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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Temperature, Humidity, and Time-Lapse Video Data from Yosemite National Park, Water Year 2024

This dataset contains time-lapse imagery and distributed measurements of air temperature, relative humidity, dew point, and soil temperature across Yosemite National Park from October 2023 to September 2024. Instruments were deployed at 10 sites in two cross-valley transects as part of the DOE Grant: Seasonal Cycles Unravel Mysteries of Missing Mountain Water organized by Jessica Lundquist (University of Washington), Rosemary Carroll (Desert Research Institute), and Ethan Gutmann (National Center for Atmospheric Research). The data are intended to support hydrologic modeling efforts to better resolve the fate of mountain water, and are published to support studies of surface climate or hydrologic processes in complex terrain. Measurements were collected with low-cost data loggers installed 2 m high on evergreen trees or buried just below the soil surface. A time-lapse camera at one site captures valley-scale seasonal snow cover variability.Dataset files are organized by site and variable (air measurements, ground measurements, or time-lapse video). Air and ground measurements are packaged in LoggerData.zip, and time-lapse imagery is compiled into a short video stored in TimelapseVideos.zip. File-level metadata contains details for each file included in the dataset. A data dictionary provides units and descriptions for column or row names in all files. The locations metadata file describes site characteristics, locations, and associated GPS methods.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Temperature, Humidity, and Time-Lapse Video Data from the East River Watershed, Water Years 2024 and 2025

This dataset contains time-lapse imagery and distributed measurements of air temperature, relative humidity, dew point, and soil temperature across the East River basin from 3 October 2023 to 8 August 2025. Instruments were deployed at 19 sites as part of the DOE Grant: Seasonal Cycles Unravel Mysteries of Missing Mountain Water organized by Jessica Lundquist (University of Washington), Rosemary Carroll (Desert Research Institute), and Ethan Gutmann (National Center for Atmospheric Research). The data are published to support studies of surface climate or hydrologic processes in complex terrain. Measurements were collected with low-cost data loggers installed 2 m high on evergreen trees or buried just below the soil surface. Time-lapse cameras were deployed at three sites. Imagery from sites AP BONUS and AP5 (Avery Picnic) provides insight into large-scale seasonal snow cover variability. Imagery from site EL2 (Emerald Lake) shows smaller-scale snow patterns across a nearby meadow. Dataset files are organized by site and variable (air measurements, ground measurements, or time-lapse video). Air and ground measurements are packaged in LoggerData.zip, and time-lapse imagery is compiled into short videos stored in TimelapseVideos.zip. File-level metadata contains details for each file included in the dataset. A data dictionary provides units and descriptions for column or row names in all files. The locations metadata file describes site characteristics, locations, and associated GPS methods.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The sound of a Martian dust devil

Dust devils (convective vortices loaded with dust) are common at the surface of Mars, particularly at Jezero crater, the landing site of the Perseverance rover. They are indicators of atmospheric turbulence and are an important lifting mechanism for the Martian dust cycle. Improving our understanding of dust lifting and atmospheric transport is key for accurate simulation of the dust cycle and for the prediction of dust storms, in addition to being important for future space exploration as grain impacts are implicated in the degradation of hardware on the surface of Mars. Here we describe the sound of a Martian dust devil as recorded by the SuperCam instrument on the Perseverance rover. The dust devil encounter was also simultaneously imaged by the Perseverance rover’s Navigation Camera and observed by several sensors in the Mars Environmental Dynamics Analyzer instrument. Combining these unique multi-sensorial data with modelling, we show that the dust devil was around 25 m large, at least 118 m tall, and passed directly over the rover travelling at approximately 5 m s –1 . Acoustic signals of grain impacts recorded during the vortex encounter provide quantitative information about the number density of particles in the vortex. The sound of a Martian dust devil was inaccessible until SuperCam microphone recordings. This chance dust devil encounter demonstrates the potential of acoustic data for resolving the rapid wind structure of the Martian atmosphere and for directly quantifying wind-blown grain fluxes on Mars.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Slope-Aspect Induced Climate Differences Influence How Water Is Exchanged Between the Land and Atmosphere

Cross-slope climate differences in the midlatitudes are ecologically important, and impact vegetation-mediated water balance between the earth surface and the atmosphere. We made high-resolution in situ observations of air temperature, relative humidity, soil moisture, insolation, and sap velocity observations on 14 Pacific madrone trees (Arbutus menziesii) spanning adjacent north and south slopes at the University of California's Angelo Coast Range Reserve. To understand the cross-slope response of sap velocity, a proxy for transpiration, to microclimate, we modeled the sap velocity on each slope using a transpiration model driven by ambient environment and parameterized with a Markov Chain Monte Carlo parameter estimation process. The results show that trees on opposing slopes do not follow a shared pattern of physiological response to transpiration drivers. This means that the observed sap velocity differences are not due entirely to observed microclimate differences, but also due to population-level physiological differences, which indicates acclimation to inhabited microclimate. While our present data set and analytical tools do not identify mechanisms of acclimation, we speculate that differing proportions of sun-adapted and shade-adapted leaves, differences in stomatal regulation, and cross-slope root zone moisture differences could explain some of the observed and modeled differences.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

The Surface Atmosphere Integrated Field Laboratory (SAIL) Campaign

The science of mountainous hydrology spans the atmosphere through the bedrock and inherently crosses physical and disciplinary boundaries: land-atmosphere interactions in complex terrain enhance clouds and precipitation, while watersheds retain and release water over a large range of spatial and temporal scales. Limited observations in complex terrain challenge efforts to improve predictive models of the hydrology in the face of rapid changes. The Upper Colorado River exemplifies these challenges, especially with ongoing mismatches between precipitation, snowpack, and discharge. Consequently, the U.S. Department of Energy’s (DOE) Atmospheric Radiation Measurement (ARM) user facility has deployed an observatory to the East River Watershed near Crested Butte, Colorado between September 2021 and June 2023 to measure the main atmospheric drivers of water resources, including precipitation, clouds, winds, aerosols, radiation, temperature and humidity. This effort, called the Surface Atmosphere Integrated Field Laboratory (SAIL), is also working in tandem with DOE-sponsored surface and subsurface hydrologists and other federal, state, and local partners. SAIL data can be benchmarks for model development by producing a wide range of observational information on precipitation and its associated processes, including those processes that impact snowpack sublimation and redistribution, aerosol direct radiative effects in the atmosphere and in the snowpack, aerosol impacts on clouds and precipitation, and processes controlling surface fluxes of energy and mass. Preliminary data from SAIL’s first year showcase the rich information content in SAIL’s many data-streams and support testing hypotheses that will ultimately improve scientific understanding and predictability of Upper Colorado River hydrology in 2023 and beyond.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Upscaling Wetland Methane Emissions From the FLUXNET–CH4 Eddy Covariance Network (UpCH4 v1.0): Model Development, Network Assessment, and Budget Comparison

Wetlands are responsible for 20%–31% of global methane (CH 4 ) emissions and account for a large source of uncertainty in the global CH 4 budget. Data-driven upscaling of CH 4 fluxes from eddy covariance measurements can provide new and independent bottom-up estimates of wetland CH 4 emissions. Here, we develop a six-predictor random forest upscaling model (UpCH4), trained on 119 site-years of eddy covariance CH 4 flux data from 43 freshwater wetland sites in the FLUXNET-CH4 Community Product. Network patterns in site-level annual means and mean seasonal cycles of CH 4 fluxes were reproduced accurately in tundra, boreal, and temperate regions (Nash-Sutcliffe Efficiency ~0.52–0.63 and 0.53). UpCH4 estimated annual global wetland CH 4 emissions of 146 ± 43 TgCH 4 y –1 for 2001–2018 which agrees closely with current bottom-up land surface models (102–181 TgCH 4 y –1 ) and overlaps with top-down atmospheric inversion models (155–200 TgCH 4 y –1 ). However, UpCH4 diverged from both types of models in the spatial pattern and seasonal dynamics of tropical wetland emissions. We conclude that upscaling of eddy covariance CH 4 fluxes has the potential to produce realistic extra-tropical wetland CH 4 emissions estimates which will improve with more flux data. To reduce uncertainty in upscaled estimates, researchers could prioritize new wetland flux sites along humid-to-arid tropical climate gradients, from major rainforest basins (Congo, Amazon, and SE Asia), into monsoon (Bangladesh and India) and savannah regions (African Sahel) and be paired with improved knowledge of wetland extent seasonal dynamics in these regions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Throughfall-Reduction Drying Effects on Soil Respiration, Moisture, and Temperature in Four Distinct Lowland Panamanian Forests from 2015-2022

Objectives: Climatic drying is predicted for many tropical forests, yet effects on soil properties across moisture and soil gradients within tropical forests remain poorly characterized, hampering predictions of forest-climate feedbacks. We hypothesized that throughfall reduction (i.e. chronic drying) would suppress soil CO2 fluxes (i.e., respiration) in already-drier tropical forests by further reductions in soil moisture, but increases CO2 fluxes in wetter tropical forests by alleviating anaerobiosis and soil saturation. We conducted a field study in four distinct lowland Panamanian forests that vary in background rainfall and soil fertility. In each forest we constructed throughfall reduction structures that reduce moisture inputs to plots by ~50% (see methods). In drying and paired control plots we measured soil CO2 fluxes, soil moisture (volume/volume), soil temperature (°C), air temperature (°C), and forest floor biomass during wet-dry cycles (2015 – 2022). We also collected litterfall biomass and surveyed all tree species for diameter at breast height (dbh), and identified tree species in 2018 and 2019.Results:We found that soil moisture peaked in the wet season and declined in the dry season. Measured soil CO2 fluxes declined in the dry season and peaked in the early wet season ahead of peak soil moisture, resulting in a lower soil moisture optimum for respiration than previously modeled. Chronic throughfall exclusion also suppressed soil moisture across the four forests, and also initially suppressed soil CO2 fluxes across forests. There was sustained suppression of soil CO2 fluxes after four years in the wettest forest only (-28 ± 4% during the dry season), but elevated soil CO2 fluxes in a fertile forest after four years (+75 ± 28% during the late wet season). The unexpected negative drying effect in the wettest, most infertile forest could have resulted from reduced vertical flushing of nutrients into soils, as the drying effect increased with time. Including hydro-nutrient interactions in ecosystem models could improve predictions of tropical forest-climate feedbacks. Datasets included: Datasets included here include .csv and .xls files for soil respiration (carbon dioxide flux) collected using the LiCor (see methods), air temperature °C , soil moisture (vl/vl), and soil temperature °C. Soil moisture and soil temperature were collected at 0-10cm depths using hand-held probes (see methods). Data are on ~3 week timescales from 2015-2022 with some gaps. There is also a .kml file that includes coordinates for all 32 plots included in the study of four forests (n = 4 throughfall reduction and n = 4 control plots per site). No special software is needed to open these files.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Data associated with “Different methods of estimating riverbed sediment grain size diverge at the basin scale ” (v2)

This data package is associated with the publication “Different methods of estimating riverbed sediment grain size diverge at the basin scale” published in Frontiers in Earth Science (Regier et al., 2025). The distribution of sediment grain size in streams and rivers is often quantified by the median grain size (d50), a key metric for understanding and predicting hydrologic and biogeochemical function of streams and rivers. Manual methods to measure d50 are time-consuming and ignore larger grains, while model-based methods to estimate d50 often over-generalize basin characteristics, and therefore cannot accurately represent site-scale heterogeneity. Here, we apply a machine learning-enabled photogrammetry methodology (You Only Look Once, or YOLO) for estimating d50 for grains > 2 mm based on images collected from streams and rivers throughout the Yakima River Basin (YRB). To understand how such methods may help bridge the gaps in resolution and accuracy between manual and catchment characteristics model-based d50 estimates, we compared YOLO d50 values to manual and model-based estimates across the YRB. We found distinct differences among methods for d50 averages and variability, and relationships between d50 estimates and basin characteristics. Source images can be found at https://data.ess-dive.lbl.gov/view/doi:10.15485/1892052. This data package was originally published in May 2023. It was updated August 2025 (v2; new and modified files). File and folder names were not revised to indicate changes. See the change history section in the readme for more details. In addition to the readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions. This dataset is comprised of one main data folder containing (1) file-level metadata; (2) data dictionary; (3) readme; (4) and subfolders containing data, figures, and scripts. The data folder contains datasets used for the analyses in the manuscript in image, text-delimited or geospatially-referenced formats. The figures folder contains the figures from the manuscript in different formats. The scripts folder contains all of the scripts used to complete the analyses in the manuscript. All files are .csv, .rds, .dbf, .prj, .shp, .shx, .jpg, .png, .R, .Rproj, or .pdf. We acknowledge the Yakama Nation as owners and caretakers of the lands where we collected some of these data. We thank the Confederated Tribes and Bands of the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

PreSens Dissolved Oxygen and Temperature Data, Old Woman Creek National Estuarine Research Reserve, Huron, OH, 2022-07-06 to 2023-12-15

This dataset contains collected dissolved oxygen (DO) and temperature measurements of the air, surface water, and underlying soil of a wetland at Old Woman Creek Estuarine Research Reserve in Huron, OH. Dissolved oxygen and temperature measurements were made in the first few centimeters of wetland sediment to assess how oxygen profiles changed over time with hydrological events. Measurements taken outside of the sediment were taken as comparison. Measurements were taken by hand with a PreSens Fibox 4 transmitter, paired with an oxygen dipping probe and temperature sensor, at each field outing. Measurements were taken approximately every 2 weeks around 10 AM EST. The PreSens_DO_Temp_Datafile.csv contains the recorded measurements, and the PreSens_InstallationMethods.csv describes the deployment of the sensor.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Atmospheric and Soil Parameters in Five Urban Sites in Knoxville, Tennessee. 2024

This dataset, which contains ten csv files, reports several meteorological conditions such as temperature, wind speed, solar radiation, and net radiation measured in several urban parks in Knoxville, Tennessee, USA: Cumberland Estates Park (CEP), Socially Equal Energy Efficient Development (SEEED), West View Park (WVP), Victor Ashe Park (VAP), and West Hills Park (WHP). Also, hourly average of soil parameters such soil temperature, moisture, and matric potential measured in the five sites are included in this dataset. Air temperature, wind speed and direction, and solar radiation data were obtained from a METER ATMOS 41 All-in-One Weather Station (Pullman, Washington, USA). Soil moisture and temperature parameters were measured the METER Teros 12 probes embedded to a depth of 5 cm into the soil, while soil matric potential was measured with a METER Teros 21 probe. Incident and emitted radiation (shortwave and longwave) measurements were made using an Apogee (Logan, Utah, USA) net radiometer (Model SN-500-SS). This work is a part of a larger study which investigates the impact of soil moisture and plant evapotranspiration on ambient temperature and relative humidity in several city parks in Knoxville, Tennessee.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

A network of soil moisture, soil temperature, air temperature, net radiation, ground heat flux and ground water for Chicago, Illinois

This dataset contains environmental monitoring data collected using solar-powered Multi-Function Research (MFR) Long Range Wide Area (LoRaWAN)-enabled nodes at 11 sites in Chicago, Illinois, as part of the DOE Urban Integrated Field Lab CROCUS project. The MFR node system consists of an Input/Output Digital Input Module (IB8) interface box (ICT International) providing wired connections for environmental sensors and an MFR-Node-L data logger that manages power, data processing, and LoRaWAN communication. The wireless data are ingested via Sage network (https://sagecontinuum.org/) nodes that contain LoRaWAN antennae. Measurements were collected from 11 MFR nodes deployed across Chicago State University (CSU), Northeastern Illinois University (NEIU), Northwestern University (NU), University of Illinois Chicago (UIC), West Woodlawn "Blacks in Green" (BIG), and Indian Boundary Prairies (IBP). Each MFR node supports a consistent suite of sensors measuring atmospheric, soil, and hydrological variables. Atmospheric measurements include 2m air temperature (°C), 2m vapor pressure deficit (kPa), and 2m shortwave/longwave radiation (incoming and outgoing, W/m²) measured using ATH-VPD and Apogee SN500 sensors. Soil measurements include volumetric water content (VWC, %) and temperature (°C) at four depths (15, 30, 45, and 60 cm below surface) using Meter Teros54 sensors, and heat flux (W/m²) at 10 cm depth using Huske HFP01-05 sensors. At selected locations, Meter Hydros21 sensors measure groundwater depth (mm), specific conductivity (dS/m), and temperature (°C). The dataset includes timestamps, site identifiers with location names, device IDs, Global Positioning System (GPS) coordinates, variable names with units, measurement depths, values, sensor names, and Sage node identifiers. All timestamps are in local Chicago time (CDT/CST). Quality control flags are provided using a 6-bit binary system indicating physical range violations, step spikes, 24-hour flat-line conditions, 6-hour jitter, 7-day ultra-low variance, and persistent high offset. Data is provided in CSV and CF-compliant NetCDF formats. This dataset is part of a larger collection of CROCUS environmental monitoring data, including linked datasets from Air Quality Transmitter (AQT) sensors, Weather Transmitter (WXT) sensors, and Sap Flow Meter (SFM1x) sensors.

Chicago↗
DOE OSTI2024

Land Processes Can Substantially Impact the Mean Climate State

Terrestrial processes influence the atmosphere by controlling land-to-atmosphere fluxes of energy, water, and carbon. Prior research has demonstrated that parameter uncertainty drives uncertainty in land surface fluxes. However, the influence of land process uncertainty on the climate system remains underexplored. Here, we quantify how assumptions about land processes impact climate using a perturbed parameter ensemble for 18 land parameters in the Community Earth System Model version 2 under preindustrial conditions. We find that an observationally-informed range of land parameters generate biogeophysical feedbacks that significantly influence the mean climate state, largely by modifying evapotranspiration. Global mean land surface temperature ranges by 2.2°C across our ensemble (σ = 0.5°C) and precipitation changes were significant and spatially variable. Our analysis demonstrates that the impacts of land parameter uncertainty on surface fluxes propagate to the entire Earth system, and provides insights into where and how land process uncertainty influences climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Implementation and Evaluation of a Unified Turbulence Parameterization Throughout the Canopy and Roughness Sublayer in Noah‐MP Snow Simulations

Abstract The Noah‐MP land surface model (LSM) relies on the Monin‐Obukhov (M‐O) Similarity Theory (MOST) to calculate land‐atmosphere exchanges of water, energy, and momentum fluxes. However, MOST flux‐profile relationships neglect canopy‐induced turbulence in the roughness sublayer (RSL) and parameterize within‐canopy turbulence in an ad hoc manner. We implement a new physics scheme (M‐O‐RSL) into Noah‐MP that explicitly parameterizes turbulence in RSL. We compare Noah‐MP simulations employing the M‐O‐RSL scheme (M‐O‐RSL simulations) and the default M‐O scheme (M‐O simulations) against observations obtained from 647 Snow Telemetry (SNOTEL) stations and two AmeriFlux stations in the western United States. M‐O‐RSL simulations of snow water equivalent (SWE) outperform M‐O simulations over 64% and 69% of SNOTEL sites in terms of root‐mean‐square‐error (RMSE) and correlation, respectively. The largest improvements in skill for M‐O‐RSL occur over closed shrubland sites, and the largest degradations in skill occur over deciduous broadleaf forest sites. Differences between M‐O and M‐O‐RSL simulated snowpack are primarily attributable to differences in aerodynamic conductance for heat underneath the canopy top, which modulates sensible heat flux. Differences between M‐O and M‐O‐RSL within‐canopy and below‐canopy sensible heat fluxes affect the amount of heat transported into snowpack and hence change snowmelt when temperatures are close to or above the melting point. The surface energy budget analysis over two AmeriFlux stations shows that differences between M‐O and M‐O‐RSL simulations can be smaller than other model biases (e.g., surface albedo). We intend for the M‐O‐RSL physics scheme to improve performance and uncertainty estimates in weather and hydrological applications that rely on Noah‐MP.

Meteorology & Atmospheric Sciences↗
DOE OSTI2021

Deforestation strengthens atmospheric transport of mineral dust and phosphorus from North Africa to the Amazon

Phosphorus contained in atmospheric mineral dust aerosol originating from Africa fertilizes tropical forests in Amazonia. However, the mechanisms influencing this nutrient transport pathway remain poorly understood. Here we use the Community Earth System Model to investigate how large-scale deforestation affects mineral dust aerosol transport and deposition in the tropics. We find that the surface biophysical changes that accompany deforestation produce a warmer, drier and windier surface environment that perturbs atmospheric circulation and enhances long-range dust transport from North Africa to the Amazon. Tropics-wide deforestation weakens the Hadley circulation, which in turn, leads to a northward expansion of the Hadley cell and increases surface air pressure over the Sahara Desert. Additionally, the high pressure anomaly over the Sahara, in turn, increases northeasterly winds across North Africa and the tropical North Atlantic Ocean, which subsequently increases dust transport to the South America continent. We estimate that the annual atmospheric phosphorus deposition from dust significantly increases by 27% (P < 0.01) in the Amazon under a scenario of complete deforestation. These interactions exemplify how land surface changes can modify tropical nutrient cycling, which in turn, may have consequences for long-term changes in tropical ecosystem productivity and biodiversity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Multi-Scale Land-Atmosphere Interactions: Modeling Convective Processes from Plants to Planet

Research accomplishments include: 1)Two case studies of the effects of heterogeneous soil moisture and surface energy budgets on organization and propagation of convective precipitation during MC3E. 2) Development and evaluation of a new approach for simulating the effect of heterogeneous soil moisture at ARM-SGP using an innovative modeling approach. 3) Investigation of the effects of spatial coupling scale using multidecade global simulations in CESM with the multiscale modeling framework. 4) Exploration of changes to future precipitation intensity resulting from two different climate change scenarios using the multiscale model. 5) Provision of the new cloud-scale coupled multiscale Earth System Model to the larger community through the CESM process.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

The Numerical Computation of Clear-Sky Transmittance and Reflectance of Solar Radiation for the Modeling of PV System Performance

Modeling of photovoltaics (PV) system performance requires numerical computation of plane-of-array (POA) irradiances that are influenced by atmospheric absorption and scattering. Clear-sky transmittance and reflectance of diffuse irradiance are essential for the understanding of solar radiation related to multiple reflection between land surface and clouds. This study analytically solves the radiative transfer equation for clear-sky conditions and numerically computes the transmittance and reflectance of diffuse irradiance as functions of the optical thickness of the atmosphere. The computation will be implemented in all-sky radiative transfer models to improve their computational efficiency.

14 SOLAR ENERGY↗
DOE OSTI2020

Observational Evidence of State‐Dependent Positive and Negative Land Surface Feedback on Afternoon Deep Convection Over the Southern Great Plains

Abstract The detection of soil moisture‐precipitation feedback is complicated by the dependence of feedback sign on atmospheric state and cloud types. We evaluated responses of convective clouds to changes in surface evaporative fraction (EF) from satellite and ground‐based measurements over the Southern Great Plains in summer months. Strong responses to EF were inferred only after accounting for tropospheric states supporting systematically positive or negative responses. State dependence (inferred from controlled model experiments) explains the weak response found in previous studies. The probability of transitions from shallow to deep cumulus, and subsequent precipitation onset, increases by 58% over drier surfaces in weakly stratified environments having deeper boundary layers. Boundary layer dynamics play a more important role than low‐level relative humidity in these environments. Wetter surfaces yield increased deep convection in moist and thermally stratified environments. The distinct pathways of convective initiation over dry and wet land surfaces can play important roles in drought onset and termination.

Qiu, Shaoyue↗
DOE OSTI2024

Deep-learning-driven simulations of boundary layer clouds over the Southern Great Plains

Abstract. Based on long-term observations at the Southern Great Plains site by the Atmospheric Radiation Measurement (ARM) program for training and validation, a deep-learning model is developed to simulate the daytime evolution of boundary layer clouds (BLCs) from the perspective of land–atmosphere coupling. The model takes ARM measurements (including early-morning soundings and diurnally varying surface meteorological conditions and heat fluxes) as inputs and predicts hourly estimates (including cloud occurrence, the positions of cloud boundaries, and the vertical profile of the cloud fraction) as outputs. The deep-learning model offers good agreement with the observed cloud fields, especially in the accuracy with which cloud occurrence and base height are reproduced. When the inputs are substituted by reanalysis data from ERA5 and MERRA-2, the outputs of the deep-learning model provide a better agreement with observation than the cloud fields extracted from ERA5 and MERRA-2 themselves. Thus, the deep-learning model shows great potential to serve as a diagnostic tool for the performance of physics-based models in simulating stratiform and cumulus clouds. By quantifying biases in clouds and attributing them to the simulated atmospheric state variables versus the model-parameterized cloud processes, this observation-based deep-learning model may offer insights into the directions needed to improve the simulation of BLCs in physics-based models for weather forecasting and climate prediction.

54 ENVIRONMENTAL SCIENCES↗