Abstract
Removing nonweather echoes is a critical component of the quality control (QC) chain used in the context of radar data assimilation for numerical weather prediction, quantitative precipitation estimation, and nowcasting applications. Recent studies show that using a simple QC method based on the depolarization ratio (DR) performs remarkably well in many situations. Nonetheless, this method may misclassify echoes in regions affected by nonuniform beamfilling or melting particles. This study presents an updated version of this QC used to remove nonweather echoes that uses the DR-based classification together with a set of physically based rules for correcting misclassifications of hail, nonuniform beamfilling, and melting particles. The potential of the new QC is evaluated using a continental-scale monitoring framework that compares the radar observations after QC with the precipitation occurrence derived from aviation routine weather reports (METARs). For this evaluation, the study uses the radar data and the METARs available over North America during the summer of 2019 and winter of 2020. In addition, the study demonstrates the usefulness of the monitoring framework to determine the optimal QC configuration. Some practical limitations of using the METAR-derived precipitation to assess radar data quality are also discussed.
Abstract
Removing nonweather echoes is a critical component of the quality control (QC) chain used in the context of radar data assimilation for numerical weather prediction, quantitative precipitation estimation, and nowcasting applications. Recent studies show that using a simple QC method based on the depolarization ratio (DR) performs remarkably well in many situations. Nonetheless, this method may misclassify echoes in regions affected by nonuniform beamfilling or melting particles. This study presents an updated version of this QC used to remove nonweather echoes that uses the DR-based classification together with a set of physically based rules for correcting misclassifications of hail, nonuniform beamfilling, and melting particles. The potential of the new QC is evaluated using a continental-scale monitoring framework that compares the radar observations after QC with the precipitation occurrence derived from aviation routine weather reports (METARs). For this evaluation, the study uses the radar data and the METARs available over North America during the summer of 2019 and winter of 2020. In addition, the study demonstrates the usefulness of the monitoring framework to determine the optimal QC configuration. Some practical limitations of using the METAR-derived precipitation to assess radar data quality are also discussed.
Abstract
The existence of significant cross-polar antenna patterns, as well as the scan-dependent measurement biases, inherent to the polarimetric phased array radar (PPAR), are among the most important risk factors for using this technology in weather observations. The cross-polar patterns on receive induce cross coupling between returns from the two orthogonal fields causing biases in polarimetric variable estimates. Furthermore, the electromagnetic coupling in hardware may exacerbate the cross-coupling effects. To address this problem, a pulse-to-pulse phase coding in either the horizontal or vertical ports of the transmission elements has been proposed. However, it does not affect the scan-dependent system biases in PPAR estimates which require corrections via calibration mechanisms. Further, the cross-coupling signals are proportional to the cross-polar pattern power levels, rendering mitigation effective only at steering angles where these levels are sufficiently low (e.g., approximately less than ~-25 dB). In that regard, any approach that augments the number of such steering angles benefits the cross-coupling mitigation effectiveness. Herein, a simple approach that has a potential to achieve this via antenna tilt is presented.
Abstract
The existence of significant cross-polar antenna patterns, as well as the scan-dependent measurement biases, inherent to the polarimetric phased array radar (PPAR), are among the most important risk factors for using this technology in weather observations. The cross-polar patterns on receive induce cross coupling between returns from the two orthogonal fields causing biases in polarimetric variable estimates. Furthermore, the electromagnetic coupling in hardware may exacerbate the cross-coupling effects. To address this problem, a pulse-to-pulse phase coding in either the horizontal or vertical ports of the transmission elements has been proposed. However, it does not affect the scan-dependent system biases in PPAR estimates which require corrections via calibration mechanisms. Further, the cross-coupling signals are proportional to the cross-polar pattern power levels, rendering mitigation effective only at steering angles where these levels are sufficiently low (e.g., approximately less than ~-25 dB). In that regard, any approach that augments the number of such steering angles benefits the cross-coupling mitigation effectiveness. Herein, a simple approach that has a potential to achieve this via antenna tilt is presented.
Abstract
Radial velocity estimates provided by Doppler weather radar are critical measurements used by operational forecasters for the detection and monitoring of life-impacting storms. The sampling methods used to produce these measurements are inherently susceptible to aliasing, which produces ambiguous velocity values in regions with high winds, and needs to be corrected using a velocity dealiasing algorithm (VDA). In the US, the Weather Surveillance Radar – 1988 Doppler (WSR-88D) Open Radar Product Generator (ORPG) is a processing environment that provides a world-class VDA; however, this algorithm is complex and can be difficult to port to other radar systems outside of the WSR-88D network. In this work, a Deep Neural Network (DNN) is used to emulate the 2-dimensionalWSR-88D ORPG dealiasing algorithm. It is shown that a DNN, specifically a customized U-Net, is highly effective for building VDAs that are accurate, fast, and portable to multiple radar types. To train the DNN model, a large dataset is generated containing aligned samples of folded and dealiased velocity pairs. This dataset contains samples collected from WSR-88D Level-II and Level-III archives, and uses the ORPG dealiasing algorithm output as a source of truth. Using this dataset, a U-Net is trained to produce the number of folds at each point of a velocity image. Several performance metrics are presented using WSR-88D data. The algorithm is also applied to other non-WSR-88D radar systems to demonstrate portability to other hardware/software interfaces. A discussion of the broad applicability of this method is presented, including how other Level-III algorithms may benefit from this approach.
Abstract
Radial velocity estimates provided by Doppler weather radar are critical measurements used by operational forecasters for the detection and monitoring of life-impacting storms. The sampling methods used to produce these measurements are inherently susceptible to aliasing, which produces ambiguous velocity values in regions with high winds, and needs to be corrected using a velocity dealiasing algorithm (VDA). In the US, the Weather Surveillance Radar – 1988 Doppler (WSR-88D) Open Radar Product Generator (ORPG) is a processing environment that provides a world-class VDA; however, this algorithm is complex and can be difficult to port to other radar systems outside of the WSR-88D network. In this work, a Deep Neural Network (DNN) is used to emulate the 2-dimensionalWSR-88D ORPG dealiasing algorithm. It is shown that a DNN, specifically a customized U-Net, is highly effective for building VDAs that are accurate, fast, and portable to multiple radar types. To train the DNN model, a large dataset is generated containing aligned samples of folded and dealiased velocity pairs. This dataset contains samples collected from WSR-88D Level-II and Level-III archives, and uses the ORPG dealiasing algorithm output as a source of truth. Using this dataset, a U-Net is trained to produce the number of folds at each point of a velocity image. Several performance metrics are presented using WSR-88D data. The algorithm is also applied to other non-WSR-88D radar systems to demonstrate portability to other hardware/software interfaces. A discussion of the broad applicability of this method is presented, including how other Level-III algorithms may benefit from this approach.
Abstract
This work describes the extension of the Global Modeling and Assimilation Office (GMAO) Observing System Simulation Experiment (OSSE) framework to use a hybrid 4D-EnVar scheme instead of 3D-Var. The original 3D-Var and hybrid 4D-EnVar OSSEs use the same version of the data assimilation system (DAS) so that a direct comparison is possible in terms of the validation with respect to their corresponding real cases. Rather than quantifying the differences between the two data assimilation methodologies, a short inter-comparison of upgrading from a 3D- to a 4D-OSSE is provided to highlight aspects where this change matters to the OSSE community and to identify particular features of data assimilation that can only be explored in a four-dimensional OSSE framework. A short validation of the hybrid 4D-EnVar OSSE shows that conclusions from previous assessments of the 3D-Var OSSE in its ability to mimic the behavior of the real system still hold with the same caveats. Furthermore, some aspects of the ensemble configuration and behavior are discussed along with forecast sensitivity to observation impacts (FSOI). Estimates of error standard deviations are shown to be smaller in the hybrid 4D-EnVar OSSE but with little impact on the character of the error. A discussion on future work directions focuses on exploring the four-dimensional aspect such as the error distribution within the assimilation window or four-dimensional handling of high-temporal density observations.
Abstract
This work describes the extension of the Global Modeling and Assimilation Office (GMAO) Observing System Simulation Experiment (OSSE) framework to use a hybrid 4D-EnVar scheme instead of 3D-Var. The original 3D-Var and hybrid 4D-EnVar OSSEs use the same version of the data assimilation system (DAS) so that a direct comparison is possible in terms of the validation with respect to their corresponding real cases. Rather than quantifying the differences between the two data assimilation methodologies, a short inter-comparison of upgrading from a 3D- to a 4D-OSSE is provided to highlight aspects where this change matters to the OSSE community and to identify particular features of data assimilation that can only be explored in a four-dimensional OSSE framework. A short validation of the hybrid 4D-EnVar OSSE shows that conclusions from previous assessments of the 3D-Var OSSE in its ability to mimic the behavior of the real system still hold with the same caveats. Furthermore, some aspects of the ensemble configuration and behavior are discussed along with forecast sensitivity to observation impacts (FSOI). Estimates of error standard deviations are shown to be smaller in the hybrid 4D-EnVar OSSE but with little impact on the character of the error. A discussion on future work directions focuses on exploring the four-dimensional aspect such as the error distribution within the assimilation window or four-dimensional handling of high-temporal density observations.
Abstract
The assimilation of satellite all-sky infrared (IR) brightness temperatures (BTs) has been shown in previous studies to improve intensity forecasts of tropical cyclones. In this study, we examine whether assimilating all-sky IR BTs can also potentially improve tropical cyclogenesis forecasts by improving the pregenesis cloud and moisture fields. By using an ensemble-based data assimilation system, we show that the assimilation of upper-tropospheric water vapor channel BTs observed by the Meteosat-10 SEVIRI instrument two days before the formation of a tropical depression improves the genesis forecast of Hurricane Irma (2017), a classic Cape Verde storm, by up to 24 h while also capturing its later rapid intensification in deterministic forecasts. In an experiment that withholds the assimilation of all-sky IR BTs, the assimilation of conventional observations from the Global Telecommunications System (GTS) leads to the premature genesis of Hurricane Irma by at least 24 h. This premature genesis is shown to result from an overestimation of the spatial coverage of deep convection within the African easterly wave (AEW) from which Irma eventually forms. The gross overestimation of deep convection without all-sky IR BTs is accompanied by higher column saturation fraction, stronger low-level convergence, and the earlier spinup of a low-level meso-β-scale vortex within the AEW that ultimately becomes Hurricane Irma. Through its adjustment to the initial moisture and cloud conditions, the assimilation of all-sky IR BTs leads to a more realistic convective evolution in forecasts and ultimately a more realistic timing of genesis.
Significance Statement
Every year hurricanes impact the lives of thousands of people living along the eastern coast of the United States. Many of these storms originate from tropical disturbances that exit the west coast of Africa. To give the public more warning time ahead of these storms, it is important to improve the forecasts of their formation. This study uses a system developed at The Pennsylvania State University to incorporate satellite observations into forecasts of a classic Cape Verde storm, Hurricane Irma (2017), two days before it formed. By using satellite-collected radiances, we improve the timing of its formation by up to 24 h due to a better representation of the mesoscale tropical disturbance from which it originated.
Abstract
The assimilation of satellite all-sky infrared (IR) brightness temperatures (BTs) has been shown in previous studies to improve intensity forecasts of tropical cyclones. In this study, we examine whether assimilating all-sky IR BTs can also potentially improve tropical cyclogenesis forecasts by improving the pregenesis cloud and moisture fields. By using an ensemble-based data assimilation system, we show that the assimilation of upper-tropospheric water vapor channel BTs observed by the Meteosat-10 SEVIRI instrument two days before the formation of a tropical depression improves the genesis forecast of Hurricane Irma (2017), a classic Cape Verde storm, by up to 24 h while also capturing its later rapid intensification in deterministic forecasts. In an experiment that withholds the assimilation of all-sky IR BTs, the assimilation of conventional observations from the Global Telecommunications System (GTS) leads to the premature genesis of Hurricane Irma by at least 24 h. This premature genesis is shown to result from an overestimation of the spatial coverage of deep convection within the African easterly wave (AEW) from which Irma eventually forms. The gross overestimation of deep convection without all-sky IR BTs is accompanied by higher column saturation fraction, stronger low-level convergence, and the earlier spinup of a low-level meso-β-scale vortex within the AEW that ultimately becomes Hurricane Irma. Through its adjustment to the initial moisture and cloud conditions, the assimilation of all-sky IR BTs leads to a more realistic convective evolution in forecasts and ultimately a more realistic timing of genesis.
Significance Statement
Every year hurricanes impact the lives of thousands of people living along the eastern coast of the United States. Many of these storms originate from tropical disturbances that exit the west coast of Africa. To give the public more warning time ahead of these storms, it is important to improve the forecasts of their formation. This study uses a system developed at The Pennsylvania State University to incorporate satellite observations into forecasts of a classic Cape Verde storm, Hurricane Irma (2017), two days before it formed. By using satellite-collected radiances, we improve the timing of its formation by up to 24 h due to a better representation of the mesoscale tropical disturbance from which it originated.
Abstract
Cloud radiative effects have long been known to play a key role in governing the mean climate. In recent years, it has become clear that they also contribute to climate variability in the tropics. Here we build on recent work and probe the role of cloud radiative effects in extratropical sea surface temperature (SST) variability. The impact of cloud radiative effects on climate variability is explored in “cloud-locking” simulations run on an Earth System Model. The method involves comparing the output from two climate simulations: one in which clouds are coupled to atmospheric dynamic and thermodynamic processes, and another in which clouds are prescribed and thus decoupled from them. The results reveal that cloud–climate coupling leads to widespread increases in the amplitudes of extratropical SST variability from monthly to decadal time scales. Notably, it leads to ∼40%–100% increases in the amplitude of monthly to decadal variability over both the North Atlantic and North Pacific Oceans. These increases are consistent with the “reddening” of cloud shortwave radiative effects that arises when clouds respond to the dynamic and thermodynamic state of the atmosphere. The results suggest that a notable fraction of observed Northern Hemisphere SST variability—including that associated with North Pacific and North Atlantic decadal variability—is due to cloud–climate coupling.
Abstract
Cloud radiative effects have long been known to play a key role in governing the mean climate. In recent years, it has become clear that they also contribute to climate variability in the tropics. Here we build on recent work and probe the role of cloud radiative effects in extratropical sea surface temperature (SST) variability. The impact of cloud radiative effects on climate variability is explored in “cloud-locking” simulations run on an Earth System Model. The method involves comparing the output from two climate simulations: one in which clouds are coupled to atmospheric dynamic and thermodynamic processes, and another in which clouds are prescribed and thus decoupled from them. The results reveal that cloud–climate coupling leads to widespread increases in the amplitudes of extratropical SST variability from monthly to decadal time scales. Notably, it leads to ∼40%–100% increases in the amplitude of monthly to decadal variability over both the North Atlantic and North Pacific Oceans. These increases are consistent with the “reddening” of cloud shortwave radiative effects that arises when clouds respond to the dynamic and thermodynamic state of the atmosphere. The results suggest that a notable fraction of observed Northern Hemisphere SST variability—including that associated with North Pacific and North Atlantic decadal variability—is due to cloud–climate coupling.
Abstract
In partial plastic mulch-covered croplands, the complicated co-existence of bare soil surface, mulched soil surface, and dynamically changing canopy surface results in challenges in accurately estimating field surface albedo (α) and its components (bare soil surface albedo, αb ; mulched soil surface albedo, αm ; and canopy surface albedo, αc ) during the whole growth period. To accurately estimate α, αb , αm , and αc , and to quantify the three surfaces’ contributions to field shortwave radiation reflections (Fb , Fm , Fc ), (1) a modified two-stream (MTS) approximation solution that considered the effect of plastic mulch has been proposed to accurately estimate α; (2) dynamic variations of αb , αm , and αc , and Fb , Fm , Fc have been characterized. Therein, αb and αm were determined from corresponding parameterization schemes, αc is determined using mulched irrigated croplands surface albedo (MICA) relationship between α and αb , αm , and αc that established in this study. Results indicated that: (1) compared with measurements, considering the effect of plastic mulch will significantly improve estimation of α when ground surface is not fully covered by crop canopy, while not will underestimate α by a mean value of 0.061 in the early growth period; (2) mean values of α, αb , αm , and αc during the whole growth period were 0.198, 0.174, 0.308, and 0.160, respectively, while the corresponding Fb , Fm , and Fc were 0.08, 0.42, and 0.50, respectively.
Abstract
In partial plastic mulch-covered croplands, the complicated co-existence of bare soil surface, mulched soil surface, and dynamically changing canopy surface results in challenges in accurately estimating field surface albedo (α) and its components (bare soil surface albedo, αb ; mulched soil surface albedo, αm ; and canopy surface albedo, αc ) during the whole growth period. To accurately estimate α, αb , αm , and αc , and to quantify the three surfaces’ contributions to field shortwave radiation reflections (Fb , Fm , Fc ), (1) a modified two-stream (MTS) approximation solution that considered the effect of plastic mulch has been proposed to accurately estimate α; (2) dynamic variations of αb , αm , and αc , and Fb , Fm , Fc have been characterized. Therein, αb and αm were determined from corresponding parameterization schemes, αc is determined using mulched irrigated croplands surface albedo (MICA) relationship between α and αb , αm , and αc that established in this study. Results indicated that: (1) compared with measurements, considering the effect of plastic mulch will significantly improve estimation of α when ground surface is not fully covered by crop canopy, while not will underestimate α by a mean value of 0.061 in the early growth period; (2) mean values of α, αb , αm , and αc during the whole growth period were 0.198, 0.174, 0.308, and 0.160, respectively, while the corresponding Fb , Fm , and Fc were 0.08, 0.42, and 0.50, respectively.
Abstract
Using the multimodel simulations from phase 6 of the Coupled Model Intercomparison Project (CMIP6), we investigate the aridity changes in China and the associated mechanisms during the three geological periods of the Last Interglacial (LIG), Last Glacial Maximum (LGM), and mid-Holocene (MH), as well as the three future scenarios of the shared socioeconomic pathways of SSP1-2.6, SSP2-4.5, and SSP5-8.5. The aridity index is used to measure terrestrial moisture, which combines the effects of both precipitation and potential evapotranspiration (PET), with the latter representing the amount of water consumed by the atmosphere. The results show that relative to the preindustrial period, the total dryland area in China varies by −15%, 6%, and −13% during the LIG, LGM, and MH, respectively, and slightly varies in the three future scenarios. Over China, LGM dryland expansion and future dryland contraction are mainly attributed to precipitation changes, MH dryland contraction is mainly caused by PET changes, and LIG dryland contraction is comparably caused by PET and precipitation changes. For the LGM and three future scenarios, temperature is the leading factor of PET changes, while during the MH and LIG, the change in relative humidity is the main factor. In comparison, the simulated aridity changes in China are generally consistent with the reconstructed moisture changes for the three past periods, although uncertainties exist in reconstructions during the LGM and MH.
Abstract
Using the multimodel simulations from phase 6 of the Coupled Model Intercomparison Project (CMIP6), we investigate the aridity changes in China and the associated mechanisms during the three geological periods of the Last Interglacial (LIG), Last Glacial Maximum (LGM), and mid-Holocene (MH), as well as the three future scenarios of the shared socioeconomic pathways of SSP1-2.6, SSP2-4.5, and SSP5-8.5. The aridity index is used to measure terrestrial moisture, which combines the effects of both precipitation and potential evapotranspiration (PET), with the latter representing the amount of water consumed by the atmosphere. The results show that relative to the preindustrial period, the total dryland area in China varies by −15%, 6%, and −13% during the LIG, LGM, and MH, respectively, and slightly varies in the three future scenarios. Over China, LGM dryland expansion and future dryland contraction are mainly attributed to precipitation changes, MH dryland contraction is mainly caused by PET changes, and LIG dryland contraction is comparably caused by PET and precipitation changes. For the LGM and three future scenarios, temperature is the leading factor of PET changes, while during the MH and LIG, the change in relative humidity is the main factor. In comparison, the simulated aridity changes in China are generally consistent with the reconstructed moisture changes for the three past periods, although uncertainties exist in reconstructions during the LGM and MH.
