Reconnaissance Observations Research Articles

Influence of CyGNSS L2 wind data on tropical cyclone analysis and forecasts in the coupled HAFS/HYCOM system

This study examines the influence of NASA Cyclone Global Navigation Satellite System (CyGNSS) Level 2-derived 10 m (near-surface) wind speed over the ocean on analyses and forecasts within the NOAA operational Hurricane Analysis and Forecast System (HAFS). HAFS is coupled with a regional configuration of the HYCOM ocean model. The primary advantages of data from the CyGNSS constellation of satellites include higher revisit frequency compared to polar-orbiting satellites, and the availability of reliable wind observations over the ocean surface during convective precipitation. CyGNSS data are available early in the life cycle of tropical cyclones (TCs) when aerial reconnaissance observations are not available. We focus on TCs whose forecasts were initialized when the TC was a depression or tropical storm. In the present study, we find first, that assimilation of CyGNSS near-surface winds improves storm track, intensity, and structure statistics in the analysis and early in the forecast, for many cases. Second, we find that assimilation of CyGNSS observations provides additional insights into the evolution of air-sea interaction in intensifying TCs: In effect, the ocean responds in the coupled model to modifications in the initial 10 m wind field, thereby impacting forecasts of intensity, storm structure, and sea surface height, as demonstrated by two case studies. We also discuss some forecasts where assimilating CYGNSS appears to degrade performance for either intensity or structure.

Liquefaction ground deformations and cascading coastal flood hazard in the 2023 Kahramanmaraş earthquake sequence

The 2023 Kahramanmaraş earthquake sequence produced extensive liquefaction-induced ground deformations and ongoing flooding along the shoreline of the Mediterranean port city of İskenderun, Türkiye. This study compiles field observations and analyses from cross-disciplinary perspectives to investigate whether earthquake-induced liquefaction was a significant factor for increasing the flood hazard in İskenderun. Geotechnical reconnaissance observations following the earthquakes included seaward lateral spreading, settlement beneath buildings, and failures of coastal infrastructure. Three presented lateral spreading case histories indicate consistent ground deformation patterns with areas of reclaimed land. Persistent scatterer interferometry (PSI) measurements from synthetic aperture radar (SAR) imagery identify a noticeably greater rate of pre- and post-earthquake subsidence within the İskenderun coastal and urban areas relative to the surrounding regions. The PSI measurements also indicate subsidence rates accelerated following the earthquakes and were typically highest near the observed liquefaction manifestations. These evaluations suggest that while the liquefaction of coastal reclaimed fill caused significant ground deformations in the shoreline area, ongoing subsidence of İskenderun and other factors likely also exacerbated the flood hazard. Insights from this work suggest the importance of evaluating multi-hazard liquefaction and flood consequences for enhancing the resilience of coastal cities.

An Automated Method to Analyze Tropical Cyclone Surface Winds from Real-Time Aircraft Reconnaissance Observations

Abstract This study describes an automated analysis of real-time tropical cyclone (TC) aircraft reconnaissance observations to estimate TC surface winds. The wind analysis uses an iterative, objective, data-weighted analysis approach with different smoothing constraints in the radial and azimuthal directions. Smoothing constraints penalize the data misfit when the solutions deviate from smoothed analyses and extend the aircraft information into areas not directly observed. The analysis composites observations following storm motion taken within 5 h prior and 3 h after analysis time and makes use of prescribed methods to move observations to a common flight level (CFL; 700 hPa) for analysis and to reduce reconnaissance observations to the surface. Comparing analyses to several observed and simulated wind fields shows that analyses fit the observations while extending observational information to poorly observed regions. However, resulting analyses tend toward greater symmetry as observational coverage decreases, and show sensitivity to the first guess information in unobserved radii. Analyses produce reasonable and useful estimates of operationally important characteristics of the wind field. But, due to the radial and azimuthal smoothing and the undersampling of typical aircraft reconnaissance flights, wind maxima are underestimated, and the radii of maximum wind are slightly overestimated. Varying observational coverage using model-based synthetic aircraft observations, these analyses improve as observational coverage increases, and for a typical observational pattern (two transects through the storm) the root-mean-square error deviation is <10 kt (<5 m s−1). Significance Statement Many applications need estimates of 2D surface winds in tropical cyclones in real time. While real-time aircraft-based observations of the winds inside tropical cyclones have been available for several decades, there have been few automated and objective methods to analyze this information to provide estimates of the strength and distribution of the surface winds. Here, we provide details of one method that fuses these unique observations to provide useful 2D analyses of the winds in and around tropical cyclones.

АНАЛИЗ ГЕОЛОГИЧЕСКИХ И ИНЖЕНЕРНО-ГЕОЛОГИЧЕСКИХ УСЛОВИЙ ОСОБО ОХРАНЯЕМОЙ ПРИРОДНОЙ ТЕРРИТОРИИ - ИЛЬМЕНСКИЙ ГЛИНТ

lmensky Klint” on the territory of the Southern Ilmen region is a complex extended natural monument of regional importance, an ornithological monument of international importance. This valuable geological object is actively being destroyed and requires the close attention of many specialists. The article shows that despite the good accessibility of research objects and the existing scientific research on various topics, no emphasis was placed on studying the geodynamic situation of a specially protected natural area — the Ilmensky Klint. A brief qualitative assessment of the condition of the Ilmensky Klint is given based on the results of reconnaissance observations. A set of available measures is recommended to save this unique geological object in the north-west of Russia

Методика полевых исследований оползней в Чеченской Республике

Introduction. Landslide engineering and geological surveys usually consist of reconnaissance observations and field work to collect the necessary material and information, and their purpose is to document surface conditions in such a way as to provide the necessary basis for subsequent forecasting of conditions and parameters of landslide geomass movement. At the same time, the main task of identifying a landslide is to determine its boundaries on the earth’s surface and other parameters, including the area of the hearth and the displaced geomass. Materials and мethods. Identification of landslides in the field is an integral part of standard geomorphological cartography. At the same time, an important approach to the study of landslides is the methods of geomorphological inventory of landslides, which are direct methods during which, as a rule, the main characteristics of the morphology of landslides, as well as provoking and predisposing factors, are recorded. Results. It has been established that shallow landslides caused by rains, earthquakes and man-made human activity have become quite frequent within the North Caucasus, serving to summarize the data of previous studies. Discussion. There are more than 120 modern active landslides within the Chechen Republic. Mapping of all landslide displacements on the territory of the Chechen Republic was not carried out, only geological surveys were carried out in certain areas and near settlements in order to prevent emergency situations for the infrastructure of settlements and mining facilities. Due to the inaccessibility of the mountainous terrain and the limited possibilities of using satellite images of high destructibility, there is currently no complete information about the spread of ancient and modern landslides in hard-to-reach areas. Conclusion. However, despite the fact that the special literature on landslide hazards is constantly updated with new data, and with increasing complexity, some methodological issues of landslide research remain unresolved. First, despite the frequent use of statistical models of landslide susceptibility, there remains uncertainty about the sampling system of landslide geomass, the resolution applied and the uncertainty of the model used. Secondly, due to the increase in computing power and the availability of specialized software tools, landslide susceptibility models are sometimes used without proper interpretation of the geomorphological reliability of the results. In addition, there are no reliable estimates of landslide susceptibility for many remote regions (especially mountainous ones). Resume: 1. The article presents the results of research on landslides in the Czech Republic. 2. It has been established that shallow landslides caused by rains, earthquakes and man-made human activity have become quite frequent within the North Caucasus, serving to summarize the data of previous studies. It is shown what ground engineering and geological studies of landslide territories consist of. 3. Future research is needed to prevent landslides in the Shatoi district in the village of Gush-Kert (Chechen Republic).

Airstream Association of Large Boundary Layer Rolls during Extratropical Transition of Post-Tropical Cyclone Sandy (2012)

Better understanding of roll vortices that often occur in the tropical cyclone (TC) boundary layer is required to improve forecasts of TC intensification and the granularity of damaging surface winds. It is especially important to characterize rolls over a wide variety of TCs, their environments, and TC development phases. Boundary layer rolls have been observed in TCs since 1998, but only recently in a TC during its extratropical transition phase. The work reported herein is the first to analyze how boundary layer rolls are distributed among the extratropical features of a transitioning TC. To this end, routine and special operational observations recorded during landfalling Post-tropical Cyclone Sandy (2012) were leveraged, including radar, surface, rawinsonde, and aircraft reconnaissance observations. Large rolls occurred in cold airstreams, both in the cold conveyor belt within the northwestern storm quadrant and in the secluding airstream within the northeastern quadrant, but roll presence was much diminished within the intervening warm sector. The large size of the rolls and their confinement to cold airstreams is attributed to an optimum inflow layer depth, which is deep enough below a strong stable layer to accommodate deep and strong positive radial wind shear to promote roll growth, yet not so deep as to limit radial wind shear magnitude, as occurred in the warm sector.

Estimating tropical cyclone intensity using dynamic balance convolutional neural network from satellite imagery

Accurate estimation of tropical cyclone (TC) intensity helps to understand the evolution of TCs throughout their life cycle and plays an essential role in mitigating TC impact. Although TC intensity estimation methods based on deep learning have made significant progress, the existing techniques do not apply good methods to overcome the intensity overestimation and underestimation problems caused by the unbalanced distribution of TC data. Therefore, we propose a dynamic balance convolutional neural network to overcome these issues. The model consists of two branches, one branch is the learning of the raw data, and the other is the learning of strong (weak) TCs that account for a few data samples. Finally, the model is dynamically adjusted by adaptive trade-off parameters, gradually from the learning of the raw data to the learning of strong (weak) TCs, thus reducing errors in underestimation (overestimation) of strong (weak) TCs. Furthermore, an attention mechanism is employed to obtain the correlation between channels to improve the accuracy of TC intensity estimation further. We used globally 1285 TC cases from 2003–2016 to train the model and globally 94 TC cases from 2017 as independent test data. The results showed that the root-mean-square error of TC intensity estimation was 8.32 kt, 35% lower than that of the advanced Dvorak technique and 26% lower than that of the deep learning method visual geometry group (VGG). For a subset of 482 samples (from East Pacific and Atlantic) analyzed with reconnaissance observations, a root-mean-square intensity difference of 7.95 kt is achieved. Finally, we explored the model’s feature learning process and the contribution of each component of the satellite image to the TC intensity estimation through model visualization.

Impacts of Assimilating Additional Reconnaissance Data on Operational GFS Tropical Cyclone Forecasts

Abstract 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.

Visible–near-infrared observations of organics and carbonates on (101955) Bennu: Classification method and search for surface context

The OSIRIS-REx Visible and InfraRed Spectrometer (OVIRS) onboard the Origins, Spectral Interpretation, Resource Identification, and Security–Regolith Explorer (OSIRIS-REx) spacecraft detected ~3.4-μm absorption features indicative of carbonates and organics on near-Earth asteroid (101955) Bennu. We apply a Kolmogorov-Smirnov similarity test to OVIRS spectra of Bennu and laboratory spectra of minerals to categorize 3.4-μm features observed on Bennu as representing either carbonates or organics. Among the 15,585 spectra acquired by OVIRS during high-resolution (4 to 9 m/spectrum footprint) reconnaissance observations of select locations on Bennu's surface, we find 544 spectral matches with carbonates and 245 spectral matches with organics (total of 789 high-confidence spectral matches). We map the locations of these matches and characterize features of Bennu's surface using corresponding image data. Image data are used to quantitatively characterize the albedo within each spectrometer footprint. We find no apparent relationships between spectral classification and surface morphological expression, and we find no correlation between carbon species classification and other spectral properties such as slope or band depth. This suggests either that carbonates and organics are ubiquitous across the surface of Bennu, independent of surface features (consistent with findings from laboratory studies of carbonaceous chondrites), or that the observations do not have the spatial resolution required to resolve differences. However, we find more organic spectral matches at certain locations, including the site from which the OSIRIS-REx mission collected a sample, than at others. Higher concentrations of organics may be explained if these materials have been more recently exposed to surface alteration processes, perhaps by recent crater formation.

Justification of the line of action for reclamation of lands disturbed by opencast mining

The article is devoted to the substantiation of the line of action for reclamationof disturbed lands. The object of reclamation is a land plot (Russia, Leningrad Region), disturbed as a result of unauthorized open-pit mining operations for the extraction of limestone. In order to obtain the information necessary to justify the reclamation project, the following works were carried out: reconnaissance and route observations, topographic, geological, and environmental surveys of the land. Based on the results of the generalized study data of the environmental and economic justification, a line of action for agricultural reclamation was chosen. In addition, an assessment of the estimated cost of the event was carried out and a list of necessary works was proposed within the framework of the technical and biological stages of reclamation.

Studies of the sirko ravine in order to prevent the development of landslide-hazardous processes

The purpose of research is to study the Sirko ravine, including the analysis of the deformation of buildings and structures, observations by the method of the Earth's natural pulse electromagnetic field to determine the possibility of the development of landslide processes on its slopes. The study of the Sirko ravine and the adjacent territory was carried out by means of reconnaissance observations, during which the sides of the ravine were described, an analysis of its development, the presence of drainage, water supply systems and the absence of a drainage system, as well as the prospects for its further use. The factors causing the development of landslide processes are the phenomena of soaking due to precipitation and leaks from the water supply system and the absence of storm drainage and sewerage systems. The main deformations of buildings and structures on the sides of the ravine are considered - the development of cracks on the walls of buildings, fences of asphalt and concrete pavements. Photo documentation is presented, confirming their development. The comparison of the Sirko ravine with the disturbances of the Serednoprydniprovskyi megablock is carried out. It is shown that the strike azimuth of the Sirko ravine coincides with the strike azimuth of the deep fault of the Serednoprydniprovskyi megablock. To assess the degree of landslide hazard of its slopes, the express method of observation of ENPEMF was chosen. The observations were carried out at the top of the ravine in the area of Gagarin Ave, as well as in the area of the widest part of its thalweg. The results of observations carried out by the method of the Earth's natural pulse electromagnetic field along two profiles traversed across the strike of the ravine thalweg, which indicate the possible development of landslide deformations of its slopes. These research will help prevent possible deformations of buildings and the development of landslide processes arising from the soaking of its slopes during the development of the slopes of the Sirko ravine. Keywords: landslide-hazardous processes, deformations of buildings, slopes, natural pulse electromagnetic field.

ATC Mw7.1 Puebla-Morelos earthquake reconnaissance observations: Structural observations and instrumentation

After the 2017 Puebla-Morelos earthquake, the Applied Technology Council (ATC) funded a mission to Mexico City to collect structural, geotechnical, seismological, and damage information on concrete structures. The collected data set includes 70 reinforced concrete buildings and contains photos, design drawings, ground motion records, ambient vibration data, and reconnaissance observations, where available. This article presents the most important structural observations and instrumentation findings. The data show that, in buildings with a flexible lateral system, the unreinforced masonry infill walls resisted substantial load and prevented more severe damage to the structural system. Many previously retrofitted buildings failed in locally unreinforced areas, because retrofits did not comprehensively strengthen all weaknesses in the building. Significant damage was also observed in buildings with weak story irregularities and buildings founded on weak soil.

ATC Mw7.1 Puebla–Morelos earthquake reconnaissance observations: Seismological, geotechnical, ground motions, site effects, and GIS mapping

This article presents the Applied Technology Council (ATC) team’s observations following the 2017 Mw7.1 Puebla–Morelos, Mexico earthquake. The team was deployed in Mexico City to collect seismological, geotechnical, structural, and overall performance information. The focus was on non-ductile concrete structures, to support implementation of recently published FEMA P-2018 procedures and to identify study buildings for incorporation into NIST-funded ATC-134 ongoing project. This article presents seismological data with 71 strong-motion records processed and ready for use in engineering analysis, geotechnical observations, and characterization of sites visited. Analyses of the response of representative soil profiles are presented in the form of acceleration response spectra and seismic amplification at the ground surface. A comparison of the same analysis using records from the 1985 Michoacán Ms8.1 (approximately Mw8.0) earthquake is also discussed. The ATC team composed GIS maps with structural and geotechnical characteristics of the inspected sites, including color-coded damage of inspected buildings and estimated soil fundamental period to correlate observed behavior with potential soil–structure interaction resonance effects. Recommendations on further detailed studies based on this comprehensive set of case histories are proposed.

Adjoint-Derived Impact of Assimilated Observations on Tropical Cyclone Intensity Forecasts of Hurricane Joaquin (2015) and Hurricane Matthew (2016)

AbstractThe adjoint-derived observation impact method is used as a diagnostic to derive the impact of assimilated observations on a metric representing the forecast intensity of a tropical cyclone (TC). Storm-centered composites of observation impact and the model background state are computed across 6-hourly analysis/forecast cycles to compute the composite observation impact throughout the life cycle of Hurricane Joaquin (2015) to evaluate the impact of in situ wind and temperature observations in the upper and lower troposphere, as well as the impact of brightness temperature and precipitable water observations, on intensity forecasts with forecast lengths from 12 to 48 h. The compositing across analysis/forecast cycles allows for the exploration of consistent relationships between the synoptic-scale state of the initial conditions and the impact of observations that are interpreted as flow-dependent interactions between model background bias and correction by assimilated observations on the TC intensity forecast. The track of Hurricane Matthew (2016), with an extended period of time near the coasts of Florida, Georgia, and the Carolinas, allows for a comparison of the impact of aircraft reconnaissance observations with the impact of nearby overland rawinsonde observations available within the same radius of the TC.

The Rapid Intensification and Eyewall Replacement Cycles of Hurricane Irma (2017)

Abstract The initiation of a rapid intensification (RI) event for a tropical cyclone (TC) at major hurricane intensity is a rare event in the North Atlantic basin. This study examined the environmental and vortex-scale processes related to such an RI event observed in Hurricane Irma (2017) using a combination of flight-level and airborne radar aircraft reconnaissance observations, microwave satellite observations, and model environmental analyses. The onset of RI was linked to an increase in sea surface temperatures and ocean heat content toward levels more commonly associated with North Atlantic RI episodes. Remarkably, Irma’s RI event comprised two rapidly evolving eyewall replacement cycle (ERC) episodes that each completed in less than 12 h. The two ERC events displayed different secondary eyewall formation (SEF) mechanisms and vortex evolutions. During the first SEF event, a secondary maximum in ascent and tangential wind was observed at the leading edge of a mesoscale descending inflow jet. During the ensuing ERC event, the primary eyewall weakened and ultimately collapsed, resulting in a brief period of weakening. The second SEF event displayed characteristics consistent with unbalanced boundary layer dynamics. Additionally, it is plausible that both SEF events were affected by the stagnation and axisymmeterization of outward-propagating vortex Rossby waves. During the second ERC event, the TC continued to rapidly intensify, which is a stark contrast to the ERC paradigm described in the literature. The differing ERC evolutions appear linked to the vortex response to changing environmental conditions. The results presented here underscore the utility of frequent aircraft reconnaissance observations for an improved understanding of TC dynamics.

Reconnaissance report on geotechnical effects and structural damage caused by the 3 January 2017 Tripura earthquake, India

An earthquake of moment magnitude Mw 5.7 shook the northeastern region of India on 3 January 2017 at 14 h:39 min:0.5 s local time. The duration of the tremor lasted for about 5–6 s and had its epicenter in Dhalai District, Tripura, India. Even though the earthquake was of moderate magnitude, it caused damage to several masonry dwellings in Tripura and triggered soil liquefaction, lateral spreading, and landslides near the epicentral area. The sand boils containing appreciable amount of silts were ejected to the ground surface at the Kanchanbari and Kumarghat area due to the liquefaction-induced upward ground water flow. This is possibly the first liquefaction evidence in India induced due to a moderate earthquake magnitude of Mw 5.7. This paper reports the field reconnaissance observations of geotechnical effects and damage to buildings following a shallow, strike-slip earthquake in northeast India on 3 January 2017. In addition, the distribution of surface peak ground acceleration of the earthquake estimated from the empirical equations based on the available data is evaluated and discussed.

Impact of Gulfstream-IV Dropsondes on Tropical Cyclone Prediction in a Regional OSSE System

Abstract Aircraft reconnaissance missions remain the primary means of collecting direct measurements of marine atmospheric conditions affecting tropical cyclone formation and evolution. The National Hurricane Center tasks the NOAA G-IV aircraft to sample environmental conditions that may impact the development of a tropical cyclone threatening to make landfall in the United States or its territories. These aircraft data are assimilated into deterministic models and used to produce real-time analyses and forecasts for a given tropical cyclone. Existing targeting techniques aim to optimize the use of reconnaissance observations and partially rely on regions of highest uncertainty in the Global Ensemble Forecast System. Evaluating the potential impact of various trade-offs in the targeting process is valuable for determining the ideal aircraft flight track for a prospective mission. AOML’s Hurricane Research Division has developed a system for performing regional observing system simulation experiments (OSSEs) to assess the potential impact of proposed observing systems on hurricane track and intensity forecasting. This study focuses on improving existing targeting methods by investigating the impact of proposed aircraft observing system designs through various sensitivity studies. G-IV dropsonde retrievals were simulated from a regional nature run, covering the life cycle of a rapidly intensifying Atlantic hurricane. Results from sensitivity studies provide insight into improvements for real-time operational synoptic surveillance targeting for hurricanes and tropical storms, where dropsondes released closer to the vortex–environment interface provide the largest impact on the track forecast. All dropsonde configurations provide a positive 2-day impact on intensity forecasts by improving the environmental conditions known to impact tropical cyclone intensity.

Using Deep Learning to Estimate Tropical Cyclone Intensity from Satellite Passive Microwave Imagery

AbstractA deep learning convolutional neural network model is used to explore the possibilities of estimating tropical cyclone (TC) intensity from satellite images in the 37- and 85–92-GHz bands. The model, called “DeepMicroNet,” has unique properties such as a probabilistic output, the ability to operate from partial scans, and resiliency to imprecise TC center fixes. The 85–92-GHz band is the more influential data source in the model, with 37 GHz adding a marginal benefit. Training the model on global best track intensities produces model estimates precise enough to replicate known best track intensity biases when compared to aircraft reconnaissance observations. Model root-mean-square error (RMSE) is 14.3 kt (1 kt ≈ 0.5144 m s−1) compared to two years of independent best track records, but this improves to an RMSE of 10.6 kt when compared to the higher-standard aircraft reconnaissance-aided best track dataset, and to 9.6 kt compared to the reconnaissance-aided best track when using the higher-resolution TRMM TMI andAquaAMSR-E microwave observations only. A shortage of training and independent testing data for category 5 TCs leaves the results at this intensity range inconclusive. Based on this initial study, the application of deep learning to TC intensity analysis holds tremendous promise for further development with more advanced methodologies and expanded training datasets.

Estimating Tropical Cyclone Intensity by Satellite Imagery Utilizing Convolutional Neural Networks

Abstract Accurately estimating tropical cyclone (TC) intensity is one of the most critical steps in TC forecasting and disaster warning/management. For over 40 years, the Dvorak technique (and several improved versions) has been applied for estimating TC intensity by forecasters worldwide. However, the operational Dvorak techniques primarily used in various agencies have several deficiencies, such as inherent subjectivity leading to inconsistent intensity estimates within various basins. This collaborative study between meteorologists and data scientists has developed a deep-learning model using satellite imagery to estimate TC intensity. The conventional convolutional neural network (CNN), which is a mature technology for object classification, requires several modifications when being used for directly estimating TC intensity (a regression task). Compared to the Dvorak technique, the CNN model proposed here is objective and consistent among various basins; it has been trained with satellite infrared brightness temperature and microwave rain-rate data from 1097 global TCs during 2003–14 and optimized with data from 188 TCs during 2015–16. This paper also introduces an upgraded version that further improves the accuracy by using additional TC information (i.e., basin, day of year, local time, longitude, and latitude) and applying a postsmoothing procedure. An independent testing dataset of 94 global TCs during 2017 has been used to evaluate the model performance. A root-mean-square intensity difference of 8.39 kt (1 kt ≈ 0.51 m s−1) is achieved relative to the best track intensities. For a subset of 482 samples analyzed with reconnaissance observations, a root-mean-square intensity difference of 8.79 kt is achieved.

Impact of Assimilating Aircraft Reconnaissance Observations on Tropical Cyclone Initialization and Prediction Using Operational HWRF and GSI Ensemble–Variational Hybrid Data Assimilation

Abstract This study evaluates the impact of assimilating high-resolution, inner-core reconnaissance observations on tropical cyclone initialization and prediction in the 2013 version of the operational Hurricane Weather Research and Forecasting (HWRF) Model. The 2013 HWRF data assimilation system is a GSI-based hybrid ensemble–variational system that, in this study, uses the Global Data Assimilation System ensemble to estimate flow-dependent background error covariance. Assimilation of inner-core observations improves track forecasts and reduces intensity error after 18–24 h. The positive impact on the intensity forecast is mainly found in weak storms, where inner-core assimilation produces more accurate tropical cyclone structures and reduces positive intensity bias. Despite such positive benefits, there is degradation in short-term intensity forecasts that is attributable to spindown of strong storms, which has also been seen in other studies. There are several reasons for the degradation of intense storms. First, a newly discovered interaction between model biases and the HWRF vortex initialization procedure causes the first-guess wind speed aloft to be too strong in the inner core. The problem worsens for the strongest storms, leading to a poor first-guess fit to observations. Though assimilation of reconnaissance observations results in analyses that better fit the observations, it also causes a negative intensity bias at the surface. In addition, the covariance provided by the NCEP global model is inaccurate for assimilating inner-core observations, and model physics biases result in a mismatch between simulated and observed structure. The model ultimately cannot maintain the analysis structure during the forecast, leading to spindown.