Suchergebnisse

Abstract. This study was performed in the framework of HyMeX (Hydrological cycle in the Mediterranean Experiment), which aimed to study the heavy precipitation that regularly affects the Mediterranean area. A reanalysis with a convective-scale model AROME-WMED (Application of Research to Operations at MEsoscale western Mediterranean) was performed, which assimilated most of the available data for a 2-month period corresponding to the first special observation period of the field campaign (Fourrié et al., 2019). Among them, observations related to the low-level humidity flow were assimilated. Such observations are important for the description of the feeding of the convective mesoscale systems with humidity (Duffourg and Ducrocq, 2011; Bresson et al., 2012; Ricard et al., 2012). Among them there were a dense reprocessed network of high-quality Global Navigation Satellite System (GNSS) zenithal total delay (ZTD) observations, reprocessed data from wind profilers, lidar-derived vertical profiles of humidity (ground and airborne) and Spanish radar data. The aim of the paper is to assess the impact of the assimilation of these four observation types on the analyses and the forecasts from the 3 h forecast range (first guess) up to the 48 h forecast range. In order to assess this impact, several observing system experiments (OSEs) or so-called denial experiments, were carried out by removing one single data set from the observation data set assimilated in the reanalysis. Among the evaluated observations, it is found that the ground-based GNSS ZTD data set provides the largest impact on the analyses and the forecasts, as it represents an evenly spread and frequent data set providing information at each analysis time over the AROME-WMED domain. The impact of the reprocessing of GNSS ZTD data also improves the forecast quality, but this impact is not statistically significant. The assimilation of the Spanish radar data improves the 3 h precipitation forecast quality as well as the short-term (30 h) precipitation forecasts, but this impact remains located over Spain. Moreover, marginal impact from wind profilers was observed on wind background quality. No impacts have been found regarding lidar data, as they represent a very small data set, mainly located over the sea.

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.