GPS – Zenith Total Delay assimilation in different resolution simulations of a heavy precipitation event over southern France

Caldas-Álvarez, Alberto; Khodayar, S.; Bock, Olivier

doi:10.5194/asr-14-157-2017

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…Initially, assimilation studies were accomplished using total precipitable water retrieved using zenith tropospheric delay (ZTD), surface pressure, and temperature (Abbasi et al, 2017;Falvey & Beavan, 2002;Guerova et al, 2006;Koizumi & Sato, 2004;Nakamura et al, 2004;Sharifi et al, 2015;Shoji et al, 2011;Smith et al, 2007;Yu et al, 2017). With the development of variational data assimilation methods, the direct assimilation of GPS ZTD has become possible (De Pondeca & Zou, 2001;Benjamin et al, 2010;Bennitt & Jupp, 2012;Boniface et al, 2009;Caldas-Alvarez et al, 2017;de Haan, 2013;Guerova et al, 2016;Faccani et al, 2005;Macpherson et al, 2008;Mahfouf et al, 2015;Poli et al, 2007;Rohm et al, 2019;Sanchez-Arriola et al, 2016;Vedel & Huang, 2004;. Most of the above-mentioned studies noticed the positive or neutral impact of the GNSS data (either with the assimilation of total precipitable water or ZTD directly) on the NWP systems.…”

Section: Introductionmentioning

confidence: 99%

Impact of GNSS Signal Delay Assimilation on Short Range Weather Forecasts Over the Indian Region

et al. 2019

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Radio signals transmitted from a satellite constellation and gathered by a ground-based Global Navigation Satellite System (GNSS) receiver allow computation of zenith tropospheric delay (ZTD), which is related to the atmospheric moisture. This study investigates the impact of assimilating ZTD obtained from a ground-based GNSS network on a numerical weather prediction model analyses and subsequent forecasts quality. The numerical data assimilation experiments are performed using three-dimensional variational data assimilation system in the Weather Research and Forecasting model, at 10-km horizontal grid spacing for the entire month of July 2017. A comparison with European Centre for Medium-Range Weather Forecast analyses shows a clear positive impact of ZTD assimilation on the lower to middle tropospheric moisture, upper air temperature, and middle and upper tropospheric wind; errors are reduced by as large as 4%, when compared to the model run without ZTD assimilation. The impact on the analyses and forecast quality of surface meteorological variables is mostly neutral with some indication of positive impact on surface pressure. An improvement in the rainfall forecasts is also noticed when model assimilates ZTD observations. In addition, the impact of the formulation of forward model, which calculates model equivalent of the GNSS ZTD, has been assessed on ZTD assimilation. A revised forward model has been implemented within the Weather Research and Forecasting assimilation system. The revised forward model outperforms the original model for ZTD assimilation. Overall result implies that GNSS ZTD data has a good potential for improving the weather prediction and advocates the strengthening of the ground-based GNSS network over the Indian region, which is currently very sparse.A number of satellites are currently equipped with humidity sensors, including Microwave Humidity Sounder, Special Sensor Microwave Imager Sounder, Advanced Microwave Scanning Radiometer, the Key Points:• A revised forward model has been implemented for the assimilation of zenith tropospheric delay (ZTD) observations into the WRF model • Assimilation of ZTD using the revised forward model shows higher positive impact as compared to the original observation operator • The assimilation of ZTD from ground-based GNSS receivers has a great potential to improve the weather prediction over the Indian regionCorrespondence to:

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Section: Introductionmentioning

confidence: 99%

Impact of GNSS Signal Delay Assimilation on Short Range Weather Forecasts Over the Indian Region

et al. 2019

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“…The model error is related to grid-scale unsolved processes in the parametrization scheme and is assessed in the models with two main techniques. Some models use stochastic perturbations of the inner-model physics scheme (Palmer et al, 2009); others use different parametrization schemes in each forecast member (Charron et al, 2009;Descamps et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Systematic error analysis of heavy-precipitation-event prediction using a 30-year hindcast dataset

Ponzano¹,

Joly²,

Descamps³

et al. 2020

Nat. Hazards Earth Syst. Sci.

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Abstract. The western Mediterranean region is prone to devastating flash floods induced by heavy-precipitation events (HPEs), which are responsible for considerable human and material losses. Quantitative precipitation forecasts have improved dramatically in recent years to produce realistic accumulated rainfall estimations. Nevertheless, there are still challenging issues which must be resolved to reduce uncertainties in the initial condition assimilation and the modelling of physical processes. In this study, we analyse the HPE forecasting ability of the multi-physics-based ensemble model Prévision d’Ensemble ARPEGE (PEARP) operational at Météo-France. The analysis is based on 30-year (1981–2010) ensemble hindcasts which implement the same 10 physical parameterizations, one per member, run every 4 d. Over the same period a 24 h precipitation dataset is used as the reference for the verification procedure. Furthermore, regional classification is performed in order to investigate the local variation in spatial properties and intensities of rainfall fields, with a particular focus on HPEs. As grid-point verification tends to be perturbed by the double penalty issue, we focus on rainfall spatial pattern verification thanks to the feature-based quality measure of structure, amplitude, and location (SAL) that is performed on the model forecast and reference rainfall fields. The length of the dataset allows us to subsample scores for very intense rainfall at a regional scale and still obtain a significant analysis, demonstrating that such a procedure is consistent to study model behaviour in HPE forecasting. In the case of PEARP, we show that the amplitude and structure of the rainfall patterns are basically driven by the deep-convection parametrization. Between the two main deep-convection schemes used in PEARP, we qualify that the Prognostic Condensates Microphysics and Transport (PCMT) parametrization scheme performs better than the B85 scheme. A further analysis of spatial features of the rainfall objects to which the SAL metric pertains shows the predominance of large objects in the verification measure. It is for the most extreme events that the model has the best representation of the distribution of object-integrated rain.

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Overview towards improved understanding of the mechanisms leading to heavy precipitation in the western Mediterranean: lessons learned from HyMeX

et al. 2021

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Abstract. Heavy precipitation (HP) constitutes a major meteorological threat in the western Mediterranean (WMed). Every year, recurrent events affect the area with fatal consequences for infrastructure and personal losses. Despite this being a well-known issue widely investigated in the past, open questions still remain. Particularly, the understanding of the underlying mechanisms and the modeling representation of the events must be improved. One of the major goals of the Hydrological Cycle in the Mediterranean Experiment (HyMeX; 2010–2020) has been to advance knowledge on this topic. In this article, we present an overview of the most recent lessons learned from HyMeX towards an improved understanding of the mechanisms leading to HP in the WMed. The unique network of instruments deployed as well as the use of finer model resolutions and coupled models provided an unprecedented opportunity to validate numerical model simulations, develop improved parameterizations, and design high-resolution ensemble modeling approaches and sophisticated assimilation techniques across scales. All in all, HyMeX, and particularly the science team heavy precipitation, favored the evidencing of theoretical results, the enrichment of our knowledge on the genesis and evolution of convection in a complex topography environment, and the improvement of precipitation forecasts. Illustratively, the intervention of cyclones and warm conveyor belts in the occurrence of heavy precipitation has been pointed out, and the crucial role of the spatiotemporal distribution of atmospheric water vapor for the understanding and accurate forecast of the timing and location of deep convection has been evidenced, as has the complex interaction among processes across scales. The importance of soil and ocean conditions and the interactions among systems were highlighted, and such systems were specifically developed in the framework of HyMeX to improve the realism of weather forecasts. Furthermore, the benefits of cross-disciplinary efforts within HyMeX have been a key asset in bringing our knowledge about heavy precipitation in the Mediterranean region a step forward.

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GPS – Zenith Total Delay assimilation in different resolution simulations of a heavy precipitation event over southern France

Cited by 3 publications

References 11 publications

Impact of GNSS Signal Delay Assimilation on Short Range Weather Forecasts Over the Indian Region

Impact of GNSS Signal Delay Assimilation on Short Range Weather Forecasts Over the Indian Region

Systematic error analysis of heavy-precipitation-event prediction using a 30-year hindcast dataset

Overview towards improved understanding of the mechanisms leading to heavy precipitation in the western Mediterranean: lessons learned from HyMeX

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