scholarly journals Retieving antenna miss-pointing for vertical pointing cloud radar and correcting the introduced Doppler velocity errors in the measurements

2021 ◽  
Author(s):  
Lukas Pfitzenmaier ◽  
Pavlos Kollias ◽  
Ulrich Löhnert
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Convective Motion ◽  
Doppler Velocity ◽  
Velocity Measurements ◽  
Wind Fields ◽  
Satellite Mission ◽  
Fall Velocity ◽  
Particle Sedimentation ◽  
Retrieval Technique

<p>In the last decades, Doppler velocity measurements from zenith pointing radars have evolved to a standard radar variable. Measuring Doppler velocities allow estimating particle sedimentation or fall velocity of hydrometeors and thus offer key information to evaluate micro-physical parametrizations in numerical weather prediction models. In the future, the joint ESA-JAXA satellite mission EarthCARE features the first Doppler capable 94-GHz Cloud Profiling Radar (CPR), with enhanced sensitivity and improved resolution compared to the CloudSat CPR. These features, especially the Doppler velocity measurements, are expected to improve the CPR-based microphysical retrievals in clouds and precipitation and for the first time provide information about convective motion in clouds.</p><p>To evaluate EarthCare CPR Doppler velocity from the ground, the Doppler velocity from five ground-based zenith pointing 94 GHz radar spread over Europa should be used in future. To increase the quality of the measured Doppler velocity the antenna miss-pointing has to be estimated. Unknown antenna miss-pointing is the main source of error in Doppler velocity measurements and can reach values on the same order as the fall velocity of pristine ice crystals. Knowing the angle of miss-pointing, the error in the measured Doppler velocity measurements can be corrected and the precision and quality improved. This is especially important for cases where Doppler velocity values are direct input for retrievals, which, e.g., employ multiple radar sensors with matching sampling(?) volumes.</p><p>Within this work we will present a retrieval technique to identify the angle of antenna miss-pointing for ground-based radar profilers and correct the measured Doppler velocity values. The retrieval technique is a statistical method requiring the uncorrected Doppler velocity measurements and additional wind information from reanalysis or in parallel measuring sensors. Evaluation of the retrieval was done using different wind input data sets, e.g., ECMWF IFS wind fields or retrieved wind information from Radar scans. Also, the retrieval was used to correct the miss-pointing angles of two in parallel measuring zenith pointing radars and, therefore, correct the velocity errors in dual Doppler velocity field.  </p>

scholarly journals Forecasting length-of-day using numerical weather prediction models

1988 ◽  
Vol 128 ◽  
pp. 285-286
Author(s):  
R. D. Rosen ◽  
D. A. Salstein ◽  
T. Nehrkorn ◽  
J. O. Dickey ◽  
T. M. Eubanks ◽  
...  
Keyword(s):  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Weather Prediction ◽  
Lead Times ◽  
Length Of Day ◽  
Wind Fields ◽  
Numerical Weather ◽  
Medium Range Forecast ◽  
Frequency Changes ◽  
Numerical Weather Prediction Models

A new approach to forecasting changes in length-of-day (δl.o.d) with lead times from one to ten days is examined. The approach is based on the high correlation that has been shown to exist between high frequency changes in l.o.d. and those in the atmosphere's angular momentum (M). Because forecasts of tropospheric values of M can be calculated from the zonal wind fields produced by operational numerical weather prediction models, it seems worth investigating whether these forecasts are sufficiently skillful to use to infer the evolution of δl.o.d. Here, we examine the quality of M forecasts made by the Medium Range Forecast (MRF) model of the U.S. National Meteorological Center (NMC). By comparing these forecasts against those based on a simple model of persistence, we find that skillful forecasts of M are being achieved on average by the MRF, although there has been much month-to-month variability in forecast quality. Overall, our results indicate that for prediction lead times of 1–10 days, dynamically-based forecasts of δl.o.d. represent a viable alternative to the empirical approaches currently in use.

scholarly journals C-Band Dual-Doppler Retrievals in Complex Terrain: Improving the Knowledge of Severe Storm Dynamics in Catalonia

2020 ◽  
Vol 12 (18) ◽  
pp. 2930 ◽  
Author(s):  
Anna del Moral ◽  
Tammy M. Weckwerth ◽  
Tomeu Rigo ◽  
Michael M. Bell ◽  
María Carmen Llasat
Keyword(s):  
Complex Terrain ◽  
Prediction Models ◽  
Weather Prediction ◽  
Warning System ◽  
Wind Fields ◽  
Severe Thunderstorm ◽  
Radar Network ◽  
Storm Dynamics ◽  
Storm Cells ◽  
First Time

Convective activity in Catalonia (northeastern Spain) mainly occurs during summer and autumn, with severe weather occurring 33 days per year on average. In some cases, the storms have unexpected propagation characteristics, likely due to a combination of the complex topography and the thunderstorms’ propagation mechanisms. Partly due to the local nature of the events, numerical weather prediction models are not able to accurately nowcast the complex mesoscale mechanisms (i.e., local influence of topography). This directly impacts the retrieved position and motion of the storms, and consequently, the likely associated storm severity. Although a successful warning system based on lightning and radar observations has been developed, there remains a lack of knowledge of storm dynamics that could lead to forecast improvements. The present study explores the capabilities of the radar network at the Meteorological Service of Catalonia to retrieve dual-Doppler wind fields to study the dynamics of Catalan thunderstorms. A severe thunderstorm that splits and a tornado-producing supercell that is channeled through a valley are used to demonstrate the capabilities of an advanced open source technique that retrieves dynamical variables from C-band operational radars in complex terrain. For the first time in the Iberian Peninsula, complete 3D storm-relative winds are obtained, providing information about the internal dynamics of the storms. This aids in the analyses of the interaction between different storm cells within a system and/or the interaction of the cells with the local topography.

scholarly journals Operational Multiple-Doppler Wind Retrieval Inferred from Long-Range Radial Velocity Measurements

2008 ◽  
Vol 47 (11) ◽  
pp. 2929-2945 ◽  
Author(s):  
Olivier Bousquet ◽  
Pierre Tabary ◽  
Jacques Parent du Châtelet
Keyword(s):  
Radial Velocity ◽  
Doppler Velocity ◽  
Velocity Measurements ◽  
Wind Fields ◽  
Pulse Rise Time ◽  
Wind Retrieval ◽  
Radar Network ◽  
Precipitation Regimes ◽  
Operational Networks ◽  
Scanning Strategies

Abstract The recent deployment of an innovative triple pulse rise time (PRT) scheme within the French operational radar network allows for the simultaneous collection of reflectivity and radial velocity measurements up to a range of 250 km with no ambiguity. This achievement brings new perspectives in terms of operational exploitation of Doppler measurements including the capability to consistently perform multiple-Doppler wind synthesis in a fully operational framework. Using real and simulated Doppler observations, the authors show that the 3D wind fields retrieved in that framework can definitely be relied upon to achieve a consistent and detailed mapping of the airflow structure in various precipitation regimes despite radar baselines averaging ∼180 km and very limited scanning strategies. This achievement could be easily transposed to other operational networks and represents a remarkable opportunity to add further value to operational Doppler velocity measurements.

Improvements to melting snow behavior in an NWP bulk microphysics scheme

2020 ◽  
Author(s):  
Emilie C. Iversen ◽  
Gregory Thompson ◽  
Bjørn Egil Nygaard
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Wrf Model ◽  
Bimodal Distribution ◽  
Fall Velocity ◽  
Microphysics Scheme ◽  
Melting Snow ◽  
Melting Layer ◽  
Computational Performance ◽  
Fall Speed

<p>Snow falling into a melting layer will eventually consist of a fraction of meltwater and hence change its characteristics in terms of size, shape, density, fall speed and stickiness. Given that these characteristics contribute to determine the phase and amount of precipitation reaching the ground, precisely predicting such are important in order to obtain accurate weather forecasts for which society depends on. For example, in hydrological modelling precipitation phase at the surface is a first-order driver of hydrological processes in a water shed. Also, melting snow exerts a possible threat to critical infrastructure because the wet, sticky snow may adhere to the structures and form heavy ice sleeves.</p><p>Most widely used bulk microphysical parameterization schemes part of numerical weather prediction models represent only purely solid or liquid hydrometeors, and so melting particle characteristics are either ignored or represented by parent species with simple conditions for behavior in the melting layer. The Thompson microphysics scheme is explicitly developed for forecasting winter conditions in real-time as part of the WRF model, and to maintain computational performance, the introduction of additional prognostic variables is undesirable. This research aims at improving the Thompson scheme with respect to melting snow characteristics using a physically based approximation for the snowflake melted fraction, as well as a new definition of melting level and melting particle fall velocity. A real 3D WRF case is set up to compare with in-situ measurements of hydrometeor size and fall velocity from a disdrometer and a vertically pointing Doppler radar deployed during the Olympic Mountain Experiment (OLYMPEX). The modified microphysics scheme is able to replicate the bimodal distribution of fall speed – diameter relations typical of mixed precipitation seen in disdrometer data, as well as the non-linear increase in snow fall speed with melted fraction through the melting layer.</p>

scholarly journals Wind Features Extracted from Weather Simulations for Wind-Wave Prediction Using High-Resolution Neural Networks

2021 ◽  
Vol 9 (11) ◽  
pp. 1257
Author(s):  
Chih-Chiang Wei
Keyword(s):  
High Resolution ◽  
Spatial Data ◽  
Prediction Models ◽  
Wind Wave ◽  
Weather Prediction ◽  
Time Series Prediction ◽  
Surface Wind ◽  
Wind Fields ◽  
Forecast Horizon ◽  
Wave Prediction

Nearshore wave forecasting is susceptible to changes in regional wind fields and environments. However, surface wind field changes are difficult to determine due to the lack of in situ observational data. Therefore, accurate wind and coastal wave forecasts during typhoon periods are necessary. The purpose of this study is to develop artificial intelligence (AI)-based techniques for forecasting wind–wave processes near coastal areas during typhoons. The proposed integrated models employ combined a numerical weather prediction (NWP) model and AI techniques, namely numerical (NUM)-AI-based wind–wave prediction models. This hybrid model comprising VGGNNet and High-Resolution Network (HRNet) was integrated with recurrent-based gated recurrent unit (GRU). Termed mVHR_GRU, this model was constructed using a convolutional layer for extracting features from spatial images with high-to-low resolution and a recurrent GRU model for time series prediction. To investigate the potential of mVHR_GRU for wind–wave prediction, VGGNet, HRNet, and Two-Step Wind-Wave Prediction (TSWP) were selected as benchmark models. The coastal waters in northeast Taiwan were the study area. The length of the forecast horizon was from 1 to 6 h. The mVHR_GRU model outperformed the HR_GRU, VGGNet, and TSWP models according to the error indicators. The coefficient of mVHR_GRU efficiency improved by 13% to 18% and by 13% to 15% at the Longdong and Guishandao buoys, respectively. In addition, in a comparison of the NUM–AI-based model and a numerical model simulating waves nearshore (SWAN), the SWAN model generated greater errors than the NUM–AI-based model. The results of the NUM–AI-based wind–wave prediction model were in favorable accordance with the observed results, indicating the feasibility of the established model in processing spatial data.

scholarly journals Evaluation of EarthCARE Cloud Profiling Radar Doppler Velocity Measurements in Particle Sedimentation Regimes

2014 ◽  
Vol 31 (2) ◽  
pp. 366-386 ◽  
Author(s):  
Pavlos Kollias ◽  
Simone Tanelli ◽  
Alessandro Battaglia ◽  
Aleksandra Tatarevic
Keyword(s):  
Large Scale ◽  
European Space Agency ◽  
Vertical Motion ◽  
Doppler Velocity ◽  
Optimum Conditions ◽  
Velocity Measurements ◽  
Particle Sedimentation ◽  
Beam Geometry ◽  
Space Agency ◽  
Nonuniform Beam

Abstract The joint European Space Agency–Japan Aerospace Exploration Agency (ESA–JAXA) Earth Clouds, Aerosols and Radiation Explorer (EarthCARE) mission is scheduled for launch in 2016 and features the first atmospheric Cloud Profiling Radar (CPR) with Doppler capability in space. Here, the uncertainty of the CPR Doppler velocity measurements in cirrus clouds and large-scale precipitation areas is discussed. These regimes are characterized by weak vertical motion and relatively horizontally homogeneous conditions and thus represent optimum conditions for acquiring high-quality CPR Doppler measurements. A large dataset of radar reflectivity observations from ground-based radars is used to examine the homogeneity of the cloud fields at the horizontal scales of interest. In addition, a CPR instrument model that uses as input ground-based radar observations and outputs simulations of CPR Doppler measurements is described. The simulator accurately accounts for the beam geometry, nonuniform beam-filling, and signal integration effects, and it is applied to representative cases of cirrus cloud and stratiform precipitation. The simulated CPR Doppler velocities are compared against those derived from the ground-based radars. The unfolding of the CPR Doppler velocity is achieved using simple conditional rules and a smoothness requirement for the CPR Doppler measurements. The application of nonuniform beam-filling Doppler velocity bias-correction algorithms is found necessary even under these optimum conditions to reduce the CPR Doppler biases. Finally, the analysis indicates that a minimum along-track integration of 5000 m is needed to reduce the uncertainty in the CPR Doppler measurements to below 0.5 m s−1 and thus enable the detection of the melting layer and the characterization of the rain- and ice-layer Doppler velocities.

scholarly journals A Quality Control Concept for Radar Reflectivity, Polarimetric Parameters, and Doppler Velocity

2006 ◽  
Vol 23 (7) ◽  
pp. 865-887 ◽  
Author(s):  
Katja Friedrich ◽  
Martin Hagen ◽  
Thomas Einfalt
Keyword(s):  
Quality Control ◽  
Quality Index ◽  
Prediction Models ◽  
Weather Prediction ◽  
Radar Data ◽  
Radar Reflectivity ◽  
Doppler Velocity ◽  
Control Procedure ◽  
End User ◽  
Weather Situation

Abstract Over the last few years the use of weather radar data has become a fundamental part of various applications like rain-rate estimation, nowcasting of severe weather events, and assimilation into numerical weather prediction models. The increasing demand for radar data necessitates an automated, flexible, and modular quality control. In this paper a quality control procedure is developed for radar reflectivity factors, polarimetric parameters, and Doppler velocity. It consists of several modules that can be extended, modified, and omitted depending on the user requirement, weather situation, and radar characteristics. Data quality is quantified on a pixel-by-pixel basis and encoded into a quality-index field that can be easily interpreted by a nontrained end user or an automated scheme that generates radar products. The quality-index algorithms detect and quantify the influence of beam broadening, the height of the first radar echo, ground clutter contamination, return from non-weather-related objects, and attenuation of electromagnetic energy by hydrometeors on the quality of the radar measurement. The quality-index field is transferred together with the radar data to the end user who chooses the amount of data and the level of quality used for further processing. The calculation of quality-index fields is based on data measured by the polarimetric C-band Doppler radar (POLDIRAD) located in the Alpine foreland in southern Germany.

scholarly journals The Characterization of Ice Cloud Properties from Doppler Radar Measurements

2007 ◽  
Vol 46 (10) ◽  
pp. 1682-1698 ◽  
Author(s):  
Julien Delanoë ◽  
A. Protat ◽  
D. Bouniol ◽  
Andrew Heymsfield ◽  
Aaron Bansemer ◽  
...  
Keyword(s):  
Water Content ◽  
Weather Prediction ◽  
Doppler Velocity ◽  
Fall Velocity ◽  
Ice Cloud ◽  
Cloud Radar ◽  
Cloud Properties ◽  
Ice Water Content ◽  
Radar Measurements ◽  
Ice Water

Abstract The paper describes an original method that is complementary to the radar–lidar algorithm method to characterize ice cloud properties. The method makes use of two measurements from a Doppler cloud radar (35 or 95 GHz), namely, the radar reflectivity and the Doppler velocity, to recover the effective radius of crystals, the terminal fall velocity of hydrometeors, the ice water content, and the visible extinction from which the optical depth can be estimated. This radar method relies on the concept of scaling the ice particle size distribution. An error analysis using an extensive in situ airborne microphysical database shows that the expected errors on ice water content and extinction are around 30%–40% and 60%, respectively, including both a calibration error and a bias on the terminal fall velocity of the particles, which all translate into errors in the retrieval of the density–diameter and area–diameter relationships. Comparisons with the radar–lidar method in areas sampled by the two instruments also demonstrate the accuracy of this new method for retrieval of the cloud properties, with a roughly unbiased estimate of all cloud properties with respect to the radar–lidar method. This method is being systematically applied to the cloud radar measurements collected over the three-instrumented sites of the European Cloudnet project to validate the representation of ice clouds in numerical weather prediction models and to build a cloud climatology.

scholarly journals An Observational Case for the Prevalence of Roll Vortices in the Hurricane Boundary Layer*

2005 ◽  
Vol 62 (8) ◽  
pp. 2662-2673 ◽  
Author(s):  
Ian Morrison ◽  
Steven Businger ◽  
Frank Marks ◽  
Peter Dodge ◽  
Joost A. Businger
Keyword(s):  
Boundary Layer ◽  
Prediction Models ◽  
Weather Prediction ◽  
Doppler Velocity ◽  
Velocity Data ◽  
Roll Vortices ◽  
Secondary Circulations ◽  
Velocity Anomalies ◽  
The Mean ◽  
Hurricane Boundary Layer

Abstract Doppler velocity data from Weather Surveillance Radar-1988 Doppler (WSR-88D) radars during four hurricane landfalls are analyzed to investigate the presence of organized vortices in the hurricane boundary layer (HBL). The wavelength, depth, magnitude, and track of velocity anomalies were compiled through analysis of Doppler velocity data. The analysis reveals alternating bands of enhanced and reduced azimuthal winds closely aligned with the mean wind direction. Resulting statistics provide compelling evidence for the presence of organized secondary circulations or boundary layer rolls across significant areas during four hurricane landfalls. The results confirm previous observations of the presence of rolls in the HBL. A potential limitation of the study presented here is the resolution of the WSR-88D data. In particular, analysis of higher-resolution data (e.g., from the Doppler on Wheels) is needed to confirm that data aliasing has not unduly impacted the statistics reported here. Momentum fluxes associated with the secondary circulations are estimated using the covariance between the horizontal and vertical components of the wind fluctuations in rolls, with resulting fluxes 2–3 times greater than estimated by parameterizations in numerical weather prediction models. The observational analysis presented here, showing a prevalence of roll vortices in the HBL, has significant implications for the vertical transport of energy in hurricanes, for the character of wind damage, and for improvements in numerical simulations of hurricanes.

scholarly journals Correction of Dual-PRF Doppler Velocity Outliers in the Presence of Aliasing

2017 ◽  
Vol 34 (7) ◽  
pp. 1529-1543 ◽  
Author(s):  
Patricia Altube ◽  
Joan Bech ◽  
Oriol Argemí ◽  
Tomeu Rigo ◽  
Nicolau Pineda ◽  
...  
Keyword(s):  
Prediction Models ◽  
Doppler Radar ◽  
Pulse Repetition Frequency ◽  
Weather Prediction ◽  
Velocity Fields ◽  
Circular Statistics ◽  
Doppler Velocity ◽  
Storm Events ◽  
Quality Issue ◽  
Improved Performance

AbstractIn Doppler weather radars, the presence of unfolding errors or outliers is a well-known quality issue for radial velocity fields estimated using the dual–pulse repetition frequency (PRF) technique. Postprocessing methods have been developed to correct dual-PRF outliers, but these need prior application of a dealiasing algorithm for an adequate correction. This paper presents an alternative procedure based on circular statistics that corrects dual-PRF errors in the presence of extended Nyquist aliasing. The correction potential of the proposed method is quantitatively tested by means of velocity field simulations and is exemplified in the application to real cases, including severe storm events. The comparison with two other existing correction methods indicates an improved performance in the correction of clustered outliers. The technique proposed is well suited for real-time applications requiring high-quality Doppler radar velocity fields, such as wind shear and mesocyclone detection algorithms, or assimilation in numerical weather prediction models.

Sign in / Sign up

Export Citation Format

Share Document