Abstract

This study examines the impact of assimilating multitime wind profiles over a single station on the numerical simulation of a warm season mesoscale convective system over the region from the Kansas and Oklahoma border to the Texas Panhandle, observed 12–13 June 2002 during the International H₂O Project (IHOP_2002). Wind profile observations, obtained from Goddard Lidar Observatory for Winds (GLOW) are assimilated into an advanced research version of the Weather Research and Forecasting (WRF) model using its four-dimensional variational data assimilation (4DVAR) system. Results indicate that the assimilation of high temporal and vertical resolution GLOW wind profiles has a significant influence on the numerical simulation of the convective initiation and evolution. Besides the wind fields, the structure of the moisture fields associated with the convective system is also improved. Data assimilation has also resulted in a more accurate prediction of the locations and timing of the convection initiations; as a consequence, the skill of quantitative precipitation forecasting is enhanced greatly.

The positive impact of 4DVAR assimilation of multitime wind profiles over a single station on the mesoscale prediction in this study presents a successful procession of the traditional technique in time to space conversion. However, when the data from conventional networks are assimilated into the model with GLOW wind profiles, the data impact is not compatible with that from the assimilation of GLOW wind profiles only, implying the need for a high temporal and spatial resolution wind profile network in order to achieve reasonable mesoscale analysis and forecasting.

Abstract

All-sky assimilation of brightness temperatures (BTs) from GOES-16 infrared water vapor channels is challenging, primarily because these channels are sensitive to cloud ice that causes large nonlinear errors in the forecast and forward models. Thus, bias correction (BC) for all-sky assimilation of GOES-16 BTs is vital. This study examines the impacts of different BC schemes, especially for a scheme with a quartic polynomial of cloud predictors (the ASRBC4 scheme), on the analysis and WRF Model forecasts of tropical cyclones when assimilating the all-sky GOES-16 channel-8 BTs using the NCEP GSI-based 3D ensemble–variational hybrid data assimilation (DA) system with variational BC (VarBC). Long-term statistics are performed during the NASA Convective Processes Experiment field campaign (2017). Results demonstrate that the ASRBC4 scheme effectively reduces the average of all-sky scaled observation-minus-backgrounds (OmBs) in a cloudy sky and alleviates their nonlinear conditional biases with respect to the symmetric cloud proxy variable, in contrast to the BC schemes without the cloud predictor or with a first-order cloud predictor. In addition, adopting the ASRBC4 scheme in DA decreases the positive temperature increments at 200 hPa and the accompanying midlevel cyclonic wind increments in the analysis of Tropical Storm (TS) Cindy (2017). Applying the ASRBC4 scheme also leads to better storm-track predictions for TS Cindy (2017) and Hurricane Laura (2022), compared to experiments with other BC schemes. Overall, this study highlights the importance of reducing nonlinear biases of OmBs in a cloudy sky for successful all-sky assimilation of BTs from GOES-16 infrared water vapor channels.

Abstract

Low-level jets (LLJs) are associated with 10%–45% of the summer precipitation in the U.S. Great Plains region (GPR). This study uses the NCEP North American Regional Reanalysis data product (1979–2017) to characterize the association between LLJs and precipitation extremes (anomalously wet versus dry) during the summer months (June–August) over the GPR. It is found that the number, distribution, and direction of LLJs are not clearly associated with the precipitation anomalies. The characteristics and structural variations of the LLJs and their large-scale and mesoscale environment are then examined to identify the links between LLJs and precipitation extremes. Results show that dry and wet summers vary by synoptic anomaly patterns. During dry summers the anomalous ridging results in a warmer and drier environment, primarily through subsidence, which inhibits precipitation near LLJs. In contrast, during wet summers, a reduction in subsidence occurs, resulting in stronger lift and a cooler and moister environment, which leads to enhanced precipitation near LLJs. The LLJ speed, orientation, and spatial properties vary according to the synoptic anomaly patterns. LLJs do not drive precipitation extremes, but instead, they respond to them. Specifically, the LLJ exit region is characterized by stronger baroclinity and higher moisture content during the wet years. The higher moisture content allows for ascending air parcels to reach saturation more quickly, while the stronger baroclinity increases the warm advection associated with the LLJ. This, in turn, leads to faster rising motion and is therefore closely associated with the location and intensity of the LLJ associated precipitation.

Abstract

The sensitivity of numerical simulations of the genesis of Typhoon Nuri (2008) to initial conditions is examined using the Advanced Research core of the Weather Research and Forecasting (WRF) Model. The initial and boundary conditions are derived from two different global analyses at different lead times. One simulation successfully captures the processes of Nuri’s genesis and early intensification, whereas other simulations fail to predict the genesis of Nuri. Discrepancies between simulations with and without Nuri’s development are diagnosed. Significant differences are found in the development and organization of the intense convection during Nuri’s pregenesis phase. In the developing case, convection evolves and organizes into a “pouch” center of a westward-propagating wavelike disturbance. In the nondeveloping case, the convection fails to develop and organize. Favorable conditions for the development of deep convection include strong closed circulation patterns with high humidity, especially at the middle levels. An additional set of sensitivity experiments is performed to examine the impact of the moisture field on numerical simulations of Nuri’s genesis. Results confirm that the enhancement of mid- to upper-level moisture is favorable for Nuri’s genesis, mainly because moist conditions benefit deep convection, which produces diabatic heating from latent heat release when vertical airmass flux maxima occur in the mid- to upper-level atmosphere. The substantial warming at upper levels induced by latent heat release from persistent deep convection contributes to the drop in Nuri’s minimum central sea level pressure. Overall, results from this study demonstrate that it is essential to accurately represent the initial conditions in numerical predictions of tropical cyclone genesis.

Abstract

As a result of rapid changes in surface conditions when a landfalling hurricane moves from ocean to land, interactions between the hurricane and surface heat and moisture fluxes become essential components of its evolution and dissipation. With a research version of the Hurricane Weather Research and Forecasting Model (HWRF), this study examines the effects of the vertical eddy diffusivity in the boundary layer on the evolution of three landfalling hurricanes (Dennis, Katrina, and Rita in 2005).

Specifically, the parameterization scheme of eddy diffusivity for momentum K _m is adjusted with the modification of the mixed-layer velocity scale in HWRF for both stable and unstable conditions. Results show that the change in the K _m parameter leads to improved simulations of hurricane track, intensity, and quantitative precipitation against observations during and after landfall, compared to the simulations with the original K _m .

Further diagnosis shows that, compared to original K _m , the modified K _m produces stronger vertical mixing in the hurricane boundary layer over land, which tends to stabilize the hurricane boundary layer. Consequently, the simulated landfalling hurricanes attenuate effectively with the modified K _m , while they mostly inherit their characteristics over the ocean and decay inefficiently with the original K _m .

Abstract

A series of numerical experiments are conducted to examine the impact of surface observations on the prediction of landfalls of Hurricane Katrina (2005), one of the deadliest disasters in U.S. history. A specific initial time (0000 UTC 25 August 2005), which led to poor prediction of Hurricane Katrina in several previous studies, is selected to begin data assimilation experiments. Quick Scatterometer (QuikSCAT) ocean surface wind vectors and surface mesonet observations are assimilated with the minimum central sea level pressure and conventional observations from NCEP into an Advanced Research version of the Weather Research and Forecasting Model (WRF) using an ensemble Kalman filter method. Impacts of data assimilation on the analyses and forecasts of Katrina’s track, landfalling time and location, intensity, structure, and rainfall are evaluated. It is found that the assimilation of QuikSCAT and mesonet surface observations can improve prediction of the hurricane track and structure through modifying low-level thermal and dynamical fields such as wind, humidity, and temperature and enhancing low-level convergence and vorticity. However, assimilation of single-level surface observations alone does not ensure reasonable intensity forecasts because of the lack of constraint on the mid- to upper troposphere. When surface observations are assimilated with other conventional data, obvious enhancements are found in the forecasts of track and intensity, realistic convection, and surface wind structures. More importantly, surface data assimilation results in significant improvements in quantitative precipitation forecasts (QPFs) during landfalls.

Abstract

An advanced research version of the Weather Research and Forecasting (ARW) Model is used to simulate the early rapid intensification of Hurricane Emily (2005) using grids nested to high resolution (3 km). A series of numerical simulations is conducted to examine the sensitivity of the simulation to available cloud microphysical (CM) and planetary boundary layer (PBL) parameterization schemes. Results indicate that the numerical simulations of the early rapid intensification of Hurricane Emily are very sensitive to the choice of CM and PBL schemes in the ARW model. Specifically, with different CM schemes, the simulated minimum central sea level pressure (MSLP) varies by up to 29 hPa, and the use of various PBL schemes has resulted in differences in the simulated MSLP of up to 19 hPa during the 30-h forecast period. Physical processes associated with the above sensitivities are investigated. It is found that the magnitude of the environmental vertical wind shear is not well correlated with simulated hurricane intensities. In contrast, the eyewall convective heating distributions and the latent heat flux and high equivalent potential temperature (θ_e ) feeding from the ocean surface are directly associated with the simulated intensities. Consistent with recognized facts, higher latent heat release in stronger eyewall convection, stronger surface energy, and high θ_e air feeding from the ocean surface into the hurricane eyewall are evident in the more enhanced convection and intense storms. The sensitivity studies in this paper also indicate that the contributions from the CM and PBL processes can only partially explain the slow intensification in the ARW simulations. Simulation at 1-km grid resolution shows a slight improvement in Emily’s intensity forecast, implying that the higher resolution is somewhat helpful, but still not enough to cause the model to reproduce the real intensity of the hurricane.

Abstract

Despite the great importance of interactions between moisture, clouds, radiation, and convection in the Madden–Julian oscillation, their role in the boreal summer intraseasonal oscillation (BSISO) has not been well established. This study investigates the moisture variation of a BSISO during its rapid redevelopment over the eastern Maritime Continent through a cloud-permitting-scale numerical simulation. It is found that moisture variation depends closely on the evolution of clouds and precipitation. Total moisture budget analysis reveals that the deepening and strengthening (lessening) of humidity before (after) the BSISO deep convection are attributed largely to zonal advection. In addition, the column moistening/drying is mostly in phase with the humidity and is related to the maintenance of BSISO. An objective cloud-type classification method and a weak temperature gradient approximation are used to further understand the column moistening/drying. Results indicate that elevated stratiform clouds play a significant role in moistening the lower troposphere through cloud water evaporation. Decreases in deep convection condensation and reevaporation of deep stratiform precipitation induce moistening during the development and after the decay of BSISO deep convection, respectively. Meanwhile, anomalous longwave radiative heating appears first in the lower troposphere during the developing stage of BSISO, further strengthens via the increase of deep stratiform clouds, and eventually deepens with elevated stratiform clouds. Accordingly, anomalous moistening largely in phase with the humidity of BSISO toward its suppressed stage is induced via compensated ascent. Owing to the anomalous decrease in the ratio of vertical moisture and potential temperature gradients, the cloud–radiation effect is further enhanced in the convective phase of BSISO.

Abstract

Strongly coupled land–atmosphere data assimilation has not yet been implemented into operational numerical weather prediction (NWP) systems. Up to now, upper-air measurements have been assimilated mainly in atmospheric analyses, while land and near-surface data have been assimilated mainly into land surface models. Thus, this study aims to explore the benefits of assimilating atmospheric and land surface observations within the framework of strongly coupled data assimilation. Specifically, we added soil moisture as a control state within the ensemble Kalman filter (EnKF)-based Gridpoint Statistical Interpolation (GSI) and conducted a series of numerical experiments through the assimilation of 2-m temperature/humidity and in situ surface soil moisture data along with conventional atmospheric measurements such as radiosondes into the Weather Research and Forecasting (WRF) Model with the Noah land surface model. The verification against in situ measurements and analyses show that compared to the assimilation of conventional data, adding soil moisture as a control state and assimilating 2-m humidity can bring additional benefits to analyses and forecasts. The impact of assimilating 2-m temperature (surface soil moisture) data is positive mainly on the temperature (soil moisture) analyses but on average marginal for other variables. On average, below 750 hPa, verification against the NCEP analysis indicates that the respective RMSE reduction in the forecasts of temperature and humidity is 5% and 2% for assimilating conventional data; 10% and 5% for including soil moisture as a control state; and 16% and 11% for simultaneously adding soil moisture as a control state and assimilating 2-m humidity data.

Abstract

This study examines the sensitivity of numerical simulations of near-surface atmospheric conditions to the initial surface albedo and snow depth during an observed ice fog event in the Heber Valley of northern Utah. Numerical simulation results from the mesoscale community Weather Research and Forecasting (WRF) Model are compared with observations from the Mountain Terrain Atmospheric Modeling and Observations (MATERHORN) Program fog field program. It is found that near-surface cooling during the nighttime is significantly underestimated by the WRF Model, resulting in the failure of the model to reproduce the observed fog episode. Meanwhile, the model also overestimates the temperature during the daytime. Nevertheless, these errors could be reduced by increasing the initial surface albedo and snow depth, which act to cool the near-surface atmosphere by increasing the reflection of downward shortwave radiation and decreasing the heating effects from the soil layer. Overall results indicate the important effects of snow representation on the simulation of near-surface atmospheric conditions and highlight the need for snow measurements in the cold season for improved model physics parameterizations.