Constant Altitude Plan Position Indicator Research Articles

Temporal characteristics of positive cloud-to-ground flashes from tropical thunderstorms

The occurrence of positive cloud-to-ground (CG) lightning flashes proves to be a challenging task when compared to the negative CG flash which common happen in tropical region. This due to the positively charged clouds to reach higher altitudes to enable the transfer of electrical charges to the Earth's surface. With several sensors that deployed in the Malacca region (2.314077°N, 102.318282°E) such as the wide band fast and slow antenna, magnetic field sensors, and the electric field mill (EFM-100). This research study focuses on the temporal characteristics of positive CG lightning activity in the tropical region during September and October 2021. A total of 203 positive cloud-to-ground flashes were meticulously analyzed in the context of 16 thunderstorm events. Key findings highlight the consistent initiation of positive cloud-to-ground flashes through initial breakdown (IB) pulses, followed by the development of stepped leaders and subsequent return strokes. Notably, most positive cloud-to-ground events (72%) were observed as single strokes and the maximum multiplicity recorded being four subsequent return strokes. Furthermore, the analysis focuses into the temporal aspects of these lightning events, revealing average durations for various parameters, including rise-time (6.26 μs), zero-crossing (26.43 μs), pulse duration of the first RS (139.76 μs), and the interval from the first IB to the first return stroke (134.19 ms). Additionally, the recorded data from the Electric Field Mill (EFM) instruments together with Constant Altitude Plan Position Indicator (CAPPI) radar demonstrate a notable correlation. Specifically, the electric field (E-field) values exhibit a discernible increase when positive cloud-to-ground lightning events are detected near the lightning sensor.

Velocity estimation of thunderstorm movement and dealiasing of single Doppler radar during convective events

The use of meteorological radars in monitoring current weather conditions is crucial regarding the observation of the evolution and dissipation of thunderstorms. Thus, Doppler velocities being measured in each radar scan and velocity vectors derived from Numerical Weather Prediction (NWP) models—that are usually not as highly resolving as radar scans—are combined, as a monitoring utility during the evolution of a convective weather event. The objective of this study is to develop a new method that allows the implementation of a thunderstorm movement velocity estimation technique combining block matching and optical flow techniques. This new method constitutes a nowcasting (NWC) application that enables the use of a single Doppler radar without the need of using NWPs. The method relies on the estimation of the thunderstorm movement vector velocities (Doppler velocity) for each constant altitude plan position indicator (CAPPI) and through correction for aliasing errors to obtain 3D vector velocity fields for convective systems. The performance of the method is evaluated for selected case studies of convective thunderstorms under different synoptic scale conditions over Greece, a geographical area with challenges in forecasting due to its sharp relief and the need for optimization of the use of radar products.

GraphAT Net: A Deep Learning Approach Combining TrajGRU and Graph Attention for Accurate Cumulonimbus Distribution Prediction

In subtropical regions, heavy rains from cumulonimbus clouds can cause disasters such as flash floods and mudslides. The accurate prediction of cumulonimbus cloud distribution is crucial for mitigating such losses. Traditional machine learning approaches have been used on radar echo data generated by constant altitude plan position indicator (CAPPI) radar systems for predicting cumulonimbus cloud distribution. However, the results are often too foggy and fuzzy. This paper proposes a novel approach that integrates graph convolutional networks (GCN) and trajectory gated recurrent units (TrajGRU) with an attention mechanism to predict cumulonimbus cloud distribution from radar echo data. Experiments were conducted using the moving modified National Institute of Standards and Technology (moving MNIST) dataset and real-world radar echo data, and the proposed model showed a 59.12% improvement in mean square error (MSE) and a 16.26% improvement in structure similarity index measure (SSIM) on average in the moving MNIST dataset, a 65.40% improvement in MSE, and an 10.29% improvement in SSIM on average in the radar echo dataset. These results demonstrate the effectiveness of the proposed approach for improving the prediction accuracy of cumulonimbus cloud distribution.

A new approach for quantitative precipitation estimation from radar reflectivity using a gated recurrent unit network

Accurate quantitative precipitation estimation (QPE) utilizing radar data plays a vital role in weather monitoring, hydrological modelling and prediction. In previous research, some regression techniques such as the reflectivity (Z) - rainfall (R) relationship and artificial intelligence methods were employed for radar QPE; however, the estimation results remain limited the spatial and temporal variability of rainfall results in large errors in radar QPE. Recently, an emerging deep learning approach that can be applied in hydrological areas for QPE, namely, the gated recurrent unit (GRU) network method, has been used. GRU networks have evolved from long short-term memory (LSTM) networks, with improved training speed and modelling accuracy. In this research, the GRU network is applied to QPE using the three-dimensional structure of reflectivity from the Constant Altitude Plan Position Indicator (3D CAPPI) for an urban catchment in Seoul, South Korea. Simultaneously, a “complete” 3D CAPPI based on a cubic grid of 4 × 4 km2 that is highly suitable for the study area is proposed. Based on the comparison of the results of the GRU model with those of the artificial neural networks (ANNs), recurrent neural networks (RNNs), and LSTM networks, and the Z-R relationship, namely the least-squares model (LSM) for 2 testing strategies including traditional test strategy and leave-one-out cross-validation (LOOCV) strategy, the results indicate that the GRU model is superior to other models in radar QPE, with improvements by 3.3 ∼ 46.2%, 25 ∼ 70% and 1.2 ∼ 44.8% in the root mean square error (RMSE), bias and correlation coefficient (CC). This study presents a valuable method for improving the accuracy of radar QPE that can be actively employed in the future.

Correlation analysis between lightning flashes and rainfall rate during a flash flood thunderstorm

This paper presents the correlation analysis between lightning flashes and the rainfall rate of a thunderstorm when a flash flood event happened in Melaka on 11 August 2020. Four types of data have been collected from the electric field mill (EFM), fast antenna (FA) system, constant altitude plan position indicator (CAPPI) radar and world wide lightning location network (WWLLN). Two storms have been detected by the EFM occurred between 04:00:00 and 14:00:00. The FA system recorded a total of 33 lightning flashes had detected with the highest number occurrence of flashes which positive narrow bipolar event (+NBE) around 21 flashes, the maximum rainfall rate and reflectivity have been detected by radar during the first storm were 8 mm h-1 and 37 dBZ, respectively (light rain). During the second storm, there was a total of 980 lightning flashes detected by the FA system with the highest number occurrence of flashes around 429 flashes (IC), the highest value of rainfall rate and reflectivity is 50 mm h-1 and 50 dBZ, respectively (heavy rain). Analyses of the lightning and rainfall rate data also show a close link between the occurrence of major thunderstorms systems and flash flooding on a regional scale.

Large hail detection using radar-based VIL calibrated with isotherms from the ERA5 reanalysis

Hail involving very large hailstones (maximum diameter ≥ 4 cm) is rare but is one of the most hazardous weather phenomena in Poland. The most useful of the numerous weather radar reflectivity indicators are automated hail detection algorithms and reflectivity criteria, which can be easily implemented in operational weather warning systems. Since the temperature at which large hail forms is known, the vertically integrated liquid water content (VIL) can be calculated for the corresponding temperature layer. This paper presents four new VIL-based hail indicators using hail formation temperatures determined by the isotherms from ERA5. We compared these indicators with the 8 km Constant Altitude Plan Position Indicator (CAPPI) and the Waldvogel hail detection method by linking the detected cells to ESWD reports. Based on the VILs, we aimed to determine which layer within the VIL integration was the most useful for large hail prediction. In the case of the VILs, the layer between −12 and −25 °C was the best for hail detection. We also analysed the lead times of these indicators to assess their potential for implementation in the operational warning system in Poland. Using data from the European Severe Weather Database and the Polish radar system, we found that most incidents involving very large hailstones were linked to supercell storms with distinctive reflectivity signatures. The best hail indicator was the Waldvogel et al. (1979) hail detection technique, which had a lead time of approximately 17,5 min. The VIL had a near-zero lead time.

Very Short-Term Rainfall Prediction Using Ground Radar Observations and Conditional Generative Adversarial Networks

Weather radars play an important role in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> rainfall monitoring owing to their ability to measure instantaneous rain rates and rainfall distributions. Currently, the Korea Meteorological Administration (KMA) provides instantaneous radar observation data and predictions based on the McGill algorithm for precipitation nowcasting by Lagrangian extrapolation (MAPLE) for up to 6 h, for short-term forecasting. This study presents a conditional generative adversarial network (CGAN)-based radar rainfall prediction method for very short-range weather forecasts from 10 min to 4 h. The CGAN-predicted model was trained and tested using KMA’s constant altitude plan position indicator (CAPPI) observation data. The qualitative comparison between the radar observation and the CGAN-predicted rain rates displayed high statistical scores, such as the probability of detection (POD) = 0.8442, false alarm ratio (FAR) = 0.2913, and critical success index (CSI) = 0.6268, in the case of a 1-h prediction for rainfall on September 5, 2019, 15:20 KST. This study demonstrates the capability of the CGAN model for short-term rainfall forecasting. Consequently, the CGAN-generated radar-based rainfall prediction could complement the KMA MAPLE system and be useful in various forecasting applications.

Microphysical and Dynamical Climatology of Precipitating Systems Inferred by Weather Radar Polarimetric Measurements in Brazil

This work presents the climatology of the microphysics and the dynamics of weather systems in two coastal areas of São Paulo and the Espírito States at high spatial-temporal resolution as measured by two dual Doppler weather radars during the summer and early fall of 2015. Averages and respective standard deviations of polarimetric variables, namely, reflectivity (Z), differential reflectivity (ZDR), differential phase (ϕDP), specific differential phase (KDP), copolar correlation coefficient (ρoHV), radial velocity (Vr), and the spectral width (W) were obtained within a 240-km range on plan position indicator (PPI), constant altitude plan position indicator (CAPPI) and vertical cross-sections to analyze overall horizontal and vertical precipitation microphysics and mesoscale circulation of prevailing weather systems, and their peculiarities over coastal and oceanic, and urban and rural areas. Overall, raindrops tend to be larger over the Metropolitan area of São Paulo from the surface to up to 6 km altitude indicating more vigorous updrafts caused by the heat island effect and the local sea breeze. The vertical microphysical structure is remarkably distinct over the Metropolitan Area of São Paulo (MASP) where thunderstorms can reach 20-km altitude in summertime under sea breeze and heat island effects. On the other hand, there is a dominancy of smaller drop sizes though larger ones observed close to the surface by the coast of Espírito Santo and at the land-ocean interface influenced by the local low-level jet and oceanic-type CCN. Convective cells tend to be smaller associated with Easterlies and more organized with Westerlies. The results indicate distinct features on hydrometeor types and circulation characteristics under these different surface and boundary-layer conditions in close agreement with previous results in the literature.

Precipitation cloud identification based on faster-RCNN for Doppler weather radar

Precipitation clouds are visible aggregates of hydrometeor in the air that floating in the atmosphere after condensation, which can be divided into stratiform cloud and convective cloud. Different precipitation clouds often accompany different precipitation processes. Accurate identification of precipitation clouds is significant for the prediction of severe precipitation processes. Traditional identification methods mostly depend on the differences of radar reflectivity distribution morphology between stratiform and convective precipitation clouds in three-dimensional space. However, all of them have a common shortcoming that the radial velocity data detected by Doppler Weather Radar has not been applied to the identification of precipitation clouds because it is insensitive to the convective movement in the vertical direction. This paper proposes a new method for precipitation clouds identification based on deep learning algorithm, which is according the distribution morphology of multiple radar data. It mainly includes three parts, which are Constant Altitude Plan Position Indicator data (CAPPI) interpolation for radar reflectivity, Radial projection of the ground horizontal wind field by using radial velocity data, and the precipitation clouds identification based on Faster-RCNN. The testing result shows that the method proposed in this paper performs better than the traditional methods in terms of precision. Moreover, this method boasts great advantages in running time and adaptive ability.

Analyses of three-dimensional weather radar data from volcanic eruption clouds

This paper demonstrates the advantages of three-dimensional (3D) analysis and visualization of weather radar data for studies on the inner structure of eruption columns and their 3D ash-fall distributions. The 3D data were collected using an operational X-band polarimetric radar located ~11 km from the Showa vent of Sakurajima, Japan. Three eruption cases were chosen for the analysis to study the effect of environmental wind conditions on the inner structure of an eruption column and horizontal ash-fall distributions at 1000 m height: a 3677-m-high eruption on 13 June 2013 under calm winds (Case 1), a 3982-m-high eruption on 7 October 2013 under strong winds (Case 2), and a 4520-m-high eruption on 18 August 2013 under moderate winds (Case 3). The Analytical Tools for Three-dimensional Weather Radar Data (ANT3D) package was developed and used for the construction of 3D constant altitude plan position indicator (3D CAPPI) data from eruption columns. The quantitative ash-fall analyses of 3D CAPPI data revealed that the total ash-fall amounts of the Case 1, Case 2, and Case 3 eruptions were 6.24 × 107, 9.99 × 107, and 6.71 × 107 kg, respectively, and the total ash-fall areas at the 1000-m height were 29.7, 151.3, and 107.7 km2, respectively. It was shown that the area of the maximum ash-fall amount at the 1000-m height depends on the environmental conditions. The accumulated ash-fall distribution area was limited to over and around the vent in the Case 1 eruption, whereas the ash distribution pattern was elongated to the downwind direction and multiple ash concentration maxima were formed in the Case 2 eruption probably due to the multiple eruptions. The accumulated ash-fall distribution was also elongated to the down wind direction in the Case 3 eruption but a single maximum concentration which was larger than that in the Case 2 eruption was found inland of Sakurajima. The 3D CAPPI data were visualized using techniques such as surface rendering, volume rendering, bird's eye viewing, and cross-sectional analysis. The 3D visualizations suggest that vertical size-sorting and aggregation occurred in the buoyancy-driven regions of the eruption columns in all three cases. The results of this study will contribute to the understanding of volcanic eruption column dynamics and horizontal ash-fall transportation.

Construction of three-dimensional weather radar data from volcanic eruption clouds

Analysis tools of three-dimensional weather radar data (ANT3D) was originally developed at the National Research Institute for Earth Science and Disaster prevention (NIED) to retrieve three-dimensional (3D) precipitation and wind fields for convective storms. In 2013, Kagoshima University significantly revised ANT3D for analyses of volcanic eruption clouds, mainly to improve the temporal and spatial interpolation of radar data and estimation of the advection vector, which is required for temporal interpolation. Detailed information pertaining to these algorithms is listed as additional information in this paper.•Procedures necessary for the construction of three-dimensional (3D) volcanic cloud weather radar data are described.•An algorithm based on temporal and elevation angle interpolation methods was used to create 3D constant altitude plan position indicator (3D CAPPI) data with high temporal and spatial resolution.•Two programs (ANT3D_GUI and the CAPPI viewer) are provided for readers interested in analyzing volcanic eruption cloud radar data.

Reconstruction of Extreme Rainfall Event on September 19-20, 2017, Using a Weather Radar in Bengkulu of Sumatra Island.

Extreme rainfall accompanied by strong winds hit the province of Bengkulu in the western coastal area of Sumatera Island during September 19-20, 2017, causing floods and landslides in Seluma and Central Bengkulu district. This extreme rainfall was recorded by Bengkulu Meteorological Station about 257.0 mm day−1 using rain-gauge observation. The spatial distribution of extreme rainfall cannot be seen if only using a rain-gauge observation in this location. The spatial distribution of extreme rainfall is needed to identify the impact of rainfall on landslides in large areas. The study aims to (1) develop the reconstruction of the spatial distribution of extreme rainfall using weather radar and (2) investigate the trigger that caused extreme rainfall by analyzing the synoptic-scale tropical waves. Each weather radar datum is saved in a Constant Altitude Plan Position Indicator (CAPPI). To get rainfall information, the CAPPI must be derived from Quantitative Precipitation Estimation (QPE) values. In this paper, we derived CAPPI using a Marshall-Palmer reflectivity-rain rate relationship. The result shows that rainfall formed on September 20, 2017, 21.00 UTC with total daily rainfall ranged between 176 and 247 mm in both districts and the mean of total daily rainfall has exceeded the average of monthly rainfall. The analysis of tropical waves suggests that only Kelvin waves were active and served as a possible trigger factor while the Madden-Julian Oscillation (MJO) and Equatorial Rossby (ER) waves were inactive during this extreme rainfall.

Nowcasting of Convective Rainfall Using Volumetric Radar Observations

Short-range forecasts (nowcasts) of rainfall facilitate providing early warning of severe rainfall and flooding, which is particularly important in densely populated urban areas. Nowcasts are conventionally obtained by the extrapolation of radar echoes from a constant altitude plan position indicator (CAPPI) or lowest-angle plan position indicator (PPI). Lacking a model for growth or decay, this approach has a limited ability to forecast the summertime convective storms. In a previous attempt to address this shortcoming, called RadVil, the predicted surface rain rate is obtained from mass balance equations of vertically integrated liquid (VIL) retrieved from volumetric reflectivity measurements. Predicting the growth and decay has also been attempted by using an autoregressive (AR) model, which led to the development of spectral prognosis (S-PROG). A novel combination of these two methodologies, called ANVIL, is proposed. In this approach, the growth and decay of VIL are modeled by an autoregressive integrated (ARI) process. It is shown that the predictability of growth and decay is scale-dependent. Thus, the key idea of ANVIL is to decompose the VIL into multiple spatial scales and apply a separate ARI model to each scale. The operational feasibility of ANVIL is evaluated using the Next-Generation Radar (NEXRAD)/WSR-88D radar that covers the Dallas–Fort Worth metropolitan area. The evaluation is done using ten convective events in 2018 and 2019. Using several verification metrics, it is shown that ANVIL has up to 25% improved skill compared to conventional nowcasting techniques. The improvement is consistent for a wide range of spatial scales (1–11 km) and rain rate thresholds (5–20 mmh−1).

Monitoring the rainfall intensity at two active volcanoes in Indonesia and Japan by small-compact X-band radars

Since 2015, collaborative research conducted by Indonesian and Japan scientists has initiated the installation of small X-band Multi-Parameter (X-MP) radars to mitigate the occurrence of rainfall-induced lahar in three active volcanoes in Indonesia and Japan: Merapi, Sinabung, and Sakurajima. This paper discusses the technical aspects of data acquisition, processing, and performance of the X-MP radar at the Merapi and Sakurajima volcanoes by comparing the estimated rainfall intensity acquired by the radar to three empirical radar-rainfall algorithms. The algorithms are based on radar reflectivity factor (ZHH), specific differential phase shift (KDP), and differential reflectivity (ZDR). A new method of Constant Altitude Plan Position Indicator (CAPPI) interpolation by linear regression is also proposed for a more efficient computation. The first algorithm by Marshall-Palmer, which relies on ZHH, gave the lowest average and maximum rainfall values compared with the other algorithms for all rainfall event cases. On the other hand, the other two algorithms, which involve the MP of radar by Bringi and Chandrasekar and Park et al., gave closer rainfall intensity values with the estimated rainfall intensity acquired by the X-MP radar. These three rain rates give a closer temporal fluctuation when they are compared to the rain gauge-based rainfall intensity.

Spatiotemporal distribution of strong convective cells over northern Serbia, 2008–2010

AbstractKnowledge of the spatial distribution of severe convective cells (SCCs) can determine the areas with the most frequent occurrence of these events, and helps in the protection of the most vulnerable areas. An objective analysis of a three year observation of strong convective cells (CCs) in northern Serbia is presented, based on data collected via meteorological radar. The CC and SCCs are defined by radar reflectivity thresholds of 45 and 55 dBZ, respectively, and are analysed at constant altitude plan position indicator (CAPPI) levels of 4.0 km (CAPPI4) and 5.5 km (CAPPI5). The analysis showed that the dominant occurrence of thunderstorm cells was directed from southwest to northeast. The spatial distribution from year to year was variable; however, the analysis identified areas with frequent thunderstorm cells. The number of grid points where CCs occur is smaller at the CAPPI5 than at the CAPPI4. There is a more frequent occurrence of CCs than SCCs: there were no CCs in only 0.58% of grid points during the entire three year period at the CAPPI4, while for SCCs it was 39.82%. This difference was even greater at the CAPPI5: 1.42% for CCs and 55.47% for SCCs. At most of the grid points (67–79% at the CAPPI4 and 78–88% at the CAPPI5, depending on the year), no SCCs were observed for any given year.

Improving Numerical Weather Prediction of Rainfall Events Using Radar Data Assimilation

Data assimilation is one of method to improve initial atmospheric conditions data in numerical weather prediction. The assimilation of weather radar data that has quite extensive and tight data is considered to be able to improve the quality of weather prediction and analysis. This study aims to investigate the effect of assimilation of Doppler weather radar data in Weather Research Forecasting (WRF) numerical model for the prediction of heavy rain events in the Jabodetabek area with dates representing four seasons respectively on 20 February 2017, 3 April 2017, 13 June 2017, and 9 November 2017. For this purpose, the reflectivity (Z) and radial velocity (V) data from Plan Position Indicator (PPI) product and reflectivity (Z) data from Constant Altitude PPI (CAPPI) product were assimilated using WRFDA (WRF Data Assimilation) numerical model with 3DVar (The Three Dimensional Variational) system. The output of radar data assimilation and without assimilation of the numerical model of WRF is verified by spatial with GSMaP data and by point with precipitation observation data. In general, WRF radar assimilation provides a better simulation of spatial and point rain events compared to the WRF model without assimilation which is improvements of rain prediction from WRF radar data assimilation would be more visible in areas close to radar sources and not echo-blocked from fixed objects, and more visible during the rainy season

Characterization of shallow oceanic precipitation using profiling and scanning radar observations at the Eastern North Atlantic ARM observatory

Abstract. Shallow oceanic precipitation variability is documented using three second-generation radar systems located at the Atmospheric Radiation Measurement (ARM) Eastern North Atlantic observatory: ARM zenith radar (KAZR2), the Ka-band scanning ARM cloud radar (KaSACR2) and the X-band scanning ARM precipitation radar (XSAPR2). First, the radar systems and measurement post-processing techniques, including sea-clutter removal and calibration against colocated disdrometer and Global Precipitation Mission (GPM) observations are described. Then, we present how a combination of profiling radar and lidar observations can be used to estimate adaptive (in both time and height) parameters that relate radar reflectivity (Z) to precipitation rate (R) in the form Z=αRβ, which we use to estimate precipitation rate over the domain observed by XSAPR2. Furthermore, constant altitude plan position indicator (CAPPI) gridded XSAPR2 precipitation rate maps are also constructed. Hourly precipitation rate statistics estimated from the three radar systems differ because KAZR2 is more sensitive to shallow virga and XSAPR2 suffers from less attenuation than KaSACR2 and as such is best suited for characterizing intermittent and mesoscale-organized precipitation. Further analysis reveals that precipitation rate statistics obtained by averaging 12 h of KAZR2 observations can be used to approximate that of a 40 km radius domain averaged over similar time periods. However, it was determined that KAZR2 is unsuitable for characterizing domain-averaged precipitation rate over shorter periods. But even more fundamentally, these results suggest that these observations cannot produce an objective domain precipitation estimate and that the simultaneous use of forward simulators is desirable to guide model evaluation studies.

Identification of Rainfall Intensity by Using Bahir Dar C-band Weather Radar products

Observation of rainfall accumulation, distribution and wind direction spatially and temporally is very essential for meteorologists and hydrologists in the Upper Blue Nile basin and different locations of Ethiopia. The Bahir Dar (BAH) Cband weather radar used to estimate the intensity, amount and distribution of precipitation with a high resolution temporally and spatially. It is dual polarization with horizontal and vertical reflectivity; six variables are used to determine the hydrometer and non-hydrometer types, shape and sizes of targets up to range of 250 km and 450 km at constant altitude plan position indicator (CAPPI) and plan position indicator (PPI) products. From our first observation, we used different types of products to observe rainfall intensity, distribution, wind direction and warnings across Upper Blue Nile. For RainN on 01 July 2016 the rainfall distribution over twenty four hours (24 Hr) UTC at Bahir Dar, parts of lake Tana, most northern parts, western parts of Gondar town, and south western parts of lake Tana a resolution of 809 m/Pixel with frequency of 600 Hz the maximum rainfall accumulation 91.6 mm was achieved. By using CAPPI product with vertical height of 1 km on 26 June 2016 the maximum reflectivity 53.5 dBZ is observed.

Identification of Precipitation-Clouds Based on the Dual-Polarization Doppler Weather Radar Echoes Using Deep–Learning Method

The dual-polarization Doppler weather radar is a kind of radio-frequency sensor that can provide abundant information about atmospheric particle scattering behavior. The identification of the precipitation cloud type based on dual-polarization Doppler weather radar echoes is a study that classifies precipitation clouds based on the scattering theory of precipitation cloud particles to polarized electromagnetic waves. In recent years, the Doppler weather radar has been widely used in quantitative precipitation estimation, and the accurate identification of precipitation cloud types plays an essential role in improving the accuracy of precipitation estimation. The accuracy of the conventional precipitation cloud identification method relies on the number of features that are identified by human eyes, and it greatly reduces the operation efficiency. In order to improve the accuracy and efficiency of the precipitation cloud identification, a methodology of precipitation cloud identification based on deep learning is proposed in this paper. The method mainly consists of three major parts, which are constant altitude plan position indicator data inversion, zero-layer bright band identification, and precipitation-cloud classification by using the deep learning network model. At last, this paper evaluates the identification effect of this method through a real precipitation process. The results show that this method can distinguish the stratiform clouds and convective cloud precipitation in the precipitation area in real time, and it is in good agreement with the ground observation data. This method is very useful for improving the accuracy of the quantitative precipitation estimation of the Doppler weather radar.

Using the c-band Doppler weather radar data to reconstruct extreme rainfall event on 11th march 2018 in Bangka island, Indonesia

Extreme weather in the form of heavy rainfall hit Bangka Island, Indonesia on 10 - 11 March 2018 caused flooding in some areas such as in Pangkal Pinang and Muntok in Bangka Barat District, Air Asam Belinyu in Bangka Induk District, and Koba in Bangka Tengah District. Observation of weather conditions at Pangkal Pinang Meteorological Station on 10 March 2018 recorded temperature ranged from 23 to 25°C; relative humidity (RH) ranged from 91 to 100% and measured rainfall reached 84.4 mm/day. In Muntok, the measured rainfall reached 257.5 mm/day which exceeds the March average rainfall 250 mm/month. This study aims to reconstruct this extreme rainfall using C-Band Doppler weather radar centered in Palembang, South Sumatera Province with Python-wradlib library. Weather radar images were displayed in Constant Altitude Plan Position Indicator (CAPPI) and Quantitative Precipitation Estimation (QPE) temporal analysis was performed in areas of extreme rainfall by applying the Marshall-Palmer reflectivity-rain rate (Z-R) relationship. The analysis was conducted by observing the movement and growth of convective clouds through the Palembang radar over Bangka Island and identifying the regional extreme rainfall using Indonesia In-House Radar Integration System (IIRIS) over Sumatra Island. The results suggest that the reconstructed rainfall reached 236.7 mm/day for Muntok, 92.1 mm/day for Pangkal Pinang, 106.0 mm/day for Koba and 80.8 mm/day for Air Asam Belinyu. Although most of the location sites are more than 200 km from the radar center, both of the reconstructed and measured rainfall is well comparable.