What is Precipitation?
Precipitation is a key hydrological and climate variable and includes both the liquid (rain) and solid (snow and ice) forms. Precipitation occurs when a particle formed by the condensation of water vapor becomes heavy enough to fall under the force of gravity. Precipitation can be measured at any vertical level in the atmosphere, but it is the amount that falls at Earth’s surface that is most relevant for people. Here at RSS, we derive estimates of surface rain rate over the ocean from microwave radiometers.
Measurement of Precipitation
Because of its importance for human civilization, many techniques have been developed for the remote sensing of precipitation. Remote sensing is especially important for characterizing the precipitation over the vast ocean regions and remote land areas where no rain gauges exist. Remote sensing techniques include two general categories, active remote sensing and passive remote sensing. Active remote sensing involves transmitting a pulse of electromagnetic radiation, and measuring the radiation that bounces off the target (rain drops in this case). Weather radar is an example of an active sensor. Passive remote sensing simply measures incoming radiation, like a camera without the flash. Microwave and infrared imagers are examples of passive sensors.
Passive remote sensing of precipitation is possible at microwave and infrared wavelengths. Microwave radiation has wavelengths around 1 cm, while infrared radiation has wavelengths about 1000 times shorter. For this reason, microwave measurements can see deep inside the cloud, while infrared measurements see just the cloud top. It also means that microwave measurements have a lower spatial resolution compared to infrared. The spatial resolution of a microwave sensor is improved by placing it in lower orbit. Microwave sensors are typically placed on satellites in a low earth orbit (350-850 km), while infrared sensors are typically placed in a geostationary orbit (36,000 km).
The physical basis for retrieving precipitation from passive microwave measurements depends on distinguishing the radiation coming from Earth’s surface with the radiation coming from precipitation. This is more difficult to do over land, so we provide estimates over the ocean only. The microwave emission from the ocean surface is strongly polarized, while the emission from rain drops is unpolarized. Thus, precipitation can be accurately distinguished from the underlying ocean surface using measurements of the vertically and horizontally polarized radiation. Liquid water produces a much stronger signal than ice, so we provide estimates of rain rate only.
Time Scales of Precipitation
Precipitation exhibits variability across a wide range of time scales. Capturing precipitation variability on time scales less than one day requires measurements from as many sensors as possible. Capturing the variability on time scales longer than one year requires continuous records from consistently-calibrated sensors. Specific atmospheric phenomena are associated with each of the time scales of precipitation variability, and examples are discussed below. These examples illustrate how different datasets can be used to study different phenomena. The analysis shown here is for the time period 1998-2012.
On time scales of less than one day is the diurnal cycle of precipitation. Figure 1 shows the hourly precipitation anomaly from the daily average. Over the ocean, precipitation is generally at a maximum in the early morning local time. This is in contrast to over land, where the maximum precipitation is generally associated with the heating of the day.
Figure 1. Daily precipitation anomaly at 6 AM local time (top) and 6 PM local time (bottom). These images were made from RSS daily TMI.
On sub-seasonal time scales (1-3 months) is the Madden-Julian Oscillation (MJO). MJO is a propagating pattern of precipitation anomalies over the tropical Indian and Pacific Oceans (Figure 2). MJO phase is given by the Real-time Multivariate MJO Index (RMM). The center of the positive rainfall anomaly is located near 90°E in phase 2 and propagates to 160°E by phase 6.
Figure 2. Precipitation anomaly for MJO phases 2 (top), 4 (center), and 6 (bottom). These images were made from RSS daily merged rain rate.
On seasonal time scales are Earth’s monsoon systems. The monsoon is a seasonally reversing pattern of winds and precipitation. Figure 3 shows this reversal for the Indian monsoon. The figure depicts the conditions in 2008 about one month after onset and several weeks after monsoon withdrawal.
Figure 3. Weekly average wind direction and precipitation during the monsoon (top) and after the monsoon (bottom) in 2008. These images were made from RSS weekly QuikScat winds and RSS weekly SSM/I F13 rain rate.
On annual time scales is the El Niño-Southern Oscillation (ENSO). This is a global scale shift in precipitation and sea surface temperature (SST) patterns over the tropical Pacific and Indian Oceans. El Niño is the warm phase of the oscillation with warmer SST and heavier precipitation over the eastern Pacific (Figure 4). La Niña is the cool phase, in which the warmer SST and heavier precipitation is located over the far western Pacific (Figure 5). ENSO phase is given by the Oceanic Niño Index (ONI) using a threshold of ±0.5°C. These changes in tropical precipitation have far reaching “teleconnections” with precipitation over land where people live.
Figure 4. Precipitation (top) and SST (bottom) January anomalies for El Niño conditions. These images were made from RSS daily merged rain rate and RSS daily OI-SST.
Figure 5. Precipitation (top) and SST (bottom) January anomalies for La Niña conditions. These images were made from RSS daily merged rain rate and RSS daily OISST.
RSS Rain Rate Data Products
Passive microwave radiometers can only derive rain rate, not precipitation. As part of our radiometer data processing, we determine a rain rate value that is included in the individual satellite gridded binary data files. For more information on the content of these files and data access, see the Mission pages.
All RSS rain rate products are derived using the algorithm described in Hilburn and Wentz (2008). Validation of Version-7 rain rate products is provided by Hilburn and Smith (2012).
| Instrument | Period of Operation | Version |
| SSM/I, SSMIS | 1987 - present | V7 |
| TMI | 1997 - 2015 | V7.1 |
| AMSR-E | 2002 - 2011 | V7 |
| AMSR-2 | 2012 - present | V7.2 |
| WindSat | 2003 - present | V7.0.1 |