Assimilating cloudy and rainy microwave observations from SAPHIR on board Megha Tropiques within the ARPEGE global model

Duruisseau, Fabrice; Chambon, Philippe; Wattrelot, Éric; Barreyat, Marylis; Mahfouf, Jean‐François

doi:10.1002/qj.3456

Cited by 21 publications

(30 citation statements)

References 49 publications

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“…Still, we can get a positive impact, especially in the case of complex meteorological events. The assimilation of cloudy radiances is now operational at ECMWF (Geer et al [39]), and Meteo France is developing a two-step techniques (1-D-Bay + 4-D-Var) similar to the method which was implemented for radar reflectivity assimilation (Caumont et al [40]; Wattrelot et al [41]) to assimilate cloudy radiances but with a dedicated quality control and dedicated observation errors (Duruisseau et al [42]). In the future, we plan to implement the ECMWF-IFS solution and expect that the impact of the microwave radiances will be larger.…”

Section: Summary and Discussionmentioning

confidence: 99%

Observing System Experiments with an Arctic Mesoscale Numerical Weather Prediction Model

2019

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In the Arctic, weather forecasting is one element of risk mitigation, helping operators to have knowledge on weather-related risk in advance through forecasting capabilities at time ranges from a few hours to days ahead. The operational numerical weather prediction is an initial value problem where the forecast quality depends both on the quality of the forecast model itself and on the quality of the specified initial state. The initial states are regularly updated using environmental observations through data assimilation. This paper assesses the impact of observations, which are accessible through the global telecommunication and the EUMETCast dissemination systems on analyses and forecasts of an Arctic limited area AROME (Application of Research to Operations at Mesoscale) model (AROME-Arctic). An assessment through the computation of degrees of freedom for signals on the analysis, the utilization of an energy norm-based approach applied to the forecasts, verifications against observations, and a case study showed similar impacts of the studied observations on the AROME-Arctic analysis and forecast systems. The AROME-Arctic assimilation system showed a relatively high sensitivity to the humidity or humidity-sensitive observations. The more radiance data were assimilated, the lower was the estimated relative sensitivity of the assimilation system to different conventional observations. Data assimilation, at least for surface parameters, is needed to produce accurate forecasts from a few hours up to days ahead over the studied Arctic region. Upper-air conventional observations are not enough to improve the forecasting capability over the AROME-Arctic domain compared to those already produced by the ECMWF (European Centre for Medium-range Weather Forecast). Each added radiance data showed a relatively positive impact on the analyses and forecasts of the AROME-Arctic. The humidity-sensitive microwave (AMSU-B/MHS) radiances, assimilated together with the conventional observations and the Infrared Atmospheric Sounding Interferometer (IASI)-assimilated on top of conventional and microwave radiances produced enough accurate one-day-ahead forecasts of polar low.

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Section: Summary and Discussionmentioning

confidence: 99%

Observing System Experiments with an Arctic Mesoscale Numerical Weather Prediction Model

2019

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“…() and Duruisseau et al . () have used the method with MW radiances; and Borderies et al . () used a similar approach to validate airborne radar measurements against AROME forecasts.…”

Section: Methods Of Assimilation Of Atmospheric Water Informationmentioning

confidence: 99%

“…, where x t is the true state. Caumont et al (2010) and Wattrelot et al (2014) used this method successfully to retrieve RH from radar reflectivity observations; Guerbette et al (2016) and Duruisseau et al (2019) have used the method with MW radiances; and Borderies et al (2018) used a similar approach to validate airborne radar measurements against AROME forecasts.…”

Section: Pre-assimilation Retrievalsmentioning

confidence: 99%

Techniques and challenges in the assimilation of atmospheric water observations for numerical weather prediction towards convective scales

Bannister

Chipilski

Martínez‐Alvarado

2019

Quart J Royal Meteoro Soc

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While contemporary numerical weather prediction models represent the large‐scale structure of moist atmospheric processes reasonably well, they often struggle to maintain accurate forecasts of small‐scale features such as convective rainfall. Even though high‐resolution models resolve more of the flow, and are therefore arguably more accurate, moist convective flow becomes increasingly nonlinear and dynamically unstable. Importantly, the models' initial conditions are typically sub‐optimal, leaving scope to improve the accuracy of forecasts with improved data assimilation. To address issues regarding the use of atmospheric water‐related observations – especially at convective scales (also known as storm scales) – this article discusses the observation and assimilation of water‐related quantities. Special emphasis is placed on background error statistics for variational and hybrid methods which need special attention for water variables. The challenges of convective‐scale data assimilation of atmospheric water information are discussed, which are more difficult to tackle than at larger scales. Some of the most important challenges include the greater degree of inhomogeneity and lower degree of smoothness of the flow, the high volume of water‐related observations (e.g. from radar, microwave and infrared instruments), the need to analyse a range of hydrometeors, the increasing importance of position errors in forecasts, the greater sophistication of forward models to allow use of indirect observations (e.g. cloud‐ and precipitation‐affected observations), the need to account for the flow‐dependent multivariate “balance” between atmospheric water and both dynamical and mass fields, and the inherent non‐Gaussian nature of atmospheric water variables.

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“…Eresmaa 2014The study of Eresmaa (2014) proposed an imager-assisted cloud detection for the global ECMWF NWP system and was based on the hypothesis that each AVHRR cluster is made of fully clear or fully cloudy pixels. Therefore, these selection criteria only intended to diagnose and retain observations when they were completely clear, using the last two infrared channels of AVHRR (10.5 and 11.5 µm).…”

Section: Homogeneity Criteria Derived Frommentioning

confidence: 99%

“…The AVHRR cluster information associated with IASI has already proven to be useful for selection purposes in the context of cloudy data assimilation, with an explicit treatment of microphysical variables in the AROME model by Martinet et al (2013). Eresmaa (2014) at ECMWF also used AVHRR cluster information for cloud detection and observation selection in the clear sky. Martinet et al (2013) selected cloudy scenes based on cloud homogeneity.…”

Section: Introductionmentioning

confidence: 99%

Homogeneity criteria from AVHRR information within IASI pixels in a numerical weather prediction context

2019

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Abstract. This article focuses on the selection of satellite infrared IASI (Infrared Atmospheric Sounding Interferometer) observations in the global numerical weather prediction (NWP) system ARPEGE (Action de Recherche Petite Echelle Grande Echelle). The observation simulation is performed with the sophisticated radiative transfer model RTTOV-CLD, which takes into account the cloud scattering and the multilayer clouds from atmospheric profiles and cloud microphysical profiles (liquid water content, ice content and cloud fraction). The aim of this work is to select homogeneous scenes by using the information of the collocated Advanced Very High Resolution Radiometer (AVHRR) pixels inside each IASI field of view and to retain the most favourable cases for the assimilation of IASI infrared radiances. Two methods to select homogeneous scenes using homogeneity criteria already proposed in the literature were adapted: the criteria derived from Martinet et al. (2013) for cloudy sky selection in the French mesoscale model AROME (Applications of Research to Operations at MEsoscale) and the criteria from Eresmaa (2014) for clear-sky selection in the global model IFS (Integrated Forecasting System). A comparison between these methods reveals considerable differences, in both the method to compute the criteria and the statistical results. From this comparison a revised method representing a kind of compromise between the different tested methods is proposed and it uses the two infrared AVHRR channels to define the homogeneity criteria in the brightness temperature space. This revised method has a positive impact on the observation minus the simulation statistics, while retaining 36 % of observations for the assimilation. It was then tested in the NWP system ARPEGE for the clear-sky assimilation. These criteria were added to the current data selection based on the McNally and Watts (2003) cloud detection scheme. It appears that the impact on analyses and forecasts is rather neutral.

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Assimilating cloudy and rainy microwave observations from SAPHIR on board Megha Tropiques within the ARPEGE global model

Cited by 21 publications

References 49 publications

Observing System Experiments with an Arctic Mesoscale Numerical Weather Prediction Model

Observing System Experiments with an Arctic Mesoscale Numerical Weather Prediction Model

Techniques and challenges in the assimilation of atmospheric water observations for numerical weather prediction towards convective scales

Homogeneity criteria from AVHRR information within IASI pixels in a numerical weather prediction context

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