Estimation of 3D wet refractivity by tomography, combining GNSS and NWP data: First results from assimilation of wet refractivity into NWP
PBN-AR
Instytucja
Wydział Inżynierii Kształtowania Środowiska i Geodezji (Uniwersytet Przyrodniczy we Wrocławiu)
Informacje podstawowe
Główny język publikacji
angielski
Czasopismo
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY
ISSN
0035-9009
EISSN
1477-870X
Wydawca
WILEY-BLACKWELL
DOI
URL
Rok publikacji
2019
Numer zeszytu
720
Strony od-do
1034-1051
Numer tomu
145
Identyfikator DOI
Liczba arkuszy
Autorzy
(liczba autorów: 2)
Słowa kluczowe
angielski
data assimilation
GNSS meteorology
GNSS troposphere tomographyn
umerical weather prediction models
Streszczenia
Język
angielski
Treść
The magnitude of water-vapour content and its temporal variability are factors that influence the thermodynamics of the atmosphere significantly and result in different meteorological phenomena or hazards. High-quality observations of water-vapour spatial and temporal distribution enable precise weather forecasts to be made. Global Navigation Satellite System (GNSS) troposphere tomography is a technique that enables derivation of a three-dimensional (3D) distribution of the wet refractivity with low cost in all weather conditions, based on GNSS slant observations of tropospheric delay. The tomographic estimations of the wet refractivity distribution have the potential to improve numerical weather prediction (NWP) models. In this study, we established a near-real-time (NRT) tomographic solution in the area of Poland using the TOMO2 model in order to verify whether tomographic products can attain the required accuracy and be assimilated into operational NWP models. The assimilation of the TOMO2 output into a weather research and forecasting (WRF) model was performed, using the WRF Data Assimilation (WRFDA) system and a GPSREF observation operator dedicated to radio occultation (RO) total refractivity assimilation. Two selected analysis periods covered summer storms and autumn rainfalls. The validation of the WRF model analysis with GNSS integrated water vapour (IWV) data, synoptic observations, radiosonde profiles, and ERA-Interim reanalysis indicated an improvement in the relative humidity in the top tropospheric layers (the bias decreased by 1.4–4.6% and the standard deviation by 0.8–2.8%). In the middle troposphere, a positive impact was noticed in the summer (the standard deviation of the relative humidity decreased by 0.15%) but not in the autumn. The forecast at lead times of 6–18 hr was visibly improved in the autumn (reduction of root-mean-square error (RMSE) by 0.5% in relative humidity and 0.25 °C in temperature, reduction in standard deviation of surface pressure by 0.5 hPa), while in the summer the results were neutral or negative (RMSE of relative humidity increased by 1.0%).
Inne
System-identifier
PX-5cf4cd53d5debaf09943f04a
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