Browse
Abstract
Using passive microwave brightness temperatures Tb from the Global Precipitation Measurement (GPM) Microwave Imager (GMI) and hydrometeor identification (HID) data from dual-polarization ground radars, empirical lookup tables are developed for a multifrequency estimation of the likelihood a precipitation column includes certain hydrometeor types, as a function of Tb . Eight years of collocated Tb and HID data from the GPM Validation Network are used for development and testing of the GMI-based HID retrieval, with 2015–20 used for training and 2021–22 used for testing the GMI-based HID retrieval. The occurrence of profiles with hail and graupel are both slightly underpredicted by the lookup tables, but the percentage of profiles predicted is highly correlated with the percentage observed (0.98 correlation coefficient for hail and 0.99 for graupel). By having snow appear before rain in the hierarchy, the sample size for rain, without ice aloft, is fairly small, and the percentage of rain profiles is less than snow for all Tb .
Abstract
Using passive microwave brightness temperatures Tb from the Global Precipitation Measurement (GPM) Microwave Imager (GMI) and hydrometeor identification (HID) data from dual-polarization ground radars, empirical lookup tables are developed for a multifrequency estimation of the likelihood a precipitation column includes certain hydrometeor types, as a function of Tb . Eight years of collocated Tb and HID data from the GPM Validation Network are used for development and testing of the GMI-based HID retrieval, with 2015–20 used for training and 2021–22 used for testing the GMI-based HID retrieval. The occurrence of profiles with hail and graupel are both slightly underpredicted by the lookup tables, but the percentage of profiles predicted is highly correlated with the percentage observed (0.98 correlation coefficient for hail and 0.99 for graupel). By having snow appear before rain in the hierarchy, the sample size for rain, without ice aloft, is fairly small, and the percentage of rain profiles is less than snow for all Tb .
Abstract
Observations of clouds and precipitation in the microwave domain from the active dual-frequency precipitation radar (DPR) and the passive Global Precipitation Measurement (GPM) Microwave Imager (GMI) onboard the GPM Core Observatory satellite are used in synergy with cloud tracking information derived from infrared imagery from the GOES-13 and Meteosat-7 geostationary satellites for analysis of the life cycle of precipitating cloud systems, in terms of temporal evolution of their macrophysical characteristics, in several oceanic and continental regions of the tropics. The life cycle of each one of the several hundred thousand cloud systems tracked during the 2-yr (2015–16) analysis period is divided into five equal-duration stages between initiation and dissipation. The average cloud size, precipitation intensity, precipitation top height, and convective and stratiform precipitating fractions are documented at each stage of the life cycle for different cloud categories (based upon lifetime duration). The average life cycle dynamics is found remarkably homogeneous across the different regions and is consistent with previous studies: systems peak in size around midlife; precipitation intensity and convective fraction tend to decrease continuously from the initiation stage to the dissipation. Over the three continental regions, Amazonia (AMZ), central Africa (CAF), and Sahel (SAH), at the early stages of clouds’ life cycle, precipitation estimates from the passive GMI instrument are systematically found to be 15%–40% lower than active radar estimates. By highlighting stage-dependent biases in state-of-the-art passive microwave precipitation estimates over land, we demonstrate the potential usefulness of cloud tracking information for improving retrievals and suggest new directions for the synergistic use of geostationary and low-Earth-orbiting satellite observations.
Abstract
Observations of clouds and precipitation in the microwave domain from the active dual-frequency precipitation radar (DPR) and the passive Global Precipitation Measurement (GPM) Microwave Imager (GMI) onboard the GPM Core Observatory satellite are used in synergy with cloud tracking information derived from infrared imagery from the GOES-13 and Meteosat-7 geostationary satellites for analysis of the life cycle of precipitating cloud systems, in terms of temporal evolution of their macrophysical characteristics, in several oceanic and continental regions of the tropics. The life cycle of each one of the several hundred thousand cloud systems tracked during the 2-yr (2015–16) analysis period is divided into five equal-duration stages between initiation and dissipation. The average cloud size, precipitation intensity, precipitation top height, and convective and stratiform precipitating fractions are documented at each stage of the life cycle for different cloud categories (based upon lifetime duration). The average life cycle dynamics is found remarkably homogeneous across the different regions and is consistent with previous studies: systems peak in size around midlife; precipitation intensity and convective fraction tend to decrease continuously from the initiation stage to the dissipation. Over the three continental regions, Amazonia (AMZ), central Africa (CAF), and Sahel (SAH), at the early stages of clouds’ life cycle, precipitation estimates from the passive GMI instrument are systematically found to be 15%–40% lower than active radar estimates. By highlighting stage-dependent biases in state-of-the-art passive microwave precipitation estimates over land, we demonstrate the potential usefulness of cloud tracking information for improving retrievals and suggest new directions for the synergistic use of geostationary and low-Earth-orbiting satellite observations.
Abstract
NASA’s multisatellite precipitation product from the Global Precipitation Measurement (GPM) mission, the Integrated Multi-satellitE Retrievals for GPM (IMERG) product, is validated over tropical and high-latitude oceans from June 2014 to August 2021. This oceanic study uses the GPM Validation Network’s island-based radars to assess IMERG when the GPM Core Observatory’s Microwave Imager (GMI) observes precipitation at these sites (i.e., IMERG-GMI). Error tracing from the Level 3 (gridded) IMERG V06B product back through to the input Level 2 (satellite footprint) Goddard Profiling Algorithm GMI V05 climate (GPROF-CLIM) product quantifies the errors separately associated with each step in the gridding and calibration of the estimates from GPROF-CLIM to IMERG-GMI. Mean relative bias results indicate that IMERG-GMI V06B overestimates Alaskan high-latitude oceanic precipitation by +147% and tropical oceanic precipitation by +12% with respect to surface radars. GPROF-CLIM V05 overestimates Alaskan oceanic precipitation by +15%, showing that the IMERG algorithm’s calibration adjustments to the input GPROF-CLIM precipitation estimates increase the mean relative bias in this region. In contrast, IMERG adjustments are minimal over tropical waters with GPROF-CLIM overestimating oceanic precipitation by +14%. This study discovered that the IMERG V06B gridding process incorrectly geolocated GPROF-CLIM V05 precipitation estimates by 0.1° eastward in the latitude band 75°N–75°S, which has been rectified in the IMERG V07 algorithm. Correcting for the geolocation error in IMERG-GMI V06B improved oceanic statistics, with improvements greater in tropical waters than Alaskan waters. This error tracing approach enables a high-precision diagnosis of how different IMERG algorithm steps contribute to and mitigate errors, demonstrating the importance of collaboration between evaluation studies and algorithm developers.
Significance Statement
Evaluation of IMERG’s oceanic performance is very limited to date. This study uses the GPM Validation Network to conduct the first extensive assessment of IMERG V06B at its native resolution over both high-latitude and tropical oceans, and traces errors in IMERG-GMI back through to the input GPROF-CLIM GMI product. IMERG-GMI overestimates tropical oceanic precipitation (+12%) and strongly overestimates Alaskan oceanic precipitation (+147%) with respect to the island-based radars studied. IMERG’s GMI estimates are assessed as these should be the optimal estimates within the multisatellite product due to the GMI’s status as calibrator of the GPM passive microwave constellation.
Abstract
NASA’s multisatellite precipitation product from the Global Precipitation Measurement (GPM) mission, the Integrated Multi-satellitE Retrievals for GPM (IMERG) product, is validated over tropical and high-latitude oceans from June 2014 to August 2021. This oceanic study uses the GPM Validation Network’s island-based radars to assess IMERG when the GPM Core Observatory’s Microwave Imager (GMI) observes precipitation at these sites (i.e., IMERG-GMI). Error tracing from the Level 3 (gridded) IMERG V06B product back through to the input Level 2 (satellite footprint) Goddard Profiling Algorithm GMI V05 climate (GPROF-CLIM) product quantifies the errors separately associated with each step in the gridding and calibration of the estimates from GPROF-CLIM to IMERG-GMI. Mean relative bias results indicate that IMERG-GMI V06B overestimates Alaskan high-latitude oceanic precipitation by +147% and tropical oceanic precipitation by +12% with respect to surface radars. GPROF-CLIM V05 overestimates Alaskan oceanic precipitation by +15%, showing that the IMERG algorithm’s calibration adjustments to the input GPROF-CLIM precipitation estimates increase the mean relative bias in this region. In contrast, IMERG adjustments are minimal over tropical waters with GPROF-CLIM overestimating oceanic precipitation by +14%. This study discovered that the IMERG V06B gridding process incorrectly geolocated GPROF-CLIM V05 precipitation estimates by 0.1° eastward in the latitude band 75°N–75°S, which has been rectified in the IMERG V07 algorithm. Correcting for the geolocation error in IMERG-GMI V06B improved oceanic statistics, with improvements greater in tropical waters than Alaskan waters. This error tracing approach enables a high-precision diagnosis of how different IMERG algorithm steps contribute to and mitigate errors, demonstrating the importance of collaboration between evaluation studies and algorithm developers.
Significance Statement
Evaluation of IMERG’s oceanic performance is very limited to date. This study uses the GPM Validation Network to conduct the first extensive assessment of IMERG V06B at its native resolution over both high-latitude and tropical oceans, and traces errors in IMERG-GMI back through to the input GPROF-CLIM GMI product. IMERG-GMI overestimates tropical oceanic precipitation (+12%) and strongly overestimates Alaskan oceanic precipitation (+147%) with respect to the island-based radars studied. IMERG’s GMI estimates are assessed as these should be the optimal estimates within the multisatellite product due to the GMI’s status as calibrator of the GPM passive microwave constellation.
Abstract
Recent voyages of the Australian R/V Investigator across the remote Southern Ocean have provided unprecedented observations of precipitation made with both an Ocean Rainfall and Ice-Phase Precipitation Measurement Network (OceanRAIN) maritime disdrometer and a dual-polarization C-band weather radar (OceanPOL). This present study employs these observations to evaluate the Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) and the fifth major global reanalysis produced by ECMWF (ERA5) precipitation products. Working at a resolution of 60 min and 0.25° (∼25 km), light rain and drizzle are most frequently observed across the region. The IMERG product overestimated precipitation intensity when evaluated against the OceanRAIN but captured the frequency of occurrence well. Looking at the synoptic/process scale, IMERG was found to be the least accurate (overestimated intensity) under warm-frontal and high-latitude cyclone conditions, where multilayer clouds were commonly present. Under postfrontal conditions, IMERG underestimated the precipitation frequency. In comparison, ERA5’s skill was more consistent across various synoptic conditions, except for high pressure conditions where the precipitation frequency (intensity) was highly overestimated (underestimated). Using the OceanPOL radar, an area-to-area analysis (fractional skill score) finds that ERA5 has greater skill than IMERG. There is little agreement in the phase classification between the OceanRAIN disdrometer, IMERG, and ERA5. The comparisons are complicated by the various assumptions for phase classification in the different datasets.
Significance Statement
Our best quantitative estimates of precipitation over the remote, pristine Southern Ocean (SO) continue to suffer from a high degree of uncertainty, with large differences present among satellite-based and reanalysis products. New instrumentation on the R/V Investigator, specifically a dual-polarization C-band weather radar (OceanPOL) and a maritime disdrometer (OceanRAIN), provide unprecedented high-quality observations of precipitation across the SO that will aid in improving precipitation estimates in this region. We use these observations to evaluate the IMERG and ERA5 precipitation products. We find that, in general, IMERG overestimated precipitation intensity, but captured the frequency of occurrence well. In comparison, ERA5 was found to overestimate the frequency of precipitation. Using the OceanPOL radar, an area-to-area analysis finds that ERA5 has greater skill than IMERG.
Abstract
Recent voyages of the Australian R/V Investigator across the remote Southern Ocean have provided unprecedented observations of precipitation made with both an Ocean Rainfall and Ice-Phase Precipitation Measurement Network (OceanRAIN) maritime disdrometer and a dual-polarization C-band weather radar (OceanPOL). This present study employs these observations to evaluate the Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) and the fifth major global reanalysis produced by ECMWF (ERA5) precipitation products. Working at a resolution of 60 min and 0.25° (∼25 km), light rain and drizzle are most frequently observed across the region. The IMERG product overestimated precipitation intensity when evaluated against the OceanRAIN but captured the frequency of occurrence well. Looking at the synoptic/process scale, IMERG was found to be the least accurate (overestimated intensity) under warm-frontal and high-latitude cyclone conditions, where multilayer clouds were commonly present. Under postfrontal conditions, IMERG underestimated the precipitation frequency. In comparison, ERA5’s skill was more consistent across various synoptic conditions, except for high pressure conditions where the precipitation frequency (intensity) was highly overestimated (underestimated). Using the OceanPOL radar, an area-to-area analysis (fractional skill score) finds that ERA5 has greater skill than IMERG. There is little agreement in the phase classification between the OceanRAIN disdrometer, IMERG, and ERA5. The comparisons are complicated by the various assumptions for phase classification in the different datasets.
Significance Statement
Our best quantitative estimates of precipitation over the remote, pristine Southern Ocean (SO) continue to suffer from a high degree of uncertainty, with large differences present among satellite-based and reanalysis products. New instrumentation on the R/V Investigator, specifically a dual-polarization C-band weather radar (OceanPOL) and a maritime disdrometer (OceanRAIN), provide unprecedented high-quality observations of precipitation across the SO that will aid in improving precipitation estimates in this region. We use these observations to evaluate the IMERG and ERA5 precipitation products. We find that, in general, IMERG overestimated precipitation intensity, but captured the frequency of occurrence well. In comparison, ERA5 was found to overestimate the frequency of precipitation. Using the OceanPOL radar, an area-to-area analysis finds that ERA5 has greater skill than IMERG.
Abstract
The Dual-Frequency Precipitation Radar (DPR), which consists of a Ku-band precipitation radar (KuPR) and a Ka-band precipitation radar (KaPR) on board the GPM Core Observatory, cannot observe precipitation at low altitudes near the ground contaminated by surface clutter. This near-surface region is called the blind zone. DPR estimates the clutter-free bottom (CFB), which is the lowest altitude not included in the blind zone, and estimates precipitation at altitudes higher than the CFB. High CFBs, which are common over mountainous areas, represent obstacles to detection of shallow precipitation and estimation of low-level enhanced precipitation. We compared KuPR data with rain gauge data from Da-Tun Mountain of northern Taiwan acquired from March 2014 to February 2020. A total of 12 cases were identified in which the KuPR missed some rainfall with intensity of >10 mm h−1 that was observed by rain gauges. Comparison of KuPR profile and ground-based radar profile revealed that shallow precipitation in the KuPR blind zone was missed because the CFB was estimated to be higher than the lower bound of the range free from surface echoes. In the original operational algorithm, CFB was estimated using only the received power data of the KuPR. In this study, the CFB was identified by the sharp increase in the difference between the received powers of the KuPR and the KaPR at altitude affected by surface clutter. By lowering the CFB, the KuPR succeeded in detection and estimation of shallow precipitation.
Significance Statement
The Dual-Frequency Precipitation Radar (DPR) on board the GPM Core Observatory cannot capture precipitation in the low-altitude region near the ground contaminated by surface clutter. This region is called the blind zone. The DPR estimates the clutter-free bottom (CFB), which is the lower bound of the range free from surface echoes, and uses data higher than CFB. DPR consists of a Ku-band precipitation radar (KuPR) and a Ka-band precipitation radar (KaPR). KuPR missed some shallow precipitation more than 10 mm h−1 in the blind zone over Da-Tun Mountain of northern Taiwan because of misjudged CFB estimation. Using both the KuPR and the KaPR, we improved the CFB estimation algorithm, which lowered the CFB, narrowed the blind zone, and improved the capability to detect shallow precipitation.
Abstract
The Dual-Frequency Precipitation Radar (DPR), which consists of a Ku-band precipitation radar (KuPR) and a Ka-band precipitation radar (KaPR) on board the GPM Core Observatory, cannot observe precipitation at low altitudes near the ground contaminated by surface clutter. This near-surface region is called the blind zone. DPR estimates the clutter-free bottom (CFB), which is the lowest altitude not included in the blind zone, and estimates precipitation at altitudes higher than the CFB. High CFBs, which are common over mountainous areas, represent obstacles to detection of shallow precipitation and estimation of low-level enhanced precipitation. We compared KuPR data with rain gauge data from Da-Tun Mountain of northern Taiwan acquired from March 2014 to February 2020. A total of 12 cases were identified in which the KuPR missed some rainfall with intensity of >10 mm h−1 that was observed by rain gauges. Comparison of KuPR profile and ground-based radar profile revealed that shallow precipitation in the KuPR blind zone was missed because the CFB was estimated to be higher than the lower bound of the range free from surface echoes. In the original operational algorithm, CFB was estimated using only the received power data of the KuPR. In this study, the CFB was identified by the sharp increase in the difference between the received powers of the KuPR and the KaPR at altitude affected by surface clutter. By lowering the CFB, the KuPR succeeded in detection and estimation of shallow precipitation.
Significance Statement
The Dual-Frequency Precipitation Radar (DPR) on board the GPM Core Observatory cannot capture precipitation in the low-altitude region near the ground contaminated by surface clutter. This region is called the blind zone. The DPR estimates the clutter-free bottom (CFB), which is the lower bound of the range free from surface echoes, and uses data higher than CFB. DPR consists of a Ku-band precipitation radar (KuPR) and a Ka-band precipitation radar (KaPR). KuPR missed some shallow precipitation more than 10 mm h−1 in the blind zone over Da-Tun Mountain of northern Taiwan because of misjudged CFB estimation. Using both the KuPR and the KaPR, we improved the CFB estimation algorithm, which lowered the CFB, narrowed the blind zone, and improved the capability to detect shallow precipitation.
Abstract
Several decades of continuous improvements in satellite precipitation algorithms have resulted in fairly accurate level-2 precipitation products for local-scale applications. Numerous studies have been carried out to quantify random and systematic errors at individual validation sites and regional networks. Understanding uncertainties at larger scales, however, has remained a challenge. Temporal changes in precipitation regional biases, regime morphology, sampling, and observation-vector information content, all play important roles in defining the accuracy of satellite rainfall retrievals. This study considers these contributors to offer a quantitative estimate of uncertainty in recently produced global precipitation climate data record. Generated from intercalibrated observations collected by a constellation of passive microwave (PMW) radiometers over the course of 30 years, this data record relies on Global Precipitation Measurement (GPM) mission enterprise PMW precipitation retrieval to offer a long-term global monthly precipitation estimates with corresponding uncertainty at 5° scales. To address changes in the information content across different constellation members the study develops synthetic datasets from GPM Microwave Imager (GMI) sensor, while sampling- and morphology-related uncertainties are quantified using GPM’s dual-frequency precipitation radar (DPR). Special attention is given to separating precipitation into self-similar states that appear to be consistent across environmental conditions. Results show that the variability of bias patterns can be explained by the relative occurrence of different precipitation states across the regions and used to calculate product’s uncertainty. It is found that at 5° spatial scale monthly mean precipitation uncertainties in tropics can exceed 10%.
Abstract
Several decades of continuous improvements in satellite precipitation algorithms have resulted in fairly accurate level-2 precipitation products for local-scale applications. Numerous studies have been carried out to quantify random and systematic errors at individual validation sites and regional networks. Understanding uncertainties at larger scales, however, has remained a challenge. Temporal changes in precipitation regional biases, regime morphology, sampling, and observation-vector information content, all play important roles in defining the accuracy of satellite rainfall retrievals. This study considers these contributors to offer a quantitative estimate of uncertainty in recently produced global precipitation climate data record. Generated from intercalibrated observations collected by a constellation of passive microwave (PMW) radiometers over the course of 30 years, this data record relies on Global Precipitation Measurement (GPM) mission enterprise PMW precipitation retrieval to offer a long-term global monthly precipitation estimates with corresponding uncertainty at 5° scales. To address changes in the information content across different constellation members the study develops synthetic datasets from GPM Microwave Imager (GMI) sensor, while sampling- and morphology-related uncertainties are quantified using GPM’s dual-frequency precipitation radar (DPR). Special attention is given to separating precipitation into self-similar states that appear to be consistent across environmental conditions. Results show that the variability of bias patterns can be explained by the relative occurrence of different precipitation states across the regions and used to calculate product’s uncertainty. It is found that at 5° spatial scale monthly mean precipitation uncertainties in tropics can exceed 10%.
Removing Interfering Signals in Spaceborne Radar Data for Precipitation Detection at Very High AltitudesAbstract
This study investigated the effects of interfering signals on high-altitude precipitation extraction from spaceborne precipitation radar data. Data analyses were performed on the products of the Tropical Rainfall Measuring Mission Precipitation Radar (TRMM PR) and the Global Precipitation Measurement Core Observatory Dual-Frequency Precipitation Radar (GPM DPR) to clarify the effects of removing radio interferences and mirror images, particularly focusing on deep precipitation detection. The TRMM PR acquired precipitation data up to an altitude of approximately 20 km and occasionally captured interferences from artificial radio transmissions in specific areas. Artifacts could be distinguished as isolated profiles exhibiting almost constant radar reflectivity. The number of interferences affecting the TRMM PR gradually increased during the operation period of 1998–2013. A filter was introduced to separate the observed profiles into deep storms that reach the upper observation altitude and contamination caused by radio interference. The former frequently appeared over the Sahel area, where the observation upper limits are lowest. The removal of the latter, radio interference, improved the detection accuracy of the mean precipitation at high altitudes and considerably influenced specific low-precipitation areas such as the Middle East. This spatial feature–based filter allowed us to evaluate the results of screening based on noise limits that are implemented in standard algorithms. The GPM DPR Ku-band radar product contained other unwanted echoes due to the mirror images appearing as second-trip echoes contaminating the high-altitude statistics. Such second-trip echoes constitute a major portion of the echoes observed near the highest altitudes of deep storms.
Significance Statement
Understanding the current state of separation of naturally occurring precipitation signals from artificial interference signals in spaceborne radar data at altitudes of approximately 20 km is critical for gaining a comprehensive picture of the intensity and structure of precipitation systems. In the case of the TRMM PR data, artifacts could be distinguished as isolated profiles with an almost constant radar reflectivity, and interferences gradually increased during the operation period. The removal of radio interference considerably affects the statistics of extremely deep storms. Improved algorithms and observation techniques have expanded the observation coverage associated with the GPM DPR KuPR data, but there are interferences (mirror images) that should be removed for a thorough discussion of very high-altitude precipitation.
Abstract
This study investigated the effects of interfering signals on high-altitude precipitation extraction from spaceborne precipitation radar data. Data analyses were performed on the products of the Tropical Rainfall Measuring Mission Precipitation Radar (TRMM PR) and the Global Precipitation Measurement Core Observatory Dual-Frequency Precipitation Radar (GPM DPR) to clarify the effects of removing radio interferences and mirror images, particularly focusing on deep precipitation detection. The TRMM PR acquired precipitation data up to an altitude of approximately 20 km and occasionally captured interferences from artificial radio transmissions in specific areas. Artifacts could be distinguished as isolated profiles exhibiting almost constant radar reflectivity. The number of interferences affecting the TRMM PR gradually increased during the operation period of 1998–2013. A filter was introduced to separate the observed profiles into deep storms that reach the upper observation altitude and contamination caused by radio interference. The former frequently appeared over the Sahel area, where the observation upper limits are lowest. The removal of the latter, radio interference, improved the detection accuracy of the mean precipitation at high altitudes and considerably influenced specific low-precipitation areas such as the Middle East. This spatial feature–based filter allowed us to evaluate the results of screening based on noise limits that are implemented in standard algorithms. The GPM DPR Ku-band radar product contained other unwanted echoes due to the mirror images appearing as second-trip echoes contaminating the high-altitude statistics. Such second-trip echoes constitute a major portion of the echoes observed near the highest altitudes of deep storms.
Significance Statement
Understanding the current state of separation of naturally occurring precipitation signals from artificial interference signals in spaceborne radar data at altitudes of approximately 20 km is critical for gaining a comprehensive picture of the intensity and structure of precipitation systems. In the case of the TRMM PR data, artifacts could be distinguished as isolated profiles with an almost constant radar reflectivity, and interferences gradually increased during the operation period. The removal of radio interference considerably affects the statistics of extremely deep storms. Improved algorithms and observation techniques have expanded the observation coverage associated with the GPM DPR KuPR data, but there are interferences (mirror images) that should be removed for a thorough discussion of very high-altitude precipitation.
Abstract
The accuracy and uncertainty of radar echo-top heights estimated by ground-based radars remain largely unknown despite their critical importance for applications ranging from aviation weather forecasting to severe weather diagnosis. Because the vantage point of space is more suited than that of ground-based radars for the estimation of echo-top heights, the use of spaceborne radar observations is explored as an external reference for cross comparison. An investigation has been carried out across the conterminous United States by comparing the NOAA/National Severe Storms Laboratory Multi-Radar Multi-Sensor (MRMS) system with the space-based radar on board the NASA–JAXA Global Precipitation Measurement satellite platform. No major bias was assessed between the two products. An annual cycle of differences is found, driven by an underestimation of the stratiform cloud echo-top heights and an overestimation of the convective ones. The investigation of the systematic biases for different radar volume coverage patterns (VCP) shows that scanning strategies with fewer tilts and greater voids as VCP 21/121/221 contribute to overestimations observed for high MRMS tops. For VCP 12/212, the automated volume scan evaluation and termination (AVSET) function increases the radar cone of silence, causing overestimations when the echo top lies above the highest elevation scan. However, it seems that for low echo tops the shorter refresh rates contribute to mitigate underestimations, especially in stratiform cases.
Abstract
The accuracy and uncertainty of radar echo-top heights estimated by ground-based radars remain largely unknown despite their critical importance for applications ranging from aviation weather forecasting to severe weather diagnosis. Because the vantage point of space is more suited than that of ground-based radars for the estimation of echo-top heights, the use of spaceborne radar observations is explored as an external reference for cross comparison. An investigation has been carried out across the conterminous United States by comparing the NOAA/National Severe Storms Laboratory Multi-Radar Multi-Sensor (MRMS) system with the space-based radar on board the NASA–JAXA Global Precipitation Measurement satellite platform. No major bias was assessed between the two products. An annual cycle of differences is found, driven by an underestimation of the stratiform cloud echo-top heights and an overestimation of the convective ones. The investigation of the systematic biases for different radar volume coverage patterns (VCP) shows that scanning strategies with fewer tilts and greater voids as VCP 21/121/221 contribute to overestimations observed for high MRMS tops. For VCP 12/212, the automated volume scan evaluation and termination (AVSET) function increases the radar cone of silence, causing overestimations when the echo top lies above the highest elevation scan. However, it seems that for low echo tops the shorter refresh rates contribute to mitigate underestimations, especially in stratiform cases.
Abstract
Satellite-based rainfall estimates are a great resource for data-scarce regions, including urban regions, because of its finer resolution. Integrated Multi-satellitE Retrievals for GPM (IMERG) is a widely used product and is evaluated at a city scale for the Hyderabad region using two different ground truths, i.e., India Meteorological Department (IMD) gridded rainfall and Telangana State Development Planning Society (TSDPS) automatic weather station (AWS) measured rainfall. The IMERG rainfall estimates are evaluated on multiple spatial and temporal scales as well as on a rainfall event scale. Both continuous and categorical verification metrics suggest good performance of IMERG on the daily scale; however, relatively decreased performance was observed on the hourly scale. Underestimated and overestimated IMERG estimates with respect to IMD gridded rainfall and AWS measured rainfall, respectively, suggest the performance depends on type of ground truth. Unlike categorical metrics, RMSE and PBIAS have a pattern implying a systematic error with respect to rainfall amount. Further, sample size, diurnal variations, and season are found to have a role in IMERG estimates’ performance. Temporal aggregation of hourly to daily time scales showed the improved IMERG performance; however, no spatial-scale dependence was observed among zonewise and Hyderabad region–wise rainfall estimates. Comparison of raw and bias-corrected IMERG rainfall-based intensity–duration–frequency (IDF) curves with corresponding hourly rain gauge IDF curves showcases the value addition via simple bias correction techniques. Overall, the study suggests the IMERG estimates can be used as an alternative data source, and it can be further improved by modifying the retrieval algorithm.
Significance Statement
Many urban regions are typically data sparse, which limits scientific understanding and reliable engineering designs of various urban hydrometeorology-relevant tasks, including climatological and extreme rainfall characterization, flood hazard assessment, and stormwater management systems. Satellite rainfall estimates come as a great resource and Integrated Multi-satellitE Retrievals for GPM (IMERG) acts as a best alternative. The Hyderabad region, the sixth-largest metropolitan area in India, is selected to analyze the widely used satellite estimates, i.e., retrievals for GPM. The study observed inaccuracies in the IMERG estimates that varied with rainfall magnitudes and space and time scales; nonetheless, the estimates can be used as an alternative data source for decision-making such as whether rain exceeds a certain threshold or not.
Abstract
Satellite-based rainfall estimates are a great resource for data-scarce regions, including urban regions, because of its finer resolution. Integrated Multi-satellitE Retrievals for GPM (IMERG) is a widely used product and is evaluated at a city scale for the Hyderabad region using two different ground truths, i.e., India Meteorological Department (IMD) gridded rainfall and Telangana State Development Planning Society (TSDPS) automatic weather station (AWS) measured rainfall. The IMERG rainfall estimates are evaluated on multiple spatial and temporal scales as well as on a rainfall event scale. Both continuous and categorical verification metrics suggest good performance of IMERG on the daily scale; however, relatively decreased performance was observed on the hourly scale. Underestimated and overestimated IMERG estimates with respect to IMD gridded rainfall and AWS measured rainfall, respectively, suggest the performance depends on type of ground truth. Unlike categorical metrics, RMSE and PBIAS have a pattern implying a systematic error with respect to rainfall amount. Further, sample size, diurnal variations, and season are found to have a role in IMERG estimates’ performance. Temporal aggregation of hourly to daily time scales showed the improved IMERG performance; however, no spatial-scale dependence was observed among zonewise and Hyderabad region–wise rainfall estimates. Comparison of raw and bias-corrected IMERG rainfall-based intensity–duration–frequency (IDF) curves with corresponding hourly rain gauge IDF curves showcases the value addition via simple bias correction techniques. Overall, the study suggests the IMERG estimates can be used as an alternative data source, and it can be further improved by modifying the retrieval algorithm.
Significance Statement
Many urban regions are typically data sparse, which limits scientific understanding and reliable engineering designs of various urban hydrometeorology-relevant tasks, including climatological and extreme rainfall characterization, flood hazard assessment, and stormwater management systems. Satellite rainfall estimates come as a great resource and Integrated Multi-satellitE Retrievals for GPM (IMERG) acts as a best alternative. The Hyderabad region, the sixth-largest metropolitan area in India, is selected to analyze the widely used satellite estimates, i.e., retrievals for GPM. The study observed inaccuracies in the IMERG estimates that varied with rainfall magnitudes and space and time scales; nonetheless, the estimates can be used as an alternative data source for decision-making such as whether rain exceeds a certain threshold or not.
Abstract
Parameters of the normalized gamma particle size distribution (PSD) have been retrieved from the Precipitation Image Package (PIP) snowfall observations collected during the International Collaborative Experiment–PyeongChang Olympic and Paralympic winter games (ICE-POP 2018). Two of the gamma PSD parameters, the mass-weighted particle diameter D mass and the normalized intercept parameter NW , have median values of 1.15–1.31 mm and 2.84–3.04 log(mm−1 m−3), respectively. This range arises from the choice of the relationship between the maximum versus equivalent diameter, D mx–D eq, and the relationship between the Reynolds and Best numbers, Re–X. Normalization of snow water equivalent rate (SWER) and ice water content W by NW reduces the range in NW , resulting in well-fitted power-law relationships between SWER/NW and D mass and between W/NW and D mass. The bulk descriptors of snowfall are calculated from PIP observations and from the gamma PSD with values of the shape parameter μ ranging from −2 to 10. NASA’s Global Precipitation Measurement (GPM) mission, which adopted the normalized gamma PSD, assumes μ = 2 and 3 in its two separate algorithms. The mean fractional bias (MFB) of the snowfall parameters changes with μ, where the functional dependence on μ depends on the specific snowfall parameter of interest. The MFB of the total concentration was underestimated by 0.23–0.34 when μ = 2 and by 0.29–0.40 when μ = 3, whereas the MFB of SWER had a much narrower range (from −0.03 to 0.04) for the same μ values.
Abstract
Parameters of the normalized gamma particle size distribution (PSD) have been retrieved from the Precipitation Image Package (PIP) snowfall observations collected during the International Collaborative Experiment–PyeongChang Olympic and Paralympic winter games (ICE-POP 2018). Two of the gamma PSD parameters, the mass-weighted particle diameter D mass and the normalized intercept parameter NW , have median values of 1.15–1.31 mm and 2.84–3.04 log(mm−1 m−3), respectively. This range arises from the choice of the relationship between the maximum versus equivalent diameter, D mx–D eq, and the relationship between the Reynolds and Best numbers, Re–X. Normalization of snow water equivalent rate (SWER) and ice water content W by NW reduces the range in NW , resulting in well-fitted power-law relationships between SWER/NW and D mass and between W/NW and D mass. The bulk descriptors of snowfall are calculated from PIP observations and from the gamma PSD with values of the shape parameter μ ranging from −2 to 10. NASA’s Global Precipitation Measurement (GPM) mission, which adopted the normalized gamma PSD, assumes μ = 2 and 3 in its two separate algorithms. The mean fractional bias (MFB) of the snowfall parameters changes with μ, where the functional dependence on μ depends on the specific snowfall parameter of interest. The MFB of the total concentration was underestimated by 0.23–0.34 when μ = 2 and by 0.29–0.40 when μ = 3, whereas the MFB of SWER had a much narrower range (from −0.03 to 0.04) for the same μ values.
