Topical cyclone rainfall characteristics as determined from a satellite passive microwave radiometer

Data from the Nimbus-5 Electrically Scanning Microwave Radiometer (ESMR-5) were used to calculate latent heat release and other rainfall parameters for over 70 satellite observations of 21 tropical cyclones in the tropical North Pacific Ocean. The results indicate that the ESMR-5 measurements can be useful in determining the rainfall characteristics of these storms and appear to be potentially useful in monitoring as well as predicting their intensity. The ESMR-5 derived total tropical cyclone rainfall estimates agree favorably with previous estimates for both the disturbance and typhoon stages. The mean typhoon rainfall rate (1.9 mm h(-1)) is approximately twice that of disturbances (1.1 mm h(-1))

Identification of storm eye from Satellite image data using fuzzy logic with machine learning

This research presents a study of a unique technique for identifying storm eye that is based on fuzzy logic and image processing with the help of cloud images. Fuzzy logic is a term that refers to complicated systems with unclear behaviour caused by a number of different circumstances. It provides the ability to model the dynamic behavior of the storm and determines the location of the best eye in an area of interest. After that, image processing is applied to enable accurate eye positioning based on the search results. The experimental results are analyzing the storm eye position with approxiamtely $98\%$ accurate compared to the India meteorological department provided best track data and Cooperative Institute for Meteorological Satellite Studies provided Advances Dvorak Technique data. As a result, the identification of storm's eye location using this technique can be found to improve significantly. Using the present technique, it is possible to determine the eye entirely automatically, which replacing the manual method that has been employed in the past

Tropical Cyclone Center Determination Algorithm by Texture and Gradient of Infrared Satellite Image

A novel algorithm for tropical cyclone (TC) center determination is presented by using texture and gradient of infrared satellite image from geostationary satellite. Except those latter disappearing TC satellite images that are little valuable to a TC center determination, generally other periods of TC, all have an inner core. And the centers are generally determined in the inner core. Based on this, an efficient TC center determination algorithm is designed. First, the inner core of a TC is obtained. Then, according to the texture and gradient information of the inner core, the center location of the TC is determined. The effectiveness of the proposed TC center determination algorithm is verified by using Chinese FY-2C stationary infrared satellite image. And the location result is compared with that of the “tropical cyclone yearbook,” which was compiled by Shanghai Typhoon Institute of China Meteorological Administration. Experimental results show that the proposed algorithm can provide a new technique that can automatically determine the center location for a TC based on infrared satellite image

Mesoscale Lightning Experiment (MLE): A view of lightning as seen from space during the STS-26 mission

Information on the data obtained from the Mesoscale Lightning Experiment flown on STS-26 is provided. The experiment used onboard TV cameras and a 35 mm film camera to obtain data. Data from the 35 mm camera are presented. During the mission, the crew had difficulty locating the various targets of opportunity with the TV cameras. To obtain as much data as possible in the short observational timeline allowed due to other commitments, the crew opted to use the hand-held 35 mm camera

An Advanced Operational Approach for Tropical Cyclone Center Estimation Using Geostationary-Satellite-Based Water Vapor and Infrared Channels

Tropical cyclones (TCs) are destructive natural disasters. Accurate prediction and monitoring are important to mitigate the effects of natural disasters. Although remarkable efforts have been made to understand TCs, operational monitoring information still depends on the experience and knowledge of forecasters. In this study, a fully automated geostationary-satellite-based TC center estimation approach is proposed. The proposed approach consists of two improved methods: the setting of regions of interest (ROI) using a score matrix (SCM) and a TC center determination method using an enhanced logarithmic spiral band (LSB) and SCM. The former enables prescreening of the regions that may be misidentified as TC centers during the ROI setting step, and the latter contributes to the determination of an accurate TC center, considering the size and length of the TC rainband in relation to its intensity. Two schemes, schemes A and B, were examined depending on whether the forecasting data or real-time observations were used to determine the initial guess of the TC centers. For each scheme, two models were evaluated to discern whether SCM was combined with LSB for TC center determination. The results were investigated based on TC intensity and phase to determine the impact of TC structural characteristics on TC center determination. While both proposed models improved the detection performance over the existing approach, the best-performing model (i.e., LSB combined with SCM) achieved skill scores (SSs) of +17.4% and +20.8% for the two schemes. In particular, the model resulted in a significant improvement for strong TCs (categories 4 and 5), with SSs of +47.8% and +72.8% and +41.2% and +72.3% for schemes A and B, respectively. The research findings provide an improved understanding of the intensity- and phase-wise spatial characteristics of TCs, which contributes to objective TC center estimation

Global Satellite Observations for Smart Cities

The smart city approach requires collection of interdisciplinary data and information from multiple sources and integration with modern technologies to provide a new and cost-effective way for researchers and decision makers to study and manage cities. In this book chapter, we introduce NASA satellite-based global and regional observations with emphasis on the hydrologic cycle (e.g., precipitation, wind, temperature, soil moisture) for smart cities. These products, consisting of both near-real-time and historical datasets, are publicly available free of charge and can be used for global and regional research and applications. Examples of using these datasets in smart cities are included. The chapter is organized as follows, first, a brief overview of NASA global satellite-based data products, followed by data services and tools, two examples of using satellite-based datasets in megacities, and finally summary and future plans

NASA Global Satellite and Model Data Products and Services for Tropical Cyclone Research

The lack of observations over vast tropical oceans is a major challenge for tropical cyclone research. Satellite observations and model reanalysis data play an important role in filling these gaps. Established in the mid-1980s, the Goddard Earth Sciences Data and Information Services Center (GES DISC), as one of the 12 NASA data centers, archives and distributes data from several Earth science disciplines such as precipitation, atmospheric dynamics, atmospheric composition, and hydrology, including well-known NASA satellite missions (e.g., TRMM, GPM) and model assimilation projects (MERRA-2). Acquiring datasets suitable for tropical cyclone research in a large data archive is a challenge for many, especially for those who are not familiar with satellite or model data. Over the years, the GES DISC has developed user-friendly data services. For example, Giovanni is an online visualization and analysis tool, allowing users to visualize and analyze over 2000 satellite- and model-based variables with a Web browser, without downloading data and software. In this chapter, we will describe data and services at the GES DISC with emphasis on tropical cyclone research. We will also present two case studies and discuss future plans

Meteorological interpretation of Nimbus High Resolution Infrared /HRIR/ data

Nimbus satellite high resolution infrared photographic data analysi