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Use of an End-to-End-Simulator to Analyze CYGNSSAbstract
Tropical convection during the onset of two Madden–Julian oscillation (MJO) events, in October and December of 2011, was simulated using the Weather Research and Forecasting (WRF) Model. Observations from the Dynamics of the MJO (DYNAMO) field campaign were assimilated into the WRF Model for an improved simulation of the mesoscale features of tropical convection. The WRF simulations with the assimilation of DYNAMO data produced realistic representations of mesoscale convection related to westerly wind bursts (WWBs) as well as downdraft-induced gust fronts. An end-to-end simulator (E2ES) for the Cyclone Global Navigation Satellite System (CYGNSS) mission was then applied to the WRF dataset, producing simulated CYGNSS near-surface wind speed data. The results indicated that CYGNSS could detect mesoscale wind features such as WWBs and gust fronts even in the presence of simulated heavy precipitation. This study has two primary conclusions as a consequence: 1) satellite simulators could be used to examine a mission’s capabilities for accomplishing secondary tasks and 2) CYGNSS likely will provide benefits to future tropical oceanic field campaigns that should be considered during their planning processes.
Abstract
Tropical convection during the onset of two Madden–Julian oscillation (MJO) events, in October and December of 2011, was simulated using the Weather Research and Forecasting (WRF) Model. Observations from the Dynamics of the MJO (DYNAMO) field campaign were assimilated into the WRF Model for an improved simulation of the mesoscale features of tropical convection. The WRF simulations with the assimilation of DYNAMO data produced realistic representations of mesoscale convection related to westerly wind bursts (WWBs) as well as downdraft-induced gust fronts. An end-to-end simulator (E2ES) for the Cyclone Global Navigation Satellite System (CYGNSS) mission was then applied to the WRF dataset, producing simulated CYGNSS near-surface wind speed data. The results indicated that CYGNSS could detect mesoscale wind features such as WWBs and gust fronts even in the presence of simulated heavy precipitation. This study has two primary conclusions as a consequence: 1) satellite simulators could be used to examine a mission’s capabilities for accomplishing secondary tasks and 2) CYGNSS likely will provide benefits to future tropical oceanic field campaigns that should be considered during their planning processes.
Abstract
The local environment during the joint Atmospheric Radiation Measurement Program (ARM) Madden–Julian oscillation (MJO) Investigation Experiment (AMIE)–Cooperative Indian Ocean Experiment on Intraseasonal Variability in Year 2011 (CINDY2011)–Dynamics of the MJO (DYNAMO) field experiments caused frequent occurrences of sidelobe artifacts in the NCAR S-Pol radar dataset. Although generally low in radar reflectivity factor value (less than 5 dBZ), this contamination still proved problematic for Bragg scattering layer (BSL) analysis, generating numerous false BSL edge detections. In this paper, a statistical filtering technique is developed that effectively removes these false BSL edge detections, utilizing a new version of BSL analysis based on range–height indicator (RHI) data instead of plan position indicator data.
Abstract
The local environment during the joint Atmospheric Radiation Measurement Program (ARM) Madden–Julian oscillation (MJO) Investigation Experiment (AMIE)–Cooperative Indian Ocean Experiment on Intraseasonal Variability in Year 2011 (CINDY2011)–Dynamics of the MJO (DYNAMO) field experiments caused frequent occurrences of sidelobe artifacts in the NCAR S-Pol radar dataset. Although generally low in radar reflectivity factor value (less than 5 dBZ), this contamination still proved problematic for Bragg scattering layer (BSL) analysis, generating numerous false BSL edge detections. In this paper, a statistical filtering technique is developed that effectively removes these false BSL edge detections, utilizing a new version of BSL analysis based on range–height indicator (RHI) data instead of plan position indicator data.
Abstract
This study examines the DigiCORA and Global Climate Observing System Reference Upper-Air Network (GRUAN) humidity corrections of Vaisala RS92 radiosondes at three sites over the tropical Indian Ocean and surrounding areas during the Dynamics of the Madden–Julian Oscillation (DYNAMO) field campaign in 2011. The proprietary DigiCORA correction algorithm is built into the ground station software provided by Vaisala, whereas the GRUAN correction is an open source algorithm. Included in the GRUAN data product are uncertainty estimates for their corrections. This information is used to examine the statistical consistency of the various corrections.
In general, the algorithms produce a positive relative humidity (RH) correction that increases with altitude related primarily to a solar radiation dry bias adjustment. For example, in daytime soundings the relative RH correction increases from a few percent for temperatures >0°C to 20%–40% between 100 and 200 hPa. Comparison of corrected RH vertical profiles show only small differences (on the order of a few percent or less at any given level) between the DigiCORA and GRUAN algorithms, such that these corrections are considered to be statistically consistent at most levels.
In evaluating corrected humidity data with independent estimates of total precipitable water (TPW), good agreement was found at all sites between corrected sounding and ground-based microwave radiometer (MWR) estimates of TPW with mean differences ≤0.9 mm (or <2%), which is well within the uncertainty of these measurements. Overall, the correction algorithms examined herein perform well over a wide range of tropical moisture conditions.
Abstract
This study examines the DigiCORA and Global Climate Observing System Reference Upper-Air Network (GRUAN) humidity corrections of Vaisala RS92 radiosondes at three sites over the tropical Indian Ocean and surrounding areas during the Dynamics of the Madden–Julian Oscillation (DYNAMO) field campaign in 2011. The proprietary DigiCORA correction algorithm is built into the ground station software provided by Vaisala, whereas the GRUAN correction is an open source algorithm. Included in the GRUAN data product are uncertainty estimates for their corrections. This information is used to examine the statistical consistency of the various corrections.
In general, the algorithms produce a positive relative humidity (RH) correction that increases with altitude related primarily to a solar radiation dry bias adjustment. For example, in daytime soundings the relative RH correction increases from a few percent for temperatures >0°C to 20%–40% between 100 and 200 hPa. Comparison of corrected RH vertical profiles show only small differences (on the order of a few percent or less at any given level) between the DigiCORA and GRUAN algorithms, such that these corrections are considered to be statistically consistent at most levels.
In evaluating corrected humidity data with independent estimates of total precipitable water (TPW), good agreement was found at all sites between corrected sounding and ground-based microwave radiometer (MWR) estimates of TPW with mean differences ≤0.9 mm (or <2%), which is well within the uncertainty of these measurements. Overall, the correction algorithms examined herein perform well over a wide range of tropical moisture conditions.
Abstract
During the Dynamics of Madden–Julian Oscillation (DYNAMO) Experiment in 2011, airborne expendable conductivity–temperature–depth (AXCTD) probes and airborne expendable bathythermographs (AXBTs) were deployed using NOAA’s WP-3D Orion aircraft over the southern tropical Indian Ocean. From initial analysis of the AXCTD data, about 95% of profiles exhibit double mixed layer structures. The presence of a mixed layer from some of these profiles were erroneous and were introduced because of the AXCTD processing software not being able to correctly identify the starting point of the probe descent. This work reveals the impact of these errors in data processing and presents an objective method to remove such erroneous data from the profiles using spectrograms from raw audio files. Reconstructed AXCTD/AXBT profiles are compared with collocated shipborne conductivity–temperature–depth (CTD) and expendable bathythermograph (XBT) profiles and are found to be in good agreement.
Abstract
During the Dynamics of Madden–Julian Oscillation (DYNAMO) Experiment in 2011, airborne expendable conductivity–temperature–depth (AXCTD) probes and airborne expendable bathythermographs (AXBTs) were deployed using NOAA’s WP-3D Orion aircraft over the southern tropical Indian Ocean. From initial analysis of the AXCTD data, about 95% of profiles exhibit double mixed layer structures. The presence of a mixed layer from some of these profiles were erroneous and were introduced because of the AXCTD processing software not being able to correctly identify the starting point of the probe descent. This work reveals the impact of these errors in data processing and presents an objective method to remove such erroneous data from the profiles using spectrograms from raw audio files. Reconstructed AXCTD/AXBT profiles are compared with collocated shipborne conductivity–temperature–depth (CTD) and expendable bathythermograph (XBT) profiles and are found to be in good agreement.
Abstract
To improve understanding of the convective processes key to the Madden–Julian oscillation (MJO) initiation, the Dynamics of the MJO (DYNAMO) and the Atmospheric Radiation Measurement Program (ARM) MJO Investigation Experiment (AMIE) collected 4 months of observations from three radars—the S-band dual-polarization Doppler radar (S-Pol), the C-band Shared Mobile Atmospheric Research and Teaching Radar (SMART-R), and Ka-band ARM zenith radar (KAZR)—along with radiosonde and comprehensive surface meteorological instruments on Addu Atoll, Maldives, in the tropical Indian Ocean. One DYNAMO/AMIE hypothesis suggests that the evolution of shallow and congestus cloud populations is essential to the initiation of the MJO. This study focuses on evaluating the ability of these three radars to document the full spectrum of cloud populations and to construct a merged cloud–precipitation radar dataset that can be used to test this hypothesis. Comparisons between collocated observations from the three radars show that KAZR provides the only reliable estimate of shallow clouds, while S-Pol/SMART-R can reasonably detect congestus within the 30–50-km range in addition to precipitating deep clouds. On the other hand, KAZR underestimates cloud-top heights due to rainfall attenuation in ~34% of the precipitating clouds, and an empirical method to correct KAZR cloud-top height bias is proposed. Finally, a merged KAZR–S-Pol dataset is produced to provide improved cloud-top height estimates, total hydrometeor microphysics, and radiative heating rate retrievals. With this dataset the full spectrum of tropical convective clouds during DYNAMO/AMIE can be reliably constructed and, together with complimentary radiosonde data, it can be used to study the role of shallow and congestus clouds in the initiation of the MJO.
Abstract
To improve understanding of the convective processes key to the Madden–Julian oscillation (MJO) initiation, the Dynamics of the MJO (DYNAMO) and the Atmospheric Radiation Measurement Program (ARM) MJO Investigation Experiment (AMIE) collected 4 months of observations from three radars—the S-band dual-polarization Doppler radar (S-Pol), the C-band Shared Mobile Atmospheric Research and Teaching Radar (SMART-R), and Ka-band ARM zenith radar (KAZR)—along with radiosonde and comprehensive surface meteorological instruments on Addu Atoll, Maldives, in the tropical Indian Ocean. One DYNAMO/AMIE hypothesis suggests that the evolution of shallow and congestus cloud populations is essential to the initiation of the MJO. This study focuses on evaluating the ability of these three radars to document the full spectrum of cloud populations and to construct a merged cloud–precipitation radar dataset that can be used to test this hypothesis. Comparisons between collocated observations from the three radars show that KAZR provides the only reliable estimate of shallow clouds, while S-Pol/SMART-R can reasonably detect congestus within the 30–50-km range in addition to precipitating deep clouds. On the other hand, KAZR underestimates cloud-top heights due to rainfall attenuation in ~34% of the precipitating clouds, and an empirical method to correct KAZR cloud-top height bias is proposed. Finally, a merged KAZR–S-Pol dataset is produced to provide improved cloud-top height estimates, total hydrometeor microphysics, and radiative heating rate retrievals. With this dataset the full spectrum of tropical convective clouds during DYNAMO/AMIE can be reliably constructed and, together with complimentary radiosonde data, it can be used to study the role of shallow and congestus clouds in the initiation of the MJO.
