bluuebunny/crossref_metadata_2025_split huggingface dataset:
Subtropical Cyclones over the Southwestern South Atlantic: Climatological Aspects and Case Study
Authors: Gozzo Luiz Felippe , da Rocha Rosmeri P. , Reboita Michelle S. , Sugahara Shigetoshi 
Published: October 2014

Abstract
Hurricane Catarina (2004) and subtropical storm Anita (2010) called attention to the development of subtropical cyclones (SCs) over the South Atlantic basin. Besides strong and organized storms, a large number of weaker, shallower cyclones with both extratropical and tropical characteristics form in the region, impacting the South American coast. The main focus of this study is to simulate a climatology of subtropical cyclones and their synoptic pattern over the South Atlantic, proposing a broader definition of these systems. In addition, a case study is presented to discuss the main characteristics of one weak SC. The Interim ECMWF Re-Analysis (ERA-Interim) and NCEP–NCAR reanalysis are used to construct the 33-yr (1979–2011) climatology, and a comparison between them is established. Both reanalyses show good agreement in the SCs’ intensity, geographical distribution, and seasonal variability, but the interannual variability is poorly correlated. Anomaly composites for austral summer show that subtropical cyclogenesis occurs under a dipole-blocking pattern in upper levels. Upward motion is enhanced by the vertical temperature gradient between a midtropospheric cold cutoff low/trough and the intense low-level warm air advection by the South Atlantic subtropical high. Turbulent fluxes in the cyclone region are not above average during cyclogenesis, but the subtropical high flow advects great amounts of moisture from distant regions to fuel the convective activity. Although most of the SCs develop during austral summer (December–February), it is in autumn (March–May) that the most “tropical” environment is found (stronger surface fluxes and weaker vertical wind shear), leading to the most intense episodes.
An Investigation into the Impact of using Various Techniques to Estimate Arctic Surface Air Temperature Anomalies*
Authors: Dodd Emma M. A. , Merchant Christopher J. , Rayner Nick A. , Morice Colin P. 
Published: October 2014

Abstract
Time series of global and regional mean surface air temperature (SAT) anomalies are a common metric used to estimate recent climate change. Various techniques can be used to create these time series from meteorological station data. The degree of difference arising from using five different techniques, based on existing temperature anomaly dataset techniques, to estimate Arctic SAT anomalies over land and sea ice was investigated using reanalysis data as a test bed. Techniques that interpolated anomalies were found to result in smaller errors than noninterpolating techniques relative to the reanalysis reference. Kriging techniques provided the smallest errors in estimates of Arctic anomalies, and simple kriging was often the best kriging method in this study, especially over sea ice. A linear interpolation technique had, on average, root-mean-square errors (RMSEs) up to 0.55 K larger than the two kriging techniques tested. Noninterpolating techniques provided the least representative anomaly estimates. Nonetheless, they serve as useful checks for confirming whether estimates from interpolating techniques are reasonable. The interaction of meteorological station coverage with estimation techniques between 1850 and 2011 was simulated using an ensemble dataset comprising repeated individual years (1979–2011). All techniques were found to have larger RMSEs for earlier station coverages. This supports calls for increased data sharing and data rescue, especially in sparsely observed regions such as the Arctic.
The Curious Case of Indian Ocean Warming*,+
Authors: Roxy Mathew Koll , Ritika Kapoor , Terray Pascal , Masson Sébastien 
Published: October 2014

Abstract
Recent studies have pointed out an increased warming over the Indian Ocean warm pool (the central-eastern Indian Ocean characterized by sea surface temperatures greater than 28.0°C) during the past half-century, although the reasons behind this monotonous warming are still debated. The results here reveal a larger picture—namely, that the western tropical Indian Ocean has been warming for more than a century, at a rate faster than any other region of the tropical oceans, and turns out to be the largest contributor to the overall trend in the global mean sea surface temperature (SST). During 1901–2012, while the Indian Ocean warm pool went through an increase of 0.7°C, the western Indian Ocean experienced anomalous warming of 1.2°C in summer SSTs. The warming of the generally cool western Indian Ocean against the rest of the tropical warm pool region alters the zonal SST gradients, and has the potential to change the Asian monsoon circulation and rainfall, as well as alter the marine food webs in this biologically productive region. The current study using observations and global coupled ocean–atmosphere model simulations gives compelling evidence that, besides direct contribution from greenhouse warming, the long-term warming trend over the western Indian Ocean during summer is highly dependent on the asymmetry in the El Niño–Southern Oscillation (ENSO) teleconnection, and the positive SST skewness associated with ENSO during recent decades.
Development of an Experimental Near-Real-Time Drought Monitor for India*
Authors: Shah Reepal D. , Mishra Vimal 
Published: October 2014

Abstract
Persistent and widespread drought hampers water resources management and crop production. India has faced frequent droughts over the last few decades. Despite the detrimental impacts of droughts in India, a real-time monitoring system at appropriate spatial and temporal resolution has been lacking. Here, an experimental drought monitor for India is developed that operates daily at a spatial resolution of 0.25° and provides near-real-time information on droughts. The real-time daily precipitation data are obtained from the Tropical Rainfall Measurement Mission (TRMM) while daily temperatures are obtained from the Global Ensemble Forecast System (GEFS), version 2. Near-real-time precipitation and temperatures are bias corrected using the historic precipitation and temperature data from the India Meteorological Department (IMD). Data extending from the past to near present were reconstructed by combining IMD (1969–2010) with real-time, bias-corrected TRMM and GEFS datasets (2010 onward). The experimental drought monitor provides information on meteorological, hydrological, and agricultural droughts using the standardized precipitation index (SPI), standardized runoff index (SRI), and standardized soil moisture index (SSI), respectively. Soil moisture and runoff are simulated using the Variable Infiltration Capacity (VIC) model in near–real time to estimate the severity and areal extent of agricultural and hydrological droughts. The severity and areal extent of droughts from the experimental drought monitor are successfully evaluated against a satellite-based drought severity index. The experimental drought monitor provides high-resolution drought information (district level) that can be valuable for natural resources management and policy making.
Land Surface Feedbacks on Spring Precipitation in the Netherlands
Authors: Daniels Emma E. , Hutjes Ronald W. A. , Lenderink Geert , Ronda Reinder J. , Holtslag Albert A. M. 
Published: October 2014

Abstract
In this paper, the Weather Research and Forecasting (WRF) Model is used to investigate the sensitivity of precipitation to soil moisture and urban areas in the Netherlands. The average output of a 4-day event during 10–13 May 1999 for which the individual days had similar synoptical forcing is analyzed. Four simulations are conducted to test the impact of soil moisture changes on precipitation. A positive soil moisture–precipitation feedback is found, that is, wet (dry) soils increase (decrease) the amount of precipitation. Three additional experiments are executed, two in which urban areas in the Netherlands are expanded and one where urban areas are completely removed. Expansion of urban areas results in an increase of the sensible heat flux and a deeper planetary boundary layer, similar to reducing soil moisture. Expanding urban areas reduces precipitation over the Netherlands as a whole, but the local response is not clear. Within existing urban areas, mean and maximum temperature increases of 0.4 and 2 K, respectively, are found under an urban coverage scenario for 2040. The ratio of evapotranspiration to precipitation (a measure of the soil moisture–precipitation feedback) in the urbanization experiments is only about one-third (23%) of that in the soil moisture experiments (67%). Triggering of precipitation, on the other hand, is relatively high in the urban expansion experiments. The effects of reduced moisture availability and enhanced triggering in the urban expansion experiments compensate each other, leading to the moderate reduction in precipitation.
A Dynamic Approach to Addressing Observation-Minus-Forecast Bias in a Land Surface Skin Temperature Data Assimilation System
Authors: Draper Clara , Reichle Rolf , De Lannoy Gabrielle , Scarino Benjamin 
Published: October 2014

Abstract
In land data assimilation, bias in the observation-minus-forecast (O − F) residuals is typically removed from the observations prior to assimilation by rescaling the observations to have the same long-term mean (and higher-order moments) as the corresponding model forecasts. Such observation rescaling approaches require a long record of observed and forecast estimates and an assumption that the O − F residuals are stationary. A two-stage observation bias and state estimation filter is presented here, as an alternative to observation rescaling that does not require a long data record or assume stationary O − F residuals. The two-stage filter removes dynamic (nonstationary) estimates of the seasonal-scale mean O − F difference from the assimilated observations, allowing the assimilation to correct the model for subseasonal-scale errors without adverse effects from observation biases. The two-stage filter is demonstrated by assimilating geostationary skin temperature Tskin observations into the Catchment land surface model. Global maps of the estimated O − F biases are presented, and the two-stage filter is evaluated for one year over the Americas. The two-stage filter effectively removed the Tskin O − F mean differences, for example, the Geostationary Operational Environmental Satellite (GOES)-West O − F mean difference at 2100 UTC was reduced from 5.1 K for a bias-blind assimilation to 0.3 K. Compared to independent in situ and remotely sensed Tskin observations, the two-stage assimilation reduced the unbiased root-mean-square difference (ubRMSD) of the modeled Tskin by 10% of the open-loop values.
A Numerical Investigation of Formation and Variability of Antarctic Bottom Water off Cape Darnley, East Antarctica
Authors: Nakayama Yoshihiro , Ohshima Kay I. , Matsumura Yoshimasa , Fukamachi Yasushi , Hasumi Hiroyasu 
Published: October 2014

Abstract
At several locations around Antarctica, dense water is formed as a result of intense sea ice formation. When this dense water becomes sufficiently denser than the surrounding water, it descends the continental slope and forms Antarctic Bottom Water (AABW). This study presents the AABW formation off the coast of Cape Darnley [Cape Darnley Bottom Water (CDBW)] in East Antarctica, using a nonhydrostatic model. The model is forced for 8 months by a temporally uniform surface salt flux (because of sea ice formation) estimated from Advanced Microwave Scanning Radiometer for Earth Observing System (EOS; AMSR-E) data and a heat budget calculation. The authors reproduce AABW formation and associated periodic downslope flows of dense water. Descending pathways of dense water are largely determined by the topography; most dense water flows into depressions on the continental shelf, advects onto the continental slope, and is steered downslope to greater depths by the canyons. Intense sea ice formation is the most important factor in the formation of AABW off Cape Darnley, and the existence of depressions is of only minor importance for the flux of CDBW. The mechanism responsible for the periodic downslope flow of dense water is further analyzed using an idealized model setup. The period of dense water outflow is regulated primarily by the topographic beta effect.
Variability of the Deep-Water Overflow in the Luzon Strait*
Authors: Zhou Chun , Zhao Wei , Tian Jiwei , Yang Qingxuan , Qu Tangdong 
Published: October 2014

Abstract
The Luzon Strait, with its deepest sills at the Bashi Channel and Luzon Trough, is the only deep connection between the Pacific Ocean and the South China Sea (SCS). To investigate the deep-water overflow through the Luzon Strait, 3.5 yr of continuous mooring observations have been conducted in the deep Bashi Channel and Luzon Trough. For the first time these observations enable us to assess the detailed variability of the deep-water overflow from the Pacific to the SCS. On average, the along-stream velocity of the overflow is at its maximum at about 120 m above the ocean bottom, reaching 19.9 ± 6.5 and 23.0 ± 11.8 cm s−1 at the central Bashi Channel and Luzon Trough, respectively. The velocity measurements can be translated to a mean volume transport for the deep-water overflow of 0.83 ± 0.46 Sverdrups (Sv; 1 Sv ≡ 106 m3 s−1) at the Bashi Channel and 0.88 ± 0.77 Sv at the Luzon Trough. Significant intraseasonal and seasonal variations are identified, with their dominant time scales ranging between 20 and 60 days and around 100 days. The intraseasonal variation is season dependent, with its maximum strength taking place in March–May. Deep-water eddies are believed to play a role in this intraseasonal variation. On the seasonal time scale, the deep-water overflow intensifies in late fall (October–December) and weakens in spring (March–May), corresponding well with the seasonal variation of the density difference between the Pacific and SCS, for which enhanced mixing in the deep SCS is possibly responsible.
Radar Beam Tracing Methods Based on Atmospheric Refractive Index
Authors: Zeng Yuefei , Blahak Ulrich , Neuper Malte , Jerger Dorit 
Published: October 2014

AbstractSimulation of radar beam propagation is an important component of numerous radar applications in meteorology, including height assignment, quality control, and especially the so-called radar forward operator. Although beam propagation in the atmosphere depends on the refractive index and its vertical variation, which themselves depend on the actual state of the atmosphere, the most common method is to apply the 4/3 earth radius model, based on climatological standard conditions. Serious deviations from the climatological value can occur under so-called ducting conditions, where radar beams at low elevations can be trapped or propagate in a waveguide-like fashion, such that this model is unsuitable in this case. To account for the actual atmospheric conditions, sophisticated methods have been developed in literature. However, concerning the practical implementation of these methods, it was determined that the description in the literature is not always complete with respect to possible pitfalls for practical implementations.In this paper, a revised version of an existing method (one example for the above-mentioned “pitfall” statement) is introduced that exploits Snell’s law for spherically stratified media. From Snell’s law, the correct sign of the local elevation is a priori ambiguous, and the revised method explicitly applies (i) a total reflection criterion and (ii) another ad hoc criterion to solve the problem.Additionally, a new method, based on an ordinary differential equation with respect to range, is proposed in this paper that has no ambiguity.Sensitivity experiments are conducted to investigate the properties of these three methods. The results show that both the revised and new methods are robust under nonstandard conditions. But considering the need to catch an elevation sign ambiguity in the revised method (which cannot be excluded to fail in rare instances), the new method is regarded as more robust and unproblematic, for example, for applications in radar forward operators.
A Comparison of Two Ground-Based Lightning Detection Networks against the Satellite-Based Lightning Imaging Sensor (LIS)
Authors: Thompson Kelsey B. , Bateman Monte G. , Carey Lawrence D. 
Published: October 2014

Abstract
Lightning stroke data from both the World Wide Lightning Location Network (WWLLN) and the Earth Networks Total Lightning Network (ENTLN) were compared to lightning group data from the Lightning Imaging Sensor (LIS) from 1 January 2010 through 30 June 2011. The region of study, from 39°S to 39°N latitude, chosen based on the orbit of LIS, and 164°E east to 17°W longitude, chosen to approximate the possible Geostationary Lightning Mapper (GLM) longitude, was considered in its entirety and then divided into geographical subregions. Over this 18-month time period, WWLLN had an 11.0% entire region, 13.2% North American, 6.2% South American, 16.4% Atlantic Ocean, and 18.9% Pacific Ocean coincidence percent (CP) value. The ENTLN CP values were 28.5%, 63.3%, 2.2%, 3.0%, and 2.5%, respectively. During the 18 months, WWLLN CP values remained rather consistent but low and often higher over ocean than land; ENTLN CP values showed large spatial and temporal variability. With both networks, North America had less variability during summer months than winter months and higher CP values during winter months than summer months. The highest ENTLN CP values were found in the southeastern United States, especially in a semicircle that extended from central Oklahoma, through Texas, along the northern Gulf of Mexico, across southern Florida, and along the U.S. East Coast. There was no significant change in CP values over time; the lowest monthly North American ENTLN CP value was found in June 2011 at 48.1%, the last month analyzed. These findings are consistent with most ENTLN sensors being located in the United States.
DCC Radiometric Sensitivity to Spatial Resolution, Cluster Size, and LWIR Calibration Bias Based on VIIRS Observations
Authors: Wang Wenhui , Cao Changyong 
Published: October 2014

AbstractThe Visible and Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi National Polar-Orbiting Partnership satellite brings new opportunities for improving scientists’ understanding of deep convective cloud (DCC) radiometry with multiple bands in the visible (VIS), near-infrared (NIR), and longwave infrared (LWIR) spectrum. This paper investigated the radiometric sensitivity of DCC reflectance to spatial resolution, brightness temperature of the LWIR band centered at ~11 μm (TB11), TB11 calibration bias, and cluster size using VIIRS VIS (M5), NIR (M7 and I2), and LWIR (M15 and I5) observations at 375- and 750-m spatial resolutions. The mean and mode of the monthly probability distribution functions of DCC reflectance are used as two important indices in using DCC for calibration, and the results show that the onboard radiometric calibration of M5, M7, and I2 are stable during May 2013–April 2014 despite severe instrument responsivity degradations. The standard deviations of the mean and mode of monthly DCC reflectance are 0.5% and 0.2%, respectively, for all bands. It was found that a TB11 calibration bias on the order of 0.5 K has minimal impact on monthly DCC reflectance, especially when the mode method is used. The mean and mode of VIS and NIR DCC reflectance are functions of spatial resolution, TB11 threshold, and DCC cluster size in all seasons. However, the mode of DCC reflectance is more stable than the mean in terms of all three factors. Therefore, the mode is more suitable as an indicator of calibration stability for individual VIS and NIR bands.
A Semisupervised Robust Hydrometeor Classification Method for Dual-Polarization Radar Applications
Authors: Bechini Renzo , Chandrasekar V. 
Published: October 2014

AbstractMost of the recent hydrometeor classification schemes are based on fuzzy logic. When the input radar observations are noisy, the output classification could also be noisy, since the process is bin based and the information from neighboring radar cells is not considered. This paper employs cluster analysis, in combination with fuzzy logic, to improve the hydrometeor classification from dual-polarization radars using a multistep approach. The first step involves a radar-based optimization of an input temperature profile from auxiliary data. Then a first-guess fuzzy logic processing produces the classification to initiate a cluster analysis with contiguity and penalty constraints. The result of the cluster analysis is eventually processed to identify the regions populated with adjacent bins assigned to the same hydrometeor class. Finally, the set of connected regions is passed to the fuzzy logic algorithm for the final classification, exploiting the statistical sample composed by the distribution of the dual-polarization and temperature observations within the regions. Example applications to radar in different environments and meteorological situations, and using different operating frequency bands—namely, S, C, and X bands—are shown. The results are discussed with specific attention to the robustness of the method and the segregation of the data space. Furthermore, the sensitivity to noise and bias in the input variables is also analyzed.
Impact of Sea Level Assimilation on ENSO Initialization and Prediction: The Role of the Sea Level Zonal Tilt and Zonal Mean
Authors: Thual Sulian , Ayoub Nadia , Dewitte Boris 
Published: October 2014

Abstract
At present, most models forecasting the El Niño–Southern Oscillation (ENSO) use data assimilation, which constrains models physics using available observations. In this article, an ENSO model of intermediate complexity is constrained by sea level observations: sea level from the Simple Ocean Data Assimilation (SODA) reanalysis is assimilated in the model forced by SODA winds, using an ensemble Kalman filter. In addition, retrospective ENSO forecasts over the period 1958–2007 are computed. The assimilation of sea level observations slightly improves the model’s predictive skill, which is due to the correction of the recharge–discharge process simulated by the model. To assess this, two indices relevant to the ENSO recharge–discharge theory are considered: the zonal tilt and zonal mean of sea level in the equatorial Pacific. The assimilation of those two observed indices alone leads to results that are qualitatively similar to the assimilation of full maps of sea level observations. This partly results from the fact that the leading statistical modes of the model errors on sea level have a zonal tilt and zonal mean structure. The data assimilation corrects in particular a too weak amplitude of the zonal mean sea level and its associated subsurface variability in the model. The authors suggest that insight on the role of the recharge–discharge process in other models could be gained by comparing the assimilation of full maps of sea level observations with the assimilation of the two indices of sea level.