipitation are widely recognized. In particular, rainfall rates can fluctuate widely in regions where the raindrops are clustered and where mean conditions are changing (statistical heterogeneity). Indeed, at times, the ambiguity associated with an estimated average rainfall rate may become very large. Therefore, in quantitative measurements of precipitation, it would be useful to identify where this occurs. In this work a technique is proposed and applied to quantify the variability in rainfall rates introduced by statistical heterogeneity and raindrop clustering using deviations from Rayleigh statistics of intensity fluctuations. This technique separates the Rayleigh contributions to the observed relative dispersion from those arising from clustering and statistical heterogeneities. Applications to conventional meteorological radar measurements are illustrated using two scans. Often, but not always, the greatest ambiguities in estimates of the average rainfall rate occur just where the rainfall rates are the largest and presumably where accurate estimates are most important. This ambiguity is not statistical; rather, it indicates the presence of important sub-beam-scale fluctuations. As a consequence, no single average value can be applied uniformly to the entire domain. The examples provided here also demonstrate that the appropriate observations are feasible using current conventional meteorological radars with adequate processing capabilities. However, changes in radar technology that improve and increase pulse-to-pulse statistical independence will permit such observations to be gathered more routinely at finer spatial resolution and with enhanced precision.

scholarly journals Detection of potentially hazardous convective clouds with a dual-polarized C-band radar

2013 ◽  
Vol 6 (10) ◽  
pp. 2741-2760 ◽  
Author(s):  
A. Adachi ◽  
T. Kobayashi ◽  
H. Yamauchi ◽  
S. Onogi
Keyword(s):  
Heavy Rainfall ◽  
Cumulus Cell ◽  
Elevation Angle ◽  
Onset Time ◽  
Convective Cloud ◽  
Estimation Algorithm ◽  
Maximum Intensity ◽  
Rainfall Rate ◽  
Polarimetric Radar ◽  
Radar Measurements

Abstract. A method for forecasting very short-term rainfall to detect potentially hazardous convective cloud that produces heavy local rainfall was developed using actual volumetric C-band polarimetric radar data. Because the rainfall estimation algorithm used in this method removed the effect of ice particles based on polarimetric measurements, it was immune to the high reflectivity associated with hail. The reliability of the algorithm was confirmed by comparing the rainfall rate estimated from the polarimetric radar measurements at the lowest elevation angle with that obtained from optical disdrometers on the ground. The rainfall rate estimated from polarimetric data agreed well with the results obtained from the disdrometers, and was much more reliable than results derived from reflectivity alone. Two small cumulus cells were analyzed, one of which developed and later produced heavy rainfall, whereas the other did not. Observations made by polarimetric radar with a volumetric scan revealed that a high vertical maximum intensity of rainfall rate and a vertical area of enhanced differential reflectivity extending above the freezing level, often termed a high ZDR column, were clearly formed about 10 min prior to the onset of heavy rainfall on the ground. The onset time of the heavy rainfall could be estimated in advance from the polarimetric data, which agreed fairly well with observations. These polarimetric characteristics were not observed for the cumulus cell that did not produce heavy rainfall. The results suggest that both the vertical maximum intensity of the rainfall rate and a high ZDR column, estimated from polarimetric measurements, can be used to identify potentially hazardous clouds. Furthermore, this study shows that polarimetric radar measurements with high spatial and temporal resolutions are invaluable for disaster reduction.

The Orchard Plot: Cultivating a Forest Plot for Use in Ecology, Evolution and Beyond

2019 ◽  
Author(s):  
Shinichi Nakagawa ◽  
Malgorzata Lagisz ◽  
Rose E O'Dea ◽  
Joanna Rutkowska ◽  
Yefeng Yang ◽  
...  
Keyword(s):  
Meta Analysis ◽  
R Package ◽  
Effect Sizes ◽  
Forest Plot ◽  
Point Estimates ◽  
Aggregate Effect ◽  
The Individual ◽  
Meta Analyses ◽  
Heterogeneous Effect ◽  
Intuitive Interpretation

‘Classic’ forest plots show the effect sizes from individual studies and the aggregate effect from a meta-analysis. However, in ecology and evolution meta-analyses routinely contain over 100 effect sizes, making the classic forest plot of limited use. We surveyed 102 meta-analyses in ecology and evolution, finding that only 11% use the classic forest plot. Instead, most used a ‘forest-like plot’, showing point estimates (with 95% confidence intervals; CIs) from a series of subgroups or categories in a meta-regression. We propose a modification of the forest-like plot, which we name the ‘orchard plot’. Orchard plots, in addition to showing overall mean effects and CIs from meta-analyses/regressions, also includes 95% prediction intervals (PIs), and the individual effect sizes scaled by their precision. The PI allows the user and reader to see the range in which an effect size from a future study may be expected to fall. The PI, therefore, provides an intuitive interpretation of any heterogeneity in the data. Supplementing the PI, the inclusion of underlying effect sizes also allows the user to see any influential or outlying effect sizes. We showcase the orchard plot with example datasets from ecology and evolution, using the R package, orchard, including several functions for visualizing meta-analytic data using forest-plot derivatives. We consider the orchard plot as a variant on the classic forest plot, cultivated to the needs of meta-analysts in ecology and evolution. Hopefully, the orchard plot will prove fruitful for visualizing large collections of heterogeneous effect sizes regardless of the field of study.

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