Polarimetric X-band Research Articles

On the extraction of the reservoirs’ waterline using polarimetric X-band SAR measurements: the case study of the San Giuliano reservoir, Italy

ABSTRACT According to the World Bank data catalogue, records of reservoirs and their associated dams are present summing up a capacity of km3 of water. They play a crucial role in providing potable and irrigation water and, therefore, it is of paramount interest to effectively monitor such critical infrastructures. An effective approach is based on satellite remote sensing and, in particular, on the Synthetic Aperture Radar (SAR). In this paper, we critically investigate the use of polarimetric SAR measurements for reservoirs’ waterline estimation. Measurements of the novel COSMO-SkyMed Second Generation (CSG) X-band quad-polarimetric SAR related to the San Giuliano reservoir, in the South of Italy, are used to carry out an electromagnetic analysis of the different polarimetric scattering returns. Experimental results show that the cross-polarized channel, as well as the inter-channel phase, are noisy and, therefore, uninformative when used to design coherent polarimetric waterline extraction methods. From an electromagnetic viewpoint, this is due to the peculiarities of the reservoirs that call for low surface roughness and negligible wave pattern that, at once, result in a joint combination of un-tilted Bragg scattering and specular reflection. This implies that a low co-polarized backscatter and a cross-polarized signal largely below the system noise floor are to be expected. As a consequence, waterline extraction approaches that do not exploit the inter-channel phase, the so-called incoherent approaches, are shown to outperform the coherent ones.

Higher order spectral analysis of weather signals

Higher Order Spectral Analysis (HOSA) is very useful technic for analysing the statistical relationships between spectral components and their distribution. HOSA can be defined in terms of bispectrum as a third order spectrum. One of the main reasons to consider HOSA on weather signals is that to characterise the Doppler spectrum obtained through the backscattered echoes which are generally skewed and having multiple peaks. The analysis also brings out any deviations from gaussianity and the performance under noisy environment. This will help in identifying and detecting the signals and their by better estimation of moments for further processing The bispectrum estimation technique has been applied to a complex time series data derived from a Polarimetric X-band Doppler Weather Radar (DWR). The results of this analysis are compared with the conventional techniques like Fourier method and pulse pair technique. It is observed that through bispectrum approach the gaussian noise components are significantly removed from the back scattered signals and improves the detectability of the weather signals under noisy condition.

Evaluation of modelled summertime convective storms using polarimetric radar observations

Abstract. Ensemble simulations with the Terrestrial Systems Modelling Platform (TSMP) covering northwestern Germany are evaluated for three summertime convective storms using polarimetric X-band radar measurements. Using a forward operator, the simulated microphysical processes have been evaluated in radar observation space. Observed differential reflectivity (ZDR) columns, which are proxies for updrafts, and multi-variate fingerprints for size sorting and aggregation processes are captured by the model, but co-located specific differential phase (KDP) columns in observations are not reproduced in the simulations. Also, the simulated ZDR columns, generated by only small-sized supercooled drops, show smaller absolute ZDR values and a reduced width compared to their observational counterparts, which points to deficiencies in the cloud microphysics scheme as well as the forward operator, which does not have explicit information of water content of ice hydrometeors. Above the melting layer, the simulated polarimetric variables also show weak variability, which can be at least partly explained by the reduced particle diversity in the model and the inability of the T-matrix method to reproduce the polarimetric signatures of snow and graupel; i.e. current forward operators need to be further developed to fully exploit radar data for model evaluation and improvement. Below the melting level, the model captures the observed increase in reflectivity, ZDR and specific differential phase (KDP) towards the ground. The contoured frequency altitude diagrams (CFADs) of the synthetic and observed polarimetric variables were also used to evaluate the model microphysical processes statistically. In general, CFADs of the cross-correlation coefficient (ρhv) were poorly simulated. CFADs of ZDR and KDP were similar but the model exhibits a relatively narrow distribution above the melting layer for both, and a bimodal distribution for ZDR below the melting layer, indicating either differences in the mechanism of precipitation formation or errors in forward operator which uses a functional form of drop size distribution. In general, the model was found to underestimate the convective area fraction, high reflectivities, and the width/magnitude of ZDR columns, all leading to an underestimation of the frequency distribution for high precipitation values.

Analysis of Two Convective Storms Using Polarimetric X-Band Radar and Satellite Data

We analyzed two convective storms that passed over or near the Milešovka meteorological observatory. The observatory is located at the top of a hill and has been recently equipped with a Doppler polarimetric X-band radar FURUNO WR2120 for cloud investigations. Our analysis was based mainly on Doppler polarimetric radar data measured in vertical cross-sections (RHI-Range-Height Indicator). Radar data was also used for classifying hydrometeors by a newly developed XCLASS (X-band radar CLASSification) algorithm. We also used rapid scan data measured by the geostationary satellite Meteosat Second Generation to validate radar measurements at the upper parts of storms. Although an attenuation correction was applied to the reflectivity and differential reflectivity measurements, the attenuation typical of X-band radars was noticeable. It was mainly manifested in the differential reflectivity, co-polar correlation coefficient and specific differential phase. Nevertheless, radar measurements can be used to analyze the internal cloud structure of severe convective storms. The XCLASS classification was developed by major innovation of a previously published algorithm. The XCLASS algorithm identifies seven types of hydrometeors: light rain, rain, wet snow, dry snow, ice, graupel, and hail. It uses measured horizontal and vertical radar reflectivity, specific differential phase, co-polar correlation coefficient, and temperature, and applies fuzzy logic to determine the type of hydrometeor. The new algorithm practically eliminates unrealistic results around and below the melting layer provided by the original algorithm. It identifies wet snow in more cases, and areas with individual hydrometeors have more realistic shapes compared to the original algorithm. The XCLASS algorithm shows reasonable results for the classification of hydrometeors and can be used to study the structure of convective storms.

Approaches for Road Surface Roughness Estimation Using Airborne Polarimetric SAR

The road surface roughness is an important parameter that determines the quality of a road network. It has a direct influence on the grip and skid resistance of the vehicles. For this reason, this parameter has to be periodically monitored to keep track of its changes. Nowadays, road surface roughness is measured by driving measurement vehicles equipped with laser scanners all over the country. But, this approach is very costly, labor-intensive, and time-consuming. This article is done to evaluate the potential of high-resolution airborne polarimetric synthetic aperture radar (SAR) to remotely estimate the road surface roughness on a wide scale. Different SAR backscatter-based semi-empirical models and SAR polarimetry-based models for surface roughness estimation are implemented in this article. Also, a new semi-empirical model is proposed in this article, which is trained specifically for the road surface roughness estimation. Additive noise subtraction, upper sigma nought threshold masking, and lower signal-to-noise ratio threshold masking techniques were implemented in this article to improve the reliability of road surface roughness estimation. The feasibility of this approach is tested using fully polarimetric X-band datasets acquired with DLR's airborne radar sensor F-SAR. The surface roughness results estimated using these airborne SAR datasets show good agreement with the ground truth surface roughness values and the results are discussed in this article.

Multisensor Characterization of the Incandescent Jet Region of Lava Fountain-Fed Tephra Plumes

Explosive basaltic eruptions eject a great amount of pyroclastic material into the atmosphere, forming columns rising to several kilometers above the eruptive vent and causing significant disruption to both proximal and distal communities. Here, we analyze data, collected by an X-band polarimetric weather radar and an L-band Doppler fixed-pointing radar, as well as by a thermal infrared (TIR) camera, in relation to lava fountain-fed tephra plumes at the Etna volcano in Italy. We clearly identify a jet, mainly composed of lapilli and bombs mixed with hot gas in the first portion of these volcanic plumes and here called the incandescent jet region (IJR). At Etna and due to the TIR camera configuration, the IJR typically corresponds to the region that saturates thermal images. We find that the IJR is correlated to a unique signature in polarimetric radar data as it represents a zone with a relatively high reflectivity and a low copolar correlation coefficient. Analyzing five recent Etna eruptions occurring in 2013 and 2015, we propose a jet region radar retrieval algorithm (JR3A), based on a decision-tree combining polarimetric X-band observables with L-band radar constraints, aiming at the IJR height detection during the explosive eruptions. The height of the IJR does not exactly correspond to the height of the lava fountain due to a different altitude, potentially reached by lapilli and blocks detected by the X-band weather radar. Nonetheless, it can be used as a proxy of the lava fountain height in order to obtain a first approximation of the exit velocity of the mixture and, therefore, of the mass eruption rate. The comparisons between the JR3A estimates of IJR heights with the corresponding values recovered from TIR imagery, show a fairly good agreement with differences of less than 20% in clear air conditions, whereas the difference between JR3A estimates of IJR height values and those derived from L-band radar data only are greater than 40%. The advantage of using an X-band polarimetric weather radar in an early warning system is that it provides information in all weather conditions. As a matter of fact, we show that JR3A retrievals can also be obtained in cloudy conditions when the TIR camera data cannot be processed.

Retrieval of snow precipitation rate from polarimetric X-band radar measurements in Southern Italy Apennine mountains

Abstract Recently, the interest on snowfall remote sensing and quantitative precipitation estimation is becoming a popular topic by both the scientific and operational communities. As a matter of fact, snow plays a key role in the hydrological cycle and Earth energy budget and clearly represents a meteorological hazard that can seriously compromise human activities and properties. In this study, we used a dual-polarization X-band weather radar to quantify the near-surface liquid equivalent snowfall rate, proposing a new parameterization based on the use of radar reflectivity factor and specific differential phase shift. This effort adds to several recent works, mainly focused on S-band weather radar systems, demonstrating that the use of the radar specific differential phase shift (Kdp) is able to enhance the estimation precision with respect to the more customary approaches making use of radar reflectivity factor alone. To demonstrate this concept also at X-band, some case studies were collected from December 2018 to May 2019 in the Southern Apennine Mountains in the area of Naples (Italy). They were used to compare the proposed radar based liquid equivalent snowfall rate estimations, based on Z and Kdp, with reference laser-optical disdrometer time series collected in the close reference site of Montevergine observatory. Findings show that also at X band the use of Kdp produces a better score between the radar-derived liquid equivalent snowfall rate and the reference one from the disdrometer.

Polarimetric Radar Variables in the Layers of Melting and Dendritic Growth at X Band—Implications for a Nowcasting Strategy in Stratiform Rain

AbstractTime series of quasi–vertical profiles (QVPs) from 52 stratiform precipitation events observed with the polarimetric X-band radar in Bonn, Germany (BoXPol), between 2013 and 2016 have been statistically analyzed to infer microphysical processes shaping the dendritic-growth-layer (DGL) and melting-layer (ML) signatures including surface rainfall. Specific differential phase KDP in the ML shows an average correlation of 0.65 with surface rainfall for these cases. Radar reflectivity decreases below the ML by about 2 dB on average while differential reflectivity ZDR is hardly affected, which suggests rain evaporation as the dominating effect. Estimated ice water content or snow water equivalent precipitation rate S in the DGL is correlated with surface rain rates with lead times of 30 min and longer, which opens a pathway for radar-based nowcasting of stratiform precipitation tendencies. Trajectories of snow generated aloft down to the surface are constructed from wind profiles derived both from the nearest radiosounding and radar-based velocity azimuth displays (VAD) to narrow down the location at which the DGL-generated snow reaches the surface as rain. The lagged correlation analysis between KDP in the DGL and reflectivity ZH at that location demonstrates the superiority of the VAD information. Correlation coefficients up to 0.80 with lead times up to 120 min provide a proof of concept for future nowcasting applications that are based on DGL monitoring. Statistical relations found in our QVP dataset provide a database for estimating intrinsic polarimetric variables from the usual azimuth and elevation scans within and in the vicinity of the ML.

Improved Estimation of the Specific Attenuation and Backscatter Differential Phase over Short Rain Paths

AbstractIn radar polarimetry, the differential phase consists of the propagation differential phase and the backscatter differential phase . While is commonly used for attenuation correction (i.e., estimation of the specific attenuation A and specific differential phase ), recent studies have demonstrated that can provide information concerning the dominant size of raindrops. However, the estimation of and is not straightforward given their coupled nature and the noisy behavior of , especially over short paths. In this work, the impacts of estimating on the estimation of A over short paths, using the extended version of the ZPHI method, are examined. Special attention is given to the optimization of the parameter α that connects and A. In addition, an improved technique is proposed to compute from and in rain. For these purposes, diverse storm events observed by a polarimetric X-band radar in the Netherlands are used. Statistical analysis based on the minimum errors associated with the optimization of α and the consistency between and A showed that more accurate and stable α and A are obtained if is estimated at range resolution, which is not possible by conventional range filtering techniques. Accurate estimates were able to depict the spatial variability of dominant raindrop size in the observed storms. By following the presented study, the ZPHI method and its variations can be employed without the need for considering long paths, leading to localized and accurate estimation of A and .

Rapid-Scan Radar Observations of an Oklahoma Tornadic Hailstorm Producing Giant Hail

AbstractRapid-scan radar observations of a supercell that produced near-record size hail in Oklahoma are examined. Data from the National Weather Radar Testbed Phased Array Radar (PAR) in Norman, Oklahoma, are used to study the overall character and evolution of the storm. Data from the nearby polarimetric KOUN WSR-88D and rapid-scanning X-band polarimetric (RaXPol) mobile radar are used to study the evolution of low- to midaltitude dual-polarization parameters above two locations where giant hailstones up to 16 cm in diameter were observed. The PAR observation of the supercell’s maximum storm-top divergent outflow is similar to the strongest previously documented value. The storm’s mesocyclone rotational velocity at midaltitudes reached a maximum that is more than double the median value for similar observations from other storms producing giant hail. For the two storm-relative areas where giant hail was observed, noteworthy findings include 1) the giant hail occurred outside the main precipitation core, in areas with low-altitude reflectivities of 40–50 dBZ; 2) the giant hail was associated with dual-polarization signatures consistent with past observations of large hail at 10-cm wavelength, namely, low ZDR, low ρHV, and low KDP; 3) the giant hail fell along both the northeast and southwest edges of the primary updraft at ranges of 6–10 km from the updraft center; and 4) with the exception of one isolated report, the giant hail fell to the northeast and northwest of the large tornado and the parent mesocyclone.

Characterisation of the melting layer variability in an Alpine valley based on polarimetric X-band radar scans

Abstract. The melting layer designates the transition region from solid to liquid precipitation, and is a typical feature of the vertical structure of stratiform precipitation. As it is characterised by a well-known signature in polarimetric radar variables, it can be identified by automatic detection algorithms. Though often assumed to be uniform in space and time for applications such as vertical profile correction, the spatial variability of the melting layer remains poorly documented. This work aims to characterise and quantify the spatial and temporal variability of the melting layer using a method based on the Fourier transform, which is applied to high-resolution X-band polarimetric radar data from two measurement campaigns in Switzerland. It is first demonstrated that the proposed method can accurately and concisely describe the spatial variability of the melting layer and may therefore be used as a tool for comparison. The method is then used to characterise the melting layer variability in summer precipitation on the relatively flat Swiss Plateau and in winter precipitation in a large inner Alpine valley (the Rhone valley in the Swiss Alps). Results indicate a higher contribution of smaller spatial scales to the total melting layer variability in the case of the Alpine environment. The same method is also applied to data from vertical scans in order to study the temporal variability of the melting layer. The variability in space and time is then compared to investigate the spatio-temporal coherence of the melting layer variability in the two study areas, which was found to be more consistent with the assumption of pure advection for the case of the plateau.

Advancing Precipitation Estimation and Streamflow Simulations in Complex Terrain with X-Band Dual-Polarization Radar Observations

In mountain basins, the use of long-range operational weather radars is often associated with poor quantitative precipitation estimation due to a number of challenges posed by the complexity of terrain. As a result, the applicability of radar-based precipitation estimates for hydrological studies is often limited over areas that are in close proximity to the radar. This study evaluates the advantages of using X-band polarimetric (XPOL) radar as a means to fill the coverage gaps and improve complex terrain precipitation estimation and associated hydrological applications based on a field experiment conducted in an area of Northeast Italian Alps characterized by large elevation differences. The corresponding rainfall estimates from two operational C-band weather radar observations are compared to the XPOL rainfall estimates for a near-range (10–35 km) mountainous basin (64 km2). In situ rainfall observations from a dense rain gauge network and two disdrometers (a 2D-video and a Parsivel) are used for ground validation of the radar-rainfall estimates. Ten storm events over a period of two years are used to explore the differences between the locally deployed XPOL vs. longer-range operational radar-rainfall error statistics. Hourly aggregate rainfall estimates by XPOL, corrected for rain-path attenuation and vertical reflectivity profile, exhibited correlations between 0.70 and 0.99 against reference rainfall data and 21% mean relative error for rainfall rates above 0.2 mm h−1. The corresponding metrics from the operational radar-network rainfall products gave a strong underestimation (50–70%) and lower correlations (0.48–0.81). For the two highest flow-peak events, a hydrological model (Kinematic Local Excess Model) was forced with the different radar-rainfall estimations and in situ rain gauge precipitation data at hourly resolution, exhibiting close agreement between the XPOL and gauge-based driven runoff simulations, while the simulations obtained by the operational radar rainfall products resulted in a greatly underestimated runoff response.

Critical scales to explain urban hydrological response: an application in Cranbrook, London

Abstract. Rainfall variability in space and time, in relation to catchment characteristics and model complexity, plays an important role in explaining the sensitivity of hydrological response in urban areas. In this work we present a new approach to classify rainfall variability in space and time and we use this classification to investigate rainfall aggregation effects on urban hydrological response. Nine rainfall events, measured with a dual polarimetric X-Band radar instrument at the CAESAR site (Cabauw Experimental Site for Atmospheric Research, NL), were aggregated in time and space in order to obtain different resolution combinations. The aim of this work was to investigate the influence that rainfall and catchment scales have on hydrological response in urban areas. Three dimensionless scaling factors were introduced to investigate the interactions between rainfall and catchment scale and rainfall input resolution in relation to the performance of the model. Results showed that (1) rainfall classification based on cluster identification well represents the storm core, (2) aggregation effects are stronger for rainfall than flow, (3) model complexity does not have a strong influence compared to catchment and rainfall scales for this case study, and (4) scaling factors allow the adequate rainfall resolution to be selected to obtain a given level of accuracy in the calculation of hydrological response.

Multifractal Comparison of Reflectivity and Polarimetric Rainfall Data from C- and X-Band Radars and Respective Hydrological Responses of a Complex Catchment Model

This paper presents a comparison between C-band and X-band radar data over an instrumented and regulated catchment of the Paris region. We study the benefits of polarimetry and the respective hydrological impacts with the help of rain gauge and flow measurements using a semi-distributed hydrological model. Both types of radar confirm the high spatial variability of the rainfall down to their space resolution (1 km and 250 m, respectively). Therefore, X-band radar data underscore the limitations of simulations using a semi-distributed model with sub-catchments of an average size of 2 km. The use of the polarimetric capacity of the Météo-France C-band radar was limited to corrections of the horizontal reflectivity, and its rainfall estimates are adjusted with the help of a rain gauge network. On the contrary, no absolute calibration and scanning optimisation were performed for the polarimetric X-band radar of the Ecole des Ponts ParisTech (hereafter referred to as the ENPC X-band radar). In spite of this and the fact that the catchment is much closer to the C-band radar than to the X-band radar (average distance of 15 km vs. 35 km, respectively), the latter seems to perform at least as well as the former, but with a higher spatial resolution. This was best highlighted with the help of a multifractal analysis, which also shows that the X-band radar was able to pick up a few rainfall extremes that were smoothed out by the C-band radar.

Adaptive and High-Resolution Estimation of Specific Differential Phase for Polarimetric X-Band Weather Radars

ABSTRACTOne of the most beneficial polarimetric variables may be the specific differential phase KDP because of its independence from power attenuation and radar miscalibration. However, conventional KDP estimation requires a substantial amount of range smoothing as a result of the noisy characteristic of the measured differential phase ΨDP. In addition, the backscatter differential phase δhv component of ΨDP, significant at C- and X-band frequency, may lead to inaccurate KDP estimates. In this work, an adaptive approach is proposed to obtain accurate KDP estimates in rain from noisy ΨDP, whose δhv is of significance, at range resolution scales. This approach uses existing relations between polarimetric variables in rain to filter δhv from ΨDP while maintaining its spatial variability. In addition, the standard deviation of the proposed KDP estimator is mathematically formulated for quality control. The adaptive approach is assessed using four storm events, associated with light and heavy rain, observed by a polarimetric X-band weather radar in the Netherlands. It is shown that this approach is able to retain the spatial variability of the storms at scales of the range resolution. Moreover, the performance of the proposed approach is compared with two different methods. The results of this comparison show that the proposed approach outperforms the other two methods in terms of the correlation between KDP and reflectivity, and KDP standard deviation reduction.

Theoretical Characterization of X-Band Multiincidence Angle and Multipolarimetric SAR Data From Rice Paddies at Late Vegetative Stage

Various polarimetric decomposition techniques have been proposed and demonstrated for more than 20 years. However, they still have never been operational applications because of insufficient validations. The true composition ratio of scattering mechanisms within a radar backscatter has to be known. To achieve this, a novel comprehensive approach to accurately identify the contribution of each scattering mechanism by a multiincidence angle and multipolarimetric (MIMP) synthetic aperture radar (SAR) observation combined with a theoretical model simulation is introduced. Rice paddies in Niigata City in Japan having a simple vegetation structure without topography were observed by X-band polarimetric and interferometric SAR 2 (Pi-SAR2), by gradually varying the flight path in terms of incidence angle. In addition to the MIMP SAR observation, a dominant scattering mechanism is reliably isolated through the theoretical characterization of the data by a discrete scatterer model. In the case of the rice paddies at a late vegetative stage, a dominant scattering on HH gradually varies from the double-bounce scattering to the volume scattering of rice grains in terms of incidence angle, whereas VV is affected by various volume scatterings of grains, leaves, and stems at the small, medium, and large incidence angles, respectively. HV is simply affected by the volume scattering of grains at the small incidence angle, and then it gradually switches to the volume scattering of the stalks. This brand new approach by the MIMP SAR observation with the theoretical modeling for this specific stage could take forward the polarimetric decomposition studies.

Initial Results of a New Composite-Weighted Algorithm for Dual-Polarized X-Band Rainfall Estimation

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(Ah, Zdr) rainfall estimator for X band, where Ah is the specific attenuation for horizontal polarization and Zdr 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.

Radar Observation of Evaporation and Implications for Quantitative Precipitation and Cooling Rate Estimation

AbstractThis study analyzes radar observations of evaporation in rain and investigates its impact on surface rainfall and atmospheric cooling rates. A 1D model is used to examine the impact of raindrop evaporation on the evolution of the initial raindrop size distribution (DSD), the resulting reflectivity (Z), and differential reflectivity (ZDR) and surface rain rates. Raindrop evaporation leads to a decrease of Z and an increase of ZDR toward the surface because of the depletion of small raindrops that evaporate first and thus enhance the mean raindrop size. The latter effect, however, can be reduced because of the increasing temperature toward the surface and may even lead to a decrease of ZDR toward the surface. Two events with significant rain evaporation, observed simultaneously by a polarimetric X-band radar and a K-band Micro Rain Radar (MRR), offer quite detailed insight into the evaporation process. During the first event, which exhibits an initial ZDR > 1.5 dB in the upper rain column, raindrops undergo relatively weak evaporation as deduced from the decrease of the small raindrop fraction observed by the MRR. The second event is characterized by a lower initial ZDR < 0.5 dB with all raindrops evaporating before reaching the ground. A retrieval scheme for estimating the evaporation-related cooling rate and surface precipitation from polarimetric radar observations below the bright band is derived based on MRR observations. The algorithm is then used to simulate polarimetric X-band radar observations, which might mitigate uncertainties in the surface rainfall retrievals due to evaporation at far distances from the radars and in the case of beam blocking.

Precipitation and microphysical processes observed by three polarimetric X-band radars and ground-based instrumentation during HOPE

Abstract. This study presents a first analysis of precipitation and related microphysical processes observed by three polarimetric X-band Doppler radars (BoXPol, JuXPol and KiXPol) in conjunction with a ground-based network of disdrometers, rain gauges and vertically pointing micro rain radars (MRRs) during the High Definition Clouds and Precipitation for advancing Climate Prediction (HD(CP)2) Observational Prototype Experiment (HOPE) during April and May 2013 in Germany. While JuXPol and KiXPol were continuously observing the central HOPE area near Forschungszentrum Jülich at a close distance, BoXPol observed the area from a distance of about 48.5 km. MRRs were deployed in the central HOPE area and one MRR close to BoXPol in Bonn, Germany. Seven disdrometers and three rain gauges providing point precipitation observations were deployed at five locations within a 5 km × 5 km region, while three other disdrometers were collocated with the MRR in Bonn. The daily rainfall accumulation at each rain gauge/disdrometer location estimated from the three X-band polarimetric radar observations showed very good agreement. Accompanying microphysical processes during the evolution of precipitation systems were well captured by the polarimetric X-band radars and corroborated by independent observations from the other ground-based instruments.

Use of X-Band Differential Reflectivity Measurements to Study Shallow Arctic Mixed-Phase Clouds

AbstractMicrophysical processes in shallow Arctic precipitation clouds are illustrated using measurements of differential reflectivity ZDR from the U.S. Department of Energy Atmospheric Radiation Measurement Program polarimetric X-band radar deployed in Barrow, Alaska. X-band hemispheric range height indicator scans used in conjunction with Ka-band radar and lidar measurements revealed prolonged periods dominated by vapor depositional, riming, and/or aggregation growth. In each case, ice precipitation fell through at least one liquid-cloud layer in a seeder–feeder situation before reaching the surface. A long period of sustained low radar reflectivity ZH (<0–5 dBZ) and high ZDR (6–7.5 dB) throughout the depth of the cloud and subcloud layer, coinciding with observations of large pristine dendrites at the surface, suggests vapor depositional growth of large dendrites at low number concentrations. In contrast, ZDR values decreased to 2–3 dB in the mean profile when surface precipitation was dominated by aggregates or rimed dendrites. Small but consistent differences in zenith Ka-band radar Doppler velocity and lidar depolarization measurements were found between aggregation- and riming-dominated periods. The clean Arctic environment can enhance ZDR signals relative to more complex midlatitude cases, producing higher values.