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Yang Cao and Robert G. Fovell

Abstract

The “Santa Ana” wind is an offshore flow that affects Southern California periodically during the winter half of the year, typically between September and May. The winds can be locally gusty, particularly in the complex terrain of San Diego County, where the winds have characteristics of downslope windstorms. These winds can cause and/or rapidly spread wildfires, the threat of which is particularly acute during the autumn season before the onset of winter rains. San Diego’s largest fires, including the Cedar fire of 2003 and Witch Creek fire of 2007, occurred during Santa Ana wind events.

A case study of downslope flow during a moderately intense Santa Ana event during mid-February 2013 is presented. Motivated by the need to forecast winds impinging on electrical lines, the authors make use of an exceptionally dense network of near-surface observations in San Diego County to calibrate and verify simulations made utilizing the Advanced Research version of the Weather Research and Forecasting (WRF) Model, which in turn is employed to augment the observations. Results demonstrate that this particular Santa Ana episode consists of two pulses separated by a protracted lull. During the first pulse, the downslope flow is characterized by a prominent hydraulic jumplike feature, while during the second one the flow possesses a clear temporal progression of winds downslope. WRF has skill in capturing the evolution and magnitude of the event at most locations, although most model configurations overpredict the observed sustained wind and the forecast bias is itself biased.

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Yang Cao and Robert G. Fovell

Abstract

The “Santa Ana” winds of Southern California represent a high-impact weather event because their dry, fast winds can significantly elevate the wildfire threat. This high-resolution numerical study of six events of moderate or greater strength employs physics parameterization and stochastic perturbation ensembles to determine the optimal model configuration for predicting winds in San Diego County, with verification performed against observations from the San Diego Gas and Electric (SDG&E) mesonet. Results demonstrate model physics can have a material effect on the strength, location, and timing of the winds, with the land surface model playing an outsized role via its specification of surface roughness lengths. Even when bias in the network-averaged sustained wind forecasts is minimized, systematic biases remain in that many stations are consistently over- or underforecasted. The argument is made that this is an “unavoidable” error that represents localized anemometer exposure issues revealed through the station gust factor. A very simple gust parameterization is proposed for the mesonet based on the discovery that the network-averaged gust factor is independent of weather conditions and results in unbiased forecasts of gusts at individual stations and the mesonet as a whole. Combined with atmospheric humidity and fuel moisture information, gust forecasts can help in the assessment of wildfire risks.

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Wen Zhuo, Zhiguo Cao, and Yang Xiao

Abstract

Cloud classification of ground-based images is a challenging task. Recent research has focused on extracting discriminative image features, which are mainly divided into two categories: 1) choosing appropriate texture features and 2) constructing structure features. However, simply using texture or structure features separately may not produce a high performance for cloud classification. In this paper, an algorithm is proposed that can capture both texture and structure information from a color sky image. The algorithm comprises three main stages. First, a preprocessing color census transform (CCT) is applied. The CCT contains two steps: converting red, green, and blue (RGB) values to opponent color space and applying census transform to each component. The CCT can capture texture and local structure information. Second, a novel automatic block assignment method is proposed that can capture global rough structure information. A histogram and image statistics are computed in every block and are concatenated to form a feature vector. Third, the feature vector is fed into a trained support vector machine (SVM) classifier to obtain the cloud type. The results show that this approach outperforms other existing cloud classification methods. In addition, several different color spaces were tested and the results show that the opponent color space is most suitable for cloud classification. Another comparison experiment on classifiers shows that the SVM classifier is more accurate than the k–nearest neighbor (k-NN) and neural networks classifiers.

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Ruowen Yang, Zhiang Xie, and Jie Cao

Abstract

Based on the ridge line of the western Pacific subtropical high (WPSH) and the theory of gradient wind approximation, a dynamic index for the westward ridge point (WRPI) of the WPSH is defined. Owing to its definition, the new dynamic index can be used to analyze the evolution of the WPSH at various time scales over most isobaric surfaces. The WRPI comprises two dimensions labeled ZWRPI and MWRPI, which depict the zonal and meridional movement, respectively, of the westward ridge point of the WPSH. The rationality and reliability of the dynamic index were validated using reanalysis atmospheric circulation, outgoing longwave radiation, surface air temperature, and rainfall data. The WRPI series revealed that the westward ridge point of the WPSH generally advances poleward while withdrawing eastward. Furthermore, there were close relationships between the WRPI, atmospheric circulation, outgoing longwave radiation, and precipitation over East Asia and the western Pacific in summer. The significant correlation coefficients indicated that the ZWRPI and the MWRPI can reflect the impact of the zonal and meridional movement of the WPSH on the climate over East Asia and the western Pacific. The ZWRPI has no significant linear trend at the interdecadal time scale, indicating that the WPSH did not significantly extend westward in summer. The slight decrease of the MWRPI suggests that the WPSH moves southward but with an insignificant trend. Compared with indices proposed in previous studies, the WRPI showed advantages in objectivity, reliability, predictability, practicability, and therefore extensive potential for application.

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Yang Xiao, Zhiguo Cao, Wen Zhuo, Liang Ye, and Lei Zhu

Abstract

In this paper, a novel Multiview CLOUD (mCLOUD) visual feature extraction mechanism is proposed for the task of categorizing clouds based on ground-based images. To completely characterize the different types of clouds, mCLOUD first extracts the raw visual descriptors from the views of texture, structure, and color simultaneously, in a densely sampled way—specifically, the scale invariant feature transform (SIFT), the census transform histogram (CENTRIST), and the statistical color features are extracted, respectively. To obtain a more descriptive cloud representation, the feature encoding of the raw descriptors is realized by using the Fisher vector. This is followed by the feature aggregation procedure. A linear support vector machine (SVM) is employed as the classifier to yield the final cloud image categorization result. The experiments on a challenging cloud dataset termed the six-class Huazhong University of Science and Technology (HUST) cloud demonstrate that mCLOUD consistently outperforms the state-of-the-art cloud classification approaches by large margins (at least 6.9%) under all the different experimental settings. It has also been verified that, compared to the single view, the multiview cloud representation generally enhances the performance.

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Jie Cao, Shu Gui, Qin Su, and Yali Yang

Abstract

The interannual zonal movement of the interface between the Indian summer monsoon and the East Asian summer monsoon (IIE), associated with the spring sea surface temperature (SST) seesaw mode (SSTSM) over the tropical Indian Ocean (TIO) and the tropical central-western Pacific (TCWP), is studied for the period 1979–2008. The observational analysis is based on Twentieth Century Reanalysis data (version 2) of atmospheric circulations, Extended Reconstructed SST data (version 3), and the Climate Prediction Center Merged Analysis of Precipitation. The results indicate that the IIE’s zonal movement is significantly and persistently correlated with the TIO–TCWP SSTSM, from spring to summer. The results of two case studies resemble those obtained by regression analysis. Experiments using an atmospheric general circulation model (ECHAM6) substantiate the key physical processes revealed in the observational analysis. When warmer (colder) SSTs appear in the TIO and colder (warmer) SSTs occur in the TCWP, the positive (negative) SSTSM forces anomalous easterly (westerly) winds over the Bay of Bengal (BOB), South China Sea (SCS), and western North Pacific (WNP). The anomalous easterly (westerly) winds further result in a weakened (strengthened) southwest summer monsoon over the BOB and a strengthened (weakened) southeast summer monsoon over the SCS and WNP. This causes the IIE to shift farther eastward (westward) than normal.

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Chang Cao, Yichen Yang, Yang Lu, Natalie Schultze, Pingyue Gu, Qi Zhou, Jiaping Xu, and Xuhui Lee

Abstract

Heat stress caused by high air temperature and high humidity is a serious health concern for urban residents. Mobile measurement of these two parameters can complement weather station observations because of its ability to capture data at fine spatial scales and in places where people live and work. In this paper, we describe a smart temperature and humidity sensor (Smart-T) for use on bicycles to characterize intracity variations in human thermal conditions. The sensor has several key characteristics of internet of things (IoT) technology, including lightweight, low cost, low power consumption, ability to communicate and geolocate the data (via the cyclist’s smartphone), and the potential to be deployed in large quantities. The sensor has a reproducibility of 0.03°–0.05°C for temperature and of 0.18%–0.33% for relative humidity (one standard deviation of variation among multiple units). The time constant with a complete radiation shelter and moving at a normal cycling speed is 9.7 and 18.5 s for temperature and humidity, respectively, corresponding to a spatial resolution of 40 and 70 m. Measurements were made with the sensor on street transects in Nanjing, China. Results show that increasing vegetation fraction causes reduction in both air temperature and absolute humidity and that increasing impervious surface fraction has the opposite effect.

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Tom Rolinski, Scott B. Capps, Robert G. Fovell, Yang Cao, Brian J. D’Agostino, and Steve Vanderburg

Abstract

Santa Ana winds, common to Southern California from the fall through early spring, are a type of downslope windstorm originating from a direction generally ranging from 360°/0° to 100° and are usually accompanied by very low humidity. Since fuel conditions tend to be driest from late September through the middle of November, Santa Ana winds occurring during this time have the greatest potential to produce large, devastating fires upon ignition. Such catastrophic fires occurred in 1993, 2003, 2007, and 2008. Because of the destructive nature of such fires, there has been a growing desire to categorize Santa Ana wind events in much the same way that tropical cyclones have been categorized. The Santa Ana wildfire threat index (SAWTI) is a tool for categorizing Santa Ana wind events with respect to anticipated fire potential. The latest version of the index has been a result of a three-and-a-half-year collaboration effort between the USDA Forest Service, the San Diego Gas and Electric utility (SDG&E), and the University of California, Los Angeles (UCLA). The SAWTI uses several meteorological and fuel moisture variables at 3-km resolution as input to the Weather Research and Forecasting (WRF) Model to generate the index out to 6 days. In addition to the index, a 30-yr climatology of weather, fuels, and the SAWTI has been developed to help put current and future events into perspective. This paper outlines the methodology for developing the SAWTI, including a discussion on the various datasets employed and its operational implementation.

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Qing Cao, Yang Hong, Jonathan J. Gourley, Youcun Qi, Jian Zhang, Yixin Wen, and Pierre-Emmanuel Kirstetter

Abstract

This study presents a statistical analysis of the vertical structure of precipitation measured by NASA–Japan Aerospace Exploration Agency’s (JAXA) Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) in the region of southern California, Arizona, and western New Mexico, where the ground-based Next-Generation Radar (NEXRAD) network finds difficulties in accurately measuring surface precipitation because of beam blockages by complex terrain. This study has applied TRMM PR version-7 products 2A23 and 2A25 from 1 January 2000 to 26 October 2011. The seasonal, spatial, intensity-related, and type-related variabilities are characterized for the PR vertical profile of reflectivity (VPR) as well as the heights of storm, freezing level, and bright band. The intensification and weakening of reflectivity at low levels in the VPR are studied through fitting physically based VPR slopes. Major findings include the following: precipitation type is the most significant factor determining the characteristics of VPRs, the shape of VPRs also influences the intensity of surface rainfall rates, the characteristics of VPRs have a seasonal dependence with strong similarities between the spring and autumn months, and the spatial variation of VPR characteristics suggests that the underlying terrain has an impact on the vertical structure. The comprehensive statistical and physical analysis strengthens the understanding of the vertical structure of precipitation and advocates for the approach of VPR correction to improve surface precipitation estimation in complex terrain.

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Sheng Chen, Jonathan J. Gourley, Yang Hong, Qing Cao, Nicholas Carr, Pierre-Emmanuel Kirstetter, Jian Zhang, and Zac Flamig

Abstract

In meteorological investigations, the reference variable or “ground truth” typically comes from an instrument. This study uses human observations of surface precipitation types to evaluate the same variables that are estimated from an automated algorithm. The NOAA/National Severe Storms Laboratory’s Multi-Radar Multi-Sensor (MRMS) system relies primarily on observations from the Next Generation Radar (NEXRAD) network and model analyses from the Earth System Research Laboratory’s Rapid Refresh (RAP) system. Each hour, MRMS yields quantitative precipitation estimates and surface precipitation types as rain or snow. To date, the surface precipitation type product has received little attention beyond case studies. This study uses precipitation type reports collected by citizen scientists who have contributed observations to the meteorological Phenomena Identification Near the Ground (mPING) project. Citizen scientist reports of rain and snow during the winter season from 19 December 2012 to 30 April 2013 across the United States are compared to the MRMS precipitation type products. Results show that while the mPING reports have a limited spatial distribution (they are concentrated in urban areas), they yield similar critical success indexes of MRMS precipitation types in different cities. The remaining disagreement is attributed to an MRMS algorithmic deficiency of yielding too much rain, as opposed to biases in the mPING reports. The study also shows reduced detectability of snow compared to rain, which is attributed to lack of sensitivity at S band and the shallow nature of winter storms. Some suggestions are provided for improving the MRMS precipitation type algorithm based on these findings.

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