A geo‐statistical observation operator for the assimilation of near‐surface wind data

Abstract

Although many near-surface wind observations are available, very few are assimilated over land mainly due to sub-grid scale topographic interactions with the flow. The main objectives of this study are to understand the impact of near-surface wind observations on the analysis and to point out aspects that need to be improved to make a better use of these observations. A geo-statistical observation operator has been developed to correct for systematic and representativeness errors. Assimilation experiments were performed in a simplified context, assimilating only near-surface wind observations over land in the ensemble-variational data assimilation system developed at Environment Canada. Due to the background-error covariances, the assimilation of near-surface wind observations impacts the lower part of the atmosphere. The resulting correction has been evaluated by verifying the analyses against non-assimilated collocated radiosonde data. This assessment also made it possible to estimate the observation error variance to strike a balance between having an important impact at the surface and maintaining a good vertical fit to upper air observations. Results from 1 month of assimilation experiments show that the geostatistical operator eliminates biases and significantly reduces representativeness errors as well as observation error correlations in the analysis, mainly over complex terrain. Results also show that flow-dependent background error covariances from ensembles provide better vertical information propagation than static error statistics. Overall, the analysis fit to non-assimilated collocated radiosonde observations is improved when assimilating wind observations from surface stations.

High‐Resolution Regional Reanalysis in China: Evaluation of 1 Year Period Experiments

“…Channels of satellite radiance data ingested by data assimilation system are shown in Table 2. It should be noted that all nearsurface observation data are not assimilated in CNRR because ingesting the near surface observations may 1826,1865,1865,1866,1868,1869,1872,1873,1876,1881,1882,1911,1917,1918,1924,1928 1001~1100 1101~1200 1201~1300 1301~1400 1401~1500 1501~1600 1601~1800 1801~2000 2001~3000 1027, 1046, 11 1121,1133, 1191,1194 1271 1479 1509,1513, 1521,1536, 1574,1579, 1585,1587 1626,1639, 1643,1652, 1658,1671, 1786 1805,1884, 1991 2019,2094, 2119,2213, 2239,2271, 2321,2398, 2701, Journal of Geophysical Research: Atmospheres 10.1002/2017JD027476 cause uncertainties to data assimilation outcomes (Bédard, Laroche, & Gauthier, 2015;Jordi et al, 2015;Lee et al, 2011;Pu, 2017).…”

“…Channels of satellite radiance data ingested by data assimilation system are shown in Table . It should be noted that all near‐surface observation data are not assimilated in CNRR because ingesting the near surface observations may cause uncertainties to data assimilation outcomes (Bédard, Laroche, & Gauthier, ; Jordi et al, ; Lee et al, ; Pu, ).…”

Challenges and Opportunities for Data Assimilation in Mountainous Environments

“…Similarly, sub-grid scale terrain variations present in the real world can systematically alter observed wind directions and speeds relative to a model. Recently, Bédard et al [27] developed a geostatistical forward operator (replacing spatial interpolation) aimed at assimilating biased wind observations. Systematic errors resulting from scales or processes that a model cannot simulate because of discretization are best viewed as observation biases rather than model biases.…”

Hybrid Background Error Covariances for a Limited-Area Deterministic Weather Prediction System

“…The resolution and the quality of background error covariances are factors limiting the assimilation of dense observations (Gustafsson et al 2018). Also, the use of climatological background error covariances limits the propagation of the information from near-surface observation networks (Bédard et al 2015(Bédard et al , 2017. Most state of the art assimilation systems use a combination of climatological and flow-dependent background error covariances (so-called hybrid covariance approach) to partially alleviate sampling issues related to estimating flowdependent background error covariances from an ensemble.…”