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DOE OSTI2022

Impacts, processes and projections of the quasi-biennial oscillation

In the tropical stratosphere, deep layers of eastward and westward winds encircle the globe and descend regularly from the upper stratosphere to the tropical tropopause. With a complete cycle typically lasting almost 2.5 years, this quasi-biennial oscillation (QBO) is arguably the most predictable mode of atmospheric variability that is not linked to the changing seasons. Here, the QBO affects climate phenomena outside the tropical stratosphere, including ozone transport, the North Atlantic Oscillation and the Madden–Julian Oscillation, and its high predictability could enable better forecasts of these phenomena if models can accurately represent the coupling processes. Climate and forecasting models are increasingly able to simulate stratospheric oscillations resembling the QBO, but exhibit common systematic errors such as weak amplitude in the lowermost tropical stratosphere. Uncertainties about the waves that force the oscillation, particularly the momentum fluxes from small-scale gravity waves excited by deep convection, make its simulation challenging. Improved representation of the processes governing the QBO is expected to lead to better forecasts of the oscillation and its impacts, increased understanding of unusual events such as the two QBO disruptions observed since 2016, and more reliable future projections of QBO behaviour under climate change.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Data-Driven Transition Path Analysis Yields a Statistical Understanding of Sudden Stratospheric Warming Events in an Idealized Model

Abstract Atmospheric regime transitions are highly impactful as drivers of extreme weather events, but pose two formidable modeling challenges: predicting the next event (weather forecasting) and characterizing the statistics of events of a given severity (the risk climatology). Each event has a different duration and spatial structure, making it hard to define an objective “average event.” We argue here that transition path theory (TPT), a stochastic process framework, is an appropriate tool for the task. We demonstrate TPT’s capacities on a wave–mean flow model of sudden stratospheric warmings (SSWs) developed by Holton and Mass, which is idealized enough for transparent TPT analysis but complex enough to demonstrate computational scalability. Whereas a recent article (Finkel et al. 2021) studied near-term SSW predictability, the present article uses TPT to link predictability to long-term SSW frequency. This requires not only forecasting forward in time from an initial condition, but also backward in time to assess the probability of the initial conditions themselves. TPT enables one to condition the dynamics on the regime transition occurring, and thus visualize its physical drivers with a vector field called the reactive current . The reactive current shows that before an SSW, dissipation and stochastic forcing drive a slow decay of vortex strength at lower altitudes. The response of upper-level winds is late and sudden, occurring only after the transition is almost complete from a probabilistic point of view. This case study demonstrates that TPT quantities, visualized in a space of physically meaningful variables, can help one understand the dynamics of regime transitions.

Meteorology & Atmospheric Sciences↗

NASA NTRS2021

The impact of Los Angeles Basin pollution and stratospheric intrusions on the surrounding San Gabriel Mountains as seen by surface measurements, lidar, and numerical models

In this work, the impact of Los Angeles Basin pollution transport and stratospheric intrusions on the surface ozone levels observed in the San Gabriel Mountains is investigated based on a combination of surface and lidar measurements as well as WRF-Chem (Weather Research and Forecasting with Chemistry) and WACCM (Whole Atmosphere Community Climate Model) runs. The number of days with observed surface ozone levels exceeding the National Ambient Air Quality Standards exhibit a clear seasonal pattern, with a maximum during summer, when models suggest a minimum influence of stratospheric intrusions and the largest impact from Los Angeles Basin pollution transport. Additionally, measured and modeled surface ozone and PM10 were analyzed as a function of season, time of the day, and wind direction. Measurements and models are in good qualitative agreement, with maximum surface ozone observed for southwest and west winds. For the prevailing summer wind direction, slightly south of the ozone maximum and corresponding to south-southwest winds, lower ozone levels were observed. Back trajectories suggest that this is associated with transport from the central Los Angeles Basin, where titration limits the amount of surface ozone. A quantitative comparison of the lidar profiles with WRF-Chem and WACCM models revealed good agreement near the surface, with models showing an increasing positive bias as function of altitude, reaching 75 % at 15 km above sea level. Finally, three selected case studies covering the different mechanisms affecting the near-surface ozone concentration over the San Gabriel Mountains, namely stratospheric intrusions and pollution transport, are analyzed based on surface and ozone lidar measurements, as well as co-located ceilometer measurements and models.

Impact↗

DOE OSTI2018

Balancing Needs Assessment Using Advanced Probabilistic Forecasts

This paper presents a comprehensive approach to predict Balancing Authority (BA) regulation and load following requirements in order to improve BA control performance. In this paper the Pacific Northwest National Laboratory’s (PNNL) “ramp and uncertainty prediction tool (RUT) and day-ahead regulation prediction (DARP) tool” were upgraded to incorporate advanced probabilistic forecast information provided by AWS Truepower. The proposed methodology has been tested and validated using actual California Independent System Operator (CAISO) data. Simulation confirmed that integration probabilistic forecast information can reduce the predicted regulation range by about 12-31%. This means that BAs can procure fewer balancing resources without compromising their reliability and control performance requirements.

Regulation, Solar Generation, Wind Generation, Con↗

DOE OSTI2023

Classification Analysis of Southwest Pacific Tropical Cyclone Intensity Changes Prior to Landfall

This study evaluates the ability of a random forest classifier to identify tropical cyclone (TC) intensification or weakening prior to landfall over the western region of the Southwest Pacific Ocean (SWPO) basin. For both Australia mainland and SWPO island cases, when a TC first crosses land after spending ≥24 h over the ocean, the closest hour prior to the intersection is considered as the landfall hour. If the maximum wind speed (V max ) at the landfall hour increased or remained the same from the 24-h mark prior to landfall, the TC is labeled as intensifying and if the V max at the landfall hour decreases, the TC is labeled as weakening. Geophysical and aerosol variables closest to the 24 h before landfall hour were collected for each sample. The random forest model with leave-one-out cross validation and the random oversampling example technique was identified as the best-performing classifier for both mainland and island cases. The model identified longitude, initial intensity, and sea skin temperature as the most important variables for the mainland and island landfall classification decisions. Incorrectly classified cases from the test data were analyzed by sorting the cases by their initial intensity hour, landfall hour, monthly distribution, and 24-h intensity changes. TC intensity changes near land strongly impact coastal preparations such as wind damage and flood damage mitigations; hence, this study will contribute to improve identifying and prioritizing prediction of important variables contributing to TC intensity change before landfall.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2023

ICARUS: in-Situ Studies of the Solar Corona Beyond Parker Solar Probe and Solar Orbiter

The primary scientific goal of ICARUS (Investigation of Coronal AcceleRation and heating of solar wind Up to the Sun), a mother-daughter satellite mission, proposed in response to the ESA “Voyage 2050” Call, will be to determine how the magnetic field and plasma dynamics in the outer solar atmosphere give rise to the corona, the solar wind, and the entire heliosphere. Reaching this goal will be a Rosetta Stone step, with results that are broadly applicable within the fields of space plasma physics and astrophysics. Within ESA’s Cosmic Vision roadmap, these science goals address Theme 2: “How does the Solar System work?” by investigating basic processes occurring “From the Sun to the edge of the Solar System”. ICARUS will not only advance our understanding of the plasma environment around our Sun, but also of the numerous magnetically active stars with hot plasma coronae. ICARUS I will perform the first direct in situ measurements of electromagnetic fields, particle acceleration, wave activity, energy distribution, and flows directly in the regions in which the solar wind emerges from the coronal plasma. ICARUS I will have a perihelion altitude of 1 solar radius and will cross the region where the major energy deposition occurs. The polar orbit of ICARUS I will enable crossing the regions where both the fast and slow winds are generated. It will probe the local characteristics of the plasma and provide unique information about the physical processes involved in the creation of the solar wind. ICARUS II will observe this region using remote-sensing instruments, providing simultaneous, contextual information about regions crossed by ICARUS I and the solar atmosphere below as observed by solar telescopes. It will thus provide bridges for understanding the magnetic links between the heliosphere and the solar atmosphere. Such information is crucial to our understanding of the plasma physics and electrodynamics of the solar atmosphere. ICARUS II will also play a very important relay role, enabling the radio-link with ICARUS I. It will receive, collect, and store information transmitted from ICARUS I during its closest approach to the Sun. It will also perform preliminary data processing before transmitting it to Earth. Performing such unique in situ observations in the area where presumably hazardous solar energetic particles are energized, ICARUS will provide fundamental advances in our capabilities to monitor and forecast the space radiation environment. Therefore, the results from the ICARUS mission will be extremely crucial for future space explorations, especially for long-term crewed space missions.

Solar wind↗

NASA NTRS1989

A case study using kinematic quantities derived from a triangle of VHF Doppler wind profilers

Horizontal divergence, relative vorticity, kinematic vertical velocity, and geostrophic and ageostrophic winds are computed from Colorado profiler network data to investigate an upslope snowstorm in northeastern Colorado. Horizontal divergence and relative vorticity are computed using the Gauss and Stokes theorems, respectively. Kinematic vertical velocities are obtained from the surface to 9 km by vertically integrating the continuity equation. The geostrophic and ageostrophic winds are computed by applying a finite differencing technique to evaluate the derivatives in the horizontal equations of motion. Comparison of the synoptic-scale data with the profiler network data reveals that the two datasets are generally consistent. Also, the profiler-derived quantities exhibit coherent vertical and temporal patterns consistent with conceptual and theoretical flow fields of various meteorological phenomena. It is suggested that the profiler-derived quantities are of potential use to weather forecasters in that they enable the dynamic and kinematic interpretation of weather system structure to be made and thus have nowcasting and short-term forecasting value.

Carlson, Catherine A.↗

NASA NTRS2019

Recent Antarctic Sea Ice Extent Variability and Its Relation to Atmospheric Circulation in Reanalyses and Seasonal Forecasts

Sea ice is considered an important indicator of climate change. Interest has focused on the trend towards reduced sea ice cover in the Arctic Ocean, particularly during summer months. In the Southern Hemisphere, sea ice has also received attention due to a trend towards increasing cover, in opposition to the Northern Hemisphere and to climate model projections. This all changed in the austral spring 2016, when the Southern Ocean sea ice cover dropped dramatically, and has remained consistently at below average values up to the present. Here I review some of the issues and perceived causes of the 2016 sea ice reduction. Several studies have suggested a change in surface winds, which would allow warmer waters to move upwards in the water column and towards the surface. Results from the GMAO (Global Modeling and Assimilation Office) ocean data assimilation system are consistent with this hypothesis, and indicate a significant warming of subsurface waters occurred in the winter of 2016, and has remained in place.

Polar↗

DOE OSTI2018

Predicting Large Ocean Wave Events Characterized by Bimodal Energy Spectra in the Presence of a Low-Level Southerly Wind Feature

Abstract Three large wave events are simulated with WaveWatch III using different wind inputs and physics packages. The modeled output, including spectral shape and bulk parameter time series, are compared with National Data Buoy Center buoy observations offshore of Newport, Oregon. The atmospheric conditions that generate these large waves include a strong southerly wind along with a distant cyclone. The energetic contributions of these simultaneously occurring atmospheric features result in a wave field characterized by bimodal energy spectra for two events and unimodal energy spectra for the third event. The analysis of model output evaluates bulk parameter time series of significant wave height, mean period, and mean wave direction derived from partitioned energy spectra. A consistent underestimation in wave energy approaching from the southwestern direction is found for the output associated with all model configurations. This wave energy is generated by the southerly wind. An overestimation in swell energy approaching from the northwest is also found for all model configurations. The model configuration that most accurately reproduces the southerly wave energy results in the best performance for the overall bulk parameters.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Harnessing stratospheric diffusion barriers for enhanced climate geoengineering

Stratospheric sulfate aerosol geoengineering is a proposed method to temporarily intervene in the climate system to increase the reflectance of shortwave radiation and reduce mean global temperature. In previous climate modeling studies, choosing injection locations for geoengineering aerosols has, thus far, only utilized the average dynamics of stratospheric wind fields instead of accounting for the essential role of time-varying material transport barriers in turbulent atmospheric flows. Here we conduct the first analysis of sulfate aerosol dispersion in the stratosphere, comparing what is now a standard fixed-injection scheme with time-varying injection locations that harness short-term stratospheric diffusion barriers. We show how diffusive transport barriers can quickly be identified, and we provide an automated injection location selection algorithm using short forecast and reanalysis data. Within the first 7 d days of transport, the dynamics-based approach is able to produce particle distributions with greater global coverage than fixed-site methods with fewer injections. Additionally, this enhanced dispersion slows aerosol microphysical growth and can reduce the effective radii of aerosols up to 200–300 d after injection. While the long-term dynamics of aerosol dispersion are accurately predicted with transport barriers calculated from short forecasts, the long-term influence on radiative forcing is more difficult to predict and warrants deeper investigation. Statistically significant changes in radiative forcing at timescales beyond the forecasting window showed mixed results, potentially increasing or decreasing forcing after 1 year when compared to fixed injections. We conclude that future feasibility studies of geoengineering should consider the cooling benefits possible by strategically injecting sulfate aerosols at optimized time-varying locations. Our method of utilizing time-varying attracting and repelling structures shows great promise for identifying optimal dispersion locations, and radiative forcing impacts can be improved by considering additional meteorological variables.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1999

Clouds in ECMWF's 30 KM Resolution Global Atmospheric Forecast Model (TL639)

Global models of the general circulation of the atmosphere resolve a wide range of length scales, and in particular cloud structures extend from planetary scales to the smallest scales resolvable, now down to 30 km in state-of-the-art models. Even the highest resolution models do not resolve small-scale cloud phenomena seen, for example, in Landsat and other high-resolution satellite images of clouds. Unresolved small-scale disturbances often grow into larger ones through non-linear processes that transfer energy upscale. Understanding upscale cascades is of crucial importance in predicting current weather, and in parameterizing cloud-radiative processes that control long term climate. Several movie animations provide examples of the temporal and spatial variation of cloud fields produced in 4-day runs of the forecast model at the European Centre for Medium-Range Weather Forecasts (ECMWF) in Reading, England, at particular times and locations of simultaneous measurement field campaigns. model resolution is approximately 30 km horizontally (triangular truncation TL639) with 31 vertical levels from surface to stratosphere. Timestep of the model is about 10 minutes, but animation frames are 3 hours apart, at timesteps when the radiation is computed. The animations were prepared from an archive of several 4-day runs at the highest available model resolution, and archived at ECMWF. Cloud, wind and temperature fields in an approximately 1000 km X 1000 km box were retrieved from the archive, then approximately 60 Mb Vis5d files were prepared with the help of Graeme Kelly of ECMWF, and were compressed into MPEG files each less than 3 Mb. We discuss the interaction of clouds and radiation in the model, and compare the variability of cloud liquid as a function of scale to that seen in cloud observations made in intensive field campaigns. Comparison of high-resolution global runs to cloud-resolving models, and to lower resolution climate models is leading to better understanding of the upscale cascade and suggesting new cloud-radiation parameterizations for climate models.

Cahalan, R. F.↗

NASA NTRSDate not supplied

Evaluating Meteorological Dust Events and Machine-Learning Based Dust Identification in Geostationary Satellite Imagery

NASA scientists in the Short-term Prediction Research and Transition Center (SPoRT) developed a physically-based machine learning approach to identify dust in satellite imagery with a focus on night-time dust detection (Berndt et al. 201; DustTracker-AI). NASA/NOAA Geostationary Environmental Operational Satellite-16 (GOES-16) imagery was used for training and model inputs. The training, testing and validation data set consists of 28 events in the Southwest United States, capturing dust and null events in the region from 2018-2020.With 83 distinct images and millions of pixels a random forest model was trained and validated, correctly labeling 85% of dust pixels.For the first time, the model was run in near-real time production during the spring of 2022 and dust probability visualizations were made available to NOAA National Weather Service (NWS) forecasters to assess its utility for dust forecasting. Results indicated the model helped increase the confidence in the presence of dust and enabled dust tracking for a longer period of time into the night-time hours. Forecaster assessment and running the model in near real-time allowed for the team to determine the types of events missed, captured, and false alarms. To gain additional context on model performance,the SPoRT team sought to gather more detailed information on the training database(e.g., meteorological characteristics and drivers). The goal of this project was to identify the meteorological drivers for the dust events and create a database which synthesized information from observations, forecaster discussions, and analyses pertaining to the dust events to understand the types of events currently used to train the model. A more detailed meteorological synopsis was created for each dust event in the training, testing, and validation datasets. Following the completion of the database and documentation, the classification details revealed that 88% of the dust events were synoptically driven while mesoscale events were less prevalent in model datasets. Meteorological conditions found such as mixing layer depth and wind velocity had mean values of 645mb and 21kt respectively.With conditions of deep mixed layers and moderate to strong surface winds a mesoscale thunderstorm outflow event was considered and subsequently added to the model training data set to test the impact of additional mesoscale training data. The model was retrained and then qualitatively tested on a sample thunderstorm outflow case that the original model was unable to identify. Preliminary results showed potential that the addition of more mesoscale events included in the training data could help to better identify indistinct and localized dust events.

Connor Welch↗

NASA NTRS2021

Grand Teton Ecological Forecasting: Assessing Forage Change and Winter Habitat Availability for Bighorn Sheep that Employ a High-Elevation Overwintering Strategy to Identify Areas for Intervention

Grand Teton National Park provides habitat for a small native population of approximately 125 bighorn sheep (Ovis canadensis). The reduction in population of this species is attributed to loss of low elevation habitat, changing local environmental and climatic conditions, and increased disturbance from backcountry recreation. In response to these changes, this population of sheep employs a unique high-elevation wintering strategy in which they amass large fat stores in the summer and expend as little energy as possible in the winter while foraging on high elevation wind-swept and snow-free areas. For this project, DEVELOP partnered with Grand Teton National Park and used NASA Earth observations including Landsat 8 Operational Land Imager (OLI), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 5 Thematic Mapper (TM), and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover data to assess habitat suitability. Landcover change analyzed between 1987 and 2020 indicated shifting trends in grass and forb cover and tree cover. These trends persisted in the 2031 prediction, with grass cover decreasing and tree cover increasing, which translates to a loss of overall favorable habitat for bighorn sheep. Snow cover analyzed between 2001 and 2020 indicated similar unfavorable trends for the sheep with a decrease in barren areas, important for winter foraging. Finally, habitat suitability was modeled for 2020 and predicted to 2031 to determine habitat gains and losses for this species across the landscape. Overall, the results predicted decreases in suitable habitat and indicated that while these sheep are highly adaptable, strategies to manage suitable bighorn habitat may need to be employed rapidly to effectively conserve this species.

Remote sensing↗

NASA NTRS2001

The Computational Complexity, Parallel Scalability, and Performance of Atmospheric Data Assimilation Algorithms

The computational complexity of algorithms for Four Dimensional Data Assimilation (4DDA) at NASA's Data Assimilation Office (DAO) is discussed. In 4DDA, observations are assimilated with the output of a dynamical model to generate best-estimates of the states of the system. It is thus a mapping problem, whereby scattered observations are converted into regular accurate maps of wind, temperature, moisture and other variables. The DAO is developing and using 4DDA algorithms that provide these datasets, or analyses, in support of Earth System Science research. Two large-scale algorithms are discussed. The first approach, the Goddard Earth Observing System Data Assimilation System (GEOS DAS), uses an atmospheric general circulation model (GCM) and an observation-space based analysis system, the Physical-space Statistical Analysis System (PSAS). GEOS DAS is very similar to global meteorological weather forecasting data assimilation systems, but is used at NASA for climate research. Systems of this size typically run at between 1 and 20 gigaflop/s. The second approach, the Kalman filter, uses a more consistent algorithm to determine the forecast error covariance matrix than does GEOS DAS. For atmospheric assimilation, the gridded dynamical fields typically have More than 10(exp 6) variables, therefore the full error covariance matrix may be in excess of a teraword. For the Kalman filter this problem can easily scale to petaflop/s proportions. We discuss the computational complexity of GEOS DAS and our implementation of the Kalman filter. We also discuss and quantify some of the technical issues and limitations in developing efficient, in terms of wall clock time, and scalable parallel implementations of the algorithms.

Lyster, Peter M.↗

NASA NTRS2011

Effects of Spatial Resolution on the Simulated Dust Aerosol Lifecycle: Implications for Dust Event Magnitude and Timing in the NASA GEOS-5 AGCM

The NASA GEOS-5 atmospheric transport model simulates global aerosol distributions with an online aerosol module. GEOS-5 may be run at various horizontal spatial resolutions depending on the research application. For example, long integration climate simulations are typically run at 2 deg or 1 deg grid spacing, whereas aerosol reanalysis and forecasting applications may be performed at O.5 deg or 0.25 deg resolutions. In this study, we assess the implications of varying spatial resolution on the simulated aerosol fields, with a particular focus on dust. Dust emissions in GEOS-5 are calculated with one of two parameterizations, one based on the Goddard Chemistry, Aerosol, Radiation, and Transport (GO CART) model and another based on the Dust Entrainment and Deposition (DEAD) model. Emission fluxes are parameterized in terms of the surface wind speed, either the 10-m (GO CART) or friction (DEAD) wind speed. We consider how surface wind speeds and thus the dust emission rates are a function of the model spatial resolution. We find that spatial resolution has a significant effect on the magnitude of dust emissions, as higher resolution versions of the model have typically higher surface wind speeds. Utilizing space-borne observations from MISR, MODIS, and CALIOP, we find that simulated Aerosol Optical Thickness (AOT) distributions respond differently to spatial resolution over the African and Asian source regions, highlighting the need to regional dust emission tuning. When compared to ground-based observations from AERONET, we found improved timing of dust events with as spatial resolution was increased. In an attempt to improve the representation of the dust aerosol lifecycle at coarse resolutions, we found that incorporating the effects of sub-grid wind variability in a course resolution simulation led to improved agreement with observed AOT magnitudes, but did not impact the timing of simulated dust events.

Nowottnick, E.↗

NASA NTRS2004

Nowcasting Thunderstorm Anvil Clouds Over KSC/CCAFS

Electrified thunderstorm anvil clouds extend the threat of natural and triggered lightning to space launch and landing operations far beyond the immediate vicinity of thunderstorm cells. The deep convective updrafts of thunderstorms transport large amounts of water vapor, super-cooled water droplets and ice crystals into the upper troposphere, forming anvil clouds, which are then carried downstream by the prevailing winds in the anvil formation layer. Electrified anvil clouds have been observed over the space launch and landing facilities of Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS), emanating from thunderstorm activity more than 200 km distant. Space launch commit criteria and flight rules require launch and landing vehicles to avoid penetration of the non-transparent portion of anvil clouds. The life cycles of 167 anvil clouds over the Florida peninsula and its coastal waters were documented using GOES-8 visible imagery on 50 anvil case days during the months of May through July 2001. Anvil clouds were found to propagate at the speed and direction of upper-tropospheric winds in the layer from 300-to-l50 mb, approximately 9.4 km to 14 km, with an effective average transport lifetime of 2 hours and a standard deviation of approximately 30 minutes. The effective lifetime refers to the time required for the nontransparent leading edge of an anvil cloud to reach its maximum extent before beginning to dissipate. The propagation and lifetime information was incorporated into the design, construction and implementation of an objective short-range anvil forecast tool based on upper-air observations, for use on the Meteorological Interactive Data Display System within the Range Weather Operations facility of the 45th Weather Squadron at CCAFS and the Spaceflight Meteorology Group at Johnson Space Center.

Short, David A.↗

NASA NTRS2012

Experimental Validation of a Forward Looking Interferometer for Detection of Clear Air Turbulence due to Mountain Waves

The Forward-Looking Interferometer (FLI) is an airborne sensor concept for detection and estimation of potential atmospheric hazards to aircraft. The FLI concept is based on high-resolution Infrared Fourier Transform Spectrometry technologies that have been developed for satellite remote sensing. The FLI is being evaluated for its potential to address multiple hazards, during all phases of flight, including clear air turbulence, volcanic ash, wake vortices, low slant range visibility, dry wind shear, and icing. In addition, the FLI is being evaluated for its potential to detect hazardous runway conditions during landing, such as wet or icy asphalt or concrete. The validation of model-based instrument and hazard simulation results is accomplished by comparing predicted performance against empirical data. In the mountain lee wave data collected in the previous FLI project, the data showed a damped, periodic mountain wave structure. The wave data itself will be of use in forecast and nowcast turbulence products such as the Graphical Turbulence Guidance and Graphical Turbulence Guidance Nowcast products. Determining how turbulence hazard estimates can be derived from FLI measurements will require further investigation.

Schaffner, Philip R.↗

DOE OSTI2021

Effects of spatial resolution on WRF v3.8.1 simulated meteorology over the central Himalaya

The sensitive ecosystem of the central Himalayan (CH) region, which is experiencing enhanced stress from anthropogenic forcing, requires adequate atmospheric observations and an improved representation of the Himalaya in the models. However, the accuracy of atmospheric models remains limited in this region due to highly complex mountainous topography. This article delineates the effects of spatial resolution on the modeled meteorology and dynamics over the CH by utilizing the Weather Research and Forecasting (WRF) model extensively evaluated against the Ganges Valley Aerosol Experiment (GVAX) observations during the summer monsoon. The WRF simulation is performed over a domain (d01) encompassing northern India at 15 km x 15 km resolution and two nests (d02 at 5 km x 5 km and d03 at 1 km x 1 km) centered over the CH, with boundary conditions from the respective parent domains. WRF simulations reveal higher variability in meteorology, e.g., relative humidity (RH = 70.3%-96.1%) and wind speed (WS = 1.1-4.2m s(-1)), compared to the ERA-Interim reanalysis (RH D 80.0%-85.0%, WS D 1.2-2.3m s(-1)) over northern India owing to the higher resolution. WRF-simulated temporal evolution of meteorological variables is found to agree with balloon-borne measurements, with stronger correlations aloft (r = 0.44-0.92) than those in the lower troposphere (r = 0.18-0.48). The model overestimates temperature (warm bias by 2.8 degrees C) and underestimates RH (dry bias by 6.4 %) at the surface in d01. Model results show a significant improvement in d03 (P = 827.6 hPa, T = 19.8 degrees C, RH = 92.3 %), closer to the GVAX observations (P = 801.4 hPa, T = 19.5 degrees C, RH = 94.7 %). Interpolating the output from the coarser domains (d01, d02) to the altitude of the station reduces the biases in pressure and temperature; however, it suppresses the diurnal variations, highlighting the importance of well-resolved terrain (d03). Temporal variations in near-surface P, T, and RH are also reproduced by WRF in d03 to an extent (r>0:5). A sensitivity simulation incorporating the feedback from the nested domain demonstrates the improvement in simulated P, T, and RH over the CH. Our study shows that the WRF model setup at finer spatial resolution can significantly reduce the biases in simulated meteorology, and such an improved representation of the CH can be adopted through domain feedback into regional-scale simulations. Interestingly, WRF simulates a dominant easterly wind component at 1 km x 1 km resolution (d03), which is missing in the coarse simulations; however, the frequency of southeasterlies remains underestimated. The model simulation implementing a highresolution (3 s) topography input (SRTM) improved the prediction of wind directions; nevertheless, further improvements are required to better reproduce the observed local-scale dynamics over the CH.

Singh, Jaydeep↗