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Accurate measurements of ice hydrometeors are required to improve the representation of clouds and precipitation in weather and climate models. In this study, a newly developed, synergistic retrieval algorithm that combines radar with passive millimeter and sub-millimeter observations is applied to observations of three frontally generated, mid-latitude cloud systems in order to validate the retrieval and assess its capabilities to constrain the properties of ice hydrometeors. To account for uncertainty in the assumed shapes of ice particles, the retrieval is run multiple times while the shape is varied. Good agreement with in situ measurements of ice water content and particle concentrations for particle maximum diameters larger than 200 µm is found for one of the flights for the large plate aggregate and the six-bullet rosette shapes. The variational retrieval fits the observations well, although small systematic deviations are observed for some of the sub-millimeter channels pointing towards issues with the sensor calibration or the modeling of gas absorption. For one of the flights the quality of the fit to the observations exhibits a weak dependency on the assumed ice particle shape, indicating that the employed combination of observations may provide limited information on the shape of ice particles in the observed clouds. Compared to a radar-only retrieval, the results show an improved sensitivity of the synergistic retrieval to the microphysical properties of ice hydrometeors at the base of the cloud. Our findings indicate that the synergy between active and passive microwave observations may improve remote-sensing measurements of ice hydrometeors and thus help to reduce uncertainties that affect currently available data products. Due to the increased sensitivity to their microphysical properties, the retrieval may also be a valuable tool to study ice hydrometeors in field campaigns. The good fits obtained to the observations increase confidence in the modeling of clouds in the Atmospheric Radiative Transfer Simulator and the corresponding single scattering database, which were used to implement the retrieval forward model. Our results demonstrate the suitability of these tools to produce realistic simulations for upcoming sub-millimeter sensors such as the Ice Cloud Image or the Arctic Weather Satellite.

To improve precipitation estimation accuracy, new methods, which are able to merge different precipitation measurement modalities, are necessary. In this study, we propose a deep learning method to merge rain gauge measurements with a ground-based radar composite and thermal infrared satellite imagery. The proposed convolutional neural network, composed of an encoder–decoder architecture, performs a multiscale analysis of the three input modalities to estimate simultaneously the rainfall probability and the precipitation rate value with a spatial resolution of 2 km. The training of our model and its performance evaluation are carried out on a dataset spanning 5 years from 2015 to 2019 and covering Belgium, the Netherlands, Germany and the North Sea. Our results for instantaneous precipitation detection, instantaneous precipitation rate estimation, and for daily rainfall accumulation estimation show that the best accuracy is obtained for the model combining all three modalities. The ablation study, done to compare every possible combination of the three modalities, shows that the combination of rain gauges measurements with radar data allows for a considerable increase in the accuracy of the precipitation estimation, and the addition of satellite imagery provides precipitation estimates where rain gauge and radar coverage are lacking. We also show that our multi-modal model significantly improves performance compared to the European radar composite product provided by OPERA and the quasi gauge-adjusted radar product RADOLAN provided by the DWD for precipitation rate estimation.

Applications like drought monitoring and forecasting can profit from the global and near real-time availability of satellite-based precipitation estimates once their related uncertainties and challenges are identified and treated. To this end, this study evaluates the IMERG V06B Late Run precipitation product from the Global Precipitation Measurement mission (GPM), a multi-satellite product that combines space-based radar, passive microwave (PMW), and infrared (IR) data into gridded precipitation estimates. The evaluation is performed on the spatiotemporal resolution of IMERG (0.1° × 0.1°, 30 min) over the Netherlands over a five-year period. A gauge-adjusted radar precipitation product from the Royal Netherlands Meteorological Institute (KNMI) is used as reference, against which IMERG shows a large positive bias. To find the origin of this systematic overestimation, the data is divided into seasons, rainfall intensity ranges, echo top height (ETH) ranges, and categories based on the relative contributions of IR, morphing, and PMW data to the IMERG estimates. Furthermore, the specific radiometer is identified for each PMW-based estimate. IMERG’s detection performance improves with higher ETH and rainfall intensity, but the associated error and relative bias increase as well. Severe overestimation occurs during low-intensity rainfall events and is especially linked to PMW observations. All individual PMW instruments show the same pattern: overestimation of low-intensity events and underestimation of high-intensity events. IMERG misses a large fraction of shallow rainfall events, which is amplified when IR data is included. Space-based retrieval of shallow and low-intensity precipitation events should improve before IMERG can be implemented over the middle and high-latitudes.

While it has been proved that multiple scattering in the microwave frequencies has to be accounted for in precipitation retrieval algorithms, the effects of the random arrangements of drops in space has seldom been investigated. The fact is, a single rain drop size distribution (RDSD) corresponds with many actual 3D distributions of those rain drops and each of those may a priori absorb and scatter radiation in a different way. Each spatial configuration is equivalent to any other in terms of the RDSD function, but not in terms of radiometric characteristics, both near and far from field, because of changes in the relative phases among the particles. Here, using the T-matrix formalism, we investigate the radiometric variability of two ensembles of 50 different 3D, stochastically-derived configurations from two consecutive measured RDSDs with 30 and 31 drops, respectively. The results show that the random distribution of drops in space has a measurable but apparently small effect in the scattering calculations with the exception of the asymmetry factor.

The purpose of this study is to describe a new algorithm based on a neural network approach (Passive microwave Neural network Precipitation Retrieval – PNPR) for precipitation rate estimation from AMSU/MHS observations, and to provide examples of its performance for specific case studies over the European/Mediterranean area. The algorithm optimally exploits the different characteristics of Advanced Microwave Sounding Unit-A (AMSU-A) and the Microwave Humidity Sounder (MHS) channels, and their combinations, including the brightness temperature (TB) differences of the 183.31 channels, with the goal of having a single neural network for different types of background surfaces (vegetated land, snow-covered surface, coast and ocean). The training of the neural network is based on the use of a cloud-radiation database, built from cloud-resolving model simulations coupled to a radiative transfer model, representative of the European and Mediterranean Basin precipitation climatology. The algorithm provides also the phase of the precipitation and a pixel-based confidence index for the evaluation of the reliability of the retrieval. Applied to different weather conditions in Europe, the algorithm shows good performance both in the identification of precipitation areas and in the retrieval of precipitation, which is particularly valuable over the extremely variable environmental and meteorological conditions of the region. The PNPR is particularly efficient in (1) screening and retrieval of precipitation over different background surfaces; (2) identification and retrieval of heavy rain for convective events; and (3) identification of precipitation over a cold/iced background, with increased uncertainties affecting light precipitation. In this paper, examples of good agreement of precipitation pattern and intensity with ground-based data (radar and rain gauges) are provided for four different case studies. The algorithm has been developed in order to be easily tailored to new radiometers as they become available (such as the cross-track scanning Suomi National Polar-orbiting Partnership (NPP) Advanced Technology Microwave Sounder (ATMS)), and it is suitable for operational use as it is computationally very efficient. PNPR has been recently extended for applications to the regions of Africa and the South Atlantic, and an extended validation over these regions (using 2 yr of data acquired by the Tropical Rainfall Measuring Mission precipitation radar for comparison) is the subject of a paper in preparation. The PNPR is currently used operationally within the EUMETSAT Hydrology Satellite Application Facility (H-SAF) to provide instantaneous precipitation from passive microwave cross-track scanning radiometers. It undergoes routinely thorough extensive validation over Europe carried out by the H-SAF Precipitation Products Validation Team.

The uncertainties associated with rainfall estimates comprise various measurement scales: from rain gauges and ground-based radars to the satellite rainfall retrievals. The quality of satellite rainfall products has improved significantly in recent decades, however, such algorithms require validation studies using observational rainfall data. For this reason, this study aims to apply the H-SAF consolidated radar data processing to the X-band radar used in the CHUVA campaigns and apply the well established H-SAF validation procedure to these data and verify the quality of EUMETSAT H-SAF operational passive microwave precipitation products in two regions of Brazil (Vale do Para&iacute, ba and Manaus). These products are based on two rainfall retrieval algorithms: the physically based Bayesian Cloud Dynamics and Radiation Database (CDRD algorithm) for SSMI/S sensors and the Passive microwave Neural network Precipitation Retrieval algorithm (PNPR) for cross-track scanning radiometers (AMSU-A/AMSU-B/MHS sensors) and for the ATMS sensor. These algorithms, optimized for Europe, Africa and the Southern Atlantic region, provide estimates for the MSG full disk area. Firstly, the radar data was treated with an overall quality index which includes corrections for different error sources like ground clutter, range distance, rain-induced attenuation, among others. Different polarimetric and non-polarimetric QPE algorithms have been tested and the Vulpiani algorithm (hereafter, R q 2 V u 15 ) presents the best precipitation retrievals when compared with independent rain gauges. Regarding the results from satellite-based algorithms, generally, all rainfall retrievals tend to detect a larger precipitation area than the ground-based radar and overestimate intense rain rates for the Manaus region. Such behavior is related to the fact that the environmental and meteorological conditions of the Amazon region are not well represented in the algorithms. Differently, for the Vale do Para&iacute, ba region, the precipitation patterns were well detected and the estimates are in accordance with the reference as indicated by the low mean bias values.

The Soil Moisture Active Passive (SMAP) mission was designed to provide global mapping of soil moisture (SM) on nested 3, 9, and 36 km earth grids measured by L-band passive and active microwave sensors. The validation of SMAP SM products is crucial for the application of the products and improvement of the retrieval algorithm. Since the SMAP SM products were released, much effort has been invested in the evaluation of the SMAP radiometer SM product (SMAP_P). However, there has been little validation of SMAP radar (SMAP_A) and active/passive combined (SMAP_AP) SM products. This paper presents an evaluation of SMAP_P, SMAP_A and SMAP_AP SM products by using distributed ground observations networks in different landscapes in the Heihe River Basin of northwestern China. The standard error metrics of SMAP products and relative error are applied to measure the products’ performances. The results show that the SMAP SM products exhibit consistent spatial-temporal variation with the ground measurements and typical precipitation events. Three products show various types of performance capability (e.g., active, passive and combined), surface coverage (e.g., bare, vegetated) and climatic region (e.g., cold, arid). Relatively, the SMAP_P shows the best performance, while the SMAP_A performs the worst. The best performances are observed over bare soils but with overestimation and the largest relative error, and unsatisfactory accuracies are observed over cold regions and woody vegetated surfaces with underestimation. The vegetation effect and the freezing-thawing cycle may be major factors that led to an unsatisfactory performance. Efforts on resolving the influence of these factors are expected to improve the accuracy and to promote the application of SMAP SM products over these regions. Overall, this evaluation provides an understanding of SMAP SM products over cold and arid regions, and suggestions for the further refinement of the SMAP SM retrieval algorithms.

This paper proposes a rain rate retrieval algorithm for conical-scanning microwave imagers (RAMARS), as an alternative to the NASA Goddard profiling (GPROF) algorithm, that does not rely on any a priori information. The fundamental basis of the RAMARS follows the concept of the GPROF algorithm, which means, being consistent with the Tropical Rainfall Measuring Mission (TRMM) precipitation radar rain rate observations, but independent of any auxiliary information. The RAMARS is built upon the combination of state-of-the-art machine learning and regression techniques, comprising of random forest algorithm, RReliefF, and multivariate adaptive regression splines. The RAMARS is applicable to both over ocean and land as well as coast surface terrains. It has been demonstrated that, when comparing with the TRMM Precipitation Radar observations, the performance of the RAMARS algorithm is comparable with the 2A12 GPROF algorithm. Furthermore, the RAMARS has been applied to two cyclonic cases, hurricane Sandy in 2012, and cyclone Mahasen in 2013, showing a very good capability to reproduce the structure and intensity of the cyclone fields. The RAMARS is highly flexible, because of its four processing components, making it extremely suitable for use to other passive microwave imagers in the global precipitation measurement (GPM) constellation.

The spatial error structure of daily precipitation derived from the latest version 7 (v7) tropical rainfall measuring mission (TRMM) level 2 data products are studied through comparison with the Asian precipitation highly resolved observational data integration toward evaluation of the water resources (APHRODITE) data over a subtropical region of the Indian subcontinent for the seasonal rainfall over 6 years from June 2002 to September 2007. The data products examined include v7 data from the TRMM radiometer Microwave Imager (TMI) and radar precipitation radar (PR), namely, 2A12, 2A25, and 2B31 (combined data from PR and TMI). The spatial distribution of uncertainty from these data products were quantified based on performance metrics derived from the contingency table. For the seasonal daily precipitation over a subtropical basin in India, the data product of 2A12 showed greater skill in detecting and quantifying the volume of rainfall when compared with the 2A25 and 2B31 data products. Error characterization using various error models revealed that random errors from multiplicative error models were homoscedastic and that they better represented rainfall estimates from 2A12 algorithm. Error decomposition techniques performed to disentangle systematic and random errors verify that the multiplicative error model representing rainfall from 2A12 algorithm successfully estimated a greater percentage of systematic error than 2A25 or 2B31 algorithms. Results verify that although the radiometer derived 2A12 rainfall data is known to suffer from many sources of uncertainties, spatial analysis over the case study region of India testifies that the 2A12 rainfall estimates are in a very good agreement with the reference estimates for the data period considered.

Several spaceborne X-band synthetic aperture radar (X-SAR) systems were launched in 2007, and more will be launched in the current decade. These sensors may significantly augment the sensors that comprise the global precipitation mission (GPM) constellation. X-SAR rainfall measurements may be beneficial particularly over land where rainfall is difficult to measure by means of satellite microwave radiometers. Inversion techniques to quantitatively derive precipitation fields over land at high spatial resolution are developed and illustrated in this paper. These inversion algorithms are the model-oriented statistical (MOS) methodology and the Volterra integral equation (VIE) approach. Simplified rain-cloud models are used to train and test the inversion algorithms by evaluating the expected error budget. Two case studies, using data obtained from measurements of SIR-C/X-SAR in 1994 over Bangladesh and the Amazon, are introduced, and retrieved precipitation maps are discussed. Even though no validation of the precipitation estimates was possible, the obtained results are encouraging, showing physically consistent retrieved structures and patterns.

This study aims to make a regional characterization of the performance of the rain retrieval algorithm BRAIN. This algorithm estimates the rain rate from brightness temperatures measured by the TRMM Microwave Imager (TMI) onboard the TRMM satellite. In this stage of the study, a comparison between the rain estimated from Precipitation Radar (PR) onboard TRMM (2A25 version 5) and the rain retrieved by the BRAIN algorithm is presented, for about 30 satellite overpasses over the Central and Eastern Mediterranean during the period October 2003–March 2004, in order to assess the behavior of the algorithm in the Eastern Mediterranean region. BRAIN was built and tested using PR rain estimates distributed randomly over the whole TRMM sampling region. Characterization of the differences between PR and BRAIN over a specific region is thus interesting because it might show some local trend for one or the other of the instrument. The checking of BRAIN results against the PR rain-estimate appears to be consistent with former results i.e. a somewhat marked discrepancy for the highest rain rates. This difference arises from a known problem that affect rain retrieval based on passive microwave radiometers measurements, but some of the higher radar rain rates could also be questioned. As an independent test, a good correlation between the rain retrieved by BRAIN and lighting data (obtained by the UK Met. Office long range detection system) is also emphasized in the paper.

Four satellite rain estimations based on microwave (MW), infrared (IR) or combined MW-IR techniques are compared with the BOlogna Limited Area Model (BOLAM) rain forecast for a severe weather event (8–13 November 2001) over the western Mediterranean Sea. Two of the investigated multi-channel MW rainfall algorithms use data from the Tropical Rainfall Measuring Mission (TRMM). The Frequency Difference Algorithm relies on data from the TRMM Microwave Imager (TMI) and the other one combines data from the Precipitation Radar (PR) with those from the nine-channel radiometer TMI, called PR Adjusted TMI Estimations of Rainfall (PATER) algorithm. The pure IR Rain Estimator uses geostationary IR METEOSAT data and the combined Naval Research Laboratory algorithm uses both MW data from low orbiting satellites and IR data from the geostationary orbit. Validation results, computed over a common grid, which is independent of the different field of view sizes of the applied data sets, indicate that there is generally a better performance for heavy rain (> 6m m h −1 ) than for light rain (< 1m m h −1 ). Both MW algorithms perform rather similarly, although PATER shows some rain detection problems due to thick aerosol loads originating from the desert. The BOLAM model presents a good agreement with the MW and only a minor location error of a heavy rain area was detected. Both IR-based algorithms have problems in identifying the correct rainy areas compared to MW. Overall, the results suggest that there are advantages in combining both techniques – the well-known rain physics of the MW channels with the high temporal resolution of IR algorithms – to retrieve precipitation from satellite data.

There is an interest for climatology and meteorology to map hydrometeors, and in particular precipitation, both over the entire globe and across large defined regions. To achieve this goal at the proper spatial and temporal resolution which are required to satisfy the end users needs it has been proposed to use satellite based observations. This paper presents the results of a theoretical study aiming to the research of new concepts for the remote sensing of hydrometeors with emphasis on precipitation. The concept proposed is a Focused Wide band millimeter wave INTERferometer (WINTER-F) comprising a constellation of two small satellites, flying close to each other. The two satellites, which are phase synchronized by the GNSS (Global Navigation Satellite System) signals, perform near field interferometry by passive (radiometer) and active (radar) means. The study comprises the realization of laboratory experiments to check the validity of the theoretical models

The Advanced Microwave Precipitation Radiometer (AMPR), an across-track scanning, four-channel (10.7, 19.35, 37.1, 85.5 GHz) total-power radiometer system, was instrumented aboard a NASA ER-2 aircraft during the 1991 CaPE (Convection and Precipitation/Electrification) project in central Florida. At a 20 km flight altitude, the AMPR provides fine-scale microwave imagery of Earth surfaces and its atmosphere, and is well-suited for diverse hydrological applications. During overflights of precipitation, coincident ground-based radar measurements were taken with the NCAR CP-2 dual-frequency, dual-polarization radar system. After remapping the radar data into a format compatible with the AMPR scanning geometry, the radar-derived profiles of rain, melting, and frozen hydrometeors are compared against the AMPR equivalent blackbody brightness temperature (TB) imagery. Microwave radiative transfer modeling procedures incorporating the radar-derived hydrometeor profiles were used to simulated the multifrequency AMPR imagery over both land and ocean background ER-2 flights. Within storm cores over land, columnar ice water paths up to 20 kgm−2 gradually depressed the 85 GHzTB as low as 100 K. The presence of tall vertical reflectivity columns encompassing > 20 kgm−2 columnar ice water path often produced 37 GHzTB