Abstract
Prolonged drought poses significant challenges for food and fiber production in the U.S. Southwest, where range livestock production has great economic and cultural significance. Sustaining rangeland agriculture in the region necessitates swift and nimble uptake of drought adaptations. While Extension serves as a promising resource to drought adaptation among ranchers, how Extension staff perceive their capacity to support ranching clientele in that endeavor is not well understood. We interviewed university Extension professionals across New Mexico to explore their perceptions of drought. We found that their perceived ability to aid in drought adaptation was dependent upon interpersonal, as well as structural, factors. These factors differed across ranching regions in New Mexico. This case study highlights the importance of Extension networks, opportunity for novel Extension training, and a need for heightened attention to structural barriers.
Abstract
Prolonged drought poses significant challenges for food and fiber production in the U.S. Southwest, where range livestock production has great economic and cultural significance. Sustaining rangeland agriculture in the region necessitates swift and nimble uptake of drought adaptations. While Extension serves as a promising resource to drought adaptation among ranchers, how Extension staff perceive their capacity to support ranching clientele in that endeavor is not well understood. We interviewed university Extension professionals across New Mexico to explore their perceptions of drought. We found that their perceived ability to aid in drought adaptation was dependent upon interpersonal, as well as structural, factors. These factors differed across ranching regions in New Mexico. This case study highlights the importance of Extension networks, opportunity for novel Extension training, and a need for heightened attention to structural barriers.
Abstract
The ability to predict changes in the right tail of daily precipitation distributions over short time periods, as well as the probability of clustering extreme values, is critical for current risk management. In the present study, we apply the well-established metric Average Value-at-Risk (AVaR) for the first time within the field of climatology. We also investigate the evolution of the return level (RL) and the extremal index (EI), which we refer to as risk measures. In the case of precipitation processes, a rise in the first two and a reduction in the third may result in increased hazards. These methods were applied to the new data on the daily sum of precipitation from the region of Upper Vistula in Poland from 1951 to 2020 to analyse the dynamics of changes across time. We found that AVaR and RL have the smallest values for the middle of the analysed period (years 1971–2010), while EI has a maximal value around 1963–1992. Both the beginning and the end of the investigated period are characterized by more extreme and clustered precipitation events. Furthermore, this paper includes some recommendations for the tools used to compute the measures.
Abstract
The ability to predict changes in the right tail of daily precipitation distributions over short time periods, as well as the probability of clustering extreme values, is critical for current risk management. In the present study, we apply the well-established metric Average Value-at-Risk (AVaR) for the first time within the field of climatology. We also investigate the evolution of the return level (RL) and the extremal index (EI), which we refer to as risk measures. In the case of precipitation processes, a rise in the first two and a reduction in the third may result in increased hazards. These methods were applied to the new data on the daily sum of precipitation from the region of Upper Vistula in Poland from 1951 to 2020 to analyse the dynamics of changes across time. We found that AVaR and RL have the smallest values for the middle of the analysed period (years 1971–2010), while EI has a maximal value around 1963–1992. Both the beginning and the end of the investigated period are characterized by more extreme and clustered precipitation events. Furthermore, this paper includes some recommendations for the tools used to compute the measures.
Abstract
Antenna-arrayed high-frequency coastal radar is widely used to monitor the ocean and obtain metocean parameters such as sea surface current, sea wave height, and surface wind. However, the accuracy of these parameters can be significantly influenced by the spectral width and Doppler velocity of the sea echo signals across azimuthal directions, and insufficient spectrum resolution increases uncertainties in the estimates of spectral width and Doppler velocity. To address this, we demonstrate an alternative approach to beamforming by utilizing the norm-constrained Capon (NC-Capon) method to enhance the Doppler spectral resolution and improve the localization accuracy of the spectral peaks. The efficacy of the NC-Capon method is exemplified through an application to a coastal radar dataset collected from 16 receiving channels, operated at a central frequency of 27.75 MHz. A comparative investigation of the NC-Capon beamforming method with the conventional Fourier beamforming method showed that the widths of the spectral peaks at different range cells and azimuthal angles are noticeably improved at lower signal-to-noise ratio (SNR) conditions. Given this, the NC-Capon beamforming method exhibits more robustness to noise and could effectively enhance the concentration of the radar sea echo signals in the Doppler-frequency spectrum, thereby reducing the uncertainties of the spectral width and Doppler/radial velocity of the first-order sea echoes. These characteristics are substantiated by the comparative analysis of spectral parameters between the two beamforming methods across various ranges, beamforming angles, and SNR levels. Finally, the computed radial velocities are benchmarked against in-situ measurements obtained from a bottom-mounted acoustic current profiler to confirm the validity of the NC-Capon method.
Abstract
Antenna-arrayed high-frequency coastal radar is widely used to monitor the ocean and obtain metocean parameters such as sea surface current, sea wave height, and surface wind. However, the accuracy of these parameters can be significantly influenced by the spectral width and Doppler velocity of the sea echo signals across azimuthal directions, and insufficient spectrum resolution increases uncertainties in the estimates of spectral width and Doppler velocity. To address this, we demonstrate an alternative approach to beamforming by utilizing the norm-constrained Capon (NC-Capon) method to enhance the Doppler spectral resolution and improve the localization accuracy of the spectral peaks. The efficacy of the NC-Capon method is exemplified through an application to a coastal radar dataset collected from 16 receiving channels, operated at a central frequency of 27.75 MHz. A comparative investigation of the NC-Capon beamforming method with the conventional Fourier beamforming method showed that the widths of the spectral peaks at different range cells and azimuthal angles are noticeably improved at lower signal-to-noise ratio (SNR) conditions. Given this, the NC-Capon beamforming method exhibits more robustness to noise and could effectively enhance the concentration of the radar sea echo signals in the Doppler-frequency spectrum, thereby reducing the uncertainties of the spectral width and Doppler/radial velocity of the first-order sea echoes. These characteristics are substantiated by the comparative analysis of spectral parameters between the two beamforming methods across various ranges, beamforming angles, and SNR levels. Finally, the computed radial velocities are benchmarked against in-situ measurements obtained from a bottom-mounted acoustic current profiler to confirm the validity of the NC-Capon method.
Abstract
This study investigates the in situ generation of planetary waves (PWs) by zonally asymmetric gravity wave drag (GWD) in the mesosphere using a fully nonlinear general circulation model extending to the lower thermosphere. To isolate the effects of GWD, we establish a highly idealized but efficient framework that excludes stationary PWs propagating from the troposphere and in situ PWs generated by barotropic and baroclinic instabilities. The GWD is prescribed in a zonally sinusoidal form with a zonal wavenumber (ZWN) of either 1 or 2 in the lower mesosphere of the Northern Hemisphere midlatitude. Our idealized simulations clearly show that zonally asymmetric GWD generates PWs by serving as a nonconservative source Z′ of linearized disturbance quasigeostrophic potential vorticity q′. While Z′ initially amplifies PWs through enhancing q′ tendency, the subsequent zonal advection of q′ gradually balances with Z′, thereby attaining steady-state PWs. The GWD-induced PWs predominantly have the same ZWN as the applied GWD with minor contributions from higher ZWN components attributed to nonlinear processes. The amplitude of the induced PWs increases in proportion with the magnitude of the peak GWD, while it decreases in proportion to the square of ZWN. Moreover, the amplitude of PWs increases as the meridional range of GWD expands and as GWD shifts toward lower latitudes. These PWs deposit substantial positive Eliassen–Palm flux divergence (EPFD) of ∼30 m s−1 day−1 at their origin and negative EPFD of 5–10 m s−1 day−1 during propagation. In addition, the in situ PWs exhibit interhemispheric propagation following westerlies that extend into the Southern Hemisphere.
Abstract
This study investigates the in situ generation of planetary waves (PWs) by zonally asymmetric gravity wave drag (GWD) in the mesosphere using a fully nonlinear general circulation model extending to the lower thermosphere. To isolate the effects of GWD, we establish a highly idealized but efficient framework that excludes stationary PWs propagating from the troposphere and in situ PWs generated by barotropic and baroclinic instabilities. The GWD is prescribed in a zonally sinusoidal form with a zonal wavenumber (ZWN) of either 1 or 2 in the lower mesosphere of the Northern Hemisphere midlatitude. Our idealized simulations clearly show that zonally asymmetric GWD generates PWs by serving as a nonconservative source Z′ of linearized disturbance quasigeostrophic potential vorticity q′. While Z′ initially amplifies PWs through enhancing q′ tendency, the subsequent zonal advection of q′ gradually balances with Z′, thereby attaining steady-state PWs. The GWD-induced PWs predominantly have the same ZWN as the applied GWD with minor contributions from higher ZWN components attributed to nonlinear processes. The amplitude of the induced PWs increases in proportion with the magnitude of the peak GWD, while it decreases in proportion to the square of ZWN. Moreover, the amplitude of PWs increases as the meridional range of GWD expands and as GWD shifts toward lower latitudes. These PWs deposit substantial positive Eliassen–Palm flux divergence (EPFD) of ∼30 m s−1 day−1 at their origin and negative EPFD of 5–10 m s−1 day−1 during propagation. In addition, the in situ PWs exhibit interhemispheric propagation following westerlies that extend into the Southern Hemisphere.
Abstract
This study uses Stage IV precipitation data over the state of Alaska to assess and cross-compare precipitation estimates from the most recent versions of multiple precipitation products, including satellite-based passive microwave (PMW; SSMIS-F17, MHS-MetOp-B, MHS-NOAA19, AMSR2, ATMS and GMI in V05 and V07), active microwave (AMW or radar; GPM DPR in V06 and V07), combined active and passive microwave (APMW; DPRGMI in V06 and V07), infrared (AIRS), reanalysis (ERA5, MERRA-2), and satellite-gauge (GPCP V1.3 and GPCP V3.2) products. PMW estimates are generally improved in V07 compared to V05 in terms of overall bias, pattern, and capturing precipitation extremes. DPR and DPRGMI show low skill in capturing different precipitation features. ERA5 and MERRA-2 show the highest agreement with Stage IV for all precipitation rate metrics. AIRS and GPCP capture the overall precipitation pattern and magnitude fairly well, performing better than the radar and comparable to the PMW V07 products, although the geographical maps suggest that they provide a relatively smoothed spatial distribution of mean precipitation rates. The outcomes of this study shed light on the performance of various precipitation products over Alaska (partly representing high-latitude regions) and can be useful to guide the development of multi-sensor products.
Abstract
This study uses Stage IV precipitation data over the state of Alaska to assess and cross-compare precipitation estimates from the most recent versions of multiple precipitation products, including satellite-based passive microwave (PMW; SSMIS-F17, MHS-MetOp-B, MHS-NOAA19, AMSR2, ATMS and GMI in V05 and V07), active microwave (AMW or radar; GPM DPR in V06 and V07), combined active and passive microwave (APMW; DPRGMI in V06 and V07), infrared (AIRS), reanalysis (ERA5, MERRA-2), and satellite-gauge (GPCP V1.3 and GPCP V3.2) products. PMW estimates are generally improved in V07 compared to V05 in terms of overall bias, pattern, and capturing precipitation extremes. DPR and DPRGMI show low skill in capturing different precipitation features. ERA5 and MERRA-2 show the highest agreement with Stage IV for all precipitation rate metrics. AIRS and GPCP capture the overall precipitation pattern and magnitude fairly well, performing better than the radar and comparable to the PMW V07 products, although the geographical maps suggest that they provide a relatively smoothed spatial distribution of mean precipitation rates. The outcomes of this study shed light on the performance of various precipitation products over Alaska (partly representing high-latitude regions) and can be useful to guide the development of multi-sensor products.
Abstract
Accurate representation of the planetary boundary layer (PBL) in numerical weather prediction is crucial to air quality, weather forecasting and climate change research and operations. Here, we evaluate the estimates of PBL Height (PBLH) from a set of convective-permitting simulations conducted using the Weather Research and Forecast (WRF) model during the Plain Elevated Convection at Night (PECAN) campaign (June to mid-July 2015). Our analysis aims to quantify the variability in PBLH, along with its associated surface variables and atmospheric profiles, to gauge the influence of model physics on the accuracy of simulated PBL properties. Specifically, we conducted twenty-four 45-day retrospective simulations encompassing different combinations of three PBL and two microphysics schemes, two initial and boundary condition (ICBC) datasets, and two model vertical grid spacings. We compare these simulations with measurements from surface sites and radiosonde launches across four sites in the Southern Great Plains during PECAN. Our findings indicate that deriving PBLH from modeled atmospheric profiles yields a narrower spread (200-300m) compared to PBLH calculated from the model's PBL scheme (400-500m) relative to radiosonde-derived PBLH under fair-weather conditions. The choice of PBL scheme and ICBCs exerts the most significant impact (by up to 400m) on the spread of modeled PBLH and associated atmospheric profiles, primarily influencing the representation of surface heating, transport, and mixing processes in the model. The model has difficulty reproducing the correct thermodynamic profile under cloud-intense conditions. These results underscore the benefit of using the physically-consistent approach in retrieving, evaluating, and assimilating PBLH estimates, as well as the utility of multi-physics to better address model uncertainties.
Abstract
Accurate representation of the planetary boundary layer (PBL) in numerical weather prediction is crucial to air quality, weather forecasting and climate change research and operations. Here, we evaluate the estimates of PBL Height (PBLH) from a set of convective-permitting simulations conducted using the Weather Research and Forecast (WRF) model during the Plain Elevated Convection at Night (PECAN) campaign (June to mid-July 2015). Our analysis aims to quantify the variability in PBLH, along with its associated surface variables and atmospheric profiles, to gauge the influence of model physics on the accuracy of simulated PBL properties. Specifically, we conducted twenty-four 45-day retrospective simulations encompassing different combinations of three PBL and two microphysics schemes, two initial and boundary condition (ICBC) datasets, and two model vertical grid spacings. We compare these simulations with measurements from surface sites and radiosonde launches across four sites in the Southern Great Plains during PECAN. Our findings indicate that deriving PBLH from modeled atmospheric profiles yields a narrower spread (200-300m) compared to PBLH calculated from the model's PBL scheme (400-500m) relative to radiosonde-derived PBLH under fair-weather conditions. The choice of PBL scheme and ICBCs exerts the most significant impact (by up to 400m) on the spread of modeled PBLH and associated atmospheric profiles, primarily influencing the representation of surface heating, transport, and mixing processes in the model. The model has difficulty reproducing the correct thermodynamic profile under cloud-intense conditions. These results underscore the benefit of using the physically-consistent approach in retrieving, evaluating, and assimilating PBLH estimates, as well as the utility of multi-physics to better address model uncertainties.
Abstract
In February 2022, an extreme wet and cold event hits South China, with the regional precipitation ranking the second highest record, while the temperature ranking the third lowest since 1979. In this study, the physical mechanisms of this extreme event are investigated from the perspective of multiple timescales interaction. Results show that the strong confrontation between the warm and moist air advection by the India-Burma trough (IBT) and the invasion of cold air activity related to strengthening of the East Asian winter monsoon (EAWM) is the key to trigger this extreme event. Further analyses show that the multi-timescale coupling of the South Asian jet wave train and Eurasian (EU) teleconnection is the main reason for the strong cold and warm-moist air flow. The EU teleconnection on both intraseasonal and synoptic timescales plays a key role in triggering this extreme event by strengthening the EAWM. On synoptic timescale, not only EU teleconnection, but also the South Asian jet wave train plays a key role. They show a stronger intensity on this timescale and their coupling are obvious. The South Asian jet wave train enhances the moisture supply by deepening the IBT, which further conflicts with the strong EAWM modulated by EU teleconnection over South China, leading to this extreme wet-cold event. The forecast skills in the Subseasonal to Seasonal (S2S) prediction project models of this event are evaluated in this paper, results show that the ECMWF model can successfully predict the extreme precipitation by capturing the coupling of the two wave trains with a 5 days lead time.
Abstract
In February 2022, an extreme wet and cold event hits South China, with the regional precipitation ranking the second highest record, while the temperature ranking the third lowest since 1979. In this study, the physical mechanisms of this extreme event are investigated from the perspective of multiple timescales interaction. Results show that the strong confrontation between the warm and moist air advection by the India-Burma trough (IBT) and the invasion of cold air activity related to strengthening of the East Asian winter monsoon (EAWM) is the key to trigger this extreme event. Further analyses show that the multi-timescale coupling of the South Asian jet wave train and Eurasian (EU) teleconnection is the main reason for the strong cold and warm-moist air flow. The EU teleconnection on both intraseasonal and synoptic timescales plays a key role in triggering this extreme event by strengthening the EAWM. On synoptic timescale, not only EU teleconnection, but also the South Asian jet wave train plays a key role. They show a stronger intensity on this timescale and their coupling are obvious. The South Asian jet wave train enhances the moisture supply by deepening the IBT, which further conflicts with the strong EAWM modulated by EU teleconnection over South China, leading to this extreme wet-cold event. The forecast skills in the Subseasonal to Seasonal (S2S) prediction project models of this event are evaluated in this paper, results show that the ECMWF model can successfully predict the extreme precipitation by capturing the coupling of the two wave trains with a 5 days lead time.
Abstract
The increasing frequency and severity of wildfires in Australia, driven by climate change, pose a significant threat to ecosystems, lives, and property. This study examines the impact of climate drivers, specifically El Niño Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), Southern Annular Mode (SAM), Madden-Julian Oscillation (MJO), and two modes of persistent high pressure in the Australia-Pacific region, on extreme fire danger. By analysing observed and simulated fire danger in relation to these climate drivers, we aim to enhance understanding of climate-fire mechanisms and contribute to Australia’s bushfire preparedness.
Our findings indicate that all assessed drivers influence extreme fire danger, with key influences related to the drivers’ established relationships with rainfall and temperature. El Niño, positive IOD, and negative SAM events generally increase extreme fire danger across most of Australia and in most seasons. The two modes of Australian blocking exhibit similar effects, varying spatially. Specific phases of the MJO have significant seasonal relationships with fire danger. In some instances, increased fire danger is not directly linked to temperature or precipitation changes but rather driven by remote teleconnections, airflow, or pressure anomalies.
Evaluating the accuracy of weather and climate forecasting systems in representing these relationships is crucial for effective prediction and mitigation of fire hazards. The leading Australian climate simulation model effectively reproduces observed relationships but reveals biases in capturing certain aspects of climate variability.
Advancements in this field would enhance fire weather forecasts, including those by the Australian Bureau of Meteorology with lead times of up to four months.
Abstract
The increasing frequency and severity of wildfires in Australia, driven by climate change, pose a significant threat to ecosystems, lives, and property. This study examines the impact of climate drivers, specifically El Niño Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), Southern Annular Mode (SAM), Madden-Julian Oscillation (MJO), and two modes of persistent high pressure in the Australia-Pacific region, on extreme fire danger. By analysing observed and simulated fire danger in relation to these climate drivers, we aim to enhance understanding of climate-fire mechanisms and contribute to Australia’s bushfire preparedness.
Our findings indicate that all assessed drivers influence extreme fire danger, with key influences related to the drivers’ established relationships with rainfall and temperature. El Niño, positive IOD, and negative SAM events generally increase extreme fire danger across most of Australia and in most seasons. The two modes of Australian blocking exhibit similar effects, varying spatially. Specific phases of the MJO have significant seasonal relationships with fire danger. In some instances, increased fire danger is not directly linked to temperature or precipitation changes but rather driven by remote teleconnections, airflow, or pressure anomalies.
Evaluating the accuracy of weather and climate forecasting systems in representing these relationships is crucial for effective prediction and mitigation of fire hazards. The leading Australian climate simulation model effectively reproduces observed relationships but reveals biases in capturing certain aspects of climate variability.
Advancements in this field would enhance fire weather forecasts, including those by the Australian Bureau of Meteorology with lead times of up to four months.
Abstract
Prediction of severe convective storms at timescales of 2–4 weeks is of interest to forecasters and stakeholders due to their impacts to life and property. Prediction of severe convective storms on this timescale is challenging, since the large-scale weather patterns that drive this activity begin to lose dynamic predictability beyond week 1. Previous work related to severe convective storms on the subseasonal timescale has mostly focused on observed relationships with teleconnections. The skill of numerical weather prediction forecasts of convective-related variables has been comparatively less explored. In this study over the United States, a forecast evaluation of variables relevant in the prediction of severe convective storms is conducted using Global Ensemble Forecast System Version 12 reforecasts at lead times up to four weeks. We find that kinematic and thermodynamic fields are predicted with skill out to week 3 in some cases, while composite parameters struggle to achieve meaningful skill into week 2. Additionally, using a novel method of weekly summations of daily maximum composite parameters, we suggest that aggregation of certain variables may assist in providing additional predictability beyond week 1. These results should serve as a reference for forecast skill for the relevant fields and help inform the development of convective forecasting tools at timescales beyond current operational products.
Abstract
Prediction of severe convective storms at timescales of 2–4 weeks is of interest to forecasters and stakeholders due to their impacts to life and property. Prediction of severe convective storms on this timescale is challenging, since the large-scale weather patterns that drive this activity begin to lose dynamic predictability beyond week 1. Previous work related to severe convective storms on the subseasonal timescale has mostly focused on observed relationships with teleconnections. The skill of numerical weather prediction forecasts of convective-related variables has been comparatively less explored. In this study over the United States, a forecast evaluation of variables relevant in the prediction of severe convective storms is conducted using Global Ensemble Forecast System Version 12 reforecasts at lead times up to four weeks. We find that kinematic and thermodynamic fields are predicted with skill out to week 3 in some cases, while composite parameters struggle to achieve meaningful skill into week 2. Additionally, using a novel method of weekly summations of daily maximum composite parameters, we suggest that aggregation of certain variables may assist in providing additional predictability beyond week 1. These results should serve as a reference for forecast skill for the relevant fields and help inform the development of convective forecasting tools at timescales beyond current operational products.
