Zenith Hydrostatic Delay (ZHD) is a crucial parameter in Global Navigation Satellite System (GNSS) navigation and positioning and GNSS meteorology. Since Saastamoinen ZHD model has a larger error in China, it is significant to improve the Saastamoinen ZHD model. This work firstly estimated the Saastamoinen model using the integrated ZHD as reference values obtained from radiosonde data collected at 73 stations in China from 2012 to 2016. Then, the residuals between the reference values and the Saastamoinen modeled ZHDs were calculated, the correlations between the residuals and meteorological parameters were explored. The continuous wavelet transform method was used to recognize the annual and semi-annual characteristics of the residuals. Because of the nonlinear variation characteristics of residuals, the nonlinear least square estimation method was introduced to establish an improved ZHD model-China Revised Zenith Hydrostatic Delay (CRZHD) adapted for China. The accuracy of CRZHD model was assessed using radiosonde data and IGS (International GNSS Service, IGS) data in 2017, the radiosonde data results show that CRZHD model is superior to Saastamoinen model with a 69.6% improvement. The three IGS stations with continuous meteorological data present that the BIAS/RMSE are decreased by 2.7 /1.5 (URUM), 5.9 /5.3 (BJFS) and 9.6 /8.8 mm (TCMS). The performance of the CRZHD model retrieving PWV was discussed using radiosonde data in 2017. It is shown that the CRZHD model retrieving PWV (CRZHD-PWV) outperforms Saastamoinen model (SAAS-PWV), which the precision is improved by 44.4%. The BIAS ranged from -1 to 1 mm and RMSE ranged from 0 to 2 mm in CRZHD-PWV account for 89.0%/95.9%, while SAAS-PWV account for 46.6%/ 58.9%.
天顶静水延迟(ZHD)是全球导航卫星系统(GNSS)导航定位和GNSS气象中的一个重要参数。由于Saastamoinen ZHD模型在中国存在较大误差，因此对Saastamoinen ZHD模型进行改进具有重要意义。本文首先利用2012 - 2016年中国73个站点的探空数据，以综合ZHD作为参考值，估算了Saastamoinen模型。然后，计算了参考值与Saastamoinen模型zhd之间的残差，并探讨了残差与气象参数之间的相关性。采用连续小波变换方法识别残差的年际和半年度特征。针对残差的非线性变化特点，引入非线性最小二乘估计方法，建立了一种适合中国国情的改进的ZHD模型——修正天顶静水时滞(CRZHD)。2017年利用探空数据和IGS (International GNSS Service, IGS)数据对CRZHD模型的精度进行了评价，结果表明，CRZHD模型优于Saastamoinen模型，提高了69.6%。具有连续气象数据的3个IGS站点的偏差/均方根误差(BIAS/RMSE)分别降低了2.7 /1.5 mm (URUM)、5.9 /5.3 mm (BJFS)和9.6 /8.8 mm (TCMS)。利用2017年的探空数据，讨论了CRZHD模型检索PWV的性能。结果表明，CRZHD模型检索PWV (CRZHD-PWV)优于Saastamoinen模型(SAAS-PWV)，精度提高44.4%。CRZHD-PWV的偏差范围为-1 ~ 1 mm, RMSE范围为0 ~ 2 mm，分别占89.0%/95.9%和46.6%/ 58.9%。
{"title":"A Precise Zenith Hydrostatic Delay Calibration Model in China Based on Nonlinear Least Square Method","authors":"Kaiyun Lv, Weifeng Yang, Zhiping Chen, Pengfei Xia, Xiaoxing He, Zhigao Chen, Tieding Lu","doi":"10.1175/jtech-d-22-0111.1","DOIUrl":"https://doi.org/10.1175/jtech-d-22-0111.1","url":null,"abstract":"\u0000Zenith Hydrostatic Delay (ZHD) is a crucial parameter in Global Navigation Satellite System (GNSS) navigation and positioning and GNSS meteorology. Since Saastamoinen ZHD model has a larger error in China, it is significant to improve the Saastamoinen ZHD model. This work firstly estimated the Saastamoinen model using the integrated ZHD as reference values obtained from radiosonde data collected at 73 stations in China from 2012 to 2016. Then, the residuals between the reference values and the Saastamoinen modeled ZHDs were calculated, the correlations between the residuals and meteorological parameters were explored. The continuous wavelet transform method was used to recognize the annual and semi-annual characteristics of the residuals. Because of the nonlinear variation characteristics of residuals, the nonlinear least square estimation method was introduced to establish an improved ZHD model-China Revised Zenith Hydrostatic Delay (CRZHD) adapted for China. The accuracy of CRZHD model was assessed using radiosonde data and IGS (International GNSS Service, IGS) data in 2017, the radiosonde data results show that CRZHD model is superior to Saastamoinen model with a 69.6% improvement. The three IGS stations with continuous meteorological data present that the BIAS/RMSE are decreased by 2.7 /1.5 (URUM), 5.9 /5.3 (BJFS) and 9.6 /8.8 mm (TCMS). The performance of the CRZHD model retrieving PWV was discussed using radiosonde data in 2017. It is shown that the CRZHD model retrieving PWV (CRZHD-PWV) outperforms Saastamoinen model (SAAS-PWV), which the precision is improved by 44.4%. The BIAS ranged from -1 to 1 mm and RMSE ranged from 0 to 2 mm in CRZHD-PWV account for 89.0%/95.9%, while SAAS-PWV account for 46.6%/ 58.9%.","PeriodicalId":15074,"journal":{"name":"Journal of Atmospheric and Oceanic Technology","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42481250","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 : 2023-01-19DOI: 10.1175/jtech-d-23-0004.1
Jordan Brook, A. Protat, C. Potvin, J. Soderholm, H. McGowan
Three-dimensional wind retrievals from ground-based Doppler radars have played an important role in meteorological research and nowcasting over the past four decades. However, in recent years, the proliferation of open-source software and increased demands from applications such as convective parameterizations in numerical weather prediction models has led to a renewed interest in these analyses. In this study, we analyze how a major, yet often-overlooked, error source effects the quality of retrieved 3D wind fields. Namely, we investigate the effects of spatial interpolation, and show how the common practice of pre-gridding radial velocity data can degrade the accuracy of the results. Alternatively, we show that assimilating radar data directly at their observation locations improves the retrieval of important dynamic features such as the rear flank downdraft and mesocyclone within supercells, while also reducing errors in vertical vorticity, horizontal divergence, and all three velocity components.
{"title":"The Effects of Spatial Interpolation on a Novel, Dual-Doppler 3D Wind Retrieval Technique","authors":"Jordan Brook, A. Protat, C. Potvin, J. Soderholm, H. McGowan","doi":"10.1175/jtech-d-23-0004.1","DOIUrl":"https://doi.org/10.1175/jtech-d-23-0004.1","url":null,"abstract":"\u0000Three-dimensional wind retrievals from ground-based Doppler radars have played an important role in meteorological research and nowcasting over the past four decades. However, in recent years, the proliferation of open-source software and increased demands from applications such as convective parameterizations in numerical weather prediction models has led to a renewed interest in these analyses. In this study, we analyze how a major, yet often-overlooked, error source effects the quality of retrieved 3D wind fields. Namely, we investigate the effects of spatial interpolation, and show how the common practice of pre-gridding radial velocity data can degrade the accuracy of the results. Alternatively, we show that assimilating radar data directly at their observation locations improves the retrieval of important dynamic features such as the rear flank downdraft and mesocyclone within supercells, while also reducing errors in vertical vorticity, horizontal divergence, and all three velocity components.","PeriodicalId":15074,"journal":{"name":"Journal of Atmospheric and Oceanic Technology","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42208943","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 : 2023-01-19DOI: 10.1175/jtech-d-22-0065.1
N. Zilberman, M. Scanderbeg, A. Gray, P. Oke
Global estimates of absolute velocities can be derived from Argo float trajectories during drift at parking depth. A new velocity dataset developed and maintained at Scripps Institution of Oceanography is presented based on all Core, Biogeochemical, and Deep Argo float trajectories collected between 2001 and 2020. Discrepancies between velocity estimates from the Scripps dataset and other existing products including YoMaHa and ANDRO are associated with quality control criteria, as well as selected parking depth and cycle time. In the Scripps product, over 1.3 million velocity estimates are used to reconstruct a time-mean velocity field for the 800-1200 dbar layer at 1-degree horizontal resolution. This dataset provides a benchmark to evaluate the veracity of the BRAN2020 reanalysis in representing the observed variability of absolute velocities and offers a compelling opportunity for improved characterization and representation in forecast and reanalysis systems.
{"title":"Scripps Argo Trajectory-Based Velocity Product: Global Estimates of Absolute Velocity Derived from Core, Biogeochemical, and Deep Argo Float Trajectories at Parking Depth","authors":"N. Zilberman, M. Scanderbeg, A. Gray, P. Oke","doi":"10.1175/jtech-d-22-0065.1","DOIUrl":"https://doi.org/10.1175/jtech-d-22-0065.1","url":null,"abstract":"\u0000Global estimates of absolute velocities can be derived from Argo float trajectories during drift at parking depth. A new velocity dataset developed and maintained at Scripps Institution of Oceanography is presented based on all Core, Biogeochemical, and Deep Argo float trajectories collected between 2001 and 2020. Discrepancies between velocity estimates from the Scripps dataset and other existing products including YoMaHa and ANDRO are associated with quality control criteria, as well as selected parking depth and cycle time. In the Scripps product, over 1.3 million velocity estimates are used to reconstruct a time-mean velocity field for the 800-1200 dbar layer at 1-degree horizontal resolution. This dataset provides a benchmark to evaluate the veracity of the BRAN2020 reanalysis in representing the observed variability of absolute velocities and offers a compelling opportunity for improved characterization and representation in forecast and reanalysis systems.","PeriodicalId":15074,"journal":{"name":"Journal of Atmospheric and Oceanic Technology","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49641980","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 : 2023-01-18DOI: 10.1175/jtech-d-22-0080.1
T. Cropper, D. Berry, R. Cornes, Elizabeth C. Kent
Marine air temperatures recorded on ships during the daytime are known to be biased warm on average due to energy storage by the superstructure of the vessels. This makes unadjusted daytime observations unsuitable for many applications including for the monitoring of long-term temperature change over the oceans. In this paper a physics-based approach is used to estimate this heating bias in ship observations from ICOADS. Under this approach, empirically determined coefficients represent the energy transfer terms of a heat budget model which quantifies the heating bias and is applied as a function of cloud cover and the relative wind speed over individual ships. The coefficients for each ship are derived from the anomalous diurnal heating relative to nighttime air temperature. Model coefficients, cloud cover and relative wind speed are then used to estimate the heating bias ship-by-ship and generate nighttime-equivalent time series. A variety of methodological approaches were tested. Application of this method enables the inclusion of some daytime observations in climate records based on marine air temperatures, allowing an earlier start date and giving an increase in spatial coverage compared to existing records that exclude daytime observations.
{"title":"Quantifying Daytime Heating Biases in Marine Air Temperature Observations from Ships","authors":"T. Cropper, D. Berry, R. Cornes, Elizabeth C. Kent","doi":"10.1175/jtech-d-22-0080.1","DOIUrl":"https://doi.org/10.1175/jtech-d-22-0080.1","url":null,"abstract":"\u0000Marine air temperatures recorded on ships during the daytime are known to be biased warm on average due to energy storage by the superstructure of the vessels. This makes unadjusted daytime observations unsuitable for many applications including for the monitoring of long-term temperature change over the oceans. In this paper a physics-based approach is used to estimate this heating bias in ship observations from ICOADS. Under this approach, empirically determined coefficients represent the energy transfer terms of a heat budget model which quantifies the heating bias and is applied as a function of cloud cover and the relative wind speed over individual ships. The coefficients for each ship are derived from the anomalous diurnal heating relative to nighttime air temperature. Model coefficients, cloud cover and relative wind speed are then used to estimate the heating bias ship-by-ship and generate nighttime-equivalent time series. A variety of methodological approaches were tested. Application of this method enables the inclusion of some daytime observations in climate records based on marine air temperatures, allowing an earlier start date and giving an increase in spatial coverage compared to existing records that exclude daytime observations.","PeriodicalId":15074,"journal":{"name":"Journal of Atmospheric and Oceanic Technology","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47574740","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 : 2023-01-12DOI: 10.1175/jtech-d-22-0034.1
T. Harrison, Nate Clemett, B. Polagye, J. Thomson
Tidal currents, particularly in narrow channels, can be challenging to characterize due to high current speeds (> 1 m s−1), strong spatial gradients, and relatively short synoptic windows. To assess tidal currents in Agate Pass, WA, we cross-evaluated data products from an array of acoustically-tracked underwater floats and from acoustic Doppler current profilers (ADCPs) in both station-keeping and drifting modes. While increasingly used in basin-scale science, underwater floats have seen limited use in coastal environments. This study presents the first application of a float array towards small-scale (< 1 km), high resolution (< 5 m) measurements of mean currents in energetic tidal channel and utilizes a new prototype float, the µFloat, designed specifically for sampling in dynamic coastal waters. We show that a float array (20 floats) can provide data with similar quality to ADCPs, with measurements of horizontal velocity differing by less than 10% of nominal velocity, except during periods of low flow (0.1 m s−1). Additionally, floats provided measurements of the three dimensional temperature field, demonstrating their unique ability to simultaneously resolve in situ properties that cannot be remotely observed.
潮流，特别是狭窄通道中的潮流，由于高流速（>1 m s−1）、强空间梯度和相对较短的天气窗口，很难表征。为了评估华盛顿州阿加特山口的潮流，我们交叉评估了声学跟踪水下漂浮物阵列和声学多普勒海流剖面仪（ADCP）在保持和漂移模式下的数据产品。虽然水下漂浮物越来越多地用于盆地规模的科学，但在沿海环境中的使用有限。这项研究首次将浮子阵列应用于高能潮汐通道中平均电流的小规模（<1公里）、高分辨率（<5米）测量，并使用了一种新的原型浮子µfloat，该浮子专为在动态沿海水域中采样而设计。我们表明，浮子阵列（20个浮子）可以提供与ADCP质量相似的数据，除低流量（0.1 m s−1）期间外，水平速度的测量值相差不到标称速度的10%。此外，浮子提供了三维温度场的测量结果，证明了它们同时解决无法远程观测的原位特性的独特能力。
{"title":"Experimental Validation of Float Array Tidal Current Measurements in Agate Pass, WA","authors":"T. Harrison, Nate Clemett, B. Polagye, J. Thomson","doi":"10.1175/jtech-d-22-0034.1","DOIUrl":"https://doi.org/10.1175/jtech-d-22-0034.1","url":null,"abstract":"\u0000Tidal currents, particularly in narrow channels, can be challenging to characterize due to high current speeds (> 1 m s−1), strong spatial gradients, and relatively short synoptic windows. To assess tidal currents in Agate Pass, WA, we cross-evaluated data products from an array of acoustically-tracked underwater floats and from acoustic Doppler current profilers (ADCPs) in both station-keeping and drifting modes. While increasingly used in basin-scale science, underwater floats have seen limited use in coastal environments. This study presents the first application of a float array towards small-scale (< 1 km), high resolution (< 5 m) measurements of mean currents in energetic tidal channel and utilizes a new prototype float, the µFloat, designed specifically for sampling in dynamic coastal waters. We show that a float array (20 floats) can provide data with similar quality to ADCPs, with measurements of horizontal velocity differing by less than 10% of nominal velocity, except during periods of low flow (0.1 m s−1). Additionally, floats provided measurements of the three dimensional temperature field, demonstrating their unique ability to simultaneously resolve in situ properties that cannot be remotely observed.","PeriodicalId":15074,"journal":{"name":"Journal of Atmospheric and Oceanic Technology","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48026285","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 : 2023-01-10DOI: 10.1175/jtech-d-22-0023.1
C. Bertossa, T. L’Ecuyer, A. Merrelli, Xianglei Huang, Xiuhong Chen
The Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) will fill a gap in our understanding of polar processes and the polar climate by offering widespread, spectrally-resolved measurements through the Far InfraRed (FIR) with two identical CubeSat spacecraft. While the polar regions are typically difficult for skillful cloud identification due to cold surface temperatures, the reflection by bright surfaces, and frequent temperature inversions, the inclusion of the FIR may offer increased spectral sensitivity, allowing for the detection of even thin ice clouds. This study assesses the potential skill, as well as limitations, of a neural network-based cloud mask using simulated spectra mimicking what the PREFIRE mission will capture. Analysis focuses on the polar regions. Clouds are found to be detected approximately 90% of time using the derived neural network. The NN’s assigned confidence for whether a scene is ‘clear’ or ‘cloudy’ proves to be a skillful way in which quality flags can be attached to predictions. Clouds with higher cloud top heights are typically more easily detected. Low-altitude clouds over polar surfaces, which are the most difficult for the NN to detect, are still detected over 80% of the time. The FIR portion of the spectrum is found to increase the detection of clear scenes and increase mid-to-high altitude cloud detection. Cloud detection skill improves through the use of the overlapping fields of view produced by the PREFIRE instrument’s sampling strategy. Overlapping fields of view increase accuracy relative to the baseline NN while simultaneously predicting on a sub-FOV scale.
{"title":"A Neural Network-based Cloud Mask for PREFIRE and Evaluation with Simulated Observations","authors":"C. Bertossa, T. L’Ecuyer, A. Merrelli, Xianglei Huang, Xiuhong Chen","doi":"10.1175/jtech-d-22-0023.1","DOIUrl":"https://doi.org/10.1175/jtech-d-22-0023.1","url":null,"abstract":"\u0000The Polar Radiant Energy in the Far InfraRed Experiment (PREFIRE) will fill a gap in our understanding of polar processes and the polar climate by offering widespread, spectrally-resolved measurements through the Far InfraRed (FIR) with two identical CubeSat spacecraft. While the polar regions are typically difficult for skillful cloud identification due to cold surface temperatures, the reflection by bright surfaces, and frequent temperature inversions, the inclusion of the FIR may offer increased spectral sensitivity, allowing for the detection of even thin ice clouds. This study assesses the potential skill, as well as limitations, of a neural network-based cloud mask using simulated spectra mimicking what the PREFIRE mission will capture. Analysis focuses on the polar regions. Clouds are found to be detected approximately 90% of time using the derived neural network. The NN’s assigned confidence for whether a scene is ‘clear’ or ‘cloudy’ proves to be a skillful way in which quality flags can be attached to predictions. Clouds with higher cloud top heights are typically more easily detected. Low-altitude clouds over polar surfaces, which are the most difficult for the NN to detect, are still detected over 80% of the time. The FIR portion of the spectrum is found to increase the detection of clear scenes and increase mid-to-high altitude cloud detection. Cloud detection skill improves through the use of the overlapping fields of view produced by the PREFIRE instrument’s sampling strategy. Overlapping fields of view increase accuracy relative to the baseline NN while simultaneously predicting on a sub-FOV scale.","PeriodicalId":15074,"journal":{"name":"Journal of Atmospheric and Oceanic Technology","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47561150","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 : 2023-01-06DOI: 10.1175/jtech-d-22-0054.1
Brian Fitzgerald, J. Broccolo, K. Garrett
The Mount Washington Observatory Regional Mesonet (MWRM) is a network of 18 remote meteorological monitoring stations (as of 2022), including the Auto Road Vertical Profile (ARVP), located across the White Mountains of Northern New Hampshire. Each station measures temperature and relative humidity, with additional variables at many locations. All stations need to withstand the frequent combination of intense cold, high precipitation amounts, icing, and hurricane-force winds in a mountain environment. Due to these challenges, the MWRM employs rugged instrumentation, an innovative radio-communications relay approach, and carefully selected sites that balance ideal measuring environments with station survivability. Data collected from the MWRM are used operationally by forecasters (including Mount Washington Observatory and National Weather Service staff) to validate model guidance, by alpine and climate scientists, by recreationalists accessing conditions in the backcountry, by groups operating on the mountain (Cog Railway, toll Auto Road) and by search and rescue organizations. This paper provides a detailed description of the network, with emphasis on how the challenging climate and terrain of this mountain region impacts sensor selection, site maintenance and overall operation.
{"title":"The Mount Washington Observatory Regional Mesonet: A Technical Overview of a Mountain-Based Mesonet","authors":"Brian Fitzgerald, J. Broccolo, K. Garrett","doi":"10.1175/jtech-d-22-0054.1","DOIUrl":"https://doi.org/10.1175/jtech-d-22-0054.1","url":null,"abstract":"\u0000The Mount Washington Observatory Regional Mesonet (MWRM) is a network of 18 remote meteorological monitoring stations (as of 2022), including the Auto Road Vertical Profile (ARVP), located across the White Mountains of Northern New Hampshire. Each station measures temperature and relative humidity, with additional variables at many locations. All stations need to withstand the frequent combination of intense cold, high precipitation amounts, icing, and hurricane-force winds in a mountain environment. Due to these challenges, the MWRM employs rugged instrumentation, an innovative radio-communications relay approach, and carefully selected sites that balance ideal measuring environments with station survivability. Data collected from the MWRM are used operationally by forecasters (including Mount Washington Observatory and National Weather Service staff) to validate model guidance, by alpine and climate scientists, by recreationalists accessing conditions in the backcountry, by groups operating on the mountain (Cog Railway, toll Auto Road) and by search and rescue organizations. This paper provides a detailed description of the network, with emphasis on how the challenging climate and terrain of this mountain region impacts sensor selection, site maintenance and overall operation.","PeriodicalId":15074,"journal":{"name":"Journal of Atmospheric and Oceanic Technology","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46069878","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 : 2023-01-01DOI: 10.1175/jtech-d-21-0168.1
Shakeel Asharaf, Derek J. Posselt, Faozi Said, Christopher S. Ruf
Abstract Global Navigation Satellite System Reflectometry (GNSS-R)-based wind retrieval techniques use the global positioning system (GPS) signals scattered from the ocean surface in the forward direction, and can potentially work in all weather conditions. An overview of recent progress made in the Cyclone Global Navigation Satellite System (CYGNSS) level-2 surface wind products is given. To this end, four publicly released CYGNSS surface wind products—Science Data Record (SDR) v2.1, SDR v3.0, Climate Data Record (CDR) v1.1, and science wind speed product NOAA v1.1—are validated quantitatively against high-quality data from tropical buoy arrays. The latest released CYGNSS wind products (e.g., CDR v1.1, SDR v3.0, NOAA v1.1), as compared with these tropical buoy data, significantly outperform the SDR v2.1. Moreover, the uncertainty among these products is found to be less than 2 m s −1 root-mean-squared difference, meeting the NASA science mission level-1 uncertainty requirement for wind speeds below 20 m s −1 . The quality of the CYGNSS wind is further assessed under different precipitation conditions in low winds, and in large-scale convective regions. Results show that the presence of rain appears to cause a slightly positive wind speed bias in all CYGNSS data. Nonetheless, the outcomes are encouraging for the recently released CYGNSS wind products in general, and for CYGNSS data in regions with precipitating deep convection. The overall comparison indicates a significant improvement in wind speed quality and sample size when going from the older version to any of the newer datasets.
基于全球导航卫星系统反射(GNSS-R)的风反演技术利用海面前方散射的全球定位系统(GPS)信号，可以在所有天气条件下工作。综述了气旋全球导航卫星系统(CYGNSS) 2级地面风产品的最新进展。为此，利用来自热带浮标阵列的高质量数据，对CYGNSS公开发布的四个地面风产品——科学数据记录(SDR) v2.1、SDR v3.0、气候数据记录(CDR) v1.1和科学风速产品NOAA v1.1进行了定量验证。最新发布的CYGNSS风产品(如CDR v1.1, SDR v3.0, NOAA v1.1)与这些热带浮标数据相比，显著优于SDR v2.1。此外，发现这些产品之间的不确定度小于2 m s−1均方根差，满足NASA科学任务对风速低于20 m s−1的不确定度要求。在低风和大尺度对流区域的不同降水条件下，进一步评估CYGNSS风的质量。结果表明，在所有CYGNSS数据中，降雨的存在似乎导致了轻微的正风速偏差。尽管如此，对于最近发布的CYGNSS风产品总体而言，以及对于深对流降水区域的CYGNSS数据来说，结果是令人鼓舞的。总体比较表明，从旧版本到任何新数据集，风速质量和样本量都有显着改善。
{"title":"Updates on CYGNSS Ocean Surface Wind Validation in the Tropics","authors":"Shakeel Asharaf, Derek J. Posselt, Faozi Said, Christopher S. Ruf","doi":"10.1175/jtech-d-21-0168.1","DOIUrl":"https://doi.org/10.1175/jtech-d-21-0168.1","url":null,"abstract":"Abstract Global Navigation Satellite System Reflectometry (GNSS-R)-based wind retrieval techniques use the global positioning system (GPS) signals scattered from the ocean surface in the forward direction, and can potentially work in all weather conditions. An overview of recent progress made in the Cyclone Global Navigation Satellite System (CYGNSS) level-2 surface wind products is given. To this end, four publicly released CYGNSS surface wind products—Science Data Record (SDR) v2.1, SDR v3.0, Climate Data Record (CDR) v1.1, and science wind speed product NOAA v1.1—are validated quantitatively against high-quality data from tropical buoy arrays. The latest released CYGNSS wind products (e.g., CDR v1.1, SDR v3.0, NOAA v1.1), as compared with these tropical buoy data, significantly outperform the SDR v2.1. Moreover, the uncertainty among these products is found to be less than 2 m s −1 root-mean-squared difference, meeting the NASA science mission level-1 uncertainty requirement for wind speeds below 20 m s −1 . The quality of the CYGNSS wind is further assessed under different precipitation conditions in low winds, and in large-scale convective regions. Results show that the presence of rain appears to cause a slightly positive wind speed bias in all CYGNSS data. Nonetheless, the outcomes are encouraging for the recently released CYGNSS wind products in general, and for CYGNSS data in regions with precipitating deep convection. The overall comparison indicates a significant improvement in wind speed quality and sample size when going from the older version to any of the newer datasets.","PeriodicalId":15074,"journal":{"name":"Journal of Atmospheric and Oceanic Technology","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136296842","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 : 2022-12-28DOI: 10.1175/jtech-d-21-0183.1
B. Sloyan, C. Chapman, R. Cowley, A. Charantonis
In situ observations are vital to improving our understanding of the variability and dynamics of the ocean. A critical component of the ocean circulation are the strong, narrow and highly variable western boundary currents. Ocean moorings that extend from the sea floor to the surface remain the most effective and efficient method to fully observe these currents. For various reasons mooring instruments may not provide continuous records. Here we assess the application of the Iterative Completion Self Organising Maps (ITCOMPSOM) machine learning technique to fill observational data gaps in a 7.5 year time-series of the East Australian Current. The method was validated by withholding parts of fully known profiles, and reconstructing them. For 20% random withholding of known velocity data validation statistics of the u- and v-velocity components are R2 coefficients of 0.70 and 0.88 and, root mean square errors of 0.038 m s−1 and 0.064 m s−1, respectively. Withholding 100 days of known velocity profiles over a depth range between 60 m to 700 m has mean profile residual differences between true and predicted u- and v-velocity of 0.009 m s−1 and 0.02 m s−1, respectively. The ITCOMPSOM also reproduces the known velocity variability. For 20% withholding of temperature and salinity data root mean square error of 0.04 and 0.38°C, respectively, are obtained. The ITCOMPSOM validation statistics are significantly better than those obtained when standard data filling methods are used. We suggest that machine learning techniques can be an appropriate method to fill missing data and enable production of observational-derived data products.
现场观测对于提高我们对海洋变化和动力学的理解至关重要。海洋环流的一个关键组成部分是强大、狭窄和高度可变的西部边界流。从海底延伸到海面的海洋系泊系统仍然是全面观察这些洋流的最有效和最有效的方法。由于各种原因，系泊仪器可能无法提供连续记录。在这里，我们评估了迭代完成自组织图（ITCOMPSOM）机器学习技术在填补东澳大利亚洋流7.5年时间序列中的观测数据空白方面的应用。该方法通过保留完全已知轮廓的部分并对其进行重建来验证。对于20%的已知速度数据随机扣留，u和v速度分量的验证统计数据为R2系数0.70和0.88，均方根误差分别为0.038 m s−1和0.064 m s−1。在60 m至700 m的深度范围内保留100天的已知速度剖面，真实和预测的u和v速度之间的平均剖面残差分别为0.009 m s−1和0.02 m s−1。ITCOMPSOM还再现了已知的速度变化。对于保留20%的温度和盐度数据，分别获得0.04和0.38°C的均方根误差。ITCOMPSOM验证统计数据明显优于使用标准数据填充方法时获得的统计数据。我们建议，机器学习技术可以是一种适当的方法来填补缺失的数据，并能够产生观测衍生的数据产品。
{"title":"Application of Machine Learning Techniques to Ocean Mooring Time-Series Data","authors":"B. Sloyan, C. Chapman, R. Cowley, A. Charantonis","doi":"10.1175/jtech-d-21-0183.1","DOIUrl":"https://doi.org/10.1175/jtech-d-21-0183.1","url":null,"abstract":"\u0000In situ observations are vital to improving our understanding of the variability and dynamics of the ocean. A critical component of the ocean circulation are the strong, narrow and highly variable western boundary currents. Ocean moorings that extend from the sea floor to the surface remain the most effective and efficient method to fully observe these currents. For various reasons mooring instruments may not provide continuous records. Here we assess the application of the Iterative Completion Self Organising Maps (ITCOMPSOM) machine learning technique to fill observational data gaps in a 7.5 year time-series of the East Australian Current. The method was validated by withholding parts of fully known profiles, and reconstructing them. For 20% random withholding of known velocity data validation statistics of the u- and v-velocity components are R2 coefficients of 0.70 and 0.88 and, root mean square errors of 0.038 m s−1 and 0.064 m s−1, respectively. Withholding 100 days of known velocity profiles over a depth range between 60 m to 700 m has mean profile residual differences between true and predicted u- and v-velocity of 0.009 m s−1 and 0.02 m s−1, respectively. The ITCOMPSOM also reproduces the known velocity variability. For 20% withholding of temperature and salinity data root mean square error of 0.04 and 0.38°C, respectively, are obtained. The ITCOMPSOM validation statistics are significantly better than those obtained when standard data filling methods are used. We suggest that machine learning techniques can be an appropriate method to fill missing data and enable production of observational-derived data products.","PeriodicalId":15074,"journal":{"name":"Journal of Atmospheric and Oceanic Technology","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2022-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49582257","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 : 2022-12-15DOI: 10.1175/jtech-d-22-0060.1
A. Amador, S. Merrifield, E. Terrill
The present work details the measurement capabilities ofWave Glider Autonomous Surface Vehicles (ASVs) for research-grade meteorology, wave, and current data. Methodologies for motion compensation are described and tested, including a correction technique to account for Doppler shifting of the wave signal. Wave Glider measurements are evaluated against observations obtained fromWorld Meteorological Organization (WMO)-compliant moored buoy assets located off the coast of Southern California. The validation spans a range of field conditions and includes multiple deployments to assess the quality of vehicle-based observations. Results indicate that Wave Gliders can accurately measure wave spectral information, bulk wave parameters, water velocities, bulk winds, and other atmospheric variables with the application of appropriate motion compensation techniques. Measurement errorswere found to be comparable to those from reference moored buoys and within WMO operational requirements. The findings of this study represent a step towards enabling the use of ASV-based data for the calibration and validation of remote observations and assimilation into forecast models.
{"title":"Assessment of Atmospheric and Oceanographic Measurements from an Autonomous Surface Vehicle","authors":"A. Amador, S. Merrifield, E. Terrill","doi":"10.1175/jtech-d-22-0060.1","DOIUrl":"https://doi.org/10.1175/jtech-d-22-0060.1","url":null,"abstract":"\u0000The present work details the measurement capabilities ofWave Glider Autonomous Surface Vehicles (ASVs) for research-grade meteorology, wave, and current data. Methodologies for motion compensation are described and tested, including a correction technique to account for Doppler shifting of the wave signal. Wave Glider measurements are evaluated against observations obtained fromWorld Meteorological Organization (WMO)-compliant moored buoy assets located off the coast of Southern California. The validation spans a range of field conditions and includes multiple deployments to assess the quality of vehicle-based observations. Results indicate that Wave Gliders can accurately measure wave spectral information, bulk wave parameters, water velocities, bulk winds, and other atmospheric variables with the application of appropriate motion compensation techniques. Measurement errorswere found to be comparable to those from reference moored buoys and within WMO operational requirements. The findings of this study represent a step towards enabling the use of ASV-based data for the calibration and validation of remote observations and assimilation into forecast models.","PeriodicalId":15074,"journal":{"name":"Journal of Atmospheric and Oceanic Technology","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44971506","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：