The international community has definitively recognized the urgent need for a holistic understanding of land systems in the context of Global Change. Our approach to the integration of climate information in the assessment of sustainability and land degradation vulnerability is based on the analysis of climate data within the geographical constraints imposed by the land cover heterogeneity. This study uses the CHIRPS dataset (Climate Hazards Group InfraRed Precipitation with Station data) to evaluate potential rainfall impacts in Basilicata (Italy), a very complex Mediterranean area. Two indices, accounting for rainfall erosivity and, by contrast, for the local degree of dryness are integrated in a unique rainfall exposition layer. For each land cover, this information layer enables us to detect the areas most exposed to rainfall erosion risk or to water scarcity and drought. This layer can be profitably used within multivariate analyses for the estimation of land degradation vulnerability.

This study proposes an Artificial Neural Network (ANN) algorithm for downscaling weather forecasts of some variables useful for agriculture in Southern Italy. Using the Weather Research and Forecasting (WRF) model at 1.2 km spatial resolution, the algorithm performs downscaling at 240 m resolution using an operation similar to bilinear interpolation, but with enhanced performance. To train the ANNs, a database was built using the WRF model in Large Eddy Simulation (LES) mode with 240 m grid spacing. Particular attention was paid to defining the architecture of the ANNs and selecting the inputs. The comparison of the algorithm’s performance against spline interpolation shows a reduction of the mean squared error (MSE) ranging from a minimum of 6% for solar irradiance to a maximum of 87% for surface pressure.

This study proposes an Artificial Neural Network approach for the detection of optically thin cirrus using observations from the Infrared Atmospheric Sounding Interferometer - New Generation (IASI-NG) and from its predecessor, IASI. The Thin Cirrus Detection Algorithm applies a Feedforward Neural Network (NN) to IASI/IASI-NG samples previously declared as clear by a cloud detection algorithm. The NN training, test and validation datasets are generated from a set of ECMWF 5-generation reanalysis (ERA5) processed with the σ-IASI radiative transfer model to simulate IASI/IASI-NG radiances. The IASI and IASI-NG Thin Cirrus detection algorithms were validated against an independent dataset showing better performances for the IASI-NG thin-cirrus-detection algorithm. Moreover, IASI thin-cirrus-detection algorithm outputs were compared against Cloudsat/CPR and SEVIRI cloud products, showing good probability of detection: 0.84 for SEVIRI and 0.77 for CPR/Cloudsat.

The identification of optically thin cirrus is crucial for their accurate parameterization in climate and Earth’s system models. This study exploits the characteristics of the infrared atmospheric sounding interferometer—new generation (IASI-NG) to develop an algorithm for the detection of optically thin cirrus. IASI-NG has been designed for the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) polar system second-generation program to continue the service of its predecessor IASI from 2024 onward. A thin-cirrus detection algorithm (TCDA) is presented here, as developed for IASI-NG, but also in parallel for IASI to evaluate its performance on currently available real observations. TCDA uses a feedforward neural network (NN) approach to detect thin cirrus eventually misidentified as clear sky by a previously applied cloud detection algorithm. TCDA also estimates the uncertainty of “clear-sky” or “thin-cirrus” detection. NN is trained and tested on a dataset of IASI-NG (or IASI) simulations obtained by processing ECMWF 5-generation reanalysis (ERA5) data with the$\sigma $-IASI radiative transfer model. TCDA validation against an independent simulated dataset provides a quantitative statistical assessment of the improvements brought by IASI-NG with respect to IASI. In fact, IASI-NG TCDA outperforms IASI TCDA by 3% in probability of detection (POD), 1% in bias, and 2% in accuracy, and the false alarm ratio (FAR) passes from 0.02 to 0.01. Moreover, IASI TCDA validation against state-of-the-art cloud products from Cloudsat/CPR and CALIPSO/Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) real observations reveals a tendency for IASI TCDA to underestimate the presence of thin cirrus (POD = 0.47) but with a low FAR (0.07), which drops to 0.0 for very thin cirrus.

Sun-tracking microwave radiometry (STMW) is a ground-based technique where the Sun is used as a beacon source to infer the atmospheric path attenuation in all-weather conditions. STMW shows an appealing potential for overcoming the difficulties to perform satellite-to-Earth radiopropagation experiments in the unexplored millimeterwave and submillimeter-wave frequency region, especially where experimental data from a beacon receiver are not available. The theoretical framework and the ad hoc procedures and data processing developed in the last 5 years will be presented, together with the estimate of the overall error budget. The application and experimental challenges during long-term deployments, such as the field campaign of the W-band radiometric study (WRad) funded by the European Space Agency (ESA), will be reviewed.

The Advanced Technology Microwave Sounder (ATMS) is a cross-track scanning microwave (MW) radiometer useful to retrieve Temperature (T) and Water Vapor (WV) atmospheric vertical profiles. Using spatial and temporal coincidences between ATMS observations and two different datasets of vertical T and WV, a global training dataset for machine learning purpose was built for the whole 2016. For each ATMS Brightness Temperatures acquisition, 32 levels of T (between 10 and 1000 hPa), and 23 levels of WV (between 200 and 1000 hPa), are used to train an algorithm based on Random Forests regression technique. A single RF was trained for each level and atmospheric variable, using the evaluation on the Out of Bag error to optimize the number of random selection of the input variables at each node splitting step, the number of trees in each forest and the minimum leaf size parameter, to avoid overfitting problem and obtain an accurate retrieval. Considering that the sounding below the precipitation level becomes unreliable, the precipitation-affected observations were removed from the training dataset by means of a pre-screening test based on BT. The results show an overall ability of the algorithm to retrieve T and WV vertical profiles in line with expectations.

The extraordinary rainfall event that affected the center of Italy on 9thand 10thSeptember 2017 was studied by examining the synoptic analysis, radar network and rain gauges' measurements. The main precipitation event took place in the area around Livorno, where more than 200 mm of precipitation was recorded in 24 hours. The case study is analyzed using Weather Research and Forecasting (WRF) model and two algorithms based on satellite observations: the Rain Class Evaluation from Infrared and Visible observation (RainCEIV) technique and the cloud Classification Mask Coupling of Statistical and Physics Methods (C-MACSP). The analysis shows that WRF is able to forecast the event, though with errors in actual structure, location, and time. For this reason, the combined use of different observational tools could support the WRF simulation to provide a better characterization of the event.

A new cloud dynamics and radiation database (CDRD) precipitation retrieval algorithm for satellite passive microwave (PMW) radiometer measurements has been developed. It represents a modification to and an improvement upon the conventional cloud radiation database (CRD) algorithms, which have always been prone to ambiguity. This part 2 paper of a series describes the methodology of the algorithm and the modeling verification analysis involved in creating a synthetic CDRD database for the Europe/Mediterranean basin region. This is followed by a proof-of-concept analysis, which demonstrates that the underlying CDRD theory based on use of meteorological parameters for reducing retrieval ambiguity is valid. This paper uses a regional/mesoscale model, applied in cloud resolving model (CRM) mode, to produce a large set of numerical simulations of precipitating storms and extended precipitating systems. The simulations are used for selection of millions of meteorological/microphysical vertical profiles within which surface rainfall is identified. For each of these profiles, top-of-atmosphere brightness temperature (TB) vectors are calculated (the vector dimension associated with the number of relevant cm-mm wavelengths and polarizations), based on an elaborate radiative-transfer equation (RTE) model system (RMS) coupled to the CRM. This entire body of simulation information is organized into the CDRD database, then used as a priori knowledge to guide a physical Bayesian retrieval algorithm in obtaining rainfall and associated precipitation parameters from the PMW satellite observations. We first prove the physical validity of our CRM-RMS simulations, by showing that the simulated TBs are in close agreement with observations. Agreement is demonstrated using dual-channel-frequency TB manifold sections, which quantify the degree of overlap between the simulated and observed TBs extracted from the full manifolds. Nevertheless, the salient result of this paper is a proof that the underlying CDRD theory is valid, found by combining subdivisions of the invoked meteorological parameter ranges of values and showing that such meteorological partitioning associates itself with distinct microphysical profiles. It is then shown that these profiles give rise to similar TB vectors, proving the existence of ambiguity in a CRD-type algorithm. Finally, we show that the CDRD methodology provides significant improvements in reducing retrieval ambiguity and retrieval error, especially for land surface backgrounds where contrasts are typically small between the rainfall TB signatures and surface emission signatures.