Forecasting extreme precipitation remains challenging due to the high computational cost of high-resolution ensemble modeling. Although high-resolution artificial intelligence (AI)-based weather models are comparably accurate and remarkably efficient compared to traditional numerical weather prediction (NWP) systems, their potential for probabilistic precipitation forecasting, critical for decision-making, remains in its early stages. Here, we present a novel framework that generates high-resolution large ensembles using AI models over the western United States. Our approach bridges NWP and AI by integrating initial condition perturbations from NWP with model uncertainty derived from diverse stretched-grid AI checkpoints. We further introduce two strategies, distribution adjustment and hierarchical modeling, to enhance the ensembles’ performance. This optimized AI ensemble outperforms state-of-the-art NWP systems and original AI ensembles in predicting extreme precipitation. By generating skillful probabilistic forecasts at a fraction of the traditional computational cost, this framework presents a promising pathway for operational real-time forecasting of extreme weather.

Forecasting of extreme precipitation in areas of complex topography remains a challenge for the numerical weather prediction community. California receives 25%-50% of its annual rainfall from just a few storms, referred to as atmospheric rivers. Accurate and timely forecasts of atmospheric rivers are critical for managing water resources and flood risk. An important consideration is whether limited computational resources are best spent on larger ensembles or on finer horizontal grid spacing. In this study, we compare two experimental versions of the operational West-WRF ensemble run at 9-and 3-km grid spacing, each with 72 members and 60 vertical levels. Decreasing the horizontal grid spacing from 9 to 3 km adds skill to the forecast but increases the computational expense by a factor of-27. We then evaluate how the skill of the 3-km ensemble degrades as we reduce the number of members to 45, 30, and 15. We find that the 45-member version of the 3-km ensemble still outperforms the 72-member 9-km ensemble; however, a 45-member 3-km ensemble is too computationally expensive for most centers to run operationally. As we move to smaller ensembles, the skill degrades signifi-cantly, especially for extreme events. The 15-member version of the 3-km ensemble, while still being almost 6 times as computationally expensive, performs worse than the 9-km ensemble in almost every metric considered. Therefore, we conclude that while there is clear value in decreasing the horizontal grid spacing in an ensemble forecast, ensemble size is also important, particularly if the focus of the prediction is on extreme weather.

A 200-member ensemble developed at the Center for Western Weather and Water Extremes based on the Weather Research and Forecast atmospheric model tailored for the prediction of atmospheric rivers and associated heavy-to-extreme precipitation events over the Western US (West-WRF) is presented. The ensemble (WW200En) is generated with initial and boundary conditions from the US National Center for Environmental Prediction's Global Ensemble Forecast System (GEFS) and the European Centre for Medium-Range Weather Forecasts' Ensemble Prediction System (EPS), 100 unique combinations of microphysics, planetary boundary layer, and cumulus schemes, as well as perturbations applied to each of the 200 members based on the stochastic kinetic-energy backscatter scheme. Each member is run with 9-km horizontal increments and 60 vertical levels for a 10-month period spanning two winters. The performance of WW200En is compared to GEFS and EPS for probabilistic forecasts of 24-h precipitation, integrated water vapor transport (IVT), and for several thresholds including high percentiles of the observed climatological distribution. The WW200En precipitation forecast skill is better than GEFS at nearly all thresholds and lead times, and comparable or better than the EPS. For larger rainfall thresholds WW200En typically exhibits the best forecast skill. Additionally, WW200En has a better spread-skill relationship than the global systems, and an improved overall reliability and resolution of the probabilistic prediction. The results for IVT are qualitatively similar to those for precipitation forecasts. A sensitivity analysis of the physics parameterizations and the number of ensemble members provides insights into possible future developments of WW200En.

This study introduces a deep learning (DL) scheme to generate reliable and skillful probabilistic quantitative precipitation forecasts (PQPFs) in a postprocessing framework. Enhanced machine learning model architecture and training mechanisms are proposed to improve the reliability and skill of PQPFs while permitting computationally ef fi cient model fi tting using a short training dataset. The methodology is applied to postprocessing of 24-h accumulated PQPFs from an ensemble forecast system recently introduced by the Center for Western Weather and Water Extremes (CW3E) and for lead times from 1 to 6 days. The ensemble system was designed based on a high-resolution version of the Weather Research and Forecasting (WRF) Model, named West-WRF, to produce a 200-member ensemble in near - real time (NRT) over the western United States during the boreal cool seasons to support Forecast-Informdayed Reservoir Operations (FIRO) and studies of prediction of heavy-to-extreme events. Postprocessed PQPFs are compared with those from the raw West-WRF ensemble, the operational Global Ensemble Forecast System version 12 (GEFSv12), and the ensemble from the European Centre for Medium-Range Weather Forecasts (ECMWF). As an additional baseline, we provide PQPF veri fi cation metrics from a recently developed neural network postprocessing scheme. The results demonstrate that the skill of postprocessed forecasts signi fi cantly outperforms PQPFs and deterministic forecasts from raw ensembles and the recently developed algorithm. The resulting PQPFs broadly improve upon the reliability and skill of baselines in predicting heavy-to-extreme precipitation (e.g., .75 mm) across all lead times while maintaining the spatial structure of the high-resolution raw ensemble.

During a 6-day intensive observing period in January 2021, Atmospheric River Reconnaissance (AR Recon) aircraft sampled a series of atmospheric rivers (ARs) over the northeastern Pacific that caused heavy precipitation over coastal California and the Sierra Nevada. Using these observations, data denial experiments were conducted with a regional modeling and data assimilation system to explore the impacts of research flight frequency and spatial resolution of dropsondes on model analyses and forecasts. Results indicate that dropsondes significantly improve the representation of ARs in the model analyses and positively impact the forecast skill of ARs and quantitative precipitation forecasts (QPF), particularly for lead times . 1 day. Both reduced mission frequency and reduced dropsonde horizontal resolution degrade forecast skill. On the other hand, experiments that assimilated only G -IV data and experiments that assimilated both G -IV and C-130 data show better forecast skill than experiments that only assimilated C-130 data, suggesting that the additional information provided by G -IV data is necessary for improving forecast skill. Although this is a case study, the 6-day period studied encompassed multiple AR events that are representative of typical AR behavior. Therefore, the results indicate that future operational AR Recon missions incorporate daily mission or back-to-back flights, maintain current dropsonde spacing, support high-resolution data transfer capacity on the C -130s, and utilize G -IV aircraft in addition to C -130s.

Post-wildfire debris flows (PFDF) threaten life and property in western North America. They are triggered by short-duration, high-intensity rainfall. Following a wildfire, rainfall thresholds are developed that, if exceeded, indicate high likelihood of a PFDF. Existing weather forecast products allow forecasters to identify favorable atmospheric conditions for rainfall intensities that may exceed established thresholds at lead times needed for decision-making (e.g., & GE;24 h). However, at these lead times, considerable uncertainty exists regarding rainfall intensity and whether the high-intensity rainfall will intersect the burn area. The approach of messaging on potential hazards given favorable conditions is generally effective in avoiding unanticipated PFDF impacts, but may lead to "messaging fatigue" if favorable triggering conditions are forecast numerous times, yet no PFDF occurs (i.e., false alarm). Forecasters and emergency managers need additional tools that increase their confidence regarding occurrence of short-duration, high-intensity rainfall as well as tools that tie rainfall forecasts to potential PFDF outcomes. We present a concept for probabilistic tools that evaluate PFDF hazards by coupling a high-resolution (1-km), large (100-member) ensemble 24-h precipitation forecast at 5-min resolution with PFDF likelihood and volume models. The observed 15-min maximum rainfall intensities are captured within the ensemble spread, though in highest & SIM;10% of members. We visualize the model output in several ways to demonstrate most likely and most extreme outcomes and to characterize uncertainty. Our experiment highlights the benefits and limitations of this approach, and provides an initial step toward further developing situational awareness and impact-based decision-support tools for forecasting PFDF hazards.

This study presents a high-resolution regional reforecast based on the Weather Research and Forecasting (WRF) Model, tailored for the prediction of extreme hydrometeorological events over the western United States (West-WRF) spanning 34 cool seasons (1 December-31 March) from 1986 to 2019. The West-WRF reforecast has a 9-km domain covering western North America and the eastern Pacific Ocean and a 3-km domain covering much of California. The West-WRF reforecast is generated by dynamically downscaling the control member of the Global Ensemble Forecasting System (GEFS) v10 reforecast. Verification of near-surface temperature, wind, and humidity highlight the added value in the reforecast relative GEFS. Analysis of geopotential height indicates that West-WRF reduces the bias throughout much of the troposphere during early lead times. The West-WRF reforecast also shows clear improvement in atmospheric river characteristics (intensity and landfall) over GEFS. Analysis of mean areal precipitation (MAP) shows that at the basin scale, the reforecast can improve MAP relative to GEFS and reveals a consistent low bias in the reforecast for a coastal watershed (Russian) and a high bias observed in a Northern Sierra watershed (Yuba). The reforecast has a dry bias in seasonal precipitation in the northern Central Valley and Coast Mountain ranges, and a wet bias in the Northern Sierra Nevada, consistent with other operational high resolution (,25 km) regional models. The applications of this high-resolution multiyear reforecast include process-based studies, assessment of model performance, and machine learning applications.

This study evaluates the efficacy of the mobile flux plane (MFP) method to derive methane emissions from oil and gas production fields using a first‐of‐its‐kind high‐resolution methane concentration data set. Transport and dispersion of methane emissions from seven hypothetical well pads generated with an oil field emission simulator is simulated every second at 10 m resolution using the Weather Research and Forecasting (WRF) model in large eddy simulation mode. The time varying WRF‐generated methane concentration data set is sampled by a simulated MFP system downwind of the seven well pads at five sampling distances of 50, 75, 100, 125, and 150 m. Several key findings highlight the significant variability in MFP emission rate estimates induced by atmospheric turbulence and variable source emission rates. Natural atmospheric turbulence alone was found to generate significant variability (33%–75%) in the MFP emission estimates with constant emission rates at the source location. It was also found that turbulent wind speed fluctuations over the duration of a transect can also affect MFP estimates up to about ±50% through convergence (divergence) that increases (decreases) methane concentrations, and by its effect on the assumption of steady winds over the duration of the transect. It was further found that the MFP method typically estimated about 19%–33% and 51%–75% of known site emission rates using the trapezoidal and Gaussian fit integration methods, respectively. Thus, methane concentrations would need to be measured to a much higher elevation to generate robust and accurate methane emission rate estimates.

Dataset consists of a single folder that includes all data used for the publication entitled: Quantifying the Spatial Variability of a Snowstorm Using Differential Airborne Lidar. Within the folder is a README file that describes the data structure.

The regional climate of the Arabian Gulf region is modeled using a set of simulations based on the Weather Research and Forecasting (WRF) Model, including a 30-yr benchmark simulation driven by reanalysis data, and two bias-corrected Community Earth System Model (CESM)-driven (BCD) WRF simulations for retrospective and future periods that both include 10-yr convection-permitting nested simulations. The modeled precipitation is cross-validated using Tropical Rainfall Measuring Mission data, rain gauge data, and the baseline dataset from the benchmark simulation. The changes in near-surface temperature, precipitation, and ambient conditions are investigated using the BCD WRF simulations. The results show that the BCD WRF simulation well captures the precipitation distribution, the precipitation variability, and the thermodynamic properties. In a warmer climate under the RCP8.5 scenario around the year 2070, the near-surface temperature warms by ~3°C. Precipitation increases over the Arabian Gulf, and decreases over most of the continental area, particularly over the Zagros Mountains. The wet index decreases while the maximum dry spell increases in most areas of the model domain. The future changes in precipitation are determined by both the thermodynamics and dynamics. The thermodynamic impact, which is controlled by the warming and moistening, results in more precipitation over the ocean but not over the land. The dynamic impact, which is controlled by changes in the large-scale circulation, results in decrease in precipitation over mountains. The simulations presented in this study provide a unique dataset to study the regional climate in the Arabian Gulf region for both retrospective and future climates.

Climate change poses serious energy, water, and health challenges for the United Arab Emirates (UAE). While closely interconnected, the development of sustainable energy, water, and health policies has typically been viewed as independent, sector-specific planning challenges. However, changing demographics, a rapidly growing economy, dependence on desalination, and worsening air quality – all taking place as climate change unfolds – suggest a need for a more integrated approach to risk management. Accounting for the interactions between "energy, water, and health nexus" is one way to ensure that development strategies are considered within a framework that addresses the range of potential trade-offs, risks, and synergies. To address the energy-water-health nexus under a changing climate, research activities were undertaken as part of the Local, National, and Regional Climate Change Programme (LNRCCP) of the Abu Dhabi Global Environmental Data Initiative (AGEDI). Climate change modeling at the regional spatial scale (Arabian Peninsula; Arabian Gulf) was first carried out to establish the atmospheric and marine physical conditions that will underlie energy, water, and health challenges in the future. The results of this modeling were then used as inputs to an analysis of policies that account for linkages across the energy-water nexus on the one hand and the energy-health nexus on the other. The modeling results show that climate change will render an extreme hyper-arid climate even more so, while the waters of the Arabian Gulf will experience heightened salinity, changing circulation patterns, and higher temperatures as desalination activities intensify. The analysis of the energy-water-health nexus shows that energy efficiency and renewable energy can lead to significant reductions in annual greenhouse gas (GHG) emissions at negative to modest societal cost while leading to substantial decreases in premature mortality and health-care facility visits in the urban environment.

Large-eddy simulations (LES) coupled to a model that simulates methane emissions from oil and gas production facilities are used to generate realistic distributions of meteorological variables and methane concentrations. These are sampled to obtain simulated observations used to develop and evaluate source term estimation (STE) methods. A widely used EPA STE method (OTM33A) is found to provide emission estimates with little bias when averaged over six time periods and seven well pads. Sixty-four percent of the emissions estimated with OTM33A are within +/- 30% of the simulated emissions, showing a slightly larger spread than the 72% found previously using controlled release experiments. A newly developed method adopts the OTM33A sampling strategy and uses a variational or a stochastic STE approach coupled to an LES to obtain a better fit to the sampled meteorological conditions and to account for multiple sources within the well pad. This method can considerably reduce the spread of the emissions estimates compared to OTM33A (92-95% within +/- 30% percent error), but it is associated with a substantial increase in computational cost due to the LES. It thus provides an alternative when the additional costs can be afforded to obtain more precise emission estimates.

The factors responsible for rare summertime rainfall over portions of the United Arab Emirates (UAE), which have not been previously explored in detail, are elucidated with the Climate Forecast System Re-analysis and WRF mesoscale model simulations. The simulations show associations between active phases of the southwest Asian monsoon and intensification of the Arabian heat low, leading up to UAE rainfall events. Variability in the location and strength of the Arabian heat low circulation, which differs from the static portrayal in climatological minimum sea level pressure (MSLP), can affect the development of deep convection over the UAE. Analysis of the vorticity equation for a two-day case study period confirms that convergence is solely responsible for the spinup and maintenance of the primary heat low circulation. Convergence is also responsible for the spinup of a separate cyclonic circulation over the eastern UAE, which propagates offshore to the Arabian Gulf during morning hours. This cyclonic circulation advects moist air onshore over the western UAE, and deep convection follows from inland horizontal convective rolls and interaction with the approaching sea-breeze front. The development of widespread deep convection is shown to be most favorable during the decay phase of the Arabian heat low, when the preconditioned moist air is not replaced by drier continental flow, and the vertical profiles of temperature and moisture are also more favorable. Three other rainfall cases are briefly discussed to illustrate how the strength and geographic position of the Arabian heat low can affect rainfall characteristics over the UAE.

INTRODUCTION:An ongoing Zika virus pandemic in Latin America and the Caribbean has raised concerns that travel-related introduction of Zika virus could initiate local transmission in the United States (U.S.) by its primary vector, the mosquito Aedes aegypti.METHODS:We employed meteorologically driven models for 2006-2015 to simulate the potential seasonal abundance of adult Aedes aegypti for fifty cities within or near the margins of its known U.S. range. Mosquito abundance results were analyzed alongside travel and socioeconomic factors that are proxies of viral introduction and vulnerability to human-vector contact.RESULTS:Meteorological conditions are largely unsuitable for Aedes aegypti over the U.S. during winter months (December-March), except in southern Florida and south Texas where comparatively warm conditions can sustain low-to-moderate potential mosquito abundance. Meteorological conditions are suitable for Aedes aegypti across all fifty cities during peak summer months (July-September), though the mosquito has not been documented in all cities. Simulations indicate the highest mosquito abundance occurs in the Southeast and south Texas where locally acquired cases of Aedes-transmitted viruses have been reported previously. Cities in southern Florida and south Texas are at the nexus of high seasonal suitability for Aedes aegypti and strong potential for travel-related virus introduction. Higher poverty rates in cities along the U.S.-Mexico border may correlate with factors that increase human exposure to Aedes aegypti.DISCUSSION:Our results can inform baseline risk for local Zika virus transmission in the U.S. and the optimal timing of vector control activities, and underscore the need for enhanced surveillance for Aedes mosquitoes and Aedes-transmitted viruses.

The mosquito virus vector Aedes (Ae.) aegypti exploits a wide range of containers as sites for egg laying and development of the immature life stages, yet the approaches for modeling meteorologically sensitive container water dynamics have been limited. This study introduces the Water Height and Temperature in Container Habitats Energy Model (WHATCH'EM), a state-of-the-science, physically based energy balance model of water height and temperature in containers that may serve as development sites for mosquitoes. The authors employ WHATCH'EM to model container water dynamics in three cities along a climatic gradient in México ranging from sea level, where Ae. aegypti is highly abundant, to ~2100 m, where Ae. aegypti is rarely found. When compared with measurements from a 1-month field experiment in two of these cities during summer 2013, WHATCH'EM realistically simulates the daily mean and range of water temperature for a variety of containers. To examine container dynamics for an entire season, WHATCH'EM is also driven with field-derived meteorological data from May to September 2011 and evaluated for three commonly encountered container types. WHATCH'EM simulates the highly nonlinear manner in which air temperature, humidity, rainfall, clouds, and container characteristics (shape, size, and color) determine water temperature and height. Sunlight exposure, modulated by clouds and shading from nearby objects, plays a first-order role. In general, simulated water temperatures are higher for containers that are larger, darker, and receive more sunlight. WHATCH'EM simulations will be helpful in understanding the limiting meteorological and container-related factors for proliferation of Ae. aegypti and may be useful for informing weather-driven early warning systems for viruses transmitted by Ae. aegypti.

The mosquito Aedes ( Ae ) . aegypti transmits the viruses that cause dengue, chikungunya, Zika and yellow fever. We investigate how choosing alternate emissions and/or socioeconomic pathways may modulate future human exposure to Ae. aegypti . Occurrence patterns for Ae. aegypti for 2061–2080 are mapped globally using empirically downscaled air temperature and precipitation projections from the Community Earth System Model, for the Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios. Population growth is quantified using gridded global population projections consistent with two Shared Socioeconomic Pathways (SSPs), SSP3 and SSP5. Change scenarios are compared to a 1950–2000 reference period. A global land area of 56.9 M km 2 is climatically suitable for Ae. aegypti during the reference period, and is projected to increase by 8 % (RCP4.5) to 13 % (RCP8.5) by 2061–2080. The annual average number of people exposed globally to Ae. aegypti for the reference period is 3794 M, a value projected to statistically significantly increase by 298–460 M (8–12 %) by 2061–2080 if only climate change is considered, and by 4805–5084 M (127–134 %) for SSP3 and 2232–2483 M (59–65 %) for SSP5 considering both climate and population change (lower and upper values of each range represent RCP4.5 and RCP8.5 respectively). Thus, taking the lower-emissions RCP4.5 pathway instead of RCP8.5 may mitigate future human exposure to Ae. aegypti globally, but the effect of population growth on exposure will likely be larger. Regionally, Australia, Europe and North America are projected to have the largest percentage increases in human exposure to Ae. aegypti considering only climate change.

BACKGROUND:Cooking over open fires using solid fuels is both common practice throughout much of the world and widely recognized to contribute to human health, environmental, and social problems. The public health burden of household air pollution includes an estimated four million premature deaths each year. To be effective and generate useful insight into potential solutions, cookstove intervention studies must select cooking technologies that are appropriate for local socioeconomic conditions and cooking culture, and include interdisciplinary measurement strategies along a continuum of outcomes.METHODS/DESIGN:REACCTING (Research on Emissions, Air quality, Climate, and Cooking Technologies in Northern Ghana) is an ongoing interdisciplinary randomized cookstove intervention study in the Kassena-Nankana District of Northern Ghana. The study tests two types of biomass burning stoves that have the potential to meet local cooking needs and represent different "rungs" in the cookstove technology ladder: a locally-made low-tech rocket stove and the imported, highly efficient Philips gasifier stove. Intervention households were randomized into four different groups, three of which received different combinations of two improved stoves, while the fourth group serves as a control for the duration of the study. Diverse measurements assess different points along the causal chain linking the intervention to final outcomes of interest. We assess stove use and cooking behavior, cooking emissions, household air pollution and personal exposure, health burden, and local to regional air quality. Integrated analysis and modeling will tackle a range of interdisciplinary science questions, including examining ambient exposures among the regional population, assessing how those exposures might change with different technologies and behaviors, and estimating the comparative impact of local behavior and technological changes versus regional climate variability and change on local air quality and health outcomes.DISCUSSION:REACCTING is well-poised to generate useful data on the impact of a cookstove intervention on a wide range of outcomes. By comparing different technologies side by side and employing an interdisciplinary approach to study this issue from multiple perspectives, this study may help to inform future efforts to improve health and quality of life for populations currently relying on open fires for their cooking needs.

We examined temporal changes in the abundance of the mosquitoes Aedes (Stegomyia) aegypti (L.) and Aedes (Ochlerotatus) epactius Dyar & Knab from June to October 2012 in one reference community at lower elevation (Rio Blanco; approximately 1,270 m) and three high-elevation communities (Acultzingo, Maltrata, and Puebla City; 1,670-2,150 m) in Veracruz and Puebla States, México. The combination of surveys for pupae in water-filled containers and trapping of adults, using BG-Sentinel traps baited with the BG-Lure, corroborated previous data from 2011 showing that Ae. aegypti is present at low abundance up to 2,150 m in this part of México. Data for Ae. aegypti adults captured through repeated trapping in fixed sites in Acultzingo--the highest elevation community (approximately 1,670 m) from which the temporal intra-annual abundance pattern for Ae. aegypti has been described--showed a gradual increase from low numbers in June to a peak occurrence in late August, and thereafter declining numbers in September. Ae. epactius adults were collected repeatedly in BG-Sentinel traps in all four study communities; this is the first recorded collection of this species with a trap aiming specifically to collect human-biting mosquitoes. We also present the first description of the temporal abundance pattern for Ae. epactius across an elevation gradient: peak abundance was reached in mid-July in the lowest elevation community (Rio Blanco) but not until mid-September in the highest elevation one (Puebla City). Finally, we present data for meteorological conditions (mean temperature and rainfall) in the examined communities during the study period, and for a cumulative measure of the abundance of adults over the full sampling period.

Meningitis remains a major health burden throughout Sahelian Africa, especially in heavily populated northwest Nigeria with an annual incidence rate ranging from 18 to 200 per 100000 people for 2000-11. Several studies have established that cases exhibit sensitivity to intra- and interannual climate variability, peaking during the hot and dry boreal spring months, raising concern that future climate change may increase the incidence of meningitis in the region. The impact of future climate change on meningitis risk in northwest Nigeria is assessed by forcing an empirical model of meningitis with monthly simulations of seven meteorological variables from an ensemble of 13 statistically downscaled global climate model projections from phase 5 of the Coupled Model Intercomparison Experiment (CMIP5) for representative concentration pathway (RCP) 2.6, 6.0, and 8.5 scenarios, with the numbers representing the globally averaged top-of-the-atmosphere radiative imbalance (in W m(-2)) in 2100. The results suggest future temperature increases due to climate change have the potential to significantly increase meningitis cases in both the early (2020-35) and late (2060-75) twenty-first century, and for the seasonal onset of meningitis to begin about a month earlier on average by late century, in October rather than November. Annual incidence may increase by 47% +/- 8%, 64% +/- 9%, and 99% +/- 12% for the RCP 2.6, 6.0, and 8.5 scenarios, respectively, in 2060-75 with respect to 1990-2005. It is noteworthy that these results represent the climatological potential for increased cases due to climate change, as it is assumed that current prevention and treatment strategies will remain similar in the future.