Subtracting the changing sky contribution from the near-infrared (NIR) spectra of faint astronomical objects is challenging and crucial to a wide range of science cases such as estimating the velocity dispersions of dwarf galaxies, studying the gas dynamics in faint galaxies, measuring accurate redshifts, and any spectroscopic study of faint targets. Since the sky background varies with time and location, NIR spectral observations, especially those employing fiber spectrometers and targeting extended sources, require frequent sky-only observations for calibration. However, sky subtraction can be optimized with sufficient a priori knowledge of the sky's variability. In this work, we explore how to optimize sky subtraction by analyzing 1075 high-resolution NIR spectra from the Canada-France-Hawaii Telescope's SPIRou on Maunakea, and we estimate the variability of 481 hydroxyl (OH) lines. These spectra were collected during two sets of three nights dedicated to obtaining sky observations every 5.5 minutes. During the first set, we observed how the Moon affects the NIR, which has not been accurately measured at these wavelengths. We suggest accounting for the Moon contribution at separation distances less than 10 degrees when (1) reconstructing the sky using principal component analysis, (2) observing targets at YJHK magnitudes fainter than similar to 15, and (3) attempting a sky subtraction better than 1%. We also identified 126 spectral doublets, or OH lines that split into at least two components, at SPIRou's resolution. In addition, we used Lomb-Scargle periodograms and Gaussian process regression to estimate that most OH lines vary on similar timescales, which provides a valuable input for IR spectroscopic survey strategies. The data (https://zenodo.org/records/13363061) and code (https://github.com/FDauphin/spirou-sky-subtraction) developed for this study are publicly available.

This paper provides a historical review of our understanding of the workings of the atmospheric boundary layer (ABL), the part of the lower atmosphere, typically <= 1 km in depth, that directly feels the influence of Earth's surface. It is written from the perspective of the career of Joost A. Businger (1924-2023), a pioneer in this area, who realized that the atmospheric boundary layer holds great practical importance in the sense that most of humankind's activities take place within it and scientific importance in the sense that the boundary layer forms the interface between the free atmosphere and Earth's surface and therefore plays a major role in our weather and climate. SIGNIFICANCE STATEMENT: This paper reviews progress in our understanding of relationships between the surface layer and circulations in the ABL and in our understanding of air-sea interaction during the past century. The relationships form a cornerstone that numerical weather and climate models rely upon.

Hawai'i sees a significant number of aviation accidents each year, many of which are caused by weather. In an effort to mitigate such incidents in the future, weather-related aviation accidents in Hawai'i between 2003 and 2022 are investigated. A data catalog is created using the National Transportation Safety Board (NTSB) reports, National Oceanic and Atmospheric Administration (NOAA) weather charts, satellite imagery, atmospheric soundings, Automated Surface Observing System/Automated Weather Observing System (ASOS/AWOS) surface winds, and Next Generation Weather Radar (NEXRAD) data. It serves as the basis for the analysis of the days on which accidents occurred. A machine learning algorithm is implemented to compare accident days to nonaccident days. Results show that most weather-related accidents take place during trade wind conditions and occur on O'ahu, Moloka'i, and Maui. When looking at the monthly distribution, there is a higher incidence of accidents during the winter months. This is due to the fact that in addition to trade wind accidents, over 75% of cases that are linked to a synoptic-scale disturbance occur during this time. Atmospheric stability and wind speeds below the trade wind inversion are deemed most crucial when assessing atmospheric conditions for aviation safety. For fatal accidents, surface winds are observed to be a factor. Shearlines and troughs are associated with the majority of fatal weather-related accidents. In particular, trade wind days during which a shearline is present account for the largest number of fatal cases. This suggests that the most hazardous days for aviation in Hawai'i are not necessarily those with the most severe weather but those that subtly deviate from the familiar trade wind regime}conditions that can be easily overlooked. SIGNIFICANCE STATEMENT: Weather is believed to play a significant role in aviation accidents in Hawai'i. This study analyzes weather-related helicopter and airplane accidents in Hawai'i from 2003 to 2022 with the objective to better understand the conditions that caused them and help minimize future accidents. We found that weather was indeed the leading cause or contributing factor for aviation accidents in Hawai'i during this period and identified atmospheric stability and wind speeds below the trade wind inversion as the atmospheric variables most critical for aviation safety among those considered. Additionally, we were able to show that machine learning models have the potential to assist in determining the factors responsible for these accidents and recommend future research to further explore this path.

The THINICE field campaign, based in Svalbard in August 2022, provided unique observations of summertime Arctic cyclones, their coupling with cloud cover, and their interactions with tropopause polar vortices and sea ice conditions. THINICE was motivated by the need to advance our understanding of these processes and to improve coupled models used to forecast weather and sea ice, as well as long-term projections of climate change in the Arctic. Two research aircraft were deployed with complementary instrumentation. The Service des Avions Fran & ccedil;ais Instrument & eacute;s pour la Recherche en Environnement (Safire) Aerei da Trasporto Regionale 42 (ATR42) aircraft, equipped with the radar-lidar (RALI) remote sensing instrumentation and in situ cloud microphysics probes, flew in the midtroposphere to observe the wind and multiphase cloud structure of Arctic cyclones. The British Antarctic Survey Meteorological Airborne Science Instrumentation (MASIN) aircraft flew at low levels measuring sea ice properties, including surface brightness temperature, albedo and roughness, and the turbulent fluxes that mediate exchange of heat and momentum between the atmosphere and the surface. Long-duration instrumented balloons, operated by WindBorne Systems, sampled meteorological conditions within both cyclones and tropospheric polar vortices across the Arctic. Several novel findings are highlighted. Intense, shallow low-level jets along warm fronts were observed within three Arctic cyclones using the Doppler radar and turbulence probes. A detailed depiction of the interweaving layers of ice crystals and supercooled liquid water in mixed-phase clouds is revealed through the synergistic

A month‐long data assimilation experiment is carried out to assess the impact of CrIS and IASI Transformed Retrievals (TRs) on the accuracy of analyses and forecasts from a 3‐hr Weather Research and Forecasting cycling system implemented over the central North Pacific Ocean. Conventional observations and satellite MicroWave (MW) radiance data are assimilated along with TRs in comparative experiments. Both the NCEP Global Forecasting System and the European Centre for Medium‐Range Weather Forecasts analyses are used in the evaluation process. The results show that the assimilation of TRs alone, and in combination with MW radiance assimilation, have the greatest impact on the characterization of the moisture field in the middle atmospheric levels (800–300 hPa), and particularly in the lower portion (800–600 hPa). The latter improvement is likely due to a refinement in the vertical definition of the trade‐wind inversion.

In the Roman era, wide-field, deep, visible-to-near infrared images will revolutionize our understanding of galaxy evolution (e.g. environments, morphologies, masses, colors). The legacy value of Roman images and low-resolution spectra (with Roman’s prism and grism) will be greatly enhanced by massively multiplexed ground-based observations in the near – future and simultaneously allow us to leverage an impressive bounty of archived spectra from Maunakea facilities. We plan to enhance ground-based NIR spectra of astrophysically interesting objects with ground-sky spectra, atmospheric data, HST spectra and images, and machine learning techniques proven to predict galaxy spectra from images.

ABSTRACT The staff at the Maunakea Weather Center (MKWC) has provided daily forecasts of optical turbulence for the summit of Maunakea for more than 20 yr. Observational measures of optical turbulence at Maunakea with which to validate official MKWC forecasts have been available since mid-2009. This paper presents a machine-learning approach to translate the MKWC experience into a forecast of the nightly average optical turbulent state of the atmosphere. Maunakea observational and forecast data were collected to build a predictive model of the total and free atmospheric seeing for the following five nights. The motivation for this work is two-fold: to provide a tool/guidance to the MKWC forecaster and allow for a dynamic calibration of the optical turbulence algorithm implemented in the MKWC Weather Research and Forecasting (WRF) model.

Rainbows contribute to human wellbeing by providing an inspiring connection to nature. Because the rainbow is an atmospheric optical phenomenon that results from the refraction of sunlight by rainwater droplets, changes in precipitation and cloud cover due to anthropogenic climate forcing will alter rainbow distribution. Yet, we lack a basic understanding of the current spatial distribution of rainbows and how climate change might alter this pattern. To assess how climate change might affect rainbow viewing opportunities, we developed a global database of crowd-sourced photographed rainbows, trained an empirical model of rainbow occurrence, and applied this model to present-day climate and three future climate scenarios. Results suggest that the average terrestrial location on Earth currently has 117 +/- 71 days per year with conditions suitable for rainbows. By 2100, climate change is likely to generate a 4.0-4.9 % net increase in mean global annual rainbow-days (i.e., days with at least one rainbow), with the greatest change under the highest emission scenario. Around 21-34 % of land areas will lose rainbow-days and 66-79 % will gain rainbow-days, with rainbow gain hotspots mainly in high -latitude and high-elevation regions with smaller human populations. Our research demonstrates that alter-ations to non-tangible environmental attributes due to climate change could be significant and are worthy of consideration and mitigation.

A series of extreme cloudbursts occurred on 14 April 2018 over the northern slopes of the island of Kaua'i, Hawaii. The storm inundated some areas with 1262 mm (similar to 50 in.) of rainfall in a 24-h period, eclipsing the previous 24-h U.S. rainfall record of 1100 mm (42 in.) set in Texas in 1979. Three periods of intense rainfall are diagnosed through detailed analysis of National Weather Service operational and special datasets. On the synoptic scale, a slowly southeastward propagating trough aloft over a deep layer of low-level moisture (>40 mm of total precipitable water) produced prolonged instability over Kaua'i. Enhanced northeast to east low-level flow impacted Kaua'i's complex terrain, which includes steep north- and eastward-facing slopes and cirques. The resulting orographic lift initiated deep convection. The wind profile exhibited significant shear in the troposphere and streamwise vorticity within the convective storm inflow. Evidence suggests that large directional shear in the boundary layer, paired with enhanced orographic vertical motion, produced rotating updrafts within the convective storms. Mesoscale rotation is manifest in the radar data during the latter two periods, and reflectivity cores are observed to propagate both to the left and to the right of the mean shear, which is characteristic of supercells. The observations suggest that the terrain configuration in combination with the wind shear separates the area of updrafts from the downdraft section of the storm, resulting in almost continuous heavy rainfall over Waipa Garden.

This paper makes a case for why Hawaii is the rainbow capital of the world. It begins by briefly touching on the cultural and historical significance of rainbows in Hawaii. Next it provides an overview of the science behind the rainbow phenomenon, which provides context for exploring the meteorology that helps explain the prevalence of Hawaiian rainbows. Last, the paper discusses the art and science of chasing rainbows.

Kīlauea volcano, located on the island of Hawaii, is one of the most active volcanoes in the world. It was in a state of nearly continuous eruption from 1983 to 2018 with copious emissions of sulfur dioxide (SO2) that affected public health, agriculture, and infrastructure over large portions of the island. Since 2010, the University of Hawaiʻi at Mānoa provides publicly available vog forecasts that began in 2010 to aid in the mitigation of volcanic smog (or “vog”) as a hazard. In September 2017, the forecast system began to produce operational ensemble forecasts. The months that preceded Kīlauea’s historic lower east rift zone eruption of 2018 provide an opportunity to evaluate the newly implemented air quality ensemble prediction system and compare it another approach to the generation of ensemble members. One of the two approaches generates perturbations in the wind field while the other perturbs the sulfur dioxide (SO2) emission rate from the volcano. This comparison has implications for the limits of forecast predictability under the particularly dynamic conditions at Kīlauea volcano. We show that for ensemble forecasts of SO2 generated under these conditions, the uncertainty associated with the SO2 emission rate approaches that of the uncertainty in the wind field. However, the inclusion of a fluctuating SO2 emission rate has the potential to improve the prediction of the changes in air quality downwind of the volcano with suitable postprocessing.

Satellite retrievals strive to exploit the information contained in thousands of channels provided by hyperspectral sensors and show promise in providing a gain in computational efficiency over current radiance assimilation methods by transferring computationally expensive radiative transfer calculations to retrieval providers. This paper describes the implementation of a new approach based on the transformation proposed in 2008 by Migliorini et al., which reduces the impact of the a priori information in the retrievals and generates transformed retrievals (TRs) whose assimilation does not require knowledge of the hyperspectral instruments characteristics. Significantly, the results confirm both the viability of Migliorini's approach and the possibility of assimilating data from different hyperspectral satellite sensors regardless of the instrument characteristics. The Weather Research and Forecasting (WRF) Model's Data Assimilation (WRFDA) 3-h cycling system was tested over the central North Pacific Ocean, and the results show that the assimilation of TRs has a greater impact in the characterization of the water vapor distribution than on the temperature field. These results are consistent with the knowledge that temperature field is well constrained by the initial and boundary conditions of the Global Forecast System (GFS), whereas the water vapor distribution is less well constrained in the GFS. While some preliminary results on the comparison between the assimilation with and without TRs in the forecasting system are presented in this paper, additional work remains to explore the impact of the new assimilation approach on forecasts and will be provided in a follow-up publication.

ABSTRACT Optical turbulence greatly impacts the range and quality of astronomical observations. Advanced knowledge of the expected atmospheric optical turbulence provides important guidance that helps astronomers decide which instrument to schedule and enables them to optimize the adaptive optics technology that improves image resolution. Along with forecasts of weather conditions, prediction of the optical observing quality on the Maunakea summit has been a goal for the Maunakea Weather Center (MKWC) since its inception more than 20 yr ago. Forecasting optical turbulence, and its derivative, ‘seeing’, has proven to be quite challenging because optical turbulence is too small and complex to directly capture with a regional weather model. Fortunately, the permanent installation of a Differential Image Motion Monitor (DIMM) and Multi-Aperture Scintillation Sensor (MASS) at the summit of Maunakea has made seeing observations available during the last decade, providing valuable feedback to the MKWC. This paper summarizes the experience at MKWC in anticipating optical turbulence for the summit of Maunakea accrued through years of daily operational forecasting, and continuous comparison between MKWC official forecasts, model guidance, and observational measures of seeing. Access to a decade seeing observations has allowed quantification the factors that impact seeing, including wind shear, atmospheric stability patterns, and optical turbulence, and to document the seasonal and intra-seasonal variations in seeing. Consequently, the combination of experience gained, and custom model guidance has led to more accurate seeing forecasts (rms errors averaging <0.25 arcsec since 2012) for the Maunakea astronomical observatories.

A new method for modeling the lowest model level vertical motion is described and validated. Instead of smoothing terrain heights, the new method calculates the terrain gradient on a high-resolution grid and averages the gradient values around a gridpoint location. In essence, the method provides a way to achieve some of the impact of very steep terrain on the flow without the computational overhead associated with the very high grid resolution needed to fully resolve complex terrain. The more accurate depiction of the terrain gradient leads to an increase in orographic vertical motion and causes rainfall to occur more often over the windward-facing mountain slopes, consistent with observations. Model results are compared with rain gauge data during the month of January 2016 as well as radar data from a case study on 9 March 2012. When implemented in the Weather Research and Forecasting (WRF) Model over the island of Oahu and compared with the current WRF method, the model precipitation forecast skill is improved. The new method produces more precipitation over the island during January 2016, which is closer to the observed value. On 9 March 2012, the new method clearly focuses the precipitation over the Ko'olau Mountains, reducing the number of false alarm forecasts by nearly one-half. Although the changes to model precipitation skill were small, they were generally positive.

High literacy rates among Native Hawaiians in the nineteenth century and publication of more than 100 Hawaiian-language newspapers from 1834 to 1948 produced the largest archive of indigenous writing in the Western Hemisphere. These newspapers extend our knowledge of historical environmental events and natural disasters back into the early nineteenth century and deeper into precontact times. Articles reporting observations of meteorological events allowed the authors to reconstruct the track and intensity of an 1871 hurricane that brought devastation to the islands of Hawaii and Maui and to discern historical patterns of droughts and floods in Hawaii. These findings illustrate the value of Hawaiian-language newspapers as resources for science research and science education.

Permafrost and perennial cave ice are preserved in Hawaii as a result of microclimates.

Renewable energy is a main avenue to reduce greenhouse gas emissions and mitigate climate change, as well as health impacts, associated with mining, refining and burning fossil fuels. Isolated locations with consistent natural energy resources patterns, such as Hawaii, have great potential to reduce their dependence on fossil fuels and generate energy locally. Using a regional atmospheric model, we explored the wind-power potential of Oahu at high resolution (1 km) and over a period (2005-2014) that allowed the assessment of variability from hourly to interannual. A validation of the model using both weather stations and wind farms showed the need for observational data at the turbine hub height to correctly estimate model errors for wind power applications because the model response can be quite different at standard near-surface wind measurement heights. The model performance at larger timescales evidences the potential for long-term assessment of wind characteristics. On the other hand, the model errors at sub-daily timescales indicated limitations of short-term planning, except for sudden changes in wind speed, which were accurately simulated. Our results identify optimal locations for wind power plants from capacity factor estimates, which include analysis of mean, variability at different timescales, ramps, and sustained periods of low generation. Published by Elsevier Ltd.

Sulfur dioxide emissions from the Kilauea Volcano on the island of Hawai'i and the subsequent formation of sulfate aerosols have caused a public health hazard across the state of Hawaii since the volcano began erupting continuously in 1983. The University of Hawaii at Manoa began to forecast the trajectory and dispersion of emissions in 2010 to help mitigate the hazards to public health. In this paper a comprehensive review of potential conversion reactions is presented with the goal of more accurately representing the sulfur dioxide chemistry in the dispersion model. Atmospheric sulfur dioxide chemistry and major process responsible for sulfate formation are well documented in urban and industrial settings. The atmosphere in the vicinity of Kilauea Volcano on the island of Hawaii differs from that in previous investigations by virtue of being far removed from both urban and industrial settings in a remote, tropical marine atmosphere. Additionally, the combination of the high rate of sulfur dioxide emissions and trace gases and metals from Kilauea Volcano creates a unique circumstance that requires a new look at potential conversion pathways to determine the dominant reactions. The theoretical analysis suggests that the dominant reaction in clear air will be between sulfur dioxide and the hydroxyl radical (0.01-5% h(-1)) and the dominant reaction in cloudy air involves hydrogen peroxide (3-50% s(-1)). Moreover, given the high SO2 emissions from the Halema'urna'u Crater vent, the oxidation of sulfur dioxide by these reactants is limited by their rate of production.

Solar power as an alternative energy source in Hawai'i has grown in recent years [1], with increasing amounts of photovoltaic panels found statewide. Power resource management and grid stability require that the variability in irradiance, hence, solar power coming into the electric grid be understood. The aim of this study is to produce an irradiance dataset for Oahu, Hawai'i, containing temporal and spatial variability, so it can be used in modeling power generation.Images from the Geostationary Operational Environmental Satellite are used to estimate surface irradiance within the Heliosat method and are validated against ground-based pyranometer measurements. The diurnal cycle was then removed to assess the accuracy of the Heliosat method in estimating atmospheric attenuation. Lastly, the irradiance data was converted into DC power to consider the dataset in terms of power generation.The irradiance dataset produced has RMSE values of 15–30% depending on location, with correction for viewing angle discrepancies found to improve the performance. Variability on sub hourly, diurnal, monthly and yearly time scales are found within the dataset, as well as events such as an El Nino year, a La Nina year and Kona lows, together with a spatial distribution consistent with previous research.

The Space Science Engineering Center, in collaboration with the Mauna Kea Weather Center at the University of Hawai'i at Manoa, has developed a regional retrieval processor for high-spectral-resolution infrared data. The core of the processor makes use of an inversion system, referred to as Mirto, which combines, in a Bayesian way, the a priori knowledge of the atmospheric state, based on available numerical weather prediction forecasts, with the physical information embedded in satellite observations. Forecast temperature and water vapor mixing ratio fields over the central North Pacific Ocean are adjusted to produce synthetic radiances closer and closer to the Suomi NPP Cross-track Infrared Sounder (CrIS) observations taken in clear-sky conditions. The paucity of synoptic observations over this area and the highly homogeneous background represented by the ocean provide a good framework for the implementation of this hyperspectral data inversion system. Nearly real-time (less than 60 min from overpass time) Internet publication of retrieved atmospheric profiles is made possible by the availability of a direct broadcast system that provides data from the Suomi NPP platform (CrIS and VIIRS). The main goal for the implemented system is to provide the forecasting community with products suitable for nowcasting applications and for optimal data assimilation. The implemented processor has been running routinely since August 2013. Validation based on the comparisons of retrievals with rawinsonde data from Hilo, Hawaii, and Lihue, Hawaii, and GPS-derived total precipitable water from four stations, performed over a time period of more than 1 year, shows a statistically significant improvement on the background atmospheric state used as a priori information.