scholarly journals Improved analysis of solar signals for differential reflectivity monitoring

2016 ◽  
Author(s):  
Asko Huuskonen ◽  
Mikko Kurri ◽  
Iwan Holleman
Keyword(s):  
Vertical Polarization ◽  
Daily Monitoring ◽  
Weather Radars ◽  
Radar Network ◽  
Angle Data ◽  
Data Points ◽  
Reflectivity Data ◽  
Differential Reflectivity ◽  
Better Than ◽  
Receiver Bias

Abstract. The method for the daily monitoring of the differential reflectivity bias for polarimetric weather radars is developed. Improved quality control is applied to the solar signals detected during the operational scanning of the radar which removes efficiently rain and clutter contamination occurring in the solar hits. The simultaneous reflectivity data are used as a proxy to determine which data points are to be removed. A number of analysis methods are compared, and methods based on surface fitting are found superior to simple averaging. A separate fit to the reflectivity of the horizontal and vertical polarization channels is recommended, because it provides in addition to the differential reflectivity the pointing difference of the polarization channels, the squint angle. Data from the Finnish weather radar network shows that the squint angles are less than 0.02° and that the differential reflectivity bias is stable and determined to better than 0.04 dB. The results are compared to those from measurements at vertical incidence, which allows to determine the differential receiver bias and the transmitter bias.

scholarly journals Improved analysis of solar signals for differential reflectivity monitoring

2016 ◽  
Vol 9 (7) ◽  
pp. 3183-3192 ◽  
Author(s):  
Asko Huuskonen ◽  
Mikko Kurri ◽  
Iwan Holleman
Keyword(s):  
Vertical Polarization ◽  
Daily Monitoring ◽  
Weather Radars ◽  
Radar Network ◽  
Total Differential ◽  
Data Points ◽  
Reflectivity Data ◽  
Differential Reflectivity ◽  
Better Than ◽  
Receiver Bias

Abstract. The method for the daily monitoring of the differential reflectivity bias for polarimetric weather radars is developed further. Improved quality control is applied to the solar signals detected during the operational scanning of the radar, which efficiently removes rain and clutter-contaminated gates occurring in the solar hits. The simultaneous reflectivity data are used as a proxy to determine which data points are to be removed. A number of analysis methods to determine the differential reflectivity bias are compared, and methods based on surface fitting are found superior to simple averaging. A separate fit to the reflectivity of the horizontal and vertical polarization channels is recommended because of stability. Separate fitting also provides, in addition to the differential reflectivity bias, the pointing difference of the polarization channels. Data from the Finnish weather radar network show that the pointing difference is less than 0.02° and that the differential reflectivity bias is stable and determined to better than 0.04 dB. The results are compared to those from measurements at vertical incidence, which allows us to determine the total differential reflectivity bias including the differential receiver bias and the transmitter bias.

scholarly journals Operational Monitoring of Radar Differential Reflectivity Using the Sun

2010 ◽  
Vol 27 (5) ◽  
pp. 881-887 ◽  
Author(s):  
Iwan Holleman ◽  
Asko Huuskonen ◽  
Rashpal Gill ◽  
Pierre Tabary
Keyword(s):  
The Sun ◽  
Volume Data ◽  
High Quality ◽  
Vertical Polarization ◽  
Monitoring Method ◽  
Daily Monitoring ◽  
Weather Radars ◽  
Radar Antenna ◽  
Differential Reflectivity ◽  
Receiver Bias

Abstract A method for the daily monitoring of the differential reflectivity bias for polarimetric weather radars is presented. Sun signals detected in polar volume data produced during operational scanning of the radar are used. This method is an extension of that for monitoring the weather radar antenna pointing at low elevations and the radar receiving chain using the sun. This “online” method is ideally suited for routine application in networks of operational radars. The online sun monitoring can be used to check the agreement between horizontal and vertical polarization lobes of the radar antenna, which is a prerequisite for high-quality polarimetric measurements. By performing both online sun monitoring and rain calibration at vertical incidence, the differential receiver bias and differential transmitter bias can be disentangled. Results from the polarimetric radars in Trappes (France) and Bornholm (Denmark), demonstrating the importance of regular monitoring of the differential reflectivity bias, are discussed. It is recommended that the online sun-monitoring method, preferably in combination with rain calibration, is routinely performed on all polarimetric weather radars because accurate calibration is a prerequisite for most polarimetric algorithms.

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

2020 ◽  
Vol 12 (24) ◽  
pp. 4061
Author(s):  
Jeong-Eun Lee ◽  
Sung-Hwa Jung ◽  
Soohyun Kwon
Keyword(s):  
Signal To Noise Ratio ◽  
Vertical Profiles ◽  
Bright Band ◽  
Weather Radars ◽  
Radar Network ◽  
Temperature Zero ◽  
The Mean ◽  
Microphysical Processes ◽  
Differential Reflectivity ◽  
Self Consistency

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

scholarly journals Monitoring the differential reflectivity and receiver calibration for the German polarimetric weather radar network

2019 ◽  
Author(s):  
Michael Frech ◽  
John Hubbert
Keyword(s):  
Temperature Sensitivity ◽  
Temperature Dependent ◽  
Temperature Range ◽  
Weather Radars ◽  
Radar Systems ◽  
Radar Network ◽  
Polarimetric Weather Radar ◽  
Using Data ◽  
Differential Reflectivity ◽  
Made In

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 operationally 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, a two dimensional scan 5° azimuth by 8° elevation that encompasses the solar disk, have been 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 crossguide coupler outside the receiver box or into the LNAs, reveal only a very weak temperature sensitivity (

scholarly journals A Statistical study of the Doppler spectral width of high-latitude ionospheric F-region echoes recorded with SuperDARN coherent HF radars

2002 ◽  
Vol 20 (11) ◽  
pp. 1769-1781 ◽  
Author(s):  
J.-P. Villain ◽  
R. André ◽  
M. Pinnock ◽  
R. A. Greenwald ◽  
C. Hanuise
Keyword(s):  
High Latitude ◽  
Statistical Study ◽  
Spectral Width ◽  
Auroral Oval ◽  
F Region ◽  
Radar Network ◽  
Period 2 ◽  
Time Period ◽  
High Latitude Ionosphere ◽  
Data Points

Abstract. The HF radars of the Super Dual Auroral Radar Network (SuperDARN) provide measurements of the E × B drift of ionospheric plasma over extended regions of the high-latitude ionosphere. We have conducted a statistical study of the associated Doppler spectral width of ionospheric F-region echoes. The study has been conducted with all available radars from the Northern Hemisphere for 2 specific periods of time. Period 1 corresponds to the winter months of 1994, while period 2 covers October 1996 to March 1997. The distributions of data points and average spectral width are presented as a function of Magnetic Latitude and Magnetic Local Time. The databases are very consistent and exhibit the same features. The most stringent features are: a region of very high spectral width, collocated with the ionospheric LLBL/cusp/mantle region; an oval shaped region of high spectral width, whose equator-ward boundary matches the poleward limit of the Holzworth and Meng auroral oval. A simulation has been conducted to evaluate the geometrical and instrumental effects on the spectral width. It shows that these effects cannot account for the observed spectral features. It is then concluded that these specific spectral width characteristics are the signature of ionospheric/magnetospheric coupling phenomena.Key words. Ionosphere (auroral ionosphere; ionosphere-magnetosphere interactions; ionospheric irregularities)

scholarly journals Hydrological Validation of a Radar-Based Nowcasting Technique

2005 ◽  
Vol 6 (4) ◽  
pp. 532-549 ◽  
Author(s):  
Marc Berenguer ◽  
Carles Corral ◽  
Rafael Sánchez-Diezma ◽  
Daniel Sempere-Torres
Keyword(s):  
Point Of View ◽  
Motion Field ◽  
Warning Systems ◽  
Rainfall Runoff ◽  
Weather Radars ◽  
Radar Echoes ◽  
Radar Measurements ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Better Than

Abstract Nowcasting precipitation is a key element in the anticipation of floods in warning systems. In this framework, weather radars are very useful because of the high resolution of their measurements both in time and space. The aim of this study is to assess the performance of a recently proposed nowcasting technique (S-PROG) from a hydrological point of view in a Mediterranean environment. S-PROG is based on the advection of weather radar fields according to the motion field derived with an algorithm based on tracking radar echoes by correlation (TREC), and it has the ability of filtering out the most unpredictable scales of these fields as the forecasting time increases. Validation of this nowcasting technique was done from two different perspectives: (i) comparing forecasted precipitation fields against radar measurements, and (ii) by means of a distributed rainfall runoff model, comparing hydrographs simulated with a hydrological model using rainfall fields forecasted by S-PROG against hydrographs generated with the model using the entire series of radar measurements. In both cases, results obtained by a simpler nowcasting technique are used as a reference to evaluate improvements. Validation showed that precipitation fields forecasted with S-PROG seem to be better than fields forecasted using simpler techniques. Additionally, hydrological validation led the authors to point out that the use of radar-based nowcasting techniques allows the anticipation window in which flow estimates are forecasted with enough quality to be sensibly extended.

Averaging Trials Versus Averaging Trial Peaks: Impact on Study Outcomes

2017 ◽  
Vol 33 (3) ◽  
pp. 233-236 ◽  
Author(s):  
Kevin D. Dames ◽  
Jeremy D. Smith ◽  
Gary D. Heise
Keyword(s):  
Phase Shift ◽  
Average Data ◽  
Paired Samples ◽  
Individual Trial ◽  
Dependent Variables ◽  
Data Points ◽  
Multiple Trials ◽  
Average Profile ◽  
Better Than ◽  
Peak Value

Gait data are commonly presented as an average of many trials or as an average across participants. Discrete data points (eg, maxima or minima) are identified and used as dependent variables in subsequent statistical analyses. However, the approach used for obtaining average data from multiple trials is inconsistent and unclear in the biomechanics literature. This study compared the statistical outcomes of averaging peaks from multiple trials versus identifying a single peak from an average profile. A series of paired-samples t tests were used to determine whether there were differences in average dependent variables from these 2 methods. Identifying a peak value from the average profile resulted in significantly smaller magnitudes of dependent variables than when peaks from multiple trials were averaged. Disagreement between the 2 methods was due to temporal differences in trial peak locations. Sine curves generated in MATLAB confirmed this misrepresentation of trial peaks in the average profile when a phase shift was introduced. Based on these results, averaging individual trial peaks represents the actual data better than choosing a peak from an average trial profile.

Evaluating the Impact of the COSMIC RO Bending Angle Data on Predicting the Heavy Precipitation Episode on 16 June 2008 during SoWMEX-IOP8

2014 ◽  
Vol 142 (11) ◽  
pp. 4139-4163 ◽  
Author(s):  
Shu-Chih Yang ◽  
Shu-Hua Chen ◽  
Shu-Ya Chen ◽  
Ching-Yuang Huang ◽  
Ching-Sen Chen
Keyword(s):  
Lower Troposphere ◽  
Heavy Precipitation ◽  
Southwest Monsoon ◽  
Weather Research And Forecasting ◽  
Heavy Rainfall Event ◽  
Bending Angle ◽  
Angle Data ◽  
Ensemble Transform Kalman Filter ◽  
The Impact ◽  
Better Than

Abstract Global positioning system (GPS) radio occultation (RO) data have been broadly used in global and regional numerical weather predictions. Assimilation with the bending angle often performs better than refractivity, which is inverted from the bending angle under spherical assumption and is sometimes associated with negative biases at the lower troposphere; however, the bending angle operator also requires a higher model top as used in global models. This study furnishes the feasibility of bending-angle assimilation in the prediction of heavy precipitation systems with a regional model. The local RO operators for simulating bending angle and refractivity are implemented in the Weather Research and Forecasting (WRF)–local ensemble transform Kalman filter (LETKF) framework. The impacts of assimilating RO data from the Constellation Observing System for Meteorology Ionosphere and Climate (COSMIC) using both operators are evaluated on the prediction of a heavy precipitation episode during Southwest Monsoon Experiment intensive observing period 8 (SoWMEX-IOP8) in 2008. Results show that both the refractivity and bending angle provide a favorable condition for generating this heavy rainfall event. In comparison with the refractivity data, the advantage of assimilating the bending angle is identified in the midtroposphere for deepening of the moist layer that leads to a rainfall forecast closer to the observations.

scholarly journals Scale Dependence of Radar Rainfall Uncertainty: Initial Evaluation of NEXRAD’s New Super-Resolution Data for Hydrologic Applications

2010 ◽  
Vol 11 (5) ◽  
pp. 1191-1198 ◽  
Author(s):  
Bong-Chul Seo ◽  
Witold F. Krajewski
Keyword(s):  
Scale Effects ◽  
Spatial Scales ◽  
Super Resolution ◽  
Rain Gauge ◽  
Radar Rainfall ◽  
Weather Radars ◽  
Level Ii ◽  
Simple Scaling ◽  
Resolution Level ◽  
Reflectivity Data

Abstract This study explores the scale effects of radar rainfall accumulation fields generated using the new super-resolution level II radar reflectivity data acquired by the Next Generation Weather Radar (NEXRAD) network of the Weather Surveillance Radar-1988 Doppler (WSR-88D) weather radars. Eleven months (May 2008–August 2009, exclusive of winter months) of high-density rain gauge network data are used to describe the uncertainty structure of radar rainfall and rain gauge representativeness with respect to five spatial scales (0.5, 1, 2, 4, and 8 km). While both uncertainties of gauge representativeness and radar rainfall show simple scaling behavior, the uncertainty of radar rainfall is characterized by an almost 3 times greater standard error at higher temporal and spatial resolutions (15 min and 0.5 km) than at lower resolutions (1 h and 8 km). These results may have implications for error propagation through distributed hydrologic models that require high-resolution rainfall input. Another interesting result of the study is that uncertainty obtained by averaging rainfall products produced from the super-resolution reflectivity data is slightly lower at smaller scales than the uncertainty of the corresponding resolution products produced using averaged (recombined) reflectivity data.

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