scholarly journals Evaluation of Wind Vectors Measured by a Bistatic Doppler Radar Network

2004 ◽  
Vol 21 (12) ◽  
pp. 1840-1854 ◽  
Author(s):  
Katja Friedrich ◽  
Martin Hagen
Keyword(s):  
Vector Fields ◽  
Doppler Radar ◽  
Wind Vector ◽  
Wind Fields ◽  
Observational Analysis ◽  
Radar Network ◽  
Research Aircraft ◽  
Wind Measurements ◽  
Instrumentation Error

Abstract By installing and linking additional receivers to a monostatic Doppler radar, several wind components can be measured and combined into a wind vector field. Such a bistatic Doppler radar network was developed in 1993 by the National Center for Atmospheric Research and has been in operation at different research departments. Since then, the accuracy of wind vectors has been investigated mainly based on theoretical examinations. Observational analysis of the accuracy has been limited to comparisons of dual-Doppler-derived wind vectors always including the monostatic Doppler radar. Intercomparisons to independent wind measurements have not yet been accomplished. In order to become an alternative to monostatic multiple–Doppler applications, the reliability of wind vector fields has to be also proven by observational analysis. In this paper wind vectors measured by a bistatic Doppler radar network are evaluated by 1) internally comparing results of bistatic receivers; 2) comparing with independent wind measurements observed by a second Doppler radar; and 3) comparing with in situ flight measurements achieved with a research aircraft during stratiform precipitation events. Investigations show how reliable bistatically measured wind fields are and how they can contribute highly to research studies, weather surveillance, and forecasting. As a result of the intercomparison, the instrumentation error of the bistatic receivers can be assumed to be within 1 m s−1. Differences between bistatic Doppler radar and independent measurements range mainly between 2 and 3 m s−1.

scholarly journals The Eyewall-Penetration Reconnaissance Observation of Typhoon Longwang (2005) with Unmanned Aerial Vehicle, Aerosonde

2008 ◽  
Vol 25 (1) ◽  
pp. 15-25 ◽  
Author(s):  
Po-Hsiung Lin ◽  
Cheng-Shang Lee
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Doppler Radar ◽  
Surface Wind ◽  
Wind Retrieval ◽  
Aerial Vehicle ◽  
Wind Measurements ◽  
Wind Profiles ◽  
Outer Perimeter ◽  
Local Weather

Abstract In this paper, a successful eye-penetration reconnaissance flight by an unmanned aerial vehicle, Aerosonde, into Typhoon Longwang (2005) and the preliminary analyses of the collected data are presented. The 10-h flight is diagnosed through four flight legs. The wind field measured along flight leg 1 provides the tangential and radial wind profiles from the outer perimeter into the eye of the typhoon at the 700-hPa layer. A vertical sounding was taken in the eye along flight leg 2 and the derived surface pressure in the eyewall is close to the estimates made by the local weather agencies. Along flight leg 3, the strongest winds during the whole flight mission were measured. These in situ wind measurements by Aerosonde are consistent with the winds observed by the Hua-lien Doppler weather radar. The maximum 10-min (1 min) wind along flight leg 3 when Aerosonde was flying around the eyewall region is 58.6 m s−1 (62 m s−1). The maximum sustained surface wind derived from this maximum wind speed is also close to the estimates made by the local weather agencies. In conclusion, this successful mission demonstrates that the Aerosonde with a trained crew can play a role in severe weather monitoring and the Aerosonde’s measurement can serve as an independent check for Doppler radar wind retrieval.

Towards Operational Downscaling of Low Resolution Wind Fields using Neural Networks

2020 ◽  
Author(s):  
Michael Kern ◽  
Kevin Höhlein ◽  
Timothy Hewson ◽  
Rüdiger Westermann
Keyword(s):  
Neural Networks ◽  
High Resolution ◽  
Wind Field ◽  
Input Data ◽  
Vector Fields ◽  
Wind Vector ◽  
Proof Of Concept ◽  
Low Resolution ◽  
Wind Fields ◽  
Resolution Simulation

<p>Numerical weather prediction models with high resolution (of order kms or less) can deliver very accurate low-level winds. The problem is that one cannot afford to run simulations at very high resolution over global or other large domains for long periods because the computational power needed is prohibitive.</p><p>Instead, we propose using neural networks to downscale low-resolution wind-field simulations (input) to high-resolution fields (targets) to try to match a high-resolution simulation. Based on short-range forecasts of wind fields (at the 100m level) from the ECMWF ERA5 reanalysis, at 31km resolution, and the HRES (deterministic) model version, at 9km resolution, we explore two complementary approaches, in an initial “proof-of-concept” study.</p><p>In a first step, we evaluate the ability of U-Net-type convolutional neural networks to learn a one-to-one mapping of low-resolution input data to high-resolution simulation results. By creating a compressed feature-space representation of the data, networks of this kind manage to encode important flow characteristics of the input fields and assimilate information from additional data sources. Next to wind vector fields, we use topographical information to inform the network, at low and high resolution, and include additional parameters that strongly influence wind-field prediction in simulations, such as vertical stability (via the simple, compact metric of boundary layer height) and the land-sea mask. We thus infer weather-situation and location-dependent wind structures that could not be retrieved otherwise.</p><p>In some situations, however, it will be inappropriate to deliver only a single estimate for the high-resolution wind field. Especially in regions where topographic complexity fosters the emergence of complex wind patterns, a variety of different high-resolution estimates may be equally compatible with the low-resolution input, and with physical reasoning. In a second step, we therefore extend the learning task from optimizing deterministic one-to-one mappings to modelling the distribution of physically reasonable high-resolution wind-vector fields, conditioned on the given low-resolution input. Using the framework of conditional variational autoencoders, we realize a generative model, based on convolutional neural networks, which is able to learn the conditional distributions from data. Sampling multiple estimates of the high-resolution wind vector fields from the model enables us to explore multimodalities in the data and to infer uncertainties in the predictand.</p><p>In a future customer-oriented extension of this proof-of-concept work, we would envisage using a target resolution higher than 9km - say in the 1-4km range - to deliver much better representivity for users. Ensembles of low resolution input data could also be used, to deliver as output an “ensemble of ensembles”, to condense into a meaningful probabilistic format for users. The many exciting applications of this work (e.g. for wind power management) will be highlighted.</p>

An Investigation of the Goshen County, Wyoming, Tornadic Supercell of 5 June 2009 Using EnKF Assimilation of Mobile Mesonet and Radar Observations Collected during VORTEX2. Part I: Experiment Design and Verification of the EnKF Analyses

2014 ◽  
Vol 142 (2) ◽  
pp. 530-554 ◽  
Author(s):  
James Marquis ◽  
Yvette Richardson ◽  
Paul Markowski ◽  
David Dowell ◽  
Joshua Wurman ◽  
...  
Keyword(s):  
Ensemble Kalman Filter ◽  
Doppler Radar ◽  
Three Dimensional ◽  
Cold Pool ◽  
Grid Spacing ◽  
Wind Fields ◽  
Sensitivity Tests ◽  
Vorticity Budget ◽  
In Situ Observations

Abstract High-resolution Doppler radar velocities and in situ surface observations collected in a tornadic supercell on 5 June 2009 during the second Verification of the Origins of Rotation in Tornadoes Experiment (VORTEX2) are assimilated into a simulated convective storm using an ensemble Kalman filter (EnKF). A series of EnKF experiments using a 1-km horizontal model grid spacing demonstrates the sensitivity of the cold pool and kinematic structure of the storm to the assimilation of these observations and to different model microphysics parameterizations. An experiment is performed using a finer grid spacing (500 m) and the most optimal data assimilation and model configurations from the sensitivity tests to produce a realistically evolving storm. Analyses from this experiment are verified against dual-Doppler and in situ observations and are evaluated for their potential to confidently evaluate mesocyclone-scale processes in the storm using trajectory analysis and calculations of Lagrangian vorticity budgets. In Part II of this study, these analyses will be further evaluated to learn the roles that mesocyclone-scale processes play in tornado formation, maintenance, and decay. The coldness of the simulated low-level outflow is generally insensitive to the choice of certain microphysical parameterizations, likely owing to the vast quantity of kinematic and in situ thermodynamic observations assimilated. The three-dimensional EnKF wind fields and parcel trajectories resemble those retrieved from dual-Doppler observations within the storm, suggesting that realistic four-dimensional mesocyclone-scale processes are captured. However, potential errors are found in trajectories and Lagrangian three-dimensional vorticity budget calculations performed within the mesocyclone that may be due to the coarse (2 min) temporal resolution of the analyses. Therefore, caution must be exercised when interpreting trajectories in this area of the storm.

scholarly journals Raindrop Size Distribution and Rain Characteristics during the 2013 Great Colorado Flood

2015 ◽  
Vol 17 (1) ◽  
pp. 53-72 ◽  
Author(s):  
Katja Friedrich ◽  
Evan A. Kalina ◽  
Joshua Aikins ◽  
Matthias Steiner ◽  
David Gochis ◽  
...  
Keyword(s):  
Drop Size ◽  
Doppler Radar ◽  
Liquid Water Content ◽  
Size Distributions ◽  
Intense Rainfall ◽  
Wind Fields ◽  
Raindrop Size Distribution ◽  
Median Volume ◽  
Drop Size Distributions ◽  
Analysis System

Abstract Drop size distributions observed by four Particle Size Velocity (PARSIVEL) disdrometers during the 2013 Great Colorado Flood are used to diagnose rain characteristics during intensive rainfall episodes. The analysis focuses on 30 h of intense rainfall in the vicinity of Boulder, Colorado, from 2200 UTC 11 September to 0400 UTC 13 September 2013. Rainfall rates R, median volume diameters D0, reflectivity Z, drop size distributions (DSDs), and gamma DSD parameters were derived and compared between the foothills and adjacent plains locations. Rainfall throughout the entire event was characterized by a large number of small- to medium-sized raindrops (diameters smaller than 1.5 mm) resulting in small values of Z (<40 dBZ), differential reflectivity Zdr (<1.3 dB), specific differential phase Kdp (<1° km−1), and D0 (<1 mm). In addition, high liquid water content was present throughout the entire event. Raindrops observed in the plains were generally larger than those in the foothills. DSDs observed in the foothills were characterized by a large concentration of small-sized drops (d < 1 mm). Heavy rainfall rates with slightly larger drops were observed during the first intense rainfall episode (0000–0800 UTC 12 September) and were associated with areas of enhanced low-level convergence and vertical velocity according to the wind fields derived from the Variational Doppler Radar Analysis System. The disdrometer-derived Z–R relationships reflect how unusual the DSDs were during the 2013 Great Colorado Flood. As a result, Z–R relations commonly used by the operational NEXRAD strongly underestimated rainfall rates by up to 43%.

scholarly journals Simultaneous and co-located wind measurements in the middle atmosphere by lidar and rocket-borne techniques

2016 ◽  
Vol 9 (8) ◽  
pp. 3911-3919 ◽  
Author(s):  
Franz-Josef Lübken ◽  
Gerd Baumgarten ◽  
Jens Hildebrand ◽  
Francis J. Schmidlin
Keyword(s):  
Middle Atmosphere ◽  
Systematic Difference ◽  
Horizontal Distance ◽  
In Situ Method ◽  
Time Period ◽  
Meridional Winds ◽  
Wind Measurements ◽  
In Situ Observations ◽  
Mean Differences

Abstract. We present the first comparison of a new lidar technique to measure winds in the middle atmosphere, called DoRIS (Doppler Rayleigh Iodine Spectrometer), with a rocket-borne in situ method, which relies on measuring the horizontal drift of a target (“starute”) by a tracking radar. The launches took place from the Andøya Space Center (ASC), very close to the ALOMAR observatory (Arctic Lidar Observatory for Middle Atmosphere Research) at 69° N. DoRIS is part of a steerable twin lidar system installed at ALOMAR. The observations were made simultaneously and with a horizontal distance between the two lidar beams and the starute trajectories of typically 0–40 km only. DoRIS measured winds from 14 March 2015, 17:00 UTC, to 15 March 2015, 11:30 UTC. A total of eight starute flights were launched successfully from 14 March, 19:00 UTC, to 15 March, 00:19 UTC. In general there is excellent agreement between DoRIS and the in situ measurements, considering the combined range of uncertainties. This concerns not only the general height structures of zonal and meridional winds and their temporal developments, but also some wavy structures. Considering the comparison between all starute flights and all DoRIS observations in a time period of ±20 min around each individual starute flight, we arrive at mean differences of typically ±5–10 m s−1 for both wind components. Part of the remaining differences are most likely due to the detection of different wave fronts of gravity waves. There is no systematic difference between DoRIS and the in situ observations above 30 km. Below ∼ 30 km, winds from DoRIS are systematically too large by up to 10–20 m s−1, which can be explained by the presence of aerosols. This is proven by deriving the backscatter ratios at two different wavelengths. These ratios are larger than unity, which is an indication of the presence of aerosols.

scholarly journals Retrieval of three-dimensional wind fields from Doppler radar data using an efficient two-step approach

2010 ◽  
Vol 3 (5) ◽  
pp. 4459-4495 ◽  
Author(s):  
C. López Carrillo ◽  
D. J. Raymond
Keyword(s):  
Variational Approach ◽  
Continuity Equation ◽  
Doppler Radar ◽  
Three Dimensional ◽  
Relevant Information ◽  
Radar Data ◽  
Wind Fields ◽  
Wind Retrieval ◽  
Complex Sampling ◽  
Doppler Radar Data

Abstract. In this work, we describe an efficient approach for wind retrieval from dual Doppler radar data. The approach produces a gridded field that not only satisfies the observations, but also satisfies the anelastic mass continuity equation. The method is based on the so-called three-dimensional variational approach to the retrieval of wind fields from radar data. The novelty consists in separating the task into steps that reduce the amount of data processed by the global minimization algorithm, while keeping the most relevant information from the radar observations. The method is flexible enough to incorporate observations from several radars, accommodate complex sampling geometries, and readily include dropsonde or sounding observations in the analysis. We demonstrate the usefulness of our method by analyzing a real case with data collected during the TPARC/TCS-08 field campaign.

scholarly journals A new pan-European dataset for gridded daily average wind speed based on in-situ observations

2021 ◽  
Author(s):  
Jouke de Baar ◽  
Gerard van der Schrier ◽  
Irene Garcia-Marti ◽  
Else van den Besselaar
Keyword(s):  
Spatial Distribution ◽  
High Resolution ◽  
Cross Validation ◽  
Data Sets ◽  
Optimal Prediction ◽  
Auxiliary Variables ◽  
Climate Data ◽  
Ensemble Spread ◽  
Wind Fields

<p><strong>Objective</strong></p><p>The purpose of the European Copernicus Climate Change Service (C3S) is to support society by providing information about the past, present and future climate. For the service related to <em>in-situ</em> observations, one of the objectives is to provide high-resolution (0.1x0.1 and 0.25x0.25 degrees) gridded wind speed fields. The gridded wind fields are based on ECA&D daily average station observations for the period 1970-2020.</p><p><strong>Research question</strong> </p><p>We address the following research questions: [1] How efficiently can we provide the gridded wind fields as a statistically reliable ensemble, in order to represent the uncertainty of the gridding? [2] How efficiently can we exploit high-resolution geographical auxiliary variables (e.g. digital elevation model, terrain roughness) to augment the station data from a sparse network, in order to provide gridded wind fields with high-resolution local features?</p><p><strong>Approach</strong></p><p>In our analysis, we apply greedy forward selection linear regression (FSLR) to include the high-resolution effects of the auxiliary variables on monthly-mean data. These data provide a ‘background’ for the daily estimates. We apply cross-validation to avoid FSLR over-fitting and use full-cycle bootstrapping to create FSLR ensemble members. Then, we apply Gaussian process regression (GPR) to regress the daily anomalies. We consider the effect of the spatial distribution of station locations on the GPR gridding uncertainty.</p><p>The goal of this work is to produce several decades of daily gridded wind fields, hence, computational efficiency is of utmost importance. We alleviate the computational cost of the FSLR and GPR analyses by incorporating greedy algorithms and sparse matrix algebra in the analyses.</p><p><strong>Novelty</strong>   </p><p>The gridded wind fields are calculated as a statistical ensemble of realizations. In the present analysis, the ensemble spread is based on uncertainties arising from the auxiliary variables as well as from the spatial distribution of stations.</p><p>Cross-validation is used to tune the GPR hyper parameters. Where conventional GPR hyperparameter tuning aims at an optimal prediction of the gridded mean, instead, we tune the GPR hyperparameters for optimal prediction of the gridded ensemble spread.</p><p>Building on our experience with providing similar gridded climate data sets, this set of gridded wind fields is a novel addition to the E-OBS climate data sets.</p>

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