C-band Weather Radar Research Articles

An Evaluation of IMERG and ERA5 Quantitative Precipitation Estimates over the Southern Ocean Using Shipborne Observations

Abstract Recent voyages of the Australian R/V Investigator across the remote Southern Ocean have provided unprecedented observations of precipitation made with both an Ocean Rainfall and Ice-Phase Precipitation Measurement Network (OceanRAIN) maritime disdrometer and a dual-polarization C-band weather radar (OceanPOL). This present study employs these observations to evaluate the Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) and the fifth major global reanalysis produced by ECMWF (ERA5) precipitation products. Working at a resolution of 60 min and 0.25° (∼25 km), light rain and drizzle are most frequently observed across the region. The IMERG product overestimated precipitation intensity when evaluated against the OceanRAIN but captured the frequency of occurrence well. Looking at the synoptic/process scale, IMERG was found to be the least accurate (overestimated intensity) under warm-frontal and high-latitude cyclone conditions, where multilayer clouds were commonly present. Under postfrontal conditions, IMERG underestimated the precipitation frequency. In comparison, ERA5’s skill was more consistent across various synoptic conditions, except for high pressure conditions where the precipitation frequency (intensity) was highly overestimated (underestimated). Using the OceanPOL radar, an area-to-area analysis (fractional skill score) finds that ERA5 has greater skill than IMERG. There is little agreement in the phase classification between the OceanRAIN disdrometer, IMERG, and ERA5. The comparisons are complicated by the various assumptions for phase classification in the different datasets. Significance Statement Our best quantitative estimates of precipitation over the remote, pristine Southern Ocean (SO) continue to suffer from a high degree of uncertainty, with large differences present among satellite-based and reanalysis products. New instrumentation on the R/V Investigator, specifically a dual-polarization C-band weather radar (OceanPOL) and a maritime disdrometer (OceanRAIN), provide unprecedented high-quality observations of precipitation across the SO that will aid in improving precipitation estimates in this region. We use these observations to evaluate the IMERG and ERA5 precipitation products. We find that, in general, IMERG overestimated precipitation intensity, but captured the frequency of occurrence well. In comparison, ERA5 was found to overestimate the frequency of precipitation. Using the OceanPOL radar, an area-to-area analysis finds that ERA5 has greater skill than IMERG.

Estimation of extreme precipitation events in Estonia and Italy using dual-polarization weather radar quantitative precipitation estimations

Abstract. Evaluating extreme rainfall for a certain location is commonly considered when designing stormwater management systems. Rain gauge data are widely used to estimate rainfall intensities for a given return period. However, the poor spatial and temporal resolution of operational gauges is the main limiting factor. Several studies have used rainfall estimates based on weather radar horizontal reflectivity (Zh), but they come with a great caveat: while proven reliable for low or moderate rainfall rates, they are subject to major errors in extreme rainfall and convective cases. It is widely known that C-band weather radar can underestimate precipitation intensity due to signal attenuation or overestimate it due to hail and clutter contamination. From the late 1990s, dual-polarization weather radar started to become operational in the national surveillance radar network in Europe, providing innovative quantitative precipitation estimation (QPE) based on polarimetric variables. This study circumvents Zh shortcomings by using specific differential-phase (Kdp) data from operational dual-polarization C-band weather radars. The rain intensity estimates based on a specific differential-phase data are immune to attenuation and less affected by hail contamination. In this study, for the first time, QPEs based on polarimetric observations by operational C-band weather radars and without any rain gauge adjustments are analyzed. The purpose is to estimate return periods for 1 h rainfall total computed from polarimetric weather radar data using non-adjusted QPEs based on R(Zh,Kdp) data and to compare the results with those derived using R(Zh) and rain gauge data. Only the warm period during the year is considered here, as most of the extreme precipitation events for such a duration occur for both places studied (Italy and Estonia) at this time. Limiting the dataset to warm periods also allows us to use the radar-based rainfall quantitative precipitation estimations, which are more reliable than the snowfall ones. Data from operational dual polarimetric C-band weather radar sites are used from both Italy and Estonia. Given climatologically homogeneous regions, this study demonstrates that polarimetric weather radar observations can provide reliable QPEs compared to single-polarization estimates with respect to rain gauges and that they can provide a reliable estimation of return periods of 1 h rainfall total, even for relatively short time series.

Insights in Hailstorm Dynamics through Polarimetric High-Resolution X-Band and Operational C-Band Radar: A Case Study for Vienna, Austria

Abstract Data from a dual-polarized, solid-state X-band radar and an operational C-band weather radar are used for high-resolution analyses of two hailstorms in the Vienna, Austria, region. The combination of both radars provides rapid-update (1 min) polarimetric data paired with wind field data of a dual-Doppler analysis. This is the first time that such an advanced setup is used to examine severe storm dynamics at the eastern Alpine fringe, where the influence of local topography is particularly challenging for thunderstorm prediction. We investigate two storms transitioning from the pre-Alps into the Vienna basin with different characteristics: 1) A rapidly evolving multicell storm producing large hail (5 cm), with observations of an intense ZDR column preceding hail formation and the rapid development of multiple pulses of hail; and 2) a cold pool–driven squall line with small hail, for which we find that the updraft location inhibited the formation of larger hailstones. For both cases, we analyzed the evolution of different ZDR column metrics as well as updraft speed and size and found that (i) the 90th percentile of ZDR within the ZDR column was highest for the cell later producing large hail, (ii) the peak 90th percentile of ZDR preceded large hailfall by 20 min and highest updraft size and speed by 10 min, and (iii) sudden drops of the 90th percentile of ZH within the ZDR column indicated imminent hailfall. Significance Statement Thunderstorm evolution on the transition from complex terrain into the Vienna basin in northeastern Austria varies strongly. In some instances, thunderstorm cells intensify once they reach flat terrain, while in most cases there is a weakening tendency. To improve our process understanding and short-term forecasting methods, we analyze two representative cases of hail-bearing storms transitioning into the Vienna basin. We mainly build our study on data from a new, cost-efficient weather radar, complemented by an operational radar, lightning observations, and ground reports. Our results show which radar variables could be well suited for early detection of intensification, and how they relate to thunderstorm updraft speeds and lightning activity.

Two-Dimensional Spectral Analysis Filter for Removal of LFM Radar Interference in Spaceborne SAR Imagery

Radio spectrum bands allocated to spaceborne synthetic aperture radar (SAR) imagery are shared by multiple missions. In practical radio spectrum environments, these bands are also used by some ground radars, e.g., C-band weather radar. Due to this fact, radio frequency interference (RFI) may occur for a spaceborne SAR when its received signals contain the transmitted waveforms from another SAR or radar operating at the same frequency band. This particular class of RFI is usually linear-frequency-modulation (LFM) signals, which can cause bright radiometric artifacts in focused SAR images. Most existing signal processing approaches designed for addressing this problem belong to the class of preprocessing methods, which removes RFI in level-0 raw radar data before SAR focusing. In this article, we propose a postprocessing kernel—2-D SPECtral ANalysis (2-D SPECAN) filter, for removing the class of LFM RFI in level-1 SLC images. The filtering consists of three main steps: Step 1: focus LFM RFI artifacts in SLC images as point-like responses in the spectral domain via 2-D SPECAN; Step 2: perform 2-D notch filtering in the spectral domain to remove the most contribution of the RFI responses; and Step 3: transform the filtered spectrum back into the SLC image domain using the inverse operation of the 2-D SPECAN. For computation efficiency, we design a simplified processing flow and adopt a blockwise processing strategy. Experiments with several Sentinel-1 SLC images demonstrate that severe RFI artifacts in SLC images can be removed significantly by the proposed method.

Application of Attenuation Correction to Quantitative Precipitation Estimation on C-Band Weather Radar in Bengkulu

A weather radar is an active system remote sensing tool that observes precipitation indirectly. Weather radar has an advantage in estimating precipitation because it has a high spatial resolution (up to 0.5 km). Reflectivity generated by weather radar still has signal interference caused by attenuation factors. Attenuation causes the Quantitative Precipitation Estimation (QPE) by the C-band weather radar to underestimate. Therefore attenuation correction on C-band weather radar is needed to eliminate precipitation estimation errors. This study aims to apply attenuation correction to determine Quantitative Precipitation Estimation (QPE) on the c-band weather radar in Bengkulu in December 2018. Gate-by-gate method attenuation correction with Kraemer approach has applied to c-band weather radar data from the Indonesian Agency for Meteorology and Geophysics (BMKG) weather radar network Bengkulu. This method uses reflectivity as the only input. Quantitative Precipitation Estimation (QPE) has obtained by comparing weather radar-based rain estimates to 10 observation rain gauges over a month with the Z-R relation equation. Root Mean Square Error (RMSE) is used to calculate the estimation error. Weather radar data are processed using Python-based libraries Wradlib and ArcGIS 10.5. As a result, the calculation between the weather radar estimate precipitation and the observed rainfall obtained equation Z=2,65R1,3. The attenuation correction process with Kreamer's approach on the c-band weather radar has reduced error in the Qualitative Precipitation Estimation (QPE). Corrected precipitation has a smaller error value (r = 0.88; RMSE = 8.38) than the uncorrected precipitation (r = 0.83; RMSE = 11.70).

Evaluation of the dual-polarization weather radar quantitative precipitation estimation using long-term datasets

Abstract. Accurate, timely, and reliable precipitation observations are mandatory for hydrological forecast and early warning systems. In the case of convective precipitation, traditional rain gauge networks often miss precipitation maxima, due to density limitations and the high spatial variability of the rainfall field. Despite several limitations like attenuation or partial beam blocking, the use of C-band weather radar has become operational in most European weather services. Traditionally, weather-radar-based quantitative precipitation estimation (QPE) is derived from horizontal reflectivity data. Nevertheless, dual-polarization weather radar can overcome several shortcomings of the conventional horizontal-reflectivity-based estimation. As weather radar archives are growing, they are becoming increasingly important for climatological purposes in addition to operational use. For the first time, the present study analyses one of the longest datasets from fully operational polarimetric C-band weather radars; these are located in Estonia and Italy, in very different climate conditions and environments. The length of the datasets used in the study is 5 years for both Estonia and Italy. The study focuses on long-term observations of summertime precipitation and their quantitative estimations by polarimetric observations. From such derived QPEs, accumulations for 1 h, 24 h, and 1-month durations are calculated and compared with reference rain gauges to quantify uncertainties and evaluate performances. Overall, the radar products showed similar results in Estonia and Italy when compared to each other. The product where radar reflectivity and specific differential phase were combined based on a threshold exhibited the best agreement with gauge values in all accumulation periods. In both countries reflectivity-based rainfall QPE underestimated and specific differential-phase-based product overestimated gauge measurements.

Weather radar detection of planetary boundary layer and smoke layer top of peatland fire in Central Kalimantan, Indonesia

During the dry period of August–October 2015, a C-band Doppler weather radar of the BMKG station in a fire-prone peatland area, Palangka Raya, detected echoes with reflectivity values between − 19 and + 34 dBZ at a height below 2–3 km and a slant range of 100 km. The MERRA-2/NASA atmospheric reanalysis database is used to obtain the vertical profiles of refractive index and equivalent potential temperature of the air. The temporal variation of the radar image is due to the tropical diurnal cycle of planetary boundary layer formation, which is consistent with the results of the database analysis. The echo images are discussed in terms of Bragg scattering of microwaves at the top of the planetary boundary layer. Weather radar monitoring of the fire smoke layer-top images has a potential feasibility to support real-time management of peatland fires.

A Study of Comb Beam Transmission on Phased Array Weather Radars

The comb beam transmission (TX) approach, which forms a power antenna radiation pattern with multiple mainlobes, was studied for application to phased array weather radars. Combining the use of comb TX and a digital beamforming receiver enables a weather radar to observe multiple directions simultaneously. Numerical simulations show that the two-way antenna radiation patterns formed by comb TXs have properties comparable to conventional weather radar observation: almost the same mainlobe widths are achieved, and with reference to the mainlobe peak, the maximum sidelobe level is less than −37 dB, and the sidelobe level reaches −60 dB at an angle of 13.77°. These properties are superior to the wide TXs that are normally utilized by phased array weather radars. A simulation that applied the comb TX approach to a current C-band weather radar showed that the volume scan time can be reduced from almost 500 s to 1 min.

Full-Year Evaluation of Nonmeteorological Echo Removal with Dual-Polarization Fuzzy Logic for Two C-Band Radars in a Temperate Climate

AbstractThe Royal Netherlands Meteorological Institute (KNMI) operates two dual-polarization C-band weather radars in simultaneous transmission and reception (STAR; i.e., horizontally and vertically polarized pulses are transmitted simultaneously) mode, providing 2D radar rainfall products. Despite the application of Doppler and speckle filtering, remaining nonmeteorological echoes (especially sea clutter) mainly due to anomalous propagation still pose a problem. This calls for additional filtering algorithms, which can be realized by means of polarimetry. Here we explore the effectiveness of the open-source wradlib fuzzy echo classification and clutter identification based on polarimetric moments. Based on our study, this has recently been extended with the depolarization ratio and clutter phase alignment as new decision variables. Optimal values for weights of the different membership functions and threshold are determined employing a 4-h calibration dataset from one radar. The method is applied to a full year of volumetric data from the two radars in the Dutch temperate climate. The verification focuses on the presence of remaining nonmeteorological echoes by mapping the number of exceedances of radar reflectivity factors for given thresholds. Moreover, accumulated rainfall maps are obtained to detect unrealistically large rainfall depths. The results are compared to those for which no further filtering has been applied. Verification against rain gauge data reveals that only a little precipitation is removed. Because the fuzzy logic algorithm removes many nonmeteorological echoes, the practice to composite data from both radars in logarithmic space to hide these echoes is abandoned and replaced by linearly averaging reflectivities.

Measured impact of different back‐off points and cooling methods on pulse‐to‐pulse stability and sidelobe level of a high‐power solid‐state amplifier

Using solid-state power amplifiers for next generation of weather radars becomes feasible by pulse compression techniques. In this study a 1.5 kW solid-state power amplifier (transmitter) for C-band weather radars is designed and fabricated by GaN high electron mobility transistor (HEMT) technology. An experimental setup based on heterodyne receiver with 16-bit digitiser is developed to investigate the behavior of the power amplifier under different cooling methods and back-off points. Several measurements with shaped LFM pulse show an approximately identical pulse to pulse (P2P) stability for 3 dB compression, P1dB and 2 dB back-off points while the best sidelobe level (SLL) is achieved for 2 dB back-off point. To study the effect of different cooling techniques on P2P stability and time SLL, two power amplifiers with the same transistors but different cooling methods are examined. Measurement results for 100 µs pulse duration indicate that the power amplifier with liquid cooling method shows 56 dBm output power with 0.1 dB droop while the forced-air cooling one shows 55.5dBm output power with 0.3 dB droop. While the liquid cooling technique shows 3 dB improvement in the overall P2P stability, its time SLL is deteriorated. To the authors' knowledge, this is the first experimental study on the time SLL under different back-off points and cooling techniques.

Identification of Rainfall Intensity by Using Bahir Dar C-band Weather Radar products

Observation of rainfall accumulation, distribution and wind direction spatially and temporally is very essential for meteorologists and hydrologists in the Upper Blue Nile basin and different locations of Ethiopia. The Bahir Dar (BAH) Cband weather radar used to estimate the intensity, amount and distribution of precipitation with a high resolution temporally and spatially. It is dual polarization with horizontal and vertical reflectivity; six variables are used to determine the hydrometer and non-hydrometer types, shape and sizes of targets up to range of 250 km and 450 km at constant altitude plan position indicator (CAPPI) and plan position indicator (PPI) products. From our first observation, we used different types of products to observe rainfall intensity, distribution, wind direction and warnings across Upper Blue Nile. For RainN on 01 July 2016 the rainfall distribution over twenty four hours (24 Hr) UTC at Bahir Dar, parts of lake Tana, most northern parts, western parts of Gondar town, and south western parts of lake Tana a resolution of 809 m/Pixel with frequency of 600 Hz the maximum rainfall accumulation 91.6 mm was achieved. By using CAPPI product with vertical height of 1 km on 26 June 2016 the maximum reflectivity 53.5 dBZ is observed.

Wind turbine impact on operational weather radar I/Q data: characterisation and filtering

Abstract. For the past 2 decades wind turbines have been growing in number all over the world as a response to the increasing demand for renewable energy. However, the rapid expansion of wind turbines presents a problem for many radar systems, including weather radars. Wind turbines in the line of sight of a weather radar can have a negative impact on the radar's measurements. As weather radars are important instruments for meteorological offices, finding a way for wind turbines and weather radars to co-exist would be of great societal value.Doppler weather radars base their measurements on in-phase and quadrature phase (I/Q) data. In this work a month's worth of recordings of high-resolution I/Q data from an operational Swedish C-band weather radar are presented. The impact of point targets, such as masts and wind turbines, on the I/Q data is analysed and characterised. It is shown that the impact of point targets on single radar pulses, when normalised by amplitude, is manifested as a distinct and highly repeatable signature. The shape of this signature is found to be independent of the size, shape and yaw angle of the wind turbine. It is further demonstrated how the robustness of the point target signature can be used to identify and filter out the impact of wind turbines in the radar's signal processor.

A Comparison between the GPM Dual-Frequency Precipitation Radar and Ground-Based Radar Precipitation Rate Estimates in the Swiss Alps and Plateau

Abstract The Global Precipitation Measurement (GPM) mission Dual-Frequency Precipitation Radar (DPR) provides a unique set of three-dimensional radar precipitation estimates across much of the globe. Both terrain and climatic conditions can have a strong influence on the reliability of these estimates. Switzerland provides an ideal testbed to evaluate the performance of the DPR in complex terrain: it consists of a mixture of very complex terrain (the Alps) and the far flatter Swiss Plateau. It is also well instrumented, covered with a dense gauge network as well as a network of four dual-polarization C-band weather radars, with the same instrument network used in both the Plateau and the Alps. Here an evaluation of the GPM DPR rainfall rate products against the MeteoSwiss radar rainfall rate product for the first two years of the GPM DPR’s operation is presented. Errors in both detection and estimation are considered, broken down by terrain complexity, season, precipitation phase, precipitation type, and precipitation rate. Errors are considered both integrated across the entire domain and spatially, and consistent underestimation of precipitation by GPM is found. This rises to −51% in complex terrain in the winter, primarily due to the predominance of DPR measurements wholly in the solid phase, where problems are caused by lower reflectivities. The smaller vertical extent of precipitation in winter is also likely a cause. Both detection and estimation performance are found to be significantly better in summer than in winter, in liquid than in solid precipitation, and in flatter terrain than in complex terrain.

Evaluating the Solar Slowly Varying Component at C-Band Using Dual- and Single-Polarization Weather Radars in Europe

Six C-band weather radars located in Europe (Finland, Netherlands, and Switzerland) have been used to monitor the slowly varying solar emission, which is an oscillation with an amplitude of several decibels and a period of approximately 27 days. It is caused by the fact that the number of active regions that enhance the solar radio emission with respect to the quiet component, as seen from Earth, varies because of the Sun’s rotation about its axis. The analysis is based on solar signals contained in the polar volume data produced during the operational weather scan strategy. This paper presents hundreds of daily comparisons between radar estimates and the Sun’s reference signal, during the current active Sun period (year 2014). The Sun’s reference values are accurately measured by the Dominion Radio Astrophysical Observatory (DRAO) at S-band and converted to C-band using a standard DRAO formula. Vertical and horizontal polarization receivers are able to capture the monthly oscillation of the solar microwave signal: the standard deviation of the log-transformed ratio between radars and the DRAO reference ranges from 0.26 to 0.4 dB. A larger coefficient (and a different value for the quiet Sun component) in the standard formula improves the agreement.

Generation and Verification of Rainfall Estimates from 10-Yr Volumetric Weather Radar Measurements

AbstractVolumetric measurements from a C-band weather radar in Belgium are reprocessed over the years 2005–14 to improve the quantitative precipitation estimation (QPE). The data quality is controlled using static clutter and beam blockage maps and clutter identification based on vertical gradients, horizontal texture, and satellite observations. A new QPE is obtained using stratiform–convective classification, a 40-min averaged vertical profile of reflectivity (VPR), a brightband identification, and a specific transformation to rain rates for each precipitation regime. The rain rates are interpolated on a 500-m Cartesian grid, linearly accumulated, and combined with hourly rain gauge measurements using mean field bias or kriging with external drift (KED). The algorithms have been fine-tuned on 13 cases with various meteorological situations. A detailed validation against independent daily rain gauge measurements reveals the importance of VPR correction. A 10-yr verification shows a significant improvement of the new QPE, especially at short and long range, with roughly 50% increase in coverage. Adding the KED allows average improvements of 38%, 35%, and 80% for the mean absolute difference, the multiplicative error spread, and the fraction of good estimates, respectively. The benefit is higher in widespread situations and increases when considering higher rainfall amounts. The mitigation of radar artifacts is clearly visible on 10-yr statistics, including mean annual totals, probabilities to exceed 10 mm, and maxima for hourly and daily accumulation. The correlation of mean totals with rain gauges increases from 0.54 to 0.66 with the new QPE and to 0.8 adding KED.

Bayesian statistical analysis of ground-clutter for the relative calibration of dual polarization weather radars

A new data processing methodology, based on the statistical analysis of ground-clutter echoes and aimed at investigating the stability of the weather radar relative calibration, is presented. A Bayesian classification scheme has been used to identify meteorological and/or ground-clutter echoes. The outcome is evaluated on a training dataset using statistical score indexes through the comparison with a deterministic clutter map. After discriminating the ground clutter areas, we have focused on the spatial analysis of robust and stable returns by using an automated region-merging algorithm. The temporal series of the ground-clutter statistical parameters, extracted from the spatial analysis and expressed in terms of percentile and mean values, have been used to estimate the relative clutter calibration and its uncertainty for both co-polar and differential reflectivity. The proposed methodology has been applied to a dataset collected by a C-band weather radar in southern Italy.

Retrieval of Tephra Size Spectra and Mass Flow Rate From C-Band Radar During the 2010 Eyjafjallajökull Eruption, Iceland

The eruption of the Eyjafjallajokull volcano in April–May 2010 was continuously monitored by the Keflavik C-band weather radar. The Keflavik radar is located at a distance of about 156 km from the volcano vent, and it has sensitivity of about −5 dBZ at 2-km range resolution over the volcanic area. The time series of radar volume data, which was available every 5 min, is quantitatively analyzed by using the Volcanic Ash Radar Retrieval (VARR) technique. The latter is a physically based methodology that is applied to estimate ash-fall rate and mass concentration within each radar volume. The VARR methodology is here extended, with respect to the previous formulation, to provide an approximate estimate of both mean particle diameter and airborne tephra particle size distribution under some assumptions. Deposited tephra at ground is also extrapolated together with an estimate of the magma mass flow rate (MFR) at the volcano vent, derived from the implementation of the mass continuity equation in the radar reference system. The VARR-based retrievals are compared with those derived from a direct tephra sampling at the ground, experimentally carried out in terms of ash grain size and loading during the Eyjafjallajokull eruption activity on May 5–7, 2010. VARR-based particle diameter estimates may suggest that a sorting of airborne particles during the downwind transport is taking place without observing aggregation processes during the ash fall. VARR-derived daily ash mass loadings in the period between April 14 and May 10 are also evaluated with respect to integrated ground and model-based data in the Eyjafjallajokull area. VARR-retrieved MFRs are finally compared with corresponding values obtained from analytical 1-D eruption models, using radar-estimated plume height and radio-sounding wind fields. A fairly good agreement is obtained, thus opening the exploitation of weather radar retrievals for volcanic eruption quantitative studies and ash dispersion model initialization.

Analysis and numerical simulation of a real cell merger using a three-dimensional cloud resolving model

A three-dimensional cloud resolving model is used to study a real cell merger case that occurred on 10 August, 2008 over north-central Greece, causing heavy rainfall, hailfall and high-frequency lightning. Firstly, the storm is observed, analyzed and recorded using a C-band weather radar. Secondly, three distinct simulations are performed using a cloud resolving model. An unseeded simulation, in order to test the ability of the model to reproduce the structural and evolutionary properties of the storm and two seeded simulations in which seeding occurred before and after cell merging. Reflectivity fields are analyzed, horizontally and vertically, at different simulation times. The 3-D numerical simulations suggest that the merger process occurred by two or three isolated single-cells and formed during their SW–NE motion. The merging process apparently alters dynamical and microphysical properties through low and middle level forcing; increases cloud diameters and cloud depths, producing more graupel and ice particles and increases radar reflectivity values. Processed radar images depict a similar view of the storm structure, evolution and interactions of such merging processes. The model calculated maximum radar reflectivity values coincide with the recorded ones. Results indicate that seeding the cloud before its merging produces more positive effects on hail suppression than seeding after merging. These findings are quite important, in order to document the value of the cloud resolving model and its capability to simulate and reproduce the realistic storm processes and to provide a better understanding of the cloud dynamical and microphysical features related to different seeding approaches.

Hail formation triggers rapid ash aggregation in volcanic plumes.

During explosive eruptions, airborne particles collide and stick together, accelerating the fallout of volcanic ash and climate-forcing aerosols. This aggregation process remains a major source of uncertainty both in ash dispersal forecasting and interpretation of eruptions from the geological record. Here we illuminate the mechanisms and timescales of particle aggregation from a well-characterized ‘wet' eruption. The 2009 eruption of Redoubt Volcano, Alaska, incorporated water from the surface (in this case, a glacier), which is a common occurrence during explosive volcanism worldwide. Observations from C-band weather radar, fall deposits and numerical modelling demonstrate that hail-forming processes in the eruption plume triggered aggregation of ∼95% of the fine ash and stripped much of the erupted mass out of the atmosphere within 30 min. Based on these findings, we propose a mechanism of hail-like ash aggregation that contributes to the anomalously rapid fallout of fine ash and occurrence of concentrically layered aggregates in volcanic deposits.

Eigenvalue Signal Processing for Weather Radar Polarimetry: Removing the Bias Induced by Antenna Coherent Cross-Channel Coupling

We present a novel digital signal processing procedure, named eigenvalue signal pocessing (henceforth ESP), patented by the first author with Brookhaven Science Associates in 2013. The method enables the removal of the bias due to antenna coherent cross-channel coupling and is applicable in the LDR mode, the ATSR mode and the STSR orthogonal mode of weather radar measurements. In this paper, we focus on the LDR mode and consider copolar reflectivity at horizontal transmit $({\rm Z}_{\rm HH})$ , cross-polar reflectivity at horizontal transmit $({\rm Z}_{\rm VH})$ , linear depolarization ratio at horizontal transmit $({\rm LDR}_{\rm H})$ and degree of polarization at horizontal transmit $({\rm DOP}_{\rm H})$ . The ESP (ESP) method is substantiated by an experiment carried out in November 2012 using C-band weather radar with a parabolic reflector located at the Selex ES—Gematronik facilities in Neuss, Germany. The experiment involved comparison of weather radar measurements taken 1.5 minutes apart in two hardware configurations, namely with cross-coupling on (cc-on) and cross-coupling off (cc-off). It is experimentally demonstrated that eigenvalue-derived variables are invariant with respect to antenna coherent cross-channel coupling. This property had to be expected, since the eigenvalues of the Coherency matrix are SU(2) invariant.