scholarly journals The Use of a Numerical Weather Prediction Model to Simulate Near-Field Volcanic Plumes

Atmosphere ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 594
Author(s):  
Ralph R. Burton ◽  
Mark J. Woodhouse ◽  
Alan M. Gadian ◽  
Stephen D. Mobbs
Keyword(s):  
Numerical Weather Prediction ◽  
Near Field ◽  
Weather Prediction ◽  
Wrf Model ◽  
Integral Model ◽  
Eruption Column ◽  
Volcanic Plumes ◽  
Numerical Weather ◽  
Essential Components ◽  
Nwp Model

In this paper, a state-of the art numerical weather prediction (NWP) model is used to simulate the near-field plume of a Plinian-type volcanic eruption. The NWP model is run at very high resolution (of the order of 100 m) and includes a representation of physical processes, including turbulence and buoyancy, that are essential components of eruption column dynamics. Results are shown that illustrate buoyant gas plume dynamics in an atmosphere at rest and in an atmosphere with background wind, and we show that these results agree well with those from theoretical models in the quiescent atmosphere. For wind-blown plumes, we show that features observed in experimental and natural settings are reproduced in our model. However, when comparing with predictions from an integral model using existing entrainment closures there are marked differences. We speculate that these are signatures of a difference in turbulent mixing for uniform and shear flow profiles in a stratified atmosphere. A more complex implementation is given to show that the model may also be used to examine the dispersion of heavy volcanic gases such as sulphur dioxide. Starting from the standard version of the weather research and forecasting (WRF) model, we show that minimal modifications are needed in order to model volcanic plumes. This suggests that the modified NWP model can be used in the forecasting of plume evolution during future volcanic events, in addition to providing a virtual laboratory for the testing of hypotheses regarding plume behaviour.

UTCI as the NWP model ALADIN (CHMI) output – first experiences

2021 ◽  
Vol 94 (2) ◽  
pp. 237-249
Author(s):  
Martin Novák
Keyword(s):  
Numerical Weather Prediction ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Climate Index ◽  
Basic Information ◽  
Universal Thermal Climate Index ◽  
Numerical Weather ◽  
Weather Forecasters ◽  
Thermal Climate ◽  
Nwp Model

The article includes a summary of basic information about the Universal Thermal Climate Index (UTCI) calculation by the numerical weather prediction (NWP) model ALADIN of the Czech Hydrometeorological Institute (CHMI). Examples of operational outputs for weather forecasters in the CHMI are shown in the first part of this work. The second part includes results of a comparison of computed UTCI values by ALADIN for selected place with UTCI values computed from real measured meteorological data from the same place.

scholarly journals Verification of Forecast Weather Surface Variables over Vietnam Using the National Numerical Weather Prediction System

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Tien Du Duc ◽  
Lars Robert Hole ◽  
Duc Tran Anh ◽  
Cuong Hoang Duc ◽  
Thuy Nguyen Ba
Keyword(s):  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Model Performance ◽  
Wrf Model ◽  
Horizontal Resolution ◽  
Prediction System ◽  
Regional Models ◽  
Global Spectral Model ◽  
Numerical Weather ◽  
The Us

The national numerical weather prediction system of Vietnam is presented and evaluated. The system is based on three main models, namely, the Japanese Global Spectral Model, the US Global Forecast System, and the US Weather Research and Forecasting (WRF) model. The global forecast products have been received at 0.25- and 0.5-degree horizontal resolution, respectively, and the WRF model has been run locally with 16 km horizontal resolution at the National Center for Hydro-Meteorological Forecasting using lateral conditions from GSM and GFS. The model performance is evaluated by comparing model output against observations of precipitation, wind speed, and temperature at 168 weather stations, with daily data from 2010 to 2014. In general, the global models provide more accurate forecasts than the regional models, probably due to the low horizontal resolution in the regional model. Also, the model performance is poorer for stations with altitudes greater than 500 meters above sea level (masl). For tropical cyclone performance validations, the maximum wind surface forecast from global and regional models is also verified against the best track of Joint Typhoon Warning Center. Finally, the model forecast skill during a recent extreme rain event in northeast Vietnam is evaluated.

scholarly journals Background Error in WRF Model

2020 ◽  
Vol 16 ◽  
Keyword(s):  
Data Assimilation ◽  
Physical Properties ◽  
Covariance Matrix ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Wrf Model ◽  
Background Error ◽  
Numerical Weather ◽  
Error Covariance ◽  
First Time

WRF model have been tuned and tested over Georgia’s territory for years. First time in Georgia theprocess of data assimilation in Numerical weather prediction is developing. This work presents how forecasterror statistics appear in the data assimilation problem through the background error covariance matrix – B, wherethe variances and correlations associated with model forecasts are estimated. Results of modeling of backgrounderror covariance matrix for control variables using WRF model over Georgia with desired domain configurationare discussed and presented. The modeling was implemented in two different 3DVAR systems (WRFDA andGSI) and results were checked by pseudo observation benchmark cases using also default global and regional BEmatrixes. The mathematical and physical properties of the covariances are also reviewed.

Estimates of the Earth surface influence on the accuracy of numerical prediction of air temperature in Belarus using the WRF model

2021 ◽  
Vol 4 ◽  
pp. 50-68
Author(s):  
S.А. Lysenko ◽  
◽  
P.О. Zaiko ◽  
Keyword(s):  
Land Use ◽  
Air Temperature ◽  
Numerical Weather Prediction ◽  
Land Surface ◽  
Lead Time ◽  
Weather Prediction ◽  
Wrf Model ◽  
Surface Model ◽  
Numerical Weather ◽  
Global Land

The spatial structure of land use and biophysical characteristics of land surface (albedo, leaf index, and vegetation cover) are updated using the GLASS (Global Land Surface Satellite) and GLC2019 (Global Land Cover, 2019) modern satellite databases for mesoscale numerical weather prediction with the WRF model for the territory of Belarus. The series of WRF-based numerical experiments was performed to verify the influence of the updated characteristics on the forecast quality for some difficult to predict winter cases. The model was initialized by the GFS (Global Forecast System, NCEP) global numerical weather prediction model. It is shown that the use of high-resolution land use data in the WRF and the consideration of the new albedo and leaf index distribution over the territory of Belarus can reduce the root-mean-square error (RMSE) of short-range (to 48 hours) forecasts of surface air temperature by 16–33% as compared to the GFS. The RMSE of the temperature forecast for the weather stations in Belarus for a forecast lead time of 12, 24, 36, and 48 hours decreased on average by 0.40°С (19%), 0.35°С (10%), 0.68°С (23%), and 0.56°С (15%), respectively. The most significant decrease in RMSE of the numerical forecast of temperature (up to 2.1 °С) was obtained for the daytime (for a lead time of 12 and 36 hours), when positive feedbacks between albedo and temperature of the land surface are manifested most. Keywords: numerical weather prediction, WRF, digital land surface model, albedo, leaf area index, forecast model validation

scholarly journals An Evaluation of Radiative Transfer Simulations of Cloudy Scenes from a Numerical Weather Prediction Model at Sub-Millimetre Frequencies Using Airborne Observations

2020 ◽  
Vol 12 (17) ◽  
pp. 2758
Author(s):  
Stuart Fox
Keyword(s):  
Optical Properties ◽  
Radiative Transfer ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Full Range ◽  
Numerical Weather Prediction Model ◽  
Atmospheric Measurements ◽  
Numerical Weather ◽  
Nwp Model ◽  
Cloud Ice

The Ice Cloud Imager (ICI) will be launched on the next generation of EUMETSAT polar-orbiting weather satellites and make passive observations between 183 and 664 GHz which are sensitive to scattering from cloud ice. These observations have the potential to improve weather forecasts through direct assimilation using "all-sky" methods which have been successfully applied to microwave observations up to 200 GHz in current operational systems. This requires sufficiently accurate representations of cloud ice in both numerical weather prediction (NWP) and radiative transfer models. In this study, atmospheric fields from a high-resolution NWP model are used to drive radiative transfer simulations using the Atmospheric Radiative Transfer Simulator (ARTS) and a recently released database of cloud ice optical properties. The simulations are evaluated using measurements between 89 and 874 GHz from five case studies of ice and mixed-phase clouds observed by the Facility for Airborne Atmospheric Measurements (FAAM) BAe-146 research aircraft. The simulations are strongly sensitive to the assumed cloud ice optical properties, but by choosing an appropriate ice crystal model it is possible to simulate realistic brightness temperatures over the full range of sub-millimetre frequencies. This suggests that sub-millimetre observations have the potential to be assimilated into NWP models using the all-sky method.

scholarly journals Evaluation of IWV from the numerical weather prediction WRF model with PPP GNSS processing for Bulgaria

2016 ◽  
Author(s):  
Tzvetan Simeonov ◽  
Dmitry Sidorov ◽  
Felix Norman Teferle ◽  
Georgi Milev ◽  
Guergana Guerova
Keyword(s):  
Surface Pressure ◽  
Numerical Weather Prediction ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Wrf Model ◽  
Mean Difference ◽  
Global Navigation Satellite Systems ◽  
Numerical Weather ◽  
Precipitation Efficiency ◽  
Good Agreement

Abstract. Global Navigation Satellite Systems (GNSS) meteorology is an established operational service providing hourly updated GNSS tropospheric products to the National Meteorologic Services (NMS) in Europe. In the last decade through the ground-based GNSS network densification and new processing strategies like Precise Point Positioning (PPP) it has become possible to obtain sub-hourly tropospheric products for monitoring severe weather events. In this work one year (January–December 2013) of sub-hourly GNSS tropospheric products (Zenith Total Delay) are computed using the PPP strategy for seven stations in Bulgaria. In order to take advantage of the sub-hourly GNSS data to derive Integrated Water Vapour (IWV) surface pressure and temperature with similar temporal resolution is required. As the surface observations are on 3 hourly basis the first step is to compare the surface pressure and temperature from numerical weather prediction model Weather Forecasting and Research (WRF) with observations at three synoptic stations in Bulgaria. The mean difference between the two data-sets for 1) surface pressure is less than 0.5 hPa and the correlation is over 0.989, 2) temperature the largest mean difference is 1.1 °C and the correlation coefficient is over 0.957 and 3) IWV mean difference is in range 0.1–1.1 mm. The evaluation of WRF on annual bases shows IWV underestimation between 0.5 and 1.5 mm at five stations and overestimation at Varna and Rozhen. Varna and Rozhen have also much smaller correlation 0.9 and 0.76. The study of the monthly IWV variation shows that at those locations the GNSS IWV has unexpected drop in April and March respectively. The reason for this drop is likely problems with station raw data. At the remaining 5 stations a very good agreement between GNSS and WRF is observed with high correlation during the cold part of 2013 i.e. March, October and December (0.95) and low correlation during the warm part of 2013 i.e. April to August (below 0.9). The diurnal cycle of the WRF model shows a dry bias in the range of 0.5-1.5 mm. Between 00 and 01 UTC the GNSS IWV tends to be underestimate IWV which is likely due to the processing window used. The precipitation efficiency from GNSS and WRF show very good agreement on monthly bases with a maximum in May-June and minimum in August–September. The annual precipitation efficiency in 2013 at Lovech and Burgas is about 6 %.

scholarly journals Tropospheric delay parameters derived from GNSS-tracking data of a fast-moving fleet of trains

2021 ◽  
Author(s):  
Matthias Aichinger-Rosenberger ◽  
Natalia Hanna ◽  
Robert Weber
Keyword(s):  
Numerical Weather Prediction ◽  
Initial Conditions ◽  
Weather Prediction ◽  
Global Navigation Satellite Systems ◽  
Single Frequency ◽  
Tropospheric Delay ◽  
Global Networks ◽  
Numerical Weather ◽  
Station Coordinates ◽  
Nwp Model

<p>Electromagnetic signals, as broadcasted by Global Navigation Satellite Systems (GNSS), are delayed when travelling through the Earth’s atmosphere due to the presence of water vapour. Parametrisations of this delay, most prominently the Zenith Total Delay (ZTD) parameter, have been studied extensively and proven to provide substantial benefits for atmospheric research and especially the Numerical Weather Prediction (NWP) model performance. Typically, regional/global networks of static reference stations are utilized to derive ZTD along with other parameters of interest in GNSS analysis (e.g. station coordinates). Results are typically used as a contributing data source for determining the initial conditions of NWP models, a process referred to as Data Assimilation (DA).</p><p>This contribution goes beyond the approach outlined above as it shows how reasonable tropospheric parameters can be derived from highly kinematic, single-frequency (SF) GNSS data. The utilized data was gathered at trains by the Austrian Federal Railways (ÖBB) and processed using the Precise Point Positioning (PPP) technique. Although the special nature of the observations yields several challenges concerning data processing, we show that reasonable results for ZTD estimates can be obtained for all four analysed test cases by using different PPP processing strategies. Comparison with ZTD calculated from ERA5 reanalysis data yields a very high correlation and an overall agreement from the low millimetre-range up to 5 cm, depending on solution and analysed travelling track. We also present the first tests of assimilation into a numerical weather prediction (NWP) model which show the reasonable quality of the results as between 30-100 % of the observations are accepted by the model. Furthermore, we investigate means to transfer the developed ideas to an operational stage in order to exploit the huge benefits (horizontal/temporal resolution) of this special dataset for operational weather forecasting. </p>

scholarly journals Toward a variational assimilation of polarimetric radar observations in a convective-scale numerical weather prediction (NWP) model

2020 ◽  
Vol 13 (5) ◽  
pp. 2279-2298
Author(s):  
Guillaume Thomas ◽  
Jean-François Mahfouf ◽  
Thibaut Montmerle
Keyword(s):  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Numerical Weather ◽  
Observation Operator ◽  
Weather Radars ◽  
Convective Scale ◽  
The Difference ◽  
Input Variables ◽  
Nwp Model

Abstract. This paper presents the potential of nonlinear and linear versions of an observation operator for simulating polarimetric variables observed by weather radars. These variables, deduced from the horizontally and vertically polarized backscattered radiations, give information about the shape, the phase and the distributions of hydrometeors. Different studies in observation space are presented as a first step toward their inclusion in a variational data assimilation context, which is not treated here. Input variables are prognostic variables forecasted by the AROME-France numerical weather prediction (NWP) model at convective scale, including liquid and solid hydrometeor contents. A nonlinear observation operator, based on the T-matrix method, allows us to simulate the horizontal and the vertical reflectivities (ZHH and ZVV), the differential reflectivity ZDR, the specific differential phase KDP and the co-polar correlation coefficient ρHV. To assess the uncertainty of such simulations, perturbations have been applied to input parameters of the operator, such as dielectric constant, shape and orientation of the scatterers. Statistics of innovations, defined by the difference between simulated and observed values, are then performed. After some specific filtering procedures, shapes close to a Gaussian distribution have been found for both reflectivities and for ZDR, contrary to KDP and ρHV. A linearized version of this observation operator has been obtained by its Jacobian matrix estimated with the finite difference method. This step allows us to study the sensitivity of polarimetric variables to hydrometeor content perturbations, in the model geometry as well as in the radar one. The polarimetric variables ZHH and ZDR appear to be good candidates for hydrometeor initialization, while KDP seems to be useful only for rain contents. Due to the weak sensitivity of ρHV, its use in data assimilation is expected to be very challenging.

Improving Numerical Weather Prediction-Based Near-Cloud Aviation Turbulence Forecasts by Diagnosing Convective Gravity Wave Breaking

2021 ◽  
Author(s):  
Soo-Hyun Kim ◽  
Hye-Yeong Chun ◽  
Dan-Bi Lee ◽  
Jung-Hoon Kim ◽  
Robert D. Sharman
Keyword(s):  
Gravity Wave ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Area Under The Curve ◽  
Wave Drag ◽  
Probability Of Detection ◽  
Numerical Weather ◽  
Air Turbulence ◽  
One Year ◽  
Nwp Model

AbstractBased on a convective gravity wave drag parameterization scheme in a Numerical Weather Prediction (NWP) model, previously proposed near-cloud turbulence (NCT) diagnostics for better detecting turbulence near convection are tested and evaluated by using global in situ flight data and outputs from operational global NWP model of the Korea Meteorological Administration for one year (from December 2016 to November 2017). For comparison, eleven widely used clear air turbulence (CAT) diagnostics currently used in operational NWP-based aviation turbulence forecasting systems are separately computed. For selected cases, NCT diagnostics predict more accurately localized turbulence events over convective regions with better intensity, which is clearly distinguished from the turbulence areas diagnosed by conventional CAT diagnostics that they mostly failed to forecast with broad areas and low magnitudes. Although overall performance of NCT diagnostics for whole one year is lower than conventional CAT diagnostics due to the fact that NCT diagnostics exclusively focus on the isolated NCT events, adding the NCT diagnostics to CAT diagnostics improves the performance of aviation turbulence forecasting. Especially in the summertime, performance in terms of an area under the curve (AUC) based on probability of detection statistics is the best (AUC = 0.837 with a 4% increase, compared to conventional CAT forecasts) when the mean of all CAT and NCT diagnostics is used, while performance in terms of root mean square error is the best when the maximum among combined CAT and single NCT diagnostic is used. This implies that including NCT diagnostics to currently used NWP-based aviation turbulence forecasting systems should be beneficial for safety of air travel.

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