Browse
Abstract
Regional weather networks–also referred to as mesonets–are imperative for filling in the spatial and temporal data gaps between nationally supported weather stations. The North Carolina Environment and Climate Observing Network (ECONet) fills this regional role; it is a mesoscale network of 44 (as of 2023) automated stations collecting 12 environmental variables every minute across North Carolina. Measured variables include air temperature, precipitation, relative humidity, barometric pressure, wind speed, wind direction, total solar radiation, photosynthetically active radiation, soil temperature, soil moisture, leaf wetness index, and black globe temperature. All data undergo quality control procedures and are made freely available to the public via data portals hosted by the State Climate Office of North Carolina at North Carolina State University. This paper provides a technical overview of ECONet, including a description of the siting criteria, station maintenance procedures, data quality control procedures, and data availability. We also summarize unique aspects of ECONet data collection as well as innovative research and applications that rely on ECONet data. ECONet data are used by many sectors including, but not limited to, emergency management, natural resources management, public health, agriculture, forestry, science education, outdoor recreation, and research. ECONet data and data-powered applications offer valuable insights to local, regional, and federal partners yet opportunities to expand ECONet research and applications remain.
Abstract
Regional weather networks–also referred to as mesonets–are imperative for filling in the spatial and temporal data gaps between nationally supported weather stations. The North Carolina Environment and Climate Observing Network (ECONet) fills this regional role; it is a mesoscale network of 44 (as of 2023) automated stations collecting 12 environmental variables every minute across North Carolina. Measured variables include air temperature, precipitation, relative humidity, barometric pressure, wind speed, wind direction, total solar radiation, photosynthetically active radiation, soil temperature, soil moisture, leaf wetness index, and black globe temperature. All data undergo quality control procedures and are made freely available to the public via data portals hosted by the State Climate Office of North Carolina at North Carolina State University. This paper provides a technical overview of ECONet, including a description of the siting criteria, station maintenance procedures, data quality control procedures, and data availability. We also summarize unique aspects of ECONet data collection as well as innovative research and applications that rely on ECONet data. ECONet data are used by many sectors including, but not limited to, emergency management, natural resources management, public health, agriculture, forestry, science education, outdoor recreation, and research. ECONet data and data-powered applications offer valuable insights to local, regional, and federal partners yet opportunities to expand ECONet research and applications remain.
Experimental Validation of Float Array Tidal Current Measurements in Agate Pass, WashingtonAbstract
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, Washington, 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 toward 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.
Significance Statement
The purpose of this research was to validate measurements of tidal currents in a fast-flowing tidal channel using a prototype technology composed of 20 drifting underwater sensors called μFloats (“microFloats”) and five surface buoys against standard devices (acoustic Doppler current profilers). Float measurements matched those from the standard devices within 10% of the mean water speed and simultaneously provided three-dimensional mapping of temperature in the test region. Results demonstrate how moderate numbers of simultaneously deployed μFloats can provide high-resolution sensing capacity that will improve our understanding of physical, chemical, and biological processes in coastal waters.
Abstract
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, Washington, 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 toward 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.
Significance Statement
The purpose of this research was to validate measurements of tidal currents in a fast-flowing tidal channel using a prototype technology composed of 20 drifting underwater sensors called μFloats (“microFloats”) and five surface buoys against standard devices (acoustic Doppler current profilers). Float measurements matched those from the standard devices within 10% of the mean water speed and simultaneously provided three-dimensional mapping of temperature in the test region. Results demonstrate how moderate numbers of simultaneously deployed μFloats can provide high-resolution sensing capacity that will improve our understanding of physical, chemical, and biological processes in coastal waters.
Abstract
Original techniques are proposed for the improvement of surface current mapping with phased-array oceanographic High-Frequency Radars. The first idea, which works only in bistatic configuration, is to take advantage of a remote transmitter to perform an automatic correction of the receiving antennas based on the signal received in the direct path, an adjustment that is designated as “self-calibration”. The second idea, which applies to both mono- and bistatic systems, consists in applying a Direction Finding (DF) technique (instead of traditional Beam Forming) not only to the full antenna array but also to subarrays made of a smaller number of sequential antennas, a method which is referred to as “antenna grouping”. In doing this, the number of sources can also be varied, leading to an increased number of DF maps that can be averaged, an operation which is designated as “source stacking”. The combination of self-calibration, antenna grouping, and source stacking makes it possible to obtain high-resolution maps with increased coverage and is found robust to damaged antennas. The third improvement concerns the mitigation of noise in the antenna signal. These methods are illustrated with the multistatic High-Frequency Radar network in Toulon and their performances are assessed with drifters. The improved DF technique is found to significantly increase the accuracy of radar-based surface current when compared to the conventional Beam Forming technique.
Abstract
Original techniques are proposed for the improvement of surface current mapping with phased-array oceanographic High-Frequency Radars. The first idea, which works only in bistatic configuration, is to take advantage of a remote transmitter to perform an automatic correction of the receiving antennas based on the signal received in the direct path, an adjustment that is designated as “self-calibration”. The second idea, which applies to both mono- and bistatic systems, consists in applying a Direction Finding (DF) technique (instead of traditional Beam Forming) not only to the full antenna array but also to subarrays made of a smaller number of sequential antennas, a method which is referred to as “antenna grouping”. In doing this, the number of sources can also be varied, leading to an increased number of DF maps that can be averaged, an operation which is designated as “source stacking”. The combination of self-calibration, antenna grouping, and source stacking makes it possible to obtain high-resolution maps with increased coverage and is found robust to damaged antennas. The third improvement concerns the mitigation of noise in the antenna signal. These methods are illustrated with the multistatic High-Frequency Radar network in Toulon and their performances are assessed with drifters. The improved DF technique is found to significantly increase the accuracy of radar-based surface current when compared to the conventional Beam Forming technique.
Abstract
The Chebyshev polynomial fitting (CPF) method has been proved to be effective to construct reliable cotidal charts for the eight major tidal constituents (M2, S2, K1, O1, N2, K2, P1, and Q1) and six minor tidal constituents (2N2, J1, L2, Mu2, Nu2, and T2) near Hawaii in Part I and Part II, respectively. In this paper, this method is extended to estimate the harmonic constants of four long-period tidal constituents (Mf, Mm, Sa, and Ssa). The harmonic constants obtained by this method were compared with those from the TPXO9, Finite Element Solutions 2014 (FES2014), and Empirical Ocean Tide 20 (EOT20) models, using benchmark data from satellite altimeters and eight tide gauges. The accuracies of the Mf and Mm constituents derived from the CPF method are comparable to those from the models, but the accuracies of the Sa and Ssa constituents are significantly higher than those from the FES2014 and EOT20 models. The results indicate that the CPF method is also effective for estimating harmonic constants of long-period tidal constituents. Furthermore, since the CPF method relies only on satellite altimeter data, it is an easier-to-use method than these ocean tide models.
Abstract
The Chebyshev polynomial fitting (CPF) method has been proved to be effective to construct reliable cotidal charts for the eight major tidal constituents (M2, S2, K1, O1, N2, K2, P1, and Q1) and six minor tidal constituents (2N2, J1, L2, Mu2, Nu2, and T2) near Hawaii in Part I and Part II, respectively. In this paper, this method is extended to estimate the harmonic constants of four long-period tidal constituents (Mf, Mm, Sa, and Ssa). The harmonic constants obtained by this method were compared with those from the TPXO9, Finite Element Solutions 2014 (FES2014), and Empirical Ocean Tide 20 (EOT20) models, using benchmark data from satellite altimeters and eight tide gauges. The accuracies of the Mf and Mm constituents derived from the CPF method are comparable to those from the models, but the accuracies of the Sa and Ssa constituents are significantly higher than those from the FES2014 and EOT20 models. The results indicate that the CPF method is also effective for estimating harmonic constants of long-period tidal constituents. Furthermore, since the CPF method relies only on satellite altimeter data, it is an easier-to-use method than these ocean tide models.
Abstract
Passive microwave sounders are critical for accurate forecasts from numerical weather prediction models. These sensors are calibrated using a traditional two-point approach, with one source typically a free-space blackbody target and the second a clear view to the cosmic microwave background, commonly referred to as “cold space.” Occasionally, one or both of these calibration sources can become corrupted, either by solar/lunar intrusion in the cold space view or by thermal instability of the blackbody calibration source. A Temporal Experiment for Storms and Tropical Systems (TEMPEST) microwave sounder instrument is currently deployed on the International Space Station (ISS) for a 3-year mission. TEMPEST is also calibrated using a blackbody target and cold space view; however, the cold space view will be routinely obstructed by objects present on the ISS. Here we test an alternative single point calibration methodology that uses only the blackbody calibration target. We find the brightness temperature difference between this new approach and the traditional two-point calibration approach to be < 0.1 K when applied to 3 years of the TEMPEST CubeSat Demonstration (TEMPEST-D) mission data from 2018-2020. This approach is applicable to other microwave radiometers that experience occasional degradation of calibration sources, such as thermal effects, intrusions or instability of noise diodes.
Abstract
Passive microwave sounders are critical for accurate forecasts from numerical weather prediction models. These sensors are calibrated using a traditional two-point approach, with one source typically a free-space blackbody target and the second a clear view to the cosmic microwave background, commonly referred to as “cold space.” Occasionally, one or both of these calibration sources can become corrupted, either by solar/lunar intrusion in the cold space view or by thermal instability of the blackbody calibration source. A Temporal Experiment for Storms and Tropical Systems (TEMPEST) microwave sounder instrument is currently deployed on the International Space Station (ISS) for a 3-year mission. TEMPEST is also calibrated using a blackbody target and cold space view; however, the cold space view will be routinely obstructed by objects present on the ISS. Here we test an alternative single point calibration methodology that uses only the blackbody calibration target. We find the brightness temperature difference between this new approach and the traditional two-point calibration approach to be < 0.1 K when applied to 3 years of the TEMPEST CubeSat Demonstration (TEMPEST-D) mission data from 2018-2020. This approach is applicable to other microwave radiometers that experience occasional degradation of calibration sources, such as thermal effects, intrusions or instability of noise diodes.
The Mount Washington Observatory Regional Mesonet: A Technical Overview of a Mountain-Based MesonetAbstract
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.
Significance Statement
The mountain environment is a heterogeneous landscape, and interactions between the atmosphere and terrain can cause a wide variety of conditions across time and space. Our network of remote stations at different elevations across the White Mountains allows data users to understand how the weather varies spatially across the mountain range where conditions on higher peaks can be drastically, and dangerously, different. Sharing information about the MWRM can help other groups establish networks in similar challenging environments, and broaden our understanding of weather and climate in mountainous regions.
Abstract
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.
Significance Statement
The mountain environment is a heterogeneous landscape, and interactions between the atmosphere and terrain can cause a wide variety of conditions across time and space. Our network of remote stations at different elevations across the White Mountains allows data users to understand how the weather varies spatially across the mountain range where conditions on higher peaks can be drastically, and dangerously, different. Sharing information about the MWRM can help other groups establish networks in similar challenging environments, and broaden our understanding of weather and climate in mountainous regions.
Abstract
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 SSTs 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 preprocessed 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: (i) 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); (ii) 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; (iii) 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 (iv) 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.
Significance Statement
Intercomparisons of gridded SST products be affected by how the products are compared with in situ observations: whether the products are in coarse (0.25°) or original (0.05°–0.10°) grids, whether the in situ SSTs are in their reported locations or gridded and how they are quality controlled, and whether the biases of satellite SSTs are corrected by localized matchups or large-scale patterns. By taking all these factors into account, our analyses indicate that the NOAA DOISST is among the best SST products for the long period (1981–present) and relatively coarse (0.25°) resolution that it was designed for.
Abstract
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 SSTs 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 preprocessed 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: (i) 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); (ii) 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; (iii) 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 (iv) 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.
Significance Statement
Intercomparisons of gridded SST products be affected by how the products are compared with in situ observations: whether the products are in coarse (0.25°) or original (0.05°–0.10°) grids, whether the in situ SSTs are in their reported locations or gridded and how they are quality controlled, and whether the biases of satellite SSTs are corrected by localized matchups or large-scale patterns. By taking all these factors into account, our analyses indicate that the NOAA DOISST is among the best SST products for the long period (1981–present) and relatively coarse (0.25°) resolution that it was designed for.
Abstract
Two multispectral satellite imagery products are presented that were developed for use within the fire management community. These products, which take the form of false color red-green-blue composites, were designed to aid fire detection and characterization, and for assessment of the environment surrounding a fire. The first, named the Fire Temperature RGB, uses spectral channels near 1.6 μm, 2.2 μm and 3.9 μm for fire detection and rapid assessment of the range of fire intensity through intuitive coloration. The second, named the Day Fire RGB, uses spectral channels near 0.64 μm, 0.86 μm and 3.9 μm for rapid scene assessment. The 0.64 μm channel provides information on smoke, the 0.86 μm channel provides information on vegetation health and burn scars, and the 3.9 μm channel provides active fire detections. Examples of these red-green-blue composite images developed from observations collected by three operational satellite imagers (VIIRS on the polar-orbiting platform and the Advanced Baseline Imager and Advanced Himawari Imager on the geostationary platform) demonstrate that both red-green-blue composites are useful for fire detection and contain valuable information that is not present within operational fire detection algorithms. In particular, it is shown that Fire Temperature RGB and Day Fire RGB images from VIIRS have similar utility for fire detection as the operational VIIRS Active Fire products, with the added benefit that the imagery provides context for more than just the fires themselves.
Abstract
Two multispectral satellite imagery products are presented that were developed for use within the fire management community. These products, which take the form of false color red-green-blue composites, were designed to aid fire detection and characterization, and for assessment of the environment surrounding a fire. The first, named the Fire Temperature RGB, uses spectral channels near 1.6 μm, 2.2 μm and 3.9 μm for fire detection and rapid assessment of the range of fire intensity through intuitive coloration. The second, named the Day Fire RGB, uses spectral channels near 0.64 μm, 0.86 μm and 3.9 μm for rapid scene assessment. The 0.64 μm channel provides information on smoke, the 0.86 μm channel provides information on vegetation health and burn scars, and the 3.9 μm channel provides active fire detections. Examples of these red-green-blue composite images developed from observations collected by three operational satellite imagers (VIIRS on the polar-orbiting platform and the Advanced Baseline Imager and Advanced Himawari Imager on the geostationary platform) demonstrate that both red-green-blue composites are useful for fire detection and contain valuable information that is not present within operational fire detection algorithms. In particular, it is shown that Fire Temperature RGB and Day Fire RGB images from VIIRS have similar utility for fire detection as the operational VIIRS Active Fire products, with the added benefit that the imagery provides context for more than just the fires themselves.
Abstract
The Ocean Color Component of the Aerosol Robotic Network (AERONET-OC) aims at supporting the assessment of satellite ocean color radiometric products with in situ reference data derived from automated above-water measurements. This study, applying metrology principles and taking advantage of recent technology and science advances, revisits the uncertainty estimates formerly provided for AERONET-OC normalized water-leaving radiances L WN. The new uncertainty values are quantified for a number of AERONET-OC sites located in marine regions representative of chlorophyll-a-dominated waters (i.e., Case 1) and a variety of optically complex waters. Results show uncertainties typically increasing with the optical complexity of water and wind speed. Relative and absolute uncertainty values are provided for the various sites together with contributions from each source of uncertainty affecting measurements. In view of supporting AERONET-OC data users, the study also suggests practical solutions to quantify uncertainties for L WN from its spectral values. Additionally, results from an evaluation of the temporal variability characterizing L WN at various AERONET-OC sites are presented to address the impact of temporal mismatches between in situ and satellite data in matchup analysis.
Abstract
The Ocean Color Component of the Aerosol Robotic Network (AERONET-OC) aims at supporting the assessment of satellite ocean color radiometric products with in situ reference data derived from automated above-water measurements. This study, applying metrology principles and taking advantage of recent technology and science advances, revisits the uncertainty estimates formerly provided for AERONET-OC normalized water-leaving radiances L WN. The new uncertainty values are quantified for a number of AERONET-OC sites located in marine regions representative of chlorophyll-a-dominated waters (i.e., Case 1) and a variety of optically complex waters. Results show uncertainties typically increasing with the optical complexity of water and wind speed. Relative and absolute uncertainty values are provided for the various sites together with contributions from each source of uncertainty affecting measurements. In view of supporting AERONET-OC data users, the study also suggests practical solutions to quantify uncertainties for L WN from its spectral values. Additionally, results from an evaluation of the temporal variability characterizing L WN at various AERONET-OC sites are presented to address the impact of temporal mismatches between in situ and satellite data in matchup analysis.
Abstract
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 that 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.
Significance Statement
Currently, the longest available record of air temperature over the oceans starts in 1880. We present an approach that enables observations of air temperatures over the oceans to be used in the creation of long-term climate records that are presently excluded. We do this by estimating the biases inherent in daytime temperature reports from ships, and adjust for these biases by implementing a numerical heat-budget model. The adjustment can be applied to the variety of ship types present in observational archives. The resulting adjusted temperatures can be used to create a more spatially complete record over the oceans, that extends further back in time, potentially into the late eighteenth century.
Abstract
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 that 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.
Significance Statement
Currently, the longest available record of air temperature over the oceans starts in 1880. We present an approach that enables observations of air temperatures over the oceans to be used in the creation of long-term climate records that are presently excluded. We do this by estimating the biases inherent in daytime temperature reports from ships, and adjust for these biases by implementing a numerical heat-budget model. The adjustment can be applied to the variety of ship types present in observational archives. The resulting adjusted temperatures can be used to create a more spatially complete record over the oceans, that extends further back in time, potentially into the late eighteenth century.
