scholarly journals Sensitivities of Quantitative Precipitation Forecasts for Typhoon Soudelor (2015) near Landfall to Polarimetric Radar Data Assimilation

2020 ◽  
Vol 12 (22) ◽  
pp. 3711
Author(s):  
Chih-Chien Tsai ◽  
Kao-Shen Chung
Keyword(s):  
Data Assimilation ◽  
Radar Data ◽  
Convective Precipitation ◽  
Polarimetric Radar ◽  
Differential Phase ◽  
Observation Operator ◽  
Quantitative Precipitation Forecasts ◽  
Series Of Experiments ◽  
Differential Reflectivity ◽  
Radar Data Assimilation

Based on the preciousness and uniqueness of polarimetric radar observations collected near the landfall of Typhoon Soudelor (2015), this study investigates the sensitivities of very short-range quantitative precipitation forecasts (QPFs) for this typhoon to polarimetric radar data assimilation. A series of experiments assimilating various combinations of radar variables are carried out for the purpose of improving a 6 h deterministic forecast for the most intense period. The results of the control simulation expose three sources of the observation operator errors, including the raindrop shape-size relation, the limitations for ice-phase hydrometeors, and the melting ice model. Nevertheless, polarimetric radar data assimilation with the unadjusted observation operator can still improve the analyses, especially rainwater, and consequent QPFs for this typhoon case. The different impacts of assimilating reflectivity, differential reflectivity, and specific differential phase are only distinguishable at the lower levels of convective precipitation areas where specific differential phase is found most helpful. The positive effect of radar data assimilation on QPFs can last three hours in this study, and further improvement can be expected by optimizing the observation operator in the future

X-Band Polarimetric Observations of Cross Coupling in the Ice Phase of Convective Storms in Taiwan

2014 ◽  
Vol 53 (6) ◽  
pp. 1678-1695 ◽  
Author(s):  
J. C. Hubbert ◽  
S. M. Ellis ◽  
W.-Y. Chang ◽  
Y.-C. Liou
Keyword(s):  
Electric Fields ◽  
Cross Coupling ◽  
Radar Data ◽  
Ice Crystals ◽  
Polarimetric Radar ◽  
Differential Phase ◽  
X Band ◽  
Linear Depolarization Ratio ◽  
Differential Reflectivity ◽  
Ice Phase

AbstractIn this paper, experimental X-band polarimetric radar data from simultaneous transmission of horizontal (H) and vertical (V) polarizations (SHV) are shown, modeled, and microphysically interpreted. Both range–height indicator data and vertical-pointing X-band data from the Taiwan Experimental Atmospheric Mobile-Radar (TEAM-R) are presented. Some of the given X-band data are biased, which is very likely caused by cross coupling of the H and V transmitted waves as a result of aligned, canted ice crystals. Modeled SHV data are used to explain the observed polarimetric signatures. Coincident data from the National Center for Atmospheric Research S-band polarimetric radar (S-Pol) are presented to augment and support the X-band polarimetric observations and interpretations. The polarimetric S-Pol data are obtained via fast-alternating transmission of horizontal and vertical polarizations (FHV), and thus the S-band data are not contaminated by the cross coupling (except the linear depolarization ratio LDR) observed in the X-band data. The radar data reveal that there are regions in the ice phase where electric fields are apparently aligning ice crystals near vertically and thus causing negative specific differential phase Kdp. The vertical-pointing data also indicate the presence of preferentially aligned ice crystals that cause differential reflectivity Zdr and differential phase ϕdp to be strong functions of azimuth angle.

scholarly journals Assimilation of Polarimetric Radar Data in Simulation of a Supercell Storm with a Variational Approach and the WRF Model

2021 ◽  
Vol 13 (16) ◽  
pp. 3060
Author(s):  
Muyun Du ◽  
Jidong Gao ◽  
Guifu Zhang ◽  
Yunheng Wang ◽  
Pamela L. Heiselman ◽  
...  
Keyword(s):  
Correlation Coefficient ◽  
Cross Correlation ◽  
Weather Prediction ◽  
Radar Data ◽  
Polarimetric Radar ◽  
Differential Phase ◽  
Supercell Storm ◽  
Convective Scale ◽  
Differential Reflectivity ◽  
Forward Operator

Polarimetric radar data (PRD) have potential to be used in numerical weather prediction (NWP) models to improve convective-scale weather forecasts. However, thus far only a few studies have been undertaken in this research direction. To assimilate PRD in NWP models, a forward operator, also called a PRD simulator, is needed to establish the relation between model physics parameters and polarimetric radar variables. Such a forward operator needs to be accurate enough to make quantitative comparisons between radar observations and model output feasible, and to be computationally efficient so that these observations can be easily incorporated into a data assimilation (DA) scheme. To address this concern, a set of parameterized PRD simulators for the horizontal reflectivity, differential reflectivity, specific differential phase, and cross-correlation coefficient were developed. In this study, we have tested the performance of these new operators in a variational DA system. Firstly, the tangent linear and adjoint (TL/AD) models for these PRD simulators have been developed and checked for the validity. Then, both the forward operator and its adjoint model have been built into the three-dimensional variational (3DVAR) system. Finally, some preliminary DA experiments have been performed with an idealized supercell storm. It is found that the assimilation of PRD, including differential reflectivity and specific differential phase, in addition to radar radial velocity and horizontal reflectivity, can enhance the accuracy of both initial conditions for model hydrometer state variables and ensuing model forecasts. The usefulness of the cross-correlation coefficient is very limited in terms of improving convective-scale data analysis and NWP.

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.

scholarly journals Modeling of the Melting Layer. Part IV: Brightband Bulk Parameterization

2008 ◽  
Vol 65 (6) ◽  
pp. 1991-2001 ◽  
Author(s):  
Catherine Heyraud ◽  
Wanda Szyrmer ◽  
Stéphane Laroche ◽  
Isztar Zawadzki
Keyword(s):  
Data Assimilation ◽  
Radar Data ◽  
Melting Layer ◽  
Observation Operator ◽  
Data Assimilation Scheme ◽  
Bulk Model ◽  
Assimilation Scheme ◽  
Bulk Parameterization ◽  
Good Agreement ◽  
Extract Information

Abstract In this paper a simplified UHF-band backscattering parameterization for individual melting snowflakes is proposed. This parameterization is a function of the density, shape, and melted fraction, and is used here in a brightband bulk modeling study. A 1D bulk model is developed where aggregation and breakup are neglected. Model results are in good agreement with detailed bin-model results and simulate the radar brightband observations well. It is shown the model can be seen as an observation operator that could be introduced into a data assimilation scheme to extract information contained in the radar data measurements.

A Comparative Study of Rainfall Retrievals Based on Specific Differential Phase Shifts at X- and S-Band Radar Frequencies

2006 ◽  
Vol 23 (7) ◽  
pp. 952-963 ◽  
Author(s):  
Sergey Y. Matrosov ◽  
Robert Cifelli ◽  
Patrick C. Kennedy ◽  
Steven W. Nesbitt ◽  
Steven A. Rutledge ◽  
...  
Keyword(s):  
Comparative Study ◽  
Radar Data ◽  
Drop Diameter ◽  
Horizontal Polarization ◽  
Differential Phase Shift ◽  
Phase Measurements ◽  
Differential Phase ◽  
X Band ◽  
Raindrop Size ◽  
Differential Reflectivity

Abstract A comparative study of the use of X- and S-band polarimetric radars for rainfall parameter retrievals is presented. The main advantage of X-band polarimetric measurements is the availability of reliable specific differential phase shift estimates, KDP, for lighter rainfalls when phase measurements at the S band are too noisy to produce usable KDP. Theoretical modeling with experimental raindrop size distributions indicates that due to some non-Rayleigh resonant effects, KDP values at a 3.2-cm wavelength (X band) are on average a factor of 3.7 greater than at 11 cm (S band), which is a somewhat larger difference than simple frequency scaling predicts. The non-Rayleigh effects also cause X-band horizontal polarization reflectivity, Zeh, and differential reflectivity, ZDR, to be larger than those at the S band. The differences between X- and S-band reflectivities can exceed measurement uncertainties for Zeh starting approximately at Zeh > 40 dBZ, and for ZDR when the mass-weighted drop diameter, Dm, exceeds about 2 mm. Simultaneous X- and S-band radar measurements of rainfall showed that consistent KDP estimates exceeding about 0.1° km−1 began to be possible at reflectivities greater than ∼26–30 dBZ while at the S band such estimates can generally be made if Zeh > ∼35–39 dBZ. Experimental radar data taken in light-to-moderate stratiform rainfalls with rain rates R in an interval from 2.5 to 15 mm h−1 showed availability of the KDP-based estimates of R for most of the data points at the X band while at the S band such estimates were available only for R greater than about 8–10 mm h−1. After correcting X-band differential reflectivity measurements for differential attenuation, ZDR measurements at both radar frequency bands were in good agreement with each other for Dm < 2 mm, which approximately corresponds to ZDR ≈ 1.6 dB. The ZDR-based retrievals of characteristic raindrop sizes also agreed well with in situ disdrometer measurements.

scholarly journals Evaluation of Combined Satellite and Radar Data Assimilation with POD-4DEnVar Method on Rainfall Forecast

2020 ◽  
Vol 10 (16) ◽  
pp. 5493 ◽  
Author(s):  
Jingnan Wang ◽  
Lifeng Zhang ◽  
Jiping Guan ◽  
Mingyang Zhang
Keyword(s):  
Data Assimilation ◽  
Weather Prediction ◽  
Weather Forecast ◽  
Radar Data ◽  
Precipitation Forecast ◽  
Rainfall Forecast ◽  
Skill Scores ◽  
The Individual ◽  
The Impact ◽  
Radar Data Assimilation

Satellite and radar observations represent two fundamentally different remote sensing observation types, providing independent information for numerical weather prediction (NWP). Because the individual impact on improving forecast has previously been examined, combining these two resources of data potentially enhances the performance of weather forecast. In this study, satellite radiance, radar radial velocity and reflectivity are simultaneously assimilated with the Proper Orthogonal Decomposition (POD)-based ensemble four-dimensional variational (4DVar) assimilation method (referred to as POD-4DEnVar). The impact is evaluated on continuous severe rainfall processes occurred from June to July in 2016 and 2017. Results show that combined assimilation of satellite and radar data with POD-4DEnVar has the potential to improve weather forecast. Averaged over 22 forecasts, RMSEs indicate that though the forecast results are sensitive to different variables, generally the improvement is found in different pressure levels with assimilation. The precipitation skill scores are generally increased when assimilation is carried out. A case study is also examined to figure out the contributions to forecast improvement. Better intensity and distribution of precipitation forecast is found in the accumulated rainfall evolution with POD-4DEnVar assimilation. These improvements are attributed to the local changes in moisture, temperature and wind field. In addition, with radar data assimilation, the initial rainwater and cloud water conditions are changed directly. Both experiments can simulate the strong hydrometeor in the precipitation area, but assimilation spins up faster, strengthening the initial intensity of the heavy rainfall. Generally, the combined assimilation of satellite and radar data results in better rainfall forecast than without data assimilation.

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