Sensing Heavy Precipitation With GNSS Polarimetric Radio Occultations

Cardellach, Estel; Oliveras, S.; Rius, A.; Tomás, S.; Ao, C. O.; Franklin, G.; Iijima, B. A.; Kuang, Da; Meehan, T. K.; Padullés, Ramon; Juárez, Manuel de la Torre; Turk, F. Joseph; Hunt, Doug; Schreiner, William S.; Sokolovskiy, Sergey; Hove, Teresa Van; Weiss, Jie W; Yoon, Yoke; Zeng, Zhen; Clapp, J.; Xia‐Serafino, Wei; Cerezo, F.

doi:10.1029/2018gl080412

Cited by 30 publications

(47 citation statements)

References 20 publications

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“…10, the precipitation and heavy precipitation groups (blue and red, respectively) exhibit large positive values below 10 km, although positive values start to be noticeable below 15 km. The positive peaks are well above the standard deviation of the no precipitation group, indicating sensitivity to precipitation and consistent with Cardellach et al (2019).…”

Section: Validation Of the ∆φsupporting

confidence: 55%

“…Once the receiving system has been calibrated and the data accordingly corrected, these new observables are validated using the GPM products mentioned above. The results of the validation are compared with what was obtained in Cardellach et al (2019) and also with the predicted performance from the simulations in Cardellach et al (2014).…”

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confidence: 99%

“…For the first analysis of the ROHP-PAZ data (Cardellach et al, 2019), where we aimed for a quick look of the sensitivity 10 of PRO observations to precipitation events, we linked every occultation with the intensity of precipitation in the surrounding areas, using cells of fixed size and circularly shaped (e.g. 2 and 0.6 deg of diameter).…”

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confidence: 99%

“…3. Using the strategy described above we can reduce the cases where the ray does not cross precipitation, but it would be labeled as rainy in a 2 • circular co-location, and reduce the uncertainty of the co-locations that might have appeared in Cardellach et al (2019).…”

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confidence: 99%

“…As it was done in Cardellach et al (2019), we can characterize each PRO observation to a single value derived from the ∆Φ(h 100m ). This is done by averaging ∆Φ(h 100m ) between two different heights.…”

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confidence: 99%

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Calibration and Validation of the Polarimetric Radio Occultation and Heavy Precipitation experiment Aboard the PAZ Satellite

Padullés¹,

Ao²,

Turk³

et al. 2019

Preprint

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This manuscript presents the calibration and validation studies for the Radio Occultations and Heavy Precipitation experiment aboard the PAZ satellite. These studies, necessary to assess and characterize the noise level and robustness of the ∆Φ observable of Polarimetric Radio Occultations (PRO), confirm the good performance of the experiment and the capability of this technique in sensing precipitation. It is shown how all the predicted effects that could have an impact into the PRO ob-5 servables (e.g. effect of metallic structures nearby the antenna, the Faraday Rotation at the ionosphere, signal impurities in the transmission, altered cross polarization isolation, etc.) are effectively calibrated and corrected, and they have a negligible effect into the final observable. The on-orbit calibration, performed using an extensive dataset of free-of-rain and low ionospheric activity observations, is successfully used to correct all the collected observations, which are further validated against independent precipitation observations confirming the sensitivity of the observables to the presence of hydrometeors. The validation 10 results also show how vertically averaged ∆Φ can be used as a proxy for precipitation.

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Section: Validation Of the ∆φsupporting

confidence: 55%

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confidence: 99%

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confidence: 99%

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confidence: 99%

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confidence: 99%

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Calibration and Validation of the Polarimetric Radio Occultation and Heavy Precipitation experiment Aboard the PAZ Satellite

Padullés¹,

Ao²,

Turk³

et al. 2019

Preprint

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Assimilation of satellite data in numerical weather prediction. Part II: Recent years

Eyre

Bell

Cotton

et al. 2022

Quart J Royal Meteoro Soc

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Developments in the assimilation of satellite data in numerical weather prediction (NWP), from the first experiments in the late 1960s to the present day, are presented in a two‐part review article. This part, Part II, reviews the progress in recent years, from about 2000. It includes summaries of advances in the relevant satellite remote‐sensing technologies and in methods to assimilate observations from these instruments into NWP systems. It also summarises impacts on forecast skill. Continued progress has been made on the assimilation of passive infrared (IR) sounding data and microwave (MW) sounding and imaging data. This has included data from hyperspectral IR sounders, which first became available during this period. Advances in the use of cloud‐affected radiances, from both IR and MW instruments, have been made. In support of this progress, further developments have been made in fast radiative transfer models and in bias correction techniques, and work has continued to improve understanding and representation of observation uncertainties. Continued progress has also been made on the use of wind information from satellites, including atmospheric motion vectors and scatterometer data. A new source of temperature and humidity information, from radio occultation observations, has become available during the period and has been exploited by many NWP centres. The impact of satellite data on NWP accuracy is continually assessed using a range of methods and metrics. Some results from recent Observing System Experiments (OSEs) and Forecast Sensitivity to Observation Impact (FSOI) assessment are presented and other methods are discussed. The role of satellite data in NWP‐based atmospheric reanalysis systems is also described.

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