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Hong, Yang

Publications and source records attributed to Hong, Yang.

31 records · Page 2

Electromagnetically induced transparency in a five-level {Lambda} system dominated by two-photon resonant transitions

We study the steady optical response of a five-level atomic system in the parametric region where resonant two-photon transitions are much stronger than far-detuned single-photon transitions. We find that the concurrent absorption of two weak probe fields can be well suppressed in a narrow spectral region to attain electromagnetically induced transparency (EIT) via quantum destructive interference between different two-photon transition pathways. To gain a deeper insight into relevant physics, we adiabatically reduce this five-level system with trivial single-photon transitions into a three-level system with vanishing single-photon transitions by deriving an effective Hamiltonian. The two systems have almost the same two-photon absorption spectra exhibiting typical EIT features but are a little different in fine details. This means that most characteristics of two-photon quantum destructive interference are reserved after the adiabatic elimination approximation. In addition, we verify by numerical calculations that the two-photon EIT spectra are insensitive to the dipole-dipole interaction of cold Rydberg atoms when the uppermost level has a high principle quantum number.

74 ATOMIC AND MOLECULAR PHYSICS↗

Advances in Landslide Nowcasting: Evaluation of a Global and Regional Modeling Approach

The increasing availability of remotely sensed data offers a new opportunity to address landslide hazard assessment at larger spatial scales. A prototype global satellite-based landslide hazard algorithm has been developed to identify areas that may experience landslide activity. This system combines a calculation of static landslide susceptibility with satellite-derived rainfall estimates and uses a threshold approach to generate a set of nowcasts that classify potentially hazardous areas. A recent evaluation of this algorithm framework found that while this tool represents an important first step in larger-scale near real-time landslide hazard assessment efforts, it requires several modifications before it can be fully realized as an operational tool. This study draws upon a prior work s recommendations to develop a new approach for considering landslide susceptibility and hazard at the regional scale. This case study calculates a regional susceptibility map using remotely sensed and in situ information and a database of landslides triggered by Hurricane Mitch in 1998 over four countries in Central America. The susceptibility map is evaluated with a regional rainfall intensity duration triggering threshold and results are compared with the global algorithm framework for the same event. Evaluation of this regional system suggests that this empirically based approach provides one plausible way to approach some of the data and resolution issues identified in the global assessment. The presented methodology is straightforward to implement, improves upon the global approach, and allows for results to be transferable between regions. The results also highlight several remaining challenges, including the empirical nature of the algorithm framework and adequate information for algorithm validation. Conclusions suggest that integrating additional triggering factors such as soil moisture may help to improve algorithm performance accuracy. The regional algorithm scenario represents an important step forward in advancing regional and global-scale landslide hazard assessment.

Kirschbaum, Dalia Bach↗

SERVIR-Africa: Developing an Integrated Platform for Floods Disaster Management in Africa

SERVIR-Africa is an ambitious regional visualization and monitoring system that integrates remotely sensed data with predictive models and field-based data to monitor ecological processes and respond to natural disasters. It aims addressing societal benefits including floods and turning data into actionable information for decision-makers. Floods are exogenous disasters that affect many parts of Africa, probably second only to drought in terms of social-economic losses. This paper looks at SERVIR-Africa's approach to floods disaster management through establishment of an integrated platform, floods prediction models, post-event flood mapping and monitoring as well as flood maps dissemination in support of flood disaster management.

Macharia, Daniel↗

Application of Satellite Observations to Manage Natural Disasters in the Lake Victoria Basin

Lake Victoria, the second largest fresh water lake in the Eastern part of Africa is a vital natural resource for the economic well being and prosperity of over 30 million people located in riparian regions of Uganda, Kenya and Tanzania. It covers a large area of about 68,870 km2 and produces a GDP of about US $30 billion per year. The region is also very much prone to natural disasters such as severe floods during heavy precipitation periods in the Eastern part of Africa. In addition to floods, the precipitation also produces large infestations of mosquito larvae due to the standing water in many areas. This further causes multiple vector borne diseases such as Malaria, Rift Valley Fever and more. These problems are of serious concern and require active and aggressive surveillance and management to minimize the loss of human and animal lives and property damage. Satellite imagery and observations along with the in situ measurements provide a great tool to analyze and study this area and inform the policy makers to make calculated policy decisions which are more beneficial to the environment. Recently, NASA and USAID have joined forces with the Regional Center for Mapping of Resources for Development (RCMRD) located in Nairobi, Kenya to utilize multiple NASA sensors such as TRMM, SRTM and MODIS to develop flood potential maps for the Lake Victoria Basin. The idea is to generate a flood forecasts and "nowcasts" that can be sent to the disaster management organizations of Uganda, Kenya, and Tanzania. Post flood event satellite imagery is becoming a common tool to assess the areas inundated by flooding. However, this work is unique undertaking by utilizing land imaging and atmospheric satellites to build credible flood potential maps. At same time, we are also studying the potential occurrence and spread of Rift Valley Fever disease based on the short term climate records and precipitation data. These activities require multi-nation coordination and agreements and multiple operational agencies within each respective country. It also requires credible in situ data such as precipitation, river flow rates and lake levels to further validate the global and regional Hood models and algorithms. This also requires a considerable amount of training and capacity building for the RCMRD experts who will help us validate the model results and eventually transition it for operational use. In a final analysis, Disaster management and humanitarian aid organizations need accurate and timely information for making decisions regarding deployment of relief teams and emergency supplies during major floods. Flood maps based on the use of satellite data have proven extremely valuable to such organizations for identifying the location, extent, and severity of these events. However, despite extraordinary efforts on the part of remote sensing data providers to rapidly deliver such maps, there is typically a delay of several days or even weeks from the on-set of flooding until such maps are available to the disaster management community. This paper summarizes efforts at NASA to address this problem through development of an integrated and automated process of a) flood forecasting b) flood detection, c) satellite data acquisition, d) rapid Hood mapping and distribution, and e) validation of Hood forecasting and detection products.

Habib, Shahid↗

Applications of TRMM-based Multi-Satellite Precipitation Estimation for Global Runoff Simulation: Prototyping a Global Flood Monitoring System

Advances in flood monitoring/forecasting have been constrained by the difficulty in estimating rainfall continuously over space (catchment-, national-, continental-, or even global-scale areas) and flood-relevant time scale. With the recent availability of satellite rainfall estimates at fine time and space resolution, this paper describes a prototype research framework for global flood monitoring by combining real-time satellite observations with a database of global terrestrial characteristics through a hydrologically relevant modeling scheme. Four major components included in the framework are (1) real-time precipitation input from NASA TRMM-based Multi-satellite Precipitation Analysis (TMPA); (2) a central geospatial database to preprocess the land surface characteristics: water divides, slopes, soils, land use, flow directions, flow accumulation, drainage network etc.; (3) a modified distributed hydrological model to convert rainfall to runoff and route the flow through the stream network in order to predict the timing and severity of the flood wave, and (4) an open-access web interface to quickly disseminate flood alerts for potential decision-making. Retrospective simulations for 1998-2006 demonstrate that the Global Flood Monitor (GFM) system performs consistently at both station and catchment levels. The GFM website (experimental version) has been running at near real-time in an effort to offer a cost-effective solution to the ultimate challenge of building natural disaster early warning systems for the data-sparse regions of the world. The interactive GFM website shows close-up maps of the flood risks overlaid on topography/population or integrated with the Google-Earth visualization tool. One additional capability, which extends forecast lead-time by assimilating QPF into the GFM, also will be implemented in the future.

Hong, Yang↗

Application of Multi-Satellite Precipitation Analysis to Floods and Landslides

Satellite data acquired and processed in real time now have the potential to provide the spacetime information on rainfall needed to monitor flood and landslide events around the world. This can be achieved by integrating the satellite-derived forcing data with hydrological models and landslide algorithms. Progress in using the TRMM Multi-satellite Precipitation Analysis (TMPA) as input to flood and landslide forecasts is outlined, with a focus on understanding limitations of the rainfall data and impacts of those limitations on flood/landslide analyses. Case studies of both successes and failures will be shown, as well as comparison with ground comparison data sets both in terms of rainfall and in terms of flood/landslide events. In addition to potential uses in real-time, the nearly ten years of TMPA data allow retrospective running of the models to examine variations in extreme events. The flood determination algorithm consists of four major components: 1) multi-satellite precipitation estimation; 2) characterization of land surface including digital elevation from NASA SRTM (Shuttle Radar Terrain Mission), topography-derived hydrologic parameters such as flow direction, flow accumulation, basin, and river network etc.; 3) a hydrological model to infiltrate rainfall and route overland runoff; and 4) an implementation interface to relay the input data to the models and display the flood inundation results to potential users and decision-makers. In terms of landslides, the satellite rainfall information is combined with a global landslide susceptibility map, derived from a combination of global surface characteristics (digital elevation topography, slope, soil types, soil texture, and land cover classification etc.) using a weighted linear combination approach. In those areas identified as "susceptible" (based on the surface characteristics), landslides are forecast where and when a rainfall intensity/duration threshold is exceeded. Results are described indicating general agreement with landslide occurrences. However, difficulties in comparing landslide event information (mostly from news reports) with the satellite-based forecasts are analyzed.

Adler, Robert↗

Real-Time Application of Multi-Satellite Precipitation Analysis for Floods and Landslides

Satellite data acquired and processed in real time now have the potential to provide the spacetime information on rainfall needed to monitor flood and landslide events around the world. This can be achieved by integrating the satellite-derived forcing data with hydrological models and landslide algorithms. Progress in using the TRMM Multi-satellite Precipitation Analysis (TMPA) as input to flood and landslide forecasts is outlined, with a focus on understanding limitations of the rainfall data and impacts of those limitations on flood/landslide analyses. Case studies of both successes and failures will be shown, as well as comparison with ground comparison data sets-- both in terms of rainfall and in terms of flood/landslide events. In addition to potential uses in real-time, the nearly ten years of TMPA data allow retrospective running of the models to examine variations in extreme events. The flood determination algorithm consists of four major components: 1) multi-satellite precipitation estimation; 2) characterization of land surface including digital elevation from NASA SRTM (Shuttle Radar Terrain Mission), topography-derived hydrologic parameters such as flow direction, flow accumulation, basin, and river network etc.; 3) a hydrological model to infiltrate rainfall and route overland runoff; and 4) an implementation interface to relay the input data to the models and display the flood inundation results to potential users and decision-makers, In terms of landslides, the satellite rainfall information is combined with a global landslide susceptibility map, derived from a combination of global surface characteristics (digital elevation topography, slope, soil types, soil texture, and land cover classification etc.) using a weighted linear combination approach. In those areas identified as "susceptible" (based on the surface characteristics), landslides are forecast where and when a rainfall intensity/duration threshold is exceeded. Results are described indicating general agreement with landslide occurrences.

Adler, Robert↗

Flood and Landslide Applications of Near Real-time Satellite Rainfall Products

Floods and associated landslides are one of the most widespread natural hazards on Earth, responsible for tens of thousands of deaths and billions of dollars in property damage every year. During 1993-2002, over 1000 of the more than 2,900 natural disasters reported were due to floods. These floods and associated landslides claimed over 90,000 lives, affected over 1.4 billion people and cost about $210 billion. The impact of these disasters is often felt most acutely in less developed regions. In many countries around the world, satellite-based precipitation estimation may be the best source of rainfall data due to lack of surface observing networks. Satellite observations can be of essential value in improving our understanding of the occurrence of hazardous events and possibly in lessening their impact on local economies and in reducing injuries, if they can be used to create reliable warning systems in cost-effective ways. This article addressed these opportunities and challenges by describing a combination of satellite-based real-time precipitation estimation with land surface characteristics as input, with empirical and numerical models to map potential of landslides and floods. In this article, a framework to detect floods and landslides related to heavy rain events in near-real-time is proposed. Key components of the framework are: a fine resolution precipitation acquisition system; a comprehensive land surface database; a hydrological modeling component; and landslide and debris flow model components. A key precipitation input dataset for the integrated applications is the NASA TRMM-based multi-satellite precipitation estimates. This dataset provides near real-time precipitation at a spatial-temporal resolution of 3 hours and 0.25deg x 0.25deg. By careful integration of remote sensing and in-situ observations, and assimilation of these observations into hydrological and landslide/debris flow models with surface topographic information, prediction of useful probabilistic maps of landslide and floods for emergency management in a timely manner is possible. Early results shows that the potential exists for successful application of satellite precipitation data in improving/developing global monitoring systems for flood/landslide disaster preparedness and management. The scientific and technological prototype can be first applied in a representative test-bed and then the information deliverables for the region can be tailored to the societal and economic needs of the represented affected countries.

Hong, Yang↗

Evaluation of the Potential of NASA Multi-satellite Precipitation Analysis in Global Landslide Hazard Assessment

Landslides are one of the most widespread natural hazards on Earth, responsible for thousands of deaths and billions of dollars in property damage every year. In the U.S. alone landslides occur in every state, causing an estimated $2 billion in damage and 25- 50 deaths each year. Annual average loss of life from landslide hazards in Japan is 170. The situation is much worse in developing countries and remote mountainous regions due to lack of financial resources and inadequate disaster management ability. Recently, a landslide buried an entire village on the Philippines Island of Leyte on Feb 17,2006, with at least 1800 reported deaths and only 3 houses left standing of the original 300. Intense storms with high-intensity , long-duration rainfall have great potential to trigger rapidly moving landslides, resulting in casualties and property damage across the world. In recent years, through the availability of remotely sensed datasets, it has become possible to conduct global-scale landslide hazard assessment. This paper evaluates the potential of the real-time NASA TRMM-based Multi-satellite Precipitation Analysis (TMPA) system to advance our understanding of and predictive ability for rainfall-triggered landslides. Early results show that the landslide occurrences are closely associated with the spatial patterns and temporal distribution of rainfall characteristics. Particularly, the number of landslide occurrences and the relative importance of rainfall in triggering landslides rely on the influence of rainfall attributes [e.g. rainfall climatology, antecedent rainfall accumulation, and intensity-duration of rainstorms). TMPA precipitation data are available in both real-time and post-real-time versions, which are useful to assess the location and timing of rainfall-triggered landslide hazards by monitoring landslide-prone areas while receiving heavy rainfall. For the purpose of identifying rainfall-triggered landslides, an empirical global rainfall intensity-duration threshold is developed by examining a number of landslide occurrences and their corresponding TMPA precipitation characteristics across the world. These early results , in combination with TRMM real-time precipitation estimation system, may form a starting point for developing an operational early warning system for rainfall-triggered landslides around the globe.

Hong, Yang↗

Use of Satellite Remote Sensing Data in the Mapping of Global Landslide Susceptibility

Satellite remote sensing data has significant potential use in analysis of natural hazards such as landslides. Relying on the recent advances in satellite remote sensing and geographic information system (GIS) techniques, this paper aims to map landslide susceptibility over most of the globe using a GIs-based weighted linear combination method. First , six relevant landslide-controlling factors are derived from geospatial remote sensing data and coded into a GIS system. Next, continuous susceptibility values from low to high are assigned to each of the six factors. Second, a continuous scale of a global landslide susceptibility index is derived using GIS weighted linear combination based on each factor's relative significance to the process of landslide occurrence (e.g., slope is the most important factor, soil types and soil texture are also primary-level parameters, while elevation, land cover types, and drainage density are secondary in importance). Finally, the continuous index map is further classified into six susceptibility categories. Results show the hot spots of landslide-prone regions include the Pacific Rim, the Himalayas and South Asia, Rocky Mountains, Appalachian Mountains, Alps, and parts of the Middle East and Africa. India, China, Nepal, Japan, the USA, and Peru are shown to have landslide-prone areas. This first-cut global landslide susceptibility map forms a starting point to provide a global view of landslide risks and may be used in conjunction with satellite-based precipitation information to potentially detect areas with significant landslide potential due to heavy rainfall. 1

Hong, Yang↗

A First Approach to Global Runoff Simulation using Satellite Rainfall Estimation

Many hydrological models have been introduced in the hydrological literature to predict runoff but few of these have become common planning or decision-making tools, either because the data requirements are substantial or because the modeling processes are too complicated for operational application. On the other hand, progress in regional or global rainfall-runoff simulation has been constrained by the difficulty of measuring spatiotemporal variability of the primary causative factor, i.e. rainfall fluxes, continuously over space and time. Building on progress in remote sensing technology, researchers have improved the accuracy, coverage, and resolution of rainfall estimates by combining imagery from infrared, passive microwave, and space-borne radar sensors. Motivated by the recent increasing availability of global remote sensing data for estimating precipitation and describing land surface characteristics, this note reports a ballpark assessment of quasi-global runoff computed by incorporating satellite rainfall data and other remote sensing products in a relatively simple rainfall-runoff simulation approach: the Natural Resources Conservation Service (NRCS) runoff Curve Number (CN) method. Using an Antecedent Precipitation Index (API) as a proxy of antecedent moisture conditions, this note estimates time-varying NRCS-CN values determined by the 5-day normalized API. Driven by multi-year (1998-2006) Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis, quasi-global runoff was retrospectively simulated with the NRCS-CN method and compared to Global Runoff Data Centre data at global and catchment scales. Results demonstrated the potential for using this simple method when diagnosing runoff values from satellite rainfall for the globe and for medium to large river basins. This work was done with the simple NRCS-CN method as a first-cut approach to understanding the challenges that lie ahead in advancing the satellite-based inference of global runoff. We expect that the successes and limitations revealed in this study will lay the basis for applying more advanced methods to capture the dynamic variability of the global hydrologic process for global runoff monlto~ngin real time. The essential ingredient in this work is the use of global satellite-based rainfall estimation.

Hong, Yang↗

An Experimental Global Monitoring System for Rainfall-triggered Landslides using Satellite Remote Sensing Information

Landslides triggered by rainfall can possibly be foreseen in real time by jointly using rainfall intensity-duration thresholds and information related to land surface susceptibility. However, no system exists at either a national or a global scale to monitor or detect rainfall conditions that may trigger landslides due to the lack of extensive ground-based observing network in many parts of the world. Recent advances in satellite remote sensing technology and increasing availability of high-resolution geospatial products around the globe have provided an unprecedented opportunity for such a study. In this paper, a framework for developing an experimental real-time monitoring system to detect rainfall-triggered landslides is proposed by combining two necessary components: surface landslide susceptibility and a real-time space-based rainfall analysis system (http://trmm.gsfc.nasa.aov). First, a global landslide susceptibility map is derived from a combination of semi-static global surface characteristics (digital elevation topography, slope, soil types, soil texture, and land cover classification etc.) using a GIs weighted linear combination approach. Second, an adjusted empirical relationship between rainfall intensity-duration and landslide occurrence is used to assess landslide risks at areas with high susceptibility. A major outcome of this work is the availability of a first-time global assessment of landslide risk, which is only possible because of the utilization of global satellite remote sensing products. This experimental system can be updated continuously due to the availability of new satellite remote sensing products. This proposed system, if pursued through wide interdisciplinary efforts as recommended herein, bears the promise to grow many local landslide hazard analyses into a global decision-making support system for landslide disaster preparedness and risk mitigation activities across the world.

Hong, Yang↗

Flood and Landslide Applications of High Time Resolution Satellite Rain Products

Experimental, potentially real-time systems to detect floods and landslides related to heavy rain events are described. A key basis for these applications is high time resolution satellite rainfall analyses. Rainfall is the primary cause for devastating floods across the world. However, in many countries, satellite-based precipitation estimation may be the best source of rainfall data due to insufficient ground networks and absence of data sharing along many trans-boundary river basins. Remotely sensed precipitation from the NASA's TRMM Multi-satellite Precipitation Analysis (TMPA) operational system (near real-time precipitation at a spatial-temporal resolution of 3 hours and 0.25deg x 0.25deg) is used to monitor extreme precipitation events. Then these data are ingested into a macro-scale hydrological model which is parameterized using spatially distributed elevation, soil and land cover datasets available globally from satellite remote sensing. Preliminary flood results appear reasonable in terms of location and frequency of events, with implementation on a quasi-global basis underway. With the availability of satellite rainfall analyses at fine time resolution, it has also become possible to assess landslide risk on a near-global basis. Early results show that landslide occurrence is closely associated with the spatial patterns and temporal distribution of TRMM rainfall characteristics. Particularly, the number of landslides triggered by rainfall is related to rainfall climatology, antecedent rainfall accumulation, and intensity-duration of rainstorms. For the purpose of prediction, an empirical TMPA-based rainfall intensity-duration threshold is developed and shown to have skill in determining potential areas of landslides. These experimental findings, in combination with landslide surface susceptibility information based on satellite-based land surface information, form a starting point towards a potential operational landslide monitoring/warning system around the globe.

Adler, Robert F.↗