A Neural Network–Based Cloud Mask for PREFIRE and Evaluation with Simulated ObservationsAbstract
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 (NN)-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.
Significance Statement
Clouds play an important role in defining the Arctic and Antarctic climates. The purpose of this study is to explore the potential of never-before systematically measured radiative properties of the atmosphere to aid in the detection of polar clouds, which are traditionally difficult to detect. Satellite measurements of emitted radiation at wavelengths longer than 15 μm, combined with complex machine learning methods, may allow us to better understand the occurrence of various cloud types at both poles. The occurrence of these clouds can determine whether the surface warms or cools, influencing surface temperatures and the rate at which ice melts or refreezes. Understanding the frequencies of these various clouds is increasingly important within the context of our rapidly changing climate.
Abstract
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 (NN)-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.
Significance Statement
Clouds play an important role in defining the Arctic and Antarctic climates. The purpose of this study is to explore the potential of never-before systematically measured radiative properties of the atmosphere to aid in the detection of polar clouds, which are traditionally difficult to detect. Satellite measurements of emitted radiation at wavelengths longer than 15 μm, combined with complex machine learning methods, may allow us to better understand the occurrence of various cloud types at both poles. The occurrence of these clouds can determine whether the surface warms or cools, influencing surface temperatures and the rate at which ice melts or refreezes. Understanding the frequencies of these various clouds is increasingly important within the context of our rapidly changing climate.
A Precise Zenith Hydrostatic Delay Calibration Model in China Based on the Nonlinear Least Square MethodAbstract
Zenith hydrostatic delay (ZHD) is a crucial parameter in Global Navigation Satellite System (GNSS) navigation and positioning and GNSS meteorology. Since the Saastamoinen ZHD model has a larger error in China, it is significant to improve the Saastamoinen ZHD model. This work first 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, and the correlations between the residuals and meteorological parameters were explored. The continuous wavelet transform method was used to recognize the annual and semiannual characteristics of the residuals. Because of the nonlinear variation characteristics of residuals, the nonlinear least squares estimation method was introduced to establish an improved ZHD model—China Revised Zenith Hydrostatic Delay (CRZHD)—adapted for China. The accuracy of the CRZHD model was assessed using radiosonde data and International GNSS Service (IGS) data in 2017; the radiosonde data results show that the CRZHD model is superior to the Saastamoinen model with a 69.6% improvement. The three IGS stations with continuous meteorological data present that the BIAS and RMSE are decreased by 2.7 and 1.5 (URUM), 5.9 and 5.3 (BJFS), and 9.6 and 8.8 mm (TCMS), respectively. 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 the Saastamoinen model (SAAS-PWV), in 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% and 95.9%, while SAAS-PWV account for 46.6% and 58.9%.
Significance Statement
Zenith hydrostatic delay (ZHD) is one of the most important parameters in Global Navigation Satellite System (GNSS) navigation and positioning and GNSS meteorology, which can be derived from a precise ZHD model due to its stability. This research established an improved ZHD model for China to obtain accurate ZHD, which is a prerequisite for pinpoint precipitable water vapor (PWV) retrieval. And the PWV value is beneficial to analyze the change in precipitation in some regions, forecast the short-term rainfall, and monitor the climate.
Abstract
Zenith hydrostatic delay (ZHD) is a crucial parameter in Global Navigation Satellite System (GNSS) navigation and positioning and GNSS meteorology. Since the Saastamoinen ZHD model has a larger error in China, it is significant to improve the Saastamoinen ZHD model. This work first 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, and the correlations between the residuals and meteorological parameters were explored. The continuous wavelet transform method was used to recognize the annual and semiannual characteristics of the residuals. Because of the nonlinear variation characteristics of residuals, the nonlinear least squares estimation method was introduced to establish an improved ZHD model—China Revised Zenith Hydrostatic Delay (CRZHD)—adapted for China. The accuracy of the CRZHD model was assessed using radiosonde data and International GNSS Service (IGS) data in 2017; the radiosonde data results show that the CRZHD model is superior to the Saastamoinen model with a 69.6% improvement. The three IGS stations with continuous meteorological data present that the BIAS and RMSE are decreased by 2.7 and 1.5 (URUM), 5.9 and 5.3 (BJFS), and 9.6 and 8.8 mm (TCMS), respectively. 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 the Saastamoinen model (SAAS-PWV), in 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% and 95.9%, while SAAS-PWV account for 46.6% and 58.9%.
Significance Statement
Zenith hydrostatic delay (ZHD) is one of the most important parameters in Global Navigation Satellite System (GNSS) navigation and positioning and GNSS meteorology, which can be derived from a precise ZHD model due to its stability. This research established an improved ZHD model for China to obtain accurate ZHD, which is a prerequisite for pinpoint precipitable water vapor (PWV) retrieval. And the PWV value is beneficial to analyze the change in precipitation in some regions, forecast the short-term rainfall, and monitor the climate.
Abstract
This study investigates the mechanisms of low-latitude intraseasonal oscillations affecting regional persistent extreme precipitation events (RPEPEs) over Southwest China (SWC) during rainy seasons. Most of the RPEPEs over SWC are dominated by 7–20-day variability. The RPEPEs over SWC are preconditioned by two different types of 7–20-day Rossby waves with almost opposite phases over the western North Pacific (WNP). The two types of 7–20-day Rossby waves have direct and indirect effects on type-1 and -2 RPEPEs, respectively. For type 1, a coupled 7–20-day low-level anticyclone and suppressed convection originating from the tropical WNP propagate northwestward and cover the region from the South China Sea (SCS) to the Bay of Bengal before the RPEPEs. The anticyclone triggers ascending motion over SWC and transports more moisture to SWC, favoring the SWC RPEPEs. Before the type-2 RPEPEs, a coupled 7–20-day low-level cyclone and enhanced convection propagates from the tropical WNP to the SCS. The enhanced convection over the SCS leads to the westward extension of the western Pacific subtropical high (WPSH) and the eastward shift of the South Asian high (SAH). The variations in the WPSH and the SAH directly cause SWC RPEPEs by inducing ascending motion and transporting moisture. The mechanisms for type-2 RPEPEs tend to work under the background with a strong WPSH. Using a Lagrangian model, we found that both the 7–20-day oscillations and their background atmospheric circulations result in significant differences in moisture sources for the two types of RPEPEs. These findings benefit a better understanding of SWC extreme precipitation events.
Abstract
This study investigates the mechanisms of low-latitude intraseasonal oscillations affecting regional persistent extreme precipitation events (RPEPEs) over Southwest China (SWC) during rainy seasons. Most of the RPEPEs over SWC are dominated by 7–20-day variability. The RPEPEs over SWC are preconditioned by two different types of 7–20-day Rossby waves with almost opposite phases over the western North Pacific (WNP). The two types of 7–20-day Rossby waves have direct and indirect effects on type-1 and -2 RPEPEs, respectively. For type 1, a coupled 7–20-day low-level anticyclone and suppressed convection originating from the tropical WNP propagate northwestward and cover the region from the South China Sea (SCS) to the Bay of Bengal before the RPEPEs. The anticyclone triggers ascending motion over SWC and transports more moisture to SWC, favoring the SWC RPEPEs. Before the type-2 RPEPEs, a coupled 7–20-day low-level cyclone and enhanced convection propagates from the tropical WNP to the SCS. The enhanced convection over the SCS leads to the westward extension of the western Pacific subtropical high (WPSH) and the eastward shift of the South Asian high (SAH). The variations in the WPSH and the SAH directly cause SWC RPEPEs by inducing ascending motion and transporting moisture. The mechanisms for type-2 RPEPEs tend to work under the background with a strong WPSH. Using a Lagrangian model, we found that both the 7–20-day oscillations and their background atmospheric circulations result in significant differences in moisture sources for the two types of RPEPEs. These findings benefit a better understanding of SWC extreme precipitation events.
Abstract
Current climate models project that Antarctic sea ice will decrease by the end of 21st century. Previous studies have suggested that Antarctic sea ice change have impacts on atmospheric circulation and the mean state of the Southern Hemisphere. However, little is known whether Antarctic sea ice loss may have a tangible impact on climate extremes over southern continents and whether ocean-atmosphere coupling plays an important role in changes of climate extremes over southern continents. In this study, we conduct a set of fully coupled and atmosphere-only model experiments forced by present and future Antarctic sea ice cover. It is found that the projected Antarctic sea ice loss by the end of 21st century leads to increase in the frequency and duration of warm extremes (especially warm nights) over southern continents, and decrease in cold extremes over most regions. The frequency and duration of wet extremes are projected to increase over South America and Antarctica, whereas changes in dry days and longest dry spell vary with regions. Further Antarctic sea ice loss under a quadrupling of CO2 leads to similar but larger changes. Comparison between the coupled and atmosphere-only model experiments suggests that ocean dynamics and their interactions with the atmosphere induced by Antarctic sea ice loss plays a key role in driving the identified changes in temperature and precipitation extremes over southern continents. By comparing with global warming experiments, we find that Antarctic sea ice loss may affect temperature and precipitation extremes for some regions under greenhouse warming, especially for Antarctica.
Abstract
Current climate models project that Antarctic sea ice will decrease by the end of 21st century. Previous studies have suggested that Antarctic sea ice change have impacts on atmospheric circulation and the mean state of the Southern Hemisphere. However, little is known whether Antarctic sea ice loss may have a tangible impact on climate extremes over southern continents and whether ocean-atmosphere coupling plays an important role in changes of climate extremes over southern continents. In this study, we conduct a set of fully coupled and atmosphere-only model experiments forced by present and future Antarctic sea ice cover. It is found that the projected Antarctic sea ice loss by the end of 21st century leads to increase in the frequency and duration of warm extremes (especially warm nights) over southern continents, and decrease in cold extremes over most regions. The frequency and duration of wet extremes are projected to increase over South America and Antarctica, whereas changes in dry days and longest dry spell vary with regions. Further Antarctic sea ice loss under a quadrupling of CO2 leads to similar but larger changes. Comparison between the coupled and atmosphere-only model experiments suggests that ocean dynamics and their interactions with the atmosphere induced by Antarctic sea ice loss plays a key role in driving the identified changes in temperature and precipitation extremes over southern continents. By comparing with global warming experiments, we find that Antarctic sea ice loss may affect temperature and precipitation extremes for some regions under greenhouse warming, especially for Antarctica.
Abstract
Which processes control the mean amounts of precipitation received by tropical land and ocean? Do large-scale constraints exist on the ratio between the two? We address these questions using a conceptual box model based on water balance equations. With empirical but physically motivated parametrizations of the water balance components, we construct a set of coupled differential equations which describe the dynamical behavior of the water vapor content over land and ocean as well as the land’s soil moisture content. For a closed model configuration with one ocean and one land box, we compute equilibrium solutions across the parameter space and analyze their sensitivity to parameter choices. The precipitation ratio χ, defined as the ratio between mean land and ocean precipitation rates, quantifies the land-sea precipitation contrast. We find that χ is bounded between zero and one as long as the presence of land does not affect the relationship between water vapor path and precipitation. However, for the tested parameter values, 95% of the obtained χ values are even larger than 0.75. The sensitivity analysis reveals that χ is primarily controlled by the efficiency of atmospheric moisture transport rather than by land surface parameters. We further investigate under which conditions precipitation enhancement over land (χ > 1) would be possible. An open model configuration with an island between two ocean boxes and nonzero external advection into the domain can yield χ values larger than one, but only for a small subset of parameter choices, characterized by small land fractions and a sufficiently large moisture influx through the windward boundary.
Abstract
Which processes control the mean amounts of precipitation received by tropical land and ocean? Do large-scale constraints exist on the ratio between the two? We address these questions using a conceptual box model based on water balance equations. With empirical but physically motivated parametrizations of the water balance components, we construct a set of coupled differential equations which describe the dynamical behavior of the water vapor content over land and ocean as well as the land’s soil moisture content. For a closed model configuration with one ocean and one land box, we compute equilibrium solutions across the parameter space and analyze their sensitivity to parameter choices. The precipitation ratio χ, defined as the ratio between mean land and ocean precipitation rates, quantifies the land-sea precipitation contrast. We find that χ is bounded between zero and one as long as the presence of land does not affect the relationship between water vapor path and precipitation. However, for the tested parameter values, 95% of the obtained χ values are even larger than 0.75. The sensitivity analysis reveals that χ is primarily controlled by the efficiency of atmospheric moisture transport rather than by land surface parameters. We further investigate under which conditions precipitation enhancement over land (χ > 1) would be possible. An open model configuration with an island between two ocean boxes and nonzero external advection into the domain can yield χ values larger than one, but only for a small subset of parameter choices, characterized by small land fractions and a sufficiently large moisture influx through the windward boundary.
Abstract
A scale-dependent dynamic Smagorinsky model is implemented in the Met Office/NERC cloud model (MONC) using two averaging flavours, along Lagrangian pathlines and local moving averages. The dynamic approaches were compared against the conventional Smagorinsky-Lilly scheme in simulating the diurnal cycle of shallow cumulus convection. The simulations spanned from the LES to the near-grey-zone and grey-zone resolutions and revealed the adaptability of the dynamic model across the scales and different stability regimes. The dynamic model can produce a scale and stability dependent profile of the subfilter turbulence length-scale across the chosen resolution range. At grey-zone resolutions the adaptive length scales can better represent the early pre-cloud boundary layer leading to temperature and moisture profiles closer to the LES compared to the standard Smagorinsky. As a result the initialisation and general representation of the cloud field in the dynamic model is in good agreement with the LES. In contrast, the standard Smagorinsky produces a less well-mixed boundary-layer which fails to ventilate moisture from the boundary layer resulting in the delayed spin-up of the cloud layer. Moreover, strong down-gradient diffusion controls the turbulent transport of scalars in the cloud layer. However, the dynamic approaches rely on the resolved field to account for non-local transports, leading to over-energetic structures when the boundary layer is fully developed and the Lagrangian model is used. Introducing the local averaging version of the model or adopting a new Lagrangian time scale provides stronger dissipation without significantly affecting model behaviour.
Abstract
A scale-dependent dynamic Smagorinsky model is implemented in the Met Office/NERC cloud model (MONC) using two averaging flavours, along Lagrangian pathlines and local moving averages. The dynamic approaches were compared against the conventional Smagorinsky-Lilly scheme in simulating the diurnal cycle of shallow cumulus convection. The simulations spanned from the LES to the near-grey-zone and grey-zone resolutions and revealed the adaptability of the dynamic model across the scales and different stability regimes. The dynamic model can produce a scale and stability dependent profile of the subfilter turbulence length-scale across the chosen resolution range. At grey-zone resolutions the adaptive length scales can better represent the early pre-cloud boundary layer leading to temperature and moisture profiles closer to the LES compared to the standard Smagorinsky. As a result the initialisation and general representation of the cloud field in the dynamic model is in good agreement with the LES. In contrast, the standard Smagorinsky produces a less well-mixed boundary-layer which fails to ventilate moisture from the boundary layer resulting in the delayed spin-up of the cloud layer. Moreover, strong down-gradient diffusion controls the turbulent transport of scalars in the cloud layer. However, the dynamic approaches rely on the resolved field to account for non-local transports, leading to over-energetic structures when the boundary layer is fully developed and the Lagrangian model is used. Introducing the local averaging version of the model or adopting a new Lagrangian time scale provides stronger dissipation without significantly affecting model behaviour.
Abstract
A new set of CMIP6 data downscaled using the Localized Constructed Analogs (LOCA) statistical method has been produced, covering central Mexico through Southern Canada at 6 km resolution. Output from 27 CMIP6 Earth System Models is included, with up to 10 ensemble members per model and 3 SSPs (245, 370, and 585). Improvements from the previous CMIP5 downscaled data result in higher daily precipitation extremes, which have significant societal and economic implications. The improvements are accomplished by using a precipitation training data set that better represents daily extremes and by implementing an ensemble bias correction that allows a more realistic representation of extreme high daily precipitation values in models with numerous ensemble members. Over Southern Canada and the CONUS exclusive of Arizona (AZ) and New Mexico (NM), seasonal increases in daily precipitation extremes are largest in winter (~25% in SSP370). Over Mexico, AZ, and NM, seasonal increases are largest in autumn (~15%). Summer is the outlier season, with low model agreement except in New England and little changes in 5-yr return values, but substantial increases in the CONUS and Canada in the 500-yr return value. 1-in-100 yr historical daily precipitation events become substantially more frequent in the future, as often as once in 30-40 years in the southeastern U.S. and Pacific Northwest by end of century under SSP 370. Impacts of the higher precipitation extremes in the LOCA version 2 downscaled CMIP6 product relative to LOCA-downscaled CMIP5 product, even for similar anthropogenic emissions, may need to be considered by end-users.
Abstract
A new set of CMIP6 data downscaled using the Localized Constructed Analogs (LOCA) statistical method has been produced, covering central Mexico through Southern Canada at 6 km resolution. Output from 27 CMIP6 Earth System Models is included, with up to 10 ensemble members per model and 3 SSPs (245, 370, and 585). Improvements from the previous CMIP5 downscaled data result in higher daily precipitation extremes, which have significant societal and economic implications. The improvements are accomplished by using a precipitation training data set that better represents daily extremes and by implementing an ensemble bias correction that allows a more realistic representation of extreme high daily precipitation values in models with numerous ensemble members. Over Southern Canada and the CONUS exclusive of Arizona (AZ) and New Mexico (NM), seasonal increases in daily precipitation extremes are largest in winter (~25% in SSP370). Over Mexico, AZ, and NM, seasonal increases are largest in autumn (~15%). Summer is the outlier season, with low model agreement except in New England and little changes in 5-yr return values, but substantial increases in the CONUS and Canada in the 500-yr return value. 1-in-100 yr historical daily precipitation events become substantially more frequent in the future, as often as once in 30-40 years in the southeastern U.S. and Pacific Northwest by end of century under SSP 370. Impacts of the higher precipitation extremes in the LOCA version 2 downscaled CMIP6 product relative to LOCA-downscaled CMIP5 product, even for similar anthropogenic emissions, may need to be considered by end-users.
