NOAA Earth System Research Laboratory

Severe convective storms pose significant challenges to societal resilience and represent a critical test for Subseasonal-to-Seasonal (S2S) forecasting at longer lead-times. This study investigates the predictability of the torrential 2015 May Texas-Oklahoma extreme rainfall event, during which record-breaking rainfall abruptly terminated a multi-year drought, only to be followed by a second wave of heavy rainfall by Tropical Storm Bill in June. We evaluated the performance of the MPAS-NoahMP S2S prediction system in capturing this extreme rainfall event. Three sets of global mesh are designed, a global 60-km uniform mesh, two regional refinement mesh centered in the US for 60-15km, and 60-4km going down to convection-permitting resolution. At the 1-week lead time, ensemble forecasts demonstrate high fidelity, skillfully capturing the timing, magnitude, and spatial pattern of precipitation anomalies. At 2- and 3-week lead times, the model maintains a persistent signal of the May wet event, albeit with a damped magnitude and significantly larger ensemble spread, which itself is a useful indicator of potential high-impact weather. We further investigate the added values of regional refinement for this extreme rainfall event, in terms of extreme precipitation distribution, diurnal cycle, and land-atmosphere interactions priori to the rainfall. This study discusses the applications of km-scale convection-permitting simulation in subseasonal forecasts (2-6 week) and the valuable findings translating probabilistic S2S forecasts into actionable intelligence for stakeholders, such as water managers, who must navigate these increasingly volatile weather regimes.

<strong class="journal-contentHeaderColor">Abstract.</strong> Thermodynamic profiles, especially in the atmospheric boundary layer (ABL), are essential for many research and operational applications. Ground-based infrared spectrometers (IRS) are commercially available, and thermodynamic profiles in the ABL can be retrieved from these observations at 5-minute resolution or better. This study deployed seven IRS systems within 5 m of each other in Boulder, Colorado, USA, in September&ndash;October 2023, providing an opportunity to evaluate the relative accuracy of the measured radiances from these systems as well as the retrieved thermodynamic profiles. The analysis demonstrates that the observed radiances from the seven instruments agree within 1 % of the ambient radiance in both opaque and more transparent channels. The differences in the spectral calibration between the instruments were smaller than 0.11 cm^-1, relative to the nominal effective wavenumber of the metrology laser of 15799 cm^-1 (i.e., better than 7.1 ppm). Further, the retrieved temperature and humidity profiles agree with each other well within the uncertainty of the retrieved profiles, and qualities derived from these thermodynamic profiles such as precipitable water vapor and height of the convective boundary layer also agree within their uncertainties. These results demonstrate a high degree of repeatability and precision, and that if these instruments were deployed as part of a network, any differences larger than the retrieval uncertainty would be associated with real environmental differences and not an artifact of the instrument calibration or retrieval.

Abstract. Aerosol particles resulting from both wildfires and dust events introduce considerable uncertainty into both climate research and public health assessments. These challenges are becoming particularly evident in the western U.S. To gain a deeper understanding of western U.S. aerosol properties, we analyzed 13 years (2011–2024) of surface in-situ aerosol optical data from Storm Peak Laboratory (SPL) in northwestern Colorado, and 6 years (2019–2024) of surface in-situ aerosol optical data from Table Mountain (BOS) in central Colorado. The aerosol optical properties at both sites demonstrate a strong summer wildfire smoke signal (peaking in August) and evidence of springtime dust events. BOS exhibited higher aerosol loading than SPL, particularly during spring and winter, consistent with the proximity of BOS to urban sources and its lower elevation. While the general patterns observed for SPL are consistent with a previous climatological analysis (covering the period 2011–2016) for the site, the longer SPL dataset used here shows that there has been a significant increase in extreme wildfire smoke events for 2017–2024 relative to 2011–2016. Both summer and fall exhibit statistically significant increasing trends in the upper percentiles of scattering coefficient with trends of 10 ± 1 % yr-1 at the 98th percentile in the summer and 2.4 ± 0.4 % yr-1 at the 96th percentile in the fall. Co-variability among some of the aerosol optical properties is used to further identify aerosol types and temporal patterns, demonstrating similarities between the two sites.

Abstract NOAA began operational assimilation of radio occultation (RO) observations from the Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC-1) mission over 15 years ago. Since then, other operational and research missions have been incorporated into the operational suite. To increase the volume of RO profiles and their geographical coverage beyond what COSMIC-2 provides, which is mostly limited to the tropical and subtropical regions, NOAA engaged in procurement activities with commercial companies capable of delivering RO data for operational use. Tests were conducted in this study to quantify the value of commercial RO data for numerical weather prediction (NWP) applications and their relative value compared to a U.S. Government RO data source (viz., COSMIC-2). When commercial RO soundings are added to a baseline that already assimilates COSMIC-2 bending angle observations, it is found that commercial RO soundings bring benefits to global weather forecast skill, mostly by reducing the temperature and wind root-mean-squared error globally. As expected, the benefits are larger in the extratropics. Statistics between COSMIC-2 and commercial RO data show similar values, indicating that higher signal-to-noise values are not necessary to improve weather forecasting—at least with current modeling capabilities. The quality of commercial RO is sufficient for current global NWP applications. Finally, and supporting earlier studies with simulated data, results show that global RO data distribution is needed to improve NWP skill globally. Significance Statement The results of this paper are significant because they inform on the value of radio occultation observations from different sources for global numerical weather prediction applications, thus helping decision-makers plan for future commercial data buys.

Abstract. Thermodynamic profiles, especially in the atmospheric boundary layer (ABL), are essential for many research and operational applications. Ground-based infrared spectrometers (IRS) are commercially available, and thermodynamic profiles in the ABL can be retrieved from these observations at 5-minute resolution or better. This study deployed seven IRS systems within 5 m of each other in Boulder, Colorado, USA, in September–October 2023, providing an opportunity to evaluate the relative accuracy of the measured radiances from these systems as well as the retrieved thermodynamic profiles. The analysis demonstrates that the observed radiances from the seven instruments agree within 1 % of the ambient radiance in both opaque and more transparent channels. The differences in the spectral calibration between the instruments were smaller than 0.11 cm-1, relative to the nominal effective wavenumber of the metrology laser of 15799 cm-1 (i.e., better than 7.1 ppm). Further, the retrieved temperature and humidity profiles agree with each other well within the uncertainty of the retrieved profiles, and qualities derived from these thermodynamic profiles such as precipitable water vapor and height of the convective boundary layer also agree within their uncertainties. These results demonstrate a high degree of repeatability and precision, and that if these instruments were deployed as part of a network, any differences larger than the retrieval uncertainty would be associated with real environmental differences and not an artifact of the instrument calibration or retrieval.

Abstract Precise measurements of 14 C:C in atmospheric CO 2 (expressed as Δ 14 C) are increasingly used in contemporary carbon cycle studies as a tracer of various processes, including anthropogenic CO 2 emissions from combustion of fossil fuels and cement production. Here we develop a complete representation of the global atmospheric Δ 14 CO 2 budget and use a combination of economic inventories, geophysical observations and process models to create monthly 1° × 1° gridded estimates of 14 C and associated CO 2 mass fluxes into the atmosphere for use in atmospheric transport models. The fluxes impacting Δ 14 C we include are (in order of impact on the global trend): fossil CO 2 emissions, cosmogenic production of 14 CO 2 , oceanic and terrestrial isotopic disequilibrium fluxes, 14 C production from nuclear power generation, and the impact of net terrestrial and ocean CO 2 fluxes. The total impact of these fluxes from 2000 through 2012 underestimated the observed decline in global average Δ 14 C indicating that the sum of derived fluxes was too positive. We thus optimize two global and time‐invariant scalars (for cosmogenic 14 C production and terrestrial disequilibrium) that allow us to match observed atmospheric trends and close the global 14 C budget. While our optimized scalars are not a unique combination, our optimized fluxes represent a geophysically plausible set that conforms with surface ocean 14 C observations and newly convergent constraints on the magnitude of global cosmogenic 14 C production. In Part 2 of this study, we implement this set of fluxes in a global atmospheric transport model and compare simulated Δ 14 CO 2 to a large array of global observations.

Abstract Accurate forecasts of the development and evolution of deep, moist convection in convection-allowing models (CAMs) remain a challenge in part owing to the difficulties inherent in modeling the microphysical and internal structures of convection, which can affect storm mode, intensity, and longevity. We hypothesize that underused Weather Surveillance Radar-1988 Doppler (WSR-88D) and GOES-16 observations can improve forecasts of deep convection in CAM ensembles. Since the upgrade to the national network of WSR-88D radars was completed in 2013, polarimetric radar data offer a wealth of information about the shape, size, and type of hydrometeors present in precipitation. Several distinct polarimetric signatures within deep convection have been identified, such as the differential reflectivity (ZDR) column, that can aid significantly in characterizing internal storm structures and improve CAM representation of convection. In addition, GOES-16 infrared all-sky brightness temperatures (BTs) provide complimentary information on cloud structures and cover that radars cannot directly measure. The CAM ensemble in this study uses the Advanced Research version of the Weather Research and Forecasting Model with the High-Resolution Rapid Refresh configuration at 3-km horizontal grid spacing and with 40 ensemble members. Observations are assimilated using an Ensemble Kalman Filter, where the radar and satellite observations are assimilated jointly and separately, and results are compared in four proof-of-concept experiments. Results indicate that the assimilation of BT observations improves forecasts of a severe convective event, which are further improved with the assimilation of ZDR observations. While BT assimilation alone improves the convective forecast, ZDR observations provide additional improvements to updraft helicity tracks, precipitation, and hail forecasts. Significance Statement There are several challenges associated with predictions of severe weather in numerical weather models, including the time and location of thunderstorm initiation as well as the hazards associated with severe thunderstorms, including hail, flash flooding, and tornadoes. This study explores using a combination of underused satellite and radar observations to better define the atmospheric state used to start the weather prediction models, with the hope that this will lead to better forecasts before and during the thunderstorms. Results show that underused observations from routinely available Doppler weather radars and a geostationary satellite, all of which are currently available, can work synergistically to improve forecasts of thunderstorms as well as their hazards.

<strong class="journal-contentHeaderColor">Abstract.</strong> This paper describes efforts to establish aerosol data assimilation capabilities for NOAA&rsquo;s National Air Quality Forecasting Capability (NAQFC), a regional online air quality modeling (AQM) system under NOAA&rsquo;s Unified Forecast System (UFS), by assimilating measurements of fine particulate matter (PM_2.5, particles with diameters less than 2.5 &micro;m). PM_2.5assimilation is developed within the Joint Effort for Data assimilation Integration (JEDI) framework and tested using its 3D-Var data assimilation (DA) component. The PM_2.5 observation operator is constructed by combining newly developed PM_2.5 transformation recipes in the JEDI Variable Derivation Repository (VADER) with a general spatial interpolation operator in the Unified Forward Operator (UFO). Cycled DA and forecast experiments were conducted from 1 to 21 September 2020, during a period of Western U.S. wildfires, to assess the impact of assimilating PM_2.5 observations from the AirNow and PurpleAir networks. The control and analysis variables include individual aerosol species, with background error standard deviations generated by scaling their respective background values. Prognostic variables such as aerosol particle number and total particulate surface area are updated accordingly following each analysis update. All DA experiments use a 3-hourly cycling interval, with PM_2.5 observations assimilated every 3 hours. The control experiment uses the same configuration but without any data assimilation. Results show that assimilating either AirNow or PurpleAir PM_2.5 data reduces 1&ndash;24 h forecast errors in terms of mean absolute error (MAE) and root mean square error (RMSE) compared to the control run over CONUS. Forecast skill, measured using the Critical Success Index (CSI) for PM_2.5 thresholds of 5, 12, and 35 &micro;g/m&sup3;, also improves. AirNow observations have a greater impact overall, while PurpleAir shows its strongest impact over Nevada, northern Utah, Colorado, and southwestern New Mexico &ndash; regions with persistent underpredictions in the control run at forecast hour 1. Overall, the assimilation of PurpleAir observations in addition to AirNow data leads to a slight reduction in 3&ndash;24 h MAE.

Abstract Models from Coupled Model Intercomparison Project Phase 6 produce too much precipitation over the Andes but too little over the Amazon or the Wet Andes‐Dry Amazon (WADA) bias pattern. Unlike the conventional view that convection parameterization and land model deficiencies can contribute to Amazonian rainfall biases, we approach this long‐standing biased model behavior through the lens of Andean convection. Using Community Earth System Model v1.1 and focusing on the wet season, our mechanism‐denial experiments demonstrate that Andean convection notably reduces precipitation over the Amazon during austral summer. The Andean forced Amazonian response operates on weather timescale. The reduction of Amazonian rainfall is detectable within a few hours after initial Andean forcing. The precipitation response is primarily driven by variations in the moisture budget and is moderated by changes in convective available potential energy over the Amazon. Changes in the total advection of moisture over the Amazon are dominated by the vertical advection term and can be attributed to discrepancies in the dynamic omega field. In the experiments, the Andean east flank region is scrutinized where the vertical velocity and moisture fields play an intermediary role for the Andean driven WADA connection. The Andean forcing induces descending anomalies on the Andean east flank. The disturbances of wind and geopotential fields over the Andean east flank propagate eastward via Kelvin waves. Over the Amazon, descending anomalies and advective drying lead to reduction of mid‐to‐high level cloud, increase of shortwave cloud forcing and surface net radiation, and enhancement of themodynamic stability and rainfall reduction.

Abstract Convective instability is affected by both surface heat fluxes into near-surface air parcels and the atmospheric profile encountered as these air parcels are lifted aloft. In this study, we investigate the evolution of buoyancy experienced by surface-based parcels, taking interstorm soil moisture drydowns as fundamental time periods. We use profile observations of atmospheric temperature and humidity from ground-based passive remote sensing and in situ observations available at the U.S. Department of Energy Atmospheric Radiation Measurement Southern Great Plains site in Oklahoma, United States, and examine drydown events during the warm seasons from 2017 to 2022. Results reveal new insights on the emergence and growth of convective available potential energy (CAPE) during drydowns. We observe that the peak convective potential appears in the afternoon and grows in the midtroposphere (68% of 66 drydown events), with the largest CAPE values toward the end of the drydown event. We also find that the near-surface parcel contributes more to the evolution of CAPE than the atmospheric environment (86% of drydown events). These findings highlight the importance of surface heat fluxes and moisture availability from surface-based parcels in modulating the evolution of atmospheric conditions favorable for convection, underscoring the interplay between surface processes and atmospheric dynamics in driving convective instability. Significance Statement Land–atmosphere interactions, particularly feedbacks between soil moisture and precipitation, are pivotal in shaping convective processes, yet their influence on weather and climate remains poorly characterized. This study leverages high-resolution thermodynamic profiles of the atmosphere at a research site in Oklahoma, United States, to investigate the evolution of buoyancy and CAPE during drydown interstorm periods. By linking surface processes, boundary layer dynamics, and parcel ascent, our findings provide critical insights into the mechanisms driving convection. These results enhance the understanding of land–atmosphere coupling, improving forecasts of moist convection and its preconditions across diverse environmental conditions.