Vertical Profile Of Reflectivity Research Articles

Rainfall Estimates with Respect to Rainfall Types Using S-Band Polarimetric Radar in Korea

To investigate the impact of rainfall type on rainfall estimation using polarimetric variables, rainfall relations such as those between rain rate (R) and specific differential phase (KDP), between R and KDP/differential reflectivity (ZDR), and between R and reflectivity (Z)/ZDR, were examined with respect to the precipitation type classified using drop size distributions (DSDs) measured by a disdrometer. The classification of rainfall type was assessed using four different methods: temporal rainfall variation; and the relations between intercept parameter (N0) and R; normalized intercept parameter (Nw) and median diameter (D0); and slope parameter (Λ) and R. The logN0–R relation discriminated between convective and stratiform rain with less standard deviation than the other methods as shown by the Z–ZDR scatter with respect to the rainfall types. The transition type from convective to stratiform and vice versa occurred in the stratiform rain region for all methods. To apply the classified rainfall relations to radar rainfall estimation, logNw and D0 were retrieved from polarimetric variables to discriminate the rainfall types in the radar domain. The DSD classification was verified with the vertical profile of reflectivity extracted at two positions corresponding to gage sites. Statistical analysis of four different rainfall events showed that rainfall estimation using the relations with precipitation type were better than those obtained without classification. The R(KDP,ZDR) relation with classification performed best on rainfall estimation for all rainfall events. The greatest improvement in rainfall estimation was obtained from R(Z,ZDR) with classification. We conclude that the classification of rainfall type leads to more accurate rainfall estimation. The different relations R(KDP), R(KDP,ZDR), and R(Z,ZDR) with respect to the rain types using polarimetric radar show improvement compared to estimation without consideration of rainfall type, in Korea.

Improving Operational Radar Rainfall Estimates Using Profiler Observations Over Complex Terrain in Northern California

Quantitative precipitation estimation (QPE) using operational weather radars in the western United States is still a challenging issue due to the beam blockage in the mountainous areas and complex rainfall microphysics induced by the orographic enhancement. This article aims to improve operational radar rainfall estimates in complex terrain by incorporating auxiliary remote sensing observations. An innovative vertical profile of reflectivity (VPR) correction scheme is developed for operational radar using observations from multiple vertically pointing profilers to represent the vertical structure of precipitation at various locations. A demonstration study in the Russian River basin in Northern California is detailed. Results show that the QPE performance is significantly improved after VPR correction, and this new VPR correction approach is superior to the conventional approach currently applied in the operational radar rainfall system. The normalized standard error of hourly rainfall estimates for the two precipitation events presented in this article is improved by ~20% after applying the proposed VPR correction scheme.

Microphysics of Snowfall Over Coastal East Antarctica Simulated by Polar WRF and Observed by Radar

AbstractThe current assessment of the Antarctic surface mass balance mostly relies on reanalysis products or climate model simulations. However, little is known about the ability of models to reliably represent the microphysical processes governing the precipitation. This study makes use of recent ground‐based precipitation measurements at Dumont d'Urville station in Adélie Land to evaluate the representation of the precipitation microphysics in the Polar version of the Weather Research Forecast (Polar WRF) atmospheric model. During two summertime snowfall events, high‐resolution simulations are compared to measurements from an X‐band polarimetric radar and from a Multi‐Angle Snowflake Camera (MASC). A radar simulator and a “MASC” simulator in Polar WRF make it possible to compare similar observed and simulated variables. Radiosoundings and surface‐meteorological observations were used to assess the representation of the regional dynamics in the model. Five different microphysical parameterizations are tested. The simulated temperature, wind, and humidity fields are in good agreement with the observations. However, the amount of simulated surface precipitation shows large discrepancies with respect to observations, and it strongly differs between the simulations themselves, evidencing the critical role of the microphysics. The inspection of vertical profiles of reflectivity and mixing ratios revealed that the representation of the sublimation process by the low‐level dry katabatic winds strongly influences the actual amount of precipitation at the ground surface. By comparing the simulated radar signal as well as MASC and model particle size distributions, it is also possible to identify the microphysical processes involved and to pinpoint shortcomings within the tested parameterizations.

Combined use of volume radar observations and high-resolution numerical weather predictions to estimate precipitation at the ground: methodology and proof of concept

Abstract. The extrapolation of the precipitation to the ground from radar reflectivities measured at the beam altitude is one of the most delicate phases of radar data processing for producing quantitative precipitation estimations (QPEs) and remains a major scientific issue. In many operational meteorological services such as Météo-France, a vertical profile of reflectivity (VPR) correction is uniformly applied over a large part or the entire radar domain. This method is computationally efficient, and the overall bias induced by the bright band is most of the time well corrected. However, this way of proceeding is questionable in situations with high spatial and vertical variability of precipitation (during the passage of a cold front or in a complex terrain, for example). This study initiates from two statements: first, radars provide information on precipitation with a high spatio-temporal resolution but still require VPR corrections to extrapolate rain rates at the ground level. Second, the horizontal resolution of some numerical weather prediction (NWP) models is now comparable with the radar one, and their dynamical core and microphysics schemes allow the production of realistic simulations of VPRs. The present paper proposes a new approach to assess surface rainfall from radar reflectivity aloft by exploiting simulated VPRs and rainfall forecasts from the high-resolution NWP model AROME-NWC. To our knowledge, this is the first time that simulated precipitation profiles from an NWP model are used to derive radar QPEs. The implementation of the new method on two stratiform situations provided significant improvements on the hourly and 6 h accumulations compared to the operational QPEs, showing the relevance of this new approach.

A Retrieval Method of Vertical Profiles of Reflectivity for Migratory Animals Using Weather Radar

Quantifying the distribution of the aerial organisms is essential for investigating the movement and behavior of migratory animals. This large-scale broad-front migration can be readily detected by weather radars. However, estimating their vertical distribution is still biased due to the vertical variability of the reflectivity in the radar beam. In this article, we establish a weather radar biological observation model and propose a retrieval method to identify the vertical profiles of reflectivity (VPRs) using regularization technique, which can eliminate the estimation bias. The performance of the method is evaluated using different radar antenna patterns and different regularization parameters, and a sensitivity analysis is performed. The improvement of the method is represented by comparing to the direct method. We apply this method to autumn migration cases over the east coast of China; the demonstration results show the potential of this method in the study of migratory animals.

Early Lessons on Combining Lidar and Multi-baseline SAR Measurements for Forest Structure Characterization

The estimation and monitoring of 3D forest structure at large scales strongly rely on the use of remote sensing techniques. Today, two of them are able to provide 3D forest structure estimates: lidar and synthetic aperture radar (SAR) configurations. The differences in wavelength, imaging geometry, and technical implementation make the measurements provided by the two configurations different and, when it comes to the sensitivity to individual 3D forest structure components, complementary. Accordingly, the potential of combining lidar and SAR measurements toward an improved 3D forest structure estimation has been recognised from the very beginning. However, until today there is no established framework for this combination. This paper attempts to review differences, commonalities, and complementarities of lidar and SAR measurements. First, vertical lidar reflectance and SAR reflectivity profiles at different wavelengths are compared in different forest types. Then, current perspectives on their combination for the generation of enhanced structure products are discussed. Two promising frameworks for combining lidar and SAR measurements are reviewed. The first one is a model-based framework where lidar-derived parameters are used to initialize SAR scattering models, and relies on both the validity of the models and on the physical equivalence of the used lidar and SAR parameters. The second one is a structure-based framework based on the ability of lidar and SAR measurements to express physical forest structure by means of appropriate indices. These indices can then be used to establish a link between the two kind of measurements. The review is supported by experimental results achieved using space- and airborne data acquired in recent relevant mission and campaigns.

Precipitation Microphysics of Tropical Cyclones Over the Western North Pacific Based on GPM DPR Observations: A Preliminary Analysis

AbstractUsing observations from the dual‐frequency precipitation radar (DPR) onboard the Global Precipitation Measurement mission (GPM) satellite, this study analyzes the microphysical structures and processes of tropical cyclone (TC) precipitation over the western North Pacific in terms of different precipitation efficiency indices (PEIs). The statistical results show that the mean mass‐weighted mean diameter of raindrops (Dm) at 2 km is 1.67 mm (1.37 mm) for convective (stratiform) precipitating clouds. Precipitating clouds with high PEI have higher liquid water path than nonliquid water path for both convective and stratiform clouds. The mean Dm of convective and stratiform precipitation increases as the PEI increases. The vertical profiles of Dm and reflectivity (Ze) for convective and stratiform precipitating clouds in TCs differ substantially as the PEI changes. Below the melting level, there is a clear decrease (increase) in Dm and Ze toward the surface for clouds with low (high) PEI. In general, clouds within TCs producing the most efficient precipitation are characterized by strong coalescence, not only for small droplets but also for relatively large raindrops; in contrast, the breakup of hydrometeors is the dominant process in convective and stratiform precipitating clouds with low PEI. These results will help validate and improve the hydrometeor parameterization schemes in cloud and climate models.

The vertical structure of precipitation at two stations in East Antarctica derived from micro rain radars

Abstract. Precipitation over Antarctica is the main term in the surface mass balance of the Antarctic ice sheet, which is crucial for the future evolution of the sea level worldwide. Precipitation, however, remains poorly documented and understood mainly because of a lack of observations in this extreme environment. Two observatories dedicated to precipitation have been set up at the Belgian station Princess Elisabeth (PE) and at the French station Dumont d'Urville (DDU) in East Antarctica. Among other instruments, both sites have a vertically pointing micro rain radar (MRR) working at the K band. Measurements have been continuously collected at DDU since the austral summer of 2015–2016, while they have been collected mostly during summer seasons at PE since 2010, with a full year of observation during 2012. In this study, the statistics of the vertical profiles of reflectivity, vertical velocity, and spectral width are analyzed for all seasons. Vertical profiles were separated into surface precipitation and virga to evaluate the impact of virga on the structure of the vertical profiles. The climatology of the study area plays an important role in the structure of the precipitation: warmer and moister atmospheric conditions at DDU favor the occurrence of more intense precipitation compared with PE, with a difference of 8 dBZ between both stations. The strong katabatic winds blowing at DDU induce a decrease in reflectivity close to the ground due to the sublimation of the snowfall particles. The vertical profiles of precipitation velocity show significant differences between the two stations. In general, at DDU the vertical velocity increases as the height decreases, while at PE the vertical velocity decreases as the height decreases. These features of the vertical profiles of reflectivity and vertical velocity could be explained by the more frequent occurrence of aggregation and riming at DDU compared to PE because of the lower temperature and relative humidity at the latter, located further in the interior. Robust and reliable statistics about the vertical profile of precipitation in Antarctica, as derived from MRRs for instance, are necessary and valuable for the evaluation of precipitation estimates derived from satellite measurements and from numerical atmospheric models.

Microphysical Characteristics and Types of Precipitation for Different Seasons over North Taiwan

In this work, long-term (10 years) raindrop size distribution (RSD) measurements from the Joss–Waldvogel Disdrometer (JWD) installed at the National Central University (NCU) (24°58′6″N, 121°11′27″E), Taiwan, and the vertical profile of radar reflectivity were used to analyze the variations in the gamma parameters of six seasons (winter, spring, mei-yu, summer, typhoon, and autumn) and types of precipitation. The normalized gamma distribution of RSD revealed that the highest mean Dm (mass-weighted average diameter) values occurred in the summer, whereas the highest mean log10 Nw (normalized intercept parameter) values were found in the winter. Furthermore, most of the rain falling at a rate of less than 20 mm h−1 occurs in Northern Taiwan. In this study, we used radar reflectivity to differentiate between convective and stratiform systems. It was revealed that the mean Dm values are higher in convective systems, whereas the mean log10 Nw values are higher in stratiform systems. The structure of RSD in stratiform systems remains constant in all seasons; however, convection is similar to maritime type. The microphysical characteristics that are responsible for the different RSD features in different seasons and types of precipitation are illustrated with the help of contoured frequency by altitude diagrams of radar reflectivity.

Evaluating the Algorithm for Correction of the Bright Band Effects in QPEs with S-, C- and X-Band Dual-Polarized Radars

The bright band, a layer of enhanced radar reflectivity associated with melting ice particles, is a major source of significant overestimation in quantitative precipitation estimation (QPE) based on the Z-R (reflectivity factor-rain rate) relationship. The effects of the bright band on radar-based QPE can be eliminated by vertical profile of reflectivity (VPR) correction. In this study, we applied bright-band correction algorithms to evaluate three different bands (S-, C- and X-band) of dual-polarized radars and to reduce overestimation errors in Z-R relationship-based QPEs. After the reflectivity was corrected by the algorithms using average VPR (AVPR) alone and a combination of average VPR and the vertical profile of the copolar correlation coefficient (AVPR+CC), the QPEs were derived. The bright-band correction and resulting QPEs were evaluated in eight precipitation events by comparing to the uncorrected reflectivity and rain-gauge observations, separately. The overestimation of Z-R relationship-based QPEs associated with the bright band was reduced after correction by the two schemes for which hourly rainfall was less than 5 mm. For the verification metrics of RMSE (root-mean-square error), RMAE (relative mean absolute error) and RMB (relative mean bias) of QPEs, averaged over all eight cases, the AVPR method improved from 2.28, 0.94 and 0.78 to 1.55, 0.60 and 0.40, respectively, while the AVPR+CC method improved to 1.44, 0.55 and 0.30, respectively. The QPEs after AVPR+CC correction had less overestimation than those after AVPR correction, and similar conclusions were drawn for all three different bands of dual-polarized radars.

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

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

3-D Scattering Characterization of Agricultural Crops at C-Band Using SAR Tomography

The aim of this paper is to interpret and characterize the changes of the 3-D polarimetric scattering signatures of agricultural crops at C-band and to relate them to temporal changes of the soil and plant parameters. For this, a time series of multibaseline (MB) synthetic aperture radar (SAR) data acquired at C-band by the airborne F-SAR system of the German Aerospace Center over the Wallerfing test site in Germany was analyzed. The availability of MB SAR data enables the resolution of scattering contributions in height by means of SAR tomography. The tomographic profiles at different polarizations were analyzed regarding temporal changes for different crop types. First, it was investigated if the center of mass (CoM) of the vertical reflectivity profiles as a single parameter enables the tracking of changes in soil and vegetation. The results show that the vertical reflectivity profiles and their CoM do not allow resolving the ambiguity if a change originates from soil or vegetation dynamics as expected. Thus, the scattering contributions from ground and volume were separated in height, using a filtering approach, and used for the estimation of the ground and volume scattering powers by means of covariance matching. Comparing the outputs with coincident ground measurements showed that dielectric as well as geometric changes in the vegetation are traceable by the separated ground and volume powers. Finally, the estimated powers were analyzed with respect to orientation effects, i.e., to polarimetric anisotropic behavior. They were found to be not significant for the crops under study at C-band.

Forest Above-Ground Biomass Estimation Using Single-Baseline Polarization Coherence Tomography with P-Band PolInSAR Data

Forest above ground biomass (AGB) extraction using Synthetic Aperture Radar (SAR) images has been widely used in global carbon cycle research. Classical AGB inversion methods using SAR images are mainly based on backscattering coefficients. The polarization coherence tomography (PCT) technology which can generate vertical profiles of forest relative reflectivity, has the potential to improve the accuracy of biomass inversion. The relationship between vertical profiles and forest AGB is modeled by some parameters defined based on geometric characteristics of the relative reflectivity distribution curve. But these parameters are defined without physical characteristics. Among these parameters, tomographic height (TomoH) is considered as the most important one. However, TomoH only corresponds to the highest volume relative reflectivity, which is lower than the actual forest height, affecting the accuracy of forest height and AGB inversion. In this paper, we introduce a new parameter, the canopy height (Hac), for AGB inversion by analyzing the vertical backscatter power loss. Then, we construct an inversion model based on the combination of the new parameter (Hac) and other parameters from the tomographic profile. The P-band polarimetric SAR datasets of the European Space Agency (ESA) BioSAR 2008 campaign acquired over Krycklan Catchment are selected for the verification experiment at two different flight directions. The results show that Hac performs better in estimating forest height and AGB than TomoH does. The inversion root mean square error (RMSE) of the proposed method is 18.325 t ha−1, and the result of using TomoH is 21.126 t ha−1.

Radar-based quantitative precipitation estimation for the identification of debris flow occurrence over earthquake-affected regions in Sichuan, China

Abstract. Both Ms 8.0 Wenchuan earthquake on 12 May 2008 and Ms 7.0 Lushan earthquake on 20 April 2013 occurred in the province of Sichuan, China. In the earthquake-affected mountainous area, a large amount of loose material caused a high occurrence of debris flow during the rainy season. In order to evaluate the rainfall intensity–duration (I–D) threshold of the debris flow in the earthquake-affected area, and to fill up the observational gaps caused by the relatively scarce and low-altitude deployment of rain gauges in this area, raw data from two S-band China New Generation Doppler Weather Radar (CINRAD) were captured for six rainfall events that triggered 519 debris flows between 2012 and 2014. Due to the challenges of radar quantitative precipitation estimation (QPE) over mountainous areas, a series of improvement measures are considered: a hybrid scan mode, a vertical reflectivity profile (VPR) correction, a mosaic of reflectivity, a merged rainfall–reflectivity (R − Z) relationship for convective and stratiform rainfall, and rainfall bias adjustment with Kalman filter (KF). For validating rainfall accumulation over complex terrains, the study areas are divided into two kinds of regions by the height threshold of 1.5 km from the ground. Three kinds of radar rainfall estimates are compared with rain gauge measurements. It is observed that the normalized mean bias (NMB) is decreased by 39 % and the fitted linear ratio between radar and rain gauge observation reaches at 0.98. Furthermore, the radar-based I–D threshold derived by the frequentist method is I = 10.1D−0.52 and is underestimated by uncorrected raw radar data. In order to verify the impacts on observations due to spatial variation, I–D thresholds are identified from the nearest rain gauge observations and radar observations at the rain gauge locations. It is found that both kinds of observations have similar I–D thresholds and likewise underestimate I–D thresholds due to undershooting at the core of convective rainfall. It is indicated that improvement of spatial resolution and measuring accuracy of radar observation will lead to the improvement of identifying debris flow occurrence, especially for events triggered by the strong small-scale rainfall process in the study area.

Radar-derived quantitative precipitation estimation in complex terrain over the eastern Tibetan Plateau

Quantitative precipitation estimation (QPE) is one of the important applications of weather radars. However, in complex terrain such as Tibetan Plateau, it is a challenging task to obtain an optimal Z–R relation due to the complex spatial and temporal variability in precipitation microphysics. This paper develops two radar QPE schemes respectively based on Reflectivity Threshold (RT) and Storm Cell Identification and Tracking (SCIT) algorithms using observations from 11 Doppler weather radars and 3264 rain gauges over the Eastern Tibetan Plateau (ETP). These two QPE methodologies are evaluated extensively using four precipitation events that are characterized by different meteorological features. Precipitation characteristics of independent storm cells associated with these four events, as well as the storm-scale differences, are investigated using short-term vertical profile of reflectivity (VPR) clusters. Evaluation results show that the SCIT-based rainfall approach performs better than the simple RT-based method for all precipitation events in terms of score comparison using validation gauge measurements as references. It is also found that the SCIT-based approach can effectively mitigate the local error of radar QPE and represent the precipitation spatiotemporal variability better than the RT-based scheme.

The Potential Use of the Linear Depolarization Ratio to Distinguish between Convective and Stratiform Rainfall to Improve Radar Rain-Rate Estimates

AbstractA major source of errors in radar-derived quantitative precipitation estimates is the inhomogeneous nature of the vertical reflectivity profile (VPR). Operational radars generally scan in azimuth at constant elevation (PPI mode) and provide limited VPR information, so predetermined VPR shapes with limited degrees of freedom are needed to correct for the VPR in real time. Typical stratiform VPRs have a sharp peak below the 0° isotherm, known as the “bright band,” caused by the presence of large melting snowflakes, but this feature is not present in convective cores where the melting ice is in the form of graupel or compact ice. Inappropriate correction assuming a brightband VPR can lead to underestimation of rain rates, with particular impacts in intense convective storms. This paper proposes the use of high values of linear depolarization ratio (LDR) measurements to confirm the presence of large melting snowflakes and lower values for melting graupel or high-density ice as a prerequisite to selecting a suitable profile shape for VPR correction. Using a climatologically representative dataset of short-range, high-resolution C-band vertical profiles, the peak value of the LDR in the melting layer is shown to have robust skill in identifying VPRs without bright band, with the “best” performance at a threshold of −20 dB. Further work is proposed to apply this result to improving corrections for VPR at longer range, where the limited effect of beam broadening on LDR peaks could provide advantages over other available methods.

How do changes in warm-phase microphysics affect deep convective clouds?

Abstract. Understanding aerosol effects on deep convective clouds and the derived effects on the radiation budget and rain patterns can largely contribute to estimations of climate uncertainties. The challenge is difficult in part because key microphysical processes in the mixed and cold phases are still not well understood. For deep convective clouds with a warm base, understanding aerosol effects on the warm processes is extremely important as they set the initial and boundary conditions for the cold processes. Therefore, the focus of this study is the warm phase, which can be better resolved. The main question is: How do aerosol-derived changes in the warm phase affect the properties of deep convective cloud systems? To explore this question, we used a weather research and forecasting (WRF) model with spectral bin microphysics to simulate a deep convective cloud system over the Marshall Islands during the Kwajalein Experiment (KWAJEX). The model results were validated against observations, showing similarities in the vertical profile of radar reflectivity and the surface rain rate. Simulations with larger aerosol loading resulted in a larger total cloud mass, a larger cloud fraction in the upper levels, and a larger frequency of strong updrafts and rain rates. Enlarged mass both below and above the zero temperature level (ZTL) contributed to the increase in cloud total mass (water and ice) in the polluted runs. Increased condensation efficiency of cloud droplets governed the gain in mass below the ZTL, while both enhanced condensational and depositional growth led to increased mass above it. The enhanced mass loading above the ZTL acted to reduce the cloud buoyancy, while the thermal buoyancy (driven by the enhanced latent heat release) increased in the polluted runs. The overall effect showed an increased upward transport (across the ZTL) of liquid water driven by both larger updrafts and larger droplet mobility. These aerosol effects were reflected in the larger ratio between the masses located above and below the ZTL in the polluted runs. When comparing the net mass flux crossing the ZTL in the clean and polluted runs, the difference was small. However, when comparing the upward and downward fluxes separately, the increase in aerosol concentration was seen to dramatically increase the fluxes in both directions, indicating the aerosol amplification effect of the convection and the affected cloud system properties, such as cloud fraction and rain rate.

Combined Space and Ground Radars for Improving Quantitative Precipitation Estimations in the Eastern Downstream Region of the Tibetan Plateau. Part I: Variability in the Vertical Structure of Precipitation in ChuanYu Analyzed from Long-Term Spaceborne Observations by TRMM PR

AbstractThis study presents a statistical analysis of the variability of the vertical structure of precipitation in the eastern downstream region of the Tibetan Plateau as measured by the Precipitation Radar (PR) on the National Aeronautics and Space Administration Tropical Rainfall Measuring Mission (TRMM) satellite. Data were analyzed over an 11-yr time span (January 2004–December 2014). The results show the seasonal and spatial variability of the storm height, freezing level, and bright band for different types of precipitation as well as the characteristics of intensity-related and type-related vertical profiles of reflectivity (VPR). Major findings were as follows: About 90% of the brightband peak reflectivity of stratiform precipitation was less than 32 dBZ, and 40% of the maximum reflectivity of convective precipitation exceeded 35 dBZ. The intensity of surface rainfall rates also depended on the shapes of VPRs. For stratiform precipitation, ice–snow aggregation was faster during moderate and heavy rainfall than it was in light rainfall. Since both the moisture and temperature are lower in winter, the transformation efficiency of hydrometeors becomes slower. Typical Ku-band representative climatological VPRs (CPRs) for stratiform precipitation have been created on the basis of the integration of normalized VPR shape for the given area and the rainfall intensity. All of the findings indicate that the developed CPRs can be used to improve surface precipitation estimates in regions with complex terrain where the ground-based radar net has limited visibility at low levels.

A terrain‐based weighted random forests method for radar quantitative precipitation estimation

ABSTRACTWhile weather radar is widely used for quantitative precipitation estimation (QPE) in China and many other countries, the performance of radar QPE is unsatisfactory. A major reason for inaccurate radar QPE is the application of conventional Z–R relationships. In this study the entire vertical profile of reflectivity (VPR) is taken into consideration and a new relationship converting the VPR to rainfall rate is developed. The new relationship is obtained by a proposed terrain‐based weighted random forests (TWRF) method. The TWRF method regards 21 levels of constant altitude plan position indicator reflectivity (from 1 to 18 km) as features. The method consists of two parts: the first is to obtain subregions based on terrain, and the second is to refine the classical random forests method by computing feature weights based on correlation co‐efficients between features and rainfall rate. Radar QPE based on the TWRF method was tested within the 45–100 km range of the radar in Hangzhou, China, on rainfall events in 2014. The proposed method showed improved performance for all verification scores over the Z–R relationship and the classical random forests method. Use of the entire VPR and the terrain‐based study proved to be effective in this example. Experimental results indicate that the proposed TWRF method can improve the accuracy of radar QPE compared to an independent network of rain gauges.

Seasonal variability of warm boundary layer cloud and precipitation properties in the Southern Ocean as diagnosed from A‐Train data

AbstractThe extensive cloudiness and resulting high albedo of the Southern Oceans (SO) are predominantly due to the occurrence of widespread marine boundary layer clouds. Recent work finds correlations between biogenically enhanced cloud condensation nuclei concentrations and cloud droplet number concentrations derived from passive satellite data. The active remote sensors in the A‐Train have created a unique and long‐term record of these clouds that include vertical profiles of radar reflectivity and microwave brightness temperature from CloudSat that can be combined with solar reflectances from Moderate Resolution Imaging Spectroradiometer. We examine this data record using a unique algorithm to infer warm‐topped cloud and precipitation properties. We find seasonal variations in cloud properties where summer season clouds demonstrate higher cloud droplet number concentrations on average and require higher liquid water contents to produce similar precipitation rates.