Image

Submerged Houston neighborhoods in the wake of Tropical Storm Harvey on August 29, 2017. Credit: Marcus Yam / Getty Images

To better understand and predict floods scientists have developed hydrological models based on how much rainfall occurs and where the water will likely go once it hits the ground. They use several satellite precipitation datasets within these models to provide near real-time estimates of when and where areas may flood on local, regional, and global scales. GPM provides frequent precipitation observations with near global coverage, of which 80% are less than 3 hours apart, exceeding the minimum deemed necessary for hydrometeorological applications. Therefore, rainfall data measured by the GPM Mission and its products, like the Integrated Multi-satellitE Retrievals for GPM (IMERG) data product, helps us better understand how changing precipitation patterns at multiple scales translates changes into hydrologic fluxes and states that can be used for flood detection and warning systems.

View floods articles

Image

Aerial view of landslide that buried Colonia las Colinas, El Salvador. Credit: USGS

Landslides are one of the most pervasive hazards in the world, resulting in more fatalities and economic damage than is generally recognized. Saturating the soil on vulnerable slopes, intense and prolonged rainfall is the most frequent landslide trigger, but seismicity, river undercutting, freeze-thaw processes, and human activity can also cause extensive and devastating landslides. Understanding where and when landslides have occurred in the past and where they may occur in the future is extremely challenging because of the lack of ground-based sensors at the landslide site to provide both triggering information (e.g. rainfall intensity and duration), and the timing and extent of the mass movement events. Precipitation measurements from remote sensing allows us to gain new insight to identify landslide activity, characterize the triggering patterns of these events spatially and temporally, assess the surface conditions for potential activity, and support the full cycle of disaster risk assessment. In particular, GPM’s more frequent and more detailed coverage of precipitation across the globe can help improve landslide model accuracy and expand potential landslide forecasting capabilities.

Learn more about GPM applications for landslides

Image

High severity fire in the western U.S. Credit: USDA Forest Service

Wildfires play an integral role in maintaining ecosystem biodiversity and structure.  Wildfires, which include any non-structure fire that occurs in vegetation or natural fuels, is an essential process that connects terrestrial systems to the atmosphere and climate.  However, the effects of fire can be disastrous, both immediately (e.g., poor air quality, loss of life and property) and through post-fire impacts (floods, debris flows/landslides, poor water quality). Wildfires can be triggered by several factors including lightning, high winds, drought, and people. 

There are several ongoing activities using remote sensing data to support pre-, active- and post-fire research, as well as the applicable use of these data and products in support of management decisions and strategies, policy planning and in setting rules and regulations. Frequent precipitation measurements from GPM along with temperature and land cover measurements from other satellites can provide key information to determine the overall dryness of an area and the potential start and spread of a vegetation fire. 

View wildfires articles
 

Image

GPM's GMI and DPR observe rainfall accumulation over the storm and 3-D vertical structure in a line of intense storms associated with the mesoscale convection system over northern New Mexico and Oklahoma on June 25, 2018. Credit: Hal Pierce (SSAI/NASA GSFC)

Many regions in the world experience severe weather such as thunderstorms, hail, tornadoes, and blizzards every year. Severe weather usually comes with heavy precipitation and causes unexpected hydrometeorological hazards, such as floods or landslides, which can affect thousands of people, posing a threat on life and property. Therefore, it is critically important to monitor severe weather and estimate heavy precipitation so that the occurrence and intensity of associated hydrometeorological hazards can be well identified, detected, and forecasted. Where ground-based instruments are sparse, remote sensing systems can be especially useful to observe and monitor these extreme events. Microwave sensors used by the GPM Mission allows scientists to map thunderstorm cores to gain insight into storm structures and mesoscale dynamics (e.g. thunderstorms to hurricanes) as well as detect light rain to moderate-to heavy rain and snowfall. Delivery of precipitation data from the GPM Mission is crucial for operational and research organizations to advance precipitation measurement science to improve weather forecasting that can subsequently benefit society for years to come. 

View severe weather articles

Image

Coast Guardsmen use a boat to assist residents during severe flooding around Baton Rouge, LA on August 14, 2016. Credit: Petty Officer 3rd Class Brandon Giles/Coast Guard

Every year, landslides wipe out roads or town, devastating floods put city blocks underwater, or a violent hurricane devastates the coastal communities. Natural hazards, like Hurricane Maria or flooding in Houston, have huge impacts on people around the world. Heavy rains and large storm systems are often significant factors that cause these disasters to have huge economic costs or even kill people. The best defense against natural hazards is accurate and early warning systems. Understanding the timing, location, and intensity of precipitation extremes using GPM data, organizations that handle disaster response and recovery can monitor, assess, and understand the damage or potential damage of a disaster. These efforts help to minimize the impact of a natural disaster as well as effectively coordinate with organizations and the public before, during, after so as many people are safe and needs are met. 

Image

A house on the Jersey Shore submerged in water in the aftermath of Hurricane Sandy.  Credit: Jim Greenhill via BU Today

The insurance and disaster management industries are closely related; dealing with the risk of natural disaster and managing the events following disasters. Reinsurance companies work to understand the need of its potential customers and the risks to which they may be exposed.  A companies’ success is generally tied to the ability to forecast the probability of natural hazards, including storms, floods, and landslides. Earth Science data and information derived from remote sensing instruments over the last decade have made it more feasible to develop climate records and understand region’s susceptibility to a natural disaster. Such data are then used to design payout triggers when natural hazards occurs. Policyholders are then compensated according to the strength of the measured event against those triggers. Specially, reinsurance companies across the world use rainfall data from GPM to develop rainfall thresholds to design insurance payouts when disasters strike.