Szczegóły publikacji
Opis bibliograficzny
1D-Var retrieval of temperature and humidity using GNSS airborne radio occultation data : [poster] / Paweł Hordyniec, Aleksandra MACIEJEWSKA // W: 2026 AR Recon workshop and 2nd observational campaigns workshop for better weather forecasts [Dokument elektroniczny] : 29 June – 3 July 2026, [Reading, United Kingdom]. — Wersja do Windows. — Dane tekstowe. — [UK : ECMWF], [2026]. — S. [1]. — Wymagania systemowe: Adobe Reader. — Tryb dostępu: https://s.agh.edu.pl/0WGSq [2026-07-20]. — Abstr.
Autorzy (2)
- Hordyniec Paweł
- AGHMaciejewska Aleksandra
Dane bibliometryczne
| ID BaDAP | 169180 |
|---|---|
| Data dodania do BaDAP | 2026-09-07 |
| Rok publikacji | 2026 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
Abstract
Atmospheric rivers (ARs) are major contributors to extreme precipitation and hydrological hazards, yet their representation in numerical weather prediction remains limited by sparse observations over oceanic regions. The Atmospheric River Reconnaissance (AR Recon) program addresses this gap by deploying targeted aircraft and buoy observations ahead of landfalling ARs along the U.S. West Coast. The campaigns enable simultaneous observations of ARs using dropsondes and complementary profiles from GNSS airborne radio occultation (ARO), both of which can improve the analysis of AR environments. The value of ARO in data assimilation has recently become the subject of extensive study, driven by the development of advanced forward models for ARO observations. This work builds on ongoing efforts to quantify the impact of ARO data on atmospheric analyses through the use of a one-dimensional variational (1D-Var) retrieval. By combining ARO observations with background information, the most probable profiles of temperature and humidity are estimated which otherwise cannot be directly observed with ARO. This approach broadens the applicability of ARO data by providing an independent benchmark for evaluating model background states and conventional observations. Operational systems can utilize 1D-Var for initial impact assessment and quality control screening prior to assimilation with 3D- or 4D-Var schemes. It is shown that background profiles derived from the ECMWF ERA5 reanalysis can be further improved through the inclusion of ARO data in 1D-Var retrieval. The impact on the temperature analysis is quantified to be on the order of 0.5 K in terms of analysis-minus-background statistics. Additional sensitivity experiments are conducted to assess the influence of different processing configurations, including observational error models, error covariances and minimization strategies. Particular attention is given to the ability to capture sharp vertical gradients associated with potential inversion layers identified from retrieved thermodynamic profiles.
