S-band Weather Radar Research Articles

A New Perspective on the Scattering Mechanism of S-Band Weather Radar Clear-Air Echoes Based on Communication Models

Clear-air echo studies are usually based on isotropic turbulence theory. But the theory has been considered incomplete by modern turbulence theory. The intermittence of turbulence can reveal obvious shortcomings in the existing studies of clear-air echoes. The mechanism of clear-air echo scattering needs to be supplemented. This paper introduces the troposcatter theory, normally used in over-the-horizon communication, to fill the gap left by Bragg scattering. By treating radar as a self-transmitted and self-received device, the equivalent transmission loss of weather radar is established and compared with the recommendations of the Radiocommunication Sector of the International Telecommunication Union (ITU-R). The results show that the S-band radar transmission loss aligns with ITU-R recommendations. There is also a linear regression relationship between the radar transmission loss and height, which conforms to the troposcatter theory. This means that the theory of troposcatter scattering is a supplement to the theory of Bragg scattering. Tropospheric scattering can be thought of as general Bragg scattering. Meanwhile, based on ITU-R recommendations, this study also provides a new way for the recognition of biological echoes.

Quantification of Migration Birds Based on Polarimetric Weather Radar

Weather radar plays an important role in monitoring aerial animal migration, providing a stable data source for biological studies with large-scale coverage and consecutive-time samples. The accurate estimation of bird density from weather radar echoes is fundamental for quantitative biological studies. We analyzed the bird observation model in weather radar, and proposed a method to build the bird quantification model by jointly utilizing dual-polarization Doppler weather radar and scanning bird radar. We designed a detailed process to remove tracks or echoes from non-bird targets, ensuring the effectiveness of bird observations. The field experiments validated the quantification method, showing that the average radar cross section of birds in Jinan was 19.09 dBscm (i.e., 81.19 cm2; 95% confidence interval, CI: 18.92–19.27 dBscm) for the S-band weather radar, with an R2 of 0.79. In addition, through the correlation analysis, we found that the ground terrain may affect the distribution pattern of aerial bird density.

Turbulence: A Significant Role in Clear-Air Echoes of CINRAD/SA at Night

It is commonly believed that clear-air echoes detected by weather radars are caused by atmobios migration. However, clear-air echoes are sometimes inconsistently related to the activity of living creatures. In some cases, the characteristics of radar products seem to conform to biological scattering, but the movement of echoes cannot be observed. For these reasons, we sought to expand the cause of clear-air echoes from a Chinese Doppler S-band Weather Radar (CINRAD/SA) in Beijing. Some contradictions were discovered in a case which diverged from previous conclusions. It was found that the progression and movement of clear-air echoes do not conform to the rules of biological activities. The frequency distribution of dual-wavelength ratio peaks is 21.5 dB, which is in accordance with Villars–Weisskopf’s turbulence theory. From 1 May to 20 May, the 58% dual-wavelength ratio between the S-band and the X-band was distributed between 18 dB and 24 dB. These results show that more than half of the clear-air echoes of CINRAD/SA at night were caused by turbulence in Beijing. A new model of troposcatter propagation, the reflecting-layers model, was then introduced to explain the radar observations. According to the reflecting-layers model, the echoes’ diurnal variation and reflectivity characteristics are attributed to the effects of turbulent mixing. Excessive turbulent mixing affects the generation of the reflective layer, thereby weakening the echo signal. It is necessary to re-examine the position of turbulence in clear-air echoes.

Study on the Backscatter Differential Phase Characteristics of X-Band Dual-Polarization Radar and its Processing Methods

The differential propagation phase (ΦDP) of X-band dual-polarization weather radar (including X-band dual-polarization phased-array weather radar, X-PAR) is important for estimating precipitation and classifying hydrometeors. However, the measured differential propagation phase contains the backscatter differential phase (δ), which poses difficulties for the application of the differential propagation phase from X-band radars. This paper presents the following: (1) the simulation and characteristics analysis of the backscatter differential phase based on disdrometer DSD (raindrop size distribution) measurement data; (2) an improved method of the specific differential propagation phase (KDP) estimation based on linear programming and backscatter differential phase elimination; (3) the effect of backscatter differential phase elimination on the specific differential propagation phase estimation of X-PAR. The results show the following: (1) For X-band weather radar, the raindrop equivalent diameters D > 2 mm may cause a backscatter differential phase between 0 and 20°; in particular, the backscatter differential phase varies sharply with raindrop size between 3.2 and 4.5 mm. (2) Using linear programming or smoothing filters to process the differential propagation phase could suppress the backscatter differential phase, but it is hard to completely eliminate the effect of the backscatter differential phase. (3) Backscatter differential phase correction may improve the calculation accuracy of the specific differential propagation phase, and the optimization was verified by the improved self-consistency of polarimetric variables, correlation between specific differential propagation phase estimations from S- and X-band radar and the accuracy of quantitative precipitation estimation. The X-PAR deployed in Shenzhen showed good observation performance and the potential to be used in radar mosaics with S-band weather radar.

Study on Attenuation Correction for the Reflectivity of X-Band Dual-Polarization Phased-Array Weather Radar Based on a Network with S-Band Weather Radar

X-band dual-polarization phased-array weather radars (X-PARs) have been used in South China extensively. Eliminating the attenuation and system bias of X-band radar data is the key to utilizing the advantage of X-PAR networks. In this paper, the disdrometer raindrop-size distribution (DSD) measurements are used to calculate the radar polarimetric variables and analyze the characteristics of precipitation attenuation. Furthermore, based on the network of S-band dual-polarization Doppler weather radar (S-POL) and X-PARs, an attenuation-correction method for X-PAR reflectivity is proposed with S-POL constraints in view of the radar-mosaic requirements of a multi-radar network. Linear programming is used to calculate the attenuation-correction parameters of different rainfall areas, which realizes the attenuation correction for X-PAR. The results show that the attenuation-correction parameters simulated based on the disdrometer DSD vary with different precipitation classification; the attenuation-corrected reflectivity of X-PARs is consistent with S-POL and can realize a more precise observation of the evolution of the convective system. Compared with previous attenuation-correction methods with constant correction parameters, the improved method can reduce the deviation between X-PAR reflectivity and that of S-POL in heavy rainfall areas and areas of strong attenuation. The method proposed in this paper is stable and effective. After effective quality control, it is found that the X-PAR network deployed in South China observes data accurately and is consistent with S-POL; thus, it is expected to achieve high temporal–spatial resolution within a radar mosaic.

Validation of IMERG Oceanic Precipitation over Kwajalein

The integrated Multi-satellitE Retrievals for GPM (IMERG) Version V05B and V06B precipitation products from the Global Precipitation Measurement (GPM) mission are validated against ground-based observations from the Kwajalein Polarimetric S-band Weather Radar (KPOL) deployed at Kwajalein Atoll in the central Pacific Ocean. Such a validation is particularly important as comprehensive surface measurements over the oceans are practically infeasible, which hampers the identification of possible errors, and improvement of future versions of IMERG and other satellite-based retrieval algorithms. The V05B and V06B IMERG products are validated at their native 0.1°, 30 min resolution from 2014 to 2018 based on both volumetric and categorical metrics. This validation study indicates that precipitation rates from both IMERG V05B and V06B are underestimated with respect to radar surface estimates, but the underestimation is much reduced from V05B to V06B. IMERG V06B outperforms V05B with reduced systematic bias and improved precipitation detectability. The IMERG performance is further traced back to its individual sensors and morphing-based algorithms. The overall underestimation in V05B is mainly driven by the negative relative biases from morphing-based algorithms which are largely corrected in V06B. Imagers perform generally better than sounders because of the usage of low-frequency channels in imagers which can better detect emission signals by the hydrometeors. Among imagers, the GPM Microwave Imager (GMI) and Advanced Microwave Scanning Radiometer Version 2 (AMSR2) are the best, followed by Special Sensor Microwave Imager/Sounder (SSMIS). Among sounders, the Microwave Humidity Sounder (MHS) is the best, followed by Advanced Technology Microwave Sounder (ATMS) and the Sounder for Atmospheric Profiling of Humidity in the Intertropics by Radiometry (SAPHIR) for V06B. Among all categories, morph-only and IR + morph only perform better than SAPHIR. SAPHIR shows the worst performance among all categories, likely due to its limited channel selection. It is envisaged that these results will improve our understanding of IMERG performance over oceans and aid in the improvement of future versions of IMERG.

Insect-Equivalent Radar Cross-Section Model Based on Field Experimental Results of Body Length and Orientation Extraction

Migratory insects constitute a valuable component of atmospheric and terrestrial biomass, and their migratory behavior provides abundant information for insect management and ecological effect assessment. Effective monitoring of migratory insects contributes to the evaluation and forecasting of catastrophic migration events, such as pest outbreaks. With a large-scale monitoring technique using S-band weather radar, the insect density is estimated based on the linear relationship between radar reflectivity and the average radar cross-section (RCS) of the insects. However, the average RCS model neglects the morphological and observation parameters of the insects, which reduces the estimation accuracy. In this paper, we established an insect-equivalent RCS model based on the joint probability distribution of “body length–incident angle”. Then, we observed and extracted the morphological and observational parameters of the migratory insects by conducting a 69-day field experiment, using a Ku-band fully polarimetric entomological radar, in Dongying, Shandong Province, China. Finally, combined with the experimental results and the simulated scattering characteristics of individual insects with different body lengths, the typical insect-equivalent RCS model was established. The RCS of the model fluctuates between 0.233 mm2 and 0.514 mm2, with different incident angles. Our results lay a data foundation for the quantitative analysis of insects by weather radar.

Health Monitoring Framework for Weather Radar Based on Long Short-Term Memory Network with a Real Case Study

The health management of weather radar plays a key role in achieving timely and accurate weather forecasting. The current practice mainly exploits a fixed threshold prespecified for some monitoring parameters for fault detection. This causes abundant false alarms due to the evolving working environments, increasing complexity of the modern weather radar, and the ignorance of the dependencies among monitoring parameters. To address the above issues, we propose a deep learning-based health monitoring framework for weather radar. First, we develop a two-stage approach for problem formulation that address issues of fault scarcity and abundant false fault alarms in processing the databases of monitoring data, fault alarm record, and maintenance records. The temporal evolution of weather radar under healthy conditions is represented by a long short-term memory network (LSTM) model. As such, any anomaly can be identified according to the deviation between the LSTM-based prediction and the actual measurement. Then, construct a health indicator based on the portion of the occurrence of deviation beyond a user-specified threshold within a time window. The proposed framework is demonstrated by a real case study for the Chinese S-band weather radar (CINRAD-SA). The results validate the effectiveness of the proposed framework in providing early fault warnings.

Solar Monitoring of the NEXRAD WSR-88D Network Using Operational Scan Data

Abstract Solar monitoring is a method in which solar interferences, recorded during operational scanning of a radar, are used to monitor antenna pointing, identify signal processor issues, track receiver chain stability, and check the balance between horizontal and vertical polarization receive channels. The method is used by EUMETNET to monitor more than 100 radars in 20 European countries and it has been adopted by many national weather services across the world. NEXRAD is a network of 160 similar S-band weather radars (WSR-88Ds), which makes it most suitable for assessing the capability of the solar monitoring method on a continental scale. The NEXRAD level-II data contain radial-by-radial noise power estimates. An increase in this estimate is observed when the antenna points close to the sun. Our decoding software extracts these noise power estimates for the horizontal and vertical receive channels (converted to solar flux units) and other relevant metadata, including azimuth, elevation, observation time, and radar location. Here we present results of analyzing one year of solar monitoring data generated by 142 radars from the contiguous United States. We show monitoring results, geographical maps, and statistical outcomes on antenna pointing, solar fluxes, and differential reflectivity biases. We also assess the quality of the radars by defining a figure of merit, which is calculated from the solar monitoring results. The results demonstrate that the solar method provides great benefit for routine monitoring and harmonization of national and transnational operational radar networks.

Estimation of Liquid Fraction of Wet Snow by Using 2-D Video Disdrometer and S-Band Weather Radar

Wet snow may cause significant damage to humans and property, and thus, it is necessary to estimate the corresponding liquid fraction (FL). Consequently, the FL of wet snow was estimated using a novel technique; specifically, the particle shape irregularity (Ir) was estimated through the particle coordinate information obtained using 2-D video disdrometer (2DVD) measurements. Moreover, the possibility of quantitively estimating FL via Ir, based on the temperature (T), was examined. Eight snowfall cases from 2014 to 2016 were observed through a 2DVD installed in Jincheon, South Korea, to analyze the dominant properties of physical variables of snowflakes (i.e., the terminal velocity (VT), particle density (ρs), Ir, and FL) and the corresponding relationships according to the T ranges (−4.5 < T (°C) < 2.5) in which wet snow can occur. It was clarified that the volume-equivalent particle diameter (D)–FL and D–Ir relationships depended on T, and a relationship existed between Ir and FL. The analysis results were verified using the Yong-In Testbed (YIT) S-band weather radar and T-matrix scattering simulation. The D–FL relationship was implemented in the scattering simulation, and the results indicated that the simulated reflectivity (ZS) was highly correlated with the observed reflectivity (ZO) under all T classes. These features can provide a basis for radar analysis and quantitative snowfall estimation for wet snow with various FL values.

Key Technologies of Hydrometeor Classification and Mosaic Algorithm for X-band Polarimetric Radar

The advantages of X-band polarimetric weather radar focus on its high spatio-temporal resolution and capability of multi-radar networking. However, the previously designed hydrometeor classification algorithm (HCA) for S-band weather radar is unsuitable for X-band weather radar due to the difference of backscattering characteristics and heavy precipitation attenuation. Therefore, the key technologies of hydrometeor classification algorithm and multi-radar mosaic algorithm for X-band polarimetric weather radar are proposed. First, it is found that the melting layer detection algorithm designed for S-band polarimetric weather radar is not suitable for X-band weather radar through analysis on the data collected by Beijing X-band radar network. A melting layer detection method based on quasi-vertical profile is proposed, which greatly improves the accuracy of obtaining the melting information. Second, a confidence threshold adjustment method is proposed to accurately estimate the data quality in the case of precipitation and clutter superposition. Third, an optimization method of membership functions based on data statistics is proposed to reconstruct the classification parameters suitable for Beijing X-band radar network. Finally, a multi-radar mosaic method based on rainfall attenuation is proposed, in which the reflectivity factors of networking radars are weighted and averaged by the data quality factor. Compared with the traditional method, it is found that the structural inhomogeneity of X-band radar mosaic result is effectively reduced. Those modifications effectively enhance the reliability of classification mosaic results of X-band weather radar network and provide technical support for the rapid deployment of X-band radar in China. Three typical precipitation cases in Beijing during the flood season in 2016 are used to compare the observational efficiency between X-band weather radar network and S-band operational radar. For the cases of convective rainfall, fine echo structures and reasonable hydrometeor distributions are found in X-band radar mosaic results. Especially for convective rainfall with short duration and small spatial scale, the advantage of X-band radar is more obvious, which alleviates the limited detection ability of S-band operational radar in urban areas. In addition, the hail falling area identified by X-band radar can be verified by manual observation in national weather stations. The performance of X-band weather radar network in large-scale stratiform precipitation, however, is not as good as S-band weather radar.

Raindrop size distribution of rainfall system indirectly affected by Typhoon Kong-Rey (2018) passed through the southern parts of Korea

The internal structure and microphysical properties of a rainband of Typhoon Kong-Rey (TY), which occurred in 2018, and of the rainfall system indirectly affected by the typhoon (IA) that passed through the southern part of the Korean Peninsula in October 2018, were analyzed by operational S-band weather radars and particle size and velocity (PARSIVEL) disdrometers.The height of maximum intensity (Hc in km) of the IA was located at an elevation of ~ 7.5 km; moreover, in the TY regime, the column maximum (Cmax in dBZ) continuously increased up to 45 dBZ at all observation sites, while Hc decreased to 5 km. The IA showed hardly any increase in temperature during precipitation, differently from the TY.In the TY, the maximum fall velocity (Vmax) of raindrops with maximum diameters (Dmax) comprised between 2 and 3 mm was about 1 m s−1 lower than that of drops during precipitation derived from the typhoon's indirect impact. Convective precipitation in the IA presented the following raindrop size distribution (DSD) characteristics within an interval of 18 h (from 21:00 local standard time (LST) on 4 October to 15:00 LST on 5 October): the mass-weighted mean diameter (Dm) and the logarithmic normalized intercept parameter (logNw) were comprised between 1.3 and 1.5 mm and 3.8–4.3, respectively. However, TY showed large temporal variation within an interval of 15 h: Dm and logNw ranged between 1.2 and 2.9 mm and 2.9–4.6, respectively. Additionally, at rainfall rates (R) of 5–15 mm h−1, the shape parameter (μ) of the IA varied between 4 and 7, while that of the TY between 2 and 10.

Vertical electrical field during decay stage of local thunderstorm near coastline in tropical island

In order to directly observe the electric field characteristics and study the charge structure in thunderstorms occurring in tropical regions, a balloon-borne strong electric field sounding is used to measure the vertical component of the electric field, temperature within the cloud and real-time location information of the sounding. Based on the principle of corona discharge, two 1-m-long metal probes are used as the sensors to detect the vertical electric field. In the summer of 2019, a result of electric field sounding within a local thunderstorm was obtained in the northeastern coastal area of Hainan Island, China. With the combination of an S-band weather radar, atmospheric electric field instrument and lightning locating network, the charge structure of the thunderstorm is analyzed in detail. The results show that the thunderstorm is a small-scaled local thunderstorm occurring in the afternoon, the sounding starting to be observed at the decay stage of the thunderstorm. In this period, lightning activities is rare, and the variation of ground electric field is similar to that of conventional summer thunderstorms. The whole sounding process lasts 34 min, during which the vertical airflow in the cloud is relatively stable, basically keeping 4–6 m/s. It can be seen from the electric field profile that the charge distribution in the thunderstorm cloud shows a complex charge structure which is composed of six charge regions. A negative charge region is lowermost, and above this the polarity alternates successively from bottom to up, where all charge regions are located above the melting-layer. Due to data interruption, it is impossible to accurately judge the upper boundary of the upper negative charge region and the information about the positive charge region above. The remaining charge regions are located in an altitude range of 6.0–6.3 km, 6.3–6.6 km, 6.9–7.3 km and 7.3–8.3 km, respectively. The charge densities in these four regions are –1.84 nC/m<sup>3</sup>, 1.80 nC/m<sup>3</sup>, –1.46 nC/m<sup>3</sup>, and 1.04 nC/m<sup>3</sup>, respectively. According to the existing data, the charge density of the uppermost negative charge area should be greater than –0.51 nC/m<sup>3</sup>. Moreover, the upper positive charge region (the fourth from bottom up) has the largest strength, followed by the negative charge region above it, both of which are more than 1 km in thickness. The electric field intensities in the other charge regions are relatively small. The pairs of positive and negative charge regions at the bottom are slightly different in strength and thickness.

Characteristics of the Bright Band Based on Quasi-Vertical Profiles of Polarimetric Observations from an S-Band Weather Radar Network

Bright band (BB) characteristics obtained via dual-polarization weather radars elucidate thermodynamic and microphysical processes within precipitation systems. This study identified BB using morphological features from quasi-vertical profiles (QVPs) of polarimetric observations, and their geometric, thermodynamic, and polarimetric characteristics were statistically examined using nine operational S-band weather radars in South Korea. For comparable analysis among weather radars in the network, the calibration biases in reflectivity (ZH) and differential reflectivity (ZDR) were corrected based on self-consistency. The cross-correlation coefficient (ρHV) bias in the weak echo regions was corrected using the signal-to-noise ratio (SNR). First, we analyzed the heights of BBPEAK derived from the ZH as a function of season and compared the heights of BBPEAK derived from the ZH, ZDR, and ρHV. The heights of BBPEAK were highest in the summer season when the surface temperature was high. However, they showed distinct differences depending on the location (e.g., latitude) within the radar network, even in the same season. The height where the size of melting particles was at a maximum (BBPEAK from the ZH) was above that where the oblateness of these particles maximized (BBPEAK from ZDR). The height at which the inhomogeneity of hydometeors was at maximum (BBPEAK from the ρHV) was also below that of BBPEAK from the ZH. Second, BB thickness and relative position of BBPEAK were investigated to characterize the geometric structure of the BBs. The BB thickness increased as the ZH at BBBOTTOM increased, which indicated that large snowflakes melt more slowly than small snowflakes. The geometrical structure of the BBs was asymmetric, since the melting particles spent more time forming the thin shell of meltwater around them, and they rapidly collapsed to form a raindrop at the final stage of melting. Third, the heights of BBTOP, BBPEAK, and BBBOTTOM were compared with the zero-isotherm heights. The dry-temperature zero-isotherm heights were between BBTOP and BBBOTTOM, while the wet-bulb temperature zero-isotherm heights were close to the height of BBPEAK. Finally, we examined the polarimetric observations to understand the involved microphysical processes. The correlation among ZH at BBTOP, BBPEAK, and BBBOTTOM was high (>0.94), and the ZDR at BBBOTTOM was high when the BB’s intensity was strong. This proved that the size and concentration of snowflakes above the BB influence the size and concentration of raindrops below the BB. There was no depression in the ρHV for a weak BB. Finally, the mean profile of the ZH and ZDR depended on the ZH at BBBOTTOM. In conclusion, the growth process of snowflakes above the BB controls polarimetric observations of BB.

The Cause of Night Clear Air Echo of S-band Weather Radar in Beijing

S-band weather radar can often detect a large number of clear air echoes at night. However, there are different views on the mechanism of clear air echoes. According to characteristics of biological migration, combined with L-band radiosonde data and Beijing S-band weather radar data from 1 March to 18 October in 2018, changes of clear air echo reflectivity factor in different seasons and wind directions are analyzed to discuss causes of clear air echo. Firstly, characteristics of time variation of clear air echo are analyzed. The intensity of clear air echo in Beijing increases after sunset, weakens before sunrise, and changes little at night. The intensity of echo gradually increases from March to the middle of May, then weakens from July to the first ten days in August, and maintains at a high level since September. In vertical profiles, the fluctuation of high-level echoes, up to the altitude of 2 kilometers, is larger than that of low-level echoes, with an obviously seasonal variation. Secondly, by comparing the relationship between the wind direction and radar data in different periods of time, it is found that the echo is not consistent with the biological activity regularity. The entropy of radar data gray level co-occurrence matrix is also calculated, and there is no rule to follow the biology. Therefore, it is considered that the turbulence is the dominant cause of night clear air echo in Beijing. And then, radiosonde data show that the temperature lapse rate and wind shear are consistent with the seasonal variation of combined reflectivity factor at the altitude from 1 to 2 km. Finally, possible influence factors of clear air echo are inferred based on some phenomena observed.

Ice Hydrometeor Shape Estimations Using Polarimetric Operational and Research Radar Measurements

A polarimetric radar method to estimate mean shapes of ice hydrometeors was applied to several snowfall and ice cloud events observed by operational and research weather radars. The hydrometeor shape information is described in terms of their aspect ratios, r, which represent the ratio of particle minor and major dimensions. The method is based on the relations between depolarization ratio (DR) estimates and aspect ratios. DR values, which are a proxy for circular depolarization ratio, were reconstructed from radar variables of reflectivity factor, Ze, differential reflectivity, ZDR, and copolar correlation coefficient ρhv, which are available from radar systems operating in either simultaneous or alternate transmutation of horizontally and vertically polarized signals. DR-r relations were developed for retrieving aspect ratios and their sensitivity to different assumptions and model uncertainties were discussed. To account for changing particle bulk density, which is a major contributor to the retrieval uncertainty, an approach is suggested to tune the DR-r relations using reflectivity-based estimates of characteristic hydrometeor size. The analyzed events include moderate snowfall observed by an operational S-band weather radar and a precipitating ice cloud observed by a scanning Ka-band cloud radar at an Arctic location. Uncertainties of the retrievals are discussed.

Planetary boundary layer evolution over the Amazon rainforest in episodes of deep moist convection at the Amazon Tall Tower Observatory

Abstract. In this study, high-frequency, multilevel measurements, performed from late October to mid-November of 2015 at a 80 m tall tower of the Amazon Tall Tower Observatory (ATTO) project in the central state of Amazonas, Brazil, were used to diagnose the evolution of thermodynamic and kinematic variables as well as scalar fluxes during the passage of outflows generated by deep moist convection (DMC). Outflow associated with DMC activity over or near the tall tower was identified through the analysis of storm echoes in base reflectivity data from an S-band weather radar at Manaus, combined with the detection of gust fronts and cold pools utilizing tower data. Four outflow events were selected, three of which took place during the early evening transition or nighttime hours and one during the early afternoon. Results show that the magnitude of the drop in virtual potential temperature and changes in wind velocity during outflow passages vary according to the type, organization, and life cycle of the convective storm. The nocturnal events had well-defined gust fronts with moderate decreases in virtual potential temperature and increases in wind speed. The early afternoon event lacked a sharp gust front and only a gradual drop in virtual potential temperature was observed, probably because of weak or undeveloped outflow. Sensible heat flux (H) increased at the time of the gust front arrival, which was possibly due to the sinking of colder air. This was followed by a prolonged period of negative H, associated with enhanced nocturnal negative H in the wake of the storms. In turn, increased latent heat flux (LE) was observed following the gust front, owing to drier air coming from the outflow; however, malfunctioning of the moisture sensors during rain precluded a better assessment of this variable. Substantial enhancements of turbulent kinetic energy (TKE) were observed during and after the gust front passage, with values comparable to those measured in grass fire experiments, evidencing the highly turbulent character of convective outflows. The early afternoon event displayed slight decreases in the aforementioned quantities in the passage of the outflow. Finally, a conceptual model of the time evolution of H in nocturnal convective outflows observed at the tower site is presented.

Hydrological appraisal of rainfall estimates from radar, satellite, raingauge and satellite–gauge combination on the Qinhuai River Basin, China

ABSTRACTMultisource rainfall products can be used to overcome the absence of gauged precipitation data for hydrological applications. This study aims to evaluate rainfall estimates from the Chinese S-band weather radar (CINRAD-SA), operational raingauges, multiple satellites (CMORPH, ERA-Interim, GPM, TRMM-3B42RT) and the merged satellite–gauge rainfall products, CMORPH-GC, as inputs to a calibrated probability distribution model (PDM) on the Qinhuai River Basin in Nanjing, China. The Qinhuai is a middle-sized catchment with an area of 799 km2. All sources used in this study are capable of recording rainfall at high spatial and temporal resolution (3 h). The discrepancies between satellite and radar data are analysed by statistical comparison with raingauge data. The streamflow simulation results from three flood events suggest that rainfall estimates using CMORPH-GC, TRMM-3B42RT and S-band radar are more accurate than those using the other rainfall sources. These findings indicate the potential to use satellite and radar data as alternatives to raingauge data in hydrological applications for ungauged or poorly gauged basins.

A novel velocity dealiasing algorithm for S-Band weather radars

Noise, especially continuous noise, is an important factor that affects the velocity dealiasing of Doppler weather radars. A challenge in the design of dealiasing algorithm is how to effectively suppress noise. Therefore, a novel anti-noise dealiasing algorithm (AND) for Doppler velocities was proposed in this paper. In comparison with traditional methods, the AND utilizes a new separation-restoration noise suppression scheme, which can significantly reduce the interference of noise on dealiasing without losing data. This algorithm utilizes three fitting curves as references to correct ambiguous echoes, and it can further suppress residual noise and dynamically match wind fields at varying scales. The AND has been applied to the CINRAD-SA network of operational radars in China, achieving a better performance on noisy echoes, isolated echoes, typhoon echoes, and tornado echoes.

Sensitivity of Rain-Rate Estimates Related to Convective Organization: Observations from the Kwajalein, RMI, Radar

AbstractGround radar rainfall, necessary for satellite rainfall product (e.g., TRMM and GPM) ground validation (GV) studies, is often retrieved using annual or climatological convective/stratiform Z–R relationships. Using the Kwajalein, Republic of the Marshall Islands (RMI), polarimetric S-band weather radar (KPOL) and gauge network during the 2009 and 2011 wet seasons, the robustness of such rain-rate relationships is assessed through comparisons with rainfall retrieved using relationships that vary as a function of precipitation regime, defined as shallow convection, isolated deep convection, and deep organized convection. It is found that the TRMM-GV 2A53 rainfall product underestimated rain gauges by −8.3% in 2009 and −13.1% in 2011, where biases are attributed to rainfall in organized precipitation regimes. To further examine these biases, 2A53 GV rain rates are compared with polarimetrically tuned rain rates, in which GV biases are found to be minimized when rain relationships are developed for each precipitation regime, where, for example, during the 2009 wet-season biases in isolated deep precipitation regimes were reduced from −16.3% to −4.7%. The regime-based improvements also exist when specific convective and stratiform Z–R relationships are developed as a function of precipitation regime, where negative biases in organized convective events (−8.7%) are reduced to −1.6% when a regime-based Z–R is implemented. Negative GV biases during the wet seasons lead to an underestimation in accumulated rainfall when compared with ground gauges, suggesting that satellite-related bias estimates could be underestimated more than originally described. Such results encourage the use of the large-scale precipitation regime along with their respective locally characterized convective or stratiform classes in precipitation validation endeavors and in development of Z–R rainfall relationships.