Various Paragraphs
George J. Huffman
13 August 2008
Acronyms
GPCP
SG
1DD
TMPA
TRMM
MW
IR
HQ
VAR
TMPA-RT
3B40RT
3B41RT
PPS
PMM
GPM
3B42RT
TOVAS
ASCII
GDISC
CoCoRaHS
Global Precipitation Climatology Project
Satellite-Gauge monthly combined precip product
One Degree Daily precip product
TRMM Multi-satellite Precipitation Analysis
Tropical Rainfall Measuring Mission
microwave (sensor channel)
infrared (sensor channel)
High-Quality (MW) precip estimate
Variable Rainrate (IR) precip estimate
Real-Time version of the TRMM Multi-satellite Precipitation Analysis
PPS identifier for HQ product in real time
PPS identifier for VAR product in real time
Precipitation Processing System
Precipitation Measuring Missions
Global Precipitation Measurement project
PPS identifier for TMPA-RT combined MW-IR product
TRMM Observations Visualization and Analysis System
Text, or character data
Goddard Data and Information Services Center
Community Cooperative Rain, Hail, and Snow network
Satellites Estimate Precip, Rather than Measuring It
Although we sometimes say that particular satellites measure precipitation, more precisely
satellites measure the radiant energy in various parts of the electromagnetic spectrum that allow
scientists to estimate precipitation. As the energy upwells from the Earth’s surface through the
atmosphere, it is modified by the gases, aerosols, clouds, and precipitation that make up the
atmosphere, and the satellite channels are chosen to be sensitive to various combinations of those
things. Some channels are “window” channels, relatively insensitive to gasses and aerosols and
mostly responding to the highest-elevation object that the satellite sees, either a dense cloud or
the surface. Examples include visible and infrared channels. Other channels sense the
precipitation particles. In some cases, the channels respond to the emission of additional radiant
energy by the liquid precipitation, while others detect reductions in the upwelling radiant energy
due to icy precipitation particles scattering away the upwelling energy. Examples include
microwave channels. The microwave sensors are more accurate, but fly only on satellites that
have “low” altitude orbits and therefore provide only occasional snapshots of any particular spot
on Earth. These and other issues prevent completely accurate estimates of precipitation from
satellites and emphasize the need to consider satellite results only as estimates.
What Does a Typical Global Precipitation Picture Look Like?
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<this could point to the lower “week accumulation image” on trmm.gsfc.nasa.gov> At the largest
scales, namely global images averaged over weeks or longer, the precipitation takes on a fairly
stable pattern. There is a narrow band of nearly continuous moderate-to-heavy precipitation that
encircles the Earth fairly close to the Equator, the Inter Tropical Convergence Zone. At slightly
higher latitudes there tend to be large areas of very light precipitation, located in the “subtropical
high pressure” centers. In mid-latitudes the precipitation is again systematically higher, due to
the repetitive occurrence of low pressure centers and frontal systems in the “storm tracks”.
Animations of average monthly data around the annual cycle reveal systematic shifts of these
basic features with the seasons, principally showing that the precipitation maxima tend to move
north-south as the Sun moves north-south during the year. The largest departure from this usual
picture occurs in the Equatorial Pacific Ocean in El Niño and La Niño events, which occur every
3-7 years and take most of a year to run their course.
<this could point to the upper “3-hr image” on trmm.gsfc.nasa.gov> Moving to successively
shorter time scales reveals that precipitation occurs in discrete patterns, as you would expect
from your personal experience, which makes it hard to infer the broadscale patterns that become
apparent with enough time averaging. In the tropics, the systems tend to appear as relatively
shapeless blobs, except for the occasional tropical cyclone. At higher latitudes the precipitation
is frequently organized into lines and arcs in the vicinity of fronts and low pressure systems.
Even at higher latitudes, in the absence of strong dynamical organization strong convection will
also appear in blobs. At time scales shorter than a day, one sees the typical cycles of
precipitation that occur over the course of the day. These diurnal cycles occur more or less
strongly depending on location and season, but land areas generally have stronger diurnal cycles
than the oceans. Tropical and subtropical coastal areas tend to show land/sea breeze effects that
are coupled. When you view the image loop of 3-hourly precipitation on trmm.gsfc.nasa.gov,
the “flashing” of individual features partly represents real short-term variability, but mostly it
arises from disagreements among the satellite estimates.
Sources of Error and Typical Errors
Errors arise from four main sources in rainfall estimation. First, the satellite data themselves can
have errors. Typically, the channels are well-enough calibrated that other errors dominate the
error budget, but rarely there are processing problems that can result in substantial artifacts. In
addition, there are a variety of transmission, reception, and archival issues that can impact the
quality of the estimates. Due to its near-real-time production, the TMPA-RT is created and
posted without human intervention, so data errors have the potential for appearing in the
precipitation products. Glaring errors are corrected when observed, sometimes by recomputing
with corrected data, and sometimes by simply dropping the offending data from the system.
The second source of error arises from limitations in the information content of the available
satellite channels. That is, the observations for any particular channel, or even all the channels
taken together, do not adequately provide information to unambiguously determine the
precipitation. Furthermore, in specific situations, such as land surfaces and icy or snow-covered
surfaces, some or most of the available channels stop providing useful information. In part, this
deficiency arises because essentially all of the sensors are “passive”, simply recording the
upwelling radiant energy. “Active” sensors, which send out a very specific signal and record its
return, provide much better information, but are too complex and power-intensive to have seen
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routine use. A few active sensors are being flown for research purposes, including the TRMM
Precipitation Radar and several lidar packages. This problem can only be addressed by adding
better sensors on future satellite mission. For example, the upcoming GPM satellite will include
channels that observe the atmosphere, but not the surface. Another aspect of the limited
information content in sensor data is that each sensor footprint yields values averaged over some
spatial area. For precipitation-related sensors, these footprints range in size from 5 to 50 km in
diameter. The sensor provides no information on smaller-sized regions within the footprint,
thereby establishing a lower limit on the size of features that data users can study. Here again,
the solution is equipping future satellites with finer-resolution sensors.
The third source of error arises from inaccuracies in the algorithms that translate that information
into precipitation estimates. To a certain extent this issue is intertwined with the limitations of
the sensors, but even when the theoretical use of the channel is clear, it is not always possible to
make full use of the sensor’s capabilities due to computation expense or lack of necessary
additional data.
Finally, errors in the combined precipitation estimates arise due to gaps between observations by
the satellites. The sensors that provide the best channels for estimating precipitation only fly on
low-Earth orbit (LEO) satellites, each of which can only provide one or two snapshots a day of
any particular place on Earth. Even taking the entire constellation of such satellites together, one
can only expect a higher-quality estimate every three hours or less often. Geosynchronous-Earth
orbit (GEO) satellites provide observations every 15-30 minutes over much of the Earth, but the
sensors that they carry provide precipitation estimates that are relatively inaccurate on a
footprint-by-footprint basis.
In the short and intermediate term, most improvements in precipitation estimates will come by
attacking the last two problems. As improvements in processing are introduced, users should
expect to occasionally see new versions of the precipitation products. The data producers strive
to inform users of such updates and are typically interested in hearing about specific problems in
the data, even if no changes can be made in the processing for several years.
Over land, errors generally are higher over coastal areas and regions covered by snow or ice.
Precipitation that occurs in relatively shallow clouds will likely have more errors, and this
includes precipitation enhancement and suppression due to wind flow over hills, mountains, and
valleys (orographic precipitation). Light and short-lived precipitation in general are harder for
the products to correctly depict.
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