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At least 181 records · Page 10

A study of planetary meteorology

Inversion techniques are applied to the few earth based observations of the Jovian emission spectrum to obtain directly the profiles of atmospheric temperature and ammonia abundance. The temperature profile is characterized by a definite tropopause region with a temperature of about 115K and a stratospheric region in which the temperature slowly increases with altitude. The derived ammonia profile indicates the presence of a saturated ammonia layer with a base temperature of approximately 14OK. The concept is described deducing the temperature and constituent profile of a planetary atmosphere from orbiter measurements of the planet's IR limb radiance profile. Analysis of the weighting functions for the Martian atmosphere indicates that a limb radiance profile in the 15 micron CO2 band can be used to determine the Martian atmospheric temperature profile from 20 to 60 km.

Ohring, G.↗

Prioritizing urban heat adaptation infrastructure based on multiple outcomes: Comfort, health, and energy

Globally, cities face increasing extreme heat, impacting comfort, health, and energy consumption. Infrastructure-based heat adaptation strategies can improve these outcomes, but each strategy has a unique mix of benefits and drawbacks. Here, we apply an urbanized meteorological model (WRF) with the newly integrated multilayer BEP-Tree street tree model to dynamically downscale Earth System Model projections and a 3-D microclimate model (TUF-Pedestrian) to simulate the street-scale radiation environment impacting pedestrians. We evaluate the performance of five heat adaptation strategies (street trees, cool roofs, green roofs, rooftop photovoltaics (PV), and reflective pavements) during extreme heat events in three cities with contrasting background climates (Toronto, Phoenix, and Miami), under contemporary and end-of-century projected climates, based on three metrics: outdoor heat stress, air conditioning (AC) energy use, and ventilation of vehicular air pollution. No single adaptation strategy improves all three outcomes. While street trees inhibit ventilation, they reduce outdoor heat stress four times more effectively than the next best strategy via shade provision, fully offsetting heat stress increases under a high-emissions end-of-century climate scenario in all cities studied. Cool roofs and green roofs moderately reduce heat stress and energy use. Alternatively, rooftop PV with energy storage can generate sufficient power for space cooling but have marginal effects on heat stress. Reflective pavements are the least effective across metrics. Where the ventilation of street-level emissions is of less concern, our results clearly support the combination of street trees and rooftop PV as a highly complementary and effective means of adaptive mitigation across different climates and neighborhood densities.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Lightning Detection Efficiency Analysis Process: Modeling Based on Empirical Data

A ground based lightning detection system employs a grid of sensors, which record and evaluate the electromagnetic signal produced by a lightning strike. Several detectors gather information on that signal s strength, time of arrival, and behavior over time. By coordinating the information from several detectors, an event solution can be generated. That solution includes the signal s point of origin, strength and polarity. Determination of the location of the lightning strike uses algorithms based on long used techniques of triangulation. Determination of the event s original signal strength relies on the behavior of the generated magnetic field over distance and time. In general the signal from the event undergoes geometric dispersion and environmental attenuation as it progresses. Our knowledge of that radial behavior together with the strength of the signal received by detecting sites permits an extrapolation and evaluation of the original strength of the lightning strike. It also limits the detection efficiency (DE) of the network. For expansive grids and with a sparse density of detectors, the DE varies widely over the area served. This limits the utility of the network in gathering information on regional lightning strike density and applying it to meteorological studies. A network of this type is a grid of four detectors in the Rondonian region of Brazil. The service area extends over a million square kilometers. Much of that area is covered by rain forests. Thus knowledge of lightning strike characteristics over the expanse is of particular value. I have been developing a process that determines the DE over the region [3]. In turn, this provides a way to produce lightning strike density maps, corrected for DE, over the entire region of interest. This report offers a survey of that development to date and a record of present activity.

Rompala, John T.↗

The Future of Planetary Climate Modeling and Weather Prediction

Modeling of planetary climate and weather has followed the development of tools for studying Earth, with lags of a few years. Early Earth climate studies were performed with 1-dimensionalradiative-convective models, which were soon fol-lowed by similar models for the climates of Mars and Venus and eventually by similar models for exoplan-ets. 3-dimensional general circulation models (GCMs) became common in Earth science soon after and within several years were applied to the meteorology of Mars, but it was several decades before a GCM was used to simulate extrasolar planets. Recent trends in Earth weather and and climate modeling serve as a useful guide to how modeling of Solar System and exoplanet weather and climate will evolve in the coming decade.

Del Genio, A. D.↗

Enhanced Monitoring and Forecasting of South Asian Air Quality Episodes with Multi-Sensor Satellite Products and Dispersion Modeling

Air pollution and particulates represent a serious environmental, public health, and overarching societal concern in the Hindu Kush Himalaya (HKH) region of south-central Asia, especially during the boreal winter and spring months. Frequent contributors to poor air quality of the region include dust and particulate matter transport from the Middle East region and western India, persistent nocturnal fog and smog during the stable dry monsoon months, and biomass burning during the pre-monsoon months of boreal spring. Nocturnal and multi-day persistent stable fog and smog events can occur during the stable conditions of the dry monsoon months that lead to hazardous visibility reductions (e.g., at major airports such as Delhi, India)and poor air quality, particularly in urban corridors.Our project goal is therefore to design and implement a robust air quality and chemistry observation and modeling system using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) and the Weather Research and Forecasting coupled with Chemistry (WRF-Chem) models. This system effectively assimilates aerosol and trace gas retrievals from geostationary and polar-orbiting satellites to advance the monitoring and prediction capabilities of air quality and visibility reductions throughout the HKH region. For this presentation, we highlight several[multi-spectral] geostationary satellite products and dispersion modeling capabilities being developed and implemented for improved situational awareness and forecasting of air quality episodes.Launched in December 2018, the Advanced Meteorological Imager (AMI) aboard the Geostationary-Korean Multi-Purpose Satellite-2A (GEO-KOMPSAT-2A, or GK2A) provides 16 channels of multi-spectral information similar to the U.S. GOES-16/17 satellites. Proven community recipes for generating multi-spectral Red-Green-Blue (RGB) composite products to detect specific meteorological phenomena are applied to the AMI data for the HKH region. We implemented four such RGB products consisting of truecolor with Rayleigh correction to aid in smoke detection; dust RGB for monitoring larger particulate matter; nighttime microphysics for fog/smog detection;and natural color fire RGB and the shortwave 3.8 micron channel for fire hot-spot detection.We additionally configured HYSPLIT for dust/sand dispersion and concentration forecasts over HKH. The HYSPLIT dispersion simulations are initially being conducted with two different methodologies: (1) dust/sand lofting based on initializing plume releases from visual inspection of the dust RGB imagery; and (2) dust/sand release and dispersion using the internal HYSPLIT algorithm that determines lofting from input wind speeds and prescribed land use. The goal is to develop a near real-time HYSPLIT modeling solution for routinely simulating the transport and dispersion of dust plumes across the HKH region. This presentation illustrates these products and capabilities for various use cases from the 2019 to 2021 period, highlighting a particularly unhealthy episode during late March 2021 across Nepal.

Remote Sensing↗

Sensitivity of the pseudo-global warming method under flood conditions: a case study from the northeastern US

Abstract. Intensified extreme precipitation and corresponding floods are the most relevant consequences of climate change over the northeastern US (NEUS). To evaluate the impacts of climate change or certain climate perturbations on future extreme weather events which are dynamically similar to historic analogs, the pseudo-global warming (PGW) method has been frequently employed; however, this method lacks precise definition and guidelines, thus limiting its application. More specifically, three key questions related to the application of the PGW method remain unanswered: at what spatial scale should climate perturbations be applied? Among the different meteorological variables available, which ones should be perturbed? And will PGW projections vary significantly when different perturbations are applied? To address these questions, we examine the sensitivity and robustness of conclusions drawn from the PGW method over the NEUS by conducting multiple PGW experiments with varied perturbation spatial scales and choice of perturbed meteorological variables. The results show that the projections of precipitation and other essential variables at the regional mean scale are consistent across the PGW simulations, with a relative difference of much less than 10 %; however, different perturbation modifications can cause significant displacements of the storm events being simulated. Several previously assumed advantages of modifying only the temperature at regional mean scale, such as the preservation of geostrophic balance, do not appear to hold. Also, for these experiments, we find the regional mean perturbation produces a positive precipitation bias because it ignores the land–ocean warming contrast, which is a robust regional response to global warming. Overall, PGW experiments with perturbations from temperature or the combination of temperature and wind at the grid point scale are both recommended, depending on the research questions. The first approach can isolate the spatially dependent thermodynamic impact, and the latter incorporates both the thermodynamic and dynamic impacts.

54 ENVIRONMENTAL SCIENCES↗

Contribution of Meteorological Downscaling to Skill and Precision of Seasonal Drought Forecasts

Research in meteorological prediction on sub-seasonal to seasonal (S2S) timescales has seen growth in recent years. Concurrent with this, demand for seasonal drought forecasting has risen. While there is obvious synergy between these fields, S2S meteorological forecasting has typically focused on low resolution global models, while the development of drought can be sensitive to the local expression of weather anomalies and their interaction with local surface properties and processes. This suggests that downscaling might play an important role in the application of meteorological S2S forecasts to skillful forecasting of drought. Here, we apply the Generalized Analog Regression Downscaling (GARD) algorithm to downscale meteorological hindcasts from the NASA Goddard Earth Observing System (GEOS) global S2S forecast system. Downscaled meteorological fields are then applied to drive offline simulations with the Catchment Land Surface Model (CLSM) to forecast United States Drought Monitor (USDM) style drought indicators derived from simulated surface hydrology variables. We compare the representation of drought in these downscaled hindcasts to hindcasts that are not downscaled, using the North American Land Data Assimilation System Phase 2 (NLDAS-2) dataset as an observational reference. We find that downscaling using GARD improves hindcasts of temperature and temperature anomalies, but the results for precipitation are mixed and generally small. Overall, GARD downscaling led to improved hindcast skill for total drought across the Contiguous United States (CONUS), and improvements were greatest for extreme (D3) and exceptional (D4) drought categories.

Ryan A Zamora↗

A study of unmanned mission opportunities to comets and asteroids

Several unmanned multiple-target mission opportunities to comets and asteroids were studied. The targets investigated include Grigg-Skjellerup, Giacobini-Zinner, Tuttle-Giacobini-Kresak, Borrelly, Halley, Schaumasse, Geographos, Eros, Icarus, and Toro, and the trajectories consist of purely ballistic flight, except that powered swingbys and deep space burns are employed when necessary. Optimum solar electric rendezvous trajectories to the comets Giacobini-Zinner/85, Borrelly/87, and Temple (2)/83 and /88 employing the 8.67 kw Sert III spacecraft modified for interplanetary flight were also investigated. The problem of optimizing electric propulsion heliocentric trajectories, including the effects of geocentric launch asymptote declination on launch vehicle performance capability, was formulated, and a solution developed using variational calculus techniques. Improvements were made to the HILTOP trajectory optimization computer program. An error analysis of high-thrust maneuvers involving spin-stabilized spacecraft was developed and applied to a synchronous meteorological satellite mission.

Mann, F. I.↗

SWOT Applications in Alaska

Use of SWOT for model calibration and assimilation are encouraging, but further work is needed: account for uncertainty in meteorological forcing (calibration) and apply ensemble Kalman Smoother (data assimilation). Lessons learned can inform development of operational NOAA National Water Model Alaskan domain and other SWOT applications.

SWOT↗

Irrigation Water Demand Sensitivity to Climate Variability across the Contiguous United States

Climate variability is an important driver of irrigation water use in many regions. Efforts to anticipate climate change impacts on future water availability can benefit from understanding how irrigation water demand has responded to these drivers to date. Here we apply satellite derived data, meteorological reanalysis, an advanced land surface model, and available state level reports to quantify irrigation demand sensitivities to temperature and precipitation across the Contiguous United States, for the period of 2002-2017. As expected, strong negative correlations are found between precipitation and irrigation withdrawals, both simulated and reported. Temperature sensitivities, however, vary by region and season, as do the interactive effects of temperature and precipitation on irrigation. Climate-induced irrigation variability is largest in transitional climate zones. These transitional zones are generally separate from the regions where rates of irrigation withdrawals are greatest, such that climate-induced variability in irrigation demand represents a water resource consideration that is distinct from chronic over pumping.

Wanshu Nie↗

DeepPhenoMem V1.0: deep learning modelling of canopy greenness dynamics accounting for multi-variate meteorological memory effects on vegetation phenology

Abstract. Vegetation phenology plays a key role in controlling the seasonality of ecosystem processes that modulate carbon, water and energy fluxes between the biosphere and atmosphere. Accurate modelling of vegetation phenology in the interplay of Earth's surface and the atmosphere is thus crucial to understand how the coupled system will respond to and shape climatic changes. Phenology is controlled by meteorological conditions at different timescales: on the one hand, changes in key meteorological variables (temperature, water, radiation) can have immediate effects on the vegetation development; on the other hand, phenological changes can be driven by past environmental conditions, known as memory effects. However, the processes governing meteorological memory effects on phenology are not completely understood, resulting in their limited performance of vegetation phenology represented in land surface models. A deep learning model, specifically a long short-term memory network (LSTM), has the potential to capture and model the meteorological memory effects on vegetation phenology. Here, we apply the LSTM to model the vegetation phenology using meteorological drivers and high-temporal-resolution canopy greenness observations through digital repeat photography by the PhenoCam network. We compare a multiple linear regression model, a no-memory-effect LSTM model and a full-memory-effect LSTM model to predict the whole seasonal greenness trajectory and the corresponding phenological transition dates across 50 sites and 317 site years during 2009–2018, covering deciduous broadleaf forests, evergreen needleleaf forests and grasslands. Results show that the deep learning model outperforms the multiple linear regression model, and the full-memory-effect LSTM model performs better than the no-memory-effect model for all three plant function types (median R2 of 0.878, 0.957 and 0.955 for broadleaf forests, evergreen needleleaf forests and grasslands). We also find that the full-memory-effect LSTM model is capable of predicting the seasonal dynamic variations of canopy greenness and reproducing trends in shifting phenological transition dates. We also performed a sensitivity analysis of the full-memory-effect LSTM model to assess its plausibility, revealing its coherence with established knowledge of vegetation phenology sensitivity to meteorological conditions, particularly changes in temperature. Our study highlights that (1) multi-variate meteorological memory effects play a crucial role in vegetation phenology, and (2) deep learning opens up new avenues for improving the representation of vegetation phenological processes in land surface models via a hybrid modelling approach.

Geology↗

Apparatus and methods for location and sizing of trace gas sources

A system for detecting gas leaks and determining their location and size. A data gathering portion of the system utilizes a chosen geometrical configuration to collect path-integrated spectroscopic data over multiple paths around an area. A processing portion of the system applies a transport model together with meteorological data of the area to generate an influence function of possible leak locations on gas detector measurement paths, and applies an inversion model to the influence function, prior data, and the spectroscopic data to generate gas source size and location.

03 NATURAL GAS↗

The Quality Control Algorithms Used in the Creation of NASA Kennedy Space Center Lightning Protection System Towers Meteorological Database

An accurate database of meteorological data is essential for designing any aerospace vehicle and for preparing launch commit criteria. Meteorological instrumentation were recently placed on the three Lightning Protection System (LPS) towers at Kennedy Space Center (KSC) launch complex 39B (LC-39B), which provide a unique meteorological dataset existing at the launch complex over an extensive altitude range. Data records of temperature, dew point, relative humidity, wind speed, and wind direction are produced at 40, 78, 116, and 139 m at each tower. The Marshall Space Flight Center Natural Environments Branch (EV44) received an archive that consists of one-minute averaged measurements for the period of record of January 2011 - April 2015. However, before the received database could be used EV44 needed to remove any erroneous data from within the database through a comprehensive quality control (QC) process. The QC process applied to the LPS towers' meteorological data is similar to other QC processes developed by EV44, which were used in the creation of meteorological databases for other towers at KSC. The QC process utilized in this study has been modified specifically for use with the LPS tower database. The QC process first includes a check of each individual sensor. This check includes removing any unrealistic data and checking the temporal consistency of each variable. Next, data from all three sensors at each height are checked against each other, checked against climatology, and checked for sensors that erroneously report a constant value. Then, a vertical consistency check of each variable at each tower is completed. Last, the upwind sensor at each level is selected to minimize the influence of the towers and other structures at LC-39B on the measurements. The selection process for the upwind sensor implemented a study of tower-induced turbulence. This paper describes in detail the QC process, QC results, and the attributes of the LPS towers meteorological database.

Orcutt, John M.↗

Mapping the Martian Meteorology

The Mars-adapted version of the NASA/GISS general circulation model (GCM) has been applied to the hourly/daily simulation of the planet's meteorology over several seasonal orbits. The current running version of the model includes a diurnal solar cycle, CO2 sublimation, and a mature parameterization of upper level wave drag with a vertical domain extending from the surface up to the 6 micro b level. The benchmark simulations provide a four-dimensional archive for the comparative evaluation of various schemes for the retrieval of winds from anticipated polar orbiter measurements of temperatures by the Pressure Modulator Infrared Radiometer.

Allison, Michael↗

Mapping the Martian Meteorology

The Mars-adapted version of the NASA/GISS general circulation model (GCM) has been applied to the hourly/daily simulation of the planet's meteorology over several seasonal orbits. The current running version of the model includes a diurnal solar cycle, CO2 sublimation, and a mature parameterization of upper level wave drag with a vertical domain extending from the surface up to the 6microb level. The benchmark simulations provide a four-dimensional archive for the comparative evaluation of various schemes for the retrieval of winds from anticipated polar orbiter measurements of temperatures by the Pressure Modulator Infrared Radiometer. Additional information is contained in the original extended abstract.

Allison, M.↗

Evaluation of aerosol–cloud interactions in E3SM using a Lagrangian framework

A Lagrangian framework is used to evaluate aerosol–cloud interactions in the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM) version 1 (E3SMv1) for measurements taken at Graciosa Island in the Azores where a U.S. Department of Energy Atmosphere Radiation Measurement (ARM) site is located. This framework uses direct measurements of cloud condensation nuclei (CCN) concentration (instead of relying on satellite retrievals of aerosol optical depth) and incorporates a suite of ground-based ARM measurements, satellite retrievals, and meteorological reanalysis products that when applied to over a 1500 trajectories provides key insights into the evolution of low-level clouds and aerosol radiative forcing that is not feasible from a traditional Eulerian analysis framework. Significantly lower concentrations (40 %) of surface CCN concentration are measured when precipitation rates in 48 h back trajectories average above 1.2 mm d -1 in the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) product. The depletion of CCN concentration when precipitation rates are elevated is nearly twice as large in the ARM observations compared to E3SMv1 simulations. The model CCN concentration bias remains significant despite modifying the autoconversion and accretion rates in warm clouds. As the clouds in trajectories associated with larger surface-based CCN concentration advect away from Graciosa Island, they maintain higher values of droplet number concentrations (N d ) over multiple days in observations and E3SM simulations compared to trajectories that start with lower CCN concentrations. The response remains robust even after controlling for meteorological factors such as lower troposphere stability, the degree of cloud coupling with the surface, and island wake effects. E3SMv1 simulates a multi-day aerosol effect on clouds and a Twomey radiative effect that is within 30 % of the ARM and satellite observations. However, the mean cloud droplet concentration is more than 2–3 times larger than in the observations. While Twomey radiative effects are similar amongst autoconversion and accretion sensitivity experiments, the liquid water path and cloud fraction adjustments are positive when using a regression model as opposed to negative when using the present-day minus pre-industrial aerosol emissions approach. This result suggests that tuning the autoconversion and accretion alone is unlikely to produce the desired aerosol susceptibilities in E3SMv1.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Connecting Large‐Scale Meteorological Patterns to Extratropical Cyclones in CMIP6 Climate Models Using Self‐Organizing Maps

Abstract Extratropical cyclones (ETCs) are responsible for the majority of cool‐season extreme events in the northeastern United States (NEUS), often leading to high‐impact weather conditions that can have wide‐ranging socioeconomic impacts. Evaluating the ability of climate models to adequately simulate ETC dynamics is essential for improving model performance and increasing confidence in future projections used by stakeholders and policymakers. ETCs are traditionally studied using techniques such as case studies and synoptic typing, however, these approaches can be time‐consuming, require subjective analysis, and do not necessarily identify the coincident large‐scale meteorological patterns (LSMPs). Here, we apply self‐organizing maps (SOMs) as an automated machine‐learning approach to characterize the LSMPs and associated frequency and intensity of discrete ETC events over NEUS. The dominant patterns of geopotential height variability are identified through SOM analysis of five reanalysis products during the last four decades. ETC events are tracked using TempestExtremes and are integrated with SOMs to classify the accumulated cyclone activity (ACA) associated with each pattern. We then evaluate the skill of CMIP6 historical experiments in simulating the LSMPs and ETC events identified in the SOM. Our results identify a robust bias toward more zonal patterns, with models struggling to reproduce the more amplified patterns typically associated with the highest cyclone activity. While model resolution has some impact on simulation credibility, model configuration appears to be more important in LSMP representation. The vast majority of CMIP6 models produce too few ETCs, although model errors are distributed around historical reanalyses when ACA is normalized by storm frequency.

54 ENVIRONMENTAL SCIENCES↗