Quantifying Water Cycle Variability in the Classroom Using CREW GDS Data

Quantifying Water Cycle Variability in the Classroom Using CREW GDS Data

Access water-cycle data from the homepage of Center for Research on Environment and Water (CREW).

This webpage was created for SERC by Xia Feng in consultation with Paul Houser and Debbie Belvedere of CREW.

The Dataset

Water-cycle data from the CREW homepage provides users the ability to access, subset and analyze water cycle related data via the GrADS Data Server (GDS), therefore easily manipulating and visualizing water cycle variation using the Grid Analysis and Display System (GrADS).

Two commonly-used dataset will be briefly introduced for the creation of a water cycle education module. The first dataset is the Global Precipitation Climatology Project (GPCP) Version-2 monthly combined precipitation from satellite measurements and surface rain gauge observations, extending from 1979 to 2005 with 2.5° latitude 2.5° longitude spatial resolution. The second dataset is the National Centers for Environmental Prediction (NCEP)/Department of Energy (DOE) Atmospheric Model Intercomparison Project (AMIP-II) reanalysis covering the period 1979 to 2005. In particularly, it provides the surface water cycle components, which overcome the inadequacy of sparsely distributed in situ network, such as evaporation, runoff and soil moisture.

Use and Relevance

CREW’s large collection of water cycle datasets can be utilized to understand water cycle variability on a broad range of spatial (global and regional) and temporal scales (diurnal, seasonal and interannual), and the complicated ways in which they interact. The CREW GDS data sever not only provides various advanced water cycle datasets to research community but also to anyone else interested in water cycle.

Use in Teaching

The datasets can be used in teaching the following topics and skills in atmosphere science, meteorology, climate, and hydrology.

Teaching Topics

Water cycle variation on multiple spatial and temporal scales.

Anomalous dry and wet periods.

Teaching Skills

Using data to make images of temporal changes of water cycle data.

Using data to make spatial plots of water cycle data.

Using linear regression to determine the trend of water cycle components.

Interpreting water cycle variation from graphs.

Exploring the Data

Data Type and Presentation

GPCP and NCEP/DOE reanalysis have provided for the period 1979-2005. Data are available in Binary format.

Accessing Data

Raw data can be accessed via the CREW GDS sever. The GDS versatile capability allows users to remotely gain access to the desired dataset and subset specified temporal and spatial subdomain without downloading a large dataset for further analysis.

Manipulating Data and Creating Visualizations

Students can process the data to create graphs from raw data using GrADS. Graphs can then be used to visualize water cycle data spatially and temporally and to display the water cycle variation.

Tools for Data Manipulation

Users access the raw data provided from the CREW GDS sever and use GrADS software to manipulate and visualize the dataset. A tutorial for using GrADS is helpful to learn the basic features of GrADS.

About the Data

Collection Methods

GPCP incorporates precipitation estimates from low-orbit satellite microwave data, geosynchronous-orbit satellite infrared data, and surface rain gauge observations. The higher accuracy of the low-orbit microwave observations is utilized to calibrate, or adjust, the more frequent geosynchronous infrared observations. Then the combined satellite-based precipitation product is adjusted to the measured rain gauge.

NCEP/DOE reanalysis aims to fix the known processing errors in NCEP/National Center for Atmospheric Research (NCAR) with newer physics and observed soil moisture forcing using an improved forecast model and updated data assimilation system. When compared with NCEP/NCAR reanalysis, significant improvements were made in NCEP/DOE reanalysis, such as land surface hydrology and land-ocean fluxes.

Limitations and Sources of Error

GPCP precipitation uncertainty is due to the algorithm and sampling error.

Reanalysis data are subject to the bias in uncertainty of model parameterization.

References and Resources

Scientific References that Describe these Datasets

The Version-2 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979-present): Research article illustrates the features of GPCP precipitation (from Journal of Hydrometeorology).

NCEP-DEO AMIP-II Reanalysis (R-2): This research article presents characteristics of reanalysis-2 dataset (from Bulletin of the American Meteorological Society).

Other Related Scientific References

Contemporary Changes of the Hydrological Cycle over the Contiguous United States: Trends Derived from In Situ Observations: A research article describes the changes of water cycle components over the contiguous United States during the twentieth century (from Journal of Hydrometeorology).

Assessing a Satellite-Era Perspective of the Global Water Cycle: This research article assesses the capability of a global data compilation to depict the global water cycle’s mean state and variability.

Other Related Scientific Educational Resources

Introduction to the Atmosphere: Students will build a model to simulate parts of the water cycle.
The Hydrological Cycle Online Meteorology Guide: Online module illustrates the water cycle components.