S-band Polarimetric Radar Research Articles

A New Paradigm for Automated Ground Clutter Removal: Global Regression Filtering

Abstract Ground clutter filtering is an important and necessary step for quality control of ground-based weather radars and this is widely done in the spectral domain via use of the Discrete Fourier Transform (DFT). To accomplish spectral clutter filtering, the I (in-phase) and Q (quadrature) time series are multiplied by a window function, such as the Blackman window, which suppresses clutter leakage. Subsequently, the clutter is filtered from the Doppler spectrum by setting spectral coefficients to zero where there is clutter signal. Recently, it has been shown with simulations that a regression clutter filter is a viable alternative. To demonstrate the regression filter, it is applied to radar data from both National Science Foundation-National Center for Atmospheric Research (NSF-NCAR) S-band polarimetric radar (S-Pol), and NEXRAD (Next Generation Weather Radar). A regression filter can be applied directly to the non-windowed time series data, thus avoiding the signal attenuation from a window function, which is required for spectral domain clutter filters such as GMAP (Gaussian Model Adaptive Processing). It is shown that the regression filter rejects clutter as effectively as the spectral technique, or better, but has the distinct advantage that standard error of estimates of the radar variables are in general improved by about 25% to 50% over a comparable spectral domain filter. The regression filter is straightforward and can be executed in real time.

Characterization and Application of S-Band Polarimetric Radar and X-Band Phased Array Radar for a Tornadic Storm Event on June 16, 2022

Characterization and Application of S-Band Polarimetric Radar and X-Band Phased Array Radar for a Tornadic Storm Event on June 16, 2022

Towards improved short-term forecasting for Lake Victoria Basin: Part I – A radar-based convective mode analysis

Abstract East African countries benefit economically from the largest freshwater lake in Africa: Lake Victoria (LV). Around 30 million people live along its coastline and 5.4 million people subsist on its fishing industry. However, more than 1,000 fishermen die annually by high-wave conditions often produced by severe convective wind phenomena, which marks this lake one of the deadliest places in the world for hazardous weather impacts. The World Meteorological Organization launched the 3-year “HIGH impact Weather lAke sYstem” (HIGHWAY) project, with the main objective to reduce loss of lives and economic goods in the lake basin and improve the resilience of the local communities. The project conducted a field campaign in 2019 aiming to provide forecasters with high-resolution observations and to study the storm life cycle over the lake basin. The research discussed here used the S-band polarimetric Tanzania radar from the field campaign to investigate the diurnal cycle of the convective mode over the lake. We classified the lake storms occurring during the two wet seasons into six different convective modes and present their diurnal evolution, organization, and main radar-based attributes, thereby extending the knowledge of convection on the lake. The result is the creation of a “convection catalog for Lake Victoria,” using the operational forecast lake sectors, and defining the exact times for the different timeslots resulting from the HIGHWAY project for the marine forecast. This will inform methods to improve the marine operational forecasts for Lake Victoria, and to provide the basis for new Standard Operation Procedures (SOP) for severe weather surveillance and warning.

An Improved S-Band Polarimetric Radar-Based QPE Algorithm for Typhoons over South China Using 2DVD Observations

Polarimetric radar data are an important tool for quantitative precipitation estimation (QPE), which is essential for monitoring and forecasting precipitation. Previous studies have shown that the drop size distribution (DSD) and polarimetric radar parameters of typhoon-induced precipitation differ significantly from those of other types of rainfall. South China is a region that frequently experiences typhoons and heavy rainfall, which can cause serious disasters. Therefore, it is critical to develop a QPE algorithm that is suitable for typhoon precipitation over South China. In this study, we constructed four simple QPE estimators, R(ZH), R(ZH, ZDR), R(KDP) and R(KDP, ZDR) based on two-dimensional video disdrometer (2DVD) DSD observations of typhoon-induced precipitation over South China in 2017–2018. We analyzed the DSD characteristics and the estimation accuracy of these four QPE estimators in the reflectivity–differential reflectivity (ZH–ZDR) space, as well as the S-band polarimetric radar (S-POL) data of seven typhoon-induced precipitation events that affected South China in 2017–2019. We used these data to quantitatively determine the optimal ranges of the estimators and establish a typhoon precipitation QPE algorithm for typhoon-induced precipitation over South China (2DVD-Typhoon). The evaluation results showed that: (1) compared to R(ZH) and R(KDP), R(ZH, ZDR) and R(KDP, ZDR) had lower performance in estimating typhoon-induced rainfall after incorporating the polarimetric parameter ZDR, as strong crosswind of the typhoon caused some bias in the raindrop-induced ZDR; (2) the 2DVD-Typhoon algorithm utilizes the respective advantages of the individual estimators to generate the best QPE results; (3) the QPE performance of 2DVD-Typhoon and the Colorado State University–Hydrometeor Identification Rainfall Optimization (CSU-HIDRO) is used as a comparison for hourly rainfall, cumulative rainfall and different rainfall intensity. The comparison shows that 2DVD-Typhoon gives a better normalized error (NE), root mean square error (RMSE) and correlation coefficient (CC), indicating its strength in rainfall estimation for typhoons over South China. The above results provide theoretical support for improving typhoon-induced rainfall monitoring and numerical weather forecasting models in South China.

Polarimetric radar observation of the melting layer during the pre-summer rainy season over South China

ABSTRACT The geometric and polarimetric characteristics of the melting layer (ML) during the pre-summer rainy season over South China (PRSC) are investigated from 389 S-band polarimetric radar volume scans. The existing ML detection algorithm is slightly improved, in which the ML is automatically detected by using vertical gradients of radar reflectivity factor for horizontal polarizations (Z H), and co-polar cross-correlation coefficient (ρ HV) from quasi-vertical profiles. And these profiles are obtained by azimuthal averaging of polarimetric radar variables at antenna elevation 19.5°. The comparison between the detected ML top and the 0°C isotherm heights from ERA5 demonstrates that the detection algorithm has high accuracy with an offset of 158 m. Due to larger snowflakes, the average ML thickness in PRSC is thicker (i.e. 150 m) than that in other climatic regions. The ML is characterized by large Z H, large differential reflectivity (Z DR) and small ρ HV, with average values of 32 dBZ, 0.91 dB and 0.94. Furthermore, the peak values of Z H and Z DR in the ML are obviously larger than that from rain regions and snow regions, while the opposite is true for ρ HV distribution.

Lightning flash rate nowcasting based on polarimetric radar data and machine learning

ABSTRACT This study investigates the prediction of lightning flash rates using a ground-based, S-band polarimetric weather radar and storm cell-based lightning prediction. Both short-term cases during multiple years and long-term cases are used for training and testing by combining the radar and ground-based lightning detection network measurements. The identified lightning-generating storm cells are tracked during the data pre-processing to initiate our machine learning (ML) algorithm. The selection of features for ML algorithm implementation is discussed, and the performance of lightning rate prediction is evaluated using Pearson’s correlation coefficient (r) and mean percentage error (MPE) metrics. The results show that using polarimetric measurement can improve prediction accuracy with short-term data sets, while for long-term datasets, the improvement is less significant. In general, reasonable prediction accuracy is achieved for up to 30 minutes of lead time.

Improving S-Band Polarimetric Radar Monsoon Rainfall Estimation with Two-Dimensional Video Disdrometer Observations in South China

The capability to estimate monsoon rainfall is investigated by using S-band polarimetric radar (S-POL) and two-dimensional Video Disdrometer (2DVD) during 2017–2018 in South China. Based on 2 years of 2DVD raindrop size distribution (DSD) observations of monsoon precipitation systems, four different quantitative precipitation estimation (QPE) algorithms were obtained, including R(ZH), R(ZH, ZDR), R(KDP), and R(KDP, ZDR). In order to clearly demarcate the optimal ranges of the four QPE algorithms by considering the impact of the monsoon precipitation system of South China, the optimal ranges of the four QPE algorithms were integrated together according to the characteristics of different QPE algorithms in the reflectivity-differential reflectivity (ZH-ZDR) space distribution by reference to 8 monsoon rainfall events from 2016 to 2020 observed in Guangzhou and Yangjiang S-POL. Then, an optimal algorithm was proposed for the quantitative estimation of monsoon precipitation in South China (2DVD-SCM) using S-POL. The 2DVD-SCM was tested by comparing it with a traditional radar QPE algorithm PPS (WSR-88D Precipitation Processing System); a classical QPE algorithm CSU-HIDRO (Colorado State University-Hydrometeor Identification Rainfall Optimization) for the polarimetric radar; a piecewise fitting algorithm LPA-PFM (Piecewise Fitting Method) based on laser raindrop spectrum. The rainfall event one-by-one test results show that the 2DVD-SCM algorithm performs obviously better than the other three algorithms in most of the rainfall events. The hourly accumulated rainfalls estimated by the 2DVD-SCM algorithm are agreed well with rain gauge observations. The normalized errors (NE) and the root mean square errors (RMSE) values of 2DVD-SCM are remarkably less than the other three algorithms, and the correlation coefficient (CC) values are higher. The results of the classified rain rate test show that the NE and RMSE values of the 2DVD-SCM algorithm are the lowest in all classified rain rates. The overall evaluation results show that the 2DVD-SCM algorithm performs obviously better than the existing three algorithms and have the potential to apply in S-band polarimetric radar monsoon rainfall estimation operational system in South China.

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.

Testing the Drop-Size Distribution-Based Separation of Stratiform and Convective Rain Using Radar and Disdrometer Data from a Mid-Latitude Coastal Region

Stratiform and convective rain are associated with different microphysical processes and generally produce drop-size distributions (DSDs) with different characteristics. Previous studies using data from (a) a tropical coastal location, (b) a mid-latitude continental location with semi-arid climate, and (c) a sub-tropical continental location, found that the two rain types could be separated in the NW–Dm space, where Dm is the mass-weighted mean diameter and NW is the normalized intercept parameter. In this paper, we investigate the same separation technique using data and observations from a mid-latitude coastal region. Three-minute DSDs from disdrometer measurements are used for the NW- versus Dm-based classification and are compared with simultaneous observations from an S-band polarimetric radar 38 km away from the disdrometer site. Specifically, RHI (range-height indicator) scans over the disdrometer were used for confirmation. Results show that there was no need to modify the separation criteria from previous studies. Three-minute DSDs from the same location were used as input to scattering calculations to derive retrieval equations for NW and Dm for the S-band radar using an improved technique and applied to the RHI scans to identify convective and stratiform rain regions. Two events are shown as illustrative examples.

Evaluation of the Specific Attenuation Method for Radar-Based Quantitative Precipitation Estimation: Improvements and Practical Challenges

AbstractThis study demonstrates an implementation of the prototype quantitative precipitation R estimation algorithm using specific attenuation A for S-band polarimetric radar. The performance of R(A) algorithm is assessed, compared to the conventional algorithm using radar reflectivity Z, at multiple temporal scales. Because the factor α, defined as the net ratio of A to specific differential phase, is a key parameter of the algorithm characterized by drop size distributions (e.g., differential reflectivity Zdr dependence on Z), the estimation equations of α and a proper number of Zdr–Z samples required for a reliable α estimation are examined. Based on the dynamic estimation of α, the event-based evaluation using hourly rain gauge observations reveals that the performance of R(A) is superior to that of R(Z), with better agreement and lower variability. Despite its superiority, the study finds that R(A) leads to quite consistent overestimations of about 10%–30%. It is demonstrated that the application of uniform α over the entire radar domain yields the observed uncertainty because of the heterogeneity of precipitation in the domain. A climatological range-dependent feature of R(A) and R(Z) is inspected in the multiyear evaluation at yearly scale using rain totals for April–October. While R(Z) exposes a systematic shift and overestimation, each of which arise from the radar miscalibration and bright band effects, R(A) combining with multiple R(Z) values for solid/mixed precipitation shows relatively robust performance without those effects. The immunity of R(A) to partial beam blockage (PBB) based on both qualitative and quantitative analyses is also verified. However, the capability of R(A) regarding PBB is limited by the presence of the melting layer and its application requirement for the total span of differential phase (e.g., 3°), which is another challenge for light rain.

A Preliminary Polarimetric Radar Comparison of Pretornadic and Nontornadic Supercell Storms

Abstract Supercell thunderstorms produce a variety of hazards, including tornadoes. A supercell will often exist for some time prior to producing a tornado, while other supercells never become tornadic. In this study, a series of hypotheses is tested regarding the ability of S-band polarimetric radar fields to distinguish pretornadic from nontornadic supercell storms. Several quantified polarimetric radar metrics are examined that are related to storm inflow, updraft, and hailfall characteristics in samples of 19–30 pretornadic and 18–31 nontornadic supercells. The results indicate that pretornadic supercells are characterized by smaller hail extent and echo appendages with larger mean drop size. Additionally, differential reflectivity ZDR column size is larger and less variable in the pretornadic storms in the 25–30 min prior to initial tornadogenesis. Many of the results indicate relatively small polarimetric differences that will likely be difficult to translate to operational use. Hail extent and ZDR column size, however, may exhibit operationally useful differences between pretornadic and nontornadic supercells.

Monitoring the differential reflectivity and receiver calibration of the German polarimetric weather radar network

Abstract. It is a challenge to calibrate differential reflectivity ZDR to within 0.1–0.2 dB uncertainty for dual-polarization weather radars that operate 24∕7 throughout the year. During operations, a temperature sensitivity of ZDR larger than 0.2 dB over a temperature range of 10 ∘C has been noted. In order to understand the source of the observed ZDR temperature sensitivity, over 2000 dedicated solar box scans, two-dimensional scans of 5∘ azimuth by 8∘ elevation that encompass the solar disk, were made in 2018 from which horizontal (H) and vertical (V) pseudo antenna patterns are calculated. This assessment is carried out using data from the Hohenpeißenberg research radar which is identical to the 17 operational radar systems of the German Meteorological Service (Deutscher Wetterdienst, DWD). ZDR antenna patterns are calculated from the H and V patterns which reveal that the ZDR bias is temperature dependent, changing about 0.2 dB over a 12 ∘C temperature range. One-point-calibration results, where a test signal is injected into the antenna cross-guide coupler outside the receiver box or into the low-noise amplifiers (LNAs), reveal only a very weak differential temperature sensitivity (<0.02 dB) of the receiver electronics. Thus, the observed temperature sensitivity is attributed to the antenna assembly. This is in agreement with the NCAR (National Center for Atmospheric Research) S-Pol (S-band polarimetric radar) system, where the primary ZDR temperature sensitivity is also related to the antenna assembly (Hubbert, 2017). Solar power measurements from a Canadian calibration observatory are used to compute the antenna gain and to validate the results with the operational DWD monitoring results. The derived gain values agree very well with the gain estimate of the antenna manufacturer. The antenna gain shows a quasi-linear dependence on temperature with different slopes for the H and V channels. There is a 0.6 dB decrease in gain for a 10 ∘C temperature increase, which directly relates to a bias in the radar reflectivity factor Z which has not been not accounted for previously. The operational methods used to monitor and calibrate ZDR for the polarimetric DWD C-band weather radar network are discussed. The prime sources for calibrating and monitoring ZDR are birdbath scans, which are executed every 5 min, and the analysis of solar spikes that occur during operational scanning. Using an automated ZDR calibration procedure on a diurnal timescale, we are able to keep ZDR bias within the target uncertainty of ±0.1 dB. This is demonstrated for data from the DWD radar network comprising over 87 years of cumulative dual-polarization radar operations.

Comparison of Raindrop Size Distribution between NASA’s S-Band Polarimetric Radar and Two-Dimensional Video Disdrometers

AbstractThe ground-based-radar-derived raindrop size distribution (DSD) parameters—mass-weighted drop diameter Dmass and normalized intercept parameter NW—are the sole resource for direct validation of the National Aeronautics and Space Administration (NASA) Global Precipitation Measurement (GPM) mission Core Observatory satellite-based retrieved DSD. Both Dmass and NW are obtained from radar-measured reflectivity ZH and differential reflectivity ZDR through empirical relationships. This study uses existing relationships that were determined for the GPM ground validation (GV) program and directly compares the NASA S-band polarimetric radar (NPOL) observables of ZH and ZDR and derived Dmass and NW with those calculated by two-dimensional video disdrometer (2DVD). The joint NPOL and 2DVD datasets were acquired during three GPM GV field campaigns conducted in eastern Iowa, southern Appalachia, and western Washington State. The comparative study quantifies the level of agreement for ZH, ZDR, Dmass, and log(NW) at an optimum distance (15–40 km) from the radar as well as at distances greater than 60 km from radar and over mountainous terrain. Interestingly, roughly 10%–15% of the NPOL ZH–ZDR pairs were well outside the envelope of 2DVD-estimated ZH–ZDR pairs. The exclusion of these pairs improved the comparisons noticeably.

Classification of Rainfall Types Using Parsivel Disdrometer and S-Band Polarimetric Radar in Central Korea

Tools to identify and classify stratiform and convective rains at various times of the 12 days from June 2015 to March 2016 in Jincheon, Korea, were developed by using a Parsivel disdrometer and S-band polarimetric (S-POL) radar data. Stratiform and convective rains were identified using three different methods (vertical profile of reflectivity (VPR), the method proposed by Bringi et al. (BR03), and a combination of the two (BR03-VPR)) by using a Parsivel disdrometer for its applications to radar as a reference. BR03-VPR exhibits a better classification scheme than the VPR and BR03 methods. The rain types were compared using the drop size distribution (DSD) retrieved from polarimetric variables and reflectivity only. By using the DSD variables, a new convective/stratiform classification line of the log-normalized droplet number concentration ( log 10 N w ) − median volume diameter ( D 0 ) was derived for this area to classify the rainfall types using DSD variables retrieved from the polarimetric radar. For the radar variables, the method by Steiner et al. (SHY95) was found to be the best method, with 0.00% misclassification of the stratiform rains. For the convective rains, the DSD retrieval method performed better. However, for both stratiform and convective rains, the fuzzy method performed better than the SHY95 and DSD retrieval methods.

Utilization of Specific Attenuation for Rainfall Estimation in Southern China

This study uses rain gauge observations to assess the performance of different radar estimators <i>R</i>(<i>Z</i>_H), <i>R</i>(<i>K</i>_DP) and <i>R</i>(<i>A</i>) in estimating precipitation based on the observations of an S-band polarimetric radar over southern China during a typical convective storm and an extremely severe typhoon, i. e., Typhoon Manghkut. These radar estimators were derived from observations of a local autonomous particle size and velocity (Parsivel) unit (APU) disdrometer. A key parameter, alpha (<i>α</i>), which is the ratio of specific attenuation <i>A</i> to specific differential phase <i>K</i>_DP with three fixed values (<i>α</i>=0.015 dB deg^-1, <i>α</i>=0.0185 dB deg^-1 and <i>α</i>=0.03 dB deg^-1) was examined to test the sensitivity of the <i>R</i>(<i>A</i>) rain retrievals. The results show that: (1) All radar estimators can capture the spatio-temporal patterns of two precipitation events, <i>R</i>(<i>A</i>) with <i>α</i>=0.0185 dB deg^-1 is well correlated with gauge measurement via higher Pearson's correlation coefficient (CC) of 0.87, lower relative bias (RB) of 16%, and lower root mean square error (RMSE) of 17.09 mm in the convective storm while it underestimates the typhoon event with RB of 35%; (2) <i>R</i>(<i>A</i>) with <i>α</i>=0.03 dB deg^-1 shows the best statistical scores with the highest CC (0.92), lowest RB (7%) and RMSE (25.74mm) corresponding to Typhoon Manghkut; (3) <i>R</i>(<i>A</i>) estimates are more efficient in mitigating the impact of partial beam blockage. The results indicate that <i>α</i> is remarkably influenced by the variation of drop size distribution. Thus, more work is needed to establish an automated and optimized <i>α</i> for the <i>R</i>(<i>A</i>) relation during different rainfall events over different regions.

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.

UAV-Aided Weather Radar Calibration

Weather radar is well recognized as an effective sensor for obtaining the microphysical and dynamical properties of precipitation at high spatial and temporal resolution. Radar calibration is one of the most important prerequisites for achieving accurate observations. In this article, a portable, cost-effective and repeatable radar calibration technique, namely, unmanned aerial vehicle (UAV)-aided radar calibration, is proposed. A UAV serves as the stable aerial platform carrying a metal sphere, flying over the radar illumination areas to complete the calibration process. The flying routine of the UAV can be pre-programmed, and thus, the antenna pattern regarding different elevation and azimuth angles can be retrieved. To obtain the position of the sphere, the real-time single-frequency precise point positioning-type global navigation satellite system solution is developed. In addition, the radar constant is calculated in the range-Doppler domain, and only the data where the metal sphere separates from clutter and other objects are selected. The S-band polarimetric Doppler transportable atmospheric radar (TARA) is used in the calibration campaign. The experiments demonstrate the following results: 1) antenna pointing calibration can be completed and 2) antenna pattern can be retrieved and weather radar constant can be accurately calculated.

Dual-Polarization Radar Observations of Deep Convection over Lake Victoria Basin in East Africa

Lake Victoria in East Africa supports the livelihood of thousands of fishermen and it is estimated that 3000–5000 human deaths occur per year over the lake. It is hypothesized that most of these fatalities are due to localized, severe winds produced by intense thunderstorms over the lake during the rainy season and larger scale, intense winds over the lake during the dry season. The intense winds produce a rough state of the lake (big wave heights) that cause fishing boats to capsize. In this region, weather radars have never been a primary tool for monitoring and nowcasting high impact weather. The Tanzania Meteorological Agency operates an S-band polarimetric radar in Mwanza, Tanzania, along the south shore of Lake Victoria. This radar collects high temporal and spatial resolution data that is now being used to detect and monitor the formation of deep convection over the lake and improve scientific understanding of storm dynamics and intensification. Nocturnal thunderstorms and convection initiation over the lake are well observed by the Mwanza radar and are strongly forced by lake and land breezes and gust fronts. Unexpected is the detection of clear air echo to ranges ≥100 km over the lake that makes it possible to observe low-level winds, gust fronts, and other convergence lines near the surface of the lake. The frequent observation of extensive clear air and low-level convergence lines opens up the opportunity to nowcast strong winds, convection initiation, and subsequent thunderstorm development and incorporate this information into a regional early warning system proposed for Lake Victoria Basin (LVB). Two weather events are presented illustrating distinctly different nocturnal convection initiation over the lake that evolve into intense morning thunderstorms. The evolution of these severe weather events was possible because of the Mwanza radar observations; satellite imagery alone was insufficient to provide prediction of storm initiation, growth, movement, and decay.

Optimized raindrop size distribution retrieval and quantitative rainfall estimation from polarimetric radar

A variational approach for optimized drop size distribution (DSD) retrieval, attenuation correction, and rainfall estimation from polarimetric radar measurements (horizontal reflectivity factor ZH, differential reflectivity ZDR, and differential phase shift ΦDP) is proposed in this study. The spatial continuity of rain is guaranteed by introducing the radial B-spine filter and azimuthal Kalman filter, which helps to mitigate the impact of the random errors in ZDR and ΦDP. The approach is evaluated using a simulated experiment and two real cases observed by a mobile C-band and an operational S-band polarimetric radar in south China. The comparative results demonstrate that the proposed variational approach can correct attenuation more accurately than the conventional method using assumed linear relationships between specific attenuation and specific differential phase. The rainfalls derived from the DSD estimates based on this proposed approach agree well with the rain gauge measurements. It is also shown that the proposed method has a better performance than the existing variational retrieval and the composite estimator.

A Prototype Quantitative Precipitation Estimation Algorithm for Operational S-Band Polarimetric Radar Utilizing Specific Attenuation and Specific Differential Phase. Part I: Algorithm Description

Abstract A prototype quantitative precipitation estimate (QPE) algorithm that utilizes specific attenuation A and specific differential phase KDP was developed for inclusion into the Multi-Radar Multi-Sensor (MRMS) system and the Weather Surveillance Radar-1988 Doppler (WSR-88D) network. Special attention is given to the optimization of the factor α used for computation of a path-integrated attenuation from a total span of differential phase along the propagation path in rain. It is suggested to estimate α from a slope of the ZDR dependence on Z in rain. The use of real-time adjusted α allows us to capture the variations of the drop size distributions, and therefore improve the QPE accuracy. It is demonstrated that the factor α is generally higher for tropical rain type compared to continental rain. Since the R(A) approach is only valid for pure rainfall, the R(KDP) relation is suggested as a complement in areas of hail contamination. The paper contains a description of the basic version of the R(A) and R(KDP) algorithm and recommendations for its further optimization.