Weather Radar Observations Research Articles

Spatial evaluation rainfall estimation on weather radar using Marshall–Palmer reflectivity–rainfall rate in Banten

Based on data from the National Disaster Management Agency (Ind.: Badan Nasional Penanggulangan Bencana – BNPB), throughout 2022, more than 91% of disaster events were hydrometeorological disasters, with floods at 43% and landslides at 17%. One of the factors for floods and landslides is high rainfall intensity. Automatic rain gauge (ARG) is a rainfall observation instrument that can accurately measure rainfall at observation points. However, it has problems such as communication systems that cause delays in data transmission, low instrument density, and inability to cover a wide spatial area, which can affect the accuracy of rainfall information. Weather radar is a remote sensing instrument that can estimate rainfall spatially so that weather radar observations can reach areas of the region that do not have ARG. However, before being used as rainfall information, estimation rainfall needs to be evaluated or calibrated. Evaluation of rainfall estimation on weather radar to ARG in Banten at a 30– 120 km distance range, shows a coefficient of determination above 0.8. Based on the studies that have been conducted, increase of root mean square error (RMSE) is due to influence of radar observation range and observation distance on ARG. Adjustment of rainfall estimation improves the accuracy of rainfall estimation. Adjusting rainfall estimation can reduce RMSE by 50%.

A Comparative Study of Cloud Microphysics Schemes in Simulating a Quasi-Linear Convective Thunderstorm Case

An investigation is undertaken to explore a sudden quasi-linear precipitation and gale event that transpired in the afternoon of 30 May 2024 over Beijing. It was situated at the southwestern periphery of a double-center low-vortex system, where a moisture-rich belt efficiently channeled abundant warm, humid air northward from the south. The interplay between dynamical lifting, convergent airflow-induced uplift, and the amplifying effects of the northern mountainous terrain’s topography creates favorable conditions that support the development and persistence of quasi-linear convective precipitation, accompanied by gale-force winds at the surface. The study also analyzes the impacts of five microphysics schemes (Lin, WSM6, Goddard, Morrison, and WDM6) employed in a weather research and forecasting (WRF) numerical model, with which the simulated rainfall and radar reflectivity are compared against ground-based rain gauge network and weather radar observations, respectively. Simulations with the five microphysics schemes demonstrate commendable skills in replicating the macroscopic quasi-linear pattern of the event. Among the schemes assessed, the WSM6 scheme exhibits its superior agreement with radar observations. The Morrison scheme demonstrates superior performance in predicting cumulative rainfall. Nevertheless, five microphysics schemes exhibit limitations in predicting the rainfall amount, the rainfall duration, and the rainfall area, with a discernible lag of approximately 30 min in predicting precipitation onset, indicating a tendency to forecast peak rainfall events slightly posterior to their true occurrence. Furthermore, substantial disparities emerge in the simulation of the vertical distribution of hydrometeors, underscoring the intricacies of microphysical processes.

Verification of weather-radar-based hail metrics with crowdsourced observations from Switzerland

Abstract. Remote hail detection and hail size estimation using weather radar observations has the advantage of wide spatial coverage and high spatial and temporal resolution. Switzerland's National Weather Service (MeteoSwiss) uses two radar-based hail metrics: the probability of hail on the ground (POH) to assess the presence of hail and the maximum expected severe hailstone size (MESHS) to estimate the largest hailstone diameter. However, radar-based metrics are not direct measurements of hail and have to be calibrated with and verified against ground-based observations of hail, such as crowdsourced hail reports. Switzerland benefits from a particularly rich and dense dataset of crowdsourced hail reports from the MeteoSwiss app. We combine a new spatiotemporal clustering method (Density-Based Spatial Clustering of Applications with Noise, ST-DBSCAN) with radar reflectivity to filter the reports and use the filtered reports to verify POH and MESHS in terms of the hit rate, false-alarm ratio (FAR), critical success index (CSI), and Heidke skill score (HSS). Using a 4 km × 4 km maximum upscaling approach, we find FAR values between 0.3 and 0.7 for POH and FAR > 0.6 for MESHS. For POH, the highest CSI (0.37) and HSS (0.52) are obtained using a 60 % threshold, while for MESHS the highest CSI (0.25) and HSS (0.4) are obtained using a 2 cm threshold. We find that the current calibration of POH does not correspond to a probability and suggest a recalibration based on the filtered reports.

Large-scale dynamics of extreme precipitation in the tropical Andes: combining weather radar observations and reanalysis data

Large-scale dynamics of extreme precipitation in the tropical Andes: combining weather radar observations and reanalysis data

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

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

Recreating Observed Convection‐Generated Gravity Waves From Weather Radar Observations via a Neural Network and a Dynamical Atmospheric Model

AbstractConvection‐generated gravity waves (CGWs) transport momentum and energy, and this momentum is a dominant driver of global features of Earth's atmosphere's general circulation (e.g., the quasi‐biennial oscillation, the pole‐to‐pole mesospheric circulation). As CGWs are not generally resolved by global weather and climate models, their effects on the circulation need to be parameterized. However, quality observations of GWs are spatiotemporally sparse, limiting understanding and preventing constraints on parameterizations. Convection‐permitting or ‐resolving simulations do generate CGWs, but validation is not possible as these simulations cannot reproduce the CGW‐forcing convection at correct times, locations, and intensities. Here, realistic convective diabatic heating, learned from full‐physics convection‐permitting Weather Research and Forecasting simulations, is predicted from weather radar observations using neural networks and a previously developed look‐up table. These heating rates are then used to force an idealized GW‐resolving dynamical model. Simulated CGWs forced in this way closely resembled those observed by the Atmospheric InfraRed Sounder in the upper stratosphere. CGW drag in these validated simulations extends 100s of kilometers away from the convective sources, highlighting errors in current gravity wave drag parameterizations due to the use of the ubiquitous single‐column approximation. Such validatable simulations have significant potential to be used to further basic understanding of CGWs, improve their parameterizations physically, and provide more restrictive constraints on tuning with confidence.

Evaluating the potential of bioacoustics in avian migration research by citizen science and weather radar observations.

The study of nocturnal bird migration brings observational challenges because of reduced visibility and observability of birds at night. Remote sensing tools, especially radars, have long been the preferred choice of scientists to study nocturnal migrations. A major downside of these remote sensing tools is the lack of species-level information. With technological advances in recent decades and with improved accessibility and affordability of acoustic tools, sound recordings have steeply increased in popularity. In Europe, there is no exhaustive qualitative and quantitative evaluation of the content of such acoustic databases and therefore the value for migration science and migration-related applications, such as bird collision hazard assessments, is mostly unknown. In the present work we compared migration schedules estimated from citizen science data with quantitative temporal occurrence of species in four years of acoustic recordings. Furthermore, we contrasted acoustic recordings with citizen science observations and weather radar data from one spring and one autumn season to assess the qualitative and quantitative yield of acoustic recordings for migration-related research and applications. Migration intensity estimated from weather radar data correlated best at low levels with acoustic records including all species in spring while in autumn passerine species showed stronger correlation than the entire species composition. Our findings identify a minor number of species whose call records may be eligible for applications derived from acoustics. Especially the highly vocal species Song thrush and Redwing showed relatively good correlations with radar and citizen science migration schedules. Most long-distance passerine migrants and many other migrants were not captured by acoustics and an estimated seasonal average of about 50% of nocturnally migrating passerine populations remained undetected. Overall, the ability of acoustic records to act as a proxy of overall migration dynamics is highly dependent on the migration period and species involved.

Multi‐Platform Observations of Severe Typhoon Koinu

AbstractSevere Typhoon Koinu passed south of Hong Kong on 8 and 9 October 2023, triggering the issuance of the Increasing Gale or Storm Signal No. 9, the second highest tropical cyclone (TC) warning signal in Hong Kong. Koinu was a difficult case for TC warning service due to its compact size and rather erratic movement over the coastal waters of Guangdong. To monitor Koinu's movement and wind structure, the Hong Kong Observatory utilized various observational platforms, including meteorological aircraft, ocean radar, and synthetic aperture radar on polar orbiting satellites. The paper presents major observations derived from these measurements. The aircraft probe and dropsonde data suggested boundary layer inflow, warm core structure, eyewall updraft, and high turbulence in the eyewall of the typhoon. The weather radar observations indicated the occurrence of a waterspout in the vicinity of the typhoon. Additionally, the study highlights the forecasting performance of the AI‐based Pangu‐Weather model, which could outperform the conventional global numerical weather prediction models in forecasting TC track in the region. The documentation of these observations aims to provide valuable references for weather forecasters and stimulate further research on forecasting this type of tropical cyclone.

Evaluating pySTEPS optical flow algorithms for convection nowcasting over the Maritime Continent using satellite data

Abstract. The Maritime Continent (MC) regularly experiences powerful convective storms that produce intense rainfall, flooding and landslides, which numerical weather prediction models struggle to forecast. Nowcasting uses observations to make more accurate predictions of convective activity over short timescales (∼ 0–6 h). Optical flow algorithms are effective nowcasting methods as they are able to accurately track clouds across observed image series and predict forward trajectories. Optical flow is generally applied to weather radar observations; however, the radar coverage network over the MC is not complete and the signal cannot penetrate the high mountainous regions. In this research, we apply optical flow algorithms from the pySTEPS nowcasting library to satellite imagery to generate both deterministic and probabilistic nowcasts over the MC. The deterministic algorithm shows skill up to 4 h on spatial scales of 10 km and coarser and outperforms a persistence nowcast for all lead times. Lowest skill is observed over the mountainous regions during the early afternoon, and highest skill is seen during the night over the sea. A key feature of the probabilistic algorithm is its attempt to reduce uncertainty in the lifetime of small-scale convection. Composite analysis of 3 h lead time nowcasts, initialised in the morning and afternoon, produces reliable ensembles but with an under-dispersive distribution and produces area under the curve scores (i.e. ratio of hit rate to false alarm rate across all probability thresholds) of 0.80 and 0.71 over the sea and land, respectively. When directly comparing the two approaches, the probabilistic nowcast shows greater skill at ≤ 60 km spatial scales, whereas the deterministic nowcast shows greater skill at larger spatial scales ∼ 200 km. Overall, the results show promise for the use of pySTEPS and satellite retrievals as an operational nowcasting tool over the MC.

Tracking Lightning Through 3D Thunder Source Location With Distributed Acoustic Sensing

AbstractInvestigating lightning is of key significance in understanding the lightning mechanism and mitigating lightning hazards. We reported an experiment of investigating lightning through three‐dimensional (3D) thunder locating using a Distributed Acoustic Sensing (DAS) array in Hefei, China. In this experiment, we recast a 7.7 km long urban telecom optical fiber cable as 3,850 sensors using the DAS technique. From dense DAS recording, we manually identified 101 thunder events during six positive cloud‐to‐ground (CG) lightning flashes within 20 min. The DAS recorded thunder signals are dominated by direct acoustic waves rather than air‐ground coupled surface waves. The thunder events were then located using the arrival times of thunder signals. The locations and amplitudes of thunder events are generally consistent with those from the conventional lightning detection data set and broadband magnetic field. There is likely a correlation between the maximum strength of thunder events and the highest peak current for individual CG flashes. Moreover, the comparison with weather radar observations indicates that lightning usually originated from areas of high reflectivity (e.g., ≥50dBz) with diffusely distributed (from ground surface to ∼5 km altitude) thunder events and extended in a narrow altitude range of 3–5 km to areas with low radar reflectivity.

Warn-on-Forecast System: From Vision to Reality

Abstract In 2009, advancements in NWP and computing power inspired a vision to advance hazardous weather warnings from a warn-on-detection to a warn-on-forecast paradigm. This vision would require not only the prediction of individual thunderstorms and their attributes but the likelihood of their occurrence in time and space. During the last decade, the warn-on-forecast research team at the NOAA National Severe Storms Laboratory met this challenge through the research and development of 1) an ensemble of high-resolution convection-allowing models; 2) ensemble- and variational-based assimilation of weather radar, satellite, and conventional observations; and 3) unique postprocessing and verification techniques, culminating in the experimental Warn-on-Forecast System (WoFS). Since 2017, we have directly engaged users in the testing, evaluation, and visualization of this system to ensure that WoFS guidance is usable and useful to operational forecasters at NOAA national centers and local offices responsible for forecasting severe weather, tornadoes, and flash floods across the watch-to-warning continuum. Although an experimental WoFS is now a reality, we close by discussing many of the exciting opportunities remaining, including folding this system into the Unified Forecast System, transitioning WoFS into NWS operations, and pursuing next-decade science goals for further advancing storm-scale prediction. Significance Statement The purpose of this research is to develop an experimental prediction system that forecasts the probability for severe weather hazards associated with individual thunderstorms up to 6 h in advance. This capability is important because some people and organizations, like those living in mobile homes, caring for patients in hospitals, or managing large outdoor events, require extended lead time to protect themselves and others from potential severe weather hazards. Our results demonstrate a prediction system that enables forecasters, for the first time, to message probabilistic hazard information associated with individual severe storms between the watch-to-warning time frame within the United States.

Detecting Signatures of Convective Storm Events in GNSS‐SNR: Two Case Studies From Summer 2021 in Switzerland

AbstractGlobal Navigation Satellite Systems (GNSS) are not only a state‐of‐the‐art sensor for positioning and navigation applications but also a valuable tool for remote sensing. Through the usage of L‐band carrier frequencies, GNSS acts as an all‐weather‐operation system, offering substantial benefits compared to optical systems. Nevertheless, severe weather can still have an impact on the strength of signals received at a ground station, as we show in this study. We investigate GNSS Signal‐to‐Noise Ratio (SNR) observations during two severe convective storm events over the city of Zurich, Switzerland. We make use of a GNSS‐SNR‐based algorithm originally developed for the detection of hail particles from volcanic eruptions. Results indicate that, although GNSS observations are considered to be fairly insensitive to the presence of hydrometeors, convective storm events are visible in SNR observations. SNR levels of affected satellites show a significant drop during event periods, which are determined by weather radar observations.

Seismic signature detection during the 2018 Anak Krakatau flank collapse and tsunami using seismic amplitudes from regional-scale monitoring

On 22 December 2018, the major flank collapse of Anak Krakatau volcano generated a tsunami that struck the surrounding coasts of Java and Sumatra islands in Indonesia without warning. It was later suggested that the corresponding seismic event lacked the body-wave arrivals typical of tectonic earthquakes, causing difficulties for the automated detection system to recognize the event. We explore the possibility of detecting the seismic signature of such events without relying on the arrival times of body waves, by measuring seismic amplitudes in a regional seismic network at the expected arrival times from a fixed, potential source and comparing them to the theoretical attenuation of surface waves. We propose a fast detection method and evaluate the method using seismograms recorded during the flank collapse and tsunami episode as well as several known tectonic earthquakes. Detailed examinations of the detection results confirm the seismic signatures of the flank collapse and teleseismic events as suggested by previous studies. We also find more seismic signatures suggesting the occurrence of two possible smaller collapse events and variations in the eruptive activity related to the major flank collapse, as well as body and surface wave signals from two teleseismic earthquakes that were present during this episode. Finally, we construct a timeline of events during this devastating episode, combining our results with previous studies as well as insights from weather radar observations. With the ability to detect and discriminate various types of seismic events from each other, the detection method can be useful in assisting the existing monitoring and early warning systems in detecting major volcano-related tsunamigenic events.Graphical

Observation and simulation study on the rapid intensification mechanism of Typhoon “Mekkhala” (2006)

Based on Doppler Weather radar observations and numerical simulations applying the Weather Research and Forecasting (WRF) system, this study focused on the rapid intensification (RI) of Typhoon “Mekkhala” (2006) in the inshore area in 2020. The simulated track of the typhoon relatively matched with the observation, with a slight eastward bias compared to the observed track. During the phase of RI, there was a slight weakening of vertical wind shear between 200–500 hPa. The temporary decrease in vertical wind shear became a favorable factor for the intensification of the typhoon. In general, vertical wind shear of the lower atmosphere is the key to supporting the RI of Typhoon Mekkhala. In the middle troposphere, the southward component of the vertical wind shear suddenly increases, indicates that the inflow of southern wind to the core of the typhoon had strengthened. Thus, the strengthening of the moisture transport by enhanced southern wind, contributed to the intensification of the typhoon. During the intensification of the typhoon, the low-level vorticity was significantly enhanced, and the high vorticity values expanded from the lower to higher troposphere. The vertical distribution of vorticity transformed from symmetry to asymmetry. The development of secondary circulation on both sides of the typhoon is a dynamic factor for intensification.

Estimation of extreme precipitation events in Estonia and Italy using dual-polarization weather radar quantitative precipitation estimations

Abstract. Evaluating extreme rainfall for a certain location is commonly considered when designing stormwater management systems. Rain gauge data are widely used to estimate rainfall intensities for a given return period. However, the poor spatial and temporal resolution of operational gauges is the main limiting factor. Several studies have used rainfall estimates based on weather radar horizontal reflectivity (Zh), but they come with a great caveat: while proven reliable for low or moderate rainfall rates, they are subject to major errors in extreme rainfall and convective cases. It is widely known that C-band weather radar can underestimate precipitation intensity due to signal attenuation or overestimate it due to hail and clutter contamination. From the late 1990s, dual-polarization weather radar started to become operational in the national surveillance radar network in Europe, providing innovative quantitative precipitation estimation (QPE) based on polarimetric variables. This study circumvents Zh shortcomings by using specific differential-phase (Kdp) data from operational dual-polarization C-band weather radars. The rain intensity estimates based on a specific differential-phase data are immune to attenuation and less affected by hail contamination. In this study, for the first time, QPEs based on polarimetric observations by operational C-band weather radars and without any rain gauge adjustments are analyzed. The purpose is to estimate return periods for 1 h rainfall total computed from polarimetric weather radar data using non-adjusted QPEs based on R(Zh,Kdp) data and to compare the results with those derived using R(Zh) and rain gauge data. Only the warm period during the year is considered here, as most of the extreme precipitation events for such a duration occur for both places studied (Italy and Estonia) at this time. Limiting the dataset to warm periods also allows us to use the radar-based rainfall quantitative precipitation estimations, which are more reliable than the snowfall ones. Data from operational dual polarimetric C-band weather radar sites are used from both Italy and Estonia. Given climatologically homogeneous regions, this study demonstrates that polarimetric weather radar observations can provide reliable QPEs compared to single-polarization estimates with respect to rain gauges and that they can provide a reliable estimation of return periods of 1 h rainfall total, even for relatively short time series.

Research on Radar Echo Extrapolation Method by Fusing Environment Grid Point Field Information

Short-term extrapolation by weather radar observations is one of the main tools for making weather forecasts. Recently, deep learning has been gradually applied to radar extrapolation techniques, achieving significant results. However, for radar echo images containing strong convective systems, it is difficult to obtain high-quality results with long-term extrapolation. Additionally, there are few attempts and discussions to incorporate environmental factors governing the occurrence and development of convective storms into the training process. To demonstrate the positive effect of environmental factors on radar echo extrapolation tasks, this paper designs a three-dimensional convolutional neural network. The paper outlines the processing steps for matching radar echo images with environmental data in the spatio–temporal dimension. Additionally, it develops an experimental study on the effectiveness of seven physical elements and their combinations in improving the quality of radar echo extrapolation. Furthermore, a loss function is adopted to guide the training process of the model to pay more attention to strong convective systems. The quantitative statistical evaluation shows the critical success index (CSI) of our model’s prediction is improved by 3.42% (threshold = 40 dBZ) and 2.35% (threshold = 30 dBZ) after incorporating specific environmental field data. Two representative cases indicate that environmental factors provide essential information about convective systems, especially in predicting the birth, extinction, merging, and splitting of convective cells.

Cloud-Resolving Modeling and Evaluation of Microphysical Schemes for Flash Flood–Producing Convection over the Black Sea

Abstract In the present study, the convective event over the Black Sea area in September 2018 is analyzed using the Weather Research and Forecasting (WRF) Model configured with a fully convective-resolving setup. We test the WRF sensitivity to the choice of sea surface temperature (SST) dataset and microphysics scheme. The simulation is verified using weather radar measurements and ground observations. Both the choice of the microphysical scheme and SST dataset have a significant impact on the dynamic properties of the maritime convective system and associated rainfall. The best results are achieved with the WDM6 microphysical scheme and a more detailed and slightly warmer (compared to the default OSTIA SST) G1SST dataset. The optimally configured WRF simulations add value to coarser driving operational analysis, with more accurate amount and pattern of rainfall and the earlier arrival of the convective system, which is in better agreement with radar and weather station measurements. The vertical structure of the reflectivity profiles in the WDM6 scheme that simulates 15%–20% larger rainwater loading compared to other schemes agrees best with the observed data. Other schemes reproduce excessive reflectivity above the freezing level. Enhanced rainfall estimates and faster convective system propagation in the G1SST WDM6 simulations are linked to stronger cold pools caused by enhanced evaporation due to the higher rainwater content and droplet number concentrations. Stronger cold pools result in the 15%–20% enhancement of latent and sensible heat fluxes, reflecting the strong sensitivity of ocean–atmosphere heat and moisture exchange to the choice of microphysics scheme and SST dataset.

An analytical representation of raindrop size distribution in a mixed convective and stratiform precipitating system as revealed by field observations

AbstractThis study investigated a rainfall event under a typhoon influence using a 2D video disdrometer and weather radar observations to characterize raindrop size distribution (DSD) in a mixed convective and stratiform precipitating system. During the time period when both convective and stratiform rainfalls existed, the DSDs generally indicated a monotonically decreasing shape with increasing particle size, with a relatively gradual decrease at intermediate particle size observed at certain times; this feature is attributed to the combined effect of convective and stratiform rainfalls. During the transitional period between convective and stratiform rainfalls, the DSDs exhibited a bimodal shape. The DSDs were well approximated by a newly proposed gamma raindrop distribution combined with exponential (GRACE) distribution function, which was defined as the sum of the exponential distribution and the gamma distribution. A comparison of the volume ratio of the exponential and gamma components of the GRACE distribution revealed that the exponential component of the DSD was larger than the gamma component in the bimodal DSD. These results suggest that the DSD became bimodal during the period when stratiform rainfall predominated because of the weakening of convective rainfall. The GRACE distribution is useful for understanding cloud‐microphysical processes in mixed stratiform and convective precipitation conditions.

Prediction of Extremely Severe Cyclonic Storm “Fani” Using Moving Nested Domain

The prediction of extremely severe cyclonic storms has been a long-standing and challenging issue due to their short life period and large variation in intensities over a short time. In this study, we predict the track, intensity, and structure of an extremely severe cyclonic storm (ESCS) named ‘Fani,’ which developed over the Bay of Bengal region from 27 April to 4 May 2019, using the Advanced Research version of the Weather Research and Forecasting (WRF-ARW) model. Two numerical experiments were conducted using the moving nested domain method with a 3 km horizontal resolution, one with the FLUX-1 air-sea flux parameterization scheme and the other with the FLUX-2 air-sea flux parameterization scheme. The NCEP operational Global Forecast System (GFS) analysis and forecast datasets with a 25 km horizontal resolution were used to derive the initial and boundary conditions. The WRF model’s predicted track and intensity were validated with the best-fit track dataset from the India Meteorological Department (IMD), and the structure was validated with different observations. The results showed that the WRF model with the FLUX-1 air-sea parameterization scheme accurately predicted the track, landfall (position and time), and intensity (minimum sea level pressure and maximum surface wind) of the storm. The track errors on days 1 to 4 were approximately 47 km, 123 km, 96 km, and 27 km in the FLUX-1 experiment and approximately 54 km, 142 km, 152 km, and 166 km in the FLUX-2 experiment, respectively. The intensity was better predicted in the FLUX-1 experiment during the first 60 h, while it was better predicted in the FLUX-2 experiment for the remaining period. The structure, in terms of relative humidity, water vapor, maximum reflectivity, and temperature anomaly of the storm, was also discussed and compared with available satellite and Doppler Weather Radar observations.

Characterization of deep convective cells during the Indian summer monsoon using C-band polarimetric Doppler Weather Radar observations over Thumba (8.5o N, 77o E)

The vertical structure and dynamics of deep convective clouds plays an important role during the Indian summer monsoon period. The characteristics of deep convective cells (DCC) and their microphysics remain unclear, especially during the Indian summer monsoon (ISM). Further, the intensity of convection is an important element, which plays a key role in the progression of the monsoon. The present study focuses on characterizing DCC over a coastal location Thumba (8.50 N, 770 E) during the Indian summer monsoon periods of 2017, 2018 and 2019 using C-band polarimetric Doppler Weather Radar (DWR) measurements in terms of their intensity, vertical extent, top heights and their microphysical properties. A method is devised to identify the DCC from the DWR observed reflectivity cross-sections. The results showed that the occurrence of reflectivities in the range of 25–35 dBZ dominates below 6 km and the reflectivities in the range of 10–20 dBZ dominates above 6 km. The diurnal evolution of 40 dBZ depth radar echoes in DCC were analysed for June–September months, which showed peaks at preferential timings. The mean diurnal evolution of DCC also been investigated and it is found that these systems peak at three local time intervals viz., 00–05, 13–16 and 19–23 h during ISM period. Further, a separate analysis has been carried out for land and oceanic region for 40 dBZ depth radar echoes in DCC. The analysis show that the occurrence of 40 dBZ depth radar echoes peak at early-morning and late-night over the ocean and afternoon and late night over the land regions. The present results provide a quantitative assessment of DCC including their diurnal evolution during the Indian summer monsoon season over Thumba using DWR observations for the first time.