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William H. Hunt, David M. Winker, Mark A. Vaughan, Kathleen A. Powell, Patricia L. Lucker, and Carl Weimer

Abstract

This paper provides background material for a collection of Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) algorithm papers that are to be published in the Journal of Atmospheric and Oceanic Technology. It provides a brief description of the design and performance of CALIOP, a three-channel elastic backscatter lidar on the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite. After more than 2 yr of on-orbit operation, CALIOP performance continues to be excellent in the key areas of laser energy, signal-to-noise ratio, polarization sensitivity, and overall long-term stability, and the instrument continues to produce high-quality data products. There are, however, some areas where performance has been less than ideal. These include short-term changes in the calibration coefficients at both wavelengths as the satellite passes between dark and sunlight, some radiation-induced effects on both the detectors and the laser when passing through the South Atlantic Anomaly, and slow transient recovery on the 532-nm channels. Although these issues require some special treatment in data analysis, they do not seriously detract from the overall quality of the level 2 data products.

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A. Addison Alford, Jun A. Zhang, Michael I. Biggerstaff, Peter Dodge, Frank D. Marks, and David J. Bodine

Abstract

The hurricane boundary layer (HBL) has been observed in great detail through aircraft investigations of tropical cyclones over the open ocean, but the coastal transition of the HBL has been less frequently observed. During the landfall of Hurricane Irene (2011), research and operational aircraft over water sampled the open-ocean HBL simultaneously with ground-based research and operational Doppler radars onshore. The location of the radars afforded 13 h of dual-Doppler analysis over the coastal region. Thus, the HBL from the coastal waterways, through the coastal transition, and onshore was observed in great detail for the first time. Three regimes of HBL structure were found. The outer bands were characterized by temporal perturbations of the HBL structure with attendant low-level wind maxima in the vicinity of rainbands. The inner core, in contrast, did not produce such perturbations, but did see a reduction of the height of the maximum wind and a more jet-like HBL wind profile. In the eyewall, a tangential wind maximum was observed within the HBL over water as in past studies and above the HBL onshore. However, the transition of the tangential wind maximum through the coastal transition showed that the maximum continued to reside in the HBL through 5 km inland, which has not been observed previously. It is shown that the adjustment of the HBL to the coastal surface roughness discontinuity does not immediately mix out the residual high-momentum jet aloft. Thus, communities closest to the coast are likely to experience the strongest winds onshore prior to the complete adjustment of the HBL.

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David M. Winker, Mark A. Vaughan, Ali Omar, Yongxiang Hu, Kathleen A. Powell, Zhaoyan Liu, William H. Hunt, and Stuart A. Young

Abstract

The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) is a two-wavelength polarization lidar that performs global profiling of aerosols and clouds in the troposphere and lower stratosphere. CALIOP is the primary instrument on the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite, which has flown in formation with the NASA A-train constellation of satellites since May 2006. The global, multiyear dataset obtained from CALIOP provides a new view of the earth’s atmosphere and will lead to an improved understanding of the role of aerosols and clouds in the climate system. A suite of algorithms has been developed to identify aerosol and cloud layers and to retrieve a variety of optical and microphysical properties. CALIOP represents a significant advance over previous space lidars, and the algorithms that have been developed have many innovative aspects to take advantage of its capabilities. This paper provides a brief overview of the CALIPSO mission, the CALIOP instrument and data products, and an overview of the algorithms used to produce these data products.

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Charles R. Sampson, Andrea B. Schumacher, John A. Knaff, Mark DeMaria, Edward M. Fukada, Chris A. Sisko, David P. Roberts, Katherine A. Winters, and Harold M. Wilson

Abstract

The Department of Defense uses a Tropical Cyclone Conditions of Readiness (TC-CORs) system to prepare bases and evacuate assets and personnel in advance of adverse weather associated with tropical cyclones (TCs). TC-CORs are recommended by weather facilities either on base or at central sites and generally are related to the timing and potential for destructive (50 kt; 1 kt ≈ 0.5144 m s−1) sustained winds. Recommendations are then considered by base or area commanders along with other factors for setting the TC-CORs. Ideally, the TC-CORs are set sequentially, from TC-COR IV (destructive winds within 72 h), through TC-COR III (destructive winds within 48 h) and TC-COR II (destructive winds within 24 h), and finally to TC-COR I (destructive winds within 12 h), if needed. Each TC-COR, once set, initiates a series of preparations and actions. Preparations for TC-COR IV can be as unobtrusive as obtaining emergency supplies, while preparations and actions leading up to TC-COR I are generally far more costly, intrusive, and labor-intensive activities. The purpose of this paper is to describe an objective aid that provides TC-COR guidance for meteorologists to use when making recommendations to base commanders. The TC-COR guidance is based on wind probability thresholds from an operational wind probability product run at the U.S. tropical cyclone forecast centers. An analysis on 113 independent cases from various bases shows the skill of the objective aid and how well it compares with the operational TC-CORs. A sensitivity analysis is also performed to demonstrate some of the advantages and pitfalls of raising or lowering the wind probability thresholds used by this objective aid.

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Chen Chen, Mark A. Cane, Naomi Henderson, Dong Eun Lee, David Chapman, Dmitri Kondrashov, and Mickaël D. Chekroun

Abstract

A suite of empirical model experiments under the empirical model reduction framework are conducted to advance the understanding of ENSO diversity, nonlinearity, seasonality, and the memory effect in the simulation and prediction of tropical Pacific sea surface temperature (SST) anomalies. The model training and evaluation are carried out using 4000-yr preindustrial control simulation data from the coupled model GFDL CM2.1. The results show that multivariate models with tropical Pacific subsurface information and multilevel models with SST history information both improve the prediction skill dramatically. These two types of models represent the ENSO memory effect based on either the recharge oscillator or the time-delayed oscillator viewpoint. Multilevel SST models are a bit more efficient, requiring fewer model coefficients. Nonlinearity is found necessary to reproduce the ENSO diversity feature for extreme events. The nonlinear models reconstruct the skewed probability density function of SST anomalies and improve the prediction of the skewed amplitude, though the role of nonlinearity may be slightly overestimated given the strong nonlinear ENSO in GFDL CM2.1. The models with periodic terms reproduce the SST seasonal phase locking but do not improve the prediction appreciably. The models with multiple ingredients capture several ENSO characteristics simultaneously and exhibit overall better prediction skill for more diverse target patterns. In particular, they alleviate the spring/autumn prediction barrier and reduce the tendency for predicted values to lag the target month value.

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David L. Randel, Thomas H. Vonder Haar, Mark A. Ringerud, Graeme L. Stephens, Thomas J. Greenwald, and Cynthia L. Combs

A comprehensive and accurate global water vapor dataset is critical to the adequate understanding of water vapor's role in the earth's climate system. To begin to satisfy this need, the authors have produced a blended dataset made up of global, 5-yr (1988–92), l°x 1° spatial resolution, atmospheric water vapor (WV) and liquid water path products. These new products consist of both the daily total column-integrated composites and a multilayered WV product at three layers (1000–700, 700–500, 500–300 mb). The analyses combine WV retrievals from the Television and Infrared Operational Satellite (TIROS) Operational Vertical Sounder (TOVS), the Special Sensor Microwave/Imager, and radiosonde observations. The global, vertical-layered water vapor dataset was developed by slicing the blended total column water vapor using layer information from TOVS and radiosonde. Also produced was a companion, over oceans only, liquid water path dataset. Satellite observations of liquid water path are growing in importance since many of the global climate models are now either incorporating or contain liquid water as an explicit variable. The complete dataset (all three products) has been named NVAP, an acronym for National Aeronautics and Space Administration Water Vapor Project.

This paper provides examples of the new dataset as well as scientific analysis of the observed annual cycle and the interannual variability of water vapor at global, hemispheric, and regional scales. A distinct global annual cycle is shown to be dominated by the Northern Hemisphere observations. Planetary-scale variations are found to relate well to recent independent estimates of tropospheric temperature variations. Maps of regional interannual variability in the 5-yr period show the effect of the 1992 ENSO and other features.

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Shawn R. Smith, Jacques Servain, David M. Legler, James N . Stricherz, Mark A. Bourassa, and James J. O'brien

Quality wind stress fields are desired for a wide range of oceanographic and atmospheric studies. An overview is presented of the monthly quick-look and research-quality tropical ocean wind (pseudostress) products produced for the Pacific and Indian Oceans by The Florida State University (FSU) and for the Atlantic Ocean by the French Institut de Recherche pour le Développement [IRD; formerly Institut Français de Recherche Scientifique pour le Développement en Coopération (ORSTOM)]. This review article briefly discusses the current state of tropical wind stress products, including an introduction to the new objective FSU analysis technique and advancements in the IRD method. The primary focus is a detailed discussion of how the FSU and IRD pseudostress fields evolved from early subjective in situ analysis techniques. The historical retrospective introduces the scientific motivation, development, and methodology for each product. Examples of the wide range of scientific research and operational applications of the FSU and IRD products are provided.

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Anu Simon, Andrew B. Penny, Mark DeMaria, James L. Franklin, Richard J. Pasch, Edward N. Rappaport, and David A. Zelinsky

Abstract

This study discusses the development of the Hurricane Forecast Improvement Program (HFIP) Corrected Consensus Approach (HCCA) for tropical cyclone track and intensity forecasts. The HCCA technique relies on the forecasts of separate input models for both track and intensity and assigns unequal weighting coefficients based on a set of training forecasts. The HCCA track and intensity forecasts for 2015 were competitive with some of the best-performing operational guidance at the National Hurricane Center (NHC); HCCA was the most skillful model for Atlantic track forecasts through 48 h. Average track input model coefficients for the 2015 forecasts in both the Atlantic and eastern North Pacific basins were largest for the European Centre for Medium-Range Weather Forecasts (ECMWF) deterministic model and the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) ensemble mean, but the relative magnitudes of the intensity coefficients were more varied. Input model sensitivity experiments conducted using retrospective HCCA forecasts from 2011 to 2015 indicate that the ECMWF deterministic model had the largest positive impact on the skill of the HCCA track forecasts in both basins. The most important input models for HCCA intensity forecasts are the Hurricane Weather Research and Forecasting (HWRF) Model and the Coupled Ocean–Atmosphere Mesoscale Prediction System-Tropical Cyclone (COAMPS-TC) model initialized from the GFS. Several updates were incorporated into the HCCA formulation prior to the 2016 season. Verification results indicate HCCA continued to be a skillful model, especially for short-range (12–48 h) track forecasts in both basins.

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Paul M. Della-Marta, Mark A. Liniger, Christof Appenzeller, David N. Bresch, Pamela Köllner-Heck, and Veruska Muccione

Abstract

Current estimates of the European windstorm climate and their associated losses are often hampered by either relatively short, coarse resolution or inhomogeneous datasets. This study tries to overcome some of these shortcomings by estimating the European windstorm climate using dynamical seasonal-to-decadal (s2d) climate forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF). The current s2d models have limited predictive skill of European storminess, making the ensemble forecasts ergodic samples on which to build pseudoclimates of 310–396 yr in length. Extended winter (October–April) windstorm climatologies are created using scalar extreme wind indices considering only data above a high threshold. The method identifies up to 2363 windstorms in s2d data and up to 380 windstorms in the 40-yr ECMWF Re-Analysis (ERA-40). Classical extreme value analysis (EVA) techniques are used to determine the windstorm climatologies. Differences between the ERA-40 and s2d windstorm climatologies require the application of calibration techniques to result in meaningful comparisons. Using a combined dynamical–statistical sampling technique, the largest influence on ERA-40 return period (RP) uncertainties is the sampling variability associated with only 45 seasons of storms. However, both maximum likelihood (ML) and L-moments (LM) methods of fitting a generalized Pareto distribution result in biased parameters and biased RP at sample sizes typically obtained from 45 seasons of reanalysis data. The authors correct the bias in the ML and LM methods and find that the ML-based ERA-40 climatology overestimates the RP of windstorms with RPs between 10 and 300 yr and underestimates the RP of windstorms with RPs greater than 300 yr. A 50-yr event in ERA-40 is approximately a 40-yr event after bias correction. Biases in the LM method result in higher RPs after bias correction although they are small when compared with those of the ML method. The climatologies are linked to the Swiss Reinsurance Company (Swiss Re) European windstorm loss model. New estimates of the risk of loss are compared with those from historical and stochastically generated windstorm fields used by Swiss Re. The resulting loss-frequency relationship matches well with the two independently modeled estimates and clearly demonstrates the added value by using alternative data and methods, as proposed in this study, to estimate the RP of high RP losses.

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Anna P. M. Michel, David J. Miller, Kang Sun, Lei Tao, Levi Stanton, and Mark A. Zondlo

Abstract

A long-path methane (CH4) sensor was developed and field deployed using an 8-μm quantum cascade laser. The high optical power (40 mW) of the laser allowed for path-integrated measurements of ambient CH4 at total pathlengths from 100 to 1200 m with the use of a retroreflector. Wavelength modulation spectroscopy was used to make high-precision measurements of atmospheric pressure–broadened CH4 absorption over these long distances. An in-line reference cell with higher harmonic detection provided metrics of system stability in rapidly changing and harsh environments. The system consumed less than 100 W of power and required no consumables. The measurements intercompared favorably (typically less than 5% difference) with a commercial in situ methane sensor when accounting for the different spatiotemporal scales of the measurements. The sensor was field deployed for 2 weeks at an arctic lake to examine the robustness of the approach in harsh field environments. Short-term precision over a 458-m pathlength was 10 ppbv at 1 Hz, equivalent to a signal from a methane enhancement above background of 5 ppmv in a 1-m length. The sensor performed well in a range of harsh environmental conditions, including snow, rain, wind, and changing temperatures. These field measurements demonstrate the capabilities of the approach for use in detecting large but highly variable emissions in arctic environments.

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