Radar Reflectivity Measurements Research Articles

Reanalysis of multi-year high-resolution X-band weather radar observations in Hamburg

Abstract. This paper presents an open-access data set of reanalysed radar reflectivities and rainfall rates at sub-kilometre spatial and minute temporal scales. Variability at these scales is a blind spot for both operational rain gauge networks and operational radar networks. In the urban area of Hamburg, precipitation measurements of a single-polarized X-band weather radar operating at high temporal (30 s), range (60 m), and azimuthal sampling (1°) resolutions are made available for a period of more than 8 years. We describe in detail the reanalysis of the raw radar data, outline the radar performance for the years 2013 to 2021, and discuss open issues and limitations of the data set. Several sources of radar-based errors were adjusted gradually, affecting the radar reflectivity and rainfall measurements, e.g. noise, alignment, non-meteorological echoes, radar calibration, and attenuation. The deployment of additional vertically pointing micro rain radars yields drop size distributions at the radar beam height, which effectively reduces errors concerning the radar calibration and attenuation correction and monitors the radar data quality. A statistical evaluation revealed that X-band radar reflectivities and rainfall rates are in very good agreement with the micro rain radar measurements. Moreover, the analyses of rainfall patterns shown for an event and accumulated rainfall of several months prove the quality of the data set. The provided radar reflectivities facilitate studies on attenuation correction and the derivation of further weather radar products, like an improved rainfall rate. The rainfall rates themselves can be used for studies on the spatial and temporal scales of precipitation and hydrological research, e.g. input data for high-resolution modelling, in an urban area. The radar reflectivities and rainfall rates are available at https://doi.org/10.26050/WDCC/LAWR_UHH_HHG_v2 (Burgemeister et al., 2024).

Change Point Detection in Radar Reflectivity Measurements Contaminated by Speckle Noise

Change Point Detection in Radar Reflectivity Measurements Contaminated by Speckle Noise

Assessing Arctic low-level clouds and precipitation from above – a radar perspective

Abstract. Most Arctic clouds occur below 2 km altitude, as revealed by CloudSat satellite observations. However, recent studies suggest that the relatively coarse spatial resolution, low sensitivity, and blind zone of the radar installed on CloudSat may not enable it to comprehensively document low-level clouds. We investigate the impact of these limitations on the Arctic low-level cloud fraction, which is the number of cloudy points with respect to all points as a function of height, derived from CloudSat radar observations. For this purpose, we leverage highly resolved vertical profiles of low-level cloud fraction derived from down-looking Microwave Radar/radiometer for Arctic Clouds (MiRAC) radar reflectivity measurements. MiRAC was operated during four aircraft campaigns that took place in the vicinity of Svalbard during different times of the year, covering more than 25 000 km. This allows us to study the dependence of CloudSat limitations on different synoptic and surface conditions. A forward simulator converts MiRAC measurements to synthetic CloudSat radar reflectivities. These forward simulations are compared with the original CloudSat observations for four satellite underflights to prove the suitability of our forward-simulation approach. Above CloudSat's blind zone of 1 km and below 2.5 km, the forward simulations reveal that CloudSat would overestimate the MiRAC cloud fraction over all campaigns by about 6 percentage points (pp) due to its horizontal resolution and by 12 pp due to its range resolution and underestimate it by 10 pp due to its sensitivity. Especially during cold-air outbreaks over open water, high-reflectivity clouds appear below 1.5 km, which are stretched by CloudSat's pulse length causing the forward-simulated cloud fraction to be 16 pp higher than that observed by MiRAC. The pulse length merges multilayer clouds, whereas thin low-reflectivity clouds remain undetected. Consequently, 48 % of clouds observed by MiRAC belong to multilayer clouds, which reduces by a factor of 4 for the forward-simulated CloudSat counterpart. Despite the overestimation between 1 and 2.5 km, the overall low-level cloud fraction is strongly reduced due to CloudSat's blind zone that misses a cloud fraction of 32 % and half of the total (mainly light) precipitation amount.

Enhanced Technique for Prediction of Z-R Relationship in Tropical Region

Over the years, research has been directed to investigate radar as potential tools for estimating rain. The radar reflectivity is measured by comparing the location of the rain gauge and the associated vertical path at certain altitude. The latitude and longitude of the rain gauge is identified to pinpoint the exact associated location by using Rainbow®5 meteorological software. In this work, radar reflectivity was estimated by using S-band Terminal Doppler Weather Radar (TWDR) data at Malaysian KLIA radar station. The rain gauge is owned by Malaysian Department of Irrigation and Drainage (DID) at several locations within the vicinity of the radar. A technique to estimate the Z-R relationship by using radar reflectivity information and rainfall rate from rain gauge values is demonstrated in this article. Comparisons between radar reflectivity and actual rain gauge measurement is presented. Preliminary findings using available Z-R relation shows similar values with the actual measurement and suitable to be used in tropical climate country such as Malaysia.

Consistency test of precipitating ice cloud retrieval properties obtained from the observations of different instruments operating at Dome C (Antarctica)

Abstract. Selected case studies of precipitating ice clouds at Dome C (Antarctic Plateau) were used to test a new approach for the estimation of ice cloud reflectivity at 24 GHz (12.37 mm wavelength) using ground-based far infrared spectral measurements from the REFIR-PAD Fourier transform spectroradiometer and backscattering/depolarization lidar profiles. The resulting reflectivity was evaluated with the direct reflectivity measurements provided by a co-located micro rain radar (MRR) operating at 24 GHz, that was able to detect falling crystals with large particle size, typically above 600 µm. To obtain the 24 GHz reflectivity, we used the particle effective diameter and the cloud optical depth retrieved from the far infrared spectral radiances provided by REFIR-PAD and the tropospheric co-located backscattering lidar to calculate the modal radius and the intercept of the particle size distribution. These parameters spanned in the wide ranges between 570–2400 µm and 10−2–104 cm−5, respectively. The retrieved effective sizes and optical depths mostly varied in the ranges 70–250 µm and 0.1–5, respectively. From these parameters, the theoretical reflectivity at 24 GHz was obtained by integrating the size distribution over different cross sections for various habit crystals provided by Eriksson et al. (2018) databases. From the comparison with the radar reflectivity measurements, we found that the hexagonal column-like habits, the columnar crystal aggregates, and the 5/6 branches bullet rosettes showed the best agreement with the MRR observations. The dispersion coefficient of the crystal particle size distribution was assumed in the range 0–2 according to the temperature dependence found in previous studies. The retrieved values of the intercept and slope were found in good agreement with these studies. The presence of the inferred habits was confirmed by the crystal images taken by the ICE-CAMERA, operating in proximity of REFIR-PAD and the MRR. In particular, the occurrence of hexagonal column-like ice crystals was confirmed by the presence of 22∘ solar halos, detected by the HALO-CAMERA. The average crystal lengths obtained from the retrieved size distribution were also compared to those estimated from the ICE-CAMERA images. The agreement between the two results confirmed that the retrieved parameters of the particle size distributions correctly reproduced the observations.

Aspects of melting layer and fall streaks in stratiform cloud system over the Western Ghats, India from Ka-band polarimetric radar observations

During the Indian summer monsoon, high temporal and spatial resolution observations from Ka-band dual-polarization radar deployed in the Western Ghats (WG), India are utilized to investigate the vertical structure of the stratiform precipitating system. First, the radar reflectivity (Z) measurements are corrected for rain attenuation, based on the relationship between specific attenuation (A) and Z. The disdrometer dataset is used to calculate the expected value of A at Ka-band for a given Z using microwave scattering simulation. The radar data are also corrected for wet radome transmission loss, gaseous, and melting layer (ML) attenuation. Within the ML, a change in Z and other radar polarimetric variables suggest hydrometeors phase change. In addition to Z, polarimetric measurements such as differential reflectivity (ZDR), linear depolarization ratio (LDR), and copolar correlation coefficient between horizontal and vertical channel (ρHV) are used to determine the ML height. Ka-band polarimetric radar signatures near the ML are characterized by a low ρHV (∼0.93), high Z (∼25–30 dBZ), ZDR (∼3 dB), and LDR (∼ −15 dB) values. In the stratiform precipitation region, well-defined fall streaks are observed above ML, which descends through 0 °C isotherm into the rain region. When the fall streaks are present above the ML, Z increases below the ML, indicating a seeder-feeder mechanism. Because there is little observational evidence concerning the vertical distribution of stratiform clouds over the Indian subcontinent, the current study may help to understand stratiform clouds and the ice processes that occur within them.

Retrievals of ice microphysical properties using dual-wavelength polarimetric radar observations during stratiform precipitation events

Abstract. Ice growth processes within clouds affect the type and amount of precipitation. Hence, the importance of an accurate representation of ice microphysics in numerical weather and numerical climate models has been confirmed by several studies. To better constrain ice processes in models, we need to study ice cloud regions before and during monitored precipitation events. For this purpose, two radar instruments facing each other were used to collect complementary measurements. The C-band POLDIRAD weather radar from the German Aerospace Center (DLR) in Oberpfaffenhofen and the Ka-band MIRA-35 cloud radar from the Ludwig Maximilians University of Munich (LMU) were used to monitor stratiform precipitation in the vertical cross-sectional area between the two instruments. The logarithmic difference of radar reflectivities at two different wavelengths (54.5 and 8.5 mm), known as the dual-wavelength ratio, was exploited to provide information about the size of the detected ice hydrometeors, taking advantage of the different scattering behavior in the Rayleigh and Mie regime. Along with the dual-wavelength ratio, differential radar reflectivity measurements from POLDIRAD provided information about the apparent shape of the detected ice hydrometeors. Scattering simulations using the T-matrix method were performed for oblate and horizontally aligned prolate ice spheroids of varying shape and size using a realistic particle size distribution and a well-established mass–size relationship. The combination of dual-wavelength ratio, radar reflectivity, and differential radar reflectivity measurements as well as scattering simulations was used for the development of a novel retrieval for ice cloud microphysics. The development of the retrieval scheme also comprised a method to estimate the hydrometeor attenuation in both radar bands. To demonstrate this approach, a feasibility study was conducted on three stratiform snow events which were monitored over Munich in January 2019. The ice retrieval can provide ice particle shape, size, and mass information which is in line with differential radar reflectivity, dual-wavelength ratio, and radar reflectivity observations, respectively, when the ice spheroids are assumed to be oblates and to follow the mass–size relation of aggregates. When combining two spatially separated radars to retrieve ice microphysics, the beam width mismatch can locally lead to significant uncertainties. However, the calibration uncertainty is found to cause the largest bias for the averaged retrieved size and mass. Moreover, the shape assumption is found to be equally important to the calibration uncertainty for the retrieved size, while it is less important than the calibration uncertainty for the retrieval of ice mass. A further finding is the importance of the differential radar reflectivity for the particle size retrieval directly above the MIRA-35 cloud radar. Especially for that observation geometry, the simultaneous slantwise observation from the polarimetric weather radar POLDIRAD can reduce ambiguities in retrieval of the ice particle size by constraining the ice particle shape.

Accurate Absolute Measurements of Liquid Water Content (LWC) and Ice Water Content (IWC) of Clouds and Precipitation with Spectrometric Water Raman Lidar

Abstract A detailed description is given of how the liquid water content (LWC) and the ice water content (IWC) can be determined accurately and absolutely from the measured water Raman spectra of clouds. All instrumental and spectroscopic parameters that affect the accuracy of the water-content measurement are discussed and quantified; specifically, these are the effective absolute differential Raman backscattering cross section of water vapor , and the molecular Raman backscattering efficiencies ηliq and ηice of liquid and frozen microparticles, respectively. The latter two are determined following rigorous theoretical approaches combined with Raman Lidar for Atmospheric Moisture Sensing (RAMSES) measurements. For ηice, this includes a new experimental method that assumes continuity of the number of water molecules across the vertical extent of the melting layer. Examples of water-content measurements are presented, including supercooled liquid-water clouds and melting layers. Error sources are discussed; one effect that stands out is interfering fluorescence by aerosols. Aerosol effects and calibration issues are the main reasons why spectral Raman measurements are required for quantitative measurements of LWC and IWC. The presented study lays the foundation for cloud microphysical investigations and for the evaluation of cloud models or the cloud data products of other instruments. As a first application, IWC retrieval methods are evaluated that are based on either lidar extinction or radar reflectivity measurements. While the lidar-based retrievals show unsatisfactory agreement with the RAMSES IWC measurements, the radar-based IWC retrieval which is used in the Cloudnet project performs reasonably well. On average, retrieved IWC agrees within 20% to 30% (dry bias) with measured IWC.

Verification of numerical modeling of atmospheric dynamics using the measurements of radar reflectivity

Numerical modeling is one of the main research tools in modern atmospheric physics. The complexity of the verification of modeling is related to the probabilistic nature of the atmospheric processes under study. We define the quality of modeling as the accuracy of describing the general dynamics, and not as the equality of measured and modeled physical quantities at any specific time moment. The paper proposes a verification method based on comparing the measured and modeled spatial distribution of radar reflectivity. The method is applied to compare the quality of modeling with different microphysical schemes for the mountainous region.

Conditional stochastic inversion of common-offset ground-penetrating radar reflection data

We have developed a stochastic inversion procedure for common-offset ground-penetrating radar (GPR) reflection measurements. Stochastic realizations of subsurface properties that offer an acceptable fit to GPR data are generated via simulated annealing optimization. The realizations are conditioned to borehole porosity measurements available along the GPR profile or equivalent measurements of another petrophysical property that can be related to the dielectric permittivity, as well as to geostatistical parameters derived from the borehole logs and the processed GPR image. Validation of our inversion procedure is performed on a pertinent synthetic data set and indicates that our method is capable of reliably recovering strongly heterogeneous porosity structures associated with surficial alluvial aquifers. This finding is largely corroborated through application of the methodology to field measurements from the Boise Hydrogeophysical Research Site near Boise, Idaho, USA.

The Impact of Reflectivity Gradients on the Performance of Range-Oversampling Processing

AbstractRange-oversampling processing is a technique that can be used to lower the variance of radar-variable estimates, reduce radar update times, or a mixture of both. There are two main assumptions for using range-oversampling processing: accurate knowledge of the range correlation and uniform reflectivity in the radar resolution volume. The first assumption has been addressed in previous research; this work focuses on the uniform reflectivity assumption. Earlier research shows that significant reflectivity gradients can occur in storms; we utilized those results to develop realistic simulations of radar returns that include effects of reflectivity gradients in range. An important consideration when using range-oversampling processing is the resulting change in the range weighting function. The range weighting function can change for different types of range-oversampling processing and some techniques, such as adaptive pseudowhitening, can lead to different range weighting functions at each range gate. To quantify the possible effects of differing range weighting functions in the presence of reflectivity gradients, we developed simulations to examine varying types of range-oversampling processing with two receiver filters: a matched receiver filter and a wider-bandwidth receiver filter (as recommended for use with range oversampling). Simulation results show that differences in range weighting functions are the only contributor to differences in radar reflectivity measurements. Results from real weather data demonstrate that the reflectivity gradients that occur in typical severe storms do not cause significant changes in reflectivity measurements, and the benefits from range-oversampling processing outweigh the possible isolated effects from large reflectivity gradients.

The Observed Water Vapor Budget in an Atmospheric River over the Northeast Pacific

AbstractCombined airborne, shipboard, and satellite measurements provide the first observational assessment of all major terms of the vertically integrated water vapor (IWV) budget for a 150 km × 160 km region within the core of a strong atmospheric river over the northeastern Pacific Ocean centered on 1930 UTC 5 February 2015. Column-integrated moisture flux convergence is estimated from eight dropsonde profiles, and surface rain rate is estimated from tail Doppler radar reflectivity measurements. Dynamical convergence of water vapor (2.20 ± 0.12 mm h−1) nearly balances estimated precipitation (2.47 ± 0.41 mm h−1), but surface evaporation (0.0 ± 0.05 mm h−1) is negligible. Advection of drier air into the budget region (−1.50 ± 0.21 mm h−1) causes IWV tendency from the sum of all terms to be negative (−1.66 ± 0.45 mm h−1). An independent estimate of IWV tendency obtained from the difference between IWV measured by dropsonde and retrieved by satellite 3 h earlier is less negative (−0.52 ± 0.24 mm h−1), suggesting the presence of substantial temporal variability that is smoothed out when averaging over several hours. The calculation of budget terms for various combinations of dropsonde subsets indicates the presence of substantial spatial variability at ~50-km scales for precipitation, moisture flux convergence, and IWV tendency that is smoothed out when averaging over the full budget region. Across subregions, surface rain rate is linearly proportional to dynamical convergence of water vapor. These observational results improve our understanding of the thermodynamic and kinematic processes that control IWV in atmospheric rivers and the scales at which they occur.

Estimating Rain Microphysical Characteristics Using S-Band Dual-Polarization Radar in South Korea

AbstractRaindrop size distribution (DSD) observed using a disdrometer can be represented by a constrained-gamma (C-G) DSD model based on the empirical relationship between shape (µ) and slope (Λ). The C-G DSD model can be used to retrieve DSDs and rain microphysical parameters from dual-polarization radar measurements of reflectivity (ZH) and differential reflectivity (ZDR). This study presents a newµ–Λ relationship to characterize rain microphysics in South Korea using a two-dimensional video disdrometer (2DVD) and Yong-in S-band dual-polarization radar. To minimize sampling errors from the 2DVD and radar measurements, measured size distributions are truncated by particle size and velocity-based filtering and compared with rain gauge measurement. The calibration biases of radarZHandZDRwere calculated using the self-consistency constraint and vertical pointing measurements. The derivedµ–Λ relationship was verified using the mass-weighted mean diameter (Dm) and standard deviation of the size distribution (σm), calculated from the 2DVD, for comparison with existingµ–Λ relationships for Florida and Oklahoma. TheDm–σmrelationship derived from the 2DVD corresponded well with theµ–Λ relationship. Theµ–Λ relationship derived for the Korean Peninsula was similar to Florida, and both generally had largerµvalues than Oklahoma for the same Λ. The derivedµ–Λ relationship was applied to retrieve DSD parameters from polarimetric radar data, and the retrieved DSDs and derived physical parameters were evaluated and compared with the 2DVD measurements. The polarization radar-based C-G DSD model characterized rain microphysics more accurately than the exponential DSD model. The C-G DSD model based on the newly derivedµ–Λ relationship performed the best at retrieving rain microphysical parameters.

Profiles of MBL Cloud and Drizzle Microphysical Properties Retrieved From Ground‐Based Observations and Validated by Aircraft In Situ Measurements Over the Azores

AbstractThe profiles of marine boundary layer (MBL) cloud and drizzle microphysical properties are important for studying the cloud‐to‐rain conversion and growth processes in MBL clouds. However, it is challenging to simultaneously retrieve both cloud and drizzle microphysical properties within an MBL cloud layer using ground‐based observations. In this study, methods were developed to first decompose drizzle and cloud reflectivity in MBL clouds from Atmospheric Radiation Measurement cloud radar reflectivity measurements and then simultaneously retrieve cloud and drizzle microphysical properties during the Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE‐ENA) campaign. These retrieved microphysical properties, such as cloud and drizzle particle size (rc and rm,d), their number concentration (Nc and Nd) and liquid water content (LWCc and LWCd), have been validated by aircraft in situ measurements during ACE‐ENA (~158 hr of aircraft data). The mean surface retrieved (in situ measured) rc, Nc, and LWCc are 10.9 μm (11.8 μm), 70 cm−3 (60 cm−3), and 0.21 g m−3 (0.22 g m−3), respectively. For drizzle microphysical properties, the retrieved (in situ measured) rd, Nd, and LWCd are 44.9 μm (45.1 μm), 0.07 cm−3 (0.08 cm−3), and 0.052 g m−3 (0.066 g m−3), respectively. Treating the aircraft in situ measurements as truth, the estimated median retrieval errors are ~15% for rc, ~35% for Nc, ~30% for LWCc and rd, and ~50% for Nd and LWCd. The findings from this study will provide insightful information for improving our understanding of warm rain processes, as well as for improving model simulations. More studies are required over other climatic regions.

Statistical analysis of ice microphysical properties in tropical mesoscale convective systems derived from cloud radar and in situ microphysical observations

Abstract. This study presents a statistical analysis of the properties of ice hydrometeors in tropical mesoscale convective systems observed during four different aircraft campaigns. Among the instruments on board the aircraft, we focus on the synergy of a 94 GHz cloud radar and two optical array probes (OAP; measuring hydrometeor sizes from 10 µm to about 1 cm). For two campaigns, an accurate simultaneous measurement of the ice water content is available, while for the two others, ice water content is retrieved from the synergy of the radar reflectivity measurements and hydrometeor size and morphological retrievals from OAP probes. The statistics of ice hydrometeor properties are calculated as a function of radar reflectivity factor measurement percentiles and temperature. Hence, mesoscale convective systems (MCS) microphysical properties (ice water content, visible extinction, mass–size relationship coefficients, total concentrations, and second and third moments of hydrometeor size distribution) are sorted in temperature (and thus altitude) zones, and each individual campaign is subsequently analyzed with respect to median microphysical properties of the merged dataset (merging all four campaign datasets). The study demonstrates that ice water content (IWC), visible extinction, total crystal concentration, and the second and third moments of hydrometeor size distributions are similar in all four types of MCS for IWC larger than 0.1 g m−3. Finally, two parameterizations are developed for deep convective systems. The first concerns the calculation of the visible extinction as a function of temperature and ice water content. The second concerns the calculation of hydrometeor size distributions as a function of ice water content and temperature that can be used in numerical weather prediction.

Microwave Radar/radiometer for Arctic Clouds (MiRAC): first insights from the ACLOUD campaign

Abstract. The Microwave Radar/radiometer for Arctic Clouds (MiRAC) is a novel instrument package developed to study the vertical structure and characteristics of clouds and precipitation on board the Polar 5 research aircraft. MiRAC combines a frequency-modulated continuous wave (FMCW) radar at 94 GHz including a 89 GHz passive channel (MiRAC-A) and an eight-channel radiometer with frequencies between 175 and 340 GHz (MiRAC-P). The radar can be flexibly operated using different chirp sequences to provide measurements of the equivalent radar reflectivity with different vertical resolution down to 5 m. MiRAC is mounted for down-looking geometry on Polar 5 to enable the synergy with lidar and radiation measurements. To mitigate the influence of the strong surface backscatter the radar is mounted with an inclination of about 25∘ backward in a belly pod under the Polar 5 aircraft. Procedures for filtering ground return and range side lobes have been developed. MiRAC-P frequencies are especially adopted for low-humidity conditions typical for the Arctic to provide information on water vapor and hydrometeor content. MiRAC has been operated on 19 research flights during the ACLOUD campaign in the vicinity of Svalbard in May–June 2017 providing in total 48 h of measurements from flight altitudes >2300 m. The radar measurements have been carefully quality controlled and corrected for surface clutter, mounting of the instrument, and aircraft orientation to provide measurements on a unified, geo-referenced vertical grid allowing the combination with the other nadir-pointing instruments. An intercomparison with CloudSat shows good agreement in terms of cloud top height of 1.5 km and radar reflectivity up to −5 dBz and demonstrates that MiRAC with its more than 10 times higher vertical resolution down to about 150 m above the surface is able to show to some extent what is missed by CloudSat when observing low-level clouds. This is especially important for the Arctic as about 40 % of the clouds during ACLOUD showed cloud tops below 1000 m, i.e., the blind zone of CloudSat. In addition, with MiRAC-A 89 GHz it is possible to get an estimate of the sea ice concentration with a much higher resolution than the daily AMSR2 sea ice product on a 6.25 km grid.

Radar Reflectivity as a Proxy for the Dust Content of Individual Layers in the Martian North Polar Layered Deposits

AbstractThe stratigraphy of the north polar layered deposits (NPLD) of Mars is believed to contain a climate record of the recent Amazonian period. However, full utilization of this record is difficult without detailed information regarding the physical properties of the constituent layers. Here we present a method for determining the fractional dust content of individual layers using a combination of orbital radar reflectivity measurements and physical modeling. We apply this method to the upper 500 m of the NPLD at 10 study sites and compare the results to a cap‐wide radar‐mapped surface. Our results show that reflectivity can vary drastically both geographically and with depth, a result we attribute to changing dust content, though the impact of variable layer thickness cannot be totally discounted. These findings imply large‐scale regional patterns in ice and dust accumulation do not remain consistent through time. We also find that current models of Mars's dust cycle and polar ice accumulation consistently underpredict the dust content of layers, indicating that our understanding of dust transport, dust sequestration, or dust preservation remains incomplete. Comparisons of study sites on the NPLD also show that some locations contain fewer radar reflectors than others, meaning they may contain a less complete record of the planet's recent paleoclimate, and any future efforts to use the polar layered deposits as a climate proxy, including in situ measurements, should take this into account by choosing study sites wisely.

A Kalman filter application for rainfall estimation using radar reflectivity measurements

The rainfall amount observed at a given location mostly depend on the cloud density, which can be quantified with the reflectivity values observed by meteorology weather radars. In this study, we aim to estimate the rainfall amount using a Kalman filter with radar reflectivity measurements. We first assume that the amount of rainfall observed at automatic weather observation stations (AWOSs) are elements of an unknown state vector and consider the Kalman filter process model as the true rainfall amounts observed at these AWOSs over time. For the measurement model of the Kalman filter, we use the radar reflectivity values observed at each AWOS location. For the execution of the Kalman filter, a number of rainfall amount and radar reflectivity value pairs are first required to learn the process and measurement models of the Kalman filter. The estimation performance of the proposed Kalman filter is then compared with empirical reflectivity (Z) - rainfall (R) relationships. Numerical results show that when the Kalman filter is executed with radar reflectivity measurements observed around a large number of AWOS locations, the mean squared errors of the Kalman filter rainfall estimates are smaller than the ones obtained with empirical ZR relationships.

In situ terminal settling velocity measurements at Stromboli volcano: Input from physical characterization of ash

Ash particle terminal settling velocity is an important parameter to measure in order to constrain the internal dynamics and dispersion of volcanic ash plumes and clouds that emplace ash fall deposits from which source eruption conditions are often inferred. Whereas the total Particle Size Distribution (PSD) is the main parameter to constrain terminal velocities, many studies have empirically highlighted the need to consider shape descriptors such as the sphericity to refine ash settling velocity as a function of size. During radar remote sensing measurements of weak volcanic plumes erupted from Stromboli volcano in 2015, an optical disdrometer was used to measure the size and settling velocities of falling ash particles over time, while six ash fallout samples were collected at different distances from the vent. We focus on the implications of the physical parameters of ash for settling velocity measurements and modeling. Two-dimensional sizes and shapes are automatically characterized for a large number of ash particles using an optical morpho-grainsizer MORPHOLOGI G3. Manually sieved ash samples show sorted, relatively coarse PSDs spanning a few microns to 2000 μm with modal values between 180 and 355 μm. Although negligible in mass, a population of fine particles below 100 μm form a distinct PSD with a mode around 5–20 μm. All size distributions are offset compared to the indicated sieve limits. Accordingly, we use the diagonal of the upper mesh sizes as the upper sieve limit. Morphologically, particles show decreasing average form factors with increasing circle-equivalent diameter, the latter being equal to 0.92 times the average size between the length and intermediate axes of ash particles. Average particle densities measured by water pycnometry are 2755 ± 50 kg m−3 and increase slightly from 2645 to 2811 kg m−3 with decreasing particle size. The measured settling velocities under laboratory conditions with no wind, <3.6 m s−1, are in agreement with the field velocities expected for particles with sizes <460 μm. The Ganser (1993) empirical model for particle settling velocity is the most consistent with our disdrometer settling velocity results. Converting disdrometer detected size into circle equivalent diameter shows similar PSDs between disdrometer measurements and G3 analyses. This validates volcanological applications of the disdrometer to monitor volcanic ash sizes and settling velocities in real-time with ideal field conditions. We discuss ideal conditions and the measurement limitations. In addition to providing sedimentation rates in-situ, calculated reflectivities can be compared with radar reflectivity measurements inside ash plumes to infer first-order ash plume concentrations. Detailed PSDs and shape parameters may be used to further refine radar-derived mass loading retrievals of the ash plumes.

Spatial variability of airborne radar reflectivity and velocity measurements of tropical rain with application to spaceborne radar

One of the challenges in accurate estimation of rainfall from spaceborne radars is achievement of adequate spatial resolution with reasonable-sized antennas. Previous studies have shown that variability of precipitation within the radar beam can result in errors in rainfall and Doppler estimation from spaceborne radars. In designing these radars, it is therefore necessary to achieve a spatial resolution that reduces such errors to acceptable levels. In this work the author considers data acquired by airborne radar in the tropics or sub-tropics, over ocean. The author confirms many of the findings of previous studies, specifically large Ku-band reflectivity fluctuations and relatively short Ku-band reflectivity correlation lengths in convective areas. This study also examines similar statistics for Ku-band path integrated attenuation and for vertical motion, the latter of which is not widely discussed in the literature. Results from these observations are then applied to spatial resolution considerations for future spaceborne precipitation radars.