Bright Band Errors in Radar Estimates of Rainfall: Identification and Correction Using Polarization Diversity

From 19 April to 19 May 2010, volcanic aerosol layers originating from the Eyjafjallajökull volcano were observed at the Institute of Methodologies for Environmental Analysis of the National Research Council of Italy Atmospheric Observatory, named CIAO (40.60°N, 15.72°E, 760 m above sea level), in Southern Italy with a multiwavelength Raman lidar. During this period, ultragiant aerosols were also observed at CIAO using a colocated 8.45 mm wavelength Doppler radar. The Ka‐band radar signatures observed in four separate days (19 April and 7, 10, and 13 May) are consistent with the observation of nonspherical ultragiant aerosols characterized by values of linear depolarization ratio (LDR) higher than –4 dB. Air mass back trajectory analysis suggests a volcanic origin of the ultragiant aerosols observed by the radar. The observed values of the radar reflectivity (Ze) are consistent with a particle effective radius (r) larger than 50–75 µm. Scattering simulations based on the T‐matrix approach show that the high LDR values can be explained if the observed particles have an absolute aspect ratio larger than 3.0 and consist of an internal aerosol core and external ice shell, with a variable radius ratio ranging between 0.2 and 0.7 depending on the shape and aspect ratio. Comparisons between daytime vertical profiles of aerosol backscatter coefficient (β) as measured by lidar and radar LDR reveal a decrease of β where ultragiant particles are observed. Scattering simulations based on Mie theory show how the lidar capability in typing ultragiant aerosols could be limited by low number concentrations or by the presence of an external ice shell covering the aerosol particles. Preferential vertical alignment of the particles is discussed as another possible reason for the decrease of β.

A new approach is presented to the problem of correcting radar estimates of surface rainfall for errors resulting from the changes in reflectivity between the surface and the height at which the radar measures reflectivity. In this approach, a set of five typical shape functions (TSF) of a sample of 89 000 vertical profiles was found. These vertical profiles were formed from volumes scan information, interpolated onto 10 levels, at 250 m spacing, with level 6 being set to the height of the top of the bright band. The TSF class for each vertical profile was determined from a limited set of observations of reflectivity aloft using Bayesian discriminant functions. Regression equations were developed for each TSF class to estimate the lowest interpolated level reflectivity (level 1) from these observations aloft. Application of the discriminant functions shows that the 62% of the dependent sample could be correctly classified into its TSF class for data at heights greater than 500 m below the bright band. In four of the five TSF classes, there was significant skill in estimating the surface reflectivity, explaining 80% of the variation of the level 1 reflectivity. The fifth class explained 49% of the level 1 variation. On average, using this approach of determining the TSF for each vertical profile rather than using a single profile for each volume scan increased the variation of the surface reflectivity explained by observations aloft from 59.5% to 77.4%. Information on the skill of estimating the surface reflectivity was also produced, using the posterior probability and the explained variance of the regression equations.

In an attempt to unravel the interactions between cloud microphysics and dynamics that make shallow clouds precipitate heavily in this region, some unique observations of rain and cloud microphysical parameters are presented here from two stations, Pune and Mahabaleshwar, one each on the lee and windward sides, respectively, of the Western Ghat (WG) mountains in peninsular India. To elucidate rain microphysics, we used the raindrop size distribution (DSD) by fitting three parameter Gamma functions to the observed raindrop spectra. Over Pune, during stratiform rain with bright band (BB) at 0°C isotherm; concave upward DSD shapes are observed below the BB which becomes concave downward at lower altitudes. It is due to breakup process of large raindrops which increases drop concentration at midsizes suggesting coalescence, collision, and breakup processes. Both slope and intercept parameters of Gamma DSD decrease during no BB condition as altitudes decrease, signifying collision and coalescence processes. Over Mahabaleshwar, bimodal and monomodal DSD are observed during light and heavy rainfall, respectively. With shallow storm heights, small raindrops mainly contribute to both types of rainfall. The DSDs are parameterized, and their radar reflectivity factor‐rainfall intensity relationships are evaluated suggesting the dominance of collision‐coalescence processes. Aircraft measurements of orographic clouds over the WG suggest interaction of cloud mass with the ambient updraft speed. The orographically forced updrafts foster rapid condensational growth of cloud droplets triggering coalescence process within few hundred meters of cloud depth. Hence, these clouds are dynamically forced to produce precipitation over the WG.

In this study, a 1D steady-state microphysical model that describes the vertical distribution of melting precipitation particles is developed. The model is driven by the ice-phase precipitation distributions just above the freezing level at applicable grid points of ''parent'' 3D cloud-resolving model (CRM) simulations. It extends these simulations by providing the number density and meltwater fraction of each particle in finely separated size categories through the melting layer. The depth of the modeled melting layer is primarily determined by the initial material density of the ice-phase precipitation. The radiative properties of melting precipitation at microwave frequencies are calculated based upon different methods for describing the dielectric properties of mixed-phase particles. Particle absorption and scattering efficiencies at the Tropical Rainfall Measuring Mission Microwave Imager frequencies (10.65-85.5 GHz) are enhanced greatly for relatively small (0.1) meltwater fractions. The relatively large number of partially melted particles just below the freezing level in stratiform regions leads to significant microwave absorption, well exceeding the absorption by rain at the base of the melting layer. Calculated precipitation backscatter efficiencies at the precipitation radar frequency (13.8 GHz) increase with particle meltwater fraction, leading to a ''bright band'' of enhanced radar reflectivities in agreement with previous studies. The radiative properties of the melting layer are determined by the choice of dielectric models and the initial water contents and material densities of the ''seeding'' ice-phase precipitation particles. Simulated melting-layer profiles based upon snow described by the Fabry-Szyrmer core-shell dielectric model and graupel described by the Maxwell-Garnett water matrix dielectric model lead to reasonable agreement with radar-derived melting-layer optical depth distributions. Moreover, control profiles that do not contain mixedphase precipitation particles yield optical depths that are systematically lower than those observed. Therefore, the use of the melting-layer model to extend 3D CRM simulations is likely justified, at least until more-realistic spectral methods for describing melting precipitation in high-resolution, 3D CRMs are implemented.

Under different water vapor and dynamic conditions, and the influence of topographies and atmospheric environments, stratiform precipitation over South China and the Tibetan Plateau can produce different features. In this study, stratiform precipitation vertical characteristics, bright-band (BB) microstructures, and the vertical variations of the raindrop size distribution (DSD) over a low-altitude site (Longmen site, 86 m) in South China and a high-altitude site (Nagqu site, 4507 m) on the Tibetan Plateau were comprehensively investigated and compared using measurements from a Ka-band millimeter-wave cloud radar (CR), a K-band microrain radar (MRR), and a Parsivel disdrometer (disdrometer). A reliable BB identification scheme was proposed on the basis of CR variables and used for stratiform precipitation sample selection and further statistics and analysis. Results indicate that melting layers over the Longmen are much higher and slightly thicker than those over the Nagqu due to significant differences in atmospheric conditions. For stratiform precipitation, vertical air motions and radar variables over the two sites show different variation trends from cloud top to the ground. Vertical air motions are very weak in the stratiform precipitation over the Longmen, whereas updrafts are more active over the Nagqu. Above the melting layer, radar equivalent reflectivity factor Ze (mean Doppler velocity VM) gradually increases (decreases) as height decreases over the two sites, but the aggregation rate for ice particles over the Longmen can be faster. In the melting layer, Ze (VM) at the BB bottom/center over the Longmen is larger (smaller) than those over the Nagqu for the reason that melted raindrops in the melting layers over the Longmen are larger than those over the Nagqu. Below the melting layer, profiles of radar variables and DSDs show completely different behaviors over the two sites, which reflects that the collision, coalescence, evaporation, and breakup processes of raindrops are different between the two sites. Over the Longmen, collision and coalescence dominate the precipitation properties; in particular, from 2.0–2.8 km, the breakup process competes with collision–coalescence processes but later is overpowered. In contrast, due to the lower BB heights over the Nagqu, collision and coalescence dominate raindrop properties. Comparisons of raindrop spectra suggest that the concentration of small (medium-to-large) raindrops over the Nagqu is much higher (slightly lower) than that over the Longmen. Therefore, the mass-weighted mean diameter Dm (the generalized intercept parameter Nw) over the Nagqu is smaller (larger) than that over the Longmen.

Accurate records of high‐resolution rainfall fields are essential in urban hydrology, and are lacking in many areas. We develop a high‐resolution (15 min, 1 km2) radar rainfall data set for Charlotte, North Carolina during the 2001‐2010 period using the Hydro‐NEXRAD system with radar reflectivity from the National Weather Service Weather Surveillance Radar 1988 Doppler weather radar located in Greer, South Carolina. A dense network of 71 rain gages is used for estimating and correcting radar rainfall biases. Radar rainfall estimates with daily mean field bias (MFB) correction accurately capture the spatial and temporal structure of extreme rainfall, but bias correction at finer timescales can improve cold‐season and tropical cyclone rainfall estimates. Approximately 25 rain gages are sufficient to estimate daily MFB over an area of at least 2,500 km2, suggesting that robust bias correction is feasible in many urban areas. Conditional (rain‐rate dependent) bias can be removed, but at the expense of other performance criteria such as mean square error. Hydro‐NEXRAD radar rainfall estimates are also compared with the coarser resolution (hourly, 16 km2) Stage IV operational rainfall product. Stage IV is adequate for flood water balance studies but is insufficient for applications such as urban flood modeling, in which the temporal and spatial scales of relevant hydrologic processes are short. We recommend the increased use of high‐resolution radar rainfall fields in urban hydrology.

Nine summers of reflectivity and Doppler data, (1993–2001), archived by the Marshall Radar Observatory of McGill University have been processed for the purpose of identifying the location, strength and frequency of occurrence of severe weather events associated with convective activity. A mesocyclone detection and tracking algorithm has identified 329 features with a lifetime of at least 10 minutes. The distribution of their duration, path length, diameter, depth, rotational velocity and shear are provided, as well as their hourly and monthly frequency. Their geographical distribution reveals an increased relative probability of occurrence over the western area of the McGill radar coverage as well as to the north, near Mirabel, and a reduced probability of occurrence near the metropolitan area of Montréal. The analysis of deep convection has been performed in terms of upper level Vertically Integrated Liquid water content maps (UVIL) where the vertical integration of reflectivity begins at a height of 5 km. UVIL maps are preferred among other types of reflectivity‐based radar products because they are the least affected by radar artefacts such as bright band, ground echoes, anomalous propagation and beam blocking. An inherent bias, present in the standard generation of UVIL maps, and due to an oxygen attenuation overcorrection and to the vertical profile of reflectivity of thunderstorms as sampled by a broadening beamwidth, has been identified and corrected. The geographical distribution of UVIL measurements in terms of the time that a UVIL threshold of 10 and 15 kg m−2 is exceeded has been obtained for both the individual years and for the entire 9‐year period. Areas of preferred convection have been identified in the latter, particularly along the flatlands of the St. Lawrence River Basin. A relative minimum is quite noticeable along a broad corridor running north and south of the radar over heavily forested mountainous regions. This combination results in a non‐negligible correlation of the order of ‐0.2 between the height of the topography (m) and deep convection. Curves revealing the relative severity of the nine years in terms of a continuous UVIL threshold have also been derived. The years 1994, 1998 and 1999 emerged as the most active years while 1996 and 2000 were clearly very quiet.

Observation of a melting layer using a 1.55 µm coherent Doppler lidar (CDL) is first presented during a stratiform precipitation event. Simultaneous radar measurements are also performed by co-located 1.24 cm micro rain radar (MRR) and 10.6 cm Doppler weather radar (DWR). As a well-known bright band in radar reflectivity appears during precipitation, an interesting dark band about 160 m below that in lidar backscattering is observed. Due to the absorption effect, the backscattering from raindrops at 1.55 µm is found much weaker than that at short wavelengths usually used in direct detection lidars. However, the CDL provides additional Doppler information which is helpful for melting layer identification. For example, a spectrum bright band with broadened width and sign conversion of skewness is detected in this case. After a deep analysis of the power spectra, the aerosol and precipitation components are separated. The fall speed of hydrometeors given by CDL is found smaller than that of MRR, with the differences of approximately 0.5 m/s and 1.5 m/s for the snow and rainfall, respectively. To illustrate the influence of absorption effect, simulations of the backscatter coefficient and extinction coefficient of aerosol and rainfall are also performed at the wavelength range of 0.3 ∼ 2.2 µm using the Mie theory.

This study examined the vertical gradient of radar reflectivity below the detected bright band in stratiform regions from the Tropics to the extratropical latitudes using data from the Ku‐band (13.6 GHz) precipitation radar on board the Global Precipitation Measurement (GPM) Core Observatory. Stratiform precipitation profiles with reflectivity decreasing (increasing) from the melting level toward the surface occur frequently in the tropical oceans (mid‐ and high‐latitude oceans). High fractions of downward increasing stratiform pixels are found over the North Pacific Ocean throughout the year and over East Asia except for winter. In contrast, the North American continent and the adjacent North Atlantic Ocean are characterized by low fractions of downward‐increasing pixels during summer. The difference is consistent with the dominant type of convection over East Asia (warm‐type clouds) and over the North American continent (cold‐type clouds). Even in the tropical oceans such as the Atlantic and eastern Pacific intertropical convergence zones, there are some areas with moderate fractions of downward‐increasing stratiform pixels where the warm rain process dominates. The downward reduction of reflectivity in the stratiform region of MCSs is obviously due to evaporation, which is a function of lower‐tropospheric relative humidity. The downward increase of reflectivity in stratiform regions over the midlatitude oceans appears the result of raindrop growth. This is achieved via the collection of cloud droplets while falling through low‐level clouds produced by large‐scale vertical motion in the lower troposphere due to large‐scale convergence associated with synoptic‐scale systems.

Radars are known for their ability to obtain a wealth of information about spatial storm field characteristics. Unfortunately, rainfall estimates obtained by this instrument are known to be affected by multiple sources of error. Especially for stratiform precipitation systems, the quality of radar rainfall estimates starts to decrease at relatively close ranges. In the current study, the hydrological potential of weather radar is analyzed during a winter half‐year for the hilly region of the Belgian Ardennes. A correction algorithm is proposed which corrects the radar data for errors related to attenuation, ground clutter, anomalous propagation, the vertical profile of reflectivity (VPR), and advection. No final bias correction with respect to rain gauge data was implemented because such an adjustment would not add to a better understanding of the quality of the radar data. The impact of the different corrections is assessed using rainfall information sampled by 42 hourly rain gauges. The largest improvement in the quality of the radar data is obtained by correcting for ground clutter. The impact of VPR correction and advection depends on the spatial variability and velocity of the precipitation system. Overall during the winter period, the radar underestimates the amount of precipitation as compared to the rain gauges. Remaining differences between both instruments can be attributed to spatial and temporal variability in the type of precipitation, which has not been taken into account.

<p>Taiwan is located at the northwest side of Pacific Ocean and also in the Circum-Pacific Seismic Belt, as a result, suffers from frequently typhoons and earthquakes. The plate collision creates steep mountains account for 70% area of Taiwan. Averaged annual rainfall is 3600 mm in Taiwan, whenever typhoon or weather front brings heavy rainfall addition with geological instability thus increase the landslide occurrence. Rainfall gauge stations are sparse in the mountainous region, and the interpolated rainfall are usually underestimated. The error sources include 1.) variation of raindrop size distribution which is rarely known and varies in time and space (James, 1979); 2.) radar beam attenuation, the rainfall estimation will be underestimated as distance from radar to gauges increase ( Joss, 1998); 3.) beam blockage by mountains, when the beam encounters terrain blocking, it will cause signal interference, which is known as ground clutter (Li and Chen, 2002). Therefore, it is necessary to overcome those issues while try to predict accurate rainfall via radar reflectivity in the mountain regions.</p><p>In this research, we use radar reflectivity combines ground rainfall gauges to compensate the forecasting rainfall. The first method uses the known radar echo intensity (Z) and the rainfall of ground stations (R) to calculate the A and b coefficients by genetic algorithm with the exponential relationship, Z=A*R<sup>b</sup>, proposed by Marshall and Palmer. However, the results are unreasonable, the value of A is varying in 0.01-1000, mostly under 1, and the value of b is varying in 0.1-30. Hence, we decide to use another method. First, we assume that for a short distance (ex: 30 Km), the raindrop size correction factor is constant without attenuation and beam blockage. Second, we estimate the correction factor with the attenuation pattern with distance. Third, the beam blockage from mountains is then considered, and it also takes the first two corrections in consideration. The approach we used is artificial neural network (ANN) to compensate the estimated rainfall from real time radar reflectivity.</p><p>The purpose of this study is to estimate the accurate rainfall of potential landslide area hours ahead typhoon or weather front reaches, we use the historical route of radar echo to infer the path of movement of next hour. If the estimated rainfall exceeds landslide thresholds, the alarm system will be activated. With these efforts the estimated rainfall in the mountain region is improved 70% from tryout experiments. It is found that is correction for radar reflectivity is not an universal transformation, it is depended on the nature of water concentration and also the drop size within the weather front.</p><p> </p><p>Key words: Radar Echo, Artificial Neural Network, Early Warning</p>

Radar calibration by gage, disdrometer, and polarimetry: Theoretical limit caused by the variability of drop size distribution and application to fast scanning operational radar data

Freezing rain is commonly caused by overrunning. The typical atmospheric structure and precipitation patterns associated with overrunning and freezing rain suggest a general association between 1) a bright band and 2) a wind-shear layer between the colder air mass near the surface and an overrunning warmer air mass above the frontal boundary. These features were easily detected by a Doppler radar, and their associated thermodynamic structure was documented by an instrumented aircraft in two freezing-rain events near Kansas City, Missouri, in February 1990. The two cases suggest that perhaps freezing rain caused by overrunning has recognizable and easily parameterized Doppler radar signatures that could be incorporated into a freezing-rain detection algorithm. A preliminary algorithm is discussed.

Radar range: far. Max rain level: moderate rain. Parent DatasetIDRA weather radar measurements - month 2017-01 Measuring instrumentIDRA atmospheric radar in CESAR observatory, CabauwIRCTR has built a high resolution radar system, IDRA (IRCTR Drizzle Radar), aimed at the detailed observation of the spatial and temporal distribution of rainfall and drizzle. The system was placed at the end of August 2007 on top of a 213 m high meteorological tower in the CESAR (Cabauw Experimental Site for Atmospheric Research) observatory in Cabauw, The Netherlands. This location has several advantages: In the first place, an increased sensitivity due to the reduction of the influence of ground clutter. Secondly, it allows direct observation of the horizontal distribution of low level clouds and fog. Finally, the presence of other instruments in the vicinity enhances the understanding of the physical processes in the atmosphere by synergistically combining their measurements. Data from other instruments at CESAR are available at .IDRA provides the horizontal distribution of reflectivity, mean Doppler velocity, Doppler spectrum width and polarimetric parameters like differential reflectivity, linear depolarization ratio or specific differential phase. The data collected is freely available to the scientific community.

Radar range: standard. Max rain level: weak rain. Parent DatasetIDRA weather radar measurements - month 2011-08 Measuring instrumentIDRA atmospheric radar in CESAR observatory, CabauwIRCTR has built a high resolution radar system, IDRA (IRCTR Drizzle Radar), aimed at the detailed observation of the spatial and temporal distribution of rainfall and drizzle. The system was placed at the end of August 2007 on top of a 213 m high meteorological tower in the CESAR (Cabauw Experimental Site for Atmospheric Research) observatory in Cabauw, The Netherlands. This location has several advantages: In the first place, an increased sensitivity due to the reduction of the influence of ground clutter. Secondly, it allows direct observation of the horizontal distribution of low level clouds and fog. Finally, the presence of other instruments in the vicinity enhances the understanding of the physical processes in the atmosphere by synergistically combining their measurements. Data from other instruments at CESAR are available at .IDRA provides the horizontal distribution of reflectivity, mean Doppler velocity, Doppler spectrum width and polarimetric parameters like differential reflectivity, linear depolarization ratio or specific differential phase. The data collected is freely available to the scientific community.