An idealized Testbed for Radar Data Assimilation

2020 ◽  
Author(s):  
Yuefei Zeng ◽  
Tijana Janjic ◽  
Alberto de Lozar ◽  
Ulrich Blahak ◽  
Axel Seifert
Keyword(s):  
Data Assimilation ◽  
Initial Conditions ◽  
Numerical Models ◽  
Radar Data ◽  
Observation Error ◽  
Supercell Storm ◽  
Highly Nonlinear ◽  
Convective Scale ◽  
Radar Data Assimilation ◽  
Made In

<p> </p> <pre class="moz-quote-pre">Data assimilation on the convective scale uses high-resolution numerical models of the atmosphere that resolve highly nonlinear dynamics and physics. These non-hydrostatic, convection permitting models are in short runs very sensitive to proper initial conditions. <br />However, the estimation of initial conditions is hampered by assumptions made in data assimilation algorithms <br />and in their models of the observation error and model error uncertainty. Within this work, an idealized testbed <br />for Radar Data Assimilation has been developed, which uses Kilometre-scale ENsemble Data Assimilation (KENDA) system <br />of the (Deutscher Wetterdienst) DWD. A series of data assimilation experiments for a supercell storm are conducted. <br />The sensitivity to the configurations of the radar forward operator and specification of the observation error <br />is investigated. Moreover, impacts of different observations (radial wind, reflectivity or both) <br />on the performance of data assimilation cycles and 6-h forecasts are shown, for instance, <br />the preservation of divergence, vorticity and mass of hydrometeors, compared to the nature run is of special interest.</pre> <p> </p>

An Examination of WRF 3DVAR Radar Data Assimilation on Its Capability in Retrieving Unobserved Variables and Forecasting Precipitation through Observing System Simulation Experiments

2009 ◽  
Vol 137 (11) ◽  
pp. 4011-4029 ◽  
Author(s):  
Soichiro Sugimoto ◽  
N. Andrew Crook ◽  
Juanzhen Sun ◽  
Qingnong Xiao ◽  
Dale M. Barker
Keyword(s):  
Data Assimilation ◽  
Initial Conditions ◽  
System Simulation ◽  
Radar Data ◽  
Wind Component ◽  
Simulation Experiments ◽  
Tangential Wind ◽  
Analysis Scheme ◽  
Radar Data Assimilation ◽  
Reflectivity Factor

Abstract The purpose of this study is to investigate the performance of 3DVAR radar data assimilation in terms of the retrievals of convective fields and their impact on subsequent quantitative precipitation forecasts (QPFs). An assimilation methodology based on the Weather Research and Forecasting (WRF) model three-dimensional variational data assimilation (3DVAR) and a cloud analysis scheme is described. Simulated data from 25 Weather Surveillance Radar-1988 Doppler (WSR-88D) radars are assimilated, and the potential benefits and limitations of the assimilation are quantitatively evaluated through observing system simulation experiments of a dryline that occurred over the southern Great Plains. Results indicate that the 3DVAR system is able to analyze certain mesoscale and convective-scale features through the incorporation of radar observations. The assimilation of all possible data (radial velocity and reflectivity factor data) results in the best performance on short-range precipitation forecasting. The wind retrieval by assimilating radial velocities is of primary importance in the 3DVAR framework and the storm case applied, and the use of multiple-Doppler observations improves the retrieval of the tangential wind component. The reflectivity factor assimilation is also beneficial especially for strong precipitation. It is demonstrated that the improved initial conditions through the 3DVAR analysis lead to improved skills on QPF.

scholarly journals Processing of 3D Weather Radar Data with Application for Assimilation in the NWP Model

2014 ◽  
Vol 18 (3) ◽  
pp. 31-39 ◽  
Author(s):  
Katarzyna Ośródka ◽  
Jan Szturc ◽  
Bogumił Jakubiak ◽  
Anna Jurczyk
Keyword(s):  
Quality Control ◽  
Data Assimilation ◽  
Prediction Models ◽  
Initial Conditions ◽  
Weather Prediction ◽  
Weather Radar ◽  
Radar Data ◽  
Field Pattern ◽  
The Impact ◽  
Radar Data Assimilation

Abstract The paper is focused on the processing of 3D weather radar data to minimize the impact of a number of errors from different sources, both meteorological and non-meteorological. The data is also quantitatively characterized in terms of its quality. A set of dedicated algorithms based on analysis of the reflectivity field pattern is described. All the developed algorithms were tested on data from the Polish radar network POLRAD. Quality control plays a key role in avoiding the introduction of incorrect information into applications using radar data. One of the quality control methods is radar data assimilation in numerical weather prediction models to estimate initial conditions of the atmosphere. The study shows an experiment with quality controlled radar data assimilation in the COAMPS model using the ensemble Kalman filter technique. The analysis proved the potential of radar data for such applications; however, further investigations will be indispensable.

Importance of Horizontally Inhomogeneous Environmental Initial Conditions to Ensemble Storm-Scale Radar Data Assimilation and Very Short-Range Forecasts

2010 ◽  
Vol 138 (4) ◽  
pp. 1250-1272 ◽  
Author(s):  
David J. Stensrud ◽  
Jidong Gao
Keyword(s):  
Data Assimilation ◽  
Short Range ◽  
Doppler Radar ◽  
Environmental Variability ◽  
Initial Conditions ◽  
Radar Data ◽  
Ensemble Member ◽  
Radar Observations ◽  
Convective Scale ◽  
Background Fields

Abstract The assimilation of operational Doppler radar observations into convection-resolving numerical weather prediction models for very short-range forecasting represents a significant scientific and technological challenge. Numerical experiments over the past few years indicate that convective-scale forecasts are sensitive to the details of the data assimilation methodology, the quality of the radar data, the parameterized microphysics, and the storm environment. In this study, the importance of horizontal environmental variability to very short-range (0–1 h) convective-scale ensemble forecasts initialized using Doppler radar observations is investigated for the 4–5 May 2007 Greensburg, Kansas, tornadic thunderstorm event. Radar observations of reflectivity and radial velocity from the operational Doppler radar network at 0230 UTC 5 May 2007, during the time of the first large tornado, are assimilated into each ensemble member using a three-dimensional variational data assimilation system (3DVAR) developed at the Center for Analysis and Prediction of Storms (CAPS). Very short-range forecasts are made using the nonhydrostatic Advanced Regional Prediction System (ARPS) model from each ensemble member and the results are compared with the observations. Explicit three-dimensional environmental variability information is provided to the convective-scale ensemble using analyses from a 30-km mesoscale ensemble data assimilation system. Comparisons between convective-scale ensembles with initial conditions produced by 3DVAR using 1) background fields that are horizontally homogeneous but vertically inhomogeneous (i.e., have different vertical environmental profiles) and 2) background fields that are horizontally and vertically inhomogeneous are undertaken. Results show that the ensemble with horizontally and vertically inhomogeneous background fields provides improved predictions of thunderstorm structure, mesocyclone track, and low-level circulation track than the ensemble with horizontally homogeneous background fields. This suggests that knowledge of horizontal environmental variability is important to successful convective-scale ensemble predictions and needs to be included in real-data experiments.

scholarly journals Assimilating visible satellite reflectances in combination with radar data in a pre-operational convective-scale seamless prediction system 

2021 ◽  
Author(s):  
Liselotte Bach ◽  
Thomas Deppisch ◽  
Leonhard Scheck ◽  
Alberto de Lozar ◽  
Christian Welzbacher ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Bias Correction ◽  
Weather Prediction ◽  
Radar Data ◽  
Observation Error ◽  
Major Step ◽  
Observation Systems ◽  
Seamless Transition ◽  
Convective Scale ◽  
Nwp Model

<p>In the framework of the SINFONY project at Deutscher Wetterdienst (DWD) we have developed data assimilation of visible satellite reflectances of the SEVIRI instrument (MSG) and radar observations in a rapid update cycle (ICON-D2-KENDA-RUC) which will be running in a first 24/7-testsuite starting in spring of this year. Our major goal related to the assimilation of these new observation systems is to improve the positioning of cloud and precipitation systems and their intensities, needed for the seamless transition of radar nowcasting to numerical weather prediction (NWP) in our SINFONY system. We give an overview of the steps undertaken in the course of developing the data assimilation of visible satellite reflectances. This includes quality control, observation error modelling, data reduction and bias correction of the reflectances. Further development and enhancement of the forward operator MFASIS is still ongoing. A major step to allow for a successful assimilation has been the improvement of microphysical consistency between the NWP model and MFASIS both with 1-moment and 2-moment microphysics to reduce the bias of first-guess departures. To further enhance and stabilize the agreement of observations and model climatologies over the course of the year and different weather regimes, an innovative histogram-based bias correction has been developed. We show results of data assimilation experiments combining visible reflectances and radar data in the ICON-D2-KENDA-Rapid Update Cycle using 2-moment microphysics. Further, we discuss the improvement of forecast skill from both observing systems and the way they complement each other – putting special emphasis to the key variable of interest in the SINFONY system, namely radar reflectivity.</p>

scholarly journals Numerical Simulations of Lightning and Storm Charge of the 29–30 May 2004 Geary, Oklahoma, Supercell Thunderstorm Using EnKF Mobile Radar Data Assimilation

2014 ◽  
Vol 142 (11) ◽  
pp. 3977-3997 ◽  
Author(s):  
Kristin M. Calhoun ◽  
Edward R. Mansell ◽  
Donald R. MacGorman ◽  
David C. Dowell
Keyword(s):  
Data Assimilation ◽  
Radar Data ◽  
Main Body ◽  
Lightning Flash ◽  
Wind Fields ◽  
Propagation Length ◽  
Kinematic Evolution ◽  
Supercell Storm ◽  
Total Lightning ◽  
Radar Data Assimilation

Abstract Results from simulations are compared with dual-Doppler and total lightning observations of the 29–30 May 2004 high-precipitation supercell storm from the Thunderstorm Electrification and Lightning Experiment (TELEX). The simulations use two-moment microphysics with six hydrometeor categories and parameterizations for electrification and lightning while employing an ensemble Kalman filter for mobile radar data assimilation. Data assimilation was utilized specifically to produce a storm similar to the observed for ancillary analysis of the electrification and lightning associated with the supercell storm. The simulated reflectivity and wind fields well approximated that of the observed storm. Additionally, the simulated lightning flash rates were very large, as was observed. The simulation reveals details of the charge distribution and dependence of lightning on storm kinematics, characteristics that could not be observed directly. Storm electrification was predominately confined to the updraft core, but the persistence of both positive and negative charging of graupel in this region, combined with the kinematic evolution, limited the extent of charged areas of the same polarity. Thus, the propagation length of lightning flashes in this region was also limited. Away from the updraft core, regions of charge had greater areal extent, allowing flashes to travel farther without termination due to unfavorable charge potential. Finally, while the simulation produced the observed lightning holes and high-altitude lightning seen in the observations, it failed to produce the observed lightning initiations (or even lightning channels) in the distant downstream anvil as seen in the observed storm. Instead, the simulated lightning was confined to the main body of the storm.

Impact of radar data assimilation on simulations of precipitable water with the Harmonie model: A case study over Cyprus

2021 ◽  
Vol 253 ◽  
pp. 105473
Author(s):  
Serguei Ivanov ◽  
Silas Michaelides ◽  
Igor Ruban ◽  
Demetris Charalambous ◽  
Filippos Tymvios
Keyword(s):  
Data Assimilation ◽  
Precipitable Water ◽  
Radar Data ◽  
Radar Data Assimilation

scholarly journals Predictability of Deep Convection in Idealized and Operational Forecasts: Effects of Radar Data Assimilation, Orography, and Synoptic Weather Regime

2019 ◽  
Vol 148 (1) ◽  
pp. 63-81 ◽  
Author(s):  
Kevin Bachmann ◽  
Christian Keil ◽  
George C. Craig ◽  
Martin Weissmann ◽  
Christian A. Welzbacher
Keyword(s):  
Data Assimilation ◽  
Deep Convection ◽  
Radar Data ◽  
Ensemble Prediction ◽  
Small Scale ◽  
Model Errors ◽  
Ensemble Prediction System ◽  
Beneficial Impact ◽  
The Impact ◽  
Radar Data Assimilation

Abstract We investigate the practical predictability limits of deep convection in a state-of-the-art, high-resolution, limited-area ensemble prediction system. A combination of sophisticated predictability measures, namely, believable and decorrelation scale, are applied to determine the predictable scales of short-term forecasts in a hierarchy of model configurations. First, we consider an idealized perfect model setup that includes both small-scale and synoptic-scale perturbations. We find increased predictability in the presence of orography and a strongly beneficial impact of radar data assimilation, which extends the forecast horizon by up to 6 h. Second, we examine realistic COSMO-KENDA simulations, including assimilation of radar and conventional data and a representation of model errors, for a convectively active two-week summer period over Germany. The results confirm increased predictability in orographic regions. We find that both latent heat nudging and ensemble Kalman filter assimilation of radar data lead to increased forecast skill, but the impact is smaller than in the idealized experiments. This highlights the need to assimilate spatially and temporally dense data, but also indicates room for further improvement. Finally, the examination of operational COSMO-DE-EPS ensemble forecasts for three summer periods confirms the beneficial impact of orography in a statistical sense and also reveals increased predictability in weather regimes controlled by synoptic forcing, as defined by the convective adjustment time scale.

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