Search NASA⌕ Search

SEARCH · Search NASA

Results for “Earth observing satellites”

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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

The Early Career Research Program Annual Progress Report: Using ARM Data and Multiscale Models to Advance the Understanding of Liquid-Phase Cloud Response to Aerosol Perturbation over Ocean and Land

This project aims to study the response of liquid-phase clouds to aerosol perturbation and reduce the related uncertainty in the DOE Earth system model. Specific objectives include: Objective 1: Quantify the aerosol indirect effect on liquid-phase clouds using ARM observations and satellite retrievals while constraining the confounding large-scale conditions; Objective 2: Harness LES case studies, untangle the confounding meteorological impacts on the observed aerosol indirect effect, and identify key cloud processes controlling the net radiative forcing of aerosol-cloud interactions at different spatiotemporal scales; Objective 3: Evaluate the aerosol indirect effect on liquid-phase clouds with process-level understanding in E3SM multiscale model configurations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Multiple Hypothesis Tracking for Opportunistic Transmitter Identification

The exploitation of radio frequency (RF) signals from Low Earth Orbit (LEO) communication satellites for the purposes of navigation is an active field of research. In this alternative navigation system, navigation observables are passively extracted from existing RF signals. Because these signals are not designed for navigation purposes and often have proprietary signal structures that are unknown to the receiver, they lack much of the information required for navigation systems or such information is unavailable to the receiver.

42 ENGINEERING↗
DOE OSTI2026

Southern Hemisphere Polar Infrared Radiation Experiment (SHIRE) Field Campaign Report

An extended-range atmospheric emitted radiance interferometer (AERI) instrument to be deployed to Scott Base (78 S, 166 E) in McMurdo Sound, Antarctica for the purpose of synergizing with the National Aeronautics and Space Administration (NASA) Polar Radiant Energy in the Far-InfraRed Experiment (PREFIRE) satellite mission (https://prefire.ssec.wisc.edu/), examining the closure of atmospheric thermal emission, and providing validation for PREFIRE. The PREFIRE mission is the first satellite mission to map Earth’s spectrally resolved thermal emission, from 5 to 50 microns, over seasonal to annual timescales. These new observations represent the beginning of an intensive examination of the nature of thermal emission from Earth (emission from both the surface and atmosphere). A primary requirement of the PREFIRE mission is to provide validation of the retrieved variables. Numerous field sites in the Arctic (such as the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) North Slope of Alaska observatory and Summit Station, Greenland) have existing instrumentation to facilitate validation, leaving an observational gap in Antarctica. Through SHIRE, we propose to establish a temporary, intensive measurement site in Antarctica to ensure that both poles are suitably represented, and benefit from this state-of-the art science. This will also allow Arctic and Antarctic atmospheric radiative states to be compared and contrasted. For SHIRE specifically, we also propose further objectives to examine the spectrally resolved thermal radiative closure of the polar atmosphere and the historical changes in Antarctic radiative forcing, using previous Antarctic field campaigns, such as the ARM West Antarctic Radiation Experiment (AWARE).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Estimation of the radiation budget during MOSAiC based on ground-based and satellite remote sensing observations

An accurate representation of the radiation budget is essential for investigating the impact of clouds on the climate system, especially in the Arctic, an environment highly sensitive to complex and rapid environmental changes. In this study, we analyse a unique dataset of observations from the central Arctic made during the MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) expedition in conjunction with state-of-the-art satellite products from CERES (Clouds and the Earth's Radiant Energy System) to investigate the radiative effect of clouds and radiative closure at the surface and the top of the atmosphere (TOA). We perform a series of radiative transfer simulations using derived cloud macro- and microphysical properties as inputs to the simulations for the entire MOSAiC period, comparing our results to collocated satellite products and ice-floe observations. The radiative closure biases were generally within the instrumental uncertainty, indicating that the simulations are sufficiently accurate to reproduce the radiation budget during MOSAiC. Comparisons of the simulated radiation budget relative to CERES show similar values in the terrestrial flux but relatively large differences in the solar flux, which are attributed to a lower surface albedo and a possible underestimation of atmospheric opacity by CERES. While the simulation results were consistent with the observations, more detailed analyses reveal an overestimation of simulated cloud opacity for cases involving geometrically thick ice clouds. In the annual mean, we found that, during the MOSAiC expedition, the presence of clouds leads to a loss of 5.2 W m −2 of the atmosphere–surface system to space, while the surface gains 25.0 W m −2 and the atmosphere is cooled by 30.2 W m −2 .

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Geophysical Retrievals in an Artificial Intelligence (AI) Framework for Illuminating Processes Controlling Water Cycle

Focal Area(s): This white paper responds to Focal Area #3: Insight gleaned from complex data (both observed and simulated) using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge-guided AI. Science Challenge: This white paper addresses the water-cycle and data-model integration grand challenge. It leverages data from the Atmospheric Radiation Measurement (ARM) Climate Research Facility, and Next-Generation Ecosystem Experiment (NGEE), and Science Focus Area (SFA). The white paper focuses on controlling cloud, precipitation, and radiative properties as observed and simulated by the Earth System Models (ESM). The described framework can be readily applied to any other ensemble of instruments, including satellites and other ground-based networks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

The CLoud–Aerosol–Radiation Interaction and Forcing: Year 2017 (CLARIFY-2017) measurement campaign

The representations of clouds, aerosols, and cloud–aerosol–radiation impacts remain some of the largest uncertainties in climate change, limiting our ability to accurately reconstruct past climate and predict future climate. The south-east Atlantic is a region where high atmospheric aerosol loadings and semi-permanent stratocumulus clouds are co-located, providing an optimum region for studying the full range of aerosol–radiation and aerosol–cloud interactions and their perturbations of the Earth's radiation budget. While satellite measurements have provided some useful insights into aerosol–radiation and aerosol–cloud interactions over the region, these observations do not have the spatial and temporal resolution, nor the required level of precision to allow for a process-level assessment. Detailed measurements from high spatial and temporal resolution airborne atmospheric measurements in the region are very sparse, limiting their use in assessing the performance of aerosol modelling in numerical weather prediction and climate models. CLARIFY-2017 was a major consortium programme consisting of five principal UK universities with project partners from the UK Met Office and European- and USA-based universities and research centres involved in the complementary ORACLES, LASIC, and AEROCLO-sA projects. The aims of CLARIFY-2017 were fourfold: (1) to improve the representation and reduce uncertainty in model estimates of the direct, semi-direct, and indirect radiative effect of absorbing biomass burning aerosols; (2) to improve our knowledge and representation of the processes determining stratocumulus cloud microphysical and radiative properties and their transition to cumulus regimes; (3) to challenge, validate, and improve satellite retrievals of cloud and aerosol properties and their radiative impacts; (4) to improve the impacts of aerosols in weather and climate numerical models. This paper describes the modelling and measurement strategies central to the CLARIFY-2017 deployment of the FAAM BAe146 instrumented aircraft campaign, summarizes the flight objectives and flight patterns, and highlights some key results from our initial analyses.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Integrating Reanalysis and Satellite Cloud Information to Estimate Surface Downward Long-Wave Radiation

The estimation of downward long-wave radiation (DLR) at the surface is very important for the understanding of the Earth’s radiative budget with implications in surface–atmosphere exchanges, climate variability, and global warming. Theoretical radiative transfer and observationally based studies identify the crucial role of clouds in modulating the temporal and spatial variability of DLR. In this study, a new machine learning algorithm that uses multivariate adaptive regression splines (MARS) and the combination of near-surface meteorological data with satellite cloud information is proposed. The new algorithm is compared with the current operational formulation used by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Land Surface Analysis (LSA-SAF). Both algorithms use near-surface temperature and dewpoint temperature along with total column water vapor from the latest European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis ERA5 and satellite cloud information from the Meteosat Second Generation. The algorithms are trained and validated using both ECMWF-ERA5 and DLR acquired from 23 ground stations as part of the Baseline Surface Radiation Network (BSRN) and the Atmospheric Radiation Measurement (ARM) user facility. Results show that the MARS algorithm generally improves DLR estimation in comparison with other model estimates, particularly when trained with observations. When considering all the validation data, root mean square errors (RMSEs) of 18.76, 23.55, and 22.08 W·m –2 are obtained for MARS, operational LSA-SAF, and ERA5, respectively. The added value of using the satellite cloud information is accessed by comparing with estimates driven by ERA5 total cloud cover, showing an increase of 17% of the RMSE. The consistency of MARS estimate is also tested against an independent dataset of 52 ground stations (from FLUXNET2015), further supporting the good performance of the proposed model.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Regime-based aerosol–cloud interactions from CALIPSO-MODIS and the Energy Exascale Earth System Model version 2 (E3SMv2) over the Eastern North Atlantic

This study investigates aerosol-cloud interactions in marine boundary layer (MBL) clouds using an advanced deep-learning-driven synoptic-regime-based framework, combining satellite data (CALIPSO vertically resolved aerosol extinction and MODIS cloud properties) with 1° nudged Energy Exascale Earth System Model version 2 (E3SMv2) simulation over the Eastern North Atlantic (ENA; ∼10°×10°, 2006–2014). The E3SMv2 captures observed seasonal variations in cloud droplet number concentrations (N d ) and liquid water path (LWP), though it systematically underestimates N d . We then partition ENA meteorology into four synoptic regimes (Pre-Trough, Post-Trough, Ridge, Trough) via a deep-learning clustering of ERA5 reanalysis fields, enabling regime-dependent aerosol-cloud interactions analyses. Both satellite and E3SMv2 exhibit an inverted-V LWP-N d relationship. In Post-Trough and Ridge regimes, the satellite shows stronger negative LWP-N d sensitivities than in Pre-Trough regime. The Trough regime displays a muted satellite LWP response. In comparison, the model predicts more exaggerated LWP responses across regimes, with LWP increasing too quickly at low N d and decreasing more sharply at high N d , especially in Pre-Trough and Trough regimes. These exaggerated model LWP sensitivities may stem from uncertainties in representing drizzle processes, entrainment, and turbulent mixing. As for N d susceptibility to aerosols, N d increases with MBL aerosol extinction in both datasets, but the simulated aerosol-cloud interactions appear oversensitive to meteorological conditions. Overall, E3SMv2 better captures aerosol effects under regimes that favor stratiform clouds (Post-Trough, Ridge), but performance deteriorates for regimes with deeper, dynamically complex clouds (Trough), highlighting the need for improved representations of those cloud processes in climate models.

Environmental sciences↗
DOE OSTI2020

Retrieval of Cloud Liquid Water Using Microwave Signals from LEO Satellites: A Feasibility Study through Simulations

A novel method, using low Earth orbit (LEO) satellite microwave communication links for cloud liquid water measurements, is introduced in this paper. The feasibility of this approach is studied through simulations of the retrieval system including a LEO satellite communicating with a group of ground receivers equipped with signal-to-noise ratio (SNR) estimators, a synthetic cloud attenuation field and a tomographic retrieval algorithm. Rectangular and Gaussian basis functions are considered to define the targeted field. Simulation results suggest that the proposed least-squares based retrieval algorithm produces satisfactory outcomes for both types of basis functions. The root-mean-square error of the retrieved field is around 0.2 dB/km, with the range of the reference field as 0 to 2.35 dB/km. It is also confirmed that the partial retrieval of the cloud field is achievable when a limited number of receivers with restricted locations are available. The retrieval outcomes exhibit properties of high resolution and low error, indicating that the proposed approach has great potential for cloud observations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

A U.S. Scientific Community Vision for Sustained Earth Observations of Greenhouse Gases to Support Local to Global Action

Managing carbon stocks in the land, ocean, and atmosphere under changing climate requires a globally‐integrated view of carbon cycle processes at local and regional scales. The growing Earth Observation (EO) record is the backbone of this multi‐scale system, providing local information with discrete coverage from surface measurements and regional information at global scale from satellites. Carbon flux information, anchored by inverse estimates from spaceborne Greenhouse Gas (GHG) concentrations, provides an important top‐down view of carbon emissions and sinks, but currently lacks global continuity at assessment and management scales (<100 km). Partial‐column data can help separate signals in the boundary layer from the overlying atmosphere, providing an opportunity to enhance surface sensitivity and bring flux resolution down from that of column‐integrated data (100–500 km). Based on a workshop held in September 2024, the carbon cycle community envisions a carbon observation system leveraging GHG partial columns in the lower and upper troposphere to weave together information across scales from surface and satellite EO data, and integration of top‐down/bottom‐up analyses to link process understanding to global assessment.

Parazoo, Nicholas C. [California Institute of Tech↗
DOE OSTI2020

Remote sensing and statistical analysis of the effects of hurricane María on the forests of Puerto Rico

Widely recognized as one of the worst natural disaster in Puerto Rico's history, hurricane María made landfall on September 20, 2017 in southeast Puerto Rico as a high-end category 4 hurricane on the Saffir-Simpson scale causing widespread destruction, fatalities and forest disturbance. This study focused on hurricane María's effect on Puerto Rico's forests as well as the effect of landform and forest characteristics on observed disturbance patterns. In this work, we used Google Earth Engine (GEE) to assess the severity of forest disturbance using a disturbance metric based on Landsat 8 satellite data composites with pre and post-hurricane María. Forest structure, tree phenology characteristics, and landforms were obtained from satellite data products, including digital elevation model and global forest canopy height. Our analyses showed that forest structure, and characteristics such as forest age and forest type affected patterns of forest disturbance. Among forest types, highest disturbance values were found in sierra palm, transitional, and tall cloud forests; seasonal evergreen forests with coconut palm; and mangrove forests. For landforms, greatest disturbance metrics was found at high elevations, steeper slopes, and windward surfaces. As expected, high levels of disturbance were also found close to the hurricane track, with disturbance less severe as hurricane María moved inland. Results demonstrated that forest and landform characteristics accounted for 34% of the variation in spatial forest spectral disturbance patterns. This study demonstrated an informative regional approach, combining remote sensing with statistical analyses to investigate factors that result in variability in hurricane effects on forest ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Remote sensing of planetary boundary layer from ground and space: Structure, thermodynamics, and boundary layer clouds

The planetary boundary layer (PBL) is a critical interface between Earth's surface and atmosphere, influencing atmospheric convection, weather patterns, and air quality. Recognized by the 2017 National Academies of Sciences, Engineering, and Medicine Earth Science Decadal Survey as an Incubation Targeted Observable, high-quality and effective observation of the PBL has become a priority. This paper reviews surface-based and satellite remote sensing techniques for characterizing PBL features, including PBL height (PBLH), boundary layer thermodynamics, turbulence, and boundary layer clouds (BLCs). These elements are treated as interconnected aspects of the PBL system, while recognizing that different instruments retrieve different physical manifestations of the PBL. The review summarizes recent advances and limitations in Micro-Pulse Lidar (MPL), Doppler lidar, Raman lidar, Differential Absorption Lidar (DIAL), ceilometers, wind profilers, GNSS Radio Occultation, radar, and hyperspectral sounders. These observations help trace dynamic processes within the PBL and link PBL structure to broader weather and climate processes. The review also highlights persistent observational gaps over oceans, remote land, and polar regions, where continuous surface-based profiling is sparse. By comparing instrument capabilities, practical limitations, and interpretation issues, this review emphasizes the need for integrated remote sensing approaches and careful definition of the retrieved PBL quantities.

Boundary layer clouds↗
DOE OSTI2024

Systematic Underestimation of Canopy Conductance Sensitivity to Drought by Earth System Models

The response of vegetation canopy conductance (g c ) to changes in moisture availability ($γ^{m}_{gc}$) is a major source of uncertainty in climate projections. While vegetation typically reduces stomatal conductance during drought, accurately modeling how and to what degree stomata respond to changes in moisture availability at global scales is particularly challenging, because no global scale g c observations exist. Here, we leverage a collection of satellite, reanalysis and station-based near-surface air and surface temperature estimates, which are physically and statistically linked to $γ^{m}_{gc}$ due to the local cooling effect of g c through transpiration, to develop a novel emergent constraint of $γ^{m}_{gc}$ in an ensemble of Earth System Models (ESMs). We find that ESMs systematically underestimate $γ^{m}_{gc}$ by ~33%, particularly in grasslands, croplands, and savannas in semi-arid and bordering regions of the Central United States, Central Europe, Southeastern South America, Southern Africa, Eastern Australia, and parts of East Asia. We show that this underestimation occurs because ESMs inadequately reduce g c when soil moisture decreases. As g c controls carbon, water and energy fluxes, the misrepresentation of modeled $γ^{m}_{gc}$ contributes to biases in ESM projections of gross primary production, transpiration, and temperature during droughts. Our results suggest that the severity and duration of droughts may be misrepresented in ESMs due to the impact of sustained g c on both soil moisture dynamics and the biosphere-atmosphere feedbacks that affect local temperatures and regional weather patterns.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

In-orbit performance of the soft X-ray imaging telescope Xtend aboard XRISM

Here, we present a summary of the in-orbit performance of the soft X-ray imaging telescope Xtend onboard the X-Ray Imaging and Spectroscopy Mission (XRISM), based on in-flight observation data, including first-light celestial objects, calibration sources, and results from the cross-calibration campaign with other currently operating X-ray observatories. XRISM/Xtend has a large field of view of ${38{^{\prime }_{.}}5}$ $\times$ ${38{^{\prime }_{.}}5}$, covering an energy range of 0.4–13 keV, as demonstrated by the first-light observation of the galaxy cluster Abell 2319. It also features an energy resolution of 170–180 eV at 6 keV, which meets the mission requirement and enables us to resolve He-like and H-like Fe K$\alpha$ lines. Throughout the observation during the performance verification phase, we confirm that two issues identified in the Soft X-ray Imager (SXI) onboard the previous Hitomi mission—light leakage and crosstalk events—are addressed and suppressed in the case of Xtend. A joint cross-calibration observation of the bright quasar 3C 273 results in an effective area measured to be $\sim$420 cm$^{2}$ at1.5 keV and $\sim$310 cm$^{2}$ at 6.0 keV, which matches values obtained in ground tests. We also continuously monitor the health of Xtend by analyzing overclocking data, calibration source spectra, and day-Earth observations; the readout noise is stable and low, and contamination is negligible even one year after launch. A low background level compared with other major X-ray instruments onboard satellites, combined with the largest grasp ($\Omega _{\rm eff}\sim 60$ cm$^2$ deg$^2$) of Xtend, will not only support Resolve analysis, but also enable significant scientific results on its own. This includes near-future follow-up observations and transient searches in the context of time-domain and multi-messenger astrophysics.

instrumentation: detectors: Xtend↗
DOE OSTI2022

Deficient precipitation sensitivity to Sahel land surface forcings among CMIP5 models

Abstract The overall performance of the simulated seasonal precipitation response to local terrestrial forcings, namely vegetation abundance and soil moisture, in the Sahel among the Coupled Model Intercomparison Project Phase Five (CMIP5) Earth System Models (ESMs) is systematically investigated and compared with its observational counterpart using a multivariate statistical method. The observed seasonal precipitation response is evaluated against a large ensemble of observational, reanalysis, and satellite data sets to provide quantification of uncertainties. The behaviour of models with and without a Dynamic Global Vegetation Model (DGVM) component is also explored, along with the mechanisms responsible for terrestrial feedback on rainfall. In general, the CMIP5 models can reasonably capture the seasonal evolution of Sahel precipitation and soil moisture, albeit with wet biases during the pre‐monsoon period and dry biases during the peak monsoon period. The non‐DGVM ESMs simulate comparable leaf area indices (LAIs) with observations, while DGVM‐enabled ESMs simulate too much year‐round LAI. The variance of precipitation that is attributed to oceanic forcings in CMIP5 is comparable with observations; however, the variance of precipitation that is attributed to terrestrial forcings is smaller in CMIP5 models than observed, especially for non‐DGVM ESMs. CMIP5 models, especially those without DGVMs, undervalue precipitation's observed response strength to soil moisture anomalies. In both observations and CMIP5 models, none of the atmospheric variables show significant responses to direct vegetation forcing, except for the response in transpiration. Although vegetation has minimal direct effect on the atmospheric state, it can affect the atmosphere by modifying soil moisture and transpiration rate indirectly, which helps explain the more realistic simulation of rainfall in DGVM‐enabled ESMs than non‐DGVM ESMs. Coupling of an ESM to a DGVM is critical in generating reasonable land–atmosphere feedback and examining future ecological and climatic changes over the Sahel.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Advances in solar forecasting: Computer vision with deep learning

Renewable energy forecasting is crucial for integrating variable energy sources into the grid. It allows power systems to address the intermittency of the energy supply at different spatiotemporal scales. To anticipate the future impact of cloud displacements on the energy generated by solar facilities, conventional modeling methods rely on numerical weather prediction or physical models, which have difficulties in assimilating cloud information and learning systematic biases. Augmenting computer vision with machine learning overcomes some of these limitations by fusing real-time cloud cover observations with surface measurements acquired from multiple sources. This Review summarizes recent progress in solar forecasting from multisensor Earth observations with a focus on deep learning, which provides the necessary theoretical framework to develop architectures capable of extracting relevant information from data generated by ground-level sky cameras, satellites, weather stations, and sensor networks. Overall, machine learning has the potential to significantly improve the accuracy and robustness of solar energy meteorology; however, more research is necessary to realize this potential and address its limitations.

14 SOLAR ENERGY↗
DOE OSTI2023

Upland forest retreat lags behind sea‐level rise in the mid‐Atlantic coast

Abstract Ghost forests consisting of dead trees adjacent to marshes are striking indicators of climate change, and marsh migration into retreating coastal forests is a primary mechanism for marsh survival in the face of global sea‐level rise. Models of coastal transgression typically assume inundation of a static topography and instantaneous conversion of forest to marsh with rising seas. In contrast, here we use four decades of satellite observations to show that many low‐elevation forests along the US mid‐Atlantic coast have survived despite undergoing relative sea‐level rise rates (RSLRR) that are among the fastest on Earth. Lateral forest retreat rates were strongly mediated by topography and seawater salinity, but not directly explained by spatial variability in RSLRR, climate, or disturbance. The elevation of coastal tree lines shifted upslope at rates correlated with, but far less than, contemporary RSLRR. Together, these findings suggest a multi‐decadal lag between RSLRR and land conversion that implies coastal ecosystem resistance. Predictions based on instantaneous conversion of uplands to wetlands may therefore overestimate future land conversion in ways that challenge the timing of greenhouse gas fluxes and marsh creation, but also imply that the full effects of historical sea‐level rise have yet to be realized.

Biodiversity & Conservation↗
DOE OSTI2024

Using Satellite and ARM Observations to Evaluate Cold Air Outbreak Cloud Transitions in E3SM Global Storm‐Resolving Simulations

Abstract This study examines marine boundary layer cloud regime transition during a cold air outbreak (CAO) over the Norwegian Sea, simulated by a global storm‐resolving model (GSRM) known as the Simple Cloud‐Resolving Energy Exascale Earth System Model Atmosphere Model (SCREAM). By selecting observational references based on a combination of large‐scale conditions rather than strict time‐matched comparisons, this study finds that SCREAM qualitatively captures the CAO cloud transition, including boundary layer growth, cloud mesoscale structure, and phase partitioning. SCREAM also accurately locates the greatest ice and liquid in the mesoscale updrafts, however, underestimates supercooled liquid water in cumulus clouds. The model evaluation approach adopted by this study takes advantages of the existing computational‐expensive global simulations of GSRM and the available observations to understand model performance and can be applied to assessments of other cloud regimes in different regions. Such practice provides valuable guidance on the future effort to correct and improve biased model behaviors.

54 ENVIRONMENTAL SCIENCES↗