Pub Date : 2023-03-16DOI: 10.1175/jtech-d-22-0128.1
N. B. Miller, A. Merrelli, T. L’Ecuyer, B. Drouin
The Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) mission will measure the Earth’s emission at wavelengths ranging from 3-54 µm. The pre-launch clear-sky retrieval algorithm, evaluated with simulated test data, indicates that PREFIRE measurements will be valuable for retrieving atmospheric water vapor and temperature profiles. Far infrared measurements provide unique retrieval information, indicated by the high ranking of select FIR channels as primary contributors to the total degrees of freedom for signal (DFS). In utilizing all the PREFIRE channels, the average total DFS of 4 test regions ranges from 1.90 - 4.71. The information content increases with higher column water vapor and in the presence of near surface temperature inversions. Using the DFS profiles for guidance, the retrieval concentrates information into 7 distinct layers to reduce the retrieval uncertainty per layer. Sensitivity tests indicate forward model error due to surface emissivity uncertainty results in about a 9% increase in column water vapor uncertainty. The clear-sky retrieval is sensitive to the presence of undetected ice clouds, especially those with optical depths larger than 0.2. Hence, in addition to a separate PREFIRE cloud mask, optimal estimation retrieval metrics are explored as possible indicators of cloudy scenes.
{"title":"Simulated Clear-Sky Water Vapor and Temperature Retrievals from PREFIRE Measurements","authors":"N. B. Miller, A. Merrelli, T. L’Ecuyer, B. Drouin","doi":"10.1175/jtech-d-22-0128.1","DOIUrl":"https://doi.org/10.1175/jtech-d-22-0128.1","url":null,"abstract":"\u0000The Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) mission will measure the Earth’s emission at wavelengths ranging from 3-54 µm. The pre-launch clear-sky retrieval algorithm, evaluated with simulated test data, indicates that PREFIRE measurements will be valuable for retrieving atmospheric water vapor and temperature profiles. Far infrared measurements provide unique retrieval information, indicated by the high ranking of select FIR channels as primary contributors to the total degrees of freedom for signal (DFS). In utilizing all the PREFIRE channels, the average total DFS of 4 test regions ranges from 1.90 - 4.71. The information content increases with higher column water vapor and in the presence of near surface temperature inversions. Using the DFS profiles for guidance, the retrieval concentrates information into 7 distinct layers to reduce the retrieval uncertainty per layer. Sensitivity tests indicate forward model error due to surface emissivity uncertainty results in about a 9% increase in column water vapor uncertainty. The clear-sky retrieval is sensitive to the presence of undetected ice clouds, especially those with optical depths larger than 0.2. Hence, in addition to a separate PREFIRE cloud mask, optimal estimation retrieval metrics are explored as possible indicators of cloudy scenes.","PeriodicalId":15074,"journal":{"name":"Journal of Atmospheric and Oceanic Technology","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45911554","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 : 2023-03-09DOI: 10.1175/jtech-d-22-0108.1
P. Jangir, K. Ewans, I. Young
Accurate ocean wave measurements are needed for the safe design and operation of offshore facilities, but despite many ocean wave measurements, the accuracy of wave measurement systems remains an ongoing issue. Of paramount importance are measurements during extreme sea states. This paper examines wave measurements made with an Optech Laser, a Rosemount WaveRadar, and a Datawell Waverider buoy at North Rankin A platform (NRA), Australia; Ekofisk, North Sea; and several South China Sea locations. We evaluate the relative performance of these instruments based upon various frequency domain comparisons, including comparisons of their 1-D frequency spectra using spectrograms, spectral moments, high-frequency tail slopes, and significant wave heights derived from their wave spectra. A spectral relationship (transfer function) in terms of mean spectral ratio of the instruments is developed, which can be used for spectral calibration. On average, Laser and Waverider spectral estimates agree well at all sea states. However, at low wind speeds, the higher frequency spectral levels of the Laser are relatively high and noisy compared with the other two instruments. Radar higher frequency spectral estimates are relatively low compared to the other two instruments, particularly at lower sea states. In additionally, the higher frequency tail slopes of all three instruments vary between f‒4 and f‒5. However, at higher sea states, the Waverider tail slopes become steeper than f‒5. The Radar produces the lowest significant wave heights (Hm0) compared to the Laser and Waverider, but its second moment period (Tm02) estimates are longer than the Laser and Waverider.
{"title":"Comparative performance of Radar, Laser, and Waverider Buoy measurements of ocean waves – Part 1: Frequency domain analysis","authors":"P. Jangir, K. Ewans, I. Young","doi":"10.1175/jtech-d-22-0108.1","DOIUrl":"https://doi.org/10.1175/jtech-d-22-0108.1","url":null,"abstract":"\u0000Accurate ocean wave measurements are needed for the safe design and operation of offshore facilities, but despite many ocean wave measurements, the accuracy of wave measurement systems remains an ongoing issue. Of paramount importance are measurements during extreme sea states. This paper examines wave measurements made with an Optech Laser, a Rosemount WaveRadar, and a Datawell Waverider buoy at North Rankin A platform (NRA), Australia; Ekofisk, North Sea; and several South China Sea locations. We evaluate the relative performance of these instruments based upon various frequency domain comparisons, including comparisons of their 1-D frequency spectra using spectrograms, spectral moments, high-frequency tail slopes, and significant wave heights derived from their wave spectra. A spectral relationship (transfer function) in terms of mean spectral ratio of the instruments is developed, which can be used for spectral calibration. On average, Laser and Waverider spectral estimates agree well at all sea states. However, at low wind speeds, the higher frequency spectral levels of the Laser are relatively high and noisy compared with the other two instruments. Radar higher frequency spectral estimates are relatively low compared to the other two instruments, particularly at lower sea states. In additionally, the higher frequency tail slopes of all three instruments vary between f‒4 and f‒5. However, at higher sea states, the Waverider tail slopes become steeper than f‒5. The Radar produces the lowest significant wave heights (Hm0) compared to the Laser and Waverider, but its second moment period (Tm02) estimates are longer than the Laser and Waverider.","PeriodicalId":15074,"journal":{"name":"Journal of Atmospheric and Oceanic Technology","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47569051","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 : 2023-03-03DOI: 10.1175/jtech-d-22-0090.1
Yuxin Zhao, Hengde Zhao, Xiong Deng
While numerical models have been developed for several years, some of these have been applied to ocean state sampling. Adaptive sampling deploys limited assets using prior information; then, observation assets are concentrated in areas of greater sampling value, which is very suitable for an extensive and dynamic marine environment. The improved resolution allows numerical models to be used on mobile platforms. However, the existing adaptive sampling framework for mobile platforms lacks regular interaction with the numerical model. And the observation scheme is easy to deviate from the optimal. This study sets up a closed-loop adaptive sampling framework for mobile platforms that realizes the optimization of model → sampling → model. Linking coupled model with the sampling points of the mobile platforms, the adaptive method configures key sampling locations to determine when and where the sampling schemes are adjusted. With the aid of a coupled model, we selected an optimization algorithm for the framework and simulated the process under the twin experimental framework. This research provides theoretical technical support for the combination of model and mobile sampling platforms.
{"title":"A Periodically Updated Adaptive Sampling Framework for Marine Mobile Observation Platforms","authors":"Yuxin Zhao, Hengde Zhao, Xiong Deng","doi":"10.1175/jtech-d-22-0090.1","DOIUrl":"https://doi.org/10.1175/jtech-d-22-0090.1","url":null,"abstract":"\u0000While numerical models have been developed for several years, some of these have been applied to ocean state sampling. Adaptive sampling deploys limited assets using prior information; then, observation assets are concentrated in areas of greater sampling value, which is very suitable for an extensive and dynamic marine environment. The improved resolution allows numerical models to be used on mobile platforms. However, the existing adaptive sampling framework for mobile platforms lacks regular interaction with the numerical model. And the observation scheme is easy to deviate from the optimal. This study sets up a closed-loop adaptive sampling framework for mobile platforms that realizes the optimization of model → sampling → model. Linking coupled model with the sampling points of the mobile platforms, the adaptive method configures key sampling locations to determine when and where the sampling schemes are adjusted. With the aid of a coupled model, we selected an optimization algorithm for the framework and simulated the process under the twin experimental framework. This research provides theoretical technical support for the combination of model and mobile sampling platforms.","PeriodicalId":15074,"journal":{"name":"Journal of Atmospheric and Oceanic Technology","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43275024","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 : 2023-02-27DOI: 10.1175/jtech-d-22-0077.1
J. R. Reeves Eyre, M. Cronin, Dongxiao Zhang, E. Thompson, C. Fairall, J. Edson
High frequency wind measurements from Saildrone autonomous surface vehicles are used to calculate wind stress in the tropical East Pacific. Comparison between direct covariance (DC) and bulk wind stress estimates demonstrates very good agreement. Building on previouswork that showed the bulk input data was reliable, our results lend credibility to the DC estimates. Wind flow distortion by Saildrones is comparable to or smaller than other platforms. Motion correction results in realistic wind spectra, albeit with signatures of swell-coherent wind fluctuations that may be unrealistically strong. Fractional differences between DC and bulk wind stress magnitude are largest at wind speeds below 4 m s−1. The size of this effect, however, depends on choice of stress direction assumptions. Past work has shown the importance of using current-relative (instead of Earth-relative) winds to achieve accurate wind stress magnitude. We show that it is also important for wind stress direction.
Saildone自主水面飞行器的高频风测量用于计算热带东太平洋的风应力。直接协方差（DC）和整体风应力估计之间的比较表明了非常好的一致性。基于先前的工作表明大量输入数据是可靠的，我们的结果为DC估计提供了可信度。赛欧无人机的气流畸变与其他平台相当或更小。运动校正产生了真实的风谱，尽管具有可能不切实际的强烈涌浪相干风波动的特征。在风速低于4 m s−1时，直流风应力和整体风应力之间的分数差异最大。然而，这种影响的大小取决于应力方向假设的选择。过去的工作表明，使用当前相对风（而不是地球相对风）来获得准确的风应力大小非常重要。我们表明，它对风应力方向也很重要。
{"title":"Saildrone direct covariance wind stress in various wind and current regimes of the tropical Pacific","authors":"J. R. Reeves Eyre, M. Cronin, Dongxiao Zhang, E. Thompson, C. Fairall, J. Edson","doi":"10.1175/jtech-d-22-0077.1","DOIUrl":"https://doi.org/10.1175/jtech-d-22-0077.1","url":null,"abstract":"\u0000High frequency wind measurements from Saildrone autonomous surface vehicles are used to calculate wind stress in the tropical East Pacific. Comparison between direct covariance (DC) and bulk wind stress estimates demonstrates very good agreement. Building on previouswork that showed the bulk input data was reliable, our results lend credibility to the DC estimates. Wind flow distortion by Saildrones is comparable to or smaller than other platforms. Motion correction results in realistic wind spectra, albeit with signatures of swell-coherent wind fluctuations that may be unrealistically strong. Fractional differences between DC and bulk wind stress magnitude are largest at wind speeds below 4 m s−1. The size of this effect, however, depends on choice of stress direction assumptions. Past work has shown the importance of using current-relative (instead of Earth-relative) winds to achieve accurate wind stress magnitude. We show that it is also important for wind stress direction.","PeriodicalId":15074,"journal":{"name":"Journal of Atmospheric and Oceanic Technology","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46635112","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 : 2023-02-24DOI: 10.1175/jtech-d-22-0058.1
J. Lyman, G. Johnson
The ocean, with its low albedo and vast thermal inertia, plays key roles in the climate system, including absorbing massive amounts of heat as atmospheric greenhouse gas concentrations rise. While the Argo array of profiling floats has vastly improved sampling of ocean temperature in the upper half of the global ocean volume since the mid-2000s, they are not sufficient in number to resolve eddy scales in the oceans. However, satellite sea-surface temperature (SST) and sea-surface height (SSH) measurements do resolve these scales. Here we use Random Forest regressions to map ocean heat content anomalies (OHCA) using in situ training data from Argo and other sources on a 7-day × ¼° grid with latitude, longitude, time, SSH, and SST as predictors. The maps display substantial patterns on eddy scales, resolving variations of ocean currents and fronts. During the well sampled Argo period, global integrals of these maps reduce noise relative to estimates based on objective mapping of in situ data alone by roughly a factor of three when compared to time series of CERES (satellite data) top-of-the-atmosphere energy flux measurements and improve correlations of anomalies with CERES on annual time scales. Prior to and early on in the Argo period, when in situ data were sparser, global integrals of these maps retain low variance, and do not relax back to a climatological mean, avoiding potential deficiencies of various methods for infilling data-sparse regions with objective maps by exploiting temporal and spatial patterns of OHCA and its correlations with SST and SSH.
{"title":"Global High-Resolution Random Forest Regression Maps of Ocean Heat Content Anomalies Using in Situ and Satellite Data","authors":"J. Lyman, G. Johnson","doi":"10.1175/jtech-d-22-0058.1","DOIUrl":"https://doi.org/10.1175/jtech-d-22-0058.1","url":null,"abstract":"\u0000The ocean, with its low albedo and vast thermal inertia, plays key roles in the climate system, including absorbing massive amounts of heat as atmospheric greenhouse gas concentrations rise. While the Argo array of profiling floats has vastly improved sampling of ocean temperature in the upper half of the global ocean volume since the mid-2000s, they are not sufficient in number to resolve eddy scales in the oceans. However, satellite sea-surface temperature (SST) and sea-surface height (SSH) measurements do resolve these scales. Here we use Random Forest regressions to map ocean heat content anomalies (OHCA) using in situ training data from Argo and other sources on a 7-day × ¼° grid with latitude, longitude, time, SSH, and SST as predictors. The maps display substantial patterns on eddy scales, resolving variations of ocean currents and fronts. During the well sampled Argo period, global integrals of these maps reduce noise relative to estimates based on objective mapping of in situ data alone by roughly a factor of three when compared to time series of CERES (satellite data) top-of-the-atmosphere energy flux measurements and improve correlations of anomalies with CERES on annual time scales. Prior to and early on in the Argo period, when in situ data were sparser, global integrals of these maps retain low variance, and do not relax back to a climatological mean, avoiding potential deficiencies of various methods for infilling data-sparse regions with objective maps by exploiting temporal and spatial patterns of OHCA and its correlations with SST and SSH.","PeriodicalId":15074,"journal":{"name":"Journal of Atmospheric and Oceanic Technology","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48539328","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 : 2023-02-10DOI: 10.1175/jtech-d-22-0091.1
Rupayan Saha, F. Testik
This study was to assess the raindrop fall speed measurement capabilities of OTT-Parsivel2 disdrometer through comparisons with measurements of a collocated High-speed Optical Disdrometer (HOD). Raindrop fall speed is often assumed to be terminal in relevant hydrological and meteorological applications, and generally predicted using terminal speed - raindrop size relationships obtained from laboratory observations. Nevertheless, recent field studies have revealed that other factors (e.g. wind, turbulence, raindrop oscillations, and collisions) significantly influence raindrop fall speed, necessitating accurate fall speed measurements for many applications instead of reliance on laboratory-based terminal speed predictions. Field observations in this study covered rainfall events with a variety of environmental conditions, including light, moderate, and heavy rainfall events. This study also involved rigorous laboratory experiments to faithfully identify the internal filtering and calculation algorithm of OTT Parsivel2. Our assessments revealed that, for the smaller diameter bins, Parsivel2 filters out many of the observed raindrops that fall faster than predicted terminal speeds, bringing down the mean fall speed for those size bins without observational evidence. Furthermore, Parsivel2 fall speed measurements exhibited a notable artificial bell-shaped deviations from the predicted terminal speeds towards sub-terminal fall starting at around 1 mm diameter raindrops with peak deviations around 1.625 mm diameter bin. Such bell-shaped fall speed deviation patterns were not present in collocated HOD measurements. Assessment results along with the faithfully identified Parsivel2 algorithm are presented with discussions on implications on reported raindrop size distributions (DSD) and rainfall kinetic energy.
{"title":"Assessment of OTT-Parsivel2 Raindrop Fall Speed Measurements","authors":"Rupayan Saha, F. Testik","doi":"10.1175/jtech-d-22-0091.1","DOIUrl":"https://doi.org/10.1175/jtech-d-22-0091.1","url":null,"abstract":"\u0000This study was to assess the raindrop fall speed measurement capabilities of OTT-Parsivel2 disdrometer through comparisons with measurements of a collocated High-speed Optical Disdrometer (HOD). Raindrop fall speed is often assumed to be terminal in relevant hydrological and meteorological applications, and generally predicted using terminal speed - raindrop size relationships obtained from laboratory observations. Nevertheless, recent field studies have revealed that other factors (e.g. wind, turbulence, raindrop oscillations, and collisions) significantly influence raindrop fall speed, necessitating accurate fall speed measurements for many applications instead of reliance on laboratory-based terminal speed predictions. Field observations in this study covered rainfall events with a variety of environmental conditions, including light, moderate, and heavy rainfall events. This study also involved rigorous laboratory experiments to faithfully identify the internal filtering and calculation algorithm of OTT Parsivel2. Our assessments revealed that, for the smaller diameter bins, Parsivel2 filters out many of the observed raindrops that fall faster than predicted terminal speeds, bringing down the mean fall speed for those size bins without observational evidence. Furthermore, Parsivel2 fall speed measurements exhibited a notable artificial bell-shaped deviations from the predicted terminal speeds towards sub-terminal fall starting at around 1 mm diameter raindrops with peak deviations around 1.625 mm diameter bin. Such bell-shaped fall speed deviation patterns were not present in collocated HOD measurements. Assessment results along with the faithfully identified Parsivel2 algorithm are presented with discussions on implications on reported raindrop size distributions (DSD) and rainfall kinetic energy.","PeriodicalId":15074,"journal":{"name":"Journal of Atmospheric and Oceanic Technology","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44304167","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 : 2023-02-01DOI: 10.1175/jtech-d-21-0125.1
K. G. Rubinshtein, I. Gubenko
The article compares four lightning detection networks, provides a brief overview of lightning observation data assimilation in numerical weather forecasts, and describes and illustrates the used procedure of lightning location and time assimilation in numerical weather forecasting. Evaluations of absolute errors in temperatures of air at 2 m, humidity at 2 m, air pressure near the surface, wind speed at 10 m, and precipitation are provided for 10 forecasts made in 2020 for days on which intensive thunderstorms were observed in the Krasnodar region of Russia. It has been found that average errors for the forecast area for 24, 48, and 72 h of the forecast decreased for all parameters when assimilation of observed lightning data is used for forecasting. It has been shown that the predicted precipitation field configuration and intensity became closer to references for both areas where thunderstorms were observed and the areas where no thunderstorms occurred.
{"title":"Impact of Thunderstorm Location Data Assimilation on Numerical Weather Forecasting","authors":"K. G. Rubinshtein, I. Gubenko","doi":"10.1175/jtech-d-21-0125.1","DOIUrl":"https://doi.org/10.1175/jtech-d-21-0125.1","url":null,"abstract":"\u0000The article compares four lightning detection networks, provides a brief overview of lightning observation data assimilation in numerical weather forecasts, and describes and illustrates the used procedure of lightning location and time assimilation in numerical weather forecasting. Evaluations of absolute errors in temperatures of air at 2 m, humidity at 2 m, air pressure near the surface, wind speed at 10 m, and precipitation are provided for 10 forecasts made in 2020 for days on which intensive thunderstorms were observed in the Krasnodar region of Russia. It has been found that average errors for the forecast area for 24, 48, and 72 h of the forecast decreased for all parameters when assimilation of observed lightning data is used for forecasting. It has been shown that the predicted precipitation field configuration and intensity became closer to references for both areas where thunderstorms were observed and the areas where no thunderstorms occurred.","PeriodicalId":15074,"journal":{"name":"Journal of Atmospheric and Oceanic Technology","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47131148","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 : 2023-02-01DOI: 10.1175/jtech-d-22-0081.1
Boyin Huang, Xungang Yin, J. Carton, Ligang Chen, Garrett Graham, Chunying Liu, T. Smith, Huai-min Zhang
Our study shows that the intercomparison among sea surface temperature (SST) products is influenced by the choice of SST reference, and the interpolation of SST products. The influence of reference SST depends on whether the reference SST are averaged to a grid or in pointwise in situ locations, including buoy or Argo observations, and filtered by first-guess or climatology quality control (QC) algorithms. The influence of the interpolation depends on whether SST products are in their original grids or pre-processed into common coarse grids. The impacts of these factors are demonstrated in our assessments of eight widely used SST products (DOISST, MUR25, MGDSST, GAMSSA, OSTIA, GPB, CCI, CMC) relative to buoy observations: (a) when the reference SSTs are averaged onto 0.25°×0.25° grid boxes, the magnitude of biases is lower in DOISST and MGDSST (<0.03°C), and magnitude of root-mean-square-differences (RMSDs) is lower in DOISST (0.38°C) and OSTIA (0.43°C); (b) when the same reference SSTs are evaluated at pointwise in situ locations, the standard deviations (SDs) are smaller in DOISST (0.38°C) and OSTIA (0.39°C) on 0.25°×0.25° grids; but the SDs become smaller in OSTIA (0.34°C) and CMC (0.37°C) on products original grids, showing the advantage of those high-resolution analyses for resolving finer scale SSTs; (c) when a loose QC algorithm is applied to the reference buoy observations, SDs increase; and vice versa; however, the relative performance of products remains the same; and (d) when the drifting-buoy or Argo observations are used as the reference, the magnitude of RMSDs and SDs become smaller, potentially due to changes in observing intervals. These results suggest that high-resolution SST analyses may take advantage in intercomparisons.
{"title":"Understanding Differences in Sea Surface Temperature Intercomparisons","authors":"Boyin Huang, Xungang Yin, J. Carton, Ligang Chen, Garrett Graham, Chunying Liu, T. Smith, Huai-min Zhang","doi":"10.1175/jtech-d-22-0081.1","DOIUrl":"https://doi.org/10.1175/jtech-d-22-0081.1","url":null,"abstract":"\u0000Our study shows that the intercomparison among sea surface temperature (SST) products is influenced by the choice of SST reference, and the interpolation of SST products. The influence of reference SST depends on whether the reference SST are averaged to a grid or in pointwise in situ locations, including buoy or Argo observations, and filtered by first-guess or climatology quality control (QC) algorithms. The influence of the interpolation depends on whether SST products are in their original grids or pre-processed into common coarse grids.\u0000The impacts of these factors are demonstrated in our assessments of eight widely used SST products (DOISST, MUR25, MGDSST, GAMSSA, OSTIA, GPB, CCI, CMC) relative to buoy observations: (a) when the reference SSTs are averaged onto 0.25°×0.25° grid boxes, the magnitude of biases is lower in DOISST and MGDSST (<0.03°C), and magnitude of root-mean-square-differences (RMSDs) is lower in DOISST (0.38°C) and OSTIA (0.43°C); (b) when the same reference SSTs are evaluated at pointwise in situ locations, the standard deviations (SDs) are smaller in DOISST (0.38°C) and OSTIA (0.39°C) on 0.25°×0.25° grids; but the SDs become smaller in OSTIA (0.34°C) and CMC (0.37°C) on products original grids, showing the advantage of those high-resolution analyses for resolving finer scale SSTs; (c) when a loose QC algorithm is applied to the reference buoy observations, SDs increase; and vice versa; however, the relative performance of products remains the same; and (d) when the drifting-buoy or Argo observations are used as the reference, the magnitude of RMSDs and SDs become smaller, potentially due to changes in observing intervals. These results suggest that high-resolution SST analyses may take advantage in intercomparisons.","PeriodicalId":15074,"journal":{"name":"Journal of Atmospheric and Oceanic Technology","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48179061","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 : 2023-02-01DOI: 10.1175/jtech-d-22-0043.1
Xiaobo Wu, G. Han, Wei Li, Qidong Shao, Lige Cao
Variation of the Kuroshio path south of Japan has an important impact on weather, climate, and ecosystems due to its distinct features. Motivated by the ever-popular deep learning methods using neural network architectures in areas where more accurate reference data for oceanographic observations and reanalysis are available, we build four deep learning models based on the long short-term memory (LSTM) neural network, combined with the empirical orthogonal function (EOF) and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), namely, the LSTM, EOF–LSTM, CEEMDAN–LSTM, and EOF–CEEMDAN–LSTM. Using these models, we conduct long-range predictions (120 days) of the Kuroshio path south of Japan based on 50-yr ocean reanalysis and nearly 15 years of satellite altimeter data. We show that the EOF–CEEMDAN–LSTM performs the best among the four models, by attaining approximately 0.739 anomaly correlation coefficient and 0.399° root-mean-square error for the 120-day prediction of the Kuroshio path south of Japan. The hindcasts of the EOF–CEEMDAN–LSTM are successful in reproducing the observed formation and decay of the Kuroshio large meander during 2004/05, and the formation of the latest large meander in 2017. Finally, we present predictions of the Kuroshio path south of Japan at 120-day lead time, which suggest that the Kuroshio will remain in the state of the large meander until November 2022.
{"title":"Deep Learning–Based Prediction of Kuroshio Path South of Japan","authors":"Xiaobo Wu, G. Han, Wei Li, Qidong Shao, Lige Cao","doi":"10.1175/jtech-d-22-0043.1","DOIUrl":"https://doi.org/10.1175/jtech-d-22-0043.1","url":null,"abstract":"\u0000Variation of the Kuroshio path south of Japan has an important impact on weather, climate, and ecosystems due to its distinct features. Motivated by the ever-popular deep learning methods using neural network architectures in areas where more accurate reference data for oceanographic observations and reanalysis are available, we build four deep learning models based on the long short-term memory (LSTM) neural network, combined with the empirical orthogonal function (EOF) and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), namely, the LSTM, EOF–LSTM, CEEMDAN–LSTM, and EOF–CEEMDAN–LSTM. Using these models, we conduct long-range predictions (120 days) of the Kuroshio path south of Japan based on 50-yr ocean reanalysis and nearly 15 years of satellite altimeter data. We show that the EOF–CEEMDAN–LSTM performs the best among the four models, by attaining approximately 0.739 anomaly correlation coefficient and 0.399° root-mean-square error for the 120-day prediction of the Kuroshio path south of Japan. The hindcasts of the EOF–CEEMDAN–LSTM are successful in reproducing the observed formation and decay of the Kuroshio large meander during 2004/05, and the formation of the latest large meander in 2017. Finally, we present predictions of the Kuroshio path south of Japan at 120-day lead time, which suggest that the Kuroshio will remain in the state of the large meander until November 2022.","PeriodicalId":15074,"journal":{"name":"Journal of Atmospheric and Oceanic Technology","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47673432","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 : 2023-01-25DOI: 10.1175/jtech-d-21-0165.1
J. Marquis, E. Dolinar, A. Garnier, J. Campbell, B. Ruston, P. Yang, Jianglong Zhang
The assimilation of hyperspectral infrared sounders (HIS) observations aboard earth-observing satellites has become vital to numerical weather prediction, yet this assimilation is predicated on the assumption of clear-sky observations. Using co-located assimilated observations from the Atmospheric Infrared Sounder (AIRS) and the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), it is found that near 7.7% of HIS observations assimilated by the Naval Research Laboratory Variational Data Assimilation System – Accelerated Representer (NAVDAS-AR) are contaminated by cirrus clouds. These contaminating clouds primarily exhibit visible cloud optical depths at 532nm (COD532nm) below 0.10 and cloud top temperatures between 240 K and 185 K as expected for cirrus clouds. These contamination statistics are consistent with simulations from the Radiative Transfer for TOVS (RTTOV) radiative transfer model showing a cirrus cloud with a COD532nm of 0.10 imparts brightness temperature differences below typical innovation thresholds used by NAVDAS-AR. Using a one-dimensional variational (1DVar) assimilation system coupled with RTTOV for forward and gradient radiative transfer, the analysis temperature and moisture impact of assimilating cirrus contaminated HIS observations is estimated. Large differences of 2.5 K in temperature and 11 K in dew point are possible for a cloud with COD532nm of 0.10 and cloud top temperature of 210 K. When normalized by the contamination statistics, global differences of near 0.11 K in temperature and 0.34 K in dew point are possible, with temperature and dew point tropospheric root-mean-squared-error (RMSD) as large as 0.06 and 0.11 K, respectively. While in isolation these global estimates are not particularly concerning, differences are likely much larger in regions with high cirrus frequency.
{"title":"Estimating the Impact of Assimilating Cirrus Cloud Contaminated Hyperspectral Infrared Radiances for Numerical Weather Prediction","authors":"J. Marquis, E. Dolinar, A. Garnier, J. Campbell, B. Ruston, P. Yang, Jianglong Zhang","doi":"10.1175/jtech-d-21-0165.1","DOIUrl":"https://doi.org/10.1175/jtech-d-21-0165.1","url":null,"abstract":"\u0000The assimilation of hyperspectral infrared sounders (HIS) observations aboard earth-observing satellites has become vital to numerical weather prediction, yet this assimilation is predicated on the assumption of clear-sky observations. Using co-located assimilated observations from the Atmospheric Infrared Sounder (AIRS) and the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), it is found that near 7.7% of HIS observations assimilated by the Naval Research Laboratory Variational Data Assimilation System – Accelerated Representer (NAVDAS-AR) are contaminated by cirrus clouds. These contaminating clouds primarily exhibit visible cloud optical depths at 532nm (COD532nm) below 0.10 and cloud top temperatures between 240 K and 185 K as expected for cirrus clouds. These contamination statistics are consistent with simulations from the Radiative Transfer for TOVS (RTTOV) radiative transfer model showing a cirrus cloud with a COD532nm of 0.10 imparts brightness temperature differences below typical innovation thresholds used by NAVDAS-AR. Using a one-dimensional variational (1DVar) assimilation system coupled with RTTOV for forward and gradient radiative transfer, the analysis temperature and moisture impact of assimilating cirrus contaminated HIS observations is estimated. Large differences of 2.5 K in temperature and 11 K in dew point are possible for a cloud with COD532nm of 0.10 and cloud top temperature of 210 K. When normalized by the contamination statistics, global differences of near 0.11 K in temperature and 0.34 K in dew point are possible, with temperature and dew point tropospheric root-mean-squared-error (RMSD) as large as 0.06 and 0.11 K, respectively. While in isolation these global estimates are not particularly concerning, differences are likely much larger in regions with high cirrus frequency.","PeriodicalId":15074,"journal":{"name":"Journal of Atmospheric and Oceanic Technology","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47692812","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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