Challenges to Satellite Sensors of Ocean Winds: Addressing Precipitation Effects

Measurements of global ocean surface winds made by orbiting satellite radars have provided valuable information to the oceanographic and meteorological communities since the launch of the Seasat in 1978, by the National Aeronautics and Space Administration (NASA). When Quick Scatterometer (QuikSCAT) was launched in 1999, it ushered in a new era of dual-polarized, pencil-beam, higher-resolution scatterometers for measuring the global ocean surface winds from space. A constant limitation on the full utilization of scatterometer-derived winds is the presence of isolated rain events, which affect about 7% of the observations. The vector wind sensors, the Ku-band scatterometers [NASA\u27s SeaWinds on the QuikSCAT and Midori-II platforms and Indian Space Research Organisation\u27s (ISRO\u27s) Ocean Satellite (Oceansat)-2], and the current C-band scatterometer [Advanced Wind Scatterometer (ASCAT), on the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)\u27s Meteorological Operation (MetOp) platform] all experience rain interference, but with different characteristics. Over this past decade, broad-based research studies have sought to better understand the physics of the rain interference problem, to search for methods to bypass the problem (using rain detection, flagging, and avoidance of affected areas), and to develop techniques to improve the quality of the derived wind vectors that are adversely affected by rain. This paper reviews the state of the art in rain flagging and rain correction and describes many of these approaches, methodologies, and summarizes the results

Revealing the Winds under the Rain. Part I: Passive Microwave Rain Retrievals Using a New Observation-Based Parameterization of Subsatellite Rain Variability and Intensity—Algorithm Description

Scatterometer ocean surface winds have been providing very valuable information to researchers and operational weather forecasters for over 10 years. However, the scatterometer wind retrievals are compromised when rain is present. Merely flagging all rain-affected areas removes the most dynamic and interesting areas from the wind analysis. Fortunately, the Advanced Earth Observing Satellite II (ADEOS-II) mission carried a radiometer [the Advanced Microwave Scanning Radiometer (AMSR)] and a scatterometer, allowing for independent, collocated retrievals of rain. The authors developed an algorithm that uses AMSR observations to estimate the rain inside the scatterometer beam. This is the first in a series of papers that describe their approach to providing rain estimation and correction to scatterometer observations. This paper describes the retrieval algorithm and evaluates it using simulated data. Part II will present its validation when applied to AMSR observations. This passive microwave rain retrieval algorithm addresses the issues of nonuniform beam filling and hydrometeor uncertainty in a novel way by 1) using a large number of soundings to develop the retrieval database, thus accounting for the geographically varying atmospheric parameters; 2) addressing the spatial inhomogeneity of rain by developing multiple retrieval databases with different built-in inhomogeneity and rain intensity, along with a ‘‘rain indicator’’ to select the most appropriate database for each observed scene; 3) developing a new cloud-versus-rain partitioning that allows the use of a variety of drop size distribution assumptions to account for some of the natural variability diagnosed from the soundings; and 4) retrieving atmospheric and surface parameters just outside the rainy areas, thus providing information about the environment to help decrease the uncertainty of the rain estimates.Keywords: Precipitation, Satellite observations, Algorithms, Radiative transfer, Microwave observations, Remote sensin

A Multi-center exercise on the sensitivity of PAZ GNSS Polarimetric RO for NWP modelling

Trabajo presentado al 7th International Workshop on Occultations for Probing Atmosphere and Climate y al 9th Workshop of the International Radio Occultation Working Group (OPAC-IROWG), celebrados del 8 al 14 de septiembre de 2022 en Leibnitz, Austria.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 the PRO profile alongside other conventional (non-polarimetric) RO data. (3) To examine the utility of PAZ GNSS PRO observations for model validation and diagnosis. The exercise includes comparisons with ECWMF reanalysis ERA-5 model, the operational NWP at the Japan Meteorological Agency, and a near-real-time implementation of the WRF regional model over the northeastern Pacific produced at the Center for Western Weather and Water Extremes (CW3E) called West WRF, among others.The ROHP-PAZ project is part of the Grant RTI2018-099008-B-C22 funded by the Spanish Ministry of Science and Innovation MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe” of the “European Union”. Part of the investigations are done under the EUMETSAT ROM SAF CDOP4. This work was partially supported by the program Unidad de Excelencia María de Maeztu CEX2020-001058-M. Part of this research has received funding from the postdoctoral fellowships program Beatriu de Pinós, funded by the Secretary of Universities and Research (Government of Catalonia) and by the Horizon 2020 program of research and innovation of the European Union under the Marie Sklodowska-Curie grant agreement No 801370.Peer reviewe

Combining wind and rain in spaceborne scatterometer observations: modelling the splash effects in the sea surface backscattering coefficient

Scatterometer wind retrievals are widely used by the oceanographic and meteorological communities for several applications. However, rain strongly affects such wind retrievals, especially at Ku band and higher frequencies. Although several semi-empirical techniques have been already developed to correct scatterometer wind retrievals, the implementation of a physical forward model representing the realistic behavior of ocean surface in presence of atmospheric events may lead to a more accurate approach in estimating winds. A physical forward model can also open the opportunity to ingest an inversion algorithm to jointly estimate wind and rain as well as to evaluate the uncertainty of the rain rate estimates which, in turn, affect the uncertainty in the wind speed and direction retrievals. In this work we present the first step of our approach in developing such physically-based model. The purpose is to include the rain rate parameter in the sea surface backscattering coefficient by physically implementing the effects due to the impact of the raindrops on the ocean surface. Numerical results confirm that the proposed model is physically consistent as it is able to reproduce the expected behavior of the surface backscattering coefficient at Ku band

Theoretical Modeling of Dual-Frequency Scatterometer Response: Improving Ocean Wind and Rainfall Effects

International Ocean Vector Winds Science Team Workshop (2017 IOVWST), 2-4 May 2017, San Diego, CaliforniaPeer Reviewe

Modeling ocean wave surface to simulate spaceborne scatterometer observations in presence of rain

Spaceborne scatterometer observations, especially at Ku-band, are affected by rain in several ways and these effects need to be corrected to avoid errors in wind retrievals. In this work we propose a model to derive the surface backscattering coefficient in presence of both wind and rain. Our approach consists in the development of an ocean surface wind wave spectrum accounting for two effects due to raindrops impact on the surface: the rain-induced wave damping and the generation of ring waves. The results show that this extended spectrum is able to model the ocean surface wave modifications due to rain so that it can be used for further study in physically representing the scatterometer observations in presence of both wind and rai

Addressing the rain effects on the ocean scattering: a theoretical model of the ocean surface backscattering coefficient in presence of both wind and rain

Atmospheric rain has a strong impact on spaceborne scatterometer observations at Ku-band and higher frequencies. Several semi-empirical techniques have been developed in the past years to address the rain effects on scatterometer backscattered signal but, a theoretical model is still an open issue [Weissman et al., 2012]. A theoretical model would contribute to establish an accurate approach in deriving winds as well as in studying the sensibility to the rain intensity. It would also ingest the opportunity to develop new techniques to estimate both wind and rain simultaneously. Therefore, in this perspective, the goal of this work is to describe our approach in developing such model. Here, we have focused on modelling the ocean surface backscattering coefficient in presence of rain. The inclusion of rain attenuation and volume backscattering will be the goal of future works. To describe the surface backscattering coefficient, we have used the Two Scale Model, where a new ocean surface roughness model is proposed to account for the ocean surface modification induced by the rain [Contreras and Plant, 2006]. This new spectrum consists on an extension of the ocean wave spectrum model proposed by Elfouhaily et al., (1997) (hereafter E). However, the E spectrum has been firstly tuned to best match the empirical geophysical model functions (GMF). The tuning strategy has been defined such that it does not depend on any instrumental parameters like frequency, incidence angle or polarization as well as on the different available GMFs, in order to allow the model to work in any conditions. Two main rain effects have been included in this spectrum, such as: the short wave damping according to the theory proposed by Nystuen, (1990) and the generation of the ring waves, as shown by Bliven et al., (1997). The results of the modeled Normalized Radar Cross Section (NRCS), in non-rainy conditions, show a very good agreement with the GMF developed for the Ku-band QuikSCAT scatterometer, at vertical polarization. Good results are also obtained at C-band, in vertical polarization, using the CMOD5.N GMF for validation but, in this case, some discrepancies can be seen at wind speed higher than 15 m/s so, further investigation are needed. Moreover, at Ku-band, the horizontal polarization shows a bias which increases with the wind speed. This bias can be ascribed to the presence of the steep breaking waves which are known to introduce an additional scattering to the wind wave backscattering coefficient. Future investigations will focus on modeling such additional scattering, according to the theory proposed by Kudryavtsev et al., (2003). On the other hand, in rainy conditions, the results are consistent to what expected. The Ku-band NRCS increases with the rain rate at low, medium and high winds, demonstrating that the ring waves are the dominant contribution. The rain smooths the directional component of the NRCS at lower winds but does not affects the directional component at moderate and high winds. Moreover, it is shown that the sensibility of the NRCS to rain decreases with the wind speed. Additional tests, using real data, are also planned

Addressing the rain effects on ocean wind scatterometry at C and Ku band: modification of the ocean surface backscattering coefficient

Ocean surface winds are the main factors affecting the scatterometer backscattered radiation. In clear weather conditions, the surface wind modifies the ocean surface roughness which plays a key role in the regulation of the backscattered radiation and, in turn, in the wind estimates. However, in presence of atmospheric precipitation, the surface roughness is also modified by the impact of the raindrops imping on the surface. Therefore, modeling the ocean surface modification combining both wind and rain offers an opportunity to physically understand the contribution of the rain in the ocean backscattering coefficient. It opens the way towards a complete theoretical forward model to simulate the scatterometer observations in both rainy and non-rainy conditions once the contributions due to rain attenuation and volume backscattering are introduced. Such model allows also possible correction techniques of the wind retrievals and synergistic estimates of rain parameters as well. In this perspective, our work focuses on the development of a physical model of the ocean surface backscattering coefficient accounting for the modification of the surface roughness due to both wind and rain. The ocean surface roughness is described by the equilibrium ocean wind wave spectrum which can be defined as the distribution of the mean waves’ energy considering the spectral sources as balanced, such as wind contributions, breaking dissipation and non-linear interactions. Several representations of the ocean surface spectrum in equilibrium range already exist and, in term of wavenumbers, they describe the ocean surface as composed by two scale of roughness such as large scale gravity waves and small scale capillary waves. Our approach consists in modifying the spectrum in the region of capillary waves in order to include the rain-induced wave damping and the generation of ring waves [Contreras and Plant, 2006]. We have modeled these effects by introducing an attenuation factor accounting for the variation of the water viscosity [Nystuen, 1990] and an additive contribution representing the increase of the surface roughness due to ring waves [Bliven et al., 1997]. The main features of our approach is that the rain model does not depend on the representation of the surface wind wave spectrum so that it can be applied to any equilibrium spectrum as long as it shows a separation between gravity and capillary waves. We have first analyze the spectrum developed by Donelan and Pierson (1987) and the preliminary results are as expected, however analysis using further spectra will follow. To compute the co-polar ocean surface backscattering coefficient, the ocean surface two scale model with the new rain affected wind wave spectrum, has been used. We have first focused on Ku band simulations in non-rainy conditions and comparison to the empirical geophysical model function developed for the NASA scatterometer QuikSCAT show good agreement especially at vertical polarization. Then, we have focused on low-medium wind regime to analyze the ocean surface backscattering coefficient at different rain intensities. As expected, the backscattering coefficient increases when the rain rate becomes higher due to the increasing roughness and this shows that our approach is physically consistent. Analysis at C band will be also carried out. The validation in non-rainy conditions will be performed using the CMOD5.N geophysical model function developed for C band scatterometer such as European Remote Sensing Satellite-2 (ERS-2) and Advanced Scatterometer (ASCAT). Analysis of the ocean response at different rain rates as well as comparisons between C and Ku bands are planned in order to study the impact on the backscattering coefficient, at different frequencies, of the rain induced surface modification

Addressing the polarization signature of the ocean scattering at Ku band in presence of both non homogenous winds and rain

Ku-band spaceborne scatterometer observations are affected by rain and these effects need to be corrected to avoid errors in wind retrievals. We propose a theoretical model of the ocean surface backscattering coefficient accounting for both wind and rain. The rain effects are included in the ocean wind wave spectrum. In this work we describe our model and the approach used to address the discrepancies found at horizontal (H) and vertical (V) polarizations