Frequently Asked Questions
- What causes precipitation to fall?
- Is there a specific time of day that a thunderstorm is most likely to occur?
- What causes thunderstorms?
- What is the difference between a tropical storm and a tropical depression?
- How does a hurricane form?
- What is the difference between a typhoon, cyclone, and hurricane?
- What determines the rate and size of falling rain?
- Where do most hurricanes originate?
- How does hail form?
- Why do the tropics have more precipitation than other locations?
- How is a hurricane’s path predicted?
- Is rain formed by the condensation of water vapor or by the melting of ice?
- What are clouds made of? Are they more likely to form in polluted air or in pristine air?
Measurement From Space
Climate, hazards, and the water cycle
- How does climate change affect precipitation?
- What is a flash flood?
- What areas are at risk from flash floods?
- What is a landslide?
- What is a drought?
- What causes drought?
- What is the water cycle?
- How does the water cycle work?
- Why are water cycle processes important?
- What is the difference between a tornado and a hurricane?
- When will GPM data be made available?
- What is the spatial and temporal resolution of GPM data?
- How do I get precipitation data for my specific location?
- Where can I find detailed documentation on the precipitation algorithms?
- What will happen to the TRMM Multi-satellite Precipitation Analysis (TMPA / 3B42x) data products?
- What is the difference between "Realtime" (RT) and "Production" (Prod) data?
- Where can I find climatology data?
- Am I allowed to use GPM data for my research?
- How do I give credit for using GPM data?
- How is the intensity of precipitation distributed within a given data value in TMPA and IMERG?
- Can I do parameter subsetting or spatial subsetting?
- Does GPM Predict the Weather?
- With the TRMM mission over, what will happen to the TRMM 3B42 (TMPA) series of data products?
- How Do I Download Precipitation Data in [X] Format?
- What is the file naming convention for GPM data products?
- Why do some 5-minute granules of real-time GPROF-GMI retrievals have "missing" for the last several scan lines?
- I am unable to log in or access files on the PPS FTP (arthurhou, jsimpson)
- How do I determine when GPM and other satellites will pass over my location?
- Are there any guarantees about the availability of GPM data?
- Am I free to use GPM data and images?
- How do the various forms of precipitation map into the IMERG "probabilityLiquidPrecipitation" data field?
- What are the IMERG variables in Giovanni?
- What are the differences between IMERG Early, Late, and Final Runs, and which should be used for research?
- What is the difference between the global (90°N-S) and full (60°N-S) coverage for IMERG?
- How important are surface precipitation gauges in combined satellite-gauge data sets?
Data from the Precipitation Measurement Missions are made freely available to the public. There are several sources where the TRMM and GPM precipitation data can be downloaded: Click here to visit the Data Access page.
Global Precipitation Measurement (GPM) is an international satellite mission to unify and advance precipitation measurements from space for scientific discovery and societal applications.
GPM measures precipitation globally; over the land and ocean, in both the tropics, mid-latitudes, and cold locations near the poles. GPM measures both light, heavy, and frozen precipitation including the microphysical properties of precipitation particles. This wide range of locations and precipitation types presents a host of challenges not encountered by TRMM, which only measures moderate to heavy rainfall in the tropics.
GPM's predecessor the Tropical Rainfall Measuring Mission (TRMM) measures heavy to moderate rain over tropical and subtropical oceans. GPM provides advanced measurements, including coverage over medium to high latitudes, improved estimates of light rain and snowfall, advanced estimates over land and ocean, and coordination of radar and microwave retrievals to unify and refine precipitation estimates from a constellation of research and operational satellites. GPM also provides more frequent observations, every 2 to 4 hours.
GPM data is primarily used by operational forecasters, but the information also benefits numerical weather prediction models, climate prediction patterns, crop monitoring, and other research applications. In addition, scientists use GPM data to advance our understanding of precipitation and its role in the Earth's environment.
The increased sensitivity of the Dual-frequency Precipitation Radar (DPR) and the high-frequency channels on the GPM Microwave Imager (GMI) enables GPM to improve forecasting by estimating light rain and falling snow outside the tropics, even in the winter seasons, over areas which other satellites and ground sensors are unable to measure. These advanced measurements extend current capabilities in monitoring and predicting hurricanes and other extreme weather events, as well as contributing to improved forecasting for floods, landslides, and droughts.
GPM advances precipitation measurement capability from space using a combination of active and passive remote-sensing techniques. These measurements are used to calibrate, unify, and improve precipitation measurements from a constellation of research and operational satellites with microwave sensors in order to create a global dataset of precipitation measurements.
The primary GPM instruments are the Dual-frequency Precipitation Radar (DPR) and GPM Microwave Imager (GMI). The DPR makes detailed 3D measurements of rainfall, while the GMI uses a set of 13 optimized frequencies to retrieve heavy, moderate, and light precipitation measurements.
GV activity is designed to support pre-launch algorithm development and post-launch product evaluation. There are be a series of pre and post-launch field campaigns to carry out validation activities. Learn more about GPM Ground Validation.
At the start of the modern passive microwave age, the SSMI was designed to give an Earth Incidence angle around 53°. The design parameters for SSMI were generated by Jim Hollinger, at NRL at the time, and meant to address several Earth science variables, not just precipitation. For EIAs around 53°, the roughness component of the wind speed signal is nearly zero in vertical polarization. Starting around wind speeds of 5 m/s wave action starts to generate foam, which depolarizes the signal, so this consideration is less important for higher speeds. Note that 53° is also in the range of giving good separation between the vertical and horizontal polarization channels (see this image). One not-relevant factor is the Brewster angle for the air/water interface, at which reflected radiant energy is purely horizontally polarized. For visible wavelengths this is close to 53°, but for microwave frequencies it's up around 80°, and so not relevant to SSMI design. Given the ~800 km altitude of the DMSP, the look angle for the SSMI has to be 45° for an EIA of 53° (due to the curvature of the Earth). Then the TMI was designed and given the same EIA, which simplifies instrument intercomparison and has the same wind speed characteristics. At TRMM's lower altitude, originally 350 km, that required a look angle of 48.5°. GPM is in a similar orbit, so its viewing geometry is the same. The gotcha on GMI, which has multiple feed horns (see this image) is that the feed horns are clustered, and therefore stare into the reflector at slightly different angles, yielding slightly different look angles and EIAs.
But, as we've gotten more sophisticated about this stuff, it turns out that having similar EIAs is nice, but in fact we now consider the EIA variations, both between sensors, and for the same sensor due to altitude changes (particularly the TRMM altitude boost to 402.5 km nominal in 2001) and the variations in Earth shape seen by the sensors in ordinary operation.
GPM can help numerical models predict some aspects of chemical / biological / nuclear (C/B/N) agent dispersal and assess removal of these agents from the air by rainfall.
Accurate estimates of precipitation amount are important for crop growth assessments and timing of precipitation is critical for assessing crop productivity.
GPM provides precipitation information that the health community can use to identify weather and climate patterns associated with disease outbreaks and provide advanced warning of outbreaks.
GPM will improve climate prediction through better understanding of surface water fluxes, soil moisture storage, cloud/precipitation microphysics, and latent heat release in the Earth’s atmosphere.
By providing four-dimensional measurements of space-time variability of global precipitation, GPM allows for a better understanding of precipitation systems, water cycle variability, and freshwater availability.
By providing more accurate and timely precipitation observations, GPM will lead to improved weather forecasting, allowing for more accurate monitoring of transportation hazards and timelier travel advisories.
Weather satellites have been used by the National Weather Service since the sixties to map clouds, sea surface temperatures, and vertical temperature and moisture distributions. These data are supplemented by crucial ground observations, and are subsequently input to numerical models which try to predict the short-term evolution of the weather. The results are then made available to the public, most directly in the form of composite weather maps. The rain shown on these maps comes mostly from ground weather radars.
Unfortunately, the crucial ground observations are often simply not available over the tropics because these regions are typically inaccessible. In addition, the large-scale numerical models are still woefully inaccurate over the tropics, mainly because we do not yet have a solid understanding of the often severe dynamics which govern the circulation in the warm moist tropical atmosphere, and which have a determining effect on the weather far away from the tropics. The unprecedented precision of the TRMM measurements is replacing the missing ground measurements over the tropics, and it is helping fill the gap in our understanding of the processes which start around the equator but affect the weather all over the globe.
Usually up to the "freezing level", where the temperature has decreased to below 0° C (over the tropics, that occurs at about 5000 meters; over Los Angeles, it fluctuates between 2000 and 4000 meters in winter, and between 4000 and 5000 meters in summer). In different types of rain, there can be frozen water such as hail mixed with the rain below the freezing level, and/or "super-cooled" liquid drops above it.
In most storms, the TRMM radar is very good at detecting the freezing level. Unfortunately, it is not very good at discriminating between ice and liquid drops. The follow-on instrument will have separate channels to identify frozen, melting, and liquid particles, thereby providing very detailed information about the evaporative cooling and/or condensational heating released into the atmosphere by rainstorms.
Not really. Raindrops start out as round cloud droplets. As they grow and start falling, they begin to experience the resistance of the air, which causes them to flatten and resemble tiny M&M candy. Further growth leads to thinning in the center of the M&M, until the eventual breakup of the drop.
The flattening of raindrops alters the echo they produce when "illuminated" from the side. But for a space-borne radar such as the one on TRMM, the effect is minimal.
Drops vary in size from the tiny cloud droplets (measuring less than 0.1 mm in diameter) to the large drops associated with heavy rainfall, and reaching up to 6 mm in diameter. Collision among drops and surface instabilities are generally thought to impose this 6-mm size limit, although drops as large as 8 mm in diameter have been reported in shallow warm showers in Hawaii.
The reflectivity of a drop when illuminated by radar is roughly proportional to the square of its volume. It is this property which radar meteorologists exploit to estimate the total volume of rain from the reflectivity observed. This estimation process is rather difficult because the radar-rain relation is not linear, and the range of drop sizes within a single storm can vary greatly.
This question is tricky because some precipitating raindrops may not fall at all, if the surrounding wind has a sufficiently strong upward component. In still air, the terminal speed of a raindrop is an increasing function of the size of the drop, reaching a maximum of about 10 meters per second (20 knots) for the largest drops. To reach the ground from, say, 4000 meters up, such a raindrop will take at least 400 seconds, or about seven minutes.
The TRMM radar does not have the capability to measure the fall speed of precipitating particles. Its follow-on, however, will. This capability is important because it helps characterize the type of rain being measured.
Hail stones vary in size. Most commonly they are 1 cm in diameter but have been observed to be as large as 10 to 15 cm. Hailstones are formed when either aggregated ice ("graupel") or large frozen raindrops grow by collecting cloud droplets with below-freezing temperatures. An important aspect of hail growth is the latent heat of fusion which is released when the collected cloud water freezes. So much liquid water is collected in the process of hail growth that the latent heat released can significantly affect the temperature of the hailstone and make it several degrees warmer than the cloud environment. As long as the temperature of the hailstone remains below 0° C, its surface remains dry and its development is called "dry growth". The heat transfer from the hailstone to the surrounding air, however, is generally too slow to keep up with the release of heat associated with the freezing of the collected cloud drops. Therefore, if a hailstone remains in a supercooled cloud long enough, its temperature can rise to 0° C. At this temperature the collected supercooled droplets no longer freeze immediately upon contact with the hailstone. Although some of the collected water may be lost to the warm hailstone by shedding, a considerable portion can remain to be incorporated into the stone forming a water-ice mesh that is called "spongy hail". This process is called "wet growth". During its lifetime, a hailstone may grow alternately by the dry and wet processes as it passes through air of varying temperature. When hailstones are sliced open, they often exhibit a layered structure, which is evidence of these alternating growth modes. Hailstones need time to grow before they become too heavy and fall to the ground. An empirical relation between the fall velocity of a hailstone and its diameter is given by
V = 9 exp(0.8ln(D)) m/s,
where D is the diameter in cm. Hence a hailstone with a diameter on the order of 15 cm will fall at 75-80 m/s (170-180 miles/hour)!! This implies that updrafts of a comparable magnitude must exist in the cloud to support the hailstones long enough for them to grow. Because of this, hail is found only in very intense thunderstorms. Therefore, hail detection in storms is a clear indicator of their severity.
The TRMM radar can precisely determine the altitude where hail may be present, but the radar cannot say for sure if the signal is coming from hail, lots of graupel, or some other [no-glossary]hydrometeor[/no-glossary]. The radar on the follow-on mission GPM will have specific channels which will detect frozen particles in general and hail in particular. This will provide crucial information about storm severity.
Precipitation forms when cloud droplets or ice particles in clouds grow and combine to become so large that the updrafts in the clouds can no longer support them, and they fall to the ground.
A thunderstorm is formed when a combination of moisture and warm air rise in the atmosphere and condense. While over land, thunderstorms are most likely to occur at the warmest, most humid part of the day, which is usually the afternoon or evening. Over the ocean they are most likely to occur in the early hours of the morning before dawn.
Thunderstorms form when an air mass becomes unstable (when air in the lowest layers is very warm and humid, or air in the upper layers is unusually cold, or if both occur). Rising near-surface air in an unstable air mass expands and cools, making it warmer than its environment, which causes it to rise even farther. If enough water vapor is present, some of this vapor condenses into a cloud, releasing heat, which makes the air parcel even warmer, forcing it to rise yet again. Water vapor fuels the storm.
A tropical depression forms when a low pressure area is accompanied by thunderstorms that produce a circular wind flow with maximum sustained winds below 39 mph. An upgrade to a tropical storm occurs when cyclonic circulation becomes more organized and maximum sustained winds gust between 39 mph and 73 mph.
As rising water vapor condenses and latent heat is released, surrounding air is warmed and made less dense, causing the air to rise. The thunderstorms that make up the hurricane’s core are strengthened by this process. As air rises within the storms, pressure at the surface decreases and moister, tropical air is drawn to the center of the circulation, providing even more water vapor to fuel the hurricane. A hurricane has sustained wind gusts of at least 74 mph.
They are different names for the same type of storm, collectively known as tropical cyclones.
What they’re called is determined by where they form. In the Atlantic Basin and east of the International Date Line in the Pacific Ocean, they’re called hurricanes. Typhoons form in the North Pacific Ocean, west of the date line. The storms are called cyclones in the Indian Ocean and in the Coral Sea off northeastern Australia.
Availability of water vapor and intensity of updrafts within a cloud determine the size of a raindrop. Larger drops tend to result from the vigorous updrafts within a thunderstorm and fall faster than smaller drops. Mist or drizzle produce smaller drops that fall at lower speeds.
Most hurricanes begin in the Atlantic as a result of tropical waves that move westward off the African coast.
Hail forms when thunderstorm updrafts are strong enough to carry water droplets well above the freezing level. This freezing process forms a hailstone, which can grow as additional water freezes onto it. Eventually, the hailstone becomes too heavy for the updrafts to support it and it falls to the ground.
The tropics receive a great amount of direct solar energy, which produces more evaporation than higher latitudes. The warm, moist air rises, condenses into clouds and thunderstorms, and falls back to earth as precipitation. More evaporation results in more precipitation.
The forecast of a hurricane's path is dependent upon the accuracy of the predicted winds from computer forecast models. The speed and direction of steering winds generally vary with altitude. Weak tropical cyclones tend to be steered more by lower-level winds, while upper-level winds usually influence the paths of stronger hurricanes.
This important question is still under investigation. Much of the rain is produced by clouds whose tops do not extend to temperatures colder than 0° C. The mechanism responsible for rain formation in these "warm" clouds is merging or "coalescence" among cloud droplets, which are first formed by vapor condensation. Coalescence is probably the dominant rain-forming mechanism in the tropics. It is also effective in some mid-latitude clouds whose tops may extend to subfreezing temperatures. However, once a cloud extends to altitudes where the temperature is colder than 0° C, ice crystals can form and "ice-phase" processes become important. In favorable conditions, ice-involving processes can initiate precipitation in half the amount of time water-only processes would need. Hence, at mid-latitudes, cumulus cloud rain is probably initiated by ice-processes and melting of ice. Observations have shown, however, that precipitation can first appear at levels warmer that 0° C, where vapor condensation and coalescence are the main rain producers. Thus, precipitation may be initiated by either process.
Depending on their type, clouds can consist of dry air mixed with liquid water drops, ice particles, or both. Low, shallow clouds are mostly made of water droplets of various sizes. Thin, upper level clouds (cirrus) are made of tiny ice particles. Deep thunderstorm clouds which can reach up to 20 km in height contain both liquid and ice in the form of cloud and raindrops, cloud ice, snow, graupel and hail.
It is important to understand that even a cloud that looks impenetrably dark is almost entirely made of dry air. Water vapor and precipitation each make up a maximum of just a couple of percent of the mass of a cloud, except in a few very intense storms.
How do these precipitation particles form? First, tiny cloud droplets are born when the water vapor in the air is cooled and starts to condense around tiny "condensation nuclei" (particles so small they are invisible to the naked eye). The presence of these aerosols is crucial: without them, in absolutely clean air, condensation would not start until the relative humidity has reached several hundred percent (this suggests that the "saturation" level of 100% humidity is poorly defined; in fact, the atmosphere always contains more than enough nuclei of all sorts for condensation to start as soon as the dew point temperature is reached). The more particles there are in the atmosphere, the easier cloud droplets will be formed and the smaller they will be (since more particles will be competing for the same amount of water, so each one of them will attract less). This is why clouds over land have more droplets of smaller sizes than clouds over oceans where the air is generally much cleaner.
The process of ice formation similarly requires the presence of nuclei. However, there are much fewer particles which make suitable ice nuclei. This is why freezing often does not start until the temperature of the air reaches -15° C (if there are no ice nuclei at all, freezing will not occur before the temperature drops to -40° C). Hence, clouds with temperatures below 0° C can still consist of water droplets called "supercooled" water. These drops freeze immediately upon contact with any surface. When they fall to the ground as freezing rain, they can form a thin layer of sleet on roadways, an almost invisible and very dangerous hazard for drivers.
Precipitation is measured from space using a combination of active and passive remote-sensing techniques, improving the spatial and temporal coverage of global precipitation observations.
Reliable ground-based precipitation measurements are difficult to obtain because most of the world is covered by water and many countries don’t have precise rain measuring equipment (i.e., rain gauges and radar). Precipitation is also difficult to measure because precipitation systems can be somewhat random and evolve very rapidly. During a storm, precipitation amounts can vary greatly over a very small area and over a short time span.
Visible and infrared space-borne sensors can provide precipitation information inferred from cloud-top radiation, and microwave sensors provide direct precipitation measurement based on radiative signatures of precipitating particles. This type of information is not available through ground-based measuring systems. GPM will advance space-based measurement even further by combining active and passive sensing capabilities.
The Tropical Rainfall Measuring Mission (TRMM) is a joint mission between NASA and the National Space Development Agency of Japan. TRMM primarily measures tropical and subtropical rainfall and is the only current satellite that carries weather radar.
Rising temperatures will intensify the Earth’s water cycle, increasing evaporation. Increased evaporation will result in more storms, but also contribute to drying over some land areas. As a result, storm-affected areas are likely to experience increases in precipitation and increased risk of flooding, while areas located far away from storm tracks are likely to experience less precipitation and increased risk of drought.
A flash flood is a rapid rise of water along a stream or low-lying urban area. Flash flooding occurs within six hours of a significant rain event and is usually caused by intense storms that produce heavy rainfall in a short amount of time.
Densely populated areas are at a high risk for flash floods. Buildings, highways, driveways, and parking lots increase runoff by reducing the amount of rain absorbed by the ground. This runoff increases potential for a flash flood.
A landslide is the movement of rock, debris, or earth down a slope.
A drought is a period of unusually persistent dry weather that continues long enough to cause serious problems such as crop damage and/or water supply shortages.
The water cycle (or hydrologic cycle) is the path that water follows as it evaporates into the air, condenses into clouds, and returns to Earth as rain, snow, sleet, or hail.
Water molecules are heated by the sun and turn into water vapor that rises into the air through a process called evaporation. Next, the water vapor cools and forms clouds, through condensation. Over time, the clouds become heavy because those cooled water particles have turned into water droplets. When the clouds become extremely heavy with water droplets, the water falls back to earth through precipitation (rain, snow, sleet, hail, etc). The process continues in a cyclical manner.
The water cycle is extremely important process because it ensures the availability of water for all living organisms and regulates weather patterns on our planet. If water didn’t naturally recycle itself, we would run out of clean water, which is essential to life.
Tornadoes and hurricanes appear to be similar in their general structure. Both are characterized by extremely strong horizontal winds swirling around the center, strong upward motion dominating the circulation with some downward motion in the center. The tangential winds far exceed the radial inflow or the vertical motion, and can cause much damage. Hurricanes always rotate counterclockwise in the northern hemisphere (clockwise in the southern), the direction of their rotation being determined by the Earth's rotation. This is almost always true of tornadoes too, although on rare occasions "anticyclonic" tornadoes spinning in the opposite direction do occur (tornadic circulation is determined by the local winds). This is where the similarities end.
The most obvious difference between tornadoes and hurricanes is that they have drastically different scales. They form under different circumstances and have different impacts on the environment. Tornadoes are "small-scale circulations", the largest observed horizontal dimensions in the most severe cases being on the order of 1 to 1.5 miles. They most often form in association with severe thunderstorms which develop in the high wind-shear environment of the Central Plains during spring and early summer, when the large-scale wind flow provides favorable conditions for the sometimes violent clash between the moist warm air from the Gulf of Mexico with the cold dry continental air coming from the northwest. However, tornadoes can form in many different circumstances and places around the globe. Hurricane landfalls are often accompanied by multiple tornadoes. While tornadoes can cause much havoc on the ground (tornadic wind speeds have been estimated at 100 to more than 300 mph), they have very short lifetimes (on the order of minutes), and travel short distances. They have very little impact on the evolution of the surrounding storm, and basically do not affect the large-scale environment at all. Hurricanes, on the other hand, are large-scale circulations with horizontal dimensions from 60 to well over 1000 miles in diameter. They form at low latitudes, generally between 5 and 20 degrees, but never right at the equator. They always form over the warm waters of the tropical oceans (sea-surface temperatures must be above 26.5° C, or about 76° F) where they draw their energy. They travel thousands of miles, persist over several days, and, during their lifetime, transport significant amounts of heat from the surface to the high altitudes of the tropical atmosphere. While their sporadic occurrence prevents them from drastically impacting the large-scale circulation, they still affect it in ways which must be accounted for and need to be better understood.
GPM project data sets, including the Core Observatory and constellation partner sensor data sets, national data sets, including multi-satellite data sets, have been released to the public and are available for download now (click here to see a table of GPM data products). These initial releases are being computed for the GPM era (February 2014 to present) using pre-launch calibrations.
Subsequently, a general reprocessing will upgrade the algorithms to fully GPM-based calibrations. This is scheduled to occur in September 2015 for Core Observatory and partner data sets, and in January 2016 for the U.S. multi-satellite algorithm (Integrated Multi-Satellite Retrievals for GPM; IMERG). After about a year of additional development work, the data sets will be retrospectively processed back to the start of TRMM (January 1998).
The resolution of Level 0, 1, and 2 data is determined by the footprint size and observation interval of the sensors involved. Level 3 products are given a grid spacing that is driven by the typical footprint size of the input data sets. See the table of GPM & TRMM Data Downloads for details on the resolution of each specific product.
There are several sources for downloading and viewing data which allow you to subset the data by longitude and latitude. These include the Simple Subset Wizard, Giovanni and STORM . In the new Giovanni 4 you can also now obtain data for a specific country, U.S. state, or watershed by using the "Show Shapes" option in the "Select Region" pane
The transition from the Tropical Rainfall Measuring Mission (TRMM) data products to the Global Precipitation Measurement (GPM) mission products has begun. The TMPA products will be replaced by the Integrated Multi-satellitE Retrievals for GPM (IMERG) products. It is tentatively planned to continue computing the TMPA products throughout the transition, into Spring 2017. Click here for more details on this transition. Click here for more details on this transition.
GPM data products can be divided into two groups (real-time and production) depending on how soon they are created after the satellite collects the observations. For applications such as weather, flood, and crop forecasting that need precipitation estimates as soon as possible, real-time data products are most appropriate. GPM real-time products are generally available within a few hours of observation. For all other applications, production data products are generally the best data sets to use because additional or improved inputs are used to increase accuracy. These other inputs are only made available several days, or in some cases, several months, after the satellite observations are taken, and the production data sets are computed after all data have arrived, making possible a more careful analysis.
The TRMM FTP has a Climatology directory which contains files in the TRMM Composite Climatology developed by Wang, Adler, Huffman, and Bolvin. A journal article on this topic is available here:http://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-13-00331.1 . Pre-generated world maps of TRMM climatology data are also available here.
For any given data TMPA data set, each data value provides a precipitation rate based on one (or perhaps two) satellite snapshots during the TMPA’s 3-hour analysis period. IMERG values are based on a single snapshot during its half-hour analysis period, or a morphed interpolation if no microwave values are available. The values are expressed in the intensive units mm/hr; it is usually best to assume that this rate applies for the entire 3- or half-hour period. If you wish to regrid to a finer time and/or space grid, note that many interpolation schemes have the property of suppressing maxima in precipitation and expanding rain events into neighboring zero-amount periods.
The GPM satellite constellation observes precipitation as it is falling, and maintains a database of precipitation records dating back to 1998. GPM is primarily focused on obtaining the highest quality precipitation measurements and studying fundamental atmospheric processes, and thus we do not focus on forecasting or predicting the weather. However, the near-real-time data collected by GPM is ingested into computer models by operational agencies such as the NWS and the ECMWF, who use it to improve their weather forecasts. Please visit the NWS and ECMWF websites for further information:
Although the TRMM satellite is no longer in service, the 3B42 series of algorithms will continue to be run using other satellites in the constellation to produce data products that are consistent with the long-term records. The current plan is to continue production into mid-2018 to give users time to transition to the newer IMERG multi-satellite data products. For more details about the status of 3B42 (and 3B42RT) and the transition to IMERG, please refer to this document: https://pmm.nasa.gov/sites/default/files/document_files/TMPA-to-IMERG_transition_170810.pdf
Please refer to our tables TRMM and GPM data downloads:
First locate the data product that meets your needs, then look to the “Format” column to find the appropriate link to download data in your desired format.
In general, GPM data products are named using the following format:
[algorithm level].[satellite].[instrument].[algorithm name].[year / month / date].[data start time hr/min/sec UTC].[data end time UTC].[sequence indicator showing orbit # (L2) or day/month (L3)].[algorithm version].[data format]
This is a Level 2A product, using the GPM satellite's GMI sensor, using the "GPROF 2008" algorithm, showing data from Novemeber 1st 2013 starting at 23:51:52 UTC and ending at 01:24:00 UTC, orbit number 352, using version 03C of the algorithm, in HDF5 format.
For a more detailed explanation of GPM file naming conventions, please refer to the following document: File Naming Convention for Precipitation Products For the Global Precipitation Measurement (GPM) Mission
The GPROF retrieval uses all the GMI channels, but these channels are recorded by multiple feed horns on the instrument, which produce data with slightly different geolocations that are systematically offset from each other. Thus, only the region with overlapping data can support GPROF retrievals. The Core Observatory data are downlinked via the NASA Tracking and Data Relay Satellite System (TDRSS) communications satellite system to the NASA White Sands Test Facility in New Mexico, and networked to PPS at NASA/Goddard as 5-minute packets. So, for GPROF to create retrievals across the entire granule, the previous and following granules are required to give all the channels over a packet's entire area of coverage. And, to satisfy the need for "real time" production, the retrieval is run no more than 11 minutes after the last observation time. (The maximum delay was recently adjusted to accommodate changes in GPROF run times.)
Episodically, the Core Observatory is out of sight of the TDRSS satellites. Depending on orbital details, the gap can be 20 minutes, and as long as an hour. When this happens, the last packet before a gap will lack timely access to the following packet and the last several scans will not have all the necessary channel data. The result is several scans of "missing" retrievals at the end of the granule. There are, of course, other ways in which scans of "missing" retrievals can occur, but the issue described is the most common.
First, ensure you have registered your email address with the PPS using this webpage: http://registration.pps.eosdis.nasa.gov/registration/
Once registered, your email address will serve as both your username AND password for logging into the FTP site. Email addresses are converted to lower case when registering, so please enter your username and password in lowercase as well.
If you need access to the near-realtime (NRT) GPM files on ftp://jsimpson.pps.eosdis.nasa.gov, please be sure to check the box labelled "Near-Realtime Products". Otherwise you will be unable to log in to the NRT FTP server.
If you have already registered but would like to change your account details (such as adding access to NRT products) please visit this page and click "Verify Email or Update Info": http://registration.pps.eosdis.nasa.gov/registration/
The PPS FTP does not work with the Safari web browser due to the way it handles FTP authentication. It is recommended you use another web browser such as Chrome or Firefox, or use the command line or a dedicated FTP client application (Click here for a list of possible FTP clients. NASA does not endorse any of these applications, they are listed merely as a suggestion).
Please visit the "PPS Satellite-Ground Coincidence Finder" website from NASA's Precipitation Processing System: https://storm.pps.eosdis.nasa.gov/storm/data/Service.jsp?serviceName=OverflightFinder#events
This tool allows you to select a location, date range, and satellite to determine when the satellite has passed over that location within those dates.
No, GPM data is provided on a "best effort" basis and should not be considered operational. By design, most GPM products, and specifically IMERG, are "best effort". We are pretty proud that our best effort has been quite effective. But, the systems are taken down for routine preventative maintenance on selected Tuesdays, and if a server crashes, a network goes down, the government shuts down, or partner satellites suddenly fall silent, we don't have hot spares, personnel with pagers, 24/7 operations, etc. that guarantee continuous operation.
The entire partner constellation processing strategy is "best effort", so no specific delivery requirements are included in the letters of agreement. The commitment is to maintain communications about the status of the sensors, data quality, and data transmissions, and recognize that GPM is an interested party.
Yes, all NASA-produced data from the GPM mission is made freely available for the public to use. In addition, all NASA media is copyright free (with the exception of the NASA logo) and can be used without explicit permission. Click here for the full NASA media usage policy.
IMERG provides a data field that estimates the probability that the retrieved precipitation amount is “liquid”, which is defined to include “mixed” (liquid and solid) precipitation. In retrospect the field name should have been “ice”, but “liquid” had already be set. The rational is that mixed precipitation is very rare and transient, so it should be lumped with “liquid” or “ice”. Furthermore, the primary effects of “ice” are to 1) prevent the falling precipitation from immediately entering the hydrological system (until it melts), and 2) to create (potentially) dangerous travel conditions. “Mixed” typically ends up not creating either of these effects, so lumping it with “liquid” seems appropriate.
Even given this basic definition, there are numerous forms of precipitation, and it might not be obvious how they end up being classified in IMERG. The key fact is that the phase is computed diagnostically at present, based on work by Guosheng Liu (Florida State University) and students. The Liu scheme uses data from a numerical model or model analysis to compute a “specification”, without reference to the satellite data, including whether or not IMERG estimates that precipitation is occurring, or even possible to estimate. Thus, probabilityLiquidPrecipitation (pLP) is a globally complete field whenever the relevant model data exist. An additional factor is that there is a conceptual difference between how the half-hourly phase is computed and how phase is defined in this probability framework for the monthly data. We will handle the half-hourly first, for which the Liu specification equation is directly relevant.
Liu determined that the primary factor for phase is the surface wet bulb temperature (Tw), a combination of temperature and humidity, with small contributions from the low-altitude Tw lapse rate and the surface pressure, and with systematic differences between ocean and land areas. In practice, the fitted probability as a function of Tw is converted to separate look-up tables for ocean and land.
Typical results for different forms of precipitation are:
- Rain: Ordinary falling liquid typically happens for Tw>0°C, so pLP is high.
- Freezing Rain: Liquid that freezes upon contact with the Earth's surface typically falls in Tw<0°C, so pLP is low.
- Snow, ice pellets, snow pellets: These frozen hydrometeors occur around or below Tw<0°C, so pLP varies from around 50% to very low.
- Sleet: Frozen droplets (U.S. definition) typically fall in Tw<0°C, so pLP is usually below 50%.
- Mixed snow and rain; falling slush: The mixed category is likely to occur around the pLP=50% mark. If one uses 50% as a liquid/solid threshold, that implies that mixed cases will end up in both categories, depending on the details.
- Hail: Hail typically occurs when the surface air temperature is well above freezing (i.e., on summer afternoons). Thus, pLP is very high. But, hail is even rarer than mixed and unlikely to be correctly specified in this scheme, and anyway, in such conditions it rapidly melts and so is properly “mixed”.
- Dew and frost: These phenomena are not forms of precipitation. They are liquid or solid water that condenses directly at the Earth's surface. For this reason, any amount of surface accumulation due to dew or frost is not included in the IMERG precip estimate.
As the time interval for the data values lengthens, it becomes increasingly likely that both liquid and solid might have fallen, at which point the meaning of pLP should change to “what fraction of the estimated precipitation amount fell as liquid or mixed?” This is the definition of pLP for both the monthly IMERG Final Run pLP and the set of GIS IMERG files (TIFF+WorldFile) providing estimated accumulations longer than three hours.
The following table provides a quick reference for the IMERG variables that can be visualized using Giovanni.
Variable and Description
30-min averaged data
Merged microwave-only precipitation estimate [Final]
Precipitation estimates from combining microwave data from the GMI, TMI, and other partner instruments.
Random error for gauge-calibrated multi-satellite precipitation [Final, Early, Late]
This is an estimate of the non-systematic component of the error. The exact variable name depends on the product, but all begin with "Random error..."
Microwave satellite observation time [Final]
Observation time of the microwave precipitation estimates given as minutes from the beginning of the current half-hour.
Microwave satellite source identifier [Final]
This is an integer between 0 and 24 that corresponds to the instrument from which the microwave precipitation estimate was taken
Weighting of IR-only precipitation relative to the morphed merged microwave-only precipitation estimate [Final]
This is the weighting of the infrared data in the final merged estimate, given in percent. Zero means either no IR weighting or no precipitation.
IR-only precipitation estimate [Final]
This is the microwave-calibrated infrared precipitation estimate.
Multi-sallite precipitation estimate with climatological gauge calibration [Final, Early, Late]
This is the precipitation estimate that has been calibrated with gauge data. This variable is recommended for most users. Note: Climatological gauge calibration is used in Early and Late.
Multi-satellite precipitation estimate [Final]
This is the precipitation estimate that has not been calibrated with gauge data.
Accumulation-weighted probability of liquid precipitation phase [Final]
This is the probability of liquid precipitation. The probabilities are calculated globally regardless of whether precipitation is actually present
1 month averaged data
Weighting of observed gauge precipitation relative to the multi-satellite precipitation estimate
This is the percent weighting of the surface gauge data.
Merged satellite-gauge precipitation estimate
This is the precipitatiotn estimate that has been calibrated with gauge data. This variable is recommended for most users.
Accumulation-weighted probability of liquid precipitation phase
This is the probability of liquid precipitation. The probabilities are calculated globally regardless of whether precipitation is actually present.
Random error for merged satellite-gauge precipitation
This is an estimate of the non-systematic component of the error.
The main difference between the IMERG Early and Late Run is that Early only has forward propagation (which basically amounts to extrapolation), while the Late has both forward and backward propogation (allowing interpolation). As well, the additional 10 hours of latency allows lagging data transmissions to make it into the Late run, even if they were not available for the Early (see below).
There are two possible factors which contribute to differences in the IMERG Late Run and Final Run datasets:
- The Late Run uses a climatological adjustment that incorporates gauge data. In Version 4 and later (scheduled to be available in November - Decemberr 2016), this will be a climatological adjustment to the Final run, which includes gauge data at the monthly scale. For Version 3 (which is the currently available data) the TRMM V7 climatological adjustment of the TMPA-RT to the production TMPA is used (which includes gauge at the monthly scale) because this at-launch algorithm didn't yet have any Late and Final data from which to build the climatological adjustment. The Final run uses a month-to-month adjustment to the monthly Final Run product, which combines the multi-satellite data for the month with GPCC gauge. Its influence in each half hour is a ratio multiplier that's fixed for the month, but spatially varying.
- The Late Run is computed about 15 hours after observation time, so sometimes a microwave overpass is not delivered in time for the Late Run, but subsequently comes in and can be used in the Final. This would affect both the half hour in which the overpass occurs, and (potentially) morphed values in nearby half hours.
The difference over the oceans has to be the first, while the difference over many land areas could be either. The satellite sensor difference could be examined by comparing the satellite sensor data field in the Late and Final Run datasets for each half hour. Since the gauge adjustment is a constant multiplier, a time series should show a constant ratio between the Late and the Final Runs for the entire month (except for cases where the satellite sensor is changing, just as for the ocean).
We always advise people to use the Final Run for research, but to be realistic; with such a short record, the extra months of Late Run might outweigh the risk of using less-accurate data. The vast majority of grid boxes have fairly similar Late and Final values, so it makes sense to stick to metrics that are more resistant to occasional data disturbances than others. Extreme values are more sensitive to these details; medians, means, and root-mean square difference are less sensitive.
Compared to previous versions, Version 05B IMERG introduces additional coverage at the high latitudes for the complete precipitation fields in all Runs -- Early, Late, and Final. IMERG continues to use a merged geosynchronous infrared brightness temperature analysis to both support computing the motion vectors in morphing and provide IR-based precipitation estimates. The requisite analysis (provided by NOAA/NWS/Climate Prediction Center) covers the latitude band 60°N-S, so a "full" IMERG analysis is possible there. At higher latitudes (in both hemispheres) both morphing and IR-based estimates are not included, so the coverage in the complete precipitation fields is "partial" -- limited to times when overpasses occur for microwave sensors and with no snow/ice on the surface. Some of the other data fields, specifically the merged microwave estimates and the precipitation phase, were already provided for the entire globe.
Q1: How closely should the monthly satellite-gauge combined precipitation datasets follow the gauge analysis?
A1: The combined precipitation research team at Goddard has major responsibility for the Global Precipitation Climatology Project monthly Satellite-Gauge combined product, the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B43 monthly product, and the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) mission (IMERG) Final Run monthly product. In each case the multi-satellite data are averaged to the monthly scale and combined with the Global Precipitation Climatology Centre's (GPCC) monthly surface precipitation gauge analysis (see https://www.dwd.de/EN/ourservices/gpcc/gpcc.html). In each case the multi-satellite data are adjusted to the large-area mean of the gauge analysis, where available (mostly over land), and then combined with the gauge analysis using a simple inverse estimated-random-error variance weighting. In all three data sets the gauge analysis has an important or dominant role in determining the final combined value for grid boxes in areas with "good" gauge coverage. Regions with poor gauge coverage, such as central Africa have a higher weight on the satellite input. The oceans are mostly devoid of gauges and therefore mostly lack such gauge input.
Q2: How closely related are the short-interval multi-satellite precipitation datasets to the monthly satellite-gauge combined precipitation datasets?
A2: The short-interval GPCP is the One-Degree Daily (1DD), the short-interval TMPA is 3B42 (which is 3-hourly), and the short-interval IMERG is the half-hourly. In each case the short-interval data are adjusted with a simple, spatially varying ratio to force the multi-satellite estimates to approximately average up to the corresponding monthly product, although with controls on the ratios to prevent unphysical results. Thus, monthly-average values for the short-interval data should be close to the mean values for the monthly datasets, which the developers consider more reliable than the short-interval datasets. In fact, compared to datasets that lack the adjustment to the monthly satellite-gauge estimates, the 1DD, 3B42, and IMERG Final half-hourly datasets tend to score better at timescales longer than a few days. This is presumably because the random error begins to cancel out as more samples are averaged together, while the bias error remains.
Bolvin, D.T., R.F. Adler, G.J. Huffman, E.J. Nelkin, J.P. Poutiainen, 2009: Comparison of GPCP Monthly and Daily Precipitation Estimates with High-Latitude Gauge Observations. J. Appl. Meteor. Climatol., 48(9), 1843–1857.