Global Precipitation Measurement (GPM): Science and Applications
Description:
Water is essential to our planet Earth. Knowing when, where and how precipitation falls is crucial for understanding the linkages between the Earth’s water and energy cycles and is extraordinarily important for sustaining life on our planet. The Global Precipitation Measurement (GPM) Core Observatory spacecraft, launched February 27, 2014 in a partnership between NASA and the Japan Aerospace Exploration Agency (JAXA), is the anchor to unify and advance precipitation measurements from a constellation of available research and operational precipitation satellite sensors.
The GPM mission provides essential 2-, 3-, and/or 4-dimensional data at scales from microphysics of rain and snow particles to regional storm events to global patterns of precipitation. The GPM products are important for both scientific investigations and societal applications and allow for detailed investigations of the distribution of precipitation and how patterns change over days, seasons, and years. GPM advances precipitation measurements from space; enhances knowledge of precipitation systems, water cycle variability and freshwater availability; and provides details essential for improving climate, weather, and hydrological modeling and prediction. GPM data are also used to model and estimate hazard impacts (e.g. floods, typhoons, and droughts), weather related disasters, agricultural forecasting, and famine warnings.
An overview paper can be found here. The GPM mission also has related AMS special collections on Retrieval Algorithms and on the ground validation field campaign called IFloodS.
Collection organizers:
Gail Skofronick-Jackson, NASA Goddard Space Flight Center
George Huffman, NASA Goddard Space Flight Center
Walter Petersen, NASA Marshall Space Flight Center
Dalia Kirschbaum, NASA Goddard Space Flight Center
Wesley Berg, Colorado State University
Yukari Takayabu, The University of Tokyo
Global Precipitation Measurement (GPM): Science and Applications
Abstract
The Atmospheric Radiative Transfer Simulator was used to conduct several simulations of Global Precipitation Measurement Microwave Imager brightness temperatures (BTs; 10.65–183.31 ± 7-GHz) over a severe hailstorm. Simulations were conducted to test the sensitivity of BTs to particle size distribution form and to the size, orientation, and shape of several hydrometeor types assuming constant S-band radar reflectivity. Results show an increase in BT (i.e., less scattering) at most frequencies when changing from a normalized gamma distribution (NGD) to exponential distribution (EXPD). This change causes a decrease in cumulative hydrometeor surface area, but not necessarily a decrease in number concentration, suggesting that surface area exerts a stronger influence on BTs than concentration. Simulated BTs at the highest frequencies (166.0–183.31 ± 7 GHz) agree better with observations when using an EXPD for cloud ice. At lower frequencies, especially 36.5–89.0 GHz, using an NGD for high-density graupel and hail leads to a better match between simulated and observed BTs. No clear preference is seen for low-density graupel, liquid precipitation, or snow. The impact of changing particle shape and/or orientation depends on the hydrometeor type. Changing the orientation of cloud ice from horizontal to a random orientation increases simulated BTs, while having no effect for high-density graupel. Assuming horizontally oriented, oblate-spheroid cloud ice results in simulated BTs that match better with observed. Finally, under the fixed reflectivity constraint, increasing the diameter of hail from 0.5 to 20 cm results in an increase in minimum BT up to 1.5-cm diameter with near constant BT at all frequencies thereafter.
Significance Statement
Accurate estimates of precipitation are important for numerous applications, and only satellite instruments can provide a global, uniform estimate of precipitation. This study seeks to use simulations to better understand how microwave radiation interacts with various hydrometeor types to improve the assumptions on which satellite-based precipitation estimates are based and ultimately improve the estimates themselves. Results indicate that an exponential distribution may be more appropriate for cloud ice and a normalized gamma distribution more appropriate for hail and high-density graupel. Other hydrometeor types (e.g., rain) show no clear preference for either distribution. Furthermore, assuming cloud ice is an oblate spheroid with horizontal orientation produces simulated brightness temperatures that better match those observed than assuming spherical ice or other orientations.
Abstract
The Atmospheric Radiative Transfer Simulator was used to conduct several simulations of Global Precipitation Measurement Microwave Imager brightness temperatures (BTs; 10.65–183.31 ± 7-GHz) over a severe hailstorm. Simulations were conducted to test the sensitivity of BTs to particle size distribution form and to the size, orientation, and shape of several hydrometeor types assuming constant S-band radar reflectivity. Results show an increase in BT (i.e., less scattering) at most frequencies when changing from a normalized gamma distribution (NGD) to exponential distribution (EXPD). This change causes a decrease in cumulative hydrometeor surface area, but not necessarily a decrease in number concentration, suggesting that surface area exerts a stronger influence on BTs than concentration. Simulated BTs at the highest frequencies (166.0–183.31 ± 7 GHz) agree better with observations when using an EXPD for cloud ice. At lower frequencies, especially 36.5–89.0 GHz, using an NGD for high-density graupel and hail leads to a better match between simulated and observed BTs. No clear preference is seen for low-density graupel, liquid precipitation, or snow. The impact of changing particle shape and/or orientation depends on the hydrometeor type. Changing the orientation of cloud ice from horizontal to a random orientation increases simulated BTs, while having no effect for high-density graupel. Assuming horizontally oriented, oblate-spheroid cloud ice results in simulated BTs that match better with observed. Finally, under the fixed reflectivity constraint, increasing the diameter of hail from 0.5 to 20 cm results in an increase in minimum BT up to 1.5-cm diameter with near constant BT at all frequencies thereafter.
Significance Statement
Accurate estimates of precipitation are important for numerous applications, and only satellite instruments can provide a global, uniform estimate of precipitation. This study seeks to use simulations to better understand how microwave radiation interacts with various hydrometeor types to improve the assumptions on which satellite-based precipitation estimates are based and ultimately improve the estimates themselves. Results indicate that an exponential distribution may be more appropriate for cloud ice and a normalized gamma distribution more appropriate for hail and high-density graupel. Other hydrometeor types (e.g., rain) show no clear preference for either distribution. Furthermore, assuming cloud ice is an oblate spheroid with horizontal orientation produces simulated brightness temperatures that better match those observed than assuming spherical ice or other orientations.
Abstract
In this study, we diagnose the onset and demise of the rainy season from the daily rainfall over West Africa. We then produce a probabilistic seasonal outlook of the rainy season over this region based on the observed variations of the onset date of the season, which verifies well with observations. We generated 101 ensemble members at every grid point by randomly perturbing the observed series of daily rainfall data obtained from the Integrated Multi-Satellite Retrievals for Global Precipitation Mission version 6 (IMERG) rainfall analysis. The generated ensemble of time series of daily rainfall accounts for uncertainties at meso to synoptic scales that could arise in the generation of the observed rainfall analysis. The ensemble members provide a robust estimate of the onset and demise dates of the rainy season. We find that the interannual variations in the onset and demise dates of the rainy season in West Africa significantly influence the corresponding anomalies of the seasonal length and rainfall. Additionally, interannual variability of the onset dates dominates over the demise dates of the rainy season across West Africa. In contrast, their association with remote, large-scale forcing is not found to be as significant. In addition, we found that the African Easterly Jet (AEJ) is displaced southward or northward in early or late onset seasons, respectively. This study highlights the effectiveness of utilizing the intrinsic relationships between onset date, seasonal length, and rainfall anomaly to produce useful seasonal outlooks of the rainy season solely by monitoring the onset date of the rainy season.
Abstract
In this study, we diagnose the onset and demise of the rainy season from the daily rainfall over West Africa. We then produce a probabilistic seasonal outlook of the rainy season over this region based on the observed variations of the onset date of the season, which verifies well with observations. We generated 101 ensemble members at every grid point by randomly perturbing the observed series of daily rainfall data obtained from the Integrated Multi-Satellite Retrievals for Global Precipitation Mission version 6 (IMERG) rainfall analysis. The generated ensemble of time series of daily rainfall accounts for uncertainties at meso to synoptic scales that could arise in the generation of the observed rainfall analysis. The ensemble members provide a robust estimate of the onset and demise dates of the rainy season. We find that the interannual variations in the onset and demise dates of the rainy season in West Africa significantly influence the corresponding anomalies of the seasonal length and rainfall. Additionally, interannual variability of the onset dates dominates over the demise dates of the rainy season across West Africa. In contrast, their association with remote, large-scale forcing is not found to be as significant. In addition, we found that the African Easterly Jet (AEJ) is displaced southward or northward in early or late onset seasons, respectively. This study highlights the effectiveness of utilizing the intrinsic relationships between onset date, seasonal length, and rainfall anomaly to produce useful seasonal outlooks of the rainy season solely by monitoring the onset date of the rainy season.
Abstract
Global numerical weather models are starting to resolve atmospheric moist convection which comes with a critical need for observational constraints. One avenue for such constraints is spaceborne radar which tends to operate at three wavelengths, Ku, Ka, and W bands. Many studies of deep convection in the past have primarily leveraged the Ku band because it is less affected by attenuation and multiple scattering. However, future spaceborne radar missions might not contain a Ku-band radar, and thus, considering the view of convection from the Ka band or W band compared to the Ku band would be useful. This study examines a coincident dataset between the Global Precipitation Measurement (GPM) mission and CloudSat as well as the entire GPM record to compare convective characteristics across various wavelengths within deep convection. We find that W-band reflectivity Z tends to maximize near the Ku-band defined echo top, while the Ka band often maximizes 4–5 km below. The height of the maximum Z above the melting level for the W band does not linearly relate to the Ku-band maximum. However, using the full GPM record, the Ka-band 30-dBZ echo tops can be linearly related to the Ku-band 40-dBZ echo top with an R 2 of 0.62 and a root-mean-squared error of about 1 km. The spatial distribution of echo tops from the Ka band corresponds well to the Ku-band echo tops, highlighting regions of relatively large ice water path. This paper suggests that Ka-band only missions, like NASA’s Investigation of Convective Updrafts, should be able to characterize global convection in a similar manner to a Ku-band system.
Significance Statement
There has been a long history of studying global storms using a Ku-band (2 cm, 13 GHz) spaceborne radar, most likely because of the least number of challenges (e.g., loss of signal) at the Ku band compared to the Ka band (8 mm, 35 GHz) and W band (3 mm, 89 GHz). However, each radar system offers different perspectives on hydrometeor profiles observed in deep convection that have remained largely unexplored, perspectives that provide insights into convective storm systems. Therefore, it is useful to know how storms measured at the Ka band and W band compare to storms measured at the Ku band. We find that many of the Ka-band convective properties can be linearly related to the Ku band, and thus, the Ka-band only mission designs should be suitable for studying convective storms.
Abstract
Global numerical weather models are starting to resolve atmospheric moist convection which comes with a critical need for observational constraints. One avenue for such constraints is spaceborne radar which tends to operate at three wavelengths, Ku, Ka, and W bands. Many studies of deep convection in the past have primarily leveraged the Ku band because it is less affected by attenuation and multiple scattering. However, future spaceborne radar missions might not contain a Ku-band radar, and thus, considering the view of convection from the Ka band or W band compared to the Ku band would be useful. This study examines a coincident dataset between the Global Precipitation Measurement (GPM) mission and CloudSat as well as the entire GPM record to compare convective characteristics across various wavelengths within deep convection. We find that W-band reflectivity Z tends to maximize near the Ku-band defined echo top, while the Ka band often maximizes 4–5 km below. The height of the maximum Z above the melting level for the W band does not linearly relate to the Ku-band maximum. However, using the full GPM record, the Ka-band 30-dBZ echo tops can be linearly related to the Ku-band 40-dBZ echo top with an R 2 of 0.62 and a root-mean-squared error of about 1 km. The spatial distribution of echo tops from the Ka band corresponds well to the Ku-band echo tops, highlighting regions of relatively large ice water path. This paper suggests that Ka-band only missions, like NASA’s Investigation of Convective Updrafts, should be able to characterize global convection in a similar manner to a Ku-band system.
Significance Statement
There has been a long history of studying global storms using a Ku-band (2 cm, 13 GHz) spaceborne radar, most likely because of the least number of challenges (e.g., loss of signal) at the Ku band compared to the Ka band (8 mm, 35 GHz) and W band (3 mm, 89 GHz). However, each radar system offers different perspectives on hydrometeor profiles observed in deep convection that have remained largely unexplored, perspectives that provide insights into convective storm systems. Therefore, it is useful to know how storms measured at the Ka band and W band compare to storms measured at the Ku band. We find that many of the Ka-band convective properties can be linearly related to the Ku band, and thus, the Ka-band only mission designs should be suitable for studying convective storms.
Abstract
Quantification of latent heating associated with precipitation at midlatitudes is essential for understanding weather and climate. While the spectral latent heating (SLH) algorithm, which retrieves heating profiles using satellite-borne precipitation radars, has been developed for tropical precipitation, it cannot be applied to midlatitude precipitation because of their different characteristics. In this study, the SLH algorithm for global midlatitude precipitation is developed. In Part I, lookup tables (LUTs) that tie heating profiles to precipitation characteristics are constructed using Local Forecast Model simulations of eight extratropical cyclones around Japan. LUTs are produced for the following six categories: convective, shallow stratiform, downward increasing (DI) deep stratiform, downward decreasing (DD) deep stratiform, SUB0 (where the 0°C level is near the surface) deep stratiform, and OTHER. The DD/DI subcategories are added to indicate different characteristics of heating profiles due to the relative positions of the cloud base and the 0°C level, as detected by the vertical gradient of precipitation profiles below the 0°C level. The precipitation-top height (the maximum precipitation rate P MAX) serves as an index for LUTs for the convective and shallow stratiform (three deep stratiform and OTHER) types. The height of P MAX roughly corresponds to the cloud-base height, which is used to separate upper-level heating and lower-level cooling. Condensates outside of precipitating areas are estimated to account for 12% of surface precipitation. Since this effect is not included in the LUTs, we applied a correction using this value to the level 3 SLH version 07 product for midlatitudes, considering the energy budget.
Abstract
Quantification of latent heating associated with precipitation at midlatitudes is essential for understanding weather and climate. While the spectral latent heating (SLH) algorithm, which retrieves heating profiles using satellite-borne precipitation radars, has been developed for tropical precipitation, it cannot be applied to midlatitude precipitation because of their different characteristics. In this study, the SLH algorithm for global midlatitude precipitation is developed. In Part I, lookup tables (LUTs) that tie heating profiles to precipitation characteristics are constructed using Local Forecast Model simulations of eight extratropical cyclones around Japan. LUTs are produced for the following six categories: convective, shallow stratiform, downward increasing (DI) deep stratiform, downward decreasing (DD) deep stratiform, SUB0 (where the 0°C level is near the surface) deep stratiform, and OTHER. The DD/DI subcategories are added to indicate different characteristics of heating profiles due to the relative positions of the cloud base and the 0°C level, as detected by the vertical gradient of precipitation profiles below the 0°C level. The precipitation-top height (the maximum precipitation rate P MAX) serves as an index for LUTs for the convective and shallow stratiform (three deep stratiform and OTHER) types. The height of P MAX roughly corresponds to the cloud-base height, which is used to separate upper-level heating and lower-level cooling. Condensates outside of precipitating areas are estimated to account for 12% of surface precipitation. Since this effect is not included in the LUTs, we applied a correction using this value to the level 3 SLH version 07 product for midlatitudes, considering the energy budget.
Abstract
A new algorithm for estimating the latent heating profile for precipitation in the extratropics was developed to extend the current spectral latent heating (SLH) algorithm that applies only to the tropics. The new algorithm incorporates normalized relative height into the heating estimation for deep stratiform precipitation. The normalized relative height successfully determines the cloud-base heights, above and below which latent heating and cooling occur, from the precipitation profile and adapts well to quite diverse precipitation and related heating in the extratropics. Another important improvement is the detection of multiple precipitation layers in the precipitation profiles that are not implemented in the algorithm for the tropics, further ensuring a reasonable estimation of the heating profile for diverse precipitation in the extratropics. The retrieval algorithm was evaluated from several perspectives. A self-consistency check using the numerical simulation data to construct the lookup tables confirmed that our algorithm can reproduce the true heating profiles well. Application to Global Precipitation Measurement (GPM) Ku-band precipitation radar (KuPR)-measured precipitation further confirmed that our algorithm works well, especially in capturing the variability of cloud-base heights for deep stratiform precipitation. A zonal-mean meridional cross section of the heating, including both tropics and extratropics up to 65° latitude, is described for the first time for the SLH product. The estimated distribution of heating in the extratropics corresponds well to midlatitude storm-track activity.
Abstract
A new algorithm for estimating the latent heating profile for precipitation in the extratropics was developed to extend the current spectral latent heating (SLH) algorithm that applies only to the tropics. The new algorithm incorporates normalized relative height into the heating estimation for deep stratiform precipitation. The normalized relative height successfully determines the cloud-base heights, above and below which latent heating and cooling occur, from the precipitation profile and adapts well to quite diverse precipitation and related heating in the extratropics. Another important improvement is the detection of multiple precipitation layers in the precipitation profiles that are not implemented in the algorithm for the tropics, further ensuring a reasonable estimation of the heating profile for diverse precipitation in the extratropics. The retrieval algorithm was evaluated from several perspectives. A self-consistency check using the numerical simulation data to construct the lookup tables confirmed that our algorithm can reproduce the true heating profiles well. Application to Global Precipitation Measurement (GPM) Ku-band precipitation radar (KuPR)-measured precipitation further confirmed that our algorithm works well, especially in capturing the variability of cloud-base heights for deep stratiform precipitation. A zonal-mean meridional cross section of the heating, including both tropics and extratropics up to 65° latitude, is described for the first time for the SLH product. The estimated distribution of heating in the extratropics corresponds well to midlatitude storm-track activity.
A Machine Learning Approach to Mitigate Ground Clutter Effects in the GPM Combined Radar–Radiometer Algorithm (CORRA) Precipitation EstimatesAbstract
In this study, a machine learning–based methodology is developed to mitigate the effects of ground clutter on precipitation estimates from the Global Precipitation Measurement Combined Radar–Radiometer Algorithm. Ground clutter can corrupt and obscure the precipitation echo in radar observations, leading to inaccuracies in precipitation estimates. To improve upon previous work, this study introduces a general machine learning (ML) approach that enables a systematic investigation and a better understanding of uncertainties in clutter mitigation. To allow for a less restrictive exploration of conditional relations between precipitation above the lowest clutter-free bin and surface precipitation, reflectivity observations above the clutter are included in a fixed-size set of predictors along with the precipitation type, surface type, and freezing level to estimate surface precipitation rates, and several ML-based estimation methods are investigated. A neural network (NN) model is ultimately identified as the best candidate for systematic evaluations, as it is computationally fast to apply while effective in applications. The NN provides unbiased estimates; however, it does not significantly outperform a simple bias correction approach in reducing random errors in the estimates. The similar performance of other ML approaches suggests that the NN’s limited improvement beyond bias removal is due to indeterminacies in the data rather than limitations in the ML approach itself.
Significance Statement
Ground clutter can obscure and corrupt the precipitation echo in the reflectivity observations by spaceborne radar, leading to inaccuracies and biases in the surface precipitation estimates. In this study, a machine learning approach is developed to mitigate the effects of ground clutter on precipitation estimates from the Global Precipitation Measurement (GPM) Combined Radar–Radiometer Algorithm (CORRA). The approach is shown to be effective in removing the biases associated with the simplest ground clutter mitigation approach and reducing the random errors associated with more complex climatologically based bias-removal approaches.
Abstract
In this study, a machine learning–based methodology is developed to mitigate the effects of ground clutter on precipitation estimates from the Global Precipitation Measurement Combined Radar–Radiometer Algorithm. Ground clutter can corrupt and obscure the precipitation echo in radar observations, leading to inaccuracies in precipitation estimates. To improve upon previous work, this study introduces a general machine learning (ML) approach that enables a systematic investigation and a better understanding of uncertainties in clutter mitigation. To allow for a less restrictive exploration of conditional relations between precipitation above the lowest clutter-free bin and surface precipitation, reflectivity observations above the clutter are included in a fixed-size set of predictors along with the precipitation type, surface type, and freezing level to estimate surface precipitation rates, and several ML-based estimation methods are investigated. A neural network (NN) model is ultimately identified as the best candidate for systematic evaluations, as it is computationally fast to apply while effective in applications. The NN provides unbiased estimates; however, it does not significantly outperform a simple bias correction approach in reducing random errors in the estimates. The similar performance of other ML approaches suggests that the NN’s limited improvement beyond bias removal is due to indeterminacies in the data rather than limitations in the ML approach itself.
Significance Statement
Ground clutter can obscure and corrupt the precipitation echo in the reflectivity observations by spaceborne radar, leading to inaccuracies and biases in the surface precipitation estimates. In this study, a machine learning approach is developed to mitigate the effects of ground clutter on precipitation estimates from the Global Precipitation Measurement (GPM) Combined Radar–Radiometer Algorithm (CORRA). The approach is shown to be effective in removing the biases associated with the simplest ground clutter mitigation approach and reducing the random errors associated with more complex climatologically based bias-removal approaches.
Abstract
The dominant microphysical processes responsible for differences in the mass-weighted mean diameter (Dm ) of deep and shallow rain during wet and dry spells of southwest monsoon (SWM) and northeast monsoon (NEM) over continental, oceanic, and orographic regions of India are inferred from Global Precipitation Measurement (GPM) dual-frequency precipitation radar (DPR) measurements made between 2014 and 2022. The deep precipitating systems’ Dm shows oceanic and continental nature during wet and dry spells of SWM and NEM. The dry spells of SWM over northwest India, all the spells except the wet spells of NEM over southeast peninsular India, and dry spells of NEM over northeast India show continental rain clusters and others show maritime rain characteristics. Mean Dm of deep systems at various rain-rate intervals shows marked intraseasonal variations over northwest India, central India, and foothills of the Himalayas during SWM and over the Western Ghats, southeast peninsular India, and northeast India during the NEM. Though Dm is larger in SWM than in NEM at seasonal scale, the dry spells of NEM show largest Dm than in other spells in SWM and NEM. The observed near-surface Dm differences between the wet and dry spells of SWM and NEM are seen from 1.5 km below the melting layer and are magnified during the descent of raindrops by the microphysical processes over all the regions except for southeast peninsular India and Myanmar coast during the SWM dry spells. Below the melting layer, collision–coalescence and breakup processes are considerable in the deep precipitating systems, and only the collision–coalescence process (>95%) dominates in shallow rain over all regions.
Significance Statement
Rainfall exhibits considerable intraseasonal variations triggering wet (or active) and dry (or break) spells during both southwest monsoon (SWM) and northeast monsoon (NEM). Significant progress has been made in understanding the wet and dry spells characteristics, while least explored is the raindrop size distribution (DSD) characteristics, which is the fundamental property of precipitation. At seasonal scale, DSDs show numerous smaller raindrops in NEM than in SWM; contrastingly, at intraseasonal time scales, abundant bigger raindrops are found in NEM dry spells than in SWM spells. At the seasonal scale, DSD characteristics show maritime precipitation features over India; however, SWM dry spells over northwest India, all the spells except the NEM wet spells over southeast peninsular India, NEM dry spells over northeast India show continental and others indicate maritime rain characteristics.
Abstract
The dominant microphysical processes responsible for differences in the mass-weighted mean diameter (Dm ) of deep and shallow rain during wet and dry spells of southwest monsoon (SWM) and northeast monsoon (NEM) over continental, oceanic, and orographic regions of India are inferred from Global Precipitation Measurement (GPM) dual-frequency precipitation radar (DPR) measurements made between 2014 and 2022. The deep precipitating systems’ Dm shows oceanic and continental nature during wet and dry spells of SWM and NEM. The dry spells of SWM over northwest India, all the spells except the wet spells of NEM over southeast peninsular India, and dry spells of NEM over northeast India show continental rain clusters and others show maritime rain characteristics. Mean Dm of deep systems at various rain-rate intervals shows marked intraseasonal variations over northwest India, central India, and foothills of the Himalayas during SWM and over the Western Ghats, southeast peninsular India, and northeast India during the NEM. Though Dm is larger in SWM than in NEM at seasonal scale, the dry spells of NEM show largest Dm than in other spells in SWM and NEM. The observed near-surface Dm differences between the wet and dry spells of SWM and NEM are seen from 1.5 km below the melting layer and are magnified during the descent of raindrops by the microphysical processes over all the regions except for southeast peninsular India and Myanmar coast during the SWM dry spells. Below the melting layer, collision–coalescence and breakup processes are considerable in the deep precipitating systems, and only the collision–coalescence process (>95%) dominates in shallow rain over all regions.
Significance Statement
Rainfall exhibits considerable intraseasonal variations triggering wet (or active) and dry (or break) spells during both southwest monsoon (SWM) and northeast monsoon (NEM). Significant progress has been made in understanding the wet and dry spells characteristics, while least explored is the raindrop size distribution (DSD) characteristics, which is the fundamental property of precipitation. At seasonal scale, DSDs show numerous smaller raindrops in NEM than in SWM; contrastingly, at intraseasonal time scales, abundant bigger raindrops are found in NEM dry spells than in SWM spells. At the seasonal scale, DSD characteristics show maritime precipitation features over India; however, SWM dry spells over northwest India, all the spells except the NEM wet spells over southeast peninsular India, NEM dry spells over northeast India show continental and others indicate maritime rain characteristics.
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.
