scholarly journals Verification of analysed and forecasted winter precipitation in complex terrain

2015 ◽  
Vol 9 (2) ◽  
pp. 587-601 ◽  
Author(s):  
M. Schirmer ◽  
B. Jamieson
Keyword(s):  
Low Cost ◽  
Weather Prediction ◽  
Winter Season ◽  
Model Performance ◽  
Winter Precipitation ◽  
Mitigation Measures ◽  
Depth Sensors ◽  
Mountainous Regions ◽  
Precipitation Analysis ◽  
Analysis System

Abstract. Numerical Weather Prediction (NWP) models are rarely verified for mountainous regions during the winter season, although avalanche forecasters and other decision makers frequently rely on NWP models. Winter precipitation from two NWP models (GEM-LAM and GEM15) and from a precipitation analysis system (CaPA) was verified at approximately 100 stations in the mountains of western Canada and the north-western US. Ultrasonic snow depth sensors and snow pillows were used to observe daily precipitation amounts. For the first time, a detailed objective validation scheme was performed highlighting many aspects of forecast quality. Overall, the models underestimated precipitation amounts, although low precipitation categories were overestimated. The finer resolution model GEM-LAM performed best in all analysed aspects of model performance, while the precipitation analysis system performed worst. An analysis of the economic value of large precipitation categories showed that only mitigation measures with low cost–loss ratios (i.e. measures that can be performed often) will benefit from these NWP models. This means that measures with large associated costs relative to anticipated losses when the measure is not performed should not or not primarily depend on forecasted precipitation.

scholarly journals Verification of forecasted winter precipitation in complex terrain

2014 ◽  
Vol 8 (6) ◽  
pp. 5727-5762 ◽  
Author(s):  
M. Schirmer ◽  
B. Jamieson
Keyword(s):  
Low Cost ◽  
Weather Prediction ◽  
Economic Value ◽  
Winter Season ◽  
Model Performance ◽  
Mitigation Measures ◽  
Depth Sensors ◽  
Mountainous Regions ◽  
Precipitation Analysis ◽  
Analysis System

Abstract. Numerical Weather Prediction (NWP) models are rarely verified for mountainous regions during the winter season, although avalanche forecasters and other decision makers frequently rely on NWP models. We verified two NWP models (GEM-LAM and GEM15) and a precipitation analysis system (CaPA) at approximately 100 stations in the mountains of western Canadian and northwestern US. Ultrasonic snow depth sensors and snow pillows were used to observe daily precipitation amounts. For the first time, a detailed objective validation scheme was performed highlighting many aspects of forecast quality. Overall, the models underestimated precipitation amounts, although low precipitation categories were overestimated. The finer resolution model GEM-LAM performed best in all analysed aspects of model performance, while the precipitation analysis system performed worst. An analysis of the economic value of large precipitation categories showed that only mitigation measures with low cost/loss ratios (i.e. measures that can be performed often) will benefit from these NWP models. This means that measures with large associated costs (relative to anticipated losses when the measure is not performed) should not or not primarily depend on forecasted precipitation.

Winter Precipitation Microphysics Characterized by Polarimetric Radar and Video Disdrometer Observations in Central Oklahoma

2011 ◽  
Vol 50 (7) ◽  
pp. 1558-1570 ◽  
Author(s):  
Guifu Zhang ◽  
Sean Luchs ◽  
Alexander Ryzhkov ◽  
Ming Xue ◽  
Lily Ryzhkova ◽  
...  
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Winter Season ◽  
Winter Precipitation ◽  
Polarimetric Radar ◽  
Particle Size Distributions ◽  
Physical Processes ◽  
Polarimetric Weather Radar ◽  
Numerical Weather Prediction Models ◽  
Precipitation Events

AbstractThe study of precipitation in different phases is important to understanding the physical processes that occur in storms, as well as to improving their representation in numerical weather prediction models. A 2D video disdrometer was deployed about 30 km from a polarimetric weather radar in Norman, Oklahoma, (KOUN) to observe winter precipitation events during the 2006/07 winter season. These events contained periods of rain, snow, and mixed-phase precipitation. Five-minute particle size distributions were generated from the disdrometer data and fitted to a gamma distribution; polarimetric radar variables were also calculated for comparison with KOUN data. It is found that snow density adjustment improves the comparison substantially, indicating the importance of accounting for the density variability in representing model microphysics.

scholarly journals Combination of ground-based and space-based GNSS tomography (2021-2025)

2021 ◽  
Author(s):  
Witold Rohm ◽  
Paweł Hordyniec ◽  
Gregor Möller ◽  
Maciej Kryza ◽  
Estera Trzcina ◽  
...  
Keyword(s):  
Weather Forecasting ◽  
Low Cost ◽  
Weather Prediction ◽  
Adjoint Operator ◽  
Model Performance ◽  
Horizontal Resolution ◽  
Global Navigation Satellite Systems ◽  
Full Potential ◽  
Satellite Systems ◽  
Weather Models

<p>Global Navigation Satellite Systems (GNSS) sense the atmosphere remotely and provide low-cost, high-quality information about its state. Nowadays, radio occultation (RO) profiles from space platforms and tropospheric delays from ground-based stations are routinely assimilated in Numerical Weather Models  (NWM).</p><p>In spite of provision of valuable information for weather forecasting, both space- and ground-based data have significant limitations. The RO technique has low horizontal resolution and does not provide reliable profiles in the first 3-5km of the troposphere. Whereas, the station-specific integrated value of troposphere are sparse and pose a problem to NWM adjoint operator for correcting model fields at different heights. These deficiencies could be resolved by the GNSS tomography technique that utilizes an inverse Radon transform to derive the 3D refractivity distribution over certain troposphere space. The combination of space-based and ground-based observations in the tomographic model will enable us to increase the number of intersections of GNSS signals and improve the refractivity solution within individual model locations. </p><p>The aim of this research is to harness the full potential of Space 4.0 era, rapidly growing numbers of RO and GNSS satellite constellations as well as low-cost GNSS ground-based networks worldwide. We will not only use current infrastructure but also examine impact of future constellations on model performance. 3D model of refractivity from dense observations should be an excellent tool in weather prediction. Our previous research proves that the assimilation of the GNSS tomography outputs into the NWM improves relative humidity and the short-term weather forecasts. Therefore, the research goal of this project is to assess the benefit of integrated tomography model on the severe weather prediction and urban scale weather models.</p>

scholarly journals Urban Air Quality Modeling Using Low-Cost Sensor Network and Data Assimilation in the Aburrá Valley, Colombia

Atmosphere ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 91
Author(s):  
Santiago Lopez-Restrepo ◽  
Andres Yarce ◽  
Nicolás Pinel ◽  
O.L. Quintero ◽  
Arjo Segers ◽  
...  
Keyword(s):  
Air Quality ◽  
Data Assimilation ◽  
Transport Model ◽  
Cost Model ◽  
Low Cost ◽  
Model Performance ◽  
Monitoring Network ◽  
Correlation Factor ◽  
Chemical Transport Model ◽  
High Quality

The use of low air quality networks has been increasing in recent years to study urban pollution dynamics. Here we show the evaluation of the operational Aburrá Valley’s low-cost network against the official monitoring network. The results show that the PM2.5 low-cost measurements are very close to those observed by the official network. Additionally, the low-cost allows a higher spatial representation of the concentrations across the valley. We integrate low-cost observations with the chemical transport model Long Term Ozone Simulation-European Operational Smog (LOTOS-EUROS) using data assimilation. Two different configurations of the low-cost network were assimilated: using the whole low-cost network (255 sensors), and a high-quality selection using just the sensors with a correlation factor greater than 0.8 with respect to the official network (115 sensors). The official stations were also assimilated to compare the more dense low-cost network’s impact on the model performance. Both simulations assimilating the low-cost model outperform the model without assimilation and assimilating the official network. The capability to issue warnings for pollution events is also improved by assimilating the low-cost network with respect to the other simulations. Finally, the simulation using the high-quality configuration has lower error values than using the complete low-cost network, showing that it is essential to consider the quality and location and not just the total number of sensors. Our results suggest that with the current advance in low-cost sensors, it is possible to improve model performance with low-cost network data assimilation.

scholarly journals A Survey on Deep Learning Based Methods and Datasets for Monocular 3D Object Detection

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 517
Author(s):  
Seong-heum Kim ◽  
Youngbae Hwang
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Low Cost ◽  
Detection Methods ◽  
Future Research ◽  
3D Object ◽  
Practical Applications ◽  
Depth Sensors ◽  
Significant Research ◽  
3D Object Detection

Owing to recent advancements in deep learning methods and relevant databases, it is becoming increasingly easier to recognize 3D objects using only RGB images from single viewpoints. This study investigates the major breakthroughs and current progress in deep learning-based monocular 3D object detection. For relatively low-cost data acquisition systems without depth sensors or cameras at multiple viewpoints, we first consider existing databases with 2D RGB photos and their relevant attributes. Based on this simple sensor modality for practical applications, deep learning-based monocular 3D object detection methods that overcome significant research challenges are categorized and summarized. We present the key concepts and detailed descriptions of representative single-stage and multiple-stage detection solutions. In addition, we discuss the effectiveness of the detection models on their baseline benchmarks. Finally, we explore several directions for future research on monocular 3D object detection.

scholarly journals 3D Affine: An Embedding of Local Image Features for Viewpoint Invariance Using RGB-D Sensor Data

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 291 ◽  
Author(s):  
Hamdi Sahloul ◽  
Shouhei Shirafuji ◽  
Jun Ota
Keyword(s):  
Low Cost ◽  
Image Features ◽  
Image Feature ◽  
Sensor Data ◽  
Invariant Representation ◽  
Depth Sensors ◽  
Viewpoint Invariance ◽  
Surface Discontinuities ◽  
Local Image ◽  
Local Image Features

Local image features are invariant to in-plane rotations and robust to minor viewpoint changes. However, the current detectors and descriptors for local image features fail to accommodate out-of-plane rotations larger than 25°–30°. Invariance to such viewpoint changes is essential for numerous applications, including wide baseline matching, 6D pose estimation, and object reconstruction. In this study, we present a general embedding that wraps a detector/descriptor pair in order to increase viewpoint invariance by exploiting input depth maps. The proposed embedding locates smooth surfaces within the input RGB-D images and projects them into a viewpoint invariant representation, enabling the detection and description of more viewpoint invariant features. Our embedding can be utilized with different combinations of descriptor/detector pairs, according to the desired application. Using synthetic and real-world objects, we evaluated the viewpoint invariance of various detectors and descriptors, for both standalone and embedded approaches. While standalone local image features fail to accommodate average viewpoint changes beyond 33.3°, our proposed embedding boosted the viewpoint invariance to different levels, depending on the scene geometry. Objects with distinct surface discontinuities were on average invariant up to 52.8°, and the overall average for all evaluated datasets was 45.4°. Similarly, out of a total of 140 combinations involving 20 local image features and various objects with distinct surface discontinuities, only a single standalone local image feature exceeded the goal of 60° viewpoint difference in just two combinations, as compared with 19 different local image features succeeding in 73 combinations when wrapped in the proposed embedding. Furthermore, the proposed approach operates robustly in the presence of input depth noise, even that of low-cost commodity depth sensors, and well beyond.

scholarly journals Brief Communication: A low-cost Arduino<sup>®</sup>-based wire extensometer for earth flow monitoring

2017 ◽  
Vol 17 (6) ◽  
pp. 881-885 ◽  
Author(s):  
Luigi Guerriero ◽  
Giovanni Guerriero ◽  
Gerardo Grelle ◽  
Francesco M. Guadagno ◽  
Paola Revellino
Keyword(s):  
High Reliability ◽  
Low Cost ◽  
Mitigation Measures ◽  
Thermal Drift ◽  
Data Logger ◽  
Test Configuration ◽  
Flow Monitoring ◽  
Earth Flow ◽  
Flow Displacement ◽  
Additional Monitoring

Abstract. Continuous monitoring of earth flow displacement is essential for the understanding of the dynamic of the process, its ongoing evolution and designing mitigation measures. Despite its importance, it is not always applied due to its expense and the need for integration with additional sensors to monitor factors controlling movement. To overcome these problems, we developed and tested a low-cost Arduino-based wire-rail extensometer integrating a data logger, a power system and multiple digital and analog inputs. The system is equipped with a high-precision position transducer that in the test configuration offers a measuring range of 1023 mm and an associated accuracy of ±1 mm, and integrates an operating temperature sensor that should allow potential thermal drift that typically affects this kind of systems to be identified and corrected. A field test, conducted at the Pietrafitta earth flow where additional monitoring systems had been installed, indicates a high reliability of the measurement and a high monitoring stability without visible thermal drift.

Processing Panorama Video in Real-time

2014 ◽  
Vol 08 (02) ◽  
pp. 209-227 ◽  
Author(s):  
Håkon Kvale Stensland ◽  
Vamsidhar Reddy Gaddam ◽  
Marius Tennøe ◽  
Espen Helgedagsrud ◽  
Mikkel Næss ◽  
...  
Keyword(s):  
Real Time ◽  
Graphics Processing Units ◽  
Low Cost ◽  
Video Camera ◽  
Wide Field ◽  
High Data ◽  
Data Rates ◽  
Camera Arrays ◽  
Analysis System ◽  
Performance Results

There are many scenarios where high resolution, wide field of view video is useful. Such panorama video may be generated using camera arrays where the feeds from multiple cameras pointing at different parts of the captured area are stitched together. However, processing the different steps of a panorama video pipeline in real-time is challenging due to the high data rates and the stringent timeliness requirements. In our research, we use panorama video in a sport analysis system called Bagadus. This system is deployed at Alfheim stadium in Tromsø, and due to live usage, the video events must be generated in real-time. In this paper, we describe our real-time panorama system built using a low-cost CCD HD video camera array. We describe how we have implemented different components and evaluated alternatives. The performance results from experiments ran on commodity hardware with and without co-processors like graphics processing units (GPUs) show that the entire pipeline is able to run in real-time.

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