Use of Microwave Radiances from Metop-C and Fengyun-3 C/D Satellites for a Northern European Limited-area Data Assimilation System

Lindskog, Magnus; Dybbroe, Adam; Randriamampianina, Roger

doi:10.1007/s00376-021-0326-5

Cited by 12 publications

(7 citation statements)

References 48 publications

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“…The radiance assimilation is commonplace in limited-area (regional) NWP systems too, albeit their relative importance in such applications tends to be less. The most common use case for radiance data is from sounders operating at microwave and infrared wavelengths (e.g., Kazumori, 2014;Lin et al, 2017;Lindskog, Dybbroe & Randriamampianina, 2021). By now, a mature status has been reached also in the area of using microwave imagers (e.g., Geer et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Variational Bias Correction of Polar-Orbiting Satellite Radiances in Convective-Scale Data Assimilation

Eresmaa

2024

Tellus A: Dynamic Meteorology and Oceanography

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Maintenance of robust bias correction is a major challenge in the assimilation of meteorological polar-orbiting satellite data into limited-area Numerical Weather Prediction (NWP) systems. This article presents a variant of the variational bias correction algorithm suitable for use in convection-resolving systems. Stable bias correction requires continuous and representative sampling of predictor variables such as satellite view angles and air-mass properties. In convection-resolving NWP systems, the sampling is often compromised because of small computing domains, short assimilation time windows, and large diurnal variation in data availability.The proposed variant is designed around the assumption of one recurring daily analysis hour at which a given satellite provides comprehensive data coverage inside the computing domain. The idea is to allow the variational algorithm to adjust the bias correction coefficients at that analysis hour only, and otherwise keep the updated coefficients constant during the analysis. The time of the daily coefficient update is to be specified separately for each satellite, taking account of the satellite orbit parameters. The proposal is an alternative to the widely-adopted operational practice where independent streams of coefficients are maintained and updated separately at each analysis hour.The proposal is evaluated by data assimilation experiments in the context of a stateof-the-art Northern-European limited-area NWP system. In comparison with the operational setup, the proposed method is found to slightly improve the satellite radiance data fit to the NWP model background. Nevertheless, verification against independent data sources indicates no solid and statistically significant impact on forecast system performance.

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Section: Introductionmentioning

confidence: 99%

Variational Bias Correction of Polar-Orbiting Satellite Radiances in Convective-Scale Data Assimilation

Eresmaa

2024

Tellus A: Dynamic Meteorology and Oceanography

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“…Despite all these challenges, satellite radiances and in particular microwave data have positive impact on analyses and forecasts in LAMs (e.g. Randriamampianina et al (2019); Lindskog et al (2021)). In general, the use of radiance measurements is more straightforward over open ocean in clear-sky conditions and the complexity of their assimilation grows with the increased sensitivity to the non-ocean surface and over areas affected by precipitation and clouds.…”

Section: Introductionmentioning

confidence: 99%

Exploring the footprint representation of microwave radiance observations in an Arctic limited-area data assimilation system

Mile,

Guedj,

Randriamampianina

2024

Preprint

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Abstract. The microwave radiances are key observations especially over data sparse regions for operational data assimilation in numerical weather prediction (NWP). An often applied simplification is that these observations are used as point measurements, however, the satellite field-of-view may cover many grid points of high-resolution models. Therefore, we examine a solution in high-resolution data assimilation to better account for the spatial representation of the radiance observations. This solution is based on a footprint operator implemented and tested in the variational assimilation scheme of the AROME-Arctic (Application of Research to Operations at Mesoscale – Arctic) limited-area model. In this paper, the design and technical challenges of the microwave radiance footprint operator are presented. In particular, implementation strategies, the representation of satellite field-of-view ellipses, and the emissivity retrieval inside the footprint area are discussed. Furthermore, the simulated brightness temperatures and the sub-footprint variability are analysed in a case study indicating particular areas where the use of the footprint operator is expected to provide significant added value. For radiances measured by the Advanced Microwave Sounding Unit-A (AMSU-A) and Microwave Humidity Sounder (MHS) sensors, the standard deviation of the observation minus background (OmB) departures are computed on a short period in order to compare the statistics of the default and the implemented footprint observation operator. For all operationally used AMSU-A and MHS channels, it is shown that the standard deviation of OmB departures is reduced when the footprint operator is applied.

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“…Singh et al [12] revealed that the assimilation of both NCEP PREPBUFR and radiance data could capture the structure of the storm and reduce tracking errors for different lead-time forecasts, which caused the improvement of the initial conditions for the storm. Lindskog et al [13] used a large number of observations from traditional observations, radars, scatterometer data, and satellite radiances to produce initial states using 3DVAR and found that the assimilation of microwave humidity sounder 2 (MWHS2) and the Advanced Microwave Sounding (AMSU-A) radiances was beneficial in reducing systematic observation errors and excluding gross errors. The impacts of the assimilation of FY-3D MWHS2 radiance data under clear skies for Tropical Storm Ampil were studied by Xu et al [14].…”

Section: Introductionmentioning

confidence: 99%

Satellite Radiance Data Assimilation Using the WRF-3DVAR System for Tropical Storm Dianmu (2021) Forecasts

Thodsan

Torsri³

et al. 2022

Atmosphere

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This study investigated the impact of the assimilation of satellite radiance observations in a three-dimensional variational data assimilation system (3DVAR) that could improve the tracking and intensity forecasts of the Tropical Storm Dianmu in 2021, which occurred over parts of southeast mainland Asia. The weather research and forecasting (WRF) model was used to conduct the assimilation experiments of the storm. Four sets of numerical experiments were performed using the WRF. In the first, the control experiment, only conventional data in Binary Universal Form for the Representation of Meteorological Data (PREPBUFR) observations from the National Centers for Environmental Prediction (NCEP) were assimilated. The second experiment (RDA1) was performed with PREPBUFR observations and satellite radiance data from the Advanced Microwave Unit-A (AMSU-A), and the Advanced Technology Microwave Sounder (ATMS). PREPBUFR observations and the High-resolution Infrared Radiation Sounder (HIRS-4) were used in the third experiment (RDA2). The fourth experiment (ALL-OBS) used the assimilation of PREPBUFR observations and all satellite radiance data (AMSU-A, ATMS, and HIRS-4). The community radiative transfer model was used on the forward operator for the satellite radiance assimilation, along with quality control and bias correction procedures, before assimilating the radiance data. To evaluate the impact of the assimilation experiments, a forecast starting on 00 UTC 23 September 2021, was produced for 72 hours. The results showed that the ALL-OBS experiment improved the short-term forecast up to ~24 hours lead time, as compared to the assimilation considering only PREPBUFR observations. When all observations were assimilated into the model, the storm’s landfall position, intensity, and structure were accurately predicted. In the deterministic forecast, the tracking errors of the ALL-OBS experiment was consistently less than 40 km within 24 hours. The case study of Tropical Storm Dianmu exhibited the significant positive impact of all observations in the numerical model, which could improve updates for initial conditions and storm forecasting.

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Use of Microwave Radiances from Metop-C and Fengyun-3 C/D Satellites for a Northern European Limited-area Data Assimilation System

Cited by 12 publications

References 48 publications

Variational Bias Correction of Polar-Orbiting Satellite Radiances in Convective-Scale Data Assimilation

Variational Bias Correction of Polar-Orbiting Satellite Radiances in Convective-Scale Data Assimilation

Exploring the footprint representation of microwave radiance observations in an Arctic limited-area data assimilation system

Satellite Radiance Data Assimilation Using the WRF-3DVAR System for Tropical Storm Dianmu (2021) Forecasts

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