Question: How many decades has satellite data been available for precipitation?
About the article "Climate change", section "Precipitation".

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Warmer temperatures in the region have brought increased precipitation to Greenland, and part of the lost mass has been offset by increased snowfall.
However, there are only a small number of weather stations on the island, and though Satellite data can examine the entire island, it has only been available since the early 1990s, making the study of trends difficult.
It has been observed that there is more precipitation where it is warmer, up to 1.5 meters per year on the SE flank, and less precipitation or none on the 25–80% (depending on the time of year) of the island that are cooler.
Actual figures for precipitation are available in "New precipitation and accumulation maps for Greenland", A. Ohmura and N. Reeh, Journal of Glaciology, 1991.
Data from NASA's Polar program confirms that the average elevation change above "was not significant". 
The IR estimates have rather low skill at short time and space scales, but are available very frequently (15 minutes or more often) from satellites in geosynchronous Earth orbit.
IR works best in cases of deep, vigorous convection—such as the tropics—and becomes progressively less useful in areas where stratiform (layered) precipitation dominates, especially in mid- and high-latitude regions.
The more-direct physical connection between hydrometeors and microwave channels gives the microwave estimates greater skill on short time and space scales than is true for IR.
However, microwave sensors fly only on low Earth orbit satellites, and there are few enough of them that the average time between observations exceeds three hours.
This several-hour interval is insufficient to adequately document precipitation because of the transient nature of most precipitation systems as well as the inability of a single satellite to appropriately capture the typical daily cycle of precipitation at a given location.
Since the late 1990s, several algorithms have been developed to combine precipitation data from multiple satellites’ sensors, seeking to emphasize the strengths and minimize the weaknesses of the individual input data sets.
The goal is to provide “best” estimates of precipitation on a uniform time/space grid, usually for as much of the globe as possible.
In some cases the long-term homogeneity of the dataset is emphasized, which is the Climate Data Record standard.
In other cases, the goal is producing the best instantaneous satellite estimate, which is the High Resolution Precipitation Product approach.
In either case, of course, the less-emphasized goal is also considered desirable.
One key result of the multi-satellite studies is that including even a small amount of surface gauge data is very useful for controlling the biases that are endemic to satellite estimates.
The difficulties in using gauge data are that 1) their availability is limited, as noted above, and 2) the best analyses of gauge data take two months or more after the observation time to undergo the necessary transmission, assembly, processing and quality control.
Thus, precipitation estimates that include gauge data tend to be produced further after the observation time than the no-gauge estimates.
As a result, while estimates that include gauge data may provide a more accurate depiction of the “true” precipitation, they are generally not suited for real- or near-real-time applications.
The work described has resulted in a variety of datasets possessing different formats, time/space grids, periods of record and regions of coverage, input datasets, and analysis procedures, as well as many different forms of dataset version designators.
In many cases, one of the modern multi-satellite data sets is the best choice for general use. 
PERSIANN, "Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks", is a satellite-based precipitation retrieval algorithm that provides near real-time rainfall information.
The algorithm uses infrared (IR) satellite data from global geosynchronous satellites as the primary source of precipitation information.
Precipitation from IR images is based on statistical relationship between cloud top temperature and precipitation rates.
The IR-based precipitation estimates are then calibrated using satellite microwave data available from low Earth orbit satellites (e.g., Tropical Rainfall Measuring Mission Microwave Imager, Special Sensor Microwave Imager, Advanced Microwave Scanning Radiometer‐Earth observing system).
The calibration technique relies on an adaptive training algorithm that updates the retrieval parameters when microwave observations become available (approximately at 3 hours intervals).
The PERSIANN satellite precipitation data sets have been validated with ground-based observations and other satellite data products.
The PERSIANN data has been used in a wide variety of studies including hydrologic modeling, drought monitoring, soil moisture analysis, and flood forecasting.
The PERSIANN data are freely available to the public. 
Past precipitation can be estimated in the modern era with the global network of precipitation gauges.
Surface coverage over oceans and remote areas is relatively sparse, but, reducing reliance on interpolation, satellite data has been available since the 1970s.
Quantification of climatological variation of precipitation in prior centuries and epochs is less complete but approximated using proxies such as marine sediments, ice cores, cave stalagmites, and tree rings.
Climatological temperatures substantially affect precipitation.
For instance, during the Last Glacial Maximum of 18,000 years ago, thermal-driven evaporation from the oceans onto continental landmasses was low, causing large areas of extreme desert, including polar deserts (cold but with low rates of precipitation).
In contrast, the world's climate was wetter than today near the start of the warm Atlantic Period of 8000 years ago.
Estimated global land precipitation increased by approximately 2% over the course of the 20th century, though the calculated trend varies if different time endpoints are chosen, complicated by ENSO and other oscillations, including greater global land precipitation in the 1950s and 1970s than the later 1980s and 1990s despite the positive trend over the century overall.
Similar slight overall increase in global river runoff and in average soil moisture has been perceived.
To characterize the drought of 1999-2005 DRI has been quantifying its atmospheric, hydrologic, and land-surface physical features at a variety of spatial and temporal scales.
This analysis will be realized by addressing three focused questions: What variables are required to quantify the characteristics of this recent drought?
What data sources and model outputs are available for quantification of these parameters?
How do we characterize and “close the budgets” of water and energy over the Prairies?
The analysis has involved the development of a four-dimensional assessment of the atmosphere during the drought over various temporal scales using a knowledge of temperature, humidity, geopotential height, wind, clouds, precipitation amount, and current weather.
At the surface, the spatial and temporal characteristics of vegetative state (in terms of water stress) for major vegetation types (crops and boreal zones), soil moisture, stream network, river flows, lake levels, wetlands, depression storage, groundwater and sub-surface flows be assessed to determine when and where drought is occurring.
Although this is a difficult task due to data gaps, and the complexity of the shallow undulations in the prairies topography, an analysis of in-situ data, satellite data and model output now provides a comprehensive overview of the evolution of the drought.