Relation between west coastal rainfall and Nimbus-6 SCAMS liquid water data over the northeastern Pacific Ocean

The application to rainfall prediction of cloud liquid water data obtained from the SCAMS experiment of Nimbus-6 is explored. The study area is the Pacific Northwest coast of the United States, where rainfall is produced by extratropical storms that approach from across the Pacific Ocean. The SCAMS data related to cloud liquid water over the ocean, and coastal rainfall data, are analyzed for 20 different storm systems in the northeastern Pacific Ocean; these produced significant rainfall from Washington to central California during the period October 1975 through March 1976. Results show that the distribution of storm cloud water analyzed from the SCAMS data over the ocean foreshadows the distribution of coastal rainfall accumulated from the storm at a later time. It is concluded that passive microwave sensor measurements of cloud water over the ocean, when used in conjunction with numerical and other objective guidance, can be used to enhance the accuracy of predictions of coastal rainfall distribution. Limitations in the SCAMS measurements and in the data analysis and interpretation are noted

Application of Nimbus-6 microwave data to problems in precipitation prediction for the Pacific west coast

The preliminary results of a research study that emphasizes the analysis and interpretation of data related to total precipitable water and nonprecipitating cloud liquid water obtained from NIMBUS-6 SCAMS are reported. Sixteen cyclonic storm situations in the northeastern Pacific Ocean that resulted in significant rainfall along the west coast of the United States during the winter season October 1975 through February 1976 are analyzed in terms of their distributions and amounts of total water vapor and liquid water, as obtained from SCAMS data. The water-substance analyses for each storm case are related to the distribution and amount of coastal precipitation observed during the subsequent time period when the storm system crosses the coastline. Concomitant precipitation predictions from the LFM are also incorporated. Techniques by which satellite microwave data over the ocean can be used to improve precipitation prediction for the Pacific West Coast are emphasized

The Use of the North Alabama Lightning Mapping Array (NALMA) in the Real-Time Operational Warning Environment During the March 2nd, 2012 Severe Weather Outbreak in Northern Alabama

The North Alabama Lightning Mapping Array (NALMA) is a three-dimensional very high frequency (VHF) detection network consisting of 11 sensors spread across north central Alabama and two sensors located in the Atlanta, Georgia region. The primary advantage of this network is that it detects total lightning, or the combination of both cloud-to-ground and intra-cloud lightning, instead of cloud-to-ground lightning alone. This helps to build a complete picture of storm evolution and development, and can serve as a proxy for storm updraft strength, particularly since intra-cloud lightning makes up the majority of all lightning in a typical thunderstorm. While the NALMA data do not directly indicate severe weather, they can indirectly indicate when a storm is strengthening (weakening) due to increases (decreases) in updraft strength, as the updraft is responsible for charging mechanisms within the storm. Data output are VHF radiation sources, which are produced during lightning breakdown processes. These sources are made into 2x2 km source density grids and are ported into the Advanced Weather Interactive Processing System (AWIPS) for National Weather Service (NWS) offices in Huntsville, AL, Nashville, TN, Morristown, TN, and Birmingham, AL, in near real-time. An increase in sources, or source densities, correlates to increased lightning activity and trends in updraft magnitude as long as the storm is within about 125 km of the center of the LMA network. Operationally, these data have been used at the Huntsville NWS office since early 2003 through a collaborative effort with NASA s Short-term Prediction Research and Transition (SPoRT) Center. Since then, total lightning observations have become an essential tool for forecasters during real-time warning operations. One of the operational advantages of the NALMA is the two-minute temporal resolution of the data. This provides forecasters with two to three updates during a typical volume scan of the WSR-88D radar

A case study of colliding tornadic storms

Abstract only availableTornadoes occur frequently across the United States each year, causing millions of dollars in damage. Meteorologists are constantly searching for new and improved methods for predicting these weather phenomenons's in order to increase public awareness and warning times. In this case study, one event was found in which two storm cells collided and produced a tornado over the Kansas City, Missouri area, causing an extensive amount of damage. The goals of this study is to first determine what caused the collision between the two storm cells, secondly, whether the collision between the two storm cells increased the intensity of the tornado using NSSL/SPC (National Severe Storms Laboratory/ Storm Prediction Center) meteorologist Stephen F. Corfidi's “vector approach.” A method that involves the use of mathematics to find the mean of the wind directions throughout the cloud layers in the storms and also the location of the low-level jet. Radar imagery was also used in determining the location, time, intensity, and other details of the two storm cells. It is our hope, that the completion of this study will produce results that are conducive to the development of more innovative methods for forecasting this type of event.Louis Stokes Alliance for Minority Participatio

Model-driven Scheduling for Distributed Stream Processing Systems

Distributed Stream Processing frameworks are being commonly used with the evolution of Internet of Things(IoT). These frameworks are designed to adapt to the dynamic input message rate by scaling in/out.Apache Storm, originally developed by Twitter is a widely used stream processing engine while others includes Flink, Spark streaming. For running the streaming applications successfully there is need to know the optimal resource requirement, as over-estimation of resources adds extra cost.So we need some strategy to come up with the optimal resource requirement for a given streaming application. In this article, we propose a model-driven approach for scheduling streaming applications that effectively utilizes a priori knowledge of the applications to provide predictable scheduling behavior. Specifically, we use application performance models to offer reliable estimates of the resource allocation required. Further, this intuition also drives resource mapping, and helps narrow the estimated and actual dataflow performance and resource utilization. Together, this model-driven scheduling approach gives a predictable application performance and resource utilization behavior for executing a given DSPS application at a target input stream rate on distributed resources.Comment: 54 page

An Eulerian-Lagrangian Coupled Model for Droplets Dispersion from Nozzle Spray

In this chapter, an Euler-Lagrangian double-way coupled model is presented for simulating the liquid particle dispersion ejected from a high-pressure nozzle. The Eulerian code is advanced regional prediction system (ARPS), developed by Center of Analysis and Prediction of Storm (CAPS) and Oklahoma University, USA, which is specialized in weather simulation. This code is the double way coupled with a Lagrangian one-particle model. The theoretical remarks of the double-way coupling, the simulation of the liquid droplet trajectory, and, finally, the droplet collision in the spray cloud using a binary collision model are descripts. The results of droplet velocities and diameters are compared with experimental laboratory measurements. Finally, agrochemical spraying over a cultivated field in weak wind and high air temperature conditions is showed

The role of the equivalent blackbody temperature in the study of Atlantic Ocean tropical cyclones

Satellite measured equivalent blackbody temperatures of Atlantic Ocean tropical cyclones are used to investigate their role in describing the convection and cloud patterns of the storms and in predicting wind intensity. The high temporal resolution of the equivalent blackbody temperature measurements afforded with the geosynchronous satellite provided sequential quantitative measurements of the tropical cyclone which reveal a diurnal pattern of convection at the inner core during the early developmental stage; a diurnal pattern of cloudiness in the storm's outer circulation throughout the life cycle; a semidiurnal pattern of cloudiness in the environmental atmosphere surrounding the storms during the weak storm stage; an outward modulating atmospheric wave originating at the inner core; and long term convective bursts at the inner core prior to wind intensification