Ground Radar Observations Research Articles

A methodology to upscale IMD ground radar observations at the same resolution with TRMM PR reflectivity using ANN

This work presents a direct comparison of Ground Radar (GR) located in Delhi against Space Radar (SR) onboard the Tropical Rainfall Measuring Mission (TRMM). At first, the GR observations made by the India Meteorological Department (IMD) for the Indian summer monsoon months of 2013 over Delhi are matched with SR observations using a three-dimensional volume matching methodology (VMM). We found that the matched GR and SR reflectivity data points at common volumes have a high correlation coefficient with a high RMS error. Further, due to the beam broadening of the GR, the matched reflectivity has the exact horizontal resolution of 5 km but a different vertical resolution based on the distance from the GR site and the elevation angle. An upscaling methodology is proposed to obtain GR observations at an equal resolution (5km×0.25km) as that of SR. We observe a better agreement between GR and SR data following our approach. Further, the high vertical resolution GR reflectivity is “corrected” for bias and other sources of error using SR observations with artificial neural networks (ANN) based approach. Our results show that the ANN-corrected high-resolution GR reflectivity observations have a root mean squared (RMS) error of 3.98 dBZ, which reduced significantly from 16.35 dBZ.

Very Short-Term Rainfall Prediction Using Ground Radar Observations and Conditional Generative Adversarial Networks

Weather radars play an important role in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> rainfall monitoring owing to their ability to measure instantaneous rain rates and rainfall distributions. Currently, the Korea Meteorological Administration (KMA) provides instantaneous radar observation data and predictions based on the McGill algorithm for precipitation nowcasting by Lagrangian extrapolation (MAPLE) for up to 6 h, for short-term forecasting. This study presents a conditional generative adversarial network (CGAN)-based radar rainfall prediction method for very short-range weather forecasts from 10 min to 4 h. The CGAN-predicted model was trained and tested using KMA’s constant altitude plan position indicator (CAPPI) observation data. The qualitative comparison between the radar observation and the CGAN-predicted rain rates displayed high statistical scores, such as the probability of detection (POD) = 0.8442, false alarm ratio (FAR) = 0.2913, and critical success index (CSI) = 0.6268, in the case of a 1-h prediction for rainfall on September 5, 2019, 15:20 KST. This study demonstrates the capability of the CGAN model for short-term rainfall forecasting. Consequently, the CGAN-generated radar-based rainfall prediction could complement the KMA MAPLE system and be useful in various forecasting applications.

Intercomparison between IMD ground radar and TRMM PR observations using alignment methodology and artificial neural network

An inter-comparison of ground radar reflectivity with space-borne TRMM’s Precipitation Radar using alignment methodology has been presented. For this purpose, reflectivity data from Dual Polarization Ground Radar (GR) maintained by the India Meteorological Department (IMD) at the IMD Delhi site is utilized. IMD Delhi has collected radar data during Continental Tropical Convergence Zone (CTCZ) programme from 2011 to 2013. The present study utilizes monsoon data collected during 4 months, namely, June, July, August, and September (JJAS) from the year 2013. The GR observables are first converted from polar coordinates to Cartesian coordinates and then spatially aligned with TRMM PR data at a near-real-time to a common volume. It was found that in all the overpass cases, IMD’s GR reflectivity has a positive bias when compared with TRMM PR. A methodology is proposed to ‘correct’ the GR reflectivity data by considering TRMM PR data as ‘truth’ using a neural network-based approach. A supervised learning algorithm based on the back-propagation neural network is used for this purpose. Ground radar reflectivity is fed as input to the network, while the TRMM PR reflectivity is the target. The trained network is then tested for its performance against data which is not used as part of the training process. The present methodology demonstrates the match up of uncalibrated ground radar measured reflectivity and a well-calibrated space-borne radar.

Promoting Earth-Based Radar Astronomical Observations of the Moon.

Earth-based radar astronomical observations provide information on surface characteristics, orbits, and rotations for a wide variety of solar system objects. Based on compound radio telescopes, both the Chinese VLBI (Very Long Baseline Interferometry) network (CVN) and the Russian VLBI network (Quasar), in cooperation with the Chinese radar transmitters, we present the current ground radar astronomical observations of the moon. The spectrum of the reflected radio signals was obtained and the Doppler frequency shift in bi-static radar mode was measured. Radar albedo of the observed region and power ratios of the reflected signals with left- and right-hand circular polarizations were determined, allowing us to study the radar reflectivity and near-surface wavelength-scale roughness of the moon. Future developments on radar astronomy are also discussed in the paper.

Using ground radar overlaps to verify the retrieval of calibration bias estimates from spaceborne platforms

Abstract. Many institutions struggle to tap into the potential of their large archives of radar reflectivity: these data are often affected by miscalibration, yet the bias is typically unknown and temporally volatile. Still, relative calibration techniques can be used to correct the measurements a posteriori. For that purpose, the usage of spaceborne reflectivity observations from the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement (GPM) platforms has become increasingly popular: the calibration bias of a ground radar (GR) is estimated from its average reflectivity difference to the spaceborne radar (SR). Recently, Crisologo et al. (2018) introduced a formal procedure to enhance the reliability of such estimates: each match between SR and GR observations is assigned a quality index, and the calibration bias is inferred as a quality-weighted average of the differences between SR and GR. The relevance of quality was exemplified for the Subic S-band radar in the Philippines, which is greatly affected by partial beam blockage. The present study extends the concept of quality-weighted averaging by accounting for path-integrated attenuation (PIA) in addition to beam blockage. This extension becomes vital for radars that operate at the C or X band. Correspondingly, the study setup includes a C-band radar that substantially overlaps with the S-band radar. Based on the extended quality-weighting approach, we retrieve, for each of the two ground radars, a time series of calibration bias estimates from suitable SR overpasses. As a result of applying these estimates to correct the ground radar observations, the consistency between the ground radars in the region of overlap increased substantially. Furthermore, we investigated if the bias estimates can be interpolated in time, so that ground radar observations can be corrected even in the absence of prompt SR overpasses. We found that a moving average approach was most suitable for that purpose, although limited by the absence of explicit records of radar maintenance operations.

Time‐Lag Correlation Between Passive Microwave Measurements and Surface Precipitation and Its Impact on Precipitation Retrieval Evaluation

AbstractThis study investigates the correlation between the upwelling microwave brightness temperature measured by satellite radiometer and surface precipitation rate from ground radar observations at different time lags. Results show that brightness temperatures correlate more strongly with the lagged surface precipitation rate than the simultaneous surface precipitation rate. The lag time for snowfall ranges from 30 to 60 min. This time lag effect has an important influence when evaluating the satellite retrieval results relative to ground observations. For example, the falsely identified pixels can decrease by as much as 23.88% when considering a 30‐min lag time. Furthermore, the satellite‐retrieved snowfall rate shows much stronger correlation with the time‐lagged surface snowfall rate than the simultaneous snowfall rate in cold environments and for tall storms. This work implies that the time of the level‐2 swath‐retrieved snowfall rate needs to shift forward when incorporated into the level‐3 gridded products.

Cross-Validation of Observations between the GPM Dual-Frequency Precipitation Radar and Ground Based Dual-Polarization Radars

The Global Precipitation Measurement (GPM) mission Core Observatory is equipped with a dual-frequency precipitation radar (DPR) with capability of measuring precipitation simultaneously at frequencies of 13.6 GHz (Ku-band) and 35.5 GHz (Ka-band). Since the GPM-DPR cannot use information from polarization diversity, radar reflectivity factor is the most important parameter used in all retrievals. In this study, GPM’s observations of reflectivity at dual-frequency and instantaneous rainfall products are compared quantitatively against dual-polarization ground-based NEXRAD radars from the GPM Validation Network (VN). The ground radars, chosen for this study, are located in the southeastern plains of the U.S.A. with altitudes varying from 5 to 210 m. It is a challenging task to quantitatively compare measurements from space-based and ground-based platforms due to their difference in resolution volumes and viewing geometry. To perform comparisons on a point-to-point basis, radar observations need to be volume matched by averaging data in common volume or by re-sampling data to a common grid system. In this study, a 3-D volume matching technique first proposed by Bolen and Chandrasekar (2003) and later modified by Schwaller and Morris (2011) is applied to both radar data. DPR and ground radar observations and products are cross validated against each other with a large data set. Over 250 GPM overpass cases at 5 NEXRAD locations, starting from April 2014 to June 2018, have been considered. Analysis shows that DPR Ku- and Ka-Band reflectivities are well matched with ground radar with correlation coefficient as high as 0.9 for Ku-band and 0.85 for Ka-band. Ground radar calibration is also checked by observing variation in mean biases of reflectivity between DPR and GR over time. DPR rainfall products are also evaluated. Though DPR underestimates higher rainfall rates in convective cases, its overall performance is found to be satisfactory.

Stochastic generation of precipitation fraction at high resolution with a multiscale constraint from satellite observations

In this work, we propose a method to generate an ensemble of equiprobable fields of rain occurrence at high resolution (1°/16 and 30 min) using a satellite observational constraint. Satellite observations are used to constrain the spatio‐temporal variations of the precipitation fraction at various scales. Spatio‐temporal averages at scales coarser than 1° and 8 h are deterministically derived from the satellite observations. At finer scales, variations are partially stochastically generated by perturbation of wavelet coefficients obtained through a three‐dimensional discrete Haar wavelet orthogonal decomposition. The proposed method can be viewed either as stochastic weather generation or as stochastic downscaling with a multiscale observational constraint. The observational constraint used here is a high‐resolution precipitation index derived from infrared cloud top temperature. As a proof of concept, the method is used here to generate a 300‐member annual ensemble covering a 12,000 km2 area in Burkina Faso in West Africa, with a parametrization derived from ground radar observations. The stochastically generated fields aim at reproducing the multiscale statistical properties of the true precipitation field (as observed by a ground radar). The ensemble mean is an optimal – in terms of mean squared error – estimation of the true precipitation fraction, with the uncertainty quantified by the ensemble dispersion.

The Passive Microwave Neural Network Precipitation Retrieval (PNPR) Algorithm for the CONICAL Scanning Global Microwave Imager (GMI) Radiometer

This paper describes a new rainfall rate retrieval algorithm, developed within the EUMETSAT H SAF program, based on the Passive microwave Neural network Precipitation Retrieval approach (PNPR v3), designed to work with the conically scanning Global Precipitation Measurement (GPM) Microwave Imager (GMI). A new rain/no-rain classification scheme, also based on the NN approach, which provides different rainfall masks for different minimum thresholds and degree of reliability, is also described. The algorithm is trained on an extremely large observational database, built from GPM global observations between 2014 and 2016, where the NASA 2B-CMB (V04) rainfall rate product is used as reference. In order to assess the performance of PNPR v3 over the globe, an independent part of the observational database is used in a verification study. The good results found over all surface types (CC &gt; 0.90, ME &lt; −0.22 mm h−1, RMSE &lt; 2.75 mm h−1 and FSE% &lt; 100% for rainfall rates lower than 1 mm h−1 and around 30–50% for moderate to high rainfall rates), demonstrate the good outcome of the input selection procedure, as well as of the training and design phase of the neural network. For further verification, two case studies over Italy are also analysed and a good consistency of PNPR v3 retrievals with simultaneous ground radar observations and with the GMI GPROF V05 estimates is found. PNPR v3 is a global rainfall retrieval algorithm, able to optimally exploit the GMI multi-channel response to different surface types and precipitation structures, that provide global rainfall retrieval in a computationally very efficient way, making the product suitable for near-real time operational applications.

Spectral Signatures of Moisture–Convection Feedbacks over the Indian Ocean

Abstract Positive feedbacks between the cloud population and the environmental moisture field are central to theoretical expositions on the Madden–Julian oscillation (MJO). This study investigates the statistical incidence of positive moisture–convection feedbacks across multiple space and time scales over the tropical Indian Ocean. This work uses vertically integrated moisture budget terms from the ECMWF interim reanalysis [ERA-Interim (ERA-I)] in a framework proposed by Hannah et al. Positive moisture–convection feedbacks are primarily a low-frequency, low-wavenumber phenomenon with significant spectral signatures in the 32–48-day time scale. The efficacy of these feedbacks, however, is subject to horizontal moisture advection variations, whose relative importance varies with scale. Wave-filtered Tropical Rainfall Measuring Mission (TRMM) satellite precipitation is used to show that these moisture–convection feedbacks contribute more to moisture increases in the MJO than in other equatorial waves. A moving-window correlation analysis suggests that instances of moisture–convection feedbacks are more frequent in drier conditions, when column water vapor (CWV) is below its climatological mean value, with the implication that positive moisture–convection feedbacks shape the mean CWV field by moistening drier air columns, but that they are less effective in moistening already moist environments. Ground radar observations show that stratiform rain damps local CWV increases on short time scales (&amp;lt;2 days) and therefore precludes positive moisture–convection feedbacks in high-CWV environments. Vertical coherence structures from ERA-I confirm that relatively bottom-heavy cloud ensembles (i.e., peaks between 700 and 850 hPa) are more effective in inducing low-frequency positive moisture–convection feedbacks than ensembles with other vertical structures. Low-frequency horizontal advective drying damps moisture increases and is strongly coherent with upper-level rising motion.

Improving Overland Precipitation Retrieval with Brightness Temperature Temporal Variation

Abstract Current microwave precipitation retrieval algorithms utilize the instantaneous brightness temperature (TB) to estimate precipitation rate. This study presents a new idea that can be used to improve existing algorithms: using TB temporal variation from the microwave radiometer constellation. As a proof of concept, microwave observations from eight polar-orbiting satellites are utilized to derive . Results show that correlates more strongly with precipitation rate than the instantaneous TB. Particularly, the correlation with precipitation rate improved to −0.6 by using over the Rocky Mountains and north of 45°N, while the correlation is only −0.1 by using TB. The underlying reason is that largely eliminates the negative influence from snow-covered land, which frequently is misidentified as precipitation. Another reason is that is less affected by environmental variation (e.g., temperature, water vapor). Further analysis shows that the magnitude of the correlation between and precipitation rate is dependent on the satellite revisit frequency. Finally, it is shown that the retrieval results from are superior to that from TB, with the largest improvement in winter. Additionally, the retrieved precipitation rate over snow-covered regions by only using at 89 GHz agrees well with the ground radar observations, which opens new opportunities to retrieve precipitation in high latitudes for sensors with the highest frequency at ~89 GHz. This study implies that a geostationary microwave radiometer can significantly improve precipitation retrieval performance. It also highlights the importance of maintaining the current passive microwave satellite constellation.

A Prototype Precipitation Retrieval Algorithm over Land for ATMS

Abstract A prototype precipitation algorithm for the Advanced Technology Microwave Sounder (ATMS) was developed by using 3-yr coincident ground radar and ATMS observations over the continental United States (CONUS). Several major improvements to a previously published algorithm for the Special Sensor Microwave Imager/Sounder (SSMIS) include 1) considering the different footprint size of ATMS pixels, 2) calculating the uncertainty associated with the precipitation estimation, and 3) extending the algorithm to the 60°S–60°N region using only CONUS observations to construct the database. It is found that the retrieved and radar-observed rain rates agree well (e.g., correlation 0.66) and the one-standard-deviation error bar provides valuable retrieval uncertainty information. The geospatial pattern from the retrieved rain rate is largely consistent with that from radar observations. For the snowfall performance, the ATMS-retrieved results clearly capture the snowfall events over the Rocky Mountain region, while radar observations almost entirely miss the snowfall events over this region. Further, this algorithm is applied to the 60°S–60°N land region. The representative nature of rainfall over CONUS permitted the application of this algorithm to 60°S–60°N for rainfall retrieval, evidenced by the progress and retreat of the major rainbands. However, an artificially large snowfall rate is observed in several regions (e.g., Tibetan Plateau and Siberia) because of frequent false detection and overestimation caused by much colder brightness temperatures.

Convective and stratiform components of the precipitation‐moisture relationship

AbstractWe reexamine the well known empirical relationship between area‐averaged surface precipitation (P) and the column moisture content (r) using ground radar and satellite observations, with an emphasis on the convective and stratiform rainfall classifications. The rapid rise in P above critical r (rc) or the “pickup” is more pronounced for stratiform rainfall on hourly and less time scales while convective rainfall, displays only a weak pickup above rc. After partitioning the area‐averaged rainfall into conditional rain rates and rain area, we find that the nonlinearity in the P‐r curve can be almost entirely explained by the nonlinear increase in rain area, especially in stratiform regions. These findings have implications for the representation of organized convective cloud systems in global circulation models.

An object-based approach for verification of precipitation estimation

Verification has become an integral component in the development of precipitation algorithms used in satellite-based precipitation products and evaluation of numerical weather prediction models. A number of object-based verification methods have been developed to quantify the errors related to spatial patterns and placement of precipitation. In this study, an image processing technique known as watershed transformation, capable of detecting closely spaced, but separable precipitation areas, is adopted in the object-based approach. Several key attributes of the segmented precipitation objects are selected and interest values of those attributes are estimated based on the distance measurement of the estimated and reference images. An overall interest score is estimated from all the selected attributes and their interest values. The proposed object-based approach is implemented to validate satellite-based precipitation estimation against ground radar observations. The results indicate that the watershed segmentation technique is capable of separating the closely spaced local-scale precipitation areas. In addition, three verification metrics, including the object-based false alarm ratio, object-based missing ratio, and overall interest score, reveal the skill of precipitation estimates in depicting the spatial and geometric characteristics of the precipitation structure against observations.

Sensitivity of Distributed Hydrologic Simulations to Ground and Satellite Based Rainfall Products

In this study, seven precipitation products (rain gauges, NEXRAD MPE, PERSIANN 0.25 degree, PERSIANN CCS-3hr, PERSIANN CCS-1hr, TRMM 3B42V7, and CMORPH) were used to force a physically-based distributed hydrologic model. The model was driven by these products to simulate the hydrologic response of a 1232 km2 watershed in the Guadalupe River basin, Texas. Storm events in 2007 were used to analyze the precipitation products. Comparison with rain gauge observations reveals that there were significant biases in the satellite rainfall products and large variations in the estimated amounts. The radar basin average precipitation compared very well with the rain gauge product while the gauge-adjusted TRMM 3B42V7 precipitation compared best with observed rainfall among all satellite precipitation products. The NEXRAD MPE simulated streamflows matched the observed ones the best yielding the highest Nash-Sutcliffe Efficiency correlation coefficient values for both the July and August 2007 events. Simulations driven by TRMM 3B42V7 matched the observed streamflow better than other satellite products for both events. The PERSIANN coarse resolution product yielded better runoff results than the higher resolution product. The study reveals that satellite rainfall products are viable alternatives when rain gauge or ground radar observations are sparse or non-existent.

Theory of longitude librations of mercury based on the ground radar observations

On the basis of the analytic theory of longitude libration of Mercury (in the elliptical orbit) and the data of determination of values of the angular velocity of Mercury rotation obtained using high-precision complex method of ground radar tracking, the amplitude, phase, and period of free libration and amplitudes of five fundamental harmonics (annual, semi-annual, third annual, …) of forced librations in longitude of the planet are determined.

REFAME: Rain Estimation Using Forward-Adjusted Advection of Microwave Estimates

Abstract A new multiplatform multisensor satellite rainfall estimation technique is proposed in which sequences of Geostationary Earth Orbit infrared (GEO-IR) images are used to advect microwave (MW)-derived precipitation estimates along cloud motion streamlines and to further adjust the rainfall rates using local cloud classification. The main objective of the Rain Estimation using Forward-Adjusted advection of Microwave Estimates (REFAME) is to investigate whether inclusion of GEO-IR information can help to improve the advected MW precipitation rate as it gets farther in time from the previous MW overpass. The technique comprises three steps. The first step incorporates a 2D cloud tracking algorithm to capture cloud motion streamlines through successive IR images. The second step classifies cloudy pixels to a number of predefined clusters using brightness temperature (Tb) gradients between successive IR images along the cloud motion streamlines in combination with IR cloud-top brightness temperatures and textural features. A mean precipitation rate for each cluster is calculated using available MW-derived precipitation estimates. In the third step, the mean cluster precipitation rates are used to adjust MW precipitation intensities advected between available MW overpasses along cloud motion streamlines. REFAME is a flexible technique, potentially capable of incorporating diverse precipitation-relevant information, such as multispectral data. Evaluated over a range of spatial and temporal scales over the conterminous United States, the performance of the full REFAME algorithm compared favorably with products incorporating either no cloud tracking or no intensity adjustment. The observed improvements in root-mean-square error and especially in correlation coefficient between REFAME outputs and ground radar observations demonstrate that the new approach is effective in reducing the uncertainties and capturing the variation of precipitation intensity along cloud advection streamlines between MW sensor overpasses. An extended REFAME algorithm combines the adjusted advected MW rainfall rates with infrared-derived precipitation rates in an attempt to capture precipitation events initiating and decaying during the interval between two consecutive MW overpasses. Evaluation statistics indicate that the extended algorithm is effective to capture the life cycle of the convective precipitation, particularly for the interval between microwave overpasses in which precipitation starts or ends.

Comparison of Deep Underwater Measurements and Radar Observations of Rainfall

Deep-water acoustical measurements of rainfall are compared to high-resolution ground radar observations for the first time. The measurements of underwater ambient sound were made from a subsurface mooring near Methoni, Greece, in 2004. The acoustical measurements were at 60-, 200-, 1000-, and 2000-m depths. Simultaneous ground-based polarimetric -band radar observations were made over the acoustic mooring. Comparisons show acoustic detection of rain events and storm structure that are in agreement with the radar observations. Results from a comparison between the underwater sound pressure level at different depths and the observed radar reflectivities are presented.

A Statistical Approach to Ground Radar-Rainfall Estimation

Abstract This paper presents development of a statistical procedure for estimation of ensemble rainfall fields from a combination of ground radar observations and in situ rain gauge measurements. The uncertainty framework characterizes radar-rainfall estimation algorithm limitation accounting for rain gauge sampling uncertainty. The procedure is applied on a multicomponent rainfall estimation algorithm, which utilizes a rain-path attenuation correction technique, a power-law reflectivity-to-rainfall (Z–R) relationship, and a parameter to differentiate between convective (C) and stratiform (S) regimes in the Z–R conversion. Uncertainty is explicitly accounted for by evaluating the algorithm’s parameter set posterior probability density function (known as parameters’ equifinality) on the basis of the Generalized Likelihood Uncertainty Estimation (GLUE) framework. The study is facilitated by NASA’s C-band Doppler radar [named the Tropical Ocean Global Atmosphere (TOGA)] observations and four dense rain gauge clusters available from the Tropical Rainfall Measuring Mission (TRMM)-Large-Scale Biosphere–Atmosphere (LBA) experiment, conducted between January and February of 1999 in Southwest Amazon. Statistics are proposed for jointly evaluating the wideness of radar retrieval uncertainty limits [uncertainty ratio (UR)] and the percentage of observations that fall within those error bounds [exceedance ratio (ER)]. Results show that the parameter range selected in GLUE could characterize the radar-rainfall estimation uncertainty. Combined assessment of UR and ER for a varying range of parameters’ equifinality provides an objective basis for comparing rain retrieval algorithms and determining uncertainty bounds. Ensemble radar-rainfall fields derived on the basis of this procedure can be used to statistically assess satellite rain retrieval algorithms and derive ensemble hydrologic predictions driven by radar-rainfall input (e.g., runoff and soil moisture).

Quantitative estimation of Tropical Rainfall Mapping Mission precipitation radar signals from ground‐based polarimetric radar observations

The Tropical Rainfall Mapping Mission (TRMM) is the first mission dedicated to measuring rainfall from space using radar. The precipitation radar (PR) is one of several instruments aboard the TRMM satellite that is operating in a nearly circular orbit with nominal altitude of 350 km, inclination of 35°, and period of 91.5 min. The PR is a single‐frequency Ku‐band instrument that is designed to yield information about the vertical storm structure so as to gain insight into the intensity and distribution of rainfall. Attenuation effects on PR measurements, however, can be significant and as high as 10–15 dB. This can seriously impair the accuracy of rain rate retrieval algorithms derived from PR signal returns. Quantitative estimation of PR attenuation is made along the PR beam via ground‐based polarimetric observations to validate attenuation correction procedures used by the PR. The reflectivity (Zh) at horizontal polarization and specific differential phase (Kdp) are found along the beam from S‐band ground radar measurements, and theoretical modeling is used to determine the expected specific attenuation (k) along the space‐Earth path at Ku‐band frequency from these measurements. A theoretical k‐Kdp relationship is determined for rain when Kdp ≥ 0.5°/km, and a power law relationship, k = a Zhb, is determined for light rain and other types of hydrometers encountered along the path. After alignment and resolution volume matching is made between ground and PR measurements, the two‐way path‐integrated attenuation (PIA) is calculated along the PR propagation path by integrating the specific attenuation along the path. The PR reflectivity derived after removing the PIA is also compared against ground radar observations.