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The Polarimetric Radio Ocultation (PRO) Research Dataset (resPrf) provides the PRO observable Delta Phi as a function of height along with the corresponding standard RO wet thermodynamic profiles. The Delta Phi observable contained in this dataset is created from the processing of the H and V ports of the polarimetric antenna aboard PAZ, that provide information about precipitation and clouds encountered along the ray-paths. The thermodynamics is obtained from the merge of UCAR's atmPrf and wetPf2 level2 retrievals. The aim of the dataset is to foster the scientfic use of the PRO data, which provide unique simultaneous measurements of precipitation and the thermodynamics of the associated environment. In addition, the dataset provides detailed collocated information from independent precipitation sources, as well as radiances from the radiometers aboard the satellites in the Global Precipitation Mission (GPM) constellation. These collocated data are used for validation but also to provide the user with additional context on meteorological events.

Currently, it remains a challenge to effectively monitor areas experiencing intense precipitation and the associated atmospheric conditions on a global scale. This challenge arises due to the limitations on both active and passive remote sensing methods. Apart from the lack of observations in remote areas, the quality of some observations deteriorates when heavy precipitation is present, making it difficult to obtain highly accurate measurements of the thermodynamic parameters driving these weather events. However, there is a promising solution in the form of the Global Navigation Satellite System (GNSS) Polarimetric Radio Occultation (PRO) technique. This approach provides a way to assess the large-scale bulk-hydrometeor characteristics of regions with heavy precipitation and the meteorological conditions associated with them. PRO offers vertical profiles of atmospheric variables, including temperature, pressure, water vapor pressure, and information about hydrometeors, all in a single fine-vertical resolution observation. To continue validating the PRO technique, we make use of polarimetric weather data from Next Generation Weather Radars (NEXRAD), focusing on comparing specific differential phase shift () structures to PRO observable differential phase shift (). We have seen that PAZ and NEXRAD exhibit a good agreement on the vertical structure of the observable and that their combination could be useful for enhancing our understanding of the

The sensitivity of PAZ’s Polarimetric Radio Occultation (PRO) observations to horizontally oriented frozen particles is assessed using coincident measurements from the Global Precipitation Measurement (GPM) core satellite’s radiometer GPM Microwave Imager (GMI) and ancillary information from Cloudsat. The difference between the horizontal and vertical polarizations of the GMI observed radiances at 166 GHz, indicative of horizontally oriented particles, is compared with the PAZ differential phase shift observations. A clear positive trend is observed, exhibiting a good correlation between the two observations. The radiometer absolute observations and polarization differences (PD) are then used to build a lookup table of ice water content (IWC) vertical profiles, using coincident observations between GPM and Cloudsat. The vertical profiles of IWC, along with the information of the radiometric PDs and some simple assumptions, are used to simulate the expected PAZ differential phase shift observations. The validation with the PAZ and GPM coincident measurements shows a high correlation between the simulated and observed differential phase shift, therefore proving the sensitivity of polarimetric radio occultations to horizontally oriented frozen particles. These results demonstrate that PRO can not only sense the precipitation near the surface (the original goal of the mission) but also the frozen particles near and well above the freezing level, providing additional detail of the cloud vertical structure.

Presentación del algoritmo de inversión de datos que estamos implementando para la misión de la ESA, HydroGNSS, para obtención de la detección de agua (inundaciones, humedales); actividades hechas hasta el momento para validar el algoritmo, y también planes de validación y calibración futuras.

This paper presents a new way of combining Abel inversion and the Chapman model with a linearly increasing scale height to retrieve ionospheric electron density vertical profiles from truncated-sounding radio-occultation data. A linear Vary–Chap model is used to cover the blind region due to data truncation, with parameters estimated by enumeration of the possible values in a grid centered around a set of parameters compatible with ionospheric physics. The resulting electron density is estimated with its corresponding error from the linear least-squares solution presenting the smaller post-fit residual on the input GNSS carrier-phase measurements. The results, tested on a set of representative GNSS RO measurements obtained by COSMIC/FORMOSAT-3, show that this method can retrieve EDVPs with a predominant absolute and relative error of 1010e−m−3 and 5%, respectively, and in less than 10 s per profile, which makes this method suitable for near real-time applications in upcoming missions such as EUMETSAT Polar System-Second Generation.

Year-round observations of the physical snow and ice properties and processes that govern the ice pack evolution and its interaction with the atmosphere and the ocean were conducted during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition of the research vessel Polarstern in the Arctic Ocean from October 2019 to September 2020. This work was embedded into the interdisciplinary design of the 5 MOSAiC teams, studying the atmosphere, the sea ice, the ocean, the ecosystem, and biogeochemical processes. The overall aim of the snow and sea ice observations during MOSAiC was to characterize the physical properties of the snow and ice cover comprehensively in the central Arctic over an entire annual cycle. This objective was achieved by detailed observations of physical properties and of energy and mass balance of snow and ice. By studying snow and sea ice dynamics over nested spatial scales from centimeters to tens of kilometers, the variability across scales can be considered. On-ice observations of in situ and remote sensing properties of the different surface types over all seasons will help to improve numerical process and climate models and to establish and validate novel satellite remote sensing methods; the linkages to accompanying airborne measurements, satellite observations, and results of numerical models are discussed. We found large spatial variabilities of snow metamorphism and thermal regimes impacting sea ice growth. We conclude that the highly variable snow cover needs to be considered in more detail (in observations, remote sensing, and models) to better understand snow-related feedback processes. The ice pack revealed rapid transformations and motions along the drift in all seasons. The number of coupled ice–ocean interface processes observed in detail are expected to guide upcoming research with respect to the changing Arctic sea ice.

During MOSAiC several remote sensing instruments designed for observing the sea ice and its snow cover were installed on the ice floe next to Polarstern and on the vessel itself. Satellite measurements constitute a few of the most important climate data records for polar regions. The MOSAiC experiments will help to improve their quality and better assess their uncertainties. In particular the following measurements were performed during MOSAiC: (i) 0.5-89 GHz microwave radiometers, (ii) L to Ka-band microwave radar scatterometers, (iii) reflected GNSS measurements, and (iv) infrared, visual, and hyperspectral cameras. The remote sensing measurements were accompanied by extensive measurements of snow and ice properties. By having these coincident multi-frequency remote sensing and in-situ observations, factors influencing the emission, reflection, and scattering of microwaves in sea ice and snow can be better understood. New remote sensing methods can be developed and contribute to new and upcoming satellite missions. Here we will present an overview of the measurement program and first results from simultaneously measuring instruments during two events in November 2019 (winter) and September 2020 (summer)

Monitoring the constantly changing shape of the sea surface is important for scientific and societal applications such as ocean current forecasting, climate research, ship routing, cable laying, and debris tracking. A project supported by the Discovery element of ESA’s Basic Activities recently investigated a new technique to measure sea surface topography very precisely. The project was based on an idea submitted by the Institute for Space Studies of Catalonia (IEEC) through the Open Space Innovation Platform (OSIP) – ESA’s place for your space ideas. The technique involves looking at satellite navigation (GNSS) signals that have been reflected off of the sea surface at very low angles. At these so-called grazing angles, waves and surface roughness have very little impact on the reflection process; the sea surface acts as a very smooth mirror. IEEC’s Estel Cardellach, principal investigator and submitter of the OSIP idea explains: “In a mirror-like reflection the phase of the signal can be tracked – it is continuous. Different surface heights result in different phase measurements. It gives a very precise measurement of the surface altitude at a few centimetres’ precision.” The ESA-funded activity involved developing a GNSS receiver and setting up an experiment in the Balaeric Islands to collect GNSS signals reflected off the sea surface. The team – made up of IEEC, imedea, SOCIB and DLR – then processed the signals for optimised measurements of the shape of the sea surface. “Thanks to OSIP and ESA Discovery we have been able to conduct this experiment on grazing GNSS reflectometry under monitored conditions,” says Manuel Martin-Neira, ESA technical officer for the project. “We have linked the coherence of the reflected signals to wave height and elevation angle of GNSS satellites. These results have been very useful for preparing the PRETTY mission.” ESA’s PRETTY (Passive REflecTomeTry and dosimetry) CubeSat mission is a small satellite that will carry out grazing

Accurate prediction and modeling of heavy precipitation events remains an issue due to gaps in our understanding of the physical processes underlying them. Such gaps arise from the limited number of good quality observations that constrain the thermodynamic parameters (e.g. temperature, moisture, etc.) within heavy precipitation, since the observations of some space-based sensors are degraded in the presence of thick clouds. The Global Navigation Satellite System (GNSS) Polarimetric Radio Occultation (PRO) technique was recently created to overcome some of these limitations, by providing vertical profiles of temperature, pressure, and water vapor, along with vertical information about hydrometeors (i.e. raindrops, snow, ice crystals, etc), simultaneously. It represents an enhancement of the standard radio occultation technique, that consists on tracking the signals emitted by GPS satellites from a low Earth orbit satellite occulting behind the Earth’s horizon. These signals cross lower and denser layers of the atmosphere as the occultation advances. The augmentation that the polarimetry provides consists on collecting these signals using two linearly and orthogonal polarized antennas (H and V), instead of the circularly polarized one used in the standard technique. Comparing the phase of the signals received at the two antenna ports, we can infer the presence of hydrometeors along the ray paths. Polarimetric Radio Occulation technique is being proved aboard the PAZ satellite, in an experiment led by the Institut de Ciències de l’Espai (ICE-CSIC,IEEC), in collaboration with NOAA, UCAR and NASA/Jet Propulsion Laboratory, operating since 2018. These profiles are obtained globally, through all kinds of clouds and over all kinds of surfaces. Such characteristics are rather unique in the current observing system. For this study, mesoscale convective systems (MCS) are particularly of interest. Given the characteristics of the observational technique and the targeted system

Machine learning algorithms build a model based on sample data, capable to make predictions without being explicitly programmed to do so. In the field of GNSS polarimetric RO (GNSS PRO), these algorithms can be used to find patterns and relationships between different variables, even before any physical model is foreseen. In this study, we present a set of investigations done with PAZ GNSS PRO observables (the vertical profile of polarimetric phase shift, DeltaPhi(h)) and their thermodynamic profiles (T(h), p(h), q(h)), sometimes complemented with ancillary (non-RO) information such as the average rain rate or infrared brightness temperatures. Both forward and backward relationships are studied: Can the thermodynamic profiles predict the polarimetric phase shift vertical profile? Are the polarimetric observables sufficient for predicting the rain rate? When is it necessary to complement with ancillary data? Which ancillary data provides useful information? In which range of altitudes these relationships and patterns are stronger? The answer to these questions might help (1) understanding the GNSS PRO observables; (2) designing optimal forward/backward models; and (3) assessing uses and applications of the GNSS PRO. Different classifiers and regressors are tested, playing with different subsets of variables. For example, it is found that presence of rain (> 1 mm) can be classified with 92% sensitivity and 85% total accuracy, or that vertical profiles of T,p,q can predict the vertical profiles of GNSS PRO polarimetric phase shift between 6 and 14 km altitude with accuracies between 85% and 90%.

A better understanding of the thermodynamics of heavy precipitation events is necessary towards improving weather and climate models and quantifying the impact of climate variability on precipitation. However, there are limited observations available to assess the model structure within heavy precipitation conditions. Recently, it has also been shown that the Radio Occultations Through Heavy Precipitation (ROHP) GNSS polarimetric radio occultation (GNSS PRO) observations are highly sensitive to hydrometeors above the freezing layer, which expands the potential uses of the GNSS PRO dataset for weather-related science and applications. An exercise is presented to analyze the sensitivity of PRO observations for NWP modeling applications. The ROHP experiment now provides over four years of coincident thermodynamic and precipitation information with high vertical resolution within regions with thick clouds. Murphy et al. (2019) simulated GNSS airborne polarimetric RO (GNSS PRO) events along an atmospheric river. These were modeled by the community WRF mesoscale model using two different microphysical parameterization schemes. The GNSS PRO observables simulated with the two schemes differed significantly, more than the actual GNSS PRO precision. The new exercise presented here reproduces this methodology for spaceborne data, using different global and regional NWP models, and it analyzes the results and divergences with the help of actual GNSS PRO data acquired aboard the PAZ satellite. The objectives of the activity are: (1) To compare simulated GNSS PRO observables, generated with models from different centers and different microphysics schemes, against actual PAZ GNSS PRO observables. Can the models reproduce the main features of the actual data? (2) To assess whether different models/schemes result in different GNSS PRO observables, and whether these differences are larger than the measurement uncertainty. This effort provides insight on future methods to assimilate t

Polarimetric radio occultation (PRO) provides refractivity and phase difference between horizontal and vertical components of the radio occultation signals. This polarimetric phase difference occurs when the occultation ray paths cross an anisotropic medium like a layer of ice particles or non-spherical precipitating droplets. PRO from the PAZ satellite have been proven to capture these effects. PAZ GPS observations are used to correlate the phase difference with the precipitating water path and ice water path. The result presented is a data derived Look-Up Table (LUT) that is compared to the predicted LUT. The empirical LUT is used to infer a non-linear logistic map function that relates the phase difference to the ice/precipitating water path as a function of refractivity. We explored profiles that do not follow the logistic function to understand what physical processes could cause such anomalies. A cluster analysis based on the polarimetric phase difference and refractivity profiles is used to relate different type of profiles to occurrence of precipitation. The analysis presented correlates the different vertical cloud structures associated with different types of precipitation. These vertical structures of radio occultation refractivity profiles inform on the combination of temperature and water vapor at different altitudes inside the clouds, including thick convective clouds. Altitude changes in the polarimetric phase difference within these PRO profiles informs on the levels at which the thermodynamic conditions lead to precipitation or ice.

This article analyzes the contribution of the delay-Doppler map (DDM) observation noise to the uncertainty of global navigation satellite system reflectometry (GNSS-R) ocean scatterometry observables and the retrieved wind speeds. For this purpose, the parameter Kp , which is commonly used in the traditional microwave scatterometer, is introduced to characterize the relative standard deviation (RSD) of the GNSS-R normalized bistatic radar cross section (NBRCS) measurement. Based on the noise covariance of the DDM measurements, the analytic expressions of RSD are derived for two cases, i.e., the NBRCS computed with one single DDM bin at the specular point and the NBRCS computed from M×N DDM bins around the specular point. By analyzing the dependence of the RSD on different system, geometry, and instrument parameters, the simplified models of RSD are derived empirically. As a simple application of the proposed models, the wind speed retrieval errors are computed using parameters from the Cyclone GNSS (CYGNSS) mission. It shows that the wind speed retrieval error due to thermal noise and speckle can be an important error source for the overall wind speed retrieval performance, especially at high wind speed and with the high incidence angle GNSS-R measurements.

Earth reflected Global Navigation Satellite System (GNSS) signals can be received by dedicated orbital receivers for remote sensing and Earth observation (EO) purposes. Different spaceborne missions have been launched during the past years, most of which can only provide the delay-Doppler map (DDM) of the power of the reflected GNSS signals as their main data products. In addition to the power DDM products, some of these missions have collected a large amount of raw intermediate frequency (IF) data, which are the bit streams of raw signal samples recorded after the analog-to-digital converters (ADCs) and prior to any onboard digital processing. The unprocessed nature of these raw IF data provides an unique opportunity to explore the potential of GNSS Reflectometry (GNSS-R) technique for advanced geophysical applications and future spaceborne missions. To facilitate such explorations, the raw IF data sets from different missions have been processed by Institute of Space Sciences (ICE-CSIC, IEEC), and the corresponding data products, i.e., the complex waveform of the reflected signal, have been generated and released through our public open-data server. These complex waveform data products provide the measurements from different GNSS constellations (e.g., GPS, Galileo and BeiDou), and include both the amplitude and carrier phase information of the reflected GNSS signal at higher sampling rate (e.g., 1000 Hz). To demonstrate these advanced features of the data products, different applications, e.g., inland water detection and surface altimetry, are introduced in this paper. By making these complex waveform data products publicly available, new EO capability of the GNSS-R technique can be further explored by the community. Such early explorations are also relevant to ESA¿s next GNSS-R mission, HydroGNSS, which will provide similar complex observations operationally and continuously in the future.