Dynamic surface-pressure changes and their significance for weather forecasting

1954 ◽  
Vol 4 (3) ◽  
pp. 350-370 ◽  
Author(s):  
Stanislav Brandejs ◽  
Jaroslav Kopáček ◽  
Vojtěch Vítek ◽  
Otakar Zikmunda
Keyword(s):  
Surface Pressure ◽  
Weather Forecasting ◽  
Dynamic Surface ◽  
Pressure Changes

Dynamic Surface Pressure Based Estimation for Flow Control

2008 ◽  
pp. 183-189 ◽  
Author(s):  
Lawrence Ukeiley ◽  
Nathan Murray ◽  
Qi Song ◽  
Louis Cattafesta
Keyword(s):  
Flow Control ◽  
Surface Pressure ◽  
Dynamic Surface

Full 3-D Unsteady Numerical Simulation of Control Stage in Partial Admission Turbine

2015 ◽  
Vol 741 ◽  
pp. 509-512
Author(s):  
Guo Ping Li ◽  
Ke Ke Gao ◽  
Ke Yang ◽  
Yong Hui Xie
Keyword(s):  
Numerical Simulation ◽  
Surface Pressure ◽  
Flow Parameters ◽  
Control Stage ◽  
Air Turbine ◽  
Unsteady Surface Pressure ◽  
Partial Admission ◽  
Unsteady Numerical Simulation ◽  
Pressure Changes ◽  
Mixing Losses

The unsteady flow parameters in control stage of partial admission are analyzed in details with full 3-D numerical simulation. The full annulus structure of air turbine in partial admission is modeled due to the unsymmetrical geometry. The partial admission is accomplished through the inlet blocked using segmental arc. The unsteady surface pressure changes of eight blades in the transition regions which demonstrate the power output ability are presented. That the entropy rise associated with the losses at different cross mainly caused by mixing losses and flow separation in partial admission is analyzed to estimate the efficiency distribution.

Dynamic surface-pressure instrumentation for rods in parallel flow

1982 ◽  
Vol 22 (1) ◽  
pp. 31-36 ◽  
Author(s):  
T. M. Mulcahy ◽  
W. Lawrence ◽  
M. W. Wambsganss
Keyword(s):  
Surface Pressure ◽  
Parallel Flow ◽  
Dynamic Surface

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 %.

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