Pub Date : 2023-05-23DOI: 10.1175/jtech-d-22-0101.1
Dorukhan Ardağ, G. Wilson, J. Lerczak, Dylan S. Winters, Adam G. Peck-Richardson, D. Lyons, R. Orben
In 2013 and 2014, multiple field excursions of varying scope were concentrated on the Columbia River, a highly energetic, partially-mixed estuary. These experiments included surface drifter and Synthetic Aperture Radar (SAR) measurements during the ONR RIVET-II experiment, and a novel animal tracking effort that samples oceanographic data by employing cormorants tagged with bio-logging devices. In the present work, several different data types from these experiments were combined in order to test an iterative, ensemble-based inversion methodology at the Mouth of the Columbia River (MCR). Results show that, despite inherent limitations of observation and model accuracy, it is possible to detect dynamically relevant bathymetric features such as large shoals and channels while originating from a linear, featureless prior bathymetry in a partially-mixed estuary by inverting surface current and gravity wave observations with a 3-D hydrostatic ocean model. Bathymetry estimation skill depends on two factors; location (i.e., differing estimation quality inside vs. outside the MCR) and observation type (e.g., surface currents vs. significant wave height). Despite not being inverted directly, temperature and salinity outputs in the hydrodynamic model improved agreement with observations after bathymetry inversion.
{"title":"Multivariate Data Assimilation at a Partially-mixed Estuary","authors":"Dorukhan Ardağ, G. Wilson, J. Lerczak, Dylan S. Winters, Adam G. Peck-Richardson, D. Lyons, R. Orben","doi":"10.1175/jtech-d-22-0101.1","DOIUrl":"https://doi.org/10.1175/jtech-d-22-0101.1","url":null,"abstract":"\u0000In 2013 and 2014, multiple field excursions of varying scope were concentrated on the Columbia River, a highly energetic, partially-mixed estuary. These experiments included surface drifter and Synthetic Aperture Radar (SAR) measurements during the ONR RIVET-II experiment, and a novel animal tracking effort that samples oceanographic data by employing cormorants tagged with bio-logging devices. In the present work, several different data types from these experiments were combined in order to test an iterative, ensemble-based inversion methodology at the Mouth of the Columbia River (MCR). Results show that, despite inherent limitations of observation and model accuracy, it is possible to detect dynamically relevant bathymetric features such as large shoals and channels while originating from a linear, featureless prior bathymetry in a partially-mixed estuary by inverting surface current and gravity wave observations with a 3-D hydrostatic ocean model. Bathymetry estimation skill depends on two factors; location (i.e., differing estimation quality inside vs. outside the MCR) and observation type (e.g., surface currents vs. significant wave height). Despite not being inverted directly, temperature and salinity outputs in the hydrodynamic model improved agreement with observations after bathymetry inversion.","PeriodicalId":15074,"journal":{"name":"Journal of Atmospheric and Oceanic Technology","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45312363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-18DOI: 10.1175/jtech-d-22-0131.1
D. Vandemark, Marc Emond, Scott D. Miller, S. Shellito, I. Bogoev, J. Covert
One long-standing technical problem affecting the accuracy of eddy correlation air-sea CO2 flux estimates has been motion contamination of the CO2 mixing ratio measurement. This sensor-related problem is well known but its source remains unresolved. This report details an attempt to identify and reduce motion-induced error and to improve the infrared gas analyzer (IRGA) design. The key finding is that a large fraction of the motion sensitivity is associated with the detection approach common to most closed- and open-path IRGA employed today for CO2 and H2O measurements. A new prototype sensor was developed to both investigate and remedy the issue. Results in laboratory and deep water tank tests show marked improvement. The prototype shows a factor of 4-10 reduction in CO2 error under typical at-sea buoy pitch and roll tilts in comparison to an off-the-shelf IRGA system. A similar noise reduction factor of 2-8 is observed in water vapor measurements. The range of platform tilt motion testing also helps to document motion-induced error characteristics of standard analyzers. Study implications are discussed including findings relevant to past field measurements and the promise for improved future flux measurements using similarly modified IRGA on moving ocean observing and aircraft platforms.
{"title":"A CO2 and H2O gas analyzer with reduced error due to platform motion","authors":"D. Vandemark, Marc Emond, Scott D. Miller, S. Shellito, I. Bogoev, J. Covert","doi":"10.1175/jtech-d-22-0131.1","DOIUrl":"https://doi.org/10.1175/jtech-d-22-0131.1","url":null,"abstract":"One long-standing technical problem affecting the accuracy of eddy correlation air-sea CO2 flux estimates has been motion contamination of the CO2 mixing ratio measurement. This sensor-related problem is well known but its source remains unresolved. This report details an attempt to identify and reduce motion-induced error and to improve the infrared gas analyzer (IRGA) design. The key finding is that a large fraction of the motion sensitivity is associated with the detection approach common to most closed- and open-path IRGA employed today for CO2 and H2O measurements. A new prototype sensor was developed to both investigate and remedy the issue. Results in laboratory and deep water tank tests show marked improvement. The prototype shows a factor of 4-10 reduction in CO2 error under typical at-sea buoy pitch and roll tilts in comparison to an off-the-shelf IRGA system. A similar noise reduction factor of 2-8 is observed in water vapor measurements. The range of platform tilt motion testing also helps to document motion-induced error characteristics of standard analyzers. Study implications are discussed including findings relevant to past field measurements and the promise for improved future flux measurements using similarly modified IRGA on moving ocean observing and aircraft platforms.","PeriodicalId":15074,"journal":{"name":"Journal of Atmospheric and Oceanic Technology","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49256655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-17DOI: 10.1175/jtech-d-22-0066.1
Ziying Yang, Jiping Liu, Chaoyuan Yang, Yongyun Hu
Sea surface temperature (SST) forecast products from the NCEP Climate Forecast System (CFSv2) that are widely used in climate research and prediction have nonstationary bias. In this study, we develop single (ANN1) and three hidden layers (ANN3) neural networks and examine their ability to correct the SST bias in the NCEP CFSv2 extended seasonal forecast starting from July in the extratropical Northern Hemisphere. Our results show that the ensemble-based ANN1 and ANN3 can reduce the uncertainty associated with parameters assigned initially and dependence on random sampling. Overall, ANN1 reduces RMSE of the CFSv2 forecasted SST substantially by 0.35°C (0.34°C) for the testing (training) data and ANN3 further reduces RMSE relatively by 0.49°C (0.47°C). Both the ensemble-based ANN1 and ANN3 can significantly reduce the spatial and temporal varying bias of the CFSv2 forecasted SST in the Pacific and Atlantic Oceans, and ANN3 shows better agreement with the observation than that of ANN1 in some subregions.
{"title":"Correcting nonstationary sea surface temperature bias in NCEP CFSv2 using Ensemble-based Neural Networks","authors":"Ziying Yang, Jiping Liu, Chaoyuan Yang, Yongyun Hu","doi":"10.1175/jtech-d-22-0066.1","DOIUrl":"https://doi.org/10.1175/jtech-d-22-0066.1","url":null,"abstract":"\u0000Sea surface temperature (SST) forecast products from the NCEP Climate Forecast System (CFSv2) that are widely used in climate research and prediction have nonstationary bias. In this study, we develop single (ANN1) and three hidden layers (ANN3) neural networks and examine their ability to correct the SST bias in the NCEP CFSv2 extended seasonal forecast starting from July in the extratropical Northern Hemisphere. Our results show that the ensemble-based ANN1 and ANN3 can reduce the uncertainty associated with parameters assigned initially and dependence on random sampling. Overall, ANN1 reduces RMSE of the CFSv2 forecasted SST substantially by 0.35°C (0.34°C) for the testing (training) data and ANN3 further reduces RMSE relatively by 0.49°C (0.47°C). Both the ensemble-based ANN1 and ANN3 can significantly reduce the spatial and temporal varying bias of the CFSv2 forecasted SST in the Pacific and Atlantic Oceans, and ANN3 shows better agreement with\u0000the observation than that of ANN1 in some subregions.","PeriodicalId":15074,"journal":{"name":"Journal of Atmospheric and Oceanic Technology","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44163050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-05DOI: 10.1175/jtech-d-22-0112.1
Jie Yu, C. Blain, P. Martin, T. Campbell
Presented is the approach, implementation, and evaluation of two-way nesting in a split-implicit ocean model, the Navy Coastal Ocean Model (NCOM). Emphasis is on the strategies applied to feed back fields from the fine-mesh nest (child grid) to the coarse-mesh (parent grid). On an appropriate separation of dynamic and feedback interfaces, attention is especially needed for the feedback interface of surface elevation. One particular issue addressed is the inconsistency between the 3D baroclinic velocities and 2D barotropic transports in the feedback. The discrepancy is inherently associated with bathymetry, depth-integration, and the need to average over spatial grid points. A simple remedy is proposed and proven to be effective and necessary in realistic coastal applications. In addition to the full two-way nesting, a simplified two-way nesting approach is provided in which only the temperature and salinity are fed back from the nest, and the velocity fields are assumed to self-adjust according to the geostrophic balance. The performance of both approaches is evaluated using the idealized benchmark, propagation of a baroclinic vortex, and an application to the Mississippi River outflowin the northeast Gulf ofMexico, including a comparison with available observations. Discussions are also made on the computational efficiency of the two-way nesting and its sensitivity to the open boundary conditions in regard to noise suppression.
{"title":"Two-way nesting in a split-implicit ocean model: NCOM","authors":"Jie Yu, C. Blain, P. Martin, T. Campbell","doi":"10.1175/jtech-d-22-0112.1","DOIUrl":"https://doi.org/10.1175/jtech-d-22-0112.1","url":null,"abstract":"\u0000Presented is the approach, implementation, and evaluation of two-way nesting in a split-implicit ocean model, the Navy Coastal Ocean Model (NCOM). Emphasis is on the strategies applied to feed back fields from the fine-mesh nest (child grid) to the coarse-mesh (parent grid). On an appropriate separation of dynamic and feedback interfaces, attention is especially needed for the feedback interface of surface elevation. One particular issue addressed is the inconsistency between the 3D baroclinic velocities and 2D barotropic transports in the feedback. The discrepancy is inherently associated with bathymetry, depth-integration, and the need to average over spatial grid points. A simple remedy is proposed and proven to be effective and necessary in realistic coastal applications. In addition to the full two-way nesting, a simplified two-way nesting approach is provided in which only the temperature and salinity are fed back from the nest, and the velocity fields are assumed to self-adjust according to the geostrophic balance. The performance of both approaches is evaluated using the idealized benchmark, propagation of a baroclinic vortex, and an application to the Mississippi River outflowin the northeast Gulf ofMexico, including a comparison with available observations. Discussions are also made on the computational efficiency of the two-way nesting and its sensitivity to the open boundary conditions in regard to noise suppression.","PeriodicalId":15074,"journal":{"name":"Journal of Atmospheric and Oceanic Technology","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45563111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-05DOI: 10.1175/jtech-d-22-0053.1
Jessie C. Moore Torres, Christopher R. Jackson, Tyler W. Ruff, S. Helfrich, R. Romeiser
Since the 1960’s, meteorological satellites have been able to monitor tropical cyclones and typhoons. Their images have been acquired by passive remote sensing instruments that operate in the visible and infrared bands, where they only display the cloud-top structure of tropical cyclones and make it a challenge to study the air-sea interaction near the sea surface. On the other hand, active remote sensors, such as spaceborne microwave scatterometers and synthetic aperture radars (SARs), can “see” through clouds and facilitate observations of the air-sea interaction processes. However, SAR acquires images and provides the wind field at a much higher resolution, where the eye of a tropical cyclone at surface level can be identified. The backscattered signals received by the SAR can be processed into a high-resolution image and calibrated to represent the normalized radar cross-section (NRCS) of the sea surface. In this study, 33 RADARSAT-2 and 102 Sentinel-1 SAR images of Atlantic and Indian Ocean tropical cyclones and Pacific typhoons from 2016-2021, which display eye structure, have been statistically analyzed with ancillary tropical cyclone intensity information. To measure the size of the eye, a 34-kt contour is defined around it and the amount and size of pixels within the eye is utilized to provide its area in km2. Additionally, an azimuthal wavenumber for each shape of the eye was assigned. Results showed that eye areas increase with decreasing wind speed and increasing wavenumber and demonstrate that SAR-derived data is useful for studying tropical cyclones at the air-sea interface and provide results of these behaviors closely to data derived from best-track archives.
{"title":"Observing Tropical Cyclone Morphology Using RADARSAT-2 and Sentinel-1 Synthetic Aperture Radar Images","authors":"Jessie C. Moore Torres, Christopher R. Jackson, Tyler W. Ruff, S. Helfrich, R. Romeiser","doi":"10.1175/jtech-d-22-0053.1","DOIUrl":"https://doi.org/10.1175/jtech-d-22-0053.1","url":null,"abstract":"\u0000Since the 1960’s, meteorological satellites have been able to monitor tropical cyclones and typhoons. Their images have been acquired by passive remote sensing instruments that operate in the visible and infrared bands, where they only display the cloud-top structure of tropical cyclones and make it a challenge to study the air-sea interaction near the sea surface. On the other hand, active remote sensors, such as spaceborne microwave scatterometers and synthetic aperture radars (SARs), can “see” through clouds and facilitate observations of the air-sea interaction processes. However, SAR acquires images and provides the wind field at a much higher resolution, where the eye of a tropical cyclone at surface level can be identified. The backscattered signals received by the SAR can be processed into a high-resolution image and calibrated to represent the normalized radar cross-section (NRCS) of the sea surface. In this study, 33 RADARSAT-2 and 102 Sentinel-1 SAR images of Atlantic and Indian Ocean tropical cyclones and Pacific typhoons from 2016-2021, which display eye structure, have been statistically analyzed with ancillary tropical cyclone intensity information. To measure the size of the eye, a 34-kt contour is defined around it and the amount and size of pixels within the eye is utilized to provide its area in km2. Additionally, an azimuthal wavenumber for each shape of the eye was assigned. Results showed that eye areas increase with decreasing wind speed and increasing wavenumber and demonstrate that SAR-derived data is useful for studying tropical cyclones at the air-sea interface and provide results of these behaviors closely to data derived from best-track archives.","PeriodicalId":15074,"journal":{"name":"Journal of Atmospheric and Oceanic Technology","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42699381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-01DOI: 10.1175/jtech-d-22-0037.1
Guo Lin, Zhien Wang, C. Ziegler, Xiao‐Ming Hu, M. Xue, B. Geerts, Yufei Chu
The magnitude of water vapor content within the near-storm inflow can either support or deter the storm’s upscale growth and maintenance. However, the heterogeneity of the moisture field near storms remains poorly understood because the operational observation network lacks detail. This observational study illustrates that near-storm inflow water vapor environments are both significantly heterogeneous and different than the far-inflow storm environment. This study also depicts the importance of temporal variation of water vapor mixing ratio (WVMR) to instability during the peak tornadic seasons in the U.S. Southeast and Great Plains regions during the Verification of the Origins of Rotation in Tornadoes Experiment Southeast 2018 (VSE18) campaign and the Targeted Observation by Radar and UAS of Supercells (TORUS) campaign, respectively. VSE18 results suggest that the surface processes control WVMR variation significantly in lower levels, with the highest WVMR mainly located near the surface in inflows in the southeast region. In contrast, TORUS results show more vertically homogeneous WVMR profiles and rather uniform water vapor distribution variation occurring in deep, moist stratified inflows in the Great Plains region. Temporal water vapor variations within 5-min periods could lead to over 1000 J kg−1 CAPE changes in both VSE18 and TORUS, which represent significant potential buoyancy perturbations for storms to intensify or decay. These temporal water vapor and instability evolutions of moving storms remain difficult to capture via radiosondes and fixed in situ or profiling instrumentation, yet may exert a strong impact on storm evolution. This study suggests that improving observations of the variability of near-storm inflow moisture can accurately refine a potential severe weather threat. It has long been recognized that better observations of the planetary boundary layer (PBL) inflow near convective storms are needed to improve severe weather forecasting. The current operational networks essentially do not provide profile measurements of the PBL, except for the sparsely spaced 12-hourly sounding network. More frequent geostationary satellite observations do not provide adequately high vertical resolution in the PBL. This study uses airborne lidar profiler measurements to examine moisture in the inflow region of convective storms in the Great Plains and the southeastern United States during their respective tornadic seasons. Rapid PBL water vapor variations on a ∼5 min time scale can lead to CAPE perturbations exceeding 1000 J kg−1, representing significant perturbations that could promote storm intensification or decay. Severe thunderstorms may generate high-impact weather phenomena, such as tornadoes, high winds, hail, and heavy rainfall, which have substantial socioeconomic impacts. Ultimately, by contrasting characteristics of the convective storm inflow in the two regions, this study may lead to a more accurate assessment of severe w
{"title":"A Comparison of Convective Storm Inflow Moisture Variability between the Great Plains and the Southeastern United States Using Multiplatform Field Campaign Observations","authors":"Guo Lin, Zhien Wang, C. Ziegler, Xiao‐Ming Hu, M. Xue, B. Geerts, Yufei Chu","doi":"10.1175/jtech-d-22-0037.1","DOIUrl":"https://doi.org/10.1175/jtech-d-22-0037.1","url":null,"abstract":"\u0000The magnitude of water vapor content within the near-storm inflow can either support or deter the storm’s upscale growth and maintenance. However, the heterogeneity of the moisture field near storms remains poorly understood because the operational observation network lacks detail. This observational study illustrates that near-storm inflow water vapor environments are both significantly heterogeneous and different than the far-inflow storm environment. This study also depicts the importance of temporal variation of water vapor mixing ratio (WVMR) to instability during the peak tornadic seasons in the U.S. Southeast and Great Plains regions during the Verification of the Origins of Rotation in Tornadoes Experiment Southeast 2018 (VSE18) campaign and the Targeted Observation by Radar and UAS of Supercells (TORUS) campaign, respectively. VSE18 results suggest that the surface processes control WVMR variation significantly in lower levels, with the highest WVMR mainly located near the surface in inflows in the southeast region. In contrast, TORUS results show more vertically homogeneous WVMR profiles and rather uniform water vapor distribution variation occurring in deep, moist stratified inflows in the Great Plains region. Temporal water vapor variations within 5-min periods could lead to over 1000 J kg−1 CAPE changes in both VSE18 and TORUS, which represent significant potential buoyancy perturbations for storms to intensify or decay. These temporal water vapor and instability evolutions of moving storms remain difficult to capture via radiosondes and fixed in situ or profiling instrumentation, yet may exert a strong impact on storm evolution. This study suggests that improving observations of the variability of near-storm inflow moisture can accurately refine a potential severe weather threat.\u0000\u0000\u0000It has long been recognized that better observations of the planetary boundary layer (PBL) inflow near convective storms are needed to improve severe weather forecasting. The current operational networks essentially do not provide profile measurements of the PBL, except for the sparsely spaced 12-hourly sounding network. More frequent geostationary satellite observations do not provide adequately high vertical resolution in the PBL. This study uses airborne lidar profiler measurements to examine moisture in the inflow region of convective storms in the Great Plains and the southeastern United States during their respective tornadic seasons. Rapid PBL water vapor variations on a ∼5 min time scale can lead to CAPE perturbations exceeding 1000 J kg−1, representing significant perturbations that could promote storm intensification or decay. Severe thunderstorms may generate high-impact weather phenomena, such as tornadoes, high winds, hail, and heavy rainfall, which have substantial socioeconomic impacts. Ultimately, by contrasting characteristics of the convective storm inflow in the two regions, this study may lead to a more accurate assessment of severe w","PeriodicalId":15074,"journal":{"name":"Journal of Atmospheric and Oceanic Technology","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45824841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-24DOI: 10.1175/jtech-d-22-0118.1
V. Louf, A. Protat
We present an integrated framework that leverages multiple weather radar calibration and monitoring techniques to provide real-time diagnostics on reflectivity calibration, antenna pointing, and dual-polarisation moments. This framework uses a volume-matching technique to track the absolute calibration of radar reflectivity with respect to the Global Precipitation Measurement (GPM) spaceborne radar; the Relative Calibration Adjustment (RCA) technique to track relative changes in the radar calibration constant; the solar calibration technique to track daily change in solar power and antenna pointing error; and techniques that track properties of light-rain medium to monitor the differential reflectivity and dual-polarisation moments. This framework allows for an evaluation of various calibration and monitoring techniques. For example, we found that a change in the RCA is highly correlated to a change in absolute calibration, with respect to GPM, if a change in antenna pointing can first be ruled out. It is currently monitoring 67+ radars from the Australian radar network. Due to the diverse and evolving nature of the Australian radar network, flexibility and modularity are at the core of the calibration framework. The framework can tailor its diagnostics to the specific characteristics of a radar (band, beamwidth, etc.). Because of its modularity, it can be expanded with new techniques to provide additional diagnostics (e.g., monitoring of radar sensitivity). The results are presented in an interactive dashboard at different level of details for a wide and diverse audience (radar engineers, researchers, forecasters, and management) an it is operational at the Australian Bureau of Meteorology.
{"title":"Real-time Monitoring of Weather Radar Network Calibration and Antenna Pointing","authors":"V. Louf, A. Protat","doi":"10.1175/jtech-d-22-0118.1","DOIUrl":"https://doi.org/10.1175/jtech-d-22-0118.1","url":null,"abstract":"\u0000We present an integrated framework that leverages multiple weather radar calibration and monitoring techniques to provide real-time diagnostics on reflectivity calibration, antenna pointing, and dual-polarisation moments. This framework uses a volume-matching technique to track the absolute calibration of radar reflectivity with respect to the Global Precipitation Measurement (GPM) spaceborne radar; the Relative Calibration Adjustment (RCA) technique to track relative changes in the radar calibration constant; the solar calibration technique to track daily change in solar power and antenna pointing error; and techniques that track properties of light-rain medium to monitor the differential reflectivity and dual-polarisation moments. This framework allows for an evaluation of various calibration and monitoring techniques. For example, we found that a change in the RCA is highly correlated to a change in absolute calibration, with respect to GPM, if a change in antenna pointing can first be ruled out. It is currently monitoring 67+ radars from the Australian radar network. Due to the diverse and evolving nature of the Australian radar network, flexibility and modularity are at the core of the calibration framework. The framework can tailor its diagnostics to the specific characteristics of a radar (band, beamwidth, etc.). Because of its modularity, it can be expanded with new techniques to provide additional diagnostics (e.g., monitoring of radar sensitivity). The results are presented in an interactive dashboard at different level of details for a wide and diverse audience (radar engineers, researchers, forecasters, and management) an it is operational at the Australian Bureau of Meteorology.","PeriodicalId":15074,"journal":{"name":"Journal of Atmospheric and Oceanic Technology","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48719584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-24DOI: 10.1175/jtech-d-22-0109.1
P. Baron, Kohei Kawashima, Dong-Kyun Kim, H. Hanado, S. Kawamura, T. Maesaka, K. Nakagawa, S. Satoh, T. Ushio
We present nowcasts of sudden heavy rains on meso-γ-scales (2–20 km) using the high spatio-temporal resolution of a Multi-Parameter Phased-Array Weather Radar (MP-PAWR) sensitive to rain droplets. The onset of typical storms is successfully predicted with 10-minute lead time, i.e., the current predictability limit of rainfall caused by individual convective cores. A supervised recurrent neural network based on Long Short-Term Memory with 3D spatial convolutions (RN3D) is used to account for the horizontal and vertical changes of the convective cells with a time resolution of 30 sec. The model uses radar reflectivity at horizontal polarization (ZH) and the differential reflectivity. The input parameters are defined in a volume of 64×64×8 km3 with the lowest level at 1.9 km and a resolution of 0.4×0.4×0.25 km3. The prediction is a 10-minute sequence of ZH at the lowest grid level. The model is trained with a large number of observations of summer 2020 and an adversarial technique. RN3D is tested with different types of rapidly evolving localized heavy rainfalls of summers 2018 and 2019. The model performance is compared to that of an advection model for 3D extrapolation of PAWR echoes (A3DM). RN3D better predicts the formation and dissipation of precipitation. However, RN3D tends to underestimate heavy rainfall especially when the storm is well developed. In this phase of the storm, A3DM nowcast scores are found slightly higher. The high skill of RN3D to predict the onset of sudden localized rainfall is illustrated with an example for which RN3D outperforms the operational precipitation nowcasting system of Japan Meteorological Agency (JMA).
{"title":"Nowcasting Multi-Parameter Phased-Array Weather Radar (MP-PAWR) echoes of localized heavy precipitation using a 3D Recurrent Neural Network trained with an adversarial technique","authors":"P. Baron, Kohei Kawashima, Dong-Kyun Kim, H. Hanado, S. Kawamura, T. Maesaka, K. Nakagawa, S. Satoh, T. Ushio","doi":"10.1175/jtech-d-22-0109.1","DOIUrl":"https://doi.org/10.1175/jtech-d-22-0109.1","url":null,"abstract":"\u0000We present nowcasts of sudden heavy rains on meso-γ-scales (2–20 km) using the high spatio-temporal resolution of a Multi-Parameter Phased-Array Weather Radar (MP-PAWR) sensitive to rain droplets. The onset of typical storms is successfully predicted with 10-minute lead time, i.e., the current predictability limit of rainfall caused by individual convective cores. A supervised recurrent neural network based on Long Short-Term Memory with 3D spatial convolutions (RN3D) is used to account for the horizontal and vertical changes of the convective cells with a time resolution of 30 sec. The model uses radar reflectivity at horizontal polarization (ZH) and the differential reflectivity. The input parameters are defined in a volume of 64×64×8 km3 with the lowest level at 1.9 km and a resolution of 0.4×0.4×0.25 km3. The prediction is a 10-minute sequence of ZH at the lowest grid level. The model is trained with a large number of observations of summer 2020 and an adversarial technique. RN3D is tested with different types of rapidly evolving localized heavy rainfalls of summers 2018 and 2019. The model performance is compared to that of an advection model for 3D extrapolation of PAWR echoes (A3DM). RN3D better predicts the formation and dissipation of precipitation. However, RN3D tends to underestimate heavy rainfall especially when the storm is well developed. In this phase of the storm, A3DM nowcast scores are found slightly higher. The high skill of RN3D to predict the onset of sudden localized rainfall is illustrated with an example for which RN3D outperforms the operational precipitation nowcasting system of Japan Meteorological Agency (JMA).","PeriodicalId":15074,"journal":{"name":"Journal of Atmospheric and Oceanic Technology","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43331800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-20DOI: 10.1175/jtech-d-22-0087.1
Susana Jorquera, Felipe Toledo Bittner, J. Delanoë, A. Berne, Anne-Claire Billault-Roux, A. Schwarzenboeck, F. Dezitter, N. Viltard, A. Martini
This article presents a calibration transfer methodology between radars of the same and different frequency bands. This method enables the absolute calibration of a meteorological radar by transferring it from another co-located instrument with known calibration, by simultaneously measuring vertical cloud reflectivity profiles. The advantage is that the added uncertainty in the newly calibrated instrument can reach the magnitude of the reference instrument calibration. This is achieved by carefully selecting comparable data, including the identification of the reflectivity range that avoids the disparities introduced by differences in sensitivity or scattering regime. The result is a correction coefficient used to compensate measurement bias in the uncalibrated instrument. Calibration transfer uncertainty can be reduced by increasing the number of sampling periods. The methodology was applied between co-located W-band radars deployed during the ICE-GENESIS campaign (Switzerland 2020-2021). A difference of 2.2 dB was found in their reflectivity measurements, with an uncertainty of 0.7 dB. The calibration transfer was also applied to radars of different frequency, an X-band radar with unknown calibration and aW-band radar with manufacturer calibration, the difference found was -16.7 dB with an uncertainty of 1.2 dB. The method was validated through closure, by transferring calibration between three different radars in two different case studies. For the first case, involving three W-band radars, the bias found was of 0.2 dB. In the second case, involving two W-band and one X-band radar, the bias found was of 0.3 dB. These results imply that the biases introduced by performing the calibration transfer with this method are negligible.
{"title":"Calibration transfer methodology for cloud radars based on ice cloud observations","authors":"Susana Jorquera, Felipe Toledo Bittner, J. Delanoë, A. Berne, Anne-Claire Billault-Roux, A. Schwarzenboeck, F. Dezitter, N. Viltard, A. Martini","doi":"10.1175/jtech-d-22-0087.1","DOIUrl":"https://doi.org/10.1175/jtech-d-22-0087.1","url":null,"abstract":"\u0000This article presents a calibration transfer methodology between radars of the same and different frequency bands. This method enables the absolute calibration of a meteorological radar by transferring it from another co-located instrument with known calibration, by simultaneously measuring vertical cloud reflectivity profiles. The advantage is that the added uncertainty in the newly calibrated instrument can reach the magnitude of the reference instrument calibration. This is achieved by carefully selecting comparable data, including the identification of the reflectivity range that avoids the disparities introduced by differences in sensitivity or scattering regime. The result is a correction coefficient used to compensate measurement bias in the uncalibrated instrument. Calibration transfer uncertainty can be reduced by increasing the number of sampling periods. The methodology was applied between co-located W-band radars deployed during the ICE-GENESIS campaign (Switzerland 2020-2021). A difference of 2.2 dB was found in their reflectivity measurements, with an uncertainty of 0.7 dB. The calibration transfer was also applied to radars of different frequency, an X-band radar with unknown calibration and aW-band radar with manufacturer calibration, the difference found was -16.7 dB with an uncertainty of 1.2 dB. The method was validated through closure, by transferring calibration between three different radars in two different case studies. For the first case, involving three W-band radars, the bias found was of 0.2 dB. In the second case, involving two W-band and one X-band radar, the bias found was of 0.3 dB. These results imply that the biases introduced by performing the calibration transfer with this method are negligible.","PeriodicalId":15074,"journal":{"name":"Journal of Atmospheric and Oceanic Technology","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45026588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-17DOI: 10.1175/jtech-d-22-0092.1
Rizana Salim, Aishwarya Singh, S. S, Kavyashree. N. Kalkura, Amar Krishna Gopinath, S. Raj, Ramesh Chand K.A, R. Krishna, S. Gunthe
Aerosol-cloud-precipitation interaction represents the largest uncertainty in climate change’s current and future understanding. Therefore, aerosol properties affecting the cloud and precipitation formation and their accurate estimation is a first step in developing improved parameterizations for the prognostic climate models. Over the last couple of decades, a commercially available Cloud Condensation Nuclei Counter (CCNC) has been deployed in the field and laboratory for characterizing CCN properties of ambient or atmospherically relevant laboratory-generated aerosols. However, most of the CCN measurements performed in the field are often compounded with the erroneous estimation of CCN concentration and other parameters due to a lack of robust and accurate CCNC calibration. CCNC is not a plug-and-play instrument and requires prudent calibration and operation, to avoid erroneous data and added parameterization uncertainties. In this work, we propose and demonstrate the usability of a global calibration equation derived from CCNC calibration experiments from 8 contrasting global environments. Significant correlationwas observed between the global calibration and each of the 16 individual experiments. A significant improvement in the correlation was observed when the calibration experiments were separated for high altitude measurements. Using these equations, we further derived the effective hygroscopicity parameter and found lower relative uncertainty in the hygroscopicity parameter at higher effective supersaturation. Our results signify that altitude-based pressure change could be of importance for accurate calibration at high altitude locations. Our results are consistent with previous studies emphasizing the criticality of the accurate CCN calibration for the lower supersaturations.
{"title":"Investigating the applicability of a global average calibration line for ambient size-resolved Cloud Condensation Nuclei (CCN) measurements: A technical note","authors":"Rizana Salim, Aishwarya Singh, S. S, Kavyashree. N. Kalkura, Amar Krishna Gopinath, S. Raj, Ramesh Chand K.A, R. Krishna, S. Gunthe","doi":"10.1175/jtech-d-22-0092.1","DOIUrl":"https://doi.org/10.1175/jtech-d-22-0092.1","url":null,"abstract":"\u0000Aerosol-cloud-precipitation interaction represents the largest uncertainty in climate change’s current and future understanding. Therefore, aerosol properties affecting the cloud and precipitation formation and their accurate estimation is a first step in developing improved parameterizations for the prognostic climate models. Over the last couple of decades, a commercially available Cloud Condensation Nuclei Counter (CCNC) has been deployed in the field and laboratory for characterizing CCN properties of ambient or atmospherically relevant laboratory-generated aerosols. However, most of the CCN measurements performed in the field are often compounded with the erroneous estimation of CCN concentration and other parameters due to a lack of robust and accurate CCNC calibration. CCNC is not a plug-and-play instrument and requires prudent calibration and operation, to avoid erroneous data and added parameterization uncertainties. In this work, we propose and demonstrate the usability of a global calibration equation derived from CCNC calibration experiments from 8 contrasting global environments. Significant correlationwas observed between the global calibration and each of the 16 individual experiments. A significant improvement in the correlation was observed when the calibration experiments were separated for high altitude measurements. Using these equations, we further derived the effective hygroscopicity parameter and found lower relative uncertainty in the hygroscopicity parameter at higher effective supersaturation. Our results signify that altitude-based pressure change could be of importance for accurate calibration at high altitude locations. Our results are consistent with previous studies emphasizing the criticality of the accurate CCN calibration for the lower supersaturations.","PeriodicalId":15074,"journal":{"name":"Journal of Atmospheric and Oceanic Technology","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48070680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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