Abstract The Multi-University Consortium for Advanced Data Assimilation Research and Education (CADRE) is a new initiative recently funded by the National Oceanic and Atmospheric Administration (NOAA) to accelerate data assimilation (DA) research, education, and workforce development. Unlike previous initiatives, CADRE fosters end-to-end, direct, and comprehensive collaboration between university faculty and government agencies. It supports innovative DA research, prepares the next-generation DA workforce, and facilitates the transition of DA research to operational applications. CADRE performs a broad scope of cutting-edge research to address multiscale, nonlinear, and coupled Earth system DA challenges to improve short-range (sub-hourly) to seasonal predictions. It achieves this task through innovative data assimilation algorithm development, novel applications of machine learning (ML) in DA, and optimizing the utilization of existing and new in-situ and remotely sensed observations. Beyond research, CADRE establishes a comprehensive education, workforce development, and community building program, which includes a novel graduate student advising model, new university class curriculum development, public training courses, community scientific workshops, an international exchange program, trans-disciplinary partnerships, an outreach program, and promotion of the open sharing of data, code, and educational materials. Through this unique holistic approach, CADRE is set to strengthen both the intellectual and software infrastructure in the broad community for DA research, increase the number of DA scientists with expanded skill sets, and revolutionize forecasting capabilities.

Abstract. This study investigates the impact of dust aerosols on the evolution of Tropical Storm Hermine (2022) using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) and observational data from the NASA Convective Processes Experiment – Cabo Verde (CPEX-CV). The objective is to evaluate how varying initial dust aerosol conditions influence storm development and to uncover the mechanisms behind these effects. Three WRF-Chem simulations were conducted with different initial aerosol concentrations: one with no aerosols, one with realistic dust concentrations, and one with intermediate aerosol levels. The simulations were compared against observational data from CPEX-CV and the best track data from the United States' National Hurricane Centre, focusing on parameters such as wind, pressure, aerosol optical depth, and radar reflectivity. The results indicate that the radiative effect of dust aerosols led to a weaker and more disorganized storm system compared to simulations without the inclusion of dust, highlighting the critical role of dustradiation interactions in modifying storm intensity. Furthermore, the study found that the ECMWF's Atmospheric Composition Reanalysis 4 (CAMS) underestimated atmospheric dust concentrations, in comparisons to observations, underlining the necessity for accurate observational data to validate aerosol-related processes and improve model predictions. These findings emphasize the complexity of dust aerosol-storm interactions and the importance of improving aerosol representations in simulations of tropical cyclones.

Accurate predictions of mountainous cold fog are crucial but challenging because poor forecasts often arise from imprecise surface initial conditions. This study evaluates how initial conditions from five U.S. weather analysis products influence the Weather Research and Forecasting (WRF) Model at gray-zone resolution (similar to 333 m) when simulating an ephemeral shallow cold fog event on 19 February 2022 in Heber Valley, northern Utah, during intensive observing period (IOP) 8 of the Cold Fog Among Complex Terrain (CFACT) campaign. Results show that all WRF simulations capture large-scale weather patterns well. However, WRF initial conditions derived from coarse-resolution datasets (>3 km) overestimate snow cover in Heber Valley, even though only trace snow was observed during CFACT IOP 8. This erroneous snow cover leads to excessive daytime shortwave radiation reflection, notable nighttime near-surface cold biases, and excessive saturation. These conditions promote the erroneous moisture release and persistent optically thick freezing fog, contradicting observations. In contrast, simulations using high-resolution analysis products (<= 3 km) show minimal biases in shortwave and longwave radiation, near-surface temperature, dewpoint, and mixing ratio-linked to better representation of observed trace-snow-cover areas, as confirmed by sensitivity tests. This study highlights the importance of adopting high-resolution snow cover to improve cold fog simulations over complex mountainous terrain. The results also underscore the critical radiative effects of snow cover on near-surface atmospheric conditions and the consequent influence on the formation and evolution of cold fog. Furthermore, the numerical results confirm that high near-surface vertical resolution is necessary for better prediction of the near-surface temperature inversions and ephemeral fog events. Significance Statement: Accurate prediction of cold fog in mountainous regions is vital for transportation safety but remains difficult, partly due to uncertainties in snow representation. We evaluated the influence of snow initial conditions from five U.S. weather analysis products on simulations of an ephemeral shallow cold fog event in Heber Valley, Utah, observed during the Cold Fog Among Complex Terrain (CFACT) field campaign. Our findings highlight that adopting high-resolution snow analysis and fine near-surface vertical resolution is critical for reliably simulating and forecasting mountainous cold fog. Our results also imply that snow cover can promote the formation of cold fog by reflecting shortwave radiation and lowering near-surface temperatures.

With extreme weather events becoming more frequent and severe, accelerating progress in Earth system predictability is urgently needed to deepen fundamental understanding, improve predictive tools, and provide reliable, actionable information for societal resilience. Building on prior and ongoing efforts by the broader community and informed by discussions at a Earth System Predictability Across Time Scales, this essay articulates a perspective on the scientific and structural priorities needed to advance Earth system predictability from short-range weather forecasts to century-scale projections, underscoring the urgency of a comprehensive, integrative approach capable of meeting emerging societal needs. Three scientific grand challenges are highlighted: understanding interactions across spatial and temporal scales, across interconnected Earth system components, and the influence of external forcing on predictability. To address these grand challenges, we identify potential implementation priorities across five key areas: 1) enhancing observations and data accessibility, 2) advancing data assimilation techniques, 3) improving modeling frameworks, 4) developing artificial intelligence (AI) and machine learning (ML) methods, and 5) applying convergence research. To support these areas, we outline four intersecting pillars of an integrated strategy: (i) a multiscale and multidisciplinary approach; (ii) closer coordination across modeling, observations, data assimilation, and AI/ML; (iii) intentional convergence research; and (iv) codevelopment of science with users. We also propose a collaborative path forward focused on strengthening scientific and technical connections, rewarding interdisciplinary and team-based science, expanding support for engagement with users, and investing in relationship building, shared language, and trust across scientific and societal domains.

Satellite-based precipitation retrievals are vital for hydrologic monitoring in the Southern Great Plains. This study assesses the cross-satellite performance of a Fourier neural operator (FNO) model trained on Geostationary Operational Environmental Satellite (GOES)-16 advanced baseline imager (ABI) and geostationary lightning mapper (GLM) inputs by applying it to GOES-19 observations of the July 4, 2025 Texas flash flood. Despite differences in training and testing data sources, the model reproduces roughly 80% of Stage IV rainfall estimates, while the operational GOES-R quantitative precipitation estimation (QPE) product overestimates by more than a factor of 2.5. The FNO approach better captures convective structure and achieves higher equitable threat scores for valid thresholds, highlighting its potential for real-time precipitation estimation during satellite transitions.

Accurate initialization of ocean states is essential for skillful prediction of Earth system variability across seasonal-to-decadal timescales. In this study, we evaluate the impact of a newly developed four-dimensional ensemble variational (4DEnVar)-based weakly coupled ocean data assimilation (WCODA) system within the DOE Energy Exascale Earth System Model version 2 (E3SMv2) on global and regional climate variability. By assimilating monthly ocean temperature and salinity from the EN4.2.1 reanalysis into the fully coupled model, we demonstrate substantial improvements in simulating both interannual and decadal climate variability. Compared with the free-running coupled simulation, the assimilation experiment exhibits markedly enhanced interannual correlations with observations for global mean surface air temperature and precipitation anomalies. The temporal variability of key climate modes, including ENSO, the Indian Ocean Dipole, and multidecadal variability in the Pacific and Atlantic Oceans, also shows markedly improved phase agreement with observations. Regional evaluation over the contiguous United States further shows enhanced skill in simulating winter surface air temperature and precipitation, particularly in the northern and southern regions, respectively, with these improvements linked to improved ENSO simulation. Additional hindcast experiments initialized from the WCODA system exhibit no appreciable initialization shock in the early years and reproduce physically coherent ENSO teleconnection patterns, suggesting the dynamical consistency of the coupled initialization framework. These findings underscore the critical role of coupled forecasts in the data assimilation cycle for propagating observational information across Earth system components. By assimilating ocean reanalysis within the fully coupled framework, the WCODA system enables cross-component information exchange among the ocean, atmosphere, and land, thereby generating dynamically consistent initial conditions that support more accurate simulations of Earth system variability and lay the foundation for seasonal-to-decadal prediction applications.

Accurate prediction of high-impact weather and hydrometeorological extremes depends on advanced data assimilation and tightly coupled modeling across spatial and temporal scales. This presentation highlights recent progress in assimilating radar, lidar, satellite, and in situ observations to improve analyses and forecasts of tropical convection and cyclones, drawing on results from NASA’s Convective Processes Experiments (CPEX-AW and CPEX-CV). These studies demonstrate how enhanced observational constraints improve the representation of convective organization, moisture transport, and interactions between convection and the large-scale circulation.Complementary investigations from the midlatitude Cold Fog Amongst Complex Terrain (CFACT) campaign illustrate the critical roles of boundary-layer thermodynamics, surface heterogeneity, and complex terrain in governing local-scale predictability. Building on these efforts, recent developments in land–atmosphere data assimilation are presented, showing how coupled initialization of surface and atmospheric states strengthens cross-interface feedbacks and improves forecast skill from short- to subseasonal timescales.Together, these results bridge tropical, midlatitude, and land–atmosphere coupling research, highlighting the value of integrated observing systems and multiscale data assimilation for improving predictions of extreme precipitation and water-related hazards. The findings contribute to broader efforts aimed at advancing the predictability of high-impact weather across scales.

Subseasonal to seasonal (S2S) scale prediction, especially precipitation prediction, depends predominantly on initial conditions. To examine the impacts of atmospheric and land initial conditions on the predictions of the Madden-Julian Oscillation (MJO) and related S2S precipitation, we conduct a novel study using coupled atmosphere-land simulations in the Energy Exascale Earth System Model (E3SM). Our findings indicate that reanalysis-based atmospheric and land initial conditions yield improved S2S precipitation simulations compared with those using a long-term spin-up equilibrium state. The impacts of initial conditions on precipitation simulation persist for approximately 40 and 50 days in the MJO and global regions, respectively, and are strongly associated with outgoing longwave radiation in the MJO region and surface latent heat flux at the global scale. Although atmospheric initial conditions exert a dominant influence on MJO simulation, improved land initial conditions provide an important secondary source of predictability by better representing land–atmosphere coupling over the Maritime Continent. More realistic surface moisture fluxes and surface temperature can modulate boundary-layer moistening and MJO-related convection, thereby contributing to improved MJO prediction. These findings have important implications for S2S precipitation prediction and provide crucial insights for the further development of Earth system models.

Satellite-based precipitation retrievals are vital for hydrologic monitoring in the Southern Great Plains. This study assesses the cross-satellite performance of a Fourier Neural Operator (FNO) model trained on GOES-16 ABI and GLM inputs by applying it to GOES-19 observations of the 4 July 2025 Texas flash flood. Despite differences training and testing data sources, the model reproduces 80% of Stage IV rainfall estimates, while the operational GOES-R QPE product overestimates by more than a factor of 2.5. The FNO approach better captures convective structure and achieves higher Equitable Threat Scores for valid thresholds, highlighting its potential for real-time precipitation estimation during satellite transitions.

The objective of this study is to characterize visibility and microphysics of cold-fog conditions during The Cold Fog Amongst Complex Terrain (CFACT) project, which was designed to investigate the life cycle of cold fog in the Heber Valley, Utah. The field campaign was conducted from 7 January to 23 February 2022 and was supported with observations and resources from the NSF Lower Atmospheric Observing Facilities (LAOF), managed by NCAR’s Earth Observing Laboratory (EOL), as well as the University of Utah and Ontario Technical University. Heber Valley is surrounded by canyons, mountains and irregular topography where the Provo River streams along the valley floor from Jordanelle Reservoir at the north to Deer Creek (DC) Reservoir at the southeastern end at 1652 m above sea level (ASL). The highest peaks surrounding the valley are at about 3500 m (ASL) to the west and southwest of the project area.The DC supersite had extensive ice and droplet microphysical as well as precipitation measurements obtained using a ground-based Gondola (composed of a Droplet Measurement Technologies (DMT) Cloud Droplet Probe - CDP and Back-scatter Cloud Probe - BCP), a DMT Fog Monitor (FM120), a Mesaphotonics Cloud Droplet Measurement System (CDMS), a DMT Ground-based Cloud Imaging Probe (GCIP), a Vaisala Present Weather Detector (PWD52), and an OTT Parsivel. During the project, aerosol measurements were performed using a GRIMM Aerosol Spectrometer, a T.S.I. Scanning Mobility Particle Sizer (SMPS), and a DMT Cloud Condensation Nuclei (CCN) counter, as well as filter samplers. These instruments covered a size range from 8 nm up to cm size range representing aerosols, fog particles, and precipitation. Measurements from a Halo Photonics doppler wind lidar, a Vaisala CL61 ceilometer, a tethered balloon system (TBS), and a 32-m turbulence tower were used to characterize vertical profiles of fog microphysics and aerosols, as well as the dynamic and thermodynamic structure. Eleven significant weather events occurred during the 47 days of the CFACT campaign that included snowfall, freezing fog, ice fog (IF), and light ice crystal precipitation when Vis

New particle formation (NPF) is a complex atmospheric phenomenon defined by the gas‐to‐particle conversion that leads to the sudden burst and growth in aerosol particles. Although chemical mechanisms for aerosol nucleation and growth are well established, the role of physical processes, such as turbulent mixing, within the atmospheric boundary layer (ABL) is beginning to emerge with recent studies. This study, based on the observations from the 2022 CFACT (Cold Fog Amongst Complex Terrain) field study in the Heber Valley of northern Utah, demonstrates an interconnection between turbulence and the occurrence of NPF. Using a spatially distributed boundary layer instrumentation, a novel feature of CFACT, three case studies depict unique boundary layer conditions that modulate the development of NPF characterized by sustained turbulence and weak intermittent turbulence. Quantitative analysis using in situ measurements and derived variables demonstrate that periods of weak intermittent turbulence hinder particle growth, whereas sustained turbulence helps modulate NPF. These findings provide new insights into the physical drivers of NPF, underscoring the role of turbulence in impacting particle formation with the ABL.

One objective of NASA's Convective Processes Experiments Aerosols and Winds (CPEX-AW, 2021) and Cabo Verde (CPEX-CV, 2022) was to assess the impact of high-spatiotemporal-resolution airborne observations on the understanding and prediction of tropical Atlantic convective systems. This study investigates the effects of assimilating observations simultaneously from two airborne lidar systems, Doppler Aerosol Wind (DAWN) lidar winds and High Altitude Lidar Observatory (HALO) water vapor profiles, as well as dropsonde profiles on short-range precipitation forecasts associated with four African easterly waves (AEWs) during CPEX-AW and CPEX-CV. Observations are assimilated into the Weather Research and Forecasting (WRF) Model using the NCEP Gridpoint Statistical Interpolation analysis system (GSI)-based three-dimensional ensemble-variational hybrid data assimilation (3DEnVAR) system. Forecasts are evaluated against Airborne Precipitation Radar (APR)-3 observations and satellite-derived precipitation datasets. Results show that assimilating DAWN winds improves forecasts of the African easterly jet (AEJ) and AEW steering flow, refining precipitation placement. DAWN assimilation also weakens low-level convergence and upper-level divergence, thereby reducing moisture convergence and suppressing spurious simulated precipitation under observed clear skies. HALO water vapor assimilation achieves similar improvements by reducing low-level moisture and total precipitable water. Additionally, DAWN wind assimilation adjusts divergence near cloud tops and enhances precipitation forecasts in observed cloudy skies. The joint assimilation of DAWN winds and HALO water vapor combines these benefits. Dropsonde profiles provide valuable in-cloud data that enhance convective system forecasts but can introduce uncertainties due to their sparse and drifting nature. These drawbacks are effectively mitigated when dropsonde data are jointly assimilated with DAWN winds and HALO water vapor, underscoring the advantages of integrated airborne data assimilation.

While large-scale climate drivers such as El Ni & ntilde;o-Southern Oscillation (ENSO) have been widely studied in relation to western U.S. droughts, the influence of synoptic-scale atmospheric patterns remains less well understood. This study examines the 2021-22 drought in the western United States by analyzing synoptic-scale conditions using the fifth major global reanalysis produced by ECMWF (ERA5) and National Centers for Environmental Prediction (NCEP)-National Center for Atmospheric Research (NCAR) reanalysis data, comparing them to the 1991-2020 climatology and the nondrought year of 2019. Results show that both winters during the drought period were dominated by anomalously strong upper-level ridging and expansive surface high pressure systems over the Pacific, producing persistent blocking patterns that limited precipitation across much of the West. In summer, while surface pressure anomalies were near climatological norms, a persistent upper-level ridge continued to reinforce regional dryness. However, the North American monsoon brought substantial moisture to the interior Southwest in both 2021 and 2022, partially alleviating drought conditions in that region. These findings underscore the critical role of synoptic-scale circulation features particularly atmospheric blocking in determining seasonal drought severity. The study highlights the importance of incorporating synoptic-scale analysis into drought forecasting and water resource management to improve preparedness and long-term resilience. SIGNIFICANCE STATEMENT: This study highlights how persistent high pressure blocking systems during the wet winter season can prolong drought conditions into the dry summer months in the western United States, exacerbating water scarcity and impacting ecosystems and agriculture. However, the North American monsoon plays a crucial role in delivering seasonal moisture to the Southwest, partially alleviating drought in that region. Understanding these synoptic patterns is essential for improving drought forecasting and water resource management, helping policymakers and stakeholders develop more effective mitigation strategies in an increasingly variable climate.

The Advanced Baseline Imager (ABI) on the Geostationary Operational Environmental Satellite-16 ( GOES-16) includes three water vapor channels (8, 9, and 10) that are specifically designed to monitor tropospheric water vapor at high, mid-, and low levels. This study investigates an effective approach for assimilating the all-sky brightness temperatures (BTs) derived from these three water vapor channels while considering the characteristics of interchannel observation-error correlations (IOECs). The NASA Unified Weather Research and Forecasting (NU-WRF) Model, alongside the NCEP Gridpoint Statistical Interpolation analysis system (GSI)-based three-dimensional ensemble-variational hybrid data assimilation system, is utilized. First, an optimal bias correction (BC) scheme and data assimilation (DA) confi guration, previously developed for all-sky assimilation of GOES-16 channel 8, are confirmed to be effective for channels 9 and 10. Then, the IOECs with respect to the symmetric cloud proxy variable C among these three channels are analyzed. It is found that the IOECs display sigmoid function characteristics, being lower and roughly invariant with respect to C under clear-sky conditions and higher in cloudy conditions. Given the unique properties of IOECs, sensitivity experiments with various configurations for assimilating the all-sky BTs from the three water vapor channels are conducted with Hurricanes Laura (2020) and Ida (2021). The results indicate that a strategy combining the assimilation of all-sky BTs from one of the GOES-16 channels, especially channel 10, with clear-sky BTs from the other water vapor channels yields superior analysis and forecasts in most scenarios, thereby highlighting the importance of appropriately estimating and accounting for cloud-dependent IOECs when assimilating all-sky BTs from the infrared channels in operational DA systems.

In the U. S. Southern Great Plains (SGP) region, severe weather occurs regularly during the warm season (e.g., June–August), causing extensive property damage and loss of life. Despite advancements in observations and numerical models, estimating precipitation associated with these severe weather events remains challenging, thereby complicating accurate public warnings. Recently, machine learning (ML) models have been employed as a data‐driven approach to quantify precipitation during severe storms. In this study, we evaluate the performance of a ML model which utilizes the Fourier Neural Operator (FNO) for obtaining hourly precipitation retrievals in the SGP region. The FNO‐based model uses water vapor‐absorbing band brightness temperatures, lightning flash counts, and lightning average flash areas from NOAA's latest generation of Geostationary Operational Environmental Satellites (known as GOES‐R) as inputs to produce hourly precipitation retrievals at approximately 4‐km horizontal resolution. The “ground truth” rainfall data are hourly National Centers for Environmental Prediction (NCEP) Stage IV precipitation analysis totals. Results demonstrate that the FNO‐based model effectively generates accurate precipitation retrieval totals, offering improvements over the operational GOES‐R Quantitative Precipitation Estimation in both estimating and detecting precipitation at hourly intervals.

The objective of this study is to analyze microphysical parameters affecting visibility parameterizations of a freezing fog case that occurred on 19 February 2022, during the Cold Fog Amongst Complex Terrain (CFACT) project conducted in a high-elevation alpine valley in Utah, USA. Observations are collected using visibility, droplet spectra, ice crystal spectra, and aerosol spectral instruments, as well as in-situ meteorological instruments. Particle phase is determined from relative humidity with respect to water (RHw) as well as ground cloud imaging probe (GCIP), ceilometer (CL61) depolarization ratio, and icing accumulation on the platforms. Results showed that freezing droplet density can affect visibility (Vis) up to 100 m during Vis less than 1 km. In addition, increasing volume can lead to up to a 2 μm increase in droplet radius due to a change in the chemical composition of aerosols from Sodium Chloride (NaCl) to Ammonium Nitrate (NH4NO3). Overall, comparisons suggested that Vis parameterizations are highly variable, and freezing fog conditions resulted in lower Vis values compared to warm fog microphysical parameterizations. Furthermore, riming of freezing fog conditions can lead to more than 50% uncertainty in Vis. It is concluded that changing aerosol composition and freezing fog droplet density and riming can play a major role in Vis simulations.

This study investigates the offshore propagation of precipitation off the west coast of Sumatra on February 1 and 4, 2018, focusing on specific events to complement previous studies on seasonality. We use ERA5, MERRA-2, IMERG, Padang radar data, and cloud-permitting scale simulations with WRF-ARW V4.1.3 to understand the roles of the land breeze, gravity waves, and convective outflow in these two events. On February 1, convection organizes north of Padang and propagates approximately 100 km offshore. On February 4, a multicellular system organizes south of Padang and travels over 150 km offshore. Large-scale environmental conditions reveal stronger westerly winds from the Indian Ocean on February 1 and strong northeasterly winds from the South China Sea on February 4. Both events occur in regions with high surface humidity and weak 900 mb winds along the coastline. WRF simulations show that on February 1, the sea breeze uniformly transitions to a land breeze, while on February 4, it forms chaotically. Convective outflow enhances offshore precipitation by interacting with the land breeze, creating additional convergence zones and intensifying convection. Gravity waves played a minor role, contributing to atmospheric instability. The results of this study conclude that offshore-moving convection is primarily driven by the land breeze and convective outflow, with gravity waves playing a minor role. The land breeze initiates convection, while the convective outflow enhances and sustains offshore propagation by creating additional convergence zones and intensifying convection. Moisture advection precedes nocturnal propagation, increasing atmospheric instability and creating favorable conditions for nighttime offshore precipitation.

Cold fog refers to a type of fog that forms when the temperature is below 0 degrees C. It can be composed of liquid, ice, and mixed-phase fog particles. Cold fog happens frequently over mountainous terrain in the cold season, but it is difficult to predict. Using observations from the Cold Fog Amongst Complex Terrain (CFACT) field campaign conducted in Heber Valley, Utah, in the western United States during January and February of 2022, this study investigates the meteorological conditions in the surface and boundary layers that support the formation of wintertime ephemeral cold fog in a local area of small-scale mountain valleys. It is found that fog formation is susceptible to subtleties in forcing conditions and is supported by several factors: (1) established high pressure over the Great Basin with associated local clear skies, calm winds, and a stable boundary layer; (2) near-surface inversion with saturation near the surface and strong moisture gradient in the boundary layer; (3) warm (above-freezing) daytime air temperature with a large diurnal range, accompanied with warm soil temperatures during the daytime; (4) a period of increased turbulence kinetic energy (above 0.5 m2s-2), followed by calm conditions throughout the fog's duration; and (5) supersaturation with respect to ice. Then, the field observations and identified supporting factors for fog formation were utilized to evaluate high-resolution (400 m horizontal grid spacing) Weather Research and Forecasting (WRF) model simulations. Results show that the WRF model accurately simulates the mesoscale conditions facilitating cold-fog formation but misses some critical surface and atmospheric boundary conditions. The overall results from this paper indicate that these identified factors that support fog formation are vital to accurately forecasting cold-fog events. At the same time, they are also critical fields for the NWP model validation. This study finds that fog formation is extremely sensitive to subtleties in forcing conditions and is supported by several factors: (1) established high pressure over the Great Basin with associated clear skies and a stable boundary layer; (2) dry, near-surface inversion paired with calm winds; (3) warm (above-freezing) air and soil temperatures during the daytime, accompanied by a period of elevated turbulence kinetic energy (above 0.5 m2s-2), followed by calm conditions throughout the fog duration; and (4) supersaturation with respect to ice. image