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GPM Applications: Weather

Using GPM Data for Weather, Climate, and Land Surface Modeling

Friday, February 8, 2019

Using GPM Data for Weather, Climate, and Land Surface Modeling

Variations in rain, snow, and other forms of precipitation are an integral part in everyday weather and long term climate trends. Initialization of short-term weather and long-term climate models with accurate precipitation information enhances their prediction skills and extends their skillful lead times. To get the resolution and temporal coverage to measure precipitation across the globe, we often rely on satellite information. Satellite data can play a fundamental role in our ability to monitor and predict weather systems as well as to forecast future changes to our climate and land surface. Satellite data from GPM’s suite of precipitation products are integrated into numerical weather prediction models that are operated by operational partners to provide and improve the observations from which the forecasts are then generated. Similarly, climate and land surface models use satellite precipitation observations from GPM to describe the conditions that exist today in order to project how conditions may change in the future. The Weather, Climate, and Land Surface Modeling applications area promotes the use of GPM data to help monitor existing weather activity and model future behavior of precipitation patterns and climate.

Overview

Variations in rain, snow, and other forms of precipitation are an integral part in everyday weather and long term climate trends. Initialization of short-term weather and long-term climate models with accurate precipitation information enhances their prediction skills and extends their skillful lead times. To get the resolution and temporal coverage to measure precipitation across the globe, we often rely on satellite information. Satellite data can play a fundamental role in our ability to monitor and predict weather systems as well as to forecast future changes to our climate and land surface. Satellite data from GPM’s suite of precipitation products are integrated into numerical weather prediction models that are operated by operational partners to provide and improve the observations from which the forecasts are then generated. Similarly, climate and land surface models use satellite precipitation observations from GPM to describe the conditions that exist today in order to project how conditions may change in the future. The Weather, Climate, and Land Surface Modeling applications area promotes the use of GPM data to help monitor existing weather activity and model future behavior of precipitation patterns and climate.

Sections

GPM Data for Decision Making

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NOAA Precipitation Forecast Example

NOAA’s Climate Prediction Center (CPC) issues extended range outlook maps for 6-10 days in the future. The above figure shows a 6-10 forecast of precipitation probability for the first week of October 2018. This product complements short-range weather forecasts issued by other components of the National Weather Service. Credit: NOAA/NCEP/CPC
 

Numerical weather prediction (NWP) is the use of computer models to predict upcoming weather. Specifically, NWP centers rely on microwave-based satellite rainfall information, such as data retrieved from GPM’s GMI, to improve short- to long-term weather forecasts and correct track forecasts for tropical cyclones. In addition, NWP centers create precipitation products for “nowcasting” weather in the immediate 1-5 hours (e.g. using near-real-time rainfall data from GPM) to meet the needs of a wider user community, including weather forecasters, hydrologists, farmers, numerical modelers, the military and the climate community. Methods for integrating rainfall data are constantly evolving and advancing, and with GPM’s advanced instruments, scientists can influence and enhance their scientific research and benefit socioeconomic activities.

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ECMWF Seasonal Forecast of Precipitation Probability

European Centre for Medium-Range Weather Forecast (ECMWF) Seasonal Forecast of precipitation probability. Percent probability is determined by using the predictive anomaly relative to 24 years of observed precipitation from 1993-2016. Credit: European Centre for Medium-Range Weather Forecast
 

To understand the changing climate and make future climate predictions, scientists need to use sophisticated computer models to recreate Earth’s climate conditions. Understanding current rainfall and snowfall variability, among other climate factors on regional and global scales, helps scientists model future behavior of precipitation patterns and climate. But for a system as complicated as the Earth, the models are only as good as the data provided. Satellite precipitation measurements from GPM and its predecessor TRMM provide global scale observational data sets that are comprehensive and consistent over long time periods, two characteristics scientists need to understand the relationships between different parts of the climate system. Specifically, organizations use GPM and TRMM data as input to verify and validate their seasonal and climate model simulations. The ultimate goal is to be able to predict changes in climate on time scales as short as the next hurricane season and as far into the future as changes that may occur in the coming decades or centuries. 

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Snowpack in the Absaroka Range

Climate change may lead to an increase in temperatures and a decrease in snowpack within the Absaroka Range, found at the eastern edge of Yellowstone National Park. Credit: National Park Service/Neal Herbert
 

Precipitation is the fundamental driver of land surface hydrological processes and a key component of the terrestrial water cycle, which in turn affects the functioning of atmospheric and climate processes. High-resolution modeling of land surface hydrological processes requires detailed rainfall estimates as inputs to improve understanding of the state of the water cycle and impacts on land-surface processes during extreme events. Satellite precipitation data from GPM is integrated into land surface models to study surface features and how they change due to manmade and natural conditions such as urbanization and erosion. The use of GPM precipitation data together with other satellite data including soil moisture within land surface models will improve weather and hydrological prediction, which will help city planners and even decision makers save lives. 

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2002

GPM IMERG Measures Rainfall from Tropical Storm Cristobal

GPM IMERG Measures Rainfall from Tropical Storm Cristobal
This animation shows NASA IMERG rain rates (blue shading) and accumulations (green shading) alongside the NOAA low-pressure center track (red line) of Tropical Storm Amanda/Cristobal. The origin of this storm was in the eastern Pacific Ocean in late May 2020, where it was named Tropical Storm Amanda as it approached the southern Mexican and Central American coast. Amanda made landfall in Guatemala on May 31, where it began to deliver the first of a series of heavy rain pulses that led to flooding in the region. After temporarily stalling over land, the system reformed over the Bay of Campeche on June 1 as Tropical Storm Cristobal and made its second landfall on June 3 in Mexico. The storm continued to deliver several pulses of heavy rainfall to southern Mexico, Guatemala, and El Salvador. Some areas of the region accumulated over 60 cm (~2 feet) of rainfall throughout Cristobal's passage. The storm then crossed the Gulf of Mexico and made landfall in Louisiana on June 7 and progressed northward as a tropical depression before being classified as an extratropical low pressure system over Wisconsin on June 10. Large swaths of the U.S. Gulf Coast and Midwest as far north as Wisconsin saw accumulations in excess of 10 cm (~4 inches), and some areas along the coasts of Florida, Alabama, and Mississippi received over 20 cm (~8 inches), during Cristobal’s passage.

Cristobal Drenches Central America

Cristobal Drenches Central America
The 2020 Atlantic hurricane season is off to a busy start. By the first week of June, Tropical Storm Arthur had already brushed North Carolina , Tropical Storm Bertha had drenched South Carolina , and the third named storm of the year— Cristobal—was dropping torrential rain on the Yucatán Peninsula. The storm first developed in the Pacific in late May as Tropical Storm Amanda, spinning off the southern end of a seasonal low-pressure pattern called the Central American Gyre . After making landfall in Guatemala and causing deadly floods in El Salvador , Amanda weakened and became less organized

Short-lived Bertha Brings Heavy Rains to Parts of Florida

Short-lived Bertha Brings Heavy Rains to Parts of Florida
Bertha was a named storm for just the briefest of periods, becoming a tropical storm on the morning of Wednesday May 27th at 8:30 am EDT just one hour before it made landfall along the South Carolina coast near Charleston. After making landfall, Bertha quickly weakened into a tropical depression and was then accelerated northward by the southerly flow between a deep trough of low pressure over the Mississippi Valley to the west and a ridge of high pressure located just off the US East Coast. Because of this, rainfall totals over the Carolina’s were not very heavy. Bertha’s biggest impact actually occurred when it was still in the formation process, before it became organized enough to be named. On Monday May 25th, a trough of low pressure became established over the Florida Straits, initiating shower and thunderstorm activity in the region. Over the next day, as this trough, which extended eastward over the warm waters of the Gulf Stream and eventually led to Bertha, slowly moved northward up the Florida peninsula, it provided a focus for showers and thunderstorms, which brought heavy rains to southeast Florida.

GPM Sees Cyclone Amphan Approach India

Cyclone Amphan IMERG Rainfall Totals
Wednesday, May 20, 2020
On May 16, 2020, NASA / JAXA's GPM Core Observatory satellite observed the early stages of Tropical Cyclone Amphan as it tracked north over the Bay of Bengal. The below GPM overpass shows precipitation within Cyclone Amphan a day before it explosively intensified into a category 4-equivalent cyclone. Even at this early stage, Amphan produced heavy rain rates near its center and to its west and southwest. View fullscreen in STORM Event Viewer NASA monitored the heavy rain associated with Tropical Cyclone Amphan as it made landfall at 0900 UTC (2:30 PM local time) on May 20, 2020. Landfall...
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Typhoon Vongfong Hits the Philippines

Typhoon Vongfong IMERG Rainfall Totals
Monday, May 18, 2020
The first typhoon of the season, Vongfong, struck the central Philippines this past week (where it is known as Ambo) as a strong category 2 storm, bringing strong winds and locally heavy rainfall. Vongfong formed into a tropical depression in the southern Philippine Sea west of Palau on Sunday May 10th from a disturbance that had been slowly making its way westward over the past several days. After becoming a depression, the system moved northward toward the central Philippine Sea and slowly began to intensify. Then, on the 12th when it reached tropical storm intensity, Vongfong’s northward...
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NASA’s New View of the Daily Cycle of Rain

The most detailed view of our daily weather has been created using NASA's newest extended precipitation record known as the Integrated Multi-satellitE Retrievals for GPM, or IMERG analysis. The IMERG analysis combines almost 20 years of rain and snow data from the Tropical Rainfall Measuring Mission (TRMM) and the joint NASA-JAXA Global Precipitation Measurement mission (GPM). The daily cycle of weather, also known as the diurnal cycle, shapes how and when our weather develops and is fundamental to regulating our climate.

Music Credits: "Battle For Our Future" and "Wonderful Orbit" by Tom...

NASA Studies Snow At The Winter Olympics

NASA engineer Manuel Vega can see one of the Olympic ski jump towers from the rooftop of the South Korean weather office where he is stationed. Vega is not watching skiers take flight, preparing for the 2018 PyeongChang Winter Olympics and Paralympic games. Instead, he’s inspecting the SUV-sized radar beside him. The instrument is one 11 NASA instruments specially transported to the Olympics to measure the quantity and type of snow falling on the slopes, tracks and halfpipes. NASA will make these observations as one of 20 agencies from eleven countries in the Republic of Korea as participants...

A New Multi-dimensional View of a Hurricane

NASA researchers now can use a combination of satellite observations to re-create multi-dimensional pictures of hurricanes and other major storms in order to study complex atmospheric interactions. In this video, they applied those techniques to Hurricane Matthew. When it occurred in the fall of 2016, Matthew was the first Category 5 Atlantic hurricane in almost ten years. Its torrential rains and winds caused significant damage and loss of life as it coursed through the Caribbean and up along the southern U.S. coast. 

Music: "Buoys," Donn Wilkerson, Killer Tracks; "Late Night Drive," Donn...

Getting Flake-y: Why All Snowflakes Have Six Sides
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NASA scientists can measure the size and shape distribution of snow particles, layer by layer, in a storm. The Global Precipitation Measurement mission is an international satellite project that provides next-generation observations of rain and snow worldwide every three hours.

GPM Observes Snow Storm over Kentucky, West Virginia, and North Carolina (February 17, 2015)

The Global Precipitation Measurement (GPM) Core Satellite captured a 3-D image of a winter storm on February 17, 2015, that left six to 12 inches of snow over much of Kentucky, southwestern West Virginia, and northwestern North Carolina. The shades of blue in the 3-D image indicate rates of snowfall with more intense snowfall shown in darker blue. Underneath where it melts into rain, the most intense rainfall is shown in red. You can see a lot of variation in precipitation types over the southeastern portion of the United States.

The GPM Core Observatory carries two instruments that show the...

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