Global precipitation measurement: Methods, datasets and applications
Introduction
Today, precipitation science is at the crossroads of different scientific disciplines including among others hydrology, numerical modeling, climate change, remote sensing, forecasting and more recent innovations such as renewable energy research. Research directions range from improving the description of rain microphysics for climate change studies to areal interpolation of surface rain for agricultural applications, to name but two quite different approaches.
Rainfall and solid precipitation at the basin scale are the primary input to hydrological models predicting stream flow when used to manage hydropower operations. Early warning systems for landslides also require a good knowledge of recent precipitation, while in agriculture, irrigation scheduling is contingent upon recent and expected rainfall in the near future, especially in semiarid environments. In the realm of weather, precipitation estimates are used for nowcasting and for assimilation into global and regional models, aiming to improve the forecasts of not only precipitation but other variables such as temperature, evaporation and wind speed.
In the field of climate change assessment, apart from the intrinsic importance of detecting changes in water availability in the future, precipitation is routinely used to gauge the skill of model simulations, a task which is realized by comparing the climatology of present-climate simulations with that of observational datasets. Also, precipitation estimates over land and the oceans are instrumental to closing the global water cycle.
Both measuring and forecasting precipitation are important for these and other applications. Prediction might be seen as clearly preferable as it allows for the preparation of future events. However, improving the skill of the forecasts is closely intertwined with the ability to measure precipitation. The better the precipitation measurement, the better the likelihood of improved forecasts of precipitation and other meteorological parameters. Here, an area of fertile exchange is model parameterization, as the advances in the physics of precipitation required to improve numerical models heavily rely on testing new hypotheses by actual measurements of precipitation. This is particularly the case of, for instance, theoretical parameterizations of rain microphysics used in models, which should be consistent with ground or in-situ observations.
Given the breadth of the applications and their importance for human activities, the interest and effort devoted to accurate precipitation monitoring is not surprising. The growing importance of the field, however, runs in parallel with difficulties in the actual measurement. Precipitation is a very difficult variable to estimate both because of its irregular spatial occurrence, and also due to very diverse physical processes. For example, while cold and warm-based clouds both eventually generate precipitation, the processes leading to the formation of the liquid water are quite different. Such diverse meteorological conditions present a challenge to space-based remote sensing techniques for estimating precipitation.
Scientific advancements in quantitative precipitation estimation and their applications throughout the last decade have crystallized into the Global Precipitation Measurement (GPM) mission, organized as an international project led by the National Aeronautics and Space Administration (NASA, USA) and the Japanese Space Agency (JAXA, Japan). Given its approaching launch date (February 14, 2014), it seems timely for this paper to organize the many links and feedbacks between precipitation measurement, estimation and modeling, focusing on those methods suitable for generating a picture of global precipitation patterns. The body of this paper is organized in three sections. First, the methods for measuring, estimating, and modeling precipitation are discussed. Then, the most relevant datasets gathering precipitation information from those three sources are presented. The third part of the paper illustrates a few of the many applications of the databases.
Within the paper, the term measurement is used depending on context as a general term or to specify the direct physical readings of precipitation, thus being restricted to rain gauges and optical and video disdrometers. Estimation refers to inferring precipitation from a measure such as brightness temperature, momentum, or reflectivity, whereas the term forecasts is used to the hours to days predictions of numerical weather prediction (NWP) models. Projections refers to predictions from seasonal models, and simulations to the computer file outputs from either Global Climate Models (GCMs) or Regional Climate Models (RCMs). Reanalysis is defined as a retrospective analysis of the atmosphere using data assimilation methods and a numerical model.
Section snippets
Ground observations of precipitation
Ground observations of precipitation include those from rain gauges, disdrometers and radars. With a few exceptions which are immaterial on the global scale, these are restricted to land and to a few atolls. Rain gauges are universally considered as the source of reference data for precipitation observations as they provide a direct physical record of the precipitation in a given spot. Disdrometers are a relatively new instrument that estimate not only the total precipitation but also the
Satellite estimates
Sensors onboard low-Earth orbiting satellites are the only instruments capable of retrieving global and relatively homogeneous estimates of precipitation. Early statistical or qualitative forms of precipitation measurement have steadily progressed towards more direct methods and more physically based algorithms. The methods used to derive precipitation from the radiances measured by the satellites have evolved from visible (VIS) and infrared (IR) based methods to active and passive microwave
Modeling
Direct observation and estimation of precipitation are important to provide a realistic picture of the several components of the water cycle. However, they cannot be used to predict ahead at the temporal and spatial scales required for most scientific studies and applications. The location and timing of model predicted precipitation, on the other hand, often bears little resemblance with observed patterns except when averaged over fairly coarse scales.
Models predict precipitation after solving
Datasets of global precipitation
The preceding sections describe satellites and models to provide precipitation data that are accumulated at several temporal and spatial scales to create databases. Such reference datasets are critical to assess the actual uncertainties in climate projections, which are primary tools for global warming studies, by comparing projections with observations and estimates. They are also valuable in their own right to analyze the many aspects of the hydrological cycle, and provide information for
Applications of global precipitation measurements
The applications of global precipitation measurement span from direct application of the databases to their use as input of tailored models such as those to estimate local water use and allocation in a global warming scenario. As examples, three very different applications are presented: hydropower assessment, data assimilation and validation of regional climate models.
Outlook
Precipitation is a meteorological variable that is difficult to measure precisely (Levizzani et al., 2007, Anagnostou et al., 1999). Disdrometers, scanners, radars and radiometers present their own sources of error, limitations and uncertainties. This results in a challenge for those aiming to provide a timely and precise estimate of how much precipitation reaches the ground, and in which state (solid, liquid, or mixed).
Large international projects such as the Global Precipitation Measurement
Acknowledgements
The work of FTJ (Tapiador) has been funded through projects CGL2010-20787-C02-01, CGL2010-20787-C02-02, CENIT PROMETEO (MICCIN), and PPII10-0162-554 (JCCM). The contributions from FJT (Turk) were performed at the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration. EGO acknowledges research project LE176A11-2 (JCyL). GJH and WP acknowledge the NASA PMM (Dr. Ramesh Kakar) and GPM Programs for funding support. FJT
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