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The IMERG data product is an optimal combination of precipitation estimates from the Global Precipitation Mission (GPM), making use of a variety of data types, primarily data from various spaceborne passive instruments. Previous versions of the IMERG product have been extensively validated by comparisons with gauge data and ground-based radars over land. However, IMERG rain rates, especially sub-daily, over open ocean are less validated due to the scarcity of comparison data, particularly with the relatively new Version 07. To address this issue, we consider IMERG V07 30-min data acquired in tropical cyclones over open ocean. We perform two tasks. The first is a straightforward comparison between IMERG precipitation rates and those retrieved from the GPM Dual-frequency Precipitation Radar (DPR). From this, we find that IMERG and DPR are close at low rain rates, while, at high rain rates, IMERG tends to be lower than DPR. The second task is the assessment of IMERG's ability to represent or detect structures commonly seen in tropical cyclones, including the annular structure and concentric eyewalls. For this, we operate on IMERG data with many machine learning algorithms and are able to achieve a 96% classification accuracy, indicating that IMERG does indeed contain TC structural information. [ABSTRACT FROM AUTHOR]

The IMERG data product is an optimal combination of precipitation estimates from the Global Precipitation Mission (GPM), making use of a variety of data types, primarily data from various spaceborne passive instruments. Previous versions of the IMERG product have been extensively validated by comparisons with gauge data and ground-based radars over land. However, IMERG rain rates, especially sub-daily, over open ocean are less validated due to the scarcity of comparison data, particularly with the relatively new Version 07. To address this issue, we consider IMERG V07 30-min data acquired in tropical cyclones over open ocean. We perform two tasks. The first is a straightforward comparison between IMERG precipitation rates and those retrieved from the GPM Dual-frequency Precipitation Radar (DPR). From this, we find that IMERG and DPR are close at low rain rates, while, at high rain rates, IMERG tends to be lower than DPR. The second task is the assessment of IMERG’s ability to represent or detect structures commonly seen in tropical cyclones, including the annular structure and concentric eyewalls. For this, we operate on IMERG data with many machine learning algorithms and are able to achieve a 96% classification accuracy, indicating that IMERG does indeed contain TC structural information.

To understand the microphysical characteristics of rainfall in four different climatological regions (called BOS, BUS, CPO, and JIN) in South Korea, DSDs and their variables, including the mass-weighted mean diameter (Dm) and normalized number concentration (logNw), were examined. To examine the characteristics of DSDs at four sites with different climatology and topography, data measured from Parsivel disdrometer and wind direction from Automatic Weather System (AWS) during rainy seasons from June to August for three years (2018 to 2020) were analyzed. The DSDs variables were calculated using Gamma distribution model. In the coastal area, larger raindrops with a lower number concentration occurred, whereas smaller raindrops with a higher number concentration dominated in the middle land and mountain region. The mountain area of CPO and middle land area of JIN had a larger contribution to the rain rate than that of the coastal area of BOS and JIN in the range of the smallest diameter. The contribution of the drop size to the total number concentration at the CPO and JIN sites was larger (smaller) than that at BOS and BUS in the smallest (larger) diameter. The average shape and slope parameter of gamma model were higher values at the mountain area than at other sites for both rain types, Z-R relation and polarimetric variables were also shown different values at the four studied sites. The intercept coefficient of Z-R relation showed higher values in the mountain area and middle land area than the coastal area. The slope values of Z-R relation were the smallest in the mountain area. The polarimetric variables of ZH and ZDR were shown highest (lowest) value at the coastal region of BOS (mountain area of CPO) site for both rain types. The Dm-rose, which shows the Dm distributions with the wind direction, was used in this study. In the coastal area (mountain and middle land area), the dominant wind was east–southeast (east) direction. The ratio of the smaller diameter to the middle size at BOS was much smaller than that at CPO. In the analysis of the hourly distribution of the Dm and logNw, there were two and four peaks of Dm at BUS and BOS, respectively. There was one peak of the Dm at the CPO and JIN sites. The time variation of the Dm was much higher than that of the logNw. [ABSTRACT FROM AUTHOR]

To understand the microphysical characteristics of rainfall in four different climatological regions (called BOS, BUS, CPO, and JIN) in South Korea, DSDs and their variables, including the mass-weighted mean diameter (Dm) and normalized number concentration (logNw), were examined. To examine the characteristics of DSDs at four sites with different climatology and topography, data measured from Parsivel disdrometer and wind direction from Automatic Weather System (AWS) during rainy seasons from June to August for three years (2018 to 2020) were analyzed. The DSDs variables were calculated using Gamma distribution model. In the coastal area, larger raindrops with a lower number concentration occurred, whereas smaller raindrops with a higher number concentration dominated in the middle land and mountain region. The mountain area of CPO and middle land area of JIN had a larger contribution to the rain rate than that of the coastal area of BOS and JIN in the range of the smallest diameter. The contribution of the drop size to the total number concentration at the CPO and JIN sites was larger (smaller) than that at BOS and BUS in the smallest (larger) diameter. The average shape and slope parameter of gamma model were higher values at the mountain area than at other sites for both rain types, Z-R relation and polarimetric variables were also shown different values at the four studied sites. The intercept coefficient of Z-R relation showed higher values in the mountain area and middle land area than the coastal area. The slope values of Z-R relation were the smallest in the mountain area. The polarimetric variables of ZH and ZDR were shown highest (lowest) value at the coastal region of BOS (mountain area of CPO) site for both rain types. The Dm-rose, which shows the Dm distributions with the wind direction, was used in this study. In the coastal area (mountain and middle land area), the dominant wind was east–southeast (east) direction. The ratio of the smaller diameter to the middle size at BOS was much smaller than that at CPO. In the analysis of the hourly distribution of the Dm and logNw, there were two and four peaks of Dm at BUS and BOS, respectively. There was one peak of the Dm at the CPO and JIN sites. The time variation of the Dm was much higher than that of the logNw.

Rain Signatures on C-band Synthetic Aperture Radar (SAR) images acquired over ocean are common and can dominate the backscattered signal from the ocean surface. In many cases, the inability to decipher between ocean and rain signatures can disturb the analysis of SAR scenes for maritime applications. This study relies on Sentinel-1 SAR acquisitions in the Interferometric Wide swath mode and high-resolution measurements from ground-based weather radar to document the rain impact on the radar backscattered signal in both co- and cross-polarization channels. The dark and bright rain signatures are found in connection with the timeliness of the rain cells. In particular, the bright patches are demonstrated by the hydrometeors (graupels, hails) in the melting layer. In general, the radar backscatter under rain increases with rain rate for a given sea state and decreases when the sea state strengthens. The rain also has a stronger impact on the radar signal in both polarizations when the incidence angle increases. The complementary sensitivity of the SAR signal of rain in both channels is then used to derive a filter to locate the areas in SAR scenes where the signal is not dominated by rain. The filter optimized to match the rain observed by the ground-based weather radar is more efficient when both polarization channels are considered. Case studies are presented to discuss the advantages and limitations of such a filtering approach.

Because of its clear physical meaning, physical methods are more often used for space-borne microwave radiometers to retrieve the rain rate, but they are rarely used for ground-based microwave radiometers that are very sensitive to rainfall. In this article, an opacity physical retrieval method is implemented to retrieve the rain rate (denoted as Opa-RR) using ground-based microwave radiometer data (21.4 and 31.5 GHz) of the tropospheric water radiometer (TROWARA) at Bern, Switzerland from 2005 to 2019. The Opa-RR firstly establishes a direct connection between the rain rate and the enhanced atmospheric opacity during rain, then iteratively adjusts the rain effective temperature to determine the rain opacity, based on the radiative transfer equation, and finally estimates the rain rate. These estimations are compared with the available simultaneous rain rate derived from rain gauge data and reanalysis data (ERA5). The results and the intercomparison demonstrate that during moderate rains and at the 31 GHz channel, the Opa-RR method was close to the actual situation and capable of the rain rate estimation. In addition, the Opa-RR method can well derive the changes in cumulative rain over time (day, month, and year), and the monthly rain rate estimation is superior, with the rain gauge validated R2 and the root-mean-square error value of 0.77 and 22.46 mm/month, respectively. Compared with ERA5, Opa-RR at 31GHz achieves a competitive performance.

Surface precipitation phase is a fundamental meteorological property with immense importance. Accurate classification of phase from satellite remotely sensed observations is difficult. This study demonstrates the ability of the Precipitation Imaging Package (PIP), a ground-based, in situ precipitation imager, to distinguish precipitation phase. The PIP precipitation phase identification capabilities are compared to observer records from the National Weather Service (NWS) office in Marquette, Michigan, as well as co-located observations from profiling and scanning radars, disdrometer data, and surface meteorological measurements. Examined are 13 events with at least one precipitation phase transition. The PIP-determined onsets and endings of the respective precipitation phase periods agree to within 15 min of NWS observer records for the vast majority of the events. Additionally, the PIP and NWS liquid water equivalent accumulations for 12 of the 13 events were within 10%. Co-located observations from scanning and profiling radars, as well as reanalysis-derived synoptic and thermodynamic conditions, support the accuracy of the precipitation phases identified by the PIP. PIP observations for the phase transition events are compared to output from a parameterization based on wet bulb and near-surface lapse rates to produce a probability of solid precipitation. The PIP phase identification and the parameterization output are consistent. This work highlights the ability of the PIP to properly characterize hydrometeor phase and provide dependable precipitation accumulations under complicated mixed-phase and rain and snow (or vice versa) transition events.

Sensing of the rain rate and the movement of the rain field over a relatively small geographical area may often be necessary for agricultural purposes, water management objectives or transport management in airports or harbors. In this paper, an entirely passive method is suggested to sense rainfall rate and extension of a rain field, with reconstruction in two dimensions using a tomographic method. In order to achieve this goal, multiple colocated satellite receivers are proposed to measure the beacon signal levels of geostationary satellites received at different azimuth angles. The tomographic reconstruction of the rain field takes the advantage of the dis-placement of the rain field due to the movement of air masses, while the rain intensity along the receiving path is estimated by the attenuation of the radio signal between the satellite and the ground receiver. To realize the proposed system, a complete receiver setup and an existing, operational group of satellites are selected. A widely known rain field modeling method is applied to generate test data and evaluate the system. The feasibility of a technically realizable system is provided, and its capabilities are compared with a hypothetical, ideal system. The methods and algorithms are tested on an existing geographical location, simulating a real operating environment.

This study assesses where tropical cyclone (TC) surface winds can be measured as a function of footprint sizes and wavelengths (Ka- Ku- and C-band). During TCs, most high-resolution surface observations are impeded by considerable ‘rain contamination.’ Under these conditions, high-resolution surface observations typically come from operational aircraft. Other techniques that provide high-resolution surface observations through rain are also hindered somewhat by rain contamination and are very sparse in space and time. The impacts of rain are functions of the remotely sensed wavelength and rain–drop size. Therefore, relative long wavelengths have been used to observe the surface, but at the cost of a larger footprint. We examine how smaller footprint sizes could be used to observe through gaps between moderate to heavy rainbands that circulate around the main low-pressure center of a TC. Aircraft data from the National Oceanic and Atmospheric Administration’s (NOAA’s) WP-3D turboprop aircraft will be used to create realistic maps of rain. Our results provide information on the satellite instrument characteristics needed to see the surface through these gaps. This information is expected to aid in developing hurricane-related applications of new higher-resolution satellites.

This Special Issue hosts papers on all aspects of remote sensing of precipitation, including applications that embrace the use of remote-sensing techniques of precipitation in tackling issues, such as precipitation estimations and retrievals, along with their methodologies and corresponding error assessment; precipitation modelling including validation, instrument comparison, and calibration; understanding of cloud and precipitation microphysical properties; precipitation downscaling; precipitation droplet size distribution; assimilation of remotely sensed precipitation into numerical weather prediction models; and measurement of precipitable water vapor.

With the operational deployment of the *SFMR, hurricane reconnaissance and research aircraft provide near real-time observations of the 10 m ocean-surface wind-speed both within and around tropical cyclones. Hurricane specialists use these data to assist in determining wind radii and maximum sustained winds—critical parameters for determining and issuing watches and warnings. These observations are also used for post-storm analysis, model validation, and ground truth for aircraft- and satellite-based wind sensors. We present observations on the current operational wind-speed and rain-rate *SFMR retrieval procedures in the tropical cyclone environment and propose suggestions to improve them based on observed wind-speed biases. Using these new models in the *SFMR retrieval process, we correct an approximate 10% low bias in the wind-speed retrievals from 15 m / s –45 m / s with respect to *GPS dropwindsondes. In doing so, we eliminate the rain-contaminated wind-speed retrievals below 45/ h at tropical storm- and hurricane-force speeds present in the current operational model. We also update the *SFMR *RTM to include recent updates to smooth-ocean emissivity and atmospheric opacity models. All corrections were designed such that no changes to the current *SFMR calibration procedures are required.

A rainfall retrieval algorithm for tropical cyclones (TCs) using 18.7 and 36.5 GHz of vertically and horizontally polarized brightness temperatures (Tbs) from the Microwave Radiation Imager (MWRI) is presented. The beamfilling effect is corrected based on ratios of the retrieved liquid water absorption and theoretical Mie absorption coefficients at 18.7 and 36.5 GHz. To assess the performance of this algorithm, MWRI measurements are matched with the National Snow and Ice Data Center (NSIDC) precipitation for six TCs. The comparison between MWRI and NSIDC rain rates is relatively encouraging, with a mean bias of −0.14 mm/h and an overall root-mean-square error (RMSE) of 1.99 mm/h. A comparison of pixel-to-pixel retrievals shows that MWRI retrievals are constrained to reasonable levels for most rain categories, with a minimum error of −1.1% in the 10⁻15 mm/h category; however, with maximum errors around −22% at the lowest (0⁻0.5 mm/h) and highest rain rates (25⁻30 mm/h). Additionally, Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) Tbs are applied to retrieve rain rates to assess the sensitivity of this algorithm, with a mean bias and RMSE of 0.90 mm/h and 3.11 mm/h, respectively. For the case study of TC Maon (2011), MWRI retrievals underestimate rain rates over 6 mm/h and overestimate rain rates below 6 mm/h compared with Precipitation Radar (PR) observations on storm scales. The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) rainfall data provided by the Remote Sensing Systems (RSS) are applied to assess the representation of mesoscale structures in intense TCs, and they show good consistency with MWRI retrievals.

Because of its clear physical meaning, physical methods are more often used for space-borne microwave radiometers to retrieve the rain rate, but they are rarely used for ground-based microwave radiometers that are very sensitive to rainfall. In this article, an opacity physical retrieval method is implemented to retrieve the rain rate (denoted as Opa-RR) using ground-based microwave radiometer data (21.4 and 31.5 GHz) of the tropospheric water radiometer (TROWARA) at Bern, Switzerland from 2005 to 2019. The Opa-RR firstly establishes a direct connection between the rain rate and the enhanced atmospheric opacity during rain, then iteratively adjusts the rain effective temperature to determine the rain opacity, based on the radiative transfer equation, and finally estimates the rain rate. These estimations are compared with the available simultaneous rain rate derived from rain gauge data and reanalysis data (ERA5). The results and the intercomparison demonstrate that during moderate rains and at the 31 GHz channel, the Opa-RR method was close to the actual situation and capable of the rain rate estimation. In addition, the Opa-RR method can well derive the changes in cumulative rain over time (day, month, and year), and the monthly rain rate estimation is superior, with the rain gauge validated R2 and the root-mean-square error value of 0.77 and 22.46 mm/month, respectively. Compared with ERA5, Opa-RR at 31GHz achieves a competitive performance. [ABSTRACT FROM AUTHOR]

Surface precipitation phase is a fundamental meteorological property with immense importance. Accurate classification of phase from satellite remotely sensed observations is difficult. This study demonstrates the ability of the Precipitation Imaging Package (PIP), a ground-based, in situ precipitation imager, to distinguish precipitation phase. The PIP precipitation phase identification capabilities are compared to observer records from the National Weather Service (NWS) office in Marquette, Michigan, as well as co-located observations from profiling and scanning radars, disdrometer data, and surface meteorological measurements. Examined are 13 events with at least one precipitation phase transition. The PIP-determined onsets and endings of the respective precipitation phase periods agree to within 15 min of NWS observer records for the vast majority of the events. Additionally, the PIP and NWS liquid water equivalent accumulations for 12 of the 13 events were within 10%. Co-located observations from scanning and profiling radars, as well as reanalysis-derived synoptic and thermodynamic conditions, support the accuracy of the precipitation phases identified by the PIP. PIP observations for the phase transition events are compared to output from a parameterization based on wet bulb and near-surface lapse rates to produce a probability of solid precipitation. The PIP phase identification and the parameterization output are consistent. This work highlights the ability of the PIP to properly characterize hydrometeor phase and provide dependable precipitation accumulations under complicated mixed-phase and rain and snow (or vice versa) transition events. [ABSTRACT FROM AUTHOR]

Sensing of the rain rate and the movement of the rain field over a relatively small geographical area may often be necessary for agricultural purposes, water management objectives or transport management in airports or harbors. In this paper, an entirely passive method is suggested to sense rainfall rate and extension of a rain field, with reconstruction in two dimensions using a tomographic method. In order to achieve this goal, multiple colocated satellite receivers are proposed to measure the beacon signal levels of geostationary satellites received at different azimuth angles. The tomographic reconstruction of the rain field takes the advantage of the dis-placement of the rain field due to the movement of air masses, while the rain intensity along the receiving path is estimated by the attenuation of the radio signal between the satellite and the ground receiver. To realize the proposed system, a complete receiver setup and an existing, operational group of satellites are selected. A widely known rain field modeling method is applied to generate test data and evaluate the system. The feasibility of a technically realizable system is provided, and its capabilities are compared with a hypothetical, ideal system. The methods and algorithms are tested on an existing geographical location, simulating a real operating environment. [ABSTRACT FROM AUTHOR]

This study assesses where tropical cyclone (TC) surface winds can be measured as a function of footprint sizes and wavelengths (Ka- Ku- and C-band). During TCs, most high-resolution surface observations are impeded by considerable 'rain contamination.' Under these conditions, high-resolution surface observations typically come from operational aircraft. Other techniques that provide high-resolution surface observations through rain are also hindered somewhat by rain contamination and are very sparse in space and time. The impacts of rain are functions of the remotely sensed wavelength and rain–drop size. Therefore, relative long wavelengths have been used to observe the surface, but at the cost of a larger footprint. We examine how smaller footprint sizes could be used to observe through gaps between moderate to heavy rainbands that circulate around the main low-pressure center of a TC. Aircraft data from the National Oceanic and Atmospheric Administration's (NOAA's) WP-3D turboprop aircraft will be used to create realistic maps of rain. Our results provide information on the satellite instrument characteristics needed to see the surface through these gaps. This information is expected to aid in developing hurricane-related applications of new higher-resolution satellites. [ABSTRACT FROM AUTHOR]

The airborne Hurricane Imaging Radiometer (HIRAD) was developed to remotely sense hurricane surface wind speed (WS) and rain rate (RR) from a high-altitude aircraft. The approach was to obtain simultaneous brightness temperature measurements over a wide frequency range to independently retrieve the WS and RR. In the absence of rain, the WS retrieval has been robust; however, for moderate to high rain rates, the joint WS/RR retrieval has not been successful. The objective of this paper was to resolve this issue by developing an improved forward radiative transfer model (RTM) for the HIRAD cross-track viewing geometry, with separated upwelling and specularly reflected downwelling atmospheric paths. Furthermore, this paper presents empirical results from an unplanned opportunity that occurred when HIRAD measured brightness temperatures over an intense tropical squall line, which was simultaneously observed by a ground based NEXRAD (Next Generation Weather Radar) radar. The independently derived NEXRAD RR created the simultaneous 3D rain field "surface truth", which was used as an input to the RTM to generate HIRAD modeled brightness temperatures. This paper presents favorable results of comparisons of theoretical and the simultaneous, collocated HIRAD brightness temperature measurements that validate the accuracy of this new HIRAD RTM. [ABSTRACT FROM AUTHOR]

This Special Issue hosts papers on all aspects of remote sensing of precipitation, including applications that embrace the use of remote-sensing techniques of precipitation in tackling issues, such as precipitation estimations and retrievals, along with their methodologies and corresponding error assessment; precipitation modelling including validation, instrument comparison, and calibration; understanding of cloud and precipitation microphysical properties; precipitation downscaling; precipitation droplet size distribution; assimilation of remotely sensed precipitation into numerical weather prediction models; and measurement of precipitable water vapor. [ABSTRACT FROM AUTHOR]

With the operational deployment of the Stepped Frequency Microwave Radiometer (SFMR), hurricane reconnaissance and research aircraft provide near real-time observations of the 10 m ocean-surface wind-speed both within and around tropical cyclones. Hurricane specialists use these data to assist in determining wind radii and maximum sustained winds—critical parameters for determining and issuing watches and warnings. These observations are also used for post-storm analysis, model validation, and ground truth for aircraft- and satellite-based wind sensors. We present observations on the current operational wind-speed and rain-rate SFMR retrieval procedures in the tropical cyclone environment and propose suggestions to improve them based on observed wind-speed biases. Using these new models in the SFMR retrieval process, we correct an approximate 10% low bias in the wind-speed retrievals from 15 to 45 m s −1 with respect to GPS dropwindsondes. In doing so, we eliminate the rain-contaminated wind-speed retrievals below 45 mm h −1 at tropical storm- and hurricane-force speeds present in the current operational model. We also update the SFMR radiative transfer model to include recent updates to smooth-ocean emissivity and atmospheric opacity models. All corrections were designed such that no changes to the current SFMR calibration procedures are required. [ABSTRACT FROM AUTHOR]

A rainfall retrieval algorithm for tropical cyclones (TCs) using 18.7 and 36.5 GHz of vertically and horizontally polarized brightness temperatures (Tbs) from the Microwave Radiation Imager (MWRI) is presented. The beamfilling effect is corrected based on ratios of the retrieved liquid water absorption and theoretical Mie absorption coefficients at 18.7 and 36.5 GHz. To assess the performance of this algorithm, MWRI measurements are matched with the National Snow and Ice Data Center (NSIDC) precipitation for six TCs. The comparison between MWRI and NSIDC rain rates is relatively encouraging, with a mean bias of −0.14 mm/h and an overall root-mean-square error (RMSE) of 1.99 mm/h. A comparison of pixel-to-pixel retrievals shows that MWRI retrievals are constrained to reasonable levels for most rain categories, with a minimum error of −1.1% in the 10–15 mm/h category; however, with maximum errors around −22% at the lowest (0–0.5 mm/h) and highest rain rates (25–30 mm/h). Additionally, Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) Tbs are applied to retrieve rain rates to assess the sensitivity of this algorithm, with a mean bias and RMSE of 0.90 mm/h and 3.11 mm/h, respectively. For the case study of TC Maon (2011), MWRI retrievals underestimate rain rates over 6 mm/h and overestimate rain rates below 6 mm/h compared with Precipitation Radar (PR) observations on storm scales. The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) rainfall data provided by the Remote Sensing Systems (RSS) are applied to assess the representation of mesoscale structures in intense TCs, and they show good consistency with MWRI retrievals. [ABSTRACT FROM AUTHOR]