scholarly journals Improving Short-Term Rainfall Forecasts by Assimilating Weather Radar Reflectivity Using Additive Ensemble Perturbations

2018 ◽  
Vol 123 (17) ◽  
pp. 9047-9062 ◽  
Author(s):  
S. Yokota ◽  
H. Seko ◽  
M. Kunii ◽  
H. Yamauchi ◽  
E. Sato
Keyword(s):  
Weather Radar ◽  
Radar Reflectivity ◽  
Short Term

scholarly journals AutoNowP: An Approach Using Deep Autoencoders for Precipitation Nowcasting Based on Weather Radar Reflectivity Prediction

Mathematics ◽  
2021 ◽  
Vol 9 (14) ◽  
pp. 1653
Author(s):  
Gabriela Czibula ◽  
Andrei Mihai ◽  
Alexandra-Ioana Albu ◽  
Istvan-Gergely Czibula ◽  
Sorin Burcea ◽  
...  
Keyword(s):  
Binary Classification ◽  
Weather Radar ◽  
Predictive Performance ◽  
Weather Conditions ◽  
Radar Data ◽  
Probability Of Detection ◽  
Radar Reflectivity ◽  
Classification Model ◽  
Precipitation Forecast ◽  
Short Term

Short-term quantitative precipitation forecast is a challenging topic in meteorology, as the number of severe meteorological phenomena is increasing in most regions of the world. Weather radar data is of utmost importance to meteorologists for issuing short-term weather forecast and warnings of severe weather phenomena. We are proposing AutoNowP, a binary classification model intended for precipitation nowcasting based on weather radar reflectivity prediction. Specifically, AutoNowP uses two convolutional autoencoders, being trained on radar data collected on both stratiform and convective weather conditions for learning to predict whether the radar reflectivity values will be above or below a certain threshold. AutoNowP is intended to be a proof of concept that autoencoders are useful in distinguishing between convective and stratiform precipitation. Real radar data provided by the Romanian National Meteorological Administration and the Norwegian Meteorological Institute is used for evaluating the effectiveness of AutoNowP. Results showed that AutoNowP surpassed other binary classifiers used in the supervised learning literature in terms of probability of detection and negative predictive value, highlighting its predictive performance.

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

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Yuanbo Ran ◽  
Haijiang Wang ◽  
Li Tian ◽  
Jiang Wu ◽  
Xiaohong Li
Keyword(s):  
Radial Velocity ◽  
Vertical Direction ◽  
Weather Radar ◽  
Radar Reflectivity ◽  
Convective Precipitation ◽  
Horizontal Wind ◽  
Doppler Weather Radar ◽  
Velocity Data ◽  
Precipitation Processes ◽  
Radial Velocity Data

AbstractPrecipitation 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.

scholarly journals Radar Quality Index for a Mosaic of Radar Reflectivity over Chao Phraya River Basin, Thailand

2020 ◽  
pp. 92-104
Author(s):  
Nattapon Mahavik ◽  
Sarintip Tantanee
Keyword(s):  
River Basin ◽  
Quality Index ◽  
Beam Propagation ◽  
Weather Radar ◽  
Radar Reflectivity ◽  
Temporal Information ◽  
Radar Beam ◽  
Spatio Temporal ◽  
Chao Phraya River ◽  
Application Fields

The weather radar is one of the tools that can provide spatio-temporal information for nowcast which is useful for hydro-meteorological disasters warning and mitigation system. The ground-based weather radar can provide spatial and temporal information to monitor severe storm over the risky area. However, the usage of multiple radars can provide more effective information over large study area where single radar beam may be blocked by surrounding terrain Even though, the investigation of the sever storm physical characteristics needs the information from multiple radars, the mosaicked radar product has not been available for Thai researcher yet. In this study, algorithm of mosaicked radar reflectivity has been developed by using data from ground-based radar of Thai Meteorological Department over the Chao Phraya river basin in the middle of Thailand. The Python script associated with OpenCV and Wradlib libraries were used in our investigations of the mosaicking processes. The radar quality index (RQI) field has been developed by implementing an equation of a quality radar index to identify the reliability of each mosaicked radar reflectivity pixels. First, the percentage of beam blockage is computed to understand the radar beam propagation obstructed by surrounding topography in order to clarify the limitations of the observed beam on producing radar reflectivity maps. Second, the elevation of beam propagation associated with distance field has been computed. Then, these three parameters and the obtained percentage of beam blockage are utilized as the parameters in the equation of RQI. Finally, the detected radar flare, non-precipitating radar area, has been included to the RQI field. Then, the RQI field has been applied to the extracted radar reflectivity to evaluate the quality of mosaicked radar reflectivity to inform end user in any application fields over the Chao Phraya river basin.

Impact of Doppler radar reflectivity and velocity data assimilation on the quality of precipitation forecasting in Belarus in different seasons

2020 ◽  
Author(s):  
Palina Zaiko ◽  
Siarhei Barodka ◽  
Aliaksandr Krasouski
Keyword(s):  
Data Assimilation ◽  
Weather Radar ◽  
Radar Data ◽  
Radar Reflectivity ◽  
Precipitation Forecast ◽  
Doppler Weather Radar ◽  
Velocity Data ◽  
Numerical Weather ◽  
Convective Processes ◽  
Different Seasons

<p>Heavy precipitation forecast remains one of the biggest problems in numerical weather prediction. Modern remote sensing systems allow tracking of rapidly developing convective processes and provide additional data for numerical weather models practically in real time. Assimilation of Doppler weather radar data also allows to specify the position and intensity of convective processes in atmospheric numerical models.</p><p>The primary objective of this study is to evaluate the impact of Doppler  radar reflectivity and velocity assimilation in the WRF-ARW mesoscale model for the territory of Belarus in different seasons of the year. Specifically, we focus on the short-range numerical forecasting of mesoscale convective systems passage over the territory of Belarus in 2017-2019 with assimilated radar data.</p><p>Proceeding with weather radar observations available for our cases, we first perform the necessary processing of the raw radar data to eliminate noise, reflections and other kinds of clutter. For identification of non-meteorological noise fuzzy echo classification was used. Then we use the WRF-DA (3D-Var) system to assimilate the processed radar observations from 3 Belarusian Doppler weather radar in the WRF model. Assimilating both radar reflectivity and radial velocity data in the model we aim to better represent not only the distribution of clouds and their moisture content, but also the detailed dynamical aspects of convective circulation. Finally, we analyze WRF modelling output obtained with assimilated radar data and compare it with available meteorological observations and with other model runs (including control runs with no data assimilation or with assimilation of conventional weather stations data only), paying special attention to the accuracy of precipitation forecast 12 hours in advance.</p>

scholarly journals A study on WRF radar data assimilation for hydrological rainfall prediction

2013 ◽  
Vol 17 (8) ◽  
pp. 3095-3110 ◽  
Author(s):  
J. Liu ◽  
M. Bray ◽  
D. Han
Keyword(s):  
Data Assimilation ◽  
Real Time ◽  
Meteorological Data ◽  
Weather Radar ◽  
Radar Data ◽  
Rain Gauge ◽  
Radar Reflectivity ◽  
Storm Event ◽  
Hydrological Applications ◽  
Radar Data Assimilation

Abstract. Mesoscale numerical weather prediction (NWP) models are gaining more attention in providing high-resolution rainfall forecasts at the catchment scale for real-time flood forecasting. The model accuracy is however negatively affected by the "spin-up" effect and errors in the initial and lateral boundary conditions. Synoptic studies in the meteorological area have shown that the assimilation of operational observations, especially the weather radar data, can improve the reliability of the rainfall forecasts from the NWP models. This study aims at investigating the potential of radar data assimilation in improving the NWP rainfall forecasts that have direct benefits for hydrological applications. The Weather Research and Forecasting (WRF) model is adopted to generate 10 km rainfall forecasts for a 24 h storm event in the Brue catchment (135.2 km2) located in southwest England. Radar reflectivity from the lowest scan elevation of a C-band weather radar is assimilated by using the three-dimensional variational (3D-Var) data-assimilation technique. Considering the unsatisfactory quality of radar data compared to the rain gauge observations, the radar data are assimilated in both the original form and an improved form based on a real-time correction ratio developed according to the rain gauge observations. Traditional meteorological observations including the surface and upper-air measurements of pressure, temperature, humidity and wind speed are also assimilated as a bench mark to better evaluate and test the potential of radar data assimilation. Four modes of data assimilation are thus carried out on different types/combinations of observations: (1) traditional meteorological data; (2) radar reflectivity; (3) corrected radar reflectivity; (4) a combination of the original reflectivity and meteorological data; and (5) a combination of the corrected reflectivity and meteorological data. The WRF rainfall forecasts before and after different modes of data assimilation are evaluated by examining the rainfall temporal variations and total amounts which have direct impacts on rainfall–runoff transformation in hydrological applications. It is found that by solely assimilating radar data, the improvement of rainfall forecasts are not as obvious as assimilating meteorological data; whereas the positive effect of radar data can be seen when combined with the traditional meteorological data, which leads to the best rainfall forecasts among the five modes. To further improve the effect of radar data assimilation, limitations of the radar correction ratio developed in this study are discussed and suggestions are made on more efficient utilisation of radar data in NWP data assimilation.

scholarly journals Development of an Inexpensive Raindrop Size Spectrometer

2004 ◽  
Vol 21 (11) ◽  
pp. 1710-1717 ◽  
Author(s):  
William Henson ◽  
Geoff Austin ◽  
Harry Oudenhoven
Keyword(s):  
Drop Size ◽  
Low Cost ◽  
Weather Radar ◽  
Field Work ◽  
Radar Reflectivity ◽  
Quantitative Relation ◽  
Mountainous Terrain ◽  
Raindrop Size

Abstract The deployment of weather radar, notably in mountainous terrain with many microclimates, requires the use of several or even many drop size spectrometers to provide confidence in the quantitative relation between radar reflectivity and rainfall. While there are several different commercial disdrometers available they are all expensive, large, or fragile, which militates against multiple deployment in the field. The design brief was for a reasonably accurate and sensitive, low-cost and rugged disdrometer to support field work. A design based on piezoceramic disks normally used in hydrophones is described. Calibration and typical field results are presented.

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