1. Introduction
The NASA dual-frequency, dual-polarization Doppler radar (D3R) is a deployable weather radar that enables synchronized, beam-aligned observations at Ku (13.91 GHz) and Ka band (35.56 GHz; Vega et al. 2014). The D3R was designed as a ground-based radar with operating frequencies analogous to the Global Precipitation Measurement (GPM) core satellite Dual-Frequency Precipitation Radar (DPR). As part of the ground validation mission for the GPM program, the D3R is a critical platform for comparison with the DPR because of its similar operational frequency and the ability to be deployed for targeted, long-term observation of meteorological events. The D3R provides high-temporal-resolution and high-spatial-resolution observations for a more detailed understanding of precipitation that will ultimately be used to refine algorithms for retrieval of microphysical properties (Vega et al. 2014).
The D3R is equipped with a solid-state transmitter system and coaligned, beam-matched antennas. The D3R allows for simultaneous dual-polarization observation of the same volume at two frequencies. The Ku band provides greater penetration into moderate and heavy convection. The Ka band has enhanced sensitivity to light rain and snow but is more susceptible to attenuation in moderate and heavy precipitation. The solid-state transmitter and receiver design supports reconfigurable transmitter waveforms and receiver filters, making the D3R an agile research platform for microphysical investigation.
The D3R was deployed collocated with NASA’s S-band polarimetric radar (NPOL) during the NASA Iowa Flood Studies (IFloodS) field campaign as part of the GPM ground validation. Both D3R and NPOL were deployed in Traer, Iowa, from 1 May to 15 June 2013 to observe precipitation in concert with an array of ground instruments, NEXRAD S-band radars, and the University of Iowa’s X-band radars in the area of the Cedar River and Iowa River basins. During the field campaign, severe flooding was observed in the surrounding areas and river basins. April and May of 2013 were the wettest on record, with the area experiencing unprecedented rainfall for the 2013 spring season in its 141 years of record (as of 2013; Hillaker 2013). The dataset collected by D3R and other instrumentation deployed for the IFloodS field experiment is, in part, being used to better understand and model the microphysical structure of precipitation and the hydrological response of the IFloodS region.
The IFloodS field campaign was the second operational D3R deployment and the first summer deployment of the D3R, demonstrating continuous operation in warm, humid conditions. The IFloodS experiment represents another milestone D3R event as it was the first deployment of the high-powered (40 W) Ka-band transmitter that replaced the temporary 1 W stand-in. The D3R operated continuously during the campaign, experiencing only one non-radar-related issue. This deployment demonstrated the D3R’s robustness and completed the operational validation of the radar over its anticipated operating temperature range [supplementing validation during winter operations in early 2012 as part of the NASA GPM Cold-Season Precipitation Experiment (GCPEx)]. The high-powered Ka-band solid-state transmitter was successfully demonstrated. The upgrade provides a 16-dB improvement in sensitivity compared to the 1 W transmitter. The upgrade increases the effective operational range of the Ka band and enhances the capability of the D3R for microphysical studies.
The D3R Ku-band radar’s rainfall mapping capability, also referred to as quantitative precipitation estimation (QPE), is examined. The IFloodS field campaign provides an ideal test bed for observation and cross validation with ground instruments. For comparison between ground instruments and the D3R observations, Autonomous Parsivel Unit (APU) disdrometers and rain gauges (Tokay et al. 2014) are considered here. For accurate microphysical investigation of hydrometeors, it is necessary to ensure that the radar estimates are well calibrated and corrected for attenuation effects. The D3R calibration results and preliminary attenuation-correction method for IFloodS are examined in section 3. The cross-validation results using dual-frequency observations and the other instruments deployed at IFloodS are compared in section 4. Quantitative precipitation estimates based on specific differential phase 
2. D3R overview at IFloodS
During the field experiment, D3R collected over 45 000 scans in coordination with NPOL and the other deployed ground instruments as part of the IFloodS field campaign. There were observations of precipitation on 36 of the 46 days during the experiment. From these, there are no less than five severe convective thunderstorms, two squall lines, and four stratiform rain cases. Table 1 provides a qualitative summary of the D3R observations during the deployment.
Summary of D3R observations (by date) during the IFloodS field campaign.
The D3R was able to observe, unobstructed, from 90° clockwise to 230° in azimuth and from 0° to 90° in elevation. Observations were made along a line of ground instruments to the southeast that included an array of disdrometers and rain gauges. The D3R’s maximum range of 39.75 km allowed coordinated observations with a multitude of instrument types: the NPOL, four APUs, two rain gauges, and three 2D video disdrometers (2DVDs). The ground instruments mentioned were deployed along a radial at approximately 130.4° in azimuth from the radar site. This radial was selected for coordinated RHI scans between D3R and NPOL to maximize the opportunity for cross validation and microphysical investigation using the diverse array of instruments. A map of the location of D3R, NPOL, and the ground instruments discussed here is shown in Fig. 1. The geographic coordinates and the relative bearing and range from the D3R to the ground instruments are presented in Table 2.
This map shows the locations of a subset of instruments deployed during the IFloodS field campaign during May and June 2013 located in eastern Iowa. The location of NPOL and D3R is shown with the blue marker. The blue circle with a 40-km radius represents D3R’s max observational range. The red dots mark the locations of four disdrometers deployed within D3R’s domain.
Citation: Journal of Hydrometeorology 16, 5; 10.1175/JHM-D-15-0023.1
Instrument locations within D3R’s observation domain. The range and bearings relative to D3R and NPOL are calculated using the World Geodetic System 1984 Earth model. APU01–APU04 are Autonomous Parsivel Unit disdrometers. SN25, SN35, and SN36 are 2DVDs. The gauges are tipping-bucket rain gauges.
3. D3R system calibration and attenuation correction
The calibration of radar systems, such as the D3R, is necessary for accurate retrievals (Vega et al. 2014). A variety of calibration methods are considered to provide consistent and redundant verification of the radar system parameters necessary for accurate and precise radar observations. These system parameters include transmitted power, receiver gain, antenna pointing angle, and antenna coalignment. The time scales in which the various calibration techniques are used depend on the rate of variation and the ability to track parameter drift during normal operation. The results specific to D3R’s deployment and operation in IFloodS are discussed, with a focus on demonstrating the accuracy of D3R’s meteorological parameter estimation.
a. System calibration
A full system characterization of the radar was performed using calibrated test equipment. This procedure is invasive and required breaking connections at reference planes in the radar system for injection and measurement of signals. Because of the complexity and invasive nature of this characterization, it is generally only performed during scheduled maintenance or when radar component changes occur (component changes may have slight performance deviations from unit to unit). The signal paths, for injection or monitoring, are broken at specific calibration planes, which allows for deterministic characterization of system performance between fixed points. After initial characterization is performed using the test equipment, the internal calibration hardware may be used to track slight variations in the system’s performance. The radar’s receiver provides a consistent monitor of the radar’s performance to estimate any parameter change in the transmitter and receiver paths from the calibration planes.
A metallic sphere is used as an overall, end-to-end radar system calibration target. The metal sphere provides a known radar cross section. The metal sphere is tethered to a balloon and released on a free-flight trajectory while being tracked and observed by the radar. The radar observations are compared against the expected radar cross section for valid observations. Deviation of the observations from the theoretical curve provides an overall radar calibration taking into account all components. Calibration of reflectivity Z with less than 1 dB absolute error is expected using this method. For the IFloodS experiment, a sphere calibration was performed using a 254-mm (10 in.) diameter metal sphere starting at 0000 UTC 14 June 2013. The sphere calibration was performed on a clear, dry, calm evening, and the sphere flight path was well clear of the sun. The sphere calibration results, in Fig. 2, show that the radar’s horizontal polarization reflectivity calibration Zh error is less than 0.5 dB for both frequencies.
D3R (top) Ku- and (bottom) Ka-band sphere calibration results from a metal sphere flight beginning at 0000 UTC 14 Jun 2013. The Ku-band results show a Zh calibration mean error of −0.002 dB with a std dev of 0.44 dB. For the Ka band, the Zh calibration mean error is −0.05 dB with a std dev of 0.51 dB. Ku- and Ka-band results show excellent reflectivity calibration.
Citation: Journal of Hydrometeorology 16, 5; 10.1175/JHM-D-15-0023.1
For D3R, solar scans are performed primarily as a means of verifying antenna absolute alignment and coalignment of the Ku- and Ka-band antennas. The relative position of the solar radiation compared to the expected position of the sun is compared. When the centroid of the relative position is zero, the antenna positioner’s absolute calibration is perfect. Using the same technique, the antenna coalignment is verified with results presented in Fig. 3. For the IFloodS campaign, the coalignment is 0.01° in azimuth and 0.11° in elevation with an absolute alignment error less than 0.06° in azimuth and 0.15° in elevation. A more detailed discussion of the D3R antenna coalignment can be found in Vega et al. (2014).
The D3R antenna coalignment and positioner error verification results at 2247 UTC 18 May 2013. Coalignment of the antennas is within 0.01° in azimuth and 0.11° in elevation. The absolute position error is within 0.06° in azimuth and 0.15° in elevation.
Citation: Journal of Hydrometeorology 16, 5; 10.1175/JHM-D-15-0023.1
Periodic differential reflectivity 
Calibration results of 
Citation: Journal of Hydrometeorology 16, 5; 10.1175/JHM-D-15-0023.1
Finally, a ray-by-ray calibration (a ray is a full integration period of N pulses) is performed using an internal calibration loop (Vega et al. 2014). This calibration loop provides a means to measure the transmitted power through the receiver path. Using the measurement of transmitted power, variations in the transmitted power and receiver gain can be corrected in the radar equation. Using the internal calibration, both Zh and 
Using this tiered calibration approach, the D3R system’s accuracy is maintained continuously during operation. The sphere calibration results show very good calibration of both the Ku- and Ka-band subsystems. Later discussions will compare meteorological estimates with other ground instruments in the IFloodS campaign to further substantiate the calibration quality of the D3R.
b. Attenuation correction
The attenuation correction of reflectivity and differential reflectivity observations is a challenging task in weather radars. However, dual-polarization-based attenuation-correction methods have been fairly successful and used widely in the recent years. Many operational systems have implemented differential phase–based attenuation correction (Bringi and Chandrasekar 2001). This can be further improved by more sophisticated attenuation-correction techniques such as the self-consistency approach from Gorgucci and Baldini (2007). The fundamental premise of this attenuation-correction technique is that horizontal and vertical polarization specific attenuation—
Simulation results at Ku band using APU-observed DSDs show the relationship between Ku-band specific attenuation and 
Citation: Journal of Hydrometeorology 16, 5; 10.1175/JHM-D-15-0023.1
The 
For observation volumes with hydrometeors in the Mie scattering regime, the specific attenuation estimates are subject to bias. For Ku-band observations of convective precipitation with intrinsic reflectivity on the order of 45 dBZ or more, the estimation accuracy of specific attenuation may be reduced. For PIA estimation, the bias may not be significant given the overall natural distribution of reflectivity and expected reflectivity estimation accuracy (typically 1 dB). For differential PIA, this bias can exceed the expected accuracy for estimates of 
(a) Scattergram of Ka- vs Ku-band attenuations based on simulations using training DSD dataset. (b) Scattergram of estimated attenuation using Eq. (8) vs theoretical attenuation at Ka band using the testing DSD dataset.
Citation: Journal of Hydrometeorology 16, 5; 10.1175/JHM-D-15-0023.1
Similarly, we use the testing DSD dataset to evaluate the accuracy of the coefficient in Eq. (8). Figure 6b shows a scatterplot of the Ka-band specific attenuation estimate from Eq. (8) versus the intrinsic Ka-band specific attenuation simulation from the testing DSD data. Evaluating the testing dataset using Eq. (8) yields an MAPE of 41.1% for Ku-band 
The attenuation-correction relationships derived here applied to the D3R observations. These results are compared to other instruments in the IFloodS domain in section 4. The relations in Eqs. (1), (2), and (8) are derived particularly for application to observations during the NASA IFloodS field experiment.
4. D3R cross validation with other instruments
The IFloodS field campaign presents a unique opportunity for multi-instrument comparison and cross validation of observations. By comparing observations of precipitation at multiple frequencies, and using ground observations from APU disdrometers, the integrity of the D3R observations will be further established. The microphysical features can also be evaluated in greater detail. As part of the cross validation, both Ku- and Ka-band observations will be compared, and NPOL observations will be considered. In addition, the APU-observed DSDs are used to simulate radar observations at the appropriate frequencies, which are then compared with real radar measurements.
a. Self-consistent attenuation correction and system calibration validation
A self-consistent validation of the radar parameters illustrates that the attenuation correction improves the D3R observations (Scarchilli et al. 1996). The self-consistent relation between reflectivity and differential reflectivity is examined for the D3R Ku-band and NPOL observations. The radar observations are compared to the intrinsic relationship expected using simulated radar parameters from APU-observed DSDs from the field campaign. The self-consistency comparison in Fig. 7 shows the two-dimensional distribution of 
(a) A histogram showing 
Citation: Journal of Hydrometeorology 16, 5; 10.1175/JHM-D-15-0023.1
b. Comparison of D3R Ku band and NPOL
The dual-polarization radar observations of the D3R Ku band and NPOL are compared using a plan position indicator (PPI) scan for 1.4° elevation at 2248 UTC 29 May 2013. The results are presented in Fig. 8. The figure compares a D3R Ku band and an NPOL PPI scan of heavy convection embedded in stratiform rain observed at the same elevation and same time. Four radar measurements are considered: 
Radar observations from PPI scans for 1.4° elevation at 2248 UTC 29 May 2013. (left) NPOL observations after attenuation correction. (right) D3R Ku-band attenuation-corrected observations. Note that the extinction of the D3R Ku-band signal results in a mismatch of observations in the southeastern quadrant. The NPOL range extends to 100 km while D3R range limit is 39.75 km.
Citation: Journal of Hydrometeorology 16, 5; 10.1175/JHM-D-15-0023.1
A threshold of 
A detailed ray profile comparison of the observed D3R Ku-band and NPOL reflectivities is shown in Fig. 9. The figure shows a ray plot comparison between the two radars after attenuation correction. Figure 9 also shows simulated S-band observations using the APUs in D3R’s observation domain. The radial selected is at azimuth 128.4°, which is in line with the APU disdrometers and at an elevation of 1.4°. The S-band simulated reflectivity, using DSD observations from APU02, APU03, and APU04 at the same time period, are shown as black markers in Fig. 9. The two radars and three APUs show excellent agreement in the reflectivity estimates. For this observation, the root-mean-square error (RMSE) between the D3R and NPOL observations is 2.8 dB for ranges after 13 km with observations having a copolar correlation greater than 0.9.
Comparison of the D3R Ku-band, NPOL, and S-band simulated reflectivity using APU-observed DSDs. All observations are at 2248 UTC 29 May 2013. The presented NPOL and D3R reflectivity are from the same radial. Note that signal extinction results in a mismatch between NPOL and D3R, followed by a loss of data after approximately the 35-km range.
Citation: Journal of Hydrometeorology 16, 5; 10.1175/JHM-D-15-0023.1
c. Dual-frequency ratio
The D3R Ku- and Ka-band observations of a convective cell with stratiform and ice aloft is shown in Fig. 10. The convective cell was observed at 0405 UTC 21 May 2013, and the Ku-band reflectivity, Ka-band reflectivity, and 
RHI observations at 0405 UTC 21 May 2013 of a convective cell with lofted ice. No attenuation correction has been applied and data with a copolar correlation coefficient greater than 0.8 are shown. The measured (a) Ku- and (b) Ka-band reflectivity is shown. (c) The D3R 
Citation: Journal of Hydrometeorology 16, 5; 10.1175/JHM-D-15-0023.1
From the 
Two radials representing the observations of lofted ice in Fig. 10. The (a),(b) observed Ku- and Ka-band reflectivity (without attenuation correction) and (c),(d) difference between the reflectivities 
Citation: Journal of Hydrometeorology 16, 5; 10.1175/JHM-D-15-0023.1
Next, the dual-frequency ratio of rain will be considered from an RHI observation of stratiform rain at 1530 UTC 27 May 2013. The attenuation-corrected reflectivity profiles at Ku and Ka bands are presented in Figs. 12a and 12b, respectively. Examination of the reflectivity plots shows signal extinction as a result of signal attenuation in rain, which is apparent in the Ka-band observations when compared to the Ku-band results.
Reflectivity observations at 1530 UTC 27 May 2013 of an RHI scan through stratiform rain for (a) Ku and (b) Ka band after attenuation correction.
Citation: Journal of Hydrometeorology 16, 5; 10.1175/JHM-D-15-0023.1
The intrinsic (left) Ka- vs Ku-band reflectivity, (middle) DFR vs Ku-band reflectivity, and (right) DFR vs 
Citation: Journal of Hydrometeorology 16, 5; 10.1175/JHM-D-15-0023.1
From observations presented in Fig. 12, a direct comparison of the D3R’s estimated reflectivities along an aligned radial is shown in Fig. 14. The radial is at approximately 1.5° in elevation with an integration period of 320 ms. Observations from 5 to 16 km in range showed good agreement between Ku- and Ka-band reflectivity estimates. At 15 km, simulated Ku-band reflectivity from APU02-observed DSDs is presented and is also in good agreement with observed reflectivities. Using the results of Fig. 13 (right) and the DFR estimates of Fig. 14b, the 
(a) Ku- and Ka-band reflectivity along a radial covering 1.0°–1.5° elevation from Fig. 12. Five consecutive rays were averaged to produce the results. (b) The DFR (Ku- minus Ka-band reflectivity) for the same observations.
Citation: Journal of Hydrometeorology 16, 5; 10.1175/JHM-D-15-0023.1
5. Ku-band QPE
The D3R Ku-band provides an effective means to quantitatively estimate precipitation rate and total rainfall accumulation, especially in light rain. It has been shown that various rainfall algorithms can be derived with respect to dual-polarization radar measurements via the DSD information (Bringi and Chandrasekar 2001). However, the choice of rainfall relations at Ku band gets complicated since 
Figure 15 compares the 5-min rainfall accumulation observations on 28 May 2015. The D3R Ku-band estimates at two locations during the same time period are considered: the location of the APU02 and APU04 disdrometers. The observation period on 28 May 2013 is of a widespread stratiform event. It can be seen from Fig. 15 that the radar measurements agree very well with the observations from the APUs and rain gauge measurements. For the 28 May 2013 sample event shown in Fig. 15, it is concluded that the NSE for the 5-min rainfall accumulations is 36.9%. The NSE statistic is computed based on observations at both the APU02 and APU04 locations.
The 5-min rainfall accumulation estimates from 28 May 2013 using D3R Ku-band, APU02 and APU04 disdrometers, and rain gauge collocated with disdrometers. The observations are of widespread stratiform rain.
Citation: Journal of Hydrometeorology 16, 5; 10.1175/JHM-D-15-0023.1
It should be mentioned that rain gauge observations are not used here for quantitative evaluation because the tipping-bucket gauges suffer from significant errors in light rain cases. The rainfall accumulation resolution of the gauge is 0.254 mm (0.01 in.). For light rain or high temporal sampling, the gauge resolution is not adequate and highlights the limited applicability of rainfall gauge data for use in light to moderate rainfall rates with high-temporal-resolution models. The rain gauge error during light rain at high temporal resolution is evident from review of Fig. 15.
Figure 16 shows example 5-min rainfall comparisons on 29 May 2013, characterizing a strong convective storm. It can be seen from Fig. 16 that the radar measurements agree with the observations from the APUs and rain gauge measurements very well for this convective case. The NSE for the 5-min rainfall accumulation is 49.4% using observations from both APU02 and APU04. Similarly, the NSE between radar rainfall accumulations and rain gauge measurements (collocated with APUs) is computed for this convective case as 45.6%.
The 5-min rainfall accumulation estimates from 29 May 2013 using D3R Ku-band, APU02 and APU04 disdrometers, and rain gauge collocated with disdrometers. The observations are of convective rain.
Citation: Journal of Hydrometeorology 16, 5; 10.1175/JHM-D-15-0023.1
The quantitative precipitation estimation results demonstrate D3R as an excellent platform for rain-rate estimation in light rain mainly because of the increased sensitivity of 
6. Summary
The D3R was deployed as part of a focused multi-instrument ground validation field experiment to support the GPM program. D3R is a well-calibrated, accurate instrument for microphysical investigation of meteorological phenomena. The collocated D3R and NPOL enabled coordinated multifrequency observations of precipitation at fine spatial and temporal resolution. The coordinated observations in IFloodS extend across multiple, diverse, and densely deployed ground instruments for in situ microphysical observations and measurement of rain rates. This experiment provided the conclusive end-to-end system validation for the D3R as a deployable radar system.
The D3R’s various methods of calibration and verification were presented for the IFloodS field campaign. All results show the D3R system operates in a state with excellent calibration, which was confirmed by self-consistent validation derived from microphysical relationships and comparison with a variety of instruments deployed in the D3R’s observational domain. Using simulation results from APU disdrometer DSD observations, attenuation-correction relations specific to the region were determined for the D3R. The attenuation-correction performance was similarly verified against other instruments. The D3R shows very good agreement with both ground-based disdrometers as well as the collocated NPOL.
From the D3R observations, examination of the sample microphysical characteristics of the IFloodS domain is presented. Dual-frequency ratio observations of ice and rain were shown for simultaneous Ku- and Ka-band observations. The 
The authors acknowledge the support of the GPM program, David Wolff and David Marks for NPOL support, and Mathew Schwaller and Walter Petersen for deployment and logistics support. In addition, the authors acknowledge all the participants of the IFloodS field campaign.
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