Search Results
You are looking at 1 - 10 of 14 items for
- Author or Editor: Merhala Thurai x
- Refine by Access: All Content x
Abstract
Results from an experiment to measure the drop shapes using a 2D video disdrometer (2DVD) are reported. Under calm conditions, drops were generated from a hose located on a bridge 80 m above ground, this height being sufficient to allow drop oscillations to reach a steady state. The disdrometer data had to be carefully processed so as to eliminate the drops mismatched by the instrument and to remove the system spreading function. The total number of drops analyzed was around 115 000. Their axis ratio distributions were obtained for diameters ranging from 1.5 to 9 mm. The mean axis ratio decreases with increasing drop diameter, in agreement with the upper bound of the Beard and Chuang equilibrium shape model. The inferred mode of oscillation appears to be dominated by the oblate–prolate axisymmetric mode for the diameter range of 1.5 to 9 mm.
The mean axis ratio agrees well with two empirically fitted formulas reported in earlier studies. In addition, a linear fit was applied to the data for radar applications relating to rain retrievals from dual-polarization measurements. The 2DVD data taken in moderate stratiform rain were also analyzed in a similar way and the results agree with the artificially generated drop experiment, at least up to 4 mm. No data for larger diameters were available for stratiform precipitation. Finally, the fall velocity was examined in terms of drop diameter. The results closely follow an empirical formula fitted to the Gunn and Kinzer data as well as the Beard and Pruppacher data including a slight decrease in the terminal velocity with a diameter beyond 7 mm.
Abstract
Results from an experiment to measure the drop shapes using a 2D video disdrometer (2DVD) are reported. Under calm conditions, drops were generated from a hose located on a bridge 80 m above ground, this height being sufficient to allow drop oscillations to reach a steady state. The disdrometer data had to be carefully processed so as to eliminate the drops mismatched by the instrument and to remove the system spreading function. The total number of drops analyzed was around 115 000. Their axis ratio distributions were obtained for diameters ranging from 1.5 to 9 mm. The mean axis ratio decreases with increasing drop diameter, in agreement with the upper bound of the Beard and Chuang equilibrium shape model. The inferred mode of oscillation appears to be dominated by the oblate–prolate axisymmetric mode for the diameter range of 1.5 to 9 mm.
The mean axis ratio agrees well with two empirically fitted formulas reported in earlier studies. In addition, a linear fit was applied to the data for radar applications relating to rain retrievals from dual-polarization measurements. The 2DVD data taken in moderate stratiform rain were also analyzed in a similar way and the results agree with the artificially generated drop experiment, at least up to 4 mm. No data for larger diameters were available for stratiform precipitation. Finally, the fall velocity was examined in terms of drop diameter. The results closely follow an empirical formula fitted to the Gunn and Kinzer data as well as the Beard and Pruppacher data including a slight decrease in the terminal velocity with a diameter beyond 7 mm.
Abstract
We report on measurements of drop size distributions (DSD) using collocated instruments (a Droplet Measurement Technologies, Inc., Meteorological Particle Spectrometer and a 2D-video disdrometer) from two locations with different rainfall climates (Greeley, Colorado, and Huntsville, Alabama, with measurements from the latter that include the outer rainbands of Hurricane Irma). The combination of the two instruments gives what we term as the “full” DSD spectra, the shape of which generally cannot be represented by the standard gamma model, but instead requires the additional flexibility of the generalized gamma model, which includes two shape parameters (μ and c). The double-moment normalization of DSDs using the third and fourth moments is used to arrive at the intrinsic shapes of the DSD with two shape parameters that are shown to capture simultaneously the drizzle mode as well as the precipitation mode, together with a “plateau” region between the two. The estimation of μ and c is done with a global search using nonlinear least squares, and the error residuals are examined to check the sensitivity of the parameters to a preselected, allowed tolerance around the minimum error in the μ, c plane. This leads to a range of plausible fits for a given normalized DSD mainly governed by the c parameter. The stability or invariance of the shape of the normalized DSDs from the two sites is examined, and on average the shapes are similar with some variability at the large normalized diameter end that is explained by the aforementioned range of plausible fits. Heuristic goodness-of-fit methods are described that demonstrate that the generalized gamma model outperforms the standard gamma model with only one shape parameter (μ).
Abstract
We report on measurements of drop size distributions (DSD) using collocated instruments (a Droplet Measurement Technologies, Inc., Meteorological Particle Spectrometer and a 2D-video disdrometer) from two locations with different rainfall climates (Greeley, Colorado, and Huntsville, Alabama, with measurements from the latter that include the outer rainbands of Hurricane Irma). The combination of the two instruments gives what we term as the “full” DSD spectra, the shape of which generally cannot be represented by the standard gamma model, but instead requires the additional flexibility of the generalized gamma model, which includes two shape parameters (μ and c). The double-moment normalization of DSDs using the third and fourth moments is used to arrive at the intrinsic shapes of the DSD with two shape parameters that are shown to capture simultaneously the drizzle mode as well as the precipitation mode, together with a “plateau” region between the two. The estimation of μ and c is done with a global search using nonlinear least squares, and the error residuals are examined to check the sensitivity of the parameters to a preselected, allowed tolerance around the minimum error in the μ, c plane. This leads to a range of plausible fits for a given normalized DSD mainly governed by the c parameter. The stability or invariance of the shape of the normalized DSDs from the two sites is examined, and on average the shapes are similar with some variability at the large normalized diameter end that is explained by the aforementioned range of plausible fits. Heuristic goodness-of-fit methods are described that demonstrate that the generalized gamma model outperforms the standard gamma model with only one shape parameter (μ).
Abstract
The algorithms used to estimate rainfall from polarimetric radar variables show significant variance in error characteristics over the range of naturally occurring rain rates. As a consequence, to improve rainfall estimation accuracy using polarimetric radar, it is necessary to optimally combine a number of different algorithms. In this study, a new composite method is proposed that weights the algorithms by the inverse of their theoretical error. A number of approaches are discussed and are investigated using simulated radar data calculated from disdrometer measurements. The resultant algorithms show modest improvement over composite methods based on decision-tree logic—in particular, at rain rates above 20 mm h−1.
Abstract
The algorithms used to estimate rainfall from polarimetric radar variables show significant variance in error characteristics over the range of naturally occurring rain rates. As a consequence, to improve rainfall estimation accuracy using polarimetric radar, it is necessary to optimally combine a number of different algorithms. In this study, a new composite method is proposed that weights the algorithms by the inverse of their theoretical error. A number of approaches are discussed and are investigated using simulated radar data calculated from disdrometer measurements. The resultant algorithms show modest improvement over composite methods based on decision-tree logic—in particular, at rain rates above 20 mm h−1.
Abstract
Two-dimensional video disdrometer (2DVD) data were analyzed from two equatorial Indian (Gan) and west Pacific Ocean (Manus) islands where precipitation is primarily organized by the intertropical convergence zone and the Madden–Julian oscillation (MJO). The 18 (3.5) months of 2DVD data from Manus (Gan) Island show that 1) the two sites have similar drop size distribution (DSD) spectra of liquid water content, median diameter, rain rate R, radar reflectivity z, normalized gamma number concentration Nw, and other integral rain parameters; 2) there is a robust Nw-based separation between convective (C) and stratiform (S) DSDs at both sites that produces consistent separation in other parameter spaces.
The 2DVD data indicate an equatorial, maritime average C/S rainfall accumulation fraction (frequency) of 81/19 (41/59) at these locations. It is hypothesized that convective fraction and frequency estimates are slightly higher than previous radar-based studies, because the ubiquitous weak, shallow convection (<10 mm h−1) characteristic of the tropical warm pool is properly resolved by this high-resolution DSD dataset and identification method. This type of convection accounted for about 30% of all rain events and 15% of total rain volume. These rain statistics were reproduced when newly derived C/S R(z) equations were applied to 2DVD-simulated reflectivity. However, the benefits of using separate C/S R(z) equations are only realizable when C/S partitioning properly classifies each rain type. A single R(z) relationship fit to all 2DVD data yielded accurate total rainfall amounts but overestimated (underestimated) the stratiform (convective) rain fraction by ±10% and overestimated (underestimated) stratiform (convective) rain accumulation by +50% (−15%).
Abstract
Two-dimensional video disdrometer (2DVD) data were analyzed from two equatorial Indian (Gan) and west Pacific Ocean (Manus) islands where precipitation is primarily organized by the intertropical convergence zone and the Madden–Julian oscillation (MJO). The 18 (3.5) months of 2DVD data from Manus (Gan) Island show that 1) the two sites have similar drop size distribution (DSD) spectra of liquid water content, median diameter, rain rate R, radar reflectivity z, normalized gamma number concentration Nw, and other integral rain parameters; 2) there is a robust Nw-based separation between convective (C) and stratiform (S) DSDs at both sites that produces consistent separation in other parameter spaces.
The 2DVD data indicate an equatorial, maritime average C/S rainfall accumulation fraction (frequency) of 81/19 (41/59) at these locations. It is hypothesized that convective fraction and frequency estimates are slightly higher than previous radar-based studies, because the ubiquitous weak, shallow convection (<10 mm h−1) characteristic of the tropical warm pool is properly resolved by this high-resolution DSD dataset and identification method. This type of convection accounted for about 30% of all rain events and 15% of total rain volume. These rain statistics were reproduced when newly derived C/S R(z) equations were applied to 2DVD-simulated reflectivity. However, the benefits of using separate C/S R(z) equations are only realizable when C/S partitioning properly classifies each rain type. A single R(z) relationship fit to all 2DVD data yielded accurate total rainfall amounts but overestimated (underestimated) the stratiform (convective) rain fraction by ±10% and overestimated (underestimated) stratiform (convective) rain accumulation by +50% (−15%).
Abstract
Commonly used disdrometers tend not to accurately measure concentrations of very small drops in the raindrop size distribution (DSD), either through truncation of the DSD at the small-drop end or because of large uncertainties on these measurements. Recent studies have shown that, as a result of these inaccuracies, many if not most ground-based disdrometers do not capture the “drizzle mode” of precipitation, which consists of large concentrations of small drops and is often separated from the main part of the DSD by a shoulder region. We present a technique for reconstructing the drizzle mode of the DSD from “incomplete” measurements in which the drizzle mode is not present. Two statistical moments of the DSD that are well measured by standard disdrometers are identified and used with a double-moment normalized DSD function that describes the DSD shape. A model representing the double-moment normalized DSD is trained using measurements of DSD spectra that contain the drizzle mode obtained using collocated Meteorological Particle Spectrometer and 2D video disdrometer instruments. The best-fitting model is shown to depend on temporal resolution. The result is a method to estimate, from truncated or uncertain measurements of the DSD, a more complete DSD that includes the drizzle mode. The technique reduces bias on low-order moments of the DSD that influence important bulk variables such as the total drop concentration and mass-weighted mean drop diameter. The reconstruction is flexible and often produces better rain-rate estimations than a previous DSD correction routine, particularly for light rain.
Abstract
Commonly used disdrometers tend not to accurately measure concentrations of very small drops in the raindrop size distribution (DSD), either through truncation of the DSD at the small-drop end or because of large uncertainties on these measurements. Recent studies have shown that, as a result of these inaccuracies, many if not most ground-based disdrometers do not capture the “drizzle mode” of precipitation, which consists of large concentrations of small drops and is often separated from the main part of the DSD by a shoulder region. We present a technique for reconstructing the drizzle mode of the DSD from “incomplete” measurements in which the drizzle mode is not present. Two statistical moments of the DSD that are well measured by standard disdrometers are identified and used with a double-moment normalized DSD function that describes the DSD shape. A model representing the double-moment normalized DSD is trained using measurements of DSD spectra that contain the drizzle mode obtained using collocated Meteorological Particle Spectrometer and 2D video disdrometer instruments. The best-fitting model is shown to depend on temporal resolution. The result is a method to estimate, from truncated or uncertain measurements of the DSD, a more complete DSD that includes the drizzle mode. The technique reduces bias on low-order moments of the DSD that influence important bulk variables such as the total drop concentration and mass-weighted mean drop diameter. The reconstruction is flexible and often produces better rain-rate estimations than a previous DSD correction routine, particularly for light rain.
Abstract
A case study in terms of variations in differential reflectivity Z DR observed at X band and snow crystal riming is presented for a light-snow event that occurred near Greeley, Colorado, on 26–27 November 2015. In the early portion of the event, Z DR values at near-surface levels were low (0–0.25 dB). During a second time period approximately 8 h later, Z DR values became distinctly positive (+2–3 dB). Digital photographs of the snow particles were obtained by a Multi-Angle Snowflake Camera (MASC) installed at a range of 13 km from the radar. Image-processing and machine-learning techniques applied to the MASC data showed that the snow particles were more heavily rimed during the low-Z DR time period. The aerodynamic effects of these rime deposits promoted a wider distribution of hydrometeor canting angles. The shift toward more random particle orientations underlies the observed reduction in Z DR during the period when more heavily rimed particles were observed in the MASC data.
Abstract
A case study in terms of variations in differential reflectivity Z DR observed at X band and snow crystal riming is presented for a light-snow event that occurred near Greeley, Colorado, on 26–27 November 2015. In the early portion of the event, Z DR values at near-surface levels were low (0–0.25 dB). During a second time period approximately 8 h later, Z DR values became distinctly positive (+2–3 dB). Digital photographs of the snow particles were obtained by a Multi-Angle Snowflake Camera (MASC) installed at a range of 13 km from the radar. Image-processing and machine-learning techniques applied to the MASC data showed that the snow particles were more heavily rimed during the low-Z DR time period. The aerodynamic effects of these rime deposits promoted a wider distribution of hydrometeor canting angles. The shift toward more random particle orientations underlies the observed reduction in Z DR during the period when more heavily rimed particles were observed in the MASC data.
Abstract
Dual-polarization radar rainfall estimation relationships have been extensively tested in continental and subtropical coastal rain regimes, with little testing over tropical oceans where the majority of rain on Earth occurs. A 1.5-yr Indo-Pacific warm pool disdrometer dataset was used to quantify the impacts of tropical oceanic drop-size distribution (DSD) variability on dual-polarization radar variables and their resulting utility for rainfall estimation. Variables that were analyzed include differential reflectivity Z dr; specific differential phase K dp; reflectivity Z h ; and specific attenuation A h . When compared with continental or coastal convection, tropical oceanic Z dr and K dp values were more often of low magnitude (<0.5 dB, <0.3° km−1) and Z dr was lower for a given K dp or Z h , consistent with observations of tropical oceanic DSDs being dominated by numerous, small, less-oblate drops. New X-, C-, and S-band R estimators were derived: R(K dp), R(A h ), R(K dp, ζ dr), R(z, ζ dr), and R(A h , ζ dr), which use linear versions of Z dr and Z h , namely ζ dr and z. Except for R(K dp), convective/stratiform partitioning was unnecessary for these estimators. All dual-polarization estimators outperformed updated R(z) estimators derived from the same dataset. The best-performing estimator was R(K dp, ζ dr), followed by R(A h , ζ dr) and R(z, ζ dr). The R error was further reduced in an updated blended algorithm choosing between R(z), R(z, ζ dr), R(K dp), and R(K dp, ζ dr) depending on Z dr > 0.25 dB and K dp > 0.3° km−1 thresholds. Because of these thresholds and the lack of hail, R(K dp) was never used. At all wavelengths, R(z) was still needed 43% of the time during light rain (R < 5 mm h−1, Z dr < 0.25 dB), composing 7% of the total rain volume. As wavelength decreased, R(K dp, ζ dr) was used more often, R(z, ζ dr) was used less often, and the blended algorithm became increasingly more accurate than R(z).
Abstract
Dual-polarization radar rainfall estimation relationships have been extensively tested in continental and subtropical coastal rain regimes, with little testing over tropical oceans where the majority of rain on Earth occurs. A 1.5-yr Indo-Pacific warm pool disdrometer dataset was used to quantify the impacts of tropical oceanic drop-size distribution (DSD) variability on dual-polarization radar variables and their resulting utility for rainfall estimation. Variables that were analyzed include differential reflectivity Z dr; specific differential phase K dp; reflectivity Z h ; and specific attenuation A h . When compared with continental or coastal convection, tropical oceanic Z dr and K dp values were more often of low magnitude (<0.5 dB, <0.3° km−1) and Z dr was lower for a given K dp or Z h , consistent with observations of tropical oceanic DSDs being dominated by numerous, small, less-oblate drops. New X-, C-, and S-band R estimators were derived: R(K dp), R(A h ), R(K dp, ζ dr), R(z, ζ dr), and R(A h , ζ dr), which use linear versions of Z dr and Z h , namely ζ dr and z. Except for R(K dp), convective/stratiform partitioning was unnecessary for these estimators. All dual-polarization estimators outperformed updated R(z) estimators derived from the same dataset. The best-performing estimator was R(K dp, ζ dr), followed by R(A h , ζ dr) and R(z, ζ dr). The R error was further reduced in an updated blended algorithm choosing between R(z), R(z, ζ dr), R(K dp), and R(K dp, ζ dr) depending on Z dr > 0.25 dB and K dp > 0.3° km−1 thresholds. Because of these thresholds and the lack of hail, R(K dp) was never used. At all wavelengths, R(z) was still needed 43% of the time during light rain (R < 5 mm h−1, Z dr < 0.25 dB), composing 7% of the total rain volume. As wavelength decreased, R(K dp, ζ dr) was used more often, R(z, ζ dr) was used less often, and the blended algorithm became increasingly more accurate than R(z).
Improved Precipitation Typing Using POSS Spectral Modal AnalysisAbstract
The Precipitation Occurrence Sensor System (POSS) is a small X-band Doppler radar that measures the Doppler velocity spectra from precipitation falling in a small volume near the sensor. The sensor records a 2D frequency of occurrence matrix of the velocity and power at the mode of each spectrum measured over 1 min. The centroid of the distribution of these modes, along with other spectral parameters, defines a data vector input to a multiple discriminant analysis (MDA) for classification of the precipitation type. This requires the a priori determination of a training set for different types, particle size distributions (PSDs), and wind speed conditions. A software model combines POSS system parameters, a particle scattering cross section, and terminal velocity models, to simulate the real-time Doppler signal measured by the system for different PSDs and wind speeds. This is processed in the same manner as the system hardware to produce bootstrap samples of the modal centroid distributions for the MDA training set. MDA results are compared to images from the Multi-Angle Snowflake Camera (MASC) at the MASCRAD site near Easton, Colorado, and to the CSU–CHILL X-band radar observations from Greeley, Colorado. In the four case studies presented, POSS successfully identified precipitation transitions through a range of types (rain, graupel, rimed dendrites, aggregates, unrimed dendrites). Also two separate events of hail were reported and confirmed by the images.
Abstract
The Precipitation Occurrence Sensor System (POSS) is a small X-band Doppler radar that measures the Doppler velocity spectra from precipitation falling in a small volume near the sensor. The sensor records a 2D frequency of occurrence matrix of the velocity and power at the mode of each spectrum measured over 1 min. The centroid of the distribution of these modes, along with other spectral parameters, defines a data vector input to a multiple discriminant analysis (MDA) for classification of the precipitation type. This requires the a priori determination of a training set for different types, particle size distributions (PSDs), and wind speed conditions. A software model combines POSS system parameters, a particle scattering cross section, and terminal velocity models, to simulate the real-time Doppler signal measured by the system for different PSDs and wind speeds. This is processed in the same manner as the system hardware to produce bootstrap samples of the modal centroid distributions for the MDA training set. MDA results are compared to images from the Multi-Angle Snowflake Camera (MASC) at the MASCRAD site near Easton, Colorado, and to the CSU–CHILL X-band radar observations from Greeley, Colorado. In the four case studies presented, POSS successfully identified precipitation transitions through a range of types (rain, graupel, rimed dendrites, aggregates, unrimed dendrites). Also two separate events of hail were reported and confirmed by the images.
Abstract
Analysis of drop size distributions (DSD) measured by collocated Meteorological Particle Spectrometer (MPS) and a third-generation, low-profile, 2D-video disdrometer (2DVD) are presented. Two events from two different regions (Greeley, Colorado, and Huntsville, Alabama) are analyzed. While the MPS, with its 50-μm resolution, enabled measurements of small drops, typically for drop diameters below about 1.1 mm, the 2DVD provided accurate measurements for drop diameters above 0.7 mm. Drop concentrations in the 0.7–1.1-mm overlap region were found to be in excellent agreement between the two instruments. Examination of the combined spectra clearly reveals a drizzle mode and a precipitation mode. The combined spectra were analyzed in terms of the DSD parameters, namely, the normalized intercept parameter N W , the mass-weighted mean diameter D m , and the standard deviation of mass spectrum σ M . The inclusion of small drops significantly affected the N W and the ratio σ M /D m toward higher values relative to using the 2DVD-based spectra alone. For each of the two events, polarimetric radar data were used to characterize the variation of radar-measured reflectivity Z h and differential reflectivity Z dr with D m from the combined spectra. In the Greeley event, this variation at S band was well captured for small values of D m (<0.5 mm) where measured Z dr tended to 0 dB but Z h showed a noticeable decrease with decreasing D m . For the Huntsville event, an overpass of the Global Precipitation Measurement mission Core Observatory satellite enabled comparison of satellite-based dual-frequency radar retrievals of D m with ground-based DSD measurements. Small differences were found between the satellite-based radar retrievals and disdrometers.
Abstract
Analysis of drop size distributions (DSD) measured by collocated Meteorological Particle Spectrometer (MPS) and a third-generation, low-profile, 2D-video disdrometer (2DVD) are presented. Two events from two different regions (Greeley, Colorado, and Huntsville, Alabama) are analyzed. While the MPS, with its 50-μm resolution, enabled measurements of small drops, typically for drop diameters below about 1.1 mm, the 2DVD provided accurate measurements for drop diameters above 0.7 mm. Drop concentrations in the 0.7–1.1-mm overlap region were found to be in excellent agreement between the two instruments. Examination of the combined spectra clearly reveals a drizzle mode and a precipitation mode. The combined spectra were analyzed in terms of the DSD parameters, namely, the normalized intercept parameter N W , the mass-weighted mean diameter D m , and the standard deviation of mass spectrum σ M . The inclusion of small drops significantly affected the N W and the ratio σ M /D m toward higher values relative to using the 2DVD-based spectra alone. For each of the two events, polarimetric radar data were used to characterize the variation of radar-measured reflectivity Z h and differential reflectivity Z dr with D m from the combined spectra. In the Greeley event, this variation at S band was well captured for small values of D m (<0.5 mm) where measured Z dr tended to 0 dB but Z h showed a noticeable decrease with decreasing D m . For the Huntsville event, an overpass of the Global Precipitation Measurement mission Core Observatory satellite enabled comparison of satellite-based dual-frequency radar retrievals of D m with ground-based DSD measurements. Small differences were found between the satellite-based radar retrievals and disdrometers.
Abstract
Data analyses for the mobile Iowa X-band polarimetric (XPOL) radar from a long-duration rain event that occurred during the NASA Iowa Flood Studies (IFloodS) field campaign are presented. A network of six 2D video disdrometers (2DVDs) is used to derive four rain-rate estimators for the XPOL-5 radar. The rain accumulation validations with a collocated network of twin and triple tipping-bucket rain gauges have highlighted the need for combined algorithms because no single estimator was found to be sufficient for all cases considered. A combined version of weighted and composite algorithms is introduced, including a new R(A h, Z dr) rainfall estimator for X band, where A h is the specific attenuation for horizontal polarization and Z dr is the differential reflectivity. Based on measurement and algorithm errors, the weights are derived to be as piecewise constant functions over reflectivity values. The weights are later turned into continuous functions using smoothing splines. A methodology to derive the weights in near–real time is proposed for the composite-weighted algorithm. Comparisons of 2-h accumulations and 8-h event totals obtained from the XPOL-5 with 12 rain gauges have shown 10%–40% improvement in normalized bias over individual rainfall estimators. The analyses have enabled the development of rain-rate estimators for the Iowa XPOL.
Abstract
Data analyses for the mobile Iowa X-band polarimetric (XPOL) radar from a long-duration rain event that occurred during the NASA Iowa Flood Studies (IFloodS) field campaign are presented. A network of six 2D video disdrometers (2DVDs) is used to derive four rain-rate estimators for the XPOL-5 radar. The rain accumulation validations with a collocated network of twin and triple tipping-bucket rain gauges have highlighted the need for combined algorithms because no single estimator was found to be sufficient for all cases considered. A combined version of weighted and composite algorithms is introduced, including a new R(A h, Z dr) rainfall estimator for X band, where A h is the specific attenuation for horizontal polarization and Z dr is the differential reflectivity. Based on measurement and algorithm errors, the weights are derived to be as piecewise constant functions over reflectivity values. The weights are later turned into continuous functions using smoothing splines. A methodology to derive the weights in near–real time is proposed for the composite-weighted algorithm. Comparisons of 2-h accumulations and 8-h event totals obtained from the XPOL-5 with 12 rain gauges have shown 10%–40% improvement in normalized bias over individual rainfall estimators. The analyses have enabled the development of rain-rate estimators for the Iowa XPOL.
