scholarly journals Assessment of Rainfall Estimates Using a StandardZ-RRelationship and the Probability Matching Method Applied to Composite Radar Data in Central Florida

1996 ◽  
Vol 35 (8) ◽  
pp. 1203-1219 ◽  
Author(s):  
William L. Crosson ◽  
Claude E. Duchon ◽  
Ravikumar Raghavan ◽  
Steven J. Goodman
Keyword(s):  
Radar Data ◽  
Probability Matching ◽  
Matching Method ◽  
Central Florida ◽  
Rainfall Estimates

scholarly journals Development of a window correlation matching method for improved radar rainfall estimation

2007 ◽  
Vol 11 (4) ◽  
pp. 1361-1372 ◽  
Author(s):  
T. Piman ◽  
M. S. Babel ◽  
A. Das Gupta ◽  
S. Weesakul
Keyword(s):  
Time Window ◽  
Environmental Modeling ◽  
Probability Matching ◽  
Matching Method ◽  
Radar Rainfall ◽  
Timing Errors ◽  
The North ◽  
Areal Rainfall ◽  
Rainfall Estimates ◽  
Correlation Matching

Abstract. The present study develops a method called window correlation matching method (WCMM) to reduce collocation and timing errors in matching pairs of radar measured reflectivity, Ze, and gauge measured rainfall intensity, R, for improving the accuracy of the estimation of Ze−R relationships. This method was compared with the traditional matching method (TMM), the probability matching method (PMM) and the window probability matching method (WPMM). The calibrated relationship Ze=18.05 R1.45 obtained from 7×7 km of space window and both present and 5 min previous time of radar observation for time window (S77T5) produces the best results for radar rainfall estimates for orographic rain over the Mae Chaem Watershed in the north of Thailand. The comparison shows that the Ze−R relationship obtained from WCMM provide more accuracy in radar rainfall estimates as compared with the other three methods. The Ze−R relationships estimated using TMM and PMM provide large overestimation and underestimation, respectively, of mean areal rainfall whereas WPMM slightly underestimated the mean areal rainfall. Based on the overall results, it can be concluded that WCMM can reduce collocation and timing errors in Ze−R pairs matching and improve the estimation of Ze−R relationships for radar rainfall. WCMM is therefore a promising method for improved radar-measured rainfall, which is an important input for hydrological and environmental modeling and water resources management.

scholarly journals Using Radar-Derived Parameters to Develop Probabilistic Guidance for Lightning Cessation within Isolated Convection near Cape Canaveral, Florida

2019 ◽  
Vol 34 (3) ◽  
pp. 559-575 ◽  
Author(s):  
Joseph R. Patton ◽  
Henry E. Fuelberg
Keyword(s):  
Generalized Linear Model ◽  
Probabilistic Method ◽  
Warm Season ◽  
Radar Data ◽  
Probabilistic Prediction ◽  
Central Florida ◽  
Maximum Reflectivity ◽  
Statistical Verification ◽  
Random Samples ◽  
High Skill

Abstract Thunderstorms in central Florida frequently halt outdoor activities, requiring that one wait some prescribed time after an assumed last flash before safely resuming activities. The goal of this research is to develop a high-skill probabilistic method that can be used in high pressure real-world operations to terminate lightning warnings more quickly while maintaining safety. Probabilistic guidance tools are created for isolated warm season storms in central Florida using dual-polarized radar data at 1-min intervals. The parameters examined are maximum reflectivity and graupel presence at the 0°, −5°, −10°, −15°, and −20°C levels as well as composite reflectivity. Random samples of the radar data are used to train a generalized linear model (GLM) to make a probabilistic prediction whether a given flash is the storm’s last flash. The most statistically significant predictors for lightning cessation are found to be the storm’s maximum reflectivity in the composite and the 0°C levels, along with graupel presence or absence at the −5°, −10°, −15°, and −20°C levels. Statistical verification is used to analyze the performance of the two GLMs at different probability thresholds (95.0%, 97.5%, and 99.0%). When applying the cessation guidance as though storms are occurring in real time, results showed ~99% of the storms produced no additional lightning after the GLM suggested cessation had already occurred. Although these results are encouraging, the procedure must be tested on much larger datasets having different convective modes and different areal coverages to prove its value compared to operational forecasters.

The Window Probability Matching Method for Rainfall Measurements with Radar

1994 ◽  
Vol 33 (6) ◽  
pp. 682-693 ◽  
Author(s):  
Daniel Rosenfeld ◽  
David B. Wolff ◽  
Eyal Amitai
Keyword(s):  
Probability Matching ◽  
Matching Method

PERSIANN-CDR: Daily Precipitation Climate Data Record from Multisatellite Observations for Hydrological and Climate Studies

2015 ◽  
Vol 96 (1) ◽  
pp. 69-83 ◽  
Author(s):  
Hamed Ashouri ◽  
Kuo-Lin Hsu ◽  
Soroosh Sorooshian ◽  
Dan K. Braithwaite ◽  
Kenneth R. Knapp ◽  
...  
Keyword(s):  
Daily Precipitation ◽  
Radar Data ◽  
Climate Data ◽  
Data Record ◽  
Global Precipitation ◽  
Climate Data Record ◽  
Climate Studies ◽  
Rainfall Estimates ◽  
Good Agreement

Abstract A new retrospective satellite-based precipitation dataset is constructed as a climate data record for hydrological and climate studies. Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) provides daily and 0.25° rainfall estimates for the latitude band 60°S–60°N for the period of 1 January 1983 to 31 December 2012 (delayed present). PERSIANN-CDR is aimed at addressing the need for a consistent, long-term, high-resolution, and global precipitation dataset for studying the changes and trends in daily precipitation, especially extreme precipitation events, due to climate change and natural variability. PERSIANN-CDR is generated from the PERSIANN algorithm using GridSat-B1 infrared data. It is adjusted using the Global Precipitation Climatology Project (GPCP) monthly product to maintain consistency of the two datasets at 2.5° monthly scale throughout the entire record. Three case studies for testing the efficacy of the dataset against available observations and satellite products are reported. The verification study over Hurricane Katrina (2005) shows that PERSIANN-CDR has good agreement with the stage IV radar data, noting that PERSIANN-CDR has more complete spatial coverage than the radar data. In addition, the comparison of PERSIANN-CDR against gauge observations during the 1986 Sydney flood in Australia reaffirms the capability of PERSIANN-CDR to provide reasonably accurate rainfall estimates. Moreover, the probability density function (PDF) of PERSIANN-CDR over the contiguous United States exhibits good agreement with the PDFs of the Climate Prediction Center (CPC) gridded gauge data and the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) product. The results indicate high potential for using PERSIANN-CDR for long-term hydroclimate studies in regional and global scales.

Combining Radar Attenuation and Partial Beam Blockage Corrections for Improved Quantitative Application

2021 ◽  
Vol 22 (1) ◽  
pp. 139-153
Author(s):  
Yabin Gou ◽  
Haonan Chen
Keyword(s):  
Radar Data ◽  
Data Quality Control ◽  
Spatial Continuity ◽  
Affected Area ◽  
Raindrop Size Distribution ◽  
Correction Technique ◽  
Precipitation Estimation ◽  
Practical Performance ◽  
Raindrop Size ◽  
Rainfall Estimates

AbstractPartial beam blockage (PBB) correction is an indispensable step in weather radar data quality control and subsequent quantitative applications, such as precipitation estimation, especially in urban and/or complex terrain environments. This paper developed a novel PBB correction procedure based on the improved ZPHI method for attenuation correction and regional specific differential propagation phase (KDP)–reflectivity (ZH) relationship derived from in situ raindrop size distribution (DSD) measurements. The practical performance of this PBB correction technique was evaluated through comparing the spatial continuity of reflectivity measurements, the consistency between radar-measured and DSD-derived KDP and ZH relationships, as well as rainfall estimates based on R(ZH) and R(KDP). The results showed that through incorporating attenuation and PBB corrections (i) the spatial continuity of ZH measurements can effectively be enhanced; (ii) the distribution of radar-measured KDP versus ZH is more consistent with the DSD-derived KDP versus ZH; (iii) the measured ZH from a C-band radar in the PBB-affected area becomes more consistent with collocated S-band measurements, particularly in the rainstorm center area where ZH is larger than 30 dBZ; and (iv) rainfall estimates based on R(ZH) in the PBB-affected area are incrementally improved with better spatial continuity and the performance tends to be more comparable with R(KDP).

scholarly journals The TAMORA algorithm: satellite rainfall estimates over West Africa using multi-spectral SEVIRI data

2010 ◽  
Vol 25 ◽  
pp. 3-9 ◽  
Author(s):  
R. S. Chadwick ◽  
D. I. F. Grimes ◽  
R. W. Saunders ◽  
P. N. Francis ◽  
T. A. Blackmore
Keyword(s):  
West Africa ◽  
Estimation Algorithm ◽  
Radar Data ◽  
Local Calibration ◽  
Accumulated Rainfall ◽  
Satellite Rainfall ◽  
Sahel Region ◽  
Rain Rates ◽  
Improved Accuracy ◽  
Rainfall Estimates

Abstract. A multi-spectral rainfall estimation algorithm has been developed for the Sahel region of West Africa with the purpose of producing accumulated rainfall estimates for drought monitoring and food security. Radar data were used to calibrate multi-channel SEVIRI data from MSG, and a probability of rainfall at several different rain-rates was established for each combination of SEVIRI radiances. Radar calibrations from both Europe (the SatPrecip algorithm) and Niger (TAMORA algorithm) were used. 10 day estimates were accumulated from SatPrecip and TAMORA and compared with kriged gauge data and TAMSAT satellite rainfall estimates over West Africa. SatPrecip was found to produce large overestimates for the region, probably because of its non-local calibration. TAMORA was negatively biased for areas of West Africa with relatively high rainfall, but its skill was comparable to TAMSAT for the low-rainfall region climatologically similar to its calibration area around Niamey. These results confirm the high importance of local calibration for satellite-derived rainfall estimates. As TAMORA shows no improvement in skill over TAMSAT for dekadal estimates, the extra cloud-microphysical information provided by multi-spectral data may not be useful in determining rainfall accumulations at a ten day timescale. Work is ongoing to determine whether it shows improved accuracy at shorter timescales.

scholarly journals Correction of Radar Reflectivity and Differential Reflectivity for Rain Attenuation at X Band. Part II: Evaluation and Application

2005 ◽  
Vol 22 (11) ◽  
pp. 1633-1655 ◽  
Author(s):  
S-G. Park ◽  
M. Maki ◽  
K. Iwanami ◽  
V. N. Bringi ◽  
V. Chandrasekar
Keyword(s):  
Drop Size ◽  
Earth Science ◽  
Radar Data ◽  
Rain Gauge ◽  
Radar Rainfall ◽  
X Band ◽  
Rain Events ◽  
Differential Reflectivity ◽  
Rainfall Estimates ◽  
Good Agreement

Abstract In this paper, the attenuation-correction methodology presented in Part I is applied to radar measurements observed by the multiparameter radar at the X-band wavelength (MP-X) of the National Research Institute for Earth Science and Disaster Prevention (NIED), and is evaluated by comparison with scattering simulations using ground-based disdrometer data. Further, effects of attenuation on the estimation of rainfall amounts and drop size distribution parameters are also investigated. The joint variability of the corrected reflectivity and differential reflectivity show good agreement with scattering simulations. In addition, specific attenuation and differential attenuation, which are derived in the correction procedure, show good agreement with scattering simulations. In addition, a composite rainfall-rate algorithm is proposed and evaluated by comparison with eight gauges. The radar-rainfall estimates from the uncorrected (or observed) ZH produce severe underestimation, even at short ranges from the radar and for stratiform rain events. On the contrary, the reflectivity-based rainfall estimates from the attenuation-corrected ZH does not show such severe underestimation and does show better agreement with rain gauge measurements. More accurate rainfall amounts can be obtained from a simple composite algorithm based on specific differential phase KDP, with the R(ZH_cor) estimates being used for low rainfall rates (KDP ≤ 0.3° km−1 or ZH_cor ≤ 35 dBZ). This improvement in accuracy of rainfall estimation based on KDP is a result of the insensitivity of the rainfall algorithm to natural variations of drop size distributions (DSDs). The ZH, ZDR, and KDP data are also used to infer the parameters (median volume diameter D0 and normalized intercept parameter Nw) of a normalized gamma DSD. The retrieval of D0 and Nw from the corrected radar data show good agreement with those from disdrometer data in terms of the respective relative frequency histograms. The results of this study demonstrate that high-quality hydrometeorological information on rain events such as rainfall amounts and DSDs can be derived from X-band polarimetric radars.

scholarly journals Effect of radar rainfall time resolution on the predictive capability of a distributed hydrologic model

2010 ◽  
Vol 7 (5) ◽  
pp. 7995-8043 ◽  
Author(s):  
A. Atencia ◽  
M. C. Llasat ◽  
L. Garrote ◽  
L. Mediero
Keyword(s):  
Hydrological Model ◽  
Radar Data ◽  
Hydrologic Model ◽  
Rainfall Data ◽  
Distributed Hydrological Model ◽  
Probability Matching ◽  
Radar Rainfall ◽  
Distributed Hydrologic Model ◽  
Surface Data ◽  
Distributed Hydrological Models

Abstract. The performance of distributed hydrological models depends on the resolution, both spatial and temporal, of the rainfall surface data introduced. The estimation of quantitative precipitation from meteorological radar or satellite can improve hydrological model results, thanks to an indirect estimation at higher spatial and temporal resolution. In this work, composed radar data from a network of three C-band radars, with 6-minutal temporal and 2 × 2 km2 spatial resolution, provided by the Catalan Meteorological Service, is used to feed the RIBS distributed hydrological model. A Window Probability Matching Method (gage-adjustment method) is applied to four cases of heavy rainfall to improve the observed rainfall sub-estimation in both convective and stratiform Z/R relations used over Catalonia. Once the rainfall field has been adequately obtained, an advection correction, based on cross-correlation between two consecutive images, was introduced to get several time resolutions from 1 min to 30 min. Each different resolution is treated as an independent event, resulting in a probable range of input rainfall data. This ensemble of rainfall data is used, together with other sources of uncertainty, such as the initial basin state or the accuracy of discharge measurements, to calibrate the RIBS model using probabilistic methodology. A sensitivity analysis of time resolutions was implemented by comparing the various results with real values from stream-flow measurement stations.

Sign in / Sign up

Export Citation Format

Share Document