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Daniel Galea
,
Kevin Hodges
, and
Bryan N. Lawrence

Abstract

Tropical cyclones (TCs) are important phenomena, and understanding their behavior requires being able to detect their presence in simulations. Detection algorithms vary; here we compare a novel deep learning–based detection algorithm (TCDetect) with a state-of-the-art tracking system (TRACK) and an observational dataset (IBTrACS) to provide context for potential use in climate simulations. Previous work has shown that TCDetect has good recall, particularly for hurricane-strength events. The primary question addressed here is to what extent the structure of the systems plays a part in detection. To compare with observations of TCs, it is necessary to apply detection techniques to reanalysis. For this purpose, we use ERA-Interim, and a key part of the comparison is the recognition that ERA-Interim itself does not fully reflect the observations. Despite that limitation, both TCDetect and TRACK applied to ERA-Interim mostly agree with each other. Also, when considering only hurricane-strength TCs, TCDetect and TRACK correspond well to the TC observations from IBTrACS. Like TRACK, TCDetect has good recall for strong systems; however, it finds a significant number of false positives associated with weaker TCs (i.e., events detected as having hurricane strength but are weaker in reality) and extratropical storms. Because TCDetect was not trained to locate TCs, a post hoc method to perform comparisons was used. Although this method was not always successful, some success in matching tracks and events in physical space was also achieved. The analysis of matches suggested that the best results were found in the Northern Hemisphere and that in most regions the detections followed the same patterns in time no matter which detection method was used.

Open access
Patrick F. Cummins
,
Lawrence A. Mysak
, and
Kevin Hamilton

Abstract

The Rossby wave field generated by the annual cycle of the observed wind stress curl over the North Pacific Ocean (15°N–53°, 100°W–175°E) has been obtained through numerical integration of the linearized, reduced-gravity vorticity equation in spherical coordinates. The dominant source region of Rossby waves is adjacent to the eastern boundary between 20°–44°N. More specifically this source is shown to be made up essentially of two distinct parts: a southern region off California-Baja California, which was first identified by White and Saur, and a northern region corresponding to a generation area first proposed by Mysak. In addition, a second, midocean generation region has been identified over the central North Pacific from 35° to 45°N, 150° to 160°W.

The behavior of the model is strongly affected by wave refraction due to the variation of phase velocity with latitude as described in Schopf et al. As waves emanate from the eastern boundary they are refracted such that the wavenumber vector, initially aligned zonally, becomes reoriented to the northwest. Associated with this is a turning of the group velocity vector and of wave rays towards the southwest.

Full access
John H. Pedlar
,
Daniel W. McKenney
,
Kevin Lawrence
,
Pia Papadopol
,
Michael F. Hutchinson
, and
David Price

Abstract

This study produced annual spatial models (or grids) of 27 growing-season variables for Canada that span two centuries (1901–2100). Temporal gaps in the availability of daily climate data—the typical and preferred source for calculating growing-season variables—necessitated the use of two approaches for generating these growing-season grids. The first approach, used only for the 1950–2010 period, employed a computer script to directly calculate the suite of growing-season variables from existing daily climate grids. Since daily grids were not available for the remaining years, a second approach, which employed a machine-learning method called boosted regression trees (BRT), was used to generate statistical models that related each growing-season variable to a suite of climate and water-related predictors. These BRT models were used to generate grids of growing-season variables for each year of the study period, including the 1950–2010 period to allow comparison between the two approaches. Mean absolute errors associated with the BRT-based grids were approximately 30% higher than those associated with the daily-based grids. The two approaches were also compared by calculating trends in growing-season length over the 1950–2010 period. Significant increases in growing-season length were obtained for nearly all ecozones across Canada, and there were no significant differences in the trends obtained from the two approaches. Although the daily-based approach tended to have lower errors, the BRT approach produced comparable map products that should be valuable for periods and regions for which daily data are not available.

Full access
Michael F. Hutchinson
,
Dan W. McKenney
,
Kevin Lawrence
,
John H. Pedlar
,
Ron F. Hopkinson
,
Ewa Milewska
, and
Pia Papadopol

Abstract

The application of trivariate thin-plate smoothing splines to the interpolation of daily weather data is investigated. The method was used to develop spatial models of daily minimum and maximum temperature and daily precipitation for all of Canada, at a spatial resolution of 300 arc s of latitude and longitude, for the period 1961–2003. Each daily model was optimized automatically by minimizing the generalized cross validation. The fitted trivariate splines incorporated a spatially varying dependence on ground elevation and were able to adapt automatically to the large variation in station density over Canada. Extensive quality control measures were performed on the source data. Error estimates for the fitted surfaces based on withheld data across southern Canada were comparable to, or smaller than, errors obtained by daily interpolation studies elsewhere with denser data networks. Mean absolute errors in daily maximum and minimum temperature averaged over all years were 1.1° and 1.6°C, respectively. Daily temperature extremes were also well matched. Daily precipitation is challenging because of short correlation length scales, the preponderance of zeros, and significant error associated with measurement of snow. A two-stage approach was adopted in which precipitation occurrence was estimated and then used in conjunction with a surface of positive precipitation values. Daily precipitation occurrence was correctly predicted 83% of the time. Withheld errors in daily precipitation were small, with mean absolute errors of 2.9 mm, although these were relatively large in percentage terms. However, mean percent absolute errors in seasonal and annual precipitation totals were 14% and 9%, respectively, and seasonal precipitation upper 95th percentiles were attenuated on average by 8%. Precipitation and daily maximum temperatures were most accurately interpolated in the autumn, consistent with the large well-organized synoptic systems that prevail in this season. Daily minimum temperatures were most accurately interpolated in summer. The withheld data tests indicate that the models can be used with confidence across southern Canada in applications that depend on daily temperature and accumulated seasonal and annual precipitation. They should be used with care in applications that depend critically on daily precipitation extremes.

Full access
Heather MacDonald
,
Daniel W. McKenney
,
Xiaolan L. Wang
,
John Pedlar
,
Pia Papadopol
,
Kevin Lawrence
,
Yang Feng
, and
Michael F. Hutchinson

Abstract

This study presents spatial models (i.e., thin-plate spatially continuous spline surfaces) of adjusted precipitation for Canada at daily, pentad (5 day), and monthly time scales from 1900 to 2015. The input data include manual observations from 3346 stations that were adjusted previously to correct for snow water equivalent (SWE) conversion and various gauge-related issues. In addition to the 42 331 models for daily total precipitation and 1392 monthly total precipitation models, 8395 pentad models were developed for the first time, depicting mean precipitation for 73 pentads annually. For much of Canada, mapped precipitation values from this study were higher than those from the corresponding unadjusted models (i.e., models fitted to the unadjusted data), reflecting predominantly the effects of the adjustments to the input data. Error estimates compared favorably to the corresponding unadjusted models. For example, root generalized cross-validation (GCV) estimate (a measure of predictive error) at the daily time scale was 3.6 mm on average for the 1960–2003 period as compared with 3.7 mm for the unadjusted models over the same period. There was a dry bias in the predictions relative to recorded values of between 1% and 6.7% of the average precipitations amounts for all time scales. Mean absolute predictive errors of the daily, pentad, and monthly models were 2.5 mm (52.7%), 0.9 mm (37.4%), and 11.2 mm (19.3%), respectively. In general, the model skill was closely tied to the density of the station network. The current adjusted models are available in grid form at ~2–10-km resolutions.

Open access
Daniel W. McKenney
,
Michael F. Hutchinson
,
Pia Papadopol
,
Kevin Lawrence
,
John Pedlar
,
Kathy Campbell
,
Ewa Milewska
,
Ron F. Hopkinson
,
David Price
, and
Tim Owen

Over the past two decades, researchers at Natural Resources Canada's Canadian Forest Service, in collaboration with the Australian National University (ANU), Environment Canada (EC), and the National Oceanic and Atmospheric Administration (NOAA), have made a concerted effort to produce spatial climate products (i.e., spatial models and grids) covering both Canada and the United States for a wide variety of climate variables and time steps (from monthly to daily), and across a range of spatial resolutions. Here we outline the method used to generate the spatial models, detail the array of products available and how they may be accessed, briefly describe some of the usage and impact of the models, and discuss anticipated further developments. Our initial motivation in developing these models was to support forestry-related applications. They have since been utilized by a wider range of agencies and researchers. This article is intended to further raise awareness of the strengths and weaknesses of these climate models and to facilitate their wider application.

Full access
Kevin R. Knupp
,
Todd A. Murphy
,
Timothy A. Coleman
,
Ryan A. Wade
,
Stephanie A. Mullins
,
Christopher J. Schultz
,
Elise V. Schultz
,
Lawrence Carey
,
Adam Sherrer
,
Eugene W. McCaul Jr.
,
Brian Carcione
,
Stephen Latimer
,
Andy Kula
,
Kevin Laws
,
Patrick T. Marsh
, and
Kim Klockow

By many metrics, the tornado outbreak on 27 April 2011 was the most significant tornado outbreak since 1950, exceeding the super outbreak of 3–4 April 1974. The number of tornadoes over a 24-h period (midnight to midnight) was 199; the tornado fatalities and injuries were 316 and more than 2,700, respectively; and the insurable loss exceeded $4 billion (U.S. dollars). In this paper, we provide a meteorological overview of this outbreak and illustrate that the event was composed of three mesoscale events: a large early morning quasi-linear convective system (QLCS), a midday QLCS, and numerous afternoon supercell storms. The main data sources include NWS and research radars, profilers, surface measurements, and photos and videos of the tornadoes. The primary motivation for this preliminary research is to document the diverse characteristics (e.g., tornado characteristics and mesoscale organization of deep convection) of this outbreak and summarize preliminary analyses that are worthy of additional research on this case.

Full access
Julia V. Manganello
,
Kevin I. Hodges
,
James L. Kinter III
,
Benjamin A. Cash
,
Lawrence Marx
,
Thomas Jung
,
Deepthi Achuthavarier
,
Jennifer M. Adams
,
Eric L. Altshuler
,
Bohua Huang
,
Emilia K. Jin
,
Cristiana Stan
,
Peter Towers
, and
Nils Wedi

Abstract

Northern Hemisphere tropical cyclone (TC) activity is investigated in multiyear global climate simulations with the ECMWF Integrated Forecast System (IFS) at 10-km resolution forced by the observed records of sea surface temperature and sea ice. The results are compared to analogous simulations with the 16-, 39-, and 125-km versions of the model as well as observations.

In the North Atlantic, mean TC frequency in the 10-km model is comparable to the observed frequency, whereas it is too low in the other versions. While spatial distributions of the genesis and track densities improve systematically with increasing resolution, the 10-km model displays qualitatively more realistic simulation of the track density in the western subtropical North Atlantic. In the North Pacific, the TC count tends to be too high in the west and too low in the east for all resolutions. These model errors appear to be associated with the errors in the large-scale environmental conditions that are fairly similar in this region for all model versions.

The largest benefits of the 10-km simulation are the dramatically more accurate representation of the TC intensity distribution and the structure of the most intense storms. The model can generate a supertyphoon with a maximum surface wind speed of 68.4 m s−1. The life cycle of an intense TC comprises intensity fluctuations that occur in apparent connection with the variations of the eyewall/rainband structure. These findings suggest that a hydrostatic model with cumulus parameterization and of high enough resolution could be efficiently used to simulate the TC intensity response (and the associated structural changes) to future climate change.

Full access
Julia V. Manganello
,
Kevin I. Hodges
,
Brandt Dirmeyer
,
James L. Kinter III
,
Benjamin A. Cash
,
Lawrence Marx
,
Thomas Jung
,
Deepthi Achuthavarier
,
Jennifer M. Adams
,
Eric L. Altshuler
,
Bohua Huang
,
Emilia K. Jin
,
Peter Towers
, and
Nils Wedi

Abstract

How tropical cyclone (TC) activity in the northwestern Pacific might change in a future climate is assessed using multidecadal Atmospheric Model Intercomparison Project (AMIP)-style and time-slice simulations with the ECMWF Integrated Forecast System (IFS) at 16-km and 125-km global resolution. Both models reproduce many aspects of the present-day TC climatology and variability well, although the 16-km IFS is far more skillful in simulating the full intensity distribution and genesis locations, including their changes in response to El Niño–Southern Oscillation. Both IFS models project a small change in TC frequency at the end of the twenty-first century related to distinct shifts in genesis locations. In the 16-km IFS, this shift is southward and is likely driven by the southeastward penetration of the monsoon trough/subtropical high circulation system and the southward shift in activity of the synoptic-scale tropical disturbances in response to the strengthening of deep convective activity over the central equatorial Pacific in a future climate. The 16-km IFS also projects about a 50% increase in the power dissipation index, mainly due to significant increases in the frequency of the more intense storms, which is comparable to the natural variability in the model. Based on composite analysis of large samples of supertyphoons, both the development rate and the peak intensities of these storms increase in a future climate, which is consistent with their tendency to develop more to the south, within an environment that is thermodynamically more favorable for faster development and higher intensities. Coherent changes in the vertical structure of supertyphoon composites show system-scale amplification of the primary and secondary circulations with signs of contraction, a deeper warm core, and an upward shift in the outflow layer and the frequency of the most intense updrafts. Considering the large differences in the projections of TC intensity change between the 16-km and 125-km IFS, this study further emphasizes the need for high-resolution modeling in assessing potential changes in TC activity.

Full access
Chidong Zhang
,
Gregory R. Foltz
,
Andy M. Chiodi
,
Calvin W. Mordy
,
Catherine R. Edwards
,
Christian Meinig
,
Dongxiao Zhang
,
Edoardo Mazza
,
Edward D. Cokelet
,
Eugene F. Burger
,
Francis Bringas
,
Gustavo J. Goni
,
Hristina G. Hristova
,
Hyun-Sook Kim
,
Joaquin A. Trinanes
,
Jun A. Zhang
,
Kathleen E. Bailey
,
Kevin M. O’Brien
,
Maria Morales-Caez
,
Noah Lawrence-Slavas
,
Richard Jenkins
,
Shuyi S. Chen
, and
Xingchao Chen

Abstract

On 30 September 2021, a saildrone uncrewed surface vehicle (USV) was steered into category 4 Hurricane Sam, the most intense storm of the 2021 Atlantic hurricane season. It measured significant wave heights up to 14 m (maximum wave height = 27 m) and near-surface winds exceeding 55 m s−1. This was the first time in more than seven decades of hurricane observations that in real time a USV transmitted scientific data, images, and videos of the dynamic ocean surface near a hurricane’s eyewall. The saildrone was part of a five-saildrone deployment of the NOAA 2021 Atlantic Hurricane Observations Mission. These saildrones observed the atmospheric and oceanic near-surface conditions of five other tropical storms, of which two became hurricanes. Such observations inside tropical cyclones help to advance the understanding and prediction of hurricanes, with the ultimate goal of saving lives and protecting property. The 2021 deployment pioneered a new practice of coordinating measurements by saildrones, underwater gliders, and airborne dropsondes to make simultaneous and near-collocated observations of the air–sea interface, the ocean immediately below, and the atmosphere immediately above. This experimental deployment opened the door to a new era of using remotely piloted uncrewed systems to observe one of the most extreme phenomena on Earth in a way previously impossible. This article provides an overview of this saildrone hurricane observations mission, describes how the saildrones were coordinated with other observing platforms, presents preliminary scientific results from these observations to demonstrate their potential utility and motivate further data analysis, and offers a vision of future hurricane observations using combined uncrewed platforms.

Open access