Doppler radar radial winds in HIRLAM. Part I: observation modelling and validation

Järvinen, Heikki; Salonen, Kirsti; Lindskog, Magnus; Huuskonen, A.; Niemelä, Sami; Eresmaa, Reima

doi:10.1111/j.1600-0870.2008.00380.x

Cited by 7 publications

(5 citation statements)

References 23 publications

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“…An approximation of (i) for low elevations is = + r/(4R E /3). Another approximation by Järvinen et al (2009)…”

Section: Simulation Of Doppler Velocity Measurementmentioning

confidence: 99%

“…As mentioned before, the microwave pulses emitted by any radar propagate through the atmosphere along a curve rather than a straight line because of atmospheric refraction (Järvinen et al , ). EMVORADO implements three computational methods, which are introduced very briefly here, and more details can be found in Zeng et al ().…”

Section: Description Of Emvoradomentioning

confidence: 99%

“…An approximation of (i) for low elevations is ε ′= ε + r /(4 R E /3). Another approximation by (Järvinen et al , ) is

ε^{'} = ε + arctan (\frac{r \cos ε}{r \sin ε + 4 / 3 R_{E} + h_{0}}) .

Of course, there are also some simplifications possible for Eq. .…”

Section: Description Of Emvoradomentioning

confidence: 99%

See 2 more Smart Citations

An efficient modular volume‐scanning radar forward operator for NWP models: description and coupling to the COSMO model

Zeng

Blahak

Jerger

2016

Quart J Royal Meteoro Soc

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Since radar observations are highly dense in spatial and temporal resolutions, they have been often used to improve short‐term numerical weather prediction (NWP) by means of detailed model verification and 3D radar data assimilation. However, the observed quantities are not directly comparable to the prognostic variables of NWP models (e.g. hydrometeor densities, wind vector, temperature, pressure, etc.), so a common approach to facilitate this comparison is to derive synthetic radar observations from model variables; this is the so‐called ‘radar forward operator’. In the present article, a new Efficient Modular VOlume scanning RADar Operator (EMVORADO) for Doppler velocity and reflectivity is introduced. Although it has been developed in the COSMO model framework, it can be also coupled online to any other NWP model. Comprehensive physical aspects of radar measurements (e.g. beam bending/broadening/shielding, Doppler velocity with fall speed and reflectivity weighting, attenuated reflectivity, detectable signal, etc.) have been implemented in a modular way, using state‐of‐the‐art methods with different levels of approximation and numerical costs that can be optionally chosen. The reflectivity derivation from the prognostic model variables is as ‘model consistent’ as possible and carefully honours the uncertainties associated with partially melted particles. Efficiency and applicability on supercomputers (MPI‐parallelism) is a major design criterion, which allows us to simulate entire networks of 3D volume‐scanning meteorological radars within one model run and makes EMVORADO well suited for operational applications. This article aims to give a thorough description of the EMVORADO and to provide a first insight to the performance of different modules by some selected case‐studies.

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“…An approximation of (i) for low elevations is = + r/(4R E /3). Another approximation by Järvinen et al (2009)…”

Section: Simulation Of Doppler Velocity Measurementmentioning

confidence: 99%

Section: Description Of Emvoradomentioning

confidence: 99%

See 1 more Smart Citation

An efficient modular volume‐scanning radar forward operator for NWP models: description and coupling to the COSMO model

Zeng

Blahak

Jerger

2016

Quart J Royal Meteoro Soc

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“…In practice, however, the vertical resolution is decreased because the radar measurement volume increases with increasing measurement range from the radar. The validation makes use of an observation operator, which produces the model counterpart for the observed quantity. The radar radial wind observation operator takes into account the broadening and bending of the radar pulse path.…”

Section: Validation Of Model Resultsmentioning

confidence: 99%

Production of the Finnish Wind Atlas

et al. 2011

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The Finnish Wind Atlas was prepared applying the mesoscale model AROME with 2.5 km horizontal resolution and the diagnostic downscaling method Wind Atlas Analysis and Application Programme (WAsP) with 250 m resolution. The latter was applied for areas most favourable for wind power production: a 30 km wide coastal/offshore zone, highlands, large lakes and large fields. The methodology included several novel aspects: (i) a climatologically representative period of real 48 months during 1989-2007 was simulated with the mesoscale model; (ii) in addition, the windiest and calmest months were simulated; (iii) the results were calculated separately for each month and for sectors 30°wide; (iv) the WAsP calculations were based on the mesoscale model outputs; (v) in addition to point measurements, also radar wind data were applied for the validation of the mesoscale model results; (vi) the parameterization method for gust factor was extended to be applicable at higher altitudes; and (vii) the dissemination of the Wind Atlas was based on new technical solutions. The AROME results were calculated for the heights of 50, 75, 100, 125, 150, 200, 300 and 400 m, and the WAsP results for the heights of 50, 75, 100, 125 and 150 m. In addition to the wind speed, the results included the values of the Weibull distribution parameters, the gust factor, wind power content and the potential power production, which was calculated for three turbine sizes. The Wind Atlas data are available for each grid point and can be downloaded free of charge from dynamic maps at www.windatlas.fi. Production of the Finnish Wind Atlas B. Tammelin et al.Accordingly, a strong need arose for a more accurate wind atlas. In Finland, the size of the country, its complex terrain and large seasonal differences generate strong demands for a wind atlas. The complexity of the terrain is not so much related to orography but to the complex shape of the almost flat coastline and archipelago, which generates a need for very high spatial resolution. Further, the differences in wind conditions between seasons are particularly large because in winter, the sea and lakes are frozen and the ground is covered by snow, which changes the surface roughness and stabilizes the atmospheric boundary layer (ABL). Stable stratification favours the generation of low-level jets. 4 In winter, wind power plants are also subject to ice accretion. The production of a new Wind Atlas for Finland has also been motivated by the need to evaluate the possible effects of climate change on wind conditions. In 2008, the Ministry of Labour and Economics released an international tender for production of the new Finnish Wind Atlas. The tender was won by the Finnish Meteorological Institute (FMI), with Risø DTU and Vaisala Ltd as subcontractors. The project started 1 June 2008, and the wind atlas was released 25 November 2009 (www.windatlas.fi).Many national wind atlases have recently been produced applying numerical weather prediction (NWP) models. In an ideal approach, all possible weather condition...

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“…The radar observation operator was developed in the HIRLAM program (Lindskog, et al , 2000, 2004; Salonen, et al 2009) and is used for several studies (Xiao, et al , 2008; Järvinen, et al , 2009; Salonen, et al 2009). The observation error used in this study was set to 6 m s −1 .…”

Section: Datamentioning

confidence: 99%

Assimilation of GNSS ZTD and radar radial velocity for the benefit of very‐short‐range regional weather forecasts

Haan

2013

Quart J Royal Meteoro Soc

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Wind, humidity and temperature observations from aircraft and radiosondes are generally used to find the best initial state of the atmosphere for numerical weather prediction (NWP). To be of use for very-short-range numerical weather forecasting (or numerical nowcasting), these observations need to be available within several minutes after observation time. Radiosondes have a typically observation latency of over 30 min and arrive too late for numerical nowcasting. Zenith Total Delay (ZTD) observations obtained from a ground-based network of Global Navigation Satellite System (GNSS) receivers can fill this gap of lacking rapid humidity information. ZTD contains information on the total amount of water vapour. Other rapidly available observations, such as radial wind estimates from Doppler weather radars, can also be exploited. Both observations are available with a delay of less than 5 min with adequate spatial resolution. In this article, the impact of assimilation of these humidity and wind observations in a very-short-range regional forecast model is assessed over a four-month summer period and a six-week winter period. As a reference for the impact, GNSS observations are also assimilated in a threehourly NWP scheme with longer observation cut-off times. The quality of the forecasts is evaluated against radiosonde observations, radar radial wind and hourly precipitation observations. Assimilation of both GNSS ZTD and radar radial winds resulted in a positive impact on humidity, rainfall and wind forecasts.Key Words: data assimilation; GNSS humidity; radar radial wind

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Doppler radar radial winds in HIRLAM. Part I: observation modelling and validation

Cited by 7 publications

References 23 publications

An efficient modular volume‐scanning radar forward operator for NWP models: description and coupling to the COSMO model

An efficient modular volume‐scanning radar forward operator for NWP models: description and coupling to the COSMO model

Production of the Finnish Wind Atlas

Assimilation of GNSS ZTD and radar radial velocity for the benefit of very‐short‐range regional weather forecasts

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