bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Hybrid Precipitation Index Inspired by the SPI, PDSI, and MCDI. Part II: Application to Investigate Precipitation Variability along the West Coast of North America
Authors: Chelton Dudley B. , Risien Craig M. 
Published: August 2020

AbstractThe hybrid precipitation index developed in Part I of this study is applied to investigate precipitation variability along the west coast of North America during the wet season November–March on monthly-to-interannual time scales. The variability in each of six regions considered in this study is negatively correlated with nearby 500-hPa geopotential height anomalies. Except in Southeast Alaska, these correlation patterns indicate that precipitation variability in each region is predominantly influenced by local atmospheric forcing analogous to the ridging of the westerly flow that has been studied extensively with regard to California drought variability. The first empirical orthogonal function (EOF) accounts for nearly all of the Southeast Alaska precipitation variability, which is controlled by the strength of the onshore flow rather than ridging. In association with this mode of variability, precipitation anomalies of opposite sign account for about 40% of the precipitation variance in Northern California and Oregon on all time scales. On short time scales, the second and third EOFs account primarily for precipitation variability in British Columbia/Washington and California, respectively. With increasing time scale, the third EOF diminishes in importance and the second EOF evolves into a pattern of synchronous precipitation anomalies of the same sign from British Columbia to Northern California. Precipitation variability in Southern California is only modestly related to precipitation elsewhere. With increasing time scale, Southern California precipitation variability becomes increasingly related to precipitation anomalies of opposite sign in Washington.
Prediction of Seasonal Meteorological Drought Onset and Termination over the Southern Great Plains in the North American Multimodel Ensemble
Authors: Seager Richard , Nakamura Jennifer , Ting Mingfang 
Published: August 2020

AbstractThe predictability on the seasonal time scale of meteorological drought onsets and terminations over the southern Great Plains is examined within the North American Multimodel Ensemble. The drought onsets and terminations were those identified based on soil moisture transitions in land data assimilation systems and shown to be driven by precipitation anomalies. Sea surface temperature (SST) forcing explains about a quarter of variance of seasonal mean precipitation in the region. However, at lead times of a season, forecast SSTs only explain about 10% of seasonal mean precipitation variance. For the three identified drought onsets, fall 2010 is confidently predicted and spring 2012 is predicted with some skill, and fall 2005 was not predicted at all. None of the drought terminations were predicted on the seasonal time scale. Predictability of drought onset arises from La Niña–like conditions, but there is no indication that El Niño conditions lead to drought terminations in the southern Great Plains. Spring 2012 and fall 2000 are further examined. The limited predictability of onset in spring 2012 arises from cool tropical Pacific SSTs, but internal atmospheric variability played a very important role. Drought termination in fall 2000 was predicted at the 1-month time scale but not at the seasonal time scale, likely because of failure to predict warm SST anomalies directly east of subtropical Asia. The work suggests that improved SST prediction offers some potential for improved prediction of both drought onsets and terminations in the southern Great Plains, but that many onsets and terminations will not be predictable even a season in advance.
Evaluation of a Physics-Based Tropical Cyclone Rainfall Model for Risk Assessment
Authors: Xi Dazhi , Lin Ning , Smith James 
Published: August 2020

AbstractHeavy rainfall generated by landfalling tropical cyclones (TCs) can cause extreme flooding. A physics-based TC rainfall model (TCRM) has been developed and coupled with a TC climatology model to study TC rainfall climatology. In this study, we evaluate TCRM with rainfall observations made by satellite (of North Atlantic TCs from 1999 to 2018) and radar (of 36 U.S. landfalling TCs); we also examine the influence on the rainfall estimation of the key input to TCRM—the wind profile. We found that TCRM can simulate relatively well the rainfall from TCs that have a coherent and compact structure and limited interaction with other meteorological systems. The model can simulate the total rainfall from TCs well, although it often overestimates rainfall in the inner core of TCs, slightly underestimates rainfall in the outer regions, and renders a less asymmetric rainfall structure than the observations. It can capture rainfall distribution in coastal areas relatively well but may underestimate rainfall maximums in mountainous regions and has less capability to accurately simulate TC rainfall in higher latitudes. Also, it can capture the interannual variability of TC rainfall and averaged features of the time series of TC rainfall but cannot accurately reproduce the probability distribution of short-term (1 h) rainfall. Among the tested theoretical wind profile inputs to TCRM, a complete wind profile that accurately describes the wind structure in both the inner ascending and outer descending regions of the storm is found to perform the best in accurately generating various rainfall metrics.
Robustness of Process-Based versus Data-Driven Modeling in Changing Climatic Conditions
Authors: O Sungmin , Dutra Emanuel , Orth Rene 
Published: August 2020

AbstractFuture climate projections require Earth system models to simulate conditions outside their calibration range. It is therefore crucial to understand the applicability of such models and their modules under transient conditions. This study assesses the robustness of different types of models in terms of rainfall–runoff modeling under changing conditions. In particular, two process-based models and one data-driven model are considered: 1) the physically based land surface model of the European Centre for Medium-Range Weather Forecasts, 2) the conceptual Simple Water Balance Model, and 3) the Long Short-Term Memory-Based Runoff model. Using streamflow data from 161 catchments across Europe, a differential split-sample test is performed, i.e., models are calibrated within a reference period (e.g., wet years) and then evaluated during a climatically contrasting period (e.g., drier years). Models show overall performance loss, which generally increases the more conditions deviate from the reference climate. Further analysis reveals that the models have difficulties in capturing temporal shifts in the hydroclimate of the catchments, e.g., between energy- and water-limited conditions. Overall, relatively high robustness is demonstrated by the physically based model. This suggests that improvements of physics-based parameterizations can be a promising avenue toward reliable climate change simulations. Further, our study illustrates that comparison across process-based and data-driven models is challenging due to their different nature. While we find rather low robustness of the data-driven model in our particular split-sample setup, this must not apply generally; by contrast, such model schemes have great potential as they can learn diverse conditions from observed spatial and temporal variability both at the same time to yield robust performance.
On the Predictability of Sea Surface Height around Palau
Authors: Andres Magdalena , Musgrave Ruth C. , Rudnick Daniel L. , Zeiden Kristin L. , Peacock Thomas , Park Jae-Hun 
Published: August 2020

AbstractAs part of the Flow Encountering Abrupt Topography (FLEAT) program, an array of pressure-sensor equipped inverted echo sounders (PIESs) was deployed north of Palau where the westward-flowing North Equatorial Current encounters the southern end of the Kyushu–Palau Ridge in the tropical North Pacific. Capitalizing on concurrent observations from satellite altimetry, FLEAT Spray gliders, and shipboard hydrography, the PIESs’ 10-month duration hourly bottom pressure p and round-trip acoustic travel time τ records are used to examine the magnitude and predictability of sea level and pycnocline depth changes and to track signal propagations through the array. Sea level and pycnocline depth are found to vary in response to a range of ocean processes, with their magnitude and predictability strongly process dependent. Signals characterized here comprise the barotropic tides, semidiurnal and diurnal internal tides, southeastward-propagating superinertial waves, westward-propagating mesoscale eddies, and a strong signature of sea level increase and pycnocline deepening associated with the region’s relaxation from El Niño to La Niña conditions. The presence of a broad band of superinertial waves just above the inertial frequency was unexpected and the FLEAT observations and output from a numerical model suggest that these waves detected near Palau are forced by remote winds east of the Philippines. The PIES-based estimates of pycnocline displacement are found to have large uncertainties relative to overall variability in pycnocline depth, as localized deep current variations arising from interactions of the large-scale currents with the abrupt topography around Palau have significant travel time variability.
Observed Eddy–Internal Wave Interactions in the Southern Ocean
Authors: Cusack Jesse M. , Brearley J. Alexander , Naveira Garabato Alberto C. , Smeed David A. , Polzin Kurt L. , Velzeboer Nick , Shakespeare Callum J. 
Published: August 2020

AbstractThe physical mechanisms that remove energy from the Southern Ocean’s vigorous mesoscale eddy field are not well understood. One proposed mechanism is direct energy transfer to the internal wave field in the ocean interior, via eddy-induced straining and shearing of preexisting internal waves. The magnitude, vertical structure, and temporal variability of the rate of energy transfer between eddies and internal waves is quantified from a 14-month deployment of a mooring cluster in the Scotia Sea. Velocity and buoyancy observations are decomposed into wave and eddy components, and the energy transfer is estimated using the Reynolds-averaged energy equation. We find that eddies gain energy from the internal wave field at a rate of −2.2 ± 0.6 mW m−2, integrated from the bottom to 566 m below the surface. This result can be decomposed into a positive (eddy to wave) component, equal to 0.2 ± 0.1 mW m−2, driven by horizontal straining of internal waves, and a negative (wave to eddy) component, equal to −2.5 ± 0.6 mW m−2, driven by vertical shearing of the wave spectrum. Temporal variability of the transfer rate is much greater than the mean value. Close to topography, large energy transfers are associated with low-frequency buoyancy fluxes, the underpinning physics of which do not conform to linear wave dynamics and are thereby in need of further research. Our work suggests that eddy–internal wave interactions may play a significant role in the energy balance of the Southern Ocean mesoscale eddy and internal wave fields.
Temporal Variations of a Natural Hydrocarbon Seep Using a Deep-Sea Camera System
Authors: Razaz Mahdi , Di Iorio Daniela , Wang Binbin , MacDonald Ian 
Published: August 2020

AbstractTwo video time-lapse cameras (VTLCs) were deployed by a remotely operated underwater vehicle (ROV) to observe the temporal and spatial variability of a natural hydrocarbon seep at 1180 m depth in the Green Canyon 600 lease block, Gulf of Mexico. The VTLCs were positioned approximately 60 and 90 cm away from the vent, each recording 15 s video bursts at 30 frames per second, illuminated by a 2000 lumen (lm) LED lamp. One camera functioned for 2 weeks; the second camera recorded 568 video bursts at 6 h intervals from 3 September 2017 to 2 February 2018 (153 days). Over the campaign period, seepage from three vents along a 10 cm cluster shifted toward a new fault line with up to nine intermittent individual vents shifting along 20 cm. We developed a semisupervised algorithm using Mathematica and ImageJ routines to resolve the rise velocity and size of individual bubbles. The algorithm was applied to the last 30 frames of each video burst. Bubble characteristics were also analyzed in the videos recorded by the ROV camera. Processing VTLC records yielded a bubble size distribution comparable (5% deviation) to the ROV camera, while the rise velocities were found to be 12% smaller than the ROV data. Hydrocarbon flux estimated from VTLC data was also compared favorably (2% difference) with synoptic physical collections of hydrocarbons into an ROV-held funnel. The long-term measurements indicate that bubble rise velocity was weakly correlated to the discharge rate as well as to the cross-flow velocity.
Evaluating Radiometric Responsivity Degradations of TANSO-FTS/GOSAT Using Principal Component Analysis
Authors: Someya Yu , Yoshida Yukio 
Published: August 2020

AbstractThe short-wavelength infrared bands of the Thermal and Near-Infrared Sensor for Carbon Observation (TANSO)–Fourier transform spectrometer (FTS) instrument on board theGreenhouse Gas Observing Satellite(GOSAT) have degraded, which affects the retrieval of data for CO2and CH4. Herein, a new algorithm that uses principal component analysis (PCA) to evaluate these degradations from on-orbit solar calibration spectra has been developed. The datasets of the spectra were decomposed using PCA, and the temporal variations of their components were fitted using the appropriate functions. Our results show that PCA is effective to construct a suitable degradation model for TANSO-FTS. Comparisons of CO2data retrieved using the new degradation model with that using the ground-based FTS indicate that the new model improves the measurement biases.
Separating Cloud and Drizzle Signals in Radar Doppler Spectra Using a Parametric Time Domain Method
Authors: Joshil Shashank S. , Nguyen Cuong M. , Chandrasekar V. , Chiu J. Christine , Blanchard Yann 
Published: August 2020

AbstractThe ability to separate cloud and drizzle returns in active remote sensing observations is important for understanding the microphysics of clouds and precipitation. Yet, robust separations remain challenging in radar remote sensing. Prior methods for cloud and drizzle separation for radar observations use the properties of the Doppler spectra such as skewness. However, these methods have challenges when the drizzle becomes dominant in the observation volume. This paper presents a parametric time domain method (PTDM) that separates cloud and drizzle using the Doppler spectra measurements without assuming any prior properties of cloud and drizzle. The advantage of PTDM is that it can estimate the signal properties in the time domain and can obtain the cloud and drizzle estimates simultaneously. Based on our radar signal simulations, the uncertainty in estimated power and velocity from PTDM are within 2 dB and 0.02 m s−1, respectively. We have also evaluated the PTDM algorithm using observations from the Atmospheric Radiation Measurement (ARM) Program W-band cloud radar in the Clouds, Aerosols, and Precipitation in the Marine Boundary Layer (CAP-MBL) campaign at the Azores in 2009–10. Two cases corresponding to light and moderate drizzling conditions are considered for the study. The statistics of the estimates obtained show that the PTDM method performs well in separating the cloud and drizzle returns. Finally, the estimated cloud and drizzle reflectivity from PTDM were used to retrieve their corresponding microphysical properties, showing that the retrieved liquid water path agrees to 25 g m−2 with the benchmark microwave method.
Demonstrating the Potential of Low-Cost GPS Units for the Remote Measurement of Tides and Water Levels Using Interferometric Reflectometry
Authors: Williams Simon D. P. , Bell Paul S. , McCann David L. , Cooke Richard , Sams Christine 
Published: August 2020

AbstractA low-cost [$30 (U.S. dollars)] consumer grade GPS receiver with a sideways-mounted antenna has been applied to measure tidal water levels at a mesotidal coastal site using an interferometric reflectometry approach. The proof-of-concept system was installed approximately 16 m above mean sea level in close proximity to a conventional bubbler tide gauge that provided validation data. The received signal-to-noise ratios (SNR) for the satellites in view were recorded for several months during two successive years and the observed frequencies of the interferometric oscillations used to calculate the difference in elevation between the receiver and the water surface. Comparisons with concurrent and historic in situ tide gauge data at the site initially helped to identify a calibration issue with the in situ gauge. The GPS-based measurements were shown to be in excellent agreement with the corrected in situ gauge, exhibiting a root-mean-square difference of 5.7 cm over a tidal range exceeding 3 m at spring tides and a daily averaged RMS of 1.7 cm. The SNR data from the low-cost GPS receivers are shown to provide significantly higher-quality data for this purpose compared with high-end geodetic grade receivers at similar sites. This low-cost, widely available technology has the potential to be applied globally for monitoring water levels in a wide variety of circumstances and applications that would otherwise be cost or situation prohibitive. It could also be applied as an independent cross check and quality control measure for conventional water-level gauges.
An adaptive Markov Chain approach for probabilistic forecasting of categorical events
Authors: Pope Edward C.D. , Stephenson David B. , Jackson David R. 
Published: August 2020

Abstract
Categorical probabilistic prediction is widely used for terrestrial and space weather forecasting as well as for other environmental forecasts. One example is a warning system for geomagnetic disturbances caused by space weather, which are often classified on a 10-level scale.
The simplest approach assumes that the transition probabilities are stationary in time – the Homogeneous Markov Chain (HMC). We extend this approach by developing a flexible Non-Homogeneous Markov Chain (NHMC) model using Bayesian non-parametric estimation to describe the time-varying transition probabilities. The transition probabilities are updated using a modified Bayes’ rule that gradually forgets transitions in the distant past, with a tunable memory parameter.
The approaches were tested by making daily geomagnetic state forecasts at lead times of 1-4 days and verified over the period 2000-2019 using the Rank Probability Score (RPS). Both HMC and NHMC models were found to be skilful at all lead times when compared with climatological forecasts. The NHMC forecasts with an optimal memory parameter of ~100 days were found to be substantially more skilful than the HMC forecasts, with an RPS skill for the NHMC of 10.5% and 5.6% for lead times of 1 and 4 days ahead, respectively. The NHMC is thus a viable alternative approach for forecasting geomagnetic disturbances, and could provide a new benchmark for producing operational forecasts.
The approach is generic and applicable to other forecasts including discrete weather regimes or hydrological conditions, e.g. wet and dry days.
Simulated Tropical Precipitation Assessed across Three Major Phases of the Coupled Model Intercomparison Project (CMIP)
Authors: Fiedler Stephanie , Crueger Traute , D’Agostino Roberta , Peters Karsten , Becker Tobias , Leutwyler David , Paccini Laura , Burdanowitz Jörg , Buehler Stefan A. , Cortes Alejandro Uribe , Dauhut Thibaut , Dommenget Dietmar , Fraedrich Klaus , Jungandreas Leonore , Maher Nicola , Naumann Ann Kristin , Rugenstein Maria , Sakradzija Mirjana , Schmidt Hauke , Sielmann Frank , Stephan Claudia , Timmreck Claudia , Zhu Xiuhua , Stevens Bjorn 
Published: August 2020

Abstract
The representation of tropical precipitation is evaluated across three generations of models participating in phases 3, 5, and 6 of the Coupled Model Intercomparison Project (CMIP). Compared to state-of-the-art observations, improvements in tropical precipitation in the CMIP6 models are identified for some metrics, but we find no general improvement in tropical precipitation on different temporal and spatial scales. Our results indicate overall little changes across the CMIP phases for the summer monsoons, the double-ITCZ bias, and the diurnal cycle of tropical precipitation. We find a reduced amount of drizzle events in CMIP6, but tropical precipitation occurs still too frequently. Continuous improvements across the CMIP phases are identified for the number of consecutive dry days, for the representation of modes of variability, namely, the Madden–Julian oscillation and El Niño–Southern Oscillation, and for the trends in dry months in the twentieth century. The observed positive trend in extreme wet months is, however, not captured by any of the CMIP phases, which simulate negative trends for extremely wet months in the twentieth century. The regional biases are larger than a climate change signal one hopes to use the models to identify. Given the pace of climate change as compared to the pace of model improvements to simulate tropical precipitation, we question the past strategy of the development of the present class of global climate models as the mainstay of the scientific response to climate change. We suggest the exploration of alternative approaches such as high-resolution storm-resolving models that can offer better prospects to inform us about how tropical precipitation might change with anthropogenic warming.
Assimilating Visible and Infrared Radiances in Idealized Simulations of Deep Convection
Authors: Schröttle Josef , Weissmann Martin , Scheck Leonhard , Hutt Axel 
Published: August 2020

AbstractCloud-affected radiances from geostationary satellite sensors provide the first area-wide observable signal of convection with high spatial resolution in the range of kilometers and high temporal resolution in the range of minutes. However, these observations are not yet assimilated in operational convection-resolving weather prediction models as the rapid, nonlinear evolution of clouds makes the assimilation of related observations very challenging. To address these challenges, we investigate the assimilation of satellite radiances from visible and infrared channels in idealized observing system simulation experiments (OSSEs) for a day with summertime deep convection in central Europe. This constitutes the first study assimilating a combination of all-sky observations from infrared and visible satellite channels, and the experiments provide the opportunity to test various assimilation settings in an environment where the observation forward operator and the numerical model exhibit no systematic errors. The experiments provide insights into appropriate settings for the assimilation of cloud-affected satellite radiances in an ensemble data assimilation system and demonstrate the potential of these observations for convective-scale weather prediction. Both infrared and visible radiances individually lead to an overall forecast improvement, but best results are achieved with a combination of both observation types that provide complementary information on atmospheric clouds. This combination strongly improves the forecast of precipitation and other quantities throughout the whole range of 8-h lead time.