Suchergebnisse

Abstract. The article reports on the impact of the assimilation of wind vertical
profile data in a kilometre-scale NWP system on predicting heavy
precipitation events in the north-western Mediterranean area. The data
collected in diverse conditions by the airborne W-band radar RASTA (Radar
Airborne System Tool for Atmosphere) during a 45-day period are assimilated
in the 3 h 3DVAR assimilation system of AROME. The impact of the length of
the assimilation window is investigated. The data assimilation experiments
are performed for a heavy rainfall event, which occurred over south-eastern
France on 26 September 2012 (IOP7a) and over a 45-day cycled period. Results
indicate that the quality of the rainfall accumulation forecasts increases
with the length of the assimilation window, which recommends using
observations with a large period centred on the assimilation time. The
positive impact of the assimilation of RASTA wind data is particularly
evidenced for the IOP7a case since results indicate an improvement in the
predicted wind at short-term ranges (2 and 3 h) and in the 11 h
precipitation forecasts. However, in the 45-day cycled period, the comparison
against other assimilated observations shows an overall neutral impact.
Results are still encouraging since a slight positive improvement in the 5,
8 and 11 h precipitation forecasts was demonstrated.

Abstract. This article investigates the potential of W-band radar reflectivity to
improve the quality of analyses and forecasts of heavy precipitation events
in the Mediterranean area. The "1D+3DVar" assimilation method,
operationally employed to assimilate ground-based precipitation radar data in
the Météo-France kilometre-scale numerical weather prediction (NWP)
model AROME, has been adapted to assimilate the W-band reflectivity measured
by the airborne cloud radar RASTA (Radar Airborne System Tool for Atmosphere)
during a 2-month period over the Mediterranean area. After applying a bias
correction, vertical profiles of relative humidity are first derived via a
1-D Bayesian retrieval, and
then used as relative humidity pseudo-observations in the 3DVar assimilation
system of AROME. The efficiency of the 1-D Bayesian method in retrieving
humidity fields is assessed using independent in-flight humidity
measurements. To complement this study, the benefit brought by consistent
thermodynamic and dynamic cloud conditions has been investigated by
separately and jointly assimilating the W-band reflectivity and horizontal
wind measurements collected by RASTA in the 3 h 3DVar assimilation system of
AROME. The data assimilation experiments are conducted for a single heavy precipitation event
and then also for 32 cases. Results indicate that the W-band reflectivity has a larger
impact on the humidity, temperature and pressure fields in the analyses compared to the
assimilation of RASTA wind data alone. Besides, the analyses get closer to independent
humidity observations if the W-band reflectivity is assimilated alone or jointly with
RASTA wind data. Nonetheless, the impact of the W-band reflectivity decreases more
rapidly as the forecast range increases when compared to the assimilation of RASTA wind
data alone. Generally, the joint assimilation of the W-band reflectivity with wind data
results in the best improvement in the rainfall precipitation forecasts. Consequently,
results of this study indicate that consistent thermodynamic and dynamic cloud conditions
in the analysis leads to an improvement of both model initial conditions and forecasts.
Even though to a lesser extent, the assimilation of the W-band reflectivity alone also
results in a slight improvement of the rainfall precipitation forecasts.

© The Author(s), 2017. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Surveys in Geophysics 38 (2017): 1529–1568, doi:10.1007/s10712-017-9428-0. ; Trade-wind cumuli constitute the cloud type with the highest frequency of occurrence on Earth, and it has been shown that their sensitivity to changing environmental conditions will critically influence the magnitude and pace of future global warming. Research over the last decade has pointed out the importance of the interplay between clouds, convection and circulation in controling this sensitivity. Numerical models represent this interplay in diverse ways, which translates into different responses of trade-cumuli to climate perturbations. Climate models predict that the area covered by shallow cumuli at cloud base is very sensitive to changes in environmental conditions, while process models suggest the opposite. To understand and resolve this contradiction, we propose to organize a field campaign aimed at quantifying the physical properties of trade-cumuli (e.g., cloud fraction and water content) as a function of the large-scale environment. Beyond a better understanding of clouds-circulation coupling processes, the campaign will provide a reference data set that may be used as a benchmark for advancing the modelling and the satellite remote sensing of clouds and circulation. It will also be an opportunity for complementary investigations such as evaluating model convective parameterizations or studying the role of ocean mesoscale eddies in air–sea interactions and convective organization. ; The EUREC4A project is supported by the European Research Council (ERC), under the European Union's Horizon 2020 research and innovation programme (Grant Agreement No. 694768), by the Max Planck Society and by DFG (Deutsche Forschungsgemeinschaft, German Research Foundation) Priority Program SPP 1294.