YAO: A Software for Variational Data Assimilation Using Numerical Models

Nardi, Luigi; Sorror, Charles; Badran, Fouad; Thiria, Sylvie

doi:10.1007/978-3-642-02457-3_53

Cited by 14 publications

(29 citation statements)

References 14 publications

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“…We use the YAO software (Thiria et al, 2006;Nardi et al, 2009), developed by the LOCEAN (Laboratoire d'Océanographie et du Climat). It provides a simple method for deriving the adjoint of the model.…”

Section: Minimization Of the Cost Function And Parameter Estimationmentioning

confidence: 99%

4-D-VAR assimilation of disdrometer data and radar spectral reflectivities for raindrop size distribution and vertical wind retrievals

et al. 2016

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Abstract. This paper presents a novel framework for retrieving the vertical raindrop size distribution (DSD) and vertical wind profiles during light rain events. This is also intended as a tool to better characterize rainfall microphysical processes. It consists in coupling K band Doppler spectra and ground disdrometer measurements (raindrop fluxes) in a 2-D numerical model propagating the DSD from the clouds to the ground level. The coupling is done via a 4-D-VAR data assimilation algorithm. As a first step, in this paper, the dynamical model and the geometry of the problem are quite simple. They do not allow the complexity implied by all rain microphysical processes to be encompassed (evaporation, coalescence breakup and horizontal air motion are not taken into account). In the end, the model is limited to the fall of droplets under gravity, modulated by the effects of vertical winds. The framework is thus illustrated with light, stratiform rain events.We firstly use simulated data sets (data assimilation twin experiment) to show that the algorithm is able to retrieve the DSD profiles and vertical winds. It also demonstrates the ability of the algorithm to deal with the atmospheric turbulence (broadening of the Doppler spectra) and the instrumental noise. The method is then applied to a real case study which was conducted in the southwest of France during the autumn 2013. The data set collected during a long, quiet event (6 h duration, rain rate between 2 and 7 mm h −1 ) comes from an optical disdrometer and a 24 GHz vertically pointing Doppler radar. We show that the algorithm is able to reproduce the observations and retrieve realistic DSD and vertical wind profiles, when compared to what could be expected for such a rain event.A goal for this study is to apply it to extended data sets for a validation with independent data, which could not be done with our limited 2013 data. Other data sets would also help to parameterize more processes needed in the model (evaporation, coalescence/breakup) to apply the algorithm to convective rain and to evaluate the adequacy of the model's parameterization.

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Section: Minimization Of the Cost Function And Parameter Estimationmentioning

confidence: 99%

4-D-VAR assimilation of disdrometer data and radar spectral reflectivities for raindrop size distribution and vertical wind retrievals

et al. 2016

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“…The difficulty in using the 4DVAR data assimilation method lies in the implementation of the adjoint model. In this study we use the YAO software developed by Laboratoire d'Océanographie et du Climat: Expérimentations et Approches Numériques (LOCEAN) and described in Nardi et al (2009). This provides a simple method for deriving the adjoint.…”

Section: ) 4dvar Theorymentioning

confidence: 99%

“…An accurate description of YAO can be found in Thiria et al (2006) and Nardi et al (2009), and on the LOCEAN website (http://www. locean-ipsl.upmc.fr/;yao/).…”

Section: ) Cost Functionmentioning

confidence: 99%

Estimation of Finescale Rainfall Fields Using Broadcast TV Satellite Links and a 4DVAR Assimilation Method

Mercier

Barthès

Mallet

2015

Journal of Atmospheric and Oceanic Technology

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This study proposes a method based on the use of a set of commercial satellite-to-Earth microwave links to rebuild finescale rainfall fields. Such microwave links exist all over the world and can be used to estimate the integrated rain attenuation over the links' first 5-7 km with a very high temporal resolution (10 s in the present case). The retrieval algorithm makes use of a four-dimensional variational data assimilation (4DVAR) method involving a numerical advection scheme. The advection velocity is recovered from the observations or from radar rainfall fields at successive time steps.This technique has been successively applied to simulated 2D rain maps and to real data recorded in the autumn of 2013 during the Hydrological Cycle in the Mediterranean Experiment (HyMeX), with one sensor receiving microwave signals from four different satellites. The performance of this system is assessed and is compared to an operational Météo-France radar and a network of 10 rain gauges. Because of the limitations of the propagation model, this study is limited to the events with strong advective characteristics (four out of eight recorded events). For these events (only), the method produces rainfall fields that are highly correlated with the radar maps at spatial resolutions greater than 2 3 2 km 2 . The point-scale results are also satisfactory for temporal resolutions greater than 10 min (mean correlation with rain gauge data equal to approximately 0.8, similar to the correlation between radar and rain gauge data).This method can also be adapted to the fusion of a rain gauge with microwave link measurements and, through the use of several sensors, it has the potential of being applied to larger areas.

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“…The YAO framework already presented in [3], [4] is a code generator dedicated to variational data assimilation. With YAO the user defines, using specific directives and C programming, the type of discretization and the specification of the numerical model.…”

Section: Introductionmentioning

confidence: 99%

“…It then generates automatically the numerical and the adjoint model codes via C++ object-oriented programming. YAO has already been used with success on several actual applications in oceanography: Shallow-water [3], [4], Marine acoustics [5], [6], Ocean color [7], PISCES [8], GYRE configuration of NEMO [9].…”

Section: Introductionmentioning

confidence: 99%

YAO: A Generator of Parallel Code for Variational Data Assimilation Applications

Nardi

Badran

Fortin³

et al. 2012

2012 IEEE 14th International Conference on High Performance Computing and Communication &Amp; 2012 IEEE 9th International Confe

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Abstract-Variational data assimilation consists in estimating control parameters of a numerical model in order to minimize the misfit between the forecast values and the actual observations. The YAO framework is a code generator that facilitates, especially for the adjoint model, the writing and the generation of a variational data assimilation program for a given numerical application. In this paper we present how the modular graph specific to YAO enables the automatic and efficient parallelization of the generated code with OpenMP on shared memory architectures. Thanks to this modular graph we are also able to completely avoid the data race conditions (write/write conflicts). Performance tests with actual applications demonstrates good speedups on a multicore CPU.

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YAO: A Software for Variational Data Assimilation Using Numerical Models

Cited by 14 publications

References 14 publications

4-D-VAR assimilation of disdrometer data and radar spectral reflectivities for raindrop size distribution and vertical wind retrievals

4-D-VAR assimilation of disdrometer data and radar spectral reflectivities for raindrop size distribution and vertical wind retrievals

Estimation of Finescale Rainfall Fields Using Broadcast TV Satellite Links and a 4DVAR Assimilation Method

YAO: A Generator of Parallel Code for Variational Data Assimilation Applications

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