Convection-permitting Weather Research Research Articles

Dependencies of Simulated Convective Cell and System Growth Biases on Atmospheric Instability and Model Resolution.

This study evaluates convective cell properties and their relationships with convective and stratiform rainfall within a season-long convection-permitting weather research and forecasting simulation over central Argentina using radar, satellite, and radiosonde measurements from the RELAMPAGO-CACTI field campaign. The simulation slightly underestimates radar-estimated rainfall over the ∼3.5-month evaluation period but underestimates stratiform rainfall by 46% and overestimates convective rainfall by 43%. As convective available potential energy (CAPE) increases, the convective rainfall overestimation decreases, but the stratiform rainfall underestimation increases such that the contribution of convective to total rainfall remains constantly high biased by ∼26%. Overestimated convective rainfall arises from the simulation generating 2.6 times more precipitating convective cells (14,299) than observed by radar (5,662) despite similar observed and simulated cell growth processes, with relatively wide cells contributing mostly to excessive convective rainfall. Relatively shallow cells, typically reaching heights of 4-7km, contribute most to the cell number bias. This cell number bias increases as CAPE decreases, potentially because cells and their updrafts become narrower and more under-resolved as CAPE decreases. The gross overproduction of precipitating shallow cells leads to overly efficient precipitation and inadequate detrainment of ice aloft, thereby diminishing the formation of robust stratiform rainfall regions. Decreasing model horizontal grid spacing from 3 to 1 or 0.333km for low (<300Jkg-1) and high CAPE (>1,000Jkg-1) cases results in minimal change to cell number, depth, and convective-to-stratiform partitioning biases. This suggests that improving prediction of these convective properties depends on factors beyond solely increasing model resolution.

Investigation of the characteristics of low-level jets over North America in a convection-permitting Weather Research and Forecasting simulation

Abstract. In this study, we utilized a high-resolution (4 km) convection-permitting Weather Research and Forecasting (WRF) simulation spanning a 13-year period (2000–2013) to investigate the climatological features of low-level jets (LLJs) over North America. The 4 km simulation enabled us to represent the effects of orography and the underlying surface on the boundary layer winds better. Focusing on the continental US and the adjacent border regions of Canada and Mexico, this study not only identified several well-known large-scale LLJs, such as the southerly Great Plains LLJ and the summer northerly California coastal LLJ, but also the winter Quebec northerly LLJ which received less focus before. All these LLJs reach their peak in the nighttime in the diurnal cycle. Thus, the different thermal and dynamic mechanisms forming these three significant LLJs are investigated in this paper. Inertial oscillation theory dominates in the Great Plain LLJ, and the California coastal LLJ is formed by the baroclinic theory, whereas the Quebec LLJ is associated with both theories. Moreover, the high-resolution simulation revealed climatic characteristics of weaker and smaller-scale LLJs or low-level wind maxima in regions with complex terrains, such as the northerly LLJs in the foothill regions of the Rocky Mountains and the Appalachians during the winter. This study provides valuable insights into the climatological features of LLJs in North America, and the high-resolution simulation offers a more detailed understanding of LLJ behavior near complex terrains and other smaller-scale features.

Simulated Diurnal Pulses in Hurricane Dorian (2019)

Abstract Radially outward-propagating, diurnal pulses in tropical cyclones (TCs) are associated with TC intensity and structural changes. The pulses are observed to feature either cloud-top cooling or warming, so-called cooling pulses (CPs) or warming pulses (WPs), respectively, with CPs posing a greater risk for hazardous weather because they often assume characteristics of tropical squall lines. The current study evaluates the characteristics and origins of simulated CPs using various convection-permitting Weather Research and Forecasting (WRF) Model simulations of Hurricane Dorian (2019), which featured several CPs and WPs over the tropical Atlantic Ocean. CP evolution is tested against choice of microphysics parameterization, whereby the Thompson and Morrison schemes present distinct mechanisms for CP creation and propagation. Specifically, the Thompson CP is convectively coupled and propagates outward with a rainband within 100–300 km of the storm center. The Morrison CP is restricted to the cirrus canopy and propagates radially outward in the upper-level outflow layer, unassociated with any rainband, within 200–600 km of the storm center. The Thompson simulation better represents the observations of this particular event, but it is speculated that CPs in nature can resemble characteristics from either MP scheme. It is, therefore, necessary to evaluate pulses beyond just brightness temperature (e.g., reflectivity, rain rate), especially within simulations where full fields are available. Significance Statement Tropical cyclone size and structure are influenced by the time of day. Identifying and predicting such characteristics is critical for evaluating hazardous weather risk of storms close to land. While satellite observations are valuable for recognizing daily fluctuations of tropical cyclone clouds as seen from space, they do not reliably capture what occurs at the surface. To investigate the relationship between upper-level cloud oscillations and rainbands, this study analyzes simulations of a major hurricane along the coast of Florida. The results show that rainbands are not always tied to changes in cloud tops, suggesting multiple pathways toward the daily oscillation of upper-level tropical cyclone clouds.

Assessing hydrological sensitivity to future climate change over the Canadian southern boreal forest

Assessing hydrological sensitivity to future climate change over the Canadian southern boreal forest

Tracking Urban Footprint on Extreme Precipitation in an African Megacity

Abstract This study contributes to the body of current knowledge about the urban effect on extreme precipitation (EP) by investigating the city–EP interaction over Lagos, Nigeria. This is a unique, first-time study that adds a “missing piece” of this information about the African continent to the comprehensive global urban precipitation “puzzle.” The convection-permitting Weather Research and Forecasting (WRF) Model is employed within an ensemble simulation framework using combinations of different physical schemes and boundary/initial conditions to detect the urban signal on an extreme rainfall event that occurred on 30 May 2006. WRF simulations are verified against satellite-estimated and in situ observations, and the results from the best-performing ensemble members are used for analysis. The results show that the control simulation with urban representation generated 20%–30% more rainfall over the urban area than the nonurban sensitivity simulation, in which the city is replaced by forest. Physical mechanisms behind the differences were revealed. We found that the urbanization in Lagos reduced evapotranspiration, resulting in the increase of sensible heating (by 75 W m−2). This further enhances the urban heat-island effect (+1.5 K of air surface temperature), facilitating horizontal convergence and boosting daytime sea breeze. As a result, more moisture is transported from the southern sea area to inland areas; the moisture then converges over Lagos city, creating favorable conditions for enhancing convection and extreme-rainfall-generating processes.

Impact of transported dust aerosols on precipitation over the Nepal Himalayas using convection-permitting WRF-Chem simulation

In this study, we implemented a convection-permitting Weather Research and Forecasting model coupled with chemistry (WRF-Chem) to assess the impact of the long-range transported dust aerosols on the precipitation and convective processes over the Nepal Himalayas. A substantial amount of dust aerosol was transported to the Central Himalayan region during the dust storm event that occurred from 12 to 15 June, 2018 in the Thar desert, northwestern India. Two different sensitivity simulations, with and without transported dust aerosols, in a cloud-resolving scale of 3 km horizontal resolution was performed. We isolated the long-range transported dust aerosols by zeroing out dust aerosols from lateral boundary conditions and keeping all other locally emitted and remotely transported aerosols from anthropogenic, biogenic, and biomass burning sources. The simulation results show the noticeable impacts of transported dust aerosols on the cloud microphysical properties and regional precipitation over the southern slopes of the Nepal Himalayas. In comparison to the simulation without dust, precipitation is enhanced by 9.6% over the mountainous terrains (500–3000 m) in the presence of dust aerosols. An activation of dust aerosols as condensation nuclei significantly increased the droplet concentration resulting in enhanced convection and upward velocity with the release of latent heat of condensation. Though we simulated one event of long-range transported dust aerosols and some biases in simulation exist, this study highlights the discernible impact of remote dust aerosol on the convection and cloud microphysical properties, which can significantly influence the regional precipitation and Himalayan hydrology.

The projection of canadian wind energy potential in future scenarios using a convection-permitting regional climate model

The distribution of wind resources in the future will be altered by climate change. The high-resolution regional climate model is a powerful tool for predicting wind power changes. We use a pair of 4-km convection-permitting Weather Research Forecasting (WRF) simulations over southern Canada to study wind energy. The high-resolution model better represents orography and the underlying surface that strongly affect near-surface winds. The simulations include a historical simulation (CTRL) of 13 years (2001–2013) and a future projection for the end of the 21st century (RCP 8.5 scenario), using the Pseudo Global Warming (PGW) approach. The wind power density (WPD) and extractable wind power (EWP) obtained from the winds from the two simulations are analyzed and compared. In the future, the wind energy potential over most areas of Canada will decrease, while the changes over current wind farms, located in optimal locations, would be less significant in terms of EWP. In fact, there will be a slight increase of EWP distributed along the coastal area of Eastern Quebec and the Great Lakes regions in southern Ontario. The changes also show seasonal and diurnal variations: In winter when wind energy growth is more significant, a larger magnitude of changes occurs at noon • A convection-permitting WRF model has been used to simulate the near-surface wind. • The future tendency of wind energy in southern Canada was assessed. • The wind power density over most regions will decrease due to global warming. • Extractable wind power would be less affected by global warming.

Evaluation of extreme precipitation in the Yangtze River Delta Region of China using a 1.5 km mesh convection‑permitting regional climate model

Realistic representation of rainfall characteristics on local scales by state-of-the-art climate models remains a key challenge, especially on sub-daily timescales. In this study, the convection-permitting Weather Research and Forecasting (WRF) model configured with 1.5 km grid spacing is used to simulate precipitation on sub-daily timescales over the Yangtze River Delta Region of China for continuous 10 years (2005–2014). The simulations are compared with rain gauge observations, reanalysis data, and the simulations of a lower resolution WRF with 9 km grid spacing that has a parameterization of convection. The results show that precipitation over the region can be well captured by using the convection-permitting model (CPM). Furthermore, the intensity, duration and coverage of these precipitation events can be more accurately described by the CPM. On the convection timescales of 1–4 h, especially for heavy rainfall events, the CPM is more accurate than the convection-parameterized model in capturing the short-duration events, which may be due to its better account of physical processes related to the convection on the convection-permitting scale. In addition, the extreme events which are more localized and with short-duration can be represented better by the CPM while the convection-parameterized model tends to produce widespread precipitation events covering more grid cells than observations Biases of the simulation by the 9-km mesh convection-parameterized mode appear to be related to the deficiencies in the representation of convections.

Uncertainties in Riverine and Coastal Flood Impacts under Climate Change

Climate change can affect different drivers of flooding in low-lying coastal areas of the world, challenging the design and planning of communities and infrastructure. The concurrent occurrence of multiple flood drivers such as high river flows and extreme sea levels can aggravate such impacts and result in catastrophic damages. In this study, the individual and compound effects of riverine and coastal flooding are investigated at Stephenville Crossing located in the coastal-estuarine region of Newfoundland and Labrador (NL), Canada. The impacts of climate change on flood extents and depths and the uncertainties associated with temporal patterns of storms, intensity–duration–frequency (IDF) projections, spatial resolution, and emission scenarios are assessed. A hydrologic model and a 2D hydraulic model are set up and calibrated to simulate the flood inundation for the historical (1976–2005) as well as the near future (2041–2070) and far future (2071–2100) periods under Representative Concentration Pathways (RCPs) 4.5 and 8.5. Future storm events are generated based on projected IDF curves from convection-permitting Weather Research and Forecasting (WRF) climate model simulations, using SCS, Huff, and alternating block design storm methods. The results are compared with simulations based on projected IDF curves derived from statistically downscaled Global Climate Models (GCMs). Both drivers of flooding are projected to intensify in the future, resulting in higher risks of flooding in the study area. Compound riverine and coastal flooding results in more severe inundation, affecting the communities on the coastline and the estuary area. Results show that the uncertainties associated with storm hyetographs are considerable, which indicate the importance of accurate representation of storm patterns. Further, simulations based on projected WRF-IDF curves show higher risks of flooding compared to the ones associated with GCM-IDFs.

Diurnal cycle of summer precipitation over the Eastern Tibetan Plateau and surrounding regions simulated in a convection-permitting model

Based on the hourly gauge-satellite merged precipitation data with the spatial resolution of 0.1° × 0.1° during 2013 ~ 2018, we have evaluated the performance of the convection-permitting Weather Research and Forecasting model at Nanjing University (WRF_NJU) in forecasting the precipitation diurnal variation and the associated atmospheric circulation over the eastern Tibetan Plateau and its surrounding regions during summer. Results indicate that WRF_NJU model can well reproduce the diurnal cycle of the summer precipitation in terms of the diurnal peak time, duration and magnitude. In addition, the eastward propagation of rainfall systems with long duration along the eastern Tibetan Plateau (ETP) and its adjacent areas can also be properly captured. The WRF_NJU model can reasonably reproduce the relevant atmospheric circulation during summer as well. However, the model tends to underestimate the summer precipitation amount (PA) and precipitation frequency (PF) over most time of a day. Relatively larger biases in the occurring time and magnitude of PA and PF diurnal peaks can be noted over the ETP and Sichuan Basin. Further analysis suggests that the underestimation of PA over the ETP is attributed to the much lower moisture supply, weaker low-level southwesterly winds and less convective available potential energy (CAPE) in the WRF_NJU model than in ERA5. Over Sichuan Basin, the underestimated PA is related to the weaker upward motion, which is corresponding to the cold biases of surface air temperature in WRF_NJU. Findings of this study provide the basic model biases and may be helpful to further improve the model physical processes.

A modeling examination of cloud seeding conditions under the warmer climate in Utah, USA

Assessment of how the wintertime cloud seeding conditions over Utah's mountains may change provides essential information for the state to work on increasing its snowpack in the warmer climate. In this study, data from the National Center for Atmospheric Research's high-resolution, convection-permitting Weather Research and Forecasting model are used to evaluate suitable conditions for wintertime cloud seeding over Utah's mountains, and to estimate future changes to these conditions under a pseudo-global warming scenario. While previous studies have examined historical cloud seeding conditions, this is the first study that explores the impact of future climate change on these conditions. Based on two atmospheric variables commonly used in cloud-seeding operations, i.e. average temperature from the surface to 1 km above ground level (−20 °C≤T≤−6 °C) and vertically integrated supercooled liquid water (SLW > 0.01 mm), Utah's mountains are suitable for seeding more than 20% of the time during winter. A greater rate of suitability exists in the northern and Uinta Mountains. In the warmer climate under a high-emissions scenario, these seeding conditions may wane as the percentage of precipitating clouds suitable for seeding, about 60% under current conditions, would decline to about 40% across the state with significance. This projected decrease is due to rising temperatures and a decreased frequency of precipitation events. These findings imply that climate warming will narrow the window of opportunity for winter cloud seeding operation in Utah. • Suitable cloud seeding condition over the Utah's mountains were examined. • Utah Mountains might get more but less frequent winter precipitation in a future warming climate. • Utah mountains are suitable for winter cloud seeding however climate warming may narrow the window of seeding opportunity.

Assessment and projection of the water budget over western Canada using convection-permitting weather research and forecasting simulations

Abstract. Water resources in cold regions in western Canada face severe risks posed by anthropogenic global warming as evapotranspiration increases and precipitation regimes shift. Although understanding the water cycle is key for addressing climate change issues, it is difficult to obtain high spatial- and temporal-resolution observations of hydroclimatic processes, especially in remote regions. Climate models are useful tools for dissecting and diagnosing these processes, especially the convection-permitting (CP) high-resolution regional climate simulation, which provides advantages over lower-resolution models by explicitly representing convection. In addition to better representing convective systems, higher spatial resolution also better represents topography, mountain meteorology, and highly heterogeneous geophysical features. However, there is little work with convection-permitting regional climate models conducted over western Canada. Focusing on the Mackenzie River and Saskatchewan River basins, this study investigated the surface water budget and atmospheric moisture balance in historical and representative concentration pathway (RCP8.5) projections using 4 km CP Weather Research and Forecasting (WRF). We compared the high-resolution 4 km CP WRF and three common reanalysis datasets, namely the North American Regional Reanalysis (NARR), the Japanese 55-year Reanalysis (JRA-55), and European Centre for Medium-Range Weather Forecasts reanalysis interim dataset (ERA-Interim). High-resolution WRF outperforms the reanalyses in balancing the surface water budget in both river basins with much lower residual terms. For the pseudo-global-warming scenario at the end of the 21st century with representative concentration pathway (RCP8.5) radiative forcing, both the Mackenzie River and Saskatchewan River basins show increases in the amplitude for precipitation and evapotranspiration and a decrease in runoff. The Saskatchewan River basin (SRB) shows a moderate increase in precipitation in the west and a small decrease in the east. Combined with a significant increase in evapotranspiration in a warmer climate, the Saskatchewan River basin would have a larger deficit of water resources than in the current climate based on the pseudo-global-warming (PGW) simulation. The high-resolution simulation also shows that the difference of atmospheric water vapour balance in the two river basins is due to flow orientation and topography differences at the western boundaries of the two basins. The sensitivity of water vapour balance to fine-scale topography and atmospheric processes shown in this study demonstrates that high-resolution dynamical downscaling is important for large-scale water balance and hydrological cycles.

Ultra-high resolution regional climate projections for assessing changes in hydrological extremes and underlying uncertainties

The frequency and intensity of extreme hydrological events (droughts and floods) have been increasing over the past few decades, which has been posing a threat to water security and agriculture production. Thus, projecting the future evolution of hydrological extremes plays a crucial role in sustainable water management and agriculture development in a changing climate. In this study, we develop the high-resolution projections of multidimensional drought characteristics and flood risks using the convection-permitting Weather Research and Forecasting (WRF) model with the horizontal grid spacing of 4 km for the Blanco and Mission River basins over South Texas. Uncertainties in model parameters are addressed explicitly, thereby leading to probabilistic assessments of hydrological extremes. Our findings reveal that the probabilistic multivariate assessments of drought and flood risks can reduce the underestimation and the biased conclusions generated from the univariate assessment. Furthermore, our findings disclose that future droughts are expected to become more severe over South Texas even though the frequency of the occurrence of droughts is projected to decrease, especially for the long-term drought episodes. In addition, South Texas region is expected to experience more floods with an increasing river discharge. Moreover, the Blanco and Mission river basins will suffer from higher flood risks as flood return periods are expected to become longer under climate change.

Radar-based characterisation of heavy precipitation in the eastern Mediterranean and its representation in a convection-permitting model

Abstract. Heavy precipitation events (HPEs) can lead to natural hazards (e.g. floods and debris flows) and contribute to water resources. Spatiotemporal rainfall patterns govern the hydrological, geomorphological, and societal effects of HPEs. Thus, a correct characterisation and prediction of rainfall patterns is crucial for coping with these events. Information from rain gauges is generally limited due to the sparseness of the networks, especially in the presence of sharp climatic gradients. Forecasting HPEs depends on the ability of weather models to generate credible rainfall patterns. This paper characterises rainfall patterns during HPEs based on high-resolution weather radar data and evaluates the performance of a high-resolution, convection-permitting Weather Research and Forecasting (WRF) model in simulating these patterns. We identified 41 HPEs in the eastern Mediterranean from a 24-year radar record using local thresholds based on quantiles for different durations, classified these events into two synoptic systems, and ran model simulations for them. For most durations, HPEs near the coastline were characterised by the highest rain intensities; however, for short durations, the highest rain intensities were found for the inland desert. During the rainy season, the rain field's centre of mass progresses from the sea inland. Rainfall during HPEs is highly localised in both space (less than a 10 km decorrelation distance) and time (less than 5 min). WRF model simulations were accurate in generating the structure and location of the rain fields in 39 out of 41 HPEs. However, they showed a positive bias relative to the radar estimates and exhibited errors in the spatial location of the heaviest precipitation. Our results indicate that convection-permitting model outputs can provide reliable climatological analyses of heavy precipitation patterns; conversely, flood forecasting requires the use of ensemble simulations to overcome the spatial location errors.

Convection-permitting regional climate simulation of warm-season precipitation over Eastern China

Convection-permitting regional climate models have been shown to improve precipitation simulation in many aspects, such as the diurnal cycle, precipitation frequency, intensity and extremes in many studies over several geographical regions of the world, but their skill in reproducing the warm-season precipitation characteristics over the East Asia has not been robustly tested yet. Motivated by recent advances in computing power, model physics and high-resolution reanalysis, we use the convection-permitting weather research and forecasting (WRF) model configured with 3 km grid spacing to simulate the warm-season precipitation in eastern China for 10 seasons (2008–2017). The hourly 31-km-resolution ERA5 reanalysis data are used to provide initial and boundary conditions for the simulations. The objectives are (1) to evaluate the model skill in simulating warm-season precipitation climatology in the East Asian monsoon region, (2) to identify the promises and problems of the convection-permitting simulation, and (3) to investigate solutions for the model deficiencies. Results demonstrate that the 3-km-resolution WRF model reasonably reproduces the spatial characteristics of seasonal and sub-seasonal precipitation, the seasonal meridional migration associated with the summer monsoon activity, the diurnal variation phase and amplitude, and the propagating convection east of the Tibetan Plateau. The major deficiency is that the model overestimates precipitation amount, especially in the afternoon. Analysis and sensitivity experiments suggest that improved treatment of sub-grid cloud fraction and the aerosol effects may help to suppress the oft-reported high precipitation bias. These results provide useful guidance for improving the model skill at simulating warm-season precipitation in East Asia.

Improving probabilistic hydroclimatic projections through high-resolution convection-permitting climate modeling and Markov chain Monte Carlo simulations

Understanding future changes in hydroclimatic variables plays a crucial role in improving resilience and adaptation to extreme weather events such as floods and droughts. In this study, we develop high-resolution climate projections over Texas by using the convection-permitting Weather Research and Forecasting (WRF) model with 4 km horizontal grid spacing, and then produce the Markov chain Monte Carlo (MCMC)-based hydrologic forecasts in the Guadalupe River basin which is the primary concern of the Texas Water Development Board and the Guadalupe-Blanco River Authority. The Parameter-elevation Regressions on Independent Slopes Model (PRISM) dataset is used to verify the WRF climate simulations. The Model Parameter Estimation Experiment (MOPEX) dataset is used to validate probabilistic hydrologic predictions. Projected changes in precipitation, potential evapotranspiration (PET) and streamflow at different temporal scales are examined by dynamically downscaling climate projections derived from 15 Coupled Model Intercomparison Project Phase 5 (CMIP5) general circulation models (GCMs). Our findings reveal that the Upper Coast Climate Division of Texas is projected to experience the most remarkable wetting caused by precipitation and PET changes, whereas the most significant drying is expected to occur for the North Central Texas Climate Division. The dry Guadalupe River basin is projected to become drier with a substantial increase in future drought risks, especially for the summer season. And the extreme precipitation events are projected to increase in frequency and intensity with a reduction in overall precipitation frequency, which may result in more frequent occurrences of flash floods and drought episodes in the Guadalupe River basin.

Comparison and Evaluation of Statistical Rainfall Disaggregation and High-Resolution Dynamical Downscaling over Complex Terrain

Abstract Representative methods of statistical disaggregation and dynamical downscaling are compared in terms of their ability to disaggregate precipitation data into hourly resolution in an urban area with complex terrain. The nonparametric statistical Method of Fragments (MoF) uses hourly data from rain gauges to split the daily data at the location of interest into hourly fragments. The high-resolution, convection-permitting Weather Research and Forecasting (WRF) regional climate model is driven by reanalysis data. The MoF can reconstruct the variance, dry proportion, wet hours per month, number and length of wet spells per rainy day, timing of the maximum rainfall burst, and intensities of extreme precipitation with errors of less than 10%. However, the MoF cannot capture the spatial coherence and temporal interday connectivity of precipitation events due to the random elements involved in the algorithm. Otherwise, the statistical method is well suited for filling gaps in subdaily historical records. The WRF Model is able to reproduce dry proportion, lag-1 autocorrelation, wet hours per month, number and length of wet spells per rainy day, spatial correlation, and 6- and 12-h intensities of extreme precipitation with errors of 10% or less. The WRF approach tends to underestimate peak rainfall of 1- and 3-h aggregates but can be used where no observations are available or when areal precipitation data are needed.

Evaluation of convection-permitting WRF CONUS simulation on the relationship between soil moisture and heatwaves

Soil moisture plays an important role in modulating regional climate from sub-seasonal to seasonal timescales. Particularly important, soil moisture deficits can amplify summer heatwaves (HWs) through soil moisture-temperature feedback which has critical impacts on society, economy and human health. In this study, we evaluate decade-long convection-permitting Weather Research and Forecast (WRF) model simulations over the contiguous US on simulating heatwaves and their relationship with antecedent soil moisture using a dense observational network. We showed that the WRF model is capable of capturing the spatial patten of temperature threshold to define HWs, though the simulation shows a warm bias in the Midwest and cold bias in western mountainous regions. Two HW indices, based on frequency (HWF) and magnitude (HWM), are evaluated. Significant anti-correlations between antecedent soil moisture and both HW indices have been found in most parts of the domain except the South Pacific Coast. A detailed study has been conducted for the Midwest and South Great Plains regions, where two heatwaves had occurred in the last decade. In both regions, the high quantile of the HWF distribution shows a strong dependence on antecedent soil moisture: drier soil leads to much larger increase on the upper quantile of HWF than it does on the lower quantile. Soil moisture effects on the higher end of HWM are not as strong as on the lower end: wetter antecedent soil corresponds to a larger decrease on the lower quantile of HWM. WRF captures the heterogeneous responses to dry soil on HWF distribution in both regions, but overestimates these HWM responses in the Midwest and underestimates them in the South Great Plains. Our results show confidence in WRF’s ability to simulate HW characteristics and the impacts of antecedent soil moisture on HWs. These are also important implications for using high-resolution convection-permitting mode to study the coupling between land and atmosphere.

Evaluating Seasonal Orographic Precipitation in the Interior Western United States Using Gauge Data, Gridded Precipitation Estimates, and a Regional Climate Simulation

Abstract There are several high-resolution (1–12 km) gridded precipitation datasets covering the interior western United States. This study cross validates seasonal orographic precipitation estimates from the Snowpack Telemetry (SNOTEL) network; the national hourly multisensor precipitation analysis Stage IV dataset (NCEP IV); four gauge-driven gridded datasets; and a 10-yr, 4-km, convection-permitting Weather Research and Forecasting (WRF) Model simulation. The NCEP IV dataset, which uses the NEXRAD network and precipitation gauges, is challenged in this region because of blockage and lack of low-level radar coverage in complex terrain. The gauge-driven gridded datasets, which statistically interpolate gauge measurements over complex terrain to better estimate orographic precipitation, are challenged by the highly heterogeneous, weather-dependent nature of precipitation in complex terrain at scales finer than can be resolved by the gauge network, such as the SNOTEL network. Gauge-driven gridded precipitation estimates disagree in areas where SNOTEL gauges are sparse, especially at higher elevations. The WRF simulation captures wintertime orographic precipitation distribution and amount well, and biases over specific mountain ranges are identical to those in an independent WRF simulation, suggesting that these biases are at least partly due to errors in the snowfall measurements or the gridding of these measurements. The substantial disagreement between WRF and the gridded datasets over some mountains may motivate reevaluation of some gauge records and installation of new SNOTEL gauges in regions marked by large discrepancies between modeled and gauge-driven precipitation estimates.

Sensitivity of Tropical Cyclone Simulations to Parametric Uncertainties in Air–Sea Fluxes and Implications for Parameter Estimation

Abstract Tropical cyclones (TCs) are strongly influenced by fluxes of momentum and moist enthalpy across the air–sea interface. These fluxes cannot be resolved explicitly by current-generation numerical weather prediction models, and therefore must be accounted for via empirical parameterizations of surface exchange coefficients (CD for momentum and Ck for moist enthalpy). The resultant model uncertainty is examined through hundreds of convection-permitting Weather Research and Forecasting Model (WRF) simulations of Hurricane Katrina (2005) by varying four key parameters found in commonly used parameterizations of the exchange coefficient formulas. Two of these parameters effectively act as multiplicative factors for the exchange coefficients over all wind speeds (one each for CD and Ck); the other two parameters control the behavior of CD at very high wind speeds (i.e., above 33 m s−1). It is found that both the intensity and the structure of TCs are highly dependent upon the two multiplicative parameters. The multiplicative parameter for CD has a considerably larger impact than the one for Ck on the relationship between maximum 10-m wind speed and minimum sea level pressure: CD alters TC structure, with higher values shifting the radius of maximum winds inward and strengthening the low-level inflow; Ck only affects structure by uniformly strengthening/weakening the primary and secondary circulations. The TC exhibits the greatest sensitivities to the two multiplicative parameters after a few hours of model integration, suggesting that these parameters could be estimated by assimilating near-surface observations. The other two parameters are likely more difficult to estimate because the TC is only marginally sensitive to them in small areas of high wind speed.