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The recent development of satellite products for observing precipitation based on different technologies (microwaves, infrared, etc.) allows for near-real-time meteorological studies. The purpose of this article is to evaluate 11 satellite products (GHE, PDIR, IMERG-Early v6, IMERG-Late v6, CMORPH v0.x, CMORPH-RT v0.x, GSMaP-NRT v7, GSMaP-NRT-GC v7, GSMaP-NOW v7, GSMaP-NOW-GC v7, and DATABOURG) currently available and compare them to 2 ground-based radar networks (PANTHERE and OPERA) and the French rain-gauge network RADOME. Two case studies of intense precipitation over France (22 to 25 April 2022 and 24 to 29 June 2022) were selected. The radar estimations are closer to the RADOME observations than the satellite-based estimations, which tend to globally underestimate the precipitation amounts over the areas of interest while OPERA tends to strongly overestimate precipitation amounts during the June case study. The PANTHERE radar product and the carrier-to-noise product DATABOURG shows promising results. Near-real-time satellite products tend to have closer precipitation amounts to the reference dataset than satellite products with a shorter latency. The use of these datasets for nowcasting developments is plausible but further analyses must be conducted beforehand. [ABSTRACT FROM AUTHOR]

In this work, Advanced SCATterometer (ASCAT) backscatter data are directly assimilated into the interactions between soil, biosphere, and atmosphere (ISBA) land surface model using Meteo-France's global Land Data Assimilation System (LDAS-Monde) tool in order to jointly analyse soil moisture and leaf area index (LAI). For the first time, observation operators based on neural networks (NNs) are trained with ISBA simulations and LAI observations from the PROBA-V satellite to predict the ASCAT backscatter signal. The trained NN-based observation operators are implemented in LDAS-Monde, which allows the sequential assimilation of backscatter observations. The impact of the assimilation is evaluated over southwestern France. The simulated and analysed backscatter signal, surface soil moisture, and LAI are evaluated using satellite observations from ASCAT and PROBA-V as well as in situ soil moisture observations. An overall improvement in the variables is observed when comparing the analysis with the open-loop simulation. The impact of the assimilation is greater over agricultural areas. [ABSTRACT FROM AUTHOR]

Cloud radars are capable of providing continuous high-resolution observations of the cloud. These observations are related to the microphysical properties of clouds. Power law relations in the form of Z = a · LWCb are generally used to estimate liquid water content (LWC) profiles. The constants a and b from the power-law relation vary with the cloud type and cloud characteristics. Due to the variety of such parameterizations, selecting the most appropriate Z-LWC relation for a continuous cloud system is complicated. Additional information such as Liquid water path (LWP) from a co-located microwave radiometer is used to scale the LWC of the cloud profile. An algorithm for estimating the LWC of warm clouds using radar-microwave radiometer synergy in a variational framework is presented. This method also accounts for attenuation due to cloud droplets and retrieves a suitable scaling factor (lna) of the profile in addition to the LWC. The optimal estimation techniques incorporate a priori information of desired variables, and the forward model converts these variables into observation parameters. In this algorithm formulation, the measure of uncertainty in observations, forward model and, a priori acts as weights in the retrieved quantities. These uncertainties in the retrieval are analyzed in the sensitivity analysis of the algorithm. The retrieval algorithm is first tested on a synthetic profile for different perturbations in sensitivity parameters. The sensitivity study has shown that this method is susceptible to LWP information. The algorithm is then implemented to various cloud and fog cases at SIRTA observatory to estimate LWC and the scaling factor. The scaling factor changes for each cloud profile, and the range of lna are consistent with suggested values in literature. The validation of such an algorithm is challenging, as we need reference measurements of LWC co-located with the retrieved values. During the SOFOG-3D campaign (South-West of France, October 2019 to March 2020), in-situ measurements of LWC were collected in the vicinity of a cloud radar and a microwave radiometer, allowing comparison of retrieved and measured LWC. The comparison demonstrated that the cloud-fog heterogeneity was playing a key role in the assessment. The proposed synergistic retrieval algorithm is applied to 39 cloud and fog cases at SIRTA, and the behavior of the scaling factor is studied. This statistical analysis of scaling is carried out to develop a radar-only retrieval method. The climatology revealed that the scaling factor can be linked to the maximum reflectivity of the profile. From climatology, the statistical relations for scaling factor are proposed for fog and cloud. Thanks to the variational framework, a stand-alone radar version of the algorithm is adapted from the synergistic retrieval algorithm, which incorporates the climatology of scaling factor as a priori information to estimate the LWC of warm cloud. [ABSTRACT FROM AUTHOR]