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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

Tropical Storm Patricia Forms Off Mexican Coast

Tropical Storm Patricia Forms Off Mexican Coast
Tropical Depression Twenty-E (TD20E) formed on October 20, 2015 off the Mexican coast southeast of Puerto Escondido, Mexico. Later that evening TD20E was upgraded to tropical storm Patricia. On October 21, 2015 at 1634 UTC (11:34 AM CDT) the GPM core observatory satellite flew over tropical storm Patricia. GPM's Microwave Imager (GMI) instrument had good coverage of the rainfall associated with the tropical storm. Rainfall data derived from GMI are shown overlaid on a GOES-EAST Visible/Infrared image captured at 1645 UTC (11:45 AM CDT). GPM's Dual-Frequency Precipitation Radar (DPR) instrument

GPM Checks on Cat 4 Hurricane Olaf

GPM Checks on Cat 4 Hurricane Olaf
On October 19, 2015 Hurricane Olaf became the eighth category four hurricane in the busy 2015 Eastern Pacific hurricane season. The GPM core observatory satellite had a good look at the distribution of rainfall around powerful hurricane Olaf on October 20, 2015 at 0716 UTC. At that time Olaf had sustained winds estimated at over 130 kts (150 mph). GPM's Microwave Imager (GMI) and Dual-Frequency Precipitation Radar (DPR) measured the intensity of rainfall within the hurricane. GPM's GMI found rain falling at a rate of over 71.5 mm (2.8 inches) per hour in Olaf's eye wall. The DPR instrument

Typhoon Koppu's Deadly Philippine Rainfall

Typhoon Koppu's Deadly Philippine Rainfall
Extremely heavy rainfall from super typhoon Koppu has caused deadly flooding and mudslides in the Philippines. Koppu hit the eastern coast of Luzon as a category four super typhoon with winds of 130kts (150 mph). Koppu weakened but was still battering the Philippines as a typhoon after reaching the Lingayen Gulf on Luzon's western coast. Typhoon Koppu then made a turn toward the north and continued to drench the northern Philippines as it followed Luzon's northwestern coast. Torrents of rain flowing from mountainous terrain magnified the effects of very high rainfall totals. A rainfall

GPM Measures Tropical Storm Champi's Heavy Rainfall

GPM Measures Tropical Storm Champi's Heavy Rainfall
On October 16, 2015 at 0216 UTC the GPM core observatory satellite passed above tropical storm Champi as it was drenching the northern Mariana Islands. GPM's Dual-Frequency Precipitation Radar (DPR) measured rain within Champi falling at a rate of over 114.8 mm (4.5 inches) per hour in a band of intense showers. GPM's radar sliced through Champi's western side and made 3-D measurements of storm top heights. Radar reflectivity values found to be greater than 15 dBZ are shown in a simulated perspective 3-D view. The lowest Heights are shaded blue and the tallest 3-D 15dBZ surfaces are colored

GPM Sees Tropical Storm Koppu Menacing The Philippines

GPM Sees Tropical Storm Koppu Menacing The Philippines
Tropical storm Koppu was approaching the Philippines with sustained winds estimated of 60 kts (69 mph) when the GPM core observatory satellite passed above on October 15, 2015 at 1436 UTC. A rainfall analysis using data collected by GPM's Microwave Imager (GMI) instrument revealed that an eye was very close to forming near Koppu's center of circulation. Rain was measured by GPM's GMI instrument dropping rain at a rate of over 133 mm (5.3 inches) per hour in intense convective thunderstorms just southwest of Koppu's center. Data from GPM's Dual-Frequency Precipitation Radar (DPR) instrument
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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