Comparison between 3D-Var and 4D-Var data assimilation methods for the simulation of a heavy rainfall case in central Italy

Mazzarella, Vincenzo; Maiello, Ida; Capozzi, Vincenzo; Budillon, Giorgio; Ferretti, Rossella

doi:10.5194/asr-14-271-2017

Cited by 26 publications

(18 citation statements)

References 21 publications

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“…A 3D-Var scheme iteratively minimizes the cost function that depends on error covariance matrices. The observations are collected at analysis time with no model integration required, therefore the analysis increment does not evolve in time [20,21]. Of the widely used data assimilation schemes, the 3D-Var scheme is the most computationally efficient [12].…”

Section: Introductionmentioning

confidence: 99%

Can Data Assimilation of Surface PM2.5 and Satellite AOD Improve WRF-Chem Forecasting? A Case Study for Two Scenarios of Particulate Air Pollution Episodes in Poland

2019

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Based on the Weather Research and Forecasting model with Chemistry (WRF-Chem) model and Gridpoint Statistical Interpolation (GSI) assimilation tool, a forecasting system was used for two selected episodes (winter and summer) over Eastern Europe. During the winter episode, very high particular matter (PM2.5, diameter less than 2.5 µm) concentrations, related to low air temperatures and increased emission from residential heating, were measured at many stations in Poland. During the summer episode, elevated aerosol optical depth (AOD), likely related to the transport of pollution from biomass fires, was observed in Southern Poland. Our aim is to verify if there is a relevant positive impact of surface and satellite data assimilation (DA) on modeled PM2.5 concentrations, and to assess whether there are significant differences in the DA’s impact on concentrations between the two seasons. The results show a significant difference in the impact of surface and satellite DA on the model results between the summer and winter episode, which to a large degree is related to the availability of the satellite data. For example, the application of satellite DA raises the factor of two statistic from 0.18 to 0.78 for the summer episode, whereas this statistic remains unchanged (0.71) for the winter. The study suggests that severe winter air pollution episodes in Poland and Eastern Europe in general, often related to the dense cover of low clouds, will benefit from the assimilation of surface observations rather than satellite data, which can be very sparse in such meteorological situations. In contrast, the assimilation of satellite data can have a greater positive impact on the model results during summer than the assimilation of surface data for the same period.

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Section: Introductionmentioning

confidence: 99%

Can Data Assimilation of Surface PM2.5 and Satellite AOD Improve WRF-Chem Forecasting? A Case Study for Two Scenarios of Particulate Air Pollution Episodes in Poland

2019

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“…The main difference between the two implementations is that the observations in 4D-var/WRF model are integrated within an assimilation window at the exact time of the observations. Consequently, greater computing resources compared to 3D-var assimilation are necessary [50]. Taking the possible application of GNSS ZTD data assimilation in NOA's operational forecasting system into account, without significantly altering the timeliness of forecast delivery, the 3D-var option was employed in the present work.…”

Section: Data Assimilation Schemementioning

confidence: 99%

Assessing the Impact of GNSS ZTD Data Assimilation into the WRF Modeling System during High-Impact Rainfall Events over Greece

et al. 2020

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The derivation of global navigation satellite systems (GNSSs) tropospheric products is nowadays a state-of-the-art technique that serves both research and operational needs in a broad range of applications in meteorology. In particular, GNSS zenith tropospheric delay (ZTD) data assimilation is widely applied in Europe to enhance numerical weather predictions (NWPs). The current study presents the first attempt at introducing assimilation of ZTDs, derived from more than 48 stations of the Hellenic GNSS network, into the operational NWP system of the National Observatory of Athens (NOA) in Greece, which is based on the mesoscale Weather Research and Forecasting (WRF) model. WRF was applied during seven high-impact precipitation events covering the dry and wet season of 2018. The simulation employing the ZTD data assimilation reproduces more accurately, compared to the control experiment, the observed heavy rainfall (especially for high precipitation events, exceeding 20 mm in 24h) during both dry and wet periods. Assimilating ZTDs also improves the simulation of intense (>20 mm) convective precipitation during the time window of its occurrence in the dry season, and provides a beneficial influence during synoptic-scale events in the wet period. The above results, which are statistically significant, highlight an important positive impact of ZTD assimilation on the model's precipitation forecast skill over Greece. Overall, the modelling system's configuration, including the assimilation of ZTD observations, satisfactorily captures the spatial and temporal distribution of the observed rainfall and can therefore be used as the basis for examining further improvements in the future. meteorological applications, including nowcasting and numerical weather prediction (NWP) [8][9][10][11], as well as weather monitoring, including extreme events [12][13][14]. Special meteorological interest derives from the near-real time (NRT) ZTDs, which are estimated based on raw GNSS observations. The ZTD is a standard GNSS product expressing the total signal delay in the zenith direction above a receiver [6,15]. This vertical lag contains information on the total columnar amount of water vapor [16].In Europe, collaborative scientific efforts over the past two decades substantially contributed to the development of networks and analysis centers collecting and processing, respectively, GNSS data to compute tropospheric delays. The establishment of the European GNSS water vapor program (E-GVAP; [17]) in 2005 allowed for the operational distribution of NRT ZTD estimates to the meteorological community [6]. This service encouraged the implementation of precipitation forecast impact studies involving the assimilation of NRT ZTD observations into NWP models. Poli et al. [18] found an improvement in the prediction of precipitation patterns over France during spring and summer when assimilating ZTD data into a global four-dimensional variational (4D-var) assimilation and forecasting system. Positive impacts of ZTD data assimilation on precipita...

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“…The 3D-VDA method collects observations during analysis time and does not require model integration. Therefore, the analysis increment does not evolve over time and requires less computing resources [61]. While, in the 4D-VDA method, all the observations in the assimilation period are incorporated into the model.…”

Section: Conflicts Of Interestmentioning

confidence: 99%

Evaluation of the Weak Constraint Data Assimilation Approach for Estimating Turbulent Heat Fluxes at Six Sites

Bateni

et al. 2018

Remote Sensing

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A number of studies have estimated turbulent heat fluxes by assimilating sequences of land surface temperature (LST) observations into the strong constraint-variational data assimilation (SC-VDA) approaches. The SC-VDA approaches do not account for the structural model errors and uncertainties in the micrometeorological variables. In contrast to the SC-VDA approaches, the WC-VDA approach (the so-called weak constraint-VDA) accounts for the effects of structural and model errors by adding a model error term. In this study, the WC-VDA approach is tested at six study sites with different climatic and vegetative conditions. Its performance is also compared with that of SC-VDA at the six study sites. The results show that the WC-VDA produces 10.16% and 10.15% lower root mean square errors (RMSEs) for sensible and latent heat flux estimates compared with the SC-VDA approach. The model error term can capture errors in the turbulent heat flux estimates due to errors in LST and micrometeorological measurements, as well as structural model errors, and does not allow those errors to adversely affect the turbulent heat flux estimates. The findings also indicate that the estimated model error term varies reasonably well, so as to capture the misfit between predicted and observed net radiation in different hydrological and vegetative conditions. Finally, synthetically generated positive (negative) noises are added to the hydrological input variables (e.g., LST, air temperature, air humidity, incoming solar radiation, and wind speed) to examine whether the WC-VDA approach can capture those errors. It was found that the WC-VDA approach accounts for the errors in the input data and reduces their effect on the turbulent heat flux estimates.

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Comparison between 3D-Var and 4D-Var data assimilation methods for the simulation of a heavy rainfall case in central Italy

Cited by 26 publications

References 21 publications

Can Data Assimilation of Surface PM2.5 and Satellite AOD Improve WRF-Chem Forecasting? A Case Study for Two Scenarios of Particulate Air Pollution Episodes in Poland

Can Data Assimilation of Surface PM2.5 and Satellite AOD Improve WRF-Chem Forecasting? A Case Study for Two Scenarios of Particulate Air Pollution Episodes in Poland

Assessing the Impact of GNSS ZTD Data Assimilation into the WRF Modeling System during High-Impact Rainfall Events over Greece

Evaluation of the Weak Constraint Data Assimilation Approach for Estimating Turbulent Heat Fluxes at Six Sites

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