Data assimilation of radar reflectivity volumes in a LETKF scheme

Abstract. Quantitative precipitation forecast (QPF) is still a challenge for numerical weather prediction (NWP), despite the continuous improvement of models and data assimilation systems. In this regard, the assimilation of radar reflectivity volumes should be beneficial, since the accuracy of analysis is the element that most affects short-term QPFs. Up to now, few attempts have been made to assimilate these observations in an operational set-up, due to the large amount of computational resources needed and due to several open issues, like the rise of imbalances in the analyses and the estimation of the observational error. In this work, we evaluate the impact of the assimilation of radar reflectivity volumes employing a local ensemble transform Kalman filter (LETKF), implemented for the convection-permitting model of the COnsortium for Small-scale MOdelling (COSMO). A 4-day test case on February 2017 is considered and the verification of QPFs is performed using the fractions skill score (FSS) and the SAL technique, an object-based method which allows one to decompose the error in precipitation fields in terms of structure (S), amplitude (A) and location (L). Results obtained assimilating both conventional data and radar reflectivity volumes are compared to those of the operational system of the Hydro-Meteo-Climate Service of the Emilia-Romagna Region (Arpae-SIMC), in which only conventional observations are employed and latent heat nudging (LHN) is applied using surface rainfall intensity (SRI) estimated from the Italian radar network data. The impact of assimilating reflectivity volumes using LETKF in combination or not with LHN is assessed. Furthermore, some sensitivity tests are performed to evaluate the effects of the length of the assimilation window and of the reflectivity observational error (roe). Moreover, balance issues are assessed in terms of kinetic energy spectra and providing some examples of how these affect prognostic fields. Results show that the assimilation of reflectivity volumes has a positive impact on QPF accuracy in the first few hours of forecast, both when it is combined with LHN or not. The improvement is further slightly enhanced when only observations collected close to the analysis time are assimilated, while the shortening of cycle length worsens QPF accuracy. Finally, the employment of too small a value of roe introduces imbalances into the analyses, resulting in a severe degradation of forecast accuracy, especially when very short assimilation cycles are used.

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Effect of the Assimilation Frequency of Radar Reflectivity on Rain Storm Prediction by Using WRF-3DVAR

Remote Sensing ◽

10.3390/rs13112103 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2103

Author(s):

Yuchen Liu ◽

Jia Liu ◽

Chuanzhe Li ◽

Fuliang Yu ◽

Wei Wang

Keyword(s):

Data Assimilation ◽

Northern China ◽

Horizontal Resolution ◽

Radar Reflectivity ◽

Small Scale ◽

Time Interval ◽

Outer Domain ◽

Storm Prediction ◽

The Impact ◽

Assimilation Time

An attempt was made to evaluate the impact of assimilating Doppler Weather Radar (DWR) reflectivity together with Global Telecommunication System (GTS) data in the three-dimensional variational data assimilation (3DVAR) system of the Weather Research Forecast (WRF) model on rain storm prediction in Daqinghe basin of northern China. The aim of this study was to explore the potential effects of data assimilation frequency and to evaluate the outputs from different domain resolutions in improving the meso-scale NWP rainfall products. In this study, four numerical experiments (no assimilation, 1 and 6 h assimilation time interval with DWR and GTS at 1 km horizontal resolution, 6 h assimilation time interval with radar reflectivity, and GTS data at 3 km horizontal resolution) are carried out to evaluate the impact of data assimilation on prediction of convective rain storms. The results show that the assimilation of radar reflectivity and GTS data collectively enhanced the performance of the WRF-3DVAR system over the Beijing-Tianjin-Hebei region of northern China. It is indicated by the experimental results that the rapid update assimilation has a positive impact on the prediction of the location, tendency, and development of rain storms associated with the study area. In order to explore the influence of data assimilation in the outer domain on the output of the inner domain, the rainfall outputs of 3 and 1 km resolution are compared. The results show that the data assimilation in the outer domain has a positive effect on the output of the inner domain. Since the 3DVAR system is able to analyze certain small-scale and convective-scale features through the incorporation of radar observations, hourly assimilation time interval does not always significantly improve precipitation forecasts because of the inaccurate radar reflectivity observations. Therefore, before data assimilation, the validity of assimilation data should be judged as far as possible in advance, which can not only improve the prediction accuracy, but also improve the assimilation efficiency.

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Impact of the assimilation of lightning data on the precipitation forecast at different forecast ranges

Advances in Science and Research ◽

10.5194/asr-14-187-2017 ◽

2017 ◽

Vol 14 ◽

pp. 187-194 ◽

Cited By ~ 5

Author(s):

Stefano Federico ◽

Marco Petracca ◽

Giulia Panegrossi ◽

Claudio Transerici ◽

Stefano Dietrich

Keyword(s):

Data Assimilation ◽

Mesoscale Model ◽

Weather Prediction ◽

Horizontal Resolution ◽

Probability Of Detection ◽

Precipitation Forecast ◽

Time Range ◽

False Alarms ◽

Forecast Time ◽

The Impact

Abstract. This study investigates the impact of the assimilation of total lightning data on the precipitation forecast of a numerical weather prediction (NWP) model. The impact of the lightning data assimilation, which uses water vapour substitution, is investigated at different forecast time ranges, namely 3, 6, 12, and 24 h, to determine how long and to what extent the assimilation affects the precipitation forecast of long lasting rainfall events (> 24 h). The methodology developed in a previous study is slightly modified here, and is applied to twenty case studies occurred over Italy by a mesoscale model run at convection-permitting horizontal resolution (4 km). The performance is quantified by dichotomous statistical scores computed using a dense raingauge network over Italy. Results show the important impact of the lightning assimilation on the precipitation forecast, especially for the 3 and 6 h forecast. The probability of detection (POD), for example, increases by 10 % for the 3 h forecast using the assimilation of lightning data compared to the simulation without lightning assimilation for all precipitation thresholds considered. The Equitable Threat Score (ETS) is also improved by the lightning assimilation, especially for thresholds below 40 mm day−1. Results show that the forecast time range is very important because the performance decreases steadily and substantially with the forecast time. The POD, for example, is improved by 1–2 % for the 24 h forecast using lightning data assimilation compared to 10 % of the 3 h forecast. The impact of the false alarms on the model performance is also evidenced by this study.

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Numerical simulations of June 7, 2020 convective precipitation over Slovakia using deterministic, probabilistic, and convection-permitting approaches

Időjárás ◽

10.28974/idojaras.2021.4.3 ◽

2021 ◽

Vol 125 (4) ◽

pp. 571-607

Author(s):

André Simon ◽

Martin Belluš ◽

Katarína Čatlošová ◽

Mária Derková ◽

Martin Dian ◽

...

Keyword(s):

Satellite Imagery ◽

Weather Prediction ◽

Severe Weather ◽

Precipitation Forecast ◽

Convective Precipitation ◽

Small Scale ◽

Limited Area ◽

Systematic Effect ◽

The Impact

The paper presented is dedicated to the evaluation of the influence of various improvements to the numerical weather prediction (NWP) systems exploited at the Slovak Hydrometeorological Institute (SHMÚ). The impact was illustrated in a case study with multicell thunderstorms and the results were confronted with the reference analyses from the INCA nowcasting system, regional radar reflectivity data, and METEOSAT satellite imagery. The convective cells evolution was diagnosed in non-hydrostatic dynamics experiments to study weak mesoscale vortices and updrafts. The growth of simulated clouds and evolution of the temperature at their top were compared with the brightness temperature analyzed from satellite imagery. The results obtained indicated the potential for modeling and diagnostics of small-scale structures within the convective cloudiness, which could be related to severe weather. Furthermore, the non-hydrostatic dynamics experiments related to the stability and performance improvement of the time scheme led to the formulation of a new approach to linear operator definition for semi-implicit scheme (in text referred as NHHY). We demonstrate that the execution efficiency has improved by more than 20%. The exploitation of several high resolution measurement types in data assimilation contributed to more precise position of predicted patterns and precipitation representation in the case study. The non-hydrostatic dynamics provided more detailed structures. On the other hand, the potential of a single deterministic forecast of prefrontal heavy precipitation was not as high as provided by the ensemble system. The prediction of a regional ensemble system A-LAEF (ALARO Limited Area Ensemble Forecast) enhanced the localization of precipitation patterns. Though, this was rather due to the simulation of uncertainty in the initial conditions and also because of the stochastic perturbation of physics tendencies. The various physical parameterization setups of A-LAEF members did not exhibit a systematic effect on precipitation forecast in the evaluated case. Moreover, the ensemble system allowed an estimation of uncertainty in a rapidly developing severe weather case, which was high even at very short range.

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

Applied Sciences ◽

10.3390/app10165493 ◽

2020 ◽

Vol 10 (16) ◽

pp. 5493 ◽

Cited By ~ 1

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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An Ice-Phase Microphysics Forward Model and Preliminary Results of Polarimetric Radar Data Assimilation

Monthly Weather Review ◽

10.1175/mwr-d-16-0035.1 ◽

2017 ◽

Vol 145 (2) ◽

pp. 683-708 ◽

Cited By ~ 10

Author(s):

Xuanli Li ◽

John R. Mecikalski ◽

Derek Posselt

Keyword(s):

Data Assimilation ◽

Radial Velocity ◽

Weather Prediction ◽

Radar Data ◽

Forward Model ◽

Precipitation Forecast ◽

Upper Troposphere ◽

The Impact ◽

Radar Data Assimilation ◽

Ice Phase

In this study, an ice-phase microphysics forward model has been developed for the Weather Research and Forecasting (WRF) Model three-dimensional variational data assimilation (WRF 3D-Var) system. Radar forward operators for reflectivity and the polarimetric variable, specific differential phase ( KDP), have been built into the ice-phase WRF 3D-Var package to allow modifications in liquid (cloud water and rain) and solid water (cloud ice and snow) fields through data assimilation. Experiments have been conducted to assimilate reflectivity and radial velocity observations collected by the Weather Surveillance Radar-1988 Doppler (WSR-88D) in Hytop, Alabama, for a mesoscale convective system (MCS) on 15 March 2008. Numerical results have been examined to assess the impact of the WSR-88D data using the ice-phase WRF 3D-Var radar data assimilation package. The main goals are to first demonstrate radar data assimilation with an ice-phase microphysics forward model and second to improve understanding on how to enhance the utilization of radar data in numerical weather prediction. Results showed that the assimilation of reflectivity and radial velocity data using the ice-phase system provided significant improvement especially in the mid- to upper troposphere. The improved initial conditions led to apparent improvement in the short-term precipitation forecast of the MCS. An additional experiment has been conducted to explore the assimilation of KDP data collected by the Advanced Radar for Meteorological and Operational Research (ARMOR). Results showed that KDP data have been successfully assimilated using the ice-phase 3D-Var package. A positive impact of the KDP data has been found on rainwater in the lower troposphere and snow in the mid- to upper troposphere.

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Impact of Assimilating China Precipitation Analysis Data Merging with Remote Sensing Products Using the 4DVar Method on the Prediction of Heavy Rainfall

Remote Sensing ◽

10.3390/rs11080973 ◽

2019 ◽

Vol 11 (8) ◽

pp. 973 ◽

Cited By ~ 1

Author(s):

Yuanbing Wang ◽

Yaodeng Chen ◽

Jinzhong Min

Keyword(s):

Data Assimilation ◽

Heavy Rainfall ◽

Weather Prediction ◽

Eastern China ◽

Wrf Model ◽

Analysis Data ◽

Precipitation Forecast ◽

Limited Area ◽

Precipitation Analysis ◽

The Impact

In this study, the China Hourly Merged Precipitation Analysis (CHMPA) data which combines the satellite-retrieved Climate Prediction Center Morphing (CMORPH) with the automatic weather station precipitation observations is firstly assimilated into the Weather Research and Forecasting (WRF) model using the Four-Dimensional Variational (4DVar) method. The analyses and subsequent forecasts of heavy rainfall during Meiyu season occurred in July 2013 over eastern China is evaluated. Besides, the sensitivity of rainfall forecast skill of assimilating the CHMPA data to the rainfall error, the rainfall thinning distance, and the rainfall accumulation time within assimilation window are investigated in this study. Then, the impact of 4DVar data assimilation with and without CHMPA rainfall data is evaluated to show how the assimilation of CHMPA impacts the precipitation simulations. It is found that assimilation of the CHMPA data helps to produce a better short-range precipitation forecast in this study. The rainfall fields after assimilation of CHMPA is closer to observations in terms of quantity and pattern. However, the leading time of improved forecast is limited to about 18 hours. It is also found that CHMPA data assimilation produces stronger realistic moisture divergence, precipitabale water field and the vertical wind field in the forecasting fields, which eventually contributes to the improved forecast of heavy rainfall. This study can provide references for the assimilation of CHMPA data into the WRF model using 4DVar, which is valuable for limited-area numerical weather prediction and hydrological applications.

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Applications of Radar Data Assimilation with Hydrometeor Control Variables within the WRFDA on the Prediction of Landfalling Hurricane IKE (2008)

Atmosphere ◽

10.3390/atmos12070853 ◽

2021 ◽

Vol 12 (7) ◽

pp. 853

Author(s):

Feifei Shen ◽

Jinzhong Min ◽

Hong Li ◽

Dongmei Xu ◽

Aiqing Shu ◽

...

Keyword(s):

Data Assimilation ◽

Inner Core ◽

Radar Data ◽

Precipitation Forecast ◽

Small Scale ◽

Hurricane Ike ◽

Control Variables ◽

Model Background ◽

The Impact ◽

Radar Data Assimilation

The impact of assimilating radar radial velocity and reflectivity on the analyses and forecast of Hurricane IKE is investigated within the framework of the WRF (Weather Research and Forecasting) model and its three-dimensional variational (3DVar) data assimilation system, including the hydrometeor control variables. Hurricane IKE in the year 2008 was chosen as the study case. It was found that assimilating radar data is able to effectively improve the small-scale information of the hurricane vortex area in the model background. Radar data assimilation experiments yield significant cyclonic wind increments in the inner-core area of the hurricane, enhancing the intensity of the hurricane in the model background. On the other hand, by extending the traditional control variables to include the hydrometeor control variables, the assimilation of radar reflectivity can effectively adjust the water vapor and hydrometeors of the background, further improving the track and intensity forecast of the hurricane. The precipitation forecast skill is also enhanced to some extent with the radar data assimilation, especially with the extended hydrometeor control variables.

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Investigating 3D and 4D variational rapid-update-cycling assimilation of weather radar reflectivity for a heavy rain event in central Italy

Natural Hazards and Earth System Science ◽

10.5194/nhess-21-2849-2021 ◽

2021 ◽

Vol 21 (9) ◽

pp. 2849-2865

Author(s):

Vincenzo Mazzarella ◽

Rossella Ferretti ◽

Errico Picciotti ◽

Frank Silvio Marzano

Keyword(s):

Data Assimilation ◽

Roc Curve ◽

Initial Conditions ◽

Central Italy ◽

Heavy Rain ◽

Radar Reflectivity ◽

Precipitation Forecast ◽

Rain Event ◽

Heavy Rain Event ◽

The Impact

Abstract. Forecasting precipitation over the Mediterranean basin is still a challenge because of the complex orographic region that amplifies the need for local observation to correctly initialize the forecast. In this context, data assimilation techniques play a key role in improving the initial conditions and consequently the timing and position of the precipitation forecast. For the first time, the ability of a cycling 4D-Var to reproduce a heavy rain event in central Italy, as well as to provide a comparison with the largely used cycling 3D-Var, is evaluated in this study. The radar reflectivity measured by the Italian ground radar network is assimilated in the Weather Research and Forecasting (WRF) model to simulate an event that occurred on 3 May 2018 in central Italy. In order to evaluate the impact of data assimilation, several simulations are objectively compared by means of a fraction skill score (FSS), which is calculated for several threshold values, and a receiver operating characteristic (ROC) curve. The results suggest that both assimilation methods in the cycling mode improve the 1-, 3- and 6-hourly quantitative precipitation estimation. More specifically, the cycling 4D-Var with a warm start initialization shows the highest FSS values in the first hours of the simulation both with light and heavy precipitation. Finally, the ROC curve confirms the benefit of 4D-Var: the area under the curve is 0.91 compared to 0.88 for the control experiment without data assimilation.

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Investigating 3D and 4D Variational Rapid-Update-Cycling Assimilation of Weather Radar Reflectivity for a Flash Flood Event in Central Italy

10.5194/nhess-2020-406 ◽

2021 ◽

Author(s):

Vincenzo Mazzarella ◽

Rossella Ferretti ◽

Errico Picciotti ◽

Frank S. Marzano

Keyword(s):

Data Assimilation ◽

Roc Curve ◽

Initial Conditions ◽

Flash Flood ◽

Central Italy ◽

Wrf Model ◽

Radar Reflectivity ◽

Precipitation Forecast ◽

Precipitation Pattern ◽

The Impact

Abstract. The precipitation forecast over the Mediterranean basin is still a challenge because of the complex orographic region which amplifies the need for local observation to correctly initialize the forecast. In this context the data assimilation techniques play a key role in improving the initial conditions and consequently the timing and position of precipitation pattern. For the first time, the ability of a cycling 4D-Var to reproduce a severe weather event in central Italy, as well as to provide a comparison with the largely used cycling 3D-Var, is evaluated in this study. The radar reflectivity measured by the Italian ground radar network is assimilated in the WRF model to simulate an event occurred on May 3, 2018 in central Italy. In order to evaluate the impact of data assimilation, several simulations are objectively compared by means of a Fraction Skill Score (FSS), which is calculated for several threshold values, and a Receiver Operating Characteristic (ROC) curve. The results suggest that both assimilation methods in cycling mode improve the 1, 3 and 6-hourly quantitative precipitation estimation. More specifically, the cycling 4D-Var with a warm start initialization shows the highest FSS values in the first hours of simulation both with light and heavy precipitation. Finally, the ROC curve confirms the benefit of 4D-Var: the area under the curve is 0.91 compared to the 0.88 of control experiment without data assimilation.

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