scholarly journals A robust low-level cloud and clutter discrimination method for ground-based millimeter-wavelength cloud radar

2020 ◽  
Author(s):  
Xiaoyu Hu ◽  
Jinming Ge ◽  
Jiajing Du ◽  
Qinghao Li ◽  
Jianping Huang ◽  
...  
Keyword(s):  
Hydrological Cycle ◽  
Spectral Width ◽  
Spatial Filter ◽  
Distribution Functions ◽  
Probability Of Detection ◽  
Climate Effect ◽  
Millimeter Wavelength ◽  
Low Level ◽  
Cloud Radar ◽  
Semi Arid

Abstract. Low-level clouds play a key role in the energy budget and hydrological cycle of the climate system. The long-term and accurate observation of low-level clouds is essential for understanding their climate effect and model constraints. Both ground-based and spaceborne millimeter-wavelength cloud radars can penetrate clouds but the detected low-level clouds are always contaminated by clutters, which needs to be removed. In this study, we develop an algorithm to accurately separate low-level clouds from clutters for ground-based cloud radar using multi-dimensional probability distribution functions along with the Bayesian method. The radar reflectivity, linear depolarization ratio, spectral width and their dependences on the time of the day, height and season are used as the discriminants. A low pass spatial filter is applied to the Bayesian undecided classification mask, considering the spatial correlation difference between clouds and clutters. The resulting feature mask shows a good agreement with lidar detection, which has a high probability of detection rate (98.45 %) and a low false alarm rate (0.37 %). This algorithm will be used to reliably detect low-level clouds at the Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL) site, to study their climate effect and the interaction with local abundant dust aerosol in semi-arid region.

scholarly journals A robust low-level cloud and clutter discrimination method for ground-based millimeter-wavelength cloud radar

2021 ◽  
Vol 14 (2) ◽  
pp. 1743-1759
Author(s):  
Xiaoyu Hu ◽  
Jinming Ge ◽  
Jiajing Du ◽  
Qinghao Li ◽  
Jianping Huang ◽  
...  
Keyword(s):  
Hydrological Cycle ◽  
Spectral Width ◽  
Spatial Filter ◽  
Distribution Functions ◽  
Probability Of Detection ◽  
Climate Effect ◽  
Millimeter Wavelength ◽  
Low Level ◽  
Cloud Radar ◽  
Semi Arid

Abstract. Low-level clouds play a key role in the energy budget and hydrological cycle of the climate system. The accurate long-term observation of low-level clouds is essential for understanding their climate effect and model constraints. Both ground-based and spaceborne millimeter-wavelength cloud radars can penetrate clouds but the detected low-level clouds are always contaminated by clutter, which needs to be removed. In this study, we develop an algorithm to accurately separate low-level clouds from clutter for ground-based cloud radar using multi-dimensional probability distribution functions along with the Bayesian method. The radar reflectivity, linear depolarization ratio, spectral width, and their dependence on the time of the day, height, and season are used as the discriminants. A low-pass spatial filter is applied to the Bayesian undecided classification mask by considering the spatial correlation difference between clouds and clutter. The final feature mask result has a good agreement with lidar detection, showing a high probability of detection rate (98.45 %) and a low false alarm rate (0.37 %). This algorithm will be used to reliably detect low-level clouds at the Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL) site for the study of their climate effect and the interaction with local abundant dust aerosol in semi-arid regions.

scholarly journals An improved hydrometeor detection method for millimeter-wavelength cloud radar

2017 ◽  
Vol 17 (14) ◽  
pp. 9035-9047 ◽  
Author(s):  
Jinming Ge ◽  
Zeen Zhu ◽  
Chuang Zheng ◽  
Hailing Xie ◽  
Tian Zhou ◽  
...  
Keyword(s):  
Spatial Filter ◽  
Department Of Energy ◽  
Weak Signal ◽  
Radar Observations ◽  
Millimeter Wavelength ◽  
Detection Algorithms ◽  
Cloud Radar ◽  
Modified Method ◽  
The Us ◽  
Radiation Measurements

Abstract. A modified method with a new noise reduction scheme that can reduce the noise distribution to a narrow range is proposed to distinguish clouds and other hydrometeors from noise and recognize more features with weak signal in cloud radar observations. A spatial filter with central weighting, which is widely used in cloud radar hydrometeor detection algorithms, is also applied in our method to examine radar return for significant levels of signals. Square clouds were constructed to test our algorithm and the method used for the US Department of Energy Atmospheric Radiation Measurements Program millimeter-wavelength cloud radar. We also applied both the methods to 6 months of cloud radar observations at the Semi-Arid Climate and Environment Observatory of Lanzhou University and compared the results. It was found that our method has significant advantages in reducing the rates of both failed negative and false positive hydrometeor identifications in simulated clouds and recognizing clouds with weak signal from our cloud radar observations.

First Doppler lidar and cloud radar measurements of orographic convection initiation from a mountain observatory in the Al Hajar Mountains of the United Arab Emirates.

2021 ◽  
Author(s):  
Oliver Branch ◽  
Andreas Behrendt ◽  
Osama Alnayef ◽  
Florian Späth ◽  
Thomas Schwitalla ◽  
...  
Keyword(s):  
United Arab Emirates ◽  
Sea Breeze ◽  
Doppler Lidar ◽  
Cloud Seeding ◽  
Low Level ◽  
Cloud Radar ◽  
Orographic Convection ◽  
Radar Measurements ◽  
Convection Initiation ◽  
Convective Cells

<p>We present exciting Doppler lidar and cloud radar measurements from a high-vantage mountain observatory in the hyper-arid United Arab Emirates (UAE) - initiated as part of the UAE Research Program for Rain Enhancement Science (UAEREP). The observatory was designed to study the clear-air pre-convective environment and subsequent convective events in the arid Al Hajar Mountains, with the overarching goal of improving understanding and nowcasting of seedable orographic clouds. During summer in the Al Hajar Mountains (June to September), weather processes are often complex, with summer convection being initiated by several phenomena acting in concert, e.g., interaction between sea breeze and horizontal convective rolls. These interactions can combine to initiate sporadic convective storms and these can be intense enough to cause flash floods and erosion. Such events here are influenced by mesoscale phenomena like the low-level jet and local sea breeze, and are constrained by larger-scale synoptic conditions.</p><p>The Doppler lidar and cloud radar were employed for approximately two years at a high vantage-point to capture valley wind flows and observe convective cells. The instruments were configured to run synchronized polar (PPI) scans at 0°, 5°, and 45° elevation angles and vertical cross-section (RHI) scans at 0°, 30°, 60, 90°, 120°, and 150° azimuth angles. Using this imagery, along with local C-band radar and satellite data, we were able to identify and analyze several convective cases. To illustrate our results, we have selected two cases under unstable conditions - the 5 and 6 September 2018. In both cases, we observed areas of low-level convergence/divergence, particularly associated with wind flow around a peak 2 km to the south-west of the observatory. The extension of these deformations are visible in the atmosphere to a height of 3 km above sea level. Subsequently, we observed convective cells developing at those approximate locations – apparently initiated because of these phenomena. The cloud radar images provided detailed observations of cloud structure, evolution, and precipitation. In both convective cases, pre-convective signatures were apparent before CI, in the form of convergence, wind shear structures, and updrafts.</p><p>These results have demonstrated the value of synergetic observations for understanding orographic convection initiation, improvement of forecast models, and cloud seeding guidance. The manuscript based on these results is now the subject of a peer review (Branch et al., 2021).</p><p> </p><p>Branch, O., Behrendt, Andreas Alnayef, O., Späth, F., Schwitalla, Thomas, Temimi, M., Weston, M., Farrah, S., Al Yazeedi, O., Tampi, S., Waal, K. de and Wulfmeyer, V.: The new Mountain Observatory of the Project “Optimizing Cloud Seeding by Advanced Remote Sensing and Land Cover Modification (OCAL)” in the United Arab Emirates: First results on Convection Initiation, J. Geophys. Res.  Atmos., 2021. In review (submitted 23.11.2020).</p>

An Analysis of 2016-2018 Tornadoes and National Weather Service Tornado Warnings across the contiguous United States

2021 ◽  
Author(s):  
Evan S. Bentley ◽  
Richard L. Thompson ◽  
Barry R. Bowers ◽  
Justin G. Gibbs ◽  
Steven E. Nelson
Keyword(s):  
United States ◽  
Rotational Velocity ◽  
Radar Data ◽  
Probability Of Detection ◽  
False Alarms ◽  
National Weather Service ◽  
Low Level ◽  
Tornado Warnings ◽  
Tornado Warning ◽  
Weather Service

AbstractPrevious work has considered tornado occurrence with respect to radar data, both WSR-88D and mobile research radars, and a few studies have examined techniques to potentially improve tornado warning performance. To date, though, there has been little work focusing on systematic, large-sample evaluation of National Weather Service (NWS) tornado warnings with respect to radar-observable quantities and the near-storm environment. In this work, three full years (2016–2018) of NWS tornado warnings across the contiguous United States were examined, in conjunction with supporting data in the few minutes preceding warning issuance, or tornado formation in the case of missed events. The investigation herein examines WSR-88D and Storm Prediction Center (SPC) mesoanalysis data associated with these tornado warnings with comparisons made to the current Warning Decision Training Division (WDTD) guidance.Combining low-level rotational velocity and the significant tornado parameter (STP), as used in prior work, shows promise as a means to estimate tornado warning performance, as well as relative changes in performance as criteria thresholds vary. For example, low-level rotational velocity peaking in excess of 30 kt (15 m s−1), in a near-storm environment which is not prohibitive for tornadoes (STP > 0), results in an increased probability of detection and reduced false alarms compared to observed NWS tornado warning metrics. Tornado warning false alarms can also be reduced through limiting warnings with weak (<30 kt), broad (>1nm) circulations in a poor (STP=0) environment, careful elimination of velocity data artifacts like sidelobe contamination, and through greater scrutiny of human-based tornado reports in otherwise questionable scenarios.

scholarly journals GEOTECNOLOGIAS COMO SUPORTE PARA ANÁLISE DA VEGETAÇÃO NATURAL NA SUB-BACIA HIDROGRÁFICA DO RIO GAVIÃO (1988 A 2015)

2020 ◽  
Vol 15 (01) ◽  
pp. 98-113
Author(s):  
Carlos Magno Santos Clemente ◽  
Pablo Santana Santos
Keyword(s):  
Hydrological Cycle ◽  
Natural Vegetation ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Palabras Clave ◽  
El Sistema ◽  
History Of ◽  
Main Canal ◽  
Del Rio ◽  
Semi Arid

O histórico de ocupação da sub-bacia do rio Gavião passou por transformações socioeconômicas expressivas nos últimos 30 anos. Desse modo,preocupações com preservação ou recuperação da cobertura vegetal influência, positivamente, na manutenção do ciclo hidrológico da sub-bacia. A presente pesquisa teve como objetivo analisar a modificação da vegetal natural entre os anos de 1988a 2015 na sub-bacia hidrográfico do rio Gavião (semiárido brasileiro). Foram utilizados as técnicas sensoriamento remoto e Processamento Digital de Imagens - PDI para aquisição e processamento dos produtos orbitais (satélites landsat5 TM e landsat 8 OLI). E o Sistema de Informações Geográficas – SIG para armazenamento e análise do banco de dados alfanumérico georreferenciado. Os resultados indicam redução da cobertura vegetal de 751,69 km², entre os anos de 1988 a 2015. Também, manchas de desmatamento em áreas de nascentes, na parte alta da rede de drenagem e no dessegue do canal principal. Assim, a presente pesquisa chama atenção para os efeitos da mudança da vegetação natural para outros usos da terra (solo exposto, plantio, entre outros), a concentração do desmatamento em áreas de fragilidade ambiental. Palavras-chave: Landsat; Desmatamento; Semiárido brasileiro.   GEOTECHNOLOGIES AS SUPPORT FOR ANALYSIS OF NATURAL VEGETATION IN THE HYDROGRAPHIC BASIN OF HAWK RIVER (1988 A 2015) Abstract  The occupation history of the Hawk River sub-basin underwent significant socioeconomic transformations in the last 30 years. Thus, concerns for preservation or recovery of vegetation cover positively influence the maintenance of the sub-basin's hydrological cycle. The present research had as objective to analyze the modification of the natural vegetal between the years of 1988 to 2015 in the hydrographic sub-basin of the river Gavião (semi-arid Brazilian).The techniques of remote sensing and Digital Image Processing (PDI) were used for the acquisition and processing of orbital products (landsat 5 TM and landsat 8 OLI satellites). The Geographic Information System - GIS for storage and analysis of the georeferenced alphanumeric database. The results indicate a reduction of the vegetal cover of 751,69 km ², between the years of 1988 to 2015. In addition, deforestation patches in areas of springs, in the upper part of the drainage network and in the main canal deregulation. Thus, the present research draws attention to the effects of changing natural vegetation to other land uses (exposed soil, planting, among others), the concentration of deforestation in areas of environmental fragility.  Keywords: Landsat; deforestation; Brazilian semi-arid.   GEOTECNOLOGÍA COMO SOPORTE PARA EL ANÁLISIS DE VEGETACIÓN NATURAL DE LA SUBCUENCA DEL RÍO GAVILÁN (1988 A 2015) Resumen La historia de laocupación de lasub-cuencadelrío Gavião fue sometido a importantes cambios socioeconómicos enlos últimos 30 años. De este modo, preocupación por lapreservación o restauración de lacubierta vegetal influencia positiva enelmantenimientodel ciclo hidrológico de lasubcuenca. Esta investigacióntuvo como objetivo analizarlamodificación de lavegetación natural entre losaños 1988-2015 enlasubcuenca hidrográfica delrío Gavião (semiárido brasileño). Como apoyo técnico, lateledetección y la técnica de imagen digital se utiliza Procesamiento - PDI para laadquisición y procesamiento de productosorbitales (satélites Landsat 5 y Landsat TM 8 OLI). Y el Sistema de Información Geográfica - SIG para elalmacenamiento y análisis de la base de datos alfanuméricos georeferenciada. Los resultados indicanlareducción de lacubierta vegetal de 751.69 km², entre losaños 1988-2015. Tambiénlas manchas de deforestaciónenlascabecerasenla parte superior del sistema de drenaje y dessegue el canal principal. Así, estainvestigaciónllamalaatención sobre losefectosdelcambio de lavegetación natural a otros usos de latierra (sueloexpuesto, ,plantación, etc.), laconcentración de ladeforestaciónen áreas ambientalmente frágiles. Palabras clave: Landsat; deforestación; semiárido brasileño.

scholarly journals Sensitivity of the West African hydrological cycle in ORCHIDEE to infiltration processes

2008 ◽  
Vol 5 (4) ◽  
pp. 2251-2292 ◽  
Author(s):  
T. d'Orgeval ◽  
J. Polcher ◽  
P. de Rosnay
Keyword(s):  
Hydrological Cycle ◽  
Equatorial Region ◽  
West African ◽  
Surface Model ◽  
African Region ◽  
Deep Soil ◽  
The West ◽  
West African Region ◽  
Semi Arid ◽  
Infiltration Processes

Abstract. The aim of this article is to test the sensitivity of the Land Surface Model (LSM) ORCHIDEE to infiltration processes in the West African region, and to validate the resulting version of ORCHIDEE against African river discharges. The parameterizations to take into account the effects of flat areas, ponds and floodplains on surface infiltration, and the effect of roots and deep-soil compactness on infiltration are first described. It is shown that the surface infiltration processes have a stronger impact in the soudano-sahelian region and more generally in semi-arid African basins, whereas the rootzone and deep-soil infiltration also play a role in the guinean region and in the intermediate basins between arid and humid ones. In the equatorial region and the semi-humid basins, infiltration processes generally play a minor role. The infiltration parameterizations may explain part of the difference between simulated and observed river discharge in semi-arid and intermediate basins. So ORCHIDEE could be recalibrated to reduce the discharge errors. However, different sources of uncertainty might also explain part of the error. Indeed, the precipitation forcing in the whole West African region, the long-term storage in the soudano-sahelian region, the soil types in the guinean region and the vegetation types in the equatorial region are significant sources of errors. Therefore, a denser monitoring of the hydrological cycle at different scales in West Africa would ensure the reliability of future calibrations for the infiltration parameterizations.

scholarly journals Evaluation of Two Cloud-Resolving Models Using Bin or Bulk Microphysics Representation for the HyMeX-IOP7a Heavy Precipitation Event

Atmosphere ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 1177
Author(s):  
Diana Arteaga ◽  
Céline Planche ◽  
Christina Kagkara ◽  
Wolfram Wobrock ◽  
Sandra Banson ◽  
...  
Keyword(s):  
Hydrological Cycle ◽  
Wrf Model ◽  
Heavy Precipitation ◽  
Sensitivity Study ◽  
Precipitation Event ◽  
Convective System ◽  
Wind Fields ◽  
Massif Central ◽  
Surface Precipitation ◽  
Low Level

The Mediterranean region is frequently affected in autumn by heavy precipitation that causes flash-floods or landslides leading to important material damage and casualties. Within the framework of the international HyMeX program (HYdrological cycle in Mediterranean EXperiment), this study aims to evaluate the capabilities of two models, WRF (Weather Research and Forecasting) and DESCAM (DEtailed SCAvenging Model), which use two different representations of the microphysics to reproduce the observed atmospheric properties (thermodynamics, wind fields, radar reflectivities and precipitation features) of the HyMeX-IOP7a intense precipitating event (26 September 2012). The DESCAM model, which uses a bin resolved representation of the microphysics, shows results comparable to the observations for the precipitation field at the surface. On the contrary, the simulations made with the WRF model using a bulk representation of the microphysics (either the Thompson scheme or the Morrison scheme), commonly employed in NWP models, reproduce neither the intensity nor the distribution of the observed precipitation—the rain amount is overestimated and the most intense cell is shifted to the East. The different simulation results show that the divergence in the surface precipitation features seems to be due to different mechanisms involved in the onset of the precipitating system: the convective system is triggered by the topography of the Cévennes mountains (i.e., south-eastern part of the Massif Central) in DESCAM and by a low-level flux convergence in WRF. A sensitivity study indicates that the microphysics properties have impacted the thermodynamics and dynamics fields inducing the low-level wind convergence simulated with WRF for this HyMeX event.

scholarly journals An Improved Algorithm for Low-Level Turbulence Forecasting

2018 ◽  
Vol 57 (6) ◽  
pp. 1249-1263 ◽  
Author(s):  
Domingo Muñoz-Esparza ◽  
Robert Sharman
Keyword(s):  
Probability Distributions ◽  
Atmospheric Stability ◽  
Energy Dissipation Rate ◽  
Probability Of Detection ◽  
Percentage Error ◽  
Unmanned Aerial Systems ◽  
Low Level ◽  
Forecasting System ◽  
The One ◽  
Aerial Systems

AbstractA low-level turbulence (LLT) forecasting algorithm is proposed and implemented within the Graphical Turbulence Guidance (GTG) turbulence forecasting system. The LLT algorithm provides predictions of energy dissipation rate (EDR; turbulence dissipation to the one-third power), which is the standard turbulence metric used by the aviation community. The algorithm is based upon the use of distinct log-Weibull and lognormal probability distributions in a statistical remapping technique to represent accurately the behavior of turbulence in the atmospheric boundary layer for daytime and nighttime conditions, respectively, thus accounting for atmospheric stability. A 1-yr-long GTG LLT calibration was performed using the High-Resolution Rapid Refresh operational model, and optimum GTG ensembles of turbulence indices for clear-air and mountain-wave turbulence that minimize the mean absolute percentage error (MAPE) were determined. Evaluation of the proposed algorithm with in situ EDR data from the Boulder Atmospheric Observatory tower covering a range of altitudes up to 300 m above the surface demonstrates a reduction in the error by a factor of approximately 2.0 (MAPE = 55%) relative to the current operational GTG system (version 3). In addition, the probability of detection of typical small and large EDR values at low levels is increased by approximately 15%–20%. The improved LLT algorithm is expected to benefit several nonconventional turbulence-prediction sectors such as unmanned aerial systems and wind energy.

Evaluation of 3M™ Molecular Detection Assay (MDA) Salmonella for the Detection of Salmonella in Selected Foods: Collaborative Study

2013 ◽  
Vol 96 (6) ◽  
pp. 1325-1335 ◽  
Author(s):  
Patrick Bird ◽  
Kiel Fisher ◽  
Megan Boyle ◽  
Travis Huffman ◽  
M Joseph Benzinger, Jr ◽  
...  
Keyword(s):  
Confidence Intervals ◽  
Molecular Detection ◽  
Collaborative Study ◽  
Ground Beef ◽  
Probability Of Detection ◽  
Inoculum Level ◽  
Test Portion ◽  
Low Level ◽  
Dog Food ◽  
Detection Assay

Abstract The 3M™ Molecular Detection Assay (MDA) Salmonella is used with the 3M™ Molecular Detection System for the detection of Salmonella spp. in food, food-related, and environmental samples after enrichment. The assay utilizes loop-mediated isothermal amplification to rapidly amplify Salmonella target DNA with high specificity and sensitivity, combined with bioluminescence to detect the amplification. The 3M MDA Salmonella method was compared using an unpaired study design in a multilaboratory collaborative study to the U. S. Department of Agriculture/Food Safety and Inspection Service-Microbiology Laboratory Guidebook (USDA/FSIS-MLG 4.05), Isolation and Identification of Salmonella from Meat, Poultry, Pasteurized Egg and Catfish Products for raw ground beef and the U.S. Food and Drug Administration/Bacteriological Analytical Manual (FDA/BAM) Chapter 5 Salmonella reference method for wet dog food following the current AOAC guidelines. A total of 20 laboratories participated. For the 3M MDA Salmonella method, raw ground beef was analyzed using 25 g test portions, and wet dog food was analyzed using 375 g test portions. For the reference methods, 25 g test portions of each matrix were analyzed. Each matrix was artificially contaminated with Salmonella at three inoculation levels: an uninoculated control level (0 CFU/test portion), a low inoculum level (0.2–2 CFU/test portion), and a high inoculum level (2–5 CFU/test portion). In this study, 1512 unpaired replicate samples were analyzed. Statistical analysis was conducted according to the probability of detection (POD). For the low-level raw ground beef test portions, the following dLPOD (difference between the POD of the reference and candidate method) values with 95% confidence intervals were obtained: –0.01 (–0.14, +0.12). For the low-level wet dog food test portions, the following dLPOD with 95% confidence intervals were obtained: –0.04 (–0.16, +0.09). No significant differences were observed in the number of positive samples detected by the 3M MDA Salmonella method versus either the USDA/FSIS-MLG or FDA/BAM methods.

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