A variety of instruments can be used to gather information about the quantity, type, distribution and composition of precipitation and the potential physical processes underlying its formation. A recent article in Reviews of Geophysics presents a comprehensive review of the data sources and estimation methods of 30 currently available global precipitation datasets. The editors asked one of the authors to explain more about precipitation data and its uses.
What are the different methods of collecting precipitation data, and what are their strengths and weaknesses?
Rain gauges are the most common tools for directly assessing point precipitation at the Earth’s surface. There are several types including accumulation gauges, tipping-bucket gauges, weighing gauges, and optical gauges. Gauge observations provide relatively accurate and trusted measurements of precipitation with long term records. However, gauge measurements provide incomplete areal coverage and are deficient over most oceanic and sparsely populated areas. Moreover, because of the effects of wind speed, evaporation, and precipitation intensity, different types of rain gauge, and observation techniques induce different errors in precipitation measurements.
Another type of ground-level instrument is a disdrometer. Unlike rain gauges, they can detect individual raindrops and measure their size.
Weather radar is an alternative to rain gauges and provides real-time measurements of precipitation with high spatial and temporal resolution, and can also capture the three-dimensional structure of precipitation.
Meanwhile, satellite measurements provide precipitation information that is more spatially homogeneous and temporally complete for vast areas of the globe. However, these measurements contain non-negligible random errors and biases owing to the indirect nature of the relationship between the observations and actual precipitation, inadequate sampling, and deficiencies in the algorithms.
How much precipitation data exists?
There are over 30 global precipitation data sets currently available. These can be categorized as gauge-based, satellite-related, and reanalysis data sets.
Products merging satellite and gauge measurements have been designed to improve the accuracy of the measurements. This approach is expected to maximize the relative benefits of each data type; however, these merged products only extend back as far as 1979.
The spatial and temporal resolution of reanalysis data may be heterogeneous. Observational constraints, model parameterizations, and complex interactions between the model and the observations all affect the subsequent precipitation forecast generated by the system. Therefore, the reliability of reanalysis data sets can vary considerably depending on the location and time period.
How do these datasets show different estimates across different temporal scales?
At the annual scale, the precipitation datasets show reasonably consistent interannual variability, although estimates of annual precipitation over global land deviate by as much as 300 mm/year among the different products. Reanalysis data sets show the greatest inconsistency in annual values.
At the seasonal scale, products that merge satellite and gauge measurements produce low precipitation estimates whereas reanalysis products produce high estimates. The seasonal contributions to the difference in annual precipitation are slightly larger for June-July-August and March-April-May than for the other seasons.
At the daily scale, light precipitation events occur more frequently than other precipitation events, and there is a large divergence in the frequency of light events estimated by the different products. Differences in extreme precipitation estimates are greater for arid regions than humid regions, and for lower latitudes than higher latitudes.
How can precipitation data become more reliable and estimates more accurate?
First, better calibrations of satellite data and better methods for the optimal combination of earth measurements, satellite estimates, and model outputs may provide a better understanding of precipitation. Second, more accurate convective parameterization schemes, reasonable representation of the physical processes, and higher resolution are required in the reanalysis models. Satellite data can be included in the data assimilation to improve the precision of reanalysis estimates. Finally, a single algorithm is not always applicable to different regions, cross-validating the differences among multiple data sets is essential for reducing discrepancies.
What are the potential uses and applications of more accurate precipitation data?
Precipitation is a crucial component of the water cycle and is the most important and active variable associated with atmospheric circulation in weather and climate studies. More accurate and reliable precipitation data would be invaluable, not only for the study of climate trends and variability, but also as inputs to hydrological and ecological models and for model validation, characterization of extreme events, and flood and drought forecasting.
—Chiyuan Miao, Faculty of Geographical Science, Beijing Normal University, China; email: [email protected]