Global assimilation of air temperature, humidity, wind and pressure from surface stations

Ingleby, Bruce

doi:10.1002/qj.2372

Cited by 32 publications

(33 citation statements)

References 63 publications

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“…It is likely related to the introduction of wind observations with significant biases and representativeness errors that can degrade the surface pressure fields through the multivariate background error statistics. These results are consistent with previous studies (e.g., Benjamin et al 2007Benjamin et al , 2010Ingleby 2015) and it explains why most operational centers do not assimilate near-surface wind observations over land.…”

Section: B Evaluation Against Upper-air Observationssupporting

confidence: 93%

“…ECCC's global deterministic prediction system (GDPS), based on the Global Environmental Multiscale model (GEM; Côté et al 1998;Charron et al 2012;Zadra et al 2014), has a relatively high resolution (0.358 3 0.238 latitudelongitude resolution: ;25 km), similar to the gridpoint spacing employed in other studies on the assimilation of near-surface winds over land [e.g., 20 km in Benjamin et al (2007); 27 km in Pu et al (2013); 25 km in Ingleby (2015)]. The GDPS was chosen because it is well developed and it relies on well-calibrated flow-dependent background error covariances from the operational EnKF (Houtekamer et al 2014).…”

Section: Experimental Frameworkmentioning

confidence: 99%

“…The model misrepresentation of surface characteristics generates socalled representativeness errors. Biases and representativeness errors limit the global influence of near-surface wind observations on operational forecast systems (Ingleby 2015). Winds over small islands, subgrid-scale headlands, and over land throughout the tropics are still excluded from the Met Office data assimilation system.…”

Section: Introductionmentioning

confidence: 99%

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Near-Surface Wind Observation Impact on Forecasts: Temporal Propagation of the Analysis Increment

Bédard

Laroche

Gauthier

2017

Monthly Weather Review

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This study examines the assimilation of near-surface wind observations over land to improve wind nowcasting and short-term tropospheric forecasts. A new geostatistical operator based on geophysical model output statistics (GMOS) is compared with a bilinear interpolation scheme (Bilin). The multivariate impact on forecasts and the temporal evolution of the analysis increments produced are examined as well as the influence of background error covariances on different components of the prediction system. Results show that Bilin significantly degrades surface and upper-air fields when assimilating only wind data from 4942 SYNOP stations. GMOS on the other hand produces smaller increments that are in better agreement with the model state. It leads to better short-term near-surface wind forecasts and does not deteriorate the upper-air forecasts. The information persists longer in the system with GMOS, although the local improvements do not propagate beyond 6-h lead time. Initial model tendencies indicate that the mass field is not significantly altered when using static error covariances and the boundary layer parameterizations damp the poorly balanced increment locally. Conversely, most of the analysis increment is propagated when using flow-dependent error statistics. It results in better balanced wind and mass fields and provides a more persistent impact on the forecasts. Forecast accuracy results from observing system experiments (assimilating SYNOP winds with all observations used operationally) are generally neutral. Nevertheless, forecasts and analyses from GMOS are more self-consistent than those from both Bilin and a control experiment (not assimilating near-surface winds over land) and the information from the observations persists up to 12-h lead time.

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Section: B Evaluation Against Upper-air Observationssupporting

confidence: 93%

Section: Experimental Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Near-Surface Wind Observation Impact on Forecasts: Temporal Propagation of the Analysis Increment

Bédard

Laroche

Gauthier

2017

Monthly Weather Review

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“…The representativeness error is caused by any physical scales, features or processes that affect the observation but are unresolved in the forecast model (Ingleby, 2015).…”

Section: Optimum Interpolationmentioning

confidence: 99%

High-resolution precipitation re-analysis system for climatological purposes

Soci

Bazile

Besson

et al. 2016

Tellus A: Dynamic Meteorology and Oceanography

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A B S T R A C TIn this article, we describe the design and the validation of the Mescan precipitation analysis system developed for climatological purposes under the EURO4M project. The system is based on an optimal interpolation algorithm using the 24-h aggregated gauge measurements from the surface network. The background fields are the total accumulated precipitation forecasts at different resolutions from the ALADIN or HIRLAM mesoscale models, downscaled to 5.5 km grid spacing, chosen to match the time period of the climatological gauge reports. The validation of the Mescan system is carried out over the French territory employing various metrics and by providing forcing to a hydrological model to produce river discharges. The investigations have shown that the precipitation analyses have almost the same quality as the well-validated SAFRAN analysis system. In addition, the analysis of the precipitation variance spectra computed on the same horizontal domain has indicated that at short wavelengths the downscaled fields have significantly lower variability than a field produced by time integrating a forecast model. The Mescan precipitation analysis system has successfully been used to produce 24-h total accumulated precipitation re-analyses on a 5.5 km grid over Europe for the period 2007Á2010.

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“…Therefore, the observational datasets of surface wind velocities are usually very limited or are avoided for operational weather prediction (e.g., Japan Meteorological Agency (JMA) 2015, in Japanese) to prevent the degradation of initial conditions. To improve near-surface wind prediction, recent studies tried to assimilate surface wind observations over land (e.g., Hacker and Snyder 2005;Benjamin et al 2010;Hacker and Rostkier-Edelstein 2007;Rostkier-Edelstein and Hacker 2010;Ancell et al 2011Ancell et al , 2015Ingleby 2015;Bédard et al 2015Bédard et al , 2017. In these studies, however, the surface wind observations have an influence only on extremely shortterm and local forecasts (less than 6 h) even if they have a positive impact.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Surface Wind Data Assimilation on the Predictability of Near-Surface Plume Advection in the Case of the Fukushima Nuclear Accident

Sekiyama¹,

Kajino²,

Kunii³

2017

Journal of the Meteorological Society of Japan

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We investigated the predictability of plume advection in the lower troposphere and the impact of AMeDAS surface wind data assimilation by using radioactive cesium emitted by the Fukushima nuclear accident in March 2011 as an atmospheric tracer. We conducted two experiments of radioactive plume predictions over eastern Japan for March 15, 2011 with a 3-km horizontal resolution using the Japan Meteorological Agency non-hydrostatic weather forecast model and local ensemble transform Kalman filter (JMANHM-LETKF) data assimilation system. The assimilated meteorological data were obtained from the standard archives collected for the Japan Meteorological Agency operational numerical weather prediction and the AMeDAS surface wind observations. The standard archives do not contain land-surface wind observations. The modeled radioactive cesium concentrations were examined for plume arrival times at 40 observatories. The mean error of the plume arrival times for the standard experiment (assimilating only the standard archives) was 82.0 min with a 13-h lead-time on an average. In contrast, the mean error of the AMeDAS experiment (assimilating both the standard archives and AMeDAS surface wind observations) was 72.8 min, which was 9.2 min (11 %) better than that of the standard experiment. This result indicates that the plume prediction has a reasonable accuracy for the environmental emergency response and the prediction can be significantly improved by the surface wind data assimilation.

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Global assimilation of air temperature, humidity, wind and pressure from surface stations

Cited by 32 publications

References 63 publications

Near-Surface Wind Observation Impact on Forecasts: Temporal Propagation of the Analysis Increment

Near-Surface Wind Observation Impact on Forecasts: Temporal Propagation of the Analysis Increment

High-resolution precipitation re-analysis system for climatological purposes

The Impact of Surface Wind Data Assimilation on the Predictability of Near-Surface Plume Advection in the Case of the Fukushima Nuclear Accident

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