Rainfall Measurement Schemes

Remote Sensing

Infrared	High frequency of monitoring Use only information of cloud-top temperature to determine surface rainfall
Microwave	Based on the distribution of hyrdrometeors within the cloud Explain instantaneous rainfalls more realistically, but can only monitor twice / day for any location Housed on low-orbiting satellites

Local Sensing

Fixed-time recording

Records the amount of rainfall over a set interval of time. In some ways this is a very inefficient method as it produces a large number of extraneous zeros in the data set

Fixed-event recording

Fixed-event recording records the time interval over which a set amount of rain falls. The method eliminates the large amount of extraneous zeros, making data sets leaner and more manageable. However, the article is dated 1991, and the authors concern for extraneous zero's seems to be outdated. Data storage due to recent advances in computer is much more economical than in the past. Nevertheless, the authors present an interesting alternative. The authors then present details on a computing device which could fulfill this task, no information on cost is provided

Merged

Climate Prediction Center merged analysis of precipitation (CMAP)

The merged analysis was composed of two kinds of data: standard precipitation (STD) and enhanced precipitation (ENH). STD consisted of gauge observatins, where as ENH consists of five kinds of satellite estimates. Specifically these estimates are:

Outgoing longwave radiation (OLR)-based precipitation index
Infrared-based Geostationary Operational Environmental Satellite (GOES) precipitation index
Microwave sounding unit
Microwave scattering from Special Sensor Microwave/Imager (SSM/I)
Microwave emission from SSM/I

Influences on rainfall

Pacific and Atlantic Ocean Surface Temperatures

It is known that precipitation patterns in the Amazon Basin are affected when the land is changed by clear-cutting and farming. Some areas suffer drought while other areas flood. Research has recently shown that the sea surface temperature of the Atlantic and Pacific oceans surrounding South America has as much of an influence on rainfall as do changes in land cover, according to Rong Fu, an atmospheric scientist at the Georgia Institute of Technology
Fu linked rainfall patterns over the Amazon with sea surface temperatures in the tropical Atlantic and Pacific oceans using a computer climate model. When he plugged El Niño data into the model, he found that the rainfall pattern in the eastern equatorial Amazon region of Brazil was extremely sensitive to temperature changes on the sea's surface. When the sea surface temperature increased, drought conditions appeared. When it dropped, flooding resulted.”
It was unexpected to find that precipitation was relatively unaffected by El Niño in Columbia and Peru, two countries west of Brazil and surprising that the Pacific's sea surface temperatures on Amazon rainfall was more influential that the Atlantic because moisture from the Pacific Ocean has to travel over the Andes Mountains before it reaches the Amazon.
“To determine which ocean had the greatest effect on rainfall changes, Fu removed Atlantic sea surface temperature readings from the model. Spring, normally Brazil's dry season, registered as its wettest. When Fu removed the eastern Pacific Ocean, sea surface temperature showed a similar, if weaker, effect on rainfall.
It wasn't until Fu removed western Pacific sea surface temperature readings that an unexpected result occurred. Water evaporating from the Atlantic Ocean Brazil and returns to Earth in the form of rain. Thus, Fu expected the Atlantic to have a greater impact on rainfall patterns in the Amazon. But the Pacific influence proved stronger even though evaporation from the Pacific must travel over mountains to reach Brazil .” takes a direct route to

Source of rainfall

64% of water vapor enters the Amazon basin from the eastern border. The remaining 34% enters through the northern border of the basin.

Variations in rainfall

On a decadal scale, water vapor input into the Amazon River basin has been experiencing a decreasing trend since the 1960's. This trend is believed to be associated with relaxed southeasterly trade winds, a decreasing east-to-west pressure gradient, and a general warming of the sea surface temperatures in the equatorial South Atlantic.

On a yearly scale, precipitation variability may be attributed to the El Niño-Southern Oscilation (ENSO) as well as several other secondary factors which include the strength of the North Atlantic high, the position of the intertropical covergence zone, and the surface temperatures of Atlantic. Precipitation lags behind ENSO by 3-4 months, with river discharge lagging an additional 3 months. This additional lag is likely due to the contribution from subsurface drainage since surface runoff tends to occur at a much shorter timescale. Soil water storage similarly follows precipitation by approximately 1-2 months.

On a season cycle, precipitation has been observed to vary up to 5mm / day, with runff vary up to 2mm / day and evapotranspiration remaining constant.

Rainfall evolution

Since the surface soil can be divided into three major layers, there exist three distinct relationships between the water saturation of those layers and rainfall. The first of these layers includes the top soil. The second layers extends to rooting depth(d₂) and the third layers extends to the toral soil depth (d₃). The sum of the water saturation of the three components is equal to the total rainfall to reach the land surface. Each of the layers can be described by the following three mathematical equations.

∂w₁ C₁
----- = ---------- (I - E_g) - d₁
∂t p_w d₁

∂w₂ 1
----- = ---------- (I - E_g - E_tr) - K₂ - d2
∂t p_w d₂

∂w₃ d₂
----- = ---------- (K₂ + d2) - K₃
∂t d₃ - d₂

A more physically realistic general circulation model (GCM) developed at the NASA / Goddard Institute for Space Science (GISS) introduces a canopy resistance and a six-layer soil system. This new scheme also allows runoff to travel from a river's headwater to its mouth according to topography and other channel characteristics. This model produces more realistic evaporation statistics. The new model takes into consideration conservation of mass, momentum, energy, and water vapor.

The water budget equation for the atmosphere is also related to precipitation (P), evapotranspiration (E), the vertically integrated moisture convergence (C).

∂w
----- = - P + E + C
∂t

Rainfall Data

Water year rainfall map - 1986

Major Region	Subregion	Rainfall (mm)
Madre de Dios basin	Andean flank	2500-7000
	Plain	1800-2500
Beni River Basin	Average	1755
	Summit in andean part	800-1000
	Upper part of hot valleys (Yungas) in andean part	400
	Most protected zones - behind upper summiits of the Cordilleera	350-500
	Main part of andean basin	1720
	Plains	1650-2000
Mamoré andean basin	Average	1850
	Most semi-arid zone	480
	Foot of the andes	600
	Average in Rio Grande basin	750
	Oriental watersheds	3000
	Amazon plain	800
	Ichilo basin	3000
	Head of Madeira river	1900
	Toward north	800-1900
	Toward west	1000-4000
Itenez River basin	Average	1375
	South	900
	East	1800
	Northeast	1900
Upper Madeira basin	Average	1705

Sources

Diurnal variability of tropical rainfall retrieved from combined GOES and TRMM satellite information
Authors: Sorooshian-S.; Gao-X.; Hsu-K.; Maddox-R.A.; Hong-Y.; Gupta-H.V.; Imam-B.
Source: Journal-of-Climate. 15(9): 983-1001
Date: May 1, 2002

EOS - Amazon Hydrological Models
Author: Greenberg, Harvery
Source: EOS Amazon Project at the University of Washington

Water and salt balances of the Bolivian Amazon
Authors: Roche, M. A., et al
Source: Water Management of the Amazon Basin, (83-94)
Editors: Braga, Benedite P. F., Jr., and Fernandez-Jauregui, Carlos A.
Date: August 1991

Sea surface temperatures impact weather in Amazon basin
Author: Shaw, Robinson

Offl-line simulation of the Amazon water balance: a sensitivity study with implications for GSWP
Authors: Chapelon, N., Douville, H., Kosuth, P., Oki, T.
Source: Climate Dynamics 19: 141-154
Date: March 2, 2002

Trends in the hydrologic cycle of the Amazon Basin
Authors: Costa, Marcos Heil and Foley, Jonathan A.
Source: Journal of Geophysical Research, Vol. 104 No. D12, P14,189-14,198
Date: June 27, 1999

Seasonal cycle and interannual variability in the Amazon hydrologic cycle
Author: Zeng, Ning
Source: Journal of Geophysical Research, Vol 104 No. D8, P9097-9106
Date: April 27, 1999

Calculations of river-runoff in the GSS GCM: impact of a new land-surface parameterization and runoff routing model on the hydrology of the Amazon River
Authors: Marengo, J. A.; Miller, J. R.; Russell, G. L., Rosenzweig, C. E.; and Abramopoulos, F.
Source: Climate Dynamics Vol 10, P349-361
Date: 19994