scholarly journals Validation of GMI Snowfall Observations by Using a Combination of Weather Radar and Surface Measurements

2018 ◽  
Vol 57 (4) ◽  
pp. 797-820 ◽  
Author(s):  
Annakaisa von Lerber ◽  
Dmitri Moisseev ◽  
David A. Marks ◽  
Walter Petersen ◽  
Ari-Matti Harri ◽  
...  
Keyword(s):  
Large Scale ◽  
Quantitative Estimation ◽  
Weather Radar ◽  
Detection Algorithm ◽  
Probability Of Detection ◽  
Research Station ◽  
Radar Observations ◽  
Microphysical Properties ◽  
Ground Validation ◽  
Microwave Imager

AbstractCurrently, there are several spaceborne microwave instruments suitable for the detection and quantitative estimation of snowfall. To test and improve retrieval snowfall algorithms, ground validation datasets that combine detailed characterization of snowfall microphysics and spatial precipitation measurements are required. To this endpoint, measurements of snow microphysics are combined with large-scale weather radar observations to generate such a dataset. The quantitative snowfall estimates are computed by applying event-specific relations between the equivalent reflectivity factor and snowfall rate to weather radar observations. The relations are derived using retrieved ice particle microphysical properties from observations that were carried out at the University of Helsinki research station in Hyytiälä, Finland, which is about 64 km east of the radar. For each event, the uncertainties of the estimate are also determined. The feasibility of using this type of data to validate spaceborne snowfall measurements and algorithms is demonstrated with the NASA GPM Microwave Imager (GMI) snowfall product. The detection skill and retrieved surface snowfall precipitation of the GPROF detection algorithm, versions V04A and V05A, are assessed over southern Finland. On the basis of the 26 studied overpasses, probability of detection (POD) is 0.90 for version V04A and 0.84 for version V05A, and corresponding false-alarm rates are 0.09 and 0.10, respectively. A clear dependence of detection skill on cloud echo top height is shown: POD increased from 0.8 to 0.99 (V04A) and from 0.61 to 0.94 (V05A) as the cloud echo top altitude increased from 2 to 5 km. Both versions underestimate the snowfall rate by factors of 6 (V04A) and 3 (V05A).

scholarly journals Monitoring the Reflectivity Calibration of a Scanning Radar Using a Profiling Radar and a Disdrometer

2005 ◽  
Vol 22 (7) ◽  
pp. 1004-1018 ◽  
Author(s):  
Christopher R. Williams ◽  
Kenneth S. Gage ◽  
Wallace Clark ◽  
Paul Kucera
Keyword(s):  
Standard Deviation ◽  
Confidence Interval ◽  
Weather Radar ◽  
Large Variability ◽  
Field Campaign ◽  
Radar Observations ◽  
Central Florida ◽  
Ground Validation ◽  
Temporal And Spatial ◽  
Small Resolution

Abstract This paper describes a method of absolutely calibrating and routinely monitoring the reflectivity calibration from a scanning weather radar using a vertically profiling radar that has been absolutely calibrated using a collocated surface disdrometer. The three instruments have different temporal and spatial resolutions, and the concept of upscaling is used to relate the small resolution volume disdrometer observations with the large resolution volume scanning radar observations. This study uses observations collected from a surface disdrometer, two profiling radars, and the National Weather Service (NWS) Weather Surveillance Radar-1988 Doppler (WSR-88D) scanning weather radar during the Texas–Florida Underflight-phase B (TEFLUN-B) ground validation field campaign held in central Florida during August and September 1998. The statistics from the 2062 matched profiling and scanning radar observations during this 2-month period indicate that the WSR-88D radar had a reflectivity 0.7 dBZ higher than the disdrometer-calibrated profiler, the standard deviation was 2.4 dBZ, and the 95% confidence interval was 0.1 dBZ. This study implies that although there is large variability between individual matched observations, the precision of a series of observations is good, allowing meaningful comparisons useful for calibration and monitoring.

Raindrop size distribution retrieval model from polarization radar observations using neural network techniques

2021 ◽  
Author(s):  
Jingxuan Zhu ◽  
Enze Chen ◽  
Qiang Dai
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Short Term Memory ◽  
Polynomial Regression ◽  
Weather Radar ◽  
Spatial And Temporal Variation ◽  
Retrieval Model ◽  
Radar Observations ◽  
Radar Meteorology ◽  
Raindrop Size

<p>Raindrop size distributions (DSD) information plays a significant role in many scientific fields, especially in radar meteorology. DSD has spatial and temporal variation across different storm types and climatic regimes. Since the development of polarimetric weather radar, the large-scale DSD estimation has been a long-standing goal in radar meteorology. Traditional polynomial regression algorithms for ground polarimetric radars are widely used to estimate DSD parameters due to their simple methodology and acceptable accuracy. However, a simple polynomial regression may not be able to deeply explore the intrinsic relationship using available observations. This study therefore proposes a DSD retrieval model that uses dual-polarization radar observations based on long short-term memory (LSTM) network techniques. Three schemes of a normalized gamma DSD parameters (LSTM- D<sub>0</sub>, LSTM- N<sub>w</sub>, and LSTM-µ) are designed with different combinations of polarimetric radar measurement inputs. Results show that all LSTM estimators exhibit better performance than the polynomial regression method. The proposed retrieval model using neural network techniques helps to improve quantitative precipitation estimation of weather radar and make sense of a better understanding of precipitation microphysics.</p>

scholarly journals Detection of Ground Clutter for Dual-Polarization Weather Radar Using a Novel 3D Discriminant Function

2019 ◽  
Vol 36 (7) ◽  
pp. 1285-1296 ◽  
Author(s):  
Mohammad-Hossein Golbon-Haghighi ◽  
Guifu Zhang
Keyword(s):  
Discriminant Function ◽  
Weather Radar ◽  
Gaussian Mixture ◽  
Radar Data ◽  
Detection Algorithm ◽  
Probability Of Detection ◽  
Optimal Decision ◽  
Model Parameters ◽  
Performance Improvements ◽  
Ground Clutter

AbstractA novel 3D discriminant function is introduced as part of a ground clutter detection algorithm for improving weather radar observations. The 3D discriminant function utilizes the phase fluctuations of the received signals for horizontal and vertical polarizations and the dual-scan cross-correlation coefficient. An optimal decision based on the 3D discriminant function is made using a simple Bayesian classifier to distinguish clutter from weather signals. For convenience of use, a multivariate Gaussian mixture model is used to represent the probability density functions of discriminant functions. The model parameters are estimated based on the maximum likelihood using the expectation–maximization (ML-EM) method. The performance improvements are demonstrated by applying the proposed detection algorithm to radar data collected by the polarimetric Norman, Oklahoma (KOUN), weather radar. This algorithm is compared to other clutter detection algorithms and the results indicate that, using the proposed detection algorithm, a better probability of detection can be achieved.

scholarly journals A Study of hailstorm of 19th April 2010 over Delhi using Doppler Weather Radar observations

MAUSAM ◽  
2021 ◽  
Vol 62 (3) ◽  
pp. 433-440
Author(s):  
HARI SINGH ◽  
R. K. DATTA ◽  
SURESH CHAND ◽  
D.P. MISHRA ◽  
B.A.M. KANNAN
Keyword(s):  
India Meteorological Department ◽  
Weather Radar ◽  
Threshold Value ◽  
Radar Data ◽  
Detection Algorithm ◽  
Doppler Weather Radar ◽  
Radar Observations ◽  
Freezing Level ◽  
Function Diagram ◽  
Very High

Hailstorm of 19th April 2010 over Delhi has been studied using observations from Doppler Weather Radar (DWR) installed at Palam. The data was analysed at Central Server located at India Meteorological Department HQ using IRIS software (of M/s SIGMET-VAISALA, Finland) installed in the server. Reflectivity of 45 dBZ level was found to be 6.3 km above freezing level at the time of hailstorm which corresponds to 100% (obtained from probability function diagram of Witt et al. (1998)) probability of hail. Reflectivity was more than 55 dBZ upto 10 km and 7 km at 1110 UTC and 1120 UTC respectively which exceeds the hail threshold limit adopted in NEXRAD (USA). Maximum of 62 dBZ was observed at about 3 km at 1110 UTC and 64 dBZ at 3.5 km at 1120 UTC in Radar Data. Very high values of Vertical Integrated Liquid (VIL) ranging from 58.7 kg/m2 to 64.1 kg/m2 were observed between 1040 UTC and 1120 UTC which is higher than 43 kg/m2, the threshold value for occurrence of hail. Severe Hail Index (SHI), Probability of Severe Hail (POSH) and Maximum Expected Hail Size (MEHS) were computed to verify the applicability of enhancedHail Detection Algorithm (HDA) outlined by Witt et al. (1998) to Indian conditions. The Maximum Expected Hail Size (MEHS) computed using Doppler Weather Radar observations were 2.5 cm, 2.6 cm and 2.0 cm respectively at 1050 UTC, 1100 UTC and 1110 UTC which are in close agreement with the reported hail size. The study confirms that HDA and other thresholds of reflectivity and VIL used for hail detection and warnings in NEXRAD (USA) can be used in Indian conditions also.

scholarly journals Ground Validation and Error Sources Identification for GPM IMERG Product over the Southeast Coastal Regions of China

2020 ◽  
Vol 12 (24) ◽  
pp. 4154
Author(s):  
Xinxin Sui ◽  
Zhi Li ◽  
Ziqiang Ma ◽  
Jintao Xu ◽  
Siyu Zhu ◽  
...  
Keyword(s):  
Coastal Areas ◽  
Probability Of Detection ◽  
Surface Type ◽  
Error Sources ◽  
Precipitation Measurement ◽  
Ground Validation ◽  
Precipitation Estimates ◽  
Microwave Imager ◽  
Type Factor ◽  
Global Precipitation

The Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement mission (IMERG) has been widely evaluated. However, most of these studies focus on the ultimate merged satellite-gauge precipitation estimate and neglect the valuable intermediate estimates which directly guide the improvement of the IMERG product. This research aims to identify the error sources of the latest IMERG version 6 by evaluating the intermediate and ultimate precipitation estimates, and further examine the influences of regional topography and surface type on these errors. Results show that among six passive microwave (PMW) sensors, the Microwave Humidity Sounder (MHS) has outstanding comprehensive behavior, and Special Sensor Microwave Imager/Sounder (SSMIS) operates advanced at precipitation detection, while the Sounder for Atmospheric Profiling of Humidity in the Intertropics by Radiometry (SAPHIR) has the worst performance. More precipitation events are detected with larger quantitative uncertainty in low-lying places than in highlands, in urban and water body areas than in other places, and more in coastal areas than in inland regions. Infrared (IR) estimate has worse performance than PMW, and the precipitation detectability of IR is more sensitive to the factors of elevation and the distance to the coast, as larger critical successful index (CSI) over lowlands and coastal areas. PMW morphing and the mixing of PMW and IR algorithms partly reverse the conservative feature of the precipitation detection of PMW and IR estimates, resulting in higher probability of detection (POD) and false alert ratio (FAR). Finally, monthly gauge calibration improves most of the statistical indicators and reduces the influence of elevation and surface type factor on these errors.

scholarly journals Static and dynamic functional connectivity supports the configuration of brain networks associated with creative cognition

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Abhishek Uday Patil ◽  
Sejal Ghate ◽  
Deepa Madathil ◽  
Ovid J. L. Tzeng ◽  
Hsu-Wen Huang ◽  
...  
Keyword(s):  
Cognitive Processes ◽  
Large Scale ◽  
Brain Networks ◽  
Blood Oxygen Level Dependent ◽  
Detection Algorithm ◽  
Brain Regions ◽  
Creative Cognition ◽  
Network Flexibility ◽  
Community Detection Algorithm ◽  
Window Approach

AbstractCreative cognition is recognized to involve the integration of multiple spontaneous cognitive processes and is manifested as complex networks within and between the distributed brain regions. We propose that the processing of creative cognition involves the static and dynamic re-configuration of brain networks associated with complex cognitive processes. We applied the sliding-window approach followed by a community detection algorithm and novel measures of network flexibility on the blood-oxygen level dependent (BOLD) signal of 8 major functional brain networks to reveal static and dynamic alterations in the network reconfiguration during creative cognition using functional magnetic resonance imaging (fMRI). Our results demonstrate the temporal connectivity of the dynamic large-scale creative networks between default mode network (DMN), salience network, and cerebellar network during creative cognition, and advance our understanding of the network neuroscience of creative cognition.

scholarly journals A New Tool to Estimate Inundation Depths by Spatial Interpolation (RAPIDE): Design, Application and Impact on Quantitative Assessment of Flood Damages

Water ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 1805 ◽  
Author(s):  
Anna Scorzini ◽  
Alessio Radice ◽  
Daniela Molinari
Keyword(s):  
River Basin ◽  
Water Depth ◽  
Spatial Interpolation ◽  
Large Scale ◽  
Quantitative Estimation ◽  
Digital Terrain Model ◽  
Water Levels ◽  
Flood Depth ◽  
Inundation Depth ◽  
Basin Scale

Rapid tools for the prediction of the spatial distribution of flood depths within inundated areas are necessary when the implementation of complex hydrodynamic models is not possible due to time constraints or lack of data. For example, similar tools may be extremely useful to obtain first estimates of flood losses in the aftermath of an event, or for large-scale river basin planning. This paper presents RAPIDE, a new GIS-based tool for the estimation of the water depth distribution that relies only on the perimeter of the inundation and a digital terrain model. RAPIDE is based on a spatial interpolation of water levels, starting from the hypothesis that the perimeter of the flooded area is the locus of points having null water depth. The interpolation is improved by (i) the use of auxiliary lines, perpendicular to the river reach, along which additional control points are placed and (ii) the possibility to introduce a mask for filtering interpolation points near critical areas. The reliability of RAPIDE is tested for the 2002 flood in Lodi (northern Italy), by comparing the inundation depth maps obtained by the rapid tool to those from 2D hydraulic modelling. The change of the results, related to the use of either method, affects the quantitative estimation of direct damages very limitedly. The results, therefore, show that RAPIDE can provide accurate flood depth predictions, with errors that are fully compatible with its use for river-basin scale flood risk assessments and civil protection purposes.

scholarly journals Simulating future precipitation extremes in a complex Alpine catchment

2013 ◽  
Vol 13 (2) ◽  
pp. 263-277 ◽  
Author(s):  
C. Dobler ◽  
G. Bürger ◽  
J. Stötter
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Precipitation Extremes ◽  
Climate Projections ◽  
Research Station ◽  
Climate Change Scenarios ◽  
Station Data

Abstract. The objectives of the present investigation are (i) to study the effects of climate change on precipitation extremes and (ii) to assess the uncertainty in the climate projections. The investigation is performed on the Lech catchment, located in the Northern Limestone Alps. In order to estimate the uncertainty in the climate projections, two statistical downscaling models as well as a number of global and regional climate models were considered. The downscaling models applied are the Expanded Downscaling (XDS) technique and the Long Ashton Research Station Weather Generator (LARS-WG). The XDS model, which is driven by analyzed or simulated large-scale synoptic fields, has been calibrated using ECMWF-interim reanalysis data and local station data. LARS-WG is controlled through stochastic parameters representing local precipitation variability, which are calibrated from station data only. Changes in precipitation mean and variability as simulated by climate models were then used to perturb the parameters of LARS-WG in order to generate climate change scenarios. In our study we use climate simulations based on the A1B emission scenario. The results show that both downscaling models perform well in reproducing observed precipitation extremes. In general, the results demonstrate that the projections are highly variable. The choice of both the GCM and the downscaling method are found to be essential sources of uncertainty. For spring and autumn, a slight tendency toward an increase in the intensity of future precipitation extremes is obtained, as a number of simulations show statistically significant increases in the intensity of 90th and 99th percentiles of precipitation on wet days as well as the 5- and 20-yr return values.

Lightning Frequency and Microphysical Properties of Precipitating Clouds over the Western North Pacific during Winter as Derived from TRMM Multisensor Observations

2007 ◽  
Vol 135 (6) ◽  
pp. 2226-2241 ◽  
Author(s):  
Yasu-Masa Kodama ◽  
Haruna Okabe ◽  
Yukie Tomisaka ◽  
Katsuya Kotono ◽  
Yoshimi Kondo ◽  
...  
Keyword(s):  
North Pacific ◽  
Western North Pacific ◽  
Large Scale ◽  
Tropical Rainfall Measuring Mission ◽  
Radar Reflectivity ◽  
Phase Layer ◽  
Convective Clouds ◽  
Microphysical Properties ◽  
The Tropics ◽  
Precipitating Clouds

Abstract Tropical Rainfall Measuring Mission observations from multiple sensors including precipitation radar, microwave and infrared radiometers, and a lightning sensor were used to describe precipitation, lightning frequency, and microphysical properties of precipitating clouds over the midlatitude ocean. Precipitation over midlatitude oceans was intense during winter and was often accompanied by frequent lightning. Case studies over the western North Pacific from January and February 2000 showed that some lightning occurred in deep precipitating clouds that developed around cyclones and their attendant fronts. Lightning also occurred in convective clouds that developed in regions of large-scale subsidence behind extratropical cyclones where cold polar air masses were strongly heated and moistened from below by the ocean. The relationships between lightning frequency and the minimum polarization corrected temperature (PCT) at 37 and 85 GHz and the profile of the maximum radar reflectivity resembled relationships derived previously for cases in the Tropics. Smaller lapse rates in the maximum radar reflectivity above the melting level indicate vigorous convection that, although shallow and relatively rare, was as strong as convection over tropical oceans. Lightning was most frequent in systems for which the minimum PCT at 37 GHz was less than 260 K. Lightning and PCT at 85 GHz were not as well correlated as lightning and PCT at 37 GHz. Thus, lightning was frequent in convective clouds that contained many large hydrometeors in the mixed-phase layer, because PCT is more sensitive to large hydrometeors at 37 than at 85 GHz. The relationship between lightning occurrence and cloud-top heights derived from infrared observations was not straightforward. Microphysical conditions that support lightning over the midlatitude ocean in winter were similar to conditions in the Tropics and are consistent with Takahashi’s theory of riming electrification.

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