Stratiform and Convective Radar Reflectivity–Rain Rate Relationships and Their Potential to Improve Radar Rainfall Estimates

2019 ◽  
Vol 58 (10) ◽  
pp. 2259-2271 ◽  
Author(s):  
Bastian Kirsch ◽  
Marco Clemens ◽  
Felix Ament
Keyword(s):  
Rain Rate ◽  
Rainfall Intensity ◽  
Estimation Error ◽  
Radar Reflectivity ◽  
Observation Data ◽  
Convective Rainfall ◽  
Estimation Errors ◽  
Radar Rainfall ◽  
Rain Rates ◽  
Rainfall Estimates

AbstractThe variability of the raindrop size distribution (DSD) contributes to large parts of the uncertainty in radar-based quantitative rainfall estimates. The variety of microphysical processes acting on the formation of rainfall generally leads to significantly different relationships between radar reflectivity Z and rain rate R for stratiform and convective rainfall. High-resolution observation data from three Micro Rain Radars in northern Germany are analyzed to quantify the potential of dual Z–R relationships to improve radar rainfall estimates under idealized rainfall type identification and separation. Stratiform and convective rainfall are separated with two methods, establishing thresholds for the rain rate-dependent mean drop size and the α coefficient of the power-law Z–R relationship. The two types of dual Z–R relationships are tested against a standard Marshall–Palmer relationship and a globally adjusted single relationship. The comparison of DSD-based and reflectivity-derived rain rates shows that the use of stratiform and convective Z–R relationships reduces the estimation error of the 6-month accumulated rainfall between 30% and 50% relative to a single Z–R relationship. Consistent results for neighboring locations are obtained at different rainfall intensity classes. The range of estimation errors narrows by between 20% and 40% for 10-s-integrated rain rates, dependent on rainfall intensity and separation method. The presented technique also considerably reduces the occurrence of extreme underestimations of the true rain rate for heavy rainfall, which is particularly relevant for operational applications and flooding predictions.

scholarly journals Estimating Rain Rates from Tipping-Bucket Rain Gauge Measurements

2008 ◽  
Vol 25 (1) ◽  
pp. 43-56 ◽  
Author(s):  
Jianxin Wang ◽  
Brad L. Fisher ◽  
David B. Wolff
Keyword(s):  
Rain Rate ◽  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Radar Reflectivity ◽  
Time Shift ◽  
Estimation Errors ◽  
Rate Estimation ◽  
Reflectivity Data ◽  
Rain Rates ◽  
Event Definition

Abstract This paper describes the cubic spline–based operational system for the generation of the Tropical Rainfall Measuring Mission (TRMM) 1-min rain-rate product 2A-56 from tipping-bucket (TB) gauge measurements. A simulated TB gauge from a Joss–Waldvogel disdrometer is employed to evaluate the errors of the TB rain-rate estimation. These errors are very sensitive to the time scale of rain rates. One-minute rain rates suffer substantial errors, especially at low rain rates. When 1-min rain rates are averaged over 4–7-min intervals or longer, the errors dramatically reduce. Estimated lower rain rates are sensitive to the event definition whereas the higher rates are not. The median relative absolute errors are about 22% and 32% for 1-min rain rates higher and lower than 3 mm h−1, respectively. These errors decrease to 5% and 14% when rain rates are used at the 7-min scale. The radar reflectivity–rain-rate distributions drawn from the large amount of 7-min rain rates and radar reflectivity data are mostly insensitive to the event definition. The time shift due to inaccurate clocks can also cause rain-rate estimation errors, which increase with the shifted time length. Finally, some recommendations are proposed for possible improvements of rainfall measurements and rain-rate estimations.

scholarly journals Impacts of Raindrop Fall Velocity and Axis Ratio Errors on Dual-Polarization Radar Rainfall Estimation

2014 ◽  
Vol 15 (5) ◽  
pp. 1849-1861 ◽  
Author(s):  
Bin Pei ◽  
Firat Y. Testik ◽  
Mekonnen Gebremichael
Keyword(s):  
Rain Rate ◽  
Estimation Error ◽  
Retrieval Algorithm ◽  
Dual Polarization ◽  
Fall Velocity ◽  
Estimation Errors ◽  
Radar Rainfall ◽  
Axis Ratio ◽  
Rate Estimation ◽  
Predicted Values

Abstract Motivated by the field observations of fall velocity and axis ratio deviations from predicted terminal velocity and equilibrium axis ratio values, the combined effects of raindrop fall velocity and axis ratio deviations on dual-polarization radar rainfall estimations were investigated. A radar rainfall retrieval algorithm [Colorado State University–Hydrometeor Identification Rainfall Optimization (CSU-HIDRO)] served as the test bed. Subsequent investigations determined that the available field measurements, which were very limited in scope, of the fall velocity and axis ratio deviations indicated rain-rate estimation errors of approximately 20%. Based on these findings, a sensitivity study was then performed using uncorrelated fall velocity and axis ratio deviations around the predicted values. Significant rain-rate estimation errors were observed for the realistic combinations of fall velocity and axis ratio deviations. It was shown that the maximum rain-rate estimation error can reach up to approximately 200% for combinations of fall velocity and axis ratio deviations (5000 drop size distribution samples were simulated for each combination) between −10% and +10% of the predicted values for each. The maximum standard deviation of errors was as great as 75% for the same combinations of fall velocity and axis ratio deviations. The authors found that use of dual-polarization radars to accurately estimate rainfall, during natural rain events, also requires a reanalysis of the parameterizations for raindrop fall velocity and axis ratio. These parameterizations should consider both the coupling between these two parameters and factors that may introduce any possible deviations of the predicted values of these parameters.

Comparison of Single- and Dual-Polarization–Based Rainfall Estimates Using NEXRAD Data for the NASA Iowa Flood Studies Project

2015 ◽  
Vol 16 (4) ◽  
pp. 1658-1675 ◽  
Author(s):  
Bong-Chul Seo ◽  
Brenda Dolan ◽  
Witold F. Krajewski ◽  
Steven A. Rutledge ◽  
Walter Petersen
Keyword(s):  
Rain Rate ◽  
Heavy Rain ◽  
Rain Gauge ◽  
Superior Performance ◽  
Dual Polarization ◽  
Radar Rainfall ◽  
Colorado State University ◽  
Precipitation Estimation ◽  
Improved Performance ◽  
Rainfall Estimates

Abstract This study compares and evaluates single-polarization (SP)- and dual-polarization (DP)-based radar-rainfall (RR) estimates using NEXRAD data acquired during Iowa Flood Studies (IFloodS), a NASA GPM ground validation field campaign carried out in May–June 2013. The objective of this study is to understand the potential benefit of the DP quantitative precipitation estimation, which selects different rain-rate estimators according to radar-identified precipitation types, and to evaluate RR estimates generated by the recent research SP and DP algorithms. The Iowa Flood Center SP (IFC-SP) and Colorado State University DP (CSU-DP) products are analyzed and assessed using two high-density, high-quality rain gauge networks as ground reference. The CSU-DP algorithm shows superior performance to the IFC-SP algorithm, especially for heavy convective rains. We verify that dynamic changes in the proportion of heavy rain during the convective period are associated with the improved performance of CSU-DP rainfall estimates. For a lighter rain case, the IFC-SP and CSU-DP products are not significantly different in statistical metrics and visual agreement with the rain gauge data. This is because both algorithms use the identical NEXRAD reflectivity–rain rate (Z–R) relation that might lead to substantial underestimation for the presented case.

scholarly journals Path-Average Rainfall Estimation from Optical Extinction Measurements Using a Large-Aperture Scintillometer

2011 ◽  
Vol 12 (5) ◽  
pp. 955-972 ◽  
Author(s):  
R. Uijlenhoet ◽  
J.-M. Cohard ◽  
M. Gosset
Keyword(s):  
Extinction Coefficient ◽  
Rain Rate ◽  
Optical Extinction ◽  
Rainfall Events ◽  
Rain Gauges ◽  
Large Aperture ◽  
Raindrop Size ◽  
Rain Rates ◽  
Average Rain ◽  
Rainfall Estimates

Abstract The potential of a near-infrared large-aperture boundary layer scintillometer as path-average rain gauge is investigated. The instrument was installed over a 2.4-km path in Benin as part of the African Monsoon Multidisciplinary Analysis (AMMA) Enhanced Observation Period during 2006 and 2007. Measurements of the one-minute-average received signal intensity were collected for 6 rainfall events during the dry season and 16 events during the rainy season. Using estimates of the signal base level just before the onset of the rainfall events, the optical extinction coefficient is estimated from the path-integrated attenuation for each minute. The corresponding path-average rain rates are computed using a power-law relation between the optical extinction coefficient and rain rate obtained from measurements of raindrop size distributions with an optical spectropluviometer and a scaling-law formalism for describing raindrop size distribution variations. Comparisons of five-minute rainfall estimates with measurements from two nearby rain gauges show that the temporal dynamics are generally captured well by the scintillometer. However, the instrument has a tendency to underestimate rain rates and event total rain amounts with respect to the gauges. It is shown that this underestimation can be explained partly by systematic differences between the actual and the employed mean power-law relation between rain rate and specific attenuation, partly by unresolved spatial and temporal rainfall variations along the scintillometer path. Occasionally, the signal may even be lost completely. It is demonstrated that if these effects are properly accounted for by employing appropriate relations between rain rate and specific attenuation and by adapting the pathlength to the local rainfall climatology, scintillometer-based rainfall estimates can be within 20% of those estimated using rain gauges. These results demonstrate the potential of large-aperture scintillometers to estimate path-average rain rates at hydrologically relevant scales.

scholarly journals Scale Dependence of Radar-Rainfall Rates—An Assessment Based on Raindrop Spectra

2004 ◽  
Vol 5 (6) ◽  
pp. 1171-1180 ◽  
Author(s):  
Matthias Steiner ◽  
James A. Smith
Keyword(s):  
Land Surface ◽  
Point Processes ◽  
Dynamic Range ◽  
Radar Reflectivity ◽  
Rainfall Rate ◽  
Contributing Factors ◽  
Radar Rainfall ◽  
Maximum Rainfall ◽  
Spatial And Temporal Scales ◽  
Rain Rates

Abstract Scale differences may introduce a bias when comparing, merging, or assimilating rainfall measurements because the dynamic range of values representing the underlying physical process strongly depends on the resolution of the data. The present study addresses this issue from the perspective of how well coarser-resolution radar-rainfall observations may be used for evaluation of hydrologic point processes occurring at the land surface, such as rainfall erosion, infiltration, ponding, and runoff. Conceptual and quantitative analyses reveal that scale differences may yield substantial biases. Even for perfect measurements, the overall bias is composed of two contributing factors: one related to a reduction of dynamic range of rain rates and the other related to a dependence of the relationship between observed radar reflectivity factor and retrieved rainfall rate on the scale of observation. The effects of scale differences are evaluated empirically from a perspective of averaging in time based on raindrop spectra observations. Averaging drop spectra over 5 min, on average over a large dataset, resulted in an underestimation of median and maximum rainfall rates of approximately 50% compared to the corresponding 1-min values. Overall, standard deviations of rain rates retrieved from 5-min-averaged radar reflectivity factors may easily be off a corresponding high-resolution (1 min) rainfall rate by a factor 2 or more. This magnitude is larger than the uncertainty resulting from limitations of the radar measurement precision. Scale-difference effects are thus important and should be considered when comparing, merging, or assimilating data from very different spatial and temporal scales. A similar challenge arises for downscaling schemes attempting to recover subgrid-scale features from coarse-resolution information.

scholarly journals Rain Rate and Water Content in Hurricanes Compared with Summer Rain in Miami, Florida

2012 ◽  
Vol 51 (12) ◽  
pp. 2218-2235 ◽  
Author(s):  
Robert A. Black ◽  
John Hallett
Keyword(s):  
Probability Distribution ◽  
Water Content ◽  
Liquid Water ◽  
Rain Rate ◽  
Liquid Water Content ◽  
Radar Reflectivity ◽  
Normal Surface ◽  
Functional Relationships ◽  
Surface Rain Rate ◽  
Rain Rates

AbstractLiquid water content (g m−3), precipitation rate (mm h−1), and radar reflectivity (dBZ) are inferred from cross sections of particle images obtained by aircraft. Each dataset is presented in a probability format to display changing functional relationships for the selected intervals. The probability of intercepting a given quantity during a flight provides guidance in required instrument sensitivity together with the frequency of precipitation and liquid water content events for given rainfall totals. These data are compared with surface rain rate obtained over two years in the May–October warm seasons in Miami, Florida, with a Hotplate rain gauge. The warm season Miami surface rain-rate probability distribution is similar to the 2005 hurricane rain-rate distribution. Rain rates > ~120 mm h−1 were responsible for over one-half of the accumulation, even though lighter rain dominated by time. Hurricane rainfall is somewhat more intense than the normal surface convective rainfall in that 10% of the 1977–2001 (old) hurricane rain rates exceeded 20 mm h−1, whereas only 10% of the surface rain rates exceeded only ~10 mm h−1. The shape of the rain-rate probability distributions from the 2005 (recent) hurricane data was nearly identical to the probability distribution of rain rates in the Miami data. The radar reflectivity distributions were similar, whose 90% level was about 45 dBZ for the old storms and about 35 dBZ for the 2005 storms. These data clearly show the low bias of the 2005 hurricane data caused by the systematic avoidance of heavy precipitation.

scholarly journals Development of method for X-band weather radar calibration

2013 ◽  
Vol 15 (4) ◽  
pp. 1326-1339 ◽  
Author(s):  
J. E. Nielsen ◽  
S. Thorndahl ◽  
M. R. Rasmussen
Keyword(s):  
Rainfall Intensity ◽  
Temporal Dynamics ◽  
Weather Radar ◽  
Calibration Method ◽  
Local Area ◽  
Transformation Model ◽  
Radar Rainfall ◽  
X Band ◽  
Radar Calibration ◽  
Rain Rates

Calibration of the X-band LAWR (Local Area Weather Radar) is traditionally based on an assumed linear relation between the LAWR radar output and the rainfall intensity. However, closer inspection of the data reveal that the validity of this linear assumption is doubtful. Previous studies of this type of weather radar have also illustrated that the radar commonly has difficulties in estimating high rain rates. Therefore, a new radar–rainfall transformation model and a calibration method have been developed. The new method is based on nonlinear assumptions and is aimed at describing the whole range of rain intensities in a more comprehensive way for the LAWR system. The new proposed calibration method improves the LAWR QPE (quantitative precipitation estimate) accuracy by reducing bias and describing the temporal dynamics better for the vast majority of the observed rainfall. However, in heavy rainfall, the LAWR system still faces significant challenges in measuring the peak intensities accurately.

A Regression-Free Rainfall Estimation Algorithm for Dual-Polarization Radars

2018 ◽  
Vol 35 (8) ◽  
pp. 1701-1721 ◽  
Author(s):  
Bin Pei ◽  
Firat Y. Testik
Keyword(s):  
Cost Function ◽  
Rain Rate ◽  
Estimation Algorithm ◽  
Cost Functions ◽  
Size Distributions ◽  
Dual Polarization ◽  
Rainfall Estimation ◽  
Radar Rainfall ◽  
Raindrop Size ◽  
Rain Rates

AbstractIn this study a new radar rainfall estimation algorithm—rainfall estimation using simulated raindrop size distributions (RESID)—was developed. This algorithm development was based upon the recent finding that measured and simulated raindrop size distributions (DSDs) with matching triplets of dual-polarization radar observables (i.e., horizontal reflectivity, differential reflectivity, and specific differential phase) produce similar rain rates. The RESID algorithm utilizes a large database of simulated gamma DSDs, theoretical rain rates calculated from the simulated DSDs, the corresponding dual-polarization radar observables, and a set of cost functions. The cost functions were developed using both the measured and simulated dual-polarization radar observables. For a given triplet of measured radar observables, RESID chooses a suitable cost function from the set and then identifies nine of the simulated DSDs from the database that minimize the value of the chosen cost function. The rain rate associated with the given radar observable triplet is estimated by averaging the calculated theoretical rain rates for the identified simulated DSDs. This algorithm is designed to reduce the effects of radar measurement noise on rain-rate retrievals and is not subject to the regression uncertainty introduced in the conventional development of the rain-rate estimators. The rainfall estimation capability of our new algorithm was demonstrated by comparing its performance with two benchmark algorithms through the use of rain gauge measurements from the Midlatitude Continental Convective Clouds Experiment (MC3E) and the Olympic Mountains Experiment (OLYMPEx). This comparison showed favorable performance of the new algorithm for the rainfall events observed during the field campaigns.

scholarly journals Sensitivity of Rain-Rate Estimates Related to Convective Organization: Observations from the Kwajalein, RMI, Radar

2017 ◽  
Vol 56 (4) ◽  
pp. 1099-1119 ◽  
Author(s):  
David S. Henderson ◽  
Christian D. Kummerow ◽  
David A. Marks
Keyword(s):  
Large Scale ◽  
Rain Rate ◽  
Deep Convection ◽  
Marshall Islands ◽  
Shallow Convection ◽  
Wet Season ◽  
Precipitation Regime ◽  
Radar Rainfall ◽  
Precipitation Regimes ◽  
Rain Rates

AbstractGround radar rainfall, necessary for satellite rainfall product (e.g., TRMM and GPM) ground validation (GV) studies, is often retrieved using annual or climatological convective/stratiform Z–R relationships. Using the Kwajalein, Republic of the Marshall Islands (RMI), polarimetric S-band weather radar (KPOL) and gauge network during the 2009 and 2011 wet seasons, the robustness of such rain-rate relationships is assessed through comparisons with rainfall retrieved using relationships that vary as a function of precipitation regime, defined as shallow convection, isolated deep convection, and deep organized convection. It is found that the TRMM-GV 2A53 rainfall product underestimated rain gauges by −8.3% in 2009 and −13.1% in 2011, where biases are attributed to rainfall in organized precipitation regimes. To further examine these biases, 2A53 GV rain rates are compared with polarimetrically tuned rain rates, in which GV biases are found to be minimized when rain relationships are developed for each precipitation regime, where, for example, during the 2009 wet-season biases in isolated deep precipitation regimes were reduced from −16.3% to −4.7%. The regime-based improvements also exist when specific convective and stratiform Z–R relationships are developed as a function of precipitation regime, where negative biases in organized convective events (−8.7%) are reduced to −1.6% when a regime-based Z–R is implemented. Negative GV biases during the wet seasons lead to an underestimation in accumulated rainfall when compared with ground gauges, suggesting that satellite-related bias estimates could be underestimated more than originally described. Such results encourage the use of the large-scale precipitation regime along with their respective locally characterized convective or stratiform classes in precipitation validation endeavors and in development of Z–R rainfall relationships.

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