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 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 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.

Assimilation of Satellite Cloud and Precipitation Observations in Numerical Weather Prediction Models: Introduction to the JAS Special Collection

2007 ◽  
Vol 64 (11) ◽  
pp. 3737-3741 ◽  
Author(s):  
Ronald M. Errico ◽  
George Ohring ◽  
Fuzhong Weng ◽  
Peter Bauer ◽  
Brad Ferrier ◽  
...  
Keyword(s):  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Weather Forecasting ◽  
International Workshop ◽  
Weather Prediction ◽  
Satellite Observations ◽  
Atmospheric Sciences ◽  
Numerical Weather ◽  
Special Collection ◽  
Numerical Weather Prediction Models

Abstract To date, the assimilation of satellite measurements in numerical weather prediction (NWP) models has focused on the clear atmosphere. But satellite observations in the visible, infrared, and microwave provide a great deal of information on clouds and precipitation. This special collection describes how to use this information to initialize clouds and precipitation in models. Since clouds and precipitation often occur in sensitive regions for forecast impacts, such improvements are likely necessary for continuing to acquire significant gains in weather forecasting. This special collection of the Journal of the Atmospheric Sciences is devoted to articles based on papers presented at the International Workshop on Assimilation of Satellite Cloud and Precipitation Observations in Numerical Weather Prediction Models, in Lansdowne, Virginia, in May 2005. This introduction summarizes the findings of the workshop. The special collection includes review articles on satellite observations of clouds and precipitation (Stephens and Kummerow), parameterizations of clouds and precipitation in NWP models (Lopez), radiative transfer in cloudy/precipitating atmospheres (Weng), and assimilation of cloud and precipitation observations (Errico et al.), as well as research papers on these topics.

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 On Producing Reliable and Affordable Numerical Weather Forecasts on Public Cloud-Computing Infrastructure

2019 ◽  
Vol 36 (3) ◽  
pp. 491-509 ◽  
Author(s):  
Timothy C. Y. Chui ◽  
David Siuta ◽  
Gregory West ◽  
Henryk Modzelewski ◽  
Roland Schigas ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Real Time ◽  
Virtual Machines ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Wrf Model ◽  
West African Monsoon ◽  
Computer Hardware ◽  
Potential Cost ◽  
Numerical Weather

AbstractCloud-computing resources are increasingly used in atmospheric research and real-time weather forecasting. The aim of this study is to explore new ways to reduce cloud-computing costs for real-time numerical weather prediction (NWP). One way is to compress output files to reduce data egress costs. File compression techniques can reduce data egress costs by over 50%. Data egress costs can be further minimized by postprocessing in the cloud and then exporting the smaller resulting files while discarding the bulk of the raw NWP output. Another way to reduce costs is to use preemptible resources, which are virtual machines (VMs) on the Google Cloud Platform (GCP) that clients can use at an 80% discount (compared to nonpreemptible VMs), but which can be turned off by the GCP without warning. By leveraging the restart functionality in the Weather Research and Forecasting (WRF) Model, preemptible resources can be used to save 60%–70% in weather simulation costs without compromising output reliability. The potential cost savings are demonstrated in forecasts over the Canadian Arctic and in a case study of NWP runs for the West African monsoon (WAM) of 2017. The choice in model physics, VM specification, and use of the aforementioned cost-saving measures enable simulation costs to be low enough such that the cloud can be a viable platform for running short-range ensemble forecasts when compared to the cost of purchasing new computer hardware.

One Year of Operational Numerical Weather Prediction, Part II *

1957 ◽  
Vol 38 (6) ◽  
pp. 315-328 ◽  
Keyword(s):  
Numerical Weather Prediction ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Numerical Weather ◽  
Level Data ◽  
Analysis Center ◽  
Numerical Weather Forecasting ◽  
One Year ◽  
Near Future

This is the second of two brief reports on the activities and results of the Joint Numerical Weather Prediction Unit since May 1955, and is concerned primarily with the accuracy and characteristic errors of the numerical forecasts described in the previous report. The quality of the barotropic and 3-level forecasts has been measured by several statistical indices of error, and compared with that of the subjective forecasts issued by the National Weather Analysis Center. A breakdown of these statistics shows the dependence of forecasting accuracy on length of forecast period, level, data coverage, and proximity of lateral boundaries. Various sources of systematic error are discussed with reference to the JNWP Unit's efforts to isolate and remedy them. After almost a year of experimentation and operational numerical weather forecasting, it is concluded that the quality of the numerical 500 millibar forecasts is not significantly different from that of the best subjective forecasts prepared by methods in current use. Recent results indicate that a significant improvement can be expected in the near future. The numerical 1000 mb forecasts are worse, but recent changes of model show promise of matching the performance of subjective methods. Finally, the most glaring systematic errors of the present numerical forecasts have adequate explanation in existing theory, and can be (or have already been) corrected by generalization of the models.

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