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Abstract
A numerical model of internal gravity waves allows momentum transport by the waves to interact with the mean flow. Momentum deposited at a critical level develops a “shelf” in the mean flow. Mean flow acceleration Doppler-shifts the wave frequency, allowing more penetration of wave energy than expected from linear theory.
Abstract
A numerical model of internal gravity waves allows momentum transport by the waves to interact with the mean flow. Momentum deposited at a critical level develops a “shelf” in the mean flow. Mean flow acceleration Doppler-shifts the wave frequency, allowing more penetration of wave energy than expected from linear theory.
Abstract
A simple numerical model is used to demonstrate that momentum exchange between wave and mean flow can substantially modify the process of “breaking” of internal gravity waves at great height. The momentum exchange results in appreciable transfer of energy from wave to mean flow.
Abstract
A simple numerical model is used to demonstrate that momentum exchange between wave and mean flow can substantially modify the process of “breaking” of internal gravity waves at great height. The momentum exchange results in appreciable transfer of energy from wave to mean flow.
Abstract
Accurate calibration of radar reflectivity is integral to quantitative radar measurements of precipitation and a myriad of other radar-based applications. A statistical method was developed that utilizes the probability distribution of clutter area reflectivity near a stationary, ground-based radar to provide near-real-time estimates of the relative calibration of reflectivity data. The relative calibration adjustment (RCA) method provides a valuable, automated near-real-time tool for maintaining consistently calibrated radar data with relative calibration uncertainty of ±0.5 dB or better. The original application was to S-band data in a tropical oceanic location, where the stability of the method was thought to be related to the relatively mild ground clutter and limited anomalous propagation (AP). This study demonstrates, however, that the RCA technique is transferable to other S-band radars at locations with more intense ground clutter and AP. This is done using data from NASA’s polarimetric (NPOL) surveillance radar data during the Iowa Flood Studies (IFloodS) Global Precipitation Measurement (GPM) field campaign during spring of 2013 and other deployments. Results indicate the RCA technique is well capable of monitoring the reflectivity calibration of NPOL, given proper generation of an areal clutter map. The main goal of this study is to generalize the RCA methodology for possible extension to other ground-based S-band surveillance radars and to show how it can be used both to monitor the reflectivity calibration and to correct previous data once an absolute calibration baseline is established.
Abstract
Accurate calibration of radar reflectivity is integral to quantitative radar measurements of precipitation and a myriad of other radar-based applications. A statistical method was developed that utilizes the probability distribution of clutter area reflectivity near a stationary, ground-based radar to provide near-real-time estimates of the relative calibration of reflectivity data. The relative calibration adjustment (RCA) method provides a valuable, automated near-real-time tool for maintaining consistently calibrated radar data with relative calibration uncertainty of ±0.5 dB or better. The original application was to S-band data in a tropical oceanic location, where the stability of the method was thought to be related to the relatively mild ground clutter and limited anomalous propagation (AP). This study demonstrates, however, that the RCA technique is transferable to other S-band radars at locations with more intense ground clutter and AP. This is done using data from NASA’s polarimetric (NPOL) surveillance radar data during the Iowa Flood Studies (IFloodS) Global Precipitation Measurement (GPM) field campaign during spring of 2013 and other deployments. Results indicate the RCA technique is well capable of monitoring the reflectivity calibration of NPOL, given proper generation of an areal clutter map. The main goal of this study is to generalize the RCA methodology for possible extension to other ground-based S-band surveillance radars and to show how it can be used both to monitor the reflectivity calibration and to correct previous data once an absolute calibration baseline is established.
Abstract
To establish a more reliable reference instrument for calibration normalization, this paper examines the differences between the various thermal infrared imager channels on a set of research and operational satellites. Mean brightness temperatures from the Visible Infrared Scanner (VIRS) on the Tropical Rainfall Measuring Mission (TRMM) satellite and the second Along-Track Scanning Radiometer (ATSR-2) on the second European Remote Sensing Satellite (ERS-2) are correlated with matched data from the eighth Geostationary Operational Environmental Satellite (GOES-8), the fifth Geostationary Meteorological Satellite (GMS-5), and with each other. VIRS data are also correlated with the Terra satellite's Moderate Resolution Imaging Spectroradiometer (MODIS) provisional data as a preliminary assessment of their relative calibrations. As an additional check on their long-term stability, the VIRS data are compared to the broadband longwave radiances of the Clouds and the Earth's Radiant Energy System (CERES) scanners on TRMM. No statistically significant trend in the calibration of any of the three (3.7, 10.8, and 12.0 μm) VIRS thermal channels could be detected from the comparisons with CERES data taken during 1998 and 2000 indicating that the VIRS channels can serve as a reliable reference for intercalibrating satellite imagers. However, a small day–night difference in the VIRS thermal channels detected at very low temperatures should be taken into account. In general, most of the channels agreed to within less than ±0.7 K over a temperature range between 200 and 300 K. Some of the smaller differences can be explained by spectral differences in the channel response functions. A few larger differences were found at 200 K for some of the channels suggesting some basic calibration differences for lower temperatures. A nearly 3-K bias in the ATSR-2 11-μm channel relative to VIRS and GOES-8 was found at the cold end of the temperature range. The intercalibrations described here are being continued on a routine basis.
Abstract
To establish a more reliable reference instrument for calibration normalization, this paper examines the differences between the various thermal infrared imager channels on a set of research and operational satellites. Mean brightness temperatures from the Visible Infrared Scanner (VIRS) on the Tropical Rainfall Measuring Mission (TRMM) satellite and the second Along-Track Scanning Radiometer (ATSR-2) on the second European Remote Sensing Satellite (ERS-2) are correlated with matched data from the eighth Geostationary Operational Environmental Satellite (GOES-8), the fifth Geostationary Meteorological Satellite (GMS-5), and with each other. VIRS data are also correlated with the Terra satellite's Moderate Resolution Imaging Spectroradiometer (MODIS) provisional data as a preliminary assessment of their relative calibrations. As an additional check on their long-term stability, the VIRS data are compared to the broadband longwave radiances of the Clouds and the Earth's Radiant Energy System (CERES) scanners on TRMM. No statistically significant trend in the calibration of any of the three (3.7, 10.8, and 12.0 μm) VIRS thermal channels could be detected from the comparisons with CERES data taken during 1998 and 2000 indicating that the VIRS channels can serve as a reliable reference for intercalibrating satellite imagers. However, a small day–night difference in the VIRS thermal channels detected at very low temperatures should be taken into account. In general, most of the channels agreed to within less than ±0.7 K over a temperature range between 200 and 300 K. Some of the smaller differences can be explained by spectral differences in the channel response functions. A few larger differences were found at 200 K for some of the channels suggesting some basic calibration differences for lower temperatures. A nearly 3-K bias in the ATSR-2 11-μm channel relative to VIRS and GOES-8 was found at the cold end of the temperature range. The intercalibrations described here are being continued on a routine basis.
Abstract
Operational meteorological satellites generally lack reliable onboard calibration systems for solar-imaging channels. Current methods for calibrating these channels and for normalizing similar channels on contemporaneous satellite imagers typically rely on a poorly calibrated reference source. To establish a more reliable reference instrument for calibration normalization, this paper examines the use of research satellite imagers that maintain their solar-channel calibrations by using onboard diffuser systems that rely on the sun as an absolute reference. The Visible Infrared Scanner (VIRS) on the Tropical Rainfall Measuring Mission (TRMM) satellite and the second Along-Track Scanning Radiometer (ATSR-2) on the second European Remote Sensing Satellite (ERS-2) are correlated with matched data from the eighth Geostationary Operational Environmental Satellite (GOES-8), the fifth Geostationary Meteorological satellite (GMS-5), and with each other to examine trends in the solar channels. VIRS data are also correlated with the Terra satellite's Moderate Resolution Imaging Spectroradiometer (MODIS) provisional data as a preliminary assessment of their relative calibrations. As an additional check on their long-term stability, the VIRS data are compared to the relevant corresponding broadband shortwave radiances of the Clouds and the Earth's Radiant Energy System (CERES) scanners on TRMM. No statistically significant trend in the calibration of the VIRS 0.65- and 1.64-μm channels could be detected from the comparisons with CERES data taken during 1998 and 2000. The VIRS-to-GOES-8 correlations revealed an annual degradation rate for the GOES-8 visible (0.67 μm) channel of ∼7.5% and an initial drop of 16% in the gain from the prelaunch value. The slopes in the GOES-8 visible-channel gain trend lines derived from VIRS data taken after January 1998 and ATSR-2 data taken between October 1995 and December 1999 differed by only 1%–2% indicating that both reference instruments are highly stable. The mean difference of 3%–4.8% between the VIRS–GOES-8 and ATSR-2–GOES-8 gains is attributed to spectral differences between ATSR-2 and VIRS and to possible biases in the ATSR-2 channel-2 calibration. A degradation rate of 1.3% per year found for the GMS-5 visible channel was confirmed by comparisons with earlier calibrations. The MODIS and VIRS calibrations agreed to within −1% to 3%. Some of the differences between VIRS and the provisional MODIS radiances can be explained by spectral differences between the two instruments. The MODIS measures greater reflectance than VIRS for bright scenes. Although both VIRS and ATSR-2 provide temporally stable calibrations, it is recommended that, at least until MODIS calibrations are finalized, VIRS should be used as a reference source for normalizing operational meteorological satellite imagers because of its broader visible filter.
Abstract
Operational meteorological satellites generally lack reliable onboard calibration systems for solar-imaging channels. Current methods for calibrating these channels and for normalizing similar channels on contemporaneous satellite imagers typically rely on a poorly calibrated reference source. To establish a more reliable reference instrument for calibration normalization, this paper examines the use of research satellite imagers that maintain their solar-channel calibrations by using onboard diffuser systems that rely on the sun as an absolute reference. The Visible Infrared Scanner (VIRS) on the Tropical Rainfall Measuring Mission (TRMM) satellite and the second Along-Track Scanning Radiometer (ATSR-2) on the second European Remote Sensing Satellite (ERS-2) are correlated with matched data from the eighth Geostationary Operational Environmental Satellite (GOES-8), the fifth Geostationary Meteorological satellite (GMS-5), and with each other to examine trends in the solar channels. VIRS data are also correlated with the Terra satellite's Moderate Resolution Imaging Spectroradiometer (MODIS) provisional data as a preliminary assessment of their relative calibrations. As an additional check on their long-term stability, the VIRS data are compared to the relevant corresponding broadband shortwave radiances of the Clouds and the Earth's Radiant Energy System (CERES) scanners on TRMM. No statistically significant trend in the calibration of the VIRS 0.65- and 1.64-μm channels could be detected from the comparisons with CERES data taken during 1998 and 2000. The VIRS-to-GOES-8 correlations revealed an annual degradation rate for the GOES-8 visible (0.67 μm) channel of ∼7.5% and an initial drop of 16% in the gain from the prelaunch value. The slopes in the GOES-8 visible-channel gain trend lines derived from VIRS data taken after January 1998 and ATSR-2 data taken between October 1995 and December 1999 differed by only 1%–2% indicating that both reference instruments are highly stable. The mean difference of 3%–4.8% between the VIRS–GOES-8 and ATSR-2–GOES-8 gains is attributed to spectral differences between ATSR-2 and VIRS and to possible biases in the ATSR-2 channel-2 calibration. A degradation rate of 1.3% per year found for the GMS-5 visible channel was confirmed by comparisons with earlier calibrations. The MODIS and VIRS calibrations agreed to within −1% to 3%. Some of the differences between VIRS and the provisional MODIS radiances can be explained by spectral differences between the two instruments. The MODIS measures greater reflectance than VIRS for bright scenes. Although both VIRS and ATSR-2 provide temporally stable calibrations, it is recommended that, at least until MODIS calibrations are finalized, VIRS should be used as a reference source for normalizing operational meteorological satellite imagers because of its broader visible filter.
Abstract
The combination of vertical lidar and in situ meteorological observations from two aircraft provide an unprecedented view of the marine atmospheric boundary layer (MABL) during a cold air outbreak. To a first approximation, the lidar reflectivity is associated with the concentration of sea salt aerosols. Across the capping inversion, the lidar reflectivity contours approximate isentropes and streamlines thereby defining the inversion. Within the mixed layer, high reflectivity cores are associated with updrafts carrying aerosol-rich air upward and conversely. These effects are enhanced by increasing humidity in updraft and decreasing humidity in downdrafts that operate to increase and decrease aerosol sizes. Narrow high reflectivity columns extend upward from the ocean indicating that organized flow exists all the way to the surface. Entrainment across the inversion is manifested by small scale perturbations (∼200–500 m) superimposed upon the large scale (&sim 1–2 km) undulations of the inversion. These occur where the local entrainment zone is sharpest; generally, this is on the upshear side of the, convective. domes where Kelvin-Helmholtz instability is triggered by local compression of the inversion,
The MABL on 20 January 1983 is highly organized. The organization takes the form of 1–2 km scale roll vortices and corresponding undulations of the inversion with amplitude of 150–200 m peak to trough. The roll circulation is very strong with up and downdrafts of 2–4 in s−-1 at the 210 m level. The axes of the rolls are essentially north-south along the direction of the strong northerly low-level winds. The rising arm of the roll coincides with a column of high lidar reflectivity and with the updraft which transport aerosols, moisture, and heat up from the surface. The presence of the rolls, driven mainly by the combination of strong transverse sheer and buoyancy, serves to produce low-level convergence which concentrates the small-scale buoyant eddies to form a single well-ordered updraft in the manner previously postulated by LeMone.
The fluxes measured by the covariance method in the undulating inversion are unreliable because of the sensitivity to detrending and inadequate sampling of the exchanges across the interfaces of the dames and troughs. The partitioning method of Wilczak and Businger provides improved insight as to the mechanisms responsible for the downward flux in the inversion. However, unlike Wilczak and Businger, who find the downward flux dominated by cold updrafts we find that it is due mainly to the entrainment of warm eddies which are then transported downward by the larger-scale roll circulations on the downshear side of the domes.
Abstract
The combination of vertical lidar and in situ meteorological observations from two aircraft provide an unprecedented view of the marine atmospheric boundary layer (MABL) during a cold air outbreak. To a first approximation, the lidar reflectivity is associated with the concentration of sea salt aerosols. Across the capping inversion, the lidar reflectivity contours approximate isentropes and streamlines thereby defining the inversion. Within the mixed layer, high reflectivity cores are associated with updrafts carrying aerosol-rich air upward and conversely. These effects are enhanced by increasing humidity in updraft and decreasing humidity in downdrafts that operate to increase and decrease aerosol sizes. Narrow high reflectivity columns extend upward from the ocean indicating that organized flow exists all the way to the surface. Entrainment across the inversion is manifested by small scale perturbations (∼200–500 m) superimposed upon the large scale (&sim 1–2 km) undulations of the inversion. These occur where the local entrainment zone is sharpest; generally, this is on the upshear side of the, convective. domes where Kelvin-Helmholtz instability is triggered by local compression of the inversion,
The MABL on 20 January 1983 is highly organized. The organization takes the form of 1–2 km scale roll vortices and corresponding undulations of the inversion with amplitude of 150–200 m peak to trough. The roll circulation is very strong with up and downdrafts of 2–4 in s−-1 at the 210 m level. The axes of the rolls are essentially north-south along the direction of the strong northerly low-level winds. The rising arm of the roll coincides with a column of high lidar reflectivity and with the updraft which transport aerosols, moisture, and heat up from the surface. The presence of the rolls, driven mainly by the combination of strong transverse sheer and buoyancy, serves to produce low-level convergence which concentrates the small-scale buoyant eddies to form a single well-ordered updraft in the manner previously postulated by LeMone.
The fluxes measured by the covariance method in the undulating inversion are unreliable because of the sensitivity to detrending and inadequate sampling of the exchanges across the interfaces of the dames and troughs. The partitioning method of Wilczak and Businger provides improved insight as to the mechanisms responsible for the downward flux in the inversion. However, unlike Wilczak and Businger, who find the downward flux dominated by cold updrafts we find that it is due mainly to the entrainment of warm eddies which are then transported downward by the larger-scale roll circulations on the downshear side of the domes.
Abstract
A comparative study of raindrop size distribution measurements has been conducted at NASA’s Goddard Space Flight Center where the focus was to evaluate the performance of the upgraded laser-optical OTT Particle Size Velocity (Parsivel2; P2) disdrometer. The experimental setup included a collocated pair of tipping-bucket rain gauges, OTT Parsivel (P1) and P2 disdrometers, and Joss–Waldvogel (JW) disdrometers. Excellent agreement between the two collocated rain gauges enabled their use as a relative reference for event rain totals. A comparison of event total showed that the P2 had a 6% absolute bias with respect to the reference gauges, considerably lower than the P1 and JW disdrometers. Good agreement was also evident between the JW and P2 in hourly raindrop spectra for drop diameters between 0.5 and 4 mm. The P2 drop concentrations mostly increased toward small sizes, and the peak concentrations were mostly observed in the first three measurable size bins. The P1, on the other hand, underestimated small drops and overestimated the large drops, particularly in heavy rain rates. From the analysis performed, it appears that the P2 is an improvement over the P1 model for both drop size and rainfall measurements. P2 mean fall velocities follow accepted terminal fall speed relationships at drop sizes less than 1 mm. As a caveat, the P2 had approximately 1 m s−1 slower mean fall speed with respect to the terminal fall speed near 1 mm, and the difference between the mean measured and terminal fall speeds reduced with increasing drop size. This caveat was recognized as a software bug by the manufacturer and is currently being investigated.
Abstract
A comparative study of raindrop size distribution measurements has been conducted at NASA’s Goddard Space Flight Center where the focus was to evaluate the performance of the upgraded laser-optical OTT Particle Size Velocity (Parsivel2; P2) disdrometer. The experimental setup included a collocated pair of tipping-bucket rain gauges, OTT Parsivel (P1) and P2 disdrometers, and Joss–Waldvogel (JW) disdrometers. Excellent agreement between the two collocated rain gauges enabled their use as a relative reference for event rain totals. A comparison of event total showed that the P2 had a 6% absolute bias with respect to the reference gauges, considerably lower than the P1 and JW disdrometers. Good agreement was also evident between the JW and P2 in hourly raindrop spectra for drop diameters between 0.5 and 4 mm. The P2 drop concentrations mostly increased toward small sizes, and the peak concentrations were mostly observed in the first three measurable size bins. The P1, on the other hand, underestimated small drops and overestimated the large drops, particularly in heavy rain rates. From the analysis performed, it appears that the P2 is an improvement over the P1 model for both drop size and rainfall measurements. P2 mean fall velocities follow accepted terminal fall speed relationships at drop sizes less than 1 mm. As a caveat, the P2 had approximately 1 m s−1 slower mean fall speed with respect to the terminal fall speed near 1 mm, and the difference between the mean measured and terminal fall speeds reduced with increasing drop size. This caveat was recognized as a software bug by the manufacturer and is currently being investigated.
Assessing Dual-Polarization Radar Estimates of Extreme Rainfall during Hurricane HarveyAbstract
Hurricane Harvey hit the Texas Gulf Coast as a major hurricane on 25 August 2017 before exiting the state as a tropical storm on 29 August 2017. Left in its wake was historic flooding, with some locations measuring more than 60 in. (150 cm) of rain over a 5-day period. The WSR-88D radar (KHGX) maintained operations for the entirety of the event. Rain gauge data from the Harris County Flood Warning System (HCFWS) was used for validation with the full radar dataset to retrieve daily and event-total precipitation estimates for the period 25–29 August 2017. The KHGX precipitation estimates were then compared with the HCFWS gauges. Three different hybrid polarimetric rainfall retrievals were used, along with attenuation-based retrieval that employs the radar-observed differential propagation. An advantage of using a attenuation-based retrieval is its immunity to partial beam blockage and calibration errors in reflectivity and differential reflectivity. All of the retrievals are susceptible to changes in the observed drop size distribution (DSD). No in situ DSD data were available over the study area, so changes in the DSD were interpreted by examining the observed radar data. We examined the parameter space of two key values in the attenuation retrieval to test the sensitivity of the rain retrieval. Selecting a value of α = 0.015 and β = 0.600 provided the best overall results, relative to the gauges, but more work needs to be done to develop an automated technique to account for changes in the ambient DSD.
Abstract
Hurricane Harvey hit the Texas Gulf Coast as a major hurricane on 25 August 2017 before exiting the state as a tropical storm on 29 August 2017. Left in its wake was historic flooding, with some locations measuring more than 60 in. (150 cm) of rain over a 5-day period. The WSR-88D radar (KHGX) maintained operations for the entirety of the event. Rain gauge data from the Harris County Flood Warning System (HCFWS) was used for validation with the full radar dataset to retrieve daily and event-total precipitation estimates for the period 25–29 August 2017. The KHGX precipitation estimates were then compared with the HCFWS gauges. Three different hybrid polarimetric rainfall retrievals were used, along with attenuation-based retrieval that employs the radar-observed differential propagation. An advantage of using a attenuation-based retrieval is its immunity to partial beam blockage and calibration errors in reflectivity and differential reflectivity. All of the retrievals are susceptible to changes in the observed drop size distribution (DSD). No in situ DSD data were available over the study area, so changes in the DSD were interpreted by examining the observed radar data. We examined the parameter space of two key values in the attenuation retrieval to test the sensitivity of the rain retrieval. Selecting a value of α = 0.015 and β = 0.600 provided the best overall results, relative to the gauges, but more work needs to be done to develop an automated technique to account for changes in the ambient DSD.
Abstract
Precipitation profiles from the Global Precipitation Measurement (GPM) Core Observatory Dual-Frequency Precipitation Radar (DPR; Ku and Ka bands) form part of the a priori database used in the Goddard profiling algorithm (GPROF) for retrievals of precipitation from passive microwave sensors, which are in turn used as high-quality precipitation estimates in gridded products. As GPROF performs precipitation retrievals as a function of surface classes, error characteristics may be dependent on surface types. In this study, the authors evaluate the rainfall estimates from DPR Ku as well as GPROF estimates from passive microwave sensors in the GPM constellation. The evaluation is conducted at the level of individual satellite pixels (5–15 km) against three dense networks of rain gauges, located over contrasting land surface types and rainfall regimes, with multiple gauges per satellite pixel and precise accumulation about overpass time to ensure a representative comparison. As expected, it was found that the active retrievals from DPR Ku generally performed better than the passive retrievals from GPROF. However, both retrievals struggle under coastal and semiarid environments. In particular, virga appears to be a serious challenge for both DPR Ku and GPROF. The authors detected the existence of lag due to the time it takes for satellite-observed precipitation to reach the ground, but the precise delay is difficult to quantify. It was also shown that subpixel variability is a contributor to the errors in GPROF. These results can pinpoint deficiencies in precipitation algorithms that may propagate into widely used gridded products.
Abstract
Precipitation profiles from the Global Precipitation Measurement (GPM) Core Observatory Dual-Frequency Precipitation Radar (DPR; Ku and Ka bands) form part of the a priori database used in the Goddard profiling algorithm (GPROF) for retrievals of precipitation from passive microwave sensors, which are in turn used as high-quality precipitation estimates in gridded products. As GPROF performs precipitation retrievals as a function of surface classes, error characteristics may be dependent on surface types. In this study, the authors evaluate the rainfall estimates from DPR Ku as well as GPROF estimates from passive microwave sensors in the GPM constellation. The evaluation is conducted at the level of individual satellite pixels (5–15 km) against three dense networks of rain gauges, located over contrasting land surface types and rainfall regimes, with multiple gauges per satellite pixel and precise accumulation about overpass time to ensure a representative comparison. As expected, it was found that the active retrievals from DPR Ku generally performed better than the passive retrievals from GPROF. However, both retrievals struggle under coastal and semiarid environments. In particular, virga appears to be a serious challenge for both DPR Ku and GPROF. The authors detected the existence of lag due to the time it takes for satellite-observed precipitation to reach the ground, but the precise delay is difficult to quantify. It was also shown that subpixel variability is a contributor to the errors in GPROF. These results can pinpoint deficiencies in precipitation algorithms that may propagate into widely used gridded products.
The System for Integrating Multiplatform Data to Build the Atmospheric Column (SIMBA) Precipitation Observation Fusion FrameworkAbstract
Researchers now have the benefit of an unprecedented suite of space- and ground-based sensors that provide multidimensional and multiparameter precipitation information. Motivated by NASA’s Global Precipitation Measurement (GPM) mission and ground validation objectives, the System for Integrating Multiplatform Data to Build the Atmospheric Column (SIMBA) has been developed as a unique multisensor precipitation data fusion tool to unify field observations recorded in a variety of formats and coordinate systems into a common reference frame. Through platform-specific modules, SIMBA processes data from native coordinates and resolutions only to the extent required to set them into a user-defined three-dimensional grid. At present, the system supports several ground-based scanning research radars, NWS NEXRAD radars, profiling Micro Rain Radars (MRRs), multiple disdrometers and rain gauges, soundings, the GPM Microwave Imager and Dual-Frequency Precipitation Radar on board the Core Observatory satellite, and Multi-Radar Multi-Sensor system quantitative precipitation estimates. SIMBA generates a new atmospheric column data product that contains a concomitant set of all available data from the supported platforms within the user-specified grid defining the column area in the versatile netCDF format. Key parameters for each data source are preserved as attributes. SIMBA provides a streamlined framework for initial research tasks, facilitating more efficient precipitation science. We demonstrate the utility of SIMBA for investigations, such as assessing spatial precipitation variability at subpixel scales and appraising satellite sensor algorithm representation of vertical precipitation structure for GPM Core Observatory overpass cases collected in the NASA Wallops Precipitation Science Research Facility and the GPM Olympic Mountain Experiment (OLYMPEX) ground validation field campaign in Washington State.
Abstract
Researchers now have the benefit of an unprecedented suite of space- and ground-based sensors that provide multidimensional and multiparameter precipitation information. Motivated by NASA’s Global Precipitation Measurement (GPM) mission and ground validation objectives, the System for Integrating Multiplatform Data to Build the Atmospheric Column (SIMBA) has been developed as a unique multisensor precipitation data fusion tool to unify field observations recorded in a variety of formats and coordinate systems into a common reference frame. Through platform-specific modules, SIMBA processes data from native coordinates and resolutions only to the extent required to set them into a user-defined three-dimensional grid. At present, the system supports several ground-based scanning research radars, NWS NEXRAD radars, profiling Micro Rain Radars (MRRs), multiple disdrometers and rain gauges, soundings, the GPM Microwave Imager and Dual-Frequency Precipitation Radar on board the Core Observatory satellite, and Multi-Radar Multi-Sensor system quantitative precipitation estimates. SIMBA generates a new atmospheric column data product that contains a concomitant set of all available data from the supported platforms within the user-specified grid defining the column area in the versatile netCDF format. Key parameters for each data source are preserved as attributes. SIMBA provides a streamlined framework for initial research tasks, facilitating more efficient precipitation science. We demonstrate the utility of SIMBA for investigations, such as assessing spatial precipitation variability at subpixel scales and appraising satellite sensor algorithm representation of vertical precipitation structure for GPM Core Observatory overpass cases collected in the NASA Wallops Precipitation Science Research Facility and the GPM Olympic Mountain Experiment (OLYMPEX) ground validation field campaign in Washington State.
