Research

17 pages, 10345 KiB

Open AccessArticle

A Novel Approach to Validate Satellite Snowfall Retrievals by Ground-Based Point Measurements

by Hwayoung Jeoung, Shangyong Shi and Guosheng Liu

Remote Sens. 2022, 14(3), 434; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14030434 - 18 Jan 2022

Cited by 2 | Viewed by 1655

A novel method has been proposed for validating satellite radar snowfall retrievals using surface station observations over the western United States mountainous region, where the mean snowfall rate at a station depends on its elevation. First, all station data within a 1° × [...] Read more.

A novel method has been proposed for validating satellite radar snowfall retrievals using surface station observations over the western United States mountainous region, where the mean snowfall rate at a station depends on its elevation. First, all station data within a 1° × 1° grid are used to develop a snowfall rate versus elevation relation. This relation is then used to compute snowfall rate in other locations within the 1° × 1° grid, as if surface observations were available everywhere in the grid. Grid mean snowfall rates are then derived, which should be more representative to the mean snowfall rate of the grid than using data at any one station or from a simple mean of all stations in the grid. Comparison of the so-derived grid mean snowfall rates with CloudSat retrievals shows that the CloudSat product underestimates snowfall by about 65% when averaged over all the 768 grids in the western United States mountainous regions. The bias does not seem to have clear dependency on elevation but strongly depends on snowfall rate. As an application of the method, we further estimated the snowfall to precipitation ratio using both ground and satellite measured data. It is found that the rates of increase with elevation of the snowfall to precipitation ratio are quite similar when calculating from ground and satellite data, being about 25% per kilometer elevation up or approximately 4% per every degree Cuisses of temperature drop. Full article

(This article belongs to the Special Issue Radar Remote Sensing of Cloud and Precipitation)

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20 pages, 4821 KiB

Open AccessArticle

Capability of GPM IMERG Products for Extreme Precipitation Analysis over the Indonesian Maritime Continent

by Ravidho Ramadhan, Marzuki Marzuki, Helmi Yusnaini, Robi Muharsyah, Wiwit Suryanto, Sholihun Sholihun, Mutya Vonnisa, Alessandro Battaglia and Hiroyuki Hashiguchi

Remote Sens. 2022, 14(2), 412; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14020412 - 17 Jan 2022

Cited by 19 | Viewed by 2857

Abstract

Integrated Multi-satellite Retrievals for GPM (IMERG) data have been widely used to analyze extreme precipitation, but the data have never been validated for the Indonesian Maritime Continent (IMC). This study evaluated the capability of IMERG Early (E), Late (L), and Final (F) data [...] Read more.

Integrated Multi-satellite Retrievals for GPM (IMERG) data have been widely used to analyze extreme precipitation, but the data have never been validated for the Indonesian Maritime Continent (IMC). This study evaluated the capability of IMERG Early (E), Late (L), and Final (F) data to observe extreme rain in the IMC using the rain gauge data within five years (2016–2020). The capability of IMERG in the observation of the extreme rain index was evaluated using Kling–Gupta efficiency (KGE) matrices. The IMERG well captured climatologic characteristics of the index of annual total precipitation (PRCPTOT), number of wet days (R85p), number of very wet days (R95p), number of rainy days (R1mm), number of heavy rain days (R10mm), number of very heavy rain days (R20mm), consecutive dry days (CDD), and max 5-day precipitation (RX5day), indicated by KGE value >0.4. Moderate performance (KGE = 0–0.4) was shown in the index of the amount of very extremely wet days (R99p), the number of extremely heavy precipitation days (R50mm), max 1-day precipitation (RX1day), and Simple Daily Intensity Index (SDII). Furthermore, low performance of IMERG (KGE < 0) was observed in the consecutive wet days (CWDs) index. Of the 13 extreme rain indices evaluated, IMERG underestimated and overestimated precipitation of nine and four indexes, respectively. IMERG tends to overestimate precipitation of indexes related to low rainfall intensity (e.g., R1mm). The highest overestimation was observed in the CWD index, related to the overestimation of light rainfall and the high false alarm ratio (FAR) from the daily data. For all indices of extreme rain, IMERG showed good capability to observe extreme rain variability in the IMC. Overall, IMERG-L showed a better capability than IMERG-E and -F but with an insignificant difference. Thus, the data of IMERG-E and IMERG-L, with a more rapid latency than IMERG-F, have great potential to be used for extreme rain observation and flood modeling in the IMC. Full article

(This article belongs to the Special Issue Radar Remote Sensing of Cloud and Precipitation)

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21 pages, 60499 KiB

Open AccessArticle

Geometrical and Microphysical Properties of Clouds Formed in the Presence of Dust above the Eastern Mediterranean

by Eleni Marinou, Kalliopi Artemis Voudouri, Ioanna Tsikoudi, Eleni Drakaki, Alexandra Tsekeri, Marco Rosoldi, Dragos Ene, Holger Baars, Ewan O’Connor, Vassilis Amiridis and Charikleia Meleti

Remote Sens. 2021, 13(24), 5001; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13245001 - 09 Dec 2021

Cited by 11 | Viewed by 3022

Abstract

In this work, collocated lidar–radar observations are used to retrieve the vertical profiles of cloud properties above the Eastern Mediterranean. Measurements were performed in the framework of the PRE-TECT experiment during April 2017 at the Greek atmospheric observatory of Finokalia, Crete. Cloud geometrical [...] Read more.

In this work, collocated lidar–radar observations are used to retrieve the vertical profiles of cloud properties above the Eastern Mediterranean. Measurements were performed in the framework of the PRE-TECT experiment during April 2017 at the Greek atmospheric observatory of Finokalia, Crete. Cloud geometrical and microphysical properties at different altitudes were derived using the Cloudnet target classification algorithm. We found that the variable atmospheric conditions that prevailed above the region during April 2017 resulted in complex cloud structures. Mid-level clouds were observed in 38% of the cases, high or convective clouds in 58% of the cases, and low-level clouds in 2% of the cases. From the observations of cloudy profiles, pure ice phase occurred in 94% of the cases, mixed-phase clouds were observed in 27% of the cases, and liquid clouds were observed in 8.7% of the cases, while Drizzle or rain occurred in 12% of the cases. The significant presence of Mixed-Phase Clouds was observed in all the clouds formed at the top of a dust layer, with three times higher abundance than the mean conditions (26% abundance at −15 °C). The low-level clouds were formed in the presence of sea salt and continental particles with ice abundance below 30%. The derived statistics on clouds’ high-resolution vertical distributions and thermodynamic phase can be combined with Cloudnet cloud products and lidar-retrieved aerosol properties to study aerosol-cloud interactions in this understudied region and evaluate microphysics parameterizations in numerical weather prediction and global climate models. Full article

(This article belongs to the Special Issue Radar Remote Sensing of Cloud and Precipitation)

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19 pages, 8407 KiB

Open AccessArticle

A Dual-Frequency Cloud Radar for Observations of Precipitation and Cloud in Tibet: Description and Preliminary Measurements

by Juan Huo, Yongheng Bi, Bo Liu, Congzheng Han and Minzheng Duan

Remote Sens. 2021, 13(22), 4685; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13224685 - 19 Nov 2021

Cited by 1 | Viewed by 1729

Abstract

A new dual-frequency Doppler polarimetric cloud radar (DDCR), working at 35-GHz (Ka-band radar, wavelength: 8.6 mm) and 94-GHz (W-band radar, wavelength: 3.2 mm) frequencies, has been in operation at Yangbajing Observatory on the Tibetan Plateau (China) for more than a year at the [...] Read more.

A new dual-frequency Doppler polarimetric cloud radar (DDCR), working at 35-GHz (Ka-band radar, wavelength: 8.6 mm) and 94-GHz (W-band radar, wavelength: 3.2 mm) frequencies, has been in operation at Yangbajing Observatory on the Tibetan Plateau (China) for more than a year at the time of writing. Calculations and field observations show that the DDCR has a high detection sensitivity of −39.2 dBZ at 10 km and −33 dBZ at 10 km for the 94-GHz radar and 35-GHz radar, respectively. The radar reflectivity measured by the two radars illustrates different characteristics for different types of cloud: for precipitation, the attenuation caused by liquid cloud droplets is obviously more serious for the 94-GHz radar than the 35-GHz radar (the difference reaches 40 dB in some cases), and the 94-GHz radar lost signals due to serious attenuation by heavy rainfall; while for clouds dominated by ice crystals where the attenuation significantly weakens, the 94-GHz radar shows better detection ability than the 35-GHz radar. Observations in the Tibetan region show that the 35-GHz radar is prone to missing cloud near the edge, such as the cloud-top portion, resulting in underestimation of the cloud-top height (CTH). Statistical analysis based on one year of observations shows that the mean CTH measured by the 94-GHz radar in the Tibetan region is approximately 600 m higher than that measured by the 35-GHz radar. The analysis in this paper shows that the DDCR, with its dual-frequency design, provides more valuable information than simpler configurations, and will therefore play an important role in improving our understanding of clouds and precipitation in the Tibetan region. Full article

(This article belongs to the Special Issue Radar Remote Sensing of Cloud and Precipitation)

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23 pages, 7870 KiB

Open AccessFeature PaperArticle

Supercooled Liquid Water Detection Capabilities from Ka-Band Doppler Profiling Radars: Moment-Based Algorithm Formulation and Assessment

by Petros Kalogeras, Alessandro Battaglia and Pavlos Kollias

Remote Sens. 2021, 13(15), 2891; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13152891 - 23 Jul 2021

Cited by 3 | Viewed by 2061

Abstract

The occurrence of supercooled liquid water in mixed-phase cloud (MPC) affects their cloud microphysical and radiative properties. The prevalence of MPCs in the mid- and high latitudes translates these effects to significant contributions to Earth’s radiative balance and hydrological cycle. The current study [...] Read more.

The occurrence of supercooled liquid water in mixed-phase cloud (MPC) affects their cloud microphysical and radiative properties. The prevalence of MPCs in the mid- and high latitudes translates these effects to significant contributions to Earth’s radiative balance and hydrological cycle. The current study develops and assesses a radar-only, moment-based phase partition technique for the demarcation of supercooled liquid water volumes in arctic, MPC conditions. The study utilizes observations from the Ka band profiling radar, the collocated high spectral resolution lidar, and ambient temperature profiles from radio sounding deployments following a statistical analysis of 5.5 years of data (January 2014–May 2019) from the Atmospheric Radiation Measurement observatory at the North Slope of Alaska. The ice/liquid phase partition occurs via a per-pixel, neighborhood-dependent algorithm based on the premise that the partitioning can be deduced by examining the mean values of locally sampled probability distributions of radar-based observables and then compare those against the means of climatologically derived, per-phase probability distributions. Analyzed radar observables include linear depolarization ratio (LDR), spectral width, and vertical gradients of reflectivity factor and radial velocity corrected for vertical air motion. Results highlight that the optimal supercooled liquid water detection skill levels are realized for the radar variable combination of spectral width and reflectivity vertical gradient, suggesting that radar-based polarimetry, in the absence of full LDR spectra, is not as critical as Doppler capabilities. The cloud phase masking technique is proven particularly reliable when applied to cloud tops with an Equitable Threat Score (ETS) of 65%; the detection of embedded supercooled layers remains much more uncertain (ETS = 27%). Full article

(This article belongs to the Special Issue Radar Remote Sensing of Cloud and Precipitation)

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10 pages, 1733 KiB

Open AccessCommunication

Distinguishing between Warm and Stratiform Rain Using Polarimetric Radar Measurements

by Sergey Y. Matrosov

Remote Sens. 2021, 13(2), 214; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13020214 - 10 Jan 2021

Cited by 2 | Viewed by 1824

Abstract

Modeled statistical differential reflectivity–reflectivity (i.e., Z_DR–Z_e) correspondences for no bright-band warm rain and stratiform bright-band rain are evaluated using measurements from an operational polarimetric weather radar and independent information about rain types from a vertically pointing profiler. It is [...] Read more.

Modeled statistical differential reflectivity–reflectivity (i.e., Z_DR–Z_e) correspondences for no bright-band warm rain and stratiform bright-band rain are evaluated using measurements from an operational polarimetric weather radar and independent information about rain types from a vertically pointing profiler. It is shown that these relations generally fit observational data satisfactorily. Due to a relative abundance of smaller drops, Z_DR values for warm rain are, on average, smaller than those for stratiform rain of the same reflectivity by a factor of about two (in the logarithmic scale). A Z_DR–Z_e relation, representing a mean of such relations for warm and stratiform rains, can be utilized to distinguish between warm and stratiform rain types using polarimetric radar measurements. When a mean offset of observational Z_DR data is accounted for and reflectivities are greater than 16 dBZ, about 70% of stratiform rains and approximately similar amounts of warm rains are classified correctly using the mean Z_DR–Z_e relation when applied to averaged data. Since rain rate estimators for warm rain are quite different from other common rain types, identifying and treating warm rain as a separate precipitation category can lead to better quantitative precipitation estimations. Full article

(This article belongs to the Special Issue Radar Remote Sensing of Cloud and Precipitation)

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24 pages, 1634 KiB

Open AccessArticle

Dynamic Differential Reflectivity Calibration Using Vertical Profiles in Rain and Snow

by Alfonso Ferrone and Alexis Berne

Remote Sens. 2021, 13(1), 8; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13010008 - 22 Dec 2020

Cited by 5 | Viewed by 2005

Abstract

The accuracy required for a correct interpretation of differential reflectivity (

Z_{D R}

) is typically estimated to be between 0.1 and 0.2 dB. This is achieved through calibration, defined as the identification of the constant or time-varying offset to be subtracted [...] Read more.

The accuracy required for a correct interpretation of differential reflectivity (

Z_{D R}

) is typically estimated to be between 0.1 and 0.2 dB. This is achieved through calibration, defined as the identification of the constant or time-varying offset to be subtracted from the measurements in order to isolate the meteorological signals. We propose two innovative steps: the automated selection of sufficiently homogeneous sections of Plan Position Indicator (PPI) scans at 90

^{\circ}

elevation, performed in both rain and snow, and the ordinary kriging interpolation of the median

Z_{D R}

value of the chosen radar volumes. This technique has been successfully applied to five field campaigns in various climatic regions. The availability of overlapping scans from two nearby radars allowed us to evaluate the calibration approach, and demonstrated the benefits of defining a time-varying offset. Even though the method has been designed to work with both solid and liquid precipitation, it particularly benefits radar systems with limited access to rain measurements due to the deployment in mountainous or polar regions or to issues affecting the lowest range gates. Full article

(This article belongs to the Special Issue Radar Remote Sensing of Cloud and Precipitation)

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17 pages, 4223 KiB

Open AccessArticle

What Can We Learn from the CloudSat Radiometric Mode Observations of Snowfall over the Ice-Free Ocean?

by Alessandro Battaglia and Giulia Panegrossi

Remote Sens. 2020, 12(20), 3285; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12203285 - 10 Oct 2020

Cited by 17 | Viewed by 2674

Abstract

The quantification of global snowfall by the current observing system remains challenging, with the CloudSat 94 GHz Cloud Profiling Radar (CPR) providing the current state-of-the-art snow climatology, especially at high latitudes. This work explores the potential of the novel Level-2 CloudSat 94 GHz [...] Read more.

The quantification of global snowfall by the current observing system remains challenging, with the CloudSat 94 GHz Cloud Profiling Radar (CPR) providing the current state-of-the-art snow climatology, especially at high latitudes. This work explores the potential of the novel Level-2 CloudSat 94 GHz Brightness Temperature Product (2B-TB94), developed in recent years by processing the noise floor data contained in the 1B-CPR product; the focus of the study is on the characterization of snow systems over the ice-free ocean, which has well constrained emissivity and backscattering properties. When used in combination with the path integrated attenuation (PIA), the radiometric mode can provide crucial information on the presence/amount of supercooled layers and on the contribution of the ice to the total attenuation. Radiative transfer simulations show that the location of the supercooled layers and the snow density are important factors affecting the warming caused by supercooled emission and the cooling induced by ice scattering. Over the ice-free ocean, the inclusion of the 2B-TB94 observations to the standard CPR observables (reflectivity profile and PIA) is recommended, should more sophisticated attenuation corrections be implemented in the snow CloudSat product to mitigate its well-known underestimation at large snowfall rates. Similar approaches will also be applicable to the upcoming EarthCARE mission. The findings of this paper are relevant for the design of future missions targeting precipitation in the polar regions. Full article

(This article belongs to the Special Issue Radar Remote Sensing of Cloud and Precipitation)

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