Calendar Year 2021 First Quarter Asbestos Report for Permit SW532
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The Clausius–Clapeyron (CC) scaling, which indicates a roughly 7% increase in saturated water vapor per 1 °C increase in temperature, can serve as a strong constraint linking the intensity of precipitation extremes and local temperature. However, the relationship between precipitation extreme and local temperature (referred to as the PE-T relationship) does not always follow the CC scaling and is highly dependent on climate regimes. In this study, we investigated the impacts of different moisture processes and precipitation types on the PE-T relationship over eastern China during the summertime based on convection-permitting model simulations. Consistent with observations, the simulated intensity of precipitation extremes increases with temperature at a rate close to CC (double-CC) scaling below (above) 20 °C. When the temperature exceeds 25 °C, precipitation intensity starts to drop. Precipitation extremes are mainly contributed by the stratiform, MCS (i.e., mesoscale convective system) convective, and non-MCS convective precipitation at low (< 20 °C), medium (20–25 °C), and high (> 25 °C) temperatures, respectively, suggesting that the double-CC scaling occurs when convective types become dominant, while the negative scaling at high temperatures is attributed to the reduced horizontal scale of convection. Corresponding to the reduced intensity of precipitation at high temperatures, there are stronger divergence and subsidence in the low-level atmosphere, which is probably caused by the net cooling associated with the enhanced melting and evaporation of falling hydrometeors due to the lower relative humidity in the low-level atmosphere. Overall, our findings contribute to a deeper understanding of the temperature dependence of precipitation extremes in eastern China.
Two sets of 48-hr hindcast experiments for Mei-yu season using non-hydrostatic global variable-resolution model (MPAS-Atmosphere) with regional refinement at 4km resolution (V4km) and uniform 60km resolution (U60km) are first-time conducted to attempt at simulating the Mei-yu rainfall in 2015 over East China. The analysis focuses on the seasonal average of diurnal variation of precipitation amount (PA), intensity (PI), and frequency (PF). Both simulations reasonably reproduce the spatial distribution of PA. V4km is more skillful in simulating the spatial distributions and magnitudes of PF and PI. The diurnal cycle of Mei-yu rainfall shows a major early morning peak and a minor afternoon peak, contributed by precipitation during two sub-periods with distinct synoptic circulations. With strong Mei-yu in the first sub-period, the diurnal variation of PA is controlled by nocturnal southwesterly jet. V4km overestimates the morning peak mainly due to its bias in simulating boundary layer inertial oscillation. Although differences in PA between the simulations are small, U60km overestimates PF and underestimates PI. With weak Mei-yu in the second sub-period, the diurnal variation of PA is controlled by both synoptic circulation and local convection. At both resolutions, deviations in the large-scale circulation modulated by a few typhoons lead to positive biases in the morning peak of PA. After removing the typhoon impacts, V4km captures the observed diurnal cycle of PA well, while U60km significantly underestimates PA and PI particularly in the afternoon. Future studies focusing on advancing modeling of southwesterly jet and typhoons may further improve convection permitting simulation of Mei-yu rainfall.
African easterly waves (AEWs) are strongly linked to Atlantic tropical cyclones (TCs) on the synoptic timescale by serving as seedling disturbances for TC genesis. However, it is unclear whether climatological TC frequency is limited by AEWs. We investigated the impact of suppressing AEWs using a 3-member ensemble of convection-permitting regional model simulations, in which AEWs were either retained or removed through the lateral boundary conditions. Suppressing AEWs did not substantially change seasonal TC number, but did influence TC intensity, genesis time and location. Suppressing AEWs produced stronger TCs, shifted peak TC genesis from September to August, and reduced (increased) TC genesis in the eastern Atlantic (Gulf of Mexico). Without AEWs, TCs generated under more favorable large-scale atmospheric conditions. Furthermore, these results indicate that AEWs may not be reliable predictors of basin-wide seasonal TC frequency. However, simulations provide evidence that AEWs could influence the large-scale environment that is important for TCs.
Accurately representing the precipitation diurnal cycle has long been a challenge for global climate models (GCMs). Here we evaluate the precipitation diurnal cycle in the DYAMOND global convection-permitting models (CPMs) and CMIP6 HighResMIP models. Comparison of the high- (25–50 km) and low-resolution (100–250 km) models with parameterized convection in HighResMIP shows that simply increasing model resolution does not noticeably improve the precipitation diurnal cycle. In contrast, CPMs can better capture the observed amplitude and timing of precipitation diurnal cycle. However, the simulated spatial variation of timing in CPMs is smaller than observed, leading to an exaggeration of the spatially averaged diurnal amplitude. The better-simulated precipitation diurnal cycle in the CPMs is tied to mesoscale convective systems (MCSs), which contribute about half of the total precipitation. The observed life cycle of MCSs, including initiation and mature stages, is well captured in the CPMs, leading to a more realistic precipitation diurnal cycle.
This study uses a convection‐permitting model simulation to describe the environmental conditions under which convective upscale growth occurs in central Argentina, particularly examining environmental parameters when deep convection initially forms that could differentiate the rate of initial upscale growth. Simulated mesoscale convective systems (MCSs) are separated into slow and rapid growth by the rate of spatial growth from convection initiation until reaching the MCS scale. A low‐level jet (LLJ) is found more frequently near the deep convection that experiences rapid growth to an MCS, but its presence alone is not predictive of rapid growth. Using spatially‐averaged parameters, we find that rapid growth to MCSs also occurs in environments that are significantly more thermodynamically favorable with greater low‐level moisture and instability. Fewer significant differences are found in the kinematic environment with only the 0–2 km vertical wind shear magnitude being significantly larger for rapid growth MCSs compared to slow growth MCSs, potentially related to LLJs often peaking near this height. When focusing only on MCSs with the slowest and fastest growth rates, elevated‐layer shear is significantly smaller for very rapid growth MCSs, suggesting elevated‐layer shear may help discriminate between the upper and lower bounds of growth rate. Finally, when upscale growth occurs near the Sierras de Córdoba (SDC) with a LLJ present, rapid growth is also supported by favorable wind shear orientation. However, this does not hold for upscale growth occurring away from the SDC, highlighting the importance of interpreting shear direction relative to the orientation of features initiating deep convection.
Here, this study investigates the warm-season extreme precipitation–temperature scaling relationship in CONUS404, a convection-permitting (4 km) Weather Research and Forecasting (WRF) Model simulation over the conterminous United States for the past four decades, and compares it with the WRF-Thermodynamic Global Warming (WRF-TGW) historical simulation at a coarser resolution (12 km) using parameterized convection. We also analyze the NCEP stage IV and NASA Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) datasets as observational benchmarks. We examine how extreme precipitation intensity (EPI) varies with temperature and saturation deficit over representative regions based on hourly data. The stage IV and IMERG data show a similar pattern of EPI variation with temperature and saturation deficit, except that the EPI peak is lower in IMERG than in stage IV. Under dry and hot conditions, EPI decreases too rapidly with elevated saturation deficit in both CONUS404 and WRF-TGW compared to observations, but the performance of CONUS404 is superior to WRF-TGW. When the near-surface atmosphere is saturated or close to saturated, both CONUS404 and WRF-TGW produce higher peak values of EPI relative to the observational references; IMERG exhibits scaling rates close to the Clausius–Clapeyron (C–C) relationship, while CONUS404, WRF-TGW, and stage IV all demonstrate super-C–C scaling behaviors. Despite marked warming over the past four decades, in both CONUS404 and WRF-TGW, the scaling relationship between EPI and temperature in a saturated atmosphere remains stable and robust. This indicates a strong potential for the EPI–temperature scaling rate under saturation to be used as an emergent constraint in reducing uncertainties of future extreme precipitation projection.
Abstract Symmetric instability (SI) extracts kinetic energy from fronts in the surface mixed layer (SML), potentially affecting the SML structure and dynamics. Here, a global submesoscale-permitting ocean model named MITgcm LLC4320 simulation is used to examine the Stone linear prediction of the maximum SI scale to estimate grid spacings needed to begin resolving SI. Furthermore, potential effects of SI on the usable wind work are estimated roughly: this estimate of SI “activity” is useful for assessing if these modes should be resolved or parameterized. The maximum SI scale varies by latitude with median values from 568 to 23 m. Strong seasonality is observed in the SI scale and activity. The median scale in winter is 188 m globally, 2.5 times of that of summer (75 m). SI is more active in winter: 15% of the time compared with 6% in summer. The strongest SI activity is found in the western Pacific, western Atlantic, and Southern Oceans. The required grid spacings for a global model to begin resolving SI eddies in the SML are 24 m (50% of regions resolved) and 7.9 m (90%) in winter, decreasing to 9.4 m (50%) and 3.6 m (90%) in summer. It is also estimated that SI may reduce usable wind work by an upper bound of 0.83 mW m −2 globally, or 5% of the global magnitude. The sensitivity of these estimates to empirical thresholds is provided in the text.
Geostationary infrared satellite observations are spatially dense [< 1/(20 km)2] and temporally frequent (< 1/hour). These suggest the possibility of using these observations to constrain sub-synoptic features over data sparse regions, such as tropical oceans. In this study, the potential impacts of assimilating water vapor channel brightness temperature (WV-BT) observations from the geostationary Meteorological Satellite 7 (Meteosat-7) on tropical convection analysis and prediction were systematically examined through a series of ensemble data assimilation experiments. WV-BT observations were assimilated hourly into convection-permitting ensembles using Penn State’s ensemble square root filter (EnSRF). Comparisons against independently observed Meteosat-7 window channel brightness temperature (Window-BT) show that the assimilation of WV-BT generally improved the intensities and locations of large-scale cloud patterns at spatial scales larger than 100 km. However, comparisons against independent soundings indicate that the EnSRF analysis produced a much stronger dry bias than the no data assimilation experiment. This strong dry bias is associated with the use of the simulated WV-BT from the prior mean during the EnSRF analysis step. A stochastic variant of the ensemble Kalman filter (NoMeanSF) is proposed. The NoMeanSF algorithm was able to assimilate the WV-BT without causing such a strong dry bias and the quality of the analyses’ horizontal cloud pattern is similar to EnSRF’s analyses. Finally, deterministic forecasts initiated from the NoMeanSF analyses possess better horizontal cloud patterns above 500 km than those of the EnSRF. These results suggest that it might be better to assimilate all-sky WV-BT through the NoMeanSF algorithm than EnSRF algorithm.
This study investigates impacts of altering subgrid-scale mixing in “convection-permitting” kilometer-scale horizontal-grid-spacing (Δ h ) simulations by applying either constant or stochastic multiplicative factors to the horizontal mixing coefficients within the Weather Research and Forecasting Model. In quasi-idealized 1-km Δ h simulations of two observationally based squall-line cases, constant enhanced mixing produces larger updraft cores that are more dilute at upper levels, weakens the cold pool, rear-inflow jet, and front-to-rear flow of the squall line, and degrades the model’s effective resolution. Reducing mixing by a constant multiplicative factor has the opposite effect on all metrics. Completely turning off parameterized horizontal mixing produces bulk updraft statistics and squall-line mesoscale structure closest to an LES “benchmark” among all 1-km simulations, although the updraft cores are too undilute. The stochastic mixing scheme, which applies a multiplicative factor to the mixing coefficients that varies stochastically in time and space, is employed at 0.5-, 1-, and 2-km Δ h . It generally reduces midlevel vertical velocities and enhances upper-level vertical velocities compared to simulations using the standard mixing scheme, with more substantial impacts at 1- and 2-km Δ h compared to 0.5-km Δ h . Further, the stochastic scheme also increases updraft dilution to better agree with the LES for one case, but has less impact on the other case. Stochastic mixing acts to weaken the cold pool but without a significant impact on squall-line propagation. It also does not affect the model’s overall effective resolution unlike applying constant multiplicative factors to the mixing coefficients.
Abstract A 6.5-month, convection-permitting simulation is conducted over Argentina covering the Remote Sensing of Electrification, Lightning, And Mesoscale/Microscale Processes with Adaptive Ground Observations and Clouds, Aerosols, and Complex Terrain Interactions (RELAMPAGO-CACTI) field campaign and is compared with observations to evaluate mesoscale convective system (MCS) growth prediction. Observed and simulated MCSs are consistently identified, tracked, and separated into growth, mature, and decay stages using top-of-the-atmosphere infrared brightness temperature and surface rainfall. Simulated MCS number, lifetime, seasonal and diurnal cycles, and various cloud-shield characteristics including growth rate are similar to those observed. However, the simulation produces smaller rainfall areas, greater proportions of heavy rainfall, and faster system propagations. Rainfall area is significantly underestimated for long-lived MCSs but not for shorter-lived MCSs, and rain rates are always overestimated. These differences result from a combination of model and satellite retrieval biases, in which simulated MCS rain rates are shifted from light to heavy, while satellite-retrieved rainfall is too frequent relative to rain gauge estimates. However, the simulation reproduces satellite-retrieved MCS cloud-shield evolution well, supporting its usage to examine environmental controls on MCS growth. MCS initiation locations are associated with removal of convective inhibition more than maximized low-level moisture convergence or instability. Rapid growth is associated with a stronger upper-level jet (ULJ) and a deeper northwestern Argentinean low that causes a stronger northerly low-level jet (LLJ), increasing heat and moisture fluxes, low-level vertical wind shear, baroclinicity, and instability. Sustained growth corresponds to similar LLJ, baroclinicity, and instability conditions but is less sensitive to the ULJ, large-scale vertical motion, or low-level shear. Growth sustenance controls MCS maximum extent more than growth rate.