scholarly journals Impact of Radar Reflectivity and Lightning Data Assimilation on the Rainfall Forecast and Predictability of a Summer Convective Thunderstorm in Southern Italy

Atmosphere ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 958
Author(s):  
Stefano Federico ◽  
Rosa Claudia Torcasio ◽  
Silvia Puca ◽  
Gianfranco Vulpiani ◽  
Albert Comellas Prat ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Southern Italy ◽  
Deep Convection ◽  
Radar Reflectivity ◽  
Precipitation Forecast ◽  
Single Event ◽  
Total Rainfall ◽  
Reflectivity Data ◽  
The City ◽  
Satisfactory Prediction

Heavy and localized summer events are very hard to predict and, at the same time, potentially dangerous for people and properties. This paper focuses on an event occurred on 15 July 2020 in Palermo, the largest city of Sicily, causing about 120 mm of rainfall in 3 h. The aim is to investigate the event predictability and a potential way to improve the precipitation forecast. To reach this aim, lightning (LDA) and radar reflectivity data assimilation (RDA) was applied. LDA was able to trigger deep convection over Palermo, with high precision, whereas the RDA had a key role in the prediction of the amount of rainfall. The simultaneous assimilation of both data sources gave the best results. An alert for a moderate–intense forecast could have been issued one hour and a half before the storm developed over the city, even if predicting only half of the total rainfall. A satisfactory prediction of the amount of rainfall could have been issued at 14:30 UTC, when precipitation was already affecting the city. Although the study is centered on a single event, it highlights the need for rapidly updated forecast cycles with data assimilation at the local scale, for a better prediction of similar events.

scholarly journals Operational Implementation of the 1D+3D-Var Assimilation Method of Radar Reflectivity Data in the AROME Model

2014 ◽  
Vol 142 (5) ◽  
pp. 1852-1873 ◽  
Author(s):  
Eric Wattrelot ◽  
Olivier Caumont ◽  
Jean-François Mahfouf
Keyword(s):  
Relative Humidity ◽  
Data Assimilation ◽  
Three Dimensional ◽  
Radar Reflectivity ◽  
Precipitation Forecast ◽  
Limited Area ◽  
Short Term ◽  
Background Error ◽  
Reflectivity Data

AbstractThis paper presents results from radar reflectivity data assimilation experiments with the nonhydrostatic limited-area model Application of Research to Operations at Mesoscale (AROME) in an operational context. A one-dimensional (1D) Bayesian retrieval of relative humidity profiles followed by a three-dimensional variational data assimilation (3D-Var) technique is adopted. Several preprocessing procedures of raw reflectivity data are presented and the use of the nonrainy signal in the assimilation is widely discussed and illustrated. This two-step methodology allows the authors to build up a screening procedure that takes into account the evaluation of the results from the 1D Bayesian retrieval. In particular, the 1D retrieval is checked by comparing a pseudoanalyzed reflectivity to the observed reflectivity. Additionally, a physical consistency between the reflectivity innovations and the 1D relative humidity increments is imposed before assimilating relative humidity pseudo-observations with other observations. This allows the authors to counteract the difficulty of the current 3D-Var system to correct strong differences between model and observed clouds from the crude specification of background-error covariances. Assimilation experiments of radar reflectivity data in a preoperational configuration are first performed over a 1-month period. Positive impacts on short-term precipitation forecast scores are systematically found. The evaluation shows improvements on the analysis and also on objective conventional forecast scores, in particular for the model wind field up to 12 h. A case study for a specific precipitating system demonstrates the capacity of the method for improving significantly short-term forecasts of organized convection.

Impact of Doppler radar reflectivity and velocity data assimilation on the quality of precipitation forecasting in Belarus in different seasons

2020 ◽  
Author(s):  
Palina Zaiko ◽  
Siarhei Barodka ◽  
Aliaksandr Krasouski
Keyword(s):  
Data Assimilation ◽  
Weather Radar ◽  
Radar Data ◽  
Radar Reflectivity ◽  
Precipitation Forecast ◽  
Doppler Weather Radar ◽  
Velocity Data ◽  
Numerical Weather ◽  
Convective Processes ◽  
Different Seasons

<p>Heavy precipitation forecast remains one of the biggest problems in numerical weather prediction. Modern remote sensing systems allow tracking of rapidly developing convective processes and provide additional data for numerical weather models practically in real time. Assimilation of Doppler weather radar data also allows to specify the position and intensity of convective processes in atmospheric numerical models.</p><p>The primary objective of this study is to evaluate the impact of Doppler  radar reflectivity and velocity assimilation in the WRF-ARW mesoscale model for the territory of Belarus in different seasons of the year. Specifically, we focus on the short-range numerical forecasting of mesoscale convective systems passage over the territory of Belarus in 2017-2019 with assimilated radar data.</p><p>Proceeding with weather radar observations available for our cases, we first perform the necessary processing of the raw radar data to eliminate noise, reflections and other kinds of clutter. For identification of non-meteorological noise fuzzy echo classification was used. Then we use the WRF-DA (3D-Var) system to assimilate the processed radar observations from 3 Belarusian Doppler weather radar in the WRF model. Assimilating both radar reflectivity and radial velocity data in the model we aim to better represent not only the distribution of clouds and their moisture content, but also the detailed dynamical aspects of convective circulation. Finally, we analyze WRF modelling output obtained with assimilated radar data and compare it with available meteorological observations and with other model runs (including control runs with no data assimilation or with assimilation of conventional weather stations data only), paying special attention to the accuracy of precipitation forecast 12 hours in advance.</p>

scholarly journals Comparison of 3DVar and EnSRF Data Assimilation Using Radar Observations for the Analysis and Prediction of an MCS

2018 ◽  
Vol 2018 ◽  
pp. 1-18 ◽  
Author(s):  
Shibo Gao ◽  
Jinzhong Min
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Mesoscale Convective System ◽  
Final Analysis ◽  
Radar Data ◽  
Radar Reflectivity ◽  
Precipitation Forecast ◽  
Convective System ◽  
Wind Fields ◽  
Radar Observations

Using radar observations, the performances of the ensemble square root filter (EnSRF) and an indirect three-dimensional variational (3DVar) data assimilation method were compared for a mesoscale convective system (MCS) that occurred in the Front Range of the Rocky Mountains, Colorado (USA). The results showed that the root mean square innovations (RMSIs) of EnSRF were lower than 3DVar for radar reflectivity and radial velocity and that the spread of EnSRF was generally consistent with its RMSIs. EnSRF substantially improved the analysis of the MCS compared with an experiment without radar data assimilation, and it produced a slight but noticeable improvement over 3DVar in terms of both coverage and intensity. Forecast results initiated from the final analysis revealed that EnSRF generally produced the best prediction of the MCS, with improved quantitative reflectivity and precipitation forecast skills. EnSRF also demonstrated better performance than 3DVar in the prediction of neighborhood probability for reflectivity at thresholds of 20 and 35 dBZ, which better matched the observed radar reflectivity in terms of both shape and extension. Additionally, the humidity, temperature, and wind fields were also improved by EnSRF; the largest error reduction was found in the water vapor field near the surface and at upper levels.

An Approach of Radar Reflectivity Data Assimilation and Its Assessment with the Inland QPF of Typhoon Rusa (2002) at Landfall

2007 ◽  
Vol 46 (1) ◽  
pp. 14-22 ◽  
Author(s):  
Qingnong Xiao ◽  
Ying-Hwa Kuo ◽  
Juanzhen Sun ◽  
Wen-Chau Lee ◽  
Dale M. Barker ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Doppler Radar ◽  
Mesoscale Model ◽  
Control Variable ◽  
Three Dimensional ◽  
Radar Data ◽  
Radar Reflectivity ◽  
Mixing Ratio ◽  
Warm Rain Process ◽  
Reflectivity Data

Abstract A radar reflectivity data assimilation scheme was developed within the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) three-dimensional variational data assimilation (3DVAR) system. The model total water mixing ratio was used as a control variable. A warm-rain process, its linear, and its adjoint were incorporated into the system to partition the moisture and hydrometeor increments. The observation operator for radar reflectivity was developed and incorporated into the 3DVAR. With a single reflectivity observation, the multivariate structures of the analysis increments that included cloud water and rainwater mixing ratio increments were examined. Using the onshore Doppler radar data from Jindo, South Korea, the capability of the radar reflectivity assimilation for the landfalling Typhoon Rusa (2002) was assessed. Verifications of inland quantitative precipitation forecasting (QPF) of Typhoon Rusa (2002) showed positive impacts of assimilating radar reflectivity data on the short-range QPF.

scholarly journals AVALIAÇÃO DO WRF-4DVAR EM UM CASO DE EVENTO SEVERO NO RIO GRANDE DO SUL

2016 ◽  
Vol 38 (2) ◽  
pp. 1077
Author(s):  
Luana Ribeiro Macedo ◽  
João Luiz Martins Basso ◽  
Yoshihiro Yamasaki
Keyword(s):  
Data Assimilation ◽  
Meteorological Data ◽  
Rio Grande ◽  
Real Data ◽  
Radar Reflectivity ◽  
Rio Grande Do Sul ◽  
Mesoscale System ◽  
Accumulated Rainfall ◽  
Reflectivity Data ◽  
The Impact

The WRF mesoscale system 4DVAR data assimilation technique have been used with the purpose of evaluating the impact of the meteorological data assimilation on the numeric time prognosis over the Rio Grande do Sul state. It has been done utilizing the surface and altitude data. The consistency analysis has been done evaluating the numerical prognosis exploring the differences between the analysis with and without data assimilation. The produced prognosis results have been compared spatially using the TRMM satellite data as well as the Canguçu radar reflectivity data. The accumulated rainfall has been validated and compared spatially with the TRMM data for the time period of 12 hours comprehended between October 29th and 30th of 2014. It was possible to realize that as well as the WRF, the WRFVAR overestimated the rainfall values. The radar reflectivity field without data assimilation for October 30th at 06:00UTC detected most accurately the reflectivity centers over the state. On the other hand this field with data assimilation did not present good skill. The temperature field analyses reveal that the 4DVAR assimilation system contributes, one way or another, presenting a little improvement for some points compared to the real data.

scholarly journals Mesoscale Resolution Radar Data Assimilation Experiments with the Harmonie Model

2018 ◽  
Vol 10 (9) ◽  
pp. 1453 ◽  
Author(s):  
Serguei Ivanov ◽  
Silas Michaelides ◽  
Igor Ruban
Keyword(s):  
Data Assimilation ◽  
Weather Prediction ◽  
Precipitable Water ◽  
Original Data ◽  
Radar Data ◽  
Numerical Weather Prediction Model ◽  
Radar Reflectivity ◽  
Horizontal Structure ◽  
Cell Patterns ◽  
Reflectivity Data

This study presents a pre-processing approach adopted for the radar reflectivity data assimilation and results of simulations with the Harmonie numerical weather prediction model. The proposed method creates a 3D regular grid in which a horizontal size of meshes coincides with the horizontal model resolution. This minimizes the representative error associated with the discrepancy between resolutions of informational sources. After such preprocessing, horizontal structure functions and their gradients for radar reflectivity maintain the sizes and shapes of precipitation patterns similar to those of the original data. The method shows an improvement of precipitation prediction within the radar location area in both the rain rates and spatial pattern presentation. It redistributes precipitable water with smoothed values over the common domain since the control runs show, among several sub-domains with increased and decreased values, correspondingly. It also reproduces the mesoscale belts and cell patterns of sizes from a few to ten kilometers in precipitation fields. With the assimilation of radar data, the model simulates larger water content in the middle troposphere within the layer from 1 km to 6 km with major variations at 2.5 km to 3 km. It also reproduces the mesoscale belt and cell patterns of precipitation fields.

Representation of model error in convective scale data assimilation

2020 ◽  
Author(s):  
Tijana Janjic ◽  
Yuefei Zeng ◽  
Alberto de Lozar ◽  
Yvonne Ruckstuhl ◽  
Ulrich Blahak ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Additive Noise ◽  
Model Error ◽  
Ensemble Member ◽  
Radar Reflectivity ◽  
Model Errors ◽  
Estimation Of Parameters ◽  
Convective Scale ◽  
Reflectivity Data ◽  
Scale Data

<p>Model error is one of major contributors to forecast uncertainty. In addition, statistical representations of possible model errors substantially affect the data assimilation results. We investigate variety of methods of taking into account model error in ensemble based convective scale data assimilation. This is done using the operational convection-permitting COSMO model and data assimilation system KENDA of German weather service, for a two-week convective period in May 2016 over Germany. Conventional and radar reflectivity observations are assimilated hourly by the LETKF. For example, to take into account the model error due to unresolved scales and processes, we use the additive noise with samples coming from the difference between high-resolution model run and low-resolution experiment. We compare this technique for assimilation of radar reflectivity data to other methods such as RTPS, warm bubble initialization, stochastic boundary layer perturbation and estimation of parameters. To further improve on additive noise technique, which consists of perturbing each ensemble member with a sample from a given distribution, we propose a more flexible approach in which the model error samples are treated as additional synthetic ensemble members that are used in the update step of data assimilation but are not forecasted. In this way, the rank of the model error covariance matrix can be chosen independently of the ensemble. This altered additive noise method is analyzed as well.</p>

scholarly journals A Cloud-Resolving 4DVAR Assimilation Experiment for a Local Heavy Rainfall Event in the Tokyo Metropolitan Area

2011 ◽  
Vol 139 (6) ◽  
pp. 1911-1931 ◽  
Author(s):  
Takuya Kawabata ◽  
Tohru Kuroda ◽  
Hiromu Seko ◽  
Kazuo Saito
Keyword(s):  
Data Assimilation ◽  
Metropolitan Area ◽  
Heavy Rainfall ◽  
Rainfall Event ◽  
Radar Reflectivity ◽  
Heavy Rainfall Event ◽  
Convective System ◽  
Single Observation ◽  
Assimilation Experiment ◽  
Reflectivity Data

Abstract A cloud-resolving nonhydrostatic four-dimensional variational data assimilation system (NHM-4DVAR) was modified to directly assimilate radar reflectivity and applied to a data assimilation experiment using actual observations of a heavy rainfall event. Modifications included development of an adjoint model of the warm rain process, extension of control variables, and development of an observation operator for radar reflectivity. The responses of the modified NHM-4DVAR were confirmed by single-observation assimilation experiments for an isolated deep convection, using pseudo-observations of rainwater at the initial and end times of the data assimilation window. The results showed that the intensity of convection could be adjusted by assimilating appropriate observations of rainwater near the convection and that undesirable convection could be suppressed by assimilating small or no reflectivity. An assimilation experiment using actual observations of a local heavy rainfall in the Tokyo, Japan, metropolitan area was conducted with a horizontal resolution of 2 km. Precipitable water vapor derived from global positioning system data was assimilated at 5-min intervals within 30-min assimilation windows, and surface and wind profiler data were assimilated at 10-min intervals. Doppler radial wind and radar-reflectivity data below the elevation angle of 5.4° were assimilated at 1-min intervals. The 4DVAR assimilation reproduced a line-shaped rainband with a shape and intensity consistent with the observation. Assimilation of radar-reflectivity data intensified the rainband and suppressed false convection. The simulated rainband lasted for 1 h in the extended forecast and then gradually decayed. Sustaining the low-level convergence produced by northerly winds in the western part of the rainband was key to prolonging the predictability of the convective system.

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