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Boundary layer cloud transformations at high latitudes play a key role for the Arctic climate and are partially controlled by large-scale dynamics such as subsidence. While measuring large-scale and mesoscale divergence on spatial scales on the order of 100 km has proven notoriously difficult, recent airborne campaigns in the subtropics have successfully applied measurement techniques using multiple dropsonde releases in circular flight patterns. In this paper, it is shown that this method can also be effectively applied at high latitudes, in spite of the considerable differences in atmospheric dynamics compared to the subtropics. To show the applicability, data collected during the airborne High Altitude and Long Range Research Aircraft–Transregional Collaborative Research Center TRR 172-Arctic Amplification: Climate Relevant Atmospheric and Surface Processes and Feedback Mechanisms [HALO–(AC)3] field campaign near Svalbard in spring 2022 were analyzed, where several flight patterns involving multiple dropsonde launches were realized by two aircraft. This study presents a first overview of the results. We find that the method indeed yields reliable estimates of mesoscale gradients in the Arctic, producing robust vertical profiles of horizontal divergence and, consequently, subsidence. Sensitivity to aspects of the method is investigated, including dependence on sampling area and the divergence calculation. Significance Statement: The aim of this work is to report encouraging results with a recently proposed aircraft-based method for measuring mesoscale vertical motions at high latitudes. Dropsonde data from the recent High Altitude and Long Range Research Aircraft–Transregional Collaborative Research Center TRR 172-Arctic Amplification: Climate Relevant Atmospheric and Surface Processes and Feedback Mechanisms [HALO–(AC)3] campaign are used for this purpose. The method has so far mainly been applied at subtropical to midlatitudes, but not in the Arctic, where the weather and climate conditions are very different. Gaining insight is significant because vertical winds play a key role in the Arctic climate system, including airmass transformations and the behavior of clouds and precipitation. The results motivate the use of the measured vertical winds in follow-up studies with process models. [ABSTRACT FROM AUTHOR]

The spatial variability of moist static energy (MSE) and its column‐integral (CMSE) around tropical cyclones (TCs) are known to help prognose TC development. Though limited in number and spatial coverage per TC, modeling work suggests that dropsondes, when strategically deployed, can reliably capture radial MSE and CMSE variability. This study uses North Atlantic upper‐level reconnaissance dropsondes from 1996 to 2021 to produce the most extensive observational analysis of MSE structure in TCs to date. MSE and CMSE decrease with distance from the TC center. MSE spatial variability is larger in Category 1 and 2 hurricanes compared to tropical storms, and generally largest after a TC's lifetime maximum intensity. Moisture dominates the MSE spatial variability but temperature contributes in the upper levels near the TC center. While differences in MSE spatial variability between weakening and intensifying TCs are less clear, raw MSE tends to be greater in intensifying TCs at most radii and vertical levels. Plain Language Summary: Moist static energy (MSE) is a thermodynamic variable combining contributions from temperature, water vapor, and gravity. MSE has been useful in describing processes important for atmospheric convection and tropical cyclone (TC) development. This study investigates the average MSE structure of observed Atlantic TCs using measurements from dropsondes, which are instruments dropped into TCs from Hurricane Hunter aircraft. MSE decreases with distance from the TC center, largely due to reduced moisture at large radii, though reduced temperature is important at higher altitudes. Further analysis was performed according to TC intensity and the rate of TC intensity change. MSE decreases more quickly from the TC center in Category 1 and 2 hurricanes compared to tropical storms. Intensifying and weakening TCs do not show substantial differences in how MSE varies in space. Instead, the MSE itself is larger in intensifying storms at most altitudes and distances, especially farther from the TC center. Key Points: Moist static energy (MSE) and its column integral decrease with distance from the TC center in observationsMoisture dominates the spatial variability of MSE but temperature contributes at upper levels near the TC centerMSE spatial variability is larger in Category 1–2 hurricanes than tropical storms and MSE itself is larger in more rapidly intensifying TCs [ABSTRACT FROM AUTHOR]

Satellites provide the largest dataset for monitoring the earth system and constraining analyses in numerical weather prediction models. A significant challenge for utilizing satellite radiances is the accurate estimation of their biases. High-accuracy nonradiance data are commonly employed to anchor radiance bias corrections. However, aside from the impacts of radio occultation data in the stratosphere, the influence of other types of "anchor" observation data on radiance assimilation remains unclear. This study provides an assessment of impacts of dropsonde data collected during the Atmospheric River (AR) Reconnaissance program, which samples ARs over the northeast Pacific, on the radiance assimilation using the Global Forecast System (GFS) and Global Data Assimilation System at the National Centers for Environmental Prediction. The assimilation of this dropsonde dataset has proven crucial for providing enhanced anchoring for bias corrections and improving the model background, leading to an increase of ∼5%–10% in the number of assimilated microwave radiance in the lower troposphere/midtroposphere over the northeast Pacific and North America. The impact on tropospheric infrared radiance is not only small but also beneficial. Impacts of dropsondes on the use of stratospheric channels are minimal due to the absence of dropsonde observations at certain altitudes, such as aircraft flight levels (e.g., 150 hPa). Results in this study underscore the usefulness of dropsondes, along with other conventional data, in optimizing the assimilation of satellite radiance. This study reinforces the importance of a diverse observing network for accurate weather forecasting and highlights the specific benefits derived from integrating dropsonde data into radiance assimilation processes. Significance Statement: This study aims to evaluate the impact of aircraft reconnaissance dropsondes on the assimilation of satellite radiance data, utilizing observations from the 2020 Atmospheric River Reconnaissance program. The key findings reveal a substantial enhancement in the model first guess and improved estimates of radiance biases. Notably, there is a significant 5%–10% increase in microwave radiance observations over the northeastern Pacific and North America, with positive yet modest effects observed in tropospheric infrared radiance. These findings underscore the crucial role of atmospheric river reconnaissance dropsondes as anchor data, enhancing the assimilation of radiance observations. In essence, the inclusion of these dropsondes in routine networks is particularly valuable for optimizing data assimilation in regions with sparse observational data. [ABSTRACT FROM AUTHOR]

This study presents a method to diagnose radial ventilation, the horizontal flux of relatively low-θe air into tropical cyclones, from dropsonde observations. We used this method to investigate ventilation changes over three consecutive sampling periods in Hurricane Sam (2021), which underwent substantial intensity changes over 3 days. During the first and last periods, coinciding with intensification, the ventilation was relatively small due to a lack of spatial correlation between radial flow and θe azimuthal asymmetries. During the second period, coinciding with weakening, the ventilation was relatively large. The increased ventilation was caused by greater shear associated with an upper-level trough, tilting the vortex, along with dry, low-θe air wrapping in upshear. The spatial correlation of the radial inflow and anomalously low-θe air resulted in large ventilation at mid- to upper levels. Additionally, at low to midlevels, there was evidence of mesoscale inflow of low-θe air in the stationary band complex. The location of these radial ventilation pathways and their effects on Sam's intensity are consistent with previous idealized and real-case modeling studies. More generally, this method offers a way to monitor ventilation changes in tropical cyclones, particularly when there is full-troposphere sampling around and within a tropical cyclone's core. Significance Statement: Ventilation, the injection of relatively dry and/or cool air into a tropical cyclone, may weaken a storm. In contrast, the lack of ventilation is favorable for intensification. The purpose of this study is to present a method to diagnose ventilation using aircraft dropsonde observations. Using dropsonde observations collected in Hurricane Sam (2021), there was a period of increased lateral ventilation in two regions around the storm that coincided with when the storm rapidly weakened. The results suggest that monitoring ventilation from dropsonde observations, when available, may be useful for anticipating ventilation-induced intensity changes in tropical cyclones and further studying ventilation pathways. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This study describes the research-to-operations process leading to a recent change in tropical cyclone (TC) reconnaissance sampling patterns as well as observing-system experiments that evaluated the impact of that change on numerical weather prediction model forecasts of TCs. A valuable part of this effort was having close, multipronged connections between the TC research and operational TC prediction communities at the National Oceanic and Atmospheric Administration (NOAA). Related to this work, NOAA's Atlantic Oceanographic and Meteorological Laboratory (AOML) and National Hurricane Center (NHC) have a long history of close collaboration to improve TC reconnaissance. Similar connections between AOML and NOAA's Environmental Modeling Center (EMC) also laid a foundation for the observing-system experiments conducted here. More specifically, AOML and NHC collaborated in 2018 to change how NHC uses NOAA's Gulfstream-IV (G-IV) jet during TC synoptic surveillance missions. That change added a second circumnavigation at approximately 1.5° from TC centers, when possible. Preliminary experiments suggest that the change improved track forecasts, though the intensity results are more mixed. Despite the somewhat small sample size over a 3-yr period, the track improvement does agree with prior work. This effort has led to additional work to more fully examine G-IV sampling strategies. [ABSTRACT FROM AUTHOR]

Atmospheric River Reconnaissance has held field campaigns during cool seasons since 2016. These campaigns have provided thousands of dropsonde data profiles, which are assimilated into multiple global operational numerical weather prediction models. Data denial experiments, conducted by running a parallel set of forecasts that exclude the dropsonde information, allow testing of the impact of the dropsonde data on model analyses and the subsequent forecasts. Here, we investigate the differences in skill between the control forecasts (with dropsonde data assimilated) and denial forecasts (without dropsonde data assimilated) in terms of both precipitation and integrated vapor transport (IVT) at multiple thresholds. The differences are considered in the times and locations where there is a reasonable expectation of influence of an intensive observation period (IOP). Results for 2019 and 2020 from both the European Centre for Medium-Range Weather Forecasts (ECMWF) model and the National Centers for Environmental Prediction (NCEP) global model show improvements with the added information from the dropsondes. In particular, significant improvements in the control forecast IVT generally occur in both models, especially at higher values. Significant improvements in the control forecast precipitation also generally occur in both models, but the improvements vary depending on the lead time and metrics used. Significance Statement: Atmospheric River Reconnaissance is a program that uses targeted aircraft flights over the northeast Pacific to take measurements of meteorological fields. These data are then ingested into global weather models with the intent of improving the initial conditions and resulting forecasts along the U.S. West Coast. The impacts of these observations on two global numerical weather models were investigated to determine their influence on the forecasts. The integrated vapor transport, a measure of both wind and humidity, saw significant improvements in both models with the additional observations. Precipitation forecasts were also improved, but with differing results between the two models. [ABSTRACT FROM AUTHOR]

This study conducts the first large-sample comparison of the impact of dropsondes in the tropical cyclone (TC) inner core, vortex, and environment on NWP-model TC forecasts. We analyze six observing-system experiments, focusing on four sensitivity experiments that denied dropsonde observations within annuli corresponding with natural breakpoints in reconnaissance sampling. These are evaluated against two other experiments detailed in a recent parallel study: one that assimilated and another that denied dropsonde observations. Experiments used a basin-scale, multistorm configuration of the Hurricane Weather Research and Forecasting (HWRF) Model and covered active periods of the 2017–20 North Atlantic hurricane seasons. Analysis focused on forecasts initialized with dropsondes that used mesoscale error covariance derived from a cycled HWRF ensemble, as these forecasts were where dropsondes had the greatest benefits in the parallel study. Some results generally support findings of previous research, while others are novel. Most notable was that removing dropsondes anywhere, particularly from the vortex, substantially degraded forecasts of maximum sustained winds. Removing in-vortex dropsondes also degraded outer-wind-radii forecasts in many instances. As such, in-vortex dropsondes contribute to a majority of the overall impacts of the dropsonde observing system. Additionally, track forecasts of weak TCs benefited more from environmental sampling, while track forecasts of strong TCs benefited more from in-vortex sampling. Finally, inner-core-only sampling strategies should be avoided, supporting a change made to the U.S. Air Force Reserve's sampling strategy in 2018 that added dropsondes outside of the inner core. Significance Statement: This study uses a regional hurricane model to conduct the most comprehensive assessment to date of the impact of dropsondes at different distances away from the center of a tropical cyclone (TC) on TC forecasts. The main finding is that in-vortex dropsondes are most important for intensity and outer-wind-radii forecasts. Particularly notable is the impact of dropsondes on TC maximum wind speed forecasts, as reducing sampling anywhere would degrade those forecasts. [ABSTRACT FROM AUTHOR]

The global positioning system dropwindsonde has provided thousands of high-resolution kinematic and thermodynamic soundings in and around tropical cyclones (TCs) since 1997. These data have revolutionized the understanding of TC structure, improved forecasts, and validated observations from remote sensing platforms. About 400 peer-reviewed studies on TCs using these data have been published to date. This paper reviews the history of dropwindsonde observations, changes to dropwindsonde technology since it was first used in TCs in 1982, and how the data have improved forecasting and changed our understanding of TCs. [ABSTRACT FROM AUTHOR]

This study comprehensively assesses the overall impact of dropsondes on tropical cyclone (TC) forecasts. We compare two experiments to quantify dropsonde impact: one that assimilated and another that denied dropsonde observations. These experiments used a basin-scale, multistorm configuration of the Hurricane Weather Research and Forecasting Model (HWRF) and covered active North Atlantic basin periods during the 2017-20 hurricane seasons. The importance of a sufficiently large sample size as well as thoroughly understanding the error distribution by stratifying results are highlighted by this work. Overall, dropsondes directly improved forecasts during sampled periods and indirectly impacted forecasts during unsampled periods. Benefits for forecasts of track, intensity, and outer wind radii were more pronounced during sampled periods. The forecast improvements of outer wind radii were most notable given the impact that TC size has on TC-hazards forecasts. Additionally, robustly observing the inner- and near-core region was necessary for hurricane-force wind radii forecasts. Yet, these benefits were heavily dependent on the data assimilation (DA) system quality. More specifically, dropsondes only improved forecasts when the analysis used mesoscale error covariance derived from a cycled HWRF ensemble, suggesting that it is a vital DA component. Further, while forecast improvements were found regardless of initial classification and in steady-state TCs, TCs undergoing an intensity change had diminished benefits. The diminished benefits during intensity change probably reflect continued DA deficiencies. Thus, improving DA system quality and observing system limitations would likely enhance dropsonde impacts. [ABSTRACT FROM AUTHOR]

The synoptic environment around tropical cyclones plays a significant role in vortex evolution. To capture the environment, the operational and research communities calculate diagnostic quantities. To aid with applications and research, the Tropical Cyclone Precipitation, Infrared, Microwave, and Environmental Dataset (TC PRIMED) combines disparate data sources. A key part of TC PRIMED is the environmental context. Often, environmental diagnostics come from multiple sources. However, TC PRIMED uses the European Centre for Medium-Range Weather Forecasts fifth-generation reanalysis (ERA5) product to provide a more complete representation of the storm environment from a single source. Reanalysis products usually poorly resolve tropical cyclones and their surrounding environment. To understand the uncertainty of large-scale diagnostics, ERA5 is compared to the Statistical Hurricane Intensity Prediction Scheme developmental dataset and the National Oceanic and Atmospheric Administration Gulfstream IV-SP dropwindsondes. This analysis highlights biases in the ERA5 environmental diagnostic quantities. Thermodynamic fields show the largest biases. The boundary layer exhibits a cold temperature bias that limits the amount of convective instability; also, the upper troposphere contains temperature biases and shows a high relative humidity bias. However, the upper-troposphere large-scale kinematic fields and derived metrics are low biased. In the lower troposphere, the temperature gradient and advection calculated from the thermal wind suggest that the low-level wind field is not representative of the observed distribution. These diagnostics comparisons provide uncertainty so that users of TC PRIMED can assess the implications for specific research and operational applications. [ABSTRACT FROM AUTHOR]

This study reviews the recent addition of dropwindsonde wind data near the tropical cyclone (TC) center as well as the first-time addition of high-density, flight-level reconnaissance observations (HDOBs) into the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS). The main finding is that the additional data have profound positive impacts on subsequent TC track forecasts. For TCs in the North Atlantic (NATL) basin, statistically significant improvements in track extend through 4–5 days during reconnaissance periods. Further assessment suggests that greater improvements might also be expected at days 6–7. This study also explores the importance of comprehensively assessing data impact. For example, model or data assimilation changes can affect the so-called "early" and "late" versions of the forecast very differently. It is also important to explore different ways to describe the error statistics. In several instances the impacts of the additional data strongly differ depending on whether one examines the mean or median errors. The results demonstrate the tremendous potential for further improving TC forecasts. The data added here were already operationally transmitted and assimilated by other systems at NCEP, and many further improvements likely await with improved use of these and other reconnaissance observations. This demonstrates the need of not only investing in data assimilation improvements, but also enhancements to observational systems in order to reach next-generation hurricane forecasting goals. Significance Statement: This study demonstrates that data gathered from reconnaissance missions into tropical cyclones substantially improves tropical cyclone track forecasts. [ABSTRACT FROM AUTHOR]

Interactions between clouds, water vapor, radiation, and circulation influence tropical cyclone (TC) development. Many of these interactions can be quantified by understanding tendencies of the spatial variance of moist static energy (MSE). Dropsondes from aircraft reconnaissance sample profiles needed to compute MSE at fine vertical resolution, and may be useful in analyzing these feedbacks on TCs in situ. However, dropsondes are spatially sparse, and sample limited column depths depending on the type of reconnaissance mission. We use idealized convection‐permitting simulations to examine how MSE variability, and the feedbacks that influence it, are resolved using selected patterns of grid points meant to resemble dropsonde launch points in reconnaissance flight patterns. We first examine the column depth necessary to capture the MSE variability of the full atmosphere. We then study how these simulated flight patterns depict MSE variance and its relevant diabatic feedbacks in TCs of varying structure and intensity. Plain Language Summary: Interactions between clouds, water vapor, radiation, and circulation influence tropical cyclones (TCs). These interactions can be quantified by understanding how moisture, or moist static energy (MSE), varies spatially. Hurricane Hunter aircraft missions use dropsondes, instruments that collect the necessary data to compute MSE as they descend. This offers an observational tool to study cloud‐water vapor‐radiation‐circulation interactions in TCs. However, only 20–40 dropsondes are released per mission, and they only sample the atmosphere below the aircraft's flight level. We examine several simulations of TCs, where patterns of grid points are chosen to resemble dropsonde release points. We calculate how MSE varies spatially and the mechanisms that influence it. With these simulations, we reveal the depth of the atmosphere needed for effective sampling, and how sensitive calculations are to the density and spatial distribution of data. Key Points: Sampling the full troposphere is necessary to sufficiently capture the moist static energy variability of the full atmospheric columnCalculations of this variability and the feedbacks that influence it are impacted by radial coverage and spatial distribution of dataFull resolution of tropical cyclone moist static energy requires inner‐core sampling not regularly conducted by upper‐air reconnaissance [ABSTRACT FROM AUTHOR]

The airborne stepped frequency microwave radiometer (SFMR) provides the measurements of 10-m ocean surface wind speed in high and extreme wind conditions. These winds are calibrated using the surface-adjusted wind estimates from the so-called dropsondes. The surface-adjusted winds are obtained from layer-averaged winds scaled to 10-m altitude to eliminate the local surface variability not associated with the storm strength. The SFMR measurements and, consequently, the surface-adjusted dropsonde winds represent a possible reference for satellite instrument and model calibration/validation at high and extreme wind conditions. To this end, representativeness errors that those measurements may introduce need to be taken into account to ensure that the storm variability is correctly resolved in satellite retrievals and modeling. In this work, we compare the SFMR winds with the dropsonde surface-adjusted winds derived from the so-called WL150 algorithm, which uses the lowest 150-m layer between 10 and 350 m. We use nine years of data from 2009 to 2017. We focus on the effects of the layer altitude and thickness. Our analysis shows that the layer altitude has a significant impact on dropsonde/SFMR wind comparisons. Moreover, the averaged winds obtained from layers thinner than the nominal 150 m and closer to the surface are more representative of the SFMR surface wind speed than the WL150 speeds. We also find that the surface-adjusted winds are more representative of 10-km horizontally averaged SFMR winds. We conclude that for calibration/validation purposes, the WL150 algorithm can introduce noise, and the use of actual 10-m dropsonde measurements should be further investigated. [ABSTRACT FROM AUTHOR]

The sporadic formation of short-lived convective clouds in the eye of Tropical Cyclone (TC) Trami (2018) is investigated using dropsonde data and simulation results from a coupled atmosphere–ocean model. According to the satellite data, top height of the convective clouds exceeds 9 km above mean sea level, considerably taller than that of typical hub clouds (2–3 km). These clouds are located 10–30 km away from the TC center. Hence, these convective clouds are called deep eye clouds (DECs) in this study. The dropsonde data reveal an increase in relative humidity in the eye region during the formation of DECs. Short-lived convective clouds are simulated up to the middle troposphere in the eye region in the coupled model. Investigation of thermodynamic conditions shows a weakened low-level warm core and associated favorable conditions for convection in the eye region during the formation of DECs. DECs are formed after the weakening and outward displacement of convective heating within the eyewall. To elucidate the influence of the changes in convective heating within the eyewall on the formation of DECs, we calculate secondary circulation and associated adiabatic warming induced by convective heating within the eyewall using the Sawyer–Eliassen equation. In the eye region, weakening of subsidence and associated vertical potential temperature advection is observed as DECs are formed. This suggests that the weakening and outward displacement of convective heating within the eyewall create favorable conditions for the sporadic formation of DECs. [ABSTRACT FROM AUTHOR]