Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “ocean wind”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 325 records · Page 18

The impacts of horizontal resolution on the seasonally-dependent biases of the northeastern Pacific ITCZ in coupled climate models.

Double-ITCZ has puzzled climate model community for more than two decades. Here, we found that over the eastern Pacific, precipitation and sea surface temperature (SST) biases in the NCAR CESM1 are seasonally-dependent, with positive (negative) precipitation/SST biases during boreal summer/fall (winter/spring), although the easterly wind bias persists around the year. This seasonally-dependent bias of precipitation/SST is found to be caused by the failure of the model to reproduce the climatological seasonal wind reversal of North American monsoon. During winter/spring, the climatological easterly wind dominates, so the stronger wind speed in the model enhances the evaporation and lowers the SST. It is opposite when the climatological wind turns to westerly during summer/fall. The easterly wind bias also occurs in the atmospheric model when the observation SST is prescribed, suggesting it is of atmospheric origin. Further, the easterly wind bias is mainly evident in the lower troposphere and quite independent of time, implying the effect of complex and narrow-ranged Central American topography, which is not depicted well in the coarse model resolution. When the model resolution is doubled, both SST and precipitation are improved with the reduced easterly wind bias. During boreal spring, when ITCZ bias is most significant, the northern and southern ITCZ can be improved by 29% and 18.8% respectively in the higher resolution. Finally, we suggest that these seasonally-dependent biases over the eastern Pacific are quite universal among 37 CMIP5 coupled models, and the ongoing CMIP6 project will provide a valuable opportunity to examine whether the bias can be reduced by increasing model resolution.

Song, Fengfei↗

Thermodynamic and kinematic drivers of atmospheric boundary layer stability in the central Arctic during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC)

Observations collected during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) provide a detailed description of the impact of thermodynamic and kinematic forcings on atmospheric boundary layer (ABL) stability in the central Arctic. This study reveals that the Arctic ABL is stable and near-neutral with similar frequencies, and strong stability is the most persistent of all stability regimes. MOSAiC radiosonde observations, in conjunction with observations from additional measurement platforms, including a 10 m meteorological tower, ceilometer, microwave radiometer, and radiation station, provide insight into the relationships between atmospheric stability and various atmospheric thermodynamic and kinematic forcings of ABL turbulence and how these relationships differ by season. We found that stronger stability largely occurs in low-wind (i.e., wind speeds are slow), low-radiation (i.e., surface radiative fluxes are minimal) environments; a very shallow mixed ABL forms in low-wind, high-radiation environments; weak stability occurs in high-wind, moderate-radiation environments; and a near-neutral ABL forms in high-wind, high-radiation environments. Surface pressure (a proxy for synoptic staging) partially explains the observed wind speeds for different stability regimes. Cloud frequency and atmospheric moisture contribute to the observed surface radiation budget. Unique to summer, stronger stability may also form when moist air is advected from over the warmer open ocean to over the colder sea ice surface, which decouples the colder near-surface atmosphere from the advected layer, and is identifiable through observations of fog and atmospheric moisture.

54 ENVIRONMENTAL SCIENCES↗

An Observational Comparison of Level of Neutral Buoyancy and Level of Maximum Detrainment in Tropical Deep Convective Clouds

Tropical deep convective clouds are important drivers of large-scale atmospheric circulation representing the main vertical transport pathway through the depth of the troposphere for heat, momentum, water, and chemical species. The strength and depth of this transport are impacted by the convective updraft size and intensity that are driven by buoyancy, dynamical forcing, and mixing of environmental air, i.e., entrainment. In this paper, we identify tropical deep convective systems with well-defined forward anvils using Atmospheric Radiation Measurement (ARM) ground-based profiling radars, at three ARM fixed-sites in the Tropical Western Pacific (TWP; i.e., Manus, Nauru, Darwin) and three ARM Mobile Facility deployments in Niamey, Niger; Gan Island, Maldives; and Manacapuru, Brazil. We use the difference between the level of neutral buoyancy (LNB) and the level of maximum detrainment (LMD) as a proxy for the effective bulk convective entrainment (εproxy). The LNB, the theoretical height that a parcel raised above the level of free convection would reach with no mixing, is calculated based on pre-convection radiosonde measurements using parcel theory. The LMD is the height of the maximum reflectivity observed in forward anvil clouds by profiling radars. Deep convective systems over the TWP show higher LNBs that extend to 16.3 km on average and larger εproxy (median LNB minus LMD up to 6.5 km) compared to their continental counterparts in the Amazon and West Africa. Oceanic conditions show larger convective available potential energy (CAPE) coupled with higher moisture at low levels which favors larger εproxy. In contrast, continental cases initiate and develop under high convective inhibition, steeper environmental lapse rate, and high wind shear conditions, which show smaller offset between LNB and LMD. Deep convective cases that promote significant cold pools at the surface experience less εproxy. Using a Random Forest regression algorithm, CAPE is associated with the highest feature importance score for predicting convective εproxy, followed by low-level relative humidity. For continental cases, the low-level wind shear also indicates higher importance.

54 ENVIRONMENTAL SCIENCES↗

S-MODE Sonde

These data are collected by NASA as part of the Sub-Mesoscale Ocean Dynamics Experiment (https://espo.nasa.gov/s-mode), providing observations of submesoscale (1-10 km) processes.

17 WIND ENERGY↗

S-MODE Saildrone

These data are collected by NASA as part of the Sub-Mesoscale Ocean Dynamics Experiment (https://espo.nasa.gov/s-mode), providing observations of submesoscale (1-10 km) processes.

17 WIND ENERGY↗

S-MODE Saildrone

These data are collected by NASA as part of the Sub-Mesoscale Ocean Dynamics Experiment (https://espo.nasa.gov/s-mode), providing observations of submesoscale (1-10 km) processes.

17 WIND ENERGY↗

S-MODE Saildrone

These data are collected by NASA as part of the Sub-Mesoscale Ocean Dynamics Experiment (https://espo.nasa.gov/s-mode), providing observations of submesoscale (1-10 km) processes.

17 WIND ENERGY↗

S-MODE Saildrone

These data are collected by NASA as part of the Sub-Mesoscale Ocean Dynamics Experiment (https://espo.nasa.gov/s-mode), providing observations of submesoscale (1-10 km) processes.

17 WIND ENERGY↗

S-MODE Saildrone

These data are collected by NASA as part of the Sub-Mesoscale Ocean Dynamics Experiment (https://espo.nasa.gov/s-mode), providing observations of submesoscale (1-10 km) processes.

17 WIND ENERGY↗

S-MODE Saildrone

These data are collected by NASA as part of the Sub-Mesoscale Ocean Dynamics Experiment (https://espo.nasa.gov/s-mode), providing observations of submesoscale (1-10 km) processes.

17 WIND ENERGY↗

S-MODE Saildrone

These data are collected by NASA as part of the Sub-Mesoscale Ocean Dynamics Experiment (https://espo.nasa.gov/s-mode), providing observations of submesoscale (1-10 km) processes.

17 WIND ENERGY↗

S-MODE Saildrone

These data are collected by NASA as part of the Sub-Mesoscale Ocean Dynamics Experiment (https://espo.nasa.gov/s-mode), providing observations of submesoscale (1-10 km) processes.

17 WIND ENERGY↗

S-MODE Saildrone

These data are collected by NASA as part of the Sub-Mesoscale Ocean Dynamics Experiment (https://espo.nasa.gov/s-mode), providing observations of submesoscale (1-10 km) processes.

17 WIND ENERGY↗

Southwest Pacific tropical cyclone development classification utilizing machine learning and synoptic composites

This study evaluates the ability of machine learning algorithms to classify tropical depressions (TDs) and tropical storms (TSs) in the western region of the southwest Pacific Ocean (SWPO). Decision rules are generated to predict the environment required for a depression to fully develop into a mature storm, and the most influential predictors in the classification decision are ranked. TD and TS are discriminated based on a maximum sustained wind speed threshold (≥17 ms -1 ). Various aerosol, thermodynamic, and dynamic parameters are extracted closest to the initiation point of each non-developing and developing sample. The covariates associated with each labelled sample are used to train a decision tree and random forest model. Results using a testing dataset suggest the random forest approach more accurately distinguishes between non-developing and developing samples. The classification accuracy of the decision tree and random forest are 72% and 91%, respectively. Random forest outperformed the decision tree by providing higher accuracy in test data. The most important variables for binary classification are sea salt aerosol optical depth (AOD), 1,000 mb relative humidity, and sea surface temperature. AOD is a quantitative estimate of the aerosols presents in the air through the extinction of a ray of light as it passes through the atmosphere. Mean composite maps constructed in an unsupervised manner have been created for the most important variables identified by the random forest classifier during TD and TS events to highlight the difference in geophysical and aerosol variables' climatology during the two different classifications. This work will advance the risk management strategies for northeastern Australia and other SWPO basin islands to control their tropical cyclone related losses through prioritizing forecasting variables that are the strongest predictors of the strengthening of tropical depressions into tropical cyclones.

54 ENVIRONMENTAL SCIENCES↗

Future projections in tropical cyclone activity over multiple CORDEX domains from RegCM4 CORDEX-CORE simulations

The characteristics of tropical cyclone (TC) activity over five TC basins lying within four Coordinated Regional Downscaling Experiment (CORDEX) domains are examined for present and future climate conditions using a new ensemble of simulations completed as part of the CORDEX-CORE initiative with the regional climate model RegCM4. The simulations are conducted at a 25 km horizontal grid spacing and are driven by three CMIP5 general circulation models (GCMs) under two Representative Concentration Pathways (RCP2.6 and RCP8.5). The RegCM4 captures most features of the observed TC climatology, except for the TC intensity, which is thus statistically adjusted using a bias correction procedure to account for the effect of the coarse model resolution. The RegCM4 exhibits an improved simulation of several TC statistics compared to the driving GCMs, over most basins analyzed. In future climate conditions we find significant increases in TC frequency over the North Indian Ocean, the Northwest Pacific and Eastern Pacific regions, which are consistent with an increase in mid-tropospheric relative humidity. The North Atlantic and Australasia regions show a decrease in TC frequency, mostly associated with an increase in wind shear. We also find a consistent increase in future storm rainfall rates associated with TCs and in the frequency of the most intense TCs over most domains. Finally, our study shows robust responses often, but not always, in line with previous studies, still implying the presence of significant uncertainties in the projection of TC characteristics, which need to be addressed using large ensembles of simulations with high-resolution models.

54 ENVIRONMENTAL SCIENCES↗

Trends in Warm Season Mesoscale Convective Systems Over Asia in 2001–2020

Mesoscale convective systems (MCSs) frequently occur over Asia during the warm season, often producing intense precipitation with associated socioeconomic impacts. Here we reveal significant trends in MCS occurrence frequency and related precipitation in Asia during the warm season (March–September) in 2001–2020, using a tracking method that combines cloud and precipitation criteria with high-resolution satellite data from the Global Precipitation Measurement mission. To examine whether there are differences between MCSs of different scales, both meso-α scales (MαCSs) and meso-β scales (MβCSs), with horizontal scales of 200–2,000 km and 20–200 km, are tracked. The distribution pattern of frequency and related precipitation of both MαCSs and MβCSs are quite similar and manifest positive trends over East Asia (EA) and Northeast Asia, and negative trend over Southeast Asia (SEA). The MCS precipitation trend contributes significantly to total precipitation trend, with MαCSs contributing the most. Our analysis indicates the trend in lower-tropospheric water vapor flux convergence has a similar spatial pattern to the MCS frequency and related precipitation trend. Based on an atmospheric moisture flux decomposition analysis, the water vapor flux convergence trend can largely be explained by the change in horizontal wind convergence, while the specific humidity trend driven largely by temperature change plays a minor role. In conclusion, the trend in wind convergence in EA and SEA is possibly related to the evident trend in the lower-tropospheric anticyclone over the western North Pacific and SEA, which might be due to the relatively stronger warming in the Indian Ocean during the past two decades.

54 ENVIRONMENTAL SCIENCES↗

An Evaluation of Tropical Cyclone Rainfall Structures in the HighResMIP Simulations against Satellite Observations

This study evaluates tropical cyclone (TC) rainfall structures in the CMIP6 HighResMIP global climate model (GCM) simulations against satellite rainfall retrievals. Here, we specifically focus on TCs within the deep tropics (25°S–25°N). Analysis of TC rain rate composites indicates that in comparison to the satellite observations at the same intensity, many HighResMIP simulations tend to overproduce rain rates around TCs, in terms of both maximum rain rate magnitude and area-averaged rain rates. In addition, as model horizontal resolution increases, the magnitude of the peak rain rate appears to increase. However, the area-averaged rain rates decrease with increasing horizontal resolution, partly due to the TC eyewall being located closer to the TC center, thus occupying a smaller area and contributing less to the area-averaged rain rates. The effect of ocean coupling is to lower the TC rain rates, bringing them closer to the satellite observations, due to reduced horizontal moisture flux convergence and surface latent heat flux beneath TCs. Examination of horizontal rain rate distributions indicates that vertical wind shear–induced rainfall asymmetries in HighResMIP-simulated TCs are qualitatively consistent with the observations. In addition, a positive relationship is observed between the area-averaged inner-core rainfall and TC intensification likelihoods across the HighResMIP simulations, as GCM simulations producing stronger TCs more frequently have the greater rainfall close to the center, in agreement with previous theoretical and GCM simulation results.

54 ENVIRONMENTAL SCIENCES↗

Lidar - ESRL WindCube 200s, Wasco Airport - Reviewed Data

The available "Readme" file introduces the basics of the Doppler lidar data and offers a detailed description of the variables present in the data files. For those with any further questions about the data and its interpretation, contact either Alan Brewer ( ) or Sunil Baidar ( ). It is highly recommended to discuss any planned use of the data with National Oceanic and Atmospheric Administration-Chemical Sciences Division (NOAA-CSD) scientists. For more information, refer to the Readme file: "noaa-esrl-wascolidar-readme-1.pdf."

17 WIND ENERGY↗