Data assimilation of GNSS zenith total delays from a Nordic processing centre

Lindskog, Magnus; Ridal, Martin; Þorsteinsson, Sigurður B.; Ning, Tong

doi:10.5194/acp-17-13983-2017

Cited by 40 publications

(27 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Authors also identify no clear benefit of 4DVAR against 3DVAR. (Lindskog et al, 2017) in their Nordic country study of GNSS ZTD impact on forecasts, confirmed that the forecasts are sensitive to thinning distance. Shorter distance between stations (below 100km) leads to a 25 larger humidity bias in the lower troposphere, which may explain the humidity bias in the (Bennitt and Jupp, 2012) solution.…”

Section: Introductionmentioning

confidence: 79%

“…Shorter distance between stations (below 100km) leads to a 25 larger humidity bias in the lower troposphere, which may explain the humidity bias in the (Bennitt and Jupp, 2012) solution. (Lindskog et al, 2017) showed that the humidity forecast is better when the GNSS ZTD is assimilated with other meteorological observations such as Advanced Microwave Sounding Unit (AMSU) or Infrared Atmospheric Sounding Interferometer (IASI) radiances. Authors also showed that the adopted bias correction strategy and GNSS ZTD estimation procedure have marginal impact on the forecasts.…”

Section: Introductionmentioning

confidence: 99%

“…The literature review shows that the impact of the GNSS ZTD/PW assimilation depends on the number of already 5 assimilated observations and applied preprocessing (Bennitt and Jupp, 2012;Lindskog et al, 2017;Poli et al, 2007) as well as on the type of weather conditions. The main aim of this paper is to quantify the impact of the GNSS data, both ZTD and PW, gathered operationally in Poland, in weather forecasting.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

4DVAR assimilation of GNSS zenith path delays and precipitable water into a numerical weather prediction model WRF

Rohm¹,

Guzikowski²,

Wilgan³

et al. 2018

Preprint

View full text Add to dashboard Cite

Abstract. The GNSS data assimilation is currently widely discussed in the literature with respect to the various applications in meteorology and numerical weather models. Data assimilation combines atmospheric measurements with knowledge of 10 atmospheric behavior as codified in computer models. With this approach, the 'best' estimate of current conditions consistent with both information sources is produced. Some approaches allow assimilating also the non-prognostic variables, including remote sensing data from radar or GNSS (Global Navigation Satellite System). These techniques are named variational data assimilation schemes and are based on a minimization of the cost function, which contains the differences between the model state (background) and the observations. The obtained WRF predictions are validated against surface meteorological measurements, including air temperature, humidity, wind speed, and rainfall rate. Results from the first experiment (May and June, 2013) show that the assimilation of 25 GNSS data (both ZTD and PW) have positive impact on the rain and humidity forecast. However, the assimilation of ZTD is more successful, and brings substantial reduction of errors in rain forecast by 8%, and a 20% improvement in bias of humidity forecast, but it has a slight negative impact on temperature bias and wind speed. Second experiment (5-23 May, 2013) reveals that the PW or ZTD assimilation leads to a similar reduction of errors as in the first experiment, moreover, adding SYNOP and RS observations to the assimilation does not improve the humidity or rain forecasts (in the 48h forecast) 30 but reduces errors in the wind speed and temperature. Furthermore, short term predictions (up to 24h) of rain and humidity are better when SYNOP and RS observations are assimilated. The impact of assimilation of ZTD and PW in severe weather Atmos. Meas. Tech. Discuss., https://doi

show abstract

Section: Introductionmentioning

confidence: 79%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

4DVAR assimilation of GNSS zenith path delays and precipitable water into a numerical weather prediction model WRF

Rohm¹,

Guzikowski²,

Wilgan³

et al. 2018

Preprint

View full text Add to dashboard Cite

show abstract

“…For this diagnostic purpose, a revised whitelist is generated with zero thinning in order to execute very first active assimilation and to collect its OMF departures. Liu and Rabier (2003) showed that horizontal thinning distance is optimal, where the observation error correlations are less than 0.2-0.3. By the visualisation of these error correlations, which can be seen in Fig.…”

Section: Results Of the Preselection Proceduresmentioning

confidence: 99%

The use of GNSS zenith total delays in operational AROME/Hungary 3D-Var over a central European domain

2019

View full text Add to dashboard Cite

Abstract. The delay of satellite signals broadcasted by Global Navigation Satellite System (GNSS) provides unique atmospheric observations which endorse numerical weather prediction from global to limited-area models. Due to the possibility of its frequent and near-real-time estimation, the zenith total delays (ZTDs) are valuable information for any state-of-the-art data assimilation system. This article introduces the data assimilation of ZTDs in a Hungarian numerical weather prediction system, which was carried out by taking into account observations from central European GNSS analysis and processing centres. The importance of ZTD observations is described and shown by a diagnostic tool in the 3-hourly updated 3D-Var assimilation scheme. Furthermore, observing system experiments are done to evaluate the impact of GNSS ZTDs on mesoscale limited-area forecasts. The results of the use of GNSS ZTDs showed a clear added value to improve screen-level temperature and humidity forecasts when the bias is accurately estimated and corrected in the data assimilation scheme. The importance of variational, i.e. adaptive bias correction, is highlighted by verification scores compared to static bias correction. Moreover, this paper reviews the quality control of GNSS ground-based stations inside the central European domain, the calculation of optimal thinning distance and the preparation of the two above-mentioned bias correction methods. Finally, conclusions are drawn on different settings of the forecast and analysis experiments with a brief future outlook.

show abstract

“…In addition, extended parallel experiments will be carried out in a preoperational context. These runs should also include various types of quality control, like a background check [40] and rejection in particular areas [39].…”

Section: Discussionmentioning

confidence: 99%

Short-Range Numerical Weather Prediction of Extreme Precipitation Events Using Enhanced Surface Data Assimilation

Lindskog

Landelius

2019

Atmosphere

Self Cite

View full text Add to dashboard Cite

A limited-area kilometre scale numerical weather prediction system is applied to evaluate the effect of refined surface data assimilation on short-range heavy precipitation forecasts. The refinements include a spatially dependent background error representation, use of a flow-dependent data assimilation technique, and use of data from a satellite-based scatterometer instrument. The effect of the enhancements on short-term prediction of intense precipitation events is confirmed through a number of case studies. Verification scores and subjective evaluation of one particular case points at a clear impact of the enhanced surface data assimilation on short-range heavy precipitation forecasts and suggest that it also tends to slightly improve them. Although this is not strictly statistically demonstrated, it is consistent with the expectation that a better surface state should improve rainfall forecasts.

show abstract

Data assimilation of GNSS zenith total delays from a Nordic processing centre

Cited by 40 publications

References 49 publications

4DVAR assimilation of GNSS zenith path delays and precipitable water into a numerical weather prediction model WRF

4DVAR assimilation of GNSS zenith path delays and precipitable water into a numerical weather prediction model WRF

The use of GNSS zenith total delays in operational AROME/Hungary 3D-Var over a central European domain

Short-Range Numerical Weather Prediction of Extreme Precipitation Events Using Enhanced Surface Data Assimilation

Contact Info

Product

Resources

About